import{S as vcs,i as bcs,s as Mcs,e as a,k as l,w as b,t as o,M as Tcs,c as n,d as t,m as i,a as s,x as M,h as r,b as d,G as e,g as v,y as T,q as F,o as E,B as y,v as Fcs,L as W}from"../../chunks/vendor-hf-doc-builder.js";import{T as eZo}from"../../chunks/Tip-hf-doc-builder.js";import{D as G}from"../../chunks/Docstring-hf-doc-builder.js";import{C as P}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as te}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as V}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Ecs(x){let h,C,u,f,p,c,g,ze,Jc,qg,Wt,Uc,Xc,nV,Og,Qe,oo,Dc,zs,sV,Hs,Ks,lV,qc,el,iV,Oc,Qg,In;return{c(){h=a("p"),C=o("If your "),u=a("code"),f=o("NewModelConfig"),p=o(" is a subclass of "),c=a("code"),g=o("~transformer.PretrainedConfig"),ze=o(`, make sure its
`),Jc=a("code"),qg=o("model_type"),Wt=o(" attribute is set to the same key you use when registering the config (here "),Uc=a("code"),Xc=o('"new-model"'),nV=o(")."),Og=l(),Qe=a("p"),oo=o("Likewise, if your "),Dc=a("code"),zs=o("NewModel"),sV=o(" is a subclass of "),Hs=a("a"),Ks=o("PreTrainedModel"),lV=o(`, make sure its
`),qc=a("code"),el=o("config_class"),iV=o(` attribute is set to the same class you use when registering the model (here
`),Oc=a("code"),Qg=o("NewModelConfig"),In=o(")."),this.h()},l(ro){h=n(ro,"P",{});var Ie=s(h);C=r(Ie,"If your "),u=n(Ie,"CODE",{});var nY=s(u);f=r(nY,"NewModelConfig"),nY.forEach(t),p=r(Ie," is a subclass of "),c=n(Ie,"CODE",{});var Qc=s(c);g=r(Qc,"~transformer.PretrainedConfig"),Qc.forEach(t),ze=r(Ie,`, make sure its
`),Jc=n(Ie,"CODE",{});var sY=s(Jc);qg=r(sY,"model_type"),sY.forEach(t),Wt=r(Ie," attribute is set to the same key you use when registering the config (here "),Uc=n(Ie,"CODE",{});var lY=s(Uc);Xc=r(lY,'"new-model"'),lY.forEach(t),nV=r(Ie,")."),Ie.forEach(t),Og=i(ro),Qe=n(ro,"P",{});var jo=s(Qe);oo=r(jo,"Likewise, if your "),Dc=n(jo,"CODE",{});var Nn=s(Dc);zs=r(Nn,"NewModel"),Nn.forEach(t),sV=r(jo," is a subclass of "),Hs=n(jo,"A",{href:!0});var iY=s(Hs);Ks=r(iY,"PreTrainedModel"),iY.forEach(t),lV=r(jo,`, make sure its
`),qc=n(jo,"CODE",{});var Yg=s(qc);el=r(Yg,"config_class"),Yg.forEach(t),iV=r(jo,` attribute is set to the same class you use when registering the model (here
`),Oc=n(jo,"CODE",{});var dY=s(Oc);Qg=r(dY,"NewModelConfig"),dY.forEach(t),In=r(jo,")."),jo.forEach(t),this.h()},h(){d(Hs,"href","/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel")},m(ro,Ie){v(ro,h,Ie),e(h,C),e(h,u),e(u,f),e(h,p),e(h,c),e(c,g),e(h,ze),e(h,Jc),e(Jc,qg),e(h,Wt),e(h,Uc),e(Uc,Xc),e(h,nV),v(ro,Og,Ie),v(ro,Qe,Ie),e(Qe,oo),e(Qe,Dc),e(Dc,zs),e(Qe,sV),e(Qe,Hs),e(Hs,Ks),e(Qe,lV),e(Qe,qc),e(qc,el),e(Qe,iV),e(Qe,Oc),e(Oc,Qg),e(Qe,In)},d(ro){ro&&t(h),ro&&t(Og),ro&&t(Qe)}}}function ycs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co (user-uploaded) and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;dbmdz/bert-base-german-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If configuration file is in a directory (e.g., was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*).</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./test/bert_saved_model/&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Load a specific configuration file.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./test/bert_saved_model/my_configuration.json&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Change some config attributes when loading a pretrained config.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-uncased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>, foo=<span class="hljs-literal">False</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>config, unused_kwargs = AutoConfig.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;bert-base-uncased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>, foo=<span class="hljs-literal">False</span>, return_unused_kwargs=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>unused_kwargs
{<span class="hljs-string">&#x27;foo&#x27;</span>: <span class="hljs-literal">False</span>}`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ccs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download vocabulary from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download vocabulary from huggingface.co (user-uploaded) and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;dbmdz/bert-base-german-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If vocabulary files are in a directory (e.g. tokenizer was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># tokenizer = AutoTokenizer.from_pretrained(&quot;./test/bert_saved_model/&quot;)</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download vocabulary from huggingface.co and define model-specific arguments</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>, add_prefix_space=<span class="hljs-literal">True</span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function wcs(x){let h,C,u,f,p;return{c(){h=a("p"),C=o("Passing "),u=a("code"),f=o("token=True"),p=o(" is required when you want to use a private model.")},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Passing "),u=n(g,"CODE",{});var ze=s(u);f=r(ze,"token=True"),ze.forEach(t),p=r(g," is required when you want to use a private model."),g.forEach(t)},m(c,g){v(c,h,g),e(h,C),e(h,u),e(u,f),e(h,p)},d(c){c&&t(h)}}}function Lcs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download feature extractor from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-base-960h&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If feature extractor files are in a directory (e.g. feature extractor was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># feature_extractor = AutoFeatureExtractor.from_pretrained(&quot;./test/saved_model/&quot;)</span>`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Acs(x){let h,C,u,f,p;return{c(){h=a("p"),C=o("Passing "),u=a("code"),f=o("token=True"),p=o(" is required when you want to use a private model.")},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Passing "),u=n(g,"CODE",{});var ze=s(u);f=r(ze,"token=True"),ze.forEach(t),p=r(g," is required when you want to use a private model."),g.forEach(t)},m(c,g){v(c,h,g),e(h,C),e(h,u),e(u,f),e(h,p)},d(c){c&&t(h)}}}function Rcs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download image processor from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;google/vit-base-patch16-224-in21k&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If image processor files are in a directory (e.g. image processor was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># image_processor = AutoImageProcessor.from_pretrained(&quot;./test/saved_model/&quot;)</span>`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function kcs(x){let h,C,u,f,p;return{c(){h=a("p"),C=o("Passing "),u=a("code"),f=o("token=True"),p=o(" is required when you want to use a private model.")},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Passing "),u=n(g,"CODE",{});var ze=s(u);f=r(ze,"token=True"),ze.forEach(t),p=r(g," is required when you want to use a private model."),g.forEach(t)},m(c,g){v(c,h,g),e(h,C),e(h,u),e(u,f),e(h,p)},d(c){c&&t(h)}}}function Scs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download processor from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-base-960h&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If processor files are in a directory (e.g. processor was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># processor = AutoProcessor.from_pretrained(&quot;./test/saved_model/&quot;)</span>`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function xcs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWwlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWwuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Gcs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Bcs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbCUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModel.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ics(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModel.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ncs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbC5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModel.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function $cs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModel.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function jcs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JQcmVUcmFpbmluZyUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclByZVRyYWluaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForPreTraining.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Pcs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForPreTraining.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Zcs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclByZVRyYWluaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JQcmVUcmFpbmluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForPreTraining.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Vcs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForPreTraining.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Wcs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yUHJlVHJhaW5pbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yUHJlVHJhaW5pbmcuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForPreTraining.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Jcs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForPreTraining.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ucs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JDYXVzYWxMTSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckNhdXNhbExNLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Xcs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Dcs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckNhdXNhbExNJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JDYXVzYWxMTS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForCausalLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function qcs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForCausalLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ocs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yQ2F1c2FsTE0lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yQ2F1c2FsTE0uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForCausalLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Qcs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForCausalLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ycs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JNYXNrZWRMTSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvck1hc2tlZExNLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function zcs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JNYXNrZWRMTSUwQSUwQSUyMyUyMERvd25sb2FkJTIwbW9kZWwlMjBhbmQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JNYXNrZWRMTS5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQSUwQSUyMyUyMFVwZGF0ZSUyMGNvbmZpZ3VyYXRpb24lMjBkdXJpbmclMjBsb2FkaW5nJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JNYXNrZWRMTS5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFRGJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwUHlUb3JjaCUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yTWFza2VkTE0uZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMi4lMkZ0Zl9tb2RlbCUyRmJlcnRfdGZfY2hlY2twb2ludC5ja3B0LmluZGV4JTIyJTJDJTIwZnJvbV90ZiUzRFRydWUlMkMlMjBjb25maWclM0Rjb25maWclMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Hcs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck1hc2tlZExNJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JNYXNrZWRMTS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Kcs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function ems(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yTWFza2VkTE0lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yTWFza2VkTE0uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMaskedLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function oms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMaskedLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function rms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTZXEyU2VxTE0lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJ0NS1iYXNlJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU2VxMlNlcUxNLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function tms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/t5_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/t5_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function ams(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNlcTJTZXFMTSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMnQ1LWJhc2UlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNlcTJTZXFMTS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function nms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/t5_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/t5_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function sms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yU2VxMlNlcUxNJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIydDUtYmFzZSUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JTZXEyU2VxTE0uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSeq2SeqLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function lms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;t5-base&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/t5_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/t5_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function ims(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function dms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function cms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function mms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function fms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSequenceClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function hms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function gms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JNdWx0aXBsZUNob2ljZSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvck11bHRpcGxlQ2hvaWNlLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMultipleChoice.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function ums(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMultipleChoice.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function pms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck11bHRpcGxlQ2hvaWNlJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JNdWx0aXBsZUNob2ljZS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMultipleChoice.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function _ms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck11bHRpcGxlQ2hvaWNlJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yTXVsdGlwbGVDaG9pY2UuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JNdWx0aXBsZUNob2ljZS5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvck11bHRpcGxlQ2hvaWNlLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGcHRfbW9kZWwlMkZiZXJ0X3B5dG9yY2hfbW9kZWwuYmluJTIyJTJDJTIwZnJvbV9wdCUzRFRydWUlMkMlMjBjb25maWclM0Rjb25maWclMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMultipleChoice.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function vms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yTXVsdGlwbGVDaG9pY2UlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yTXVsdGlwbGVDaG9pY2UuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMultipleChoice.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function bms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMultipleChoice.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Mms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JOZXh0U2VudGVuY2VQcmVkaWN0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForNextSentencePrediction.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Tms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForNextSentencePrediction.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Fms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck5leHRTZW50ZW5jZVByZWRpY3Rpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvck5leHRTZW50ZW5jZVByZWRpY3Rpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForNextSentencePrediction.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ems(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForNextSentencePrediction.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function yms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JOZXh0U2VudGVuY2VQcmVkaWN0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForNextSentencePrediction.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Cms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForNextSentencePrediction.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function wms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Lms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFRGJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwUHlUb3JjaCUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnRmX21vZGVsJTJGYmVydF90Zl9jaGVja3BvaW50LmNrcHQuaW5kZXglMjIlMkMlMjBmcm9tX3RmJTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ams(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Rms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function kms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForTokenClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Sms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForTokenClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function xms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JRdWVzdGlvbkFuc3dlcmluZyUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForQuestionAnswering.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Gms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Bms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclF1ZXN0aW9uQW5zd2VyaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JRdWVzdGlvbkFuc3dlcmluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForQuestionAnswering.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ims(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Nms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yUXVlc3Rpb25BbnN3ZXJpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yUXVlc3Rpb25BbnN3ZXJpbmcuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForQuestionAnswering.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function $ms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function jms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JEZXB0aEVzdGltYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JEZXB0aEVzdGltYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForDepthEstimation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDepthEstimation.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Pms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JEZXB0aEVzdGltYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yRGVwdGhFc3RpbWF0aW9uLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckRlcHRoRXN0aW1hdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFRGJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwUHlUb3JjaCUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yRGVwdGhFc3RpbWF0aW9uLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX2NoZWNrcG9pbnQuY2twdC5pbmRleCUyMiUyQyUyMGZyb21fdGYlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForDepthEstimation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDepthEstimation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDepthEstimation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDepthEstimation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Zms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JJbWFnZUNsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9ySW1hZ2VDbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Vms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Wms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckltYWdlQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvckltYWdlQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForImageClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Jms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ums(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9ySW1hZ2VDbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JJbWFnZUNsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForImageClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Xms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Dms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JWaWRlb0NsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVmlkZW9DbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVideoClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVideoClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function qms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVideoClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVideoClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVideoClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVideoClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Oms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JNYXNrZWRJbWFnZU1vZGVsaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yTWFza2VkSW1hZ2VNb2RlbGluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMaskedImageModeling

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedImageModeling.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Qms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMaskedImageModeling

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedImageModeling.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedImageModeling.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedImageModeling.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Yms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck1hc2tlZEltYWdlTW9kZWxpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvck1hc2tlZEltYWdlTW9kZWxpbmcuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMaskedImageModeling

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedImageModeling.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function zms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMaskedImageModeling

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedImageModeling.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedImageModeling.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedImageModeling.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Hms(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JPYmplY3REZXRlY3Rpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JPYmplY3REZXRlY3Rpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForObjectDetection

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Kms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForObjectDetection

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function efs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JJbWFnZVNlZ21lbnRhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckltYWdlU2VnbWVudGF0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForImageSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageSegmentation.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function ofs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForImageSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function rfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTZW1hbnRpY1NlZ21lbnRhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclNlbWFudGljU2VnbWVudGF0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSemanticSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSemanticSegmentation.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function tfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSemanticSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSemanticSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function afs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNlbWFudGljU2VnbWVudGF0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JTZW1hbnRpY1NlZ21lbnRhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSemanticSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSemanticSegmentation.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function nfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSemanticSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSemanticSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function sfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JJbnN0YW5jZVNlZ21lbnRhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckluc3RhbmNlU2VnbWVudGF0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForInstanceSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForInstanceSegmentation.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function lfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForInstanceSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForInstanceSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForInstanceSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForInstanceSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function ifs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JVbml2ZXJzYWxTZWdtZW50YXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JVbml2ZXJzYWxTZWdtZW50YXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForUniversalSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForUniversalSegmentation.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function dfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForUniversalSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForUniversalSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForUniversalSegmentation.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForUniversalSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function cfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JaZXJvU2hvdEltYWdlQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JaZXJvU2hvdEltYWdlQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForZeroShotImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotImageClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function mfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForZeroShotImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function ffs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclplcm9TaG90SW1hZ2VDbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yWmVyb1Nob3RJbWFnZUNsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForZeroShotImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForZeroShotImageClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function hfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForZeroShotImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForZeroShotImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForZeroShotImageClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForZeroShotImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function gfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JaZXJvU2hvdE9iamVjdERldGVjdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclplcm9TaG90T2JqZWN0RGV0ZWN0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForZeroShotObjectDetection

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotObjectDetection.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function ufs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForZeroShotObjectDetection

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotObjectDetection.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotObjectDetection.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotObjectDetection.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function pfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JBdWRpb0NsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yQXVkaW9DbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function _fs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function vfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckF1ZGlvQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvckF1ZGlvQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForAudioClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForAudioClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function bfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForAudioClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForAudioClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Mfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JBdWRpb0ZyYW1lQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JBdWRpb0ZyYW1lQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioFrameClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioFrameClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Tfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioFrameClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioFrameClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioFrameClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioFrameClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ffs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JDVEMlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JDVEMuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForCTC

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Efs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForCTC

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function yfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTcGVlY2hTZXEyU2VxJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU3BlZWNoU2VxMlNlcS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSpeechSeq2Seq.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Cfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSpeechSeq2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function wfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNwZWVjaFNlcTJTZXElMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNwZWVjaFNlcTJTZXEuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSpeechSeq2Seq.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Lfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNwZWVjaFNlcTJTZXElMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JTcGVlY2hTZXEyU2VxLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yU3BlZWNoU2VxMlNlcS5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNwZWVjaFNlcTJTZXEuZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHl0b3JjaF9tb2RlbC5iaW4lMjIlMkMlMjBmcm9tX3B0JTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSpeechSeq2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Afs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yU3BlZWNoU2VxMlNlcSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JTcGVlY2hTZXEyU2VxLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSpeechSeq2Seq.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Rfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yU3BlZWNoU2VxMlNlcSUwQSUwQSUyMyUyMERvd25sb2FkJTIwbW9kZWwlMjBhbmQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yU3BlZWNoU2VxMlNlcS5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQSUwQSUyMyUyMFVwZGF0ZSUyMGNvbmZpZ3VyYXRpb24lMjBkdXJpbmclMjBsb2FkaW5nJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yU3BlZWNoU2VxMlNlcS5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yU3BlZWNoU2VxMlNlcS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnB0X21vZGVsJTJGYmVydF9weXRvcmNoX21vZGVsLmJpbiUyMiUyQyUyMGZyb21fcHQlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function kfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JBdWRpb1hWZWN0b3IlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JBdWRpb1hWZWN0b3IuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioXVector

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioXVector.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Sfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioXVector

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioXVector.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioXVector.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioXVector.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function xfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JUYWJsZVF1ZXN0aW9uQW5zd2VyaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlJTJGdGFwYXMtYmFzZS1maW5ldHVuZWQtd3RxJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVGFibGVRdWVzdGlvbkFuc3dlcmluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForTableQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTableQuestionAnswering.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Gfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForTableQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTableQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTableQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/tapas_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTableQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/tapas_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Bfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclRhYmxlUXVlc3Rpb25BbnN3ZXJpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUlMkZ0YXBhcy1iYXNlLWZpbmV0dW5lZC13dHElMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclRhYmxlUXVlc3Rpb25BbnN3ZXJpbmcuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForTableQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTableQuestionAnswering.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ifs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForTableQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTableQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTableQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/tapas_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTableQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/tapas_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Nfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JEb2N1bWVudFF1ZXN0aW9uQW5zd2VyaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyaW1waXJhJTJGbGF5b3V0bG0tZG9jdW1lbnQtcWElMjIlMkMlMjByZXZpc2lvbiUzRCUyMjUyZTAxYjMlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JEb2N1bWVudFF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForDocumentQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDocumentQuestionAnswering.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function $fs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForDocumentQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDocumentQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDocumentQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/layoutlm_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDocumentQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/layoutlm_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function jfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckRvY3VtZW50UXVlc3Rpb25BbnN3ZXJpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJpbXBpcmElMkZsYXlvdXRsbS1kb2N1bWVudC1xYSUyMiUyQyUyMHJldmlzaW9uJTNEJTIyNTJlMDFiMyUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yRG9jdW1lbnRRdWVzdGlvbkFuc3dlcmluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForDocumentQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForDocumentQuestionAnswering.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Pfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckRvY3VtZW50UXVlc3Rpb25BbnN3ZXJpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JEb2N1bWVudFF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fcHJldHJhaW5lZCglMjJpbXBpcmElMkZsYXlvdXRsbS1kb2N1bWVudC1xYSUyMiUyQyUyMHJldmlzaW9uJTNEJTIyNTJlMDFiMyUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JEb2N1bWVudFF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fcHJldHJhaW5lZCglMjJpbXBpcmElMkZsYXlvdXRsbS1kb2N1bWVudC1xYSUyMiUyQyUyMHJldmlzaW9uJTNEJTIyNTJlMDFiMyUyMiUyQyUyMG91dHB1dF9hdHRlbnRpb25zJTNEVHJ1ZSklMEFtb2RlbC5jb25maWcub3V0cHV0X2F0dGVudGlvbnMlMEElMEElMjMlMjBMb2FkaW5nJTIwZnJvbSUyMGElMjBQeVRvcmNoJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwVGVuc29yRmxvdyUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGcHRfbW9kZWwlMkZsYXlvdXRsbV9wdF9tb2RlbF9jb25maWcuanNvbiUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yRG9jdW1lbnRRdWVzdGlvbkFuc3dlcmluZy5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnB0X21vZGVsJTJGbGF5b3V0bG1fcHl0b3JjaF9tb2RlbC5iaW4lMjIlMkMlMjBmcm9tX3B0JTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForDocumentQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForDocumentQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForDocumentQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/layoutlm_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForDocumentQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/layoutlm_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Zfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JWaXN1YWxRdWVzdGlvbkFuc3dlcmluZyUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmRhbmRlbGluJTJGdmlsdC1iMzItZmluZXR1bmVkLXZxYSUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclZpc3VhbFF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVisualQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-finetuned-vqa&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVisualQuestionAnswering.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Vfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVisualQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVisualQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-finetuned-vqa&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVisualQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-finetuned-vqa&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/vilt_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVisualQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/vilt_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Wfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JWaXNpb24yU2VxJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVmlzaW9uMlNlcS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVision2Seq.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Jfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVision2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ufs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclZpc2lvbjJTZXElMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclZpc2lvbjJTZXEuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForVision2Seq.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Xfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForVision2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Dfs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yVmlzaW9uMlNlcSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JWaXNpb24yU2VxLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForVision2Seq.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function qfs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForVision2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:W,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ofs(x){let 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c=new te({}),In=new P({props:{code:"bW9kZWwlMjAlM0QlMjBBdXRvTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMik=",highlighted:'model = AutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)'}}),dV=new te({}),cV=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWwlMEElMEFBdXRvQ29uZmlnLnJlZ2lzdGVyKCUyMm5ldy1tb2RlbCUyMiUyQyUyME5ld01vZGVsQ29uZmlnKSUwQUF1dG9Nb2RlbC5yZWdpc3RlcihOZXdNb2RlbENvbmZpZyUyQyUyME5ld01vZGVsKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModel

AutoConfig.register(<span class="hljs-string">&quot;new-model&quot;</span>, NewModelConfig)
AutoModel.register(NewModelConfig, NewModel)`}}),Hg=new eZo({props:{warning:!0,$$slots:{default:[Ecs]},$$scope:{ctx:x}}}),mV=new te({}),fV=new G({props:{name:"class transformers.AutoConfig",anchor:"transformers.AutoConfig",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/configuration_auto.py#L896"}}),uV=new G({props:{name:"from_pretrained",anchor:"transformers.AutoConfig.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoConfig.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model configuration hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing a configuration file saved using the
<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.save_pretrained">save_pretrained()</a> method, or the <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> method,
e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a saved configuration JSON <em>file</em>, e.g.,
<code>./my_model_directory/configuration.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoConfig.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoConfig.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download the model weights and configuration files and override the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoConfig.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoConfig.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoConfig.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoConfig.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final configuration object.</p>
<p>If <code>True</code>, then this functions returns a <code>Tuple(config, unused_kwargs)</code> where <em>unused_kwargs</em> is a
dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
part of <code>kwargs</code> which has not been used to update <code>config</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoConfig.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoConfig.from_pretrained.kwargs(additional",description:`<strong>kwargs(additional</strong> keyword arguments, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are configuration attributes will be used to override the loaded
values. Behavior concerning key/value pairs whose keys are <em>not</em> configuration attributes is controlled
by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs(additional"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/configuration_auto.py#L919"}}),Jv=new V({props:{anchor:"transformers.AutoConfig.from_pretrained.example",$$slots:{default:[ycs]},$$scope:{ctx:x}}}),pV=new G({props:{name:"register",anchor:"transformers.AutoConfig.register",parameters:[{name:"model_type",val:""},{name:"config",val:""},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoConfig.register.model_type",description:"<strong>model_type</strong> (<code>str</code>) &#x2014; The model type like &#x201C;bert&#x201D; or &#x201C;gpt&#x201D;.",name:"model_type"},{anchor:"transformers.AutoConfig.register.config",description:'<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014; The config to register.',name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/configuration_auto.py#L1056"}}),_V=new te({}),vV=new G({props:{name:"class transformers.AutoTokenizer",anchor:"transformers.AutoTokenizer",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/tokenization_auto.py#L545"}}),TV=new G({props:{name:"from_pretrained",anchor:"transformers.AutoTokenizer.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"*inputs",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoTokenizer.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a predefined tokenizer hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing vocabulary files required by the tokenizer, for instance saved
using the <a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.save_pretrained">save_pretrained()</a> method, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a single saved vocabulary file if and only if the tokenizer only requires a
single vocabulary file (like Bert or XLNet), e.g.: <code>./my_model_directory/vocab.txt</code>. (Not
applicable to all derived classes)</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoTokenizer.from_pretrained.inputs",description:`<strong>inputs</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the Tokenizer <code>__init__()</code> method.`,name:"inputs"},{anchor:"transformers.AutoTokenizer.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
The configuration object used to determine the tokenizer class to instantiate.`,name:"config"},{anchor:"transformers.AutoTokenizer.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoTokenizer.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download the model weights and configuration files and override the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoTokenizer.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoTokenizer.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoTokenizer.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoTokenizer.from_pretrained.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>) &#x2014;
In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for
facebook/rag-token-base), specify it here.`,name:"subfolder"},{anchor:"transformers.AutoTokenizer.from_pretrained.use_fast",description:`<strong>use_fast</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Use a <a href="https://huggingface.co/docs/tokenizers/index" rel="nofollow">fast Rust-based tokenizer</a> if it is supported for
a given model. If a fast tokenizer is not available for a given model, a normal Python-based tokenizer
is returned instead.`,name:"use_fast"},{anchor:"transformers.AutoTokenizer.from_pretrained.tokenizer_type",description:`<strong>tokenizer_type</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Tokenizer type to be loaded.`,name:"tokenizer_type"},{anchor:"transformers.AutoTokenizer.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoTokenizer.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Will be passed to the Tokenizer <code>__init__()</code> method. Can be used to set special tokens like
<code>bos_token</code>, <code>eos_token</code>, <code>unk_token</code>, <code>sep_token</code>, <code>pad_token</code>, <code>cls_token</code>, <code>mask_token</code>,
<code>additional_special_tokens</code>. See parameters in the <code>__init__()</code> for more details.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/tokenization_auto.py#L559"}}),Z3=new V({props:{anchor:"transformers.AutoTokenizer.from_pretrained.example",$$slots:{default:[Ccs]},$$scope:{ctx:x}}}),FV=new G({props:{name:"register",anchor:"transformers.AutoTokenizer.register",parameters:[{name:"config_class",val:""},{name:"slow_tokenizer_class",val:" = None"},{name:"fast_tokenizer_class",val:" = None"},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoTokenizer.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoTokenizer.register.slow_tokenizer_class",description:`<strong>slow_tokenizer_class</strong> (<code>PretrainedTokenizer</code>, <em>optional</em>) &#x2014;
The slow tokenizer to register.`,name:"slow_tokenizer_class"},{anchor:"transformers.AutoTokenizer.register.slow_tokenizer_class",description:`<strong>slow_tokenizer_class</strong> (<code>PretrainedTokenizerFast</code>, <em>optional</em>) &#x2014;
The fast tokenizer to register.`,name:"slow_tokenizer_class"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/tokenization_auto.py#L769"}}),EV=new te({}),yV=new G({props:{name:"class transformers.AutoFeatureExtractor",anchor:"transformers.AutoFeatureExtractor",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/feature_extraction_auto.py#L233"}}),LV=new G({props:{name:"from_pretrained",anchor:"transformers.AutoFeatureExtractor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoFeatureExtractor.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>a path to a <em>directory</em> containing a feature extractor file saved using the
<a href="/docs/transformers/v4.33.0/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained">save_pretrained()</a> method, e.g.,
<code>./my_model_directory/</code>.</li>
<li>a path or url to a saved feature extractor JSON <em>file</em>, e.g.,
<code>./my_model_directory/preprocessor_config.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the feature extractor files and override the cached versions
if they exist.`,name:"force_download"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.`,name:"resume_download"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final feature extractor object. If <code>True</code>, then this
functions returns a <code>Tuple(feature_extractor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
<code>kwargs</code> which has not been used to update <code>feature_extractor</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are feature extractor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are <em>not</em> feature extractor attributes is
controlled by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/feature_extraction_auto.py#L247"}}),H1=new eZo({props:{$$slots:{default:[wcs]},$$scope:{ctx:x}}}),K1=new V({props:{anchor:"transformers.AutoFeatureExtractor.from_pretrained.example",$$slots:{default:[Lcs]},$$scope:{ctx:x}}}),AV=new G({props:{name:"register",anchor:"transformers.AutoFeatureExtractor.register",parameters:[{name:"config_class",val:""},{name:"feature_extractor_class",val:""},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoFeatureExtractor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoFeatureExtractor.register.feature_extractor_class",description:"<strong>feature_extractor_class</strong> (<code>FeatureExtractorMixin</code>) &#x2014; The feature extractor to register.",name:"feature_extractor_class"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/feature_extraction_auto.py#L381"}}),RV=new te({}),kV=new G({props:{name:"class transformers.AutoImageProcessor",anchor:"transformers.AutoImageProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/image_processing_auto.py#L241"}}),GV=new G({props:{name:"from_pretrained",anchor:"transformers.AutoImageProcessor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoImageProcessor.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained image_processor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>a path to a <em>directory</em> containing a image processor file saved using the
<a href="/docs/transformers/v4.33.0/en/internal/image_processing_utils#transformers.ImageProcessingMixin.save_pretrained">save_pretrained()</a> method, e.g.,
<code>./my_model_directory/</code>.</li>
<li>a path or url to a saved image processor JSON <em>file</em>, e.g.,
<code>./my_model_directory/preprocessor_config.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoImageProcessor.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model image processor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoImageProcessor.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the image processor files and override the cached versions if
they exist.`,name:"force_download"},{anchor:"transformers.AutoImageProcessor.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.`,name:"resume_download"},{anchor:"transformers.AutoImageProcessor.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoImageProcessor.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"transformers.AutoImageProcessor.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoImageProcessor.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final image processor object. If <code>True</code>, then this
functions returns a <code>Tuple(image_processor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
consisting of the key/value pairs whose keys are not image processor attributes: i.e., the part of
<code>kwargs</code> which has not been used to update <code>image_processor</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoImageProcessor.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoImageProcessor.from_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are image processor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are <em>not</em> image processor attributes is
controlled by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/image_processing_auto.py#L255"}}),v2=new eZo({props:{$$slots:{default:[Acs]},$$scope:{ctx:x}}}),b2=new V({props:{anchor:"transformers.AutoImageProcessor.from_pretrained.example",$$slots:{default:[Rcs]},$$scope:{ctx:x}}}),BV=new G({props:{name:"register",anchor:"transformers.AutoImageProcessor.register",parameters:[{name:"config_class",val:""},{name:"image_processor_class",val:""},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoImageProcessor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoImageProcessor.register.image_processor_class",description:'<strong>image_processor_class</strong> (<a href="/docs/transformers/v4.33.0/en/internal/image_processing_utils#transformers.ImageProcessingMixin">ImageProcessingMixin</a>) &#x2014; The image processor to register.',name:"image_processor_class"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/image_processing_auto.py#L407"}}),IV=new te({}),NV=new G({props:{name:"class transformers.AutoProcessor",anchor:"transformers.AutoProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/processing_auto.py#L118"}}),PV=new G({props:{name:"from_pretrained",anchor:"transformers.AutoProcessor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoProcessor.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>a path to a <em>directory</em> containing a processor files saved using the <code>save_pretrained()</code> method,
e.g., <code>./my_model_directory/</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoProcessor.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoProcessor.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the feature extractor files and override the cached versions
if they exist.`,name:"force_download"},{anchor:"transformers.AutoProcessor.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.`,name:"resume_download"},{anchor:"transformers.AutoProcessor.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoProcessor.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"transformers.AutoProcessor.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoProcessor.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final feature extractor object. If <code>True</code>, then this
functions returns a <code>Tuple(feature_extractor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
<code>kwargs</code> which has not been used to update <code>feature_extractor</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoProcessor.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoProcessor.from_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are feature extractor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are <em>not</em> feature extractor attributes is
controlled by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/processing_auto.py#L132"}}),c0=new eZo({props:{$$slots:{default:[kcs]},$$scope:{ctx:x}}}),m0=new V({props:{anchor:"transformers.AutoProcessor.from_pretrained.example",$$slots:{default:[Scs]},$$scope:{ctx:x}}}),ZV=new G({props:{name:"register",anchor:"transformers.AutoProcessor.register",parameters:[{name:"config_class",val:""},{name:"processor_class",val:""},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoProcessor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoProcessor.register.processor_class",description:"<strong>processor_class</strong> (<code>FeatureExtractorMixin</code>) &#x2014; The processor to register.",name:"processor_class"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/processing_auto.py#L321"}}),VV=new te({}),WV=new te({}),JV=new G({props:{name:"class transformers.AutoModel",anchor:"transformers.AutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1204"}}),XV=new G({props:{name:"from_config",anchor:"transformers.AutoModel.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModel.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/audio-spectrogram-transformer#transformers.ASTConfig">ASTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/audio-spectrogram-transformer#transformers.ASTModel">ASTModel</a> (Audio Spectrogram Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertModel">AlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/align#transformers.AlignConfig">AlignConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/align#transformers.AlignModel">AlignModel</a> (ALIGN model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/altclip#transformers.AltCLIPConfig">AltCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/autoformer#transformers.AutoformerConfig">AutoformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/autoformer#transformers.AutoformerModel">AutoformerModel</a> (Autoformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bark#transformers.BarkConfig">BarkConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bark#transformers.BarkModel">BarkModel</a> (Bark model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartModel">BartModel</a> (BART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/beit#transformers.BeitModel">BeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertModel">BertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert-generation#transformers.BertGenerationEncoder">BertGenerationEncoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdModel">BigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusModel">BigBirdPegasusModel</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/biogpt#transformers.BioGptModel">BioGptModel</a> (BioGpt model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bit#transformers.BitConfig">BitConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bit#transformers.BitModel">BitModel</a> (BiT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot#transformers.BlenderbotModel">BlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallModel">BlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blip-2#transformers.Blip2Model">Blip2Model</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bloom#transformers.BloomModel">BloomModel</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bridgetower#transformers.BridgeTowerConfig">BridgeTowerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bridgetower#transformers.BridgeTowerModel">BridgeTowerModel</a> (BridgeTower model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/clipseg#transformers.CLIPSegConfig">CLIPSegConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/clipseg#transformers.CLIPSegModel">CLIPSegModel</a> (CLIPSeg model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ctrl#transformers.CTRLModel">CTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertModel">CamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/canine#transformers.CanineModel">CanineModel</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/chinese_clip#transformers.ChineseCLIPConfig">ChineseCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/chinese_clip#transformers.ChineseCLIPModel">ChineseCLIPModel</a> (Chinese-CLIP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/clap#transformers.ClapConfig">ClapConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/clap#transformers.ClapModel">ClapModel</a> (CLAP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/codegen#transformers.CodeGenConfig">CodeGenConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/codegen#transformers.CodeGenModel">CodeGenModel</a> (CodeGen model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/conditional_detr#transformers.ConditionalDetrConfig">ConditionalDetrConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/conditional_detr#transformers.ConditionalDetrModel">ConditionalDetrModel</a> (Conditional DETR model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.ConvBertModel">ConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/convnext#transformers.ConvNextModel">ConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/convnextv2#transformers.ConvNextV2Config">ConvNextV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/convnextv2#transformers.ConvNextV2Model">ConvNextV2Model</a> (ConvNeXTV2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/cpmant#transformers.CpmAntConfig">CpmAntConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/cpmant#transformers.CpmAntModel">CpmAntModel</a> (CPM-Ant model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/cvt#transformers.CvtModel">CvtModel</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/dpt#transformers.DPTModel">DPTModel</a> (DPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecAudioModel">Data2VecAudioModel</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecTextModel">Data2VecTextModel</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecVisionModel">Data2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.DebertaModel">DebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.DebertaV2Model">DebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/decision_transformer#transformers.DecisionTransformerConfig">DecisionTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/decision_transformer#transformers.DecisionTransformerModel">DecisionTransformerModel</a> (Decision Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deformable_detr#transformers.DeformableDetrConfig">DeformableDetrConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deformable_detr#transformers.DeformableDetrModel">DeformableDetrModel</a> (Deformable DETR model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deit#transformers.DeiTModel">DeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deta#transformers.DetaConfig">DetaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deta#transformers.DetaModel">DetaModel</a> (DETA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/detr#transformers.DetrModel">DetrModel</a> (DETR model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/dinat#transformers.DinatConfig">DinatConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/dinat#transformers.DinatModel">DinatModel</a> (DiNAT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/dinov2#transformers.Dinov2Config">Dinov2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/dinov2#transformers.Dinov2Model">Dinov2Model</a> (DINOv2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/donut#transformers.DonutSwinConfig">DonutSwinConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/donut#transformers.DonutSwinModel">DonutSwinModel</a> (DonutSwin model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/efficientformer#transformers.EfficientFormerModel">EfficientFormerModel</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/efficientnet#transformers.EfficientNetConfig">EfficientNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/efficientnet#transformers.EfficientNetModel">EfficientNetModel</a> (EfficientNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraModel">ElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/encodec#transformers.EncodecConfig">EncodecConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/encodec#transformers.EncodecModel">EncodecModel</a> (EnCodec model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ernie#transformers.ErnieModel">ErnieModel</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ernie_m#transformers.ErnieMModel">ErnieMModel</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/esm#transformers.EsmModel">EsmModel</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/fnet#transformers.FNetModel">FNetModel</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/fsmt#transformers.FSMTModel">FSMTModel</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/falcon#transformers.FalconConfig">FalconConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/falcon#transformers.FalconModel">FalconModel</a> (Falcon model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertModel">FlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/flava#transformers.FlavaModel">FlavaModel</a> (FLAVA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/focalnet#transformers.FocalNetConfig">FocalNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/focalnet#transformers.FocalNetModel">FocalNetModel</a> (FocalNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelModel">FunnelModel</a> or <a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelBaseModel">FunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/glpn#transformers.GLPNModel">GLPNModel</a> (GLPN model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.GPT2Model">GPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeModel">GPTBigCodeModel</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.GPTJModel">GPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neo#transformers.GPTNeoModel">GPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neox#transformers.GPTNeoXModel">GPTNeoXModel</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseModel">GPTNeoXJapaneseModel</a> (GPT NeoX Japanese model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/git#transformers.GitConfig">GitConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/git#transformers.GitModel">GitModel</a> (GIT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/graphormer#transformers.GraphormerConfig">GraphormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/graphormer#transformers.GraphormerModel">GraphormerModel</a> (Graphormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.GroupViTModel">GroupViTModel</a> (GroupViT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/hubert#transformers.HubertModel">HubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ibert#transformers.IBertModel">IBertModel</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/idefics#transformers.IdeficsConfig">IdeficsConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/idefics#transformers.IdeficsModel">IdeficsModel</a> (IDEFICS model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/imagegpt#transformers.ImageGPTModel">ImageGPTModel</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/informer#transformers.InformerConfig">InformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/informer#transformers.InformerModel">InformerModel</a> (Informer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/jukebox#transformers.JukeboxConfig">JukeboxConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/jukebox#transformers.JukeboxModel">JukeboxModel</a> (Jukebox model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/led#transformers.LEDModel">LEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.LayoutLMModel">LayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2Model">LayoutLMv2Model</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Model">LayoutLMv3Model</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/levit#transformers.LevitModel">LevitModel</a> (LeViT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/lilt#transformers.LiltModel">LiltModel</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/llama2#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/llama2#transformers.LlamaModel">LlamaModel</a> (LLaMA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/longt5#transformers.LongT5Model">LongT5Model</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerModel">LongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/luke#transformers.LukeModel">LukeModel</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/lxmert#transformers.LxmertModel">LxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/m2m_100#transformers.M2M100Model">M2M100Model</a> (M2M100 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartModel">MBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mctct#transformers.MCTCTModel">MCTCTModel</a> (M-CTC-T model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetModel">MPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mt5#transformers.MT5Model">MT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/marian#transformers.MarianModel">MarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/markuplm#transformers.MarkupLMModel">MarkupLMModel</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mask2former#transformers.Mask2FormerConfig">Mask2FormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mask2former#transformers.Mask2FormerModel">Mask2FormerModel</a> (Mask2Former model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/maskformer#transformers.MaskFormerModel">MaskFormerModel</a> (MaskFormer model)</li>
<li><code>MaskFormerSwinConfig</code> configuration class: <code>MaskFormerSwinModel</code> (MaskFormerSwin model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mega#transformers.MegaModel">MegaModel</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/megatron-bert#transformers.MegatronBertModel">MegatronBertModel</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mgp-str#transformers.MgpstrConfig">MgpstrConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mgp-str#transformers.MgpstrForSceneTextRecognition">MgpstrForSceneTextRecognition</a> (MGP-STR model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertModel">MobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config">MobileNetV1Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilenet_v1#transformers.MobileNetV1Model">MobileNetV1Model</a> (MobileNetV1 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2Model">MobileNetV2Model</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilevit#transformers.MobileViTModel">MobileViTModel</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilevitv2#transformers.MobileViTV2Config">MobileViTV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilevitv2#transformers.MobileViTV2Model">MobileViTV2Model</a> (MobileViTV2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpt#transformers.MptModel">MptModel</a> (MPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mra#transformers.MraModel">MraModel</a> (MRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mvp#transformers.MvpModel">MvpModel</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/nat#transformers.NatConfig">NatConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/nat#transformers.NatModel">NatModel</a> (NAT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/nezha#transformers.NezhaModel">NezhaModel</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/nllb-moe#transformers.NllbMoeConfig">NllbMoeConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/nllb-moe#transformers.NllbMoeModel">NllbMoeModel</a> (NLLB-MOE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/nystromformer#transformers.NystromformerModel">NystromformerModel</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/opt#transformers.OPTModel">OPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/oneformer#transformers.OneFormerConfig">OneFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/oneformer#transformers.OneFormerModel">OneFormerModel</a> (OneFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/openai-gpt#transformers.OpenAIGPTModel">OpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/open-llama#transformers.OpenLlamaModel">OpenLlamaModel</a> (OpenLlama model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/owlvit#transformers.OwlViTModel">OwlViTModel</a> (OWL-ViT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/plbart#transformers.PLBartModel">PLBartModel</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/pegasus#transformers.PegasusModel">PegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/pegasus_x#transformers.PegasusXConfig">PegasusXConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/pegasus_x#transformers.PegasusXModel">PegasusXModel</a> (PEGASUS-X model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/perceiver#transformers.PerceiverModel">PerceiverModel</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/poolformer#transformers.PoolFormerModel">PoolFormerModel</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/prophetnet#transformers.ProphetNetModel">ProphetNetModel</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/pvt#transformers.PvtConfig">PvtConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/pvt#transformers.PvtModel">PvtModel</a> (PVT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/qdqbert#transformers.QDQBertModel">QDQBertModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/reformer#transformers.ReformerModel">ReformerModel</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/regnet#transformers.RegNetModel">RegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertModel">RemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/resnet#transformers.ResNetModel">ResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roc_bert#transformers.RoCBertModel">RoCBertModel</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerModel">RoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaModel">RobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormModel">RobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/rwkv#transformers.RwkvConfig">RwkvConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/rwkv#transformers.RwkvModel">RwkvModel</a> (RWKV model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/sew#transformers.SEWModel">SEWModel</a> (SEW model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/sew-d#transformers.SEWDModel">SEWDModel</a> (SEW-D model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/sam#transformers.SamConfig">SamConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/sam#transformers.SamModel">SamModel</a> (SAM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/segformer#transformers.SegformerModel">SegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/speech_to_text#transformers.Speech2TextModel">Speech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/speecht5#transformers.SpeechT5Config">SpeechT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/speecht5#transformers.SpeechT5Model">SpeechT5Model</a> (SpeechT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/splinter#transformers.SplinterModel">SplinterModel</a> (Splinter model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/squeezebert#transformers.SqueezeBertModel">SqueezeBertModel</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/swiftformer#transformers.SwiftFormerConfig">SwiftFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/swiftformer#transformers.SwiftFormerModel">SwiftFormerModel</a> (SwiftFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/swin2sr#transformers.Swin2SRConfig">Swin2SRConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/swin2sr#transformers.Swin2SRModel">Swin2SRModel</a> (Swin2SR model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/swin#transformers.SwinModel">SwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/swinv2#transformers.Swinv2Model">Swinv2Model</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/switch_transformers#transformers.SwitchTransformersModel">SwitchTransformersModel</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.T5Model">T5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/table-transformer#transformers.TableTransformerConfig">TableTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/table-transformer#transformers.TableTransformerModel">TableTransformerModel</a> (Table Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TapasModel">TapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerConfig">TimeSeriesTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerModel">TimeSeriesTransformerModel</a> (Time Series Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/timesformer#transformers.TimesformerModel">TimesformerModel</a> (TimeSformer model)</li>
<li><code>TimmBackboneConfig</code> configuration class: <code>TimmBackbone</code> (TimmBackbone model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerConfig">TrajectoryTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerModel">TrajectoryTransformerModel</a> (Trajectory Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/transfo-xl#transformers.TransfoXLModel">TransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/tvlt#transformers.TvltConfig">TvltConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/tvlt#transformers.TvltModel">TvltModel</a> (TVLT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/umt5#transformers.UMT5Model">UMT5Model</a> (UMT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/unispeech#transformers.UniSpeechModel">UniSpeechModel</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel">UniSpeechSatModel</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/van#transformers.VanModel">VanModel</a> (VAN model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vit#transformers.ViTModel">ViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vit_hybrid#transformers.ViTHybridConfig">ViTHybridConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vit_hybrid#transformers.ViTHybridModel">ViTHybridModel</a> (ViT Hybrid model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vit_mae#transformers.ViTMAEModel">ViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vit_msn#transformers.ViTMSNModel">ViTMSNModel</a> (ViTMSN model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/videomae#transformers.VideoMAEModel">VideoMAEModel</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vilt#transformers.ViltModel">ViltModel</a> (ViLT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderModel">VisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/visual_bert#transformers.VisualBertModel">VisualBertModel</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vitdet#transformers.VitDetConfig">VitDetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vitdet#transformers.VitDetModel">VitDetModel</a> (VitDet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vits#transformers.VitsConfig">VitsConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vits#transformers.VitsModel">VitsModel</a> (VITS model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vivit#transformers.VivitConfig">VivitConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vivit#transformers.VivitModel">VivitModel</a> (ViViT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Model">Wav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerModel">Wav2Vec2ConformerModel</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wavlm#transformers.WavLMModel">WavLMModel</a> (WavLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/whisper#transformers.WhisperModel">WhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xclip#transformers.XCLIPConfig">XCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xclip#transformers.XCLIPModel">XCLIPModel</a> (X-CLIP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xglm#transformers.XGLMModel">XGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMModel">XLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetModel">XLMProphetNetModel</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaModel">XLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLModel">XLMRobertaXLModel</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetModel">XLNetModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xmod#transformers.XmodModel">XmodModel</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/yolos#transformers.YolosModel">YolosModel</a> (YOLOS model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/yoso#transformers.YosoModel">YosoModel</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),u0=new V({props:{anchor:"transformers.AutoModel.from_config.example",$$slots:{default:[xcs]},$$scope:{ctx:x}}}),DV=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModel.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModel.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModel.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModel.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModel.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModel.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModel.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModel.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModel.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModel.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModel.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModel.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModel.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModel.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModel.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),qT=new V({props:{anchor:"transformers.AutoModel.from_pretrained.example",$$slots:{default:[Gcs]},$$scope:{ctx:x}}}),qV=new te({}),OV=new G({props:{name:"class transformers.TFAutoModel",anchor:"transformers.TFAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L527"}}),YV=new G({props:{name:"from_config",anchor:"transformers.TFAutoModel.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModel.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.TFAlbertModel">TFAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.TFBartModel">TFBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.TFBertModel">TFBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot#transformers.TFBlenderbotModel">TFBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallModel">TFBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blip#transformers.TFBlipModel">TFBlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ctrl#transformers.TFCTRLModel">TFCTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.TFCamembertModel">TFCamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.TFConvBertModel">TFConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/convnext#transformers.TFConvNextModel">TFConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/cvt#transformers.TFCvtModel">TFCvtModel</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.TFDPRQuestionEncoder">TFDPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.TFData2VecVisionModel">TFData2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.TFDebertaModel">TFDebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.TFDebertaV2Model">TFDebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deit#transformers.TFDeiTModel">TFDeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.TFDistilBertModel">TFDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/efficientformer#transformers.TFEfficientFormerModel">TFEfficientFormerModel</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.TFElectraModel">TFElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/esm#transformers.TFEsmModel">TFEsmModel</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.TFFlaubertModel">TFFlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.TFFunnelModel">TFFunnelModel</a> or <a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.TFFunnelBaseModel">TFFunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.TFGPT2Model">TFGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.TFGPTJModel">TFGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.TFGroupViTModel">TFGroupViTModel</a> (GroupViT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/hubert#transformers.TFHubertModel">TFHubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/led#transformers.TFLEDModel">TFLEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.TFLayoutLMModel">TFLayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3Model">TFLayoutLMv3Model</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.TFLongformerModel">TFLongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/lxmert#transformers.TFLxmertModel">TFLxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.TFMBartModel">TFMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.TFMPNetModel">TFMPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mt5#transformers.TFMT5Model">TFMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/marian#transformers.TFMarianModel">TFMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.TFMobileBertModel">TFMobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilevit#transformers.TFMobileViTModel">TFMobileViTModel</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/opt#transformers.TFOPTModel">TFOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/openai-gpt#transformers.TFOpenAIGPTModel">TFOpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/pegasus#transformers.TFPegasusModel">TFPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/regnet#transformers.TFRegNetModel">TFRegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.TFRemBertModel">TFRemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/resnet#transformers.TFResNetModel">TFResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.TFRoFormerModel">TFRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.TFRobertaModel">TFRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormModel">TFRobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/sam#transformers.SamConfig">SamConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/sam#transformers.TFSamModel">TFSamModel</a> (SAM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/segformer#transformers.TFSegformerModel">TFSegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/speech_to_text#transformers.TFSpeech2TextModel">TFSpeech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/swin#transformers.TFSwinModel">TFSwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.TFT5Model">TFT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TFTapasModel">TFTapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/transfo-xl#transformers.TFTransfoXLModel">TFTransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vit#transformers.TFViTModel">TFViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vit_mae#transformers.TFViTMAEModel">TFViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vision-text-dual-encoder#transformers.TFVisionTextDualEncoderModel">TFVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.TFWav2Vec2Model">TFWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/whisper#transformers.TFWhisperModel">TFWhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xglm#transformers.TFXGLMModel">TFXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.TFXLMModel">TFXLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaModel">TFXLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.TFXLNetModel">TFXLNetModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),QT=new V({props:{anchor:"transformers.TFAutoModel.from_config.example",$$slots:{default:[Bcs]},$$scope:{ctx:x}}}),zV=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModel.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModel.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModel.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModel.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModel.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModel.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModel.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModel.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModel.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModel.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModel.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModel.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModel.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModel.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),nE=new V({props:{anchor:"transformers.TFAutoModel.from_pretrained.example",$$slots:{default:[Ics]},$$scope:{ctx:x}}}),HV=new te({}),KV=new G({props:{name:"class transformers.FlaxAutoModel",anchor:"transformers.FlaxAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_flax_auto.py#L274"}}),oW=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModel.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModel.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.FlaxAlbertModel">FlaxAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.FlaxBartModel">FlaxBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/beit#transformers.FlaxBeitModel">FlaxBeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.FlaxBertModel">FlaxBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.FlaxBigBirdModel">FlaxBigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot#transformers.FlaxBlenderbotModel">FlaxBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallModel">FlaxBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bloom#transformers.FlaxBloomModel">FlaxBloomModel</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/clip#transformers.FlaxCLIPModel">FlaxCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.FlaxDistilBertModel">FlaxDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.FlaxElectraModel">FlaxElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.FlaxGPT2Model">FlaxGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.FlaxGPTJModel">FlaxGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neo#transformers.FlaxGPTNeoModel">FlaxGPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/longt5#transformers.FlaxLongT5Model">FlaxLongT5Model</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.FlaxMBartModel">FlaxMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mt5#transformers.FlaxMT5Model">FlaxMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/marian#transformers.FlaxMarianModel">FlaxMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/opt#transformers.FlaxOPTModel">FlaxOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/pegasus#transformers.FlaxPegasusModel">FlaxPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/regnet#transformers.FlaxRegNetModel">FlaxRegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/resnet#transformers.FlaxResNetModel">FlaxResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.FlaxRoFormerModel">FlaxRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.FlaxRobertaModel">FlaxRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormModel">FlaxRobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.FlaxT5Model">FlaxT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vit#transformers.FlaxViTModel">FlaxViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vision-text-dual-encoder#transformers.FlaxVisionTextDualEncoderModel">FlaxVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2Model">FlaxWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/whisper#transformers.FlaxWhisperModel">FlaxWhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xglm#transformers.FlaxXGLMModel">FlaxXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaModel">FlaxXLMRobertaModel</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),lE=new V({props:{anchor:"transformers.FlaxAutoModel.from_config.example",$$slots:{default:[Ncs]},$$scope:{ctx:x}}}),rW=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModel.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModel.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModel.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModel.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModel.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModel.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModel.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModel.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModel.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModel.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModel.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModel.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModel.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModel.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),VE=new V({props:{anchor:"transformers.FlaxAutoModel.from_pretrained.example",$$slots:{default:[$cs]},$$scope:{ctx:x}}}),tW=new te({}),aW=new te({}),nW=new G({props:{name:"class transformers.AutoModelForPreTraining",anchor:"transformers.AutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1211"}}),lW=new G({props:{name:"from_config",anchor:"transformers.AutoModelForPreTraining.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForPreTraining.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertForPreTraining">AlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertForPreTraining">BertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdForPreTraining">BigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraForPreTraining">ElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ernie#transformers.ErnieForPreTraining">ErnieForPreTraining</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/fnet#transformers.FNetForPreTraining">FNetForPreTraining</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/flava#transformers.FlavaForPreTraining">FlavaForPreTraining</a> (FLAVA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelForPreTraining">FunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForCausalLM">GPTBigCodeForCausalLM</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/idefics#transformers.IdeficsConfig">IdeficsConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/idefics#transformers.IdeficsForVisionText2Text">IdeficsForVisionText2Text</a> (IDEFICS model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/lxmert#transformers.LxmertForPreTraining">LxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mega#transformers.MegaForMaskedLM">MegaForMaskedLM</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/megatron-bert#transformers.MegatronBertForPreTraining">MegatronBertForPreTraining</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertForPreTraining">MobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpt#transformers.MptForCausalLM">MptForCausalLM</a> (MPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mra#transformers.MraForMaskedLM">MraForMaskedLM</a> (MRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/nezha#transformers.NezhaForPreTraining">NezhaForPreTraining</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/nllb-moe#transformers.NllbMoeConfig">NllbMoeConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/nllb-moe#transformers.NllbMoeForConditionalGeneration">NllbMoeForConditionalGeneration</a> (NLLB-MOE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roc_bert#transformers.RoCBertForPreTraining">RoCBertForPreTraining</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMaskedLM">RobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/rwkv#transformers.RwkvConfig">RwkvConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/rwkv#transformers.RwkvForCausalLM">RwkvForCausalLM</a> (RWKV model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/splinter#transformers.SplinterForPreTraining">SplinterForPreTraining</a> (Splinter model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/switch_transformers#transformers.SwitchTransformersForConditionalGeneration">SwitchTransformersForConditionalGeneration</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/tvlt#transformers.TvltConfig">TvltConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/tvlt#transformers.TvltForPreTraining">TvltForPreTraining</a> (TVLT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/unispeech#transformers.UniSpeechForPreTraining">UniSpeechForPreTraining</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatForPreTraining">UniSpeechSatForPreTraining</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vit_mae#transformers.ViTMAEForPreTraining">ViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/videomae#transformers.VideoMAEForPreTraining">VideoMAEForPreTraining</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/visual_bert#transformers.VisualBertForPreTraining">VisualBertForPreTraining</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.Wav2Vec2ForPreTraining">Wav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForPreTraining">Wav2Vec2ConformerForPreTraining</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xmod#transformers.XmodForMaskedLM">XmodForMaskedLM</a> (X-MOD model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),UE=new V({props:{anchor:"transformers.AutoModelForPreTraining.from_config.example",$$slots:{default:[jcs]},$$scope:{ctx:x}}}),iW=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForPreTraining.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForPreTraining.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),e5=new V({props:{anchor:"transformers.AutoModelForPreTraining.from_pretrained.example",$$slots:{default:[Pcs]},$$scope:{ctx:x}}}),dW=new te({}),cW=new G({props:{name:"class transformers.TFAutoModelForPreTraining",anchor:"transformers.TFAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L543"}}),fW=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForPreTraining.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForPreTraining.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.TFAlbertForPreTraining">TFAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.TFBertForPreTraining">TFBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.TFElectraForPreTraining">TFElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.TFFunnelForPreTraining">TFFunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/lxmert#transformers.TFLxmertForPreTraining">TFLxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.TFMobileBertForPreTraining">TFMobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMaskedLM">TFRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vit_mae#transformers.TFViTMAEForPreTraining">TFViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),r5=new V({props:{anchor:"transformers.TFAutoModelForPreTraining.from_config.example",$$slots:{default:[Zcs]},$$scope:{ctx:x}}}),hW=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForPreTraining.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),A5=new V({props:{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[Vcs]},$$scope:{ctx:x}}}),gW=new te({}),uW=new G({props:{name:"class transformers.FlaxAutoModelForPreTraining",anchor:"transformers.FlaxAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_flax_auto.py#L281"}}),_W=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForPreTraining.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.FlaxAlbertForPreTraining">FlaxAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.FlaxBertForPreTraining">FlaxBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.FlaxBigBirdForPreTraining">FlaxBigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.FlaxElectraForPreTraining">FlaxElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMaskedLM">FlaxRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2ForPreTraining">FlaxWav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),k5=new V({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.example",$$slots:{default:[Wcs]},$$scope:{ctx:x}}}),vW=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),D5=new V({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[Jcs]},$$scope:{ctx:x}}}),bW=new te({}),MW=new te({}),TW=new G({props:{name:"class transformers.AutoModelForCausalLM",anchor:"transformers.AutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1226"}}),EW=new G({props:{name:"from_config",anchor:"transformers.AutoModelForCausalLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCausalLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartForCausalLM">BartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertLMHeadModel">BertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert-generation#transformers.BertGenerationDecoder">BertGenerationDecoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdForCausalLM">BigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForCausalLM">BigBirdPegasusForCausalLM</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/biogpt#transformers.BioGptForCausalLM">BioGptForCausalLM</a> (BioGpt model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot#transformers.BlenderbotForCausalLM">BlenderbotForCausalLM</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForCausalLM">BlenderbotSmallForCausalLM</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertForCausalLM">CamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/codegen#transformers.CodeGenConfig">CodeGenConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/codegen#transformers.CodeGenForCausalLM">CodeGenForCausalLM</a> (CodeGen model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/cpmant#transformers.CpmAntConfig">CpmAntConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/cpmant#transformers.CpmAntForCausalLM">CpmAntForCausalLM</a> (CPM-Ant model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecTextForCausalLM">Data2VecTextForCausalLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraForCausalLM">ElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ernie#transformers.ErnieForCausalLM">ErnieForCausalLM</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/falcon#transformers.FalconConfig">FalconConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/falcon#transformers.FalconForCausalLM">FalconForCausalLM</a> (Falcon model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForCausalLM">GPTBigCodeForCausalLM</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.GPTJForCausalLM">GPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neo#transformers.GPTNeoForCausalLM">GPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neox#transformers.GPTNeoXForCausalLM">GPTNeoXForCausalLM</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseForCausalLM">GPTNeoXJapaneseForCausalLM</a> (GPT NeoX Japanese model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/git#transformers.GitConfig">GitConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/git#transformers.GitForCausalLM">GitForCausalLM</a> (GIT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/llama2#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/llama2#transformers.LlamaForCausalLM">LlamaForCausalLM</a> (LLaMA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartForCausalLM">MBartForCausalLM</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/marian#transformers.MarianForCausalLM">MarianForCausalLM</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mega#transformers.MegaForCausalLM">MegaForCausalLM</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/megatron-bert#transformers.MegatronBertForCausalLM">MegatronBertForCausalLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpt#transformers.MptForCausalLM">MptForCausalLM</a> (MPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/musicgen#transformers.MusicgenConfig">MusicgenConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/musicgen#transformers.MusicgenForCausalLM">MusicgenForCausalLM</a> (MusicGen model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mvp#transformers.MvpForCausalLM">MvpForCausalLM</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/opt#transformers.OPTForCausalLM">OPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/open-llama#transformers.OpenLlamaForCausalLM">OpenLlamaForCausalLM</a> (OpenLlama model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/plbart#transformers.PLBartForCausalLM">PLBartForCausalLM</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/pegasus#transformers.PegasusForCausalLM">PegasusForCausalLM</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/prophetnet#transformers.ProphetNetForCausalLM">ProphetNetForCausalLM</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/qdqbert#transformers.QDQBertLMHeadModel">QDQBertLMHeadModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/reformer#transformers.ReformerModelWithLMHead">ReformerModelWithLMHead</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertForCausalLM">RemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roc_bert#transformers.RoCBertForCausalLM">RoCBertForCausalLM</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerForCausalLM">RoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaForCausalLM">RobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForCausalLM">RobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/rwkv#transformers.RwkvConfig">RwkvConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/rwkv#transformers.RwkvForCausalLM">RwkvForCausalLM</a> (RWKV model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/speech_to_text_2#transformers.Speech2Text2Config">Speech2Text2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/speech_to_text_2#transformers.Speech2Text2ForCausalLM">Speech2Text2ForCausalLM</a> (Speech2Text2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/trocr#transformers.TrOCRConfig">TrOCRConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/trocr#transformers.TrOCRForCausalLM">TrOCRForCausalLM</a> (TrOCR model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xglm#transformers.XGLMForCausalLM">XGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForCausalLM">XLMProphetNetForCausalLM</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForCausalLM">XLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForCausalLM">XLMRobertaXLForCausalLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xmod#transformers.XmodForCausalLM">XmodForCausalLM</a> (X-MOD model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),Q5=new V({props:{anchor:"transformers.AutoModelForCausalLM.from_config.example",$$slots:{default:[Ucs]},$$scope:{ctx:x}}}),yW=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForCausalLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCausalLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),oC=new V({props:{anchor:"transformers.AutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Xcs]},$$scope:{ctx:x}}}),CW=new te({}),wW=new G({props:{name:"class transformers.TFAutoModelForCausalLM",anchor:"transformers.TFAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L558"}}),AW=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForCausalLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForCausalLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.TFBertLMHeadModel">TFBertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.TFCamembertForCausalLM">TFCamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.TFGPTJForCausalLM">TFGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/opt#transformers.TFOPTForCausalLM">TFOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.TFRemBertForCausalLM">TFRemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.TFRoFormerForCausalLM">TFRoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.TFRobertaForCausalLM">TFRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForCausalLM">TFRobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xglm#transformers.TFXGLMForCausalLM">TFXGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForCausalLM">TFXLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),tC=new V({props:{anchor:"transformers.TFAutoModelForCausalLM.from_config.example",$$slots:{default:[Dcs]},$$scope:{ctx:x}}}),RW=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForCausalLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),TC=new V({props:{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[qcs]},$$scope:{ctx:x}}}),kW=new te({}),SW=new G({props:{name:"class transformers.FlaxAutoModelForCausalLM",anchor:"transformers.FlaxAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_flax_auto.py#L288"}}),GW=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForCausalLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.FlaxBartForCausalLM">FlaxBartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.FlaxBertForCausalLM">FlaxBertForCausalLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.FlaxBigBirdForCausalLM">FlaxBigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bloom#transformers.FlaxBloomForCausalLM">FlaxBloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.FlaxElectraForCausalLM">FlaxElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.FlaxGPT2LMHeadModel">FlaxGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.FlaxGPTJForCausalLM">FlaxGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neo#transformers.FlaxGPTNeoForCausalLM">FlaxGPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/opt#transformers.FlaxOPTForCausalLM">FlaxOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.FlaxRobertaForCausalLM">FlaxRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForCausalLM">FlaxRobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xglm#transformers.FlaxXGLMForCausalLM">FlaxXGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForCausalLM">FlaxXLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),EC=new V({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.example",$$slots:{default:[Ocs]},$$scope:{ctx:x}}}),BW=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),jC=new V({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Qcs]},$$scope:{ctx:x}}}),IW=new te({}),NW=new G({props:{name:"class transformers.AutoModelForMaskedLM",anchor:"transformers.AutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1233"}}),jW=new G({props:{name:"from_config",anchor:"transformers.AutoModelForMaskedLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertForMaskedLM">AlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertForMaskedLM">BertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdForMaskedLM">BigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.ConvBertForMaskedLM">ConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraForMaskedLM">ElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ernie#transformers.ErnieForMaskedLM">ErnieForMaskedLM</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/esm#transformers.EsmForMaskedLM">EsmForMaskedLM</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/fnet#transformers.FNetForMaskedLM">FNetForMaskedLM</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelForMaskedLM">FunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mega#transformers.MegaForMaskedLM">MegaForMaskedLM</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/megatron-bert#transformers.MegatronBertForMaskedLM">MegatronBertForMaskedLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertForMaskedLM">MobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mra#transformers.MraForMaskedLM">MraForMaskedLM</a> (MRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/nezha#transformers.NezhaForMaskedLM">NezhaForMaskedLM</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/nystromformer#transformers.NystromformerForMaskedLM">NystromformerForMaskedLM</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/perceiver#transformers.PerceiverForMaskedLM">PerceiverForMaskedLM</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/qdqbert#transformers.QDQBertForMaskedLM">QDQBertForMaskedLM</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/reformer#transformers.ReformerForMaskedLM">ReformerForMaskedLM</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertForMaskedLM">RemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roc_bert#transformers.RoCBertForMaskedLM">RoCBertForMaskedLM</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerForMaskedLM">RoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMaskedLM">RobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <code>Wav2Vec2ForMaskedLM</code> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xmod#transformers.XmodForMaskedLM">XmodForMaskedLM</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/yoso#transformers.YosoForMaskedLM">YosoForMaskedLM</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),ZC=new V({props:{anchor:"transformers.AutoModelForMaskedLM.from_config.example",$$slots:{default:[Ycs]},$$scope:{ctx:x}}}),PW=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForMaskedLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),Gw=new V({props:{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[zcs]},$$scope:{ctx:x}}}),ZW=new te({}),VW=new G({props:{name:"class transformers.TFAutoModelForMaskedLM",anchor:"transformers.TFAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L608"}}),JW=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForMaskedLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMaskedLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.TFAlbertForMaskedLM">TFAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.TFBertForMaskedLM">TFBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.TFConvBertForMaskedLM">TFConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.TFDebertaForMaskedLM">TFDebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.TFDebertaV2ForMaskedLM">TFDebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.TFElectraForMaskedLM">TFElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/esm#transformers.TFEsmForMaskedLM">TFEsmForMaskedLM</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.TFFunnelForMaskedLM">TFFunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.TFLongformerForMaskedLM">TFLongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.TFMobileBertForMaskedLM">TFMobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.TFRemBertForMaskedLM">TFRemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.TFRoFormerForMaskedLM">TFRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMaskedLM">TFRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),Iw=new V({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_config.example",$$slots:{default:[Hcs]},$$scope:{ctx:x}}}),UW=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),a6=new V({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[Kcs]},$$scope:{ctx:x}}}),XW=new te({}),DW=new G({props:{name:"class transformers.FlaxAutoModelForMaskedLM",anchor:"transformers.FlaxAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_flax_auto.py#L295"}}),OW=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForMaskedLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.FlaxAlbertForMaskedLM">FlaxAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.FlaxBertForMaskedLM">FlaxBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.FlaxBigBirdForMaskedLM">FlaxBigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.FlaxDistilBertForMaskedLM">FlaxDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.FlaxElectraForMaskedLM">FlaxElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMaskedLM">FlaxRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),s6=new V({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.example",$$slots:{default:[ems]},$$scope:{ctx:x}}}),QW=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),v6=new V({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[oms]},$$scope:{ctx:x}}}),YW=new te({}),HW=new G({props:{name:"class transformers.AutoModelForMaskGeneration",anchor:"transformers.AutoModelForMaskGeneration",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1196"}}),KW=new te({}),oJ=new G({props:{name:"class transformers.TFAutoModelForMaskGeneration",anchor:"transformers.TFAutoModelForMaskGeneration",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L519"}}),rJ=new te({}),tJ=new G({props:{name:"class transformers.AutoModelForSeq2SeqLM",anchor:"transformers.AutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1240"}}),nJ=new G({props:{name:"from_config",anchor:"transformers.AutoModelForSeq2SeqLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForConditionalGeneration">BigBirdPegasusForConditionalGeneration</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot#transformers.BlenderbotForConditionalGeneration">BlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForConditionalGeneration">BlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/encoder-decoder#transformers.EncoderDecoderModel">EncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/led#transformers.LEDForConditionalGeneration">LEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/longt5#transformers.LongT5ForConditionalGeneration">LongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/m2m_100#transformers.M2M100ForConditionalGeneration">M2M100ForConditionalGeneration</a> (M2M100 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mt5#transformers.MT5ForConditionalGeneration">MT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/marian#transformers.MarianMTModel">MarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/nllb-moe#transformers.NllbMoeConfig">NllbMoeConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/nllb-moe#transformers.NllbMoeForConditionalGeneration">NllbMoeForConditionalGeneration</a> (NLLB-MOE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/plbart#transformers.PLBartForConditionalGeneration">PLBartForConditionalGeneration</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/pegasus#transformers.PegasusForConditionalGeneration">PegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/pegasus_x#transformers.PegasusXConfig">PegasusXConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/pegasus_x#transformers.PegasusXForConditionalGeneration">PegasusXForConditionalGeneration</a> (PEGASUS-X model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/prophetnet#transformers.ProphetNetForConditionalGeneration">ProphetNetForConditionalGeneration</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/switch_transformers#transformers.SwitchTransformersForConditionalGeneration">SwitchTransformersForConditionalGeneration</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/umt5#transformers.UMT5ForConditionalGeneration">UMT5ForConditionalGeneration</a> (UMT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForConditionalGeneration">XLMProphetNetForConditionalGeneration</a> (XLM-ProphetNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),F6=new V({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[rms]},$$scope:{ctx:x}}}),sJ=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),q6=new V({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[tms]},$$scope:{ctx:x}}}),lJ=new te({}),iJ=new G({props:{name:"class transformers.TFAutoModelForSeq2SeqLM",anchor:"transformers.TFAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L615"}}),cJ=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot#transformers.TFBlenderbotForConditionalGeneration">TFBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallForConditionalGeneration">TFBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/encoder-decoder#transformers.TFEncoderDecoderModel">TFEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/led#transformers.TFLEDForConditionalGeneration">TFLEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.TFMBartForConditionalGeneration">TFMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mt5#transformers.TFMT5ForConditionalGeneration">TFMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/marian#transformers.TFMarianMTModel">TFMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/pegasus#transformers.TFPegasusForConditionalGeneration">TFPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),Q6=new V({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[ams]},$$scope:{ctx:x}}}),mJ=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),s8=new V({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[nms]},$$scope:{ctx:x}}}),fJ=new te({}),hJ=new G({props:{name:"class transformers.FlaxAutoModelForSeq2SeqLM",anchor:"transformers.FlaxAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_flax_auto.py#L302"}}),uJ=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot#transformers.FlaxBlenderbotForConditionalGeneration">FlaxBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallForConditionalGeneration">FlaxBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/encoder-decoder#transformers.FlaxEncoderDecoderModel">FlaxEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/marian#transformers.FlaxMarianMTModel">FlaxMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/pegasus#transformers.FlaxPegasusForConditionalGeneration">FlaxPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),i8=new V({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[sms]},$$scope:{ctx:x}}}),pJ=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),b8=new V({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[lms]},$$scope:{ctx:x}}}),_J=new te({}),vJ=new G({props:{name:"class transformers.AutoModelForSequenceClassification",anchor:"transformers.AutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1249"}}),MJ=new G({props:{name:"from_config",anchor:"transformers.AutoModelForSequenceClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSequenceClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertForSequenceClassification">AlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartForSequenceClassification">BartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertForSequenceClassification">BertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdForSequenceClassification">BigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForSequenceClassification">BigBirdPegasusForSequenceClassification</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/biogpt#transformers.BioGptForSequenceClassification">BioGptForSequenceClassification</a> (BioGpt model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bloom#transformers.BloomForSequenceClassification">BloomForSequenceClassification</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ctrl#transformers.CTRLForSequenceClassification">CTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertForSequenceClassification">CamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/canine#transformers.CanineForSequenceClassification">CanineForSequenceClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.ConvBertForSequenceClassification">ConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecTextForSequenceClassification">Data2VecTextForSequenceClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.DebertaForSequenceClassification">DebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.DebertaV2ForSequenceClassification">DebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraForSequenceClassification">ElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ernie#transformers.ErnieForSequenceClassification">ErnieForSequenceClassification</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ernie_m#transformers.ErnieMForSequenceClassification">ErnieMForSequenceClassification</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/esm#transformers.EsmForSequenceClassification">EsmForSequenceClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/fnet#transformers.FNetForSequenceClassification">FNetForSequenceClassification</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/falcon#transformers.FalconConfig">FalconConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/falcon#transformers.FalconForSequenceClassification">FalconForSequenceClassification</a> (Falcon model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertForSequenceClassification">FlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelForSequenceClassification">FunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.GPT2ForSequenceClassification">GPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForSequenceClassification">GPTBigCodeForSequenceClassification</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.GPTJForSequenceClassification">GPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neo#transformers.GPTNeoForSequenceClassification">GPTNeoForSequenceClassification</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neox#transformers.GPTNeoXForSequenceClassification">GPTNeoXForSequenceClassification</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ibert#transformers.IBertForSequenceClassification">IBertForSequenceClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/led#transformers.LEDForSequenceClassification">LEDForSequenceClassification</a> (LED model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.LayoutLMForSequenceClassification">LayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForSequenceClassification">LayoutLMv2ForSequenceClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForSequenceClassification">LayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/lilt#transformers.LiltForSequenceClassification">LiltForSequenceClassification</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/llama2#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/llama2#transformers.LlamaForSequenceClassification">LlamaForSequenceClassification</a> (LLaMA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerForSequenceClassification">LongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/luke#transformers.LukeForSequenceClassification">LukeForSequenceClassification</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartForSequenceClassification">MBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetForSequenceClassification">MPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mt5#transformers.MT5ForSequenceClassification">MT5ForSequenceClassification</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/markuplm#transformers.MarkupLMForSequenceClassification">MarkupLMForSequenceClassification</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mega#transformers.MegaForSequenceClassification">MegaForSequenceClassification</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/megatron-bert#transformers.MegatronBertForSequenceClassification">MegatronBertForSequenceClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertForSequenceClassification">MobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpt#transformers.MptForSequenceClassification">MptForSequenceClassification</a> (MPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mra#transformers.MraForSequenceClassification">MraForSequenceClassification</a> (MRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mvp#transformers.MvpForSequenceClassification">MvpForSequenceClassification</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/nezha#transformers.NezhaForSequenceClassification">NezhaForSequenceClassification</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/nystromformer#transformers.NystromformerForSequenceClassification">NystromformerForSequenceClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/opt#transformers.OPTForSequenceClassification">OPTForSequenceClassification</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/openai-gpt#transformers.OpenAIGPTForSequenceClassification">OpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/open-llama#transformers.OpenLlamaForSequenceClassification">OpenLlamaForSequenceClassification</a> (OpenLlama model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/plbart#transformers.PLBartForSequenceClassification">PLBartForSequenceClassification</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/perceiver#transformers.PerceiverForSequenceClassification">PerceiverForSequenceClassification</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/qdqbert#transformers.QDQBertForSequenceClassification">QDQBertForSequenceClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/reformer#transformers.ReformerForSequenceClassification">ReformerForSequenceClassification</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertForSequenceClassification">RemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roc_bert#transformers.RoCBertForSequenceClassification">RoCBertForSequenceClassification</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerForSequenceClassification">RoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaForSequenceClassification">RobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForSequenceClassification">RobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/squeezebert#transformers.SqueezeBertForSequenceClassification">SqueezeBertForSequenceClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.T5ForSequenceClassification">T5ForSequenceClassification</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TapasForSequenceClassification">TapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/transfo-xl#transformers.TransfoXLForSequenceClassification">TransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/umt5#transformers.UMT5ForSequenceClassification">UMT5ForSequenceClassification</a> (UMT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMForSequenceClassification">XLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForSequenceClassification">XLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForSequenceClassification">XLMRobertaXLForSequenceClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetForSequenceClassification">XLNetForSequenceClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xmod#transformers.XmodForSequenceClassification">XmodForSequenceClassification</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/yoso#transformers.YosoForSequenceClassification">YosoForSequenceClassification</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),T8=new V({props:{anchor:"transformers.AutoModelForSequenceClassification.from_config.example",$$slots:{default:[ims]},$$scope:{ctx:x}}}),TJ=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSequenceClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),J7=new V({props:{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[dms]},$$scope:{ctx:x}}}),FJ=new te({}),EJ=new G({props:{name:"class transformers.TFAutoModelForSequenceClassification",anchor:"transformers.TFAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L624"}}),CJ=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForSequenceClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.TFAlbertForSequenceClassification">TFAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.TFBartForSequenceClassification">TFBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.TFBertForSequenceClassification">TFBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ctrl#transformers.TFCTRLForSequenceClassification">TFCTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.TFCamembertForSequenceClassification">TFCamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.TFConvBertForSequenceClassification">TFConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.TFDebertaForSequenceClassification">TFDebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.TFDebertaV2ForSequenceClassification">TFDebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.TFDistilBertForSequenceClassification">TFDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.TFElectraForSequenceClassification">TFElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/esm#transformers.TFEsmForSequenceClassification">TFEsmForSequenceClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.TFFlaubertForSequenceClassification">TFFlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.TFFunnelForSequenceClassification">TFFunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.TFGPT2ForSequenceClassification">TFGPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.TFGPTJForSequenceClassification">TFGPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.TFLayoutLMForSequenceClassification">TFLayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForSequenceClassification">TFLayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.TFLongformerForSequenceClassification">TFLongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.TFMPNetForSequenceClassification">TFMPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.TFMobileBertForSequenceClassification">TFMobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/openai-gpt#transformers.TFOpenAIGPTForSequenceClassification">TFOpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.TFRemBertForSequenceClassification">TFRemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.TFRoFormerForSequenceClassification">TFRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.TFRobertaForSequenceClassification">TFRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForSequenceClassification">TFRobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TFTapasForSequenceClassification">TFTapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/transfo-xl#transformers.TFTransfoXLForSequenceClassification">TFTransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.TFXLMForSequenceClassification">TFXLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForSequenceClassification">TFXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.TFXLNetForSequenceClassification">TFXLNetForSequenceClassification</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),X7=new V({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.example",$$slots:{default:[cms]},$$scope:{ctx:x}}}),wJ=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),EL=new V({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[mms]},$$scope:{ctx:x}}}),LJ=new te({}),AJ=new G({props:{name:"class transformers.FlaxAutoModelForSequenceClassification",anchor:"transformers.FlaxAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_flax_auto.py#L311"}}),kJ=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.FlaxAlbertForSequenceClassification">FlaxAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.FlaxBartForSequenceClassification">FlaxBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.FlaxBertForSequenceClassification">FlaxBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.FlaxBigBirdForSequenceClassification">FlaxBigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.FlaxDistilBertForSequenceClassification">FlaxDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.FlaxElectraForSequenceClassification">FlaxElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.FlaxMBartForSequenceClassification">FlaxMBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.FlaxRoFormerForSequenceClassification">FlaxRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.FlaxRobertaForSequenceClassification">FlaxRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForSequenceClassification">FlaxRobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForSequenceClassification">FlaxXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),CL=new V({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.example",$$slots:{default:[fms]},$$scope:{ctx:x}}}),SJ=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),$L=new V({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[hms]},$$scope:{ctx:x}}}),xJ=new te({}),GJ=new G({props:{name:"class transformers.AutoModelForMultipleChoice",anchor:"transformers.AutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1305"}}),IJ=new G({props:{name:"from_config",anchor:"transformers.AutoModelForMultipleChoice.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMultipleChoice.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertForMultipleChoice">AlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertForMultipleChoice">BertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdForMultipleChoice">BigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertForMultipleChoice">CamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/canine#transformers.CanineForMultipleChoice">CanineForMultipleChoice</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.ConvBertForMultipleChoice">ConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecTextForMultipleChoice">Data2VecTextForMultipleChoice</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.DebertaV2ForMultipleChoice">DebertaV2ForMultipleChoice</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertForMultipleChoice">DistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraForMultipleChoice">ElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ernie#transformers.ErnieForMultipleChoice">ErnieForMultipleChoice</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ernie_m#transformers.ErnieMForMultipleChoice">ErnieMForMultipleChoice</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/fnet#transformers.FNetForMultipleChoice">FNetForMultipleChoice</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertForMultipleChoice">FlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelForMultipleChoice">FunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ibert#transformers.IBertForMultipleChoice">IBertForMultipleChoice</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerForMultipleChoice">LongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/luke#transformers.LukeForMultipleChoice">LukeForMultipleChoice</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetForMultipleChoice">MPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mega#transformers.MegaForMultipleChoice">MegaForMultipleChoice</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/megatron-bert#transformers.MegatronBertForMultipleChoice">MegatronBertForMultipleChoice</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertForMultipleChoice">MobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mra#transformers.MraForMultipleChoice">MraForMultipleChoice</a> (MRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/nezha#transformers.NezhaForMultipleChoice">NezhaForMultipleChoice</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/nystromformer#transformers.NystromformerForMultipleChoice">NystromformerForMultipleChoice</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/qdqbert#transformers.QDQBertForMultipleChoice">QDQBertForMultipleChoice</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertForMultipleChoice">RemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roc_bert#transformers.RoCBertForMultipleChoice">RoCBertForMultipleChoice</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerForMultipleChoice">RoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaForMultipleChoice">RobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMultipleChoice">RobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/squeezebert#transformers.SqueezeBertForMultipleChoice">SqueezeBertForMultipleChoice</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMForMultipleChoice">XLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForMultipleChoice">XLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMultipleChoice">XLMRobertaXLForMultipleChoice</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetForMultipleChoice">XLNetForMultipleChoice</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xmod#transformers.XmodForMultipleChoice">XmodForMultipleChoice</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/yoso#transformers.YosoForMultipleChoice">YosoForMultipleChoice</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),PL=new V({props:{anchor:"transformers.AutoModelForMultipleChoice.from_config.example",$$slots:{default:[gms]},$$scope:{ctx:x}}}),NJ=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForMultipleChoice.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),CA=new V({props:{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[ums]},$$scope:{ctx:x}}}),$J=new te({}),jJ=new G({props:{name:"class transformers.TFAutoModelForMultipleChoice",anchor:"transformers.TFAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L671"}}),ZJ=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForMultipleChoice.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.TFAlbertForMultipleChoice">TFAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.TFBertForMultipleChoice">TFBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.TFCamembertForMultipleChoice">TFCamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.TFConvBertForMultipleChoice">TFConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.TFDistilBertForMultipleChoice">TFDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.TFElectraForMultipleChoice">TFElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.TFFlaubertForMultipleChoice">TFFlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.TFFunnelForMultipleChoice">TFFunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.TFLongformerForMultipleChoice">TFLongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.TFMPNetForMultipleChoice">TFMPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.TFMobileBertForMultipleChoice">TFMobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.TFRemBertForMultipleChoice">TFRemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.TFRoFormerForMultipleChoice">TFRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.TFRobertaForMultipleChoice">TFRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMultipleChoice">TFRobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.TFXLMForMultipleChoice">TFXLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMultipleChoice">TFXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.TFXLNetForMultipleChoice">TFXLNetForMultipleChoice</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),LA=new V({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.example",$$slots:{default:[pms]},$$scope:{ctx:x}}}),VJ=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),DA=new V({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[_ms]},$$scope:{ctx:x}}}),WJ=new te({}),JJ=new G({props:{name:"class transformers.FlaxAutoModelForMultipleChoice",anchor:"transformers.FlaxAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_flax_auto.py#L336"}}),XJ=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.FlaxAlbertForMultipleChoice">FlaxAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.FlaxBertForMultipleChoice">FlaxBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.FlaxBigBirdForMultipleChoice">FlaxBigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.FlaxDistilBertForMultipleChoice">FlaxDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.FlaxElectraForMultipleChoice">FlaxElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.FlaxRoFormerForMultipleChoice">FlaxRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.FlaxRobertaForMultipleChoice">FlaxRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMultipleChoice">FlaxRobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMultipleChoice">FlaxXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),OA=new V({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.example",$$slots:{default:[vms]},$$scope:{ctx:x}}}),DJ=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),aR=new V({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[bms]},$$scope:{ctx:x}}}),qJ=new te({}),OJ=new G({props:{name:"class transformers.AutoModelForNextSentencePrediction",anchor:"transformers.AutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1312"}}),YJ=new G({props:{name:"from_config",anchor:"transformers.AutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertForNextSentencePrediction">BertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ernie#transformers.ErnieForNextSentencePrediction">ErnieForNextSentencePrediction</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/fnet#transformers.FNetForNextSentencePrediction">FNetForNextSentencePrediction</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/megatron-bert#transformers.MegatronBertForNextSentencePrediction">MegatronBertForNextSentencePrediction</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertForNextSentencePrediction">MobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/nezha#transformers.NezhaForNextSentencePrediction">NezhaForNextSentencePrediction</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/qdqbert#transformers.QDQBertForNextSentencePrediction">QDQBertForNextSentencePrediction</a> (QDQBert model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),sR=new V({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[Mms]},$$scope:{ctx:x}}}),zJ=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),uR=new V({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[Tms]},$$scope:{ctx:x}}}),HJ=new te({}),KJ=new G({props:{name:"class transformers.TFAutoModelForNextSentencePrediction",anchor:"transformers.TFAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L678"}}),oU=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.TFBertForNextSentencePrediction">TFBertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.TFMobileBertForNextSentencePrediction">TFMobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),_R=new V({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[Fms]},$$scope:{ctx:x}}}),rU=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),MR=new V({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[Ems]},$$scope:{ctx:x}}}),aU=new te({}),nU=new G({props:{name:"class transformers.FlaxAutoModelForNextSentencePrediction",anchor:"transformers.FlaxAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_flax_auto.py#L343"}}),lU=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.FlaxBertForNextSentencePrediction">FlaxBertForNextSentencePrediction</a> (BERT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),FR=new V({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[yms]},$$scope:{ctx:x}}}),iU=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),yR=new V({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[Cms]},$$scope:{ctx:x}}}),dU=new te({}),cU=new G({props:{name:"class transformers.AutoModelForTokenClassification",anchor:"transformers.AutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1298"}}),fU=new G({props:{name:"from_config",anchor:"transformers.AutoModelForTokenClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTokenClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertForTokenClassification">AlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertForTokenClassification">BertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdForTokenClassification">BigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/biogpt#transformers.BioGptForTokenClassification">BioGptForTokenClassification</a> (BioGpt model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bloom#transformers.BloomForTokenClassification">BloomForTokenClassification</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertForTokenClassification">CamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/canine#transformers.CanineForTokenClassification">CanineForTokenClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.ConvBertForTokenClassification">ConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecTextForTokenClassification">Data2VecTextForTokenClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.DebertaForTokenClassification">DebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.DebertaV2ForTokenClassification">DebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertForTokenClassification">DistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraForTokenClassification">ElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ernie#transformers.ErnieForTokenClassification">ErnieForTokenClassification</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ernie_m#transformers.ErnieMForTokenClassification">ErnieMForTokenClassification</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/esm#transformers.EsmForTokenClassification">EsmForTokenClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/fnet#transformers.FNetForTokenClassification">FNetForTokenClassification</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/falcon#transformers.FalconConfig">FalconConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/falcon#transformers.FalconForTokenClassification">FalconForTokenClassification</a> (Falcon model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertForTokenClassification">FlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelForTokenClassification">FunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.GPT2ForTokenClassification">GPT2ForTokenClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForTokenClassification">GPTBigCodeForTokenClassification</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neo#transformers.GPTNeoForTokenClassification">GPTNeoForTokenClassification</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neox#transformers.GPTNeoXForTokenClassification">GPTNeoXForTokenClassification</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ibert#transformers.IBertForTokenClassification">IBertForTokenClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.LayoutLMForTokenClassification">LayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForTokenClassification">LayoutLMv2ForTokenClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForTokenClassification">LayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/lilt#transformers.LiltForTokenClassification">LiltForTokenClassification</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerForTokenClassification">LongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/luke#transformers.LukeForTokenClassification">LukeForTokenClassification</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetForTokenClassification">MPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/markuplm#transformers.MarkupLMForTokenClassification">MarkupLMForTokenClassification</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mega#transformers.MegaForTokenClassification">MegaForTokenClassification</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/megatron-bert#transformers.MegatronBertForTokenClassification">MegatronBertForTokenClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertForTokenClassification">MobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpt#transformers.MptForTokenClassification">MptForTokenClassification</a> (MPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mra#transformers.MraForTokenClassification">MraForTokenClassification</a> (MRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/nezha#transformers.NezhaForTokenClassification">NezhaForTokenClassification</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/nystromformer#transformers.NystromformerForTokenClassification">NystromformerForTokenClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/qdqbert#transformers.QDQBertForTokenClassification">QDQBertForTokenClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertForTokenClassification">RemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roc_bert#transformers.RoCBertForTokenClassification">RoCBertForTokenClassification</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerForTokenClassification">RoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaForTokenClassification">RobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForTokenClassification">RobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/squeezebert#transformers.SqueezeBertForTokenClassification">SqueezeBertForTokenClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMForTokenClassification">XLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForTokenClassification">XLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForTokenClassification">XLMRobertaXLForTokenClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetForTokenClassification">XLNetForTokenClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xmod#transformers.XmodForTokenClassification">XmodForTokenClassification</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/yoso#transformers.YosoForTokenClassification">YosoForTokenClassification</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),wR=new V({props:{anchor:"transformers.AutoModelForTokenClassification.from_config.example",$$slots:{default:[wms]},$$scope:{ctx:x}}}),hU=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForTokenClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),Ak=new V({props:{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[Lms]},$$scope:{ctx:x}}}),gU=new te({}),uU=new G({props:{name:"class transformers.TFAutoModelForTokenClassification",anchor:"transformers.TFAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L662"}}),_U=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForTokenClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTokenClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.TFAlbertForTokenClassification">TFAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.TFBertForTokenClassification">TFBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.TFCamembertForTokenClassification">TFCamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.TFConvBertForTokenClassification">TFConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.TFDebertaForTokenClassification">TFDebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.TFDebertaV2ForTokenClassification">TFDebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.TFDistilBertForTokenClassification">TFDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.TFElectraForTokenClassification">TFElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/esm#transformers.TFEsmForTokenClassification">TFEsmForTokenClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.TFFlaubertForTokenClassification">TFFlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.TFFunnelForTokenClassification">TFFunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.TFLayoutLMForTokenClassification">TFLayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForTokenClassification">TFLayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.TFLongformerForTokenClassification">TFLongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.TFMPNetForTokenClassification">TFMPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.TFMobileBertForTokenClassification">TFMobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.TFRemBertForTokenClassification">TFRemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.TFRoFormerForTokenClassification">TFRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.TFRobertaForTokenClassification">TFRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForTokenClassification">TFRobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.TFXLMForTokenClassification">TFXLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForTokenClassification">TFXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.TFXLNetForTokenClassification">TFXLNetForTokenClassification</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),kk=new V({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_config.example",$$slots:{default:[Ams]},$$scope:{ctx:x}}}),vU=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),eS=new V({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[Rms]},$$scope:{ctx:x}}}),bU=new te({}),MU=new G({props:{name:"class transformers.FlaxAutoModelForTokenClassification",anchor:"transformers.FlaxAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_flax_auto.py#L327"}}),FU=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForTokenClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.FlaxAlbertForTokenClassification">FlaxAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.FlaxBertForTokenClassification">FlaxBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.FlaxBigBirdForTokenClassification">FlaxBigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.FlaxDistilBertForTokenClassification">FlaxDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.FlaxElectraForTokenClassification">FlaxElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.FlaxRoFormerForTokenClassification">FlaxRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.FlaxRobertaForTokenClassification">FlaxRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForTokenClassification">FlaxRobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForTokenClassification">FlaxXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),rS=new V({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.example",$$slots:{default:[kms]},$$scope:{ctx:x}}}),EU=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),fS=new V({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[Sms]},$$scope:{ctx:x}}}),yU=new te({}),CU=new G({props:{name:"class transformers.AutoModelForQuestionAnswering",anchor:"transformers.AutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1258"}}),LU=new G({props:{name:"from_config",anchor:"transformers.AutoModelForQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertForQuestionAnswering">AlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartForQuestionAnswering">BartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertForQuestionAnswering">BertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdForQuestionAnswering">BigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForQuestionAnswering">BigBirdPegasusForQuestionAnswering</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bloom#transformers.BloomForQuestionAnswering">BloomForQuestionAnswering</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertForQuestionAnswering">CamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/canine#transformers.CanineForQuestionAnswering">CanineForQuestionAnswering</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.ConvBertForQuestionAnswering">ConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecTextForQuestionAnswering">Data2VecTextForQuestionAnswering</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.DebertaForQuestionAnswering">DebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.DebertaV2ForQuestionAnswering">DebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraForQuestionAnswering">ElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ernie#transformers.ErnieForQuestionAnswering">ErnieForQuestionAnswering</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ernie_m#transformers.ErnieMForQuestionAnswering">ErnieMForQuestionAnswering</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/fnet#transformers.FNetForQuestionAnswering">FNetForQuestionAnswering</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/falcon#transformers.FalconConfig">FalconConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/falcon#transformers.FalconForQuestionAnswering">FalconForQuestionAnswering</a> (Falcon model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertForQuestionAnsweringSimple">FlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelForQuestionAnswering">FunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt2#transformers.GPT2ForQuestionAnswering">GPT2ForQuestionAnswering</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.GPTJForQuestionAnswering">GPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neo#transformers.GPTNeoForQuestionAnswering">GPTNeoForQuestionAnswering</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gpt_neox#transformers.GPTNeoXForQuestionAnswering">GPTNeoXForQuestionAnswering</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/ibert#transformers.IBertForQuestionAnswering">IBertForQuestionAnswering</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/led#transformers.LEDForQuestionAnswering">LEDForQuestionAnswering</a> (LED model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/lilt#transformers.LiltForQuestionAnswering">LiltForQuestionAnswering</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerForQuestionAnswering">LongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/luke#transformers.LukeForQuestionAnswering">LukeForQuestionAnswering</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/lxmert#transformers.LxmertForQuestionAnswering">LxmertForQuestionAnswering</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartForQuestionAnswering">MBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetForQuestionAnswering">MPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mt5#transformers.MT5ForQuestionAnswering">MT5ForQuestionAnswering</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/markuplm#transformers.MarkupLMForQuestionAnswering">MarkupLMForQuestionAnswering</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mega#transformers.MegaForQuestionAnswering">MegaForQuestionAnswering</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/megatron-bert#transformers.MegatronBertForQuestionAnswering">MegatronBertForQuestionAnswering</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertForQuestionAnswering">MobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpt#transformers.MptForQuestionAnswering">MptForQuestionAnswering</a> (MPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mra#transformers.MraForQuestionAnswering">MraForQuestionAnswering</a> (MRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mvp#transformers.MvpForQuestionAnswering">MvpForQuestionAnswering</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/nezha#transformers.NezhaForQuestionAnswering">NezhaForQuestionAnswering</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/nystromformer#transformers.NystromformerForQuestionAnswering">NystromformerForQuestionAnswering</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/opt#transformers.OPTForQuestionAnswering">OPTForQuestionAnswering</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/qdqbert#transformers.QDQBertForQuestionAnswering">QDQBertForQuestionAnswering</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/reformer#transformers.ReformerForQuestionAnswering">ReformerForQuestionAnswering</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertForQuestionAnswering">RemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roc_bert#transformers.RoCBertForQuestionAnswering">RoCBertForQuestionAnswering</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerForQuestionAnswering">RoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaForQuestionAnswering">RobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForQuestionAnswering">RobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/splinter#transformers.SplinterForQuestionAnswering">SplinterForQuestionAnswering</a> (Splinter model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/squeezebert#transformers.SqueezeBertForQuestionAnswering">SqueezeBertForQuestionAnswering</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/t5#transformers.T5ForQuestionAnswering">T5ForQuestionAnswering</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/umt5#transformers.UMT5ForQuestionAnswering">UMT5ForQuestionAnswering</a> (UMT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMForQuestionAnsweringSimple">XLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForQuestionAnswering">XLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForQuestionAnswering">XLMRobertaXLForQuestionAnswering</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetForQuestionAnsweringSimple">XLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xmod#transformers.XmodForQuestionAnswering">XmodForQuestionAnswering</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/yoso#transformers.YosoForQuestionAnswering">YosoForQuestionAnswering</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),gS=new V({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_config.example",$$slots:{default:[xms]},$$scope:{ctx:x}}}),AU=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),yx=new V({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[Gms]},$$scope:{ctx:x}}}),RU=new te({}),kU=new G({props:{name:"class transformers.TFAutoModelForQuestionAnswering",anchor:"transformers.TFAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L633"}}),xU=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.TFAlbertForQuestionAnswering">TFAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.TFBertForQuestionAnswering">TFBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/camembert#transformers.TFCamembertForQuestionAnswering">TFCamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/convbert#transformers.TFConvBertForQuestionAnswering">TFConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta#transformers.TFDebertaForQuestionAnswering">TFDebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deberta-v2#transformers.TFDebertaV2ForQuestionAnswering">TFDebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.TFDistilBertForQuestionAnswering">TFDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.TFElectraForQuestionAnswering">TFElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/flaubert#transformers.TFFlaubertForQuestionAnsweringSimple">TFFlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/funnel#transformers.TFFunnelForQuestionAnswering">TFFunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/gptj#transformers.TFGPTJForQuestionAnswering">TFGPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForQuestionAnswering">TFLayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/longformer#transformers.TFLongformerForQuestionAnswering">TFLongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mpnet#transformers.TFMPNetForQuestionAnswering">TFMPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilebert#transformers.TFMobileBertForQuestionAnswering">TFMobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/rembert#transformers.TFRemBertForQuestionAnswering">TFRemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.TFRoFormerForQuestionAnswering">TFRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.TFRobertaForQuestionAnswering">TFRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForQuestionAnswering">TFRobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm#transformers.TFXLMForQuestionAnsweringSimple">TFXLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForQuestionAnswering">TFXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlnet#transformers.TFXLNetForQuestionAnsweringSimple">TFXLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),wx=new V({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[Bms]},$$scope:{ctx:x}}}),GU=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),Qx=new V({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[Ims]},$$scope:{ctx:x}}}),BU=new te({}),IU=new G({props:{name:"class transformers.FlaxAutoModelForQuestionAnswering",anchor:"transformers.FlaxAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_flax_auto.py#L320"}}),$U=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/albert#transformers.FlaxAlbertForQuestionAnswering">FlaxAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bart#transformers.FlaxBartForQuestionAnswering">FlaxBartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.FlaxBertForQuestionAnswering">FlaxBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/big_bird#transformers.FlaxBigBirdForQuestionAnswering">FlaxBigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/distilbert#transformers.FlaxDistilBertForQuestionAnswering">FlaxDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/electra#transformers.FlaxElectraForQuestionAnswering">FlaxElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mbart#transformers.FlaxMBartForQuestionAnswering">FlaxMBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roformer#transformers.FlaxRoFormerForQuestionAnswering">FlaxRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta#transformers.FlaxRobertaForQuestionAnswering">FlaxRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForQuestionAnswering">FlaxRobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForQuestionAnswering">FlaxXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),zx=new V({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[Nms]},$$scope:{ctx:x}}}),jU=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),dG=new V({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[$ms]},$$scope:{ctx:x}}}),PU=new te({}),VU=new G({props:{name:"class transformers.AutoModelForTextEncoding",anchor:"transformers.AutoModelForTextEncoding",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1200"}}),WU=new te({}),UU=new G({props:{name:"class transformers.TFAutoModelForTextEncoding",anchor:"transformers.TFAutoModelForTextEncoding",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L523"}}),XU=new te({}),DU=new te({}),qU=new G({props:{name:"class transformers.AutoModelForDepthEstimation",anchor:"transformers.AutoModelForDepthEstimation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1387"}}),QU=new G({props:{name:"from_config",anchor:"transformers.AutoModelForDepthEstimation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDepthEstimation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/dpt#transformers.DPTForDepthEstimation">DPTForDepthEstimation</a> (DPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/glpn#transformers.GLPNForDepthEstimation">GLPNForDepthEstimation</a> (GLPN model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),gG=new V({props:{anchor:"transformers.AutoModelForDepthEstimation.from_config.example",$$slots:{default:[jms]},$$scope:{ctx:x}}}),YU=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForDepthEstimation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),vG=new V({props:{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.example",$$slots:{default:[Pms]},$$scope:{ctx:x}}}),HU=new te({}),KU=new G({props:{name:"class transformers.AutoModelForImageClassification",anchor:"transformers.AutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1321"}}),oX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/beit#transformers.BeitForImageClassification">BeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/bit#transformers.BitConfig">BitConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/bit#transformers.BitForImageClassification">BitForImageClassification</a> (BiT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/convnext#transformers.ConvNextForImageClassification">ConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/convnextv2#transformers.ConvNextV2Config">ConvNextV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/convnextv2#transformers.ConvNextV2ForImageClassification">ConvNextV2ForImageClassification</a> (ConvNeXTV2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/cvt#transformers.CvtForImageClassification">CvtForImageClassification</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecVisionForImageClassification">Data2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deit#transformers.DeiTForImageClassification">DeiTForImageClassification</a> or <a href="/docs/transformers/v4.33.0/en/model_doc/deit#transformers.DeiTForImageClassificationWithTeacher">DeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/dinat#transformers.DinatConfig">DinatConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/dinat#transformers.DinatForImageClassification">DinatForImageClassification</a> (DiNAT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/dinov2#transformers.Dinov2Config">Dinov2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/dinov2#transformers.Dinov2ForImageClassification">Dinov2ForImageClassification</a> (DINOv2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassification">EfficientFormerForImageClassification</a> or <a href="/docs/transformers/v4.33.0/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassificationWithTeacher">EfficientFormerForImageClassificationWithTeacher</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/efficientnet#transformers.EfficientNetConfig">EfficientNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/efficientnet#transformers.EfficientNetForImageClassification">EfficientNetForImageClassification</a> (EfficientNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/focalnet#transformers.FocalNetConfig">FocalNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/focalnet#transformers.FocalNetForImageClassification">FocalNetForImageClassification</a> (FocalNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/imagegpt#transformers.ImageGPTForImageClassification">ImageGPTForImageClassification</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/levit#transformers.LevitForImageClassification">LevitForImageClassification</a> or <a href="/docs/transformers/v4.33.0/en/model_doc/levit#transformers.LevitForImageClassificationWithTeacher">LevitForImageClassificationWithTeacher</a> (LeViT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config">MobileNetV1Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilenet_v1#transformers.MobileNetV1ForImageClassification">MobileNetV1ForImageClassification</a> (MobileNetV1 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForImageClassification">MobileNetV2ForImageClassification</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilevit#transformers.MobileViTForImageClassification">MobileViTForImageClassification</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilevitv2#transformers.MobileViTV2Config">MobileViTV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilevitv2#transformers.MobileViTV2ForImageClassification">MobileViTV2ForImageClassification</a> (MobileViTV2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/nat#transformers.NatConfig">NatConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/nat#transformers.NatForImageClassification">NatForImageClassification</a> (NAT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/perceiver#transformers.PerceiverForImageClassificationLearned">PerceiverForImageClassificationLearned</a> or <a href="/docs/transformers/v4.33.0/en/model_doc/perceiver#transformers.PerceiverForImageClassificationFourier">PerceiverForImageClassificationFourier</a> or <a href="/docs/transformers/v4.33.0/en/model_doc/perceiver#transformers.PerceiverForImageClassificationConvProcessing">PerceiverForImageClassificationConvProcessing</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/poolformer#transformers.PoolFormerForImageClassification">PoolFormerForImageClassification</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/pvt#transformers.PvtConfig">PvtConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/pvt#transformers.PvtForImageClassification">PvtForImageClassification</a> (PVT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/regnet#transformers.RegNetForImageClassification">RegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/resnet#transformers.ResNetForImageClassification">ResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/segformer#transformers.SegformerForImageClassification">SegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/swiftformer#transformers.SwiftFormerConfig">SwiftFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/swiftformer#transformers.SwiftFormerForImageClassification">SwiftFormerForImageClassification</a> (SwiftFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/swin#transformers.SwinForImageClassification">SwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/swinv2#transformers.Swinv2ForImageClassification">Swinv2ForImageClassification</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/van#transformers.VanForImageClassification">VanForImageClassification</a> (VAN model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vit#transformers.ViTForImageClassification">ViTForImageClassification</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vit_hybrid#transformers.ViTHybridConfig">ViTHybridConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vit_hybrid#transformers.ViTHybridForImageClassification">ViTHybridForImageClassification</a> (ViT Hybrid model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vit_msn#transformers.ViTMSNForImageClassification">ViTMSNForImageClassification</a> (ViTMSN model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),MG=new V({props:{anchor:"transformers.AutoModelForImageClassification.from_config.example",$$slots:{default:[Zms]},$$scope:{ctx:x}}}),rX=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),YG=new V({props:{anchor:"transformers.AutoModelForImageClassification.from_pretrained.example",$$slots:{default:[Vms]},$$scope:{ctx:x}}}),tX=new te({}),aX=new G({props:{name:"class transformers.TFAutoModelForImageClassification",anchor:"transformers.TFAutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L574"}}),sX=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/convnext#transformers.TFConvNextForImageClassification">TFConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/cvt#transformers.TFCvtForImageClassification">TFCvtForImageClassification</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.TFData2VecVisionForImageClassification">TFData2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deit#transformers.TFDeiTForImageClassification">TFDeiTForImageClassification</a> or <a href="/docs/transformers/v4.33.0/en/model_doc/deit#transformers.TFDeiTForImageClassificationWithTeacher">TFDeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/efficientformer#transformers.TFEfficientFormerForImageClassification">TFEfficientFormerForImageClassification</a> or <a href="/docs/transformers/v4.33.0/en/model_doc/efficientformer#transformers.TFEfficientFormerForImageClassificationWithTeacher">TFEfficientFormerForImageClassificationWithTeacher</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilevit#transformers.TFMobileViTForImageClassification">TFMobileViTForImageClassification</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/regnet#transformers.TFRegNetForImageClassification">TFRegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/resnet#transformers.TFResNetForImageClassification">TFResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/segformer#transformers.TFSegformerForImageClassification">TFSegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/swin#transformers.TFSwinForImageClassification">TFSwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vit#transformers.TFViTForImageClassification">TFViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),HG=new V({props:{anchor:"transformers.TFAutoModelForImageClassification.from_config.example",$$slots:{default:[Wms]},$$scope:{ctx:x}}}),lX=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),iB=new V({props:{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[Jms]},$$scope:{ctx:x}}}),iX=new te({}),dX=new G({props:{name:"class transformers.FlaxAutoModelForImageClassification",anchor:"transformers.FlaxAutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_flax_auto.py#L352"}}),mX=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/beit#transformers.FlaxBeitForImageClassification">FlaxBeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/regnet#transformers.FlaxRegNetForImageClassification">FlaxRegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/resnet#transformers.FlaxResNetForImageClassification">FlaxResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vit#transformers.FlaxViTForImageClassification">FlaxViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),cB=new V({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_config.example",$$slots:{default:[Ums]},$$scope:{ctx:x}}}),fX=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),uB=new V({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[Xms]},$$scope:{ctx:x}}}),hX=new te({}),gX=new G({props:{name:"class transformers.AutoModelForVideoClassification",anchor:"transformers.AutoModelForVideoClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1394"}}),pX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForVideoClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVideoClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/timesformer#transformers.TimesformerForVideoClassification">TimesformerForVideoClassification</a> (TimeSformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/videomae#transformers.VideoMAEForVideoClassification">VideoMAEForVideoClassification</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vivit#transformers.VivitConfig">VivitConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vivit#transformers.VivitForVideoClassification">VivitForVideoClassification</a> (ViViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),_B=new V({props:{anchor:"transformers.AutoModelForVideoClassification.from_config.example",$$slots:{default:[Dms]},$$scope:{ctx:x}}}),_X=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForVideoClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),FB=new V({props:{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.example",$$slots:{default:[qms]},$$scope:{ctx:x}}}),vX=new te({}),bX=new G({props:{name:"class transformers.AutoModelForMaskedImageModeling",anchor:"transformers.AutoModelForMaskedImageModeling",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1459"}}),TX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForMaskedImageModeling.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deit#transformers.DeiTForMaskedImageModeling">DeiTForMaskedImageModeling</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/focalnet#transformers.FocalNetConfig">FocalNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/focalnet#transformers.FocalNetForMaskedImageModeling">FocalNetForMaskedImageModeling</a> (FocalNet model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/swin#transformers.SwinForMaskedImageModeling">SwinForMaskedImageModeling</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/swinv2#transformers.Swinv2ForMaskedImageModeling">Swinv2ForMaskedImageModeling</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vit#transformers.ViTForMaskedImageModeling">ViTForMaskedImageModeling</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),yB=new V({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[Oms]},$$scope:{ctx:x}}}),FX=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),SB=new V({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.example",$$slots:{default:[Qms]},$$scope:{ctx:x}}}),EX=new te({}),yX=new G({props:{name:"class transformers.TFAutoModelForMaskedImageModeling",anchor:"transformers.TFAutoModelForMaskedImageModeling",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L565"}}),wX=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForMaskedImageModeling.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deit#transformers.TFDeiTForMaskedImageModeling">TFDeiTForMaskedImageModeling</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/swin#transformers.TFSwinForMaskedImageModeling">TFSwinForMaskedImageModeling</a> (Swin Transformer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),GB=new V({props:{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[Yms]},$$scope:{ctx:x}}}),LX=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),NB=new V({props:{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.example",$$slots:{default:[zms]},$$scope:{ctx:x}}}),RX=new te({}),kX=new G({props:{name:"class transformers.AutoModelForObjectDetection",anchor:"transformers.AutoModelForObjectDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1371"}}),xX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForObjectDetection.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForObjectDetection.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/conditional_detr#transformers.ConditionalDetrConfig">ConditionalDetrConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/conditional_detr#transformers.ConditionalDetrForObjectDetection">ConditionalDetrForObjectDetection</a> (Conditional DETR model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deformable_detr#transformers.DeformableDetrConfig">DeformableDetrConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deformable_detr#transformers.DeformableDetrForObjectDetection">DeformableDetrForObjectDetection</a> (Deformable DETR model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/deta#transformers.DetaConfig">DetaConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/deta#transformers.DetaForObjectDetection">DetaForObjectDetection</a> (DETA model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/detr#transformers.DetrForObjectDetection">DetrForObjectDetection</a> (DETR model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/table-transformer#transformers.TableTransformerConfig">TableTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/table-transformer#transformers.TableTransformerForObjectDetection">TableTransformerForObjectDetection</a> (Table Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/yolos#transformers.YolosForObjectDetection">YolosForObjectDetection</a> (YOLOS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),jB=new V({props:{anchor:"transformers.AutoModelForObjectDetection.from_config.example",$$slots:{default:[Hms]},$$scope:{ctx:x}}}),GX=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForObjectDetection.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
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Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
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Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
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automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
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already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
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corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR model)</li>
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Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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</ul>`,name:"config"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/beit#transformers.BeitForSemanticSegmentation">BeitForSemanticSegmentation</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/dpt#transformers.DPTForSemanticSegmentation">DPTForSemanticSegmentation</a> (DPT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecVisionForSemanticSegmentation">Data2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForSemanticSegmentation">MobileNetV2ForSemanticSegmentation</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilevit#transformers.MobileViTForSemanticSegmentation">MobileViTForSemanticSegmentation</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilevitv2#transformers.MobileViTV2Config">MobileViTV2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilevitv2#transformers.MobileViTV2ForSemanticSegmentation">MobileViTV2ForSemanticSegmentation</a> (MobileViTV2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/segformer#transformers.SegformerForSemanticSegmentation">SegformerForSemanticSegmentation</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/upernet#transformers.UperNetConfig">UperNetConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation">UperNetForSemanticSegmentation</a> (UPerNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),KB=new V({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[rfs]},$$scope:{ctx:x}}}),JX=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),dI=new V({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[tfs]},$$scope:{ctx:x}}}),UX=new te({}),XX=new G({props:{name:"class transformers.TFAutoModelForSemanticSegmentation",anchor:"transformers.TFAutoModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L592"}}),qX=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.TFData2VecVisionForSemanticSegmentation">TFData2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mobilevit#transformers.TFMobileViTForSemanticSegmentation">TFMobileViTForSemanticSegmentation</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/segformer#transformers.TFSegformerForSemanticSegmentation">TFSegformerForSemanticSegmentation</a> (SegFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),mI=new V({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[afs]},$$scope:{ctx:x}}}),OX=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),uI=new V({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[nfs]},$$scope:{ctx:x}}}),QX=new te({}),YX=new G({props:{name:"class transformers.AutoModelForInstanceSegmentation",anchor:"transformers.AutoModelForInstanceSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1362"}}),HX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForInstanceSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),_I=new V({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.example",$$slots:{default:[sfs]},$$scope:{ctx:x}}}),KX=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),MI=new V({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.example",$$slots:{default:[lfs]},$$scope:{ctx:x}}}),eD=new te({}),oD=new G({props:{name:"class transformers.AutoModelForUniversalSegmentation",anchor:"transformers.AutoModelForUniversalSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1353"}}),tD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForUniversalSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForUniversalSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mask2former#transformers.Mask2FormerConfig">Mask2FormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mask2former#transformers.Mask2FormerForUniversalSegmentation">Mask2FormerForUniversalSegmentation</a> (Mask2Former model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/oneformer#transformers.OneFormerConfig">OneFormerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/oneformer#transformers.OneFormerForUniversalSegmentation">OneFormerForUniversalSegmentation</a> (OneFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),FI=new V({props:{anchor:"transformers.AutoModelForUniversalSegmentation.from_config.example",$$slots:{default:[ifs]},$$scope:{ctx:x}}}),aD=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),AI=new V({props:{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.example",$$slots:{default:[dfs]},$$scope:{ctx:x}}}),nD=new te({}),sD=new G({props:{name:"class transformers.AutoModelForZeroShotImageClassification",anchor:"transformers.AutoModelForZeroShotImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1328"}}),iD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForZeroShotImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/align#transformers.AlignConfig">AlignConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/align#transformers.AlignModel">AlignModel</a> (ALIGN model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/altclip#transformers.AltCLIPConfig">AltCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/clipseg#transformers.CLIPSegConfig">CLIPSegConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/clipseg#transformers.CLIPSegModel">CLIPSegModel</a> (CLIPSeg model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/chinese_clip#transformers.ChineseCLIPConfig">ChineseCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/chinese_clip#transformers.ChineseCLIPModel">ChineseCLIPModel</a> (Chinese-CLIP model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),kI=new V({props:{anchor:"transformers.AutoModelForZeroShotImageClassification.from_config.example",$$slots:{default:[cfs]},$$scope:{ctx:x}}}),dD=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),jI=new V({props:{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.example",$$slots:{default:[mfs]},$$scope:{ctx:x}}}),cD=new te({}),mD=new G({props:{name:"class transformers.TFAutoModelForZeroShotImageClassification",anchor:"transformers.TFAutoModelForZeroShotImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L583"}}),hD=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blip#transformers.TFBlipModel">TFBlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),ZI=new V({props:{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_config.example",$$slots:{default:[ffs]},$$scope:{ctx:x}}}),gD=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),JI=new V({props:{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.example",$$slots:{default:[hfs]},$$scope:{ctx:x}}}),pD=new te({}),_D=new G({props:{name:"class transformers.AutoModelForZeroShotObjectDetection",anchor:"transformers.AutoModelForZeroShotObjectDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1378"}}),bD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/owlvit#transformers.OwlViTForObjectDetection">OwlViTForObjectDetection</a> (OWL-ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),XI=new V({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config.example",$$slots:{default:[gfs]},$$scope:{ctx:x}}}),MD=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),OI=new V({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.example",$$slots:{default:[ufs]},$$scope:{ctx:x}}}),TD=new te({}),FD=new te({}),ED=new G({props:{name:"class transformers.AutoModelForAudioClassification",anchor:"transformers.AutoModelForAudioClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1408"}}),CD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForAudioClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/audio-spectrogram-transformer#transformers.ASTConfig">ASTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/audio-spectrogram-transformer#transformers.ASTForAudioClassification">ASTForAudioClassification</a> (Audio Spectrogram Transformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecAudioForSequenceClassification">Data2VecAudioForSequenceClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/hubert#transformers.HubertForSequenceClassification">HubertForSequenceClassification</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/sew#transformers.SEWForSequenceClassification">SEWForSequenceClassification</a> (SEW model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/sew-d#transformers.SEWDForSequenceClassification">SEWDForSequenceClassification</a> (SEW-D model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/unispeech#transformers.UniSpeechForSequenceClassification">UniSpeechForSequenceClassification</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatForSequenceClassification">UniSpeechSatForSequenceClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.Wav2Vec2ForSequenceClassification">Wav2Vec2ForSequenceClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForSequenceClassification">Wav2Vec2ConformerForSequenceClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wavlm#transformers.WavLMForSequenceClassification">WavLMForSequenceClassification</a> (WavLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/whisper#transformers.WhisperForAudioClassification">WhisperForAudioClassification</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),zI=new V({props:{anchor:"transformers.AutoModelForAudioClassification.from_config.example",$$slots:{default:[pfs]},$$scope:{ctx:x}}}),wD=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForAudioClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),cN=new V({props:{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[_fs]},$$scope:{ctx:x}}}),LD=new te({}),AD=new G({props:{name:"class transformers.TFAutoModelForAudioClassification",anchor:"transformers.TFAutoModelForAudioClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L534"}}),kD=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForAudioClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForAudioClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.TFWav2Vec2ForSequenceClassification">TFWav2Vec2ForSequenceClassification</a> (Wav2Vec2 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),fN=new V({props:{anchor:"transformers.TFAutoModelForAudioClassification.from_config.example",$$slots:{default:[vfs]},$$scope:{ctx:x}}}),SD=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),gN=new V({props:{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[bfs]},$$scope:{ctx:x}}}),xD=new te({}),GD=new G({props:{name:"class transformers.AutoModelForAudioFrameClassification",anchor:"transformers.AutoModelForAudioFrameClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1431"}}),ID=new G({props:{name:"from_config",anchor:"transformers.AutoModelForAudioFrameClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecAudioForAudioFrameClassification">Data2VecAudioForAudioFrameClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatForAudioFrameClassification">UniSpeechSatForAudioFrameClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.Wav2Vec2ForAudioFrameClassification">Wav2Vec2ForAudioFrameClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForAudioFrameClassification">Wav2Vec2ConformerForAudioFrameClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wavlm#transformers.WavLMForAudioFrameClassification">WavLMForAudioFrameClassification</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),pN=new V({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.example",$$slots:{default:[Mfs]},$$scope:{ctx:x}}}),ND=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),EN=new V({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.example",$$slots:{default:[Tfs]},$$scope:{ctx:x}}}),$D=new te({}),jD=new G({props:{name:"class transformers.AutoModelForCTC",anchor:"transformers.AutoModelForCTC",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1415"}}),ZD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForCTC.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCTC.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecAudioForCTC">Data2VecAudioForCTC</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/hubert#transformers.HubertForCTC">HubertForCTC</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/mctct#transformers.MCTCTForCTC">MCTCTForCTC</a> (M-CTC-T model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/sew#transformers.SEWForCTC">SEWForCTC</a> (SEW model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/sew-d#transformers.SEWDForCTC">SEWDForCTC</a> (SEW-D model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/unispeech#transformers.UniSpeechForCTC">UniSpeechForCTC</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatForCTC">UniSpeechSatForCTC</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC">Wav2Vec2ForCTC</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForCTC">Wav2Vec2ConformerForCTC</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wavlm#transformers.WavLMForCTC">WavLMForCTC</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),CN=new V({props:{anchor:"transformers.AutoModelForCTC.from_config.example",$$slots:{default:[Ffs]},$$scope:{ctx:x}}}),VD=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForCTC.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCTC.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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</ul>`,name:"config"},{anchor:"transformers.AutoModelForCTC.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
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<p>This option can be used if you want to create a model from a pretrained configuration but load your own
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForCTC.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForCTC.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
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Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
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<ul>
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underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/pop2piano#transformers.Pop2PianoConfig">Pop2PianoConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/pop2piano#transformers.Pop2PianoForConditionalGeneration">Pop2PianoForConditionalGeneration</a> (Pop2Piano model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/speech_to_text#transformers.Speech2TextForConditionalGeneration">Speech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderConfig">SpeechEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderModel">SpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/speecht5#transformers.SpeechT5Config">SpeechT5Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/speecht5#transformers.SpeechT5ForSpeechToText">SpeechT5ForSpeechToText</a> (SpeechT5 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/whisper#transformers.WhisperForConditionalGeneration">WhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),PN=new V({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[yfs]},$$scope:{ctx:x}}}),DD=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),DN=new V({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[Cfs]},$$scope:{ctx:x}}}),qD=new te({}),OD=new G({props:{name:"class transformers.TFAutoModelForSpeechSeq2Seq",anchor:"transformers.TFAutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L687"}}),YD=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/speech_to_text#transformers.TFSpeech2TextForConditionalGeneration">TFSpeech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/whisper#transformers.TFWhisperForConditionalGeneration">TFWhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),ON=new V({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[wfs]},$$scope:{ctx:x}}}),zD=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),zN=new V({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[Lfs]},$$scope:{ctx:x}}}),KD=new te({}),eq=new G({props:{name:"class transformers.FlaxAutoModelForSpeechSeq2Seq",anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_flax_auto.py#L368"}}),rq=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderConfig">SpeechEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/speech-encoder-decoder#transformers.FlaxSpeechEncoderDecoderModel">FlaxSpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),KN=new V({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[Afs]},$$scope:{ctx:x}}}),tq=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),r$=new V({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[Rfs]},$$scope:{ctx:x}}}),nq=new te({}),sq=new G({props:{name:"class transformers.AutoModelForAudioXVector",anchor:"transformers.AutoModelForAudioXVector",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1440"}}),iq=new G({props:{name:"from_config",anchor:"transformers.AutoModelForAudioXVector.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioXVector.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/data2vec#transformers.Data2VecAudioForXVector">Data2VecAudioForXVector</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatForXVector">UniSpeechSatForXVector</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2#transformers.Wav2Vec2ForXVector">Wav2Vec2ForXVector</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForXVector">Wav2Vec2ConformerForXVector</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/wavlm#transformers.WavLMForXVector">WavLMForXVector</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),a$=new V({props:{anchor:"transformers.AutoModelForAudioXVector.from_config.example",$$slots:{default:[kfs]},$$scope:{ctx:x}}}),dq=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForAudioXVector.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),m$=new V({props:{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.example",$$slots:{default:[Sfs]},$$scope:{ctx:x}}}),cq=new te({}),fq=new G({props:{name:"class transformers.AutoModelForTextToSpectrogram",anchor:"transformers.AutoModelForTextToSpectrogram",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1444"}}),hq=new te({}),uq=new G({props:{name:"class transformers.AutoModelForTextToWaveform",anchor:"transformers.AutoModelForTextToWaveform",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1448"}}),pq=new te({}),_q=new te({}),vq=new G({props:{name:"class transformers.AutoModelForTableQuestionAnswering",anchor:"transformers.AutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1265"}}),Mq=new G({props:{name:"from_config",anchor:"transformers.AutoModelForTableQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TapasForQuestionAnswering">TapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),p$=new V({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[xfs]},$$scope:{ctx:x}}}),Tq=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),b$=new V({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[Gfs]},$$scope:{ctx:x}}}),Fq=new te({}),Eq=new G({props:{name:"class transformers.TFAutoModelForTableQuestionAnswering",anchor:"transformers.TFAutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L651"}}),Cq=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/tapas#transformers.TFTapasForQuestionAnswering">TFTapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),T$=new V({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[Bfs]},$$scope:{ctx:x}}}),wq=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),E$=new V({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[Ifs]},$$scope:{ctx:x}}}),Lq=new te({}),Aq=new G({props:{name:"class transformers.AutoModelForDocumentQuestionAnswering",anchor:"transformers.AutoModelForDocumentQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1287"}}),kq=new G({props:{name:"from_config",anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.LayoutLMForQuestionAnswering">LayoutLMForQuestionAnswering</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),C$=new V({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[Nfs]},$$scope:{ctx:x}}}),Sq=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),k$=new V({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[$fs]},$$scope:{ctx:x}}}),xq=new te({}),Gq=new G({props:{name:"class transformers.TFAutoModelForDocumentQuestionAnswering",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L640"}}),Iq=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/layoutlm#transformers.TFLayoutLMForQuestionAnswering">TFLayoutLMForQuestionAnswering</a> (LayoutLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),x$=new V({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[jfs]},$$scope:{ctx:x}}}),Nq=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),B$=new V({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[Pfs]},$$scope:{ctx:x}}}),$q=new te({}),jq=new G({props:{name:"class transformers.AutoModelForVisualQuestionAnswering",anchor:"transformers.AutoModelForVisualQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1276"}}),Zq=new G({props:{name:"from_config",anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration">Blip2ForConditionalGeneration</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vilt#transformers.ViltForQuestionAnswering">ViltForQuestionAnswering</a> (ViLT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),N$=new V({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.example",$$slots:{default:[Zfs]},$$scope:{ctx:x}}}),Vq=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),Z$=new V({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.example",$$slots:{default:[Vfs]},$$scope:{ctx:x}}}),Jq=new te({}),Uq=new G({props:{name:"class transformers.AutoModelForVision2Seq",anchor:"transformers.AutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_auto.py#L1401"}}),Dq=new G({props:{name:"from_config",anchor:"transformers.AutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration">Blip2ForConditionalGeneration</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blip#transformers.BlipForConditionalGeneration">BlipForConditionalGeneration</a> (BLIP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/git#transformers.GitConfig">GitConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/git#transformers.GitForCausalLM">GitForCausalLM</a> (GIT model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/instructblip#transformers.InstructBlipConfig">InstructBlipConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/instructblip#transformers.InstructBlipForConditionalGeneration">InstructBlipForConditionalGeneration</a> (InstructBLIP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/pix2struct#transformers.Pix2StructConfig">Pix2StructConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/pix2struct#transformers.Pix2StructForConditionalGeneration">Pix2StructForConditionalGeneration</a> (Pix2Struct model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderModel">VisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),W$=new V({props:{anchor:"transformers.AutoModelForVision2Seq.from_config.example",$$slots:{default:[Wfs]},$$scope:{ctx:x}}}),qq=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForVision2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),Y$=new V({props:{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[Jfs]},$$scope:{ctx:x}}}),Oq=new te({}),Qq=new G({props:{name:"class transformers.TFAutoModelForVision2Seq",anchor:"transformers.TFAutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_tf_auto.py#L601"}}),zq=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/blip#transformers.TFBlipForConditionalGeneration">TFBlipForConditionalGeneration</a> (BLIP model)</li>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vision-encoder-decoder#transformers.TFVisionEncoderDecoderModel">TFVisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),H$=new V({props:{anchor:"transformers.TFAutoModelForVision2Seq.from_config.example",$$slots:{default:[Ufs]},$$scope:{ctx:x}}}),Hq=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),oj=new V({props:{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[Xfs]},$$scope:{ctx:x}}}),eO=new te({}),oO=new G({props:{name:"class transformers.FlaxAutoModelForVision2Seq",anchor:"transformers.FlaxAutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/modeling_flax_auto.py#L361"}}),tO=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.33.0/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.33.0/en/model_doc/vision-encoder-decoder#transformers.FlaxVisionEncoderDecoderModel">FlaxVisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L422"}}),tj=new V({props:{anchor:"transformers.FlaxAutoModelForVision2Seq.from_config.example",$$slots:{default:[Dfs]},$$scope:{ctx:x}}}),aO=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
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the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
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<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.33.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/auto/auto_factory.py#L453"}}),nj=new V({props:{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[qfs]},$$scope:{ctx:x}}}),{c(){h=a("meta"),C=l(),u=a("h1"),f=a("a"),p=a("span"),b(c.$$.fragment),g=l(),ze=a("span"),Jc=o("Auto Classes"),qg=l(),Wt=a("p"),Uc=o(`In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you
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with the `),Ile=a("a"),vkr=o("from_pretrained()"),bkr=o(" class method or the "),Nle=a("a"),Mkr=o("from_config()"),Tkr=o(` class
method.`),Fkr=l(),UV=a("p"),Ekr=o("This class cannot be instantiated directly using "),eIe=a("code"),ykr=o("__init__()"),Ckr=o(" (throws an error)."),wkr=l(),Jt=a("div"),b(XV.$$.fragment),Lkr=l(),oIe=a("p"),Akr=o("Instantiates one of the base model classes of the library from a configuration."),Rkr=l(),lm=a("p"),kkr=o(`Note:
Loading a model from its configuration file does `),rIe=a("strong"),Skr=o("not"),xkr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),$le=a("a"),Gkr=o("from_pretrained()"),Bkr=o(" to load the model weights."),Ikr=l(),b(u0.$$.fragment),Nkr=l(),so=a("div"),b(DV.$$.fragment),$kr=l(),tIe=a("p"),jkr=o("Instantiate one of the base model classes of the library from a pretrained model."),Pkr=l(),Zn=a("p"),Zkr=o("The model class to instantiate is selected based on the "),aIe=a("code"),Vkr=o("model_type"),Wkr=o(` property of the config object (either
passed as an argument or loaded from `),nIe=a("code"),Jkr=o("pretrained_model_name_or_path"),Ukr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),sIe=a("code"),Xkr=o("pretrained_model_name_or_path"),Dkr=o(":"),qkr=l(),A=a("ul"),p0=a("li"),lIe=a("strong"),Okr=o("albert"),Qkr=o(" \u2014 "),jle=a("a"),Ykr=o("AlbertModel"),zkr=o(" (ALBERT model)"),Hkr=l(),_0=a("li"),iIe=a("strong"),Kkr=o("align"),eSr=o(" \u2014 "),Ple=a("a"),oSr=o("AlignModel"),rSr=o(" (ALIGN model)"),tSr=l(),v0=a("li"),dIe=a("strong"),aSr=o("altclip"),nSr=o(" \u2014 "),Zle=a("a"),sSr=o("AltCLIPModel"),lSr=o(" (AltCLIP model)"),iSr=l(),b0=a("li"),cIe=a("strong"),dSr=o("audio-spectrogram-transformer"),cSr=o(" \u2014 "),Vle=a("a"),mSr=o("ASTModel"),fSr=o(" (Audio Spectrogram Transformer model)"),hSr=l(),M0=a("li"),mIe=a("strong"),gSr=o("autoformer"),uSr=o(" \u2014 "),Wle=a("a"),pSr=o("AutoformerModel"),_Sr=o(" (Autoformer model)"),vSr=l(),T0=a("li"),fIe=a("strong"),bSr=o("bark"),MSr=o(" \u2014 "),Jle=a("a"),TSr=o("BarkModel"),FSr=o(" (Bark model)"),ESr=l(),F0=a("li"),hIe=a("strong"),ySr=o("bart"),CSr=o(" \u2014 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method.`),rQr=l(),QV=a("p"),tQr=o("This class cannot be instantiated directly using "),Uje=a("code"),aQr=o("__init__()"),nQr=o(" (throws an error)."),sQr=l(),Ut=a("div"),b(YV.$$.fragment),lQr=l(),Xje=a("p"),iQr=o("Instantiates one of the base model classes of the library from a configuration."),dQr=l(),cm=a("p"),cQr=o(`Note:
Loading a model from its configuration file does `),Dje=a("strong"),mQr=o("not"),fQr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),pme=a("a"),hQr=o("from_pretrained()"),gQr=o(" to load the model weights."),uQr=l(),b(QT.$$.fragment),pQr=l(),tt=a("div"),b(zV.$$.fragment),_Qr=l(),qje=a("p"),vQr=o("Instantiate one of the base model classes of the library from a pretrained model."),bQr=l(),Vn=a("p"),MQr=o("The model class to instantiate is selected based on the "),Oje=a("code"),TQr=o("model_type"),FQr=o(` property of the config object (either
passed as an argument or loaded from `),Qje=a("code"),EQr=o("pretrained_model_name_or_path"),yQr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Yje=a("code"),CQr=o("pretrained_model_name_or_path"),wQr=o(":"),LQr=l(),j=a("ul"),YT=a("li"),zje=a("strong"),AQr=o("albert"),RQr=o(" \u2014 "),_me=a("a"),kQr=o("TFAlbertModel"),SQr=o(" (ALBERT model)"),xQr=l(),zT=a("li"),Hje=a("strong"),GQr=o("bart"),BQr=o(" \u2014 "),vme=a("a"),IQr=o("TFBartModel"),NQr=o(" (BART model)"),$Qr=l(),HT=a("li"),Kje=a("strong"),jQr=o("bert"),PQr=o(" \u2014 "),bme=a("a"),ZQr=o("TFBertModel"),VQr=o(" (BERT model)"),WQr=l(),KT=a("li"),ePe=a("strong"),JQr=o("blenderbot"),UQr=o(" \u2014 "),Mme=a("a"),XQr=o("TFBlenderbotModel"),DQr=o(" (Blenderbot model)"),qQr=l(),eF=a("li"),oPe=a("strong"),OQr=o("blenderbot-small"),QQr=o(" \u2014 "),Tme=a("a"),YQr=o("TFBlenderbotSmallModel"),zQr=o(" (BlenderbotSmall model)"),HQr=l(),oF=a("li"),rPe=a("strong"),KQr=o("blip"),eYr=o(" \u2014 "),Fme=a("a"),oYr=o("TFBlipModel"),rYr=o(" (BLIP model)"),tYr=l(),rF=a("li"),tPe=a("strong"),aYr=o("camembert"),nYr=o(" \u2014 "),Eme=a("a"),sYr=o("TFCamembertModel"),lYr=o(" (CamemBERT model)"),iYr=l(),tF=a("li"),aPe=a("strong"),dYr=o("clip"),cYr=o(" \u2014 "),yme=a("a"),mYr=o("TFCLIPModel"),fYr=o(" (CLIP model)"),hYr=l(),aF=a("li"),nPe=a("strong"),gYr=o("convbert"),uYr=o(" \u2014 "),Cme=a("a"),pYr=o("TFConvBertModel"),_Yr=o(" (ConvBERT model)"),vYr=l(),nF=a("li"),sPe=a("strong"),bYr=o("convnext"),MYr=o(" \u2014 "),wme=a("a"),TYr=o("TFConvNextModel"),FYr=o(" (ConvNeXT model)"),EYr=l(),sF=a("li"),lPe=a("strong"),yYr=o("ctrl"),CYr=o(" \u2014 "),Lme=a("a"),wYr=o("TFCTRLModel"),LYr=o(" (CTRL model)"),AYr=l(),lF=a("li"),iPe=a("strong"),RYr=o("cvt"),kYr=o(" \u2014 "),Ame=a("a"),SYr=o("TFCvtModel"),xYr=o(" (CvT model)"),GYr=l(),iF=a("li"),dPe=a("strong"),BYr=o("data2vec-vision"),IYr=o(" \u2014 "),Rme=a("a"),NYr=o("TFData2VecVisionModel"),$Yr=o(" (Data2VecVision model)"),jYr=l(),dF=a("li"),cPe=a("strong"),PYr=o("deberta"),ZYr=o(" \u2014 "),kme=a("a"),VYr=o("TFDebertaModel"),WYr=o(" (DeBERTa model)"),JYr=l(),cF=a("li"),mPe=a("strong"),UYr=o("deberta-v2"),XYr=o(" \u2014 "),Sme=a("a"),DYr=o("TFDebertaV2Model"),qYr=o(" (DeBERTa-v2 model)"),OYr=l(),mF=a("li"),fPe=a("strong"),QYr=o("deit"),YYr=o(" \u2014 "),xme=a("a"),zYr=o("TFDeiTModel"),HYr=o(" (DeiT model)"),KYr=l(),fF=a("li"),hPe=a("strong"),ezr=o("distilbert"),ozr=o(" \u2014 "),Gme=a("a"),rzr=o("TFDistilBertModel"),tzr=o(" (DistilBERT model)"),azr=l(),hF=a("li"),gPe=a("strong"),nzr=o("dpr"),szr=o(" \u2014 "),Bme=a("a"),lzr=o("TFDPRQuestionEncoder"),izr=o(" (DPR model)"),dzr=l(),gF=a("li"),uPe=a("strong"),czr=o("efficientformer"),mzr=o(" \u2014 "),Ime=a("a"),fzr=o("TFEfficientFormerModel"),hzr=o(" (EfficientFormer model)"),gzr=l(),uF=a("li"),pPe=a("strong"),uzr=o("electra"),pzr=o(" \u2014 "),Nme=a("a"),_zr=o("TFElectraModel"),vzr=o(" (ELECTRA model)"),bzr=l(),pF=a("li"),_Pe=a("strong"),Mzr=o("esm"),Tzr=o(" \u2014 "),$me=a("a"),Fzr=o("TFEsmModel"),Ezr=o(" (ESM model)"),yzr=l(),_F=a("li"),vPe=a("strong"),Czr=o("flaubert"),wzr=o(" \u2014 "),jme=a("a"),Lzr=o("TFFlaubertModel"),Azr=o(" (FlauBERT model)"),Rzr=l(),Wi=a("li"),bPe=a("strong"),kzr=o("funnel"),Szr=o(" \u2014 "),Pme=a("a"),xzr=o("TFFunnelModel"),Gzr=o(" or "),Zme=a("a"),Bzr=o("TFFunnelBaseModel"),Izr=o(" (Funnel Transformer model)"),Nzr=l(),vF=a("li"),MPe=a("strong"),$zr=o("gpt-sw3"),jzr=o(" \u2014 "),Vme=a("a"),Pzr=o("TFGPT2Model"),Zzr=o(" (GPT-Sw3 model)"),Vzr=l(),bF=a("li"),TPe=a("strong"),Wzr=o("gpt2"),Jzr=o(" \u2014 "),Wme=a("a"),Uzr=o("TFGPT2Model"),Xzr=o(" (OpenAI GPT-2 model)"),Dzr=l(),MF=a("li"),FPe=a("strong"),qzr=o("gptj"),Ozr=o(" \u2014 "),Jme=a("a"),Qzr=o("TFGPTJModel"),Yzr=o(" (GPT-J model)"),zzr=l(),TF=a("li"),EPe=a("strong"),Hzr=o("groupvit"),Kzr=o(" \u2014 "),Ume=a("a"),eHr=o("TFGroupViTModel"),oHr=o(" (GroupViT model)"),rHr=l(),FF=a("li"),yPe=a("strong"),tHr=o("hubert"),aHr=o(" \u2014 "),Xme=a("a"),nHr=o("TFHubertModel"),sHr=o(" (Hubert 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model)"),AKr=l(),jF=a("li"),ZPe=a("strong"),RKr=o("rembert"),kKr=o(" \u2014 "),lfe=a("a"),SKr=o("TFRemBertModel"),xKr=o(" (RemBERT model)"),GKr=l(),PF=a("li"),VPe=a("strong"),BKr=o("resnet"),IKr=o(" \u2014 "),ife=a("a"),NKr=o("TFResNetModel"),$Kr=o(" (ResNet model)"),jKr=l(),ZF=a("li"),WPe=a("strong"),PKr=o("roberta"),ZKr=o(" \u2014 "),dfe=a("a"),VKr=o("TFRobertaModel"),WKr=o(" (RoBERTa model)"),JKr=l(),VF=a("li"),JPe=a("strong"),UKr=o("roberta-prelayernorm"),XKr=o(" \u2014 "),cfe=a("a"),DKr=o("TFRobertaPreLayerNormModel"),qKr=o(" (RoBERTa-PreLayerNorm model)"),OKr=l(),WF=a("li"),UPe=a("strong"),QKr=o("roformer"),YKr=o(" \u2014 "),mfe=a("a"),zKr=o("TFRoFormerModel"),HKr=o(" (RoFormer model)"),KKr=l(),JF=a("li"),XPe=a("strong"),eet=o("sam"),oet=o(" \u2014 "),ffe=a("a"),ret=o("TFSamModel"),tet=o(" (SAM model)"),aet=l(),UF=a("li"),DPe=a("strong"),net=o("segformer"),set=o(" \u2014 "),hfe=a("a"),iet=o("TFSegformerModel"),det=o(" (SegFormer 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model)"),Xet=l(),HF=a("li"),eZe=a("strong"),Det=o("vit_mae"),qet=o(" \u2014 "),Tfe=a("a"),Oet=o("TFViTMAEModel"),Qet=o(" (ViTMAE model)"),Yet=l(),KF=a("li"),oZe=a("strong"),zet=o("wav2vec2"),Het=o(" \u2014 "),Ffe=a("a"),Ket=o("TFWav2Vec2Model"),eot=o(" (Wav2Vec2 model)"),oot=l(),eE=a("li"),rZe=a("strong"),rot=o("whisper"),tot=o(" \u2014 "),Efe=a("a"),aot=o("TFWhisperModel"),not=o(" (Whisper model)"),sot=l(),oE=a("li"),tZe=a("strong"),lot=o("xglm"),iot=o(" \u2014 "),yfe=a("a"),dot=o("TFXGLMModel"),cot=o(" (XGLM model)"),mot=l(),rE=a("li"),aZe=a("strong"),fot=o("xlm"),hot=o(" \u2014 "),Cfe=a("a"),got=o("TFXLMModel"),uot=o(" (XLM model)"),pot=l(),tE=a("li"),nZe=a("strong"),_ot=o("xlm-roberta"),vot=o(" \u2014 "),wfe=a("a"),bot=o("TFXLMRobertaModel"),Mot=o(" (XLM-RoBERTa model)"),Tot=l(),aE=a("li"),sZe=a("strong"),Fot=o("xlnet"),Eot=o(" \u2014 "),Lfe=a("a"),yot=o("TFXLNetModel"),Cot=o(" (XLNet model)"),wot=l(),b(nE.$$.fragment),MBo=l(),mm=a("h3"),sE=a("a"),lZe=a("span"),b(HV.$$.fragment),Lot=l(),iZe=a("span"),Aot=o("FlaxAutoModel"),TBo=l(),Do=a("div"),b(KV.$$.fragment),Rot=l(),fm=a("p"),kot=o(`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),Afe=a("a"),Sot=o("from_pretrained()"),xot=o(" class method or the "),Rfe=a("a"),Got=o("from_config()"),Bot=o(` class
method.`),Iot=l(),eW=a("p"),Not=o("This class cannot be instantiated directly using "),dZe=a("code"),$ot=o("__init__()"),jot=o(" (throws an error)."),Pot=l(),Xt=a("div"),b(oW.$$.fragment),Zot=l(),cZe=a("p"),Vot=o("Instantiates one of the base model classes of the library from a configuration."),Wot=l(),hm=a("p"),Jot=o(`Note:
Loading a model from its configuration file does `),mZe=a("strong"),Uot=o("not"),Xot=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),kfe=a("a"),Dot=o("from_pretrained()"),qot=o(" to load the model weights."),Oot=l(),b(lE.$$.fragment),Qot=l(),at=a("div"),b(rW.$$.fragment),Yot=l(),fZe=a("p"),zot=o("Instantiate one of the base model classes of the library from a pretrained model."),Hot=l(),Wn=a("p"),Kot=o("The model class to instantiate is selected based on the "),hZe=a("code"),ert=o("model_type"),ort=o(` property of the config object (either
passed as an argument or loaded from `),gZe=a("code"),rrt=o("pretrained_model_name_or_path"),trt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),uZe=a("code"),art=o("pretrained_model_name_or_path"),nrt=o(":"),srt=l(),se=a("ul"),iE=a("li"),pZe=a("strong"),lrt=o("albert"),irt=o(" \u2014 "),Sfe=a("a"),drt=o("FlaxAlbertModel"),crt=o(" (ALBERT model)"),mrt=l(),dE=a("li"),_Ze=a("strong"),frt=o("bart"),hrt=o(" \u2014 "),xfe=a("a"),grt=o("FlaxBartModel"),urt=o(" (BART model)"),prt=l(),cE=a("li"),vZe=a("strong"),_rt=o("beit"),vrt=o(" \u2014 "),Gfe=a("a"),brt=o("FlaxBeitModel"),Mrt=o(" (BEiT model)"),Trt=l(),mE=a("li"),bZe=a("strong"),Frt=o("bert"),Ert=o(" \u2014 "),Bfe=a("a"),yrt=o("FlaxBertModel"),Crt=o(" (BERT model)"),wrt=l(),fE=a("li"),MZe=a("strong"),Lrt=o("big_bird"),Art=o(" \u2014 "),Ife=a("a"),Rrt=o("FlaxBigBirdModel"),krt=o(" (BigBird model)"),Srt=l(),hE=a("li"),TZe=a("strong"),xrt=o("blenderbot"),Grt=o(" \u2014 "),Nfe=a("a"),Brt=o("FlaxBlenderbotModel"),Irt=o(" (Blenderbot model)"),Nrt=l(),gE=a("li"),FZe=a("strong"),$rt=o("blenderbot-small"),jrt=o(" \u2014 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with the `),mhe=a("a"),_nt=o("from_pretrained()"),vnt=o(" class method or the "),fhe=a("a"),bnt=o("from_config()"),Mnt=o(` class
method.`),Tnt=l(),sW=a("p"),Fnt=o("This class cannot be instantiated directly using "),KZe=a("code"),Ent=o("__init__()"),ynt=o(" (throws an error)."),Cnt=l(),Dt=a("div"),b(lW.$$.fragment),wnt=l(),eVe=a("p"),Lnt=o("Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),Ant=l(),_m=a("p"),Rnt=o(`Note:
Loading a model from its configuration file does `),oVe=a("strong"),knt=o("not"),Snt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),hhe=a("a"),xnt=o("from_pretrained()"),Gnt=o(" to load the model weights."),Bnt=l(),b(UE.$$.fragment),Int=l(),lo=a("div"),b(iW.$$.fragment),Nnt=l(),rVe=a("p"),$nt=o("Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),jnt=l(),Jn=a("p"),Pnt=o("The model class to instantiate is selected based on the "),tVe=a("code"),Znt=o("model_type"),Vnt=o(` property of the config object (either
passed as an argument or loaded from `),aVe=a("code"),Wnt=o("pretrained_model_name_or_path"),Jnt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nVe=a("code"),Unt=o("pretrained_model_name_or_path"),Xnt=o(":"),Dnt=l(),U=a("ul"),XE=a("li"),sVe=a("strong"),qnt=o("albert"),Ont=o(" \u2014 "),ghe=a("a"),Qnt=o("AlbertForPreTraining"),Ynt=o(" (ALBERT model)"),znt=l(),DE=a("li"),lVe=a("strong"),Hnt=o("bart"),Knt=o(" \u2014 "),uhe=a("a"),est=o("BartForConditionalGeneration"),ost=o(" (BART model)"),rst=l(),qE=a("li"),iVe=a("strong"),tst=o("bert"),ast=o(" \u2014 "),phe=a("a"),nst=o("BertForPreTraining"),sst=o(" (BERT model)"),lst=l(),OE=a("li"),dVe=a("strong"),ist=o("big_bird"),dst=o(" \u2014 "),_he=a("a"),cst=o("BigBirdForPreTraining"),mst=o(" (BigBird model)"),fst=l(),QE=a("li"),cVe=a("strong"),hst=o("bloom"),gst=o(" \u2014 "),vhe=a("a"),ust=o("BloomForCausalLM"),pst=o(" (BLOOM model)"),_st=l(),YE=a("li"),mVe=a("strong"),vst=o("camembert"),bst=o(" \u2014 "),bhe=a("a"),Mst=o("CamembertForMaskedLM"),Tst=o(" (CamemBERT model)"),Fst=l(),zE=a("li"),fVe=a("strong"),Est=o("ctrl"),yst=o(" \u2014 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"),Bhe=a("a"),Vlt=o("GPTBigCodeForCausalLM"),Wlt=o(" (GPTBigCode model)"),Jlt=l(),fy=a("li"),LVe=a("strong"),Ult=o("gptsan-japanese"),Xlt=o(" \u2014 "),Ihe=a("a"),Dlt=o("GPTSanJapaneseForConditionalGeneration"),qlt=o(" (GPTSAN-japanese model)"),Olt=l(),hy=a("li"),AVe=a("strong"),Qlt=o("ibert"),Ylt=o(" \u2014 "),Nhe=a("a"),zlt=o("IBertForMaskedLM"),Hlt=o(" (I-BERT model)"),Klt=l(),gy=a("li"),RVe=a("strong"),eit=o("idefics"),oit=o(" \u2014 "),$he=a("a"),rit=o("IdeficsForVisionText2Text"),tit=o(" (IDEFICS model)"),ait=l(),uy=a("li"),kVe=a("strong"),nit=o("layoutlm"),sit=o(" \u2014 "),jhe=a("a"),lit=o("LayoutLMForMaskedLM"),iit=o(" (LayoutLM model)"),dit=l(),py=a("li"),SVe=a("strong"),cit=o("longformer"),mit=o(" \u2014 "),Phe=a("a"),fit=o("LongformerForMaskedLM"),hit=o(" (Longformer model)"),git=l(),_y=a("li"),xVe=a("strong"),uit=o("luke"),pit=o(" \u2014 "),Zhe=a("a"),_it=o("LukeForMaskedLM"),vit=o(" (LUKE model)"),bit=l(),vy=a("li"),GVe=a("strong"),Mit=o("lxmert"),Tit=o(" \u2014 "),Vhe=a("a"),Fit=o("LxmertForPreTraining"),Eit=o(" (LXMERT model)"),yit=l(),by=a("li"),BVe=a("strong"),Cit=o("mega"),wit=o(" \u2014 "),Whe=a("a"),Lit=o("MegaForMaskedLM"),Ait=o(" (MEGA model)"),Rit=l(),My=a("li"),IVe=a("strong"),kit=o("megatron-bert"),Sit=o(" \u2014 "),Jhe=a("a"),xit=o("MegatronBertForPreTraining"),Git=o(" (Megatron-BERT model)"),Bit=l(),Ty=a("li"),NVe=a("strong"),Iit=o("mobilebert"),Nit=o(" \u2014 "),Uhe=a("a"),$it=o("MobileBertForPreTraining"),jit=o(" (MobileBERT model)"),Pit=l(),Fy=a("li"),$Ve=a("strong"),Zit=o("mpnet"),Vit=o(" \u2014 "),Xhe=a("a"),Wit=o("MPNetForMaskedLM"),Jit=o(" (MPNet model)"),Uit=l(),Ey=a("li"),jVe=a("strong"),Xit=o("mpt"),Dit=o(" \u2014 "),Dhe=a("a"),qit=o("MptForCausalLM"),Oit=o(" (MPT model)"),Qit=l(),yy=a("li"),PVe=a("strong"),Yit=o("mra"),zit=o(" \u2014 "),qhe=a("a"),Hit=o("MraForMaskedLM"),Kit=o(" (MRA model)"),edt=l(),Cy=a("li"),ZVe=a("strong"),odt=o("mvp"),rdt=o(" \u2014 "),Ohe=a("a"),tdt=o("MvpForConditionalGeneration"),adt=o(" (MVP 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(RoCBert model)"),Zdt=l(),Gy=a("li"),OVe=a("strong"),Vdt=o("rwkv"),Wdt=o(" \u2014 "),rge=a("a"),Jdt=o("RwkvForCausalLM"),Udt=o(" (RWKV model)"),Xdt=l(),By=a("li"),QVe=a("strong"),Ddt=o("splinter"),qdt=o(" \u2014 "),tge=a("a"),Odt=o("SplinterForPreTraining"),Qdt=o(" (Splinter model)"),Ydt=l(),Iy=a("li"),YVe=a("strong"),zdt=o("squeezebert"),Hdt=o(" \u2014 "),age=a("a"),Kdt=o("SqueezeBertForMaskedLM"),ect=o(" (SqueezeBERT model)"),oct=l(),Ny=a("li"),zVe=a("strong"),rct=o("switch_transformers"),tct=o(" \u2014 "),nge=a("a"),act=o("SwitchTransformersForConditionalGeneration"),nct=o(" (SwitchTransformers model)"),sct=l(),$y=a("li"),HVe=a("strong"),lct=o("t5"),ict=o(" \u2014 "),sge=a("a"),dct=o("T5ForConditionalGeneration"),cct=o(" (T5 model)"),mct=l(),jy=a("li"),KVe=a("strong"),fct=o("tapas"),hct=o(" \u2014 "),lge=a("a"),gct=o("TapasForMaskedLM"),uct=o(" (TAPAS model)"),pct=l(),Py=a("li"),eWe=a("strong"),_ct=o("transfo-xl"),vct=o(" \u2014 "),ige=a("a"),bct=o("TransfoXLLMHeadModel"),Mct=o(" 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method.`),Dmt=l(),mW=a("p"),qmt=o("This class cannot be instantiated directly using "),vWe=a("code"),Omt=o("__init__()"),Qmt=o(" (throws an error)."),Ymt=l(),qt=a("div"),b(fW.$$.fragment),zmt=l(),bWe=a("p"),Hmt=o("Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),Kmt=l(),Mm=a("p"),eft=o(`Note:
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model\u2019s configuration. Use `),yge=a("a"),tft=o("from_pretrained()"),aft=o(" to load the model weights."),nft=l(),b(r5.$$.fragment),sft=l(),nt=a("div"),b(hW.$$.fragment),lft=l(),TWe=a("p"),ift=o("Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),dft=l(),Un=a("p"),cft=o("The model class to instantiate is selected based on the "),FWe=a("code"),mft=o("model_type"),fft=o(` property of the config object (either
passed as an argument or loaded from `),EWe=a("code"),hft=o("pretrained_model_name_or_path"),gft=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),yWe=a("code"),uft=o("pretrained_model_name_or_path"),pft=o(":"),_ft=l(),fe=a("ul"),t5=a("li"),CWe=a("strong"),vft=o("albert"),bft=o(" \u2014 "),Cge=a("a"),Mft=o("TFAlbertForPreTraining"),Tft=o(" (ALBERT model)"),Fft=l(),a5=a("li"),wWe=a("strong"),Eft=o("bart"),yft=o(" \u2014 "),wge=a("a"),Cft=o("TFBartForConditionalGeneration"),wft=o(" (BART model)"),Lft=l(),n5=a("li"),LWe=a("strong"),Aft=o("bert"),Rft=o(" \u2014 "),Lge=a("a"),kft=o("TFBertForPreTraining"),Sft=o(" (BERT model)"),xft=l(),s5=a("li"),AWe=a("strong"),Gft=o("camembert"),Bft=o(" \u2014 "),Age=a("a"),Ift=o("TFCamembertForMaskedLM"),Nft=o(" (CamemBERT model)"),$ft=l(),l5=a("li"),RWe=a("strong"),jft=o("ctrl"),Pft=o(" \u2014 "),Rge=a("a"),Zft=o("TFCTRLLMHeadModel"),Vft=o(" (CTRL model)"),Wft=l(),i5=a("li"),kWe=a("strong"),Jft=o("distilbert"),Uft=o(" \u2014 "),kge=a("a"),Xft=o("TFDistilBertForMaskedLM"),Dft=o(" (DistilBERT model)"),qft=l(),d5=a("li"),SWe=a("strong"),Oft=o("electra"),Qft=o(" \u2014 "),Sge=a("a"),Yft=o("TFElectraForPreTraining"),zft=o(" (ELECTRA model)"),Hft=l(),c5=a("li"),xWe=a("strong"),Kft=o("flaubert"),eht=o(" \u2014 "),xge=a("a"),oht=o("TFFlaubertWithLMHeadModel"),rht=o(" (FlauBERT model)"),tht=l(),m5=a("li"),GWe=a("strong"),aht=o("funnel"),nht=o(" \u2014 "),Gge=a("a"),sht=o("TFFunnelForPreTraining"),lht=o(" (Funnel Transformer model)"),iht=l(),f5=a("li"),BWe=a("strong"),dht=o("gpt-sw3"),cht=o(" \u2014 "),Bge=a("a"),mht=o("TFGPT2LMHeadModel"),fht=o(" (GPT-Sw3 model)"),hht=l(),h5=a("li"),IWe=a("strong"),ght=o("gpt2"),uht=o(" \u2014 "),Ige=a("a"),pht=o("TFGPT2LMHeadModel"),_ht=o(" (OpenAI GPT-2 model)"),vht=l(),g5=a("li"),NWe=a("strong"),bht=o("layoutlm"),Mht=o(" \u2014 "),Nge=a("a"),Tht=o("TFLayoutLMForMaskedLM"),Fht=o(" (LayoutLM model)"),Eht=l(),u5=a("li"),$We=a("strong"),yht=o("lxmert"),Cht=o(" \u2014 "),$ge=a("a"),wht=o("TFLxmertForPreTraining"),Lht=o(" (LXMERT model)"),Aht=l(),p5=a("li"),jWe=a("strong"),Rht=o("mobilebert"),kht=o(" \u2014 "),jge=a("a"),Sht=o("TFMobileBertForPreTraining"),xht=o(" (MobileBERT model)"),Ght=l(),_5=a("li"),PWe=a("strong"),Bht=o("mpnet"),Iht=o(" \u2014 "),Pge=a("a"),Nht=o("TFMPNetForMaskedLM"),$ht=o(" (MPNet model)"),jht=l(),v5=a("li"),ZWe=a("strong"),Pht=o("openai-gpt"),Zht=o(" \u2014 "),Zge=a("a"),Vht=o("TFOpenAIGPTLMHeadModel"),Wht=o(" (OpenAI GPT model)"),Jht=l(),b5=a("li"),VWe=a("strong"),Uht=o("roberta"),Xht=o(" \u2014 "),Vge=a("a"),Dht=o("TFRobertaForMaskedLM"),qht=o(" (RoBERTa model)"),Oht=l(),M5=a("li"),WWe=a("strong"),Qht=o("roberta-prelayernorm"),Yht=o(" \u2014 "),Wge=a("a"),zht=o("TFRobertaPreLayerNormForMaskedLM"),Hht=o(" (RoBERTa-PreLayerNorm model)"),Kht=l(),T5=a("li"),JWe=a("strong"),egt=o("t5"),ogt=o(" \u2014 "),Jge=a("a"),rgt=o("TFT5ForConditionalGeneration"),tgt=o(" (T5 model)"),agt=l(),F5=a("li"),UWe=a("strong"),ngt=o("tapas"),sgt=o(" \u2014 "),Uge=a("a"),lgt=o("TFTapasForMaskedLM"),igt=o(" (TAPAS model)"),dgt=l(),E5=a("li"),XWe=a("strong"),cgt=o("transfo-xl"),mgt=o(" \u2014 "),Xge=a("a"),fgt=o("TFTransfoXLLMHeadModel"),hgt=o(" (Transformer-XL model)"),ggt=l(),y5=a("li"),DWe=a("strong"),ugt=o("vit_mae"),pgt=o(" \u2014 "),Dge=a("a"),_gt=o("TFViTMAEForPreTraining"),vgt=o(" (ViTMAE model)"),bgt=l(),C5=a("li"),qWe=a("strong"),Mgt=o("xlm"),Tgt=o(" \u2014 "),qge=a("a"),Fgt=o("TFXLMWithLMHeadModel"),Egt=o(" (XLM model)"),ygt=l(),w5=a("li"),OWe=a("strong"),Cgt=o("xlm-roberta"),wgt=o(" \u2014 "),Oge=a("a"),Lgt=o("TFXLMRobertaForMaskedLM"),Agt=o(" (XLM-RoBERTa model)"),Rgt=l(),L5=a("li"),QWe=a("strong"),kgt=o("xlnet"),Sgt=o(" \u2014 "),Qge=a("a"),xgt=o("TFXLNetLMHeadModel"),Ggt=o(" (XLNet model)"),Bgt=l(),b(A5.$$.fragment),ABo=l(),Tm=a("h3"),R5=a("a"),YWe=a("span"),b(gW.$$.fragment),Igt=l(),zWe=a("span"),Ngt=o("FlaxAutoModelForPreTraining"),RBo=l(),Qo=a("div"),b(uW.$$.fragment),$gt=l(),Fm=a("p"),jgt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),Yge=a("a"),Pgt=o("from_pretrained()"),Zgt=o(" class method or the "),zge=a("a"),Vgt=o("from_config()"),Wgt=o(` class
method.`),Jgt=l(),pW=a("p"),Ugt=o("This class cannot be instantiated directly using "),HWe=a("code"),Xgt=o("__init__()"),Dgt=o(" (throws an error)."),qgt=l(),Ot=a("div"),b(_W.$$.fragment),Ogt=l(),KWe=a("p"),Qgt=o("Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),Ygt=l(),Em=a("p"),zgt=o(`Note:
Loading a model from its configuration file does `),eJe=a("strong"),Hgt=o("not"),Kgt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Hge=a("a"),eut=o("from_pretrained()"),out=o(" to load the model weights."),rut=l(),b(k5.$$.fragment),tut=l(),st=a("div"),b(vW.$$.fragment),aut=l(),oJe=a("p"),nut=o("Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),sut=l(),Xn=a("p"),lut=o("The model class to instantiate is selected based on the "),rJe=a("code"),iut=o("model_type"),dut=o(` property of the config object (either
passed as an argument or loaded from `),tJe=a("code"),cut=o("pretrained_model_name_or_path"),mut=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),aJe=a("code"),fut=o("pretrained_model_name_or_path"),hut=o(":"),gut=l(),we=a("ul"),S5=a("li"),nJe=a("strong"),uut=o("albert"),put=o(" \u2014 "),Kge=a("a"),_ut=o("FlaxAlbertForPreTraining"),vut=o(" (ALBERT model)"),but=l(),x5=a("li"),sJe=a("strong"),Mut=o("bart"),Tut=o(" \u2014 "),eue=a("a"),Fut=o("FlaxBartForConditionalGeneration"),Eut=o(" (BART model)"),yut=l(),G5=a("li"),lJe=a("strong"),Cut=o("bert"),wut=o(" \u2014 "),oue=a("a"),Lut=o("FlaxBertForPreTraining"),Aut=o(" (BERT model)"),Rut=l(),B5=a("li"),iJe=a("strong"),kut=o("big_bird"),Sut=o(" \u2014 "),rue=a("a"),xut=o("FlaxBigBirdForPreTraining"),Gut=o(" (BigBird model)"),But=l(),I5=a("li"),dJe=a("strong"),Iut=o("electra"),Nut=o(" \u2014 "),tue=a("a"),$ut=o("FlaxElectraForPreTraining"),jut=o(" (ELECTRA model)"),Put=l(),N5=a("li"),cJe=a("strong"),Zut=o("longt5"),Vut=o(" \u2014 "),aue=a("a"),Wut=o("FlaxLongT5ForConditionalGeneration"),Jut=o(" (LongT5 model)"),Uut=l(),$5=a("li"),mJe=a("strong"),Xut=o("mbart"),Dut=o(" \u2014 "),nue=a("a"),qut=o("FlaxMBartForConditionalGeneration"),Out=o(" (mBART model)"),Qut=l(),j5=a("li"),fJe=a("strong"),Yut=o("mt5"),zut=o(" \u2014 "),sue=a("a"),Hut=o("FlaxMT5ForConditionalGeneration"),Kut=o(" (MT5 model)"),ept=l(),P5=a("li"),hJe=a("strong"),opt=o("roberta"),rpt=o(" \u2014 "),lue=a("a"),tpt=o("FlaxRobertaForMaskedLM"),apt=o(" (RoBERTa model)"),npt=l(),Z5=a("li"),gJe=a("strong"),spt=o("roberta-prelayernorm"),lpt=o(" \u2014 "),iue=a("a"),ipt=o("FlaxRobertaPreLayerNormForMaskedLM"),dpt=o(" (RoBERTa-PreLayerNorm model)"),cpt=l(),V5=a("li"),uJe=a("strong"),mpt=o("roformer"),fpt=o(" \u2014 "),due=a("a"),hpt=o("FlaxRoFormerForMaskedLM"),gpt=o(" (RoFormer model)"),upt=l(),W5=a("li"),pJe=a("strong"),ppt=o("t5"),_pt=o(" \u2014 "),cue=a("a"),vpt=o("FlaxT5ForConditionalGeneration"),bpt=o(" (T5 model)"),Mpt=l(),J5=a("li"),_Je=a("strong"),Tpt=o("wav2vec2"),Fpt=o(" \u2014 "),mue=a("a"),Ept=o("FlaxWav2Vec2ForPreTraining"),ypt=o(" (Wav2Vec2 model)"),Cpt=l(),U5=a("li"),vJe=a("strong"),wpt=o("whisper"),Lpt=o(" \u2014 "),fue=a("a"),Apt=o("FlaxWhisperForConditionalGeneration"),Rpt=o(" (Whisper model)"),kpt=l(),X5=a("li"),bJe=a("strong"),Spt=o("xlm-roberta"),xpt=o(" \u2014 "),hue=a("a"),Gpt=o("FlaxXLMRobertaForMaskedLM"),Bpt=o(" (XLM-RoBERTa model)"),Ipt=l(),b(D5.$$.fragment),kBo=l(),ym=a("h2"),q5=a("a"),MJe=a("span"),b(bW.$$.fragment),Npt=l(),TJe=a("span"),$pt=o("Natural Language Processing"),SBo=l(),gue=a("p"),jpt=o("The following auto classes are available for the following natural language processing tasks."),xBo=l(),Cm=a("h3"),O5=a("a"),FJe=a("span"),b(MW.$$.fragment),Ppt=l(),EJe=a("span"),Zpt=o("AutoModelForCausalLM"),GBo=l(),Yo=a("div"),b(TW.$$.fragment),Vpt=l(),wm=a("p"),Wpt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),uue=a("a"),Jpt=o("from_pretrained()"),Upt=o(" class method or the "),pue=a("a"),Xpt=o("from_config()"),Dpt=o(` class
method.`),qpt=l(),FW=a("p"),Opt=o("This class cannot be instantiated directly using "),yJe=a("code"),Qpt=o("__init__()"),Ypt=o(" (throws an error)."),zpt=l(),Qt=a("div"),b(EW.$$.fragment),Hpt=l(),CJe=a("p"),Kpt=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),e_t=l(),Lm=a("p"),o_t=o(`Note:
Loading a model from its configuration file does `),wJe=a("strong"),r_t=o("not"),t_t=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),_ue=a("a"),a_t=o("from_pretrained()"),n_t=o(" to load the model weights."),s_t=l(),b(Q5.$$.fragment),l_t=l(),io=a("div"),b(yW.$$.fragment),i_t=l(),LJe=a("p"),d_t=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),c_t=l(),Dn=a("p"),m_t=o("The model class to instantiate is selected based on the "),AJe=a("code"),f_t=o("model_type"),h_t=o(` property of the config object (either
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falling back to using pattern matching on `),kJe=a("code"),p_t=o("pretrained_model_name_or_path"),__t=o(":"),v_t=l(),Q=a("ul"),Y5=a("li"),SJe=a("strong"),b_t=o("bart"),M_t=o(" \u2014 "),vue=a("a"),T_t=o("BartForCausalLM"),F_t=o(" (BART model)"),E_t=l(),z5=a("li"),xJe=a("strong"),y_t=o("bert"),C_t=o(" \u2014 "),bue=a("a"),w_t=o("BertLMHeadModel"),L_t=o(" (BERT model)"),A_t=l(),H5=a("li"),GJe=a("strong"),R_t=o("bert-generation"),k_t=o(" \u2014 "),Mue=a("a"),S_t=o("BertGenerationDecoder"),x_t=o(" (Bert Generation model)"),G_t=l(),K5=a("li"),BJe=a("strong"),B_t=o("big_bird"),I_t=o(" \u2014 "),Tue=a("a"),N_t=o("BigBirdForCausalLM"),$_t=o(" (BigBird model)"),j_t=l(),e9=a("li"),IJe=a("strong"),P_t=o("bigbird_pegasus"),Z_t=o(" \u2014 "),Fue=a("a"),V_t=o("BigBirdPegasusForCausalLM"),W_t=o(" (BigBird-Pegasus model)"),J_t=l(),o9=a("li"),NJe=a("strong"),U_t=o("biogpt"),X_t=o(" \u2014 "),Eue=a("a"),D_t=o("BioGptForCausalLM"),q_t=o(" (BioGpt 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model\u2019s configuration. Use `),ype=a("a"),d0t=o("from_pretrained()"),c0t=o(" to load the model weights."),m0t=l(),b(tC.$$.fragment),f0t=l(),lt=a("div"),b(RW.$$.fragment),h0t=l(),WUe=a("p"),g0t=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),u0t=l(),qn=a("p"),p0t=o("The model class to instantiate is selected based on the "),JUe=a("code"),_0t=o("model_type"),v0t=o(` property of the config object (either
passed as an argument or loaded from `),UUe=a("code"),b0t=o("pretrained_model_name_or_path"),M0t=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),XUe=a("code"),T0t=o("pretrained_model_name_or_path"),F0t=o(":"),E0t=l(),Ee=a("ul"),aC=a("li"),DUe=a("strong"),y0t=o("bert"),C0t=o(" \u2014 "),Cpe=a("a"),w0t=o("TFBertLMHeadModel"),L0t=o(" (BERT model)"),A0t=l(),nC=a("li"),qUe=a("strong"),R0t=o("camembert"),k0t=o(" \u2014 "),wpe=a("a"),S0t=o("TFCamembertForCausalLM"),x0t=o(" (CamemBERT model)"),G0t=l(),sC=a("li"),OUe=a("strong"),B0t=o("ctrl"),I0t=o(" \u2014 "),Lpe=a("a"),N0t=o("TFCTRLLMHeadModel"),$0t=o(" (CTRL model)"),j0t=l(),lC=a("li"),QUe=a("strong"),P0t=o("gpt-sw3"),Z0t=o(" \u2014 "),Ape=a("a"),V0t=o("TFGPT2LMHeadModel"),W0t=o(" (GPT-Sw3 model)"),J0t=l(),iC=a("li"),YUe=a("strong"),U0t=o("gpt2"),X0t=o(" \u2014 "),Rpe=a("a"),D0t=o("TFGPT2LMHeadModel"),q0t=o(" (OpenAI GPT-2 model)"),O0t=l(),dC=a("li"),zUe=a("strong"),Q0t=o("gptj"),Y0t=o(" \u2014 "),kpe=a("a"),z0t=o("TFGPTJForCausalLM"),H0t=o(" (GPT-J model)"),K0t=l(),cC=a("li"),HUe=a("strong"),e4t=o("openai-gpt"),o4t=o(" \u2014 "),Spe=a("a"),r4t=o("TFOpenAIGPTLMHeadModel"),t4t=o(" (OpenAI GPT model)"),a4t=l(),mC=a("li"),KUe=a("strong"),n4t=o("opt"),s4t=o(" \u2014 "),xpe=a("a"),l4t=o("TFOPTForCausalLM"),i4t=o(" (OPT model)"),d4t=l(),fC=a("li"),eXe=a("strong"),c4t=o("rembert"),m4t=o(" \u2014 "),Gpe=a("a"),f4t=o("TFRemBertForCausalLM"),h4t=o(" (RemBERT model)"),g4t=l(),hC=a("li"),oXe=a("strong"),u4t=o("roberta"),p4t=o(" \u2014 "),Bpe=a("a"),_4t=o("TFRobertaForCausalLM"),v4t=o(" (RoBERTa model)"),b4t=l(),gC=a("li"),rXe=a("strong"),M4t=o("roberta-prelayernorm"),T4t=o(" \u2014 "),Ipe=a("a"),F4t=o("TFRobertaPreLayerNormForCausalLM"),E4t=o(" (RoBERTa-PreLayerNorm model)"),y4t=l(),uC=a("li"),tXe=a("strong"),C4t=o("roformer"),w4t=o(" \u2014 "),Npe=a("a"),L4t=o("TFRoFormerForCausalLM"),A4t=o(" (RoFormer model)"),R4t=l(),pC=a("li"),aXe=a("strong"),k4t=o("transfo-xl"),S4t=o(" \u2014 "),$pe=a("a"),x4t=o("TFTransfoXLLMHeadModel"),G4t=o(" (Transformer-XL model)"),B4t=l(),_C=a("li"),nXe=a("strong"),I4t=o("xglm"),N4t=o(" \u2014 "),jpe=a("a"),$4t=o("TFXGLMForCausalLM"),j4t=o(" (XGLM model)"),P4t=l(),vC=a("li"),sXe=a("strong"),Z4t=o("xlm"),V4t=o(" \u2014 "),Ppe=a("a"),W4t=o("TFXLMWithLMHeadModel"),J4t=o(" (XLM model)"),U4t=l(),bC=a("li"),lXe=a("strong"),X4t=o("xlm-roberta"),D4t=o(" \u2014 "),Zpe=a("a"),q4t=o("TFXLMRobertaForCausalLM"),O4t=o(" (XLM-RoBERTa model)"),Q4t=l(),MC=a("li"),iXe=a("strong"),Y4t=o("xlnet"),z4t=o(" \u2014 "),Vpe=a("a"),H4t=o("TFXLNetLMHeadModel"),K4t=o(" (XLNet model)"),eMt=l(),b(TC.$$.fragment),NBo=l(),Sm=a("h3"),FC=a("a"),dXe=a("span"),b(kW.$$.fragment),oMt=l(),cXe=a("span"),rMt=o("FlaxAutoModelForCausalLM"),$Bo=l(),Ho=a("div"),b(SW.$$.fragment),tMt=l(),xm=a("p"),aMt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),Wpe=a("a"),nMt=o("from_pretrained()"),sMt=o(" class method or the "),Jpe=a("a"),lMt=o("from_config()"),iMt=o(` class
method.`),dMt=l(),xW=a("p"),cMt=o("This class cannot be instantiated directly using "),mXe=a("code"),mMt=o("__init__()"),fMt=o(" (throws an error)."),hMt=l(),zt=a("div"),b(GW.$$.fragment),gMt=l(),fXe=a("p"),uMt=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),pMt=l(),Gm=a("p"),_Mt=o(`Note:
Loading a model from its configuration file does `),hXe=a("strong"),vMt=o("not"),bMt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Upe=a("a"),MMt=o("from_pretrained()"),TMt=o(" to load the model weights."),FMt=l(),b(EC.$$.fragment),EMt=l(),it=a("div"),b(BW.$$.fragment),yMt=l(),gXe=a("p"),CMt=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),wMt=l(),On=a("p"),LMt=o("The model class to instantiate is selected based on the "),uXe=a("code"),AMt=o("model_type"),RMt=o(` property of the config object (either
passed as an argument or loaded from `),pXe=a("code"),kMt=o("pretrained_model_name_or_path"),SMt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),_Xe=a("code"),xMt=o("pretrained_model_name_or_path"),GMt=o(":"),BMt=l(),Le=a("ul"),yC=a("li"),vXe=a("strong"),IMt=o("bart"),NMt=o(" \u2014 "),Xpe=a("a"),$Mt=o("FlaxBartForCausalLM"),jMt=o(" (BART model)"),PMt=l(),CC=a("li"),bXe=a("strong"),ZMt=o("bert"),VMt=o(" \u2014 "),Dpe=a("a"),WMt=o("FlaxBertForCausalLM"),JMt=o(" (BERT model)"),UMt=l(),wC=a("li"),MXe=a("strong"),XMt=o("big_bird"),DMt=o(" \u2014 "),qpe=a("a"),qMt=o("FlaxBigBirdForCausalLM"),OMt=o(" (BigBird model)"),QMt=l(),LC=a("li"),TXe=a("strong"),YMt=o("bloom"),zMt=o(" \u2014 "),Ope=a("a"),HMt=o("FlaxBloomForCausalLM"),KMt=o(" (BLOOM model)"),eTt=l(),AC=a("li"),FXe=a("strong"),oTt=o("electra"),rTt=o(" \u2014 "),Qpe=a("a"),tTt=o("FlaxElectraForCausalLM"),aTt=o(" (ELECTRA model)"),nTt=l(),RC=a("li"),EXe=a("strong"),sTt=o("gpt-sw3"),lTt=o(" \u2014 "),Ype=a("a"),iTt=o("FlaxGPT2LMHeadModel"),dTt=o(" (GPT-Sw3 model)"),cTt=l(),kC=a("li"),yXe=a("strong"),mTt=o("gpt2"),fTt=o(" \u2014 "),zpe=a("a"),hTt=o("FlaxGPT2LMHeadModel"),gTt=o(" (OpenAI GPT-2 model)"),uTt=l(),SC=a("li"),CXe=a("strong"),pTt=o("gpt_neo"),_Tt=o(" \u2014 "),Hpe=a("a"),vTt=o("FlaxGPTNeoForCausalLM"),bTt=o(" (GPT Neo model)"),MTt=l(),xC=a("li"),wXe=a("strong"),TTt=o("gptj"),FTt=o(" \u2014 "),Kpe=a("a"),ETt=o("FlaxGPTJForCausalLM"),yTt=o(" (GPT-J model)"),CTt=l(),GC=a("li"),LXe=a("strong"),wTt=o("opt"),LTt=o(" \u2014 "),e_e=a("a"),ATt=o("FlaxOPTForCausalLM"),RTt=o(" (OPT model)"),kTt=l(),BC=a("li"),AXe=a("strong"),STt=o("roberta"),xTt=o(" \u2014 "),o_e=a("a"),GTt=o("FlaxRobertaForCausalLM"),BTt=o(" (RoBERTa model)"),ITt=l(),IC=a("li"),RXe=a("strong"),NTt=o("roberta-prelayernorm"),$Tt=o(" \u2014 "),r_e=a("a"),jTt=o("FlaxRobertaPreLayerNormForCausalLM"),PTt=o(" (RoBERTa-PreLayerNorm model)"),ZTt=l(),NC=a("li"),kXe=a("strong"),VTt=o("xglm"),WTt=o(" \u2014 "),t_e=a("a"),JTt=o("FlaxXGLMForCausalLM"),UTt=o(" (XGLM model)"),XTt=l(),$C=a("li"),SXe=a("strong"),DTt=o("xlm-roberta"),qTt=o(" \u2014 "),a_e=a("a"),OTt=o("FlaxXLMRobertaForCausalLM"),QTt=o(" (XLM-RoBERTa model)"),YTt=l(),b(jC.$$.fragment),jBo=l(),Bm=a("h3"),PC=a("a"),xXe=a("span"),b(IW.$$.fragment),zTt=l(),GXe=a("span"),HTt=o("AutoModelForMaskedLM"),PBo=l(),Ko=a("div"),b(NW.$$.fragment),KTt=l(),Im=a("p"),eFt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
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method.`),nFt=l(),$W=a("p"),sFt=o("This class cannot be instantiated directly using "),BXe=a("code"),lFt=o("__init__()"),iFt=o(" (throws an error)."),dFt=l(),Ht=a("div"),b(jW.$$.fragment),cFt=l(),IXe=a("p"),mFt=o("Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),fFt=l(),Nm=a("p"),hFt=o(`Note:
Loading a model from its configuration file does `),NXe=a("strong"),gFt=o("not"),uFt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),l_e=a("a"),pFt=o("from_pretrained()"),_Ft=o(" to load the model weights."),vFt=l(),b(ZC.$$.fragment),bFt=l(),co=a("div"),b(PW.$$.fragment),MFt=l(),$Xe=a("p"),TFt=o("Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),FFt=l(),Qn=a("p"),EFt=o("The model class to instantiate is selected based on the "),jXe=a("code"),yFt=o("model_type"),CFt=o(` property of the config object (either
passed as an argument or loaded from `),PXe=a("code"),wFt=o("pretrained_model_name_or_path"),LFt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ZXe=a("code"),AFt=o("pretrained_model_name_or_path"),RFt=o(":"),kFt=l(),K=a("ul"),VC=a("li"),VXe=a("strong"),SFt=o("albert"),xFt=o(" \u2014 "),i_e=a("a"),GFt=o("AlbertForMaskedLM"),BFt=o(" (ALBERT model)"),IFt=l(),WC=a("li"),WXe=a("strong"),NFt=o("bart"),$Ft=o(" \u2014 "),d_e=a("a"),jFt=o("BartForConditionalGeneration"),PFt=o(" (BART model)"),ZFt=l(),JC=a("li"),JXe=a("strong"),VFt=o("bert"),WFt=o(" \u2014 "),c_e=a("a"),JFt=o("BertForMaskedLM"),UFt=o(" (BERT model)"),XFt=l(),UC=a("li"),UXe=a("strong"),DFt=o("big_bird"),qFt=o(" \u2014 "),m_e=a("a"),OFt=o("BigBirdForMaskedLM"),QFt=o(" (BigBird model)"),YFt=l(),XC=a("li"),XXe=a("strong"),zFt=o("camembert"),HFt=o(" \u2014 "),f_e=a("a"),KFt=o("CamembertForMaskedLM"),eEt=o(" (CamemBERT model)"),oEt=l(),DC=a("li"),DXe=a("strong"),rEt=o("convbert"),tEt=o(" \u2014 "),h_e=a("a"),aEt=o("ConvBertForMaskedLM"),nEt=o(" (ConvBERT model)"),sEt=l(),qC=a("li"),qXe=a("strong"),lEt=o("data2vec-text"),iEt=o(" 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model\u2019s configuration. Use `),eve=a("a"),cCt=o("from_pretrained()"),mCt=o(" to load the model weights."),fCt=l(),b(Iw.$$.fragment),hCt=l(),dt=a("div"),b(UW.$$.fragment),gCt=l(),ZDe=a("p"),uCt=o("Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),pCt=l(),Yn=a("p"),_Ct=o("The model class to instantiate is selected based on the "),VDe=a("code"),vCt=o("model_type"),bCt=o(` property of the config object (either
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falling back to using pattern matching on `),JDe=a("code"),FCt=o("pretrained_model_name_or_path"),ECt=o(":"),yCt=l(),pe=a("ul"),Nw=a("li"),UDe=a("strong"),CCt=o("albert"),wCt=o(" \u2014 "),ove=a("a"),LCt=o("TFAlbertForMaskedLM"),ACt=o(" (ALBERT model)"),RCt=l(),$w=a("li"),XDe=a("strong"),kCt=o("bert"),SCt=o(" \u2014 "),rve=a("a"),xCt=o("TFBertForMaskedLM"),GCt=o(" (BERT model)"),BCt=l(),jw=a("li"),DDe=a("strong"),ICt=o("camembert"),NCt=o(" \u2014 "),tve=a("a"),$Ct=o("TFCamembertForMaskedLM"),jCt=o(" (CamemBERT model)"),PCt=l(),Pw=a("li"),qDe=a("strong"),ZCt=o("convbert"),VCt=o(" \u2014 "),ave=a("a"),WCt=o("TFConvBertForMaskedLM"),JCt=o(" (ConvBERT model)"),UCt=l(),Zw=a("li"),ODe=a("strong"),XCt=o("deberta"),DCt=o(" \u2014 "),nve=a("a"),qCt=o("TFDebertaForMaskedLM"),OCt=o(" (DeBERTa model)"),QCt=l(),Vw=a("li"),QDe=a("strong"),YCt=o("deberta-v2"),zCt=o(" \u2014 "),sve=a("a"),HCt=o("TFDebertaV2ForMaskedLM"),KCt=o(" (DeBERTa-v2 model)"),ewt=l(),Ww=a("li"),YDe=a("strong"),owt=o("distilbert"),rwt=o(" \u2014 "),lve=a("a"),twt=o("TFDistilBertForMaskedLM"),awt=o(" (DistilBERT model)"),nwt=l(),Jw=a("li"),zDe=a("strong"),swt=o("electra"),lwt=o(" \u2014 "),ive=a("a"),iwt=o("TFElectraForMaskedLM"),dwt=o(" (ELECTRA model)"),cwt=l(),Uw=a("li"),HDe=a("strong"),mwt=o("esm"),fwt=o(" \u2014 "),dve=a("a"),hwt=o("TFEsmForMaskedLM"),gwt=o(" (ESM model)"),uwt=l(),Xw=a("li"),KDe=a("strong"),pwt=o("flaubert"),_wt=o(" \u2014 "),cve=a("a"),vwt=o("TFFlaubertWithLMHeadModel"),bwt=o(" (FlauBERT model)"),Mwt=l(),Dw=a("li"),eqe=a("strong"),Twt=o("funnel"),Fwt=o(" \u2014 "),mve=a("a"),Ewt=o("TFFunnelForMaskedLM"),ywt=o(" (Funnel Transformer model)"),Cwt=l(),qw=a("li"),oqe=a("strong"),wwt=o("layoutlm"),Lwt=o(" \u2014 "),fve=a("a"),Awt=o("TFLayoutLMForMaskedLM"),Rwt=o(" (LayoutLM model)"),kwt=l(),Ow=a("li"),rqe=a("strong"),Swt=o("longformer"),xwt=o(" \u2014 "),hve=a("a"),Gwt=o("TFLongformerForMaskedLM"),Bwt=o(" (Longformer model)"),Iwt=l(),Qw=a("li"),tqe=a("strong"),Nwt=o("mobilebert"),$wt=o(" \u2014 "),gve=a("a"),jwt=o("TFMobileBertForMaskedLM"),Pwt=o(" (MobileBERT model)"),Zwt=l(),Yw=a("li"),aqe=a("strong"),Vwt=o("mpnet"),Wwt=o(" \u2014 "),uve=a("a"),Jwt=o("TFMPNetForMaskedLM"),Uwt=o(" (MPNet model)"),Xwt=l(),zw=a("li"),nqe=a("strong"),Dwt=o("rembert"),qwt=o(" \u2014 "),pve=a("a"),Owt=o("TFRemBertForMaskedLM"),Qwt=o(" (RemBERT model)"),Ywt=l(),Hw=a("li"),sqe=a("strong"),zwt=o("roberta"),Hwt=o(" \u2014 "),_ve=a("a"),Kwt=o("TFRobertaForMaskedLM"),e6t=o(" (RoBERTa model)"),o6t=l(),Kw=a("li"),lqe=a("strong"),r6t=o("roberta-prelayernorm"),t6t=o(" \u2014 "),vve=a("a"),a6t=o("TFRobertaPreLayerNormForMaskedLM"),n6t=o(" (RoBERTa-PreLayerNorm model)"),s6t=l(),e6=a("li"),iqe=a("strong"),l6t=o("roformer"),i6t=o(" \u2014 "),bve=a("a"),d6t=o("TFRoFormerForMaskedLM"),c6t=o(" (RoFormer model)"),m6t=l(),o6=a("li"),dqe=a("strong"),f6t=o("tapas"),h6t=o(" \u2014 "),Mve=a("a"),g6t=o("TFTapasForMaskedLM"),u6t=o(" (TAPAS model)"),p6t=l(),r6=a("li"),cqe=a("strong"),_6t=o("xlm"),v6t=o(" \u2014 "),Tve=a("a"),b6t=o("TFXLMWithLMHeadModel"),M6t=o(" (XLM model)"),T6t=l(),t6=a("li"),mqe=a("strong"),F6t=o("xlm-roberta"),E6t=o(" \u2014 "),Fve=a("a"),y6t=o("TFXLMRobertaForMaskedLM"),C6t=o(" (XLM-RoBERTa model)"),w6t=l(),b(a6.$$.fragment),WBo=l(),Zm=a("h3"),n6=a("a"),fqe=a("span"),b(XW.$$.fragment),L6t=l(),hqe=a("span"),A6t=o("FlaxAutoModelForMaskedLM"),JBo=l(),or=a("div"),b(DW.$$.fragment),R6t=l(),Vm=a("p"),k6t=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
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method.`),I6t=l(),qW=a("p"),N6t=o("This class cannot be instantiated directly using "),gqe=a("code"),$6t=o("__init__()"),j6t=o(" (throws an error)."),P6t=l(),ea=a("div"),b(OW.$$.fragment),Z6t=l(),uqe=a("p"),V6t=o("Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),W6t=l(),Wm=a("p"),J6t=o(`Note:
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falling back to using pattern matching on `),Mqe=a("code"),a8t=o("pretrained_model_name_or_path"),n8t=o(":"),s8t=l(),ke=a("ul"),l6=a("li"),Tqe=a("strong"),l8t=o("albert"),i8t=o(" \u2014 "),wve=a("a"),d8t=o("FlaxAlbertForMaskedLM"),c8t=o(" (ALBERT model)"),m8t=l(),i6=a("li"),Fqe=a("strong"),f8t=o("bart"),h8t=o(" \u2014 "),Lve=a("a"),g8t=o("FlaxBartForConditionalGeneration"),u8t=o(" (BART model)"),p8t=l(),d6=a("li"),Eqe=a("strong"),_8t=o("bert"),v8t=o(" \u2014 "),Ave=a("a"),b8t=o("FlaxBertForMaskedLM"),M8t=o(" (BERT model)"),T8t=l(),c6=a("li"),yqe=a("strong"),F8t=o("big_bird"),E8t=o(" \u2014 "),Rve=a("a"),y8t=o("FlaxBigBirdForMaskedLM"),C8t=o(" (BigBird model)"),w8t=l(),m6=a("li"),Cqe=a("strong"),L8t=o("distilbert"),A8t=o(" \u2014 "),kve=a("a"),R8t=o("FlaxDistilBertForMaskedLM"),k8t=o(" (DistilBERT model)"),S8t=l(),f6=a("li"),wqe=a("strong"),x8t=o("electra"),G8t=o(" \u2014 "),Sve=a("a"),B8t=o("FlaxElectraForMaskedLM"),I8t=o(" (ELECTRA model)"),N8t=l(),h6=a("li"),Lqe=a("strong"),$8t=o("mbart"),j8t=o(" \u2014 "),xve=a("a"),P8t=o("FlaxMBartForConditionalGeneration"),Z8t=o(" (mBART model)"),V8t=l(),g6=a("li"),Aqe=a("strong"),W8t=o("roberta"),J8t=o(" \u2014 "),Gve=a("a"),U8t=o("FlaxRobertaForMaskedLM"),X8t=o(" (RoBERTa model)"),D8t=l(),u6=a("li"),Rqe=a("strong"),q8t=o("roberta-prelayernorm"),O8t=o(" \u2014 "),Bve=a("a"),Q8t=o("FlaxRobertaPreLayerNormForMaskedLM"),Y8t=o(" (RoBERTa-PreLayerNorm model)"),z8t=l(),p6=a("li"),kqe=a("strong"),H8t=o("roformer"),K8t=o(" \u2014 "),Ive=a("a"),e7t=o("FlaxRoFormerForMaskedLM"),o7t=o(" (RoFormer model)"),r7t=l(),_6=a("li"),Sqe=a("strong"),t7t=o("xlm-roberta"),a7t=o(" \u2014 "),Nve=a("a"),n7t=o("FlaxXLMRobertaForMaskedLM"),s7t=o(" (XLM-RoBERTa model)"),l7t=l(),b(v6.$$.fragment),UBo=l(),Jm=a("h3"),b6=a("a"),xqe=a("span"),b(YW.$$.fragment),i7t=l(),Gqe=a("span"),d7t=o("AutoModelForMaskGeneration"),XBo=l(),zW=a("div"),b(HW.$$.fragment),DBo=l(),Um=a("h3"),M6=a("a"),Bqe=a("span"),b(KW.$$.fragment),c7t=l(),Iqe=a("span"),m7t=o("TFAutoModelForMaskGeneration"),qBo=l(),eJ=a("div"),b(oJ.$$.fragment),OBo=l(),Xm=a("h3"),T6=a("a"),Nqe=a("span"),b(rJ.$$.fragment),f7t=l(),$qe=a("span"),h7t=o("AutoModelForSeq2SeqLM"),QBo=l(),rr=a("div"),b(tJ.$$.fragment),g7t=l(),Dm=a("p"),u7t=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
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passed as an argument or loaded from `),Jqe=a("code"),V7t=o("pretrained_model_name_or_path"),W7t=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Uqe=a("code"),J7t=o("pretrained_model_name_or_path"),U7t=o(":"),X7t=l(),ge=a("ul"),E6=a("li"),Xqe=a("strong"),D7t=o("bart"),q7t=o(" \u2014 "),Zve=a("a"),O7t=o("BartForConditionalGeneration"),Q7t=o(" (BART model)"),Y7t=l(),y6=a("li"),Dqe=a("strong"),z7t=o("bigbird_pegasus"),H7t=o(" \u2014 "),Vve=a("a"),K7t=o("BigBirdPegasusForConditionalGeneration"),eLt=o(" (BigBird-Pegasus model)"),oLt=l(),C6=a("li"),qqe=a("strong"),rLt=o("blenderbot"),tLt=o(" \u2014 "),Wve=a("a"),aLt=o("BlenderbotForConditionalGeneration"),nLt=o(" (Blenderbot model)"),sLt=l(),w6=a("li"),Oqe=a("strong"),lLt=o("blenderbot-small"),iLt=o(" \u2014 "),Jve=a("a"),dLt=o("BlenderbotSmallForConditionalGeneration"),cLt=o(" (BlenderbotSmall model)"),mLt=l(),L6=a("li"),Qqe=a("strong"),fLt=o("encoder-decoder"),hLt=o(" \u2014 "),Uve=a("a"),gLt=o("EncoderDecoderModel"),uLt=o(" (Encoder decoder model)"),pLt=l(),A6=a("li"),Yqe=a("strong"),_Lt=o("fsmt"),vLt=o(" \u2014 "),Xve=a("a"),bLt=o("FSMTForConditionalGeneration"),MLt=o(" (FairSeq Machine-Translation model)"),TLt=l(),R6=a("li"),zqe=a("strong"),FLt=o("gptsan-japanese"),ELt=o(" \u2014 "),Dve=a("a"),yLt=o("GPTSanJapaneseForConditionalGeneration"),CLt=o(" (GPTSAN-japanese model)"),wLt=l(),k6=a("li"),Hqe=a("strong"),LLt=o("led"),ALt=o(" \u2014 "),qve=a("a"),RLt=o("LEDForConditionalGeneration"),kLt=o(" (LED model)"),SLt=l(),S6=a("li"),Kqe=a("strong"),xLt=o("longt5"),GLt=o(" \u2014 "),Ove=a("a"),BLt=o("LongT5ForConditionalGeneration"),ILt=o(" (LongT5 model)"),NLt=l(),x6=a("li"),eOe=a("strong"),$Lt=o("m2m_100"),jLt=o(" \u2014 "),Qve=a("a"),PLt=o("M2M100ForConditionalGeneration"),ZLt=o(" (M2M100 model)"),VLt=l(),G6=a("li"),oOe=a("strong"),WLt=o("marian"),JLt=o(" \u2014 "),Yve=a("a"),ULt=o("MarianMTModel"),XLt=o(" (Marian model)"),DLt=l(),B6=a("li"),rOe=a("strong"),qLt=o("mbart"),OLt=o(" \u2014 "),zve=a("a"),QLt=o("MBartForConditionalGeneration"),YLt=o(" (mBART model)"),zLt=l(),I6=a("li"),tOe=a("strong"),HLt=o("mt5"),KLt=o(" \u2014 "),Hve=a("a"),eAt=o("MT5ForConditionalGeneration"),oAt=o(" (MT5 model)"),rAt=l(),N6=a("li"),aOe=a("strong"),tAt=o("mvp"),aAt=o(" \u2014 "),Kve=a("a"),nAt=o("MvpForConditionalGeneration"),sAt=o(" (MVP model)"),lAt=l(),$6=a("li"),nOe=a("strong"),iAt=o("nllb-moe"),dAt=o(" \u2014 "),e3e=a("a"),cAt=o("NllbMoeForConditionalGeneration"),mAt=o(" (NLLB-MOE model)"),fAt=l(),j6=a("li"),sOe=a("strong"),hAt=o("pegasus"),gAt=o(" \u2014 "),o3e=a("a"),uAt=o("PegasusForConditionalGeneration"),pAt=o(" (Pegasus model)"),_At=l(),P6=a("li"),lOe=a("strong"),vAt=o("pegasus_x"),bAt=o(" \u2014 "),r3e=a("a"),MAt=o("PegasusXForConditionalGeneration"),TAt=o(" (PEGASUS-X model)"),FAt=l(),Z6=a("li"),iOe=a("strong"),EAt=o("plbart"),yAt=o(" \u2014 "),t3e=a("a"),CAt=o("PLBartForConditionalGeneration"),wAt=o(" (PLBart model)"),LAt=l(),V6=a("li"),dOe=a("strong"),AAt=o("prophetnet"),RAt=o(" \u2014 "),a3e=a("a"),kAt=o("ProphetNetForConditionalGeneration"),SAt=o(" (ProphetNet model)"),xAt=l(),W6=a("li"),cOe=a("strong"),GAt=o("switch_transformers"),BAt=o(" \u2014 "),n3e=a("a"),IAt=o("SwitchTransformersForConditionalGeneration"),NAt=o(" (SwitchTransformers model)"),$At=l(),J6=a("li"),mOe=a("strong"),jAt=o("t5"),PAt=o(" \u2014 "),s3e=a("a"),ZAt=o("T5ForConditionalGeneration"),VAt=o(" (T5 model)"),WAt=l(),U6=a("li"),fOe=a("strong"),JAt=o("umt5"),UAt=o(" \u2014 "),l3e=a("a"),XAt=o("UMT5ForConditionalGeneration"),DAt=o(" (UMT5 model)"),qAt=l(),X6=a("li"),hOe=a("strong"),OAt=o("xlm-prophetnet"),QAt=o(" \u2014 "),i3e=a("a"),YAt=o("XLMProphetNetForConditionalGeneration"),zAt=o(" (XLM-ProphetNet model)"),HAt=l(),D6=a("p"),KAt=o("The model is set in evaluation mode by default using "),gOe=a("code"),eRt=o("model.eval()"),oRt=o(` (so for instance, dropout modules are
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falling back to using pattern matching on `),JOe=a("code"),mSt=o("pretrained_model_name_or_path"),fSt=o(":"),hSt=l(),$e=a("ul"),d8=a("li"),UOe=a("strong"),gSt=o("bart"),uSt=o(" \u2014 "),C3e=a("a"),pSt=o("FlaxBartForConditionalGeneration"),_St=o(" (BART model)"),vSt=l(),c8=a("li"),XOe=a("strong"),bSt=o("blenderbot"),MSt=o(" \u2014 "),w3e=a("a"),TSt=o("FlaxBlenderbotForConditionalGeneration"),FSt=o(" (Blenderbot model)"),ESt=l(),m8=a("li"),DOe=a("strong"),ySt=o("blenderbot-small"),CSt=o(" \u2014 "),L3e=a("a"),wSt=o("FlaxBlenderbotSmallForConditionalGeneration"),LSt=o(" (BlenderbotSmall model)"),ASt=l(),f8=a("li"),qOe=a("strong"),RSt=o("encoder-decoder"),kSt=o(" \u2014 "),A3e=a("a"),SSt=o("FlaxEncoderDecoderModel"),xSt=o(" (Encoder decoder model)"),GSt=l(),h8=a("li"),OOe=a("strong"),BSt=o("longt5"),ISt=o(" \u2014 "),R3e=a("a"),NSt=o("FlaxLongT5ForConditionalGeneration"),$St=o(" (LongT5 model)"),jSt=l(),g8=a("li"),QOe=a("strong"),PSt=o("marian"),ZSt=o(" \u2014 "),k3e=a("a"),VSt=o("FlaxMarianMTModel"),WSt=o(" (Marian model)"),JSt=l(),u8=a("li"),YOe=a("strong"),USt=o("mbart"),XSt=o(" \u2014 "),S3e=a("a"),DSt=o("FlaxMBartForConditionalGeneration"),qSt=o(" (mBART model)"),OSt=l(),p8=a("li"),zOe=a("strong"),QSt=o("mt5"),YSt=o(" \u2014 "),x3e=a("a"),zSt=o("FlaxMT5ForConditionalGeneration"),HSt=o(" (MT5 model)"),KSt=l(),_8=a("li"),HOe=a("strong"),ext=o("pegasus"),oxt=o(" \u2014 "),G3e=a("a"),rxt=o("FlaxPegasusForConditionalGeneration"),txt=o(" (Pegasus model)"),axt=l(),v8=a("li"),KOe=a("strong"),nxt=o("t5"),sxt=o(" \u2014 "),B3e=a("a"),lxt=o("FlaxT5ForConditionalGeneration"),ixt=o(" (T5 model)"),dxt=l(),b(b8.$$.fragment),eIo=l(),ef=a("h3"),M8=a("a"),eQe=a("span"),b(_J.$$.fragment),cxt=l(),oQe=a("span"),mxt=o("AutoModelForSequenceClassification"),oIo=l(),nr=a("div"),b(vJ.$$.fragment),fxt=l(),of=a("p"),hxt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the `),I3e=a("a"),gxt=o("from_pretrained()"),uxt=o(" class method or the "),N3e=a("a"),pxt=o("from_config()"),_xt=o(` class
method.`),vxt=l(),bJ=a("p"),bxt=o("This class cannot be instantiated directly using "),rQe=a("code"),Mxt=o("__init__()"),Txt=o(" (throws an error)."),Fxt=l(),aa=a("div"),b(MJ.$$.fragment),Ext=l(),tQe=a("p"),yxt=o("Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),Cxt=l(),rf=a("p"),wxt=o(`Note:
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model\u2019s configuration. Use `),$3e=a("a"),Rxt=o("from_pretrained()"),kxt=o(" to load the model weights."),Sxt=l(),b(T8.$$.fragment),xxt=l(),fo=a("div"),b(TJ.$$.fragment),Gxt=l(),nQe=a("p"),Bxt=o("Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),Ixt=l(),os=a("p"),Nxt=o("The model class to instantiate is selected based on the "),sQe=a("code"),$xt=o("model_type"),jxt=o(` property of the config object (either
passed as an argument or loaded from `),lQe=a("code"),Pxt=o("pretrained_model_name_or_path"),Zxt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),iQe=a("code"),Vxt=o("pretrained_model_name_or_path"),Wxt=o(":"),Jxt=l(),B=a("ul"),F8=a("li"),dQe=a("strong"),Uxt=o("albert"),Xxt=o(" \u2014 "),j3e=a("a"),Dxt=o("AlbertForSequenceClassification"),qxt=o(" (ALBERT model)"),Oxt=l(),E8=a("li"),cQe=a("strong"),Qxt=o("bart"),Yxt=o(" \u2014 "),P3e=a("a"),zxt=o("BartForSequenceClassification"),Hxt=o(" (BART model)"),Kxt=l(),y8=a("li"),mQe=a("strong"),eGt=o("bert"),oGt=o(" \u2014 "),Z3e=a("a"),rGt=o("BertForSequenceClassification"),tGt=o(" (BERT model)"),aGt=l(),C8=a("li"),fQe=a("strong"),nGt=o("big_bird"),sGt=o(" \u2014 "),V3e=a("a"),lGt=o("BigBirdForSequenceClassification"),iGt=o(" (BigBird model)"),dGt=l(),w8=a("li"),hQe=a("strong"),cGt=o("bigbird_pegasus"),mGt=o(" \u2014 "),W3e=a("a"),fGt=o("BigBirdPegasusForSequenceClassification"),hGt=o(" (BigBird-Pegasus model)"),gGt=l(),L8=a("li"),gQe=a("strong"),uGt=o("biogpt"),pGt=o(" \u2014 "),J3e=a("a"),_Gt=o("BioGptForSequenceClassification"),vGt=o(" (BioGpt model)"),bGt=l(),A8=a("li"),uQe=a("strong"),MGt=o("bloom"),TGt=o(" \u2014 "),U3e=a("a"),FGt=o("BloomForSequenceClassification"),EGt=o(" (BLOOM model)"),yGt=l(),R8=a("li"),pQe=a("strong"),CGt=o("camembert"),wGt=o(" \u2014 "),X3e=a("a"),LGt=o("CamembertForSequenceClassification"),AGt=o(" (CamemBERT model)"),RGt=l(),k8=a("li"),_Qe=a("strong"),kGt=o("canine"),SGt=o(" \u2014 "),D3e=a("a"),xGt=o("CanineForSequenceClassification"),GGt=o(" (CANINE model)"),BGt=l(),S8=a("li"),vQe=a("strong"),IGt=o("code_llama"),NGt=o(" \u2014 "),q3e=a("a"),$Gt=o("LlamaForSequenceClassification"),jGt=o(" (CodeLlama model)"),PGt=l(),x8=a("li"),bQe=a("strong"),ZGt=o("convbert"),VGt=o(" \u2014 "),O3e=a("a"),WGt=o("ConvBertForSequenceClassification"),JGt=o(" (ConvBERT model)"),UGt=l(),G8=a("li"),MQe=a("strong"),XGt=o("ctrl"),DGt=o(" \u2014 "),Q3e=a("a"),qGt=o("CTRLForSequenceClassification"),OGt=o(" (CTRL model)"),QGt=l(),B8=a("li"),TQe=a("strong"),YGt=o("data2vec-text"),zGt=o(" \u2014 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model)"),djt=l(),w7=a("li"),hYe=a("strong"),cjt=o("rembert"),mjt=o(" \u2014 "),W1e=a("a"),fjt=o("RemBertForSequenceClassification"),hjt=o(" (RemBERT model)"),gjt=l(),L7=a("li"),gYe=a("strong"),ujt=o("roberta"),pjt=o(" \u2014 "),J1e=a("a"),_jt=o("RobertaForSequenceClassification"),vjt=o(" (RoBERTa model)"),bjt=l(),A7=a("li"),uYe=a("strong"),Mjt=o("roberta-prelayernorm"),Tjt=o(" \u2014 "),U1e=a("a"),Fjt=o("RobertaPreLayerNormForSequenceClassification"),Ejt=o(" (RoBERTa-PreLayerNorm model)"),yjt=l(),R7=a("li"),pYe=a("strong"),Cjt=o("roc_bert"),wjt=o(" \u2014 "),X1e=a("a"),Ljt=o("RoCBertForSequenceClassification"),Ajt=o(" (RoCBert model)"),Rjt=l(),k7=a("li"),_Ye=a("strong"),kjt=o("roformer"),Sjt=o(" \u2014 "),D1e=a("a"),xjt=o("RoFormerForSequenceClassification"),Gjt=o(" (RoFormer model)"),Bjt=l(),S7=a("li"),vYe=a("strong"),Ijt=o("squeezebert"),Njt=o(" \u2014 "),q1e=a("a"),$jt=o("SqueezeBertForSequenceClassification"),jjt=o(" (SqueezeBERT 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falling back to using pattern matching on `),ZHe=a("code"),PQt=o("pretrained_model_name_or_path"),ZQt=o(":"),VQt=l(),Fe=a("ul"),AA=a("li"),VHe=a("strong"),WQt=o("albert"),JQt=o(" \u2014 "),W2e=a("a"),UQt=o("TFAlbertForMultipleChoice"),XQt=o(" (ALBERT model)"),DQt=l(),RA=a("li"),WHe=a("strong"),qQt=o("bert"),OQt=o(" \u2014 "),J2e=a("a"),QQt=o("TFBertForMultipleChoice"),YQt=o(" (BERT model)"),zQt=l(),kA=a("li"),JHe=a("strong"),HQt=o("camembert"),KQt=o(" \u2014 "),U2e=a("a"),eYt=o("TFCamembertForMultipleChoice"),oYt=o(" (CamemBERT model)"),rYt=l(),SA=a("li"),UHe=a("strong"),tYt=o("convbert"),aYt=o(" \u2014 "),X2e=a("a"),nYt=o("TFConvBertForMultipleChoice"),sYt=o(" (ConvBERT model)"),lYt=l(),xA=a("li"),XHe=a("strong"),iYt=o("distilbert"),dYt=o(" \u2014 "),D2e=a("a"),cYt=o("TFDistilBertForMultipleChoice"),mYt=o(" (DistilBERT model)"),fYt=l(),GA=a("li"),DHe=a("strong"),hYt=o("electra"),gYt=o(" \u2014 "),q2e=a("a"),uYt=o("TFElectraForMultipleChoice"),pYt=o(" (ELECTRA model)"),_Yt=l(),BA=a("li"),qHe=a("strong"),vYt=o("flaubert"),bYt=o(" \u2014 "),O2e=a("a"),MYt=o("TFFlaubertForMultipleChoice"),TYt=o(" (FlauBERT model)"),FYt=l(),IA=a("li"),OHe=a("strong"),EYt=o("funnel"),yYt=o(" \u2014 "),Q2e=a("a"),CYt=o("TFFunnelForMultipleChoice"),wYt=o(" (Funnel Transformer model)"),LYt=l(),NA=a("li"),QHe=a("strong"),AYt=o("longformer"),RYt=o(" \u2014 "),Y2e=a("a"),kYt=o("TFLongformerForMultipleChoice"),SYt=o(" (Longformer model)"),xYt=l(),$A=a("li"),YHe=a("strong"),GYt=o("mobilebert"),BYt=o(" \u2014 "),z2e=a("a"),IYt=o("TFMobileBertForMultipleChoice"),NYt=o(" (MobileBERT model)"),$Yt=l(),jA=a("li"),zHe=a("strong"),jYt=o("mpnet"),PYt=o(" \u2014 "),H2e=a("a"),ZYt=o("TFMPNetForMultipleChoice"),VYt=o(" (MPNet model)"),WYt=l(),PA=a("li"),HHe=a("strong"),JYt=o("rembert"),UYt=o(" \u2014 "),K2e=a("a"),XYt=o("TFRemBertForMultipleChoice"),DYt=o(" (RemBERT model)"),qYt=l(),ZA=a("li"),KHe=a("strong"),OYt=o("roberta"),QYt=o(" \u2014 "),e0e=a("a"),YYt=o("TFRobertaForMultipleChoice"),zYt=o(" (RoBERTa model)"),HYt=l(),VA=a("li"),eKe=a("strong"),KYt=o("roberta-prelayernorm"),ezt=o(" \u2014 "),o0e=a("a"),ozt=o("TFRobertaPreLayerNormForMultipleChoice"),rzt=o(" (RoBERTa-PreLayerNorm model)"),tzt=l(),WA=a("li"),oKe=a("strong"),azt=o("roformer"),nzt=o(" \u2014 "),r0e=a("a"),szt=o("TFRoFormerForMultipleChoice"),lzt=o(" (RoFormer model)"),izt=l(),JA=a("li"),rKe=a("strong"),dzt=o("xlm"),czt=o(" \u2014 "),t0e=a("a"),mzt=o("TFXLMForMultipleChoice"),fzt=o(" (XLM model)"),hzt=l(),UA=a("li"),tKe=a("strong"),gzt=o("xlm-roberta"),uzt=o(" \u2014 "),a0e=a("a"),pzt=o("TFXLMRobertaForMultipleChoice"),_zt=o(" (XLM-RoBERTa model)"),vzt=l(),XA=a("li"),aKe=a("strong"),bzt=o("xlnet"),Mzt=o(" \u2014 "),n0e=a("a"),Tzt=o("TFXLNetForMultipleChoice"),Fzt=o(" (XLNet model)"),Ezt=l(),b(DA.$$.fragment),cIo=l(),pf=a("h3"),qA=a("a"),nKe=a("span"),b(WJ.$$.fragment),yzt=l(),sKe=a("span"),Czt=o("FlaxAutoModelForMultipleChoice"),mIo=l(),cr=a("div"),b(JJ.$$.fragment),wzt=l(),_f=a("p"),Lzt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
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method.`),xzt=l(),UJ=a("p"),Gzt=o("This class cannot be instantiated directly using "),lKe=a("code"),Bzt=o("__init__()"),Izt=o(" (throws an error)."),Nzt=l(),da=a("div"),b(XJ.$$.fragment),$zt=l(),iKe=a("p"),jzt=o("Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),Pzt=l(),vf=a("p"),Zzt=o(`Note:
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model\u2019s configuration. Use `),i0e=a("a"),Jzt=o("from_pretrained()"),Uzt=o(" to load the model weights."),Xzt=l(),b(OA.$$.fragment),Dzt=l(),pt=a("div"),b(DJ.$$.fragment),qzt=l(),cKe=a("p"),Ozt=o("Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),Qzt=l(),ss=a("p"),Yzt=o("The model class to instantiate is selected based on the "),mKe=a("code"),zzt=o("model_type"),Hzt=o(` property of the config object (either
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falling back to using pattern matching on `),hKe=a("code"),oHt=o("pretrained_model_name_or_path"),rHt=o(":"),tHt=l(),Ue=a("ul"),QA=a("li"),gKe=a("strong"),aHt=o("albert"),nHt=o(" \u2014 "),d0e=a("a"),sHt=o("FlaxAlbertForMultipleChoice"),lHt=o(" (ALBERT model)"),iHt=l(),YA=a("li"),uKe=a("strong"),dHt=o("bert"),cHt=o(" \u2014 "),c0e=a("a"),mHt=o("FlaxBertForMultipleChoice"),fHt=o(" (BERT model)"),hHt=l(),zA=a("li"),pKe=a("strong"),gHt=o("big_bird"),uHt=o(" \u2014 "),m0e=a("a"),pHt=o("FlaxBigBirdForMultipleChoice"),_Ht=o(" (BigBird model)"),vHt=l(),HA=a("li"),_Ke=a("strong"),bHt=o("distilbert"),MHt=o(" \u2014 "),f0e=a("a"),THt=o("FlaxDistilBertForMultipleChoice"),FHt=o(" (DistilBERT model)"),EHt=l(),KA=a("li"),vKe=a("strong"),yHt=o("electra"),CHt=o(" \u2014 "),h0e=a("a"),wHt=o("FlaxElectraForMultipleChoice"),LHt=o(" (ELECTRA model)"),AHt=l(),eR=a("li"),bKe=a("strong"),RHt=o("roberta"),kHt=o(" \u2014 "),g0e=a("a"),SHt=o("FlaxRobertaForMultipleChoice"),xHt=o(" (RoBERTa model)"),GHt=l(),oR=a("li"),MKe=a("strong"),BHt=o("roberta-prelayernorm"),IHt=o(" \u2014 "),u0e=a("a"),NHt=o("FlaxRobertaPreLayerNormForMultipleChoice"),$Ht=o(" (RoBERTa-PreLayerNorm model)"),jHt=l(),rR=a("li"),TKe=a("strong"),PHt=o("roformer"),ZHt=o(" \u2014 "),p0e=a("a"),VHt=o("FlaxRoFormerForMultipleChoice"),WHt=o(" (RoFormer model)"),JHt=l(),tR=a("li"),FKe=a("strong"),UHt=o("xlm-roberta"),XHt=o(" \u2014 "),_0e=a("a"),DHt=o("FlaxXLMRobertaForMultipleChoice"),qHt=o(" (XLM-RoBERTa model)"),OHt=l(),b(aR.$$.fragment),fIo=l(),bf=a("h3"),nR=a("a"),EKe=a("span"),b(qJ.$$.fragment),QHt=l(),yKe=a("span"),YHt=o("AutoModelForNextSentencePrediction"),hIo=l(),mr=a("div"),b(OJ.$$.fragment),zHt=l(),Mf=a("p"),HHt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),v0e=a("a"),KHt=o("from_pretrained()"),eKt=o(" class method or the "),b0e=a("a"),oKt=o("from_config()"),rKt=o(` class
method.`),tKt=l(),QJ=a("p"),aKt=o("This class cannot be instantiated directly using "),CKe=a("code"),nKt=o("__init__()"),sKt=o(" (throws an error)."),lKt=l(),ca=a("div"),b(YJ.$$.fragment),iKt=l(),wKe=a("p"),dKt=o("Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),cKt=l(),Tf=a("p"),mKt=o(`Note:
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passed as an argument or loaded from `),kKe=a("code"),yKt=o("pretrained_model_name_or_path"),CKt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),SKe=a("code"),wKt=o("pretrained_model_name_or_path"),LKt=o(":"),AKt=l(),eo=a("ul"),lR=a("li"),xKe=a("strong"),RKt=o("bert"),kKt=o(" \u2014 "),T0e=a("a"),SKt=o("BertForNextSentencePrediction"),xKt=o(" (BERT model)"),GKt=l(),iR=a("li"),GKe=a("strong"),BKt=o("ernie"),IKt=o(" \u2014 "),F0e=a("a"),NKt=o("ErnieForNextSentencePrediction"),$Kt=o(" (ERNIE model)"),jKt=l(),dR=a("li"),BKe=a("strong"),PKt=o("fnet"),ZKt=o(" \u2014 "),E0e=a("a"),VKt=o("FNetForNextSentencePrediction"),WKt=o(" (FNet model)"),JKt=l(),cR=a("li"),IKe=a("strong"),UKt=o("megatron-bert"),XKt=o(" \u2014 "),y0e=a("a"),DKt=o("MegatronBertForNextSentencePrediction"),qKt=o(" (Megatron-BERT model)"),OKt=l(),mR=a("li"),NKe=a("strong"),QKt=o("mobilebert"),YKt=o(" \u2014 "),C0e=a("a"),zKt=o("MobileBertForNextSentencePrediction"),HKt=o(" (MobileBERT model)"),KKt=l(),fR=a("li"),$Ke=a("strong"),eea=o("nezha"),oea=o(" \u2014 "),w0e=a("a"),rea=o("NezhaForNextSentencePrediction"),tea=o(" (Nezha model)"),aea=l(),hR=a("li"),jKe=a("strong"),nea=o("qdqbert"),sea=o(" \u2014 "),L0e=a("a"),lea=o("QDQBertForNextSentencePrediction"),iea=o(" (QDQBert model)"),dea=l(),gR=a("p"),cea=o("The model is set in evaluation mode by default using "),PKe=a("code"),mea=o("model.eval()"),fea=o(` (so for instance, dropout modules are
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model\u2019s configuration. Use `),k0e=a("a"),Bea=o("from_pretrained()"),Iea=o(" to load the model weights."),Nea=l(),b(_R.$$.fragment),$ea=l(),_t=a("div"),b(rU.$$.fragment),jea=l(),DKe=a("p"),Pea=o("Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),Zea=l(),is=a("p"),Vea=o("The model class to instantiate is selected based on the "),qKe=a("code"),Wea=o("model_type"),Jea=o(` property of the config object (either
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falling back to using pattern matching on `),QKe=a("code"),Dea=o("pretrained_model_name_or_path"),qea=o(":"),Oea=l(),tU=a("ul"),vR=a("li"),YKe=a("strong"),Qea=o("bert"),Yea=o(" \u2014 "),S0e=a("a"),zea=o("TFBertForNextSentencePrediction"),Hea=o(" (BERT model)"),Kea=l(),bR=a("li"),zKe=a("strong"),eoa=o("mobilebert"),ooa=o(" \u2014 "),x0e=a("a"),roa=o("TFMobileBertForNextSentencePrediction"),toa=o(" (MobileBERT model)"),aoa=l(),b(MR.$$.fragment),pIo=l(),Cf=a("h3"),TR=a("a"),HKe=a("span"),b(aU.$$.fragment),noa=l(),KKe=a("span"),soa=o("FlaxAutoModelForNextSentencePrediction"),_Io=l(),hr=a("div"),b(nU.$$.fragment),loa=l(),wf=a("p"),ioa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
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method.`),Hoa=l(),mU=a("p"),Koa=o("This class cannot be instantiated directly using "),meo=a("code"),era=o("__init__()"),ora=o(" (throws an error)."),rra=l(),ha=a("div"),b(fU.$$.fragment),tra=l(),feo=a("p"),ara=o("Instantiates one of the model classes of the library (with a token classification head) from a configuration."),nra=l(),kf=a("p"),sra=o(`Note:
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model\u2019s configuration. Use `),P0e=a("a"),dra=o("from_pretrained()"),cra=o(" to load the model weights."),mra=l(),b(wR.$$.fragment),fra=l(),uo=a("div"),b(hU.$$.fragment),hra=l(),geo=a("p"),gra=o("Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),ura=l(),cs=a("p"),pra=o("The model class to instantiate is selected based on the "),ueo=a("code"),_ra=o("model_type"),vra=o(` property of the config object (either
passed as an argument or loaded from `),peo=a("code"),bra=o("pretrained_model_name_or_path"),Mra=o(` if possible), or when it\u2019s missing, by
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model\u2019s configuration. Use `),iFe=a("a"),hba=o("from_pretrained()"),gba=o(" to load the model weights."),uba=l(),b(zx.$$.fragment),pba=l(),Ft=a("div"),b(jU.$$.fragment),_ba=l(),_ao=a("p"),vba=o("Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),bba=l(),us=a("p"),Mba=o("The model class to instantiate is selected based on the "),vao=a("code"),Tba=o("model_type"),Fba=o(` property of the config object (either
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falling back to using pattern matching on `),Mao=a("code"),Cba=o("pretrained_model_name_or_path"),wba=o(":"),Lba=l(),xe=a("ul"),Hx=a("li"),Tao=a("strong"),Aba=o("albert"),Rba=o(" \u2014 "),dFe=a("a"),kba=o("FlaxAlbertForQuestionAnswering"),Sba=o(" (ALBERT model)"),xba=l(),Kx=a("li"),Fao=a("strong"),Gba=o("bart"),Bba=o(" \u2014 "),cFe=a("a"),Iba=o("FlaxBartForQuestionAnswering"),Nba=o(" (BART model)"),$ba=l(),eG=a("li"),Eao=a("strong"),jba=o("bert"),Pba=o(" \u2014 "),mFe=a("a"),Zba=o("FlaxBertForQuestionAnswering"),Vba=o(" (BERT model)"),Wba=l(),oG=a("li"),yao=a("strong"),Jba=o("big_bird"),Uba=o(" \u2014 "),fFe=a("a"),Xba=o("FlaxBigBirdForQuestionAnswering"),Dba=o(" (BigBird model)"),qba=l(),rG=a("li"),Cao=a("strong"),Oba=o("distilbert"),Qba=o(" \u2014 "),hFe=a("a"),Yba=o("FlaxDistilBertForQuestionAnswering"),zba=o(" (DistilBERT model)"),Hba=l(),tG=a("li"),wao=a("strong"),Kba=o("electra"),e2a=o(" \u2014 "),gFe=a("a"),o2a=o("FlaxElectraForQuestionAnswering"),r2a=o(" (ELECTRA model)"),t2a=l(),aG=a("li"),Lao=a("strong"),a2a=o("mbart"),n2a=o(" \u2014 "),uFe=a("a"),s2a=o("FlaxMBartForQuestionAnswering"),l2a=o(" (mBART model)"),i2a=l(),nG=a("li"),Aao=a("strong"),d2a=o("roberta"),c2a=o(" \u2014 "),pFe=a("a"),m2a=o("FlaxRobertaForQuestionAnswering"),f2a=o(" (RoBERTa model)"),h2a=l(),sG=a("li"),Rao=a("strong"),g2a=o("roberta-prelayernorm"),u2a=o(" \u2014 "),_Fe=a("a"),p2a=o("FlaxRobertaPreLayerNormForQuestionAnswering"),_2a=o(" (RoBERTa-PreLayerNorm model)"),v2a=l(),lG=a("li"),kao=a("strong"),b2a=o("roformer"),M2a=o(" \u2014 "),vFe=a("a"),T2a=o("FlaxRoFormerForQuestionAnswering"),F2a=o(" (RoFormer model)"),E2a=l(),iG=a("li"),Sao=a("strong"),y2a=o("xlm-roberta"),C2a=o(" \u2014 "),bFe=a("a"),w2a=o("FlaxXLMRobertaForQuestionAnswering"),L2a=o(" (XLM-RoBERTa model)"),A2a=l(),b(dG.$$.fragment),kIo=l(),Df=a("h3"),cG=a("a"),xao=a("span"),b(PU.$$.fragment),R2a=l(),Gao=a("span"),k2a=o("AutoModelForTextEncoding"),SIo=l(),ZU=a("div"),b(VU.$$.fragment),xIo=l(),qf=a("h3"),mG=a("a"),Bao=a("span"),b(WU.$$.fragment),S2a=l(),Iao=a("span"),x2a=o("TFAutoModelForTextEncoding"),GIo=l(),JU=a("div"),b(UU.$$.fragment),BIo=l(),Of=a("h2"),fG=a("a"),Nao=a("span"),b(XU.$$.fragment),G2a=l(),$ao=a("span"),B2a=o("Computer vision"),IIo=l(),MFe=a("p"),I2a=o("The following auto classes are available for the following computer vision tasks."),NIo=l(),Qf=a("h3"),hG=a("a"),jao=a("span"),b(DU.$$.fragment),N2a=l(),Pao=a("span"),$2a=o("AutoModelForDepthEstimation"),$Io=l(),Mr=a("div"),b(qU.$$.fragment),j2a=l(),Yf=a("p"),P2a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a depth estimation head) when created
with the `),TFe=a("a"),Z2a=o("from_pretrained()"),V2a=o(" class method or the "),FFe=a("a"),W2a=o("from_config()"),J2a=o(` class
method.`),U2a=l(),OU=a("p"),X2a=o("This class cannot be instantiated directly using "),Zao=a("code"),D2a=o("__init__()"),q2a=o(" (throws an error)."),O2a=l(),ba=a("div"),b(QU.$$.fragment),Q2a=l(),Vao=a("p"),Y2a=o("Instantiates one of the model classes of the library (with a depth estimation head) from a configuration."),z2a=l(),zf=a("p"),H2a=o(`Note:
Loading a model from its configuration file does `),Wao=a("strong"),K2a=o("not"),e0a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),EFe=a("a"),o0a=o("from_pretrained()"),r0a=o(" to load the model weights."),t0a=l(),b(gG.$$.fragment),a0a=l(),_o=a("div"),b(YU.$$.fragment),n0a=l(),Jao=a("p"),s0a=o("Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model."),l0a=l(),ps=a("p"),i0a=o("The model class to instantiate is selected based on the "),Uao=a("code"),d0a=o("model_type"),c0a=o(` property of the config object (either
passed as an argument or loaded from `),Xao=a("code"),m0a=o("pretrained_model_name_or_path"),f0a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Dao=a("code"),h0a=o("pretrained_model_name_or_path"),g0a=o(":"),u0a=l(),zU=a("ul"),uG=a("li"),qao=a("strong"),p0a=o("dpt"),_0a=o(" \u2014 "),yFe=a("a"),v0a=o("DPTForDepthEstimation"),b0a=o(" (DPT model)"),M0a=l(),pG=a("li"),Oao=a("strong"),T0a=o("glpn"),F0a=o(" \u2014 "),CFe=a("a"),E0a=o("GLPNForDepthEstimation"),y0a=o(" (GLPN model)"),C0a=l(),_G=a("p"),w0a=o("The model is set in evaluation mode by default using "),Qao=a("code"),L0a=o("model.eval()"),A0a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Yao=a("code"),R0a=o("model.train()"),k0a=l(),b(vG.$$.fragment),jIo=l(),Hf=a("h3"),bG=a("a"),zao=a("span"),b(HU.$$.fragment),S0a=l(),Hao=a("span"),x0a=o("AutoModelForImageClassification"),PIo=l(),Tr=a("div"),b(KU.$$.fragment),G0a=l(),Kf=a("p"),B0a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),wFe=a("a"),I0a=o("from_pretrained()"),N0a=o(" class method or the "),LFe=a("a"),$0a=o("from_config()"),j0a=o(` class
method.`),P0a=l(),eX=a("p"),Z0a=o("This class cannot be instantiated directly using "),Kao=a("code"),V0a=o("__init__()"),W0a=o(" (throws an error)."),J0a=l(),Ma=a("div"),b(oX.$$.fragment),U0a=l(),eno=a("p"),X0a=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),D0a=l(),eh=a("p"),q0a=o(`Note:
Loading a model from its configuration file does `),ono=a("strong"),O0a=o("not"),Q0a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),AFe=a("a"),Y0a=o("from_pretrained()"),z0a=o(" to load the model weights."),H0a=l(),b(MG.$$.fragment),K0a=l(),vo=a("div"),b(rX.$$.fragment),e4a=l(),rno=a("p"),o4a=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),r4a=l(),_s=a("p"),t4a=o("The model class to instantiate is selected based on the "),tno=a("code"),a4a=o("model_type"),n4a=o(` property of the config object (either
passed as an argument or loaded from `),ano=a("code"),s4a=o("pretrained_model_name_or_path"),l4a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nno=a("code"),i4a=o("pretrained_model_name_or_path"),d4a=o(":"),c4a=l(),le=a("ul"),TG=a("li"),sno=a("strong"),m4a=o("beit"),f4a=o(" \u2014 "),RFe=a("a"),h4a=o("BeitForImageClassification"),g4a=o(" (BEiT model)"),u4a=l(),FG=a("li"),lno=a("strong"),p4a=o("bit"),_4a=o(" \u2014 "),kFe=a("a"),v4a=o("BitForImageClassification"),b4a=o(" (BiT model)"),M4a=l(),EG=a("li"),ino=a("strong"),T4a=o("convnext"),F4a=o(" \u2014 "),SFe=a("a"),E4a=o("ConvNextForImageClassification"),y4a=o(" (ConvNeXT model)"),C4a=l(),yG=a("li"),dno=a("strong"),w4a=o("convnextv2"),L4a=o(" \u2014 "),xFe=a("a"),A4a=o("ConvNextV2ForImageClassification"),R4a=o(" (ConvNeXTV2 model)"),k4a=l(),CG=a("li"),cno=a("strong"),S4a=o("cvt"),x4a=o(" \u2014 "),GFe=a("a"),G4a=o("CvtForImageClassification"),B4a=o(" (CvT model)"),I4a=l(),wG=a("li"),mno=a("strong"),N4a=o("data2vec-vision"),$4a=o(" \u2014 "),BFe=a("a"),j4a=o("Data2VecVisionForImageClassification"),P4a=o(" (Data2VecVision model)"),Z4a=l(),Ji=a("li"),fno=a("strong"),V4a=o("deit"),W4a=o(" \u2014 "),IFe=a("a"),J4a=o("DeiTForImageClassification"),U4a=o(" or "),NFe=a("a"),X4a=o("DeiTForImageClassificationWithTeacher"),D4a=o(" (DeiT model)"),q4a=l(),LG=a("li"),hno=a("strong"),O4a=o("dinat"),Q4a=o(" \u2014 "),$Fe=a("a"),Y4a=o("DinatForImageClassification"),z4a=o(" (DiNAT model)"),H4a=l(),AG=a("li"),gno=a("strong"),K4a=o("dinov2"),eMa=o(" \u2014 "),jFe=a("a"),oMa=o("Dinov2ForImageClassification"),rMa=o(" (DINOv2 model)"),tMa=l(),Ui=a("li"),uno=a("strong"),aMa=o("efficientformer"),nMa=o(" \u2014 "),PFe=a("a"),sMa=o("EfficientFormerForImageClassification"),lMa=o(" or "),ZFe=a("a"),iMa=o("EfficientFormerForImageClassificationWithTeacher"),dMa=o(" (EfficientFormer model)"),cMa=l(),RG=a("li"),pno=a("strong"),mMa=o("efficientnet"),fMa=o(" \u2014 "),VFe=a("a"),hMa=o("EfficientNetForImageClassification"),gMa=o(" (EfficientNet model)"),uMa=l(),kG=a("li"),_no=a("strong"),pMa=o("focalnet"),_Ma=o(" \u2014 "),WFe=a("a"),vMa=o("FocalNetForImageClassification"),bMa=o(" (FocalNet model)"),MMa=l(),SG=a("li"),vno=a("strong"),TMa=o("imagegpt"),FMa=o(" \u2014 "),JFe=a("a"),EMa=o("ImageGPTForImageClassification"),yMa=o(" (ImageGPT model)"),CMa=l(),Xi=a("li"),bno=a("strong"),wMa=o("levit"),LMa=o(" \u2014 "),UFe=a("a"),AMa=o("LevitForImageClassification"),RMa=o(" or "),XFe=a("a"),kMa=o("LevitForImageClassificationWithTeacher"),SMa=o(" (LeViT model)"),xMa=l(),xG=a("li"),Mno=a("strong"),GMa=o("mobilenet_v1"),BMa=o(" \u2014 "),DFe=a("a"),IMa=o("MobileNetV1ForImageClassification"),NMa=o(" (MobileNetV1 model)"),$Ma=l(),GG=a("li"),Tno=a("strong"),jMa=o("mobilenet_v2"),PMa=o(" \u2014 "),qFe=a("a"),ZMa=o("MobileNetV2ForImageClassification"),VMa=o(" (MobileNetV2 model)"),WMa=l(),BG=a("li"),Fno=a("strong"),JMa=o("mobilevit"),UMa=o(" \u2014 "),OFe=a("a"),XMa=o("MobileViTForImageClassification"),DMa=o(" (MobileViT model)"),qMa=l(),IG=a("li"),Eno=a("strong"),OMa=o("mobilevitv2"),QMa=o(" \u2014 "),QFe=a("a"),YMa=o("MobileViTV2ForImageClassification"),zMa=o(" (MobileViTV2 model)"),HMa=l(),NG=a("li"),yno=a("strong"),KMa=o("nat"),eTa=o(" \u2014 "),YFe=a("a"),oTa=o("NatForImageClassification"),rTa=o(" (NAT model)"),tTa=l(),Ta=a("li"),Cno=a("strong"),aTa=o("perceiver"),nTa=o(" \u2014 "),zFe=a("a"),sTa=o("PerceiverForImageClassificationLearned"),lTa=o(" or "),HFe=a("a"),iTa=o("PerceiverForImageClassificationFourier"),dTa=o(" or "),KFe=a("a"),cTa=o("PerceiverForImageClassificationConvProcessing"),mTa=o(" (Perceiver model)"),fTa=l(),$G=a("li"),wno=a("strong"),hTa=o("poolformer"),gTa=o(" \u2014 "),eEe=a("a"),uTa=o("PoolFormerForImageClassification"),pTa=o(" (PoolFormer model)"),_Ta=l(),jG=a("li"),Lno=a("strong"),vTa=o("pvt"),bTa=o(" \u2014 "),oEe=a("a"),MTa=o("PvtForImageClassification"),TTa=o(" (PVT model)"),FTa=l(),PG=a("li"),Ano=a("strong"),ETa=o("regnet"),yTa=o(" \u2014 "),rEe=a("a"),CTa=o("RegNetForImageClassification"),wTa=o(" (RegNet 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"),dEe=a("a"),sFa=o("ViTForImageClassification"),lFa=o(" (ViT model)"),iFa=l(),qG=a("li"),Nno=a("strong"),dFa=o("vit_hybrid"),cFa=o(" \u2014 "),cEe=a("a"),mFa=o("ViTHybridForImageClassification"),fFa=o(" (ViT Hybrid model)"),hFa=l(),OG=a("li"),$no=a("strong"),gFa=o("vit_msn"),uFa=o(" \u2014 "),mEe=a("a"),pFa=o("ViTMSNForImageClassification"),_Fa=o(" (ViTMSN model)"),vFa=l(),QG=a("p"),bFa=o("The model is set in evaluation mode by default using "),jno=a("code"),MFa=o("model.eval()"),TFa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Pno=a("code"),FFa=o("model.train()"),EFa=l(),b(YG.$$.fragment),ZIo=l(),oh=a("h3"),zG=a("a"),Zno=a("span"),b(tX.$$.fragment),yFa=l(),Vno=a("span"),CFa=o("TFAutoModelForImageClassification"),VIo=l(),Fr=a("div"),b(aX.$$.fragment),wFa=l(),rh=a("p"),LFa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),fEe=a("a"),AFa=o("from_pretrained()"),RFa=o(" class method or the "),hEe=a("a"),kFa=o("from_config()"),SFa=o(` class
method.`),xFa=l(),nX=a("p"),GFa=o("This class cannot be instantiated directly using "),Wno=a("code"),BFa=o("__init__()"),IFa=o(" (throws an error)."),NFa=l(),Fa=a("div"),b(sX.$$.fragment),$Fa=l(),Jno=a("p"),jFa=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),PFa=l(),th=a("p"),ZFa=o(`Note:
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model\u2019s configuration. Use `),gEe=a("a"),JFa=o("from_pretrained()"),UFa=o(" to load the model weights."),XFa=l(),b(HG.$$.fragment),DFa=l(),Et=a("div"),b(lX.$$.fragment),qFa=l(),Xno=a("p"),OFa=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),QFa=l(),vs=a("p"),YFa=o("The model class to instantiate is selected based on the "),Dno=a("code"),zFa=o("model_type"),HFa=o(` property of the config object (either
passed as an argument or loaded from `),qno=a("code"),KFa=o("pretrained_model_name_or_path"),eEa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ono=a("code"),oEa=o("pretrained_model_name_or_path"),rEa=o(":"),tEa=l(),Ge=a("ul"),KG=a("li"),Qno=a("strong"),aEa=o("convnext"),nEa=o(" \u2014 "),uEe=a("a"),sEa=o("TFConvNextForImageClassification"),lEa=o(" (ConvNeXT model)"),iEa=l(),eB=a("li"),Yno=a("strong"),dEa=o("cvt"),cEa=o(" \u2014 "),pEe=a("a"),mEa=o("TFCvtForImageClassification"),fEa=o(" (CvT model)"),hEa=l(),oB=a("li"),zno=a("strong"),gEa=o("data2vec-vision"),uEa=o(" \u2014 "),_Ee=a("a"),pEa=o("TFData2VecVisionForImageClassification"),_Ea=o(" (Data2VecVision model)"),vEa=l(),Di=a("li"),Hno=a("strong"),bEa=o("deit"),MEa=o(" \u2014 "),vEe=a("a"),TEa=o("TFDeiTForImageClassification"),FEa=o(" or "),bEe=a("a"),EEa=o("TFDeiTForImageClassificationWithTeacher"),yEa=o(" (DeiT model)"),CEa=l(),qi=a("li"),Kno=a("strong"),wEa=o("efficientformer"),LEa=o(" \u2014 "),MEe=a("a"),AEa=o("TFEfficientFormerForImageClassification"),REa=o(" or "),TEe=a("a"),kEa=o("TFEfficientFormerForImageClassificationWithTeacher"),SEa=o(" (EfficientFormer model)"),xEa=l(),rB=a("li"),eso=a("strong"),GEa=o("mobilevit"),BEa=o(" \u2014 "),FEe=a("a"),IEa=o("TFMobileViTForImageClassification"),NEa=o(" (MobileViT model)"),$Ea=l(),tB=a("li"),oso=a("strong"),jEa=o("regnet"),PEa=o(" \u2014 "),EEe=a("a"),ZEa=o("TFRegNetForImageClassification"),VEa=o(" (RegNet model)"),WEa=l(),aB=a("li"),rso=a("strong"),JEa=o("resnet"),UEa=o(" \u2014 "),yEe=a("a"),XEa=o("TFResNetForImageClassification"),DEa=o(" (ResNet model)"),qEa=l(),nB=a("li"),tso=a("strong"),OEa=o("segformer"),QEa=o(" \u2014 "),CEe=a("a"),YEa=o("TFSegformerForImageClassification"),zEa=o(" (SegFormer model)"),HEa=l(),sB=a("li"),aso=a("strong"),KEa=o("swin"),eya=o(" \u2014 "),wEe=a("a"),oya=o("TFSwinForImageClassification"),rya=o(" (Swin Transformer model)"),tya=l(),lB=a("li"),nso=a("strong"),aya=o("vit"),nya=o(" \u2014 "),LEe=a("a"),sya=o("TFViTForImageClassification"),lya=o(" (ViT model)"),iya=l(),b(iB.$$.fragment),WIo=l(),ah=a("h3"),dB=a("a"),sso=a("span"),b(iX.$$.fragment),dya=l(),lso=a("span"),cya=o("FlaxAutoModelForImageClassification"),JIo=l(),Er=a("div"),b(dX.$$.fragment),mya=l(),nh=a("p"),fya=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
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method.`),_ya=l(),cX=a("p"),vya=o("This class cannot be instantiated directly using "),iso=a("code"),bya=o("__init__()"),Mya=o(" (throws an error)."),Tya=l(),Ea=a("div"),b(mX.$$.fragment),Fya=l(),dso=a("p"),Eya=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),yya=l(),sh=a("p"),Cya=o(`Note:
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model\u2019s configuration. Use `),kEe=a("a"),Aya=o("from_pretrained()"),Rya=o(" to load the model weights."),kya=l(),b(cB.$$.fragment),Sya=l(),yt=a("div"),b(fX.$$.fragment),xya=l(),mso=a("p"),Gya=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),Bya=l(),bs=a("p"),Iya=o("The model class to instantiate is selected based on the "),fso=a("code"),Nya=o("model_type"),$ya=o(` property of the config object (either
passed as an argument or loaded from `),hso=a("code"),jya=o("pretrained_model_name_or_path"),Pya=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),gso=a("code"),Zya=o("pretrained_model_name_or_path"),Vya=o(":"),Wya=l(),Ms=a("ul"),mB=a("li"),uso=a("strong"),Jya=o("beit"),Uya=o(" \u2014 "),SEe=a("a"),Xya=o("FlaxBeitForImageClassification"),Dya=o(" (BEiT model)"),qya=l(),fB=a("li"),pso=a("strong"),Oya=o("regnet"),Qya=o(" \u2014 "),xEe=a("a"),Yya=o("FlaxRegNetForImageClassification"),zya=o(" (RegNet model)"),Hya=l(),hB=a("li"),_so=a("strong"),Kya=o("resnet"),e5a=o(" \u2014 "),GEe=a("a"),o5a=o("FlaxResNetForImageClassification"),r5a=o(" (ResNet model)"),t5a=l(),gB=a("li"),vso=a("strong"),a5a=o("vit"),n5a=o(" \u2014 "),BEe=a("a"),s5a=o("FlaxViTForImageClassification"),l5a=o(" (ViT model)"),i5a=l(),b(uB.$$.fragment),UIo=l(),lh=a("h3"),pB=a("a"),bso=a("span"),b(hX.$$.fragment),d5a=l(),Mso=a("span"),c5a=o("AutoModelForVideoClassification"),XIo=l(),yr=a("div"),b(gX.$$.fragment),m5a=l(),ih=a("p"),f5a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a video classification head) when created
with the `),IEe=a("a"),h5a=o("from_pretrained()"),g5a=o(" class method or the "),NEe=a("a"),u5a=o("from_config()"),p5a=o(` class
method.`),_5a=l(),uX=a("p"),v5a=o("This class cannot be instantiated directly using "),Tso=a("code"),b5a=o("__init__()"),M5a=o(" (throws an error)."),T5a=l(),ya=a("div"),b(pX.$$.fragment),F5a=l(),Fso=a("p"),E5a=o("Instantiates one of the model classes of the library (with a video classification head) from a configuration."),y5a=l(),dh=a("p"),C5a=o(`Note:
Loading a model from its configuration file does `),Eso=a("strong"),w5a=o("not"),L5a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),$Ee=a("a"),A5a=o("from_pretrained()"),R5a=o(" to load the model weights."),k5a=l(),b(_B.$$.fragment),S5a=l(),bo=a("div"),b(_X.$$.fragment),x5a=l(),yso=a("p"),G5a=o("Instantiate one of the model classes of the library (with a video classification head) from a pretrained model."),B5a=l(),Ts=a("p"),I5a=o("The model class to instantiate is selected based on the "),Cso=a("code"),N5a=o("model_type"),$5a=o(` property of the config object (either
passed as an argument or loaded from `),wso=a("code"),j5a=o("pretrained_model_name_or_path"),P5a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Lso=a("code"),Z5a=o("pretrained_model_name_or_path"),V5a=o(":"),W5a=l(),ch=a("ul"),vB=a("li"),Aso=a("strong"),J5a=o("timesformer"),U5a=o(" \u2014 "),jEe=a("a"),X5a=o("TimesformerForVideoClassification"),D5a=o(" (TimeSformer model)"),q5a=l(),bB=a("li"),Rso=a("strong"),O5a=o("videomae"),Q5a=o(" \u2014 "),PEe=a("a"),Y5a=o("VideoMAEForVideoClassification"),z5a=o(" (VideoMAE model)"),H5a=l(),MB=a("li"),kso=a("strong"),K5a=o("vivit"),e9a=o(" \u2014 "),ZEe=a("a"),o9a=o("VivitForVideoClassification"),r9a=o(" (ViViT model)"),t9a=l(),TB=a("p"),a9a=o("The model is set in evaluation mode by default using "),Sso=a("code"),n9a=o("model.eval()"),s9a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),xso=a("code"),l9a=o("model.train()"),i9a=l(),b(FB.$$.fragment),DIo=l(),mh=a("h3"),EB=a("a"),Gso=a("span"),b(vX.$$.fragment),d9a=l(),Bso=a("span"),c9a=o("AutoModelForMaskedImageModeling"),qIo=l(),Cr=a("div"),b(bX.$$.fragment),m9a=l(),fh=a("p"),f9a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),VEe=a("a"),h9a=o("from_pretrained()"),g9a=o(" class method or the "),WEe=a("a"),u9a=o("from_config()"),p9a=o(` class
method.`),_9a=l(),MX=a("p"),v9a=o("This class cannot be instantiated directly using "),Iso=a("code"),b9a=o("__init__()"),M9a=o(" (throws an error)."),T9a=l(),Ca=a("div"),b(TX.$$.fragment),F9a=l(),Nso=a("p"),E9a=o("Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),y9a=l(),hh=a("p"),C9a=o(`Note:
Loading a model from its configuration file does `),$so=a("strong"),w9a=o("not"),L9a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),JEe=a("a"),A9a=o("from_pretrained()"),R9a=o(" to load the model weights."),k9a=l(),b(yB.$$.fragment),S9a=l(),Mo=a("div"),b(FX.$$.fragment),x9a=l(),jso=a("p"),G9a=o("Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),B9a=l(),Fs=a("p"),I9a=o("The model class to instantiate is selected based on the "),Pso=a("code"),N9a=o("model_type"),$9a=o(` property of the config object (either
passed as an argument or loaded from `),Zso=a("code"),j9a=o("pretrained_model_name_or_path"),P9a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Vso=a("code"),Z9a=o("pretrained_model_name_or_path"),V9a=o(":"),W9a=l(),Nt=a("ul"),CB=a("li"),Wso=a("strong"),J9a=o("deit"),U9a=o(" \u2014 "),UEe=a("a"),X9a=o("DeiTForMaskedImageModeling"),D9a=o(" (DeiT model)"),q9a=l(),wB=a("li"),Jso=a("strong"),O9a=o("focalnet"),Q9a=o(" \u2014 "),XEe=a("a"),Y9a=o("FocalNetForMaskedImageModeling"),z9a=o(" (FocalNet model)"),H9a=l(),LB=a("li"),Uso=a("strong"),K9a=o("swin"),eCa=o(" \u2014 "),DEe=a("a"),oCa=o("SwinForMaskedImageModeling"),rCa=o(" (Swin Transformer model)"),tCa=l(),AB=a("li"),Xso=a("strong"),aCa=o("swinv2"),nCa=o(" \u2014 "),qEe=a("a"),sCa=o("Swinv2ForMaskedImageModeling"),lCa=o(" (Swin Transformer V2 model)"),iCa=l(),RB=a("li"),Dso=a("strong"),dCa=o("vit"),cCa=o(" \u2014 "),OEe=a("a"),mCa=o("ViTForMaskedImageModeling"),fCa=o(" (ViT model)"),hCa=l(),kB=a("p"),gCa=o("The model is set in evaluation mode by default using "),qso=a("code"),uCa=o("model.eval()"),pCa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Oso=a("code"),_Ca=o("model.train()"),vCa=l(),b(SB.$$.fragment),OIo=l(),gh=a("h3"),xB=a("a"),Qso=a("span"),b(EX.$$.fragment),bCa=l(),Yso=a("span"),MCa=o("TFAutoModelForMaskedImageModeling"),QIo=l(),wr=a("div"),b(yX.$$.fragment),TCa=l(),uh=a("p"),FCa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),QEe=a("a"),ECa=o("from_pretrained()"),yCa=o(" class method or the "),YEe=a("a"),CCa=o("from_config()"),wCa=o(` class
method.`),LCa=l(),CX=a("p"),ACa=o("This class cannot be instantiated directly using "),zso=a("code"),RCa=o("__init__()"),kCa=o(" (throws an error)."),SCa=l(),wa=a("div"),b(wX.$$.fragment),xCa=l(),Hso=a("p"),GCa=o("Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),BCa=l(),ph=a("p"),ICa=o(`Note:
Loading a model from its configuration file does `),Kso=a("strong"),NCa=o("not"),$Ca=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),zEe=a("a"),jCa=o("from_pretrained()"),PCa=o(" to load the model weights."),ZCa=l(),b(GB.$$.fragment),VCa=l(),Ct=a("div"),b(LX.$$.fragment),WCa=l(),elo=a("p"),JCa=o("Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),UCa=l(),Es=a("p"),XCa=o("The model class to instantiate is selected based on the "),olo=a("code"),DCa=o("model_type"),qCa=o(` property of the config object (either
passed as an argument or loaded from `),rlo=a("code"),OCa=o("pretrained_model_name_or_path"),QCa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),tlo=a("code"),YCa=o("pretrained_model_name_or_path"),zCa=o(":"),HCa=l(),AX=a("ul"),BB=a("li"),alo=a("strong"),KCa=o("deit"),ewa=o(" \u2014 "),HEe=a("a"),owa=o("TFDeiTForMaskedImageModeling"),rwa=o(" (DeiT model)"),twa=l(),IB=a("li"),nlo=a("strong"),awa=o("swin"),nwa=o(" \u2014 "),KEe=a("a"),swa=o("TFSwinForMaskedImageModeling"),lwa=o(" (Swin Transformer model)"),iwa=l(),b(NB.$$.fragment),YIo=l(),_h=a("h3"),$B=a("a"),slo=a("span"),b(RX.$$.fragment),dwa=l(),llo=a("span"),cwa=o("AutoModelForObjectDetection"),zIo=l(),Lr=a("div"),b(kX.$$.fragment),mwa=l(),vh=a("p"),fwa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created
with the `),eye=a("a"),hwa=o("from_pretrained()"),gwa=o(" class method or the "),oye=a("a"),uwa=o("from_config()"),pwa=o(` class
method.`),_wa=l(),SX=a("p"),vwa=o("This class cannot be instantiated directly using "),ilo=a("code"),bwa=o("__init__()"),Mwa=o(" (throws an error)."),Twa=l(),La=a("div"),b(xX.$$.fragment),Fwa=l(),dlo=a("p"),Ewa=o("Instantiates one of the model classes of the library (with a object detection head) from a configuration."),ywa=l(),bh=a("p"),Cwa=o(`Note:
Loading a model from its configuration file does `),clo=a("strong"),wwa=o("not"),Lwa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),rye=a("a"),Awa=o("from_pretrained()"),Rwa=o(" to load the model weights."),kwa=l(),b(jB.$$.fragment),Swa=l(),To=a("div"),b(GX.$$.fragment),xwa=l(),mlo=a("p"),Gwa=o("Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),Bwa=l(),ys=a("p"),Iwa=o("The model class to instantiate is selected based on the "),flo=a("code"),Nwa=o("model_type"),$wa=o(` property of the config object (either
passed as an argument or loaded from `),hlo=a("code"),jwa=o("pretrained_model_name_or_path"),Pwa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),glo=a("code"),Zwa=o("pretrained_model_name_or_path"),Vwa=o(":"),Wwa=l(),Ar=a("ul"),PB=a("li"),ulo=a("strong"),Jwa=o("conditional_detr"),Uwa=o(" \u2014 "),tye=a("a"),Xwa=o("ConditionalDetrForObjectDetection"),Dwa=o(" (Conditional DETR model)"),qwa=l(),ZB=a("li"),plo=a("strong"),Owa=o("deformable_detr"),Qwa=o(" \u2014 "),aye=a("a"),Ywa=o("DeformableDetrForObjectDetection"),zwa=o(" (Deformable DETR model)"),Hwa=l(),VB=a("li"),_lo=a("strong"),Kwa=o("deta"),e6a=o(" \u2014 "),nye=a("a"),o6a=o("DetaForObjectDetection"),r6a=o(" (DETA model)"),t6a=l(),WB=a("li"),vlo=a("strong"),a6a=o("detr"),n6a=o(" \u2014 "),sye=a("a"),s6a=o("DetrForObjectDetection"),l6a=o(" (DETR model)"),i6a=l(),JB=a("li"),blo=a("strong"),d6a=o("table-transformer"),c6a=o(" \u2014 "),lye=a("a"),m6a=o("TableTransformerForObjectDetection"),f6a=o(" (Table Transformer model)"),h6a=l(),UB=a("li"),Mlo=a("strong"),g6a=o("yolos"),u6a=o(" \u2014 "),iye=a("a"),p6a=o("YolosForObjectDetection"),_6a=o(" (YOLOS model)"),v6a=l(),XB=a("p"),b6a=o("The model is set in evaluation mode by default using "),Tlo=a("code"),M6a=o("model.eval()"),T6a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Flo=a("code"),F6a=o("model.train()"),E6a=l(),b(DB.$$.fragment),HIo=l(),Mh=a("h3"),qB=a("a"),Elo=a("span"),b(BX.$$.fragment),y6a=l(),ylo=a("span"),C6a=o("AutoModelForImageSegmentation"),KIo=l(),Rr=a("div"),b(IX.$$.fragment),w6a=l(),Th=a("p"),L6a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
with the `),dye=a("a"),A6a=o("from_pretrained()"),R6a=o(" class method or the "),cye=a("a"),k6a=o("from_config()"),S6a=o(` class
method.`),x6a=l(),NX=a("p"),G6a=o("This class cannot be instantiated directly using "),Clo=a("code"),B6a=o("__init__()"),I6a=o(" (throws an error)."),N6a=l(),Aa=a("div"),b($X.$$.fragment),$6a=l(),wlo=a("p"),j6a=o("Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),P6a=l(),Fh=a("p"),Z6a=o(`Note:
Loading a model from its configuration file does `),Llo=a("strong"),V6a=o("not"),W6a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),mye=a("a"),J6a=o("from_pretrained()"),U6a=o(" to load the model weights."),X6a=l(),b(OB.$$.fragment),D6a=l(),Fo=a("div"),b(jX.$$.fragment),q6a=l(),Alo=a("p"),O6a=o("Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),Q6a=l(),Cs=a("p"),Y6a=o("The model class to instantiate is selected based on the "),Rlo=a("code"),z6a=o("model_type"),H6a=o(` property of the config object (either
passed as an argument or loaded from `),klo=a("code"),K6a=o("pretrained_model_name_or_path"),e8a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Slo=a("code"),o8a=o("pretrained_model_name_or_path"),r8a=o(":"),t8a=l(),xlo=a("ul"),QB=a("li"),Glo=a("strong"),a8a=o("detr"),n8a=o(" \u2014 "),fye=a("a"),s8a=o("DetrForSegmentation"),l8a=o(" (DETR model)"),i8a=l(),YB=a("p"),d8a=o("The model is set in evaluation mode by default using "),Blo=a("code"),c8a=o("model.eval()"),m8a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Ilo=a("code"),f8a=o("model.train()"),h8a=l(),b(zB.$$.fragment),eNo=l(),Eh=a("h3"),HB=a("a"),Nlo=a("span"),b(PX.$$.fragment),g8a=l(),$lo=a("span"),u8a=o("AutoModelForSemanticSegmentation"),oNo=l(),kr=a("div"),b(ZX.$$.fragment),p8a=l(),yh=a("p"),_8a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),hye=a("a"),v8a=o("from_pretrained()"),b8a=o(" class method or the "),gye=a("a"),M8a=o("from_config()"),T8a=o(` class
method.`),F8a=l(),VX=a("p"),E8a=o("This class cannot be instantiated directly using "),jlo=a("code"),y8a=o("__init__()"),C8a=o(" (throws an error)."),w8a=l(),Ra=a("div"),b(WX.$$.fragment),L8a=l(),Plo=a("p"),A8a=o("Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),R8a=l(),Ch=a("p"),k8a=o(`Note:
Loading a model from its configuration file does `),Zlo=a("strong"),S8a=o("not"),x8a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),uye=a("a"),G8a=o("from_pretrained()"),B8a=o(" to load the model weights."),I8a=l(),b(KB.$$.fragment),N8a=l(),Eo=a("div"),b(JX.$$.fragment),$8a=l(),Vlo=a("p"),j8a=o("Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),P8a=l(),ws=a("p"),Z8a=o("The model class to instantiate is selected based on the "),Wlo=a("code"),V8a=o("model_type"),W8a=o(` property of the config object (either
passed as an argument or loaded from `),Jlo=a("code"),J8a=o("pretrained_model_name_or_path"),U8a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ulo=a("code"),X8a=o("pretrained_model_name_or_path"),D8a=o(":"),q8a=l(),Ye=a("ul"),eI=a("li"),Xlo=a("strong"),O8a=o("beit"),Q8a=o(" \u2014 "),pye=a("a"),Y8a=o("BeitForSemanticSegmentation"),z8a=o(" (BEiT model)"),H8a=l(),oI=a("li"),Dlo=a("strong"),K8a=o("data2vec-vision"),e7a=o(" \u2014 "),_ye=a("a"),o7a=o("Data2VecVisionForSemanticSegmentation"),r7a=o(" (Data2VecVision model)"),t7a=l(),rI=a("li"),qlo=a("strong"),a7a=o("dpt"),n7a=o(" \u2014 "),vye=a("a"),s7a=o("DPTForSemanticSegmentation"),l7a=o(" (DPT model)"),i7a=l(),tI=a("li"),Olo=a("strong"),d7a=o("mobilenet_v2"),c7a=o(" \u2014 "),bye=a("a"),m7a=o("MobileNetV2ForSemanticSegmentation"),f7a=o(" (MobileNetV2 model)"),h7a=l(),aI=a("li"),Qlo=a("strong"),g7a=o("mobilevit"),u7a=o(" \u2014 "),Mye=a("a"),p7a=o("MobileViTForSemanticSegmentation"),_7a=o(" (MobileViT model)"),v7a=l(),nI=a("li"),Ylo=a("strong"),b7a=o("mobilevitv2"),M7a=o(" \u2014 "),Tye=a("a"),T7a=o("MobileViTV2ForSemanticSegmentation"),F7a=o(" (MobileViTV2 model)"),E7a=l(),sI=a("li"),zlo=a("strong"),y7a=o("segformer"),C7a=o(" \u2014 "),Fye=a("a"),w7a=o("SegformerForSemanticSegmentation"),L7a=o(" (SegFormer model)"),A7a=l(),lI=a("li"),Hlo=a("strong"),R7a=o("upernet"),k7a=o(" \u2014 "),Eye=a("a"),S7a=o("UperNetForSemanticSegmentation"),x7a=o(" (UPerNet model)"),G7a=l(),iI=a("p"),B7a=o("The model is set in evaluation mode by default using "),Klo=a("code"),I7a=o("model.eval()"),N7a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),eio=a("code"),$7a=o("model.train()"),j7a=l(),b(dI.$$.fragment),rNo=l(),wh=a("h3"),cI=a("a"),oio=a("span"),b(UX.$$.fragment),P7a=l(),rio=a("span"),Z7a=o("TFAutoModelForSemanticSegmentation"),tNo=l(),Sr=a("div"),b(XX.$$.fragment),V7a=l(),Lh=a("p"),W7a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),yye=a("a"),J7a=o("from_pretrained()"),U7a=o(" class method or the "),Cye=a("a"),X7a=o("from_config()"),D7a=o(` class
method.`),q7a=l(),DX=a("p"),O7a=o("This class cannot be instantiated directly using "),tio=a("code"),Q7a=o("__init__()"),Y7a=o(" (throws an error)."),z7a=l(),ka=a("div"),b(qX.$$.fragment),H7a=l(),aio=a("p"),K7a=o("Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),eLa=l(),Ah=a("p"),oLa=o(`Note:
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model\u2019s configuration. Use `),wye=a("a"),aLa=o("from_pretrained()"),nLa=o(" to load the model weights."),sLa=l(),b(mI.$$.fragment),lLa=l(),wt=a("div"),b(OX.$$.fragment),iLa=l(),sio=a("p"),dLa=o("Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),cLa=l(),Ls=a("p"),mLa=o("The model class to instantiate is selected based on the "),lio=a("code"),fLa=o("model_type"),hLa=o(` property of the config object (either
passed as an argument or loaded from `),iio=a("code"),gLa=o("pretrained_model_name_or_path"),uLa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),dio=a("code"),pLa=o("pretrained_model_name_or_path"),_La=o(":"),vLa=l(),Rh=a("ul"),fI=a("li"),cio=a("strong"),bLa=o("data2vec-vision"),MLa=o(" \u2014 "),Lye=a("a"),TLa=o("TFData2VecVisionForSemanticSegmentation"),FLa=o(" (Data2VecVision model)"),ELa=l(),hI=a("li"),mio=a("strong"),yLa=o("mobilevit"),CLa=o(" \u2014 "),Aye=a("a"),wLa=o("TFMobileViTForSemanticSegmentation"),LLa=o(" (MobileViT model)"),ALa=l(),gI=a("li"),fio=a("strong"),RLa=o("segformer"),kLa=o(" \u2014 "),Rye=a("a"),SLa=o("TFSegformerForSemanticSegmentation"),xLa=o(" (SegFormer model)"),GLa=l(),b(uI.$$.fragment),aNo=l(),kh=a("h3"),pI=a("a"),hio=a("span"),b(QX.$$.fragment),BLa=l(),gio=a("span"),ILa=o("AutoModelForInstanceSegmentation"),nNo=l(),xr=a("div"),b(YX.$$.fragment),NLa=l(),Sh=a("p"),$La=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created
with the `),kye=a("a"),jLa=o("from_pretrained()"),PLa=o(" class method or the "),Sye=a("a"),ZLa=o("from_config()"),VLa=o(` class
method.`),WLa=l(),zX=a("p"),JLa=o("This class cannot be instantiated directly using "),uio=a("code"),ULa=o("__init__()"),XLa=o(" (throws an error)."),DLa=l(),Sa=a("div"),b(HX.$$.fragment),qLa=l(),pio=a("p"),OLa=o("Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),QLa=l(),xh=a("p"),YLa=o(`Note:
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model\u2019s configuration. Use `),xye=a("a"),KLa=o("from_pretrained()"),eAa=o(" to load the model weights."),oAa=l(),b(_I.$$.fragment),rAa=l(),yo=a("div"),b(KX.$$.fragment),tAa=l(),vio=a("p"),aAa=o("Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),nAa=l(),As=a("p"),sAa=o("The model class to instantiate is selected based on the "),bio=a("code"),lAa=o("model_type"),iAa=o(` property of the config object (either
passed as an argument or loaded from `),Mio=a("code"),dAa=o("pretrained_model_name_or_path"),cAa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Tio=a("code"),mAa=o("pretrained_model_name_or_path"),fAa=o(":"),hAa=l(),Fio=a("ul"),vI=a("li"),Eio=a("strong"),gAa=o("maskformer"),uAa=o(" \u2014 "),Gye=a("a"),pAa=o("MaskFormerForInstanceSegmentation"),_Aa=o(" (MaskFormer model)"),vAa=l(),bI=a("p"),bAa=o("The model is set in evaluation mode by default using "),yio=a("code"),MAa=o("model.eval()"),TAa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Cio=a("code"),FAa=o("model.train()"),EAa=l(),b(MI.$$.fragment),sNo=l(),Gh=a("h3"),TI=a("a"),wio=a("span"),b(eD.$$.fragment),yAa=l(),Lio=a("span"),CAa=o("AutoModelForUniversalSegmentation"),lNo=l(),Gr=a("div"),b(oD.$$.fragment),wAa=l(),Bh=a("p"),LAa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a universal image segmentation head) when created
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method.`),xAa=l(),rD=a("p"),GAa=o("This class cannot be instantiated directly using "),Aio=a("code"),BAa=o("__init__()"),IAa=o(" (throws an error)."),NAa=l(),xa=a("div"),b(tD.$$.fragment),$Aa=l(),Rio=a("p"),jAa=o("Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration."),PAa=l(),Ih=a("p"),ZAa=o(`Note:
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model\u2019s configuration. Use `),Nye=a("a"),JAa=o("from_pretrained()"),UAa=o(" to load the model weights."),XAa=l(),b(FI.$$.fragment),DAa=l(),Co=a("div"),b(aD.$$.fragment),qAa=l(),Sio=a("p"),OAa=o("Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model."),QAa=l(),Rs=a("p"),YAa=o("The model class to instantiate is selected based on the "),xio=a("code"),zAa=o("model_type"),HAa=o(` property of the config object (either
passed as an argument or loaded from `),Gio=a("code"),KAa=o("pretrained_model_name_or_path"),eRa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Bio=a("code"),oRa=o("pretrained_model_name_or_path"),rRa=o(":"),tRa=l(),ks=a("ul"),EI=a("li"),Iio=a("strong"),aRa=o("detr"),nRa=o(" \u2014 "),$ye=a("a"),sRa=o("DetrForSegmentation"),lRa=o(" (DETR model)"),iRa=l(),yI=a("li"),Nio=a("strong"),dRa=o("mask2former"),cRa=o(" \u2014 "),jye=a("a"),mRa=o("Mask2FormerForUniversalSegmentation"),fRa=o(" (Mask2Former model)"),hRa=l(),CI=a("li"),$io=a("strong"),gRa=o("maskformer"),uRa=o(" \u2014 "),Pye=a("a"),pRa=o("MaskFormerForInstanceSegmentation"),_Ra=o(" (MaskFormer model)"),vRa=l(),wI=a("li"),jio=a("strong"),bRa=o("oneformer"),MRa=o(" \u2014 "),Zye=a("a"),TRa=o("OneFormerForUniversalSegmentation"),FRa=o(" (OneFormer model)"),ERa=l(),LI=a("p"),yRa=o("The model is set in evaluation mode by default using "),Pio=a("code"),CRa=o("model.eval()"),wRa=o(` (so for instance, dropout modules are
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method.`),$Ra=l(),lD=a("p"),jRa=o("This class cannot be instantiated directly using "),Jio=a("code"),PRa=o("__init__()"),ZRa=o(" (throws an error)."),VRa=l(),Ga=a("div"),b(iD.$$.fragment),WRa=l(),Uio=a("p"),JRa=o("Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),URa=l(),jh=a("p"),XRa=o(`Note:
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model\u2019s configuration. Use `),Jye=a("a"),ORa=o("from_pretrained()"),QRa=o(" to load the model weights."),YRa=l(),b(kI.$$.fragment),zRa=l(),wo=a("div"),b(dD.$$.fragment),HRa=l(),Dio=a("p"),KRa=o("Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),eka=l(),Ss=a("p"),oka=o("The model class to instantiate is selected based on the "),qio=a("code"),rka=o("model_type"),tka=o(` property of the config object (either
passed as an argument or loaded from `),Oio=a("code"),aka=o("pretrained_model_name_or_path"),nka=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Qio=a("code"),ska=o("pretrained_model_name_or_path"),lka=o(":"),ika=l(),Ir=a("ul"),SI=a("li"),Yio=a("strong"),dka=o("align"),cka=o(" \u2014 "),Uye=a("a"),mka=o("AlignModel"),fka=o(" (ALIGN model)"),hka=l(),xI=a("li"),zio=a("strong"),gka=o("altclip"),uka=o(" \u2014 "),Xye=a("a"),pka=o("AltCLIPModel"),_ka=o(" (AltCLIP model)"),vka=l(),GI=a("li"),Hio=a("strong"),bka=o("blip"),Mka=o(" \u2014 "),Dye=a("a"),Tka=o("BlipModel"),Fka=o(" (BLIP model)"),Eka=l(),BI=a("li"),Kio=a("strong"),yka=o("chinese_clip"),Cka=o(" \u2014 "),qye=a("a"),wka=o("ChineseCLIPModel"),Lka=o(" (Chinese-CLIP model)"),Aka=l(),II=a("li"),edo=a("strong"),Rka=o("clip"),kka=o(" \u2014 "),Oye=a("a"),Ska=o("CLIPModel"),xka=o(" (CLIP model)"),Gka=l(),NI=a("li"),odo=a("strong"),Bka=o("clipseg"),Ika=o(" \u2014 "),Qye=a("a"),Nka=o("CLIPSegModel"),$ka=o(" (CLIPSeg model)"),jka=l(),$I=a("p"),Pka=o("The model is set in evaluation mode by default using "),rdo=a("code"),Zka=o("model.eval()"),Vka=o(` (so for instance, dropout modules are
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method.`),Hka=l(),fD=a("p"),Kka=o("This class cannot be instantiated directly using "),sdo=a("code"),eSa=o("__init__()"),oSa=o(" (throws an error)."),rSa=l(),Ba=a("div"),b(hD.$$.fragment),tSa=l(),ldo=a("p"),aSa=o("Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),nSa=l(),Vh=a("p"),sSa=o(`Note:
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model\u2019s configuration. Use `),Hye=a("a"),dSa=o("from_pretrained()"),cSa=o(" to load the model weights."),mSa=l(),b(ZI.$$.fragment),fSa=l(),Lt=a("div"),b(gD.$$.fragment),hSa=l(),ddo=a("p"),gSa=o("Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),uSa=l(),xs=a("p"),pSa=o("The model class to instantiate is selected based on the "),cdo=a("code"),_Sa=o("model_type"),vSa=o(` property of the config object (either
passed as an argument or loaded from `),mdo=a("code"),bSa=o("pretrained_model_name_or_path"),MSa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),fdo=a("code"),TSa=o("pretrained_model_name_or_path"),FSa=o(":"),ESa=l(),uD=a("ul"),VI=a("li"),hdo=a("strong"),ySa=o("blip"),CSa=o(" \u2014 "),Kye=a("a"),wSa=o("TFBlipModel"),LSa=o(" (BLIP model)"),ASa=l(),WI=a("li"),gdo=a("strong"),RSa=o("clip"),kSa=o(" \u2014 "),e5e=a("a"),SSa=o("TFCLIPModel"),xSa=o(" (CLIP model)"),GSa=l(),b(JI.$$.fragment),fNo=l(),Wh=a("h3"),UI=a("a"),udo=a("span"),b(pD.$$.fragment),BSa=l(),pdo=a("span"),ISa=o("AutoModelForZeroShotObjectDetection"),hNo=l(),$r=a("div"),b(_D.$$.fragment),NSa=l(),Jh=a("p"),$Sa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot object detection head) when created
with the `),o5e=a("a"),jSa=o("from_pretrained()"),PSa=o(" class method or the "),r5e=a("a"),ZSa=o("from_config()"),VSa=o(` class
method.`),WSa=l(),vD=a("p"),JSa=o("This class cannot be instantiated directly using "),_do=a("code"),USa=o("__init__()"),XSa=o(" (throws an error)."),DSa=l(),Ia=a("div"),b(bD.$$.fragment),qSa=l(),vdo=a("p"),OSa=o("Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration."),QSa=l(),Uh=a("p"),YSa=o(`Note:
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passed as an argument or loaded from `),Fdo=a("code"),dxa=o("pretrained_model_name_or_path"),cxa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Edo=a("code"),mxa=o("pretrained_model_name_or_path"),fxa=o(":"),hxa=l(),ydo=a("ul"),DI=a("li"),Cdo=a("strong"),gxa=o("owlvit"),uxa=o(" \u2014 "),a5e=a("a"),pxa=o("OwlViTForObjectDetection"),_xa=o(" (OWL-ViT model)"),vxa=l(),qI=a("p"),bxa=o("The model is set in evaluation mode by default using "),wdo=a("code"),Mxa=o("model.eval()"),Txa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Ldo=a("code"),Fxa=o("model.train()"),Exa=l(),b(OI.$$.fragment),gNo=l(),Xh=a("h2"),QI=a("a"),Ado=a("span"),b(TD.$$.fragment),yxa=l(),Rdo=a("span"),Cxa=o("Audio"),uNo=l(),n5e=a("p"),wxa=o("The following auto classes are available for the following audio tasks."),pNo=l(),Dh=a("h3"),YI=a("a"),kdo=a("span"),b(FD.$$.fragment),Lxa=l(),Sdo=a("span"),Axa=o("AutoModelForAudioClassification"),_No=l(),jr=a("div"),b(ED.$$.fragment),Rxa=l(),qh=a("p"),kxa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
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method.`),Ixa=l(),yD=a("p"),Nxa=o("This class cannot be instantiated directly using "),xdo=a("code"),$xa=o("__init__()"),jxa=o(" (throws an error)."),Pxa=l(),Na=a("div"),b(CD.$$.fragment),Zxa=l(),Gdo=a("p"),Vxa=o("Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),Wxa=l(),Oh=a("p"),Jxa=o(`Note:
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model\u2019s configuration. Use `),i5e=a("a"),Dxa=o("from_pretrained()"),qxa=o(" to load the model weights."),Oxa=l(),b(zI.$$.fragment),Qxa=l(),Ao=a("div"),b(wD.$$.fragment),Yxa=l(),Ido=a("p"),zxa=o("Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),Hxa=l(),Bs=a("p"),Kxa=o("The model class to instantiate is selected based on the "),Ndo=a("code"),eGa=o("model_type"),oGa=o(` property of the config object (either
passed as an argument or loaded from `),$do=a("code"),rGa=o("pretrained_model_name_or_path"),tGa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),jdo=a("code"),aGa=o("pretrained_model_name_or_path"),nGa=o(":"),sGa=l(),Be=a("ul"),HI=a("li"),Pdo=a("strong"),lGa=o("audio-spectrogram-transformer"),iGa=o(" \u2014 "),d5e=a("a"),dGa=o("ASTForAudioClassification"),cGa=o(" (Audio Spectrogram Transformer model)"),mGa=l(),KI=a("li"),Zdo=a("strong"),fGa=o("data2vec-audio"),hGa=o(" \u2014 "),c5e=a("a"),gGa=o("Data2VecAudioForSequenceClassification"),uGa=o(" (Data2VecAudio model)"),pGa=l(),eN=a("li"),Vdo=a("strong"),_Ga=o("hubert"),vGa=o(" \u2014 "),m5e=a("a"),bGa=o("HubertForSequenceClassification"),MGa=o(" (Hubert model)"),TGa=l(),oN=a("li"),Wdo=a("strong"),FGa=o("sew"),EGa=o(" \u2014 "),f5e=a("a"),yGa=o("SEWForSequenceClassification"),CGa=o(" (SEW model)"),wGa=l(),rN=a("li"),Jdo=a("strong"),LGa=o("sew-d"),AGa=o(" \u2014 "),h5e=a("a"),RGa=o("SEWDForSequenceClassification"),kGa=o(" (SEW-D model)"),SGa=l(),tN=a("li"),Udo=a("strong"),xGa=o("unispeech"),GGa=o(" \u2014 "),g5e=a("a"),BGa=o("UniSpeechForSequenceClassification"),IGa=o(" (UniSpeech model)"),NGa=l(),aN=a("li"),Xdo=a("strong"),$Ga=o("unispeech-sat"),jGa=o(" \u2014 "),u5e=a("a"),PGa=o("UniSpeechSatForSequenceClassification"),ZGa=o(" (UniSpeechSat model)"),VGa=l(),nN=a("li"),Ddo=a("strong"),WGa=o("wav2vec2"),JGa=o(" \u2014 "),p5e=a("a"),UGa=o("Wav2Vec2ForSequenceClassification"),XGa=o(" (Wav2Vec2 model)"),DGa=l(),sN=a("li"),qdo=a("strong"),qGa=o("wav2vec2-conformer"),OGa=o(" \u2014 "),_5e=a("a"),QGa=o("Wav2Vec2ConformerForSequenceClassification"),YGa=o(" (Wav2Vec2-Conformer model)"),zGa=l(),lN=a("li"),Odo=a("strong"),HGa=o("wavlm"),KGa=o(" \u2014 "),v5e=a("a"),eBa=o("WavLMForSequenceClassification"),oBa=o(" (WavLM model)"),rBa=l(),iN=a("li"),Qdo=a("strong"),tBa=o("whisper"),aBa=o(" \u2014 "),b5e=a("a"),nBa=o("WhisperForAudioClassification"),sBa=o(" (Whisper model)"),lBa=l(),dN=a("p"),iBa=o("The model is set in evaluation mode by default using "),Ydo=a("code"),dBa=o("model.eval()"),cBa=o(` (so for instance, dropout modules are
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falling back to using pattern matching on `),_co=a("code"),RIa=o("pretrained_model_name_or_path"),kIa=o(":"),SIa=l(),$t=a("ul"),_N=a("li"),vco=a("strong"),xIa=o("data2vec-audio"),GIa=o(" \u2014 "),L5e=a("a"),BIa=o("Data2VecAudioForAudioFrameClassification"),IIa=o(" (Data2VecAudio model)"),NIa=l(),vN=a("li"),bco=a("strong"),$Ia=o("unispeech-sat"),jIa=o(" \u2014 "),A5e=a("a"),PIa=o("UniSpeechSatForAudioFrameClassification"),ZIa=o(" (UniSpeechSat model)"),VIa=l(),bN=a("li"),Mco=a("strong"),WIa=o("wav2vec2"),JIa=o(" \u2014 "),R5e=a("a"),UIa=o("Wav2Vec2ForAudioFrameClassification"),XIa=o(" (Wav2Vec2 model)"),DIa=l(),MN=a("li"),Tco=a("strong"),qIa=o("wav2vec2-conformer"),OIa=o(" \u2014 "),k5e=a("a"),QIa=o("Wav2Vec2ConformerForAudioFrameClassification"),YIa=o(" (Wav2Vec2-Conformer model)"),zIa=l(),TN=a("li"),Fco=a("strong"),HIa=o("wavlm"),KIa=o(" \u2014 "),S5e=a("a"),eNa=o("WavLMForAudioFrameClassification"),oNa=o(" (WavLM model)"),rNa=l(),FN=a("p"),tNa=o("The model is set in evaluation mode by default using "),Eco=a("code"),aNa=o("model.eval()"),nNa=o(` (so for instance, dropout modules are
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falling back to using pattern matching on `),kfo=a("code"),rUa=o("pretrained_model_name_or_path"),tUa=o(":"),aUa=l(),Ag=a("ul"),w$=a("li"),Sfo=a("strong"),nUa=o("layoutlm"),sUa=o(" \u2014 "),R9e=a("a"),lUa=o("LayoutLMForQuestionAnswering"),iUa=o(" (LayoutLM model)"),dUa=l(),L$=a("li"),xfo=a("strong"),cUa=o("layoutlmv2"),mUa=o(" \u2014 "),k9e=a("a"),fUa=o("LayoutLMv2ForQuestionAnswering"),hUa=o(" (LayoutLMv2 model)"),gUa=l(),A$=a("li"),Gfo=a("strong"),uUa=o("layoutlmv3"),pUa=o(" \u2014 "),S9e=a("a"),_Ua=o("LayoutLMv3ForQuestionAnswering"),vUa=o(" (LayoutLMv3 model)"),bUa=l(),R$=a("p"),MUa=o("The model is set in evaluation mode by default using "),Bfo=a("code"),TUa=o("model.eval()"),FUa=o(` (so for instance, dropout modules are
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bon=s(LAe);M(_V.$$.fragment,bon),bon.forEach(t),von.forEach(t),Bnr=i(r$o),AAe=n(r$o,"SPAN",{});var Mon=s(AAe);Inr=r(Mon,"AutoTokenizer"),Mon.forEach(t),r$o.forEach(t),lBo=i(m),Zo=n(m,"DIV",{class:!0});var Oi=s(Zo);M(vV.$$.fragment,Oi),Nnr=i(Oi),bV=n(Oi,"P",{});var t$o=s(bV);$nr=r(t$o,`This is a generic tokenizer class that will be instantiated as one of the tokenizer classes of the library when
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iin=s(lae);Xbr=r(iin,"XLMProphetNetTokenizer"),iin.forEach(t),Dbr=r(A3o," (XLM-ProphetNet model)"),A3o.forEach(t),qbr=i(S),Ni=n(S,"LI",{});var qQ=s(Ni);iSe=n(qQ,"STRONG",{});var din=s(iSe);Obr=r(din,"xlm-roberta"),din.forEach(t),Qbr=r(qQ," \u2014 "),iae=n(qQ,"A",{href:!0});var cin=s(iae);Ybr=r(cin,"XLMRobertaTokenizer"),cin.forEach(t),zbr=r(qQ," or "),dae=n(qQ,"A",{href:!0});var min=s(dae);Hbr=r(min,"XLMRobertaTokenizerFast"),min.forEach(t),Kbr=r(qQ," (XLM-RoBERTa model)"),qQ.forEach(t),e2r=i(S),$i=n(S,"LI",{});var OQ=s($i);dSe=n(OQ,"STRONG",{});var fin=s(dSe);o2r=r(fin,"xlm-roberta-xl"),fin.forEach(t),r2r=r(OQ," \u2014 "),cae=n(OQ,"A",{href:!0});var hin=s(cae);t2r=r(hin,"XLMRobertaTokenizer"),hin.forEach(t),a2r=r(OQ," or "),mae=n(OQ,"A",{href:!0});var gin=s(mae);n2r=r(gin,"XLMRobertaTokenizerFast"),gin.forEach(t),s2r=r(OQ," (XLM-RoBERTa-XL model)"),OQ.forEach(t),l2r=i(S),ji=n(S,"LI",{});var QQ=s(ji);cSe=n(QQ,"STRONG",{});var uin=s(cSe);i2r=r(uin,"xlnet"),uin.forEach(t),d2r=r(QQ," 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library when created with the `),vae=n(l$o,"A",{href:!0});var Ain=s(vae);N2r=r(Ain,"AutoFeatureExtractor.from_pretrained()"),Ain.forEach(t),$2r=r(l$o," class method."),l$o.forEach(t),j2r=i(Yi),wV=n(Yi,"P",{});var i$o=s(wV);P2r=r(i$o,"This class cannot be instantiated directly using "),pSe=n(i$o,"CODE",{});var Rin=s(pSe);Z2r=r(Rin,"__init__()"),Rin.forEach(t),V2r=r(i$o," (throws an error)."),i$o.forEach(t),W2r=i(Yi),to=n(Yi,"DIV",{class:!0});var Ha=s(to);M(LV.$$.fragment,Ha),J2r=i(Ha),_Se=n(Ha,"P",{});var kin=s(_Se);U2r=r(kin,"Instantiate one of the feature extractor classes of the library from a pretrained model vocabulary."),kin.forEach(t),X2r=i(Ha),jn=n(Ha,"P",{});var Pj=s(jn);D2r=r(Pj,"The feature extractor class to instantiate is selected based on the "),vSe=n(Pj,"CODE",{});var Sin=s(vSe);q2r=r(Sin,"model_type"),Sin.forEach(t),O2r=r(Pj,` property of the config object
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T2o=s(J2);ABe=n(T2o,"STRONG",{});var $gn=s(ABe);cAr=r($gn,"oneformer"),$gn.forEach(t),mAr=r(T2o," \u2014 "),hle=n(T2o,"A",{href:!0});var jgn=s(hle);fAr=r(jgn,"OneFormerProcessor"),jgn.forEach(t),hAr=r(T2o," (OneFormer model)"),T2o.forEach(t),gAr=i(re),U2=n(re,"LI",{});var F2o=s(U2);RBe=n(F2o,"STRONG",{});var Pgn=s(RBe);uAr=r(Pgn,"owlvit"),Pgn.forEach(t),pAr=r(F2o," \u2014 "),gle=n(F2o,"A",{href:!0});var Zgn=s(gle);_Ar=r(Zgn,"OwlViTProcessor"),Zgn.forEach(t),vAr=r(F2o," (OWL-ViT model)"),F2o.forEach(t),bAr=i(re),X2=n(re,"LI",{});var E2o=s(X2);kBe=n(E2o,"STRONG",{});var Vgn=s(kBe);MAr=r(Vgn,"pix2struct"),Vgn.forEach(t),TAr=r(E2o," \u2014 "),ule=n(E2o,"A",{href:!0});var Wgn=s(ule);FAr=r(Wgn,"Pix2StructProcessor"),Wgn.forEach(t),EAr=r(E2o," (Pix2Struct model)"),E2o.forEach(t),yAr=i(re),D2=n(re,"LI",{});var y2o=s(D2);SBe=n(y2o,"STRONG",{});var Jgn=s(SBe);CAr=r(Jgn,"pop2piano"),Jgn.forEach(t),wAr=r(y2o," \u2014 "),ple=n(y2o,"A",{href:!0});var Ugn=s(ple);LAr=r(Ugn,"Pop2PianoProcessor"),Ugn.forEach(t),AAr=r(y2o," (Pop2Piano model)"),y2o.forEach(t),RAr=i(re),q2=n(re,"LI",{});var C2o=s(q2);xBe=n(C2o,"STRONG",{});var Xgn=s(xBe);kAr=r(Xgn,"sam"),Xgn.forEach(t),SAr=r(C2o," \u2014 "),_le=n(C2o,"A",{href:!0});var Dgn=s(_le);xAr=r(Dgn,"SamProcessor"),Dgn.forEach(t),GAr=r(C2o," (SAM model)"),C2o.forEach(t),BAr=i(re),O2=n(re,"LI",{});var w2o=s(O2);GBe=n(w2o,"STRONG",{});var qgn=s(GBe);IAr=r(qgn,"sew"),qgn.forEach(t),NAr=r(w2o," \u2014 "),vle=n(w2o,"A",{href:!0});var Ogn=s(vle);$Ar=r(Ogn,"Wav2Vec2Processor"),Ogn.forEach(t),jAr=r(w2o," (SEW model)"),w2o.forEach(t),PAr=i(re),Q2=n(re,"LI",{});var L2o=s(Q2);BBe=n(L2o,"STRONG",{});var Qgn=s(BBe);ZAr=r(Qgn,"sew-d"),Qgn.forEach(t),VAr=r(L2o," \u2014 "),ble=n(L2o,"A",{href:!0});var Ygn=s(ble);WAr=r(Ygn,"Wav2Vec2Processor"),Ygn.forEach(t),JAr=r(L2o," (SEW-D model)"),L2o.forEach(t),UAr=i(re),Y2=n(re,"LI",{});var A2o=s(Y2);IBe=n(A2o,"STRONG",{});var zgn=s(IBe);XAr=r(zgn,"speech_to_text"),zgn.forEach(t),DAr=r(A2o," \u2014 "),Mle=n(A2o,"A",{href:!0});var Hgn=s(Mle);qAr=r(Hgn,"Speech2TextProcessor"),Hgn.forEach(t),OAr=r(A2o," (Speech2Text model)"),A2o.forEach(t),QAr=i(re),z2=n(re,"LI",{});var R2o=s(z2);NBe=n(R2o,"STRONG",{});var Kgn=s(NBe);YAr=r(Kgn,"speech_to_text_2"),Kgn.forEach(t),zAr=r(R2o," \u2014 "),Tle=n(R2o,"A",{href:!0});var eun=s(Tle);HAr=r(eun,"Speech2Text2Processor"),eun.forEach(t),KAr=r(R2o," (Speech2Text2 model)"),R2o.forEach(t),eRr=i(re),H2=n(re,"LI",{});var k2o=s(H2);$Be=n(k2o,"STRONG",{});var oun=s($Be);oRr=r(oun,"speecht5"),oun.forEach(t),rRr=r(k2o," \u2014 "),Fle=n(k2o,"A",{href:!0});var run=s(Fle);tRr=r(run,"SpeechT5Processor"),run.forEach(t),aRr=r(k2o," (SpeechT5 model)"),k2o.forEach(t),nRr=i(re),K2=n(re,"LI",{});var S2o=s(K2);jBe=n(S2o,"STRONG",{});var tun=s(jBe);sRr=r(tun,"trocr"),tun.forEach(t),lRr=r(S2o," \u2014 "),Ele=n(S2o,"A",{href:!0});var aun=s(Ele);iRr=r(aun,"TrOCRProcessor"),aun.forEach(t),dRr=r(S2o," (TrOCR model)"),S2o.forEach(t),cRr=i(re),e0=n(re,"LI",{});var x2o=s(e0);PBe=n(x2o,"STRONG",{});var nun=s(PBe);mRr=r(nun,"tvlt"),nun.forEach(t),fRr=r(x2o," \u2014 "),yle=n(x2o,"A",{href:!0});var sun=s(yle);hRr=r(sun,"TvltProcessor"),sun.forEach(t),gRr=r(x2o," (TVLT model)"),x2o.forEach(t),uRr=i(re),o0=n(re,"LI",{});var G2o=s(o0);ZBe=n(G2o,"STRONG",{});var lun=s(ZBe);pRr=r(lun,"unispeech"),lun.forEach(t),_Rr=r(G2o," \u2014 "),Cle=n(G2o,"A",{href:!0});var iun=s(Cle);vRr=r(iun,"Wav2Vec2Processor"),iun.forEach(t),bRr=r(G2o," (UniSpeech model)"),G2o.forEach(t),MRr=i(re),r0=n(re,"LI",{});var B2o=s(r0);VBe=n(B2o,"STRONG",{});var dun=s(VBe);TRr=r(dun,"unispeech-sat"),dun.forEach(t),FRr=r(B2o," \u2014 "),wle=n(B2o,"A",{href:!0});var cun=s(wle);ERr=r(cun,"Wav2Vec2Processor"),cun.forEach(t),yRr=r(B2o," (UniSpeechSat model)"),B2o.forEach(t),CRr=i(re),t0=n(re,"LI",{});var I2o=s(t0);WBe=n(I2o,"STRONG",{});var mun=s(WBe);wRr=r(mun,"vilt"),mun.forEach(t),LRr=r(I2o," \u2014 "),Lle=n(I2o,"A",{href:!0});var fun=s(Lle);ARr=r(fun,"ViltProcessor"),fun.forEach(t),RRr=r(I2o," (ViLT model)"),I2o.forEach(t),kRr=i(re),a0=n(re,"LI",{});var N2o=s(a0);JBe=n(N2o,"STRONG",{});var hun=s(JBe);SRr=r(hun,"vision-text-dual-encoder"),hun.forEach(t),xRr=r(N2o," \u2014 "),Ale=n(N2o,"A",{href:!0});var gun=s(Ale);GRr=r(gun,"VisionTextDualEncoderProcessor"),gun.forEach(t),BRr=r(N2o," (VisionTextDualEncoder model)"),N2o.forEach(t),IRr=i(re),n0=n(re,"LI",{});var $2o=s(n0);UBe=n($2o,"STRONG",{});var uun=s(UBe);NRr=r(uun,"wav2vec2"),uun.forEach(t),$Rr=r($2o," \u2014 "),Rle=n($2o,"A",{href:!0});var pun=s(Rle);jRr=r(pun,"Wav2Vec2Processor"),pun.forEach(t),PRr=r($2o," (Wav2Vec2 model)"),$2o.forEach(t),ZRr=i(re),s0=n(re,"LI",{});var j2o=s(s0);XBe=n(j2o,"STRONG",{});var _un=s(XBe);VRr=r(_un,"wav2vec2-conformer"),_un.forEach(t),WRr=r(j2o," \u2014 "),kle=n(j2o,"A",{href:!0});var vun=s(kle);JRr=r(vun,"Wav2Vec2Processor"),vun.forEach(t),URr=r(j2o," (Wav2Vec2-Conformer model)"),j2o.forEach(t),XRr=i(re),l0=n(re,"LI",{});var P2o=s(l0);DBe=n(P2o,"STRONG",{});var bun=s(DBe);DRr=r(bun,"wavlm"),bun.forEach(t),qRr=r(P2o," \u2014 "),Sle=n(P2o,"A",{href:!0});var Mun=s(Sle);ORr=r(Mun,"Wav2Vec2Processor"),Mun.forEach(t),QRr=r(P2o," (WavLM model)"),P2o.forEach(t),YRr=i(re),i0=n(re,"LI",{});var Z2o=s(i0);qBe=n(Z2o,"STRONG",{});var Tun=s(qBe);zRr=r(Tun,"whisper"),Tun.forEach(t),HRr=r(Z2o," \u2014 "),xle=n(Z2o,"A",{href:!0});var Fun=s(xle);KRr=r(Fun,"WhisperProcessor"),Fun.forEach(t),ekr=r(Z2o," (Whisper model)"),Z2o.forEach(t),okr=i(re),d0=n(re,"LI",{});var V2o=s(d0);OBe=n(V2o,"STRONG",{});var Eun=s(OBe);rkr=r(Eun,"xclip"),Eun.forEach(t),tkr=r(V2o," \u2014 "),Gle=n(V2o,"A",{href:!0});var yun=s(Gle);akr=r(yun,"XCLIPProcessor"),yun.forEach(t),nkr=r(V2o," (X-CLIP model)"),V2o.forEach(t),re.forEach(t),skr=i(en),M(c0.$$.fragment,en),lkr=i(en),M(m0.$$.fragment,en),en.forEach(t),ikr=i(Hi),f0=n(Hi,"DIV",{class:!0});var _$o=s(f0);M(ZV.$$.fragment,_$o),dkr=i(_$o),QBe=n(_$o,"P",{});var Cun=s(QBe);ckr=r(Cun,"Register a new processor for this class."),Cun.forEach(t),_$o.forEach(t),Hi.forEach(t),gBo=i(m),am=n(m,"H2",{class:!0});var v$o=s(am);h0=n(v$o,"A",{id:!0,class:!0,href:!0});var wun=s(h0);YBe=n(wun,"SPAN",{});var Lun=s(YBe);M(VV.$$.fragment,Lun),Lun.forEach(t),wun.forEach(t),mkr=i(v$o),zBe=n(v$o,"SPAN",{});var Aun=s(zBe);fkr=r(Aun,"Generic model classes"),Aun.forEach(t),v$o.forEach(t),uBo=i(m),Ble=n(m,"P",{});var Run=s(Ble);hkr=r(Run,"The following auto classes are available for instantiating a base model class without a specific head."),Run.forEach(t),pBo=i(m),nm=n(m,"H3",{class:!0});var b$o=s(nm);g0=n(b$o,"A",{id:!0,class:!0,href:!0});var kun=s(g0);HBe=n(kun,"SPAN",{});var Sun=s(HBe);M(WV.$$.fragment,Sun),Sun.forEach(t),kun.forEach(t),gkr=i(b$o),KBe=n(b$o,"SPAN",{});var xun=s(KBe);ukr=r(xun,"AutoModel"),xun.forEach(t),b$o.forEach(t),_Bo=i(m),Uo=n(m,"DIV",{class:!0});var Ki=s(Uo);M(JV.$$.fragment,Ki),pkr=i(Ki),sm=n(Ki,"P",{});var sCe=s(sm);_kr=r(sCe,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),Ile=n(sCe,"A",{href:!0});var Gun=s(Ile);vkr=r(Gun,"from_pretrained()"),Gun.forEach(t),bkr=r(sCe," class method or the "),Nle=n(sCe,"A",{href:!0});var Bun=s(Nle);Mkr=r(Bun,"from_config()"),Bun.forEach(t),Tkr=r(sCe,` class
method.`),sCe.forEach(t),Fkr=i(Ki),UV=n(Ki,"P",{});var M$o=s(UV);Ekr=r(M$o,"This class cannot be instantiated directly using "),eIe=n(M$o,"CODE",{});var Iun=s(eIe);ykr=r(Iun,"__init__()"),Iun.forEach(t),Ckr=r(M$o," (throws an error)."),M$o.forEach(t),wkr=i(Ki),Jt=n(Ki,"DIV",{class:!0});var Vj=s(Jt);M(XV.$$.fragment,Vj),Lkr=i(Vj),oIe=n(Vj,"P",{});var Nun=s(oIe);Akr=r(Nun,"Instantiates one of the base model classes of the library from a configuration."),Nun.forEach(t),Rkr=i(Vj),lm=n(Vj,"P",{});var lCe=s(lm);kkr=r(lCe,`Note:
Loading a model from its configuration file does `),rIe=n(lCe,"STRONG",{});var $un=s(rIe);Skr=r($un,"not"),$un.forEach(t),xkr=r(lCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),$le=n(lCe,"A",{href:!0});var jun=s($le);Gkr=r(jun,"from_pretrained()"),jun.forEach(t),Bkr=r(lCe," to load the model weights."),lCe.forEach(t),Ikr=i(Vj),M(u0.$$.fragment,Vj),Vj.forEach(t),Nkr=i(Ki),so=n(Ki,"DIV",{class:!0});var on=s(so);M(DV.$$.fragment,on),$kr=i(on),tIe=n(on,"P",{});var Pun=s(tIe);jkr=r(Pun,"Instantiate one of the base model classes of the library from a pretrained model."),Pun.forEach(t),Pkr=i(on),Zn=n(on,"P",{});var Wj=s(Zn);Zkr=r(Wj,"The model class to instantiate is selected based on the "),aIe=n(Wj,"CODE",{});var Zun=s(aIe);Vkr=r(Zun,"model_type"),Zun.forEach(t),Wkr=r(Wj,` property of the config object (either
passed as an argument or loaded from `),nIe=n(Wj,"CODE",{});var Vun=s(nIe);Jkr=r(Vun,"pretrained_model_name_or_path"),Vun.forEach(t),Ukr=r(Wj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),sIe=n(Wj,"CODE",{});var Wun=s(sIe);Xkr=r(Wun,"pretrained_model_name_or_path"),Wun.forEach(t),Dkr=r(Wj,":"),Wj.forEach(t),qkr=i(on),A=n(on,"UL",{});var R=s(A);p0=n(R,"LI",{});var W2o=s(p0);lIe=n(W2o,"STRONG",{});var Jun=s(lIe);Okr=r(Jun,"albert"),Jun.forEach(t),Qkr=r(W2o," \u2014 "),jle=n(W2o,"A",{href:!0});var Uun=s(jle);Ykr=r(Uun,"AlbertModel"),Uun.forEach(t),zkr=r(W2o," (ALBERT model)"),W2o.forEach(t),Hkr=i(R),_0=n(R,"LI",{});var J2o=s(_0);iIe=n(J2o,"STRONG",{});var Xun=s(iIe);Kkr=r(Xun,"align"),Xun.forEach(t),eSr=r(J2o," \u2014 "),Ple=n(J2o,"A",{href:!0});var Dun=s(Ple);oSr=r(Dun,"AlignModel"),Dun.forEach(t),rSr=r(J2o," (ALIGN model)"),J2o.forEach(t),tSr=i(R),v0=n(R,"LI",{});var U2o=s(v0);dIe=n(U2o,"STRONG",{});var qun=s(dIe);aSr=r(qun,"altclip"),qun.forEach(t),nSr=r(U2o," \u2014 "),Zle=n(U2o,"A",{href:!0});var Oun=s(Zle);sSr=r(Oun,"AltCLIPModel"),Oun.forEach(t),lSr=r(U2o," (AltCLIP model)"),U2o.forEach(t),iSr=i(R),b0=n(R,"LI",{});var X2o=s(b0);cIe=n(X2o,"STRONG",{});var Qun=s(cIe);dSr=r(Qun,"audio-spectrogram-transformer"),Qun.forEach(t),cSr=r(X2o," \u2014 "),Vle=n(X2o,"A",{href:!0});var Yun=s(Vle);mSr=r(Yun,"ASTModel"),Yun.forEach(t),fSr=r(X2o," (Audio Spectrogram Transformer model)"),X2o.forEach(t),hSr=i(R),M0=n(R,"LI",{});var D2o=s(M0);mIe=n(D2o,"STRONG",{});var zun=s(mIe);gSr=r(zun,"autoformer"),zun.forEach(t),uSr=r(D2o," \u2014 "),Wle=n(D2o,"A",{href:!0});var Hun=s(Wle);pSr=r(Hun,"AutoformerModel"),Hun.forEach(t),_Sr=r(D2o," (Autoformer model)"),D2o.forEach(t),vSr=i(R),T0=n(R,"LI",{});var q2o=s(T0);fIe=n(q2o,"STRONG",{});var Kun=s(fIe);bSr=r(Kun,"bark"),Kun.forEach(t),MSr=r(q2o," \u2014 "),Jle=n(q2o,"A",{href:!0});var epn=s(Jle);TSr=r(epn,"BarkModel"),epn.forEach(t),FSr=r(q2o," (Bark model)"),q2o.forEach(t),ESr=i(R),F0=n(R,"LI",{});var O2o=s(F0);hIe=n(O2o,"STRONG",{});var opn=s(hIe);ySr=r(opn,"bart"),opn.forEach(t),CSr=r(O2o," \u2014 "),Ule=n(O2o,"A",{href:!0});var 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deactivated). To train the model, you should first set it back in training mode with `),Vje=n(vTo,"CODE",{});var K2n=s(Vje);DOr=r(K2n,"model.train()"),K2n.forEach(t),vTo.forEach(t),qOr=i(on),M(qT.$$.fragment,on),on.forEach(t),Ki.forEach(t),vBo=i(m),im=n(m,"H3",{class:!0});var T$o=s(im);OT=n(T$o,"A",{id:!0,class:!0,href:!0});var e0n=s(OT);Wje=n(e0n,"SPAN",{});var o0n=s(Wje);M(qV.$$.fragment,o0n),o0n.forEach(t),e0n.forEach(t),OOr=i(T$o),Jje=n(T$o,"SPAN",{});var r0n=s(Jje);QOr=r(r0n,"TFAutoModel"),r0n.forEach(t),T$o.forEach(t),bBo=i(m),Xo=n(m,"DIV",{class:!0});var ed=s(Xo);M(OV.$$.fragment,ed),YOr=i(ed),dm=n(ed,"P",{});var iCe=s(dm);zOr=r(iCe,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),gme=n(iCe,"A",{href:!0});var t0n=s(gme);HOr=r(t0n,"from_pretrained()"),t0n.forEach(t),KOr=r(iCe," class method or the "),ume=n(iCe,"A",{href:!0});var a0n=s(ume);eQr=r(a0n,"from_config()"),a0n.forEach(t),oQr=r(iCe,` class
method.`),iCe.forEach(t),rQr=i(ed),QV=n(ed,"P",{});var F$o=s(QV);tQr=r(F$o,"This class cannot be instantiated directly using "),Uje=n(F$o,"CODE",{});var n0n=s(Uje);aQr=r(n0n,"__init__()"),n0n.forEach(t),nQr=r(F$o," (throws an error)."),F$o.forEach(t),sQr=i(ed),Ut=n(ed,"DIV",{class:!0});var Jj=s(Ut);M(YV.$$.fragment,Jj),lQr=i(Jj),Xje=n(Jj,"P",{});var s0n=s(Xje);iQr=r(s0n,"Instantiates one of the base model classes of the library from a configuration."),s0n.forEach(t),dQr=i(Jj),cm=n(Jj,"P",{});var dCe=s(cm);cQr=r(dCe,`Note:
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model\u2019s configuration. Use `),pme=n(dCe,"A",{href:!0});var i0n=s(pme);hQr=r(i0n,"from_pretrained()"),i0n.forEach(t),gQr=r(dCe," to load the model weights."),dCe.forEach(t),uQr=i(Jj),M(QT.$$.fragment,Jj),Jj.forEach(t),pQr=i(ed),tt=n(ed,"DIV",{class:!0});var od=s(tt);M(zV.$$.fragment,od),_Qr=i(od),qje=n(od,"P",{});var d0n=s(qje);vQr=r(d0n,"Instantiate one of the base model classes of the library from a pretrained model."),d0n.forEach(t),bQr=i(od),Vn=n(od,"P",{});var Uj=s(Vn);MQr=r(Uj,"The model class to instantiate is selected based on the "),Oje=n(Uj,"CODE",{});var c0n=s(Oje);TQr=r(c0n,"model_type"),c0n.forEach(t),FQr=r(Uj,` property of the config object (either
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Loading a model from its configuration file does `),mZe=n(mCe,"STRONG",{});var VMn=s(mZe);Uot=r(VMn,"not"),VMn.forEach(t),Xot=r(mCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),kfe=n(mCe,"A",{href:!0});var WMn=s(kfe);Dot=r(WMn,"from_pretrained()"),WMn.forEach(t),qot=r(mCe," to load the model weights."),mCe.forEach(t),Oot=i(Xj),M(lE.$$.fragment,Xj),Xj.forEach(t),Qot=i(rd),at=n(rd,"DIV",{class:!0});var td=s(at);M(rW.$$.fragment,td),Yot=i(td),fZe=n(td,"P",{});var JMn=s(fZe);zot=r(JMn,"Instantiate one of the base model classes of the library from a pretrained model."),JMn.forEach(t),Hot=i(td),Wn=n(td,"P",{});var Dj=s(Wn);Kot=r(Dj,"The model class to instantiate is selected based on the "),hZe=n(Dj,"CODE",{});var UMn=s(hZe);ert=r(UMn,"model_type"),UMn.forEach(t),ort=r(Dj,` property of the config object (either
passed as an argument or loaded from `),gZe=n(Dj,"CODE",{});var XMn=s(gZe);rrt=r(XMn,"pretrained_model_name_or_path"),XMn.forEach(t),trt=r(Dj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),uZe=n(Dj,"CODE",{});var DMn=s(uZe);art=r(DMn,"pretrained_model_name_or_path"),DMn.forEach(t),nrt=r(Dj,":"),Dj.forEach(t),srt=i(td),se=n(td,"UL",{});var ie=s(se);iE=n(ie,"LI",{});var AFo=s(iE);pZe=n(AFo,"STRONG",{});var qMn=s(pZe);lrt=r(qMn,"albert"),qMn.forEach(t),irt=r(AFo," \u2014 "),Sfe=n(AFo,"A",{href:!0});var OMn=s(Sfe);drt=r(OMn,"FlaxAlbertModel"),OMn.forEach(t),crt=r(AFo," (ALBERT model)"),AFo.forEach(t),mrt=i(ie),dE=n(ie,"LI",{});var RFo=s(dE);_Ze=n(RFo,"STRONG",{});var QMn=s(_Ze);frt=r(QMn,"bart"),QMn.forEach(t),hrt=r(RFo," \u2014 "),xfe=n(RFo,"A",{href:!0});var YMn=s(xfe);grt=r(YMn,"FlaxBartModel"),YMn.forEach(t),urt=r(RFo," (BART model)"),RFo.forEach(t),prt=i(ie),cE=n(ie,"LI",{});var kFo=s(cE);vZe=n(kFo,"STRONG",{});var zMn=s(vZe);_rt=r(zMn,"beit"),zMn.forEach(t),vrt=r(kFo," \u2014 "),Gfe=n(kFo,"A",{href:!0});var HMn=s(Gfe);brt=r(HMn,"FlaxBeitModel"),HMn.forEach(t),Mrt=r(kFo," (BEiT model)"),kFo.forEach(t),Trt=i(ie),mE=n(ie,"LI",{});var SFo=s(mE);bZe=n(SFo,"STRONG",{});var KMn=s(bZe);Frt=r(KMn,"bert"),KMn.forEach(t),Ert=r(SFo," \u2014 "),Bfe=n(SFo,"A",{href:!0});var eTn=s(Bfe);yrt=r(eTn,"FlaxBertModel"),eTn.forEach(t),Crt=r(SFo," (BERT model)"),SFo.forEach(t),wrt=i(ie),fE=n(ie,"LI",{});var xFo=s(fE);MZe=n(xFo,"STRONG",{});var oTn=s(MZe);Lrt=r(oTn,"big_bird"),oTn.forEach(t),Art=r(xFo," \u2014 "),Ife=n(xFo,"A",{href:!0});var rTn=s(Ife);Rrt=r(rTn,"FlaxBigBirdModel"),rTn.forEach(t),krt=r(xFo," (BigBird model)"),xFo.forEach(t),Srt=i(ie),hE=n(ie,"LI",{});var GFo=s(hE);TZe=n(GFo,"STRONG",{});var tTn=s(TZe);xrt=r(tTn,"blenderbot"),tTn.forEach(t),Grt=r(GFo," \u2014 "),Nfe=n(GFo,"A",{href:!0});var aTn=s(Nfe);Brt=r(aTn,"FlaxBlenderbotModel"),aTn.forEach(t),Irt=r(GFo," (Blenderbot model)"),GFo.forEach(t),Nrt=i(ie),gE=n(ie,"LI",{});var BFo=s(gE);FZe=n(BFo,"STRONG",{});var nTn=s(FZe);$rt=r(nTn,"blenderbot-small"),nTn.forEach(t),jrt=r(BFo," \u2014 "),$fe=n(BFo,"A",{href:!0});var sTn=s($fe);Prt=r(sTn,"FlaxBlenderbotSmallModel"),sTn.forEach(t),Zrt=r(BFo," (BlenderbotSmall model)"),BFo.forEach(t),Vrt=i(ie),uE=n(ie,"LI",{});var IFo=s(uE);EZe=n(IFo,"STRONG",{});var lTn=s(EZe);Wrt=r(lTn,"bloom"),lTn.forEach(t),Jrt=r(IFo," \u2014 "),jfe=n(IFo,"A",{href:!0});var iTn=s(jfe);Urt=r(iTn,"FlaxBloomModel"),iTn.forEach(t),Xrt=r(IFo," (BLOOM model)"),IFo.forEach(t),Drt=i(ie),pE=n(ie,"LI",{});var NFo=s(pE);yZe=n(NFo,"STRONG",{});var dTn=s(yZe);qrt=r(dTn,"clip"),dTn.forEach(t),Ort=r(NFo," \u2014 "),Pfe=n(NFo,"A",{href:!0});var cTn=s(Pfe);Qrt=r(cTn,"FlaxCLIPModel"),cTn.forEach(t),Yrt=r(NFo," (CLIP model)"),NFo.forEach(t),zrt=i(ie),_E=n(ie,"LI",{});var $Fo=s(_E);CZe=n($Fo,"STRONG",{});var mTn=s(CZe);Hrt=r(mTn,"distilbert"),mTn.forEach(t),Krt=r($Fo," \u2014 "),Zfe=n($Fo,"A",{href:!0});var fTn=s(Zfe);ett=r(fTn,"FlaxDistilBertModel"),fTn.forEach(t),ott=r($Fo," (DistilBERT model)"),$Fo.forEach(t),rtt=i(ie),vE=n(ie,"LI",{});var jFo=s(vE);wZe=n(jFo,"STRONG",{});var hTn=s(wZe);ttt=r(hTn,"electra"),hTn.forEach(t),att=r(jFo," \u2014 "),Vfe=n(jFo,"A",{href:!0});var gTn=s(Vfe);ntt=r(gTn,"FlaxElectraModel"),gTn.forEach(t),stt=r(jFo," (ELECTRA model)"),jFo.forEach(t),ltt=i(ie),bE=n(ie,"LI",{});var PFo=s(bE);LZe=n(PFo,"STRONG",{});var uTn=s(LZe);itt=r(uTn,"gpt-sw3"),uTn.forEach(t),dtt=r(PFo," \u2014 "),Wfe=n(PFo,"A",{href:!0});var pTn=s(Wfe);ctt=r(pTn,"FlaxGPT2Model"),pTn.forEach(t),mtt=r(PFo," (GPT-Sw3 model)"),PFo.forEach(t),ftt=i(ie),ME=n(ie,"LI",{});var ZFo=s(ME);AZe=n(ZFo,"STRONG",{});var _Tn=s(AZe);htt=r(_Tn,"gpt2"),_Tn.forEach(t),gtt=r(ZFo," \u2014 "),Jfe=n(ZFo,"A",{href:!0});var vTn=s(Jfe);utt=r(vTn,"FlaxGPT2Model"),vTn.forEach(t),ptt=r(ZFo," (OpenAI GPT-2 model)"),ZFo.forEach(t),_tt=i(ie),TE=n(ie,"LI",{});var VFo=s(TE);RZe=n(VFo,"STRONG",{});var bTn=s(RZe);vtt=r(bTn,"gpt_neo"),bTn.forEach(t),btt=r(VFo," \u2014 "),Ufe=n(VFo,"A",{href:!0});var MTn=s(Ufe);Mtt=r(MTn,"FlaxGPTNeoModel"),MTn.forEach(t),Ttt=r(VFo," (GPT Neo model)"),VFo.forEach(t),Ftt=i(ie),FE=n(ie,"LI",{});var WFo=s(FE);kZe=n(WFo,"STRONG",{});var TTn=s(kZe);Ett=r(TTn,"gptj"),TTn.forEach(t),ytt=r(WFo," \u2014 "),Xfe=n(WFo,"A",{href:!0});var FTn=s(Xfe);Ctt=r(FTn,"FlaxGPTJModel"),FTn.forEach(t),wtt=r(WFo," (GPT-J model)"),WFo.forEach(t),Ltt=i(ie),EE=n(ie,"LI",{});var JFo=s(EE);SZe=n(JFo,"STRONG",{});var ETn=s(SZe);Att=r(ETn,"longt5"),ETn.forEach(t),Rtt=r(JFo," \u2014 "),Dfe=n(JFo,"A",{href:!0});var yTn=s(Dfe);ktt=r(yTn,"FlaxLongT5Model"),yTn.forEach(t),Stt=r(JFo," (LongT5 model)"),JFo.forEach(t),xtt=i(ie),yE=n(ie,"LI",{});var UFo=s(yE);xZe=n(UFo,"STRONG",{});var CTn=s(xZe);Gtt=r(CTn,"marian"),CTn.forEach(t),Btt=r(UFo," \u2014 "),qfe=n(UFo,"A",{href:!0});var wTn=s(qfe);Itt=r(wTn,"FlaxMarianModel"),wTn.forEach(t),Ntt=r(UFo," (Marian model)"),UFo.forEach(t),$tt=i(ie),CE=n(ie,"LI",{});var XFo=s(CE);GZe=n(XFo,"STRONG",{});var LTn=s(GZe);jtt=r(LTn,"mbart"),LTn.forEach(t),Ptt=r(XFo," \u2014 "),Ofe=n(XFo,"A",{href:!0});var ATn=s(Ofe);Ztt=r(ATn,"FlaxMBartModel"),ATn.forEach(t),Vtt=r(XFo," (mBART model)"),XFo.forEach(t),Wtt=i(ie),wE=n(ie,"LI",{});var DFo=s(wE);BZe=n(DFo,"STRONG",{});var RTn=s(BZe);Jtt=r(RTn,"mt5"),RTn.forEach(t),Utt=r(DFo," \u2014 "),Qfe=n(DFo,"A",{href:!0});var kTn=s(Qfe);Xtt=r(kTn,"FlaxMT5Model"),kTn.forEach(t),Dtt=r(DFo," (MT5 model)"),DFo.forEach(t),qtt=i(ie),LE=n(ie,"LI",{});var qFo=s(LE);IZe=n(qFo,"STRONG",{});var STn=s(IZe);Ott=r(STn,"opt"),STn.forEach(t),Qtt=r(qFo," \u2014 "),Yfe=n(qFo,"A",{href:!0});var xTn=s(Yfe);Ytt=r(xTn,"FlaxOPTModel"),xTn.forEach(t),ztt=r(qFo," (OPT model)"),qFo.forEach(t),Htt=i(ie),AE=n(ie,"LI",{});var OFo=s(AE);NZe=n(OFo,"STRONG",{});var GTn=s(NZe);Ktt=r(GTn,"pegasus"),GTn.forEach(t),eat=r(OFo," \u2014 "),zfe=n(OFo,"A",{href:!0});var BTn=s(zfe);oat=r(BTn,"FlaxPegasusModel"),BTn.forEach(t),rat=r(OFo," (Pegasus model)"),OFo.forEach(t),tat=i(ie),RE=n(ie,"LI",{});var QFo=s(RE);$Ze=n(QFo,"STRONG",{});var ITn=s($Ze);aat=r(ITn,"regnet"),ITn.forEach(t),nat=r(QFo," \u2014 "),Hfe=n(QFo,"A",{href:!0});var NTn=s(Hfe);sat=r(NTn,"FlaxRegNetModel"),NTn.forEach(t),lat=r(QFo," (RegNet model)"),QFo.forEach(t),iat=i(ie),kE=n(ie,"LI",{});var YFo=s(kE);jZe=n(YFo,"STRONG",{});var $Tn=s(jZe);dat=r($Tn,"resnet"),$Tn.forEach(t),cat=r(YFo," \u2014 "),Kfe=n(YFo,"A",{href:!0});var jTn=s(Kfe);mat=r(jTn,"FlaxResNetModel"),jTn.forEach(t),fat=r(YFo," (ResNet model)"),YFo.forEach(t),hat=i(ie),SE=n(ie,"LI",{});var zFo=s(SE);PZe=n(zFo,"STRONG",{});var PTn=s(PZe);gat=r(PTn,"roberta"),PTn.forEach(t),uat=r(zFo," \u2014 "),ehe=n(zFo,"A",{href:!0});var ZTn=s(ehe);pat=r(ZTn,"FlaxRobertaModel"),ZTn.forEach(t),_at=r(zFo," (RoBERTa model)"),zFo.forEach(t),vat=i(ie),xE=n(ie,"LI",{});var HFo=s(xE);ZZe=n(HFo,"STRONG",{});var VTn=s(ZZe);bat=r(VTn,"roberta-prelayernorm"),VTn.forEach(t),Mat=r(HFo," \u2014 "),ohe=n(HFo,"A",{href:!0});var WTn=s(ohe);Tat=r(WTn,"FlaxRobertaPreLayerNormModel"),WTn.forEach(t),Fat=r(HFo," (RoBERTa-PreLayerNorm model)"),HFo.forEach(t),Eat=i(ie),GE=n(ie,"LI",{});var KFo=s(GE);VZe=n(KFo,"STRONG",{});var JTn=s(VZe);yat=r(JTn,"roformer"),JTn.forEach(t),Cat=r(KFo," \u2014 "),rhe=n(KFo,"A",{href:!0});var UTn=s(rhe);wat=r(UTn,"FlaxRoFormerModel"),UTn.forEach(t),Lat=r(KFo," (RoFormer model)"),KFo.forEach(t),Aat=i(ie),BE=n(ie,"LI",{});var eEo=s(BE);WZe=n(eEo,"STRONG",{});var XTn=s(WZe);Rat=r(XTn,"t5"),XTn.forEach(t),kat=r(eEo," \u2014 "),the=n(eEo,"A",{href:!0});var DTn=s(the);Sat=r(DTn,"FlaxT5Model"),DTn.forEach(t),xat=r(eEo," (T5 model)"),eEo.forEach(t),Gat=i(ie),IE=n(ie,"LI",{});var oEo=s(IE);JZe=n(oEo,"STRONG",{});var qTn=s(JZe);Bat=r(qTn,"vision-text-dual-encoder"),qTn.forEach(t),Iat=r(oEo," \u2014 "),ahe=n(oEo,"A",{href:!0});var OTn=s(ahe);Nat=r(OTn,"FlaxVisionTextDualEncoderModel"),OTn.forEach(t),$at=r(oEo," (VisionTextDualEncoder model)"),oEo.forEach(t),jat=i(ie),NE=n(ie,"LI",{});var rEo=s(NE);UZe=n(rEo,"STRONG",{});var QTn=s(UZe);Pat=r(QTn,"vit"),QTn.forEach(t),Zat=r(rEo," \u2014 "),nhe=n(rEo,"A",{href:!0});var YTn=s(nhe);Vat=r(YTn,"FlaxViTModel"),YTn.forEach(t),Wat=r(rEo," (ViT model)"),rEo.forEach(t),Jat=i(ie),$E=n(ie,"LI",{});var tEo=s($E);XZe=n(tEo,"STRONG",{});var zTn=s(XZe);Uat=r(zTn,"wav2vec2"),zTn.forEach(t),Xat=r(tEo," \u2014 "),she=n(tEo,"A",{href:!0});var HTn=s(she);Dat=r(HTn,"FlaxWav2Vec2Model"),HTn.forEach(t),qat=r(tEo," (Wav2Vec2 model)"),tEo.forEach(t),Oat=i(ie),jE=n(ie,"LI",{});var aEo=s(jE);DZe=n(aEo,"STRONG",{});var KTn=s(DZe);Qat=r(KTn,"whisper"),KTn.forEach(t),Yat=r(aEo," \u2014 "),lhe=n(aEo,"A",{href:!0});var eFn=s(lhe);zat=r(eFn,"FlaxWhisperModel"),eFn.forEach(t),Hat=r(aEo," (Whisper model)"),aEo.forEach(t),Kat=i(ie),PE=n(ie,"LI",{});var nEo=s(PE);qZe=n(nEo,"STRONG",{});var oFn=s(qZe);ent=r(oFn,"xglm"),oFn.forEach(t),ont=r(nEo," \u2014 "),ihe=n(nEo,"A",{href:!0});var rFn=s(ihe);rnt=r(rFn,"FlaxXGLMModel"),rFn.forEach(t),tnt=r(nEo," (XGLM model)"),nEo.forEach(t),ant=i(ie),ZE=n(ie,"LI",{});var sEo=s(ZE);OZe=n(sEo,"STRONG",{});var tFn=s(OZe);nnt=r(tFn,"xlm-roberta"),tFn.forEach(t),snt=r(sEo," \u2014 "),dhe=n(sEo,"A",{href:!0});var aFn=s(dhe);lnt=r(aFn,"FlaxXLMRobertaModel"),aFn.forEach(t),int=r(sEo," (XLM-RoBERTa model)"),sEo.forEach(t),ie.forEach(t),dnt=i(td),M(VE.$$.fragment,td),td.forEach(t),rd.forEach(t),FBo=i(m),gm=n(m,"H2",{class:!0});var C$o=s(gm);WE=n(C$o,"A",{id:!0,class:!0,href:!0});var nFn=s(WE);QZe=n(nFn,"SPAN",{});var sFn=s(QZe);M(tW.$$.fragment,sFn),sFn.forEach(t),nFn.forEach(t),cnt=i(C$o),YZe=n(C$o,"SPAN",{});var lFn=s(YZe);mnt=r(lFn,"Generic pretraining classes"),lFn.forEach(t),C$o.forEach(t),EBo=i(m),che=n(m,"P",{});var iFn=s(che);fnt=r(iFn,"The following auto classes are available for instantiating a model with a pretraining head."),iFn.forEach(t),yBo=i(m),um=n(m,"H3",{class:!0});var w$o=s(um);JE=n(w$o,"A",{id:!0,class:!0,href:!0});var dFn=s(JE);zZe=n(dFn,"SPAN",{});var cFn=s(zZe);M(aW.$$.fragment,cFn),cFn.forEach(t),dFn.forEach(t),hnt=i(w$o),HZe=n(w$o,"SPAN",{});var mFn=s(HZe);gnt=r(mFn,"AutoModelForPreTraining"),mFn.forEach(t),w$o.forEach(t),CBo=i(m),qo=n(m,"DIV",{class:!0});var ad=s(qo);M(nW.$$.fragment,ad),unt=i(ad),pm=n(ad,"P",{});var fCe=s(pm);pnt=r(fCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),mhe=n(fCe,"A",{href:!0});var fFn=s(mhe);_nt=r(fFn,"from_pretrained()"),fFn.forEach(t),vnt=r(fCe," class method or the "),fhe=n(fCe,"A",{href:!0});var hFn=s(fhe);bnt=r(hFn,"from_config()"),hFn.forEach(t),Mnt=r(fCe,` class
method.`),fCe.forEach(t),Tnt=i(ad),sW=n(ad,"P",{});var L$o=s(sW);Fnt=r(L$o,"This class cannot be instantiated directly using "),KZe=n(L$o,"CODE",{});var gFn=s(KZe);Ent=r(gFn,"__init__()"),gFn.forEach(t),ynt=r(L$o," (throws an error)."),L$o.forEach(t),Cnt=i(ad),Dt=n(ad,"DIV",{class:!0});var qj=s(Dt);M(lW.$$.fragment,qj),wnt=i(qj),eVe=n(qj,"P",{});var uFn=s(eVe);Lnt=r(uFn,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),uFn.forEach(t),Ant=i(qj),_m=n(qj,"P",{});var hCe=s(_m);Rnt=r(hCe,`Note:
Loading a model from its configuration file does `),oVe=n(hCe,"STRONG",{});var pFn=s(oVe);knt=r(pFn,"not"),pFn.forEach(t),Snt=r(hCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),hhe=n(hCe,"A",{href:!0});var _Fn=s(hhe);xnt=r(_Fn,"from_pretrained()"),_Fn.forEach(t),Gnt=r(hCe," to load the model weights."),hCe.forEach(t),Bnt=i(qj),M(UE.$$.fragment,qj),qj.forEach(t),Int=i(ad),lo=n(ad,"DIV",{class:!0});var rn=s(lo);M(iW.$$.fragment,rn),Nnt=i(rn),rVe=n(rn,"P",{});var vFn=s(rVe);$nt=r(vFn,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),vFn.forEach(t),jnt=i(rn),Jn=n(rn,"P",{});var Oj=s(Jn);Pnt=r(Oj,"The model class to instantiate is selected based on the "),tVe=n(Oj,"CODE",{});var bFn=s(tVe);Znt=r(bFn,"model_type"),bFn.forEach(t),Vnt=r(Oj,` property of the config object (either
passed as an argument or loaded from `),aVe=n(Oj,"CODE",{});var MFn=s(aVe);Wnt=r(MFn,"pretrained_model_name_or_path"),MFn.forEach(t),Jnt=r(Oj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nVe=n(Oj,"CODE",{});var TFn=s(nVe);Unt=r(TFn,"pretrained_model_name_or_path"),TFn.forEach(t),Xnt=r(Oj,":"),Oj.forEach(t),Dnt=i(rn),U=n(rn,"UL",{});var q=s(U);XE=n(q,"LI",{});var lEo=s(XE);sVe=n(lEo,"STRONG",{});var FFn=s(sVe);qnt=r(FFn,"albert"),FFn.forEach(t),Ont=r(lEo," \u2014 "),ghe=n(lEo,"A",{href:!0});var EFn=s(ghe);Qnt=r(EFn,"AlbertForPreTraining"),EFn.forEach(t),Ynt=r(lEo," (ALBERT model)"),lEo.forEach(t),znt=i(q),DE=n(q,"LI",{});var iEo=s(DE);lVe=n(iEo,"STRONG",{});var yFn=s(lVe);Hnt=r(yFn,"bart"),yFn.forEach(t),Knt=r(iEo," \u2014 "),uhe=n(iEo,"A",{href:!0});var CFn=s(uhe);est=r(CFn,"BartForConditionalGeneration"),CFn.forEach(t),ost=r(iEo," (BART model)"),iEo.forEach(t),rst=i(q),qE=n(q,"LI",{});var dEo=s(qE);iVe=n(dEo,"STRONG",{});var wFn=s(iVe);tst=r(wFn,"bert"),wFn.forEach(t),ast=r(dEo," \u2014 "),phe=n(dEo,"A",{href:!0});var LFn=s(phe);nst=r(LFn,"BertForPreTraining"),LFn.forEach(t),sst=r(dEo," (BERT model)"),dEo.forEach(t),lst=i(q),OE=n(q,"LI",{});var cEo=s(OE);dVe=n(cEo,"STRONG",{});var AFn=s(dVe);ist=r(AFn,"big_bird"),AFn.forEach(t),dst=r(cEo," \u2014 "),_he=n(cEo,"A",{href:!0});var RFn=s(_he);cst=r(RFn,"BigBirdForPreTraining"),RFn.forEach(t),mst=r(cEo," (BigBird model)"),cEo.forEach(t),fst=i(q),QE=n(q,"LI",{});var mEo=s(QE);cVe=n(mEo,"STRONG",{});var kFn=s(cVe);hst=r(kFn,"bloom"),kFn.forEach(t),gst=r(mEo," \u2014 "),vhe=n(mEo,"A",{href:!0});var SFn=s(vhe);ust=r(SFn,"BloomForCausalLM"),SFn.forEach(t),pst=r(mEo," (BLOOM model)"),mEo.forEach(t),_st=i(q),YE=n(q,"LI",{});var fEo=s(YE);mVe=n(fEo,"STRONG",{});var xFn=s(mVe);vst=r(xFn,"camembert"),xFn.forEach(t),bst=r(fEo," \u2014 "),bhe=n(fEo,"A",{href:!0});var GFn=s(bhe);Mst=r(GFn,"CamembertForMaskedLM"),GFn.forEach(t),Tst=r(fEo," (CamemBERT model)"),fEo.forEach(t),Fst=i(q),zE=n(q,"LI",{});var hEo=s(zE);fVe=n(hEo,"STRONG",{});var BFn=s(fVe);Est=r(BFn,"ctrl"),BFn.forEach(t),yst=r(hEo," \u2014 "),Mhe=n(hEo,"A",{href:!0});var 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Myn=s(nWe);Wct=r(Myn,"visual_bert"),Myn.forEach(t),Jct=r(syo," \u2014 "),hge=n(syo,"A",{href:!0});var Tyn=s(hge);Uct=r(Tyn,"VisualBertForPreTraining"),Tyn.forEach(t),Xct=r(syo," (VisualBERT model)"),syo.forEach(t),Dct=i(q),Xy=n(q,"LI",{});var lyo=s(Xy);sWe=n(lyo,"STRONG",{});var Fyn=s(sWe);qct=r(Fyn,"vit_mae"),Fyn.forEach(t),Oct=r(lyo," \u2014 "),gge=n(lyo,"A",{href:!0});var Eyn=s(gge);Qct=r(Eyn,"ViTMAEForPreTraining"),Eyn.forEach(t),Yct=r(lyo," (ViTMAE model)"),lyo.forEach(t),zct=i(q),Dy=n(q,"LI",{});var iyo=s(Dy);lWe=n(iyo,"STRONG",{});var yyn=s(lWe);Hct=r(yyn,"wav2vec2"),yyn.forEach(t),Kct=r(iyo," \u2014 "),uge=n(iyo,"A",{href:!0});var Cyn=s(uge);emt=r(Cyn,"Wav2Vec2ForPreTraining"),Cyn.forEach(t),omt=r(iyo," (Wav2Vec2 model)"),iyo.forEach(t),rmt=i(q),qy=n(q,"LI",{});var dyo=s(qy);iWe=n(dyo,"STRONG",{});var wyn=s(iWe);tmt=r(wyn,"wav2vec2-conformer"),wyn.forEach(t),amt=r(dyo," \u2014 "),pge=n(dyo,"A",{href:!0});var Lyn=s(pge);nmt=r(Lyn,"Wav2Vec2ConformerForPreTraining"),Lyn.forEach(t),smt=r(dyo," (Wav2Vec2-Conformer model)"),dyo.forEach(t),lmt=i(q),Oy=n(q,"LI",{});var cyo=s(Oy);dWe=n(cyo,"STRONG",{});var Ayn=s(dWe);imt=r(Ayn,"xlm"),Ayn.forEach(t),dmt=r(cyo," \u2014 "),_ge=n(cyo,"A",{href:!0});var Ryn=s(_ge);cmt=r(Ryn,"XLMWithLMHeadModel"),Ryn.forEach(t),mmt=r(cyo," (XLM model)"),cyo.forEach(t),fmt=i(q),Qy=n(q,"LI",{});var myo=s(Qy);cWe=n(myo,"STRONG",{});var kyn=s(cWe);hmt=r(kyn,"xlm-roberta"),kyn.forEach(t),gmt=r(myo," \u2014 "),vge=n(myo,"A",{href:!0});var Syn=s(vge);umt=r(Syn,"XLMRobertaForMaskedLM"),Syn.forEach(t),pmt=r(myo," (XLM-RoBERTa model)"),myo.forEach(t),_mt=i(q),Yy=n(q,"LI",{});var fyo=s(Yy);mWe=n(fyo,"STRONG",{});var xyn=s(mWe);vmt=r(xyn,"xlm-roberta-xl"),xyn.forEach(t),bmt=r(fyo," \u2014 "),bge=n(fyo,"A",{href:!0});var Gyn=s(bge);Mmt=r(Gyn,"XLMRobertaXLForMaskedLM"),Gyn.forEach(t),Tmt=r(fyo," (XLM-RoBERTa-XL model)"),fyo.forEach(t),Fmt=i(q),zy=n(q,"LI",{});var hyo=s(zy);fWe=n(hyo,"STRONG",{});var Byn=s(fWe);Emt=r(Byn,"xlnet"),Byn.forEach(t),ymt=r(hyo," \u2014 "),Mge=n(hyo,"A",{href:!0});var Iyn=s(Mge);Cmt=r(Iyn,"XLNetLMHeadModel"),Iyn.forEach(t),wmt=r(hyo," (XLNet model)"),hyo.forEach(t),Lmt=i(q),Hy=n(q,"LI",{});var gyo=s(Hy);hWe=n(gyo,"STRONG",{});var Nyn=s(hWe);Amt=r(Nyn,"xmod"),Nyn.forEach(t),Rmt=r(gyo," \u2014 "),Tge=n(gyo,"A",{href:!0});var $yn=s(Tge);kmt=r($yn,"XmodForMaskedLM"),$yn.forEach(t),Smt=r(gyo," (X-MOD model)"),gyo.forEach(t),q.forEach(t),xmt=i(rn),Ky=n(rn,"P",{});var uyo=s(Ky);Gmt=r(uyo,"The model is set in evaluation mode by default using "),gWe=n(uyo,"CODE",{});var jyn=s(gWe);Bmt=r(jyn,"model.eval()"),jyn.forEach(t),Imt=r(uyo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),uWe=n(uyo,"CODE",{});var Pyn=s(uWe);Nmt=r(Pyn,"model.train()"),Pyn.forEach(t),uyo.forEach(t),$mt=i(rn),M(e5.$$.fragment,rn),rn.forEach(t),ad.forEach(t),wBo=i(m),vm=n(m,"H3",{class:!0});var A$o=s(vm);o5=n(A$o,"A",{id:!0,class:!0,href:!0});var Zyn=s(o5);pWe=n(Zyn,"SPAN",{});var Vyn=s(pWe);M(dW.$$.fragment,Vyn),Vyn.forEach(t),Zyn.forEach(t),jmt=i(A$o),_We=n(A$o,"SPAN",{});var Wyn=s(_We);Pmt=r(Wyn,"TFAutoModelForPreTraining"),Wyn.forEach(t),A$o.forEach(t),LBo=i(m),Oo=n(m,"DIV",{class:!0});var nd=s(Oo);M(cW.$$.fragment,nd),Zmt=i(nd),bm=n(nd,"P",{});var gCe=s(bm);Vmt=r(gCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),Fge=n(gCe,"A",{href:!0});var Jyn=s(Fge);Wmt=r(Jyn,"from_pretrained()"),Jyn.forEach(t),Jmt=r(gCe," class method or the "),Ege=n(gCe,"A",{href:!0});var Uyn=s(Ege);Umt=r(Uyn,"from_config()"),Uyn.forEach(t),Xmt=r(gCe,` class
method.`),gCe.forEach(t),Dmt=i(nd),mW=n(nd,"P",{});var R$o=s(mW);qmt=r(R$o,"This class cannot be instantiated directly using "),vWe=n(R$o,"CODE",{});var Xyn=s(vWe);Omt=r(Xyn,"__init__()"),Xyn.forEach(t),Qmt=r(R$o," (throws an error)."),R$o.forEach(t),Ymt=i(nd),qt=n(nd,"DIV",{class:!0});var Qj=s(qt);M(fW.$$.fragment,Qj),zmt=i(Qj),bWe=n(Qj,"P",{});var Dyn=s(bWe);Hmt=r(Dyn,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),Dyn.forEach(t),Kmt=i(Qj),Mm=n(Qj,"P",{});var uCe=s(Mm);eft=r(uCe,`Note:
Loading a model from its configuration file does `),MWe=n(uCe,"STRONG",{});var qyn=s(MWe);oft=r(qyn,"not"),qyn.forEach(t),rft=r(uCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),yge=n(uCe,"A",{href:!0});var Oyn=s(yge);tft=r(Oyn,"from_pretrained()"),Oyn.forEach(t),aft=r(uCe," to load the model weights."),uCe.forEach(t),nft=i(Qj),M(r5.$$.fragment,Qj),Qj.forEach(t),sft=i(nd),nt=n(nd,"DIV",{class:!0});var sd=s(nt);M(hW.$$.fragment,sd),lft=i(sd),TWe=n(sd,"P",{});var Qyn=s(TWe);ift=r(Qyn,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),Qyn.forEach(t),dft=i(sd),Un=n(sd,"P",{});var Yj=s(Un);cft=r(Yj,"The model class to instantiate is selected based on the "),FWe=n(Yj,"CODE",{});var Yyn=s(FWe);mft=r(Yyn,"model_type"),Yyn.forEach(t),fft=r(Yj,` property of the config object (either
passed as an argument or loaded from `),EWe=n(Yj,"CODE",{});var zyn=s(EWe);hft=r(zyn,"pretrained_model_name_or_path"),zyn.forEach(t),gft=r(Yj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),yWe=n(Yj,"CODE",{});var Hyn=s(yWe);uft=r(Hyn,"pretrained_model_name_or_path"),Hyn.forEach(t),pft=r(Yj,":"),Yj.forEach(t),_ft=i(sd),fe=n(sd,"UL",{});var he=s(fe);t5=n(he,"LI",{});var pyo=s(t5);CWe=n(pyo,"STRONG",{});var Kyn=s(CWe);vft=r(Kyn,"albert"),Kyn.forEach(t),bft=r(pyo," \u2014 "),Cge=n(pyo,"A",{href:!0});var e5n=s(Cge);Mft=r(e5n,"TFAlbertForPreTraining"),e5n.forEach(t),Tft=r(pyo," (ALBERT model)"),pyo.forEach(t),Fft=i(he),a5=n(he,"LI",{});var _yo=s(a5);wWe=n(_yo,"STRONG",{});var o5n=s(wWe);Eft=r(o5n,"bart"),o5n.forEach(t),yft=r(_yo," \u2014 "),wge=n(_yo,"A",{href:!0});var r5n=s(wge);Cft=r(r5n,"TFBartForConditionalGeneration"),r5n.forEach(t),wft=r(_yo," (BART model)"),_yo.forEach(t),Lft=i(he),n5=n(he,"LI",{});var vyo=s(n5);LWe=n(vyo,"STRONG",{});var t5n=s(LWe);Aft=r(t5n,"bert"),t5n.forEach(t),Rft=r(vyo," \u2014 "),Lge=n(vyo,"A",{href:!0});var a5n=s(Lge);kft=r(a5n,"TFBertForPreTraining"),a5n.forEach(t),Sft=r(vyo," (BERT model)"),vyo.forEach(t),xft=i(he),s5=n(he,"LI",{});var byo=s(s5);AWe=n(byo,"STRONG",{});var n5n=s(AWe);Gft=r(n5n,"camembert"),n5n.forEach(t),Bft=r(byo," \u2014 "),Age=n(byo,"A",{href:!0});var s5n=s(Age);Ift=r(s5n,"TFCamembertForMaskedLM"),s5n.forEach(t),Nft=r(byo," (CamemBERT model)"),byo.forEach(t),$ft=i(he),l5=n(he,"LI",{});var Myo=s(l5);RWe=n(Myo,"STRONG",{});var l5n=s(RWe);jft=r(l5n,"ctrl"),l5n.forEach(t),Pft=r(Myo," \u2014 "),Rge=n(Myo,"A",{href:!0});var i5n=s(Rge);Zft=r(i5n,"TFCTRLLMHeadModel"),i5n.forEach(t),Vft=r(Myo," (CTRL model)"),Myo.forEach(t),Wft=i(he),i5=n(he,"LI",{});var Tyo=s(i5);kWe=n(Tyo,"STRONG",{});var d5n=s(kWe);Jft=r(d5n,"distilbert"),d5n.forEach(t),Uft=r(Tyo," \u2014 "),kge=n(Tyo,"A",{href:!0});var c5n=s(kge);Xft=r(c5n,"TFDistilBertForMaskedLM"),c5n.forEach(t),Dft=r(Tyo," (DistilBERT model)"),Tyo.forEach(t),qft=i(he),d5=n(he,"LI",{});var Fyo=s(d5);SWe=n(Fyo,"STRONG",{});var m5n=s(SWe);Oft=r(m5n,"electra"),m5n.forEach(t),Qft=r(Fyo," \u2014 "),Sge=n(Fyo,"A",{href:!0});var f5n=s(Sge);Yft=r(f5n,"TFElectraForPreTraining"),f5n.forEach(t),zft=r(Fyo," (ELECTRA model)"),Fyo.forEach(t),Hft=i(he),c5=n(he,"LI",{});var Eyo=s(c5);xWe=n(Eyo,"STRONG",{});var h5n=s(xWe);Kft=r(h5n,"flaubert"),h5n.forEach(t),eht=r(Eyo," \u2014 "),xge=n(Eyo,"A",{href:!0});var g5n=s(xge);oht=r(g5n,"TFFlaubertWithLMHeadModel"),g5n.forEach(t),rht=r(Eyo," (FlauBERT model)"),Eyo.forEach(t),tht=i(he),m5=n(he,"LI",{});var yyo=s(m5);GWe=n(yyo,"STRONG",{});var u5n=s(GWe);aht=r(u5n,"funnel"),u5n.forEach(t),nht=r(yyo," \u2014 "),Gge=n(yyo,"A",{href:!0});var p5n=s(Gge);sht=r(p5n,"TFFunnelForPreTraining"),p5n.forEach(t),lht=r(yyo," (Funnel Transformer model)"),yyo.forEach(t),iht=i(he),f5=n(he,"LI",{});var Cyo=s(f5);BWe=n(Cyo,"STRONG",{});var _5n=s(BWe);dht=r(_5n,"gpt-sw3"),_5n.forEach(t),cht=r(Cyo," \u2014 "),Bge=n(Cyo,"A",{href:!0});var v5n=s(Bge);mht=r(v5n,"TFGPT2LMHeadModel"),v5n.forEach(t),fht=r(Cyo," (GPT-Sw3 model)"),Cyo.forEach(t),hht=i(he),h5=n(he,"LI",{});var wyo=s(h5);IWe=n(wyo,"STRONG",{});var b5n=s(IWe);ght=r(b5n,"gpt2"),b5n.forEach(t),uht=r(wyo," \u2014 "),Ige=n(wyo,"A",{href:!0});var M5n=s(Ige);pht=r(M5n,"TFGPT2LMHeadModel"),M5n.forEach(t),_ht=r(wyo," (OpenAI GPT-2 model)"),wyo.forEach(t),vht=i(he),g5=n(he,"LI",{});var Lyo=s(g5);NWe=n(Lyo,"STRONG",{});var T5n=s(NWe);bht=r(T5n,"layoutlm"),T5n.forEach(t),Mht=r(Lyo," \u2014 "),Nge=n(Lyo,"A",{href:!0});var F5n=s(Nge);Tht=r(F5n,"TFLayoutLMForMaskedLM"),F5n.forEach(t),Fht=r(Lyo," (LayoutLM model)"),Lyo.forEach(t),Eht=i(he),u5=n(he,"LI",{});var Ayo=s(u5);$We=n(Ayo,"STRONG",{});var E5n=s($We);yht=r(E5n,"lxmert"),E5n.forEach(t),Cht=r(Ayo," \u2014 "),$ge=n(Ayo,"A",{href:!0});var y5n=s($ge);wht=r(y5n,"TFLxmertForPreTraining"),y5n.forEach(t),Lht=r(Ayo," (LXMERT model)"),Ayo.forEach(t),Aht=i(he),p5=n(he,"LI",{});var Ryo=s(p5);jWe=n(Ryo,"STRONG",{});var C5n=s(jWe);Rht=r(C5n,"mobilebert"),C5n.forEach(t),kht=r(Ryo," \u2014 "),jge=n(Ryo,"A",{href:!0});var w5n=s(jge);Sht=r(w5n,"TFMobileBertForPreTraining"),w5n.forEach(t),xht=r(Ryo," (MobileBERT model)"),Ryo.forEach(t),Ght=i(he),_5=n(he,"LI",{});var kyo=s(_5);PWe=n(kyo,"STRONG",{});var L5n=s(PWe);Bht=r(L5n,"mpnet"),L5n.forEach(t),Iht=r(kyo," \u2014 "),Pge=n(kyo,"A",{href:!0});var A5n=s(Pge);Nht=r(A5n,"TFMPNetForMaskedLM"),A5n.forEach(t),$ht=r(kyo," (MPNet model)"),kyo.forEach(t),jht=i(he),v5=n(he,"LI",{});var Syo=s(v5);ZWe=n(Syo,"STRONG",{});var R5n=s(ZWe);Pht=r(R5n,"openai-gpt"),R5n.forEach(t),Zht=r(Syo," \u2014 "),Zge=n(Syo,"A",{href:!0});var k5n=s(Zge);Vht=r(k5n,"TFOpenAIGPTLMHeadModel"),k5n.forEach(t),Wht=r(Syo," (OpenAI GPT model)"),Syo.forEach(t),Jht=i(he),b5=n(he,"LI",{});var xyo=s(b5);VWe=n(xyo,"STRONG",{});var S5n=s(VWe);Uht=r(S5n,"roberta"),S5n.forEach(t),Xht=r(xyo," \u2014 "),Vge=n(xyo,"A",{href:!0});var x5n=s(Vge);Dht=r(x5n,"TFRobertaForMaskedLM"),x5n.forEach(t),qht=r(xyo," (RoBERTa model)"),xyo.forEach(t),Oht=i(he),M5=n(he,"LI",{});var Gyo=s(M5);WWe=n(Gyo,"STRONG",{});var G5n=s(WWe);Qht=r(G5n,"roberta-prelayernorm"),G5n.forEach(t),Yht=r(Gyo," \u2014 "),Wge=n(Gyo,"A",{href:!0});var B5n=s(Wge);zht=r(B5n,"TFRobertaPreLayerNormForMaskedLM"),B5n.forEach(t),Hht=r(Gyo," (RoBERTa-PreLayerNorm model)"),Gyo.forEach(t),Kht=i(he),T5=n(he,"LI",{});var Byo=s(T5);JWe=n(Byo,"STRONG",{});var I5n=s(JWe);egt=r(I5n,"t5"),I5n.forEach(t),ogt=r(Byo," \u2014 "),Jge=n(Byo,"A",{href:!0});var N5n=s(Jge);rgt=r(N5n,"TFT5ForConditionalGeneration"),N5n.forEach(t),tgt=r(Byo," (T5 model)"),Byo.forEach(t),agt=i(he),F5=n(he,"LI",{});var Iyo=s(F5);UWe=n(Iyo,"STRONG",{});var $5n=s(UWe);ngt=r($5n,"tapas"),$5n.forEach(t),sgt=r(Iyo," \u2014 "),Uge=n(Iyo,"A",{href:!0});var j5n=s(Uge);lgt=r(j5n,"TFTapasForMaskedLM"),j5n.forEach(t),igt=r(Iyo," (TAPAS model)"),Iyo.forEach(t),dgt=i(he),E5=n(he,"LI",{});var Nyo=s(E5);XWe=n(Nyo,"STRONG",{});var P5n=s(XWe);cgt=r(P5n,"transfo-xl"),P5n.forEach(t),mgt=r(Nyo," \u2014 "),Xge=n(Nyo,"A",{href:!0});var Z5n=s(Xge);fgt=r(Z5n,"TFTransfoXLLMHeadModel"),Z5n.forEach(t),hgt=r(Nyo," (Transformer-XL model)"),Nyo.forEach(t),ggt=i(he),y5=n(he,"LI",{});var $yo=s(y5);DWe=n($yo,"STRONG",{});var V5n=s(DWe);ugt=r(V5n,"vit_mae"),V5n.forEach(t),pgt=r($yo," \u2014 "),Dge=n($yo,"A",{href:!0});var W5n=s(Dge);_gt=r(W5n,"TFViTMAEForPreTraining"),W5n.forEach(t),vgt=r($yo," (ViTMAE model)"),$yo.forEach(t),bgt=i(he),C5=n(he,"LI",{});var jyo=s(C5);qWe=n(jyo,"STRONG",{});var J5n=s(qWe);Mgt=r(J5n,"xlm"),J5n.forEach(t),Tgt=r(jyo," \u2014 "),qge=n(jyo,"A",{href:!0});var U5n=s(qge);Fgt=r(U5n,"TFXLMWithLMHeadModel"),U5n.forEach(t),Egt=r(jyo," (XLM model)"),jyo.forEach(t),ygt=i(he),w5=n(he,"LI",{});var Pyo=s(w5);OWe=n(Pyo,"STRONG",{});var X5n=s(OWe);Cgt=r(X5n,"xlm-roberta"),X5n.forEach(t),wgt=r(Pyo," \u2014 "),Oge=n(Pyo,"A",{href:!0});var D5n=s(Oge);Lgt=r(D5n,"TFXLMRobertaForMaskedLM"),D5n.forEach(t),Agt=r(Pyo," (XLM-RoBERTa model)"),Pyo.forEach(t),Rgt=i(he),L5=n(he,"LI",{});var Zyo=s(L5);QWe=n(Zyo,"STRONG",{});var q5n=s(QWe);kgt=r(q5n,"xlnet"),q5n.forEach(t),Sgt=r(Zyo," \u2014 "),Qge=n(Zyo,"A",{href:!0});var O5n=s(Qge);xgt=r(O5n,"TFXLNetLMHeadModel"),O5n.forEach(t),Ggt=r(Zyo," (XLNet model)"),Zyo.forEach(t),he.forEach(t),Bgt=i(sd),M(A5.$$.fragment,sd),sd.forEach(t),nd.forEach(t),ABo=i(m),Tm=n(m,"H3",{class:!0});var k$o=s(Tm);R5=n(k$o,"A",{id:!0,class:!0,href:!0});var Q5n=s(R5);YWe=n(Q5n,"SPAN",{});var Y5n=s(YWe);M(gW.$$.fragment,Y5n),Y5n.forEach(t),Q5n.forEach(t),Igt=i(k$o),zWe=n(k$o,"SPAN",{});var z5n=s(zWe);Ngt=r(z5n,"FlaxAutoModelForPreTraining"),z5n.forEach(t),k$o.forEach(t),RBo=i(m),Qo=n(m,"DIV",{class:!0});var ld=s(Qo);M(uW.$$.fragment,ld),$gt=i(ld),Fm=n(ld,"P",{});var pCe=s(Fm);jgt=r(pCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),Yge=n(pCe,"A",{href:!0});var H5n=s(Yge);Pgt=r(H5n,"from_pretrained()"),H5n.forEach(t),Zgt=r(pCe," class method or the "),zge=n(pCe,"A",{href:!0});var K5n=s(zge);Vgt=r(K5n,"from_config()"),K5n.forEach(t),Wgt=r(pCe,` class
method.`),pCe.forEach(t),Jgt=i(ld),pW=n(ld,"P",{});var S$o=s(pW);Ugt=r(S$o,"This class cannot be instantiated directly using "),HWe=n(S$o,"CODE",{});var e9n=s(HWe);Xgt=r(e9n,"__init__()"),e9n.forEach(t),Dgt=r(S$o," (throws an error)."),S$o.forEach(t),qgt=i(ld),Ot=n(ld,"DIV",{class:!0});var zj=s(Ot);M(_W.$$.fragment,zj),Ogt=i(zj),KWe=n(zj,"P",{});var o9n=s(KWe);Qgt=r(o9n,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),o9n.forEach(t),Ygt=i(zj),Em=n(zj,"P",{});var _Ce=s(Em);zgt=r(_Ce,`Note:
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model\u2019s configuration. Use `),Hge=n(_Ce,"A",{href:!0});var t9n=s(Hge);eut=r(t9n,"from_pretrained()"),t9n.forEach(t),out=r(_Ce," to load the model weights."),_Ce.forEach(t),rut=i(zj),M(k5.$$.fragment,zj),zj.forEach(t),tut=i(ld),st=n(ld,"DIV",{class:!0});var id=s(st);M(vW.$$.fragment,id),aut=i(id),oJe=n(id,"P",{});var a9n=s(oJe);nut=r(a9n,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),a9n.forEach(t),sut=i(id),Xn=n(id,"P",{});var Hj=s(Xn);lut=r(Hj,"The model class to instantiate is selected based on the "),rJe=n(Hj,"CODE",{});var n9n=s(rJe);iut=r(n9n,"model_type"),n9n.forEach(t),dut=r(Hj,` property of the config object (either
passed as an argument or loaded from `),tJe=n(Hj,"CODE",{});var s9n=s(tJe);cut=r(s9n,"pretrained_model_name_or_path"),s9n.forEach(t),mut=r(Hj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),aJe=n(Hj,"CODE",{});var l9n=s(aJe);fut=r(l9n,"pretrained_model_name_or_path"),l9n.forEach(t),hut=r(Hj,":"),Hj.forEach(t),gut=i(id),we=n(id,"UL",{});var Ae=s(we);S5=n(Ae,"LI",{});var Vyo=s(S5);nJe=n(Vyo,"STRONG",{});var i9n=s(nJe);uut=r(i9n,"albert"),i9n.forEach(t),put=r(Vyo," \u2014 "),Kge=n(Vyo,"A",{href:!0});var d9n=s(Kge);_ut=r(d9n,"FlaxAlbertForPreTraining"),d9n.forEach(t),vut=r(Vyo," (ALBERT model)"),Vyo.forEach(t),but=i(Ae),x5=n(Ae,"LI",{});var Wyo=s(x5);sJe=n(Wyo,"STRONG",{});var c9n=s(sJe);Mut=r(c9n,"bart"),c9n.forEach(t),Tut=r(Wyo," \u2014 "),eue=n(Wyo,"A",{href:!0});var m9n=s(eue);Fut=r(m9n,"FlaxBartForConditionalGeneration"),m9n.forEach(t),Eut=r(Wyo," (BART model)"),Wyo.forEach(t),yut=i(Ae),G5=n(Ae,"LI",{});var Jyo=s(G5);lJe=n(Jyo,"STRONG",{});var f9n=s(lJe);Cut=r(f9n,"bert"),f9n.forEach(t),wut=r(Jyo," \u2014 "),oue=n(Jyo,"A",{href:!0});var h9n=s(oue);Lut=r(h9n,"FlaxBertForPreTraining"),h9n.forEach(t),Aut=r(Jyo," (BERT 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method.`),vCe.forEach(t),qpt=i(dd),FW=n(dd,"P",{});var B$o=s(FW);Opt=r(B$o,"This class cannot be instantiated directly using "),yJe=n(B$o,"CODE",{});var q9n=s(yJe);Qpt=r(q9n,"__init__()"),q9n.forEach(t),Ypt=r(B$o," (throws an error)."),B$o.forEach(t),zpt=i(dd),Qt=n(dd,"DIV",{class:!0});var Kj=s(Qt);M(EW.$$.fragment,Kj),Hpt=i(Kj),CJe=n(Kj,"P",{});var O9n=s(CJe);Kpt=r(O9n,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),O9n.forEach(t),e_t=i(Kj),Lm=n(Kj,"P",{});var bCe=s(Lm);o_t=r(bCe,`Note:
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model\u2019s configuration. Use `),_ue=n(bCe,"A",{href:!0});var Y9n=s(_ue);a_t=r(Y9n,"from_pretrained()"),Y9n.forEach(t),n_t=r(bCe," to load the model weights."),bCe.forEach(t),s_t=i(Kj),M(Q5.$$.fragment,Kj),Kj.forEach(t),l_t=i(dd),io=n(dd,"DIV",{class:!0});var tn=s(io);M(yW.$$.fragment,tn),i_t=i(tn),LJe=n(tn,"P",{});var z9n=s(LJe);d_t=r(z9n,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),z9n.forEach(t),c_t=i(tn),Dn=n(tn,"P",{});var eP=s(Dn);m_t=r(eP,"The model class to instantiate is selected based on the "),AJe=n(eP,"CODE",{});var H9n=s(AJe);f_t=r(H9n,"model_type"),H9n.forEach(t),h_t=r(eP,` property of the config object (either
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falling back to using pattern matching on `),kJe=n(eP,"CODE",{});var eCn=s(kJe);p_t=r(eCn,"pretrained_model_name_or_path"),eCn.forEach(t),__t=r(eP,":"),eP.forEach(t),v_t=i(tn),Q=n(tn,"UL",{});var Y=s(Q);Y5=n(Y,"LI",{});var r5o=s(Y5);SJe=n(r5o,"STRONG",{});var oCn=s(SJe);b_t=r(oCn,"bart"),oCn.forEach(t),M_t=r(r5o," \u2014 "),vue=n(r5o,"A",{href:!0});var rCn=s(vue);T_t=r(rCn,"BartForCausalLM"),rCn.forEach(t),F_t=r(r5o," (BART model)"),r5o.forEach(t),E_t=i(Y),z5=n(Y,"LI",{});var t5o=s(z5);xJe=n(t5o,"STRONG",{});var tCn=s(xJe);y_t=r(tCn,"bert"),tCn.forEach(t),C_t=r(t5o," \u2014 "),bue=n(t5o,"A",{href:!0});var aCn=s(bue);w_t=r(aCn,"BertLMHeadModel"),aCn.forEach(t),L_t=r(t5o," (BERT model)"),t5o.forEach(t),A_t=i(Y),H5=n(Y,"LI",{});var a5o=s(H5);GJe=n(a5o,"STRONG",{});var nCn=s(GJe);R_t=r(nCn,"bert-generation"),nCn.forEach(t),k_t=r(a5o," \u2014 "),Mue=n(a5o,"A",{href:!0});var sCn=s(Mue);S_t=r(sCn,"BertGenerationDecoder"),sCn.forEach(t),x_t=r(a5o," (Bert Generation 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qwn=s(LUe);Kbt=r(qwn,"trocr"),qwn.forEach(t),e2t=r(H5o," \u2014 "),gpe=n(H5o,"A",{href:!0});var Own=s(gpe);o2t=r(Own,"TrOCRForCausalLM"),Own.forEach(t),r2t=r(H5o," (TrOCR model)"),H5o.forEach(t),t2t=i(Y),q9=n(Y,"LI",{});var K5o=s(q9);AUe=n(K5o,"STRONG",{});var Qwn=s(AUe);a2t=r(Qwn,"xglm"),Qwn.forEach(t),n2t=r(K5o," \u2014 "),upe=n(K5o,"A",{href:!0});var Ywn=s(upe);s2t=r(Ywn,"XGLMForCausalLM"),Ywn.forEach(t),l2t=r(K5o," (XGLM model)"),K5o.forEach(t),i2t=i(Y),O9=n(Y,"LI",{});var e9o=s(O9);RUe=n(e9o,"STRONG",{});var zwn=s(RUe);d2t=r(zwn,"xlm"),zwn.forEach(t),c2t=r(e9o," \u2014 "),ppe=n(e9o,"A",{href:!0});var Hwn=s(ppe);m2t=r(Hwn,"XLMWithLMHeadModel"),Hwn.forEach(t),f2t=r(e9o," (XLM model)"),e9o.forEach(t),h2t=i(Y),Q9=n(Y,"LI",{});var o9o=s(Q9);kUe=n(o9o,"STRONG",{});var Kwn=s(kUe);g2t=r(Kwn,"xlm-prophetnet"),Kwn.forEach(t),u2t=r(o9o," \u2014 "),_pe=n(o9o,"A",{href:!0});var e6n=s(_pe);p2t=r(e6n,"XLMProphetNetForCausalLM"),e6n.forEach(t),_2t=r(o9o," (XLM-ProphetNet 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"),Tpe=n(n9o,"A",{href:!0});var i6n=s(Tpe);N2t=r(i6n,"XmodForCausalLM"),i6n.forEach(t),$2t=r(n9o," (X-MOD model)"),n9o.forEach(t),Y.forEach(t),j2t=i(tn),eC=n(tn,"P",{});var s9o=s(eC);P2t=r(s9o,"The model is set in evaluation mode by default using "),IUe=n(s9o,"CODE",{});var d6n=s(IUe);Z2t=r(d6n,"model.eval()"),d6n.forEach(t),V2t=r(s9o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),NUe=n(s9o,"CODE",{});var c6n=s(NUe);W2t=r(c6n,"model.train()"),c6n.forEach(t),s9o.forEach(t),J2t=i(tn),M(oC.$$.fragment,tn),tn.forEach(t),dd.forEach(t),BBo=i(m),Am=n(m,"H3",{class:!0});var I$o=s(Am);rC=n(I$o,"A",{id:!0,class:!0,href:!0});var m6n=s(rC);$Ue=n(m6n,"SPAN",{});var f6n=s($Ue);M(CW.$$.fragment,f6n),f6n.forEach(t),m6n.forEach(t),U2t=i(I$o),jUe=n(I$o,"SPAN",{});var h6n=s(jUe);X2t=r(h6n,"TFAutoModelForCausalLM"),h6n.forEach(t),I$o.forEach(t),IBo=i(m),zo=n(m,"DIV",{class:!0});var cd=s(zo);M(wW.$$.fragment,cd),D2t=i(cd),Rm=n(cd,"P",{});var MCe=s(Rm);q2t=r(MCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),Fpe=n(MCe,"A",{href:!0});var g6n=s(Fpe);O2t=r(g6n,"from_pretrained()"),g6n.forEach(t),Q2t=r(MCe," class method or the "),Epe=n(MCe,"A",{href:!0});var u6n=s(Epe);Y2t=r(u6n,"from_config()"),u6n.forEach(t),z2t=r(MCe,` class
method.`),MCe.forEach(t),H2t=i(cd),LW=n(cd,"P",{});var N$o=s(LW);K2t=r(N$o,"This class cannot be instantiated directly using "),PUe=n(N$o,"CODE",{});var p6n=s(PUe);e0t=r(p6n,"__init__()"),p6n.forEach(t),o0t=r(N$o," (throws an error)."),N$o.forEach(t),r0t=i(cd),Yt=n(cd,"DIV",{class:!0});var oP=s(Yt);M(AW.$$.fragment,oP),t0t=i(oP),ZUe=n(oP,"P",{});var _6n=s(ZUe);a0t=r(_6n,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),_6n.forEach(t),n0t=i(oP),km=n(oP,"P",{});var TCe=s(km);s0t=r(TCe,`Note:
Loading a model from its configuration file does `),VUe=n(TCe,"STRONG",{});var v6n=s(VUe);l0t=r(v6n,"not"),v6n.forEach(t),i0t=r(TCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),ype=n(TCe,"A",{href:!0});var b6n=s(ype);d0t=r(b6n,"from_pretrained()"),b6n.forEach(t),c0t=r(TCe," to load the model weights."),TCe.forEach(t),m0t=i(oP),M(tC.$$.fragment,oP),oP.forEach(t),f0t=i(cd),lt=n(cd,"DIV",{class:!0});var md=s(lt);M(RW.$$.fragment,md),h0t=i(md),WUe=n(md,"P",{});var M6n=s(WUe);g0t=r(M6n,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),M6n.forEach(t),u0t=i(md),qn=n(md,"P",{});var rP=s(qn);p0t=r(rP,"The model class to instantiate is selected based on the "),JUe=n(rP,"CODE",{});var T6n=s(JUe);_0t=r(T6n,"model_type"),T6n.forEach(t),v0t=r(rP,` property of the config object (either
passed as an argument or loaded from `),UUe=n(rP,"CODE",{});var F6n=s(UUe);b0t=r(F6n,"pretrained_model_name_or_path"),F6n.forEach(t),M0t=r(rP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),XUe=n(rP,"CODE",{});var E6n=s(XUe);T0t=r(E6n,"pretrained_model_name_or_path"),E6n.forEach(t),F0t=r(rP,":"),rP.forEach(t),E0t=i(md),Ee=n(md,"UL",{});var Ce=s(Ee);aC=n(Ce,"LI",{});var l9o=s(aC);DUe=n(l9o,"STRONG",{});var y6n=s(DUe);y0t=r(y6n,"bert"),y6n.forEach(t),C0t=r(l9o," \u2014 "),Cpe=n(l9o,"A",{href:!0});var C6n=s(Cpe);w0t=r(C6n,"TFBertLMHeadModel"),C6n.forEach(t),L0t=r(l9o," (BERT model)"),l9o.forEach(t),A0t=i(Ce),nC=n(Ce,"LI",{});var i9o=s(nC);qUe=n(i9o,"STRONG",{});var w6n=s(qUe);R0t=r(w6n,"camembert"),w6n.forEach(t),k0t=r(i9o," \u2014 "),wpe=n(i9o,"A",{href:!0});var L6n=s(wpe);S0t=r(L6n,"TFCamembertForCausalLM"),L6n.forEach(t),x0t=r(i9o," (CamemBERT model)"),i9o.forEach(t),G0t=i(Ce),sC=n(Ce,"LI",{});var d9o=s(sC);OUe=n(d9o,"STRONG",{});var A6n=s(OUe);B0t=r(A6n,"ctrl"),A6n.forEach(t),I0t=r(d9o," \u2014 "),Lpe=n(d9o,"A",{href:!0});var R6n=s(Lpe);N0t=r(R6n,"TFCTRLLMHeadModel"),R6n.forEach(t),$0t=r(d9o," (CTRL 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$6n=s(Spe);r4t=r($6n,"TFOpenAIGPTLMHeadModel"),$6n.forEach(t),t4t=r(h9o," (OpenAI GPT model)"),h9o.forEach(t),a4t=i(Ce),mC=n(Ce,"LI",{});var g9o=s(mC);KUe=n(g9o,"STRONG",{});var j6n=s(KUe);n4t=r(j6n,"opt"),j6n.forEach(t),s4t=r(g9o," \u2014 "),xpe=n(g9o,"A",{href:!0});var P6n=s(xpe);l4t=r(P6n,"TFOPTForCausalLM"),P6n.forEach(t),i4t=r(g9o," (OPT model)"),g9o.forEach(t),d4t=i(Ce),fC=n(Ce,"LI",{});var u9o=s(fC);eXe=n(u9o,"STRONG",{});var Z6n=s(eXe);c4t=r(Z6n,"rembert"),Z6n.forEach(t),m4t=r(u9o," \u2014 "),Gpe=n(u9o,"A",{href:!0});var V6n=s(Gpe);f4t=r(V6n,"TFRemBertForCausalLM"),V6n.forEach(t),h4t=r(u9o," (RemBERT model)"),u9o.forEach(t),g4t=i(Ce),hC=n(Ce,"LI",{});var p9o=s(hC);oXe=n(p9o,"STRONG",{});var W6n=s(oXe);u4t=r(W6n,"roberta"),W6n.forEach(t),p4t=r(p9o," \u2014 "),Bpe=n(p9o,"A",{href:!0});var J6n=s(Bpe);_4t=r(J6n,"TFRobertaForCausalLM"),J6n.forEach(t),v4t=r(p9o," (RoBERTa model)"),p9o.forEach(t),b4t=i(Ce),gC=n(Ce,"LI",{});var _9o=s(gC);rXe=n(_9o,"STRONG",{});var U6n=s(rXe);M4t=r(U6n,"roberta-prelayernorm"),U6n.forEach(t),T4t=r(_9o," \u2014 "),Ipe=n(_9o,"A",{href:!0});var X6n=s(Ipe);F4t=r(X6n,"TFRobertaPreLayerNormForCausalLM"),X6n.forEach(t),E4t=r(_9o," (RoBERTa-PreLayerNorm model)"),_9o.forEach(t),y4t=i(Ce),uC=n(Ce,"LI",{});var v9o=s(uC);tXe=n(v9o,"STRONG",{});var D6n=s(tXe);C4t=r(D6n,"roformer"),D6n.forEach(t),w4t=r(v9o," \u2014 "),Npe=n(v9o,"A",{href:!0});var q6n=s(Npe);L4t=r(q6n,"TFRoFormerForCausalLM"),q6n.forEach(t),A4t=r(v9o," (RoFormer model)"),v9o.forEach(t),R4t=i(Ce),pC=n(Ce,"LI",{});var b9o=s(pC);aXe=n(b9o,"STRONG",{});var O6n=s(aXe);k4t=r(O6n,"transfo-xl"),O6n.forEach(t),S4t=r(b9o," \u2014 "),$pe=n(b9o,"A",{href:!0});var Q6n=s($pe);x4t=r(Q6n,"TFTransfoXLLMHeadModel"),Q6n.forEach(t),G4t=r(b9o," (Transformer-XL model)"),b9o.forEach(t),B4t=i(Ce),_C=n(Ce,"LI",{});var M9o=s(_C);nXe=n(M9o,"STRONG",{});var Y6n=s(nXe);I4t=r(Y6n,"xglm"),Y6n.forEach(t),N4t=r(M9o," \u2014 "),jpe=n(M9o,"A",{href:!0});var z6n=s(jpe);$4t=r(z6n,"TFXGLMForCausalLM"),z6n.forEach(t),j4t=r(M9o," (XGLM model)"),M9o.forEach(t),P4t=i(Ce),vC=n(Ce,"LI",{});var T9o=s(vC);sXe=n(T9o,"STRONG",{});var H6n=s(sXe);Z4t=r(H6n,"xlm"),H6n.forEach(t),V4t=r(T9o," \u2014 "),Ppe=n(T9o,"A",{href:!0});var K6n=s(Ppe);W4t=r(K6n,"TFXLMWithLMHeadModel"),K6n.forEach(t),J4t=r(T9o," (XLM model)"),T9o.forEach(t),U4t=i(Ce),bC=n(Ce,"LI",{});var F9o=s(bC);lXe=n(F9o,"STRONG",{});var e8n=s(lXe);X4t=r(e8n,"xlm-roberta"),e8n.forEach(t),D4t=r(F9o," \u2014 "),Zpe=n(F9o,"A",{href:!0});var o8n=s(Zpe);q4t=r(o8n,"TFXLMRobertaForCausalLM"),o8n.forEach(t),O4t=r(F9o," (XLM-RoBERTa model)"),F9o.forEach(t),Q4t=i(Ce),MC=n(Ce,"LI",{});var E9o=s(MC);iXe=n(E9o,"STRONG",{});var r8n=s(iXe);Y4t=r(r8n,"xlnet"),r8n.forEach(t),z4t=r(E9o," \u2014 "),Vpe=n(E9o,"A",{href:!0});var t8n=s(Vpe);H4t=r(t8n,"TFXLNetLMHeadModel"),t8n.forEach(t),K4t=r(E9o," (XLNet 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with the `),Wpe=n(FCe,"A",{href:!0});var l8n=s(Wpe);nMt=r(l8n,"from_pretrained()"),l8n.forEach(t),sMt=r(FCe," class method or the "),Jpe=n(FCe,"A",{href:!0});var i8n=s(Jpe);lMt=r(i8n,"from_config()"),i8n.forEach(t),iMt=r(FCe,` class
method.`),FCe.forEach(t),dMt=i(fd),xW=n(fd,"P",{});var j$o=s(xW);cMt=r(j$o,"This class cannot be instantiated directly using "),mXe=n(j$o,"CODE",{});var d8n=s(mXe);mMt=r(d8n,"__init__()"),d8n.forEach(t),fMt=r(j$o," (throws an error)."),j$o.forEach(t),hMt=i(fd),zt=n(fd,"DIV",{class:!0});var tP=s(zt);M(GW.$$.fragment,tP),gMt=i(tP),fXe=n(tP,"P",{});var c8n=s(fXe);uMt=r(c8n,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),c8n.forEach(t),pMt=i(tP),Gm=n(tP,"P",{});var ECe=s(Gm);_Mt=r(ECe,`Note:
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model\u2019s configuration. Use `),Upe=n(ECe,"A",{href:!0});var f8n=s(Upe);MMt=r(f8n,"from_pretrained()"),f8n.forEach(t),TMt=r(ECe," to load the model weights."),ECe.forEach(t),FMt=i(tP),M(EC.$$.fragment,tP),tP.forEach(t),EMt=i(fd),it=n(fd,"DIV",{class:!0});var hd=s(it);M(BW.$$.fragment,hd),yMt=i(hd),gXe=n(hd,"P",{});var h8n=s(gXe);CMt=r(h8n,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),h8n.forEach(t),wMt=i(hd),On=n(hd,"P",{});var aP=s(On);LMt=r(aP,"The model class to instantiate is selected based on the "),uXe=n(aP,"CODE",{});var g8n=s(uXe);AMt=r(g8n,"model_type"),g8n.forEach(t),RMt=r(aP,` property of the config object (either
passed as an argument or loaded from `),pXe=n(aP,"CODE",{});var u8n=s(pXe);kMt=r(u8n,"pretrained_model_name_or_path"),u8n.forEach(t),SMt=r(aP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),_Xe=n(aP,"CODE",{});var p8n=s(_Xe);xMt=r(p8n,"pretrained_model_name_or_path"),p8n.forEach(t),GMt=r(aP,":"),aP.forEach(t),BMt=i(hd),Le=n(hd,"UL",{});var Re=s(Le);yC=n(Re,"LI",{});var y9o=s(yC);vXe=n(y9o,"STRONG",{});var _8n=s(vXe);IMt=r(_8n,"bart"),_8n.forEach(t),NMt=r(y9o," \u2014 "),Xpe=n(y9o,"A",{href:!0});var v8n=s(Xpe);$Mt=r(v8n,"FlaxBartForCausalLM"),v8n.forEach(t),jMt=r(y9o," (BART model)"),y9o.forEach(t),PMt=i(Re),CC=n(Re,"LI",{});var C9o=s(CC);bXe=n(C9o,"STRONG",{});var b8n=s(bXe);ZMt=r(b8n,"bert"),b8n.forEach(t),VMt=r(C9o," \u2014 "),Dpe=n(C9o,"A",{href:!0});var M8n=s(Dpe);WMt=r(M8n,"FlaxBertForCausalLM"),M8n.forEach(t),JMt=r(C9o," (BERT model)"),C9o.forEach(t),UMt=i(Re),wC=n(Re,"LI",{});var w9o=s(wC);MXe=n(w9o,"STRONG",{});var T8n=s(MXe);XMt=r(T8n,"big_bird"),T8n.forEach(t),DMt=r(w9o," \u2014 "),qpe=n(w9o,"A",{href:!0});var F8n=s(qpe);qMt=r(F8n,"FlaxBigBirdForCausalLM"),F8n.forEach(t),OMt=r(w9o," (BigBird model)"),w9o.forEach(t),QMt=i(Re),LC=n(Re,"LI",{});var L9o=s(LC);TXe=n(L9o,"STRONG",{});var E8n=s(TXe);YMt=r(E8n,"bloom"),E8n.forEach(t),zMt=r(L9o," \u2014 "),Ope=n(L9o,"A",{href:!0});var y8n=s(Ope);HMt=r(y8n,"FlaxBloomForCausalLM"),y8n.forEach(t),KMt=r(L9o," (BLOOM model)"),L9o.forEach(t),eTt=i(Re),AC=n(Re,"LI",{});var A9o=s(AC);FXe=n(A9o,"STRONG",{});var C8n=s(FXe);oTt=r(C8n,"electra"),C8n.forEach(t),rTt=r(A9o," \u2014 "),Qpe=n(A9o,"A",{href:!0});var w8n=s(Qpe);tTt=r(w8n,"FlaxElectraForCausalLM"),w8n.forEach(t),aTt=r(A9o," (ELECTRA model)"),A9o.forEach(t),nTt=i(Re),RC=n(Re,"LI",{});var R9o=s(RC);EXe=n(R9o,"STRONG",{});var L8n=s(EXe);sTt=r(L8n,"gpt-sw3"),L8n.forEach(t),lTt=r(R9o," \u2014 "),Ype=n(R9o,"A",{href:!0});var A8n=s(Ype);iTt=r(A8n,"FlaxGPT2LMHeadModel"),A8n.forEach(t),dTt=r(R9o," (GPT-Sw3 model)"),R9o.forEach(t),cTt=i(Re),kC=n(Re,"LI",{});var k9o=s(kC);yXe=n(k9o,"STRONG",{});var R8n=s(yXe);mTt=r(R8n,"gpt2"),R8n.forEach(t),fTt=r(k9o," \u2014 "),zpe=n(k9o,"A",{href:!0});var k8n=s(zpe);hTt=r(k8n,"FlaxGPT2LMHeadModel"),k8n.forEach(t),gTt=r(k9o," (OpenAI GPT-2 model)"),k9o.forEach(t),uTt=i(Re),SC=n(Re,"LI",{});var S9o=s(SC);CXe=n(S9o,"STRONG",{});var S8n=s(CXe);pTt=r(S8n,"gpt_neo"),S8n.forEach(t),_Tt=r(S9o," \u2014 "),Hpe=n(S9o,"A",{href:!0});var x8n=s(Hpe);vTt=r(x8n,"FlaxGPTNeoForCausalLM"),x8n.forEach(t),bTt=r(S9o," (GPT Neo model)"),S9o.forEach(t),MTt=i(Re),xC=n(Re,"LI",{});var x9o=s(xC);wXe=n(x9o,"STRONG",{});var G8n=s(wXe);TTt=r(G8n,"gptj"),G8n.forEach(t),FTt=r(x9o," \u2014 "),Kpe=n(x9o,"A",{href:!0});var B8n=s(Kpe);ETt=r(B8n,"FlaxGPTJForCausalLM"),B8n.forEach(t),yTt=r(x9o," (GPT-J model)"),x9o.forEach(t),CTt=i(Re),GC=n(Re,"LI",{});var G9o=s(GC);LXe=n(G9o,"STRONG",{});var I8n=s(LXe);wTt=r(I8n,"opt"),I8n.forEach(t),LTt=r(G9o," \u2014 "),e_e=n(G9o,"A",{href:!0});var N8n=s(e_e);ATt=r(N8n,"FlaxOPTForCausalLM"),N8n.forEach(t),RTt=r(G9o," (OPT model)"),G9o.forEach(t),kTt=i(Re),BC=n(Re,"LI",{});var B9o=s(BC);AXe=n(B9o,"STRONG",{});var $8n=s(AXe);STt=r($8n,"roberta"),$8n.forEach(t),xTt=r(B9o," \u2014 "),o_e=n(B9o,"A",{href:!0});var j8n=s(o_e);GTt=r(j8n,"FlaxRobertaForCausalLM"),j8n.forEach(t),BTt=r(B9o," (RoBERTa model)"),B9o.forEach(t),ITt=i(Re),IC=n(Re,"LI",{});var I9o=s(IC);RXe=n(I9o,"STRONG",{});var P8n=s(RXe);NTt=r(P8n,"roberta-prelayernorm"),P8n.forEach(t),$Tt=r(I9o," \u2014 "),r_e=n(I9o,"A",{href:!0});var Z8n=s(r_e);jTt=r(Z8n,"FlaxRobertaPreLayerNormForCausalLM"),Z8n.forEach(t),PTt=r(I9o," (RoBERTa-PreLayerNorm model)"),I9o.forEach(t),ZTt=i(Re),NC=n(Re,"LI",{});var N9o=s(NC);kXe=n(N9o,"STRONG",{});var V8n=s(kXe);VTt=r(V8n,"xglm"),V8n.forEach(t),WTt=r(N9o," \u2014 "),t_e=n(N9o,"A",{href:!0});var W8n=s(t_e);JTt=r(W8n,"FlaxXGLMForCausalLM"),W8n.forEach(t),UTt=r(N9o," (XGLM model)"),N9o.forEach(t),XTt=i(Re),$C=n(Re,"LI",{});var $9o=s($C);SXe=n($9o,"STRONG",{});var J8n=s(SXe);DTt=r(J8n,"xlm-roberta"),J8n.forEach(t),qTt=r($9o," \u2014 "),a_e=n($9o,"A",{href:!0});var U8n=s(a_e);OTt=r(U8n,"FlaxXLMRobertaForCausalLM"),U8n.forEach(t),QTt=r($9o," (XLM-RoBERTa model)"),$9o.forEach(t),Re.forEach(t),YTt=i(hd),M(jC.$$.fragment,hd),hd.forEach(t),fd.forEach(t),jBo=i(m),Bm=n(m,"H3",{class:!0});var P$o=s(Bm);PC=n(P$o,"A",{id:!0,class:!0,href:!0});var X8n=s(PC);xXe=n(X8n,"SPAN",{});var D8n=s(xXe);M(IW.$$.fragment,D8n),D8n.forEach(t),X8n.forEach(t),zTt=i(P$o),GXe=n(P$o,"SPAN",{});var q8n=s(GXe);HTt=r(q8n,"AutoModelForMaskedLM"),q8n.forEach(t),P$o.forEach(t),PBo=i(m),Ko=n(m,"DIV",{class:!0});var gd=s(Ko);M(NW.$$.fragment,gd),KTt=i(gd),Im=n(gd,"P",{});var yCe=s(Im);eFt=r(yCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the `),n_e=n(yCe,"A",{href:!0});var O8n=s(n_e);oFt=r(O8n,"from_pretrained()"),O8n.forEach(t),rFt=r(yCe," class method or the "),s_e=n(yCe,"A",{href:!0});var Q8n=s(s_e);tFt=r(Q8n,"from_config()"),Q8n.forEach(t),aFt=r(yCe,` class
method.`),yCe.forEach(t),nFt=i(gd),$W=n(gd,"P",{});var Z$o=s($W);sFt=r(Z$o,"This class cannot be instantiated directly using "),BXe=n(Z$o,"CODE",{});var Y8n=s(BXe);lFt=r(Y8n,"__init__()"),Y8n.forEach(t),iFt=r(Z$o," (throws an error)."),Z$o.forEach(t),dFt=i(gd),Ht=n(gd,"DIV",{class:!0});var nP=s(Ht);M(jW.$$.fragment,nP),cFt=i(nP),IXe=n(nP,"P",{});var z8n=s(IXe);mFt=r(z8n,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),z8n.forEach(t),fFt=i(nP),Nm=n(nP,"P",{});var CCe=s(Nm);hFt=r(CCe,`Note:
Loading a model from its configuration file does `),NXe=n(CCe,"STRONG",{});var H8n=s(NXe);gFt=r(H8n,"not"),H8n.forEach(t),uFt=r(CCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),l_e=n(CCe,"A",{href:!0});var K8n=s(l_e);pFt=r(K8n,"from_pretrained()"),K8n.forEach(t),_Ft=r(CCe," to load the model weights."),CCe.forEach(t),vFt=i(nP),M(ZC.$$.fragment,nP),nP.forEach(t),bFt=i(gd),co=n(gd,"DIV",{class:!0});var an=s(co);M(PW.$$.fragment,an),MFt=i(an),$Xe=n(an,"P",{});var e7n=s($Xe);TFt=r(e7n,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),e7n.forEach(t),FFt=i(an),Qn=n(an,"P",{});var sP=s(Qn);EFt=r(sP,"The model class to instantiate is selected based on the "),jXe=n(sP,"CODE",{});var o7n=s(jXe);yFt=r(o7n,"model_type"),o7n.forEach(t),CFt=r(sP,` property of the config object (either
passed as an argument or loaded from `),PXe=n(sP,"CODE",{});var r7n=s(PXe);wFt=r(r7n,"pretrained_model_name_or_path"),r7n.forEach(t),LFt=r(sP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ZXe=n(sP,"CODE",{});var t7n=s(ZXe);AFt=r(t7n,"pretrained_model_name_or_path"),t7n.forEach(t),RFt=r(sP,":"),sP.forEach(t),kFt=i(an),K=n(an,"UL",{});var ee=s(K);VC=n(ee,"LI",{});var j9o=s(VC);VXe=n(j9o,"STRONG",{});var a7n=s(VXe);SFt=r(a7n,"albert"),a7n.forEach(t),xFt=r(j9o," \u2014 "),i_e=n(j9o,"A",{href:!0});var n7n=s(i_e);GFt=r(n7n,"AlbertForMaskedLM"),n7n.forEach(t),BFt=r(j9o," (ALBERT model)"),j9o.forEach(t),IFt=i(ee),WC=n(ee,"LI",{});var P9o=s(WC);WXe=n(P9o,"STRONG",{});var s7n=s(WXe);NFt=r(s7n,"bart"),s7n.forEach(t),$Ft=r(P9o," \u2014 "),d_e=n(P9o,"A",{href:!0});var l7n=s(d_e);jFt=r(l7n,"BartForConditionalGeneration"),l7n.forEach(t),PFt=r(P9o," (BART model)"),P9o.forEach(t),ZFt=i(ee),JC=n(ee,"LI",{});var Z9o=s(JC);JXe=n(Z9o,"STRONG",{});var i7n=s(JXe);VFt=r(i7n,"bert"),i7n.forEach(t),WFt=r(Z9o," \u2014 "),c_e=n(Z9o,"A",{href:!0});var d7n=s(c_e);JFt=r(d7n,"BertForMaskedLM"),d7n.forEach(t),UFt=r(Z9o," (BERT model)"),Z9o.forEach(t),XFt=i(ee),UC=n(ee,"LI",{});var V9o=s(UC);UXe=n(V9o,"STRONG",{});var c7n=s(UXe);DFt=r(c7n,"big_bird"),c7n.forEach(t),qFt=r(V9o," \u2014 "),m_e=n(V9o,"A",{href:!0});var m7n=s(m_e);OFt=r(m7n,"BigBirdForMaskedLM"),m7n.forEach(t),QFt=r(V9o," (BigBird model)"),V9o.forEach(t),YFt=i(ee),XC=n(ee,"LI",{});var W9o=s(XC);XXe=n(W9o,"STRONG",{});var f7n=s(XXe);zFt=r(f7n,"camembert"),f7n.forEach(t),HFt=r(W9o," \u2014 "),f_e=n(W9o,"A",{href:!0});var h7n=s(f_e);KFt=r(h7n,"CamembertForMaskedLM"),h7n.forEach(t),eEt=r(W9o," (CamemBERT model)"),W9o.forEach(t),oEt=i(ee),DC=n(ee,"LI",{});var J9o=s(DC);DXe=n(J9o,"STRONG",{});var g7n=s(DXe);rEt=r(g7n,"convbert"),g7n.forEach(t),tEt=r(J9o," \u2014 "),h_e=n(J9o,"A",{href:!0});var u7n=s(h_e);aEt=r(u7n,"ConvBertForMaskedLM"),u7n.forEach(t),nEt=r(J9o," (ConvBERT model)"),J9o.forEach(t),sEt=i(ee),qC=n(ee,"LI",{});var U9o=s(qC);qXe=n(U9o,"STRONG",{});var p7n=s(qXe);lEt=r(p7n,"data2vec-text"),p7n.forEach(t),iEt=r(U9o," \u2014 "),g_e=n(U9o,"A",{href:!0});var _7n=s(g_e);dEt=r(_7n,"Data2VecTextForMaskedLM"),_7n.forEach(t),cEt=r(U9o," (Data2VecText model)"),U9o.forEach(t),mEt=i(ee),OC=n(ee,"LI",{});var X9o=s(OC);OXe=n(X9o,"STRONG",{});var v7n=s(OXe);fEt=r(v7n,"deberta"),v7n.forEach(t),hEt=r(X9o," \u2014 "),u_e=n(X9o,"A",{href:!0});var b7n=s(u_e);gEt=r(b7n,"DebertaForMaskedLM"),b7n.forEach(t),uEt=r(X9o," (DeBERTa model)"),X9o.forEach(t),pEt=i(ee),QC=n(ee,"LI",{});var D9o=s(QC);QXe=n(D9o,"STRONG",{});var M7n=s(QXe);_Et=r(M7n,"deberta-v2"),M7n.forEach(t),vEt=r(D9o," \u2014 "),p_e=n(D9o,"A",{href:!0});var T7n=s(p_e);bEt=r(T7n,"DebertaV2ForMaskedLM"),T7n.forEach(t),MEt=r(D9o," (DeBERTa-v2 model)"),D9o.forEach(t),TEt=i(ee),YC=n(ee,"LI",{});var q9o=s(YC);YXe=n(q9o,"STRONG",{});var F7n=s(YXe);FEt=r(F7n,"distilbert"),F7n.forEach(t),EEt=r(q9o," \u2014 "),__e=n(q9o,"A",{href:!0});var E7n=s(__e);yEt=r(E7n,"DistilBertForMaskedLM"),E7n.forEach(t),CEt=r(q9o," (DistilBERT model)"),q9o.forEach(t),wEt=i(ee),zC=n(ee,"LI",{});var O9o=s(zC);zXe=n(O9o,"STRONG",{});var y7n=s(zXe);LEt=r(y7n,"electra"),y7n.forEach(t),AEt=r(O9o," \u2014 "),v_e=n(O9o,"A",{href:!0});var C7n=s(v_e);REt=r(C7n,"ElectraForMaskedLM"),C7n.forEach(t),kEt=r(O9o," (ELECTRA model)"),O9o.forEach(t),SEt=i(ee),HC=n(ee,"LI",{});var Q9o=s(HC);HXe=n(Q9o,"STRONG",{});var w7n=s(HXe);xEt=r(w7n,"ernie"),w7n.forEach(t),GEt=r(Q9o," \u2014 "),b_e=n(Q9o,"A",{href:!0});var L7n=s(b_e);BEt=r(L7n,"ErnieForMaskedLM"),L7n.forEach(t),IEt=r(Q9o," (ERNIE model)"),Q9o.forEach(t),NEt=i(ee),KC=n(ee,"LI",{});var Y9o=s(KC);KXe=n(Y9o,"STRONG",{});var A7n=s(KXe);$Et=r(A7n,"esm"),A7n.forEach(t),jEt=r(Y9o," \u2014 "),M_e=n(Y9o,"A",{href:!0});var R7n=s(M_e);PEt=r(R7n,"EsmForMaskedLM"),R7n.forEach(t),ZEt=r(Y9o," (ESM model)"),Y9o.forEach(t),VEt=i(ee),ew=n(ee,"LI",{});var z9o=s(ew);eDe=n(z9o,"STRONG",{});var k7n=s(eDe);WEt=r(k7n,"flaubert"),k7n.forEach(t),JEt=r(z9o," \u2014 "),T_e=n(z9o,"A",{href:!0});var S7n=s(T_e);UEt=r(S7n,"FlaubertWithLMHeadModel"),S7n.forEach(t),XEt=r(z9o," 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"),C_e=n(oCo,"A",{href:!0});var P7n=s(C_e);cyt=r(P7n,"LayoutLMForMaskedLM"),P7n.forEach(t),myt=r(oCo," (LayoutLM model)"),oCo.forEach(t),fyt=i(ee),nw=n(ee,"LI",{});var rCo=s(nw);nDe=n(rCo,"STRONG",{});var Z7n=s(nDe);hyt=r(Z7n,"longformer"),Z7n.forEach(t),gyt=r(rCo," \u2014 "),w_e=n(rCo,"A",{href:!0});var V7n=s(w_e);uyt=r(V7n,"LongformerForMaskedLM"),V7n.forEach(t),pyt=r(rCo," (Longformer model)"),rCo.forEach(t),_yt=i(ee),sw=n(ee,"LI",{});var tCo=s(sw);sDe=n(tCo,"STRONG",{});var W7n=s(sDe);vyt=r(W7n,"luke"),W7n.forEach(t),byt=r(tCo," \u2014 "),L_e=n(tCo,"A",{href:!0});var J7n=s(L_e);Myt=r(J7n,"LukeForMaskedLM"),J7n.forEach(t),Tyt=r(tCo," (LUKE model)"),tCo.forEach(t),Fyt=i(ee),lw=n(ee,"LI",{});var aCo=s(lw);lDe=n(aCo,"STRONG",{});var U7n=s(lDe);Eyt=r(U7n,"mbart"),U7n.forEach(t),yyt=r(aCo," \u2014 "),A_e=n(aCo,"A",{href:!0});var X7n=s(A_e);Cyt=r(X7n,"MBartForConditionalGeneration"),X7n.forEach(t),wyt=r(aCo," (mBART model)"),aCo.forEach(t),Lyt=i(ee),iw=n(ee,"LI",{});var 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ZCo=s(Dw);eqe=n(ZCo,"STRONG",{});var TAn=s(eqe);Twt=r(TAn,"funnel"),TAn.forEach(t),Fwt=r(ZCo," \u2014 "),mve=n(ZCo,"A",{href:!0});var FAn=s(mve);Ewt=r(FAn,"TFFunnelForMaskedLM"),FAn.forEach(t),ywt=r(ZCo," (Funnel Transformer model)"),ZCo.forEach(t),Cwt=i(Me),qw=n(Me,"LI",{});var VCo=s(qw);oqe=n(VCo,"STRONG",{});var EAn=s(oqe);wwt=r(EAn,"layoutlm"),EAn.forEach(t),Lwt=r(VCo," \u2014 "),fve=n(VCo,"A",{href:!0});var yAn=s(fve);Awt=r(yAn,"TFLayoutLMForMaskedLM"),yAn.forEach(t),Rwt=r(VCo," (LayoutLM model)"),VCo.forEach(t),kwt=i(Me),Ow=n(Me,"LI",{});var WCo=s(Ow);rqe=n(WCo,"STRONG",{});var CAn=s(rqe);Swt=r(CAn,"longformer"),CAn.forEach(t),xwt=r(WCo," \u2014 "),hve=n(WCo,"A",{href:!0});var wAn=s(hve);Gwt=r(wAn,"TFLongformerForMaskedLM"),wAn.forEach(t),Bwt=r(WCo," (Longformer model)"),WCo.forEach(t),Iwt=i(Me),Qw=n(Me,"LI",{});var JCo=s(Qw);tqe=n(JCo,"STRONG",{});var LAn=s(tqe);Nwt=r(LAn,"mobilebert"),LAn.forEach(t),$wt=r(JCo," \u2014 "),gve=n(JCo,"A",{href:!0});var AAn=s(gve);jwt=r(AAn,"TFMobileBertForMaskedLM"),AAn.forEach(t),Pwt=r(JCo," (MobileBERT model)"),JCo.forEach(t),Zwt=i(Me),Yw=n(Me,"LI",{});var UCo=s(Yw);aqe=n(UCo,"STRONG",{});var RAn=s(aqe);Vwt=r(RAn,"mpnet"),RAn.forEach(t),Wwt=r(UCo," \u2014 "),uve=n(UCo,"A",{href:!0});var kAn=s(uve);Jwt=r(kAn,"TFMPNetForMaskedLM"),kAn.forEach(t),Uwt=r(UCo," (MPNet model)"),UCo.forEach(t),Xwt=i(Me),zw=n(Me,"LI",{});var XCo=s(zw);nqe=n(XCo,"STRONG",{});var SAn=s(nqe);Dwt=r(SAn,"rembert"),SAn.forEach(t),qwt=r(XCo," \u2014 "),pve=n(XCo,"A",{href:!0});var xAn=s(pve);Owt=r(xAn,"TFRemBertForMaskedLM"),xAn.forEach(t),Qwt=r(XCo," (RemBERT model)"),XCo.forEach(t),Ywt=i(Me),Hw=n(Me,"LI",{});var DCo=s(Hw);sqe=n(DCo,"STRONG",{});var GAn=s(sqe);zwt=r(GAn,"roberta"),GAn.forEach(t),Hwt=r(DCo," \u2014 "),_ve=n(DCo,"A",{href:!0});var BAn=s(_ve);Kwt=r(BAn,"TFRobertaForMaskedLM"),BAn.forEach(t),e6t=r(DCo," (RoBERTa model)"),DCo.forEach(t),o6t=i(Me),Kw=n(Me,"LI",{});var qCo=s(Kw);lqe=n(qCo,"STRONG",{});var IAn=s(lqe);r6t=r(IAn,"roberta-prelayernorm"),IAn.forEach(t),t6t=r(qCo," \u2014 "),vve=n(qCo,"A",{href:!0});var NAn=s(vve);a6t=r(NAn,"TFRobertaPreLayerNormForMaskedLM"),NAn.forEach(t),n6t=r(qCo," (RoBERTa-PreLayerNorm model)"),qCo.forEach(t),s6t=i(Me),e6=n(Me,"LI",{});var OCo=s(e6);iqe=n(OCo,"STRONG",{});var $An=s(iqe);l6t=r($An,"roformer"),$An.forEach(t),i6t=r(OCo," \u2014 "),bve=n(OCo,"A",{href:!0});var jAn=s(bve);d6t=r(jAn,"TFRoFormerForMaskedLM"),jAn.forEach(t),c6t=r(OCo," (RoFormer model)"),OCo.forEach(t),m6t=i(Me),o6=n(Me,"LI",{});var QCo=s(o6);dqe=n(QCo,"STRONG",{});var PAn=s(dqe);f6t=r(PAn,"tapas"),PAn.forEach(t),h6t=r(QCo," \u2014 "),Mve=n(QCo,"A",{href:!0});var ZAn=s(Mve);g6t=r(ZAn,"TFTapasForMaskedLM"),ZAn.forEach(t),u6t=r(QCo," (TAPAS model)"),QCo.forEach(t),p6t=i(Me),r6=n(Me,"LI",{});var YCo=s(r6);cqe=n(YCo,"STRONG",{});var VAn=s(cqe);_6t=r(VAn,"xlm"),VAn.forEach(t),v6t=r(YCo," \u2014 "),Tve=n(YCo,"A",{href:!0});var WAn=s(Tve);b6t=r(WAn,"TFXLMWithLMHeadModel"),WAn.forEach(t),M6t=r(YCo," (XLM model)"),YCo.forEach(t),T6t=i(Me),t6=n(Me,"LI",{});var zCo=s(t6);mqe=n(zCo,"STRONG",{});var JAn=s(mqe);F6t=r(JAn,"xlm-roberta"),JAn.forEach(t),E6t=r(zCo," \u2014 "),Fve=n(zCo,"A",{href:!0});var UAn=s(Fve);y6t=r(UAn,"TFXLMRobertaForMaskedLM"),UAn.forEach(t),C6t=r(zCo," (XLM-RoBERTa model)"),zCo.forEach(t),Me.forEach(t),w6t=i(pd),M(a6.$$.fragment,pd),pd.forEach(t),ud.forEach(t),WBo=i(m),Zm=n(m,"H3",{class:!0});var J$o=s(Zm);n6=n(J$o,"A",{id:!0,class:!0,href:!0});var XAn=s(n6);fqe=n(XAn,"SPAN",{});var DAn=s(fqe);M(XW.$$.fragment,DAn),DAn.forEach(t),XAn.forEach(t),L6t=i(J$o),hqe=n(J$o,"SPAN",{});var qAn=s(hqe);A6t=r(qAn,"FlaxAutoModelForMaskedLM"),qAn.forEach(t),J$o.forEach(t),JBo=i(m),or=n(m,"DIV",{class:!0});var _d=s(or);M(DW.$$.fragment,_d),R6t=i(_d),Vm=n(_d,"P",{});var ACe=s(Vm);k6t=r(ACe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language 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with the `),Eve=n(ACe,"A",{href:!0});var OAn=s(Eve);S6t=r(OAn,"from_pretrained()"),OAn.forEach(t),x6t=r(ACe," class method or the "),yve=n(ACe,"A",{href:!0});var QAn=s(yve);G6t=r(QAn,"from_config()"),QAn.forEach(t),B6t=r(ACe,` class
method.`),ACe.forEach(t),I6t=i(_d),qW=n(_d,"P",{});var U$o=s(qW);N6t=r(U$o,"This class cannot be instantiated directly using "),gqe=n(U$o,"CODE",{});var YAn=s(gqe);$6t=r(YAn,"__init__()"),YAn.forEach(t),j6t=r(U$o," (throws an error)."),U$o.forEach(t),P6t=i(_d),ea=n(_d,"DIV",{class:!0});var dP=s(ea);M(OW.$$.fragment,dP),Z6t=i(dP),uqe=n(dP,"P",{});var zAn=s(uqe);V6t=r(zAn,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),zAn.forEach(t),W6t=i(dP),Wm=n(dP,"P",{});var RCe=s(Wm);J6t=r(RCe,`Note:
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model\u2019s configuration. Use `),Cve=n(RCe,"A",{href:!0});var KAn=s(Cve);D6t=r(KAn,"from_pretrained()"),KAn.forEach(t),q6t=r(RCe," to load the model weights."),RCe.forEach(t),O6t=i(dP),M(s6.$$.fragment,dP),dP.forEach(t),Q6t=i(_d),ct=n(_d,"DIV",{class:!0});var vd=s(ct);M(QW.$$.fragment,vd),Y6t=i(vd),_qe=n(vd,"P",{});var eRn=s(_qe);z6t=r(eRn,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),eRn.forEach(t),H6t=i(vd),zn=n(vd,"P",{});var cP=s(zn);K6t=r(cP,"The model class to instantiate is selected based on the "),vqe=n(cP,"CODE",{});var oRn=s(vqe);e8t=r(oRn,"model_type"),oRn.forEach(t),o8t=r(cP,` property of the config object (either
passed as an argument or loaded from `),bqe=n(cP,"CODE",{});var rRn=s(bqe);r8t=r(rRn,"pretrained_model_name_or_path"),rRn.forEach(t),t8t=r(cP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Mqe=n(cP,"CODE",{});var tRn=s(Mqe);a8t=r(tRn,"pretrained_model_name_or_path"),tRn.forEach(t),n8t=r(cP,":"),cP.forEach(t),s8t=i(vd),ke=n(vd,"UL",{});var Pe=s(ke);l6=n(Pe,"LI",{});var HCo=s(l6);Tqe=n(HCo,"STRONG",{});var aRn=s(Tqe);l8t=r(aRn,"albert"),aRn.forEach(t),i8t=r(HCo," \u2014 "),wve=n(HCo,"A",{href:!0});var nRn=s(wve);d8t=r(nRn,"FlaxAlbertForMaskedLM"),nRn.forEach(t),c8t=r(HCo," (ALBERT model)"),HCo.forEach(t),m8t=i(Pe),i6=n(Pe,"LI",{});var KCo=s(i6);Fqe=n(KCo,"STRONG",{});var sRn=s(Fqe);f8t=r(sRn,"bart"),sRn.forEach(t),h8t=r(KCo," \u2014 "),Lve=n(KCo,"A",{href:!0});var lRn=s(Lve);g8t=r(lRn,"FlaxBartForConditionalGeneration"),lRn.forEach(t),u8t=r(KCo," (BART model)"),KCo.forEach(t),p8t=i(Pe),d6=n(Pe,"LI",{});var ewo=s(d6);Eqe=n(ewo,"STRONG",{});var iRn=s(Eqe);_8t=r(iRn,"bert"),iRn.forEach(t),v8t=r(ewo," \u2014 "),Ave=n(ewo,"A",{href:!0});var dRn=s(Ave);b8t=r(dRn,"FlaxBertForMaskedLM"),dRn.forEach(t),M8t=r(ewo," (BERT model)"),ewo.forEach(t),T8t=i(Pe),c6=n(Pe,"LI",{});var owo=s(c6);yqe=n(owo,"STRONG",{});var cRn=s(yqe);F8t=r(cRn,"big_bird"),cRn.forEach(t),E8t=r(owo," \u2014 "),Rve=n(owo,"A",{href:!0});var mRn=s(Rve);y8t=r(mRn,"FlaxBigBirdForMaskedLM"),mRn.forEach(t),C8t=r(owo," (BigBird model)"),owo.forEach(t),w8t=i(Pe),m6=n(Pe,"LI",{});var rwo=s(m6);Cqe=n(rwo,"STRONG",{});var fRn=s(Cqe);L8t=r(fRn,"distilbert"),fRn.forEach(t),A8t=r(rwo," \u2014 "),kve=n(rwo,"A",{href:!0});var hRn=s(kve);R8t=r(hRn,"FlaxDistilBertForMaskedLM"),hRn.forEach(t),k8t=r(rwo," (DistilBERT model)"),rwo.forEach(t),S8t=i(Pe),f6=n(Pe,"LI",{});var two=s(f6);wqe=n(two,"STRONG",{});var gRn=s(wqe);x8t=r(gRn,"electra"),gRn.forEach(t),G8t=r(two," \u2014 "),Sve=n(two,"A",{href:!0});var uRn=s(Sve);B8t=r(uRn,"FlaxElectraForMaskedLM"),uRn.forEach(t),I8t=r(two," (ELECTRA model)"),two.forEach(t),N8t=i(Pe),h6=n(Pe,"LI",{});var awo=s(h6);Lqe=n(awo,"STRONG",{});var pRn=s(Lqe);$8t=r(pRn,"mbart"),pRn.forEach(t),j8t=r(awo," \u2014 "),xve=n(awo,"A",{href:!0});var _Rn=s(xve);P8t=r(_Rn,"FlaxMBartForConditionalGeneration"),_Rn.forEach(t),Z8t=r(awo," (mBART model)"),awo.forEach(t),V8t=i(Pe),g6=n(Pe,"LI",{});var nwo=s(g6);Aqe=n(nwo,"STRONG",{});var vRn=s(Aqe);W8t=r(vRn,"roberta"),vRn.forEach(t),J8t=r(nwo," \u2014 "),Gve=n(nwo,"A",{href:!0});var bRn=s(Gve);U8t=r(bRn,"FlaxRobertaForMaskedLM"),bRn.forEach(t),X8t=r(nwo," (RoBERTa model)"),nwo.forEach(t),D8t=i(Pe),u6=n(Pe,"LI",{});var swo=s(u6);Rqe=n(swo,"STRONG",{});var MRn=s(Rqe);q8t=r(MRn,"roberta-prelayernorm"),MRn.forEach(t),O8t=r(swo," \u2014 "),Bve=n(swo,"A",{href:!0});var TRn=s(Bve);Q8t=r(TRn,"FlaxRobertaPreLayerNormForMaskedLM"),TRn.forEach(t),Y8t=r(swo," (RoBERTa-PreLayerNorm model)"),swo.forEach(t),z8t=i(Pe),p6=n(Pe,"LI",{});var lwo=s(p6);kqe=n(lwo,"STRONG",{});var FRn=s(kqe);H8t=r(FRn,"roformer"),FRn.forEach(t),K8t=r(lwo," \u2014 "),Ive=n(lwo,"A",{href:!0});var ERn=s(Ive);e7t=r(ERn,"FlaxRoFormerForMaskedLM"),ERn.forEach(t),o7t=r(lwo," (RoFormer model)"),lwo.forEach(t),r7t=i(Pe),_6=n(Pe,"LI",{});var iwo=s(_6);Sqe=n(iwo,"STRONG",{});var yRn=s(Sqe);t7t=r(yRn,"xlm-roberta"),yRn.forEach(t),a7t=r(iwo," \u2014 "),Nve=n(iwo,"A",{href:!0});var CRn=s(Nve);n7t=r(CRn,"FlaxXLMRobertaForMaskedLM"),CRn.forEach(t),s7t=r(iwo," (XLM-RoBERTa model)"),iwo.forEach(t),Pe.forEach(t),l7t=i(vd),M(v6.$$.fragment,vd),vd.forEach(t),_d.forEach(t),UBo=i(m),Jm=n(m,"H3",{class:!0});var X$o=s(Jm);b6=n(X$o,"A",{id:!0,class:!0,href:!0});var wRn=s(b6);xqe=n(wRn,"SPAN",{});var LRn=s(xqe);M(YW.$$.fragment,LRn),LRn.forEach(t),wRn.forEach(t),i7t=i(X$o),Gqe=n(X$o,"SPAN",{});var ARn=s(Gqe);d7t=r(ARn,"AutoModelForMaskGeneration"),ARn.forEach(t),X$o.forEach(t),XBo=i(m),zW=n(m,"DIV",{class:!0});var RRn=s(zW);M(HW.$$.fragment,RRn),RRn.forEach(t),DBo=i(m),Um=n(m,"H3",{class:!0});var D$o=s(Um);M6=n(D$o,"A",{id:!0,class:!0,href:!0});var kRn=s(M6);Bqe=n(kRn,"SPAN",{});var SRn=s(Bqe);M(KW.$$.fragment,SRn),SRn.forEach(t),kRn.forEach(t),c7t=i(D$o),Iqe=n(D$o,"SPAN",{});var xRn=s(Iqe);m7t=r(xRn,"TFAutoModelForMaskGeneration"),xRn.forEach(t),D$o.forEach(t),qBo=i(m),eJ=n(m,"DIV",{class:!0});var GRn=s(eJ);M(oJ.$$.fragment,GRn),GRn.forEach(t),OBo=i(m),Xm=n(m,"H3",{class:!0});var q$o=s(Xm);T6=n(q$o,"A",{id:!0,class:!0,href:!0});var BRn=s(T6);Nqe=n(BRn,"SPAN",{});var IRn=s(Nqe);M(rJ.$$.fragment,IRn),IRn.forEach(t),BRn.forEach(t),f7t=i(q$o),$qe=n(q$o,"SPAN",{});var NRn=s($qe);h7t=r(NRn,"AutoModelForSeq2SeqLM"),NRn.forEach(t),q$o.forEach(t),QBo=i(m),rr=n(m,"DIV",{class:!0});var bd=s(rr);M(tJ.$$.fragment,bd),g7t=i(bd),Dm=n(bd,"P",{});var kCe=s(Dm);u7t=r(kCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
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method.`),kCe.forEach(t),M7t=i(bd),aJ=n(bd,"P",{});var O$o=s(aJ);T7t=r(O$o,"This class cannot be instantiated directly using "),jqe=n(O$o,"CODE",{});var PRn=s(jqe);F7t=r(PRn,"__init__()"),PRn.forEach(t),E7t=r(O$o," (throws an error)."),O$o.forEach(t),y7t=i(bd),oa=n(bd,"DIV",{class:!0});var mP=s(oa);M(nJ.$$.fragment,mP),C7t=i(mP),Pqe=n(mP,"P",{});var ZRn=s(Pqe);w7t=r(ZRn,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),ZRn.forEach(t),L7t=i(mP),qm=n(mP,"P",{});var SCe=s(qm);A7t=r(SCe,`Note:
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model\u2019s configuration. Use `),Pve=n(SCe,"A",{href:!0});var WRn=s(Pve);S7t=r(WRn,"from_pretrained()"),WRn.forEach(t),x7t=r(SCe," to load the model weights."),SCe.forEach(t),G7t=i(mP),M(F6.$$.fragment,mP),mP.forEach(t),B7t=i(bd),mo=n(bd,"DIV",{class:!0});var nn=s(mo);M(sJ.$$.fragment,nn),I7t=i(nn),Vqe=n(nn,"P",{});var JRn=s(Vqe);N7t=r(JRn,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),JRn.forEach(t),$7t=i(nn),Hn=n(nn,"P",{});var fP=s(Hn);j7t=r(fP,"The model class to instantiate is selected based on the "),Wqe=n(fP,"CODE",{});var URn=s(Wqe);P7t=r(URn,"model_type"),URn.forEach(t),Z7t=r(fP,` property of the config object (either
passed as an argument or loaded from `),Jqe=n(fP,"CODE",{});var XRn=s(Jqe);V7t=r(XRn,"pretrained_model_name_or_path"),XRn.forEach(t),W7t=r(fP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Uqe=n(fP,"CODE",{});var DRn=s(Uqe);J7t=r(DRn,"pretrained_model_name_or_path"),DRn.forEach(t),U7t=r(fP,":"),fP.forEach(t),X7t=i(nn),ge=n(nn,"UL",{});var ve=s(ge);E6=n(ve,"LI",{});var dwo=s(E6);Xqe=n(dwo,"STRONG",{});var qRn=s(Xqe);D7t=r(qRn,"bart"),qRn.forEach(t),q7t=r(dwo," \u2014 "),Zve=n(dwo,"A",{href:!0});var ORn=s(Zve);O7t=r(ORn,"BartForConditionalGeneration"),ORn.forEach(t),Q7t=r(dwo," (BART model)"),dwo.forEach(t),Y7t=i(ve),y6=n(ve,"LI",{});var cwo=s(y6);Dqe=n(cwo,"STRONG",{});var QRn=s(Dqe);z7t=r(QRn,"bigbird_pegasus"),QRn.forEach(t),H7t=r(cwo," \u2014 "),Vve=n(cwo,"A",{href:!0});var YRn=s(Vve);K7t=r(YRn,"BigBirdPegasusForConditionalGeneration"),YRn.forEach(t),eLt=r(cwo," (BigBird-Pegasus model)"),cwo.forEach(t),oLt=i(ve),C6=n(ve,"LI",{});var mwo=s(C6);qqe=n(mwo,"STRONG",{});var zRn=s(qqe);rLt=r(zRn,"blenderbot"),zRn.forEach(t),tLt=r(mwo," \u2014 "),Wve=n(mwo,"A",{href:!0});var HRn=s(Wve);aLt=r(HRn,"BlenderbotForConditionalGeneration"),HRn.forEach(t),nLt=r(mwo," (Blenderbot model)"),mwo.forEach(t),sLt=i(ve),w6=n(ve,"LI",{});var fwo=s(w6);Oqe=n(fwo,"STRONG",{});var KRn=s(Oqe);lLt=r(KRn,"blenderbot-small"),KRn.forEach(t),iLt=r(fwo," \u2014 "),Jve=n(fwo,"A",{href:!0});var ekn=s(Jve);dLt=r(ekn,"BlenderbotSmallForConditionalGeneration"),ekn.forEach(t),cLt=r(fwo," (BlenderbotSmall model)"),fwo.forEach(t),mLt=i(ve),L6=n(ve,"LI",{});var hwo=s(L6);Qqe=n(hwo,"STRONG",{});var okn=s(Qqe);fLt=r(okn,"encoder-decoder"),okn.forEach(t),hLt=r(hwo," \u2014 "),Uve=n(hwo,"A",{href:!0});var rkn=s(Uve);gLt=r(rkn,"EncoderDecoderModel"),rkn.forEach(t),uLt=r(hwo," (Encoder decoder model)"),hwo.forEach(t),pLt=i(ve),A6=n(ve,"LI",{});var gwo=s(A6);Yqe=n(gwo,"STRONG",{});var tkn=s(Yqe);_Lt=r(tkn,"fsmt"),tkn.forEach(t),vLt=r(gwo," \u2014 "),Xve=n(gwo,"A",{href:!0});var akn=s(Xve);bLt=r(akn,"FSMTForConditionalGeneration"),akn.forEach(t),MLt=r(gwo," (FairSeq Machine-Translation model)"),gwo.forEach(t),TLt=i(ve),R6=n(ve,"LI",{});var uwo=s(R6);zqe=n(uwo,"STRONG",{});var nkn=s(zqe);FLt=r(nkn,"gptsan-japanese"),nkn.forEach(t),ELt=r(uwo," \u2014 "),Dve=n(uwo,"A",{href:!0});var skn=s(Dve);yLt=r(skn,"GPTSanJapaneseForConditionalGeneration"),skn.forEach(t),CLt=r(uwo," (GPTSAN-japanese model)"),uwo.forEach(t),wLt=i(ve),k6=n(ve,"LI",{});var pwo=s(k6);Hqe=n(pwo,"STRONG",{});var lkn=s(Hqe);LLt=r(lkn,"led"),lkn.forEach(t),ALt=r(pwo," \u2014 "),qve=n(pwo,"A",{href:!0});var ikn=s(qve);RLt=r(ikn,"LEDForConditionalGeneration"),ikn.forEach(t),kLt=r(pwo," (LED model)"),pwo.forEach(t),SLt=i(ve),S6=n(ve,"LI",{});var _wo=s(S6);Kqe=n(_wo,"STRONG",{});var dkn=s(Kqe);xLt=r(dkn,"longt5"),dkn.forEach(t),GLt=r(_wo," \u2014 "),Ove=n(_wo,"A",{href:!0});var ckn=s(Ove);BLt=r(ckn,"LongT5ForConditionalGeneration"),ckn.forEach(t),ILt=r(_wo," (LongT5 model)"),_wo.forEach(t),NLt=i(ve),x6=n(ve,"LI",{});var vwo=s(x6);eOe=n(vwo,"STRONG",{});var mkn=s(eOe);$Lt=r(mkn,"m2m_100"),mkn.forEach(t),jLt=r(vwo," \u2014 "),Qve=n(vwo,"A",{href:!0});var fkn=s(Qve);PLt=r(fkn,"M2M100ForConditionalGeneration"),fkn.forEach(t),ZLt=r(vwo," (M2M100 model)"),vwo.forEach(t),VLt=i(ve),G6=n(ve,"LI",{});var bwo=s(G6);oOe=n(bwo,"STRONG",{});var hkn=s(oOe);WLt=r(hkn,"marian"),hkn.forEach(t),JLt=r(bwo," \u2014 "),Yve=n(bwo,"A",{href:!0});var gkn=s(Yve);ULt=r(gkn,"MarianMTModel"),gkn.forEach(t),XLt=r(bwo," (Marian model)"),bwo.forEach(t),DLt=i(ve),B6=n(ve,"LI",{});var Mwo=s(B6);rOe=n(Mwo,"STRONG",{});var ukn=s(rOe);qLt=r(ukn,"mbart"),ukn.forEach(t),OLt=r(Mwo," \u2014 "),zve=n(Mwo,"A",{href:!0});var pkn=s(zve);QLt=r(pkn,"MBartForConditionalGeneration"),pkn.forEach(t),YLt=r(Mwo," (mBART model)"),Mwo.forEach(t),zLt=i(ve),I6=n(ve,"LI",{});var Two=s(I6);tOe=n(Two,"STRONG",{});var _kn=s(tOe);HLt=r(_kn,"mt5"),_kn.forEach(t),KLt=r(Two," \u2014 "),Hve=n(Two,"A",{href:!0});var vkn=s(Hve);eAt=r(vkn,"MT5ForConditionalGeneration"),vkn.forEach(t),oAt=r(Two," (MT5 model)"),Two.forEach(t),rAt=i(ve),N6=n(ve,"LI",{});var Fwo=s(N6);aOe=n(Fwo,"STRONG",{});var bkn=s(aOe);tAt=r(bkn,"mvp"),bkn.forEach(t),aAt=r(Fwo," \u2014 "),Kve=n(Fwo,"A",{href:!0});var Mkn=s(Kve);nAt=r(Mkn,"MvpForConditionalGeneration"),Mkn.forEach(t),sAt=r(Fwo," (MVP model)"),Fwo.forEach(t),lAt=i(ve),$6=n(ve,"LI",{});var Ewo=s($6);nOe=n(Ewo,"STRONG",{});var Tkn=s(nOe);iAt=r(Tkn,"nllb-moe"),Tkn.forEach(t),dAt=r(Ewo," \u2014 "),e3e=n(Ewo,"A",{href:!0});var Fkn=s(e3e);cAt=r(Fkn,"NllbMoeForConditionalGeneration"),Fkn.forEach(t),mAt=r(Ewo," (NLLB-MOE model)"),Ewo.forEach(t),fAt=i(ve),j6=n(ve,"LI",{});var ywo=s(j6);sOe=n(ywo,"STRONG",{});var Ekn=s(sOe);hAt=r(Ekn,"pegasus"),Ekn.forEach(t),gAt=r(ywo," \u2014 "),o3e=n(ywo,"A",{href:!0});var ykn=s(o3e);uAt=r(ykn,"PegasusForConditionalGeneration"),ykn.forEach(t),pAt=r(ywo," (Pegasus model)"),ywo.forEach(t),_At=i(ve),P6=n(ve,"LI",{});var Cwo=s(P6);lOe=n(Cwo,"STRONG",{});var Ckn=s(lOe);vAt=r(Ckn,"pegasus_x"),Ckn.forEach(t),bAt=r(Cwo," \u2014 "),r3e=n(Cwo,"A",{href:!0});var wkn=s(r3e);MAt=r(wkn,"PegasusXForConditionalGeneration"),wkn.forEach(t),TAt=r(Cwo," (PEGASUS-X model)"),Cwo.forEach(t),FAt=i(ve),Z6=n(ve,"LI",{});var wwo=s(Z6);iOe=n(wwo,"STRONG",{});var Lkn=s(iOe);EAt=r(Lkn,"plbart"),Lkn.forEach(t),yAt=r(wwo," \u2014 "),t3e=n(wwo,"A",{href:!0});var Akn=s(t3e);CAt=r(Akn,"PLBartForConditionalGeneration"),Akn.forEach(t),wAt=r(wwo," (PLBart model)"),wwo.forEach(t),LAt=i(ve),V6=n(ve,"LI",{});var Lwo=s(V6);dOe=n(Lwo,"STRONG",{});var Rkn=s(dOe);AAt=r(Rkn,"prophetnet"),Rkn.forEach(t),RAt=r(Lwo," \u2014 "),a3e=n(Lwo,"A",{href:!0});var kkn=s(a3e);kAt=r(kkn,"ProphetNetForConditionalGeneration"),kkn.forEach(t),SAt=r(Lwo," (ProphetNet model)"),Lwo.forEach(t),xAt=i(ve),W6=n(ve,"LI",{});var Awo=s(W6);cOe=n(Awo,"STRONG",{});var Skn=s(cOe);GAt=r(Skn,"switch_transformers"),Skn.forEach(t),BAt=r(Awo," \u2014 "),n3e=n(Awo,"A",{href:!0});var xkn=s(n3e);IAt=r(xkn,"SwitchTransformersForConditionalGeneration"),xkn.forEach(t),NAt=r(Awo," (SwitchTransformers model)"),Awo.forEach(t),$At=i(ve),J6=n(ve,"LI",{});var Rwo=s(J6);mOe=n(Rwo,"STRONG",{});var Gkn=s(mOe);jAt=r(Gkn,"t5"),Gkn.forEach(t),PAt=r(Rwo," \u2014 "),s3e=n(Rwo,"A",{href:!0});var Bkn=s(s3e);ZAt=r(Bkn,"T5ForConditionalGeneration"),Bkn.forEach(t),VAt=r(Rwo," (T5 model)"),Rwo.forEach(t),WAt=i(ve),U6=n(ve,"LI",{});var kwo=s(U6);fOe=n(kwo,"STRONG",{});var Ikn=s(fOe);JAt=r(Ikn,"umt5"),Ikn.forEach(t),UAt=r(kwo," \u2014 "),l3e=n(kwo,"A",{href:!0});var Nkn=s(l3e);XAt=r(Nkn,"UMT5ForConditionalGeneration"),Nkn.forEach(t),DAt=r(kwo," (UMT5 model)"),kwo.forEach(t),qAt=i(ve),X6=n(ve,"LI",{});var Swo=s(X6);hOe=n(Swo,"STRONG",{});var $kn=s(hOe);OAt=r($kn,"xlm-prophetnet"),$kn.forEach(t),QAt=r(Swo," \u2014 "),i3e=n(Swo,"A",{href:!0});var jkn=s(i3e);YAt=r(jkn,"XLMProphetNetForConditionalGeneration"),jkn.forEach(t),zAt=r(Swo," (XLM-ProphetNet model)"),Swo.forEach(t),ve.forEach(t),HAt=i(nn),D6=n(nn,"P",{});var xwo=s(D6);KAt=r(xwo,"The model is set in evaluation mode by default using "),gOe=n(xwo,"CODE",{});var Pkn=s(gOe);eRt=r(Pkn,"model.eval()"),Pkn.forEach(t),oRt=r(xwo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),uOe=n(xwo,"CODE",{});var Zkn=s(uOe);rRt=r(Zkn,"model.train()"),Zkn.forEach(t),xwo.forEach(t),tRt=i(nn),M(q6.$$.fragment,nn),nn.forEach(t),bd.forEach(t),YBo=i(m),Om=n(m,"H3",{class:!0});var Q$o=s(Om);O6=n(Q$o,"A",{id:!0,class:!0,href:!0});var Vkn=s(O6);pOe=n(Vkn,"SPAN",{});var Wkn=s(pOe);M(lJ.$$.fragment,Wkn),Wkn.forEach(t),Vkn.forEach(t),aRt=i(Q$o),_Oe=n(Q$o,"SPAN",{});var Jkn=s(_Oe);nRt=r(Jkn,"TFAutoModelForSeq2SeqLM"),Jkn.forEach(t),Q$o.forEach(t),zBo=i(m),tr=n(m,"DIV",{class:!0});var Md=s(tr);M(iJ.$$.fragment,Md),sRt=i(Md),Qm=n(Md,"P",{});var xCe=s(Qm);lRt=r(xCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),d3e=n(xCe,"A",{href:!0});var Ukn=s(d3e);iRt=r(Ukn,"from_pretrained()"),Ukn.forEach(t),dRt=r(xCe," class method or the "),c3e=n(xCe,"A",{href:!0});var Xkn=s(c3e);cRt=r(Xkn,"from_config()"),Xkn.forEach(t),mRt=r(xCe,` class
method.`),xCe.forEach(t),fRt=i(Md),dJ=n(Md,"P",{});var Y$o=s(dJ);hRt=r(Y$o,"This class cannot be instantiated directly using "),vOe=n(Y$o,"CODE",{});var Dkn=s(vOe);gRt=r(Dkn,"__init__()"),Dkn.forEach(t),uRt=r(Y$o," (throws an error)."),Y$o.forEach(t),pRt=i(Md),ra=n(Md,"DIV",{class:!0});var hP=s(ra);M(cJ.$$.fragment,hP),_Rt=i(hP),bOe=n(hP,"P",{});var qkn=s(bOe);vRt=r(qkn,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),qkn.forEach(t),bRt=i(hP),Ym=n(hP,"P",{});var GCe=s(Ym);MRt=r(GCe,`Note:
Loading a model from its configuration file does `),MOe=n(GCe,"STRONG",{});var Okn=s(MOe);TRt=r(Okn,"not"),Okn.forEach(t),FRt=r(GCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),m3e=n(GCe,"A",{href:!0});var Qkn=s(m3e);ERt=r(Qkn,"from_pretrained()"),Qkn.forEach(t),yRt=r(GCe," to load the model weights."),GCe.forEach(t),CRt=i(hP),M(Q6.$$.fragment,hP),hP.forEach(t),wRt=i(Md),mt=n(Md,"DIV",{class:!0});var Td=s(mt);M(mJ.$$.fragment,Td),LRt=i(Td),TOe=n(Td,"P",{});var Ykn=s(TOe);ARt=r(Ykn,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),Ykn.forEach(t),RRt=i(Td),Kn=n(Td,"P",{});var gP=s(Kn);kRt=r(gP,"The model class to instantiate is selected based on the "),FOe=n(gP,"CODE",{});var zkn=s(FOe);SRt=r(zkn,"model_type"),zkn.forEach(t),xRt=r(gP,` property of the config object (either
passed as an argument or loaded from `),EOe=n(gP,"CODE",{});var Hkn=s(EOe);GRt=r(Hkn,"pretrained_model_name_or_path"),Hkn.forEach(t),BRt=r(gP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),yOe=n(gP,"CODE",{});var Kkn=s(yOe);IRt=r(Kkn,"pretrained_model_name_or_path"),Kkn.forEach(t),NRt=r(gP,":"),gP.forEach(t),$Rt=i(Td),Ne=n(Td,"UL",{});var De=s(Ne);Y6=n(De,"LI",{});var Gwo=s(Y6);COe=n(Gwo,"STRONG",{});var eSn=s(COe);jRt=r(eSn,"bart"),eSn.forEach(t),PRt=r(Gwo," \u2014 "),f3e=n(Gwo,"A",{href:!0});var oSn=s(f3e);ZRt=r(oSn,"TFBartForConditionalGeneration"),oSn.forEach(t),VRt=r(Gwo," (BART model)"),Gwo.forEach(t),WRt=i(De),z6=n(De,"LI",{});var Bwo=s(z6);wOe=n(Bwo,"STRONG",{});var rSn=s(wOe);JRt=r(rSn,"blenderbot"),rSn.forEach(t),URt=r(Bwo," \u2014 "),h3e=n(Bwo,"A",{href:!0});var tSn=s(h3e);XRt=r(tSn,"TFBlenderbotForConditionalGeneration"),tSn.forEach(t),DRt=r(Bwo," (Blenderbot model)"),Bwo.forEach(t),qRt=i(De),H6=n(De,"LI",{});var Iwo=s(H6);LOe=n(Iwo,"STRONG",{});var aSn=s(LOe);ORt=r(aSn,"blenderbot-small"),aSn.forEach(t),QRt=r(Iwo," \u2014 "),g3e=n(Iwo,"A",{href:!0});var nSn=s(g3e);YRt=r(nSn,"TFBlenderbotSmallForConditionalGeneration"),nSn.forEach(t),zRt=r(Iwo," (BlenderbotSmall model)"),Iwo.forEach(t),HRt=i(De),K6=n(De,"LI",{});var Nwo=s(K6);AOe=n(Nwo,"STRONG",{});var sSn=s(AOe);KRt=r(sSn,"encoder-decoder"),sSn.forEach(t),ekt=r(Nwo," \u2014 "),u3e=n(Nwo,"A",{href:!0});var lSn=s(u3e);okt=r(lSn,"TFEncoderDecoderModel"),lSn.forEach(t),rkt=r(Nwo," (Encoder decoder model)"),Nwo.forEach(t),tkt=i(De),e8=n(De,"LI",{});var $wo=s(e8);ROe=n($wo,"STRONG",{});var iSn=s(ROe);akt=r(iSn,"led"),iSn.forEach(t),nkt=r($wo," \u2014 "),p3e=n($wo,"A",{href:!0});var dSn=s(p3e);skt=r(dSn,"TFLEDForConditionalGeneration"),dSn.forEach(t),lkt=r($wo," (LED model)"),$wo.forEach(t),ikt=i(De),o8=n(De,"LI",{});var jwo=s(o8);kOe=n(jwo,"STRONG",{});var cSn=s(kOe);dkt=r(cSn,"marian"),cSn.forEach(t),ckt=r(jwo," \u2014 "),_3e=n(jwo,"A",{href:!0});var mSn=s(_3e);mkt=r(mSn,"TFMarianMTModel"),mSn.forEach(t),fkt=r(jwo," (Marian model)"),jwo.forEach(t),hkt=i(De),r8=n(De,"LI",{});var Pwo=s(r8);SOe=n(Pwo,"STRONG",{});var fSn=s(SOe);gkt=r(fSn,"mbart"),fSn.forEach(t),ukt=r(Pwo," \u2014 "),v3e=n(Pwo,"A",{href:!0});var hSn=s(v3e);pkt=r(hSn,"TFMBartForConditionalGeneration"),hSn.forEach(t),_kt=r(Pwo," (mBART model)"),Pwo.forEach(t),vkt=i(De),t8=n(De,"LI",{});var Zwo=s(t8);xOe=n(Zwo,"STRONG",{});var gSn=s(xOe);bkt=r(gSn,"mt5"),gSn.forEach(t),Mkt=r(Zwo," \u2014 "),b3e=n(Zwo,"A",{href:!0});var uSn=s(b3e);Tkt=r(uSn,"TFMT5ForConditionalGeneration"),uSn.forEach(t),Fkt=r(Zwo," (MT5 model)"),Zwo.forEach(t),Ekt=i(De),a8=n(De,"LI",{});var Vwo=s(a8);GOe=n(Vwo,"STRONG",{});var pSn=s(GOe);ykt=r(pSn,"pegasus"),pSn.forEach(t),Ckt=r(Vwo," \u2014 "),M3e=n(Vwo,"A",{href:!0});var _Sn=s(M3e);wkt=r(_Sn,"TFPegasusForConditionalGeneration"),_Sn.forEach(t),Lkt=r(Vwo," (Pegasus model)"),Vwo.forEach(t),Akt=i(De),n8=n(De,"LI",{});var Wwo=s(n8);BOe=n(Wwo,"STRONG",{});var vSn=s(BOe);Rkt=r(vSn,"t5"),vSn.forEach(t),kkt=r(Wwo," \u2014 "),T3e=n(Wwo,"A",{href:!0});var bSn=s(T3e);Skt=r(bSn,"TFT5ForConditionalGeneration"),bSn.forEach(t),xkt=r(Wwo," (T5 model)"),Wwo.forEach(t),De.forEach(t),Gkt=i(Td),M(s8.$$.fragment,Td),Td.forEach(t),Md.forEach(t),HBo=i(m),zm=n(m,"H3",{class:!0});var z$o=s(zm);l8=n(z$o,"A",{id:!0,class:!0,href:!0});var MSn=s(l8);IOe=n(MSn,"SPAN",{});var TSn=s(IOe);M(fJ.$$.fragment,TSn),TSn.forEach(t),MSn.forEach(t),Bkt=i(z$o),NOe=n(z$o,"SPAN",{});var FSn=s(NOe);Ikt=r(FSn,"FlaxAutoModelForSeq2SeqLM"),FSn.forEach(t),z$o.forEach(t),KBo=i(m),ar=n(m,"DIV",{class:!0});var Fd=s(ar);M(hJ.$$.fragment,Fd),Nkt=i(Fd),Hm=n(Fd,"P",{});var BCe=s(Hm);$kt=r(BCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),F3e=n(BCe,"A",{href:!0});var ESn=s(F3e);jkt=r(ESn,"from_pretrained()"),ESn.forEach(t),Pkt=r(BCe," class method or the "),E3e=n(BCe,"A",{href:!0});var ySn=s(E3e);Zkt=r(ySn,"from_config()"),ySn.forEach(t),Vkt=r(BCe,` class
method.`),BCe.forEach(t),Wkt=i(Fd),gJ=n(Fd,"P",{});var H$o=s(gJ);Jkt=r(H$o,"This class cannot be instantiated directly using "),$Oe=n(H$o,"CODE",{});var CSn=s($Oe);Ukt=r(CSn,"__init__()"),CSn.forEach(t),Xkt=r(H$o," (throws an error)."),H$o.forEach(t),Dkt=i(Fd),ta=n(Fd,"DIV",{class:!0});var uP=s(ta);M(uJ.$$.fragment,uP),qkt=i(uP),jOe=n(uP,"P",{});var wSn=s(jOe);Okt=r(wSn,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),wSn.forEach(t),Qkt=i(uP),Km=n(uP,"P",{});var ICe=s(Km);Ykt=r(ICe,`Note:
Loading a model from its configuration file does `),POe=n(ICe,"STRONG",{});var LSn=s(POe);zkt=r(LSn,"not"),LSn.forEach(t),Hkt=r(ICe,` load the model weights. It only affects the
model\u2019s configuration. Use `),y3e=n(ICe,"A",{href:!0});var ASn=s(y3e);Kkt=r(ASn,"from_pretrained()"),ASn.forEach(t),eSt=r(ICe," to load the model weights."),ICe.forEach(t),oSt=i(uP),M(i8.$$.fragment,uP),uP.forEach(t),rSt=i(Fd),ft=n(Fd,"DIV",{class:!0});var Ed=s(ft);M(pJ.$$.fragment,Ed),tSt=i(Ed),ZOe=n(Ed,"P",{});var RSn=s(ZOe);aSt=r(RSn,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),RSn.forEach(t),nSt=i(Ed),es=n(Ed,"P",{});var pP=s(es);sSt=r(pP,"The model class to instantiate is selected based on the "),VOe=n(pP,"CODE",{});var kSn=s(VOe);lSt=r(kSn,"model_type"),kSn.forEach(t),iSt=r(pP,` property of the config object (either
passed as an argument or loaded from `),WOe=n(pP,"CODE",{});var SSn=s(WOe);dSt=r(SSn,"pretrained_model_name_or_path"),SSn.forEach(t),cSt=r(pP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),JOe=n(pP,"CODE",{});var xSn=s(JOe);mSt=r(xSn,"pretrained_model_name_or_path"),xSn.forEach(t),fSt=r(pP,":"),pP.forEach(t),hSt=i(Ed),$e=n(Ed,"UL",{});var qe=s($e);d8=n(qe,"LI",{});var Jwo=s(d8);UOe=n(Jwo,"STRONG",{});var GSn=s(UOe);gSt=r(GSn,"bart"),GSn.forEach(t),uSt=r(Jwo," \u2014 "),C3e=n(Jwo,"A",{href:!0});var BSn=s(C3e);pSt=r(BSn,"FlaxBartForConditionalGeneration"),BSn.forEach(t),_St=r(Jwo," (BART model)"),Jwo.forEach(t),vSt=i(qe),c8=n(qe,"LI",{});var Uwo=s(c8);XOe=n(Uwo,"STRONG",{});var ISn=s(XOe);bSt=r(ISn,"blenderbot"),ISn.forEach(t),MSt=r(Uwo," \u2014 "),w3e=n(Uwo,"A",{href:!0});var NSn=s(w3e);TSt=r(NSn,"FlaxBlenderbotForConditionalGeneration"),NSn.forEach(t),FSt=r(Uwo," (Blenderbot model)"),Uwo.forEach(t),ESt=i(qe),m8=n(qe,"LI",{});var Xwo=s(m8);DOe=n(Xwo,"STRONG",{});var $Sn=s(DOe);ySt=r($Sn,"blenderbot-small"),$Sn.forEach(t),CSt=r(Xwo," \u2014 "),L3e=n(Xwo,"A",{href:!0});var jSn=s(L3e);wSt=r(jSn,"FlaxBlenderbotSmallForConditionalGeneration"),jSn.forEach(t),LSt=r(Xwo," (BlenderbotSmall model)"),Xwo.forEach(t),ASt=i(qe),f8=n(qe,"LI",{});var Dwo=s(f8);qOe=n(Dwo,"STRONG",{});var PSn=s(qOe);RSt=r(PSn,"encoder-decoder"),PSn.forEach(t),kSt=r(Dwo," \u2014 "),A3e=n(Dwo,"A",{href:!0});var ZSn=s(A3e);SSt=r(ZSn,"FlaxEncoderDecoderModel"),ZSn.forEach(t),xSt=r(Dwo," (Encoder decoder model)"),Dwo.forEach(t),GSt=i(qe),h8=n(qe,"LI",{});var qwo=s(h8);OOe=n(qwo,"STRONG",{});var VSn=s(OOe);BSt=r(VSn,"longt5"),VSn.forEach(t),ISt=r(qwo," \u2014 "),R3e=n(qwo,"A",{href:!0});var WSn=s(R3e);NSt=r(WSn,"FlaxLongT5ForConditionalGeneration"),WSn.forEach(t),$St=r(qwo," (LongT5 model)"),qwo.forEach(t),jSt=i(qe),g8=n(qe,"LI",{});var Owo=s(g8);QOe=n(Owo,"STRONG",{});var JSn=s(QOe);PSt=r(JSn,"marian"),JSn.forEach(t),ZSt=r(Owo," \u2014 "),k3e=n(Owo,"A",{href:!0});var USn=s(k3e);VSt=r(USn,"FlaxMarianMTModel"),USn.forEach(t),WSt=r(Owo," (Marian model)"),Owo.forEach(t),JSt=i(qe),u8=n(qe,"LI",{});var Qwo=s(u8);YOe=n(Qwo,"STRONG",{});var XSn=s(YOe);USt=r(XSn,"mbart"),XSn.forEach(t),XSt=r(Qwo," \u2014 "),S3e=n(Qwo,"A",{href:!0});var DSn=s(S3e);DSt=r(DSn,"FlaxMBartForConditionalGeneration"),DSn.forEach(t),qSt=r(Qwo," (mBART model)"),Qwo.forEach(t),OSt=i(qe),p8=n(qe,"LI",{});var Ywo=s(p8);zOe=n(Ywo,"STRONG",{});var qSn=s(zOe);QSt=r(qSn,"mt5"),qSn.forEach(t),YSt=r(Ywo," \u2014 "),x3e=n(Ywo,"A",{href:!0});var OSn=s(x3e);zSt=r(OSn,"FlaxMT5ForConditionalGeneration"),OSn.forEach(t),HSt=r(Ywo," (MT5 model)"),Ywo.forEach(t),KSt=i(qe),_8=n(qe,"LI",{});var zwo=s(_8);HOe=n(zwo,"STRONG",{});var QSn=s(HOe);ext=r(QSn,"pegasus"),QSn.forEach(t),oxt=r(zwo," \u2014 "),G3e=n(zwo,"A",{href:!0});var YSn=s(G3e);rxt=r(YSn,"FlaxPegasusForConditionalGeneration"),YSn.forEach(t),txt=r(zwo," (Pegasus model)"),zwo.forEach(t),axt=i(qe),v8=n(qe,"LI",{});var Hwo=s(v8);KOe=n(Hwo,"STRONG",{});var zSn=s(KOe);nxt=r(zSn,"t5"),zSn.forEach(t),sxt=r(Hwo," \u2014 "),B3e=n(Hwo,"A",{href:!0});var HSn=s(B3e);lxt=r(HSn,"FlaxT5ForConditionalGeneration"),HSn.forEach(t),ixt=r(Hwo," (T5 model)"),Hwo.forEach(t),qe.forEach(t),dxt=i(Ed),M(b8.$$.fragment,Ed),Ed.forEach(t),Fd.forEach(t),eIo=i(m),ef=n(m,"H3",{class:!0});var K$o=s(ef);M8=n(K$o,"A",{id:!0,class:!0,href:!0});var KSn=s(M8);eQe=n(KSn,"SPAN",{});var exn=s(eQe);M(_J.$$.fragment,exn),exn.forEach(t),KSn.forEach(t),cxt=i(K$o),oQe=n(K$o,"SPAN",{});var oxn=s(oQe);mxt=r(oxn,"AutoModelForSequenceClassification"),oxn.forEach(t),K$o.forEach(t),oIo=i(m),nr=n(m,"DIV",{class:!0});var yd=s(nr);M(vJ.$$.fragment,yd),fxt=i(yd),of=n(yd,"P",{});var NCe=s(of);hxt=r(NCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the `),I3e=n(NCe,"A",{href:!0});var rxn=s(I3e);gxt=r(rxn,"from_pretrained()"),rxn.forEach(t),uxt=r(NCe," class method or the "),N3e=n(NCe,"A",{href:!0});var txn=s(N3e);pxt=r(txn,"from_config()"),txn.forEach(t),_xt=r(NCe,` class
method.`),NCe.forEach(t),vxt=i(yd),bJ=n(yd,"P",{});var ejo=s(bJ);bxt=r(ejo,"This class cannot be instantiated directly using "),rQe=n(ejo,"CODE",{});var axn=s(rQe);Mxt=r(axn,"__init__()"),axn.forEach(t),Txt=r(ejo," (throws an error)."),ejo.forEach(t),Fxt=i(yd),aa=n(yd,"DIV",{class:!0});var _P=s(aa);M(MJ.$$.fragment,_P),Ext=i(_P),tQe=n(_P,"P",{});var nxn=s(tQe);yxt=r(nxn,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),nxn.forEach(t),Cxt=i(_P),rf=n(_P,"P",{});var $Ce=s(rf);wxt=r($Ce,`Note:
Loading a model from its configuration file does `),aQe=n($Ce,"STRONG",{});var sxn=s(aQe);Lxt=r(sxn,"not"),sxn.forEach(t),Axt=r($Ce,` load the model weights. It only affects the
model\u2019s configuration. Use `),$3e=n($Ce,"A",{href:!0});var lxn=s($3e);Rxt=r(lxn,"from_pretrained()"),lxn.forEach(t),kxt=r($Ce," to load the model weights."),$Ce.forEach(t),Sxt=i(_P),M(T8.$$.fragment,_P),_P.forEach(t),xxt=i(yd),fo=n(yd,"DIV",{class:!0});var sn=s(fo);M(TJ.$$.fragment,sn),Gxt=i(sn),nQe=n(sn,"P",{});var ixn=s(nQe);Bxt=r(ixn,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),ixn.forEach(t),Ixt=i(sn),os=n(sn,"P",{});var vP=s(os);Nxt=r(vP,"The model class to instantiate is selected based on the "),sQe=n(vP,"CODE",{});var dxn=s(sQe);$xt=r(dxn,"model_type"),dxn.forEach(t),jxt=r(vP,` property of the config object (either
passed as an argument or loaded from `),lQe=n(vP,"CODE",{});var cxn=s(lQe);Pxt=r(cxn,"pretrained_model_name_or_path"),cxn.forEach(t),Zxt=r(vP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),iQe=n(vP,"CODE",{});var mxn=s(iQe);Vxt=r(mxn,"pretrained_model_name_or_path"),mxn.forEach(t),Wxt=r(vP,":"),vP.forEach(t),Jxt=i(sn),B=n(sn,"UL",{});var I=s(B);F8=n(I,"LI",{});var Kwo=s(F8);dQe=n(Kwo,"STRONG",{});var fxn=s(dQe);Uxt=r(fxn,"albert"),fxn.forEach(t),Xxt=r(Kwo," \u2014 "),j3e=n(Kwo,"A",{href:!0});var hxn=s(j3e);Dxt=r(hxn,"AlbertForSequenceClassification"),hxn.forEach(t),qxt=r(Kwo," (ALBERT model)"),Kwo.forEach(t),Oxt=i(I),E8=n(I,"LI",{});var e6o=s(E8);cQe=n(e6o,"STRONG",{});var gxn=s(cQe);Qxt=r(gxn,"bart"),gxn.forEach(t),Yxt=r(e6o," \u2014 "),P3e=n(e6o,"A",{href:!0});var uxn=s(P3e);zxt=r(uxn,"BartForSequenceClassification"),uxn.forEach(t),Hxt=r(e6o," (BART model)"),e6o.forEach(t),Kxt=i(I),y8=n(I,"LI",{});var o6o=s(y8);mQe=n(o6o,"STRONG",{});var pxn=s(mQe);eGt=r(pxn,"bert"),pxn.forEach(t),oGt=r(o6o," \u2014 "),Z3e=n(o6o,"A",{href:!0});var _xn=s(Z3e);rGt=r(_xn,"BertForSequenceClassification"),_xn.forEach(t),tGt=r(o6o," (BERT model)"),o6o.forEach(t),aGt=i(I),C8=n(I,"LI",{});var r6o=s(C8);fQe=n(r6o,"STRONG",{});var vxn=s(fQe);nGt=r(vxn,"big_bird"),vxn.forEach(t),sGt=r(r6o," \u2014 "),V3e=n(r6o,"A",{href:!0});var bxn=s(V3e);lGt=r(bxn,"BigBirdForSequenceClassification"),bxn.forEach(t),iGt=r(r6o," (BigBird model)"),r6o.forEach(t),dGt=i(I),w8=n(I,"LI",{});var t6o=s(w8);hQe=n(t6o,"STRONG",{});var Mxn=s(hQe);cGt=r(Mxn,"bigbird_pegasus"),Mxn.forEach(t),mGt=r(t6o," \u2014 "),W3e=n(t6o,"A",{href:!0});var Txn=s(W3e);fGt=r(Txn,"BigBirdPegasusForSequenceClassification"),Txn.forEach(t),hGt=r(t6o," (BigBird-Pegasus model)"),t6o.forEach(t),gGt=i(I),L8=n(I,"LI",{});var a6o=s(L8);gQe=n(a6o,"STRONG",{});var Fxn=s(gQe);uGt=r(Fxn,"biogpt"),Fxn.forEach(t),pGt=r(a6o," \u2014 "),J3e=n(a6o,"A",{href:!0});var Exn=s(J3e);_Gt=r(Exn,"BioGptForSequenceClassification"),Exn.forEach(t),vGt=r(a6o," (BioGpt model)"),a6o.forEach(t),bGt=i(I),A8=n(I,"LI",{});var n6o=s(A8);uQe=n(n6o,"STRONG",{});var yxn=s(uQe);MGt=r(yxn,"bloom"),yxn.forEach(t),TGt=r(n6o," \u2014 "),U3e=n(n6o,"A",{href:!0});var Cxn=s(U3e);FGt=r(Cxn,"BloomForSequenceClassification"),Cxn.forEach(t),EGt=r(n6o," (BLOOM model)"),n6o.forEach(t),yGt=i(I),R8=n(I,"LI",{});var s6o=s(R8);pQe=n(s6o,"STRONG",{});var wxn=s(pQe);CGt=r(wxn,"camembert"),wxn.forEach(t),wGt=r(s6o," \u2014 "),X3e=n(s6o,"A",{href:!0});var Lxn=s(X3e);LGt=r(Lxn,"CamembertForSequenceClassification"),Lxn.forEach(t),AGt=r(s6o," (CamemBERT model)"),s6o.forEach(t),RGt=i(I),k8=n(I,"LI",{});var l6o=s(k8);_Qe=n(l6o,"STRONG",{});var Axn=s(_Qe);kGt=r(Axn,"canine"),Axn.forEach(t),SGt=r(l6o," \u2014 "),D3e=n(l6o,"A",{href:!0});var Rxn=s(D3e);xGt=r(Rxn,"CanineForSequenceClassification"),Rxn.forEach(t),GGt=r(l6o," (CANINE model)"),l6o.forEach(t),BGt=i(I),S8=n(I,"LI",{});var i6o=s(S8);vQe=n(i6o,"STRONG",{});var kxn=s(vQe);IGt=r(kxn,"code_llama"),kxn.forEach(t),NGt=r(i6o," \u2014 "),q3e=n(i6o,"A",{href:!0});var Sxn=s(q3e);$Gt=r(Sxn,"LlamaForSequenceClassification"),Sxn.forEach(t),jGt=r(i6o," (CodeLlama model)"),i6o.forEach(t),PGt=i(I),x8=n(I,"LI",{});var d6o=s(x8);bQe=n(d6o,"STRONG",{});var xxn=s(bQe);ZGt=r(xxn,"convbert"),xxn.forEach(t),VGt=r(d6o," \u2014 "),O3e=n(d6o,"A",{href:!0});var Gxn=s(O3e);WGt=r(Gxn,"ConvBertForSequenceClassification"),Gxn.forEach(t),JGt=r(d6o," (ConvBERT model)"),d6o.forEach(t),UGt=i(I),G8=n(I,"LI",{});var c6o=s(G8);MQe=n(c6o,"STRONG",{});var Bxn=s(MQe);XGt=r(Bxn,"ctrl"),Bxn.forEach(t),DGt=r(c6o," \u2014 "),Q3e=n(c6o,"A",{href:!0});var Ixn=s(Q3e);qGt=r(Ixn,"CTRLForSequenceClassification"),Ixn.forEach(t),OGt=r(c6o," (CTRL model)"),c6o.forEach(t),QGt=i(I),B8=n(I,"LI",{});var m6o=s(B8);TQe=n(m6o,"STRONG",{});var Nxn=s(TQe);YGt=r(Nxn,"data2vec-text"),Nxn.forEach(t),zGt=r(m6o," \u2014 "),Y3e=n(m6o,"A",{href:!0});var $xn=s(Y3e);HGt=r($xn,"Data2VecTextForSequenceClassification"),$xn.forEach(t),KGt=r(m6o," (Data2VecText model)"),m6o.forEach(t),eBt=i(I),I8=n(I,"LI",{});var f6o=s(I8);FQe=n(f6o,"STRONG",{});var jxn=s(FQe);oBt=r(jxn,"deberta"),jxn.forEach(t),rBt=r(f6o," \u2014 "),z3e=n(f6o,"A",{href:!0});var Pxn=s(z3e);tBt=r(Pxn,"DebertaForSequenceClassification"),Pxn.forEach(t),aBt=r(f6o," (DeBERTa model)"),f6o.forEach(t),nBt=i(I),N8=n(I,"LI",{});var h6o=s(N8);EQe=n(h6o,"STRONG",{});var Zxn=s(EQe);sBt=r(Zxn,"deberta-v2"),Zxn.forEach(t),lBt=r(h6o," \u2014 "),H3e=n(h6o,"A",{href:!0});var Vxn=s(H3e);iBt=r(Vxn,"DebertaV2ForSequenceClassification"),Vxn.forEach(t),dBt=r(h6o," (DeBERTa-v2 model)"),h6o.forEach(t),cBt=i(I),$8=n(I,"LI",{});var g6o=s($8);yQe=n(g6o,"STRONG",{});var Wxn=s(yQe);mBt=r(Wxn,"distilbert"),Wxn.forEach(t),fBt=r(g6o," \u2014 "),K3e=n(g6o,"A",{href:!0});var Jxn=s(K3e);hBt=r(Jxn,"DistilBertForSequenceClassification"),Jxn.forEach(t),gBt=r(g6o," (DistilBERT model)"),g6o.forEach(t),uBt=i(I),j8=n(I,"LI",{});var u6o=s(j8);CQe=n(u6o,"STRONG",{});var Uxn=s(CQe);pBt=r(Uxn,"electra"),Uxn.forEach(t),_Bt=r(u6o," \u2014 "),e1e=n(u6o,"A",{href:!0});var Xxn=s(e1e);vBt=r(Xxn,"ElectraForSequenceClassification"),Xxn.forEach(t),bBt=r(u6o," (ELECTRA model)"),u6o.forEach(t),MBt=i(I),P8=n(I,"LI",{});var p6o=s(P8);wQe=n(p6o,"STRONG",{});var Dxn=s(wQe);TBt=r(Dxn,"ernie"),Dxn.forEach(t),FBt=r(p6o," \u2014 "),o1e=n(p6o,"A",{href:!0});var qxn=s(o1e);EBt=r(qxn,"ErnieForSequenceClassification"),qxn.forEach(t),yBt=r(p6o," (ERNIE model)"),p6o.forEach(t),CBt=i(I),Z8=n(I,"LI",{});var _6o=s(Z8);LQe=n(_6o,"STRONG",{});var Oxn=s(LQe);wBt=r(Oxn,"ernie_m"),Oxn.forEach(t),LBt=r(_6o," \u2014 "),r1e=n(_6o,"A",{href:!0});var Qxn=s(r1e);ABt=r(Qxn,"ErnieMForSequenceClassification"),Qxn.forEach(t),RBt=r(_6o," (ErnieM model)"),_6o.forEach(t),kBt=i(I),V8=n(I,"LI",{});var v6o=s(V8);AQe=n(v6o,"STRONG",{});var Yxn=s(AQe);SBt=r(Yxn,"esm"),Yxn.forEach(t),xBt=r(v6o," \u2014 "),t1e=n(v6o,"A",{href:!0});var zxn=s(t1e);GBt=r(zxn,"EsmForSequenceClassification"),zxn.forEach(t),BBt=r(v6o," (ESM model)"),v6o.forEach(t),IBt=i(I),W8=n(I,"LI",{});var 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with the `),abe=n(jCe,"A",{href:!0});var tIn=s(abe);XPt=r(tIn,"from_pretrained()"),tIn.forEach(t),DPt=r(jCe," class method or the "),nbe=n(jCe,"A",{href:!0});var aIn=s(nbe);qPt=r(aIn,"from_config()"),aIn.forEach(t),OPt=r(jCe,` class
method.`),jCe.forEach(t),QPt=i(Cd),yJ=n(Cd,"P",{});var rjo=s(yJ);YPt=r(rjo,"This class cannot be instantiated directly using "),GYe=n(rjo,"CODE",{});var nIn=s(GYe);zPt=r(nIn,"__init__()"),nIn.forEach(t),HPt=r(rjo," (throws an error)."),rjo.forEach(t),KPt=i(Cd),na=n(Cd,"DIV",{class:!0});var bP=s(na);M(CJ.$$.fragment,bP),eZt=i(bP),BYe=n(bP,"P",{});var sIn=s(BYe);oZt=r(sIn,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),sIn.forEach(t),rZt=i(bP),nf=n(bP,"P",{});var PCe=s(nf);tZt=r(PCe,`Note:
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model\u2019s configuration. Use `),sbe=n(PCe,"A",{href:!0});var iIn=s(sbe);sZt=r(iIn,"from_pretrained()"),iIn.forEach(t),lZt=r(PCe," to load the model weights."),PCe.forEach(t),iZt=i(bP),M(X7.$$.fragment,bP),bP.forEach(t),dZt=i(Cd),ht=n(Cd,"DIV",{class:!0});var wd=s(ht);M(wJ.$$.fragment,wd),cZt=i(wd),NYe=n(wd,"P",{});var dIn=s(NYe);mZt=r(dIn,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),dIn.forEach(t),fZt=i(wd),rs=n(wd,"P",{});var MP=s(rs);hZt=r(MP,"The model class to instantiate is selected based on the "),$Ye=n(MP,"CODE",{});var cIn=s($Ye);gZt=r(cIn,"model_type"),cIn.forEach(t),uZt=r(MP,` property of the config object (either
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mNn=s(fze);JWt=r(mNn,"tapas"),mNn.forEach(t),UWt=r(D8o," \u2014 "),Gbe=n(D8o,"A",{href:!0});var fNn=s(Gbe);XWt=r(fNn,"TFTapasForSequenceClassification"),fNn.forEach(t),DWt=r(D8o," (TAPAS model)"),D8o.forEach(t),qWt=i(me),bL=n(me,"LI",{});var q8o=s(bL);hze=n(q8o,"STRONG",{});var hNn=s(hze);OWt=r(hNn,"transfo-xl"),hNn.forEach(t),QWt=r(q8o," \u2014 "),Bbe=n(q8o,"A",{href:!0});var gNn=s(Bbe);YWt=r(gNn,"TFTransfoXLForSequenceClassification"),gNn.forEach(t),zWt=r(q8o," (Transformer-XL model)"),q8o.forEach(t),HWt=i(me),ML=n(me,"LI",{});var O8o=s(ML);gze=n(O8o,"STRONG",{});var uNn=s(gze);KWt=r(uNn,"xlm"),uNn.forEach(t),eJt=r(O8o," \u2014 "),Ibe=n(O8o,"A",{href:!0});var pNn=s(Ibe);oJt=r(pNn,"TFXLMForSequenceClassification"),pNn.forEach(t),rJt=r(O8o," (XLM model)"),O8o.forEach(t),tJt=i(me),TL=n(me,"LI",{});var Q8o=s(TL);uze=n(Q8o,"STRONG",{});var _Nn=s(uze);aJt=r(_Nn,"xlm-roberta"),_Nn.forEach(t),nJt=r(Q8o," \u2014 "),Nbe=n(Q8o,"A",{href:!0});var vNn=s(Nbe);sJt=r(vNn,"TFXLMRobertaForSequenceClassification"),vNn.forEach(t),lJt=r(Q8o," (XLM-RoBERTa model)"),Q8o.forEach(t),iJt=i(me),FL=n(me,"LI",{});var Y8o=s(FL);pze=n(Y8o,"STRONG",{});var bNn=s(pze);dJt=r(bNn,"xlnet"),bNn.forEach(t),cJt=r(Y8o," \u2014 "),$be=n(Y8o,"A",{href:!0});var MNn=s($be);mJt=r(MNn,"TFXLNetForSequenceClassification"),MNn.forEach(t),fJt=r(Y8o," (XLNet model)"),Y8o.forEach(t),me.forEach(t),hJt=i(wd),M(EL.$$.fragment,wd),wd.forEach(t),Cd.forEach(t),aIo=i(m),sf=n(m,"H3",{class:!0});var tjo=s(sf);yL=n(tjo,"A",{id:!0,class:!0,href:!0});var TNn=s(yL);_ze=n(TNn,"SPAN",{});var FNn=s(_ze);M(LJ.$$.fragment,FNn),FNn.forEach(t),TNn.forEach(t),gJt=i(tjo),vze=n(tjo,"SPAN",{});var ENn=s(vze);uJt=r(ENn,"FlaxAutoModelForSequenceClassification"),ENn.forEach(t),tjo.forEach(t),nIo=i(m),lr=n(m,"DIV",{class:!0});var Ld=s(lr);M(AJ.$$.fragment,Ld),pJt=i(Ld),lf=n(Ld,"P",{});var ZCe=s(lf);_Jt=r(ZCe,`This is a generic model class that will be instantiated as one of the model classes of 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with the `),jbe=n(ZCe,"A",{href:!0});var yNn=s(jbe);vJt=r(yNn,"from_pretrained()"),yNn.forEach(t),bJt=r(ZCe," class method or the "),Pbe=n(ZCe,"A",{href:!0});var CNn=s(Pbe);MJt=r(CNn,"from_config()"),CNn.forEach(t),TJt=r(ZCe,` class
method.`),ZCe.forEach(t),FJt=i(Ld),RJ=n(Ld,"P",{});var ajo=s(RJ);EJt=r(ajo,"This class cannot be instantiated directly using "),bze=n(ajo,"CODE",{});var wNn=s(bze);yJt=r(wNn,"__init__()"),wNn.forEach(t),CJt=r(ajo," (throws an error)."),ajo.forEach(t),wJt=i(Ld),sa=n(Ld,"DIV",{class:!0});var TP=s(sa);M(kJ.$$.fragment,TP),LJt=i(TP),Mze=n(TP,"P",{});var LNn=s(Mze);AJt=r(LNn,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),LNn.forEach(t),RJt=i(TP),df=n(TP,"P",{});var VCe=s(df);kJt=r(VCe,`Note:
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model\u2019s configuration. Use `),Zbe=n(VCe,"A",{href:!0});var RNn=s(Zbe);GJt=r(RNn,"from_pretrained()"),RNn.forEach(t),BJt=r(VCe," to load the model weights."),VCe.forEach(t),IJt=i(TP),M(CL.$$.fragment,TP),TP.forEach(t),NJt=i(Ld),gt=n(Ld,"DIV",{class:!0});var Ad=s(gt);M(SJ.$$.fragment,Ad),$Jt=i(Ad),Fze=n(Ad,"P",{});var kNn=s(Fze);jJt=r(kNn,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),kNn.forEach(t),PJt=i(Ad),ts=n(Ad,"P",{});var FP=s(ts);ZJt=r(FP,"The model class to instantiate is selected based on the "),Eze=n(FP,"CODE",{});var SNn=s(Eze);VJt=r(SNn,"model_type"),SNn.forEach(t),WJt=r(FP,` property of the config object (either
passed as an argument or loaded from `),yze=n(FP,"CODE",{});var xNn=s(yze);JJt=r(xNn,"pretrained_model_name_or_path"),xNn.forEach(t),UJt=r(FP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Cze=n(FP,"CODE",{});var GNn=s(Cze);XJt=r(GNn,"pretrained_model_name_or_path"),GNn.forEach(t),DJt=r(FP,":"),FP.forEach(t),qJt=i(Ad),Se=n(Ad,"UL",{});var Ze=s(Se);wL=n(Ze,"LI",{});var z8o=s(wL);wze=n(z8o,"STRONG",{});var BNn=s(wze);OJt=r(BNn,"albert"),BNn.forEach(t),QJt=r(z8o," \u2014 "),Vbe=n(z8o,"A",{href:!0});var INn=s(Vbe);YJt=r(INn,"FlaxAlbertForSequenceClassification"),INn.forEach(t),zJt=r(z8o," (ALBERT model)"),z8o.forEach(t),HJt=i(Ze),LL=n(Ze,"LI",{});var H8o=s(LL);Lze=n(H8o,"STRONG",{});var NNn=s(Lze);KJt=r(NNn,"bart"),NNn.forEach(t),eUt=r(H8o," \u2014 "),Wbe=n(H8o,"A",{href:!0});var $Nn=s(Wbe);oUt=r($Nn,"FlaxBartForSequenceClassification"),$Nn.forEach(t),rUt=r(H8o," (BART model)"),H8o.forEach(t),tUt=i(Ze),AL=n(Ze,"LI",{});var K8o=s(AL);Aze=n(K8o,"STRONG",{});var jNn=s(Aze);aUt=r(jNn,"bert"),jNn.forEach(t),nUt=r(K8o," \u2014 "),Jbe=n(K8o,"A",{href:!0});var PNn=s(Jbe);sUt=r(PNn,"FlaxBertForSequenceClassification"),PNn.forEach(t),lUt=r(K8o," (BERT model)"),K8o.forEach(t),iUt=i(Ze),RL=n(Ze,"LI",{});var e7o=s(RL);Rze=n(e7o,"STRONG",{});var ZNn=s(Rze);dUt=r(ZNn,"big_bird"),ZNn.forEach(t),cUt=r(e7o," \u2014 "),Ube=n(e7o,"A",{href:!0});var VNn=s(Ube);mUt=r(VNn,"FlaxBigBirdForSequenceClassification"),VNn.forEach(t),fUt=r(e7o," (BigBird model)"),e7o.forEach(t),hUt=i(Ze),kL=n(Ze,"LI",{});var o7o=s(kL);kze=n(o7o,"STRONG",{});var WNn=s(kze);gUt=r(WNn,"distilbert"),WNn.forEach(t),uUt=r(o7o," \u2014 "),Xbe=n(o7o,"A",{href:!0});var JNn=s(Xbe);pUt=r(JNn,"FlaxDistilBertForSequenceClassification"),JNn.forEach(t),_Ut=r(o7o," (DistilBERT model)"),o7o.forEach(t),vUt=i(Ze),SL=n(Ze,"LI",{});var r7o=s(SL);Sze=n(r7o,"STRONG",{});var UNn=s(Sze);bUt=r(UNn,"electra"),UNn.forEach(t),MUt=r(r7o," \u2014 "),Dbe=n(r7o,"A",{href:!0});var XNn=s(Dbe);TUt=r(XNn,"FlaxElectraForSequenceClassification"),XNn.forEach(t),FUt=r(r7o," (ELECTRA model)"),r7o.forEach(t),EUt=i(Ze),xL=n(Ze,"LI",{});var t7o=s(xL);xze=n(t7o,"STRONG",{});var DNn=s(xze);yUt=r(DNn,"mbart"),DNn.forEach(t),CUt=r(t7o," \u2014 "),qbe=n(t7o,"A",{href:!0});var qNn=s(qbe);wUt=r(qNn,"FlaxMBartForSequenceClassification"),qNn.forEach(t),LUt=r(t7o," (mBART model)"),t7o.forEach(t),AUt=i(Ze),GL=n(Ze,"LI",{});var a7o=s(GL);Gze=n(a7o,"STRONG",{});var ONn=s(Gze);RUt=r(ONn,"roberta"),ONn.forEach(t),kUt=r(a7o," \u2014 "),Obe=n(a7o,"A",{href:!0});var QNn=s(Obe);SUt=r(QNn,"FlaxRobertaForSequenceClassification"),QNn.forEach(t),xUt=r(a7o," (RoBERTa model)"),a7o.forEach(t),GUt=i(Ze),BL=n(Ze,"LI",{});var n7o=s(BL);Bze=n(n7o,"STRONG",{});var YNn=s(Bze);BUt=r(YNn,"roberta-prelayernorm"),YNn.forEach(t),IUt=r(n7o," \u2014 "),Qbe=n(n7o,"A",{href:!0});var zNn=s(Qbe);NUt=r(zNn,"FlaxRobertaPreLayerNormForSequenceClassification"),zNn.forEach(t),$Ut=r(n7o," (RoBERTa-PreLayerNorm model)"),n7o.forEach(t),jUt=i(Ze),IL=n(Ze,"LI",{});var s7o=s(IL);Ize=n(s7o,"STRONG",{});var HNn=s(Ize);PUt=r(HNn,"roformer"),HNn.forEach(t),ZUt=r(s7o," \u2014 "),Ybe=n(s7o,"A",{href:!0});var KNn=s(Ybe);VUt=r(KNn,"FlaxRoFormerForSequenceClassification"),KNn.forEach(t),WUt=r(s7o," (RoFormer model)"),s7o.forEach(t),JUt=i(Ze),NL=n(Ze,"LI",{});var l7o=s(NL);Nze=n(l7o,"STRONG",{});var e$n=s(Nze);UUt=r(e$n,"xlm-roberta"),e$n.forEach(t),XUt=r(l7o," \u2014 "),zbe=n(l7o,"A",{href:!0});var o$n=s(zbe);DUt=r(o$n,"FlaxXLMRobertaForSequenceClassification"),o$n.forEach(t),qUt=r(l7o," (XLM-RoBERTa model)"),l7o.forEach(t),Ze.forEach(t),OUt=i(Ad),M($L.$$.fragment,Ad),Ad.forEach(t),Ld.forEach(t),sIo=i(m),cf=n(m,"H3",{class:!0});var njo=s(cf);jL=n(njo,"A",{id:!0,class:!0,href:!0});var r$n=s(jL);$ze=n(r$n,"SPAN",{});var t$n=s($ze);M(xJ.$$.fragment,t$n),t$n.forEach(t),r$n.forEach(t),QUt=i(njo),jze=n(njo,"SPAN",{});var a$n=s(jze);YUt=r(a$n,"AutoModelForMultipleChoice"),a$n.forEach(t),njo.forEach(t),lIo=i(m),ir=n(m,"DIV",{class:!0});var Rd=s(ir);M(GJ.$$.fragment,Rd),zUt=i(Rd),mf=n(Rd,"P",{});var WCe=s(mf);HUt=r(WCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
with the `),Hbe=n(WCe,"A",{href:!0});var n$n=s(Hbe);KUt=r(n$n,"from_pretrained()"),n$n.forEach(t),eXt=r(WCe," class method or the "),Kbe=n(WCe,"A",{href:!0});var s$n=s(Kbe);oXt=r(s$n,"from_config()"),s$n.forEach(t),rXt=r(WCe,` class
method.`),WCe.forEach(t),tXt=i(Rd),BJ=n(Rd,"P",{});var sjo=s(BJ);aXt=r(sjo,"This class cannot be instantiated directly using "),Pze=n(sjo,"CODE",{});var l$n=s(Pze);nXt=r(l$n,"__init__()"),l$n.forEach(t),sXt=r(sjo," (throws an error)."),sjo.forEach(t),lXt=i(Rd),la=n(Rd,"DIV",{class:!0});var EP=s(la);M(IJ.$$.fragment,EP),iXt=i(EP),Zze=n(EP,"P",{});var i$n=s(Zze);dXt=r(i$n,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),i$n.forEach(t),cXt=i(EP),ff=n(EP,"P",{});var JCe=s(ff);mXt=r(JCe,`Note:
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falling back to using pattern matching on `),Xze=n(yP,"CODE",{});var g$n=s(Xze);wXt=r(g$n,"pretrained_model_name_or_path"),g$n.forEach(t),LXt=r(yP,":"),yP.forEach(t),AXt=i(ln),ae=n(ln,"UL",{});var ne=s(ae);ZL=n(ne,"LI",{});var i7o=s(ZL);Dze=n(i7o,"STRONG",{});var u$n=s(Dze);RXt=r(u$n,"albert"),u$n.forEach(t),kXt=r(i7o," \u2014 "),o2e=n(i7o,"A",{href:!0});var p$n=s(o2e);SXt=r(p$n,"AlbertForMultipleChoice"),p$n.forEach(t),xXt=r(i7o," (ALBERT model)"),i7o.forEach(t),GXt=i(ne),VL=n(ne,"LI",{});var d7o=s(VL);qze=n(d7o,"STRONG",{});var _$n=s(qze);BXt=r(_$n,"bert"),_$n.forEach(t),IXt=r(d7o," \u2014 "),r2e=n(d7o,"A",{href:!0});var v$n=s(r2e);NXt=r(v$n,"BertForMultipleChoice"),v$n.forEach(t),$Xt=r(d7o," (BERT model)"),d7o.forEach(t),jXt=i(ne),WL=n(ne,"LI",{});var c7o=s(WL);Oze=n(c7o,"STRONG",{});var b$n=s(Oze);PXt=r(b$n,"big_bird"),b$n.forEach(t),ZXt=r(c7o," \u2014 "),t2e=n(c7o,"A",{href:!0});var M$n=s(t2e);VXt=r(M$n,"BigBirdForMultipleChoice"),M$n.forEach(t),WXt=r(c7o," (BigBird 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\u2014 "),O2e=n(K7o,"A",{href:!0});var mPn=s(O2e);MYt=r(mPn,"TFFlaubertForMultipleChoice"),mPn.forEach(t),TYt=r(K7o," (FlauBERT model)"),K7o.forEach(t),FYt=i(ye),IA=n(ye,"LI",{});var eLo=s(IA);OHe=n(eLo,"STRONG",{});var fPn=s(OHe);EYt=r(fPn,"funnel"),fPn.forEach(t),yYt=r(eLo," \u2014 "),Q2e=n(eLo,"A",{href:!0});var hPn=s(Q2e);CYt=r(hPn,"TFFunnelForMultipleChoice"),hPn.forEach(t),wYt=r(eLo," (Funnel Transformer model)"),eLo.forEach(t),LYt=i(ye),NA=n(ye,"LI",{});var oLo=s(NA);QHe=n(oLo,"STRONG",{});var gPn=s(QHe);AYt=r(gPn,"longformer"),gPn.forEach(t),RYt=r(oLo," \u2014 "),Y2e=n(oLo,"A",{href:!0});var uPn=s(Y2e);kYt=r(uPn,"TFLongformerForMultipleChoice"),uPn.forEach(t),SYt=r(oLo," (Longformer model)"),oLo.forEach(t),xYt=i(ye),$A=n(ye,"LI",{});var rLo=s($A);YHe=n(rLo,"STRONG",{});var pPn=s(YHe);GYt=r(pPn,"mobilebert"),pPn.forEach(t),BYt=r(rLo," \u2014 "),z2e=n(rLo,"A",{href:!0});var _Pn=s(z2e);IYt=r(_Pn,"TFMobileBertForMultipleChoice"),_Pn.forEach(t),NYt=r(rLo," (MobileBERT 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"),o0e=n(sLo,"A",{href:!0});var CPn=s(o0e);ozt=r(CPn,"TFRobertaPreLayerNormForMultipleChoice"),CPn.forEach(t),rzt=r(sLo," (RoBERTa-PreLayerNorm model)"),sLo.forEach(t),tzt=i(ye),WA=n(ye,"LI",{});var lLo=s(WA);oKe=n(lLo,"STRONG",{});var wPn=s(oKe);azt=r(wPn,"roformer"),wPn.forEach(t),nzt=r(lLo," \u2014 "),r0e=n(lLo,"A",{href:!0});var LPn=s(r0e);szt=r(LPn,"TFRoFormerForMultipleChoice"),LPn.forEach(t),lzt=r(lLo," (RoFormer model)"),lLo.forEach(t),izt=i(ye),JA=n(ye,"LI",{});var iLo=s(JA);rKe=n(iLo,"STRONG",{});var APn=s(rKe);dzt=r(APn,"xlm"),APn.forEach(t),czt=r(iLo," \u2014 "),t0e=n(iLo,"A",{href:!0});var RPn=s(t0e);mzt=r(RPn,"TFXLMForMultipleChoice"),RPn.forEach(t),fzt=r(iLo," (XLM model)"),iLo.forEach(t),hzt=i(ye),UA=n(ye,"LI",{});var dLo=s(UA);tKe=n(dLo,"STRONG",{});var kPn=s(tKe);gzt=r(kPn,"xlm-roberta"),kPn.forEach(t),uzt=r(dLo," \u2014 "),a0e=n(dLo,"A",{href:!0});var SPn=s(a0e);pzt=r(SPn,"TFXLMRobertaForMultipleChoice"),SPn.forEach(t),_zt=r(dLo," (XLM-RoBERTa 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with the `),s0e=n(DCe,"A",{href:!0});var $Pn=s(s0e);Azt=r($Pn,"from_pretrained()"),$Pn.forEach(t),Rzt=r(DCe," class method or the "),l0e=n(DCe,"A",{href:!0});var jPn=s(l0e);kzt=r(jPn,"from_config()"),jPn.forEach(t),Szt=r(DCe,` class
method.`),DCe.forEach(t),xzt=i(xd),UJ=n(xd,"P",{});var cjo=s(UJ);Gzt=r(cjo,"This class cannot be instantiated directly using "),lKe=n(cjo,"CODE",{});var PPn=s(lKe);Bzt=r(PPn,"__init__()"),PPn.forEach(t),Izt=r(cjo," (throws an error)."),cjo.forEach(t),Nzt=i(xd),da=n(xd,"DIV",{class:!0});var LP=s(da);M(XJ.$$.fragment,LP),$zt=i(LP),iKe=n(LP,"P",{});var ZPn=s(iKe);jzt=r(ZPn,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),ZPn.forEach(t),Pzt=i(LP),vf=n(LP,"P",{});var qCe=s(vf);Zzt=r(qCe,`Note:
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model\u2019s configuration. Use `),i0e=n(qCe,"A",{href:!0});var WPn=s(i0e);Jzt=r(WPn,"from_pretrained()"),WPn.forEach(t),Uzt=r(qCe," to load the model weights."),qCe.forEach(t),Xzt=i(LP),M(OA.$$.fragment,LP),LP.forEach(t),Dzt=i(xd),pt=n(xd,"DIV",{class:!0});var Gd=s(pt);M(DJ.$$.fragment,Gd),qzt=i(Gd),cKe=n(Gd,"P",{});var JPn=s(cKe);Ozt=r(JPn,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),JPn.forEach(t),Qzt=i(Gd),ss=n(Gd,"P",{});var AP=s(ss);Yzt=r(AP,"The model class to instantiate is selected based on the "),mKe=n(AP,"CODE",{});var UPn=s(mKe);zzt=r(UPn,"model_type"),UPn.forEach(t),Hzt=r(AP,` property of the config object (either
passed as an argument or loaded from `),fKe=n(AP,"CODE",{});var XPn=s(fKe);Kzt=r(XPn,"pretrained_model_name_or_path"),XPn.forEach(t),eHt=r(AP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),hKe=n(AP,"CODE",{});var DPn=s(hKe);oHt=r(DPn,"pretrained_model_name_or_path"),DPn.forEach(t),rHt=r(AP,":"),AP.forEach(t),tHt=i(Gd),Ue=n(Gd,"UL",{});var He=s(Ue);QA=n(He,"LI",{});var mLo=s(QA);gKe=n(mLo,"STRONG",{});var qPn=s(gKe);aHt=r(qPn,"albert"),qPn.forEach(t),nHt=r(mLo," \u2014 "),d0e=n(mLo,"A",{href:!0});var OPn=s(d0e);sHt=r(OPn,"FlaxAlbertForMultipleChoice"),OPn.forEach(t),lHt=r(mLo," (ALBERT model)"),mLo.forEach(t),iHt=i(He),YA=n(He,"LI",{});var fLo=s(YA);uKe=n(fLo,"STRONG",{});var QPn=s(uKe);dHt=r(QPn,"bert"),QPn.forEach(t),cHt=r(fLo," \u2014 "),c0e=n(fLo,"A",{href:!0});var YPn=s(c0e);mHt=r(YPn,"FlaxBertForMultipleChoice"),YPn.forEach(t),fHt=r(fLo," (BERT model)"),fLo.forEach(t),hHt=i(He),zA=n(He,"LI",{});var hLo=s(zA);pKe=n(hLo,"STRONG",{});var zPn=s(pKe);gHt=r(zPn,"big_bird"),zPn.forEach(t),uHt=r(hLo," \u2014 "),m0e=n(hLo,"A",{href:!0});var HPn=s(m0e);pHt=r(HPn,"FlaxBigBirdForMultipleChoice"),HPn.forEach(t),_Ht=r(hLo," (BigBird model)"),hLo.forEach(t),vHt=i(He),HA=n(He,"LI",{});var gLo=s(HA);_Ke=n(gLo,"STRONG",{});var KPn=s(_Ke);bHt=r(KPn,"distilbert"),KPn.forEach(t),MHt=r(gLo," \u2014 "),f0e=n(gLo,"A",{href:!0});var eZn=s(f0e);THt=r(eZn,"FlaxDistilBertForMultipleChoice"),eZn.forEach(t),FHt=r(gLo," (DistilBERT model)"),gLo.forEach(t),EHt=i(He),KA=n(He,"LI",{});var uLo=s(KA);vKe=n(uLo,"STRONG",{});var oZn=s(vKe);yHt=r(oZn,"electra"),oZn.forEach(t),CHt=r(uLo," \u2014 "),h0e=n(uLo,"A",{href:!0});var rZn=s(h0e);wHt=r(rZn,"FlaxElectraForMultipleChoice"),rZn.forEach(t),LHt=r(uLo," (ELECTRA model)"),uLo.forEach(t),AHt=i(He),eR=n(He,"LI",{});var pLo=s(eR);bKe=n(pLo,"STRONG",{});var tZn=s(bKe);RHt=r(tZn,"roberta"),tZn.forEach(t),kHt=r(pLo," \u2014 "),g0e=n(pLo,"A",{href:!0});var aZn=s(g0e);SHt=r(aZn,"FlaxRobertaForMultipleChoice"),aZn.forEach(t),xHt=r(pLo," (RoBERTa model)"),pLo.forEach(t),GHt=i(He),oR=n(He,"LI",{});var _Lo=s(oR);MKe=n(_Lo,"STRONG",{});var nZn=s(MKe);BHt=r(nZn,"roberta-prelayernorm"),nZn.forEach(t),IHt=r(_Lo," \u2014 "),u0e=n(_Lo,"A",{href:!0});var sZn=s(u0e);NHt=r(sZn,"FlaxRobertaPreLayerNormForMultipleChoice"),sZn.forEach(t),$Ht=r(_Lo," (RoBERTa-PreLayerNorm model)"),_Lo.forEach(t),jHt=i(He),rR=n(He,"LI",{});var vLo=s(rR);TKe=n(vLo,"STRONG",{});var lZn=s(TKe);PHt=r(lZn,"roformer"),lZn.forEach(t),ZHt=r(vLo," \u2014 "),p0e=n(vLo,"A",{href:!0});var iZn=s(p0e);VHt=r(iZn,"FlaxRoFormerForMultipleChoice"),iZn.forEach(t),WHt=r(vLo," (RoFormer model)"),vLo.forEach(t),JHt=i(He),tR=n(He,"LI",{});var bLo=s(tR);FKe=n(bLo,"STRONG",{});var dZn=s(FKe);UHt=r(dZn,"xlm-roberta"),dZn.forEach(t),XHt=r(bLo," \u2014 "),_0e=n(bLo,"A",{href:!0});var cZn=s(_0e);DHt=r(cZn,"FlaxXLMRobertaForMultipleChoice"),cZn.forEach(t),qHt=r(bLo," (XLM-RoBERTa model)"),bLo.forEach(t),He.forEach(t),OHt=i(Gd),M(aR.$$.fragment,Gd),Gd.forEach(t),xd.forEach(t),fIo=i(m),bf=n(m,"H3",{class:!0});var mjo=s(bf);nR=n(mjo,"A",{id:!0,class:!0,href:!0});var mZn=s(nR);EKe=n(mZn,"SPAN",{});var fZn=s(EKe);M(qJ.$$.fragment,fZn),fZn.forEach(t),mZn.forEach(t),QHt=i(mjo),yKe=n(mjo,"SPAN",{});var hZn=s(yKe);YHt=r(hZn,"AutoModelForNextSentencePrediction"),hZn.forEach(t),mjo.forEach(t),hIo=i(m),mr=n(m,"DIV",{class:!0});var Bd=s(mr);M(OJ.$$.fragment,Bd),zHt=i(Bd),Mf=n(Bd,"P",{});var OCe=s(Mf);HHt=r(OCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
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method.`),OCe.forEach(t),tKt=i(Bd),QJ=n(Bd,"P",{});var fjo=s(QJ);aKt=r(fjo,"This class cannot be instantiated directly using "),CKe=n(fjo,"CODE",{});var pZn=s(CKe);nKt=r(pZn,"__init__()"),pZn.forEach(t),sKt=r(fjo," (throws an error)."),fjo.forEach(t),lKt=i(Bd),ca=n(Bd,"DIV",{class:!0});var RP=s(ca);M(YJ.$$.fragment,RP),iKt=i(RP),wKe=n(RP,"P",{});var _Zn=s(wKe);dKt=r(_Zn,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),_Zn.forEach(t),cKt=i(RP),Tf=n(RP,"P",{});var QCe=s(Tf);mKt=r(QCe,`Note:
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model\u2019s configuration. Use `),M0e=n(QCe,"A",{href:!0});var bZn=s(M0e);gKt=r(bZn,"from_pretrained()"),bZn.forEach(t),uKt=r(QCe," to load the model weights."),QCe.forEach(t),pKt=i(RP),M(sR.$$.fragment,RP),RP.forEach(t),_Kt=i(Bd),go=n(Bd,"DIV",{class:!0});var dn=s(go);M(zJ.$$.fragment,dn),vKt=i(dn),AKe=n(dn,"P",{});var MZn=s(AKe);bKt=r(MZn,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),MZn.forEach(t),MKt=i(dn),ls=n(dn,"P",{});var kP=s(ls);TKt=r(kP,"The model class to instantiate is selected based on the "),RKe=n(kP,"CODE",{});var TZn=s(RKe);FKt=r(TZn,"model_type"),TZn.forEach(t),EKt=r(kP,` property of the config object (either
passed as an argument or loaded from `),kKe=n(kP,"CODE",{});var FZn=s(kKe);yKt=r(FZn,"pretrained_model_name_or_path"),FZn.forEach(t),CKt=r(kP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),SKe=n(kP,"CODE",{});var EZn=s(SKe);wKt=r(EZn,"pretrained_model_name_or_path"),EZn.forEach(t),LKt=r(kP,":"),kP.forEach(t),AKt=i(dn),eo=n(dn,"UL",{});var It=s(eo);lR=n(It,"LI",{});var MLo=s(lR);xKe=n(MLo,"STRONG",{});var yZn=s(xKe);RKt=r(yZn,"bert"),yZn.forEach(t),kKt=r(MLo," \u2014 "),T0e=n(MLo,"A",{href:!0});var CZn=s(T0e);SKt=r(CZn,"BertForNextSentencePrediction"),CZn.forEach(t),xKt=r(MLo," (BERT model)"),MLo.forEach(t),GKt=i(It),iR=n(It,"LI",{});var TLo=s(iR);GKe=n(TLo,"STRONG",{});var wZn=s(GKe);BKt=r(wZn,"ernie"),wZn.forEach(t),IKt=r(TLo," \u2014 "),F0e=n(TLo,"A",{href:!0});var LZn=s(F0e);NKt=r(LZn,"ErnieForNextSentencePrediction"),LZn.forEach(t),$Kt=r(TLo," (ERNIE model)"),TLo.forEach(t),jKt=i(It),dR=n(It,"LI",{});var FLo=s(dR);BKe=n(FLo,"STRONG",{});var AZn=s(BKe);PKt=r(AZn,"fnet"),AZn.forEach(t),ZKt=r(FLo," \u2014 "),E0e=n(FLo,"A",{href:!0});var RZn=s(E0e);VKt=r(RZn,"FNetForNextSentencePrediction"),RZn.forEach(t),WKt=r(FLo," (FNet model)"),FLo.forEach(t),JKt=i(It),cR=n(It,"LI",{});var ELo=s(cR);IKe=n(ELo,"STRONG",{});var kZn=s(IKe);UKt=r(kZn,"megatron-bert"),kZn.forEach(t),XKt=r(ELo," \u2014 "),y0e=n(ELo,"A",{href:!0});var SZn=s(y0e);DKt=r(SZn,"MegatronBertForNextSentencePrediction"),SZn.forEach(t),qKt=r(ELo," (Megatron-BERT model)"),ELo.forEach(t),OKt=i(It),mR=n(It,"LI",{});var yLo=s(mR);NKe=n(yLo,"STRONG",{});var xZn=s(NKe);QKt=r(xZn,"mobilebert"),xZn.forEach(t),YKt=r(yLo," \u2014 "),C0e=n(yLo,"A",{href:!0});var GZn=s(C0e);zKt=r(GZn,"MobileBertForNextSentencePrediction"),GZn.forEach(t),HKt=r(yLo," (MobileBERT model)"),yLo.forEach(t),KKt=i(It),fR=n(It,"LI",{});var CLo=s(fR);$Ke=n(CLo,"STRONG",{});var BZn=s($Ke);eea=r(BZn,"nezha"),BZn.forEach(t),oea=r(CLo," \u2014 "),w0e=n(CLo,"A",{href:!0});var IZn=s(w0e);rea=r(IZn,"NezhaForNextSentencePrediction"),IZn.forEach(t),tea=r(CLo," (Nezha model)"),CLo.forEach(t),aea=i(It),hR=n(It,"LI",{});var wLo=s(hR);jKe=n(wLo,"STRONG",{});var NZn=s(jKe);nea=r(NZn,"qdqbert"),NZn.forEach(t),sea=r(wLo," \u2014 "),L0e=n(wLo,"A",{href:!0});var $Zn=s(L0e);lea=r($Zn,"QDQBertForNextSentencePrediction"),$Zn.forEach(t),iea=r(wLo," (QDQBert model)"),wLo.forEach(t),It.forEach(t),dea=i(dn),gR=n(dn,"P",{});var LLo=s(gR);cea=r(LLo,"The model is set in evaluation mode by default using "),PKe=n(LLo,"CODE",{});var jZn=s(PKe);mea=r(jZn,"model.eval()"),jZn.forEach(t),fea=r(LLo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),ZKe=n(LLo,"CODE",{});var PZn=s(ZKe);hea=r(PZn,"model.train()"),PZn.forEach(t),LLo.forEach(t),gea=i(dn),M(uR.$$.fragment,dn),dn.forEach(t),Bd.forEach(t),gIo=i(m),Ff=n(m,"H3",{class:!0});var hjo=s(Ff);pR=n(hjo,"A",{id:!0,class:!0,href:!0});var ZZn=s(pR);VKe=n(ZZn,"SPAN",{});var VZn=s(VKe);M(HJ.$$.fragment,VZn),VZn.forEach(t),ZZn.forEach(t),uea=i(hjo),WKe=n(hjo,"SPAN",{});var WZn=s(WKe);pea=r(WZn,"TFAutoModelForNextSentencePrediction"),WZn.forEach(t),hjo.forEach(t),uIo=i(m),fr=n(m,"DIV",{class:!0});var Id=s(fr);M(KJ.$$.fragment,Id),_ea=i(Id),Ef=n(Id,"P",{});var YCe=s(Ef);vea=r(YCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),A0e=n(YCe,"A",{href:!0});var JZn=s(A0e);bea=r(JZn,"from_pretrained()"),JZn.forEach(t),Mea=r(YCe," class method or the "),R0e=n(YCe,"A",{href:!0});var UZn=s(R0e);Tea=r(UZn,"from_config()"),UZn.forEach(t),Fea=r(YCe,` class
method.`),YCe.forEach(t),Eea=i(Id),eU=n(Id,"P",{});var gjo=s(eU);yea=r(gjo,"This class cannot be instantiated directly using "),JKe=n(gjo,"CODE",{});var XZn=s(JKe);Cea=r(XZn,"__init__()"),XZn.forEach(t),wea=r(gjo," (throws an error)."),gjo.forEach(t),Lea=i(Id),ma=n(Id,"DIV",{class:!0});var SP=s(ma);M(oU.$$.fragment,SP),Aea=i(SP),UKe=n(SP,"P",{});var DZn=s(UKe);Rea=r(DZn,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),DZn.forEach(t),kea=i(SP),yf=n(SP,"P",{});var zCe=s(yf);Sea=r(zCe,`Note:
Loading a model from its configuration file does `),XKe=n(zCe,"STRONG",{});var qZn=s(XKe);xea=r(qZn,"not"),qZn.forEach(t),Gea=r(zCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),k0e=n(zCe,"A",{href:!0});var OZn=s(k0e);Bea=r(OZn,"from_pretrained()"),OZn.forEach(t),Iea=r(zCe," to load the model weights."),zCe.forEach(t),Nea=i(SP),M(_R.$$.fragment,SP),SP.forEach(t),$ea=i(Id),_t=n(Id,"DIV",{class:!0});var Nd=s(_t);M(rU.$$.fragment,Nd),jea=i(Nd),DKe=n(Nd,"P",{});var QZn=s(DKe);Pea=r(QZn,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),QZn.forEach(t),Zea=i(Nd),is=n(Nd,"P",{});var xP=s(is);Vea=r(xP,"The model class to instantiate is selected based on the "),qKe=n(xP,"CODE",{});var YZn=s(qKe);Wea=r(YZn,"model_type"),YZn.forEach(t),Jea=r(xP,` property of the config object (either
passed as an argument or loaded from `),OKe=n(xP,"CODE",{});var zZn=s(OKe);Uea=r(zZn,"pretrained_model_name_or_path"),zZn.forEach(t),Xea=r(xP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),QKe=n(xP,"CODE",{});var HZn=s(QKe);Dea=r(HZn,"pretrained_model_name_or_path"),HZn.forEach(t),qea=r(xP,":"),xP.forEach(t),Oea=i(Nd),tU=n(Nd,"UL",{});var ujo=s(tU);vR=n(ujo,"LI",{});var ALo=s(vR);YKe=n(ALo,"STRONG",{});var KZn=s(YKe);Qea=r(KZn,"bert"),KZn.forEach(t),Yea=r(ALo," \u2014 "),S0e=n(ALo,"A",{href:!0});var eVn=s(S0e);zea=r(eVn,"TFBertForNextSentencePrediction"),eVn.forEach(t),Hea=r(ALo," (BERT model)"),ALo.forEach(t),Kea=i(ujo),bR=n(ujo,"LI",{});var RLo=s(bR);zKe=n(RLo,"STRONG",{});var oVn=s(zKe);eoa=r(oVn,"mobilebert"),oVn.forEach(t),ooa=r(RLo," \u2014 "),x0e=n(RLo,"A",{href:!0});var rVn=s(x0e);roa=r(rVn,"TFMobileBertForNextSentencePrediction"),rVn.forEach(t),toa=r(RLo," (MobileBERT model)"),RLo.forEach(t),ujo.forEach(t),aoa=i(Nd),M(MR.$$.fragment,Nd),Nd.forEach(t),Id.forEach(t),pIo=i(m),Cf=n(m,"H3",{class:!0});var pjo=s(Cf);TR=n(pjo,"A",{id:!0,class:!0,href:!0});var tVn=s(TR);HKe=n(tVn,"SPAN",{});var aVn=s(HKe);M(aU.$$.fragment,aVn),aVn.forEach(t),tVn.forEach(t),noa=i(pjo),KKe=n(pjo,"SPAN",{});var nVn=s(KKe);soa=r(nVn,"FlaxAutoModelForNextSentencePrediction"),nVn.forEach(t),pjo.forEach(t),_Io=i(m),hr=n(m,"DIV",{class:!0});var $d=s(hr);M(nU.$$.fragment,$d),loa=i($d),wf=n($d,"P",{});var HCe=s(wf);ioa=r(HCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),G0e=n(HCe,"A",{href:!0});var sVn=s(G0e);doa=r(sVn,"from_pretrained()"),sVn.forEach(t),coa=r(HCe," class method or the "),B0e=n(HCe,"A",{href:!0});var lVn=s(B0e);moa=r(lVn,"from_config()"),lVn.forEach(t),foa=r(HCe,` class
method.`),HCe.forEach(t),hoa=i($d),sU=n($d,"P",{});var _jo=s(sU);goa=r(_jo,"This class cannot be instantiated directly using "),eeo=n(_jo,"CODE",{});var iVn=s(eeo);uoa=r(iVn,"__init__()"),iVn.forEach(t),poa=r(_jo," (throws an error)."),_jo.forEach(t),_oa=i($d),fa=n($d,"DIV",{class:!0});var GP=s(fa);M(lU.$$.fragment,GP),voa=i(GP),oeo=n(GP,"P",{});var dVn=s(oeo);boa=r(dVn,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),dVn.forEach(t),Moa=i(GP),Lf=n(GP,"P",{});var KCe=s(Lf);Toa=r(KCe,`Note:
Loading a model from its configuration file does `),reo=n(KCe,"STRONG",{});var cVn=s(reo);Foa=r(cVn,"not"),cVn.forEach(t),Eoa=r(KCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),I0e=n(KCe,"A",{href:!0});var mVn=s(I0e);yoa=r(mVn,"from_pretrained()"),mVn.forEach(t),Coa=r(KCe," to load the model weights."),KCe.forEach(t),woa=i(GP),M(FR.$$.fragment,GP),GP.forEach(t),Loa=i($d),vt=n($d,"DIV",{class:!0});var jd=s(vt);M(iU.$$.fragment,jd),Aoa=i(jd),teo=n(jd,"P",{});var fVn=s(teo);Roa=r(fVn,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),fVn.forEach(t),koa=i(jd),ds=n(jd,"P",{});var BP=s(ds);Soa=r(BP,"The model class to instantiate is selected based on the "),aeo=n(BP,"CODE",{});var hVn=s(aeo);xoa=r(hVn,"model_type"),hVn.forEach(t),Goa=r(BP,` property of the config object (either
passed as an argument or loaded from `),neo=n(BP,"CODE",{});var gVn=s(neo);Boa=r(gVn,"pretrained_model_name_or_path"),gVn.forEach(t),Ioa=r(BP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),seo=n(BP,"CODE",{});var uVn=s(seo);Noa=r(uVn,"pretrained_model_name_or_path"),uVn.forEach(t),$oa=r(BP,":"),BP.forEach(t),joa=i(jd),leo=n(jd,"UL",{});var pVn=s(leo);ER=n(pVn,"LI",{});var kLo=s(ER);ieo=n(kLo,"STRONG",{});var _Vn=s(ieo);Poa=r(_Vn,"bert"),_Vn.forEach(t),Zoa=r(kLo," \u2014 "),N0e=n(kLo,"A",{href:!0});var vVn=s(N0e);Voa=r(vVn,"FlaxBertForNextSentencePrediction"),vVn.forEach(t),Woa=r(kLo," (BERT model)"),kLo.forEach(t),pVn.forEach(t),Joa=i(jd),M(yR.$$.fragment,jd),jd.forEach(t),$d.forEach(t),vIo=i(m),Af=n(m,"H3",{class:!0});var vjo=s(Af);CR=n(vjo,"A",{id:!0,class:!0,href:!0});var bVn=s(CR);deo=n(bVn,"SPAN",{});var MVn=s(deo);M(dU.$$.fragment,MVn),MVn.forEach(t),bVn.forEach(t),Uoa=i(vjo),ceo=n(vjo,"SPAN",{});var TVn=s(ceo);Xoa=r(TVn,"AutoModelForTokenClassification"),TVn.forEach(t),vjo.forEach(t),bIo=i(m),gr=n(m,"DIV",{class:!0});var Pd=s(gr);M(cU.$$.fragment,Pd),Doa=i(Pd),Rf=n(Pd,"P",{});var ewe=s(Rf);qoa=r(ewe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the `),$0e=n(ewe,"A",{href:!0});var FVn=s($0e);Ooa=r(FVn,"from_pretrained()"),FVn.forEach(t),Qoa=r(ewe," class method or the "),j0e=n(ewe,"A",{href:!0});var EVn=s(j0e);Yoa=r(EVn,"from_config()"),EVn.forEach(t),zoa=r(ewe,` class
method.`),ewe.forEach(t),Hoa=i(Pd),mU=n(Pd,"P",{});var bjo=s(mU);Koa=r(bjo,"This class cannot be instantiated directly using "),meo=n(bjo,"CODE",{});var yVn=s(meo);era=r(yVn,"__init__()"),yVn.forEach(t),ora=r(bjo," (throws an error)."),bjo.forEach(t),rra=i(Pd),ha=n(Pd,"DIV",{class:!0});var IP=s(ha);M(fU.$$.fragment,IP),tra=i(IP),feo=n(IP,"P",{});var CVn=s(feo);ara=r(CVn,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),CVn.forEach(t),nra=i(IP),kf=n(IP,"P",{});var owe=s(kf);sra=r(owe,`Note:
Loading a model from its configuration file does `),heo=n(owe,"STRONG",{});var wVn=s(heo);lra=r(wVn,"not"),wVn.forEach(t),ira=r(owe,` load the model weights. It only affects the
model\u2019s configuration. Use `),P0e=n(owe,"A",{href:!0});var LVn=s(P0e);dra=r(LVn,"from_pretrained()"),LVn.forEach(t),cra=r(owe," to load the model weights."),owe.forEach(t),mra=i(IP),M(wR.$$.fragment,IP),IP.forEach(t),fra=i(Pd),uo=n(Pd,"DIV",{class:!0});var cn=s(uo);M(hU.$$.fragment,cn),hra=i(cn),geo=n(cn,"P",{});var AVn=s(geo);gra=r(AVn,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),AVn.forEach(t),ura=i(cn),cs=n(cn,"P",{});var NP=s(cs);pra=r(NP,"The model class to instantiate is selected based on the "),ueo=n(NP,"CODE",{});var RVn=s(ueo);_ra=r(RVn,"model_type"),RVn.forEach(t),vra=r(NP,` property of the config object (either
passed as an argument or loaded from `),peo=n(NP,"CODE",{});var kVn=s(peo);bra=r(kVn,"pretrained_model_name_or_path"),kVn.forEach(t),Mra=r(NP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),_eo=n(NP,"CODE",{});var SVn=s(_eo);Tra=r(SVn,"pretrained_model_name_or_path"),SVn.forEach(t),Fra=r(NP,":"),NP.forEach(t),Era=i(cn),z=n(cn,"UL",{});var H=s(z);LR=n(H,"LI",{});var SLo=s(LR);veo=n(SLo,"STRONG",{});var xVn=s(veo);yra=r(xVn,"albert"),xVn.forEach(t),Cra=r(SLo," \u2014 "),Z0e=n(SLo,"A",{href:!0});var GVn=s(Z0e);wra=r(GVn,"AlbertForTokenClassification"),GVn.forEach(t),Lra=r(SLo," (ALBERT model)"),SLo.forEach(t),Ara=i(H),AR=n(H,"LI",{});var xLo=s(AR);beo=n(xLo,"STRONG",{});var BVn=s(beo);Rra=r(BVn,"bert"),BVn.forEach(t),kra=r(xLo," \u2014 "),V0e=n(xLo,"A",{href:!0});var IVn=s(V0e);Sra=r(IVn,"BertForTokenClassification"),IVn.forEach(t),xra=r(xLo," (BERT model)"),xLo.forEach(t),Gra=i(H),RR=n(H,"LI",{});var GLo=s(RR);Meo=n(GLo,"STRONG",{});var NVn=s(Meo);Bra=r(NVn,"big_bird"),NVn.forEach(t),Ira=r(GLo," \u2014 "),W0e=n(GLo,"A",{href:!0});var $Vn=s(W0e);Nra=r($Vn,"BigBirdForTokenClassification"),$Vn.forEach(t),$ra=r(GLo," (BigBird model)"),GLo.forEach(t),jra=i(H),kR=n(H,"LI",{});var BLo=s(kR);Teo=n(BLo,"STRONG",{});var jVn=s(Teo);Pra=r(jVn,"biogpt"),jVn.forEach(t),Zra=r(BLo," \u2014 "),J0e=n(BLo,"A",{href:!0});var PVn=s(J0e);Vra=r(PVn,"BioGptForTokenClassification"),PVn.forEach(t),Wra=r(BLo," (BioGpt model)"),BLo.forEach(t),Jra=i(H),SR=n(H,"LI",{});var ILo=s(SR);Feo=n(ILo,"STRONG",{});var ZVn=s(Feo);Ura=r(ZVn,"bloom"),ZVn.forEach(t),Xra=r(ILo," \u2014 "),U0e=n(ILo,"A",{href:!0});var VVn=s(U0e);Dra=r(VVn,"BloomForTokenClassification"),VVn.forEach(t),qra=r(ILo," (BLOOM model)"),ILo.forEach(t),Ora=i(H),xR=n(H,"LI",{});var NLo=s(xR);Eeo=n(NLo,"STRONG",{});var WVn=s(Eeo);Qra=r(WVn,"camembert"),WVn.forEach(t),Yra=r(NLo," \u2014 "),X0e=n(NLo,"A",{href:!0});var JVn=s(X0e);zra=r(JVn,"CamembertForTokenClassification"),JVn.forEach(t),Hra=r(NLo," (CamemBERT model)"),NLo.forEach(t),Kra=i(H),GR=n(H,"LI",{});var $Lo=s(GR);yeo=n($Lo,"STRONG",{});var UVn=s(yeo);eta=r(UVn,"canine"),UVn.forEach(t),ota=r($Lo," \u2014 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deactivated). To train the model, you should first set it back in training mode with `),boo=n(SAo,"CODE",{});var GJn=s(boo);Lla=r(GJn,"model.train()"),GJn.forEach(t),SAo.forEach(t),Ala=i(cn),M(Ak.$$.fragment,cn),cn.forEach(t),Pd.forEach(t),MIo=i(m),Sf=n(m,"H3",{class:!0});var Mjo=s(Sf);Rk=n(Mjo,"A",{id:!0,class:!0,href:!0});var BJn=s(Rk);Moo=n(BJn,"SPAN",{});var IJn=s(Moo);M(gU.$$.fragment,IJn),IJn.forEach(t),BJn.forEach(t),Rla=i(Mjo),Too=n(Mjo,"SPAN",{});var NJn=s(Too);kla=r(NJn,"TFAutoModelForTokenClassification"),NJn.forEach(t),Mjo.forEach(t),TIo=i(m),ur=n(m,"DIV",{class:!0});var Zd=s(ur);M(uU.$$.fragment,Zd),Sla=i(Zd),xf=n(Zd,"P",{});var rwe=s(xf);xla=r(rwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
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method.`),rwe.forEach(t),$la=i(Zd),pU=n(Zd,"P",{});var Tjo=s(pU);jla=r(Tjo,"This class cannot be instantiated directly using "),Foo=n(Tjo,"CODE",{});var PJn=s(Foo);Pla=r(PJn,"__init__()"),PJn.forEach(t),Zla=r(Tjo," (throws an error)."),Tjo.forEach(t),Vla=i(Zd),ga=n(Zd,"DIV",{class:!0});var $P=s(ga);M(_U.$$.fragment,$P),Wla=i($P),Eoo=n($P,"P",{});var ZJn=s(Eoo);Jla=r(ZJn,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),ZJn.forEach(t),Ula=i($P),Gf=n($P,"P",{});var twe=s(Gf);Xla=r(twe,`Note:
Loading a model from its configuration file does `),yoo=n(twe,"STRONG",{});var VJn=s(yoo);Dla=r(VJn,"not"),VJn.forEach(t),qla=r(twe,` load the model weights. It only affects the
model\u2019s configuration. Use `),W4e=n(twe,"A",{href:!0});var WJn=s(W4e);Ola=r(WJn,"from_pretrained()"),WJn.forEach(t),Qla=r(twe," to load the model weights."),twe.forEach(t),Yla=i($P),M(kk.$$.fragment,$P),$P.forEach(t),zla=i(Zd),bt=n(Zd,"DIV",{class:!0});var Vd=s(bt);M(vU.$$.fragment,Vd),Hla=i(Vd),Coo=n(Vd,"P",{});var JJn=s(Coo);Kla=r(JJn,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),JJn.forEach(t),eia=i(Vd),ms=n(Vd,"P",{});var jP=s(ms);oia=r(jP,"The model class to instantiate is selected based on the "),woo=n(jP,"CODE",{});var UJn=s(woo);ria=r(UJn,"model_type"),UJn.forEach(t),tia=r(jP,` property of the config object (either
passed as an argument or loaded from `),Loo=n(jP,"CODE",{});var XJn=s(Loo);aia=r(XJn,"pretrained_model_name_or_path"),XJn.forEach(t),nia=r(jP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Aoo=n(jP,"CODE",{});var DJn=s(Aoo);sia=r(DJn,"pretrained_model_name_or_path"),DJn.forEach(t),lia=r(jP,":"),jP.forEach(t),iia=i(Vd),ue=n(Vd,"UL",{});var be=s(ue);Sk=n(be,"LI",{});var xAo=s(Sk);Roo=n(xAo,"STRONG",{});var qJn=s(Roo);dia=r(qJn,"albert"),qJn.forEach(t),cia=r(xAo," \u2014 "),J4e=n(xAo,"A",{href:!0});var OJn=s(J4e);mia=r(OJn,"TFAlbertForTokenClassification"),OJn.forEach(t),fia=r(xAo," (ALBERT model)"),xAo.forEach(t),hia=i(be),xk=n(be,"LI",{});var GAo=s(xk);koo=n(GAo,"STRONG",{});var QJn=s(koo);gia=r(QJn,"bert"),QJn.forEach(t),uia=r(GAo," \u2014 "),U4e=n(GAo,"A",{href:!0});var YJn=s(U4e);pia=r(YJn,"TFBertForTokenClassification"),YJn.forEach(t),_ia=r(GAo," (BERT model)"),GAo.forEach(t),via=i(be),Gk=n(be,"LI",{});var BAo=s(Gk);Soo=n(BAo,"STRONG",{});var zJn=s(Soo);bia=r(zJn,"camembert"),zJn.forEach(t),Mia=r(BAo," \u2014 "),X4e=n(BAo,"A",{href:!0});var HJn=s(X4e);Tia=r(HJn,"TFCamembertForTokenClassification"),HJn.forEach(t),Fia=r(BAo," (CamemBERT model)"),BAo.forEach(t),Eia=i(be),Bk=n(be,"LI",{});var IAo=s(Bk);xoo=n(IAo,"STRONG",{});var KJn=s(xoo);yia=r(KJn,"convbert"),KJn.forEach(t),Cia=r(IAo," \u2014 "),D4e=n(IAo,"A",{href:!0});var eUn=s(D4e);wia=r(eUn,"TFConvBertForTokenClassification"),eUn.forEach(t),Lia=r(IAo," (ConvBERT model)"),IAo.forEach(t),Aia=i(be),Ik=n(be,"LI",{});var NAo=s(Ik);Goo=n(NAo,"STRONG",{});var oUn=s(Goo);Ria=r(oUn,"deberta"),oUn.forEach(t),kia=r(NAo," \u2014 "),q4e=n(NAo,"A",{href:!0});var rUn=s(q4e);Sia=r(rUn,"TFDebertaForTokenClassification"),rUn.forEach(t),xia=r(NAo," (DeBERTa model)"),NAo.forEach(t),Gia=i(be),Nk=n(be,"LI",{});var $Ao=s(Nk);Boo=n($Ao,"STRONG",{});var tUn=s(Boo);Bia=r(tUn,"deberta-v2"),tUn.forEach(t),Iia=r($Ao," \u2014 "),O4e=n($Ao,"A",{href:!0});var aUn=s(O4e);Nia=r(aUn,"TFDebertaV2ForTokenClassification"),aUn.forEach(t),$ia=r($Ao," (DeBERTa-v2 model)"),$Ao.forEach(t),jia=i(be),$k=n(be,"LI",{});var jAo=s($k);Ioo=n(jAo,"STRONG",{});var 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method.`),awe.forEach(t),yca=i(Wd),TU=n(Wd,"P",{});var Ejo=s(TU);Cca=r(Ejo,"This class cannot be instantiated directly using "),ero=n(Ejo,"CODE",{});var UUn=s(ero);wca=r(UUn,"__init__()"),UUn.forEach(t),Lca=r(Ejo," (throws an error)."),Ejo.forEach(t),Aca=i(Wd),ua=n(Wd,"DIV",{class:!0});var PP=s(ua);M(FU.$$.fragment,PP),Rca=i(PP),oro=n(PP,"P",{});var XUn=s(oro);kca=r(XUn,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),XUn.forEach(t),Sca=i(PP),Nf=n(PP,"P",{});var nwe=s(Nf);xca=r(nwe,`Note:
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model\u2019s configuration. Use `),gMe=n(nwe,"A",{href:!0});var qUn=s(gMe);Ica=r(qUn,"from_pretrained()"),qUn.forEach(t),Nca=r(nwe," to load the model weights."),nwe.forEach(t),$ca=i(PP),M(rS.$$.fragment,PP),PP.forEach(t),jca=i(Wd),Mt=n(Wd,"DIV",{class:!0});var Jd=s(Mt);M(EU.$$.fragment,Jd),Pca=i(Jd),tro=n(Jd,"P",{});var OUn=s(tro);Zca=r(OUn,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),OUn.forEach(t),Vca=i(Jd),fs=n(Jd,"P",{});var ZP=s(fs);Wca=r(ZP,"The model class to instantiate is selected based on the "),aro=n(ZP,"CODE",{});var QUn=s(aro);Jca=r(QUn,"model_type"),QUn.forEach(t),Uca=r(ZP,` property of the config object (either
passed as an argument or loaded from `),nro=n(ZP,"CODE",{});var YUn=s(nro);Xca=r(YUn,"pretrained_model_name_or_path"),YUn.forEach(t),Dca=r(ZP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),sro=n(ZP,"CODE",{});var zUn=s(sro);qca=r(zUn,"pretrained_model_name_or_path"),zUn.forEach(t),Oca=r(ZP,":"),ZP.forEach(t),Qca=i(Jd),Xe=n(Jd,"UL",{});var Ke=s(Xe);tS=n(Ke,"LI",{});var oRo=s(tS);lro=n(oRo,"STRONG",{});var HUn=s(lro);Yca=r(HUn,"albert"),HUn.forEach(t),zca=r(oRo," \u2014 "),uMe=n(oRo,"A",{href:!0});var KUn=s(uMe);Hca=r(KUn,"FlaxAlbertForTokenClassification"),KUn.forEach(t),Kca=r(oRo," (ALBERT model)"),oRo.forEach(t),ema=i(Ke),aS=n(Ke,"LI",{});var rRo=s(aS);iro=n(rRo,"STRONG",{});var eXn=s(iro);oma=r(eXn,"bert"),eXn.forEach(t),rma=r(rRo," \u2014 "),pMe=n(rRo,"A",{href:!0});var oXn=s(pMe);tma=r(oXn,"FlaxBertForTokenClassification"),oXn.forEach(t),ama=r(rRo," (BERT model)"),rRo.forEach(t),nma=i(Ke),nS=n(Ke,"LI",{});var tRo=s(nS);dro=n(tRo,"STRONG",{});var rXn=s(dro);sma=r(rXn,"big_bird"),rXn.forEach(t),lma=r(tRo," \u2014 "),_Me=n(tRo,"A",{href:!0});var tXn=s(_Me);ima=r(tXn,"FlaxBigBirdForTokenClassification"),tXn.forEach(t),dma=r(tRo," (BigBird model)"),tRo.forEach(t),cma=i(Ke),sS=n(Ke,"LI",{});var aRo=s(sS);cro=n(aRo,"STRONG",{});var aXn=s(cro);mma=r(aXn,"distilbert"),aXn.forEach(t),fma=r(aRo," \u2014 "),vMe=n(aRo,"A",{href:!0});var nXn=s(vMe);hma=r(nXn,"FlaxDistilBertForTokenClassification"),nXn.forEach(t),gma=r(aRo," (DistilBERT model)"),aRo.forEach(t),uma=i(Ke),lS=n(Ke,"LI",{});var nRo=s(lS);mro=n(nRo,"STRONG",{});var sXn=s(mro);pma=r(sXn,"electra"),sXn.forEach(t),_ma=r(nRo," \u2014 "),bMe=n(nRo,"A",{href:!0});var lXn=s(bMe);vma=r(lXn,"FlaxElectraForTokenClassification"),lXn.forEach(t),bma=r(nRo," (ELECTRA model)"),nRo.forEach(t),Mma=i(Ke),iS=n(Ke,"LI",{});var sRo=s(iS);fro=n(sRo,"STRONG",{});var iXn=s(fro);Tma=r(iXn,"roberta"),iXn.forEach(t),Fma=r(sRo," \u2014 "),MMe=n(sRo,"A",{href:!0});var dXn=s(MMe);Ema=r(dXn,"FlaxRobertaForTokenClassification"),dXn.forEach(t),yma=r(sRo," (RoBERTa model)"),sRo.forEach(t),Cma=i(Ke),dS=n(Ke,"LI",{});var lRo=s(dS);hro=n(lRo,"STRONG",{});var cXn=s(hro);wma=r(cXn,"roberta-prelayernorm"),cXn.forEach(t),Lma=r(lRo," \u2014 "),TMe=n(lRo,"A",{href:!0});var mXn=s(TMe);Ama=r(mXn,"FlaxRobertaPreLayerNormForTokenClassification"),mXn.forEach(t),Rma=r(lRo," (RoBERTa-PreLayerNorm model)"),lRo.forEach(t),kma=i(Ke),cS=n(Ke,"LI",{});var iRo=s(cS);gro=n(iRo,"STRONG",{});var fXn=s(gro);Sma=r(fXn,"roformer"),fXn.forEach(t),xma=r(iRo," \u2014 "),FMe=n(iRo,"A",{href:!0});var hXn=s(FMe);Gma=r(hXn,"FlaxRoFormerForTokenClassification"),hXn.forEach(t),Bma=r(iRo," (RoFormer model)"),iRo.forEach(t),Ima=i(Ke),mS=n(Ke,"LI",{});var dRo=s(mS);uro=n(dRo,"STRONG",{});var gXn=s(uro);Nma=r(gXn,"xlm-roberta"),gXn.forEach(t),$ma=r(dRo," \u2014 "),EMe=n(dRo,"A",{href:!0});var uXn=s(EMe);jma=r(uXn,"FlaxXLMRobertaForTokenClassification"),uXn.forEach(t),Pma=r(dRo," (XLM-RoBERTa model)"),dRo.forEach(t),Ke.forEach(t),Zma=i(Jd),M(fS.$$.fragment,Jd),Jd.forEach(t),Wd.forEach(t),yIo=i(m),$f=n(m,"H3",{class:!0});var yjo=s($f);hS=n(yjo,"A",{id:!0,class:!0,href:!0});var pXn=s(hS);pro=n(pXn,"SPAN",{});var _Xn=s(pro);M(yU.$$.fragment,_Xn),_Xn.forEach(t),pXn.forEach(t),Vma=i(yjo),_ro=n(yjo,"SPAN",{});var vXn=s(_ro);Wma=r(vXn,"AutoModelForQuestionAnswering"),vXn.forEach(t),yjo.forEach(t),CIo=i(m),_r=n(m,"DIV",{class:!0});var Ud=s(_r);M(CU.$$.fragment,Ud),Jma=i(Ud),jf=n(Ud,"P",{});var swe=s(jf);Uma=r(swe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
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method.`),swe.forEach(t),Qma=i(Ud),wU=n(Ud,"P",{});var Cjo=s(wU);Yma=r(Cjo,"This class cannot be instantiated directly using "),vro=n(Cjo,"CODE",{});var TXn=s(vro);zma=r(TXn,"__init__()"),TXn.forEach(t),Hma=r(Cjo," (throws an error)."),Cjo.forEach(t),Kma=i(Ud),pa=n(Ud,"DIV",{class:!0});var VP=s(pa);M(LU.$$.fragment,VP),efa=i(VP),bro=n(VP,"P",{});var FXn=s(bro);ofa=r(FXn,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),FXn.forEach(t),rfa=i(VP),Pf=n(VP,"P",{});var lwe=s(Pf);tfa=r(lwe,`Note:
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model\u2019s configuration. Use `),wMe=n(lwe,"A",{href:!0});var yXn=s(wMe);sfa=r(yXn,"from_pretrained()"),yXn.forEach(t),lfa=r(lwe," to load the model weights."),lwe.forEach(t),ifa=i(VP),M(gS.$$.fragment,VP),VP.forEach(t),dfa=i(Ud),po=n(Ud,"DIV",{class:!0});var mn=s(po);M(AU.$$.fragment,mn),cfa=i(mn),Tro=n(mn,"P",{});var CXn=s(Tro);mfa=r(CXn,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),CXn.forEach(t),ffa=i(mn),hs=n(mn,"P",{});var WP=s(hs);hfa=r(WP,"The model class to instantiate is selected based on the "),Fro=n(WP,"CODE",{});var wXn=s(Fro);gfa=r(wXn,"model_type"),wXn.forEach(t),ufa=r(WP,` property of the config object (either
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with the `),ITe=n(iwe,"A",{href:!0});var Yqn=s(ITe);pva=r(Yqn,"from_pretrained()"),Yqn.forEach(t),_va=r(iwe," class method or the "),NTe=n(iwe,"A",{href:!0});var zqn=s(NTe);vva=r(zqn,"from_config()"),zqn.forEach(t),bva=r(iwe,` class
method.`),iwe.forEach(t),Mva=i(Xd),SU=n(Xd,"P",{});var Ljo=s(SU);Tva=r(Ljo,"This class cannot be instantiated directly using "),$to=n(Ljo,"CODE",{});var Hqn=s($to);Fva=r(Hqn,"__init__()"),Hqn.forEach(t),Eva=r(Ljo," (throws an error)."),Ljo.forEach(t),yva=i(Xd),_a=n(Xd,"DIV",{class:!0});var JP=s(_a);M(xU.$$.fragment,JP),Cva=i(JP),jto=n(JP,"P",{});var Kqn=s(jto);wva=r(Kqn,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),Kqn.forEach(t),Lva=i(JP),Wf=n(JP,"P",{});var dwe=s(Wf);Ava=r(dwe,`Note:
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model\u2019s configuration. Use `),$Te=n(dwe,"A",{href:!0});var oOn=s($Te);Sva=r(oOn,"from_pretrained()"),oOn.forEach(t),xva=r(dwe," to load the model weights."),dwe.forEach(t),Gva=i(JP),M(wx.$$.fragment,JP),JP.forEach(t),Bva=i(Xd),Tt=n(Xd,"DIV",{class:!0});var Dd=s(Tt);M(GU.$$.fragment,Dd),Iva=i(Dd),Zto=n(Dd,"P",{});var rOn=s(Zto);Nva=r(rOn,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),rOn.forEach(t),$va=i(Dd),gs=n(Dd,"P",{});var UP=s(gs);jva=r(UP,"The model class to instantiate is selected based on the "),Vto=n(UP,"CODE",{});var tOn=s(Vto);Pva=r(tOn,"model_type"),tOn.forEach(t),Zva=r(UP,` property of the config object (either
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(CamemBERT model)"),Tko.forEach(t),s3a=i(Te),kx=n(Te,"LI",{});var Fko=s(kx);qto=n(Fko,"STRONG",{});var fOn=s(qto);l3a=r(fOn,"convbert"),fOn.forEach(t),i3a=r(Fko," \u2014 "),VTe=n(Fko,"A",{href:!0});var hOn=s(VTe);d3a=r(hOn,"TFConvBertForQuestionAnswering"),hOn.forEach(t),c3a=r(Fko," (ConvBERT model)"),Fko.forEach(t),m3a=i(Te),Sx=n(Te,"LI",{});var Eko=s(Sx);Oto=n(Eko,"STRONG",{});var gOn=s(Oto);f3a=r(gOn,"deberta"),gOn.forEach(t),h3a=r(Eko," \u2014 "),WTe=n(Eko,"A",{href:!0});var uOn=s(WTe);g3a=r(uOn,"TFDebertaForQuestionAnswering"),uOn.forEach(t),u3a=r(Eko," (DeBERTa model)"),Eko.forEach(t),p3a=i(Te),xx=n(Te,"LI",{});var yko=s(xx);Qto=n(yko,"STRONG",{});var pOn=s(Qto);_3a=r(pOn,"deberta-v2"),pOn.forEach(t),v3a=r(yko," \u2014 "),JTe=n(yko,"A",{href:!0});var _On=s(JTe);b3a=r(_On,"TFDebertaV2ForQuestionAnswering"),_On.forEach(t),M3a=r(yko," (DeBERTa-v2 model)"),yko.forEach(t),T3a=i(Te),Gx=n(Te,"LI",{});var Cko=s(Gx);Yto=n(Cko,"STRONG",{});var vOn=s(Yto);F3a=r(vOn,"distilbert"),vOn.forEach(t),E3a=r(Cko," \u2014 "),UTe=n(Cko,"A",{href:!0});var bOn=s(UTe);y3a=r(bOn,"TFDistilBertForQuestionAnswering"),bOn.forEach(t),C3a=r(Cko," (DistilBERT model)"),Cko.forEach(t),w3a=i(Te),Bx=n(Te,"LI",{});var wko=s(Bx);zto=n(wko,"STRONG",{});var MOn=s(zto);L3a=r(MOn,"electra"),MOn.forEach(t),A3a=r(wko," \u2014 "),XTe=n(wko,"A",{href:!0});var TOn=s(XTe);R3a=r(TOn,"TFElectraForQuestionAnswering"),TOn.forEach(t),k3a=r(wko," (ELECTRA model)"),wko.forEach(t),S3a=i(Te),Ix=n(Te,"LI",{});var Lko=s(Ix);Hto=n(Lko,"STRONG",{});var FOn=s(Hto);x3a=r(FOn,"flaubert"),FOn.forEach(t),G3a=r(Lko," \u2014 "),DTe=n(Lko,"A",{href:!0});var EOn=s(DTe);B3a=r(EOn,"TFFlaubertForQuestionAnsweringSimple"),EOn.forEach(t),I3a=r(Lko," (FlauBERT model)"),Lko.forEach(t),N3a=i(Te),Nx=n(Te,"LI",{});var Ako=s(Nx);Kto=n(Ako,"STRONG",{});var yOn=s(Kto);$3a=r(yOn,"funnel"),yOn.forEach(t),j3a=r(Ako," \u2014 "),qTe=n(Ako,"A",{href:!0});var COn=s(qTe);P3a=r(COn,"TFFunnelForQuestionAnswering"),COn.forEach(t),Z3a=r(Ako," (Funnel Transformer model)"),Ako.forEach(t),V3a=i(Te),$x=n(Te,"LI",{});var Rko=s($x);eao=n(Rko,"STRONG",{});var wOn=s(eao);W3a=r(wOn,"gptj"),wOn.forEach(t),J3a=r(Rko," \u2014 "),OTe=n(Rko,"A",{href:!0});var LOn=s(OTe);U3a=r(LOn,"TFGPTJForQuestionAnswering"),LOn.forEach(t),X3a=r(Rko," (GPT-J model)"),Rko.forEach(t),D3a=i(Te),jx=n(Te,"LI",{});var kko=s(jx);oao=n(kko,"STRONG",{});var AOn=s(oao);q3a=r(AOn,"layoutlmv3"),AOn.forEach(t),O3a=r(kko," \u2014 "),QTe=n(kko,"A",{href:!0});var ROn=s(QTe);Q3a=r(ROn,"TFLayoutLMv3ForQuestionAnswering"),ROn.forEach(t),Y3a=r(kko," (LayoutLMv3 model)"),kko.forEach(t),z3a=i(Te),Px=n(Te,"LI",{});var Sko=s(Px);rao=n(Sko,"STRONG",{});var kOn=s(rao);H3a=r(kOn,"longformer"),kOn.forEach(t),K3a=r(Sko," \u2014 "),YTe=n(Sko,"A",{href:!0});var SOn=s(YTe);e1a=r(SOn,"TFLongformerForQuestionAnswering"),SOn.forEach(t),o1a=r(Sko," (Longformer model)"),Sko.forEach(t),r1a=i(Te),Zx=n(Te,"LI",{});var xko=s(Zx);tao=n(xko,"STRONG",{});var xOn=s(tao);t1a=r(xOn,"mobilebert"),xOn.forEach(t),a1a=r(xko," \u2014 "),zTe=n(xko,"A",{href:!0});var GOn=s(zTe);n1a=r(GOn,"TFMobileBertForQuestionAnswering"),GOn.forEach(t),s1a=r(xko," (MobileBERT model)"),xko.forEach(t),l1a=i(Te),Vx=n(Te,"LI",{});var Gko=s(Vx);aao=n(Gko,"STRONG",{});var BOn=s(aao);i1a=r(BOn,"mpnet"),BOn.forEach(t),d1a=r(Gko," \u2014 "),HTe=n(Gko,"A",{href:!0});var IOn=s(HTe);c1a=r(IOn,"TFMPNetForQuestionAnswering"),IOn.forEach(t),m1a=r(Gko," (MPNet model)"),Gko.forEach(t),f1a=i(Te),Wx=n(Te,"LI",{});var Bko=s(Wx);nao=n(Bko,"STRONG",{});var NOn=s(nao);h1a=r(NOn,"rembert"),NOn.forEach(t),g1a=r(Bko," \u2014 "),KTe=n(Bko,"A",{href:!0});var $On=s(KTe);u1a=r($On,"TFRemBertForQuestionAnswering"),$On.forEach(t),p1a=r(Bko," (RemBERT model)"),Bko.forEach(t),_1a=i(Te),Jx=n(Te,"LI",{});var Iko=s(Jx);sao=n(Iko,"STRONG",{});var jOn=s(sao);v1a=r(jOn,"roberta"),jOn.forEach(t),b1a=r(Iko," \u2014 "),eFe=n(Iko,"A",{href:!0});var POn=s(eFe);M1a=r(POn,"TFRobertaForQuestionAnswering"),POn.forEach(t),T1a=r(Iko," (RoBERTa model)"),Iko.forEach(t),F1a=i(Te),Ux=n(Te,"LI",{});var Nko=s(Ux);lao=n(Nko,"STRONG",{});var ZOn=s(lao);E1a=r(ZOn,"roberta-prelayernorm"),ZOn.forEach(t),y1a=r(Nko," \u2014 "),oFe=n(Nko,"A",{href:!0});var VOn=s(oFe);C1a=r(VOn,"TFRobertaPreLayerNormForQuestionAnswering"),VOn.forEach(t),w1a=r(Nko," (RoBERTa-PreLayerNorm model)"),Nko.forEach(t),L1a=i(Te),Xx=n(Te,"LI",{});var $ko=s(Xx);iao=n($ko,"STRONG",{});var WOn=s(iao);A1a=r(WOn,"roformer"),WOn.forEach(t),R1a=r($ko," \u2014 "),rFe=n($ko,"A",{href:!0});var JOn=s(rFe);k1a=r(JOn,"TFRoFormerForQuestionAnswering"),JOn.forEach(t),S1a=r($ko," (RoFormer model)"),$ko.forEach(t),x1a=i(Te),Dx=n(Te,"LI",{});var jko=s(Dx);dao=n(jko,"STRONG",{});var UOn=s(dao);G1a=r(UOn,"xlm"),UOn.forEach(t),B1a=r(jko," \u2014 "),tFe=n(jko,"A",{href:!0});var XOn=s(tFe);I1a=r(XOn,"TFXLMForQuestionAnsweringSimple"),XOn.forEach(t),N1a=r(jko," (XLM model)"),jko.forEach(t),$1a=i(Te),qx=n(Te,"LI",{});var Pko=s(qx);cao=n(Pko,"STRONG",{});var DOn=s(cao);j1a=r(DOn,"xlm-roberta"),DOn.forEach(t),P1a=r(Pko," \u2014 "),aFe=n(Pko,"A",{href:!0});var qOn=s(aFe);Z1a=r(qOn,"TFXLMRobertaForQuestionAnswering"),qOn.forEach(t),V1a=r(Pko," (XLM-RoBERTa model)"),Pko.forEach(t),W1a=i(Te),Ox=n(Te,"LI",{});var Zko=s(Ox);mao=n(Zko,"STRONG",{});var OOn=s(mao);J1a=r(OOn,"xlnet"),OOn.forEach(t),U1a=r(Zko," \u2014 "),nFe=n(Zko,"A",{href:!0});var QOn=s(nFe);X1a=r(QOn,"TFXLNetForQuestionAnsweringSimple"),QOn.forEach(t),D1a=r(Zko," (XLNet model)"),Zko.forEach(t),Te.forEach(t),q1a=i(Dd),M(Qx.$$.fragment,Dd),Dd.forEach(t),Xd.forEach(t),AIo=i(m),Jf=n(m,"H3",{class:!0});var Ajo=s(Jf);Yx=n(Ajo,"A",{id:!0,class:!0,href:!0});var YOn=s(Yx);fao=n(YOn,"SPAN",{});var zOn=s(fao);M(BU.$$.fragment,zOn),zOn.forEach(t),YOn.forEach(t),O1a=i(Ajo),hao=n(Ajo,"SPAN",{});var HOn=s(hao);Q1a=r(HOn,"FlaxAutoModelForQuestionAnswering"),HOn.forEach(t),Ajo.forEach(t),RIo=i(m),br=n(m,"DIV",{class:!0});var qd=s(br);M(IU.$$.fragment,qd),Y1a=i(qd),Uf=n(qd,"P",{});var cwe=s(Uf);z1a=r(cwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
with the `),sFe=n(cwe,"A",{href:!0});var KOn=s(sFe);H1a=r(KOn,"from_pretrained()"),KOn.forEach(t),K1a=r(cwe," class method or the "),lFe=n(cwe,"A",{href:!0});var eQn=s(lFe);eba=r(eQn,"from_config()"),eQn.forEach(t),oba=r(cwe,` class
method.`),cwe.forEach(t),rba=i(qd),NU=n(qd,"P",{});var Rjo=s(NU);tba=r(Rjo,"This class cannot be instantiated directly using "),gao=n(Rjo,"CODE",{});var oQn=s(gao);aba=r(oQn,"__init__()"),oQn.forEach(t),nba=r(Rjo," (throws an error)."),Rjo.forEach(t),sba=i(qd),va=n(qd,"DIV",{class:!0});var XP=s(va);M($U.$$.fragment,XP),lba=i(XP),uao=n(XP,"P",{});var rQn=s(uao);iba=r(rQn,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),rQn.forEach(t),dba=i(XP),Xf=n(XP,"P",{});var mwe=s(Xf);cba=r(mwe,`Note:
Loading a model from its configuration file does `),pao=n(mwe,"STRONG",{});var tQn=s(pao);mba=r(tQn,"not"),tQn.forEach(t),fba=r(mwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),iFe=n(mwe,"A",{href:!0});var aQn=s(iFe);hba=r(aQn,"from_pretrained()"),aQn.forEach(t),gba=r(mwe," to load the model weights."),mwe.forEach(t),uba=i(XP),M(zx.$$.fragment,XP),XP.forEach(t),pba=i(qd),Ft=n(qd,"DIV",{class:!0});var Od=s(Ft);M(jU.$$.fragment,Od),_ba=i(Od),_ao=n(Od,"P",{});var nQn=s(_ao);vba=r(nQn,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),nQn.forEach(t),bba=i(Od),us=n(Od,"P",{});var DP=s(us);Mba=r(DP,"The model class to instantiate is selected based on the "),vao=n(DP,"CODE",{});var sQn=s(vao);Tba=r(sQn,"model_type"),sQn.forEach(t),Fba=r(DP,` property of the config object (either
passed as an argument or loaded from `),bao=n(DP,"CODE",{});var lQn=s(bao);Eba=r(lQn,"pretrained_model_name_or_path"),lQn.forEach(t),yba=r(DP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Mao=n(DP,"CODE",{});var iQn=s(Mao);Cba=r(iQn,"pretrained_model_name_or_path"),iQn.forEach(t),wba=r(DP,":"),DP.forEach(t),Lba=i(Od),xe=n(Od,"UL",{});var Ve=s(xe);Hx=n(Ve,"LI",{});var Vko=s(Hx);Tao=n(Vko,"STRONG",{});var dQn=s(Tao);Aba=r(dQn,"albert"),dQn.forEach(t),Rba=r(Vko," \u2014 "),dFe=n(Vko,"A",{href:!0});var cQn=s(dFe);kba=r(cQn,"FlaxAlbertForQuestionAnswering"),cQn.forEach(t),Sba=r(Vko," (ALBERT model)"),Vko.forEach(t),xba=i(Ve),Kx=n(Ve,"LI",{});var Wko=s(Kx);Fao=n(Wko,"STRONG",{});var mQn=s(Fao);Gba=r(mQn,"bart"),mQn.forEach(t),Bba=r(Wko," \u2014 "),cFe=n(Wko,"A",{href:!0});var fQn=s(cFe);Iba=r(fQn,"FlaxBartForQuestionAnswering"),fQn.forEach(t),Nba=r(Wko," (BART model)"),Wko.forEach(t),$ba=i(Ve),eG=n(Ve,"LI",{});var Jko=s(eG);Eao=n(Jko,"STRONG",{});var hQn=s(Eao);jba=r(hQn,"bert"),hQn.forEach(t),Pba=r(Jko," \u2014 "),mFe=n(Jko,"A",{href:!0});var gQn=s(mFe);Zba=r(gQn,"FlaxBertForQuestionAnswering"),gQn.forEach(t),Vba=r(Jko," (BERT model)"),Jko.forEach(t),Wba=i(Ve),oG=n(Ve,"LI",{});var Uko=s(oG);yao=n(Uko,"STRONG",{});var uQn=s(yao);Jba=r(uQn,"big_bird"),uQn.forEach(t),Uba=r(Uko," \u2014 "),fFe=n(Uko,"A",{href:!0});var pQn=s(fFe);Xba=r(pQn,"FlaxBigBirdForQuestionAnswering"),pQn.forEach(t),Dba=r(Uko," (BigBird model)"),Uko.forEach(t),qba=i(Ve),rG=n(Ve,"LI",{});var Xko=s(rG);Cao=n(Xko,"STRONG",{});var _Qn=s(Cao);Oba=r(_Qn,"distilbert"),_Qn.forEach(t),Qba=r(Xko," \u2014 "),hFe=n(Xko,"A",{href:!0});var vQn=s(hFe);Yba=r(vQn,"FlaxDistilBertForQuestionAnswering"),vQn.forEach(t),zba=r(Xko," (DistilBERT model)"),Xko.forEach(t),Hba=i(Ve),tG=n(Ve,"LI",{});var Dko=s(tG);wao=n(Dko,"STRONG",{});var bQn=s(wao);Kba=r(bQn,"electra"),bQn.forEach(t),e2a=r(Dko," \u2014 "),gFe=n(Dko,"A",{href:!0});var MQn=s(gFe);o2a=r(MQn,"FlaxElectraForQuestionAnswering"),MQn.forEach(t),r2a=r(Dko," (ELECTRA model)"),Dko.forEach(t),t2a=i(Ve),aG=n(Ve,"LI",{});var qko=s(aG);Lao=n(qko,"STRONG",{});var TQn=s(Lao);a2a=r(TQn,"mbart"),TQn.forEach(t),n2a=r(qko," \u2014 "),uFe=n(qko,"A",{href:!0});var FQn=s(uFe);s2a=r(FQn,"FlaxMBartForQuestionAnswering"),FQn.forEach(t),l2a=r(qko," (mBART model)"),qko.forEach(t),i2a=i(Ve),nG=n(Ve,"LI",{});var Oko=s(nG);Aao=n(Oko,"STRONG",{});var EQn=s(Aao);d2a=r(EQn,"roberta"),EQn.forEach(t),c2a=r(Oko," \u2014 "),pFe=n(Oko,"A",{href:!0});var yQn=s(pFe);m2a=r(yQn,"FlaxRobertaForQuestionAnswering"),yQn.forEach(t),f2a=r(Oko," (RoBERTa model)"),Oko.forEach(t),h2a=i(Ve),sG=n(Ve,"LI",{});var Qko=s(sG);Rao=n(Qko,"STRONG",{});var CQn=s(Rao);g2a=r(CQn,"roberta-prelayernorm"),CQn.forEach(t),u2a=r(Qko," \u2014 "),_Fe=n(Qko,"A",{href:!0});var wQn=s(_Fe);p2a=r(wQn,"FlaxRobertaPreLayerNormForQuestionAnswering"),wQn.forEach(t),_2a=r(Qko," (RoBERTa-PreLayerNorm model)"),Qko.forEach(t),v2a=i(Ve),lG=n(Ve,"LI",{});var Yko=s(lG);kao=n(Yko,"STRONG",{});var LQn=s(kao);b2a=r(LQn,"roformer"),LQn.forEach(t),M2a=r(Yko," \u2014 "),vFe=n(Yko,"A",{href:!0});var AQn=s(vFe);T2a=r(AQn,"FlaxRoFormerForQuestionAnswering"),AQn.forEach(t),F2a=r(Yko," (RoFormer model)"),Yko.forEach(t),E2a=i(Ve),iG=n(Ve,"LI",{});var zko=s(iG);Sao=n(zko,"STRONG",{});var RQn=s(Sao);y2a=r(RQn,"xlm-roberta"),RQn.forEach(t),C2a=r(zko," \u2014 "),bFe=n(zko,"A",{href:!0});var kQn=s(bFe);w2a=r(kQn,"FlaxXLMRobertaForQuestionAnswering"),kQn.forEach(t),L2a=r(zko," (XLM-RoBERTa model)"),zko.forEach(t),Ve.forEach(t),A2a=i(Od),M(dG.$$.fragment,Od),Od.forEach(t),qd.forEach(t),kIo=i(m),Df=n(m,"H3",{class:!0});var kjo=s(Df);cG=n(kjo,"A",{id:!0,class:!0,href:!0});var SQn=s(cG);xao=n(SQn,"SPAN",{});var xQn=s(xao);M(PU.$$.fragment,xQn),xQn.forEach(t),SQn.forEach(t),R2a=i(kjo),Gao=n(kjo,"SPAN",{});var GQn=s(Gao);k2a=r(GQn,"AutoModelForTextEncoding"),GQn.forEach(t),kjo.forEach(t),SIo=i(m),ZU=n(m,"DIV",{class:!0});var BQn=s(ZU);M(VU.$$.fragment,BQn),BQn.forEach(t),xIo=i(m),qf=n(m,"H3",{class:!0});var Sjo=s(qf);mG=n(Sjo,"A",{id:!0,class:!0,href:!0});var IQn=s(mG);Bao=n(IQn,"SPAN",{});var NQn=s(Bao);M(WU.$$.fragment,NQn),NQn.forEach(t),IQn.forEach(t),S2a=i(Sjo),Iao=n(Sjo,"SPAN",{});var $Qn=s(Iao);x2a=r($Qn,"TFAutoModelForTextEncoding"),$Qn.forEach(t),Sjo.forEach(t),GIo=i(m),JU=n(m,"DIV",{class:!0});var jQn=s(JU);M(UU.$$.fragment,jQn),jQn.forEach(t),BIo=i(m),Of=n(m,"H2",{class:!0});var xjo=s(Of);fG=n(xjo,"A",{id:!0,class:!0,href:!0});var PQn=s(fG);Nao=n(PQn,"SPAN",{});var ZQn=s(Nao);M(XU.$$.fragment,ZQn),ZQn.forEach(t),PQn.forEach(t),G2a=i(xjo),$ao=n(xjo,"SPAN",{});var VQn=s($ao);B2a=r(VQn,"Computer vision"),VQn.forEach(t),xjo.forEach(t),IIo=i(m),MFe=n(m,"P",{});var WQn=s(MFe);I2a=r(WQn,"The following auto classes are available for the following computer vision tasks."),WQn.forEach(t),NIo=i(m),Qf=n(m,"H3",{class:!0});var Gjo=s(Qf);hG=n(Gjo,"A",{id:!0,class:!0,href:!0});var JQn=s(hG);jao=n(JQn,"SPAN",{});var UQn=s(jao);M(DU.$$.fragment,UQn),UQn.forEach(t),JQn.forEach(t),N2a=i(Gjo),Pao=n(Gjo,"SPAN",{});var XQn=s(Pao);$2a=r(XQn,"AutoModelForDepthEstimation"),XQn.forEach(t),Gjo.forEach(t),$Io=i(m),Mr=n(m,"DIV",{class:!0});var Qd=s(Mr);M(qU.$$.fragment,Qd),j2a=i(Qd),Yf=n(Qd,"P",{});var fwe=s(Yf);P2a=r(fwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a depth estimation head) when created
with the `),TFe=n(fwe,"A",{href:!0});var DQn=s(TFe);Z2a=r(DQn,"from_pretrained()"),DQn.forEach(t),V2a=r(fwe," class method or the "),FFe=n(fwe,"A",{href:!0});var qQn=s(FFe);W2a=r(qQn,"from_config()"),qQn.forEach(t),J2a=r(fwe,` class
method.`),fwe.forEach(t),U2a=i(Qd),OU=n(Qd,"P",{});var Bjo=s(OU);X2a=r(Bjo,"This class cannot be instantiated directly using "),Zao=n(Bjo,"CODE",{});var OQn=s(Zao);D2a=r(OQn,"__init__()"),OQn.forEach(t),q2a=r(Bjo," (throws an error)."),Bjo.forEach(t),O2a=i(Qd),ba=n(Qd,"DIV",{class:!0});var qP=s(ba);M(QU.$$.fragment,qP),Q2a=i(qP),Vao=n(qP,"P",{});var QQn=s(Vao);Y2a=r(QQn,"Instantiates one of the model classes of the library (with a depth estimation head) from a configuration."),QQn.forEach(t),z2a=i(qP),zf=n(qP,"P",{});var hwe=s(zf);H2a=r(hwe,`Note:
Loading a model from its configuration file does `),Wao=n(hwe,"STRONG",{});var YQn=s(Wao);K2a=r(YQn,"not"),YQn.forEach(t),e0a=r(hwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),EFe=n(hwe,"A",{href:!0});var zQn=s(EFe);o0a=r(zQn,"from_pretrained()"),zQn.forEach(t),r0a=r(hwe," to load the model weights."),hwe.forEach(t),t0a=i(qP),M(gG.$$.fragment,qP),qP.forEach(t),a0a=i(Qd),_o=n(Qd,"DIV",{class:!0});var fn=s(_o);M(YU.$$.fragment,fn),n0a=i(fn),Jao=n(fn,"P",{});var HQn=s(Jao);s0a=r(HQn,"Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model."),HQn.forEach(t),l0a=i(fn),ps=n(fn,"P",{});var OP=s(ps);i0a=r(OP,"The model class to instantiate is selected based on the "),Uao=n(OP,"CODE",{});var KQn=s(Uao);d0a=r(KQn,"model_type"),KQn.forEach(t),c0a=r(OP,` property of the config object (either
passed as an argument or loaded from `),Xao=n(OP,"CODE",{});var eYn=s(Xao);m0a=r(eYn,"pretrained_model_name_or_path"),eYn.forEach(t),f0a=r(OP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Dao=n(OP,"CODE",{});var oYn=s(Dao);h0a=r(oYn,"pretrained_model_name_or_path"),oYn.forEach(t),g0a=r(OP,":"),OP.forEach(t),u0a=i(fn),zU=n(fn,"UL",{});var Ijo=s(zU);uG=n(Ijo,"LI",{});var Hko=s(uG);qao=n(Hko,"STRONG",{});var rYn=s(qao);p0a=r(rYn,"dpt"),rYn.forEach(t),_0a=r(Hko," \u2014 "),yFe=n(Hko,"A",{href:!0});var tYn=s(yFe);v0a=r(tYn,"DPTForDepthEstimation"),tYn.forEach(t),b0a=r(Hko," (DPT model)"),Hko.forEach(t),M0a=i(Ijo),pG=n(Ijo,"LI",{});var Kko=s(pG);Oao=n(Kko,"STRONG",{});var aYn=s(Oao);T0a=r(aYn,"glpn"),aYn.forEach(t),F0a=r(Kko," \u2014 "),CFe=n(Kko,"A",{href:!0});var nYn=s(CFe);E0a=r(nYn,"GLPNForDepthEstimation"),nYn.forEach(t),y0a=r(Kko," (GLPN model)"),Kko.forEach(t),Ijo.forEach(t),C0a=i(fn),_G=n(fn,"P",{});var eSo=s(_G);w0a=r(eSo,"The model is set in evaluation mode by default using "),Qao=n(eSo,"CODE",{});var sYn=s(Qao);L0a=r(sYn,"model.eval()"),sYn.forEach(t),A0a=r(eSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Yao=n(eSo,"CODE",{});var lYn=s(Yao);R0a=r(lYn,"model.train()"),lYn.forEach(t),eSo.forEach(t),k0a=i(fn),M(vG.$$.fragment,fn),fn.forEach(t),Qd.forEach(t),jIo=i(m),Hf=n(m,"H3",{class:!0});var Njo=s(Hf);bG=n(Njo,"A",{id:!0,class:!0,href:!0});var iYn=s(bG);zao=n(iYn,"SPAN",{});var dYn=s(zao);M(HU.$$.fragment,dYn),dYn.forEach(t),iYn.forEach(t),S0a=i(Njo),Hao=n(Njo,"SPAN",{});var cYn=s(Hao);x0a=r(cYn,"AutoModelForImageClassification"),cYn.forEach(t),Njo.forEach(t),PIo=i(m),Tr=n(m,"DIV",{class:!0});var Yd=s(Tr);M(KU.$$.fragment,Yd),G0a=i(Yd),Kf=n(Yd,"P",{});var gwe=s(Kf);B0a=r(gwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),wFe=n(gwe,"A",{href:!0});var mYn=s(wFe);I0a=r(mYn,"from_pretrained()"),mYn.forEach(t),N0a=r(gwe," class method or the "),LFe=n(gwe,"A",{href:!0});var fYn=s(LFe);$0a=r(fYn,"from_config()"),fYn.forEach(t),j0a=r(gwe,` class
method.`),gwe.forEach(t),P0a=i(Yd),eX=n(Yd,"P",{});var $jo=s(eX);Z0a=r($jo,"This class cannot be instantiated directly using "),Kao=n($jo,"CODE",{});var hYn=s(Kao);V0a=r(hYn,"__init__()"),hYn.forEach(t),W0a=r($jo," (throws an error)."),$jo.forEach(t),J0a=i(Yd),Ma=n(Yd,"DIV",{class:!0});var QP=s(Ma);M(oX.$$.fragment,QP),U0a=i(QP),eno=n(QP,"P",{});var gYn=s(eno);X0a=r(gYn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),gYn.forEach(t),D0a=i(QP),eh=n(QP,"P",{});var uwe=s(eh);q0a=r(uwe,`Note:
Loading a model from its configuration file does `),ono=n(uwe,"STRONG",{});var uYn=s(ono);O0a=r(uYn,"not"),uYn.forEach(t),Q0a=r(uwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),AFe=n(uwe,"A",{href:!0});var pYn=s(AFe);Y0a=r(pYn,"from_pretrained()"),pYn.forEach(t),z0a=r(uwe," to load the model weights."),uwe.forEach(t),H0a=i(QP),M(MG.$$.fragment,QP),QP.forEach(t),K0a=i(Yd),vo=n(Yd,"DIV",{class:!0});var hn=s(vo);M(rX.$$.fragment,hn),e4a=i(hn),rno=n(hn,"P",{});var _Yn=s(rno);o4a=r(_Yn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),_Yn.forEach(t),r4a=i(hn),_s=n(hn,"P",{});var YP=s(_s);t4a=r(YP,"The model class to instantiate is selected based on the "),tno=n(YP,"CODE",{});var vYn=s(tno);a4a=r(vYn,"model_type"),vYn.forEach(t),n4a=r(YP,` property of the config object (either
passed as an argument or loaded from `),ano=n(YP,"CODE",{});var bYn=s(ano);s4a=r(bYn,"pretrained_model_name_or_path"),bYn.forEach(t),l4a=r(YP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nno=n(YP,"CODE",{});var MYn=s(nno);i4a=r(MYn,"pretrained_model_name_or_path"),MYn.forEach(t),d4a=r(YP,":"),YP.forEach(t),c4a=i(hn),le=n(hn,"UL",{});var ce=s(le);TG=n(ce,"LI",{});var oSo=s(TG);sno=n(oSo,"STRONG",{});var TYn=s(sno);m4a=r(TYn,"beit"),TYn.forEach(t),f4a=r(oSo," \u2014 "),RFe=n(oSo,"A",{href:!0});var FYn=s(RFe);h4a=r(FYn,"BeitForImageClassification"),FYn.forEach(t),g4a=r(oSo," (BEiT model)"),oSo.forEach(t),u4a=i(ce),FG=n(ce,"LI",{});var rSo=s(FG);lno=n(rSo,"STRONG",{});var EYn=s(lno);p4a=r(EYn,"bit"),EYn.forEach(t),_4a=r(rSo," \u2014 "),kFe=n(rSo,"A",{href:!0});var yYn=s(kFe);v4a=r(yYn,"BitForImageClassification"),yYn.forEach(t),b4a=r(rSo," (BiT model)"),rSo.forEach(t),M4a=i(ce),EG=n(ce,"LI",{});var tSo=s(EG);ino=n(tSo,"STRONG",{});var CYn=s(ino);T4a=r(CYn,"convnext"),CYn.forEach(t),F4a=r(tSo," \u2014 "),SFe=n(tSo,"A",{href:!0});var wYn=s(SFe);E4a=r(wYn,"ConvNextForImageClassification"),wYn.forEach(t),y4a=r(tSo," (ConvNeXT model)"),tSo.forEach(t),C4a=i(ce),yG=n(ce,"LI",{});var aSo=s(yG);dno=n(aSo,"STRONG",{});var LYn=s(dno);w4a=r(LYn,"convnextv2"),LYn.forEach(t),L4a=r(aSo," \u2014 "),xFe=n(aSo,"A",{href:!0});var AYn=s(xFe);A4a=r(AYn,"ConvNextV2ForImageClassification"),AYn.forEach(t),R4a=r(aSo," (ConvNeXTV2 model)"),aSo.forEach(t),k4a=i(ce),CG=n(ce,"LI",{});var nSo=s(CG);cno=n(nSo,"STRONG",{});var RYn=s(cno);S4a=r(RYn,"cvt"),RYn.forEach(t),x4a=r(nSo," \u2014 "),GFe=n(nSo,"A",{href:!0});var kYn=s(GFe);G4a=r(kYn,"CvtForImageClassification"),kYn.forEach(t),B4a=r(nSo," (CvT model)"),nSo.forEach(t),I4a=i(ce),wG=n(ce,"LI",{});var sSo=s(wG);mno=n(sSo,"STRONG",{});var SYn=s(mno);N4a=r(SYn,"data2vec-vision"),SYn.forEach(t),$4a=r(sSo," \u2014 "),BFe=n(sSo,"A",{href:!0});var xYn=s(BFe);j4a=r(xYn,"Data2VecVisionForImageClassification"),xYn.forEach(t),P4a=r(sSo," (Data2VecVision model)"),sSo.forEach(t),Z4a=i(ce),Ji=n(ce,"LI",{});var eY=s(Ji);fno=n(eY,"STRONG",{});var GYn=s(fno);V4a=r(GYn,"deit"),GYn.forEach(t),W4a=r(eY," \u2014 "),IFe=n(eY,"A",{href:!0});var BYn=s(IFe);J4a=r(BYn,"DeiTForImageClassification"),BYn.forEach(t),U4a=r(eY," or "),NFe=n(eY,"A",{href:!0});var IYn=s(NFe);X4a=r(IYn,"DeiTForImageClassificationWithTeacher"),IYn.forEach(t),D4a=r(eY," (DeiT model)"),eY.forEach(t),q4a=i(ce),LG=n(ce,"LI",{});var lSo=s(LG);hno=n(lSo,"STRONG",{});var NYn=s(hno);O4a=r(NYn,"dinat"),NYn.forEach(t),Q4a=r(lSo," \u2014 "),$Fe=n(lSo,"A",{href:!0});var $Yn=s($Fe);Y4a=r($Yn,"DinatForImageClassification"),$Yn.forEach(t),z4a=r(lSo," (DiNAT model)"),lSo.forEach(t),H4a=i(ce),AG=n(ce,"LI",{});var iSo=s(AG);gno=n(iSo,"STRONG",{});var jYn=s(gno);K4a=r(jYn,"dinov2"),jYn.forEach(t),eMa=r(iSo," \u2014 "),jFe=n(iSo,"A",{href:!0});var PYn=s(jFe);oMa=r(PYn,"Dinov2ForImageClassification"),PYn.forEach(t),rMa=r(iSo," (DINOv2 model)"),iSo.forEach(t),tMa=i(ce),Ui=n(ce,"LI",{});var oY=s(Ui);uno=n(oY,"STRONG",{});var ZYn=s(uno);aMa=r(ZYn,"efficientformer"),ZYn.forEach(t),nMa=r(oY," \u2014 "),PFe=n(oY,"A",{href:!0});var VYn=s(PFe);sMa=r(VYn,"EfficientFormerForImageClassification"),VYn.forEach(t),lMa=r(oY," or "),ZFe=n(oY,"A",{href:!0});var WYn=s(ZFe);iMa=r(WYn,"EfficientFormerForImageClassificationWithTeacher"),WYn.forEach(t),dMa=r(oY," (EfficientFormer model)"),oY.forEach(t),cMa=i(ce),RG=n(ce,"LI",{});var dSo=s(RG);pno=n(dSo,"STRONG",{});var JYn=s(pno);mMa=r(JYn,"efficientnet"),JYn.forEach(t),fMa=r(dSo," \u2014 "),VFe=n(dSo,"A",{href:!0});var UYn=s(VFe);hMa=r(UYn,"EfficientNetForImageClassification"),UYn.forEach(t),gMa=r(dSo," (EfficientNet model)"),dSo.forEach(t),uMa=i(ce),kG=n(ce,"LI",{});var cSo=s(kG);_no=n(cSo,"STRONG",{});var XYn=s(_no);pMa=r(XYn,"focalnet"),XYn.forEach(t),_Ma=r(cSo," \u2014 "),WFe=n(cSo,"A",{href:!0});var DYn=s(WFe);vMa=r(DYn,"FocalNetForImageClassification"),DYn.forEach(t),bMa=r(cSo," (FocalNet model)"),cSo.forEach(t),MMa=i(ce),SG=n(ce,"LI",{});var mSo=s(SG);vno=n(mSo,"STRONG",{});var qYn=s(vno);TMa=r(qYn,"imagegpt"),qYn.forEach(t),FMa=r(mSo," \u2014 "),JFe=n(mSo,"A",{href:!0});var OYn=s(JFe);EMa=r(OYn,"ImageGPTForImageClassification"),OYn.forEach(t),yMa=r(mSo," (ImageGPT model)"),mSo.forEach(t),CMa=i(ce),Xi=n(ce,"LI",{});var rY=s(Xi);bno=n(rY,"STRONG",{});var QYn=s(bno);wMa=r(QYn,"levit"),QYn.forEach(t),LMa=r(rY," \u2014 "),UFe=n(rY,"A",{href:!0});var YYn=s(UFe);AMa=r(YYn,"LevitForImageClassification"),YYn.forEach(t),RMa=r(rY," or "),XFe=n(rY,"A",{href:!0});var zYn=s(XFe);kMa=r(zYn,"LevitForImageClassificationWithTeacher"),zYn.forEach(t),SMa=r(rY," (LeViT model)"),rY.forEach(t),xMa=i(ce),xG=n(ce,"LI",{});var fSo=s(xG);Mno=n(fSo,"STRONG",{});var HYn=s(Mno);GMa=r(HYn,"mobilenet_v1"),HYn.forEach(t),BMa=r(fSo," \u2014 "),DFe=n(fSo,"A",{href:!0});var KYn=s(DFe);IMa=r(KYn,"MobileNetV1ForImageClassification"),KYn.forEach(t),NMa=r(fSo," (MobileNetV1 model)"),fSo.forEach(t),$Ma=i(ce),GG=n(ce,"LI",{});var hSo=s(GG);Tno=n(hSo,"STRONG",{});var ezn=s(Tno);jMa=r(ezn,"mobilenet_v2"),ezn.forEach(t),PMa=r(hSo," \u2014 "),qFe=n(hSo,"A",{href:!0});var ozn=s(qFe);ZMa=r(ozn,"MobileNetV2ForImageClassification"),ozn.forEach(t),VMa=r(hSo," (MobileNetV2 model)"),hSo.forEach(t),WMa=i(ce),BG=n(ce,"LI",{});var gSo=s(BG);Fno=n(gSo,"STRONG",{});var rzn=s(Fno);JMa=r(rzn,"mobilevit"),rzn.forEach(t),UMa=r(gSo," \u2014 "),OFe=n(gSo,"A",{href:!0});var tzn=s(OFe);XMa=r(tzn,"MobileViTForImageClassification"),tzn.forEach(t),DMa=r(gSo," (MobileViT model)"),gSo.forEach(t),qMa=i(ce),IG=n(ce,"LI",{});var uSo=s(IG);Eno=n(uSo,"STRONG",{});var azn=s(Eno);OMa=r(azn,"mobilevitv2"),azn.forEach(t),QMa=r(uSo," \u2014 "),QFe=n(uSo,"A",{href:!0});var nzn=s(QFe);YMa=r(nzn,"MobileViTV2ForImageClassification"),nzn.forEach(t),zMa=r(uSo," (MobileViTV2 model)"),uSo.forEach(t),HMa=i(ce),NG=n(ce,"LI",{});var pSo=s(NG);yno=n(pSo,"STRONG",{});var szn=s(yno);KMa=r(szn,"nat"),szn.forEach(t),eTa=r(pSo," \u2014 "),YFe=n(pSo,"A",{href:!0});var lzn=s(YFe);oTa=r(lzn,"NatForImageClassification"),lzn.forEach(t),rTa=r(pSo," (NAT model)"),pSo.forEach(t),tTa=i(ce),Ta=n(ce,"LI",{});var Dg=s(Ta);Cno=n(Dg,"STRONG",{});var izn=s(Cno);aTa=r(izn,"perceiver"),izn.forEach(t),nTa=r(Dg," \u2014 "),zFe=n(Dg,"A",{href:!0});var dzn=s(zFe);sTa=r(dzn,"PerceiverForImageClassificationLearned"),dzn.forEach(t),lTa=r(Dg," or "),HFe=n(Dg,"A",{href:!0});var czn=s(HFe);iTa=r(czn,"PerceiverForImageClassificationFourier"),czn.forEach(t),dTa=r(Dg," or "),KFe=n(Dg,"A",{href:!0});var mzn=s(KFe);cTa=r(mzn,"PerceiverForImageClassificationConvProcessing"),mzn.forEach(t),mTa=r(Dg," (Perceiver model)"),Dg.forEach(t),fTa=i(ce),$G=n(ce,"LI",{});var _So=s($G);wno=n(_So,"STRONG",{});var fzn=s(wno);hTa=r(fzn,"poolformer"),fzn.forEach(t),gTa=r(_So," \u2014 "),eEe=n(_So,"A",{href:!0});var hzn=s(eEe);uTa=r(hzn,"PoolFormerForImageClassification"),hzn.forEach(t),pTa=r(_So," (PoolFormer model)"),_So.forEach(t),_Ta=i(ce),jG=n(ce,"LI",{});var vSo=s(jG);Lno=n(vSo,"STRONG",{});var gzn=s(Lno);vTa=r(gzn,"pvt"),gzn.forEach(t),bTa=r(vSo," \u2014 "),oEe=n(vSo,"A",{href:!0});var uzn=s(oEe);MTa=r(uzn,"PvtForImageClassification"),uzn.forEach(t),TTa=r(vSo," (PVT model)"),vSo.forEach(t),FTa=i(ce),PG=n(ce,"LI",{});var bSo=s(PG);Ano=n(bSo,"STRONG",{});var pzn=s(Ano);ETa=r(pzn,"regnet"),pzn.forEach(t),yTa=r(bSo," \u2014 "),rEe=n(bSo,"A",{href:!0});var _zn=s(rEe);CTa=r(_zn,"RegNetForImageClassification"),_zn.forEach(t),wTa=r(bSo," (RegNet model)"),bSo.forEach(t),LTa=i(ce),ZG=n(ce,"LI",{});var MSo=s(ZG);Rno=n(MSo,"STRONG",{});var vzn=s(Rno);ATa=r(vzn,"resnet"),vzn.forEach(t),RTa=r(MSo," \u2014 "),tEe=n(MSo,"A",{href:!0});var bzn=s(tEe);kTa=r(bzn,"ResNetForImageClassification"),bzn.forEach(t),STa=r(MSo," (ResNet model)"),MSo.forEach(t),xTa=i(ce),VG=n(ce,"LI",{});var TSo=s(VG);kno=n(TSo,"STRONG",{});var Mzn=s(kno);GTa=r(Mzn,"segformer"),Mzn.forEach(t),BTa=r(TSo," \u2014 "),aEe=n(TSo,"A",{href:!0});var Tzn=s(aEe);ITa=r(Tzn,"SegformerForImageClassification"),Tzn.forEach(t),NTa=r(TSo," (SegFormer model)"),TSo.forEach(t),$Ta=i(ce),WG=n(ce,"LI",{});var FSo=s(WG);Sno=n(FSo,"STRONG",{});var Fzn=s(Sno);jTa=r(Fzn,"swiftformer"),Fzn.forEach(t),PTa=r(FSo," \u2014 "),nEe=n(FSo,"A",{href:!0});var Ezn=s(nEe);ZTa=r(Ezn,"SwiftFormerForImageClassification"),Ezn.forEach(t),VTa=r(FSo," (SwiftFormer model)"),FSo.forEach(t),WTa=i(ce),JG=n(ce,"LI",{});var ESo=s(JG);xno=n(ESo,"STRONG",{});var yzn=s(xno);JTa=r(yzn,"swin"),yzn.forEach(t),UTa=r(ESo," \u2014 "),sEe=n(ESo,"A",{href:!0});var Czn=s(sEe);XTa=r(Czn,"SwinForImageClassification"),Czn.forEach(t),DTa=r(ESo," (Swin Transformer model)"),ESo.forEach(t),qTa=i(ce),UG=n(ce,"LI",{});var ySo=s(UG);Gno=n(ySo,"STRONG",{});var wzn=s(Gno);OTa=r(wzn,"swinv2"),wzn.forEach(t),QTa=r(ySo," \u2014 "),lEe=n(ySo,"A",{href:!0});var Lzn=s(lEe);YTa=r(Lzn,"Swinv2ForImageClassification"),Lzn.forEach(t),zTa=r(ySo," (Swin Transformer V2 model)"),ySo.forEach(t),HTa=i(ce),XG=n(ce,"LI",{});var CSo=s(XG);Bno=n(CSo,"STRONG",{});var Azn=s(Bno);KTa=r(Azn,"van"),Azn.forEach(t),eFa=r(CSo," \u2014 "),iEe=n(CSo,"A",{href:!0});var Rzn=s(iEe);oFa=r(Rzn,"VanForImageClassification"),Rzn.forEach(t),rFa=r(CSo," (VAN model)"),CSo.forEach(t),tFa=i(ce),DG=n(ce,"LI",{});var wSo=s(DG);Ino=n(wSo,"STRONG",{});var kzn=s(Ino);aFa=r(kzn,"vit"),kzn.forEach(t),nFa=r(wSo," \u2014 "),dEe=n(wSo,"A",{href:!0});var Szn=s(dEe);sFa=r(Szn,"ViTForImageClassification"),Szn.forEach(t),lFa=r(wSo," (ViT model)"),wSo.forEach(t),iFa=i(ce),qG=n(ce,"LI",{});var LSo=s(qG);Nno=n(LSo,"STRONG",{});var xzn=s(Nno);dFa=r(xzn,"vit_hybrid"),xzn.forEach(t),cFa=r(LSo," \u2014 "),cEe=n(LSo,"A",{href:!0});var Gzn=s(cEe);mFa=r(Gzn,"ViTHybridForImageClassification"),Gzn.forEach(t),fFa=r(LSo," (ViT Hybrid model)"),LSo.forEach(t),hFa=i(ce),OG=n(ce,"LI",{});var ASo=s(OG);$no=n(ASo,"STRONG",{});var Bzn=s($no);gFa=r(Bzn,"vit_msn"),Bzn.forEach(t),uFa=r(ASo," \u2014 "),mEe=n(ASo,"A",{href:!0});var Izn=s(mEe);pFa=r(Izn,"ViTMSNForImageClassification"),Izn.forEach(t),_Fa=r(ASo," (ViTMSN model)"),ASo.forEach(t),ce.forEach(t),vFa=i(hn),QG=n(hn,"P",{});var RSo=s(QG);bFa=r(RSo,"The model is set in evaluation mode by default using "),jno=n(RSo,"CODE",{});var Nzn=s(jno);MFa=r(Nzn,"model.eval()"),Nzn.forEach(t),TFa=r(RSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Pno=n(RSo,"CODE",{});var $zn=s(Pno);FFa=r($zn,"model.train()"),$zn.forEach(t),RSo.forEach(t),EFa=i(hn),M(YG.$$.fragment,hn),hn.forEach(t),Yd.forEach(t),ZIo=i(m),oh=n(m,"H3",{class:!0});var jjo=s(oh);zG=n(jjo,"A",{id:!0,class:!0,href:!0});var jzn=s(zG);Zno=n(jzn,"SPAN",{});var Pzn=s(Zno);M(tX.$$.fragment,Pzn),Pzn.forEach(t),jzn.forEach(t),yFa=i(jjo),Vno=n(jjo,"SPAN",{});var Zzn=s(Vno);CFa=r(Zzn,"TFAutoModelForImageClassification"),Zzn.forEach(t),jjo.forEach(t),VIo=i(m),Fr=n(m,"DIV",{class:!0});var zd=s(Fr);M(aX.$$.fragment,zd),wFa=i(zd),rh=n(zd,"P",{});var pwe=s(rh);LFa=r(pwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),fEe=n(pwe,"A",{href:!0});var Vzn=s(fEe);AFa=r(Vzn,"from_pretrained()"),Vzn.forEach(t),RFa=r(pwe," class method or the "),hEe=n(pwe,"A",{href:!0});var Wzn=s(hEe);kFa=r(Wzn,"from_config()"),Wzn.forEach(t),SFa=r(pwe,` class
method.`),pwe.forEach(t),xFa=i(zd),nX=n(zd,"P",{});var Pjo=s(nX);GFa=r(Pjo,"This class cannot be instantiated directly using "),Wno=n(Pjo,"CODE",{});var Jzn=s(Wno);BFa=r(Jzn,"__init__()"),Jzn.forEach(t),IFa=r(Pjo," (throws an error)."),Pjo.forEach(t),NFa=i(zd),Fa=n(zd,"DIV",{class:!0});var zP=s(Fa);M(sX.$$.fragment,zP),$Fa=i(zP),Jno=n(zP,"P",{});var Uzn=s(Jno);jFa=r(Uzn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),Uzn.forEach(t),PFa=i(zP),th=n(zP,"P",{});var _we=s(th);ZFa=r(_we,`Note:
Loading a model from its configuration file does `),Uno=n(_we,"STRONG",{});var Xzn=s(Uno);VFa=r(Xzn,"not"),Xzn.forEach(t),WFa=r(_we,` load the model weights. It only affects the
model\u2019s configuration. Use `),gEe=n(_we,"A",{href:!0});var Dzn=s(gEe);JFa=r(Dzn,"from_pretrained()"),Dzn.forEach(t),UFa=r(_we," to load the model weights."),_we.forEach(t),XFa=i(zP),M(HG.$$.fragment,zP),zP.forEach(t),DFa=i(zd),Et=n(zd,"DIV",{class:!0});var Hd=s(Et);M(lX.$$.fragment,Hd),qFa=i(Hd),Xno=n(Hd,"P",{});var qzn=s(Xno);OFa=r(qzn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),qzn.forEach(t),QFa=i(Hd),vs=n(Hd,"P",{});var HP=s(vs);YFa=r(HP,"The model class to instantiate is selected based on the "),Dno=n(HP,"CODE",{});var Ozn=s(Dno);zFa=r(Ozn,"model_type"),Ozn.forEach(t),HFa=r(HP,` property of the config object (either
passed as an argument or loaded from `),qno=n(HP,"CODE",{});var Qzn=s(qno);KFa=r(Qzn,"pretrained_model_name_or_path"),Qzn.forEach(t),eEa=r(HP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ono=n(HP,"CODE",{});var Yzn=s(Ono);oEa=r(Yzn,"pretrained_model_name_or_path"),Yzn.forEach(t),rEa=r(HP,":"),HP.forEach(t),tEa=i(Hd),Ge=n(Hd,"UL",{});var We=s(Ge);KG=n(We,"LI",{});var kSo=s(KG);Qno=n(kSo,"STRONG",{});var zzn=s(Qno);aEa=r(zzn,"convnext"),zzn.forEach(t),nEa=r(kSo," \u2014 "),uEe=n(kSo,"A",{href:!0});var Hzn=s(uEe);sEa=r(Hzn,"TFConvNextForImageClassification"),Hzn.forEach(t),lEa=r(kSo," (ConvNeXT model)"),kSo.forEach(t),iEa=i(We),eB=n(We,"LI",{});var SSo=s(eB);Yno=n(SSo,"STRONG",{});var Kzn=s(Yno);dEa=r(Kzn,"cvt"),Kzn.forEach(t),cEa=r(SSo," \u2014 "),pEe=n(SSo,"A",{href:!0});var eHn=s(pEe);mEa=r(eHn,"TFCvtForImageClassification"),eHn.forEach(t),fEa=r(SSo," (CvT model)"),SSo.forEach(t),hEa=i(We),oB=n(We,"LI",{});var xSo=s(oB);zno=n(xSo,"STRONG",{});var oHn=s(zno);gEa=r(oHn,"data2vec-vision"),oHn.forEach(t),uEa=r(xSo," \u2014 "),_Ee=n(xSo,"A",{href:!0});var rHn=s(_Ee);pEa=r(rHn,"TFData2VecVisionForImageClassification"),rHn.forEach(t),_Ea=r(xSo," (Data2VecVision model)"),xSo.forEach(t),vEa=i(We),Di=n(We,"LI",{});var tY=s(Di);Hno=n(tY,"STRONG",{});var tHn=s(Hno);bEa=r(tHn,"deit"),tHn.forEach(t),MEa=r(tY," \u2014 "),vEe=n(tY,"A",{href:!0});var aHn=s(vEe);TEa=r(aHn,"TFDeiTForImageClassification"),aHn.forEach(t),FEa=r(tY," or "),bEe=n(tY,"A",{href:!0});var nHn=s(bEe);EEa=r(nHn,"TFDeiTForImageClassificationWithTeacher"),nHn.forEach(t),yEa=r(tY," (DeiT model)"),tY.forEach(t),CEa=i(We),qi=n(We,"LI",{});var aY=s(qi);Kno=n(aY,"STRONG",{});var sHn=s(Kno);wEa=r(sHn,"efficientformer"),sHn.forEach(t),LEa=r(aY," \u2014 "),MEe=n(aY,"A",{href:!0});var lHn=s(MEe);AEa=r(lHn,"TFEfficientFormerForImageClassification"),lHn.forEach(t),REa=r(aY," or "),TEe=n(aY,"A",{href:!0});var iHn=s(TEe);kEa=r(iHn,"TFEfficientFormerForImageClassificationWithTeacher"),iHn.forEach(t),SEa=r(aY," (EfficientFormer model)"),aY.forEach(t),xEa=i(We),rB=n(We,"LI",{});var GSo=s(rB);eso=n(GSo,"STRONG",{});var dHn=s(eso);GEa=r(dHn,"mobilevit"),dHn.forEach(t),BEa=r(GSo," \u2014 "),FEe=n(GSo,"A",{href:!0});var cHn=s(FEe);IEa=r(cHn,"TFMobileViTForImageClassification"),cHn.forEach(t),NEa=r(GSo," (MobileViT model)"),GSo.forEach(t),$Ea=i(We),tB=n(We,"LI",{});var BSo=s(tB);oso=n(BSo,"STRONG",{});var mHn=s(oso);jEa=r(mHn,"regnet"),mHn.forEach(t),PEa=r(BSo," \u2014 "),EEe=n(BSo,"A",{href:!0});var fHn=s(EEe);ZEa=r(fHn,"TFRegNetForImageClassification"),fHn.forEach(t),VEa=r(BSo," (RegNet model)"),BSo.forEach(t),WEa=i(We),aB=n(We,"LI",{});var ISo=s(aB);rso=n(ISo,"STRONG",{});var hHn=s(rso);JEa=r(hHn,"resnet"),hHn.forEach(t),UEa=r(ISo," \u2014 "),yEe=n(ISo,"A",{href:!0});var gHn=s(yEe);XEa=r(gHn,"TFResNetForImageClassification"),gHn.forEach(t),DEa=r(ISo," (ResNet model)"),ISo.forEach(t),qEa=i(We),nB=n(We,"LI",{});var NSo=s(nB);tso=n(NSo,"STRONG",{});var uHn=s(tso);OEa=r(uHn,"segformer"),uHn.forEach(t),QEa=r(NSo," \u2014 "),CEe=n(NSo,"A",{href:!0});var pHn=s(CEe);YEa=r(pHn,"TFSegformerForImageClassification"),pHn.forEach(t),zEa=r(NSo," (SegFormer model)"),NSo.forEach(t),HEa=i(We),sB=n(We,"LI",{});var $So=s(sB);aso=n($So,"STRONG",{});var _Hn=s(aso);KEa=r(_Hn,"swin"),_Hn.forEach(t),eya=r($So," \u2014 "),wEe=n($So,"A",{href:!0});var vHn=s(wEe);oya=r(vHn,"TFSwinForImageClassification"),vHn.forEach(t),rya=r($So," (Swin Transformer model)"),$So.forEach(t),tya=i(We),lB=n(We,"LI",{});var jSo=s(lB);nso=n(jSo,"STRONG",{});var bHn=s(nso);aya=r(bHn,"vit"),bHn.forEach(t),nya=r(jSo," \u2014 "),LEe=n(jSo,"A",{href:!0});var MHn=s(LEe);sya=r(MHn,"TFViTForImageClassification"),MHn.forEach(t),lya=r(jSo," (ViT model)"),jSo.forEach(t),We.forEach(t),iya=i(Hd),M(iB.$$.fragment,Hd),Hd.forEach(t),zd.forEach(t),WIo=i(m),ah=n(m,"H3",{class:!0});var Zjo=s(ah);dB=n(Zjo,"A",{id:!0,class:!0,href:!0});var THn=s(dB);sso=n(THn,"SPAN",{});var FHn=s(sso);M(iX.$$.fragment,FHn),FHn.forEach(t),THn.forEach(t),dya=i(Zjo),lso=n(Zjo,"SPAN",{});var EHn=s(lso);cya=r(EHn,"FlaxAutoModelForImageClassification"),EHn.forEach(t),Zjo.forEach(t),JIo=i(m),Er=n(m,"DIV",{class:!0});var Kd=s(Er);M(dX.$$.fragment,Kd),mya=i(Kd),nh=n(Kd,"P",{});var vwe=s(nh);fya=r(vwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),AEe=n(vwe,"A",{href:!0});var yHn=s(AEe);hya=r(yHn,"from_pretrained()"),yHn.forEach(t),gya=r(vwe," class method or the "),REe=n(vwe,"A",{href:!0});var CHn=s(REe);uya=r(CHn,"from_config()"),CHn.forEach(t),pya=r(vwe,` class
method.`),vwe.forEach(t),_ya=i(Kd),cX=n(Kd,"P",{});var Vjo=s(cX);vya=r(Vjo,"This class cannot be instantiated directly using "),iso=n(Vjo,"CODE",{});var wHn=s(iso);bya=r(wHn,"__init__()"),wHn.forEach(t),Mya=r(Vjo," (throws an error)."),Vjo.forEach(t),Tya=i(Kd),Ea=n(Kd,"DIV",{class:!0});var KP=s(Ea);M(mX.$$.fragment,KP),Fya=i(KP),dso=n(KP,"P",{});var LHn=s(dso);Eya=r(LHn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),LHn.forEach(t),yya=i(KP),sh=n(KP,"P",{});var bwe=s(sh);Cya=r(bwe,`Note:
Loading a model from its configuration file does `),cso=n(bwe,"STRONG",{});var AHn=s(cso);wya=r(AHn,"not"),AHn.forEach(t),Lya=r(bwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),kEe=n(bwe,"A",{href:!0});var RHn=s(kEe);Aya=r(RHn,"from_pretrained()"),RHn.forEach(t),Rya=r(bwe," to load the model weights."),bwe.forEach(t),kya=i(KP),M(cB.$$.fragment,KP),KP.forEach(t),Sya=i(Kd),yt=n(Kd,"DIV",{class:!0});var ec=s(yt);M(fX.$$.fragment,ec),xya=i(ec),mso=n(ec,"P",{});var kHn=s(mso);Gya=r(kHn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),kHn.forEach(t),Bya=i(ec),bs=n(ec,"P",{});var eZ=s(bs);Iya=r(eZ,"The model class to instantiate is selected based on the "),fso=n(eZ,"CODE",{});var SHn=s(fso);Nya=r(SHn,"model_type"),SHn.forEach(t),$ya=r(eZ,` property of the config object (either
passed as an argument or loaded from `),hso=n(eZ,"CODE",{});var xHn=s(hso);jya=r(xHn,"pretrained_model_name_or_path"),xHn.forEach(t),Pya=r(eZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),gso=n(eZ,"CODE",{});var GHn=s(gso);Zya=r(GHn,"pretrained_model_name_or_path"),GHn.forEach(t),Vya=r(eZ,":"),eZ.forEach(t),Wya=i(ec),Ms=n(ec,"UL",{});var oZ=s(Ms);mB=n(oZ,"LI",{});var PSo=s(mB);uso=n(PSo,"STRONG",{});var BHn=s(uso);Jya=r(BHn,"beit"),BHn.forEach(t),Uya=r(PSo," \u2014 "),SEe=n(PSo,"A",{href:!0});var IHn=s(SEe);Xya=r(IHn,"FlaxBeitForImageClassification"),IHn.forEach(t),Dya=r(PSo," (BEiT model)"),PSo.forEach(t),qya=i(oZ),fB=n(oZ,"LI",{});var ZSo=s(fB);pso=n(ZSo,"STRONG",{});var NHn=s(pso);Oya=r(NHn,"regnet"),NHn.forEach(t),Qya=r(ZSo," \u2014 "),xEe=n(ZSo,"A",{href:!0});var $Hn=s(xEe);Yya=r($Hn,"FlaxRegNetForImageClassification"),$Hn.forEach(t),zya=r(ZSo," (RegNet model)"),ZSo.forEach(t),Hya=i(oZ),hB=n(oZ,"LI",{});var VSo=s(hB);_so=n(VSo,"STRONG",{});var jHn=s(_so);Kya=r(jHn,"resnet"),jHn.forEach(t),e5a=r(VSo," \u2014 "),GEe=n(VSo,"A",{href:!0});var PHn=s(GEe);o5a=r(PHn,"FlaxResNetForImageClassification"),PHn.forEach(t),r5a=r(VSo," (ResNet model)"),VSo.forEach(t),t5a=i(oZ),gB=n(oZ,"LI",{});var WSo=s(gB);vso=n(WSo,"STRONG",{});var ZHn=s(vso);a5a=r(ZHn,"vit"),ZHn.forEach(t),n5a=r(WSo," \u2014 "),BEe=n(WSo,"A",{href:!0});var VHn=s(BEe);s5a=r(VHn,"FlaxViTForImageClassification"),VHn.forEach(t),l5a=r(WSo," (ViT model)"),WSo.forEach(t),oZ.forEach(t),i5a=i(ec),M(uB.$$.fragment,ec),ec.forEach(t),Kd.forEach(t),UIo=i(m),lh=n(m,"H3",{class:!0});var Wjo=s(lh);pB=n(Wjo,"A",{id:!0,class:!0,href:!0});var WHn=s(pB);bso=n(WHn,"SPAN",{});var JHn=s(bso);M(hX.$$.fragment,JHn),JHn.forEach(t),WHn.forEach(t),d5a=i(Wjo),Mso=n(Wjo,"SPAN",{});var UHn=s(Mso);c5a=r(UHn,"AutoModelForVideoClassification"),UHn.forEach(t),Wjo.forEach(t),XIo=i(m),yr=n(m,"DIV",{class:!0});var oc=s(yr);M(gX.$$.fragment,oc),m5a=i(oc),ih=n(oc,"P",{});var Mwe=s(ih);f5a=r(Mwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a video classification head) when created
with the `),IEe=n(Mwe,"A",{href:!0});var XHn=s(IEe);h5a=r(XHn,"from_pretrained()"),XHn.forEach(t),g5a=r(Mwe," class method or the "),NEe=n(Mwe,"A",{href:!0});var DHn=s(NEe);u5a=r(DHn,"from_config()"),DHn.forEach(t),p5a=r(Mwe,` class
method.`),Mwe.forEach(t),_5a=i(oc),uX=n(oc,"P",{});var Jjo=s(uX);v5a=r(Jjo,"This class cannot be instantiated directly using "),Tso=n(Jjo,"CODE",{});var qHn=s(Tso);b5a=r(qHn,"__init__()"),qHn.forEach(t),M5a=r(Jjo," (throws an error)."),Jjo.forEach(t),T5a=i(oc),ya=n(oc,"DIV",{class:!0});var rZ=s(ya);M(pX.$$.fragment,rZ),F5a=i(rZ),Fso=n(rZ,"P",{});var OHn=s(Fso);E5a=r(OHn,"Instantiates one of the model classes of the library (with a video classification head) from a configuration."),OHn.forEach(t),y5a=i(rZ),dh=n(rZ,"P",{});var Twe=s(dh);C5a=r(Twe,`Note:
Loading a model from its configuration file does `),Eso=n(Twe,"STRONG",{});var QHn=s(Eso);w5a=r(QHn,"not"),QHn.forEach(t),L5a=r(Twe,` load the model weights. It only affects the
model\u2019s configuration. Use `),$Ee=n(Twe,"A",{href:!0});var YHn=s($Ee);A5a=r(YHn,"from_pretrained()"),YHn.forEach(t),R5a=r(Twe," to load the model weights."),Twe.forEach(t),k5a=i(rZ),M(_B.$$.fragment,rZ),rZ.forEach(t),S5a=i(oc),bo=n(oc,"DIV",{class:!0});var gn=s(bo);M(_X.$$.fragment,gn),x5a=i(gn),yso=n(gn,"P",{});var zHn=s(yso);G5a=r(zHn,"Instantiate one of the model classes of the library (with a video classification head) from a pretrained model."),zHn.forEach(t),B5a=i(gn),Ts=n(gn,"P",{});var tZ=s(Ts);I5a=r(tZ,"The model class to instantiate is selected based on the "),Cso=n(tZ,"CODE",{});var HHn=s(Cso);N5a=r(HHn,"model_type"),HHn.forEach(t),$5a=r(tZ,` property of the config object (either
passed as an argument or loaded from `),wso=n(tZ,"CODE",{});var KHn=s(wso);j5a=r(KHn,"pretrained_model_name_or_path"),KHn.forEach(t),P5a=r(tZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Lso=n(tZ,"CODE",{});var eKn=s(Lso);Z5a=r(eKn,"pretrained_model_name_or_path"),eKn.forEach(t),V5a=r(tZ,":"),tZ.forEach(t),W5a=i(gn),ch=n(gn,"UL",{});var Fwe=s(ch);vB=n(Fwe,"LI",{});var JSo=s(vB);Aso=n(JSo,"STRONG",{});var oKn=s(Aso);J5a=r(oKn,"timesformer"),oKn.forEach(t),U5a=r(JSo," \u2014 "),jEe=n(JSo,"A",{href:!0});var rKn=s(jEe);X5a=r(rKn,"TimesformerForVideoClassification"),rKn.forEach(t),D5a=r(JSo," (TimeSformer model)"),JSo.forEach(t),q5a=i(Fwe),bB=n(Fwe,"LI",{});var USo=s(bB);Rso=n(USo,"STRONG",{});var tKn=s(Rso);O5a=r(tKn,"videomae"),tKn.forEach(t),Q5a=r(USo," \u2014 "),PEe=n(USo,"A",{href:!0});var aKn=s(PEe);Y5a=r(aKn,"VideoMAEForVideoClassification"),aKn.forEach(t),z5a=r(USo," (VideoMAE model)"),USo.forEach(t),H5a=i(Fwe),MB=n(Fwe,"LI",{});var XSo=s(MB);kso=n(XSo,"STRONG",{});var nKn=s(kso);K5a=r(nKn,"vivit"),nKn.forEach(t),e9a=r(XSo," \u2014 "),ZEe=n(XSo,"A",{href:!0});var sKn=s(ZEe);o9a=r(sKn,"VivitForVideoClassification"),sKn.forEach(t),r9a=r(XSo," (ViViT model)"),XSo.forEach(t),Fwe.forEach(t),t9a=i(gn),TB=n(gn,"P",{});var DSo=s(TB);a9a=r(DSo,"The model is set in evaluation mode by default using "),Sso=n(DSo,"CODE",{});var lKn=s(Sso);n9a=r(lKn,"model.eval()"),lKn.forEach(t),s9a=r(DSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),xso=n(DSo,"CODE",{});var iKn=s(xso);l9a=r(iKn,"model.train()"),iKn.forEach(t),DSo.forEach(t),i9a=i(gn),M(FB.$$.fragment,gn),gn.forEach(t),oc.forEach(t),DIo=i(m),mh=n(m,"H3",{class:!0});var Ujo=s(mh);EB=n(Ujo,"A",{id:!0,class:!0,href:!0});var dKn=s(EB);Gso=n(dKn,"SPAN",{});var cKn=s(Gso);M(vX.$$.fragment,cKn),cKn.forEach(t),dKn.forEach(t),d9a=i(Ujo),Bso=n(Ujo,"SPAN",{});var mKn=s(Bso);c9a=r(mKn,"AutoModelForMaskedImageModeling"),mKn.forEach(t),Ujo.forEach(t),qIo=i(m),Cr=n(m,"DIV",{class:!0});var rc=s(Cr);M(bX.$$.fragment,rc),m9a=i(rc),fh=n(rc,"P",{});var Ewe=s(fh);f9a=r(Ewe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),VEe=n(Ewe,"A",{href:!0});var fKn=s(VEe);h9a=r(fKn,"from_pretrained()"),fKn.forEach(t),g9a=r(Ewe," class method or the "),WEe=n(Ewe,"A",{href:!0});var hKn=s(WEe);u9a=r(hKn,"from_config()"),hKn.forEach(t),p9a=r(Ewe,` class
method.`),Ewe.forEach(t),_9a=i(rc),MX=n(rc,"P",{});var Xjo=s(MX);v9a=r(Xjo,"This class cannot be instantiated directly using "),Iso=n(Xjo,"CODE",{});var gKn=s(Iso);b9a=r(gKn,"__init__()"),gKn.forEach(t),M9a=r(Xjo," (throws an error)."),Xjo.forEach(t),T9a=i(rc),Ca=n(rc,"DIV",{class:!0});var aZ=s(Ca);M(TX.$$.fragment,aZ),F9a=i(aZ),Nso=n(aZ,"P",{});var uKn=s(Nso);E9a=r(uKn,"Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),uKn.forEach(t),y9a=i(aZ),hh=n(aZ,"P",{});var ywe=s(hh);C9a=r(ywe,`Note:
Loading a model from its configuration file does `),$so=n(ywe,"STRONG",{});var pKn=s($so);w9a=r(pKn,"not"),pKn.forEach(t),L9a=r(ywe,` load the model weights. It only affects the
model\u2019s configuration. Use `),JEe=n(ywe,"A",{href:!0});var _Kn=s(JEe);A9a=r(_Kn,"from_pretrained()"),_Kn.forEach(t),R9a=r(ywe," to load the model weights."),ywe.forEach(t),k9a=i(aZ),M(yB.$$.fragment,aZ),aZ.forEach(t),S9a=i(rc),Mo=n(rc,"DIV",{class:!0});var un=s(Mo);M(FX.$$.fragment,un),x9a=i(un),jso=n(un,"P",{});var vKn=s(jso);G9a=r(vKn,"Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),vKn.forEach(t),B9a=i(un),Fs=n(un,"P",{});var nZ=s(Fs);I9a=r(nZ,"The model class to instantiate is selected based on the "),Pso=n(nZ,"CODE",{});var bKn=s(Pso);N9a=r(bKn,"model_type"),bKn.forEach(t),$9a=r(nZ,` property of the config object (either
passed as an argument or loaded from `),Zso=n(nZ,"CODE",{});var MKn=s(Zso);j9a=r(MKn,"pretrained_model_name_or_path"),MKn.forEach(t),P9a=r(nZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Vso=n(nZ,"CODE",{});var TKn=s(Vso);Z9a=r(TKn,"pretrained_model_name_or_path"),TKn.forEach(t),V9a=r(nZ,":"),nZ.forEach(t),W9a=i(un),Nt=n(un,"UL",{});var tc=s(Nt);CB=n(tc,"LI",{});var qSo=s(CB);Wso=n(qSo,"STRONG",{});var FKn=s(Wso);J9a=r(FKn,"deit"),FKn.forEach(t),U9a=r(qSo," \u2014 "),UEe=n(qSo,"A",{href:!0});var EKn=s(UEe);X9a=r(EKn,"DeiTForMaskedImageModeling"),EKn.forEach(t),D9a=r(qSo," (DeiT model)"),qSo.forEach(t),q9a=i(tc),wB=n(tc,"LI",{});var OSo=s(wB);Jso=n(OSo,"STRONG",{});var yKn=s(Jso);O9a=r(yKn,"focalnet"),yKn.forEach(t),Q9a=r(OSo," \u2014 "),XEe=n(OSo,"A",{href:!0});var CKn=s(XEe);Y9a=r(CKn,"FocalNetForMaskedImageModeling"),CKn.forEach(t),z9a=r(OSo," (FocalNet model)"),OSo.forEach(t),H9a=i(tc),LB=n(tc,"LI",{});var QSo=s(LB);Uso=n(QSo,"STRONG",{});var wKn=s(Uso);K9a=r(wKn,"swin"),wKn.forEach(t),eCa=r(QSo," \u2014 "),DEe=n(QSo,"A",{href:!0});var LKn=s(DEe);oCa=r(LKn,"SwinForMaskedImageModeling"),LKn.forEach(t),rCa=r(QSo," (Swin Transformer model)"),QSo.forEach(t),tCa=i(tc),AB=n(tc,"LI",{});var YSo=s(AB);Xso=n(YSo,"STRONG",{});var AKn=s(Xso);aCa=r(AKn,"swinv2"),AKn.forEach(t),nCa=r(YSo," \u2014 "),qEe=n(YSo,"A",{href:!0});var RKn=s(qEe);sCa=r(RKn,"Swinv2ForMaskedImageModeling"),RKn.forEach(t),lCa=r(YSo," (Swin Transformer V2 model)"),YSo.forEach(t),iCa=i(tc),RB=n(tc,"LI",{});var zSo=s(RB);Dso=n(zSo,"STRONG",{});var kKn=s(Dso);dCa=r(kKn,"vit"),kKn.forEach(t),cCa=r(zSo," \u2014 "),OEe=n(zSo,"A",{href:!0});var SKn=s(OEe);mCa=r(SKn,"ViTForMaskedImageModeling"),SKn.forEach(t),fCa=r(zSo," (ViT model)"),zSo.forEach(t),tc.forEach(t),hCa=i(un),kB=n(un,"P",{});var HSo=s(kB);gCa=r(HSo,"The model is set in evaluation mode by default using "),qso=n(HSo,"CODE",{});var xKn=s(qso);uCa=r(xKn,"model.eval()"),xKn.forEach(t),pCa=r(HSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Oso=n(HSo,"CODE",{});var GKn=s(Oso);_Ca=r(GKn,"model.train()"),GKn.forEach(t),HSo.forEach(t),vCa=i(un),M(SB.$$.fragment,un),un.forEach(t),rc.forEach(t),OIo=i(m),gh=n(m,"H3",{class:!0});var Djo=s(gh);xB=n(Djo,"A",{id:!0,class:!0,href:!0});var BKn=s(xB);Qso=n(BKn,"SPAN",{});var IKn=s(Qso);M(EX.$$.fragment,IKn),IKn.forEach(t),BKn.forEach(t),bCa=i(Djo),Yso=n(Djo,"SPAN",{});var NKn=s(Yso);MCa=r(NKn,"TFAutoModelForMaskedImageModeling"),NKn.forEach(t),Djo.forEach(t),QIo=i(m),wr=n(m,"DIV",{class:!0});var ac=s(wr);M(yX.$$.fragment,ac),TCa=i(ac),uh=n(ac,"P",{});var Cwe=s(uh);FCa=r(Cwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),QEe=n(Cwe,"A",{href:!0});var $Kn=s(QEe);ECa=r($Kn,"from_pretrained()"),$Kn.forEach(t),yCa=r(Cwe," class method or the "),YEe=n(Cwe,"A",{href:!0});var jKn=s(YEe);CCa=r(jKn,"from_config()"),jKn.forEach(t),wCa=r(Cwe,` class
method.`),Cwe.forEach(t),LCa=i(ac),CX=n(ac,"P",{});var qjo=s(CX);ACa=r(qjo,"This class cannot be instantiated directly using "),zso=n(qjo,"CODE",{});var PKn=s(zso);RCa=r(PKn,"__init__()"),PKn.forEach(t),kCa=r(qjo," (throws an error)."),qjo.forEach(t),SCa=i(ac),wa=n(ac,"DIV",{class:!0});var sZ=s(wa);M(wX.$$.fragment,sZ),xCa=i(sZ),Hso=n(sZ,"P",{});var ZKn=s(Hso);GCa=r(ZKn,"Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),ZKn.forEach(t),BCa=i(sZ),ph=n(sZ,"P",{});var wwe=s(ph);ICa=r(wwe,`Note:
Loading a model from its configuration file does `),Kso=n(wwe,"STRONG",{});var VKn=s(Kso);NCa=r(VKn,"not"),VKn.forEach(t),$Ca=r(wwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),zEe=n(wwe,"A",{href:!0});var WKn=s(zEe);jCa=r(WKn,"from_pretrained()"),WKn.forEach(t),PCa=r(wwe," to load the model weights."),wwe.forEach(t),ZCa=i(sZ),M(GB.$$.fragment,sZ),sZ.forEach(t),VCa=i(ac),Ct=n(ac,"DIV",{class:!0});var nc=s(Ct);M(LX.$$.fragment,nc),WCa=i(nc),elo=n(nc,"P",{});var JKn=s(elo);JCa=r(JKn,"Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),JKn.forEach(t),UCa=i(nc),Es=n(nc,"P",{});var lZ=s(Es);XCa=r(lZ,"The model class to instantiate is selected based on the "),olo=n(lZ,"CODE",{});var UKn=s(olo);DCa=r(UKn,"model_type"),UKn.forEach(t),qCa=r(lZ,` property of the config object (either
passed as an argument or loaded from `),rlo=n(lZ,"CODE",{});var XKn=s(rlo);OCa=r(XKn,"pretrained_model_name_or_path"),XKn.forEach(t),QCa=r(lZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),tlo=n(lZ,"CODE",{});var DKn=s(tlo);YCa=r(DKn,"pretrained_model_name_or_path"),DKn.forEach(t),zCa=r(lZ,":"),lZ.forEach(t),HCa=i(nc),AX=n(nc,"UL",{});var Ojo=s(AX);BB=n(Ojo,"LI",{});var KSo=s(BB);alo=n(KSo,"STRONG",{});var qKn=s(alo);KCa=r(qKn,"deit"),qKn.forEach(t),ewa=r(KSo," \u2014 "),HEe=n(KSo,"A",{href:!0});var OKn=s(HEe);owa=r(OKn,"TFDeiTForMaskedImageModeling"),OKn.forEach(t),rwa=r(KSo," (DeiT model)"),KSo.forEach(t),twa=i(Ojo),IB=n(Ojo,"LI",{});var exo=s(IB);nlo=n(exo,"STRONG",{});var QKn=s(nlo);awa=r(QKn,"swin"),QKn.forEach(t),nwa=r(exo," \u2014 "),KEe=n(exo,"A",{href:!0});var YKn=s(KEe);swa=r(YKn,"TFSwinForMaskedImageModeling"),YKn.forEach(t),lwa=r(exo," (Swin Transformer model)"),exo.forEach(t),Ojo.forEach(t),iwa=i(nc),M(NB.$$.fragment,nc),nc.forEach(t),ac.forEach(t),YIo=i(m),_h=n(m,"H3",{class:!0});var Qjo=s(_h);$B=n(Qjo,"A",{id:!0,class:!0,href:!0});var zKn=s($B);slo=n(zKn,"SPAN",{});var HKn=s(slo);M(RX.$$.fragment,HKn),HKn.forEach(t),zKn.forEach(t),dwa=i(Qjo),llo=n(Qjo,"SPAN",{});var KKn=s(llo);cwa=r(KKn,"AutoModelForObjectDetection"),KKn.forEach(t),Qjo.forEach(t),zIo=i(m),Lr=n(m,"DIV",{class:!0});var sc=s(Lr);M(kX.$$.fragment,sc),mwa=i(sc),vh=n(sc,"P",{});var Lwe=s(vh);fwa=r(Lwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created
with the `),eye=n(Lwe,"A",{href:!0});var ees=s(eye);hwa=r(ees,"from_pretrained()"),ees.forEach(t),gwa=r(Lwe," class method or the "),oye=n(Lwe,"A",{href:!0});var oes=s(oye);uwa=r(oes,"from_config()"),oes.forEach(t),pwa=r(Lwe,` class
method.`),Lwe.forEach(t),_wa=i(sc),SX=n(sc,"P",{});var Yjo=s(SX);vwa=r(Yjo,"This class cannot be instantiated directly using "),ilo=n(Yjo,"CODE",{});var res=s(ilo);bwa=r(res,"__init__()"),res.forEach(t),Mwa=r(Yjo," (throws an error)."),Yjo.forEach(t),Twa=i(sc),La=n(sc,"DIV",{class:!0});var iZ=s(La);M(xX.$$.fragment,iZ),Fwa=i(iZ),dlo=n(iZ,"P",{});var tes=s(dlo);Ewa=r(tes,"Instantiates one of the model classes of the library (with a object detection head) from a configuration."),tes.forEach(t),ywa=i(iZ),bh=n(iZ,"P",{});var Awe=s(bh);Cwa=r(Awe,`Note:
Loading a model from its configuration file does `),clo=n(Awe,"STRONG",{});var aes=s(clo);wwa=r(aes,"not"),aes.forEach(t),Lwa=r(Awe,` load the model weights. It only affects the
model\u2019s configuration. Use `),rye=n(Awe,"A",{href:!0});var nes=s(rye);Awa=r(nes,"from_pretrained()"),nes.forEach(t),Rwa=r(Awe," to load the model weights."),Awe.forEach(t),kwa=i(iZ),M(jB.$$.fragment,iZ),iZ.forEach(t),Swa=i(sc),To=n(sc,"DIV",{class:!0});var pn=s(To);M(GX.$$.fragment,pn),xwa=i(pn),mlo=n(pn,"P",{});var ses=s(mlo);Gwa=r(ses,"Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),ses.forEach(t),Bwa=i(pn),ys=n(pn,"P",{});var dZ=s(ys);Iwa=r(dZ,"The model class to instantiate is selected based on the "),flo=n(dZ,"CODE",{});var les=s(flo);Nwa=r(les,"model_type"),les.forEach(t),$wa=r(dZ,` property of the config object (either
passed as an argument or loaded from `),hlo=n(dZ,"CODE",{});var ies=s(hlo);jwa=r(ies,"pretrained_model_name_or_path"),ies.forEach(t),Pwa=r(dZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),glo=n(dZ,"CODE",{});var des=s(glo);Zwa=r(des,"pretrained_model_name_or_path"),des.forEach(t),Vwa=r(dZ,":"),dZ.forEach(t),Wwa=i(pn),Ar=n(pn,"UL",{});var _n=s(Ar);PB=n(_n,"LI",{});var oxo=s(PB);ulo=n(oxo,"STRONG",{});var ces=s(ulo);Jwa=r(ces,"conditional_detr"),ces.forEach(t),Uwa=r(oxo," \u2014 "),tye=n(oxo,"A",{href:!0});var mes=s(tye);Xwa=r(mes,"ConditionalDetrForObjectDetection"),mes.forEach(t),Dwa=r(oxo," (Conditional DETR model)"),oxo.forEach(t),qwa=i(_n),ZB=n(_n,"LI",{});var rxo=s(ZB);plo=n(rxo,"STRONG",{});var fes=s(plo);Owa=r(fes,"deformable_detr"),fes.forEach(t),Qwa=r(rxo," \u2014 "),aye=n(rxo,"A",{href:!0});var hes=s(aye);Ywa=r(hes,"DeformableDetrForObjectDetection"),hes.forEach(t),zwa=r(rxo," (Deformable DETR model)"),rxo.forEach(t),Hwa=i(_n),VB=n(_n,"LI",{});var txo=s(VB);_lo=n(txo,"STRONG",{});var ges=s(_lo);Kwa=r(ges,"deta"),ges.forEach(t),e6a=r(txo," \u2014 "),nye=n(txo,"A",{href:!0});var ues=s(nye);o6a=r(ues,"DetaForObjectDetection"),ues.forEach(t),r6a=r(txo," (DETA model)"),txo.forEach(t),t6a=i(_n),WB=n(_n,"LI",{});var axo=s(WB);vlo=n(axo,"STRONG",{});var pes=s(vlo);a6a=r(pes,"detr"),pes.forEach(t),n6a=r(axo," \u2014 "),sye=n(axo,"A",{href:!0});var _es=s(sye);s6a=r(_es,"DetrForObjectDetection"),_es.forEach(t),l6a=r(axo," (DETR model)"),axo.forEach(t),i6a=i(_n),JB=n(_n,"LI",{});var nxo=s(JB);blo=n(nxo,"STRONG",{});var ves=s(blo);d6a=r(ves,"table-transformer"),ves.forEach(t),c6a=r(nxo," \u2014 "),lye=n(nxo,"A",{href:!0});var bes=s(lye);m6a=r(bes,"TableTransformerForObjectDetection"),bes.forEach(t),f6a=r(nxo," (Table Transformer model)"),nxo.forEach(t),h6a=i(_n),UB=n(_n,"LI",{});var sxo=s(UB);Mlo=n(sxo,"STRONG",{});var Mes=s(Mlo);g6a=r(Mes,"yolos"),Mes.forEach(t),u6a=r(sxo," \u2014 "),iye=n(sxo,"A",{href:!0});var Tes=s(iye);p6a=r(Tes,"YolosForObjectDetection"),Tes.forEach(t),_6a=r(sxo," (YOLOS model)"),sxo.forEach(t),_n.forEach(t),v6a=i(pn),XB=n(pn,"P",{});var lxo=s(XB);b6a=r(lxo,"The model is set in evaluation mode by default using "),Tlo=n(lxo,"CODE",{});var Fes=s(Tlo);M6a=r(Fes,"model.eval()"),Fes.forEach(t),T6a=r(lxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Flo=n(lxo,"CODE",{});var Ees=s(Flo);F6a=r(Ees,"model.train()"),Ees.forEach(t),lxo.forEach(t),E6a=i(pn),M(DB.$$.fragment,pn),pn.forEach(t),sc.forEach(t),HIo=i(m),Mh=n(m,"H3",{class:!0});var zjo=s(Mh);qB=n(zjo,"A",{id:!0,class:!0,href:!0});var yes=s(qB);Elo=n(yes,"SPAN",{});var Ces=s(Elo);M(BX.$$.fragment,Ces),Ces.forEach(t),yes.forEach(t),y6a=i(zjo),ylo=n(zjo,"SPAN",{});var wes=s(ylo);C6a=r(wes,"AutoModelForImageSegmentation"),wes.forEach(t),zjo.forEach(t),KIo=i(m),Rr=n(m,"DIV",{class:!0});var lc=s(Rr);M(IX.$$.fragment,lc),w6a=i(lc),Th=n(lc,"P",{});var Rwe=s(Th);L6a=r(Rwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
with the `),dye=n(Rwe,"A",{href:!0});var Les=s(dye);A6a=r(Les,"from_pretrained()"),Les.forEach(t),R6a=r(Rwe," class method or the "),cye=n(Rwe,"A",{href:!0});var Aes=s(cye);k6a=r(Aes,"from_config()"),Aes.forEach(t),S6a=r(Rwe,` class
method.`),Rwe.forEach(t),x6a=i(lc),NX=n(lc,"P",{});var Hjo=s(NX);G6a=r(Hjo,"This class cannot be instantiated directly using "),Clo=n(Hjo,"CODE",{});var Res=s(Clo);B6a=r(Res,"__init__()"),Res.forEach(t),I6a=r(Hjo," (throws an error)."),Hjo.forEach(t),N6a=i(lc),Aa=n(lc,"DIV",{class:!0});var cZ=s(Aa);M($X.$$.fragment,cZ),$6a=i(cZ),wlo=n(cZ,"P",{});var kes=s(wlo);j6a=r(kes,"Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),kes.forEach(t),P6a=i(cZ),Fh=n(cZ,"P",{});var kwe=s(Fh);Z6a=r(kwe,`Note:
Loading a model from its configuration file does `),Llo=n(kwe,"STRONG",{});var Ses=s(Llo);V6a=r(Ses,"not"),Ses.forEach(t),W6a=r(kwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),mye=n(kwe,"A",{href:!0});var xes=s(mye);J6a=r(xes,"from_pretrained()"),xes.forEach(t),U6a=r(kwe," to load the model weights."),kwe.forEach(t),X6a=i(cZ),M(OB.$$.fragment,cZ),cZ.forEach(t),D6a=i(lc),Fo=n(lc,"DIV",{class:!0});var vn=s(Fo);M(jX.$$.fragment,vn),q6a=i(vn),Alo=n(vn,"P",{});var Ges=s(Alo);O6a=r(Ges,"Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),Ges.forEach(t),Q6a=i(vn),Cs=n(vn,"P",{});var mZ=s(Cs);Y6a=r(mZ,"The model class to instantiate is selected based on the "),Rlo=n(mZ,"CODE",{});var Bes=s(Rlo);z6a=r(Bes,"model_type"),Bes.forEach(t),H6a=r(mZ,` property of the config object (either
passed as an argument or loaded from `),klo=n(mZ,"CODE",{});var Ies=s(klo);K6a=r(Ies,"pretrained_model_name_or_path"),Ies.forEach(t),e8a=r(mZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Slo=n(mZ,"CODE",{});var Nes=s(Slo);o8a=r(Nes,"pretrained_model_name_or_path"),Nes.forEach(t),r8a=r(mZ,":"),mZ.forEach(t),t8a=i(vn),xlo=n(vn,"UL",{});var $es=s(xlo);QB=n($es,"LI",{});var ixo=s(QB);Glo=n(ixo,"STRONG",{});var jes=s(Glo);a8a=r(jes,"detr"),jes.forEach(t),n8a=r(ixo," \u2014 "),fye=n(ixo,"A",{href:!0});var Pes=s(fye);s8a=r(Pes,"DetrForSegmentation"),Pes.forEach(t),l8a=r(ixo," (DETR model)"),ixo.forEach(t),$es.forEach(t),i8a=i(vn),YB=n(vn,"P",{});var dxo=s(YB);d8a=r(dxo,"The model is set in evaluation mode by default using "),Blo=n(dxo,"CODE",{});var Zes=s(Blo);c8a=r(Zes,"model.eval()"),Zes.forEach(t),m8a=r(dxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Ilo=n(dxo,"CODE",{});var Ves=s(Ilo);f8a=r(Ves,"model.train()"),Ves.forEach(t),dxo.forEach(t),h8a=i(vn),M(zB.$$.fragment,vn),vn.forEach(t),lc.forEach(t),eNo=i(m),Eh=n(m,"H3",{class:!0});var Kjo=s(Eh);HB=n(Kjo,"A",{id:!0,class:!0,href:!0});var Wes=s(HB);Nlo=n(Wes,"SPAN",{});var Jes=s(Nlo);M(PX.$$.fragment,Jes),Jes.forEach(t),Wes.forEach(t),g8a=i(Kjo),$lo=n(Kjo,"SPAN",{});var Ues=s($lo);u8a=r(Ues,"AutoModelForSemanticSegmentation"),Ues.forEach(t),Kjo.forEach(t),oNo=i(m),kr=n(m,"DIV",{class:!0});var ic=s(kr);M(ZX.$$.fragment,ic),p8a=i(ic),yh=n(ic,"P",{});var Swe=s(yh);_8a=r(Swe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),hye=n(Swe,"A",{href:!0});var Xes=s(hye);v8a=r(Xes,"from_pretrained()"),Xes.forEach(t),b8a=r(Swe," class method or the "),gye=n(Swe,"A",{href:!0});var Des=s(gye);M8a=r(Des,"from_config()"),Des.forEach(t),T8a=r(Swe,` class
method.`),Swe.forEach(t),F8a=i(ic),VX=n(ic,"P",{});var ePo=s(VX);E8a=r(ePo,"This class cannot be instantiated directly using "),jlo=n(ePo,"CODE",{});var qes=s(jlo);y8a=r(qes,"__init__()"),qes.forEach(t),C8a=r(ePo," (throws an error)."),ePo.forEach(t),w8a=i(ic),Ra=n(ic,"DIV",{class:!0});var fZ=s(Ra);M(WX.$$.fragment,fZ),L8a=i(fZ),Plo=n(fZ,"P",{});var Oes=s(Plo);A8a=r(Oes,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),Oes.forEach(t),R8a=i(fZ),Ch=n(fZ,"P",{});var xwe=s(Ch);k8a=r(xwe,`Note:
Loading a model from its configuration file does `),Zlo=n(xwe,"STRONG",{});var Qes=s(Zlo);S8a=r(Qes,"not"),Qes.forEach(t),x8a=r(xwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),uye=n(xwe,"A",{href:!0});var Yes=s(uye);G8a=r(Yes,"from_pretrained()"),Yes.forEach(t),B8a=r(xwe," to load the model weights."),xwe.forEach(t),I8a=i(fZ),M(KB.$$.fragment,fZ),fZ.forEach(t),N8a=i(ic),Eo=n(ic,"DIV",{class:!0});var bn=s(Eo);M(JX.$$.fragment,bn),$8a=i(bn),Vlo=n(bn,"P",{});var zes=s(Vlo);j8a=r(zes,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),zes.forEach(t),P8a=i(bn),ws=n(bn,"P",{});var hZ=s(ws);Z8a=r(hZ,"The model class to instantiate is selected based on the "),Wlo=n(hZ,"CODE",{});var Hes=s(Wlo);V8a=r(Hes,"model_type"),Hes.forEach(t),W8a=r(hZ,` property of the config object (either
passed as an argument or loaded from `),Jlo=n(hZ,"CODE",{});var Kes=s(Jlo);J8a=r(Kes,"pretrained_model_name_or_path"),Kes.forEach(t),U8a=r(hZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ulo=n(hZ,"CODE",{});var eos=s(Ulo);X8a=r(eos,"pretrained_model_name_or_path"),eos.forEach(t),D8a=r(hZ,":"),hZ.forEach(t),q8a=i(bn),Ye=n(bn,"UL",{});var $o=s(Ye);eI=n($o,"LI",{});var cxo=s(eI);Xlo=n(cxo,"STRONG",{});var oos=s(Xlo);O8a=r(oos,"beit"),oos.forEach(t),Q8a=r(cxo," \u2014 "),pye=n(cxo,"A",{href:!0});var ros=s(pye);Y8a=r(ros,"BeitForSemanticSegmentation"),ros.forEach(t),z8a=r(cxo," (BEiT model)"),cxo.forEach(t),H8a=i($o),oI=n($o,"LI",{});var mxo=s(oI);Dlo=n(mxo,"STRONG",{});var tos=s(Dlo);K8a=r(tos,"data2vec-vision"),tos.forEach(t),e7a=r(mxo," \u2014 "),_ye=n(mxo,"A",{href:!0});var aos=s(_ye);o7a=r(aos,"Data2VecVisionForSemanticSegmentation"),aos.forEach(t),r7a=r(mxo," (Data2VecVision model)"),mxo.forEach(t),t7a=i($o),rI=n($o,"LI",{});var fxo=s(rI);qlo=n(fxo,"STRONG",{});var nos=s(qlo);a7a=r(nos,"dpt"),nos.forEach(t),n7a=r(fxo," \u2014 "),vye=n(fxo,"A",{href:!0});var sos=s(vye);s7a=r(sos,"DPTForSemanticSegmentation"),sos.forEach(t),l7a=r(fxo," (DPT model)"),fxo.forEach(t),i7a=i($o),tI=n($o,"LI",{});var hxo=s(tI);Olo=n(hxo,"STRONG",{});var los=s(Olo);d7a=r(los,"mobilenet_v2"),los.forEach(t),c7a=r(hxo," \u2014 "),bye=n(hxo,"A",{href:!0});var ios=s(bye);m7a=r(ios,"MobileNetV2ForSemanticSegmentation"),ios.forEach(t),f7a=r(hxo," (MobileNetV2 model)"),hxo.forEach(t),h7a=i($o),aI=n($o,"LI",{});var gxo=s(aI);Qlo=n(gxo,"STRONG",{});var dos=s(Qlo);g7a=r(dos,"mobilevit"),dos.forEach(t),u7a=r(gxo," \u2014 "),Mye=n(gxo,"A",{href:!0});var cos=s(Mye);p7a=r(cos,"MobileViTForSemanticSegmentation"),cos.forEach(t),_7a=r(gxo," (MobileViT model)"),gxo.forEach(t),v7a=i($o),nI=n($o,"LI",{});var uxo=s(nI);Ylo=n(uxo,"STRONG",{});var mos=s(Ylo);b7a=r(mos,"mobilevitv2"),mos.forEach(t),M7a=r(uxo," \u2014 "),Tye=n(uxo,"A",{href:!0});var fos=s(Tye);T7a=r(fos,"MobileViTV2ForSemanticSegmentation"),fos.forEach(t),F7a=r(uxo," (MobileViTV2 model)"),uxo.forEach(t),E7a=i($o),sI=n($o,"LI",{});var pxo=s(sI);zlo=n(pxo,"STRONG",{});var hos=s(zlo);y7a=r(hos,"segformer"),hos.forEach(t),C7a=r(pxo," \u2014 "),Fye=n(pxo,"A",{href:!0});var gos=s(Fye);w7a=r(gos,"SegformerForSemanticSegmentation"),gos.forEach(t),L7a=r(pxo," (SegFormer model)"),pxo.forEach(t),A7a=i($o),lI=n($o,"LI",{});var _xo=s(lI);Hlo=n(_xo,"STRONG",{});var uos=s(Hlo);R7a=r(uos,"upernet"),uos.forEach(t),k7a=r(_xo," \u2014 "),Eye=n(_xo,"A",{href:!0});var pos=s(Eye);S7a=r(pos,"UperNetForSemanticSegmentation"),pos.forEach(t),x7a=r(_xo," (UPerNet model)"),_xo.forEach(t),$o.forEach(t),G7a=i(bn),iI=n(bn,"P",{});var vxo=s(iI);B7a=r(vxo,"The model is set in evaluation mode by default using "),Klo=n(vxo,"CODE",{});var _os=s(Klo);I7a=r(_os,"model.eval()"),_os.forEach(t),N7a=r(vxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),eio=n(vxo,"CODE",{});var vos=s(eio);$7a=r(vos,"model.train()"),vos.forEach(t),vxo.forEach(t),j7a=i(bn),M(dI.$$.fragment,bn),bn.forEach(t),ic.forEach(t),rNo=i(m),wh=n(m,"H3",{class:!0});var oPo=s(wh);cI=n(oPo,"A",{id:!0,class:!0,href:!0});var bos=s(cI);oio=n(bos,"SPAN",{});var Mos=s(oio);M(UX.$$.fragment,Mos),Mos.forEach(t),bos.forEach(t),P7a=i(oPo),rio=n(oPo,"SPAN",{});var Tos=s(rio);Z7a=r(Tos,"TFAutoModelForSemanticSegmentation"),Tos.forEach(t),oPo.forEach(t),tNo=i(m),Sr=n(m,"DIV",{class:!0});var dc=s(Sr);M(XX.$$.fragment,dc),V7a=i(dc),Lh=n(dc,"P",{});var Gwe=s(Lh);W7a=r(Gwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),yye=n(Gwe,"A",{href:!0});var Fos=s(yye);J7a=r(Fos,"from_pretrained()"),Fos.forEach(t),U7a=r(Gwe," class method or the "),Cye=n(Gwe,"A",{href:!0});var Eos=s(Cye);X7a=r(Eos,"from_config()"),Eos.forEach(t),D7a=r(Gwe,` class
method.`),Gwe.forEach(t),q7a=i(dc),DX=n(dc,"P",{});var rPo=s(DX);O7a=r(rPo,"This class cannot be instantiated directly using "),tio=n(rPo,"CODE",{});var yos=s(tio);Q7a=r(yos,"__init__()"),yos.forEach(t),Y7a=r(rPo," (throws an error)."),rPo.forEach(t),z7a=i(dc),ka=n(dc,"DIV",{class:!0});var gZ=s(ka);M(qX.$$.fragment,gZ),H7a=i(gZ),aio=n(gZ,"P",{});var Cos=s(aio);K7a=r(Cos,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),Cos.forEach(t),eLa=i(gZ),Ah=n(gZ,"P",{});var Bwe=s(Ah);oLa=r(Bwe,`Note:
Loading a model from its configuration file does `),nio=n(Bwe,"STRONG",{});var wos=s(nio);rLa=r(wos,"not"),wos.forEach(t),tLa=r(Bwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),wye=n(Bwe,"A",{href:!0});var Los=s(wye);aLa=r(Los,"from_pretrained()"),Los.forEach(t),nLa=r(Bwe," to load the model weights."),Bwe.forEach(t),sLa=i(gZ),M(mI.$$.fragment,gZ),gZ.forEach(t),lLa=i(dc),wt=n(dc,"DIV",{class:!0});var cc=s(wt);M(OX.$$.fragment,cc),iLa=i(cc),sio=n(cc,"P",{});var Aos=s(sio);dLa=r(Aos,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),Aos.forEach(t),cLa=i(cc),Ls=n(cc,"P",{});var uZ=s(Ls);mLa=r(uZ,"The model class to instantiate is selected based on the "),lio=n(uZ,"CODE",{});var Ros=s(lio);fLa=r(Ros,"model_type"),Ros.forEach(t),hLa=r(uZ,` property of the config object (either
passed as an argument or loaded from `),iio=n(uZ,"CODE",{});var kos=s(iio);gLa=r(kos,"pretrained_model_name_or_path"),kos.forEach(t),uLa=r(uZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),dio=n(uZ,"CODE",{});var Sos=s(dio);pLa=r(Sos,"pretrained_model_name_or_path"),Sos.forEach(t),_La=r(uZ,":"),uZ.forEach(t),vLa=i(cc),Rh=n(cc,"UL",{});var Iwe=s(Rh);fI=n(Iwe,"LI",{});var bxo=s(fI);cio=n(bxo,"STRONG",{});var xos=s(cio);bLa=r(xos,"data2vec-vision"),xos.forEach(t),MLa=r(bxo," \u2014 "),Lye=n(bxo,"A",{href:!0});var Gos=s(Lye);TLa=r(Gos,"TFData2VecVisionForSemanticSegmentation"),Gos.forEach(t),FLa=r(bxo," (Data2VecVision model)"),bxo.forEach(t),ELa=i(Iwe),hI=n(Iwe,"LI",{});var Mxo=s(hI);mio=n(Mxo,"STRONG",{});var Bos=s(mio);yLa=r(Bos,"mobilevit"),Bos.forEach(t),CLa=r(Mxo," \u2014 "),Aye=n(Mxo,"A",{href:!0});var Ios=s(Aye);wLa=r(Ios,"TFMobileViTForSemanticSegmentation"),Ios.forEach(t),LLa=r(Mxo," (MobileViT model)"),Mxo.forEach(t),ALa=i(Iwe),gI=n(Iwe,"LI",{});var Txo=s(gI);fio=n(Txo,"STRONG",{});var Nos=s(fio);RLa=r(Nos,"segformer"),Nos.forEach(t),kLa=r(Txo," \u2014 "),Rye=n(Txo,"A",{href:!0});var $os=s(Rye);SLa=r($os,"TFSegformerForSemanticSegmentation"),$os.forEach(t),xLa=r(Txo," (SegFormer model)"),Txo.forEach(t),Iwe.forEach(t),GLa=i(cc),M(uI.$$.fragment,cc),cc.forEach(t),dc.forEach(t),aNo=i(m),kh=n(m,"H3",{class:!0});var tPo=s(kh);pI=n(tPo,"A",{id:!0,class:!0,href:!0});var jos=s(pI);hio=n(jos,"SPAN",{});var Pos=s(hio);M(QX.$$.fragment,Pos),Pos.forEach(t),jos.forEach(t),BLa=i(tPo),gio=n(tPo,"SPAN",{});var Zos=s(gio);ILa=r(Zos,"AutoModelForInstanceSegmentation"),Zos.forEach(t),tPo.forEach(t),nNo=i(m),xr=n(m,"DIV",{class:!0});var mc=s(xr);M(YX.$$.fragment,mc),NLa=i(mc),Sh=n(mc,"P",{});var Nwe=s(Sh);$La=r(Nwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created
with the `),kye=n(Nwe,"A",{href:!0});var Vos=s(kye);jLa=r(Vos,"from_pretrained()"),Vos.forEach(t),PLa=r(Nwe," class method or the "),Sye=n(Nwe,"A",{href:!0});var Wos=s(Sye);ZLa=r(Wos,"from_config()"),Wos.forEach(t),VLa=r(Nwe,` class
method.`),Nwe.forEach(t),WLa=i(mc),zX=n(mc,"P",{});var aPo=s(zX);JLa=r(aPo,"This class cannot be instantiated directly using "),uio=n(aPo,"CODE",{});var Jos=s(uio);ULa=r(Jos,"__init__()"),Jos.forEach(t),XLa=r(aPo," (throws an error)."),aPo.forEach(t),DLa=i(mc),Sa=n(mc,"DIV",{class:!0});var pZ=s(Sa);M(HX.$$.fragment,pZ),qLa=i(pZ),pio=n(pZ,"P",{});var Uos=s(pio);OLa=r(Uos,"Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),Uos.forEach(t),QLa=i(pZ),xh=n(pZ,"P",{});var $we=s(xh);YLa=r($we,`Note:
Loading a model from its configuration file does `),_io=n($we,"STRONG",{});var Xos=s(_io);zLa=r(Xos,"not"),Xos.forEach(t),HLa=r($we,` load the model weights. It only affects the
model\u2019s configuration. Use `),xye=n($we,"A",{href:!0});var Dos=s(xye);KLa=r(Dos,"from_pretrained()"),Dos.forEach(t),eAa=r($we," to load the model weights."),$we.forEach(t),oAa=i(pZ),M(_I.$$.fragment,pZ),pZ.forEach(t),rAa=i(mc),yo=n(mc,"DIV",{class:!0});var Mn=s(yo);M(KX.$$.fragment,Mn),tAa=i(Mn),vio=n(Mn,"P",{});var qos=s(vio);aAa=r(qos,"Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),qos.forEach(t),nAa=i(Mn),As=n(Mn,"P",{});var _Z=s(As);sAa=r(_Z,"The model class to instantiate is selected based on the "),bio=n(_Z,"CODE",{});var Oos=s(bio);lAa=r(Oos,"model_type"),Oos.forEach(t),iAa=r(_Z,` property of the config object (either
passed as an argument or loaded from `),Mio=n(_Z,"CODE",{});var Qos=s(Mio);dAa=r(Qos,"pretrained_model_name_or_path"),Qos.forEach(t),cAa=r(_Z,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Tio=n(_Z,"CODE",{});var Yos=s(Tio);mAa=r(Yos,"pretrained_model_name_or_path"),Yos.forEach(t),fAa=r(_Z,":"),_Z.forEach(t),hAa=i(Mn),Fio=n(Mn,"UL",{});var zos=s(Fio);vI=n(zos,"LI",{});var Fxo=s(vI);Eio=n(Fxo,"STRONG",{});var Hos=s(Eio);gAa=r(Hos,"maskformer"),Hos.forEach(t),uAa=r(Fxo," \u2014 "),Gye=n(Fxo,"A",{href:!0});var Kos=s(Gye);pAa=r(Kos,"MaskFormerForInstanceSegmentation"),Kos.forEach(t),_Aa=r(Fxo," (MaskFormer model)"),Fxo.forEach(t),zos.forEach(t),vAa=i(Mn),bI=n(Mn,"P",{});var Exo=s(bI);bAa=r(Exo,"The model is set in evaluation mode by default using "),yio=n(Exo,"CODE",{});var ers=s(yio);MAa=r(ers,"model.eval()"),ers.forEach(t),TAa=r(Exo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Cio=n(Exo,"CODE",{});var ors=s(Cio);FAa=r(ors,"model.train()"),ors.forEach(t),Exo.forEach(t),EAa=i(Mn),M(MI.$$.fragment,Mn),Mn.forEach(t),mc.forEach(t),sNo=i(m),Gh=n(m,"H3",{class:!0});var nPo=s(Gh);TI=n(nPo,"A",{id:!0,class:!0,href:!0});var rrs=s(TI);wio=n(rrs,"SPAN",{});var trs=s(wio);M(eD.$$.fragment,trs),trs.forEach(t),rrs.forEach(t),yAa=i(nPo),Lio=n(nPo,"SPAN",{});var ars=s(Lio);CAa=r(ars,"AutoModelForUniversalSegmentation"),ars.forEach(t),nPo.forEach(t),lNo=i(m),Gr=n(m,"DIV",{class:!0});var fc=s(Gr);M(oD.$$.fragment,fc),wAa=i(fc),Bh=n(fc,"P",{});var jwe=s(Bh);LAa=r(jwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a universal image segmentation head) when created
with the `),Bye=n(jwe,"A",{href:!0});var nrs=s(Bye);AAa=r(nrs,"from_pretrained()"),nrs.forEach(t),RAa=r(jwe," class method or the "),Iye=n(jwe,"A",{href:!0});var srs=s(Iye);kAa=r(srs,"from_config()"),srs.forEach(t),SAa=r(jwe,` class
method.`),jwe.forEach(t),xAa=i(fc),rD=n(fc,"P",{});var sPo=s(rD);GAa=r(sPo,"This class cannot be instantiated directly using "),Aio=n(sPo,"CODE",{});var lrs=s(Aio);BAa=r(lrs,"__init__()"),lrs.forEach(t),IAa=r(sPo," (throws an error)."),sPo.forEach(t),NAa=i(fc),xa=n(fc,"DIV",{class:!0});var vZ=s(xa);M(tD.$$.fragment,vZ),$Aa=i(vZ),Rio=n(vZ,"P",{});var irs=s(Rio);jAa=r(irs,"Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration."),irs.forEach(t),PAa=i(vZ),Ih=n(vZ,"P",{});var Pwe=s(Ih);ZAa=r(Pwe,`Note:
Loading a model from its configuration file does `),kio=n(Pwe,"STRONG",{});var drs=s(kio);VAa=r(drs,"not"),drs.forEach(t),WAa=r(Pwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Nye=n(Pwe,"A",{href:!0});var crs=s(Nye);JAa=r(crs,"from_pretrained()"),crs.forEach(t),UAa=r(Pwe," to load the model weights."),Pwe.forEach(t),XAa=i(vZ),M(FI.$$.fragment,vZ),vZ.forEach(t),DAa=i(fc),Co=n(fc,"DIV",{class:!0});var Tn=s(Co);M(aD.$$.fragment,Tn),qAa=i(Tn),Sio=n(Tn,"P",{});var mrs=s(Sio);OAa=r(mrs,"Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model."),mrs.forEach(t),QAa=i(Tn),Rs=n(Tn,"P",{});var bZ=s(Rs);YAa=r(bZ,"The model class to instantiate is selected based on the "),xio=n(bZ,"CODE",{});var frs=s(xio);zAa=r(frs,"model_type"),frs.forEach(t),HAa=r(bZ,` property of the config object (either
passed as an argument or loaded from `),Gio=n(bZ,"CODE",{});var hrs=s(Gio);KAa=r(hrs,"pretrained_model_name_or_path"),hrs.forEach(t),eRa=r(bZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Bio=n(bZ,"CODE",{});var grs=s(Bio);oRa=r(grs,"pretrained_model_name_or_path"),grs.forEach(t),rRa=r(bZ,":"),bZ.forEach(t),tRa=i(Tn),ks=n(Tn,"UL",{});var MZ=s(ks);EI=n(MZ,"LI",{});var yxo=s(EI);Iio=n(yxo,"STRONG",{});var urs=s(Iio);aRa=r(urs,"detr"),urs.forEach(t),nRa=r(yxo," \u2014 "),$ye=n(yxo,"A",{href:!0});var prs=s($ye);sRa=r(prs,"DetrForSegmentation"),prs.forEach(t),lRa=r(yxo," (DETR model)"),yxo.forEach(t),iRa=i(MZ),yI=n(MZ,"LI",{});var Cxo=s(yI);Nio=n(Cxo,"STRONG",{});var _rs=s(Nio);dRa=r(_rs,"mask2former"),_rs.forEach(t),cRa=r(Cxo," \u2014 "),jye=n(Cxo,"A",{href:!0});var vrs=s(jye);mRa=r(vrs,"Mask2FormerForUniversalSegmentation"),vrs.forEach(t),fRa=r(Cxo," (Mask2Former model)"),Cxo.forEach(t),hRa=i(MZ),CI=n(MZ,"LI",{});var wxo=s(CI);$io=n(wxo,"STRONG",{});var brs=s($io);gRa=r(brs,"maskformer"),brs.forEach(t),uRa=r(wxo," \u2014 "),Pye=n(wxo,"A",{href:!0});var Mrs=s(Pye);pRa=r(Mrs,"MaskFormerForInstanceSegmentation"),Mrs.forEach(t),_Ra=r(wxo," (MaskFormer model)"),wxo.forEach(t),vRa=i(MZ),wI=n(MZ,"LI",{});var Lxo=s(wI);jio=n(Lxo,"STRONG",{});var Trs=s(jio);bRa=r(Trs,"oneformer"),Trs.forEach(t),MRa=r(Lxo," \u2014 "),Zye=n(Lxo,"A",{href:!0});var Frs=s(Zye);TRa=r(Frs,"OneFormerForUniversalSegmentation"),Frs.forEach(t),FRa=r(Lxo," (OneFormer model)"),Lxo.forEach(t),MZ.forEach(t),ERa=i(Tn),LI=n(Tn,"P",{});var Axo=s(LI);yRa=r(Axo,"The model is set in evaluation mode by default using "),Pio=n(Axo,"CODE",{});var Ers=s(Pio);CRa=r(Ers,"model.eval()"),Ers.forEach(t),wRa=r(Axo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Zio=n(Axo,"CODE",{});var yrs=s(Zio);LRa=r(yrs,"model.train()"),yrs.forEach(t),Axo.forEach(t),ARa=i(Tn),M(AI.$$.fragment,Tn),Tn.forEach(t),fc.forEach(t),iNo=i(m),Nh=n(m,"H3",{class:!0});var lPo=s(Nh);RI=n(lPo,"A",{id:!0,class:!0,href:!0});var Crs=s(RI);Vio=n(Crs,"SPAN",{});var wrs=s(Vio);M(nD.$$.fragment,wrs),wrs.forEach(t),Crs.forEach(t),RRa=i(lPo),Wio=n(lPo,"SPAN",{});var Lrs=s(Wio);kRa=r(Lrs,"AutoModelForZeroShotImageClassification"),Lrs.forEach(t),lPo.forEach(t),dNo=i(m),Br=n(m,"DIV",{class:!0});var hc=s(Br);M(sD.$$.fragment,hc),SRa=i(hc),$h=n(hc,"P",{});var Zwe=s($h);xRa=r(Zwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created
with the `),Vye=n(Zwe,"A",{href:!0});var Ars=s(Vye);GRa=r(Ars,"from_pretrained()"),Ars.forEach(t),BRa=r(Zwe," class method or the "),Wye=n(Zwe,"A",{href:!0});var Rrs=s(Wye);IRa=r(Rrs,"from_config()"),Rrs.forEach(t),NRa=r(Zwe,` class
method.`),Zwe.forEach(t),$Ra=i(hc),lD=n(hc,"P",{});var iPo=s(lD);jRa=r(iPo,"This class cannot be instantiated directly using "),Jio=n(iPo,"CODE",{});var krs=s(Jio);PRa=r(krs,"__init__()"),krs.forEach(t),ZRa=r(iPo," (throws an error)."),iPo.forEach(t),VRa=i(hc),Ga=n(hc,"DIV",{class:!0});var TZ=s(Ga);M(iD.$$.fragment,TZ),WRa=i(TZ),Uio=n(TZ,"P",{});var Srs=s(Uio);JRa=r(Srs,"Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),Srs.forEach(t),URa=i(TZ),jh=n(TZ,"P",{});var Vwe=s(jh);XRa=r(Vwe,`Note:
Loading a model from its configuration file does `),Xio=n(Vwe,"STRONG",{});var xrs=s(Xio);DRa=r(xrs,"not"),xrs.forEach(t),qRa=r(Vwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Jye=n(Vwe,"A",{href:!0});var Grs=s(Jye);ORa=r(Grs,"from_pretrained()"),Grs.forEach(t),QRa=r(Vwe," to load the model weights."),Vwe.forEach(t),YRa=i(TZ),M(kI.$$.fragment,TZ),TZ.forEach(t),zRa=i(hc),wo=n(hc,"DIV",{class:!0});var Fn=s(wo);M(dD.$$.fragment,Fn),HRa=i(Fn),Dio=n(Fn,"P",{});var Brs=s(Dio);KRa=r(Brs,"Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),Brs.forEach(t),eka=i(Fn),Ss=n(Fn,"P",{});var FZ=s(Ss);oka=r(FZ,"The model class to instantiate is selected based on the "),qio=n(FZ,"CODE",{});var Irs=s(qio);rka=r(Irs,"model_type"),Irs.forEach(t),tka=r(FZ,` property of the config object (either
passed as an argument or loaded from `),Oio=n(FZ,"CODE",{});var Nrs=s(Oio);aka=r(Nrs,"pretrained_model_name_or_path"),Nrs.forEach(t),nka=r(FZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Qio=n(FZ,"CODE",{});var $rs=s(Qio);ska=r($rs,"pretrained_model_name_or_path"),$rs.forEach(t),lka=r(FZ,":"),FZ.forEach(t),ika=i(Fn),Ir=n(Fn,"UL",{});var En=s(Ir);SI=n(En,"LI",{});var Rxo=s(SI);Yio=n(Rxo,"STRONG",{});var jrs=s(Yio);dka=r(jrs,"align"),jrs.forEach(t),cka=r(Rxo," \u2014 "),Uye=n(Rxo,"A",{href:!0});var Prs=s(Uye);mka=r(Prs,"AlignModel"),Prs.forEach(t),fka=r(Rxo," (ALIGN model)"),Rxo.forEach(t),hka=i(En),xI=n(En,"LI",{});var kxo=s(xI);zio=n(kxo,"STRONG",{});var Zrs=s(zio);gka=r(Zrs,"altclip"),Zrs.forEach(t),uka=r(kxo," \u2014 "),Xye=n(kxo,"A",{href:!0});var Vrs=s(Xye);pka=r(Vrs,"AltCLIPModel"),Vrs.forEach(t),_ka=r(kxo," (AltCLIP model)"),kxo.forEach(t),vka=i(En),GI=n(En,"LI",{});var Sxo=s(GI);Hio=n(Sxo,"STRONG",{});var Wrs=s(Hio);bka=r(Wrs,"blip"),Wrs.forEach(t),Mka=r(Sxo," \u2014 "),Dye=n(Sxo,"A",{href:!0});var Jrs=s(Dye);Tka=r(Jrs,"BlipModel"),Jrs.forEach(t),Fka=r(Sxo," (BLIP model)"),Sxo.forEach(t),Eka=i(En),BI=n(En,"LI",{});var xxo=s(BI);Kio=n(xxo,"STRONG",{});var Urs=s(Kio);yka=r(Urs,"chinese_clip"),Urs.forEach(t),Cka=r(xxo," \u2014 "),qye=n(xxo,"A",{href:!0});var Xrs=s(qye);wka=r(Xrs,"ChineseCLIPModel"),Xrs.forEach(t),Lka=r(xxo," (Chinese-CLIP model)"),xxo.forEach(t),Aka=i(En),II=n(En,"LI",{});var Gxo=s(II);edo=n(Gxo,"STRONG",{});var Drs=s(edo);Rka=r(Drs,"clip"),Drs.forEach(t),kka=r(Gxo," \u2014 "),Oye=n(Gxo,"A",{href:!0});var qrs=s(Oye);Ska=r(qrs,"CLIPModel"),qrs.forEach(t),xka=r(Gxo," (CLIP model)"),Gxo.forEach(t),Gka=i(En),NI=n(En,"LI",{});var Bxo=s(NI);odo=n(Bxo,"STRONG",{});var Ors=s(odo);Bka=r(Ors,"clipseg"),Ors.forEach(t),Ika=r(Bxo," \u2014 "),Qye=n(Bxo,"A",{href:!0});var Qrs=s(Qye);Nka=r(Qrs,"CLIPSegModel"),Qrs.forEach(t),$ka=r(Bxo," (CLIPSeg model)"),Bxo.forEach(t),En.forEach(t),jka=i(Fn),$I=n(Fn,"P",{});var Ixo=s($I);Pka=r(Ixo,"The model is set in evaluation mode by default using "),rdo=n(Ixo,"CODE",{});var Yrs=s(rdo);Zka=r(Yrs,"model.eval()"),Yrs.forEach(t),Vka=r(Ixo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),tdo=n(Ixo,"CODE",{});var zrs=s(tdo);Wka=r(zrs,"model.train()"),zrs.forEach(t),Ixo.forEach(t),Jka=i(Fn),M(jI.$$.fragment,Fn),Fn.forEach(t),hc.forEach(t),cNo=i(m),Ph=n(m,"H3",{class:!0});var dPo=s(Ph);PI=n(dPo,"A",{id:!0,class:!0,href:!0});var Hrs=s(PI);ado=n(Hrs,"SPAN",{});var Krs=s(ado);M(cD.$$.fragment,Krs),Krs.forEach(t),Hrs.forEach(t),Uka=i(dPo),ndo=n(dPo,"SPAN",{});var ets=s(ndo);Xka=r(ets,"TFAutoModelForZeroShotImageClassification"),ets.forEach(t),dPo.forEach(t),mNo=i(m),Nr=n(m,"DIV",{class:!0});var gc=s(Nr);M(mD.$$.fragment,gc),Dka=i(gc),Zh=n(gc,"P",{});var Wwe=s(Zh);qka=r(Wwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created
with the `),Yye=n(Wwe,"A",{href:!0});var ots=s(Yye);Oka=r(ots,"from_pretrained()"),ots.forEach(t),Qka=r(Wwe," class method or the "),zye=n(Wwe,"A",{href:!0});var rts=s(zye);Yka=r(rts,"from_config()"),rts.forEach(t),zka=r(Wwe,` class
method.`),Wwe.forEach(t),Hka=i(gc),fD=n(gc,"P",{});var cPo=s(fD);Kka=r(cPo,"This class cannot be instantiated directly using "),sdo=n(cPo,"CODE",{});var tts=s(sdo);eSa=r(tts,"__init__()"),tts.forEach(t),oSa=r(cPo," (throws an error)."),cPo.forEach(t),rSa=i(gc),Ba=n(gc,"DIV",{class:!0});var EZ=s(Ba);M(hD.$$.fragment,EZ),tSa=i(EZ),ldo=n(EZ,"P",{});var ats=s(ldo);aSa=r(ats,"Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),ats.forEach(t),nSa=i(EZ),Vh=n(EZ,"P",{});var Jwe=s(Vh);sSa=r(Jwe,`Note:
Loading a model from its configuration file does `),ido=n(Jwe,"STRONG",{});var nts=s(ido);lSa=r(nts,"not"),nts.forEach(t),iSa=r(Jwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Hye=n(Jwe,"A",{href:!0});var sts=s(Hye);dSa=r(sts,"from_pretrained()"),sts.forEach(t),cSa=r(Jwe," to load the model weights."),Jwe.forEach(t),mSa=i(EZ),M(ZI.$$.fragment,EZ),EZ.forEach(t),fSa=i(gc),Lt=n(gc,"DIV",{class:!0});var uc=s(Lt);M(gD.$$.fragment,uc),hSa=i(uc),ddo=n(uc,"P",{});var lts=s(ddo);gSa=r(lts,"Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),lts.forEach(t),uSa=i(uc),xs=n(uc,"P",{});var yZ=s(xs);pSa=r(yZ,"The model class to instantiate is selected based on the "),cdo=n(yZ,"CODE",{});var its=s(cdo);_Sa=r(its,"model_type"),its.forEach(t),vSa=r(yZ,` property of the config object (either
passed as an argument or loaded from `),mdo=n(yZ,"CODE",{});var dts=s(mdo);bSa=r(dts,"pretrained_model_name_or_path"),dts.forEach(t),MSa=r(yZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),fdo=n(yZ,"CODE",{});var cts=s(fdo);TSa=r(cts,"pretrained_model_name_or_path"),cts.forEach(t),FSa=r(yZ,":"),yZ.forEach(t),ESa=i(uc),uD=n(uc,"UL",{});var mPo=s(uD);VI=n(mPo,"LI",{});var Nxo=s(VI);hdo=n(Nxo,"STRONG",{});var mts=s(hdo);ySa=r(mts,"blip"),mts.forEach(t),CSa=r(Nxo," \u2014 "),Kye=n(Nxo,"A",{href:!0});var fts=s(Kye);wSa=r(fts,"TFBlipModel"),fts.forEach(t),LSa=r(Nxo," (BLIP model)"),Nxo.forEach(t),ASa=i(mPo),WI=n(mPo,"LI",{});var $xo=s(WI);gdo=n($xo,"STRONG",{});var hts=s(gdo);RSa=r(hts,"clip"),hts.forEach(t),kSa=r($xo," \u2014 "),e5e=n($xo,"A",{href:!0});var gts=s(e5e);SSa=r(gts,"TFCLIPModel"),gts.forEach(t),xSa=r($xo," (CLIP model)"),$xo.forEach(t),mPo.forEach(t),GSa=i(uc),M(JI.$$.fragment,uc),uc.forEach(t),gc.forEach(t),fNo=i(m),Wh=n(m,"H3",{class:!0});var fPo=s(Wh);UI=n(fPo,"A",{id:!0,class:!0,href:!0});var uts=s(UI);udo=n(uts,"SPAN",{});var pts=s(udo);M(pD.$$.fragment,pts),pts.forEach(t),uts.forEach(t),BSa=i(fPo),pdo=n(fPo,"SPAN",{});var _ts=s(pdo);ISa=r(_ts,"AutoModelForZeroShotObjectDetection"),_ts.forEach(t),fPo.forEach(t),hNo=i(m),$r=n(m,"DIV",{class:!0});var pc=s($r);M(_D.$$.fragment,pc),NSa=i(pc),Jh=n(pc,"P",{});var Uwe=s(Jh);$Sa=r(Uwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot object detection head) when created
with the `),o5e=n(Uwe,"A",{href:!0});var vts=s(o5e);jSa=r(vts,"from_pretrained()"),vts.forEach(t),PSa=r(Uwe," class method or the "),r5e=n(Uwe,"A",{href:!0});var bts=s(r5e);ZSa=r(bts,"from_config()"),bts.forEach(t),VSa=r(Uwe,` class
method.`),Uwe.forEach(t),WSa=i(pc),vD=n(pc,"P",{});var hPo=s(vD);JSa=r(hPo,"This class cannot be instantiated directly using "),_do=n(hPo,"CODE",{});var Mts=s(_do);USa=r(Mts,"__init__()"),Mts.forEach(t),XSa=r(hPo," (throws an error)."),hPo.forEach(t),DSa=i(pc),Ia=n(pc,"DIV",{class:!0});var CZ=s(Ia);M(bD.$$.fragment,CZ),qSa=i(CZ),vdo=n(CZ,"P",{});var Tts=s(vdo);OSa=r(Tts,"Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration."),Tts.forEach(t),QSa=i(CZ),Uh=n(CZ,"P",{});var Xwe=s(Uh);YSa=r(Xwe,`Note:
Loading a model from its configuration file does `),bdo=n(Xwe,"STRONG",{});var Fts=s(bdo);zSa=r(Fts,"not"),Fts.forEach(t),HSa=r(Xwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),t5e=n(Xwe,"A",{href:!0});var Ets=s(t5e);KSa=r(Ets,"from_pretrained()"),Ets.forEach(t),exa=r(Xwe," to load the model weights."),Xwe.forEach(t),oxa=i(CZ),M(XI.$$.fragment,CZ),CZ.forEach(t),rxa=i(pc),Lo=n(pc,"DIV",{class:!0});var yn=s(Lo);M(MD.$$.fragment,yn),txa=i(yn),Mdo=n(yn,"P",{});var yts=s(Mdo);axa=r(yts,"Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model."),yts.forEach(t),nxa=i(yn),Gs=n(yn,"P",{});var wZ=s(Gs);sxa=r(wZ,"The model class to instantiate is selected based on the "),Tdo=n(wZ,"CODE",{});var Cts=s(Tdo);lxa=r(Cts,"model_type"),Cts.forEach(t),ixa=r(wZ,` property of the config object (either
passed as an argument or loaded from `),Fdo=n(wZ,"CODE",{});var wts=s(Fdo);dxa=r(wts,"pretrained_model_name_or_path"),wts.forEach(t),cxa=r(wZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Edo=n(wZ,"CODE",{});var Lts=s(Edo);mxa=r(Lts,"pretrained_model_name_or_path"),Lts.forEach(t),fxa=r(wZ,":"),wZ.forEach(t),hxa=i(yn),ydo=n(yn,"UL",{});var Ats=s(ydo);DI=n(Ats,"LI",{});var jxo=s(DI);Cdo=n(jxo,"STRONG",{});var Rts=s(Cdo);gxa=r(Rts,"owlvit"),Rts.forEach(t),uxa=r(jxo," \u2014 "),a5e=n(jxo,"A",{href:!0});var kts=s(a5e);pxa=r(kts,"OwlViTForObjectDetection"),kts.forEach(t),_xa=r(jxo," (OWL-ViT model)"),jxo.forEach(t),Ats.forEach(t),vxa=i(yn),qI=n(yn,"P",{});var Pxo=s(qI);bxa=r(Pxo,"The model is set in evaluation mode by default using "),wdo=n(Pxo,"CODE",{});var Sts=s(wdo);Mxa=r(Sts,"model.eval()"),Sts.forEach(t),Txa=r(Pxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Ldo=n(Pxo,"CODE",{});var xts=s(Ldo);Fxa=r(xts,"model.train()"),xts.forEach(t),Pxo.forEach(t),Exa=i(yn),M(OI.$$.fragment,yn),yn.forEach(t),pc.forEach(t),gNo=i(m),Xh=n(m,"H2",{class:!0});var gPo=s(Xh);QI=n(gPo,"A",{id:!0,class:!0,href:!0});var Gts=s(QI);Ado=n(Gts,"SPAN",{});var Bts=s(Ado);M(TD.$$.fragment,Bts),Bts.forEach(t),Gts.forEach(t),yxa=i(gPo),Rdo=n(gPo,"SPAN",{});var Its=s(Rdo);Cxa=r(Its,"Audio"),Its.forEach(t),gPo.forEach(t),uNo=i(m),n5e=n(m,"P",{});var Nts=s(n5e);wxa=r(Nts,"The following auto classes are available for the following audio tasks."),Nts.forEach(t),pNo=i(m),Dh=n(m,"H3",{class:!0});var uPo=s(Dh);YI=n(uPo,"A",{id:!0,class:!0,href:!0});var $ts=s(YI);kdo=n($ts,"SPAN",{});var jts=s(kdo);M(FD.$$.fragment,jts),jts.forEach(t),$ts.forEach(t),Lxa=i(uPo),Sdo=n(uPo,"SPAN",{});var Pts=s(Sdo);Axa=r(Pts,"AutoModelForAudioClassification"),Pts.forEach(t),uPo.forEach(t),_No=i(m),jr=n(m,"DIV",{class:!0});var _c=s(jr);M(ED.$$.fragment,_c),Rxa=i(_c),qh=n(_c,"P",{});var Dwe=s(qh);kxa=r(Dwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the `),s5e=n(Dwe,"A",{href:!0});var Zts=s(s5e);Sxa=r(Zts,"from_pretrained()"),Zts.forEach(t),xxa=r(Dwe," class method or the "),l5e=n(Dwe,"A",{href:!0});var Vts=s(l5e);Gxa=r(Vts,"from_config()"),Vts.forEach(t),Bxa=r(Dwe,` class
method.`),Dwe.forEach(t),Ixa=i(_c),yD=n(_c,"P",{});var pPo=s(yD);Nxa=r(pPo,"This class cannot be instantiated directly using "),xdo=n(pPo,"CODE",{});var Wts=s(xdo);$xa=r(Wts,"__init__()"),Wts.forEach(t),jxa=r(pPo," (throws an error)."),pPo.forEach(t),Pxa=i(_c),Na=n(_c,"DIV",{class:!0});var LZ=s(Na);M(CD.$$.fragment,LZ),Zxa=i(LZ),Gdo=n(LZ,"P",{});var Jts=s(Gdo);Vxa=r(Jts,"Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),Jts.forEach(t),Wxa=i(LZ),Oh=n(LZ,"P",{});var qwe=s(Oh);Jxa=r(qwe,`Note:
Loading a model from its configuration file does `),Bdo=n(qwe,"STRONG",{});var Uts=s(Bdo);Uxa=r(Uts,"not"),Uts.forEach(t),Xxa=r(qwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),i5e=n(qwe,"A",{href:!0});var Xts=s(i5e);Dxa=r(Xts,"from_pretrained()"),Xts.forEach(t),qxa=r(qwe," to load the model weights."),qwe.forEach(t),Oxa=i(LZ),M(zI.$$.fragment,LZ),LZ.forEach(t),Qxa=i(_c),Ao=n(_c,"DIV",{class:!0});var Cn=s(Ao);M(wD.$$.fragment,Cn),Yxa=i(Cn),Ido=n(Cn,"P",{});var Dts=s(Ido);zxa=r(Dts,"Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),Dts.forEach(t),Hxa=i(Cn),Bs=n(Cn,"P",{});var AZ=s(Bs);Kxa=r(AZ,"The model class to instantiate is selected based on the "),Ndo=n(AZ,"CODE",{});var qts=s(Ndo);eGa=r(qts,"model_type"),qts.forEach(t),oGa=r(AZ,` property of the config object (either
passed as an argument or loaded from `),$do=n(AZ,"CODE",{});var Ots=s($do);rGa=r(Ots,"pretrained_model_name_or_path"),Ots.forEach(t),tGa=r(AZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),jdo=n(AZ,"CODE",{});var Qts=s(jdo);aGa=r(Qts,"pretrained_model_name_or_path"),Qts.forEach(t),nGa=r(AZ,":"),AZ.forEach(t),sGa=i(Cn),Be=n(Cn,"UL",{});var Je=s(Be);HI=n(Je,"LI",{});var Zxo=s(HI);Pdo=n(Zxo,"STRONG",{});var Yts=s(Pdo);lGa=r(Yts,"audio-spectrogram-transformer"),Yts.forEach(t),iGa=r(Zxo," \u2014 "),d5e=n(Zxo,"A",{href:!0});var zts=s(d5e);dGa=r(zts,"ASTForAudioClassification"),zts.forEach(t),cGa=r(Zxo," (Audio Spectrogram Transformer model)"),Zxo.forEach(t),mGa=i(Je),KI=n(Je,"LI",{});var Vxo=s(KI);Zdo=n(Vxo,"STRONG",{});var Hts=s(Zdo);fGa=r(Hts,"data2vec-audio"),Hts.forEach(t),hGa=r(Vxo," \u2014 "),c5e=n(Vxo,"A",{href:!0});var Kts=s(c5e);gGa=r(Kts,"Data2VecAudioForSequenceClassification"),Kts.forEach(t),uGa=r(Vxo," (Data2VecAudio model)"),Vxo.forEach(t),pGa=i(Je),eN=n(Je,"LI",{});var Wxo=s(eN);Vdo=n(Wxo,"STRONG",{});var eas=s(Vdo);_Ga=r(eas,"hubert"),eas.forEach(t),vGa=r(Wxo," \u2014 "),m5e=n(Wxo,"A",{href:!0});var oas=s(m5e);bGa=r(oas,"HubertForSequenceClassification"),oas.forEach(t),MGa=r(Wxo," (Hubert model)"),Wxo.forEach(t),TGa=i(Je),oN=n(Je,"LI",{});var Jxo=s(oN);Wdo=n(Jxo,"STRONG",{});var ras=s(Wdo);FGa=r(ras,"sew"),ras.forEach(t),EGa=r(Jxo," \u2014 "),f5e=n(Jxo,"A",{href:!0});var tas=s(f5e);yGa=r(tas,"SEWForSequenceClassification"),tas.forEach(t),CGa=r(Jxo," (SEW model)"),Jxo.forEach(t),wGa=i(Je),rN=n(Je,"LI",{});var Uxo=s(rN);Jdo=n(Uxo,"STRONG",{});var aas=s(Jdo);LGa=r(aas,"sew-d"),aas.forEach(t),AGa=r(Uxo," \u2014 "),h5e=n(Uxo,"A",{href:!0});var nas=s(h5e);RGa=r(nas,"SEWDForSequenceClassification"),nas.forEach(t),kGa=r(Uxo," (SEW-D model)"),Uxo.forEach(t),SGa=i(Je),tN=n(Je,"LI",{});var Xxo=s(tN);Udo=n(Xxo,"STRONG",{});var sas=s(Udo);xGa=r(sas,"unispeech"),sas.forEach(t),GGa=r(Xxo," \u2014 "),g5e=n(Xxo,"A",{href:!0});var las=s(g5e);BGa=r(las,"UniSpeechForSequenceClassification"),las.forEach(t),IGa=r(Xxo," (UniSpeech model)"),Xxo.forEach(t),NGa=i(Je),aN=n(Je,"LI",{});var Dxo=s(aN);Xdo=n(Dxo,"STRONG",{});var ias=s(Xdo);$Ga=r(ias,"unispeech-sat"),ias.forEach(t),jGa=r(Dxo," \u2014 "),u5e=n(Dxo,"A",{href:!0});var das=s(u5e);PGa=r(das,"UniSpeechSatForSequenceClassification"),das.forEach(t),ZGa=r(Dxo," (UniSpeechSat model)"),Dxo.forEach(t),VGa=i(Je),nN=n(Je,"LI",{});var qxo=s(nN);Ddo=n(qxo,"STRONG",{});var cas=s(Ddo);WGa=r(cas,"wav2vec2"),cas.forEach(t),JGa=r(qxo," \u2014 "),p5e=n(qxo,"A",{href:!0});var mas=s(p5e);UGa=r(mas,"Wav2Vec2ForSequenceClassification"),mas.forEach(t),XGa=r(qxo," (Wav2Vec2 model)"),qxo.forEach(t),DGa=i(Je),sN=n(Je,"LI",{});var Oxo=s(sN);qdo=n(Oxo,"STRONG",{});var fas=s(qdo);qGa=r(fas,"wav2vec2-conformer"),fas.forEach(t),OGa=r(Oxo," \u2014 "),_5e=n(Oxo,"A",{href:!0});var has=s(_5e);QGa=r(has,"Wav2Vec2ConformerForSequenceClassification"),has.forEach(t),YGa=r(Oxo," (Wav2Vec2-Conformer model)"),Oxo.forEach(t),zGa=i(Je),lN=n(Je,"LI",{});var Qxo=s(lN);Odo=n(Qxo,"STRONG",{});var gas=s(Odo);HGa=r(gas,"wavlm"),gas.forEach(t),KGa=r(Qxo," \u2014 "),v5e=n(Qxo,"A",{href:!0});var uas=s(v5e);eBa=r(uas,"WavLMForSequenceClassification"),uas.forEach(t),oBa=r(Qxo," (WavLM model)"),Qxo.forEach(t),rBa=i(Je),iN=n(Je,"LI",{});var Yxo=s(iN);Qdo=n(Yxo,"STRONG",{});var pas=s(Qdo);tBa=r(pas,"whisper"),pas.forEach(t),aBa=r(Yxo," \u2014 "),b5e=n(Yxo,"A",{href:!0});var _as=s(b5e);nBa=r(_as,"WhisperForAudioClassification"),_as.forEach(t),sBa=r(Yxo," (Whisper model)"),Yxo.forEach(t),Je.forEach(t),lBa=i(Cn),dN=n(Cn,"P",{});var zxo=s(dN);iBa=r(zxo,"The model is set in evaluation mode by default using "),Ydo=n(zxo,"CODE",{});var vas=s(Ydo);dBa=r(vas,"model.eval()"),vas.forEach(t),cBa=r(zxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),zdo=n(zxo,"CODE",{});var bas=s(zdo);mBa=r(bas,"model.train()"),bas.forEach(t),zxo.forEach(t),fBa=i(Cn),M(cN.$$.fragment,Cn),Cn.forEach(t),_c.forEach(t),vNo=i(m),Qh=n(m,"H3",{class:!0});var _Po=s(Qh);mN=n(_Po,"A",{id:!0,class:!0,href:!0});var Mas=s(mN);Hdo=n(Mas,"SPAN",{});var Tas=s(Hdo);M(LD.$$.fragment,Tas),Tas.forEach(t),Mas.forEach(t),hBa=i(_Po),Kdo=n(_Po,"SPAN",{});var Fas=s(Kdo);gBa=r(Fas,"AutoModelForAudioFrameClassification"),Fas.forEach(t),_Po.forEach(t),bNo=i(m),Pr=n(m,"DIV",{class:!0});var vc=s(Pr);M(AD.$$.fragment,vc),uBa=i(vc),Yh=n(vc,"P",{});var Owe=s(Yh);pBa=r(Owe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the `),M5e=n(Owe,"A",{href:!0});var Eas=s(M5e);_Ba=r(Eas,"from_pretrained()"),Eas.forEach(t),vBa=r(Owe," class method or the "),T5e=n(Owe,"A",{href:!0});var yas=s(T5e);bBa=r(yas,"from_config()"),yas.forEach(t),MBa=r(Owe,` class
method.`),Owe.forEach(t),TBa=i(vc),RD=n(vc,"P",{});var vPo=s(RD);FBa=r(vPo,"This class cannot be instantiated directly using "),eco=n(vPo,"CODE",{});var Cas=s(eco);EBa=r(Cas,"__init__()"),Cas.forEach(t),yBa=r(vPo," (throws an error)."),vPo.forEach(t),CBa=i(vc),$a=n(vc,"DIV",{class:!0});var RZ=s($a);M(kD.$$.fragment,RZ),wBa=i(RZ),oco=n(RZ,"P",{});var was=s(oco);LBa=r(was,"Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),was.forEach(t),ABa=i(RZ),zh=n(RZ,"P",{});var Qwe=s(zh);RBa=r(Qwe,`Note:
Loading a model from its configuration file does `),rco=n(Qwe,"STRONG",{});var Las=s(rco);kBa=r(Las,"not"),Las.forEach(t),SBa=r(Qwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),F5e=n(Qwe,"A",{href:!0});var Aas=s(F5e);xBa=r(Aas,"from_pretrained()"),Aas.forEach(t),GBa=r(Qwe," to load the model weights."),Qwe.forEach(t),BBa=i(RZ),M(fN.$$.fragment,RZ),RZ.forEach(t),IBa=i(vc),At=n(vc,"DIV",{class:!0});var bc=s(At);M(SD.$$.fragment,bc),NBa=i(bc),tco=n(bc,"P",{});var Ras=s(tco);$Ba=r(Ras,"Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),Ras.forEach(t),jBa=i(bc),Is=n(bc,"P",{});var kZ=s(Is);PBa=r(kZ,"The model class to instantiate is selected based on the "),aco=n(kZ,"CODE",{});var kas=s(aco);ZBa=r(kas,"model_type"),kas.forEach(t),VBa=r(kZ,` property of the config object (either
passed as an argument or loaded from `),nco=n(kZ,"CODE",{});var Sas=s(nco);WBa=r(Sas,"pretrained_model_name_or_path"),Sas.forEach(t),JBa=r(kZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),sco=n(kZ,"CODE",{});var xas=s(sco);UBa=r(xas,"pretrained_model_name_or_path"),xas.forEach(t),XBa=r(kZ,":"),kZ.forEach(t),DBa=i(bc),lco=n(bc,"UL",{});var Gas=s(lco);hN=n(Gas,"LI",{});var Hxo=s(hN);ico=n(Hxo,"STRONG",{});var Bas=s(ico);qBa=r(Bas,"wav2vec2"),Bas.forEach(t),OBa=r(Hxo," \u2014 "),E5e=n(Hxo,"A",{href:!0});var Ias=s(E5e);QBa=r(Ias,"TFWav2Vec2ForSequenceClassification"),Ias.forEach(t),YBa=r(Hxo," (Wav2Vec2 model)"),Hxo.forEach(t),Gas.forEach(t),zBa=i(bc),M(gN.$$.fragment,bc),bc.forEach(t),vc.forEach(t),MNo=i(m),Hh=n(m,"H3",{class:!0});var bPo=s(Hh);uN=n(bPo,"A",{id:!0,class:!0,href:!0});var Nas=s(uN);dco=n(Nas,"SPAN",{});var $as=s(dco);M(xD.$$.fragment,$as),$as.forEach(t),Nas.forEach(t),HBa=i(bPo),cco=n(bPo,"SPAN",{});var jas=s(cco);KBa=r(jas,"TFAutoModelForAudioFrameClassification"),jas.forEach(t),bPo.forEach(t),TNo=i(m),Zr=n(m,"DIV",{class:!0});var Mc=s(Zr);M(GD.$$.fragment,Mc),eIa=i(Mc),Kh=n(Mc,"P",{});var Ywe=s(Kh);oIa=r(Ywe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio frame (token) classification head) when created
with the `),y5e=n(Ywe,"A",{href:!0});var Pas=s(y5e);rIa=r(Pas,"from_pretrained()"),Pas.forEach(t),tIa=r(Ywe," class method or the "),C5e=n(Ywe,"A",{href:!0});var Zas=s(C5e);aIa=r(Zas,"from_config()"),Zas.forEach(t),nIa=r(Ywe,` class
method.`),Ywe.forEach(t),sIa=i(Mc),BD=n(Mc,"P",{});var MPo=s(BD);lIa=r(MPo,"This class cannot be instantiated directly using "),mco=n(MPo,"CODE",{});var Vas=s(mco);iIa=r(Vas,"__init__()"),Vas.forEach(t),dIa=r(MPo," (throws an error)."),MPo.forEach(t),cIa=i(Mc),ja=n(Mc,"DIV",{class:!0});var SZ=s(ja);M(ID.$$.fragment,SZ),mIa=i(SZ),fco=n(SZ,"P",{});var Was=s(fco);fIa=r(Was,"Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),Was.forEach(t),hIa=i(SZ),eg=n(SZ,"P",{});var zwe=s(eg);gIa=r(zwe,`Note:
Loading a model from its configuration file does `),hco=n(zwe,"STRONG",{});var Jas=s(hco);uIa=r(Jas,"not"),Jas.forEach(t),pIa=r(zwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),w5e=n(zwe,"A",{href:!0});var Uas=s(w5e);_Ia=r(Uas,"from_pretrained()"),Uas.forEach(t),vIa=r(zwe," to load the model weights."),zwe.forEach(t),bIa=i(SZ),M(pN.$$.fragment,SZ),SZ.forEach(t),MIa=i(Mc),Ro=n(Mc,"DIV",{class:!0});var wn=s(Ro);M(ND.$$.fragment,wn),TIa=i(wn),gco=n(wn,"P",{});var Xas=s(gco);FIa=r(Xas,"Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),Xas.forEach(t),EIa=i(wn),Ns=n(wn,"P",{});var xZ=s(Ns);yIa=r(xZ,"The model class to instantiate is selected based on the "),uco=n(xZ,"CODE",{});var Das=s(uco);CIa=r(Das,"model_type"),Das.forEach(t),wIa=r(xZ,` property of the config object (either
passed as an argument or loaded from `),pco=n(xZ,"CODE",{});var qas=s(pco);LIa=r(qas,"pretrained_model_name_or_path"),qas.forEach(t),AIa=r(xZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),_co=n(xZ,"CODE",{});var Oas=s(_co);RIa=r(Oas,"pretrained_model_name_or_path"),Oas.forEach(t),kIa=r(xZ,":"),xZ.forEach(t),SIa=i(wn),$t=n(wn,"UL",{});var Tc=s($t);_N=n(Tc,"LI",{});var Kxo=s(_N);vco=n(Kxo,"STRONG",{});var Qas=s(vco);xIa=r(Qas,"data2vec-audio"),Qas.forEach(t),GIa=r(Kxo," \u2014 "),L5e=n(Kxo,"A",{href:!0});var Yas=s(L5e);BIa=r(Yas,"Data2VecAudioForAudioFrameClassification"),Yas.forEach(t),IIa=r(Kxo," (Data2VecAudio model)"),Kxo.forEach(t),NIa=i(Tc),vN=n(Tc,"LI",{});var eGo=s(vN);bco=n(eGo,"STRONG",{});var zas=s(bco);$Ia=r(zas,"unispeech-sat"),zas.forEach(t),jIa=r(eGo," \u2014 "),A5e=n(eGo,"A",{href:!0});var Has=s(A5e);PIa=r(Has,"UniSpeechSatForAudioFrameClassification"),Has.forEach(t),ZIa=r(eGo," (UniSpeechSat model)"),eGo.forEach(t),VIa=i(Tc),bN=n(Tc,"LI",{});var oGo=s(bN);Mco=n(oGo,"STRONG",{});var Kas=s(Mco);WIa=r(Kas,"wav2vec2"),Kas.forEach(t),JIa=r(oGo," \u2014 "),R5e=n(oGo,"A",{href:!0});var ens=s(R5e);UIa=r(ens,"Wav2Vec2ForAudioFrameClassification"),ens.forEach(t),XIa=r(oGo," (Wav2Vec2 model)"),oGo.forEach(t),DIa=i(Tc),MN=n(Tc,"LI",{});var rGo=s(MN);Tco=n(rGo,"STRONG",{});var ons=s(Tco);qIa=r(ons,"wav2vec2-conformer"),ons.forEach(t),OIa=r(rGo," \u2014 "),k5e=n(rGo,"A",{href:!0});var rns=s(k5e);QIa=r(rns,"Wav2Vec2ConformerForAudioFrameClassification"),rns.forEach(t),YIa=r(rGo," (Wav2Vec2-Conformer model)"),rGo.forEach(t),zIa=i(Tc),TN=n(Tc,"LI",{});var tGo=s(TN);Fco=n(tGo,"STRONG",{});var tns=s(Fco);HIa=r(tns,"wavlm"),tns.forEach(t),KIa=r(tGo," \u2014 "),S5e=n(tGo,"A",{href:!0});var ans=s(S5e);eNa=r(ans,"WavLMForAudioFrameClassification"),ans.forEach(t),oNa=r(tGo," (WavLM model)"),tGo.forEach(t),Tc.forEach(t),rNa=i(wn),FN=n(wn,"P",{});var aGo=s(FN);tNa=r(aGo,"The model is set in evaluation mode by default using "),Eco=n(aGo,"CODE",{});var nns=s(Eco);aNa=r(nns,"model.eval()"),nns.forEach(t),nNa=r(aGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),yco=n(aGo,"CODE",{});var sns=s(yco);sNa=r(sns,"model.train()"),sns.forEach(t),aGo.forEach(t),lNa=i(wn),M(EN.$$.fragment,wn),wn.forEach(t),Mc.forEach(t),FNo=i(m),og=n(m,"H3",{class:!0});var TPo=s(og);yN=n(TPo,"A",{id:!0,class:!0,href:!0});var lns=s(yN);Cco=n(lns,"SPAN",{});var ins=s(Cco);M($D.$$.fragment,ins),ins.forEach(t),lns.forEach(t),iNa=i(TPo),wco=n(TPo,"SPAN",{});var dns=s(wco);dNa=r(dns,"AutoModelForCTC"),dns.forEach(t),TPo.forEach(t),ENo=i(m),Vr=n(m,"DIV",{class:!0});var Fc=s(Vr);M(jD.$$.fragment,Fc),cNa=i(Fc),rg=n(Fc,"P",{});var Hwe=s(rg);mNa=r(Hwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a connectionist temporal classification head) when created
with the `),x5e=n(Hwe,"A",{href:!0});var cns=s(x5e);fNa=r(cns,"from_pretrained()"),cns.forEach(t),hNa=r(Hwe," class method or the "),G5e=n(Hwe,"A",{href:!0});var mns=s(G5e);gNa=r(mns,"from_config()"),mns.forEach(t),uNa=r(Hwe,` class
method.`),Hwe.forEach(t),pNa=i(Fc),PD=n(Fc,"P",{});var FPo=s(PD);_Na=r(FPo,"This class cannot be instantiated directly using "),Lco=n(FPo,"CODE",{});var fns=s(Lco);vNa=r(fns,"__init__()"),fns.forEach(t),bNa=r(FPo," (throws an error)."),FPo.forEach(t),MNa=i(Fc),Pa=n(Fc,"DIV",{class:!0});var GZ=s(Pa);M(ZD.$$.fragment,GZ),TNa=i(GZ),Aco=n(GZ,"P",{});var hns=s(Aco);FNa=r(hns,"Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),hns.forEach(t),ENa=i(GZ),tg=n(GZ,"P",{});var Kwe=s(tg);yNa=r(Kwe,`Note:
Loading a model from its configuration file does `),Rco=n(Kwe,"STRONG",{});var gns=s(Rco);CNa=r(gns,"not"),gns.forEach(t),wNa=r(Kwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),B5e=n(Kwe,"A",{href:!0});var uns=s(B5e);LNa=r(uns,"from_pretrained()"),uns.forEach(t),ANa=r(Kwe," to load the model weights."),Kwe.forEach(t),RNa=i(GZ),M(CN.$$.fragment,GZ),GZ.forEach(t),kNa=i(Fc),ko=n(Fc,"DIV",{class:!0});var Ln=s(ko);M(VD.$$.fragment,Ln),SNa=i(Ln),kco=n(Ln,"P",{});var pns=s(kco);xNa=r(pns,"Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model."),pns.forEach(t),GNa=i(Ln),$s=n(Ln,"P",{});var BZ=s($s);BNa=r(BZ,"The model class to instantiate is selected based on the "),Sco=n(BZ,"CODE",{});var _ns=s(Sco);INa=r(_ns,"model_type"),_ns.forEach(t),NNa=r(BZ,` property of the config object (either
passed as an argument or loaded from `),xco=n(BZ,"CODE",{});var vns=s(xco);$Na=r(vns,"pretrained_model_name_or_path"),vns.forEach(t),jNa=r(BZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Gco=n(BZ,"CODE",{});var bns=s(Gco);PNa=r(bns,"pretrained_model_name_or_path"),bns.forEach(t),ZNa=r(BZ,":"),BZ.forEach(t),VNa=i(Ln),je=n(Ln,"UL",{});var Oe=s(je);wN=n(Oe,"LI",{});var nGo=s(wN);Bco=n(nGo,"STRONG",{});var Mns=s(Bco);WNa=r(Mns,"data2vec-audio"),Mns.forEach(t),JNa=r(nGo," \u2014 "),I5e=n(nGo,"A",{href:!0});var Tns=s(I5e);UNa=r(Tns,"Data2VecAudioForCTC"),Tns.forEach(t),XNa=r(nGo," (Data2VecAudio model)"),nGo.forEach(t),DNa=i(Oe),LN=n(Oe,"LI",{});var sGo=s(LN);Ico=n(sGo,"STRONG",{});var Fns=s(Ico);qNa=r(Fns,"hubert"),Fns.forEach(t),ONa=r(sGo," \u2014 "),N5e=n(sGo,"A",{href:!0});var Ens=s(N5e);QNa=r(Ens,"HubertForCTC"),Ens.forEach(t),YNa=r(sGo," (Hubert model)"),sGo.forEach(t),zNa=i(Oe),AN=n(Oe,"LI",{});var lGo=s(AN);Nco=n(lGo,"STRONG",{});var yns=s(Nco);HNa=r(yns,"mctct"),yns.forEach(t),KNa=r(lGo," \u2014 "),$5e=n(lGo,"A",{href:!0});var Cns=s($5e);e$a=r(Cns,"MCTCTForCTC"),Cns.forEach(t),o$a=r(lGo," (M-CTC-T model)"),lGo.forEach(t),r$a=i(Oe),RN=n(Oe,"LI",{});var iGo=s(RN);$co=n(iGo,"STRONG",{});var wns=s($co);t$a=r(wns,"sew"),wns.forEach(t),a$a=r(iGo," \u2014 "),j5e=n(iGo,"A",{href:!0});var Lns=s(j5e);n$a=r(Lns,"SEWForCTC"),Lns.forEach(t),s$a=r(iGo," (SEW model)"),iGo.forEach(t),l$a=i(Oe),kN=n(Oe,"LI",{});var dGo=s(kN);jco=n(dGo,"STRONG",{});var Ans=s(jco);i$a=r(Ans,"sew-d"),Ans.forEach(t),d$a=r(dGo," \u2014 "),P5e=n(dGo,"A",{href:!0});var Rns=s(P5e);c$a=r(Rns,"SEWDForCTC"),Rns.forEach(t),m$a=r(dGo," (SEW-D model)"),dGo.forEach(t),f$a=i(Oe),SN=n(Oe,"LI",{});var cGo=s(SN);Pco=n(cGo,"STRONG",{});var kns=s(Pco);h$a=r(kns,"unispeech"),kns.forEach(t),g$a=r(cGo," \u2014 "),Z5e=n(cGo,"A",{href:!0});var Sns=s(Z5e);u$a=r(Sns,"UniSpeechForCTC"),Sns.forEach(t),p$a=r(cGo," (UniSpeech model)"),cGo.forEach(t),_$a=i(Oe),xN=n(Oe,"LI",{});var mGo=s(xN);Zco=n(mGo,"STRONG",{});var xns=s(Zco);v$a=r(xns,"unispeech-sat"),xns.forEach(t),b$a=r(mGo," \u2014 "),V5e=n(mGo,"A",{href:!0});var Gns=s(V5e);M$a=r(Gns,"UniSpeechSatForCTC"),Gns.forEach(t),T$a=r(mGo," (UniSpeechSat model)"),mGo.forEach(t),F$a=i(Oe),GN=n(Oe,"LI",{});var fGo=s(GN);Vco=n(fGo,"STRONG",{});var Bns=s(Vco);E$a=r(Bns,"wav2vec2"),Bns.forEach(t),y$a=r(fGo," \u2014 "),W5e=n(fGo,"A",{href:!0});var Ins=s(W5e);C$a=r(Ins,"Wav2Vec2ForCTC"),Ins.forEach(t),w$a=r(fGo," (Wav2Vec2 model)"),fGo.forEach(t),L$a=i(Oe),BN=n(Oe,"LI",{});var hGo=s(BN);Wco=n(hGo,"STRONG",{});var Nns=s(Wco);A$a=r(Nns,"wav2vec2-conformer"),Nns.forEach(t),R$a=r(hGo," \u2014 "),J5e=n(hGo,"A",{href:!0});var $ns=s(J5e);k$a=r($ns,"Wav2Vec2ConformerForCTC"),$ns.forEach(t),S$a=r(hGo," (Wav2Vec2-Conformer model)"),hGo.forEach(t),x$a=i(Oe),IN=n(Oe,"LI",{});var gGo=s(IN);Jco=n(gGo,"STRONG",{});var jns=s(Jco);G$a=r(jns,"wavlm"),jns.forEach(t),B$a=r(gGo," \u2014 "),U5e=n(gGo,"A",{href:!0});var Pns=s(U5e);I$a=r(Pns,"WavLMForCTC"),Pns.forEach(t),N$a=r(gGo," (WavLM model)"),gGo.forEach(t),Oe.forEach(t),$$a=i(Ln),NN=n(Ln,"P",{});var uGo=s(NN);j$a=r(uGo,"The model is set in evaluation mode by default using "),Uco=n(uGo,"CODE",{});var Zns=s(Uco);P$a=r(Zns,"model.eval()"),Zns.forEach(t),Z$a=r(uGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Xco=n(uGo,"CODE",{});var Vns=s(Xco);V$a=r(Vns,"model.train()"),Vns.forEach(t),uGo.forEach(t),W$a=i(Ln),M($N.$$.fragment,Ln),Ln.forEach(t),Fc.forEach(t),yNo=i(m),ag=n(m,"H3",{class:!0});var EPo=s(ag);jN=n(EPo,"A",{id:!0,class:!0,href:!0});var Wns=s(jN);Dco=n(Wns,"SPAN",{});var Jns=s(Dco);M(WD.$$.fragment,Jns),Jns.forEach(t),Wns.forEach(t),J$a=i(EPo),qco=n(EPo,"SPAN",{});var Uns=s(qco);U$a=r(Uns,"AutoModelForSpeechSeq2Seq"),Uns.forEach(t),EPo.forEach(t),CNo=i(m),Wr=n(m,"DIV",{class:!0});var Ec=s(Wr);M(JD.$$.fragment,Ec),X$a=i(Ec),ng=n(Ec,"P",{});var e6e=s(ng);D$a=r(e6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),X5e=n(e6e,"A",{href:!0});var Xns=s(X5e);q$a=r(Xns,"from_pretrained()"),Xns.forEach(t),O$a=r(e6e," class method or the "),D5e=n(e6e,"A",{href:!0});var Dns=s(D5e);Q$a=r(Dns,"from_config()"),Dns.forEach(t),Y$a=r(e6e,` class
method.`),e6e.forEach(t),z$a=i(Ec),UD=n(Ec,"P",{});var yPo=s(UD);H$a=r(yPo,"This class cannot be instantiated directly using "),Oco=n(yPo,"CODE",{});var qns=s(Oco);K$a=r(qns,"__init__()"),qns.forEach(t),eja=r(yPo," (throws an error)."),yPo.forEach(t),oja=i(Ec),Za=n(Ec,"DIV",{class:!0});var IZ=s(Za);M(XD.$$.fragment,IZ),rja=i(IZ),Qco=n(IZ,"P",{});var Ons=s(Qco);tja=r(Ons,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),Ons.forEach(t),aja=i(IZ),sg=n(IZ,"P",{});var o6e=s(sg);nja=r(o6e,`Note:
Loading a model from its configuration file does `),Yco=n(o6e,"STRONG",{});var Qns=s(Yco);sja=r(Qns,"not"),Qns.forEach(t),lja=r(o6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),q5e=n(o6e,"A",{href:!0});var Yns=s(q5e);ija=r(Yns,"from_pretrained()"),Yns.forEach(t),dja=r(o6e," to load the model weights."),o6e.forEach(t),cja=i(IZ),M(PN.$$.fragment,IZ),IZ.forEach(t),mja=i(Ec),So=n(Ec,"DIV",{class:!0});var An=s(So);M(DD.$$.fragment,An),fja=i(An),zco=n(An,"P",{});var zns=s(zco);hja=r(zns,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),zns.forEach(t),gja=i(An),js=n(An,"P",{});var NZ=s(js);uja=r(NZ,"The model class to instantiate is selected based on the "),Hco=n(NZ,"CODE",{});var Hns=s(Hco);pja=r(Hns,"model_type"),Hns.forEach(t),_ja=r(NZ,` property of the config object (either
passed as an argument or loaded from `),Kco=n(NZ,"CODE",{});var Kns=s(Kco);vja=r(Kns,"pretrained_model_name_or_path"),Kns.forEach(t),bja=r(NZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),emo=n(NZ,"CODE",{});var ess=s(emo);Mja=r(ess,"pretrained_model_name_or_path"),ess.forEach(t),Tja=r(NZ,":"),NZ.forEach(t),Fja=i(An),jt=n(An,"UL",{});var yc=s(jt);ZN=n(yc,"LI",{});var pGo=s(ZN);omo=n(pGo,"STRONG",{});var oss=s(omo);Eja=r(oss,"pop2piano"),oss.forEach(t),yja=r(pGo," \u2014 "),O5e=n(pGo,"A",{href:!0});var rss=s(O5e);Cja=r(rss,"Pop2PianoForConditionalGeneration"),rss.forEach(t),wja=r(pGo," (Pop2Piano model)"),pGo.forEach(t),Lja=i(yc),VN=n(yc,"LI",{});var _Go=s(VN);rmo=n(_Go,"STRONG",{});var tss=s(rmo);Aja=r(tss,"speech-encoder-decoder"),tss.forEach(t),Rja=r(_Go," \u2014 "),Q5e=n(_Go,"A",{href:!0});var ass=s(Q5e);kja=r(ass,"SpeechEncoderDecoderModel"),ass.forEach(t),Sja=r(_Go," (Speech Encoder decoder model)"),_Go.forEach(t),xja=i(yc),WN=n(yc,"LI",{});var vGo=s(WN);tmo=n(vGo,"STRONG",{});var nss=s(tmo);Gja=r(nss,"speech_to_text"),nss.forEach(t),Bja=r(vGo," \u2014 "),Y5e=n(vGo,"A",{href:!0});var sss=s(Y5e);Ija=r(sss,"Speech2TextForConditionalGeneration"),sss.forEach(t),Nja=r(vGo," (Speech2Text model)"),vGo.forEach(t),$ja=i(yc),JN=n(yc,"LI",{});var bGo=s(JN);amo=n(bGo,"STRONG",{});var lss=s(amo);jja=r(lss,"speecht5"),lss.forEach(t),Pja=r(bGo," \u2014 "),z5e=n(bGo,"A",{href:!0});var iss=s(z5e);Zja=r(iss,"SpeechT5ForSpeechToText"),iss.forEach(t),Vja=r(bGo," (SpeechT5 model)"),bGo.forEach(t),Wja=i(yc),UN=n(yc,"LI",{});var MGo=s(UN);nmo=n(MGo,"STRONG",{});var dss=s(nmo);Jja=r(dss,"whisper"),dss.forEach(t),Uja=r(MGo," \u2014 "),H5e=n(MGo,"A",{href:!0});var css=s(H5e);Xja=r(css,"WhisperForConditionalGeneration"),css.forEach(t),Dja=r(MGo," (Whisper model)"),MGo.forEach(t),yc.forEach(t),qja=i(An),XN=n(An,"P",{});var TGo=s(XN);Oja=r(TGo,"The model is set in evaluation mode by default using "),smo=n(TGo,"CODE",{});var mss=s(smo);Qja=r(mss,"model.eval()"),mss.forEach(t),Yja=r(TGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),lmo=n(TGo,"CODE",{});var fss=s(lmo);zja=r(fss,"model.train()"),fss.forEach(t),TGo.forEach(t),Hja=i(An),M(DN.$$.fragment,An),An.forEach(t),Ec.forEach(t),wNo=i(m),lg=n(m,"H3",{class:!0});var CPo=s(lg);qN=n(CPo,"A",{id:!0,class:!0,href:!0});var hss=s(qN);imo=n(hss,"SPAN",{});var gss=s(imo);M(qD.$$.fragment,gss),gss.forEach(t),hss.forEach(t),Kja=i(CPo),dmo=n(CPo,"SPAN",{});var uss=s(dmo);ePa=r(uss,"TFAutoModelForSpeechSeq2Seq"),uss.forEach(t),CPo.forEach(t),LNo=i(m),Jr=n(m,"DIV",{class:!0});var Cc=s(Jr);M(OD.$$.fragment,Cc),oPa=i(Cc),ig=n(Cc,"P",{});var r6e=s(ig);rPa=r(r6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),K5e=n(r6e,"A",{href:!0});var pss=s(K5e);tPa=r(pss,"from_pretrained()"),pss.forEach(t),aPa=r(r6e," class method or the "),e9e=n(r6e,"A",{href:!0});var _ss=s(e9e);nPa=r(_ss,"from_config()"),_ss.forEach(t),sPa=r(r6e,` class
method.`),r6e.forEach(t),lPa=i(Cc),QD=n(Cc,"P",{});var wPo=s(QD);iPa=r(wPo,"This class cannot be instantiated directly using "),cmo=n(wPo,"CODE",{});var vss=s(cmo);dPa=r(vss,"__init__()"),vss.forEach(t),cPa=r(wPo," (throws an error)."),wPo.forEach(t),mPa=i(Cc),Va=n(Cc,"DIV",{class:!0});var $Z=s(Va);M(YD.$$.fragment,$Z),fPa=i($Z),mmo=n($Z,"P",{});var bss=s(mmo);hPa=r(bss,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),bss.forEach(t),gPa=i($Z),dg=n($Z,"P",{});var t6e=s(dg);uPa=r(t6e,`Note:
Loading a model from its configuration file does `),fmo=n(t6e,"STRONG",{});var Mss=s(fmo);pPa=r(Mss,"not"),Mss.forEach(t),_Pa=r(t6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),o9e=n(t6e,"A",{href:!0});var Tss=s(o9e);vPa=r(Tss,"from_pretrained()"),Tss.forEach(t),bPa=r(t6e," to load the model weights."),t6e.forEach(t),MPa=i($Z),M(ON.$$.fragment,$Z),$Z.forEach(t),TPa=i(Cc),Rt=n(Cc,"DIV",{class:!0});var wc=s(Rt);M(zD.$$.fragment,wc),FPa=i(wc),hmo=n(wc,"P",{});var Fss=s(hmo);EPa=r(Fss,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),Fss.forEach(t),yPa=i(wc),Ps=n(wc,"P",{});var jZ=s(Ps);CPa=r(jZ,"The model class to instantiate is selected based on the "),gmo=n(jZ,"CODE",{});var Ess=s(gmo);wPa=r(Ess,"model_type"),Ess.forEach(t),LPa=r(jZ,` property of the config object (either
passed as an argument or loaded from `),umo=n(jZ,"CODE",{});var yss=s(umo);APa=r(yss,"pretrained_model_name_or_path"),yss.forEach(t),RPa=r(jZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),pmo=n(jZ,"CODE",{});var Css=s(pmo);kPa=r(Css,"pretrained_model_name_or_path"),Css.forEach(t),SPa=r(jZ,":"),jZ.forEach(t),xPa=i(wc),HD=n(wc,"UL",{});var LPo=s(HD);QN=n(LPo,"LI",{});var FGo=s(QN);_mo=n(FGo,"STRONG",{});var wss=s(_mo);GPa=r(wss,"speech_to_text"),wss.forEach(t),BPa=r(FGo," \u2014 "),r9e=n(FGo,"A",{href:!0});var Lss=s(r9e);IPa=r(Lss,"TFSpeech2TextForConditionalGeneration"),Lss.forEach(t),NPa=r(FGo," (Speech2Text model)"),FGo.forEach(t),$Pa=i(LPo),YN=n(LPo,"LI",{});var EGo=s(YN);vmo=n(EGo,"STRONG",{});var Ass=s(vmo);jPa=r(Ass,"whisper"),Ass.forEach(t),PPa=r(EGo," \u2014 "),t9e=n(EGo,"A",{href:!0});var Rss=s(t9e);ZPa=r(Rss,"TFWhisperForConditionalGeneration"),Rss.forEach(t),VPa=r(EGo," (Whisper model)"),EGo.forEach(t),LPo.forEach(t),WPa=i(wc),M(zN.$$.fragment,wc),wc.forEach(t),Cc.forEach(t),ANo=i(m),cg=n(m,"H3",{class:!0});var APo=s(cg);HN=n(APo,"A",{id:!0,class:!0,href:!0});var kss=s(HN);bmo=n(kss,"SPAN",{});var Sss=s(bmo);M(KD.$$.fragment,Sss),Sss.forEach(t),kss.forEach(t),JPa=i(APo),Mmo=n(APo,"SPAN",{});var xss=s(Mmo);UPa=r(xss,"FlaxAutoModelForSpeechSeq2Seq"),xss.forEach(t),APo.forEach(t),RNo=i(m),Ur=n(m,"DIV",{class:!0});var Lc=s(Ur);M(eq.$$.fragment,Lc),XPa=i(Lc),mg=n(Lc,"P",{});var a6e=s(mg);DPa=r(a6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),a9e=n(a6e,"A",{href:!0});var Gss=s(a9e);qPa=r(Gss,"from_pretrained()"),Gss.forEach(t),OPa=r(a6e," class method or the "),n9e=n(a6e,"A",{href:!0});var Bss=s(n9e);QPa=r(Bss,"from_config()"),Bss.forEach(t),YPa=r(a6e,` class
method.`),a6e.forEach(t),zPa=i(Lc),oq=n(Lc,"P",{});var RPo=s(oq);HPa=r(RPo,"This class cannot be instantiated directly using "),Tmo=n(RPo,"CODE",{});var Iss=s(Tmo);KPa=r(Iss,"__init__()"),Iss.forEach(t),eZa=r(RPo," (throws an error)."),RPo.forEach(t),oZa=i(Lc),Wa=n(Lc,"DIV",{class:!0});var PZ=s(Wa);M(rq.$$.fragment,PZ),rZa=i(PZ),Fmo=n(PZ,"P",{});var Nss=s(Fmo);tZa=r(Nss,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),Nss.forEach(t),aZa=i(PZ),fg=n(PZ,"P",{});var n6e=s(fg);nZa=r(n6e,`Note:
Loading a model from its configuration file does `),Emo=n(n6e,"STRONG",{});var $ss=s(Emo);sZa=r($ss,"not"),$ss.forEach(t),lZa=r(n6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),s9e=n(n6e,"A",{href:!0});var jss=s(s9e);iZa=r(jss,"from_pretrained()"),jss.forEach(t),dZa=r(n6e," to load the model weights."),n6e.forEach(t),cZa=i(PZ),M(KN.$$.fragment,PZ),PZ.forEach(t),mZa=i(Lc),kt=n(Lc,"DIV",{class:!0});var Ac=s(kt);M(tq.$$.fragment,Ac),fZa=i(Ac),ymo=n(Ac,"P",{});var Pss=s(ymo);hZa=r(Pss,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),Pss.forEach(t),gZa=i(Ac),Zs=n(Ac,"P",{});var ZZ=s(Zs);uZa=r(ZZ,"The model class to instantiate is selected based on the "),Cmo=n(ZZ,"CODE",{});var Zss=s(Cmo);pZa=r(Zss,"model_type"),Zss.forEach(t),_Za=r(ZZ,` property of the config object (either
passed as an argument or loaded from `),wmo=n(ZZ,"CODE",{});var Vss=s(wmo);vZa=r(Vss,"pretrained_model_name_or_path"),Vss.forEach(t),bZa=r(ZZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Lmo=n(ZZ,"CODE",{});var Wss=s(Lmo);MZa=r(Wss,"pretrained_model_name_or_path"),Wss.forEach(t),TZa=r(ZZ,":"),ZZ.forEach(t),FZa=i(Ac),aq=n(Ac,"UL",{});var kPo=s(aq);e$=n(kPo,"LI",{});var yGo=s(e$);Amo=n(yGo,"STRONG",{});var Jss=s(Amo);EZa=r(Jss,"speech-encoder-decoder"),Jss.forEach(t),yZa=r(yGo," \u2014 "),l9e=n(yGo,"A",{href:!0});var Uss=s(l9e);CZa=r(Uss,"FlaxSpeechEncoderDecoderModel"),Uss.forEach(t),wZa=r(yGo," (Speech Encoder decoder model)"),yGo.forEach(t),LZa=i(kPo),o$=n(kPo,"LI",{});var CGo=s(o$);Rmo=n(CGo,"STRONG",{});var Xss=s(Rmo);AZa=r(Xss,"whisper"),Xss.forEach(t),RZa=r(CGo," \u2014 "),i9e=n(CGo,"A",{href:!0});var Dss=s(i9e);kZa=r(Dss,"FlaxWhisperForConditionalGeneration"),Dss.forEach(t),SZa=r(CGo," (Whisper model)"),CGo.forEach(t),kPo.forEach(t),xZa=i(Ac),M(r$.$$.fragment,Ac),Ac.forEach(t),Lc.forEach(t),kNo=i(m),hg=n(m,"H3",{class:!0});var SPo=s(hg);t$=n(SPo,"A",{id:!0,class:!0,href:!0});var qss=s(t$);kmo=n(qss,"SPAN",{});var Oss=s(kmo);M(nq.$$.fragment,Oss),Oss.forEach(t),qss.forEach(t),GZa=i(SPo),Smo=n(SPo,"SPAN",{});var Qss=s(Smo);BZa=r(Qss,"AutoModelForAudioXVector"),Qss.forEach(t),SPo.forEach(t),SNo=i(m),Xr=n(m,"DIV",{class:!0});var Rc=s(Xr);M(sq.$$.fragment,Rc),IZa=i(Rc),gg=n(Rc,"P",{});var s6e=s(gg);NZa=r(s6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio retrieval via x-vector head) when created
with the `),d9e=n(s6e,"A",{href:!0});var Yss=s(d9e);$Za=r(Yss,"from_pretrained()"),Yss.forEach(t),jZa=r(s6e," class method or the "),c9e=n(s6e,"A",{href:!0});var zss=s(c9e);PZa=r(zss,"from_config()"),zss.forEach(t),ZZa=r(s6e,` class
method.`),s6e.forEach(t),VZa=i(Rc),lq=n(Rc,"P",{});var xPo=s(lq);WZa=r(xPo,"This class cannot be instantiated directly using "),xmo=n(xPo,"CODE",{});var Hss=s(xmo);JZa=r(Hss,"__init__()"),Hss.forEach(t),UZa=r(xPo," (throws an error)."),xPo.forEach(t),XZa=i(Rc),Ja=n(Rc,"DIV",{class:!0});var VZ=s(Ja);M(iq.$$.fragment,VZ),DZa=i(VZ),Gmo=n(VZ,"P",{});var Kss=s(Gmo);qZa=r(Kss,"Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration."),Kss.forEach(t),OZa=i(VZ),ug=n(VZ,"P",{});var l6e=s(ug);QZa=r(l6e,`Note:
Loading a model from its configuration file does `),Bmo=n(l6e,"STRONG",{});var els=s(Bmo);YZa=r(els,"not"),els.forEach(t),zZa=r(l6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),m9e=n(l6e,"A",{href:!0});var ols=s(m9e);HZa=r(ols,"from_pretrained()"),ols.forEach(t),KZa=r(l6e," to load the model weights."),l6e.forEach(t),eVa=i(VZ),M(a$.$$.fragment,VZ),VZ.forEach(t),oVa=i(Rc),xo=n(Rc,"DIV",{class:!0});var Rn=s(xo);M(dq.$$.fragment,Rn),rVa=i(Rn),Imo=n(Rn,"P",{});var rls=s(Imo);tVa=r(rls,"Instantiate one of the model classes of the library (with a audio retrieval via x-vector head) from a pretrained model."),rls.forEach(t),aVa=i(Rn),Vs=n(Rn,"P",{});var WZ=s(Vs);nVa=r(WZ,"The model class to instantiate is selected based on the "),Nmo=n(WZ,"CODE",{});var tls=s(Nmo);sVa=r(tls,"model_type"),tls.forEach(t),lVa=r(WZ,` property of the config object (either
passed as an argument or loaded from `),$mo=n(WZ,"CODE",{});var als=s($mo);iVa=r(als,"pretrained_model_name_or_path"),als.forEach(t),dVa=r(WZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),jmo=n(WZ,"CODE",{});var nls=s(jmo);cVa=r(nls,"pretrained_model_name_or_path"),nls.forEach(t),mVa=r(WZ,":"),WZ.forEach(t),fVa=i(Rn),Pt=n(Rn,"UL",{});var kc=s(Pt);n$=n(kc,"LI",{});var wGo=s(n$);Pmo=n(wGo,"STRONG",{});var sls=s(Pmo);hVa=r(sls,"data2vec-audio"),sls.forEach(t),gVa=r(wGo," \u2014 "),f9e=n(wGo,"A",{href:!0});var lls=s(f9e);uVa=r(lls,"Data2VecAudioForXVector"),lls.forEach(t),pVa=r(wGo," (Data2VecAudio model)"),wGo.forEach(t),_Va=i(kc),s$=n(kc,"LI",{});var LGo=s(s$);Zmo=n(LGo,"STRONG",{});var ils=s(Zmo);vVa=r(ils,"unispeech-sat"),ils.forEach(t),bVa=r(LGo," \u2014 "),h9e=n(LGo,"A",{href:!0});var dls=s(h9e);MVa=r(dls,"UniSpeechSatForXVector"),dls.forEach(t),TVa=r(LGo," (UniSpeechSat model)"),LGo.forEach(t),FVa=i(kc),l$=n(kc,"LI",{});var AGo=s(l$);Vmo=n(AGo,"STRONG",{});var cls=s(Vmo);EVa=r(cls,"wav2vec2"),cls.forEach(t),yVa=r(AGo," \u2014 "),g9e=n(AGo,"A",{href:!0});var mls=s(g9e);CVa=r(mls,"Wav2Vec2ForXVector"),mls.forEach(t),wVa=r(AGo," (Wav2Vec2 model)"),AGo.forEach(t),LVa=i(kc),i$=n(kc,"LI",{});var RGo=s(i$);Wmo=n(RGo,"STRONG",{});var fls=s(Wmo);AVa=r(fls,"wav2vec2-conformer"),fls.forEach(t),RVa=r(RGo," \u2014 "),u9e=n(RGo,"A",{href:!0});var hls=s(u9e);kVa=r(hls,"Wav2Vec2ConformerForXVector"),hls.forEach(t),SVa=r(RGo," (Wav2Vec2-Conformer model)"),RGo.forEach(t),xVa=i(kc),d$=n(kc,"LI",{});var kGo=s(d$);Jmo=n(kGo,"STRONG",{});var gls=s(Jmo);GVa=r(gls,"wavlm"),gls.forEach(t),BVa=r(kGo," \u2014 "),p9e=n(kGo,"A",{href:!0});var uls=s(p9e);IVa=r(uls,"WavLMForXVector"),uls.forEach(t),NVa=r(kGo," (WavLM model)"),kGo.forEach(t),kc.forEach(t),$Va=i(Rn),c$=n(Rn,"P",{});var SGo=s(c$);jVa=r(SGo,"The model is set in evaluation mode by default using "),Umo=n(SGo,"CODE",{});var pls=s(Umo);PVa=r(pls,"model.eval()"),pls.forEach(t),ZVa=r(SGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Xmo=n(SGo,"CODE",{});var _ls=s(Xmo);VVa=r(_ls,"model.train()"),_ls.forEach(t),SGo.forEach(t),WVa=i(Rn),M(m$.$$.fragment,Rn),Rn.forEach(t),Rc.forEach(t),xNo=i(m),pg=n(m,"H3",{class:!0});var GPo=s(pg);f$=n(GPo,"A",{id:!0,class:!0,href:!0});var vls=s(f$);Dmo=n(vls,"SPAN",{});var bls=s(Dmo);M(cq.$$.fragment,bls),bls.forEach(t),vls.forEach(t),JVa=i(GPo),qmo=n(GPo,"SPAN",{});var Mls=s(qmo);UVa=r(Mls,"AutoModelForTextToSpectrogram"),Mls.forEach(t),GPo.forEach(t),GNo=i(m),mq=n(m,"DIV",{class:!0});var Tls=s(mq);M(fq.$$.fragment,Tls),Tls.forEach(t),BNo=i(m),_g=n(m,"H3",{class:!0});var BPo=s(_g);h$=n(BPo,"A",{id:!0,class:!0,href:!0});var Fls=s(h$);Omo=n(Fls,"SPAN",{});var Els=s(Omo);M(hq.$$.fragment,Els),Els.forEach(t),Fls.forEach(t),XVa=i(BPo),Qmo=n(BPo,"SPAN",{});var yls=s(Qmo);DVa=r(yls,"AutoModelForTextToWaveform"),yls.forEach(t),BPo.forEach(t),INo=i(m),gq=n(m,"DIV",{class:!0});var Cls=s(gq);M(uq.$$.fragment,Cls),Cls.forEach(t),NNo=i(m),vg=n(m,"H2",{class:!0});var IPo=s(vg);g$=n(IPo,"A",{id:!0,class:!0,href:!0});var wls=s(g$);Ymo=n(wls,"SPAN",{});var Lls=s(Ymo);M(pq.$$.fragment,Lls),Lls.forEach(t),wls.forEach(t),qVa=i(IPo),zmo=n(IPo,"SPAN",{});var Als=s(zmo);OVa=r(Als,"Multimodal"),Als.forEach(t),IPo.forEach(t),$No=i(m),_9e=n(m,"P",{});var Rls=s(_9e);QVa=r(Rls,"The following auto classes are available for the following multimodal tasks."),Rls.forEach(t),jNo=i(m),bg=n(m,"H3",{class:!0});var NPo=s(bg);u$=n(NPo,"A",{id:!0,class:!0,href:!0});var kls=s(u$);Hmo=n(kls,"SPAN",{});var Sls=s(Hmo);M(_q.$$.fragment,Sls),Sls.forEach(t),kls.forEach(t),YVa=i(NPo),Kmo=n(NPo,"SPAN",{});var xls=s(Kmo);zVa=r(xls,"AutoModelForTableQuestionAnswering"),xls.forEach(t),NPo.forEach(t),PNo=i(m),Dr=n(m,"DIV",{class:!0});var Sc=s(Dr);M(vq.$$.fragment,Sc),HVa=i(Sc),Mg=n(Sc,"P",{});var i6e=s(Mg);KVa=r(i6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the `),v9e=n(i6e,"A",{href:!0});var Gls=s(v9e);eWa=r(Gls,"from_pretrained()"),Gls.forEach(t),oWa=r(i6e," class method or the "),b9e=n(i6e,"A",{href:!0});var Bls=s(b9e);rWa=r(Bls,"from_config()"),Bls.forEach(t),tWa=r(i6e,` class
method.`),i6e.forEach(t),aWa=i(Sc),bq=n(Sc,"P",{});var $Po=s(bq);nWa=r($Po,"This class cannot be instantiated directly using "),efo=n($Po,"CODE",{});var Ils=s(efo);sWa=r(Ils,"__init__()"),Ils.forEach(t),lWa=r($Po," (throws an error)."),$Po.forEach(t),iWa=i(Sc),Ua=n(Sc,"DIV",{class:!0});var JZ=s(Ua);M(Mq.$$.fragment,JZ),dWa=i(JZ),ofo=n(JZ,"P",{});var Nls=s(ofo);cWa=r(Nls,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),Nls.forEach(t),mWa=i(JZ),Tg=n(JZ,"P",{});var d6e=s(Tg);fWa=r(d6e,`Note:
Loading a model from its configuration file does `),rfo=n(d6e,"STRONG",{});var $ls=s(rfo);hWa=r($ls,"not"),$ls.forEach(t),gWa=r(d6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),M9e=n(d6e,"A",{href:!0});var jls=s(M9e);uWa=r(jls,"from_pretrained()"),jls.forEach(t),pWa=r(d6e," to load the model weights."),d6e.forEach(t),_Wa=i(JZ),M(p$.$$.fragment,JZ),JZ.forEach(t),vWa=i(Sc),Go=n(Sc,"DIV",{class:!0});var kn=s(Go);M(Tq.$$.fragment,kn),bWa=i(kn),tfo=n(kn,"P",{});var Pls=s(tfo);MWa=r(Pls,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),Pls.forEach(t),TWa=i(kn),Ws=n(kn,"P",{});var UZ=s(Ws);FWa=r(UZ,"The model class to instantiate is selected based on the "),afo=n(UZ,"CODE",{});var Zls=s(afo);EWa=r(Zls,"model_type"),Zls.forEach(t),yWa=r(UZ,` property of the config object (either
passed as an argument or loaded from `),nfo=n(UZ,"CODE",{});var Vls=s(nfo);CWa=r(Vls,"pretrained_model_name_or_path"),Vls.forEach(t),wWa=r(UZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),sfo=n(UZ,"CODE",{});var Wls=s(sfo);LWa=r(Wls,"pretrained_model_name_or_path"),Wls.forEach(t),AWa=r(UZ,":"),UZ.forEach(t),RWa=i(kn),lfo=n(kn,"UL",{});var Jls=s(lfo);_$=n(Jls,"LI",{});var xGo=s(_$);ifo=n(xGo,"STRONG",{});var Uls=s(ifo);kWa=r(Uls,"tapas"),Uls.forEach(t),SWa=r(xGo," \u2014 "),T9e=n(xGo,"A",{href:!0});var Xls=s(T9e);xWa=r(Xls,"TapasForQuestionAnswering"),Xls.forEach(t),GWa=r(xGo," (TAPAS model)"),xGo.forEach(t),Jls.forEach(t),BWa=i(kn),v$=n(kn,"P",{});var GGo=s(v$);IWa=r(GGo,"The model is set in evaluation mode by default using "),dfo=n(GGo,"CODE",{});var Dls=s(dfo);NWa=r(Dls,"model.eval()"),Dls.forEach(t),$Wa=r(GGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),cfo=n(GGo,"CODE",{});var qls=s(cfo);jWa=r(qls,"model.train()"),qls.forEach(t),GGo.forEach(t),PWa=i(kn),M(b$.$$.fragment,kn),kn.forEach(t),Sc.forEach(t),ZNo=i(m),Fg=n(m,"H3",{class:!0});var jPo=s(Fg);M$=n(jPo,"A",{id:!0,class:!0,href:!0});var Ols=s(M$);mfo=n(Ols,"SPAN",{});var Qls=s(mfo);M(Fq.$$.fragment,Qls),Qls.forEach(t),Ols.forEach(t),ZWa=i(jPo),ffo=n(jPo,"SPAN",{});var Yls=s(ffo);VWa=r(Yls,"TFAutoModelForTableQuestionAnswering"),Yls.forEach(t),jPo.forEach(t),VNo=i(m),qr=n(m,"DIV",{class:!0});var xc=s(qr);M(Eq.$$.fragment,xc),WWa=i(xc),Eg=n(xc,"P",{});var c6e=s(Eg);JWa=r(c6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the `),F9e=n(c6e,"A",{href:!0});var zls=s(F9e);UWa=r(zls,"from_pretrained()"),zls.forEach(t),XWa=r(c6e," class method or the "),E9e=n(c6e,"A",{href:!0});var Hls=s(E9e);DWa=r(Hls,"from_config()"),Hls.forEach(t),qWa=r(c6e,` class
method.`),c6e.forEach(t),OWa=i(xc),yq=n(xc,"P",{});var PPo=s(yq);QWa=r(PPo,"This class cannot be instantiated directly using "),hfo=n(PPo,"CODE",{});var Kls=s(hfo);YWa=r(Kls,"__init__()"),Kls.forEach(t),zWa=r(PPo," (throws an error)."),PPo.forEach(t),HWa=i(xc),Xa=n(xc,"DIV",{class:!0});var XZ=s(Xa);M(Cq.$$.fragment,XZ),KWa=i(XZ),gfo=n(XZ,"P",{});var eis=s(gfo);eJa=r(eis,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),eis.forEach(t),oJa=i(XZ),yg=n(XZ,"P",{});var m6e=s(yg);rJa=r(m6e,`Note:
Loading a model from its configuration file does `),ufo=n(m6e,"STRONG",{});var ois=s(ufo);tJa=r(ois,"not"),ois.forEach(t),aJa=r(m6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),y9e=n(m6e,"A",{href:!0});var ris=s(y9e);nJa=r(ris,"from_pretrained()"),ris.forEach(t),sJa=r(m6e," to load the model weights."),m6e.forEach(t),lJa=i(XZ),M(T$.$$.fragment,XZ),XZ.forEach(t),iJa=i(xc),St=n(xc,"DIV",{class:!0});var Gc=s(St);M(wq.$$.fragment,Gc),dJa=i(Gc),pfo=n(Gc,"P",{});var tis=s(pfo);cJa=r(tis,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),tis.forEach(t),mJa=i(Gc),Js=n(Gc,"P",{});var DZ=s(Js);fJa=r(DZ,"The model class to instantiate is selected based on the "),_fo=n(DZ,"CODE",{});var ais=s(_fo);hJa=r(ais,"model_type"),ais.forEach(t),gJa=r(DZ,` property of the config object (either
passed as an argument or loaded from `),vfo=n(DZ,"CODE",{});var nis=s(vfo);uJa=r(nis,"pretrained_model_name_or_path"),nis.forEach(t),pJa=r(DZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),bfo=n(DZ,"CODE",{});var sis=s(bfo);_Ja=r(sis,"pretrained_model_name_or_path"),sis.forEach(t),vJa=r(DZ,":"),DZ.forEach(t),bJa=i(Gc),Mfo=n(Gc,"UL",{});var lis=s(Mfo);F$=n(lis,"LI",{});var BGo=s(F$);Tfo=n(BGo,"STRONG",{});var iis=s(Tfo);MJa=r(iis,"tapas"),iis.forEach(t),TJa=r(BGo," \u2014 "),C9e=n(BGo,"A",{href:!0});var dis=s(C9e);FJa=r(dis,"TFTapasForQuestionAnswering"),dis.forEach(t),EJa=r(BGo," (TAPAS model)"),BGo.forEach(t),lis.forEach(t),yJa=i(Gc),M(E$.$$.fragment,Gc),Gc.forEach(t),xc.forEach(t),WNo=i(m),Cg=n(m,"H3",{class:!0});var ZPo=s(Cg);y$=n(ZPo,"A",{id:!0,class:!0,href:!0});var cis=s(y$);Ffo=n(cis,"SPAN",{});var mis=s(Ffo);M(Lq.$$.fragment,mis),mis.forEach(t),cis.forEach(t),CJa=i(ZPo),Efo=n(ZPo,"SPAN",{});var fis=s(Efo);wJa=r(fis,"AutoModelForDocumentQuestionAnswering"),fis.forEach(t),ZPo.forEach(t),JNo=i(m),Or=n(m,"DIV",{class:!0});var Bc=s(Or);M(Aq.$$.fragment,Bc),LJa=i(Bc),wg=n(Bc,"P",{});var f6e=s(wg);AJa=r(f6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created
with the `),w9e=n(f6e,"A",{href:!0});var his=s(w9e);RJa=r(his,"from_pretrained()"),his.forEach(t),kJa=r(f6e," class method or the "),L9e=n(f6e,"A",{href:!0});var gis=s(L9e);SJa=r(gis,"from_config()"),gis.forEach(t),xJa=r(f6e,` class
method.`),f6e.forEach(t),GJa=i(Bc),Rq=n(Bc,"P",{});var VPo=s(Rq);BJa=r(VPo,"This class cannot be instantiated directly using "),yfo=n(VPo,"CODE",{});var uis=s(yfo);IJa=r(uis,"__init__()"),uis.forEach(t),NJa=r(VPo," (throws an error)."),VPo.forEach(t),$Ja=i(Bc),Da=n(Bc,"DIV",{class:!0});var qZ=s(Da);M(kq.$$.fragment,qZ),jJa=i(qZ),Cfo=n(qZ,"P",{});var pis=s(Cfo);PJa=r(pis,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),pis.forEach(t),ZJa=i(qZ),Lg=n(qZ,"P",{});var h6e=s(Lg);VJa=r(h6e,`Note:
Loading a model from its configuration file does `),wfo=n(h6e,"STRONG",{});var _is=s(wfo);WJa=r(_is,"not"),_is.forEach(t),JJa=r(h6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),A9e=n(h6e,"A",{href:!0});var vis=s(A9e);UJa=r(vis,"from_pretrained()"),vis.forEach(t),XJa=r(h6e," to load the model weights."),h6e.forEach(t),DJa=i(qZ),M(C$.$$.fragment,qZ),qZ.forEach(t),qJa=i(Bc),Bo=n(Bc,"DIV",{class:!0});var Sn=s(Bo);M(Sq.$$.fragment,Sn),OJa=i(Sn),Lfo=n(Sn,"P",{});var bis=s(Lfo);QJa=r(bis,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),bis.forEach(t),YJa=i(Sn),Us=n(Sn,"P",{});var OZ=s(Us);zJa=r(OZ,"The model class to instantiate is selected based on the "),Afo=n(OZ,"CODE",{});var Mis=s(Afo);HJa=r(Mis,"model_type"),Mis.forEach(t),KJa=r(OZ,` property of the config object (either
passed as an argument or loaded from `),Rfo=n(OZ,"CODE",{});var Tis=s(Rfo);eUa=r(Tis,"pretrained_model_name_or_path"),Tis.forEach(t),oUa=r(OZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),kfo=n(OZ,"CODE",{});var Fis=s(kfo);rUa=r(Fis,"pretrained_model_name_or_path"),Fis.forEach(t),tUa=r(OZ,":"),OZ.forEach(t),aUa=i(Sn),Ag=n(Sn,"UL",{});var g6e=s(Ag);w$=n(g6e,"LI",{});var IGo=s(w$);Sfo=n(IGo,"STRONG",{});var Eis=s(Sfo);nUa=r(Eis,"layoutlm"),Eis.forEach(t),sUa=r(IGo," \u2014 "),R9e=n(IGo,"A",{href:!0});var yis=s(R9e);lUa=r(yis,"LayoutLMForQuestionAnswering"),yis.forEach(t),iUa=r(IGo," (LayoutLM model)"),IGo.forEach(t),dUa=i(g6e),L$=n(g6e,"LI",{});var NGo=s(L$);xfo=n(NGo,"STRONG",{});var Cis=s(xfo);cUa=r(Cis,"layoutlmv2"),Cis.forEach(t),mUa=r(NGo," \u2014 "),k9e=n(NGo,"A",{href:!0});var wis=s(k9e);fUa=r(wis,"LayoutLMv2ForQuestionAnswering"),wis.forEach(t),hUa=r(NGo," (LayoutLMv2 model)"),NGo.forEach(t),gUa=i(g6e),A$=n(g6e,"LI",{});var $Go=s(A$);Gfo=n($Go,"STRONG",{});var Lis=s(Gfo);uUa=r(Lis,"layoutlmv3"),Lis.forEach(t),pUa=r($Go," \u2014 "),S9e=n($Go,"A",{href:!0});var Ais=s(S9e);_Ua=r(Ais,"LayoutLMv3ForQuestionAnswering"),Ais.forEach(t),vUa=r($Go," (LayoutLMv3 model)"),$Go.forEach(t),g6e.forEach(t),bUa=i(Sn),R$=n(Sn,"P",{});var jGo=s(R$);MUa=r(jGo,"The model is set in evaluation mode by default using "),Bfo=n(jGo,"CODE",{});var Ris=s(Bfo);TUa=r(Ris,"model.eval()"),Ris.forEach(t),FUa=r(jGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Ifo=n(jGo,"CODE",{});var kis=s(Ifo);EUa=r(kis,"model.train()"),kis.forEach(t),jGo.forEach(t),yUa=i(Sn),M(k$.$$.fragment,Sn),Sn.forEach(t),Bc.forEach(t),UNo=i(m),Rg=n(m,"H3",{class:!0});var WPo=s(Rg);S$=n(WPo,"A",{id:!0,class:!0,href:!0});var Sis=s(S$);Nfo=n(Sis,"SPAN",{});var xis=s(Nfo);M(xq.$$.fragment,xis),xis.forEach(t),Sis.forEach(t),CUa=i(WPo),$fo=n(WPo,"SPAN",{});var Gis=s($fo);wUa=r(Gis,"TFAutoModelForDocumentQuestionAnswering"),Gis.forEach(t),WPo.forEach(t),XNo=i(m),Qr=n(m,"DIV",{class:!0});var Ic=s(Qr);M(Gq.$$.fragment,Ic),LUa=i(Ic),kg=n(Ic,"P",{});var u6e=s(kg);AUa=r(u6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created
with the `),x9e=n(u6e,"A",{href:!0});var Bis=s(x9e);RUa=r(Bis,"from_pretrained()"),Bis.forEach(t),kUa=r(u6e," class method or the "),G9e=n(u6e,"A",{href:!0});var Iis=s(G9e);SUa=r(Iis,"from_config()"),Iis.forEach(t),xUa=r(u6e,` class
method.`),u6e.forEach(t),GUa=i(Ic),Bq=n(Ic,"P",{});var JPo=s(Bq);BUa=r(JPo,"This class cannot be instantiated directly using "),jfo=n(JPo,"CODE",{});var Nis=s(jfo);IUa=r(Nis,"__init__()"),Nis.forEach(t),NUa=r(JPo," (throws an error)."),JPo.forEach(t),$Ua=i(Ic),qa=n(Ic,"DIV",{class:!0});var QZ=s(qa);M(Iq.$$.fragment,QZ),jUa=i(QZ),Pfo=n(QZ,"P",{});var $is=s(Pfo);PUa=r($is,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),$is.forEach(t),ZUa=i(QZ),Sg=n(QZ,"P",{});var p6e=s(Sg);VUa=r(p6e,`Note:
Loading a model from its configuration file does `),Zfo=n(p6e,"STRONG",{});var jis=s(Zfo);WUa=r(jis,"not"),jis.forEach(t),JUa=r(p6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),B9e=n(p6e,"A",{href:!0});var Pis=s(B9e);UUa=r(Pis,"from_pretrained()"),Pis.forEach(t),XUa=r(p6e," to load the model weights."),p6e.forEach(t),DUa=i(QZ),M(x$.$$.fragment,QZ),QZ.forEach(t),qUa=i(Ic),xt=n(Ic,"DIV",{class:!0});var Nc=s(xt);M(Nq.$$.fragment,Nc),OUa=i(Nc),Vfo=n(Nc,"P",{});var Zis=s(Vfo);QUa=r(Zis,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),Zis.forEach(t),YUa=i(Nc),Xs=n(Nc,"P",{});var YZ=s(Xs);zUa=r(YZ,"The model class to instantiate is selected based on the "),Wfo=n(YZ,"CODE",{});var Vis=s(Wfo);HUa=r(Vis,"model_type"),Vis.forEach(t),KUa=r(YZ,` property of the config object (either
passed as an argument or loaded from `),Jfo=n(YZ,"CODE",{});var Wis=s(Jfo);eXa=r(Wis,"pretrained_model_name_or_path"),Wis.forEach(t),oXa=r(YZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ufo=n(YZ,"CODE",{});var Jis=s(Ufo);rXa=r(Jis,"pretrained_model_name_or_path"),Jis.forEach(t),tXa=r(YZ,":"),YZ.forEach(t),aXa=i(Nc),Xfo=n(Nc,"UL",{});var Uis=s(Xfo);G$=n(Uis,"LI",{});var PGo=s(G$);Dfo=n(PGo,"STRONG",{});var Xis=s(Dfo);nXa=r(Xis,"layoutlm"),Xis.forEach(t),sXa=r(PGo," \u2014 "),I9e=n(PGo,"A",{href:!0});var Dis=s(I9e);lXa=r(Dis,"TFLayoutLMForQuestionAnswering"),Dis.forEach(t),iXa=r(PGo," (LayoutLM model)"),PGo.forEach(t),Uis.forEach(t),dXa=i(Nc),M(B$.$$.fragment,Nc),Nc.forEach(t),Ic.forEach(t),DNo=i(m),xg=n(m,"H3",{class:!0});var UPo=s(xg);I$=n(UPo,"A",{id:!0,class:!0,href:!0});var qis=s(I$);qfo=n(qis,"SPAN",{});var Ois=s(qfo);M($q.$$.fragment,Ois),Ois.forEach(t),qis.forEach(t),cXa=i(UPo),Ofo=n(UPo,"SPAN",{});var Qis=s(Ofo);mXa=r(Qis,"AutoModelForVisualQuestionAnswering"),Qis.forEach(t),UPo.forEach(t),qNo=i(m),Yr=n(m,"DIV",{class:!0});var $c=s(Yr);M(jq.$$.fragment,$c),fXa=i($c),Gg=n($c,"P",{});var _6e=s(Gg);hXa=r(_6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a visual question answering head) when created
with the `),N9e=n(_6e,"A",{href:!0});var Yis=s(N9e);gXa=r(Yis,"from_pretrained()"),Yis.forEach(t),uXa=r(_6e," class method or the "),$9e=n(_6e,"A",{href:!0});var zis=s($9e);pXa=r(zis,"from_config()"),zis.forEach(t),_Xa=r(_6e,` class
method.`),_6e.forEach(t),vXa=i($c),Pq=n($c,"P",{});var XPo=s(Pq);bXa=r(XPo,"This class cannot be instantiated directly using "),Qfo=n(XPo,"CODE",{});var His=s(Qfo);MXa=r(His,"__init__()"),His.forEach(t),TXa=r(XPo," (throws an error)."),XPo.forEach(t),FXa=i($c),Oa=n($c,"DIV",{class:!0});var zZ=s(Oa);M(Zq.$$.fragment,zZ),EXa=i(zZ),Yfo=n(zZ,"P",{});var Kis=s(Yfo);yXa=r(Kis,"Instantiates one of the model classes of the library (with a visual question answering head) from a configuration."),Kis.forEach(t),CXa=i(zZ),Bg=n(zZ,"P",{});var v6e=s(Bg);wXa=r(v6e,`Note:
Loading a model from its configuration file does `),zfo=n(v6e,"STRONG",{});var eds=s(zfo);LXa=r(eds,"not"),eds.forEach(t),AXa=r(v6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),j9e=n(v6e,"A",{href:!0});var ods=s(j9e);RXa=r(ods,"from_pretrained()"),ods.forEach(t),kXa=r(v6e," to load the model weights."),v6e.forEach(t),SXa=i(zZ),M(N$.$$.fragment,zZ),zZ.forEach(t),xXa=i($c),Io=n($c,"DIV",{class:!0});var xn=s(Io);M(Vq.$$.fragment,xn),GXa=i(xn),Hfo=n(xn,"P",{});var rds=s(Hfo);BXa=r(rds,"Instantiate one of the model classes of the library (with a visual question answering head) from a pretrained model."),rds.forEach(t),IXa=i(xn),Ds=n(xn,"P",{});var HZ=s(Ds);NXa=r(HZ,"The model class to instantiate is selected based on the "),Kfo=n(HZ,"CODE",{});var tds=s(Kfo);$Xa=r(tds,"model_type"),tds.forEach(t),jXa=r(HZ,` property of the config object (either
passed as an argument or loaded from `),eho=n(HZ,"CODE",{});var ads=s(eho);PXa=r(ads,"pretrained_model_name_or_path"),ads.forEach(t),ZXa=r(HZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),oho=n(HZ,"CODE",{});var nds=s(oho);VXa=r(nds,"pretrained_model_name_or_path"),nds.forEach(t),WXa=r(HZ,":"),HZ.forEach(t),JXa=i(xn),Wq=n(xn,"UL",{});var DPo=s(Wq);$$=n(DPo,"LI",{});var ZGo=s($$);rho=n(ZGo,"STRONG",{});var sds=s(rho);UXa=r(sds,"blip-2"),sds.forEach(t),XXa=r(ZGo," \u2014 "),P9e=n(ZGo,"A",{href:!0});var lds=s(P9e);DXa=r(lds,"Blip2ForConditionalGeneration"),lds.forEach(t),qXa=r(ZGo," (BLIP-2 model)"),ZGo.forEach(t),OXa=i(DPo),j$=n(DPo,"LI",{});var VGo=s(j$);tho=n(VGo,"STRONG",{});var ids=s(tho);QXa=r(ids,"vilt"),ids.forEach(t),YXa=r(VGo," \u2014 "),Z9e=n(VGo,"A",{href:!0});var dds=s(Z9e);zXa=r(dds,"ViltForQuestionAnswering"),dds.forEach(t),HXa=r(VGo," (ViLT model)"),VGo.forEach(t),DPo.forEach(t),KXa=i(xn),P$=n(xn,"P",{});var WGo=s(P$);eDa=r(WGo,"The model is set in evaluation mode by default using "),aho=n(WGo,"CODE",{});var cds=s(aho);oDa=r(cds,"model.eval()"),cds.forEach(t),rDa=r(WGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),nho=n(WGo,"CODE",{});var mds=s(nho);tDa=r(mds,"model.train()"),mds.forEach(t),WGo.forEach(t),aDa=i(xn),M(Z$.$$.fragment,xn),xn.forEach(t),$c.forEach(t),ONo=i(m),Ig=n(m,"H3",{class:!0});var qPo=s(Ig);V$=n(qPo,"A",{id:!0,class:!0,href:!0});var fds=s(V$);sho=n(fds,"SPAN",{});var hds=s(sho);M(Jq.$$.fragment,hds),hds.forEach(t),fds.forEach(t),nDa=i(qPo),lho=n(qPo,"SPAN",{});var gds=s(lho);sDa=r(gds,"AutoModelForVision2Seq"),gds.forEach(t),qPo.forEach(t),QNo=i(m),zr=n(m,"DIV",{class:!0});var jc=s(zr);M(Uq.$$.fragment,jc),lDa=i(jc),Ng=n(jc,"P",{});var b6e=s(Ng);iDa=r(b6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
with the `),V9e=n(b6e,"A",{href:!0});var uds=s(V9e);dDa=r(uds,"from_pretrained()"),uds.forEach(t),cDa=r(b6e," class method or the "),W9e=n(b6e,"A",{href:!0});var pds=s(W9e);mDa=r(pds,"from_config()"),pds.forEach(t),fDa=r(b6e,` class
method.`),b6e.forEach(t),hDa=i(jc),Xq=n(jc,"P",{});var OPo=s(Xq);gDa=r(OPo,"This class cannot be instantiated directly using "),iho=n(OPo,"CODE",{});var _ds=s(iho);uDa=r(_ds,"__init__()"),_ds.forEach(t),pDa=r(OPo," (throws an error)."),OPo.forEach(t),_Da=i(jc),Qa=n(jc,"DIV",{class:!0});var KZ=s(Qa);M(Dq.$$.fragment,KZ),vDa=i(KZ),dho=n(KZ,"P",{});var vds=s(dho);bDa=r(vds,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),vds.forEach(t),MDa=i(KZ),$g=n(KZ,"P",{});var M6e=s($g);TDa=r(M6e,`Note:
Loading a model from its configuration file does `),cho=n(M6e,"STRONG",{});var bds=s(cho);FDa=r(bds,"not"),bds.forEach(t),EDa=r(M6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),J9e=n(M6e,"A",{href:!0});var Mds=s(J9e);yDa=r(Mds,"from_pretrained()"),Mds.forEach(t),CDa=r(M6e," to load the model weights."),M6e.forEach(t),wDa=i(KZ),M(W$.$$.fragment,KZ),KZ.forEach(t),LDa=i(jc),No=n(jc,"DIV",{class:!0});var Gn=s(No);M(qq.$$.fragment,Gn),ADa=i(Gn),mho=n(Gn,"P",{});var Tds=s(mho);RDa=r(Tds,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),Tds.forEach(t),kDa=i(Gn),qs=n(Gn,"P",{});var eV=s(qs);SDa=r(eV,"The model class to instantiate is selected based on the "),fho=n(eV,"CODE",{});var Fds=s(fho);xDa=r(Fds,"model_type"),Fds.forEach(t),GDa=r(eV,` property of the config object (either
passed as an argument or loaded from `),hho=n(eV,"CODE",{});var Eds=s(hho);BDa=r(Eds,"pretrained_model_name_or_path"),Eds.forEach(t),IDa=r(eV,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),gho=n(eV,"CODE",{});var yds=s(gho);NDa=r(yds,"pretrained_model_name_or_path"),yds.forEach(t),$Da=r(eV,":"),eV.forEach(t),jDa=i(Gn),Hr=n(Gn,"UL",{});var Bn=s(Hr);J$=n(Bn,"LI",{});var JGo=s(J$);uho=n(JGo,"STRONG",{});var Cds=s(uho);PDa=r(Cds,"blip"),Cds.forEach(t),ZDa=r(JGo," \u2014 "),U9e=n(JGo,"A",{href:!0});var wds=s(U9e);VDa=r(wds,"BlipForConditionalGeneration"),wds.forEach(t),WDa=r(JGo," (BLIP model)"),JGo.forEach(t),JDa=i(Bn),U$=n(Bn,"LI",{});var UGo=s(U$);pho=n(UGo,"STRONG",{});var Lds=s(pho);UDa=r(Lds,"blip-2"),Lds.forEach(t),XDa=r(UGo," \u2014 "),X9e=n(UGo,"A",{href:!0});var Ads=s(X9e);DDa=r(Ads,"Blip2ForConditionalGeneration"),Ads.forEach(t),qDa=r(UGo," (BLIP-2 model)"),UGo.forEach(t),ODa=i(Bn),X$=n(Bn,"LI",{});var XGo=s(X$);_ho=n(XGo,"STRONG",{});var Rds=s(_ho);QDa=r(Rds,"git"),Rds.forEach(t),YDa=r(XGo," \u2014 "),D9e=n(XGo,"A",{href:!0});var kds=s(D9e);zDa=r(kds,"GitForCausalLM"),kds.forEach(t),HDa=r(XGo," (GIT model)"),XGo.forEach(t),KDa=i(Bn),D$=n(Bn,"LI",{});var DGo=s(D$);vho=n(DGo,"STRONG",{});var Sds=s(vho);eqa=r(Sds,"instructblip"),Sds.forEach(t),oqa=r(DGo," \u2014 "),q9e=n(DGo,"A",{href:!0});var xds=s(q9e);rqa=r(xds,"InstructBlipForConditionalGeneration"),xds.forEach(t),tqa=r(DGo," (InstructBLIP model)"),DGo.forEach(t),aqa=i(Bn),q$=n(Bn,"LI",{});var qGo=s(q$);bho=n(qGo,"STRONG",{});var Gds=s(bho);nqa=r(Gds,"pix2struct"),Gds.forEach(t),sqa=r(qGo," \u2014 "),O9e=n(qGo,"A",{href:!0});var Bds=s(O9e);lqa=r(Bds,"Pix2StructForConditionalGeneration"),Bds.forEach(t),iqa=r(qGo," (Pix2Struct model)"),qGo.forEach(t),dqa=i(Bn),O$=n(Bn,"LI",{});var OGo=s(O$);Mho=n(OGo,"STRONG",{});var Ids=s(Mho);cqa=r(Ids,"vision-encoder-decoder"),Ids.forEach(t),mqa=r(OGo," \u2014 "),Q9e=n(OGo,"A",{href:!0});var Nds=s(Q9e);fqa=r(Nds,"VisionEncoderDecoderModel"),Nds.forEach(t),hqa=r(OGo," (Vision Encoder decoder model)"),OGo.forEach(t),Bn.forEach(t),gqa=i(Gn),Q$=n(Gn,"P",{});var QGo=s(Q$);uqa=r(QGo,"The model is set in evaluation mode by default using "),Tho=n(QGo,"CODE",{});var $ds=s(Tho);pqa=r($ds,"model.eval()"),$ds.forEach(t),_qa=r(QGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Fho=n(QGo,"CODE",{});var jds=s(Fho);vqa=r(jds,"model.train()"),jds.forEach(t),QGo.forEach(t),bqa=i(Gn),M(Y$.$$.fragment,Gn),Gn.forEach(t),jc.forEach(t),YNo=i(m),jg=n(m,"H3",{class:!0});var QPo=s(jg);z$=n(QPo,"A",{id:!0,class:!0,href:!0});var Pds=s(z$);Eho=n(Pds,"SPAN",{});var Zds=s(Eho);M(Oq.$$.fragment,Zds),Zds.forEach(t),Pds.forEach(t),Mqa=i(QPo),yho=n(QPo,"SPAN",{});var Vds=s(yho);Tqa=r(Vds,"TFAutoModelForVision2Seq"),Vds.forEach(t),QPo.forEach(t),zNo=i(m),Kr=n(m,"DIV",{class:!0});var Pc=s(Kr);M(Qq.$$.fragment,Pc),Fqa=i(Pc),Pg=n(Pc,"P",{});var T6e=s(Pg);Eqa=r(T6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
with the `),Y9e=n(T6e,"A",{href:!0});var Wds=s(Y9e);yqa=r(Wds,"from_pretrained()"),Wds.forEach(t),Cqa=r(T6e," class method or the "),z9e=n(T6e,"A",{href:!0});var Jds=s(z9e);wqa=r(Jds,"from_config()"),Jds.forEach(t),Lqa=r(T6e,` class
method.`),T6e.forEach(t),Aqa=i(Pc),Yq=n(Pc,"P",{});var YPo=s(Yq);Rqa=r(YPo,"This class cannot be instantiated directly using "),Cho=n(YPo,"CODE",{});var Uds=s(Cho);kqa=r(Uds,"__init__()"),Uds.forEach(t),Sqa=r(YPo," (throws an error)."),YPo.forEach(t),xqa=i(Pc),Ya=n(Pc,"DIV",{class:!0});var oV=s(Ya);M(zq.$$.fragment,oV),Gqa=i(oV),who=n(oV,"P",{});var Xds=s(who);Bqa=r(Xds,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),Xds.forEach(t),Iqa=i(oV),Zg=n(oV,"P",{});var F6e=s(Zg);Nqa=r(F6e,`Note:
Loading a model from its configuration file does `),Lho=n(F6e,"STRONG",{});var Dds=s(Lho);$qa=r(Dds,"not"),Dds.forEach(t),jqa=r(F6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),H9e=n(F6e,"A",{href:!0});var qds=s(H9e);Pqa=r(qds,"from_pretrained()"),qds.forEach(t),Zqa=r(F6e," to load the model weights."),F6e.forEach(t),Vqa=i(oV),M(H$.$$.fragment,oV),oV.forEach(t),Wqa=i(Pc),Gt=n(Pc,"DIV",{class:!0});var Zc=s(Gt);M(Hq.$$.fragment,Zc),Jqa=i(Zc),Aho=n(Zc,"P",{});var Ods=s(Aho);Uqa=r(Ods,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),Ods.forEach(t),Xqa=i(Zc),Os=n(Zc,"P",{});var rV=s(Os);Dqa=r(rV,"The model class to instantiate is selected based on the "),Rho=n(rV,"CODE",{});var Qds=s(Rho);qqa=r(Qds,"model_type"),Qds.forEach(t),Oqa=r(rV,` property of the config object (either
passed as an argument or loaded from `),kho=n(rV,"CODE",{});var Yds=s(kho);Qqa=r(Yds,"pretrained_model_name_or_path"),Yds.forEach(t),Yqa=r(rV,` if possible), or when it\u2019s missing, by
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Classes"};function Yfs(x){return Fcs(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ths extends vcs{constructor(h){super();bcs(this,h,Yfs,Ofs,Mcs,{})}}export{ths as default,Qfs as metadata};
