import{S as SBn,i as kBn,s as $Bn,e as a,k as l,w as b,t as o,M as PBn,c as n,d as t,m as i,a as s,x as T,h as r,b as d,G as e,g as v,y as F,q as M,o as E,B as C,v as RBn,L as j}from"../../chunks/vendor-hf-doc-builder.js";import{T as $xo}from"../../chunks/Tip-hf-doc-builder.js";import{D as P}from"../../chunks/Docstring-hf-doc-builder.js";import{C as q}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as ne}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as D}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function IBn($){let g,w,u,m,p,c,h,Je,Fc,hh,Nt,Mc,Ec,TG,uh,He,oo,Cc,js,FG,Os,Vs,MG,wc,Xs,EG,Ac,ph,An;return{c(){g=a("p"),w=o("If your "),u=a("code"),m=o("NewModelConfig"),p=o(" is a subclass of "),c=a("code"),h=o("~transformer.PretrainedConfig"),Je=o(`, make sure its
`),Fc=a("code"),hh=o("model_type"),Nt=o(" attribute is set to the same key you use when registering the config (here "),Mc=a("code"),Ec=o('"new-model"'),TG=o(")."),uh=l(),He=a("p"),oo=o("Likewise, if your "),Cc=a("code"),js=o("NewModel"),FG=o(" is a subclass of "),Os=a("a"),Vs=o("PreTrainedModel"),MG=o(`, make sure its
`),wc=a("code"),Xs=o("config_class"),EG=o(` attribute is set to the same class you use when registering the model (here
`),Ac=a("code"),ph=o("NewModelConfig"),An=o(")."),this.h()},l(ro){g=n(ro,"P",{});var Re=s(g);w=r(Re,"If your "),u=n(Re,"CODE",{});var jW=s(u);m=r(jW,"NewModelConfig"),jW.forEach(t),p=r(Re," is a subclass of "),c=n(Re,"CODE",{});var Lc=s(c);h=r(Lc,"~transformer.PretrainedConfig"),Lc.forEach(t),Je=r(Re,`, make sure its
`),Fc=n(Re,"CODE",{});var OW=s(Fc);hh=r(OW,"model_type"),OW.forEach(t),Nt=r(Re," attribute is set to the same key you use when registering the config (here "),Mc=n(Re,"CODE",{});var VW=s(Mc);Ec=r(VW,'"new-model"'),VW.forEach(t),TG=r(Re,")."),Re.forEach(t),uh=i(ro),He=n(ro,"P",{});var Bo=s(He);oo=r(Bo,"Likewise, if your "),Cc=n(Bo,"CODE",{});var Ln=s(Cc);js=r(Ln,"NewModel"),Ln.forEach(t),FG=r(Bo," is a subclass of "),Os=n(Bo,"A",{href:!0});var XW=s(Os);Vs=r(XW,"PreTrainedModel"),XW.forEach(t),MG=r(Bo,`, make sure its
`),wc=n(Bo,"CODE",{});var _h=s(wc);Xs=r(_h,"config_class"),_h.forEach(t),EG=r(Bo,` attribute is set to the same class you use when registering the model (here
`),Ac=n(Bo,"CODE",{});var zW=s(Ac);ph=r(zW,"NewModelConfig"),zW.forEach(t),An=r(Bo,")."),Bo.forEach(t),this.h()},h(){d(Os,"href","/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel")},m(ro,Re){v(ro,g,Re),e(g,w),e(g,u),e(u,m),e(g,p),e(g,c),e(c,h),e(g,Je),e(g,Fc),e(Fc,hh),e(g,Nt),e(g,Mc),e(Mc,Ec),e(g,TG),v(ro,uh,Re),v(ro,He,Re),e(He,oo),e(He,Cc),e(Cc,js),e(He,FG),e(He,Os),e(Os,Vs),e(He,MG),e(He,wc),e(wc,Xs),e(He,EG),e(He,Ac),e(Ac,ph),e(He,An)},d(ro){ro&&t(g),ro&&t(uh),ro&&t(He)}}}function NBn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-uncased")

# Download configuration from huggingface.co (user-uploaded) and cache.
config = AutoConfig.from_pretrained("dbmdz/bert-base-german-cased")

# If configuration file is in a directory (e.g., was saved using *save_pretrained('./test/saved_model/')*).
config = AutoConfig.from_pretrained("./test/bert_saved_model/")

# Load a specific configuration file.
config = AutoConfig.from_pretrained("./test/bert_saved_model/my_configuration.json")

# Change some config attributes when loading a pretrained config.
config = AutoConfig.from_pretrained("bert-base-uncased", output_attentions=True, foo=False)
config.output_attentions

config, unused_kwargs = AutoConfig.from_pretrained(
    "bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True
)
config.output_attentions

unused_kwargs`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function BBn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoTokenizer

# Download vocabulary from huggingface.co and cache.
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

# Download vocabulary from huggingface.co (user-uploaded) and cache.
tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-cased")

# If vocabulary files are in a directory (e.g. tokenizer was saved using *save_pretrained('./test/saved_model/')*)
# tokenizer = AutoTokenizer.from_pretrained("./test/bert_saved_model/")

# Download vocabulary from huggingface.co and define model-specific arguments
tokenizer = AutoTokenizer.from_pretrained("roberta-base", add_prefix_space=True)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function GBn($){let g,w,u,m,p;return{c(){g=a("p"),w=o("Passing "),u=a("code"),m=o("use_auth_token=True"),p=o(" is required when you want to use a private model.")},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Passing "),u=n(h,"CODE",{});var Je=s(u);m=r(Je,"use_auth_token=True"),Je.forEach(t),p=r(h," is required when you want to use a private model."),h.forEach(t)},m(c,h){v(c,g,h),e(g,w),e(g,u),e(u,m),e(g,p)},d(c){c&&t(g)}}}function qBn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoFeatureExtractor

# Download feature extractor from huggingface.co and cache.
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")

# If feature extractor files are in a directory (e.g. feature extractor was saved using *save_pretrained('./test/saved_model/')*)
# feature_extractor = AutoFeatureExtractor.from_pretrained("./test/saved_model/")`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function DBn($){let g,w,u,m,p;return{c(){g=a("p"),w=o("Passing "),u=a("code"),m=o("use_auth_token=True"),p=o(" is required when you want to use a private model.")},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Passing "),u=n(h,"CODE",{});var Je=s(u);m=r(Je,"use_auth_token=True"),Je.forEach(t),p=r(h," is required when you want to use a private model."),h.forEach(t)},m(c,h){v(c,g,h),e(g,w),e(g,u),e(u,m),e(g,p)},d(c){c&&t(g)}}}function jBn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoImageProcessor

# Download image processor from huggingface.co and cache.
image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")

# If image processor files are in a directory (e.g. image processor was saved using *save_pretrained('./test/saved_model/')*)
# image_processor = AutoImageProcessor.from_pretrained("./test/saved_model/")`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function OBn($){let g,w,u,m,p;return{c(){g=a("p"),w=o("Passing "),u=a("code"),m=o("use_auth_token=True"),p=o(" is required when you want to use a private model.")},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Passing "),u=n(h,"CODE",{});var Je=s(u);m=r(Je,"use_auth_token=True"),Je.forEach(t),p=r(h," is required when you want to use a private model."),h.forEach(t)},m(c,h){v(c,g,h),e(g,w),e(g,u),e(u,m),e(g,p)},d(c){c&&t(g)}}}function VBn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoProcessor

# Download processor from huggingface.co and cache.
processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")

# If processor files are in a directory (e.g. processor was saved using *save_pretrained('./test/saved_model/')*)
# processor = AutoProcessor.from_pretrained("./test/saved_model/")`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function XBn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModel

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModel.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function zBn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModel

# Download model and configuration from huggingface.co and cache.
model = AutoModel.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModel.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModel.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function WBn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModel

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModel.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function QBn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModel

# Download model and configuration from huggingface.co and cache.
model = TFAutoModel.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModel.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModel.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function UBn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModel

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModel.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function HBn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModel

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModel.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModel.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModel.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function JBn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForPreTraining

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForPreTraining.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function ZBn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForPreTraining

# Download model and configuration from huggingface.co and cache.
model = AutoModelForPreTraining.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForPreTraining.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForPreTraining.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function YBn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForPreTraining

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForPreTraining.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function KBn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForPreTraining

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForPreTraining.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForPreTraining.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForPreTraining.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function eGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForPreTraining

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForPreTraining.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function oGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForPreTraining

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForPreTraining.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForPreTraining.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForPreTraining.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function rGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForCausalLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForCausalLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function tGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForCausalLM

# Download model and configuration from huggingface.co and cache.
model = AutoModelForCausalLM.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForCausalLM.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForCausalLM.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function aGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForCausalLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForCausalLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function nGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForCausalLM

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForCausalLM.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForCausalLM.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForCausalLM.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function sGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForCausalLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForCausalLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function lGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForCausalLM

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForCausalLM.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForCausalLM.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForCausalLM.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function iGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForMaskedLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForMaskedLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function dGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForMaskedLM

# Download model and configuration from huggingface.co and cache.
model = AutoModelForMaskedLM.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForMaskedLM.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForMaskedLM.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function cGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForMaskedLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForMaskedLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function fGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForMaskedLM

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForMaskedLM.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForMaskedLM.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForMaskedLM.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function mGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForMaskedLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForMaskedLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function gGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForMaskedLM

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForMaskedLM.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForMaskedLM.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForMaskedLM.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function hGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForSeq2SeqLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("t5-base")
model = AutoModelForSeq2SeqLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function uGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForSeq2SeqLM

# Download model and configuration from huggingface.co and cache.
model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")

# Update configuration during loading
model = AutoModelForSeq2SeqLM.from_pretrained("t5-base", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/t5_tf_model_config.json")
model = AutoModelForSeq2SeqLM.from_pretrained(
    "./tf_model/t5_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function pGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForSeq2SeqLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("t5-base")
model = TFAutoModelForSeq2SeqLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function _Gn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForSeq2SeqLM

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-base")

# Update configuration during loading
model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-base", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/t5_pt_model_config.json")
model = TFAutoModelForSeq2SeqLM.from_pretrained(
    "./pt_model/t5_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function vGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForSeq2SeqLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("t5-base")
model = FlaxAutoModelForSeq2SeqLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function bGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForSeq2SeqLM

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForSeq2SeqLM.from_pretrained("t5-base")

# Update configuration during loading
model = FlaxAutoModelForSeq2SeqLM.from_pretrained("t5-base", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/t5_pt_model_config.json")
model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
    "./pt_model/t5_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function TGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForSequenceClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForSequenceClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function FGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForSequenceClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForSequenceClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function MGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForSequenceClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForSequenceClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function EGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForSequenceClassification

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForSequenceClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function CGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForSequenceClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForSequenceClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function wGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForSequenceClassification

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForSequenceClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForSequenceClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForSequenceClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function AGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForMultipleChoice

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForMultipleChoice.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function LGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForMultipleChoice

# Download model and configuration from huggingface.co and cache.
model = AutoModelForMultipleChoice.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForMultipleChoice.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForMultipleChoice.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function yGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForMultipleChoice

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForMultipleChoice.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function xGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForMultipleChoice

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForMultipleChoice.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForMultipleChoice.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForMultipleChoice.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function SGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForMultipleChoice

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForMultipleChoice.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function kGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForMultipleChoice

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForMultipleChoice.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForMultipleChoice.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForMultipleChoice.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function $Gn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForNextSentencePrediction

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForNextSentencePrediction.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function PGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForNextSentencePrediction

# Download model and configuration from huggingface.co and cache.
model = AutoModelForNextSentencePrediction.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForNextSentencePrediction.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForNextSentencePrediction.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function RGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForNextSentencePrediction

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForNextSentencePrediction.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function IGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForNextSentencePrediction

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForNextSentencePrediction.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForNextSentencePrediction.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForNextSentencePrediction.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function NGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForNextSentencePrediction

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForNextSentencePrediction.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function BGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForNextSentencePrediction

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForNextSentencePrediction.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForNextSentencePrediction.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForNextSentencePrediction.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function GGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForTokenClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForTokenClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function qGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForTokenClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForTokenClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForTokenClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForTokenClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function DGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForTokenClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForTokenClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function jGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForTokenClassification

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForTokenClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForTokenClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForTokenClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function OGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForTokenClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForTokenClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function VGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForTokenClassification

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForTokenClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForTokenClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForTokenClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function XGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function zGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = AutoModelForQuestionAnswering.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForQuestionAnswering.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForQuestionAnswering.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function WGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function QGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForQuestionAnswering.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForQuestionAnswering.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForQuestionAnswering.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function UGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function HGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForQuestionAnswering.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForQuestionAnswering.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForQuestionAnswering.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function JGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForDepthEstimation

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForDepthEstimation.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function ZGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForDepthEstimation

# Download model and configuration from huggingface.co and cache.
model = AutoModelForDepthEstimation.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForDepthEstimation.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForDepthEstimation.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function YGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForImageClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForImageClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function KGn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForImageClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForImageClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForImageClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForImageClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function eqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForImageClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForImageClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function oqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForImageClassification

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForImageClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForImageClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForImageClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function rqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForImageClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForImageClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function tqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForImageClassification

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForImageClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForImageClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForImageClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function aqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForVideoClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForVideoClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function nqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForVideoClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForVideoClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForVideoClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForVideoClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function sqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForMaskedImageModeling

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForMaskedImageModeling.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function lqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForMaskedImageModeling

# Download model and configuration from huggingface.co and cache.
model = AutoModelForMaskedImageModeling.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForMaskedImageModeling.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForMaskedImageModeling.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function iqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForObjectDetection

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForObjectDetection.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function dqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForObjectDetection

# Download model and configuration from huggingface.co and cache.
model = AutoModelForObjectDetection.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForObjectDetection.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForObjectDetection.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function cqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForImageSegmentation

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForImageSegmentation.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function fqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForImageSegmentation

# Download model and configuration from huggingface.co and cache.
model = AutoModelForImageSegmentation.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForImageSegmentation.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForImageSegmentation.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function mqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForSemanticSegmentation

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForSemanticSegmentation.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function gqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForSemanticSegmentation

# Download model and configuration from huggingface.co and cache.
model = AutoModelForSemanticSegmentation.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForSemanticSegmentation.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForSemanticSegmentation.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function hqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForSemanticSegmentation

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForSemanticSegmentation.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function uqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForSemanticSegmentation

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForSemanticSegmentation.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForSemanticSegmentation.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForSemanticSegmentation.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function pqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForInstanceSegmentation

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForInstanceSegmentation.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function _qn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForInstanceSegmentation

# Download model and configuration from huggingface.co and cache.
model = AutoModelForInstanceSegmentation.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForInstanceSegmentation.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForInstanceSegmentation.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function vqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForUniversalSegmentation

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForUniversalSegmentation.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function bqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForUniversalSegmentation

# Download model and configuration from huggingface.co and cache.
model = AutoModelForUniversalSegmentation.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForUniversalSegmentation.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForUniversalSegmentation.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Tqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForZeroShotImageClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForZeroShotImageClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Fqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForZeroShotImageClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForZeroShotImageClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForZeroShotImageClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForZeroShotImageClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Mqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForZeroShotImageClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForZeroShotImageClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Eqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForZeroShotImageClassification

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForZeroShotImageClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForZeroShotImageClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForZeroShotImageClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Cqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForZeroShotObjectDetection

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForZeroShotObjectDetection.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function wqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForZeroShotObjectDetection

# Download model and configuration from huggingface.co and cache.
model = AutoModelForZeroShotObjectDetection.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForZeroShotObjectDetection.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForZeroShotObjectDetection.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Aqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForAudioClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForAudioClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Lqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForAudioClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForAudioClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForAudioClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForAudioClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function yqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForAudioFrameClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForAudioFrameClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function xqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForAudioFrameClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForAudioFrameClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForAudioFrameClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForAudioFrameClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Sqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForCTC

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForCTC.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function kqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForCTC

# Download model and configuration from huggingface.co and cache.
model = AutoModelForCTC.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForCTC.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForCTC.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function $qn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForSpeechSeq2Seq

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForSpeechSeq2Seq.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Pqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForSpeechSeq2Seq

# Download model and configuration from huggingface.co and cache.
model = AutoModelForSpeechSeq2Seq.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForSpeechSeq2Seq.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForSpeechSeq2Seq.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Rqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForSpeechSeq2Seq

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForSpeechSeq2Seq.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Iqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForSpeechSeq2Seq

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForSpeechSeq2Seq.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForSpeechSeq2Seq.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForSpeechSeq2Seq.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Nqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForSpeechSeq2Seq

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForSpeechSeq2Seq.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Bqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForSpeechSeq2Seq

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Gqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForAudioXVector

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForAudioXVector.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function qqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForAudioXVector

# Download model and configuration from huggingface.co and cache.
model = AutoModelForAudioXVector.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForAudioXVector.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForAudioXVector.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Dqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForTableQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("google/tapas-base-finetuned-wtq")
model = AutoModelForTableQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function jqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForTableQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq")

# Update configuration during loading
model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/tapas_tf_model_config.json")
model = AutoModelForTableQuestionAnswering.from_pretrained(
    "./tf_model/tapas_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Oqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForTableQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("google/tapas-base-finetuned-wtq")
model = TFAutoModelForTableQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Vqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForTableQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq")

# Update configuration during loading
model = TFAutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/tapas_pt_model_config.json")
model = TFAutoModelForTableQuestionAnswering.from_pretrained(
    "./pt_model/tapas_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Xqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForDocumentQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")
model = AutoModelForDocumentQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function zqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForDocumentQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = AutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")

# Update configuration during loading
model = AutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/layoutlm_tf_model_config.json")
model = AutoModelForDocumentQuestionAnswering.from_pretrained(
    "./tf_model/layoutlm_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Wqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForDocumentQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")
model = TFAutoModelForDocumentQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Qqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForDocumentQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")

# Update configuration during loading
model = TFAutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/layoutlm_pt_model_config.json")
model = TFAutoModelForDocumentQuestionAnswering.from_pretrained(
    "./pt_model/layoutlm_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Uqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForVisualQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
model = AutoModelForVisualQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Hqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForVisualQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = AutoModelForVisualQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")

# Update configuration during loading
model = AutoModelForVisualQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/vilt_tf_model_config.json")
model = AutoModelForVisualQuestionAnswering.from_pretrained(
    "./tf_model/vilt_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Jqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForVision2Seq

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForVision2Seq.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Zqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, AutoModelForVision2Seq

# Download model and configuration from huggingface.co and cache.
model = AutoModelForVision2Seq.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForVision2Seq.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForVision2Seq.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Yqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForVision2Seq

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForVision2Seq.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function Kqn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, TFAutoModelForVision2Seq

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForVision2Seq.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForVision2Seq.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForVision2Seq.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function eDn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForVision2Seq

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForVision2Seq.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function oDn($){let g,w,u,m,p;return m=new q({props:{code:`from transformers import AutoConfig, FlaxAutoModelForVision2Seq

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForVision2Seq.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForVision2Seq.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForVision2Seq.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),b(m.$$.fragment)},l(c){g=n(c,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(c),T(m.$$.fragment,c)},m(c,h){v(c,g,h),e(g,w),v(c,u,h),F(m,c,h),p=!0},p:j,i(c){p||(M(m.$$.fragment,c),p=!0)},o(c){E(m.$$.fragment,c),p=!1},d(c){c&&t(g),c&&t(u),C(m,c)}}}function rDn($){let 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c=new ne({}),An=new q({props:{code:'model = AutoModel.from_pretrained("bert-base-cased")',highlighted:'model = AutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)'}}),CG=new ne({}),wG=new q({props:{code:`from transformers import AutoConfig, AutoModel

AutoConfig.register("new-model", NewModelConfig)
AutoModel.register(NewModelConfig, NewModel)`,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)`}}),bh=new $xo({props:{warning:!0,$$slots:{default:[IBn]},$$scope:{ctx:$}}}),AG=new ne({}),LG=new P({props:{name:"class transformers.AutoConfig",anchor:"transformers.AutoConfig",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/configuration_auto.py#L817"}}),SG=new P({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/v4.29.0/en/main_classes/configuration#transformers.PretrainedConfig.save_pretrained">save_pretrained()</a> method, or the <a href="/docs/transformers/v4.29.0/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/v4.29.0/src/transformers/models/auto/configuration_auto.py#L840"}}),X_=new D({props:{anchor:"transformers.AutoConfig.from_pretrained.example",$$slots:{default:[NBn]},$$scope:{ctx:$}}}),kG=new P({props:{name:"register",anchor:"transformers.AutoConfig.register",parameters:[{name:"model_type",val:""},{name:"config",val:""}],parametersDescription:[{anchor:"transformers.AutoConfig.register.model_type",description:"<strong>model_type</strong> (<code>str</code>) &#x2014; The model type like &#x201C;bert&#x201D; or &#x201C;gpt&#x201D;.",name:"model_type"},{anchor:"transformers.AutoConfig.register.config",description:'<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014; The config to register.',name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/configuration_auto.py#L955"}}),$G=new ne({}),PG=new P({props:{name:"class transformers.AutoTokenizer",anchor:"transformers.AutoTokenizer",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/tokenization_auto.py#L513"}}),NG=new P({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/v4.29.0/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/v4.29.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
The configuration object used to dertermine 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/v4.29.0/src/transformers/models/auto/tokenization_auto.py#L527"}}),B2=new D({props:{anchor:"transformers.AutoTokenizer.from_pretrained.example",$$slots:{default:[BBn]},$$scope:{ctx:$}}}),BG=new P({props:{name:"register",anchor:"transformers.AutoTokenizer.register",parameters:[{name:"config_class",val:""},{name:"slow_tokenizer_class",val:" = None"},{name:"fast_tokenizer_class",val:" = None"}],parametersDescription:[{anchor:"transformers.AutoTokenizer.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/v4.29.0/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/v4.29.0/src/transformers/models/auto/tokenization_auto.py#L726"}}),GG=new ne({}),qG=new P({props:{name:"class transformers.AutoFeatureExtractor",anchor:"transformers.AutoFeatureExtractor",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/feature_extraction_auto.py#L220"}}),OG=new P({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/v4.29.0/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.use_auth_token",description:`<strong>use_auth_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:"use_auth_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/v4.29.0/src/transformers/models/auto/feature_extraction_auto.py#L234"}}),Wv=new $xo({props:{$$slots:{default:[GBn]},$$scope:{ctx:$}}}),Qv=new D({props:{anchor:"transformers.AutoFeatureExtractor.from_pretrained.example",$$slots:{default:[qBn]},$$scope:{ctx:$}}}),VG=new P({props:{name:"register",anchor:"transformers.AutoFeatureExtractor.register",parameters:[{name:"config_class",val:""},{name:"feature_extractor_class",val:""}],parametersDescription:[{anchor:"transformers.AutoFeatureExtractor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/v4.29.0/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/v4.29.0/src/transformers/models/auto/feature_extraction_auto.py#L354"}}),XG=new ne({}),zG=new P({props:{name:"class transformers.AutoImageProcessor",anchor:"transformers.AutoImageProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/image_processing_auto.py#L226"}}),UG=new P({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/v4.29.0/en/main_classes/image_processor#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.use_auth_token",description:`<strong>use_auth_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:"use_auth_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/v4.29.0/src/transformers/models/auto/image_processing_auto.py#L240"}}),s4=new $xo({props:{$$slots:{default:[DBn]},$$scope:{ctx:$}}}),l4=new D({props:{anchor:"transformers.AutoImageProcessor.from_pretrained.example",$$slots:{default:[jBn]},$$scope:{ctx:$}}}),HG=new P({props:{name:"register",anchor:"transformers.AutoImageProcessor.register",parameters:[{name:"config_class",val:""},{name:"image_processor_class",val:""}],parametersDescription:[{anchor:"transformers.AutoImageProcessor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/v4.29.0/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/v4.29.0/en/main_classes/image_processor#transformers.ImageProcessingMixin">ImageProcessingMixin</a>) &#x2014; The image processor to register.',name:"image_processor_class"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/image_processing_auto.py#L378"}}),JG=new ne({}),ZG=new P({props:{name:"class transformers.AutoProcessor",anchor:"transformers.AutoProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/processing_auto.py#L111"}}),eq=new P({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.use_auth_token",description:`<strong>use_auth_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:"use_auth_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/v4.29.0/src/transformers/models/auto/processing_auto.py#L125"}}),Q4=new $xo({props:{$$slots:{default:[OBn]},$$scope:{ctx:$}}}),U4=new D({props:{anchor:"transformers.AutoProcessor.from_pretrained.example",$$slots:{default:[VBn]},$$scope:{ctx:$}}}),oq=new P({props:{name:"register",anchor:"transformers.AutoProcessor.register",parameters:[{name:"config_class",val:""},{name:"processor_class",val:""}],parametersDescription:[{anchor:"transformers.AutoProcessor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/v4.29.0/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/v4.29.0/src/transformers/models/auto/processing_auto.py#L300"}}),rq=new ne({}),tq=new ne({}),aq=new P({props:{name:"class transformers.AutoModel",anchor:"transformers.AutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1085"}}),sq=new P({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/v4.29.0/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/v4.29.0/en/model_doc/audio-spectrogram-transformer#transformers.ASTConfig">ASTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/audio-spectrogram-transformer#transformers.ASTModel">ASTModel</a> (Audio Spectrogram Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.AlbertModel">AlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/align#transformers.AlignConfig">AlignConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/align#transformers.AlignModel">AlignModel</a> (ALIGN model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/altclip#transformers.AltCLIPConfig">AltCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartModel">BartModel</a> (BART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/beit#transformers.BeitModel">BeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertModel">BertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert-generation#transformers.BertGenerationEncoder">BertGenerationEncoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdModel">BigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusModel">BigBirdPegasusModel</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/biogpt#transformers.BioGptModel">BioGptModel</a> (BioGpt model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bit#transformers.BitConfig">BitConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bit#transformers.BitModel">BitModel</a> (BiT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot#transformers.BlenderbotModel">BlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallModel">BlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blip-2#transformers.Blip2Model">Blip2Model</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bloom#transformers.BloomModel">BloomModel</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bridgetower#transformers.BridgeTowerConfig">BridgeTowerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bridgetower#transformers.BridgeTowerModel">BridgeTowerModel</a> (BridgeTower model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/clipseg#transformers.CLIPSegConfig">CLIPSegConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/clipseg#transformers.CLIPSegModel">CLIPSegModel</a> (CLIPSeg model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ctrl#transformers.CTRLModel">CTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertModel">CamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/canine#transformers.CanineModel">CanineModel</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/chinese_clip#transformers.ChineseCLIPConfig">ChineseCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/chinese_clip#transformers.ChineseCLIPModel">ChineseCLIPModel</a> (Chinese-CLIP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/clap#transformers.ClapConfig">ClapConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/clap#transformers.ClapModel">ClapModel</a> (CLAP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/codegen#transformers.CodeGenConfig">CodeGenConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/codegen#transformers.CodeGenModel">CodeGenModel</a> (CodeGen model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/conditional_detr#transformers.ConditionalDetrConfig">ConditionalDetrConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/conditional_detr#transformers.ConditionalDetrModel">ConditionalDetrModel</a> (Conditional DETR model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.ConvBertModel">ConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/convnext#transformers.ConvNextModel">ConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/convnextv2#transformers.ConvNextV2Config">ConvNextV2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/convnextv2#transformers.ConvNextV2Model">ConvNextV2Model</a> (ConvNeXTV2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/cpmant#transformers.CpmAntConfig">CpmAntConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/cpmant#transformers.CpmAntModel">CpmAntModel</a> (CPM-Ant model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/cvt#transformers.CvtModel">CvtModel</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/dpt#transformers.DPTModel">DPTModel</a> (DPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecAudioModel">Data2VecAudioModel</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecTextModel">Data2VecTextModel</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecVisionModel">Data2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.DebertaModel">DebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.DebertaV2Model">DebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/decision_transformer#transformers.DecisionTransformerConfig">DecisionTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/decision_transformer#transformers.DecisionTransformerModel">DecisionTransformerModel</a> (Decision Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deformable_detr#transformers.DeformableDetrConfig">DeformableDetrConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deformable_detr#transformers.DeformableDetrModel">DeformableDetrModel</a> (Deformable DETR model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deit#transformers.DeiTModel">DeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deta#transformers.DetaConfig">DetaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deta#transformers.DetaModel">DetaModel</a> (DETA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/detr#transformers.DetrModel">DetrModel</a> (DETR model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/dinat#transformers.DinatConfig">DinatConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/dinat#transformers.DinatModel">DinatModel</a> (DiNAT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/donut#transformers.DonutSwinConfig">DonutSwinConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/donut#transformers.DonutSwinModel">DonutSwinModel</a> (DonutSwin model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/efficientformer#transformers.EfficientFormerModel">EfficientFormerModel</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/efficientnet#transformers.EfficientNetConfig">EfficientNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/efficientnet#transformers.EfficientNetModel">EfficientNetModel</a> (EfficientNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraModel">ElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ernie#transformers.ErnieModel">ErnieModel</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ernie_m#transformers.ErnieMModel">ErnieMModel</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/esm#transformers.EsmModel">EsmModel</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/fnet#transformers.FNetModel">FNetModel</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/fsmt#transformers.FSMTModel">FSMTModel</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertModel">FlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/flava#transformers.FlavaModel">FlavaModel</a> (FLAVA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/focalnet#transformers.FocalNetConfig">FocalNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/focalnet#transformers.FocalNetModel">FocalNetModel</a> (FocalNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelModel">FunnelModel</a> or <a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelBaseModel">FunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/glpn#transformers.GLPNModel">GLPNModel</a> (GLPN model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.GPT2Model">GPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeModel">GPTBigCodeModel</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.GPTJModel">GPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neo#transformers.GPTNeoModel">GPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neox#transformers.GPTNeoXModel">GPTNeoXModel</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseModel">GPTNeoXJapaneseModel</a> (GPT NeoX Japanese model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/git#transformers.GitConfig">GitConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/git#transformers.GitModel">GitModel</a> (GIT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/graphormer#transformers.GraphormerConfig">GraphormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/graphormer#transformers.GraphormerModel">GraphormerModel</a> (Graphormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/groupvit#transformers.GroupViTModel">GroupViTModel</a> (GroupViT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/hubert#transformers.HubertModel">HubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ibert#transformers.IBertModel">IBertModel</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/imagegpt#transformers.ImageGPTModel">ImageGPTModel</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/informer#transformers.InformerConfig">InformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/informer#transformers.InformerModel">InformerModel</a> (Informer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/jukebox#transformers.JukeboxConfig">JukeboxConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/jukebox#transformers.JukeboxModel">JukeboxModel</a> (Jukebox model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/led#transformers.LEDModel">LEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.LayoutLMModel">LayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2Model">LayoutLMv2Model</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Model">LayoutLMv3Model</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/levit#transformers.LevitModel">LevitModel</a> (LeViT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/lilt#transformers.LiltModel">LiltModel</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/llama#transformers.LlamaModel">LlamaModel</a> (LLaMA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/longt5#transformers.LongT5Model">LongT5Model</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerModel">LongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/luke#transformers.LukeModel">LukeModel</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/lxmert#transformers.LxmertModel">LxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/m2m_100#transformers.M2M100Model">M2M100Model</a> (M2M100 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartModel">MBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mctct#transformers.MCTCTModel">MCTCTModel</a> (M-CTC-T model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetModel">MPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mt5#transformers.MT5Model">MT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/marian#transformers.MarianModel">MarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/markuplm#transformers.MarkupLMModel">MarkupLMModel</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mask2former#transformers.Mask2FormerConfig">Mask2FormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mask2former#transformers.Mask2FormerModel">Mask2FormerModel</a> (Mask2Former model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/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/v4.29.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mega#transformers.MegaModel">MegaModel</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/megatron-bert#transformers.MegatronBertModel">MegatronBertModel</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mgp-str#transformers.MgpstrConfig">MgpstrConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mgp-str#transformers.MgpstrForSceneTextRecognition">MgpstrForSceneTextRecognition</a> (MGP-STR model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertModel">MobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config">MobileNetV1Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilenet_v1#transformers.MobileNetV1Model">MobileNetV1Model</a> (MobileNetV1 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2Model">MobileNetV2Model</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilevit#transformers.MobileViTModel">MobileViTModel</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mvp#transformers.MvpModel">MvpModel</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/nat#transformers.NatConfig">NatConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/nat#transformers.NatModel">NatModel</a> (NAT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/nezha#transformers.NezhaModel">NezhaModel</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/nllb-moe#transformers.NllbMoeConfig">NllbMoeConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/nllb-moe#transformers.NllbMoeModel">NllbMoeModel</a> (NLLB-MOE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/nystromformer#transformers.NystromformerModel">NystromformerModel</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/opt#transformers.OPTModel">OPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/oneformer#transformers.OneFormerConfig">OneFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/oneformer#transformers.OneFormerModel">OneFormerModel</a> (OneFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/openai-gpt#transformers.OpenAIGPTModel">OpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/open-llama#transformers.OpenLlamaModel">OpenLlamaModel</a> (OpenLlama model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/owlvit#transformers.OwlViTModel">OwlViTModel</a> (OWL-ViT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/plbart#transformers.PLBartModel">PLBartModel</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/pegasus#transformers.PegasusModel">PegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/pegasus_x#transformers.PegasusXConfig">PegasusXConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/pegasus_x#transformers.PegasusXModel">PegasusXModel</a> (PEGASUS-X model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/perceiver#transformers.PerceiverModel">PerceiverModel</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/poolformer#transformers.PoolFormerModel">PoolFormerModel</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/prophetnet#transformers.ProphetNetModel">ProphetNetModel</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/qdqbert#transformers.QDQBertModel">QDQBertModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/reformer#transformers.ReformerModel">ReformerModel</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/regnet#transformers.RegNetModel">RegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertModel">RemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/resnet#transformers.ResNetModel">ResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roc_bert#transformers.RoCBertModel">RoCBertModel</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerModel">RoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaModel">RobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormModel">RobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/rwkv#transformers.RwkvConfig">RwkvConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/rwkv#transformers.RwkvModel">RwkvModel</a> (RWKV model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/sew#transformers.SEWModel">SEWModel</a> (SEW model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/sew-d#transformers.SEWDModel">SEWDModel</a> (SEW-D model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/sam#transformers.SamConfig">SamConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/sam#transformers.SamModel">SamModel</a> (SAM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/segformer#transformers.SegformerModel">SegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/speech_to_text#transformers.Speech2TextModel">Speech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/speecht5#transformers.SpeechT5Config">SpeechT5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/speecht5#transformers.SpeechT5Model">SpeechT5Model</a> (SpeechT5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/splinter#transformers.SplinterModel">SplinterModel</a> (Splinter model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/squeezebert#transformers.SqueezeBertModel">SqueezeBertModel</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/swin2sr#transformers.Swin2SRConfig">Swin2SRConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/swin2sr#transformers.Swin2SRModel">Swin2SRModel</a> (Swin2SR model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/swin#transformers.SwinModel">SwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/swinv2#transformers.Swinv2Model">Swinv2Model</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/switch_transformers#transformers.SwitchTransformersModel">SwitchTransformersModel</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/t5#transformers.T5Model">T5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/table-transformer#transformers.TableTransformerConfig">TableTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/table-transformer#transformers.TableTransformerModel">TableTransformerModel</a> (Table Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/tapas#transformers.TapasModel">TapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerConfig">TimeSeriesTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerModel">TimeSeriesTransformerModel</a> (Time Series Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/timesformer#transformers.TimesformerModel">TimesformerModel</a> (TimeSformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerConfig">TrajectoryTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerModel">TrajectoryTransformerModel</a> (Trajectory Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/transfo-xl#transformers.TransfoXLModel">TransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/tvlt#transformers.TvltConfig">TvltConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/tvlt#transformers.TvltModel">TvltModel</a> (TVLT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/unispeech#transformers.UniSpeechModel">UniSpeechModel</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel">UniSpeechSatModel</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/van#transformers.VanModel">VanModel</a> (VAN model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vit#transformers.ViTModel">ViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vit_hybrid#transformers.ViTHybridConfig">ViTHybridConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vit_hybrid#transformers.ViTHybridModel">ViTHybridModel</a> (ViT Hybrid model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vit_mae#transformers.ViTMAEModel">ViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vit_msn#transformers.ViTMSNModel">ViTMSNModel</a> (ViTMSN model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/videomae#transformers.VideoMAEModel">VideoMAEModel</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vilt#transformers.ViltModel">ViltModel</a> (ViLT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderModel">VisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/visual_bert#transformers.VisualBertModel">VisualBertModel</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Model">Wav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerModel">Wav2Vec2ConformerModel</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wavlm#transformers.WavLMModel">WavLMModel</a> (WavLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/whisper#transformers.WhisperModel">WhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xclip#transformers.XCLIPConfig">XCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xclip#transformers.XCLIPModel">XCLIPModel</a> (X-CLIP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xglm#transformers.XGLMModel">XGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMModel">XLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetModel">XLMProphetNetModel</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaModel">XLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLModel">XLMRobertaXLModel</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetModel">XLNetModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xmod#transformers.XmodModel">XmodModel</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/yolos#transformers.YolosModel">YolosModel</a> (YOLOS model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/yoso#transformers.YosoModel">YosoModel</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),Y4=new D({props:{anchor:"transformers.AutoModel.from_config.example",$$slots:{default:[XBn]},$$scope:{ctx:$}}}),lq=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),gT=new D({props:{anchor:"transformers.AutoModel.from_pretrained.example",$$slots:{default:[zBn]},$$scope:{ctx:$}}}),iq=new ne({}),dq=new P({props:{name:"class transformers.TFAutoModel",anchor:"transformers.TFAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_tf_auto.py#L480"}}),fq=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.TFAlbertModel">TFAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.TFBartModel">TFBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.TFBertModel">TFBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot#transformers.TFBlenderbotModel">TFBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallModel">TFBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blip#transformers.TFBlipModel">TFBlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ctrl#transformers.TFCTRLModel">TFCTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.TFCamembertModel">TFCamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.TFConvBertModel">TFConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/convnext#transformers.TFConvNextModel">TFConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/cvt#transformers.TFCvtModel">TFCvtModel</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/dpr#transformers.TFDPRQuestionEncoder">TFDPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.TFData2VecVisionModel">TFData2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.TFDebertaModel">TFDebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.TFDebertaV2Model">TFDebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deit#transformers.TFDeiTModel">TFDeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.TFDistilBertModel">TFDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.TFElectraModel">TFElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/esm#transformers.TFEsmModel">TFEsmModel</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.TFFlaubertModel">TFFlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.TFFunnelModel">TFFunnelModel</a> or <a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.TFFunnelBaseModel">TFFunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.TFGPT2Model">TFGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.TFGPTJModel">TFGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/groupvit#transformers.TFGroupViTModel">TFGroupViTModel</a> (GroupViT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/hubert#transformers.TFHubertModel">TFHubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/led#transformers.TFLEDModel">TFLEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.TFLayoutLMModel">TFLayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3Model">TFLayoutLMv3Model</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.TFLongformerModel">TFLongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/lxmert#transformers.TFLxmertModel">TFLxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.TFMBartModel">TFMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.TFMPNetModel">TFMPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mt5#transformers.TFMT5Model">TFMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/marian#transformers.TFMarianModel">TFMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.TFMobileBertModel">TFMobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilevit#transformers.TFMobileViTModel">TFMobileViTModel</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/opt#transformers.TFOPTModel">TFOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/openai-gpt#transformers.TFOpenAIGPTModel">TFOpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/pegasus#transformers.TFPegasusModel">TFPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/regnet#transformers.TFRegNetModel">TFRegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.TFRemBertModel">TFRemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/resnet#transformers.TFResNetModel">TFResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.TFRoFormerModel">TFRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.TFRobertaModel">TFRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormModel">TFRobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/segformer#transformers.TFSegformerModel">TFSegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/speech_to_text#transformers.TFSpeech2TextModel">TFSpeech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/swin#transformers.TFSwinModel">TFSwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/t5#transformers.TFT5Model">TFT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/tapas#transformers.TFTapasModel">TFTapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/transfo-xl#transformers.TFTransfoXLModel">TFTransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vit#transformers.TFViTModel">TFViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vit_mae#transformers.TFViTMAEModel">TFViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vision-text-dual-encoder#transformers.TFVisionTextDualEncoderModel">TFVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2#transformers.TFWav2Vec2Model">TFWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/whisper#transformers.TFWhisperModel">TFWhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xglm#transformers.TFXGLMModel">TFXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.TFXLMModel">TFXLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaModel">TFXLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.TFXLNetModel">TFXLNetModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),uT=new D({props:{anchor:"transformers.TFAutoModel.from_config.example",$$slots:{default:[WBn]},$$scope:{ctx:$}}}),mq=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),EF=new D({props:{anchor:"transformers.TFAutoModel.from_pretrained.example",$$slots:{default:[QBn]},$$scope:{ctx:$}}}),gq=new ne({}),hq=new P({props:{name:"class transformers.FlaxAutoModel",anchor:"transformers.FlaxAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_flax_auto.py#L269"}}),pq=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.FlaxAlbertModel">FlaxAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.FlaxBartModel">FlaxBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/beit#transformers.FlaxBeitModel">FlaxBeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.FlaxBertModel">FlaxBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.FlaxBigBirdModel">FlaxBigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot#transformers.FlaxBlenderbotModel">FlaxBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallModel">FlaxBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/clip#transformers.FlaxCLIPModel">FlaxCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.FlaxDistilBertModel">FlaxDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.FlaxElectraModel">FlaxElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.FlaxGPT2Model">FlaxGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.FlaxGPTJModel">FlaxGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neo#transformers.FlaxGPTNeoModel">FlaxGPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/longt5#transformers.FlaxLongT5Model">FlaxLongT5Model</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.FlaxMBartModel">FlaxMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mt5#transformers.FlaxMT5Model">FlaxMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/marian#transformers.FlaxMarianModel">FlaxMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/opt#transformers.FlaxOPTModel">FlaxOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/pegasus#transformers.FlaxPegasusModel">FlaxPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/regnet#transformers.FlaxRegNetModel">FlaxRegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/resnet#transformers.FlaxResNetModel">FlaxResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.FlaxRoFormerModel">FlaxRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.FlaxRobertaModel">FlaxRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormModel">FlaxRobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/t5#transformers.FlaxT5Model">FlaxT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vit#transformers.FlaxViTModel">FlaxViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vision-text-dual-encoder#transformers.FlaxVisionTextDualEncoderModel">FlaxVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2Model">FlaxWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/whisper#transformers.FlaxWhisperModel">FlaxWhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xglm#transformers.FlaxXGLMModel">FlaxXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaModel">FlaxXLMRobertaModel</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),wF=new D({props:{anchor:"transformers.FlaxAutoModel.from_config.example",$$slots:{default:[UBn]},$$scope:{ctx:$}}}),_q=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),aM=new D({props:{anchor:"transformers.FlaxAutoModel.from_pretrained.example",$$slots:{default:[HBn]},$$scope:{ctx:$}}}),vq=new ne({}),bq=new ne({}),Tq=new P({props:{name:"class transformers.AutoModelForPreTraining",anchor:"transformers.AutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1092"}}),Mq=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.AlbertForPreTraining">AlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertForPreTraining">BertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdForPreTraining">BigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraForPreTraining">ElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ernie#transformers.ErnieForPreTraining">ErnieForPreTraining</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/fnet#transformers.FNetForPreTraining">FNetForPreTraining</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/flava#transformers.FlavaForPreTraining">FlavaForPreTraining</a> (FLAVA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelForPreTraining">FunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForCausalLM">GPTBigCodeForCausalLM</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/lxmert#transformers.LxmertForPreTraining">LxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mega#transformers.MegaForMaskedLM">MegaForMaskedLM</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/megatron-bert#transformers.MegatronBertForPreTraining">MegatronBertForPreTraining</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertForPreTraining">MobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/nezha#transformers.NezhaForPreTraining">NezhaForPreTraining</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/nllb-moe#transformers.NllbMoeConfig">NllbMoeConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/nllb-moe#transformers.NllbMoeForConditionalGeneration">NllbMoeForConditionalGeneration</a> (NLLB-MOE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roc_bert#transformers.RoCBertForPreTraining">RoCBertForPreTraining</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMaskedLM">RobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/rwkv#transformers.RwkvConfig">RwkvConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/rwkv#transformers.RwkvForCausalLM">RwkvForCausalLM</a> (RWKV model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/splinter#transformers.SplinterForPreTraining">SplinterForPreTraining</a> (Splinter model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/switch_transformers#transformers.SwitchTransformersForConditionalGeneration">SwitchTransformersForConditionalGeneration</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/tvlt#transformers.TvltConfig">TvltConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/tvlt#transformers.TvltForPreTraining">TvltForPreTraining</a> (TVLT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/unispeech#transformers.UniSpeechForPreTraining">UniSpeechForPreTraining</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatForPreTraining">UniSpeechSatForPreTraining</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vit_mae#transformers.ViTMAEForPreTraining">ViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/videomae#transformers.VideoMAEForPreTraining">VideoMAEForPreTraining</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/visual_bert#transformers.VisualBertForPreTraining">VisualBertForPreTraining</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2#transformers.Wav2Vec2ForPreTraining">Wav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForPreTraining">Wav2Vec2ConformerForPreTraining</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xmod#transformers.XmodForMaskedLM">XmodForMaskedLM</a> (X-MOD model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),lM=new D({props:{anchor:"transformers.AutoModelForPreTraining.from_config.example",$$slots:{default:[JBn]},$$scope:{ctx:$}}}),Eq=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),hE=new D({props:{anchor:"transformers.AutoModelForPreTraining.from_pretrained.example",$$slots:{default:[ZBn]},$$scope:{ctx:$}}}),Cq=new ne({}),wq=new P({props:{name:"class transformers.TFAutoModelForPreTraining",anchor:"transformers.TFAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_tf_auto.py#L496"}}),Lq=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.TFAlbertForPreTraining">TFAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.TFBertForPreTraining">TFBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.TFElectraForPreTraining">TFElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.TFFunnelForPreTraining">TFFunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/lxmert#transformers.TFLxmertForPreTraining">TFLxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.TFMobileBertForPreTraining">TFMobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMaskedLM">TFRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vit_mae#transformers.TFViTMAEForPreTraining">TFViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),pE=new D({props:{anchor:"transformers.TFAutoModelForPreTraining.from_config.example",$$slots:{default:[YBn]},$$scope:{ctx:$}}}),yq=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),OE=new D({props:{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[KBn]},$$scope:{ctx:$}}}),xq=new ne({}),Sq=new P({props:{name:"class transformers.FlaxAutoModelForPreTraining",anchor:"transformers.FlaxAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_flax_auto.py#L276"}}),$q=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.FlaxAlbertForPreTraining">FlaxAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.FlaxBertForPreTraining">FlaxBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.FlaxBigBirdForPreTraining">FlaxBigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.FlaxElectraForPreTraining">FlaxElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMaskedLM">FlaxRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2ForPreTraining">FlaxWav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),XE=new D({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.example",$$slots:{default:[eGn]},$$scope:{ctx:$}}}),Pq=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),s3=new D({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[oGn]},$$scope:{ctx:$}}}),Rq=new ne({}),Iq=new ne({}),Nq=new P({props:{name:"class transformers.AutoModelForCausalLM",anchor:"transformers.AutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1107"}}),Gq=new P({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/v4.29.0/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/v4.29.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartForCausalLM">BartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertLMHeadModel">BertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert-generation#transformers.BertGenerationDecoder">BertGenerationDecoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdForCausalLM">BigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForCausalLM">BigBirdPegasusForCausalLM</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/biogpt#transformers.BioGptForCausalLM">BioGptForCausalLM</a> (BioGpt model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot#transformers.BlenderbotForCausalLM">BlenderbotForCausalLM</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForCausalLM">BlenderbotSmallForCausalLM</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertForCausalLM">CamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/codegen#transformers.CodeGenConfig">CodeGenConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/codegen#transformers.CodeGenForCausalLM">CodeGenForCausalLM</a> (CodeGen model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/cpmant#transformers.CpmAntConfig">CpmAntConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/cpmant#transformers.CpmAntForCausalLM">CpmAntForCausalLM</a> (CPM-Ant model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecTextForCausalLM">Data2VecTextForCausalLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraForCausalLM">ElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ernie#transformers.ErnieForCausalLM">ErnieForCausalLM</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForCausalLM">GPTBigCodeForCausalLM</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.GPTJForCausalLM">GPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neo#transformers.GPTNeoForCausalLM">GPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neox#transformers.GPTNeoXForCausalLM">GPTNeoXForCausalLM</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseForCausalLM">GPTNeoXJapaneseForCausalLM</a> (GPT NeoX Japanese model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/git#transformers.GitConfig">GitConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/git#transformers.GitForCausalLM">GitForCausalLM</a> (GIT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/llama#transformers.LlamaForCausalLM">LlamaForCausalLM</a> (LLaMA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartForCausalLM">MBartForCausalLM</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/marian#transformers.MarianForCausalLM">MarianForCausalLM</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mega#transformers.MegaForCausalLM">MegaForCausalLM</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/megatron-bert#transformers.MegatronBertForCausalLM">MegatronBertForCausalLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mvp#transformers.MvpForCausalLM">MvpForCausalLM</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/opt#transformers.OPTForCausalLM">OPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/open-llama#transformers.OpenLlamaForCausalLM">OpenLlamaForCausalLM</a> (OpenLlama model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/plbart#transformers.PLBartForCausalLM">PLBartForCausalLM</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/pegasus#transformers.PegasusForCausalLM">PegasusForCausalLM</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/prophetnet#transformers.ProphetNetForCausalLM">ProphetNetForCausalLM</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/qdqbert#transformers.QDQBertLMHeadModel">QDQBertLMHeadModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/reformer#transformers.ReformerModelWithLMHead">ReformerModelWithLMHead</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertForCausalLM">RemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roc_bert#transformers.RoCBertForCausalLM">RoCBertForCausalLM</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerForCausalLM">RoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaForCausalLM">RobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForCausalLM">RobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/rwkv#transformers.RwkvConfig">RwkvConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/rwkv#transformers.RwkvForCausalLM">RwkvForCausalLM</a> (RWKV model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/speech_to_text_2#transformers.Speech2Text2Config">Speech2Text2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/speech_to_text_2#transformers.Speech2Text2ForCausalLM">Speech2Text2ForCausalLM</a> (Speech2Text2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/trocr#transformers.TrOCRConfig">TrOCRConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/trocr#transformers.TrOCRForCausalLM">TrOCRForCausalLM</a> (TrOCR model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xglm#transformers.XGLMForCausalLM">XGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForCausalLM">XLMProphetNetForCausalLM</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForCausalLM">XLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForCausalLM">XLMRobertaXLForCausalLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xmod#transformers.XmodForCausalLM">XmodForCausalLM</a> (X-MOD model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),d3=new D({props:{anchor:"transformers.AutoModelForCausalLM.from_config.example",$$slots:{default:[rGn]},$$scope:{ctx:$}}}),qq=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),fC=new D({props:{anchor:"transformers.AutoModelForCausalLM.from_pretrained.example",$$slots:{default:[tGn]},$$scope:{ctx:$}}}),Dq=new ne({}),jq=new P({props:{name:"class transformers.TFAutoModelForCausalLM",anchor:"transformers.TFAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_tf_auto.py#L511"}}),Vq=new P({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/v4.29.0/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/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.TFBertLMHeadModel">TFBertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.TFCamembertForCausalLM">TFCamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.TFGPTJForCausalLM">TFGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/opt#transformers.TFOPTForCausalLM">TFOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.TFRemBertForCausalLM">TFRemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.TFRoFormerForCausalLM">TFRoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.TFRobertaForCausalLM">TFRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForCausalLM">TFRobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xglm#transformers.TFXGLMForCausalLM">TFXGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForCausalLM">TFXLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),gC=new D({props:{anchor:"transformers.TFAutoModelForCausalLM.from_config.example",$$slots:{default:[aGn]},$$scope:{ctx:$}}}),Xq=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),kC=new D({props:{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[nGn]},$$scope:{ctx:$}}}),zq=new ne({}),Wq=new P({props:{name:"class transformers.FlaxAutoModelForCausalLM",anchor:"transformers.FlaxAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_flax_auto.py#L283"}}),Uq=new P({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/v4.29.0/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/v4.29.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.FlaxBartForCausalLM">FlaxBartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.FlaxBertForCausalLM">FlaxBertForCausalLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.FlaxBigBirdForCausalLM">FlaxBigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.FlaxElectraForCausalLM">FlaxElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.FlaxGPT2LMHeadModel">FlaxGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.FlaxGPTJForCausalLM">FlaxGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neo#transformers.FlaxGPTNeoForCausalLM">FlaxGPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/opt#transformers.FlaxOPTForCausalLM">FlaxOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.FlaxRobertaForCausalLM">FlaxRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForCausalLM">FlaxRobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xglm#transformers.FlaxXGLMForCausalLM">FlaxXGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForCausalLM">FlaxXLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),PC=new D({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.example",$$slots:{default:[sGn]},$$scope:{ctx:$}}}),Hq=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),QC=new D({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[lGn]},$$scope:{ctx:$}}}),Jq=new ne({}),Zq=new P({props:{name:"class transformers.AutoModelForMaskedLM",anchor:"transformers.AutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1114"}}),Kq=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.AlbertForMaskedLM">AlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertForMaskedLM">BertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdForMaskedLM">BigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.ConvBertForMaskedLM">ConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraForMaskedLM">ElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ernie#transformers.ErnieForMaskedLM">ErnieForMaskedLM</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/esm#transformers.EsmForMaskedLM">EsmForMaskedLM</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/fnet#transformers.FNetForMaskedLM">FNetForMaskedLM</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelForMaskedLM">FunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mega#transformers.MegaForMaskedLM">MegaForMaskedLM</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/megatron-bert#transformers.MegatronBertForMaskedLM">MegatronBertForMaskedLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertForMaskedLM">MobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/nezha#transformers.NezhaForMaskedLM">NezhaForMaskedLM</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/nystromformer#transformers.NystromformerForMaskedLM">NystromformerForMaskedLM</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/perceiver#transformers.PerceiverForMaskedLM">PerceiverForMaskedLM</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/qdqbert#transformers.QDQBertForMaskedLM">QDQBertForMaskedLM</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/reformer#transformers.ReformerForMaskedLM">ReformerForMaskedLM</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertForMaskedLM">RemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roc_bert#transformers.RoCBertForMaskedLM">RoCBertForMaskedLM</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerForMaskedLM">RoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMaskedLM">RobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <code>Wav2Vec2ForMaskedLM</code> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xmod#transformers.XmodForMaskedLM">XmodForMaskedLM</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/yoso#transformers.YosoForMaskedLM">YosoForMaskedLM</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),HC=new D({props:{anchor:"transformers.AutoModelForMaskedLM.from_config.example",$$slots:{default:[iGn]},$$scope:{ctx:$}}}),eD=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),j5=new D({props:{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[dGn]},$$scope:{ctx:$}}}),oD=new ne({}),rD=new P({props:{name:"class transformers.TFAutoModelForMaskedLM",anchor:"transformers.TFAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_tf_auto.py#L561"}}),aD=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.TFAlbertForMaskedLM">TFAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.TFBertForMaskedLM">TFBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.TFConvBertForMaskedLM">TFConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.TFDebertaForMaskedLM">TFDebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.TFDebertaV2ForMaskedLM">TFDebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.TFElectraForMaskedLM">TFElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/esm#transformers.TFEsmForMaskedLM">TFEsmForMaskedLM</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.TFFunnelForMaskedLM">TFFunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.TFLongformerForMaskedLM">TFLongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.TFMobileBertForMaskedLM">TFMobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.TFRemBertForMaskedLM">TFRemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.TFRoFormerForMaskedLM">TFRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMaskedLM">TFRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),V5=new D({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_config.example",$$slots:{default:[cGn]},$$scope:{ctx:$}}}),nD=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),m8=new D({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[fGn]},$$scope:{ctx:$}}}),sD=new ne({}),lD=new P({props:{name:"class transformers.FlaxAutoModelForMaskedLM",anchor:"transformers.FlaxAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_flax_auto.py#L290"}}),dD=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.FlaxAlbertForMaskedLM">FlaxAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.FlaxBertForMaskedLM">FlaxBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.FlaxBigBirdForMaskedLM">FlaxBigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.FlaxDistilBertForMaskedLM">FlaxDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.FlaxElectraForMaskedLM">FlaxElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMaskedLM">FlaxRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),h8=new D({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.example",$$slots:{default:[mGn]},$$scope:{ctx:$}}}),cD=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),A8=new D({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[gGn]},$$scope:{ctx:$}}}),fD=new ne({}),gD=new P({props:{name:"class transformers.AutoModelForMaskGeneration",anchor:"transformers.AutoModelForMaskGeneration",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1081"}}),hD=new ne({}),uD=new P({props:{name:"class transformers.AutoModelForSeq2SeqLM",anchor:"transformers.AutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1121"}}),_D=new P({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/v4.29.0/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/v4.29.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForConditionalGeneration">BigBirdPegasusForConditionalGeneration</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot#transformers.BlenderbotForConditionalGeneration">BlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForConditionalGeneration">BlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/encoder-decoder#transformers.EncoderDecoderModel">EncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/led#transformers.LEDForConditionalGeneration">LEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/longt5#transformers.LongT5ForConditionalGeneration">LongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/m2m_100#transformers.M2M100ForConditionalGeneration">M2M100ForConditionalGeneration</a> (M2M100 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mt5#transformers.MT5ForConditionalGeneration">MT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/marian#transformers.MarianMTModel">MarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/nllb-moe#transformers.NllbMoeConfig">NllbMoeConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/nllb-moe#transformers.NllbMoeForConditionalGeneration">NllbMoeForConditionalGeneration</a> (NLLB-MOE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/plbart#transformers.PLBartForConditionalGeneration">PLBartForConditionalGeneration</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/pegasus#transformers.PegasusForConditionalGeneration">PegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/pegasus_x#transformers.PegasusXConfig">PegasusXConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/pegasus_x#transformers.PegasusXForConditionalGeneration">PegasusXForConditionalGeneration</a> (PEGASUS-X model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/prophetnet#transformers.ProphetNetForConditionalGeneration">ProphetNetForConditionalGeneration</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/switch_transformers#transformers.SwitchTransformersForConditionalGeneration">SwitchTransformersForConditionalGeneration</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForConditionalGeneration">XLMProphetNetForConditionalGeneration</a> (XLM-ProphetNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),x8=new D({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[hGn]},$$scope:{ctx:$}}}),vD=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),K8=new D({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[uGn]},$$scope:{ctx:$}}}),bD=new ne({}),TD=new P({props:{name:"class transformers.TFAutoModelForSeq2SeqLM",anchor:"transformers.TFAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_tf_auto.py#L568"}}),MD=new P({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/v4.29.0/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/v4.29.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot#transformers.TFBlenderbotForConditionalGeneration">TFBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallForConditionalGeneration">TFBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/encoder-decoder#transformers.TFEncoderDecoderModel">TFEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/led#transformers.TFLEDForConditionalGeneration">TFLEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.TFMBartForConditionalGeneration">TFMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mt5#transformers.TFMT5ForConditionalGeneration">TFMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/marian#transformers.TFMarianMTModel">TFMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/pegasus#transformers.TFPegasusForConditionalGeneration">TFPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),o6=new D({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[pGn]},$$scope:{ctx:$}}}),ED=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),m6=new D({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[_Gn]},$$scope:{ctx:$}}}),CD=new ne({}),wD=new P({props:{name:"class transformers.FlaxAutoModelForSeq2SeqLM",anchor:"transformers.FlaxAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_flax_auto.py#L297"}}),LD=new P({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/v4.29.0/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/v4.29.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot#transformers.FlaxBlenderbotForConditionalGeneration">FlaxBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallForConditionalGeneration">FlaxBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/encoder-decoder#transformers.FlaxEncoderDecoderModel">FlaxEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/marian#transformers.FlaxMarianMTModel">FlaxMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/pegasus#transformers.FlaxPegasusForConditionalGeneration">FlaxPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),h6=new D({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[vGn]},$$scope:{ctx:$}}}),yD=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),w6=new D({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[bGn]},$$scope:{ctx:$}}}),xD=new ne({}),SD=new P({props:{name:"class transformers.AutoModelForSequenceClassification",anchor:"transformers.AutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1130"}}),$D=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.AlbertForSequenceClassification">AlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartForSequenceClassification">BartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertForSequenceClassification">BertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdForSequenceClassification">BigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForSequenceClassification">BigBirdPegasusForSequenceClassification</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/biogpt#transformers.BioGptForSequenceClassification">BioGptForSequenceClassification</a> (BioGpt model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bloom#transformers.BloomForSequenceClassification">BloomForSequenceClassification</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ctrl#transformers.CTRLForSequenceClassification">CTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertForSequenceClassification">CamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/canine#transformers.CanineForSequenceClassification">CanineForSequenceClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.ConvBertForSequenceClassification">ConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecTextForSequenceClassification">Data2VecTextForSequenceClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.DebertaForSequenceClassification">DebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.DebertaV2ForSequenceClassification">DebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraForSequenceClassification">ElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ernie#transformers.ErnieForSequenceClassification">ErnieForSequenceClassification</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ernie_m#transformers.ErnieMForSequenceClassification">ErnieMForSequenceClassification</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/esm#transformers.EsmForSequenceClassification">EsmForSequenceClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/fnet#transformers.FNetForSequenceClassification">FNetForSequenceClassification</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertForSequenceClassification">FlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelForSequenceClassification">FunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.GPT2ForSequenceClassification">GPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForSequenceClassification">GPTBigCodeForSequenceClassification</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.GPTJForSequenceClassification">GPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neo#transformers.GPTNeoForSequenceClassification">GPTNeoForSequenceClassification</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neox#transformers.GPTNeoXForSequenceClassification">GPTNeoXForSequenceClassification</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ibert#transformers.IBertForSequenceClassification">IBertForSequenceClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/led#transformers.LEDForSequenceClassification">LEDForSequenceClassification</a> (LED model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.LayoutLMForSequenceClassification">LayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForSequenceClassification">LayoutLMv2ForSequenceClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForSequenceClassification">LayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/lilt#transformers.LiltForSequenceClassification">LiltForSequenceClassification</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/llama#transformers.LlamaForSequenceClassification">LlamaForSequenceClassification</a> (LLaMA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerForSequenceClassification">LongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/luke#transformers.LukeForSequenceClassification">LukeForSequenceClassification</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartForSequenceClassification">MBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetForSequenceClassification">MPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/markuplm#transformers.MarkupLMForSequenceClassification">MarkupLMForSequenceClassification</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mega#transformers.MegaForSequenceClassification">MegaForSequenceClassification</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/megatron-bert#transformers.MegatronBertForSequenceClassification">MegatronBertForSequenceClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertForSequenceClassification">MobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mvp#transformers.MvpForSequenceClassification">MvpForSequenceClassification</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/nezha#transformers.NezhaForSequenceClassification">NezhaForSequenceClassification</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/nystromformer#transformers.NystromformerForSequenceClassification">NystromformerForSequenceClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/opt#transformers.OPTForSequenceClassification">OPTForSequenceClassification</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/openai-gpt#transformers.OpenAIGPTForSequenceClassification">OpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/open-llama#transformers.OpenLlamaForSequenceClassification">OpenLlamaForSequenceClassification</a> (OpenLlama model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/plbart#transformers.PLBartForSequenceClassification">PLBartForSequenceClassification</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/perceiver#transformers.PerceiverForSequenceClassification">PerceiverForSequenceClassification</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/qdqbert#transformers.QDQBertForSequenceClassification">QDQBertForSequenceClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/reformer#transformers.ReformerForSequenceClassification">ReformerForSequenceClassification</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertForSequenceClassification">RemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roc_bert#transformers.RoCBertForSequenceClassification">RoCBertForSequenceClassification</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerForSequenceClassification">RoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaForSequenceClassification">RobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForSequenceClassification">RobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/squeezebert#transformers.SqueezeBertForSequenceClassification">SqueezeBertForSequenceClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/tapas#transformers.TapasForSequenceClassification">TapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/transfo-xl#transformers.TransfoXLForSequenceClassification">TransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMForSequenceClassification">XLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForSequenceClassification">XLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForSequenceClassification">XLMRobertaXLForSequenceClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetForSequenceClassification">XLNetForSequenceClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xmod#transformers.XmodForSequenceClassification">XmodForSequenceClassification</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/yoso#transformers.YosoForSequenceClassification">YosoForSequenceClassification</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),L6=new D({props:{anchor:"transformers.AutoModelForSequenceClassification.from_config.example",$$slots:{default:[TGn]},$$scope:{ctx:$}}}),PD=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),Ow=new D({props:{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[FGn]},$$scope:{ctx:$}}}),RD=new ne({}),ID=new P({props:{name:"class transformers.TFAutoModelForSequenceClassification",anchor:"transformers.TFAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_tf_auto.py#L577"}}),BD=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.TFAlbertForSequenceClassification">TFAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.TFBartForSequenceClassification">TFBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.TFBertForSequenceClassification">TFBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ctrl#transformers.TFCTRLForSequenceClassification">TFCTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.TFCamembertForSequenceClassification">TFCamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.TFConvBertForSequenceClassification">TFConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.TFDebertaForSequenceClassification">TFDebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.TFDebertaV2ForSequenceClassification">TFDebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.TFDistilBertForSequenceClassification">TFDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.TFElectraForSequenceClassification">TFElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/esm#transformers.TFEsmForSequenceClassification">TFEsmForSequenceClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.TFFlaubertForSequenceClassification">TFFlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.TFFunnelForSequenceClassification">TFFunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.TFGPT2ForSequenceClassification">TFGPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.TFGPTJForSequenceClassification">TFGPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.TFLayoutLMForSequenceClassification">TFLayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForSequenceClassification">TFLayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.TFLongformerForSequenceClassification">TFLongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.TFMPNetForSequenceClassification">TFMPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.TFMobileBertForSequenceClassification">TFMobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/openai-gpt#transformers.TFOpenAIGPTForSequenceClassification">TFOpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.TFRemBertForSequenceClassification">TFRemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.TFRoFormerForSequenceClassification">TFRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.TFRobertaForSequenceClassification">TFRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForSequenceClassification">TFRobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/tapas#transformers.TFTapasForSequenceClassification">TFTapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/transfo-xl#transformers.TFTransfoXLForSequenceClassification">TFTransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.TFXLMForSequenceClassification">TFXLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForSequenceClassification">TFXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.TFXLNetForSequenceClassification">TFXLNetForSequenceClassification</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),Xw=new D({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.example",$$slots:{default:[MGn]},$$scope:{ctx:$}}}),GD=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),MA=new D({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[EGn]},$$scope:{ctx:$}}}),qD=new ne({}),DD=new P({props:{name:"class transformers.FlaxAutoModelForSequenceClassification",anchor:"transformers.FlaxAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_flax_auto.py#L306"}}),OD=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.FlaxAlbertForSequenceClassification">FlaxAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.FlaxBartForSequenceClassification">FlaxBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.FlaxBertForSequenceClassification">FlaxBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.FlaxBigBirdForSequenceClassification">FlaxBigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.FlaxDistilBertForSequenceClassification">FlaxDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.FlaxElectraForSequenceClassification">FlaxElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.FlaxMBartForSequenceClassification">FlaxMBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.FlaxRoFormerForSequenceClassification">FlaxRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.FlaxRobertaForSequenceClassification">FlaxRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForSequenceClassification">FlaxRobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForSequenceClassification">FlaxXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),CA=new D({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.example",$$slots:{default:[CGn]},$$scope:{ctx:$}}}),VD=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),NA=new D({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[wGn]},$$scope:{ctx:$}}}),XD=new ne({}),zD=new P({props:{name:"class transformers.AutoModelForMultipleChoice",anchor:"transformers.AutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1186"}}),QD=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.AlbertForMultipleChoice">AlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertForMultipleChoice">BertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdForMultipleChoice">BigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertForMultipleChoice">CamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/canine#transformers.CanineForMultipleChoice">CanineForMultipleChoice</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.ConvBertForMultipleChoice">ConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecTextForMultipleChoice">Data2VecTextForMultipleChoice</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.DebertaV2ForMultipleChoice">DebertaV2ForMultipleChoice</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertForMultipleChoice">DistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraForMultipleChoice">ElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ernie#transformers.ErnieForMultipleChoice">ErnieForMultipleChoice</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ernie_m#transformers.ErnieMForMultipleChoice">ErnieMForMultipleChoice</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/fnet#transformers.FNetForMultipleChoice">FNetForMultipleChoice</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertForMultipleChoice">FlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelForMultipleChoice">FunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ibert#transformers.IBertForMultipleChoice">IBertForMultipleChoice</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerForMultipleChoice">LongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/luke#transformers.LukeForMultipleChoice">LukeForMultipleChoice</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetForMultipleChoice">MPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mega#transformers.MegaForMultipleChoice">MegaForMultipleChoice</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/megatron-bert#transformers.MegatronBertForMultipleChoice">MegatronBertForMultipleChoice</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertForMultipleChoice">MobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/nezha#transformers.NezhaForMultipleChoice">NezhaForMultipleChoice</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/nystromformer#transformers.NystromformerForMultipleChoice">NystromformerForMultipleChoice</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/qdqbert#transformers.QDQBertForMultipleChoice">QDQBertForMultipleChoice</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertForMultipleChoice">RemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roc_bert#transformers.RoCBertForMultipleChoice">RoCBertForMultipleChoice</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerForMultipleChoice">RoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaForMultipleChoice">RobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMultipleChoice">RobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/squeezebert#transformers.SqueezeBertForMultipleChoice">SqueezeBertForMultipleChoice</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMForMultipleChoice">XLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForMultipleChoice">XLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMultipleChoice">XLMRobertaXLForMultipleChoice</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetForMultipleChoice">XLNetForMultipleChoice</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xmod#transformers.XmodForMultipleChoice">XmodForMultipleChoice</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/yoso#transformers.YosoForMultipleChoice">YosoForMultipleChoice</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),GA=new D({props:{anchor:"transformers.AutoModelForMultipleChoice.from_config.example",$$slots:{default:[AGn]},$$scope:{ctx:$}}}),UD=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),E7=new D({props:{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[LGn]},$$scope:{ctx:$}}}),HD=new ne({}),JD=new P({props:{name:"class transformers.TFAutoModelForMultipleChoice",anchor:"transformers.TFAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_tf_auto.py#L624"}}),YD=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.TFAlbertForMultipleChoice">TFAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.TFBertForMultipleChoice">TFBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.TFCamembertForMultipleChoice">TFCamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.TFConvBertForMultipleChoice">TFConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.TFDistilBertForMultipleChoice">TFDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.TFElectraForMultipleChoice">TFElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.TFFlaubertForMultipleChoice">TFFlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.TFFunnelForMultipleChoice">TFFunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.TFLongformerForMultipleChoice">TFLongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.TFMPNetForMultipleChoice">TFMPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.TFMobileBertForMultipleChoice">TFMobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.TFRemBertForMultipleChoice">TFRemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.TFRoFormerForMultipleChoice">TFRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.TFRobertaForMultipleChoice">TFRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMultipleChoice">TFRobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.TFXLMForMultipleChoice">TFXLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMultipleChoice">TFXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.TFXLNetForMultipleChoice">TFXLNetForMultipleChoice</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),w7=new D({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.example",$$slots:{default:[yGn]},$$scope:{ctx:$}}}),KD=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),X7=new D({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[xGn]},$$scope:{ctx:$}}}),ej=new ne({}),oj=new P({props:{name:"class transformers.FlaxAutoModelForMultipleChoice",anchor:"transformers.FlaxAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_flax_auto.py#L331"}}),tj=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.FlaxAlbertForMultipleChoice">FlaxAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.FlaxBertForMultipleChoice">FlaxBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.FlaxBigBirdForMultipleChoice">FlaxBigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.FlaxDistilBertForMultipleChoice">FlaxDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.FlaxElectraForMultipleChoice">FlaxElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.FlaxRoFormerForMultipleChoice">FlaxRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.FlaxRobertaForMultipleChoice">FlaxRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMultipleChoice">FlaxRobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMultipleChoice">FlaxXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),W7=new D({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.example",$$slots:{default:[SGn]},$$scope:{ctx:$}}}),aj=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),rL=new D({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[kGn]},$$scope:{ctx:$}}}),nj=new ne({}),sj=new P({props:{name:"class transformers.AutoModelForNextSentencePrediction",anchor:"transformers.AutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1193"}}),ij=new P({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/v4.29.0/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/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertForNextSentencePrediction">BertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ernie#transformers.ErnieForNextSentencePrediction">ErnieForNextSentencePrediction</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/fnet#transformers.FNetForNextSentencePrediction">FNetForNextSentencePrediction</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/megatron-bert#transformers.MegatronBertForNextSentencePrediction">MegatronBertForNextSentencePrediction</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertForNextSentencePrediction">MobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/nezha#transformers.NezhaForNextSentencePrediction">NezhaForNextSentencePrediction</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/qdqbert#transformers.QDQBertForNextSentencePrediction">QDQBertForNextSentencePrediction</a> (QDQBert model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),aL=new D({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[$Gn]},$$scope:{ctx:$}}}),dj=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),gL=new D({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[PGn]},$$scope:{ctx:$}}}),cj=new ne({}),fj=new P({props:{name:"class transformers.TFAutoModelForNextSentencePrediction",anchor:"transformers.TFAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_tf_auto.py#L631"}}),gj=new P({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/v4.29.0/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/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.TFBertForNextSentencePrediction">TFBertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.TFMobileBertForNextSentencePrediction">TFMobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),uL=new D({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[RGn]},$$scope:{ctx:$}}}),hj=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),vL=new D({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[IGn]},$$scope:{ctx:$}}}),pj=new ne({}),_j=new P({props:{name:"class transformers.FlaxAutoModelForNextSentencePrediction",anchor:"transformers.FlaxAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_flax_auto.py#L338"}}),bj=new P({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/v4.29.0/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/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.FlaxBertForNextSentencePrediction">FlaxBertForNextSentencePrediction</a> (BERT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),TL=new D({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[NGn]},$$scope:{ctx:$}}}),Tj=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),ML=new D({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[BGn]},$$scope:{ctx:$}}}),Fj=new ne({}),Mj=new P({props:{name:"class transformers.AutoModelForTokenClassification",anchor:"transformers.AutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1179"}}),Cj=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.AlbertForTokenClassification">AlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertForTokenClassification">BertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdForTokenClassification">BigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/biogpt#transformers.BioGptForTokenClassification">BioGptForTokenClassification</a> (BioGpt model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bloom#transformers.BloomForTokenClassification">BloomForTokenClassification</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertForTokenClassification">CamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/canine#transformers.CanineForTokenClassification">CanineForTokenClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.ConvBertForTokenClassification">ConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecTextForTokenClassification">Data2VecTextForTokenClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.DebertaForTokenClassification">DebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.DebertaV2ForTokenClassification">DebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertForTokenClassification">DistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraForTokenClassification">ElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ernie#transformers.ErnieForTokenClassification">ErnieForTokenClassification</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ernie_m#transformers.ErnieMForTokenClassification">ErnieMForTokenClassification</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/esm#transformers.EsmForTokenClassification">EsmForTokenClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/fnet#transformers.FNetForTokenClassification">FNetForTokenClassification</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertForTokenClassification">FlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelForTokenClassification">FunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.GPT2ForTokenClassification">GPT2ForTokenClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForTokenClassification">GPTBigCodeForTokenClassification</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neo#transformers.GPTNeoForTokenClassification">GPTNeoForTokenClassification</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neox#transformers.GPTNeoXForTokenClassification">GPTNeoXForTokenClassification</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ibert#transformers.IBertForTokenClassification">IBertForTokenClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.LayoutLMForTokenClassification">LayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForTokenClassification">LayoutLMv2ForTokenClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForTokenClassification">LayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/lilt#transformers.LiltForTokenClassification">LiltForTokenClassification</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerForTokenClassification">LongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/luke#transformers.LukeForTokenClassification">LukeForTokenClassification</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetForTokenClassification">MPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/markuplm#transformers.MarkupLMForTokenClassification">MarkupLMForTokenClassification</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mega#transformers.MegaForTokenClassification">MegaForTokenClassification</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/megatron-bert#transformers.MegatronBertForTokenClassification">MegatronBertForTokenClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertForTokenClassification">MobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/nezha#transformers.NezhaForTokenClassification">NezhaForTokenClassification</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/nystromformer#transformers.NystromformerForTokenClassification">NystromformerForTokenClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/qdqbert#transformers.QDQBertForTokenClassification">QDQBertForTokenClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertForTokenClassification">RemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roc_bert#transformers.RoCBertForTokenClassification">RoCBertForTokenClassification</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerForTokenClassification">RoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaForTokenClassification">RobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForTokenClassification">RobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/squeezebert#transformers.SqueezeBertForTokenClassification">SqueezeBertForTokenClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMForTokenClassification">XLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForTokenClassification">XLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForTokenClassification">XLMRobertaXLForTokenClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetForTokenClassification">XLNetForTokenClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xmod#transformers.XmodForTokenClassification">XmodForTokenClassification</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/yoso#transformers.YosoForTokenClassification">YosoForTokenClassification</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),CL=new D({props:{anchor:"transformers.AutoModelForTokenClassification.from_config.example",$$slots:{default:[GGn]},$$scope:{ctx:$}}}),wj=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),Ey=new D({props:{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[qGn]},$$scope:{ctx:$}}}),Aj=new ne({}),Lj=new P({props:{name:"class transformers.TFAutoModelForTokenClassification",anchor:"transformers.TFAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_tf_auto.py#L615"}}),xj=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.TFAlbertForTokenClassification">TFAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.TFBertForTokenClassification">TFBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.TFCamembertForTokenClassification">TFCamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.TFConvBertForTokenClassification">TFConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.TFDebertaForTokenClassification">TFDebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.TFDebertaV2ForTokenClassification">TFDebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.TFDistilBertForTokenClassification">TFDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.TFElectraForTokenClassification">TFElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/esm#transformers.TFEsmForTokenClassification">TFEsmForTokenClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.TFFlaubertForTokenClassification">TFFlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.TFFunnelForTokenClassification">TFFunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.TFLayoutLMForTokenClassification">TFLayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForTokenClassification">TFLayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.TFLongformerForTokenClassification">TFLongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.TFMPNetForTokenClassification">TFMPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.TFMobileBertForTokenClassification">TFMobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.TFRemBertForTokenClassification">TFRemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.TFRoFormerForTokenClassification">TFRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.TFRobertaForTokenClassification">TFRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForTokenClassification">TFRobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.TFXLMForTokenClassification">TFXLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForTokenClassification">TFXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.TFXLNetForTokenClassification">TFXLNetForTokenClassification</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),wy=new D({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_config.example",$$slots:{default:[DGn]},$$scope:{ctx:$}}}),Sj=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),Hy=new D({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[jGn]},$$scope:{ctx:$}}}),kj=new ne({}),$j=new P({props:{name:"class transformers.FlaxAutoModelForTokenClassification",anchor:"transformers.FlaxAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_flax_auto.py#L322"}}),Rj=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.FlaxAlbertForTokenClassification">FlaxAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.FlaxBertForTokenClassification">FlaxBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.FlaxBigBirdForTokenClassification">FlaxBigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.FlaxDistilBertForTokenClassification">FlaxDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.FlaxElectraForTokenClassification">FlaxElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.FlaxRoFormerForTokenClassification">FlaxRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.FlaxRobertaForTokenClassification">FlaxRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForTokenClassification">FlaxRobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForTokenClassification">FlaxXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),Zy=new D({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.example",$$slots:{default:[OGn]},$$scope:{ctx:$}}}),Ij=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),lx=new D({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[VGn]},$$scope:{ctx:$}}}),Nj=new ne({}),Bj=new P({props:{name:"class transformers.AutoModelForQuestionAnswering",anchor:"transformers.AutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1139"}}),qj=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.AlbertForQuestionAnswering">AlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartForQuestionAnswering">BartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertForQuestionAnswering">BertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdForQuestionAnswering">BigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForQuestionAnswering">BigBirdPegasusForQuestionAnswering</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bloom#transformers.BloomForQuestionAnswering">BloomForQuestionAnswering</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertForQuestionAnswering">CamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/canine#transformers.CanineForQuestionAnswering">CanineForQuestionAnswering</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.ConvBertForQuestionAnswering">ConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecTextForQuestionAnswering">Data2VecTextForQuestionAnswering</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.DebertaForQuestionAnswering">DebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.DebertaV2ForQuestionAnswering">DebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraForQuestionAnswering">ElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ernie#transformers.ErnieForQuestionAnswering">ErnieForQuestionAnswering</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ernie_m#transformers.ErnieMForQuestionAnswering">ErnieMForQuestionAnswering</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/fnet#transformers.FNetForQuestionAnswering">FNetForQuestionAnswering</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertForQuestionAnsweringSimple">FlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelForQuestionAnswering">FunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt2#transformers.GPT2ForQuestionAnswering">GPT2ForQuestionAnswering</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.GPTJForQuestionAnswering">GPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neo#transformers.GPTNeoForQuestionAnswering">GPTNeoForQuestionAnswering</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gpt_neox#transformers.GPTNeoXForQuestionAnswering">GPTNeoXForQuestionAnswering</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/ibert#transformers.IBertForQuestionAnswering">IBertForQuestionAnswering</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/led#transformers.LEDForQuestionAnswering">LEDForQuestionAnswering</a> (LED model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/lilt#transformers.LiltForQuestionAnswering">LiltForQuestionAnswering</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerForQuestionAnswering">LongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/luke#transformers.LukeForQuestionAnswering">LukeForQuestionAnswering</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/lxmert#transformers.LxmertForQuestionAnswering">LxmertForQuestionAnswering</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartForQuestionAnswering">MBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetForQuestionAnswering">MPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/markuplm#transformers.MarkupLMForQuestionAnswering">MarkupLMForQuestionAnswering</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mega#transformers.MegaForQuestionAnswering">MegaForQuestionAnswering</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/megatron-bert#transformers.MegatronBertForQuestionAnswering">MegatronBertForQuestionAnswering</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertForQuestionAnswering">MobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mvp#transformers.MvpForQuestionAnswering">MvpForQuestionAnswering</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/nezha#transformers.NezhaForQuestionAnswering">NezhaForQuestionAnswering</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/nystromformer#transformers.NystromformerForQuestionAnswering">NystromformerForQuestionAnswering</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/opt#transformers.OPTForQuestionAnswering">OPTForQuestionAnswering</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/qdqbert#transformers.QDQBertForQuestionAnswering">QDQBertForQuestionAnswering</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/reformer#transformers.ReformerForQuestionAnswering">ReformerForQuestionAnswering</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertForQuestionAnswering">RemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roc_bert#transformers.RoCBertForQuestionAnswering">RoCBertForQuestionAnswering</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerForQuestionAnswering">RoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaForQuestionAnswering">RobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForQuestionAnswering">RobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/splinter#transformers.SplinterForQuestionAnswering">SplinterForQuestionAnswering</a> (Splinter model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/squeezebert#transformers.SqueezeBertForQuestionAnswering">SqueezeBertForQuestionAnswering</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMForQuestionAnsweringSimple">XLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForQuestionAnswering">XLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForQuestionAnswering">XLMRobertaXLForQuestionAnswering</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetForQuestionAnsweringSimple">XLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xmod#transformers.XmodForQuestionAnswering">XmodForQuestionAnswering</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/yoso#transformers.YosoForQuestionAnswering">YosoForQuestionAnswering</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),dx=new D({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_config.example",$$slots:{default:[XGn]},$$scope:{ctx:$}}}),Dj=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),gS=new D({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[zGn]},$$scope:{ctx:$}}}),jj=new ne({}),Oj=new P({props:{name:"class transformers.TFAutoModelForQuestionAnswering",anchor:"transformers.TFAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_tf_auto.py#L586"}}),Xj=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.TFAlbertForQuestionAnswering">TFAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.TFBertForQuestionAnswering">TFBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/camembert#transformers.TFCamembertForQuestionAnswering">TFCamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/convbert#transformers.TFConvBertForQuestionAnswering">TFConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta#transformers.TFDebertaForQuestionAnswering">TFDebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deberta-v2#transformers.TFDebertaV2ForQuestionAnswering">TFDebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.TFDistilBertForQuestionAnswering">TFDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.TFElectraForQuestionAnswering">TFElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/flaubert#transformers.TFFlaubertForQuestionAnsweringSimple">TFFlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/funnel#transformers.TFFunnelForQuestionAnswering">TFFunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/gptj#transformers.TFGPTJForQuestionAnswering">TFGPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForQuestionAnswering">TFLayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/longformer#transformers.TFLongformerForQuestionAnswering">TFLongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mpnet#transformers.TFMPNetForQuestionAnswering">TFMPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilebert#transformers.TFMobileBertForQuestionAnswering">TFMobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/rembert#transformers.TFRemBertForQuestionAnswering">TFRemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.TFRoFormerForQuestionAnswering">TFRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.TFRobertaForQuestionAnswering">TFRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForQuestionAnswering">TFRobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm#transformers.TFXLMForQuestionAnsweringSimple">TFXLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForQuestionAnswering">TFXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlnet#transformers.TFXLNetForQuestionAnsweringSimple">TFXLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),uS=new D({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[WGn]},$$scope:{ctx:$}}}),zj=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),GS=new D({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[QGn]},$$scope:{ctx:$}}}),Wj=new ne({}),Qj=new P({props:{name:"class transformers.FlaxAutoModelForQuestionAnswering",anchor:"transformers.FlaxAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_flax_auto.py#L315"}}),Hj=new P({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/v4.29.0/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/v4.29.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/albert#transformers.FlaxAlbertForQuestionAnswering">FlaxAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bart#transformers.FlaxBartForQuestionAnswering">FlaxBartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bert#transformers.FlaxBertForQuestionAnswering">FlaxBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/big_bird#transformers.FlaxBigBirdForQuestionAnswering">FlaxBigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/distilbert#transformers.FlaxDistilBertForQuestionAnswering">FlaxDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/electra#transformers.FlaxElectraForQuestionAnswering">FlaxElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mbart#transformers.FlaxMBartForQuestionAnswering">FlaxMBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roformer#transformers.FlaxRoFormerForQuestionAnswering">FlaxRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta#transformers.FlaxRobertaForQuestionAnswering">FlaxRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForQuestionAnswering">FlaxRobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForQuestionAnswering">FlaxXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),DS=new D({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[UGn]},$$scope:{ctx:$}}}),Jj=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),YS=new D({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[HGn]},$$scope:{ctx:$}}}),Zj=new ne({}),Yj=new ne({}),Kj=new P({props:{name:"class transformers.AutoModelForDepthEstimation",anchor:"transformers.AutoModelForDepthEstimation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1268"}}),oO=new P({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/v4.29.0/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/v4.29.0/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/dpt#transformers.DPTForDepthEstimation">DPTForDepthEstimation</a> (DPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/glpn#transformers.GLPNForDepthEstimation">GLPNForDepthEstimation</a> (GLPN model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),ok=new D({props:{anchor:"transformers.AutoModelForDepthEstimation.from_config.example",$$slots:{default:[JGn]},$$scope:{ctx:$}}}),rO=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),nk=new D({props:{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.example",$$slots:{default:[ZGn]},$$scope:{ctx:$}}}),aO=new ne({}),nO=new P({props:{name:"class transformers.AutoModelForImageClassification",anchor:"transformers.AutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1202"}}),lO=new P({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/v4.29.0/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/v4.29.0/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/beit#transformers.BeitForImageClassification">BeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/bit#transformers.BitConfig">BitConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/bit#transformers.BitForImageClassification">BitForImageClassification</a> (BiT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/convnext#transformers.ConvNextForImageClassification">ConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/convnextv2#transformers.ConvNextV2Config">ConvNextV2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/convnextv2#transformers.ConvNextV2ForImageClassification">ConvNextV2ForImageClassification</a> (ConvNeXTV2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/cvt#transformers.CvtForImageClassification">CvtForImageClassification</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecVisionForImageClassification">Data2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deit#transformers.DeiTForImageClassification">DeiTForImageClassification</a> or <a href="/docs/transformers/v4.29.0/en/model_doc/deit#transformers.DeiTForImageClassificationWithTeacher">DeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/dinat#transformers.DinatConfig">DinatConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/dinat#transformers.DinatForImageClassification">DinatForImageClassification</a> (DiNAT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassification">EfficientFormerForImageClassification</a> or <a href="/docs/transformers/v4.29.0/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassificationWithTeacher">EfficientFormerForImageClassificationWithTeacher</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/efficientnet#transformers.EfficientNetConfig">EfficientNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/efficientnet#transformers.EfficientNetForImageClassification">EfficientNetForImageClassification</a> (EfficientNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/focalnet#transformers.FocalNetConfig">FocalNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/focalnet#transformers.FocalNetForImageClassification">FocalNetForImageClassification</a> (FocalNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/imagegpt#transformers.ImageGPTForImageClassification">ImageGPTForImageClassification</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/levit#transformers.LevitForImageClassification">LevitForImageClassification</a> or <a href="/docs/transformers/v4.29.0/en/model_doc/levit#transformers.LevitForImageClassificationWithTeacher">LevitForImageClassificationWithTeacher</a> (LeViT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config">MobileNetV1Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilenet_v1#transformers.MobileNetV1ForImageClassification">MobileNetV1ForImageClassification</a> (MobileNetV1 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForImageClassification">MobileNetV2ForImageClassification</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilevit#transformers.MobileViTForImageClassification">MobileViTForImageClassification</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/nat#transformers.NatConfig">NatConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/nat#transformers.NatForImageClassification">NatForImageClassification</a> (NAT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/perceiver#transformers.PerceiverForImageClassificationLearned">PerceiverForImageClassificationLearned</a> or <a href="/docs/transformers/v4.29.0/en/model_doc/perceiver#transformers.PerceiverForImageClassificationFourier">PerceiverForImageClassificationFourier</a> or <a href="/docs/transformers/v4.29.0/en/model_doc/perceiver#transformers.PerceiverForImageClassificationConvProcessing">PerceiverForImageClassificationConvProcessing</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/poolformer#transformers.PoolFormerForImageClassification">PoolFormerForImageClassification</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/regnet#transformers.RegNetForImageClassification">RegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/resnet#transformers.ResNetForImageClassification">ResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/segformer#transformers.SegformerForImageClassification">SegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/swin#transformers.SwinForImageClassification">SwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/swinv2#transformers.Swinv2ForImageClassification">Swinv2ForImageClassification</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/van#transformers.VanForImageClassification">VanForImageClassification</a> (VAN model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vit#transformers.ViTForImageClassification">ViTForImageClassification</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vit_hybrid#transformers.ViTHybridConfig">ViTHybridConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vit_hybrid#transformers.ViTHybridForImageClassification">ViTHybridForImageClassification</a> (ViT Hybrid model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vit_msn#transformers.ViTMSNForImageClassification">ViTMSNForImageClassification</a> (ViTMSN model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),lk=new D({props:{anchor:"transformers.AutoModelForImageClassification.from_config.example",$$slots:{default:[YGn]},$$scope:{ctx:$}}}),iO=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),Pk=new D({props:{anchor:"transformers.AutoModelForImageClassification.from_pretrained.example",$$slots:{default:[KGn]},$$scope:{ctx:$}}}),dO=new ne({}),cO=new P({props:{name:"class transformers.TFAutoModelForImageClassification",anchor:"transformers.TFAutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_tf_auto.py#L527"}}),mO=new P({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/v4.29.0/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/v4.29.0/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/convnext#transformers.TFConvNextForImageClassification">TFConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/cvt#transformers.TFCvtForImageClassification">TFCvtForImageClassification</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.TFData2VecVisionForImageClassification">TFData2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deit#transformers.TFDeiTForImageClassification">TFDeiTForImageClassification</a> or <a href="/docs/transformers/v4.29.0/en/model_doc/deit#transformers.TFDeiTForImageClassificationWithTeacher">TFDeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilevit#transformers.TFMobileViTForImageClassification">TFMobileViTForImageClassification</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/regnet#transformers.TFRegNetForImageClassification">TFRegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/resnet#transformers.TFResNetForImageClassification">TFResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/segformer#transformers.TFSegformerForImageClassification">TFSegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/swin#transformers.TFSwinForImageClassification">TFSwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vit#transformers.TFViTForImageClassification">TFViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),Ik=new D({props:{anchor:"transformers.TFAutoModelForImageClassification.from_config.example",$$slots:{default:[eqn]},$$scope:{ctx:$}}}),gO=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),zk=new D({props:{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[oqn]},$$scope:{ctx:$}}}),hO=new ne({}),uO=new P({props:{name:"class transformers.FlaxAutoModelForImageClassification",anchor:"transformers.FlaxAutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_flax_auto.py#L347"}}),_O=new P({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/v4.29.0/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/v4.29.0/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/beit#transformers.FlaxBeitForImageClassification">FlaxBeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/regnet#transformers.FlaxRegNetForImageClassification">FlaxRegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/resnet#transformers.FlaxResNetForImageClassification">FlaxResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vit#transformers.FlaxViTForImageClassification">FlaxViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),Qk=new D({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_config.example",$$slots:{default:[rqn]},$$scope:{ctx:$}}}),vO=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),Yk=new D({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[tqn]},$$scope:{ctx:$}}}),bO=new ne({}),TO=new P({props:{name:"class transformers.AutoModelForVideoClassification",anchor:"transformers.AutoModelForVideoClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1275"}}),MO=new P({props:{name:"from_config",anchor:"transformers.AutoModelForVideoClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVideoClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/timesformer#transformers.TimesformerForVideoClassification">TimesformerForVideoClassification</a> (TimeSformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/videomae#transformers.VideoMAEForVideoClassification">VideoMAEForVideoClassification</a> (VideoMAE model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),e$=new D({props:{anchor:"transformers.AutoModelForVideoClassification.from_config.example",$$slots:{default:[aqn]},$$scope:{ctx:$}}}),EO=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),a$=new D({props:{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.example",$$slots:{default:[nqn]},$$scope:{ctx:$}}}),wO=new ne({}),AO=new P({props:{name:"class transformers.AutoModelForMaskedImageModeling",anchor:"transformers.AutoModelForMaskedImageModeling",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1332"}}),yO=new P({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/v4.29.0/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/v4.29.0/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deit#transformers.DeiTForMaskedImageModeling">DeiTForMaskedImageModeling</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/focalnet#transformers.FocalNetConfig">FocalNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/focalnet#transformers.FocalNetForMaskedImageModeling">FocalNetForMaskedImageModeling</a> (FocalNet model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/swin#transformers.SwinForMaskedImageModeling">SwinForMaskedImageModeling</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/swinv2#transformers.Swinv2ForMaskedImageModeling">Swinv2ForMaskedImageModeling</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vit#transformers.ViTForMaskedImageModeling">ViTForMaskedImageModeling</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),s$=new D({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[sqn]},$$scope:{ctx:$}}}),xO=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),g$=new D({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.example",$$slots:{default:[lqn]},$$scope:{ctx:$}}}),SO=new ne({}),kO=new P({props:{name:"class transformers.AutoModelForObjectDetection",anchor:"transformers.AutoModelForObjectDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1252"}}),PO=new P({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/v4.29.0/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/v4.29.0/en/model_doc/conditional_detr#transformers.ConditionalDetrConfig">ConditionalDetrConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/conditional_detr#transformers.ConditionalDetrForObjectDetection">ConditionalDetrForObjectDetection</a> (Conditional DETR model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deformable_detr#transformers.DeformableDetrConfig">DeformableDetrConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deformable_detr#transformers.DeformableDetrForObjectDetection">DeformableDetrForObjectDetection</a> (Deformable DETR model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/deta#transformers.DetaConfig">DetaConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/deta#transformers.DetaForObjectDetection">DetaForObjectDetection</a> (DETA model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/detr#transformers.DetrForObjectDetection">DetrForObjectDetection</a> (DETR model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/table-transformer#transformers.TableTransformerConfig">TableTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/table-transformer#transformers.TableTransformerForObjectDetection">TableTransformerForObjectDetection</a> (Table Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/yolos#transformers.YolosForObjectDetection">YolosForObjectDetection</a> (YOLOS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),u$=new D({props:{anchor:"transformers.AutoModelForObjectDetection.from_config.example",$$slots:{default:[iqn]},$$scope:{ctx:$}}}),RO=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForObjectDetection.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.AutoModelForObjectDetection.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.AutoModelForObjectDetection.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.AutoModelForObjectDetection.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),E$=new D({props:{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.example",$$slots:{default:[dqn]},$$scope:{ctx:$}}}),IO=new ne({}),NO=new P({props:{name:"class transformers.AutoModelForImageSegmentation",anchor:"transformers.AutoModelForImageSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1218"}}),GO=new P({props:{name:"from_config",anchor:"transformers.AutoModelForImageSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),w$=new D({props:{anchor:"transformers.AutoModelForImageSegmentation.from_config.example",$$slots:{default:[cqn]},$$scope:{ctx:$}}}),qO=new P({props:{name:"from_pretrained",anchor:"transformers.AutoModelForImageSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageSegmentation.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/v4.29.0/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.AutoModelForImageSegmentation.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.AutoModelForImageSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/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/v4.29.0/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.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
weights. In this case though, you should check if using <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForImageSegmentation.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.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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForImageSegmentation.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.AutoModelForImageSegmentation.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.AutoModelForImageSegmentation.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.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;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForImageSegmentation.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.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
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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),y$=new D({props:{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.example",$$slots:{default:[fqn]},$$scope:{ctx:$}}}),DO=new ne({}),jO=new P({props:{name:"class transformers.AutoModelForSemanticSegmentation",anchor:"transformers.AutoModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1225"}}),VO=new P({props:{name:"from_config",anchor:"transformers.AutoModelForSemanticSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/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/v4.29.0/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/beit#transformers.BeitForSemanticSegmentation">BeitForSemanticSegmentation</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/dpt#transformers.DPTForSemanticSegmentation">DPTForSemanticSegmentation</a> (DPT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecVisionForSemanticSegmentation">Data2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForSemanticSegmentation">MobileNetV2ForSemanticSegmentation</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilevit#transformers.MobileViTForSemanticSegmentation">MobileViTForSemanticSegmentation</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/segformer#transformers.SegformerForSemanticSegmentation">SegformerForSemanticSegmentation</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/upernet#transformers.UperNetConfig">UperNetConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation">UperNetForSemanticSegmentation</a> (UPerNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),S$=new D({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[mqn]},$$scope:{ctx:$}}}),XO=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSemanticSegmentation.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.AutoModelForSemanticSegmentation.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.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;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSemanticSegmentation.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.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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),q$=new D({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[gqn]},$$scope:{ctx:$}}}),zO=new ne({}),WO=new P({props:{name:"class transformers.TFAutoModelForSemanticSegmentation",anchor:"transformers.TFAutoModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_tf_auto.py#L545"}}),UO=new P({props:{name:"from_config",anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.TFData2VecVisionForSemanticSegmentation">TFData2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mobilevit#transformers.TFMobileViTForSemanticSegmentation">TFMobileViTForSemanticSegmentation</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/segformer#transformers.TFSegformerForSemanticSegmentation">TFSegformerForSemanticSegmentation</a> (SegFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),j$=new D({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[hqn]},$$scope:{ctx:$}}}),HO=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),z$=new D({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[uqn]},$$scope:{ctx:$}}}),JO=new ne({}),ZO=new P({props:{name:"class transformers.AutoModelForInstanceSegmentation",anchor:"transformers.AutoModelForInstanceSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1243"}}),KO=new P({props:{name:"from_config",anchor:"transformers.AutoModelForInstanceSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),Q$=new D({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.example",$$slots:{default:[pqn]},$$scope:{ctx:$}}}),eV=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),J$=new D({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.example",$$slots:{default:[_qn]},$$scope:{ctx:$}}}),oV=new ne({}),rV=new P({props:{name:"class transformers.AutoModelForUniversalSegmentation",anchor:"transformers.AutoModelForUniversalSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1234"}}),aV=new P({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/v4.29.0/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/v4.29.0/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mask2former#transformers.Mask2FormerConfig">Mask2FormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mask2former#transformers.Mask2FormerForUniversalSegmentation">Mask2FormerForUniversalSegmentation</a> (Mask2Former model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/oneformer#transformers.OneFormerConfig">OneFormerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/oneformer#transformers.OneFormerForUniversalSegmentation">OneFormerForUniversalSegmentation</a> (OneFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),Y$=new D({props:{anchor:"transformers.AutoModelForUniversalSegmentation.from_config.example",$$slots:{default:[vqn]},$$scope:{ctx:$}}}),nV=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),aP=new D({props:{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.example",$$slots:{default:[bqn]},$$scope:{ctx:$}}}),sV=new ne({}),lV=new P({props:{name:"class transformers.AutoModelForZeroShotImageClassification",anchor:"transformers.AutoModelForZeroShotImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1209"}}),dV=new P({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/v4.29.0/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/v4.29.0/en/model_doc/align#transformers.AlignConfig">AlignConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/align#transformers.AlignModel">AlignModel</a> (ALIGN model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/altclip#transformers.AltCLIPConfig">AltCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/clipseg#transformers.CLIPSegConfig">CLIPSegConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/clipseg#transformers.CLIPSegModel">CLIPSegModel</a> (CLIPSeg model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/chinese_clip#transformers.ChineseCLIPConfig">ChineseCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/chinese_clip#transformers.ChineseCLIPModel">ChineseCLIPModel</a> (Chinese-CLIP model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),sP=new D({props:{anchor:"transformers.AutoModelForZeroShotImageClassification.from_config.example",$$slots:{default:[Tqn]},$$scope:{ctx:$}}}),cV=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),hP=new D({props:{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.example",$$slots:{default:[Fqn]},$$scope:{ctx:$}}}),fV=new ne({}),mV=new P({props:{name:"class transformers.TFAutoModelForZeroShotImageClassification",anchor:"transformers.TFAutoModelForZeroShotImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_tf_auto.py#L536"}}),hV=new P({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/v4.29.0/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/v4.29.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blip#transformers.TFBlipModel">TFBlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),pP=new D({props:{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_config.example",$$slots:{default:[Mqn]},$$scope:{ctx:$}}}),uV=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),bP=new D({props:{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.example",$$slots:{default:[Eqn]},$$scope:{ctx:$}}}),_V=new ne({}),vV=new P({props:{name:"class transformers.AutoModelForZeroShotObjectDetection",anchor:"transformers.AutoModelForZeroShotObjectDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1259"}}),TV=new P({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/v4.29.0/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/v4.29.0/en/model_doc/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/owlvit#transformers.OwlViTForObjectDetection">OwlViTForObjectDetection</a> (OWL-ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),FP=new D({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config.example",$$slots:{default:[Cqn]},$$scope:{ctx:$}}}),FV=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),CP=new D({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.example",$$slots:{default:[wqn]},$$scope:{ctx:$}}}),MV=new ne({}),EV=new ne({}),CV=new P({props:{name:"class transformers.AutoModelForAudioClassification",anchor:"transformers.AutoModelForAudioClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1289"}}),AV=new P({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/v4.29.0/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/v4.29.0/en/model_doc/audio-spectrogram-transformer#transformers.ASTConfig">ASTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/audio-spectrogram-transformer#transformers.ASTForAudioClassification">ASTForAudioClassification</a> (Audio Spectrogram Transformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecAudioForSequenceClassification">Data2VecAudioForSequenceClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/hubert#transformers.HubertForSequenceClassification">HubertForSequenceClassification</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/sew#transformers.SEWForSequenceClassification">SEWForSequenceClassification</a> (SEW model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/sew-d#transformers.SEWDForSequenceClassification">SEWDForSequenceClassification</a> (SEW-D model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/unispeech#transformers.UniSpeechForSequenceClassification">UniSpeechForSequenceClassification</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatForSequenceClassification">UniSpeechSatForSequenceClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2#transformers.Wav2Vec2ForSequenceClassification">Wav2Vec2ForSequenceClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForSequenceClassification">Wav2Vec2ConformerForSequenceClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wavlm#transformers.WavLMForSequenceClassification">WavLMForSequenceClassification</a> (WavLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/whisper#transformers.WhisperForAudioClassification">WhisperForAudioClassification</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),LP=new D({props:{anchor:"transformers.AutoModelForAudioClassification.from_config.example",$$slots:{default:[Aqn]},$$scope:{ctx:$}}}),LV=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),DP=new D({props:{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[Lqn]},$$scope:{ctx:$}}}),yV=new ne({}),xV=new P({props:{name:"class transformers.AutoModelForAudioFrameClassification",anchor:"transformers.AutoModelForAudioFrameClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1312"}}),kV=new P({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/v4.29.0/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/v4.29.0/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecAudioForAudioFrameClassification">Data2VecAudioForAudioFrameClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatForAudioFrameClassification">UniSpeechSatForAudioFrameClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2#transformers.Wav2Vec2ForAudioFrameClassification">Wav2Vec2ForAudioFrameClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForAudioFrameClassification">Wav2Vec2ConformerForAudioFrameClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wavlm#transformers.WavLMForAudioFrameClassification">WavLMForAudioFrameClassification</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),OP=new D({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.example",$$slots:{default:[yqn]},$$scope:{ctx:$}}}),$V=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),HP=new D({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.example",$$slots:{default:[xqn]},$$scope:{ctx:$}}}),PV=new ne({}),RV=new P({props:{name:"class transformers.AutoModelForCTC",anchor:"transformers.AutoModelForCTC",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1296"}}),NV=new P({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/v4.29.0/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/v4.29.0/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecAudioForCTC">Data2VecAudioForCTC</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/hubert#transformers.HubertForCTC">HubertForCTC</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/mctct#transformers.MCTCTForCTC">MCTCTForCTC</a> (M-CTC-T model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/sew#transformers.SEWForCTC">SEWForCTC</a> (SEW model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/sew-d#transformers.SEWDForCTC">SEWDForCTC</a> (SEW-D model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/unispeech#transformers.UniSpeechForCTC">UniSpeechForCTC</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatForCTC">UniSpeechSatForCTC</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC">Wav2Vec2ForCTC</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForCTC">Wav2Vec2ConformerForCTC</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wavlm#transformers.WavLMForCTC">WavLMForCTC</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),ZP=new D({props:{anchor:"transformers.AutoModelForCTC.from_config.example",$$slots:{default:[Sqn]},$$scope:{ctx:$}}}),BV=new P({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/v4.29.0/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.AutoModelForCTC.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.AutoModelForCTC.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/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/v4.29.0/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.AutoModelForCTC.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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForCTC.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.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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForCTC.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.AutoModelForCTC.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.AutoModelForCTC.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.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;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForCTC.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.AutoModelForCTC.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.AutoModelForCTC.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.AutoModelForCTC.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),dR=new D({props:{anchor:"transformers.AutoModelForCTC.from_pretrained.example",$$slots:{default:[kqn]},$$scope:{ctx:$}}}),GV=new ne({}),qV=new P({props:{name:"class transformers.AutoModelForSpeechSeq2Seq",anchor:"transformers.AutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1303"}}),jV=new P({props:{name:"from_config",anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/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/v4.29.0/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/speech_to_text#transformers.Speech2TextForConditionalGeneration">Speech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderConfig">SpeechEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderModel">SpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/speecht5#transformers.SpeechT5Config">SpeechT5Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/speecht5#transformers.SpeechT5ForSpeechToText">SpeechT5ForSpeechToText</a> (SpeechT5 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/whisper#transformers.WhisperForConditionalGeneration">WhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),fR=new D({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[$qn]},$$scope:{ctx:$}}}),OV=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.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.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;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.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.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
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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),_R=new D({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[Pqn]},$$scope:{ctx:$}}}),VV=new ne({}),XV=new P({props:{name:"class transformers.TFAutoModelForSpeechSeq2Seq",anchor:"transformers.TFAutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_tf_auto.py#L640"}}),WV=new P({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/v4.29.0/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/v4.29.0/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/speech_to_text#transformers.TFSpeech2TextForConditionalGeneration">TFSpeech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/whisper#transformers.TFWhisperForConditionalGeneration">TFWhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),bR=new D({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[Rqn]},$$scope:{ctx:$}}}),QV=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),MR=new D({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[Iqn]},$$scope:{ctx:$}}}),HV=new ne({}),JV=new P({props:{name:"class transformers.FlaxAutoModelForSpeechSeq2Seq",anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_flax_auto.py#L363"}}),YV=new P({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/v4.29.0/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/v4.29.0/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderConfig">SpeechEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/speech-encoder-decoder#transformers.FlaxSpeechEncoderDecoderModel">FlaxSpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),CR=new D({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[Nqn]},$$scope:{ctx:$}}}),KV=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),LR=new D({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[Bqn]},$$scope:{ctx:$}}}),oX=new ne({}),rX=new P({props:{name:"class transformers.AutoModelForAudioXVector",anchor:"transformers.AutoModelForAudioXVector",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1321"}}),aX=new P({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/v4.29.0/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/v4.29.0/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/data2vec#transformers.Data2VecAudioForXVector">Data2VecAudioForXVector</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatForXVector">UniSpeechSatForXVector</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2#transformers.Wav2Vec2ForXVector">Wav2Vec2ForXVector</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForXVector">Wav2Vec2ConformerForXVector</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/wavlm#transformers.WavLMForXVector">WavLMForXVector</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),xR=new D({props:{anchor:"transformers.AutoModelForAudioXVector.from_config.example",$$slots:{default:[Gqn]},$$scope:{ctx:$}}}),nX=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),NR=new D({props:{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.example",$$slots:{default:[qqn]},$$scope:{ctx:$}}}),sX=new ne({}),lX=new ne({}),iX=new P({props:{name:"class transformers.AutoModelForTableQuestionAnswering",anchor:"transformers.AutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1146"}}),cX=new P({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/v4.29.0/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/v4.29.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/tapas#transformers.TapasForQuestionAnswering">TapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),qR=new D({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[Dqn]},$$scope:{ctx:$}}}),fX=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),OR=new D({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[jqn]},$$scope:{ctx:$}}}),mX=new ne({}),gX=new P({props:{name:"class transformers.TFAutoModelForTableQuestionAnswering",anchor:"transformers.TFAutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_tf_auto.py#L604"}}),uX=new P({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/v4.29.0/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/v4.29.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/tapas#transformers.TFTapasForQuestionAnswering">TFTapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),XR=new D({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[Oqn]},$$scope:{ctx:$}}}),pX=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),WR=new D({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[Vqn]},$$scope:{ctx:$}}}),_X=new ne({}),vX=new P({props:{name:"class transformers.AutoModelForDocumentQuestionAnswering",anchor:"transformers.AutoModelForDocumentQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1168"}}),TX=new P({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/v4.29.0/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/v4.29.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.LayoutLMForQuestionAnswering">LayoutLMForQuestionAnswering</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),UR=new D({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[Xqn]},$$scope:{ctx:$}}}),FX=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),KR=new D({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[zqn]},$$scope:{ctx:$}}}),MX=new ne({}),EX=new P({props:{name:"class transformers.TFAutoModelForDocumentQuestionAnswering",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_tf_auto.py#L593"}}),wX=new P({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/v4.29.0/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/v4.29.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/layoutlm#transformers.TFLayoutLMForQuestionAnswering">TFLayoutLMForQuestionAnswering</a> (LayoutLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),oI=new D({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[Wqn]},$$scope:{ctx:$}}}),AX=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),tI=new D({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[Qqn]},$$scope:{ctx:$}}}),LX=new ne({}),yX=new P({props:{name:"class transformers.AutoModelForVisualQuestionAnswering",anchor:"transformers.AutoModelForVisualQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1157"}}),SX=new P({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/v4.29.0/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/v4.29.0/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vilt#transformers.ViltForQuestionAnswering">ViltForQuestionAnswering</a> (ViLT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),nI=new D({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.example",$$slots:{default:[Uqn]},$$scope:{ctx:$}}}),kX=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.29.0/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.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/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L421"}}),iI=new D({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.example",$$slots:{default:[Hqn]},$$scope:{ctx:$}}}),$X=new ne({}),PX=new P({props:{name:"class transformers.AutoModelForVision2Seq",anchor:"transformers.AutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/auto/modeling_auto.py#L1282"}}),IX=new P({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/v4.29.0/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/v4.29.0/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration">Blip2ForConditionalGeneration</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/blip#transformers.BlipForConditionalGeneration">BlipForConditionalGeneration</a> (BLIP model)</li>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/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/v4.29.0/src/transformers/models/auto/auto_factory.py#L390"}}),cI=new D({props:{anchor:"transformers.AutoModelForVision2Seq.from_config.example",$$slots:{default:[Jqn]},$$scope:{ctx:$}}}),NX=new P({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/v4.29.0/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/v4.29.0/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/v4.29.0/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/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
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Load the model weights from a TensorFlow checkpoint save file (see docstring of
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<li><a href="/docs/transformers/v4.29.0/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vision-encoder-decoder#transformers.TFVisionEncoderDecoderModel">TFVisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
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<li>A path to a <em>directory</em> containing model weights saved using
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<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.29.0/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.29.0/en/model_doc/vision-encoder-decoder#transformers.FlaxVisionEncoderDecoderModel">FlaxVisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
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<li>A path to a <em>directory</em> containing model weights saved using
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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 is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
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<li>The model was saved using <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
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"),Xre=a("a"),$0r=o("ClapProcessor"),P0r=o(" (CLAP model)"),R0r=l(),_4=a("li"),ZLe=a("strong"),I0r=o("clip"),N0r=o(" \u2014 "),zre=a("a"),B0r=o("CLIPProcessor"),G0r=o(" (CLIP model)"),q0r=l(),v4=a("li"),YLe=a("strong"),D0r=o("clipseg"),j0r=o(" \u2014 "),Wre=a("a"),O0r=o("CLIPSegProcessor"),V0r=o(" (CLIPSeg model)"),X0r=l(),b4=a("li"),KLe=a("strong"),z0r=o("flava"),W0r=o(" \u2014 "),Qre=a("a"),Q0r=o("FlavaProcessor"),U0r=o(" (FLAVA model)"),H0r=l(),T4=a("li"),eye=a("strong"),J0r=o("git"),Z0r=o(" \u2014 "),Ure=a("a"),Y0r=o("GitProcessor"),K0r=o(" (GIT model)"),ebr=l(),F4=a("li"),oye=a("strong"),obr=o("groupvit"),rbr=o(" \u2014 "),Hre=a("a"),tbr=o("CLIPProcessor"),abr=o(" (GroupViT model)"),nbr=l(),M4=a("li"),rye=a("strong"),sbr=o("hubert"),lbr=o(" \u2014 "),Jre=a("a"),ibr=o("Wav2Vec2Processor"),dbr=o(" (Hubert model)"),cbr=l(),E4=a("li"),tye=a("strong"),fbr=o("layoutlmv2"),mbr=o(" \u2014 "),Zre=a("a"),gbr=o("LayoutLMv2Processor"),hbr=o(" (LayoutLMv2 model)"),ubr=l(),C4=a("li"),aye=a("strong"),pbr=o("layoutlmv3"),_br=o(" \u2014 "),Yre=a("a"),vbr=o("LayoutLMv3Processor"),bbr=o(" (LayoutLMv3 model)"),Tbr=l(),w4=a("li"),nye=a("strong"),Fbr=o("markuplm"),Mbr=o(" \u2014 "),Kre=a("a"),Ebr=o("MarkupLMProcessor"),Cbr=o(" (MarkupLM model)"),wbr=l(),A4=a("li"),sye=a("strong"),Abr=o("mgp-str"),Lbr=o(" \u2014 "),ete=a("a"),ybr=o("MgpstrProcessor"),xbr=o(" (MGP-STR model)"),Sbr=l(),L4=a("li"),lye=a("strong"),kbr=o("oneformer"),$br=o(" \u2014 "),ote=a("a"),Pbr=o("OneFormerProcessor"),Rbr=o(" (OneFormer model)"),Ibr=l(),y4=a("li"),iye=a("strong"),Nbr=o("owlvit"),Bbr=o(" \u2014 "),rte=a("a"),Gbr=o("OwlViTProcessor"),qbr=o(" (OWL-ViT model)"),Dbr=l(),x4=a("li"),dye=a("strong"),jbr=o("pix2struct"),Obr=o(" \u2014 "),tte=a("a"),Vbr=o("Pix2StructProcessor"),Xbr=o(" (Pix2Struct model)"),zbr=l(),S4=a("li"),cye=a("strong"),Wbr=o("sam"),Qbr=o(" \u2014 "),ate=a("a"),Ubr=o("SamProcessor"),Hbr=o(" (SAM model)"),Jbr=l(),k4=a("li"),fye=a("strong"),Zbr=o("sew"),Ybr=o(" \u2014 "),nte=a("a"),Kbr=o("Wav2Vec2Processor"),e9r=o(" (SEW model)"),o9r=l(),$4=a("li"),mye=a("strong"),r9r=o("sew-d"),t9r=o(" \u2014 "),ste=a("a"),a9r=o("Wav2Vec2Processor"),n9r=o(" (SEW-D model)"),s9r=l(),P4=a("li"),gye=a("strong"),l9r=o("speech_to_text"),i9r=o(" \u2014 "),lte=a("a"),d9r=o("Speech2TextProcessor"),c9r=o(" (Speech2Text model)"),f9r=l(),R4=a("li"),hye=a("strong"),m9r=o("speech_to_text_2"),g9r=o(" \u2014 "),ite=a("a"),h9r=o("Speech2Text2Processor"),u9r=o(" (Speech2Text2 model)"),p9r=l(),I4=a("li"),uye=a("strong"),_9r=o("speecht5"),v9r=o(" \u2014 "),dte=a("a"),b9r=o("SpeechT5Processor"),T9r=o(" (SpeechT5 model)"),F9r=l(),N4=a("li"),pye=a("strong"),M9r=o("trocr"),E9r=o(" \u2014 "),cte=a("a"),C9r=o("TrOCRProcessor"),w9r=o(" (TrOCR model)"),A9r=l(),B4=a("li"),_ye=a("strong"),L9r=o("tvlt"),y9r=o(" \u2014 "),fte=a("a"),x9r=o("TvltProcessor"),S9r=o(" (TVLT model)"),k9r=l(),G4=a("li"),vye=a("strong"),$9r=o("unispeech"),P9r=o(" \u2014 "),mte=a("a"),R9r=o("Wav2Vec2Processor"),I9r=o(" (UniSpeech model)"),N9r=l(),q4=a("li"),bye=a("strong"),B9r=o("unispeech-sat"),G9r=o(" \u2014 "),gte=a("a"),q9r=o("Wav2Vec2Processor"),D9r=o(" (UniSpeechSat model)"),j9r=l(),D4=a("li"),Tye=a("strong"),O9r=o("vilt"),V9r=o(" \u2014 "),hte=a("a"),X9r=o("ViltProcessor"),z9r=o(" (ViLT model)"),W9r=l(),j4=a("li"),Fye=a("strong"),Q9r=o("vision-text-dual-encoder"),U9r=o(" \u2014 "),ute=a("a"),H9r=o("VisionTextDualEncoderProcessor"),J9r=o(" (VisionTextDualEncoder model)"),Z9r=l(),O4=a("li"),Mye=a("strong"),Y9r=o("wav2vec2"),K9r=o(" \u2014 "),pte=a("a"),eTr=o("Wav2Vec2Processor"),oTr=o(" (Wav2Vec2 model)"),rTr=l(),V4=a("li"),Eye=a("strong"),tTr=o("wav2vec2-conformer"),aTr=o(" \u2014 "),_te=a("a"),nTr=o("Wav2Vec2Processor"),sTr=o(" (Wav2Vec2-Conformer model)"),lTr=l(),X4=a("li"),Cye=a("strong"),iTr=o("wavlm"),dTr=o(" \u2014 "),vte=a("a"),cTr=o("Wav2Vec2Processor"),fTr=o(" (WavLM model)"),mTr=l(),z4=a("li"),wye=a("strong"),gTr=o("whisper"),hTr=o(" \u2014 "),bte=a("a"),uTr=o("WhisperProcessor"),pTr=o(" (Whisper model)"),_Tr=l(),W4=a("li"),Aye=a("strong"),vTr=o("xclip"),bTr=o(" \u2014 "),Tte=a("a"),TTr=o("XCLIPProcessor"),FTr=o(" (X-CLIP model)"),MTr=l(),b(Q4.$$.fragment),ETr=l(),b(U4.$$.fragment),CTr=l(),H4=a("div"),b(oq.$$.fragment),wTr=l(),Lye=a("p"),ATr=o("Register a new processor for this class."),pwo=l(),Nc=a("h2"),J4=a("a"),yye=a("span"),b(rq.$$.fragment),LTr=l(),xye=a("span"),yTr=o("Generic model classes"),_wo=l(),Fte=a("p"),xTr=o("The following auto classes are available for instantiating a base model class without a specific head."),vwo=l(),Bc=a("h3"),Z4=a("a"),Sye=a("span"),b(tq.$$.fragment),STr=l(),kye=a("span"),kTr=o("AutoModel"),bwo=l(),Vo=a("div"),b(aq.$$.fragment),$Tr=l(),Gc=a("p"),PTr=o(`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),Mte=a("a"),RTr=o("from_pretrained()"),ITr=o(" class method or the "),Ete=a("a"),NTr=o("from_config()"),BTr=o(` class
method.`),GTr=l(),nq=a("p"),qTr=o("This class cannot be instantiated directly using "),$ye=a("code"),DTr=o("__init__()"),jTr=o(" (throws an error)."),OTr=l(),Bt=a("div"),b(sq.$$.fragment),VTr=l(),Pye=a("p"),XTr=o("Instantiates one of the base model classes of the library from a configuration."),zTr=l(),qc=a("p"),WTr=o(`Note:
Loading a model from its configuration file does `),Rye=a("strong"),QTr=o("not"),UTr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Cte=a("a"),HTr=o("from_pretrained()"),JTr=o(" to load the model weights."),ZTr=l(),b(Y4.$$.fragment),YTr=l(),so=a("div"),b(lq.$$.fragment),KTr=l(),Iye=a("p"),eFr=o("Instantiate one of the base model classes of the library from a pretrained model."),oFr=l(),kn=a("p"),rFr=o("The model class to instantiate is selected based on the "),Nye=a("code"),tFr=o("model_type"),aFr=o(` property of the config object (either
passed as an argument or loaded from `),Bye=a("code"),nFr=o("pretrained_model_name_or_path"),sFr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Gye=a("code"),lFr=o("pretrained_model_name_or_path"),iFr=o(":"),dFr=l(),y=a("ul"),K4=a("li"),qye=a("strong"),cFr=o("albert"),fFr=o(" \u2014 "),wte=a("a"),mFr=o("AlbertModel"),gFr=o(" (ALBERT model)"),hFr=l(),e0=a("li"),Dye=a("strong"),uFr=o("align"),pFr=o(" \u2014 "),Ate=a("a"),_Fr=o("AlignModel"),vFr=o(" (ALIGN model)"),bFr=l(),o0=a("li"),jye=a("strong"),TFr=o("altclip"),FFr=o(" \u2014 "),Lte=a("a"),MFr=o("AltCLIPModel"),EFr=o(" (AltCLIP model)"),CFr=l(),r0=a("li"),Oye=a("strong"),wFr=o("audio-spectrogram-transformer"),AFr=o(" \u2014 "),yte=a("a"),LFr=o("ASTModel"),yFr=o(" (Audio Spectrogram Transformer model)"),xFr=l(),t0=a("li"),Vye=a("strong"),SFr=o("bart"),kFr=o(" \u2014 "),xte=a("a"),$Fr=o("BartModel"),PFr=o(" (BART model)"),RFr=l(),a0=a("li"),Xye=a("strong"),IFr=o("beit"),NFr=o(" \u2014 "),Ste=a("a"),BFr=o("BeitModel"),GFr=o(" (BEiT model)"),qFr=l(),n0=a("li"),zye=a("strong"),DFr=o("bert"),jFr=o(" \u2014 "),kte=a("a"),OFr=o("BertModel"),VFr=o(" (BERT model)"),XFr=l(),s0=a("li"),Wye=a("strong"),zFr=o("bert-generation"),WFr=o(" \u2014 "),$te=a("a"),QFr=o("BertGenerationEncoder"),UFr=o(" (Bert Generation model)"),HFr=l(),l0=a("li"),Qye=a("strong"),JFr=o("big_bird"),ZFr=o(" \u2014 "),Pte=a("a"),YFr=o("BigBirdModel"),KFr=o(" (BigBird model)"),eMr=l(),i0=a("li"),Uye=a("strong"),oMr=o("bigbird_pegasus"),rMr=o(" \u2014 "),Rte=a("a"),tMr=o("BigBirdPegasusModel"),aMr=o(" (BigBird-Pegasus model)"),nMr=l(),d0=a("li"),Hye=a("strong"),sMr=o("biogpt"),lMr=o(" \u2014 "),Ite=a("a"),iMr=o("BioGptModel"),dMr=o(" (BioGpt model)"),cMr=l(),c0=a("li"),Jye=a("strong"),fMr=o("bit"),mMr=o(" \u2014 "),Nte=a("a"),gMr=o("BitModel"),hMr=o(" (BiT model)"),uMr=l(),f0=a("li"),Zye=a("strong"),pMr=o("blenderbot"),_Mr=o(" \u2014 "),Bte=a("a"),vMr=o("BlenderbotModel"),bMr=o(" (Blenderbot model)"),TMr=l(),m0=a("li"),Yye=a("strong"),FMr=o("blenderbot-small"),MMr=o(" \u2014 "),Gte=a("a"),EMr=o("BlenderbotSmallModel"),CMr=o(" (BlenderbotSmall model)"),wMr=l(),g0=a("li"),Kye=a("strong"),AMr=o("blip"),LMr=o(" \u2014 "),qte=a("a"),yMr=o("BlipModel"),xMr=o(" (BLIP model)"),SMr=l(),h0=a("li"),exe=a("strong"),kMr=o("blip-2"),$Mr=o(" \u2014 "),Dte=a("a"),PMr=o("Blip2Model"),RMr=o(" (BLIP-2 model)"),IMr=l(),u0=a("li"),oxe=a("strong"),NMr=o("bloom"),BMr=o(" \u2014 "),jte=a("a"),GMr=o("BloomModel"),qMr=o(" (BLOOM model)"),DMr=l(),p0=a("li"),rxe=a("strong"),jMr=o("bridgetower"),OMr=o(" \u2014 "),Ote=a("a"),VMr=o("BridgeTowerModel"),XMr=o(" (BridgeTower model)"),zMr=l(),_0=a("li"),txe=a("strong"),WMr=o("camembert"),QMr=o(" \u2014 "),Vte=a("a"),UMr=o("CamembertModel"),HMr=o(" (CamemBERT model)"),JMr=l(),v0=a("li"),axe=a("strong"),ZMr=o("canine"),YMr=o(" \u2014 "),Xte=a("a"),KMr=o("CanineModel"),eEr=o(" (CANINE model)"),oEr=l(),b0=a("li"),nxe=a("strong"),rEr=o("chinese_clip"),tEr=o(" \u2014 "),zte=a("a"),aEr=o("ChineseCLIPModel"),nEr=o(" (Chinese-CLIP model)"),sEr=l(),T0=a("li"),sxe=a("strong"),lEr=o("clap"),iEr=o(" \u2014 "),Wte=a("a"),dEr=o("ClapModel"),cEr=o(" (CLAP model)"),fEr=l(),F0=a("li"),lxe=a("strong"),mEr=o("clip"),gEr=o(" \u2014 "),Qte=a("a"),hEr=o("CLIPModel"),uEr=o(" (CLIP model)"),pEr=l(),M0=a("li"),ixe=a("strong"),_Er=o("clipseg"),vEr=o(" \u2014 "),Ute=a("a"),bEr=o("CLIPSegModel"),TEr=o(" (CLIPSeg model)"),FEr=l(),E0=a("li"),dxe=a("strong"),MEr=o("codegen"),EEr=o(" \u2014 "),Hte=a("a"),CEr=o("CodeGenModel"),wEr=o(" (CodeGen model)"),AEr=l(),C0=a("li"),cxe=a("strong"),LEr=o("conditional_detr"),yEr=o(" \u2014 "),Jte=a("a"),xEr=o("ConditionalDetrModel"),SEr=o(" (Conditional DETR model)"),kEr=l(),w0=a("li"),fxe=a("strong"),$Er=o("convbert"),PEr=o(" \u2014 "),Zte=a("a"),REr=o("ConvBertModel"),IEr=o(" (ConvBERT model)"),NEr=l(),A0=a("li"),mxe=a("strong"),BEr=o("convnext"),GEr=o(" \u2014 "),Yte=a("a"),qEr=o("ConvNextModel"),DEr=o(" (ConvNeXT model)"),jEr=l(),L0=a("li"),gxe=a("strong"),OEr=o("convnextv2"),VEr=o(" \u2014 "),Kte=a("a"),XEr=o("ConvNextV2Model"),zEr=o(" (ConvNeXTV2 model)"),WEr=l(),y0=a("li"),hxe=a("strong"),QEr=o("cpmant"),UEr=o(" \u2014 "),eae=a("a"),HEr=o("CpmAntModel"),JEr=o(" (CPM-Ant model)"),ZEr=l(),x0=a("li"),uxe=a("strong"),YEr=o("ctrl"),KEr=o(" \u2014 "),oae=a("a"),e3r=o("CTRLModel"),o3r=o(" (CTRL model)"),r3r=l(),S0=a("li"),pxe=a("strong"),t3r=o("cvt"),a3r=o(" \u2014 "),rae=a("a"),n3r=o("CvtModel"),s3r=o(" (CvT model)"),l3r=l(),k0=a("li"),_xe=a("strong"),i3r=o("data2vec-audio"),d3r=o(" \u2014 "),tae=a("a"),c3r=o("Data2VecAudioModel"),f3r=o(" (Data2VecAudio model)"),m3r=l(),$0=a("li"),vxe=a("strong"),g3r=o("data2vec-text"),h3r=o(" \u2014 "),aae=a("a"),u3r=o("Data2VecTextModel"),p3r=o(" (Data2VecText model)"),_3r=l(),P0=a("li"),bxe=a("strong"),v3r=o("data2vec-vision"),b3r=o(" \u2014 "),nae=a("a"),T3r=o("Data2VecVisionModel"),F3r=o(" (Data2VecVision model)"),M3r=l(),R0=a("li"),Txe=a("strong"),E3r=o("deberta"),C3r=o(" \u2014 "),sae=a("a"),w3r=o("DebertaModel"),A3r=o(" (DeBERTa model)"),L3r=l(),I0=a("li"),Fxe=a("strong"),y3r=o("deberta-v2"),x3r=o(" \u2014 "),lae=a("a"),S3r=o("DebertaV2Model"),k3r=o(" (DeBERTa-v2 model)"),$3r=l(),N0=a("li"),Mxe=a("strong"),P3r=o("decision_transformer"),R3r=o(" \u2014 "),iae=a("a"),I3r=o("DecisionTransformerModel"),N3r=o(" (Decision Transformer model)"),B3r=l(),B0=a("li"),Exe=a("strong"),G3r=o("deformable_detr"),q3r=o(" \u2014 "),dae=a("a"),D3r=o("DeformableDetrModel"),j3r=o(" (Deformable DETR model)"),O3r=l(),G0=a("li"),Cxe=a("strong"),V3r=o("deit"),X3r=o(" \u2014 "),cae=a("a"),z3r=o("DeiTModel"),W3r=o(" (DeiT model)"),Q3r=l(),q0=a("li"),wxe=a("strong"),U3r=o("deta"),H3r=o(" \u2014 "),fae=a("a"),J3r=o("DetaModel"),Z3r=o(" (DETA model)"),Y3r=l(),D0=a("li"),Axe=a("strong"),K3r=o("detr"),eCr=o(" \u2014 "),mae=a("a"),oCr=o("DetrModel"),rCr=o(" (DETR model)"),tCr=l(),j0=a("li"),Lxe=a("strong"),aCr=o("dinat"),nCr=o(" \u2014 "),gae=a("a"),sCr=o("DinatModel"),lCr=o(" (DiNAT model)"),iCr=l(),O0=a("li"),yxe=a("strong"),dCr=o("distilbert"),cCr=o(" \u2014 "),hae=a("a"),fCr=o("DistilBertModel"),mCr=o(" (DistilBERT model)"),gCr=l(),V0=a("li"),xxe=a("strong"),hCr=o("donut-swin"),uCr=o(" \u2014 "),uae=a("a"),pCr=o("DonutSwinModel"),_Cr=o(" (DonutSwin model)"),vCr=l(),X0=a("li"),Sxe=a("strong"),bCr=o("dpr"),TCr=o(" \u2014 "),pae=a("a"),FCr=o("DPRQuestionEncoder"),MCr=o(" (DPR model)"),ECr=l(),z0=a("li"),kxe=a("strong"),CCr=o("dpt"),wCr=o(" \u2014 "),_ae=a("a"),ACr=o("DPTModel"),LCr=o(" (DPT model)"),yCr=l(),W0=a("li"),$xe=a("strong"),xCr=o("efficientformer"),SCr=o(" \u2014 "),vae=a("a"),kCr=o("EfficientFormerModel"),$Cr=o(" (EfficientFormer model)"),PCr=l(),Q0=a("li"),Pxe=a("strong"),RCr=o("efficientnet"),ICr=o(" \u2014 "),bae=a("a"),NCr=o("EfficientNetModel"),BCr=o(" (EfficientNet model)"),GCr=l(),U0=a("li"),Rxe=a("strong"),qCr=o("electra"),DCr=o(" \u2014 "),Tae=a("a"),jCr=o("ElectraModel"),OCr=o(" (ELECTRA 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model\u2019s configuration. Use `),Kle=a("a"),Tqr=o("from_pretrained()"),Fqr=o(" to load the model weights."),Mqr=l(),b(wF.$$.fragment),Eqr=l(),et=a("div"),b(_q.$$.fragment),Cqr=l(),CPe=a("p"),wqr=o("Instantiate one of the base model classes of the library from a pretrained model."),Aqr=l(),Pn=a("p"),Lqr=o("The model class to instantiate is selected based on the "),wPe=a("code"),yqr=o("model_type"),xqr=o(` property of the config object (either
passed as an argument or loaded from `),APe=a("code"),Sqr=o("pretrained_model_name_or_path"),kqr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),LPe=a("code"),$qr=o("pretrained_model_name_or_path"),Pqr=o(":"),Rqr=l(),se=a("ul"),AF=a("li"),yPe=a("strong"),Iqr=o("albert"),Nqr=o(" \u2014 "),eie=a("a"),Bqr=o("FlaxAlbertModel"),Gqr=o(" (ALBERT model)"),qqr=l(),LF=a("li"),xPe=a("strong"),Dqr=o("bart"),jqr=o(" \u2014 "),oie=a("a"),Oqr=o("FlaxBartModel"),Vqr=o(" (BART model)"),Xqr=l(),yF=a("li"),SPe=a("strong"),zqr=o("beit"),Wqr=o(" \u2014 "),rie=a("a"),Qqr=o("FlaxBeitModel"),Uqr=o(" (BEiT model)"),Hqr=l(),xF=a("li"),kPe=a("strong"),Jqr=o("bert"),Zqr=o(" \u2014 "),tie=a("a"),Yqr=o("FlaxBertModel"),Kqr=o(" (BERT model)"),eDr=l(),SF=a("li"),$Pe=a("strong"),oDr=o("big_bird"),rDr=o(" \u2014 "),aie=a("a"),tDr=o("FlaxBigBirdModel"),aDr=o(" (BigBird model)"),nDr=l(),kF=a("li"),PPe=a("strong"),sDr=o("blenderbot"),lDr=o(" \u2014 "),nie=a("a"),iDr=o("FlaxBlenderbotModel"),dDr=o(" (Blenderbot model)"),cDr=l(),$F=a("li"),RPe=a("strong"),fDr=o("blenderbot-small"),mDr=o(" \u2014 "),sie=a("a"),gDr=o("FlaxBlenderbotSmallModel"),hDr=o(" (BlenderbotSmall model)"),uDr=l(),PF=a("li"),IPe=a("strong"),pDr=o("clip"),_Dr=o(" \u2014 "),lie=a("a"),vDr=o("FlaxCLIPModel"),bDr=o(" (CLIP model)"),TDr=l(),RF=a("li"),NPe=a("strong"),FDr=o("distilbert"),MDr=o(" \u2014 "),iie=a("a"),EDr=o("FlaxDistilBertModel"),CDr=o(" (DistilBERT model)"),wDr=l(),IF=a("li"),BPe=a("strong"),ADr=o("electra"),LDr=o(" \u2014 "),die=a("a"),yDr=o("FlaxElectraModel"),xDr=o(" (ELECTRA model)"),SDr=l(),NF=a("li"),GPe=a("strong"),kDr=o("gpt-sw3"),$Dr=o(" \u2014 "),cie=a("a"),PDr=o("FlaxGPT2Model"),RDr=o(" (GPT-Sw3 model)"),IDr=l(),BF=a("li"),qPe=a("strong"),NDr=o("gpt2"),BDr=o(" \u2014 "),fie=a("a"),GDr=o("FlaxGPT2Model"),qDr=o(" (OpenAI GPT-2 model)"),DDr=l(),GF=a("li"),DPe=a("strong"),jDr=o("gpt_neo"),ODr=o(" \u2014 "),mie=a("a"),VDr=o("FlaxGPTNeoModel"),XDr=o(" (GPT Neo model)"),zDr=l(),qF=a("li"),jPe=a("strong"),WDr=o("gptj"),QDr=o(" \u2014 "),gie=a("a"),UDr=o("FlaxGPTJModel"),HDr=o(" (GPT-J model)"),JDr=l(),DF=a("li"),OPe=a("strong"),ZDr=o("longt5"),YDr=o(" \u2014 "),hie=a("a"),KDr=o("FlaxLongT5Model"),ejr=o(" (LongT5 model)"),ojr=l(),jF=a("li"),VPe=a("strong"),rjr=o("marian"),tjr=o(" \u2014 "),uie=a("a"),ajr=o("FlaxMarianModel"),njr=o(" (Marian model)"),sjr=l(),OF=a("li"),XPe=a("strong"),ljr=o("mbart"),ijr=o(" \u2014 "),pie=a("a"),djr=o("FlaxMBartModel"),cjr=o(" (mBART model)"),fjr=l(),VF=a("li"),zPe=a("strong"),mjr=o("mt5"),gjr=o(" \u2014 "),_ie=a("a"),hjr=o("FlaxMT5Model"),ujr=o(" (MT5 model)"),pjr=l(),XF=a("li"),WPe=a("strong"),_jr=o("opt"),vjr=o(" \u2014 "),vie=a("a"),bjr=o("FlaxOPTModel"),Tjr=o(" (OPT model)"),Fjr=l(),zF=a("li"),QPe=a("strong"),Mjr=o("pegasus"),Ejr=o(" \u2014 "),bie=a("a"),Cjr=o("FlaxPegasusModel"),wjr=o(" (Pegasus model)"),Ajr=l(),WF=a("li"),UPe=a("strong"),Ljr=o("regnet"),yjr=o(" \u2014 "),Tie=a("a"),xjr=o("FlaxRegNetModel"),Sjr=o(" (RegNet model)"),kjr=l(),QF=a("li"),HPe=a("strong"),$jr=o("resnet"),Pjr=o(" \u2014 "),Fie=a("a"),Rjr=o("FlaxResNetModel"),Ijr=o(" (ResNet model)"),Njr=l(),UF=a("li"),JPe=a("strong"),Bjr=o("roberta"),Gjr=o(" \u2014 "),Mie=a("a"),qjr=o("FlaxRobertaModel"),Djr=o(" (RoBERTa model)"),jjr=l(),HF=a("li"),ZPe=a("strong"),Ojr=o("roberta-prelayernorm"),Vjr=o(" \u2014 "),Eie=a("a"),Xjr=o("FlaxRobertaPreLayerNormModel"),zjr=o(" (RoBERTa-PreLayerNorm model)"),Wjr=l(),JF=a("li"),YPe=a("strong"),Qjr=o("roformer"),Ujr=o(" \u2014 "),Cie=a("a"),Hjr=o("FlaxRoFormerModel"),Jjr=o(" (RoFormer model)"),Zjr=l(),ZF=a("li"),KPe=a("strong"),Yjr=o("t5"),Kjr=o(" \u2014 "),wie=a("a"),eOr=o("FlaxT5Model"),oOr=o(" (T5 model)"),rOr=l(),YF=a("li"),eRe=a("strong"),tOr=o("vision-text-dual-encoder"),aOr=o(" \u2014 "),Aie=a("a"),nOr=o("FlaxVisionTextDualEncoderModel"),sOr=o(" (VisionTextDualEncoder model)"),lOr=l(),KF=a("li"),oRe=a("strong"),iOr=o("vit"),dOr=o(" \u2014 "),Lie=a("a"),cOr=o("FlaxViTModel"),fOr=o(" (ViT model)"),mOr=l(),eM=a("li"),rRe=a("strong"),gOr=o("wav2vec2"),hOr=o(" \u2014 "),yie=a("a"),uOr=o("FlaxWav2Vec2Model"),pOr=o(" (Wav2Vec2 model)"),_Or=l(),oM=a("li"),tRe=a("strong"),vOr=o("whisper"),bOr=o(" \u2014 "),xie=a("a"),TOr=o("FlaxWhisperModel"),FOr=o(" (Whisper model)"),MOr=l(),rM=a("li"),aRe=a("strong"),EOr=o("xglm"),COr=o(" \u2014 "),Sie=a("a"),wOr=o("FlaxXGLMModel"),AOr=o(" (XGLM model)"),LOr=l(),tM=a("li"),nRe=a("strong"),yOr=o("xlm-roberta"),xOr=o(" \u2014 "),kie=a("a"),SOr=o("FlaxXLMRobertaModel"),kOr=o(" (XLM-RoBERTa model)"),$Or=l(),b(aM.$$.fragment),Cwo=l(),Wc=a("h2"),nM=a("a"),sRe=a("span"),b(vq.$$.fragment),POr=l(),lRe=a("span"),ROr=o("Generic pretraining classes"),wwo=l(),$ie=a("p"),IOr=o("The following auto classes are available for instantiating a model with a pretraining head."),Awo=l(),Qc=a("h3"),sM=a("a"),iRe=a("span"),b(bq.$$.fragment),NOr=l(),dRe=a("span"),BOr=o("AutoModelForPreTraining"),Lwo=l(),Wo=a("div"),b(Tq.$$.fragment),GOr=l(),Uc=a("p"),qOr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),Pie=a("a"),DOr=o("from_pretrained()"),jOr=o(" class method or the "),Rie=a("a"),OOr=o("from_config()"),VOr=o(` class
method.`),XOr=l(),Fq=a("p"),zOr=o("This class cannot be instantiated directly using "),cRe=a("code"),WOr=o("__init__()"),QOr=o(" (throws an error)."),UOr=l(),Dt=a("div"),b(Mq.$$.fragment),HOr=l(),fRe=a("p"),JOr=o("Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),ZOr=l(),Hc=a("p"),YOr=o(`Note:
Loading a model from its configuration file does `),mRe=a("strong"),KOr=o("not"),eVr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Iie=a("a"),oVr=o("from_pretrained()"),rVr=o(" to load the model weights."),tVr=l(),b(lM.$$.fragment),aVr=l(),lo=a("div"),b(Eq.$$.fragment),nVr=l(),gRe=a("p"),sVr=o("Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),lVr=l(),Rn=a("p"),iVr=o("The model class to instantiate is selected based on the "),hRe=a("code"),dVr=o("model_type"),cVr=o(` property of the config object (either
passed as an argument or loaded from `),uRe=a("code"),fVr=o("pretrained_model_name_or_path"),mVr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),pRe=a("code"),gVr=o("pretrained_model_name_or_path"),hVr=o(":"),uVr=l(),X=a("ul"),iM=a("li"),_Re=a("strong"),pVr=o("albert"),_Vr=o(" \u2014 "),Nie=a("a"),vVr=o("AlbertForPreTraining"),bVr=o(" (ALBERT model)"),TVr=l(),dM=a("li"),vRe=a("strong"),FVr=o("bart"),MVr=o(" \u2014 "),Bie=a("a"),EVr=o("BartForConditionalGeneration"),CVr=o(" (BART model)"),wVr=l(),cM=a("li"),bRe=a("strong"),AVr=o("bert"),LVr=o(" \u2014 "),Gie=a("a"),yVr=o("BertForPreTraining"),xVr=o(" (BERT model)"),SVr=l(),fM=a("li"),TRe=a("strong"),kVr=o("big_bird"),$Vr=o(" \u2014 "),qie=a("a"),PVr=o("BigBirdForPreTraining"),RVr=o(" (BigBird model)"),IVr=l(),mM=a("li"),FRe=a("strong"),NVr=o("bloom"),BVr=o(" \u2014 "),Die=a("a"),GVr=o("BloomForCausalLM"),qVr=o(" (BLOOM model)"),DVr=l(),gM=a("li"),MRe=a("strong"),jVr=o("camembert"),OVr=o(" \u2014 "),jie=a("a"),VVr=o("CamembertForMaskedLM"),XVr=o(" (CamemBERT model)"),zVr=l(),hM=a("li"),ERe=a("strong"),WVr=o("ctrl"),QVr=o(" \u2014 "),Oie=a("a"),UVr=o("CTRLLMHeadModel"),HVr=o(" (CTRL model)"),JVr=l(),uM=a("li"),CRe=a("strong"),ZVr=o("data2vec-text"),YVr=o(" \u2014 "),Vie=a("a"),KVr=o("Data2VecTextForMaskedLM"),eXr=o(" (Data2VecText model)"),oXr=l(),pM=a("li"),wRe=a("strong"),rXr=o("deberta"),tXr=o(" \u2014 "),Xie=a("a"),aXr=o("DebertaForMaskedLM"),nXr=o(" (DeBERTa model)"),sXr=l(),_M=a("li"),ARe=a("strong"),lXr=o("deberta-v2"),iXr=o(" \u2014 "),zie=a("a"),dXr=o("DebertaV2ForMaskedLM"),cXr=o(" (DeBERTa-v2 model)"),fXr=l(),vM=a("li"),LRe=a("strong"),mXr=o("distilbert"),gXr=o(" \u2014 "),Wie=a("a"),hXr=o("DistilBertForMaskedLM"),uXr=o(" (DistilBERT model)"),pXr=l(),bM=a("li"),yRe=a("strong"),_Xr=o("electra"),vXr=o(" \u2014 "),Qie=a("a"),bXr=o("ElectraForPreTraining"),TXr=o(" (ELECTRA model)"),FXr=l(),TM=a("li"),xRe=a("strong"),MXr=o("ernie"),EXr=o(" \u2014 "),Uie=a("a"),CXr=o("ErnieForPreTraining"),wXr=o(" (ERNIE model)"),AXr=l(),FM=a("li"),SRe=a("strong"),LXr=o("flaubert"),yXr=o(" \u2014 "),Hie=a("a"),xXr=o("FlaubertWithLMHeadModel"),SXr=o(" (FlauBERT model)"),kXr=l(),MM=a("li"),kRe=a("strong"),$Xr=o("flava"),PXr=o(" \u2014 "),Jie=a("a"),RXr=o("FlavaForPreTraining"),IXr=o(" (FLAVA model)"),NXr=l(),EM=a("li"),$Re=a("strong"),BXr=o("fnet"),GXr=o(" \u2014 "),Zie=a("a"),qXr=o("FNetForPreTraining"),DXr=o(" (FNet model)"),jXr=l(),CM=a("li"),PRe=a("strong"),OXr=o("fsmt"),VXr=o(" \u2014 "),Yie=a("a"),XXr=o("FSMTForConditionalGeneration"),zXr=o(" (FairSeq Machine-Translation model)"),WXr=l(),wM=a("li"),RRe=a("strong"),QXr=o("funnel"),UXr=o(" \u2014 "),Kie=a("a"),HXr=o("FunnelForPreTraining"),JXr=o(" (Funnel Transformer model)"),ZXr=l(),AM=a("li"),IRe=a("strong"),YXr=o("gpt-sw3"),KXr=o(" \u2014 "),ede=a("a"),ezr=o("GPT2LMHeadModel"),ozr=o(" (GPT-Sw3 model)"),rzr=l(),LM=a("li"),NRe=a("strong"),tzr=o("gpt2"),azr=o(" \u2014 "),ode=a("a"),nzr=o("GPT2LMHeadModel"),szr=o(" (OpenAI GPT-2 model)"),lzr=l(),yM=a("li"),BRe=a("strong"),izr=o("gpt_bigcode"),dzr=o(" \u2014 "),rde=a("a"),czr=o("GPTBigCodeForCausalLM"),fzr=o(" (GPTBigCode model)"),mzr=l(),xM=a("li"),GRe=a("strong"),gzr=o("gptsan-japanese"),hzr=o(" \u2014 "),tde=a("a"),uzr=o("GPTSanJapaneseForConditionalGeneration"),pzr=o(" (GPTSAN-japanese model)"),_zr=l(),SM=a("li"),qRe=a("strong"),vzr=o("ibert"),bzr=o(" \u2014 "),ade=a("a"),Tzr=o("IBertForMaskedLM"),Fzr=o(" (I-BERT model)"),Mzr=l(),kM=a("li"),DRe=a("strong"),Ezr=o("layoutlm"),Czr=o(" \u2014 "),nde=a("a"),wzr=o("LayoutLMForMaskedLM"),Azr=o(" (LayoutLM model)"),Lzr=l(),$M=a("li"),jRe=a("strong"),yzr=o("longformer"),xzr=o(" \u2014 "),sde=a("a"),Szr=o("LongformerForMaskedLM"),kzr=o(" (Longformer model)"),$zr=l(),PM=a("li"),ORe=a("strong"),Pzr=o("luke"),Rzr=o(" \u2014 "),lde=a("a"),Izr=o("LukeForMaskedLM"),Nzr=o(" (LUKE model)"),Bzr=l(),RM=a("li"),VRe=a("strong"),Gzr=o("lxmert"),qzr=o(" \u2014 "),ide=a("a"),Dzr=o("LxmertForPreTraining"),jzr=o(" (LXMERT model)"),Ozr=l(),IM=a("li"),XRe=a("strong"),Vzr=o("mega"),Xzr=o(" \u2014 "),dde=a("a"),zzr=o("MegaForMaskedLM"),Wzr=o(" (MEGA model)"),Qzr=l(),NM=a("li"),zRe=a("strong"),Uzr=o("megatron-bert"),Hzr=o(" \u2014 "),cde=a("a"),Jzr=o("MegatronBertForPreTraining"),Zzr=o(" (Megatron-BERT model)"),Yzr=l(),BM=a("li"),WRe=a("strong"),Kzr=o("mobilebert"),eWr=o(" \u2014 "),fde=a("a"),oWr=o("MobileBertForPreTraining"),rWr=o(" (MobileBERT model)"),tWr=l(),GM=a("li"),QRe=a("strong"),aWr=o("mpnet"),nWr=o(" \u2014 "),mde=a("a"),sWr=o("MPNetForMaskedLM"),lWr=o(" (MPNet model)"),iWr=l(),qM=a("li"),URe=a("strong"),dWr=o("mvp"),cWr=o(" \u2014 "),gde=a("a"),fWr=o("MvpForConditionalGeneration"),mWr=o(" (MVP model)"),gWr=l(),DM=a("li"),HRe=a("strong"),hWr=o("nezha"),uWr=o(" \u2014 "),hde=a("a"),pWr=o("NezhaForPreTraining"),_Wr=o(" (Nezha model)"),vWr=l(),jM=a("li"),JRe=a("strong"),bWr=o("nllb-moe"),TWr=o(" \u2014 "),ude=a("a"),FWr=o("NllbMoeForConditionalGeneration"),MWr=o(" (NLLB-MOE model)"),EWr=l(),OM=a("li"),ZRe=a("strong"),CWr=o("openai-gpt"),wWr=o(" \u2014 "),pde=a("a"),AWr=o("OpenAIGPTLMHeadModel"),LWr=o(" (OpenAI GPT model)"),yWr=l(),VM=a("li"),YRe=a("strong"),xWr=o("retribert"),SWr=o(" \u2014 "),_de=a("a"),kWr=o("RetriBertModel"),$Wr=o(" (RetriBERT model)"),PWr=l(),XM=a("li"),KRe=a("strong"),RWr=o("roberta"),IWr=o(" \u2014 "),vde=a("a"),NWr=o("RobertaForMaskedLM"),BWr=o(" (RoBERTa model)"),GWr=l(),zM=a("li"),eIe=a("strong"),qWr=o("roberta-prelayernorm"),DWr=o(" \u2014 "),bde=a("a"),jWr=o("RobertaPreLayerNormForMaskedLM"),OWr=o(" (RoBERTa-PreLayerNorm model)"),VWr=l(),WM=a("li"),oIe=a("strong"),XWr=o("roc_bert"),zWr=o(" \u2014 "),Tde=a("a"),WWr=o("RoCBertForPreTraining"),QWr=o(" (RoCBert model)"),UWr=l(),QM=a("li"),rIe=a("strong"),HWr=o("rwkv"),JWr=o(" \u2014 "),Fde=a("a"),ZWr=o("RwkvForCausalLM"),YWr=o(" (RWKV model)"),KWr=l(),UM=a("li"),tIe=a("strong"),eQr=o("splinter"),oQr=o(" \u2014 "),Mde=a("a"),rQr=o("SplinterForPreTraining"),tQr=o(" (Splinter model)"),aQr=l(),HM=a("li"),aIe=a("strong"),nQr=o("squeezebert"),sQr=o(" \u2014 "),Ede=a("a"),lQr=o("SqueezeBertForMaskedLM"),iQr=o(" (SqueezeBERT model)"),dQr=l(),JM=a("li"),nIe=a("strong"),cQr=o("switch_transformers"),fQr=o(" \u2014 "),Cde=a("a"),mQr=o("SwitchTransformersForConditionalGeneration"),gQr=o(" (SwitchTransformers model)"),hQr=l(),ZM=a("li"),sIe=a("strong"),uQr=o("t5"),pQr=o(" \u2014 "),wde=a("a"),_Qr=o("T5ForConditionalGeneration"),vQr=o(" (T5 model)"),bQr=l(),YM=a("li"),lIe=a("strong"),TQr=o("tapas"),FQr=o(" \u2014 "),Ade=a("a"),MQr=o("TapasForMaskedLM"),EQr=o(" (TAPAS model)"),CQr=l(),KM=a("li"),iIe=a("strong"),wQr=o("transfo-xl"),AQr=o(" \u2014 "),Lde=a("a"),LQr=o("TransfoXLLMHeadModel"),yQr=o(" (Transformer-XL model)"),xQr=l(),eE=a("li"),dIe=a("strong"),SQr=o("tvlt"),kQr=o(" \u2014 "),yde=a("a"),$Qr=o("TvltForPreTraining"),PQr=o(" (TVLT model)"),RQr=l(),oE=a("li"),cIe=a("strong"),IQr=o("unispeech"),NQr=o(" \u2014 "),xde=a("a"),BQr=o("UniSpeechForPreTraining"),GQr=o(" (UniSpeech model)"),qQr=l(),rE=a("li"),fIe=a("strong"),DQr=o("unispeech-sat"),jQr=o(" \u2014 "),Sde=a("a"),OQr=o("UniSpeechSatForPreTraining"),VQr=o(" (UniSpeechSat model)"),XQr=l(),tE=a("li"),mIe=a("strong"),zQr=o("videomae"),WQr=o(" \u2014 "),kde=a("a"),QQr=o("VideoMAEForPreTraining"),UQr=o(" (VideoMAE model)"),HQr=l(),aE=a("li"),gIe=a("strong"),JQr=o("visual_bert"),ZQr=o(" \u2014 "),$de=a("a"),YQr=o("VisualBertForPreTraining"),KQr=o(" (VisualBERT model)"),eUr=l(),nE=a("li"),hIe=a("strong"),oUr=o("vit_mae"),rUr=o(" \u2014 "),Pde=a("a"),tUr=o("ViTMAEForPreTraining"),aUr=o(" (ViTMAE model)"),nUr=l(),sE=a("li"),uIe=a("strong"),sUr=o("wav2vec2"),lUr=o(" \u2014 "),Rde=a("a"),iUr=o("Wav2Vec2ForPreTraining"),dUr=o(" (Wav2Vec2 model)"),cUr=l(),lE=a("li"),pIe=a("strong"),fUr=o("wav2vec2-conformer"),mUr=o(" \u2014 "),Ide=a("a"),gUr=o("Wav2Vec2ConformerForPreTraining"),hUr=o(" (Wav2Vec2-Conformer model)"),uUr=l(),iE=a("li"),_Ie=a("strong"),pUr=o("xlm"),_Ur=o(" \u2014 "),Nde=a("a"),vUr=o("XLMWithLMHeadModel"),bUr=o(" (XLM model)"),TUr=l(),dE=a("li"),vIe=a("strong"),FUr=o("xlm-roberta"),MUr=o(" \u2014 "),Bde=a("a"),EUr=o("XLMRobertaForMaskedLM"),CUr=o(" (XLM-RoBERTa model)"),wUr=l(),cE=a("li"),bIe=a("strong"),AUr=o("xlm-roberta-xl"),LUr=o(" \u2014 "),Gde=a("a"),yUr=o("XLMRobertaXLForMaskedLM"),xUr=o(" (XLM-RoBERTa-XL model)"),SUr=l(),fE=a("li"),TIe=a("strong"),kUr=o("xlnet"),$Ur=o(" \u2014 "),qde=a("a"),PUr=o("XLNetLMHeadModel"),RUr=o(" (XLNet model)"),IUr=l(),mE=a("li"),FIe=a("strong"),NUr=o("xmod"),BUr=o(" \u2014 "),Dde=a("a"),GUr=o("XmodForMaskedLM"),qUr=o(" (X-MOD model)"),DUr=l(),gE=a("p"),jUr=o("The model is set in evaluation mode by default using "),MIe=a("code"),OUr=o("model.eval()"),VUr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),EIe=a("code"),XUr=o("model.train()"),zUr=l(),b(hE.$$.fragment),ywo=l(),Jc=a("h3"),uE=a("a"),CIe=a("span"),b(Cq.$$.fragment),WUr=l(),wIe=a("span"),QUr=o("TFAutoModelForPreTraining"),xwo=l(),Qo=a("div"),b(wq.$$.fragment),UUr=l(),Zc=a("p"),HUr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),jde=a("a"),JUr=o("from_pretrained()"),ZUr=o(" class method or the "),Ode=a("a"),YUr=o("from_config()"),KUr=o(` class
method.`),eHr=l(),Aq=a("p"),oHr=o("This class cannot be instantiated directly using "),AIe=a("code"),rHr=o("__init__()"),tHr=o(" (throws an error)."),aHr=l(),jt=a("div"),b(Lq.$$.fragment),nHr=l(),LIe=a("p"),sHr=o("Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),lHr=l(),Yc=a("p"),iHr=o(`Note:
Loading a model from its configuration file does `),yIe=a("strong"),dHr=o("not"),cHr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Vde=a("a"),fHr=o("from_pretrained()"),mHr=o(" to load the model weights."),gHr=l(),b(pE.$$.fragment),hHr=l(),ot=a("div"),b(yq.$$.fragment),uHr=l(),xIe=a("p"),pHr=o("Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),_Hr=l(),In=a("p"),vHr=o("The model class to instantiate is selected based on the "),SIe=a("code"),bHr=o("model_type"),THr=o(` property of the config object (either
passed as an argument or loaded from `),kIe=a("code"),FHr=o("pretrained_model_name_or_path"),MHr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),$Ie=a("code"),EHr=o("pretrained_model_name_or_path"),CHr=o(":"),wHr=l(),me=a("ul"),_E=a("li"),PIe=a("strong"),AHr=o("albert"),LHr=o(" \u2014 "),Xde=a("a"),yHr=o("TFAlbertForPreTraining"),xHr=o(" (ALBERT model)"),SHr=l(),vE=a("li"),RIe=a("strong"),kHr=o("bart"),$Hr=o(" \u2014 "),zde=a("a"),PHr=o("TFBartForConditionalGeneration"),RHr=o(" (BART model)"),IHr=l(),bE=a("li"),IIe=a("strong"),NHr=o("bert"),BHr=o(" \u2014 "),Wde=a("a"),GHr=o("TFBertForPreTraining"),qHr=o(" (BERT model)"),DHr=l(),TE=a("li"),NIe=a("strong"),jHr=o("camembert"),OHr=o(" \u2014 "),Qde=a("a"),VHr=o("TFCamembertForMaskedLM"),XHr=o(" (CamemBERT model)"),zHr=l(),FE=a("li"),BIe=a("strong"),WHr=o("ctrl"),QHr=o(" \u2014 "),Ude=a("a"),UHr=o("TFCTRLLMHeadModel"),HHr=o(" (CTRL model)"),JHr=l(),ME=a("li"),GIe=a("strong"),ZHr=o("distilbert"),YHr=o(" \u2014 "),Hde=a("a"),KHr=o("TFDistilBertForMaskedLM"),eJr=o(" (DistilBERT model)"),oJr=l(),EE=a("li"),qIe=a("strong"),rJr=o("electra"),tJr=o(" \u2014 "),Jde=a("a"),aJr=o("TFElectraForPreTraining"),nJr=o(" (ELECTRA model)"),sJr=l(),CE=a("li"),DIe=a("strong"),lJr=o("flaubert"),iJr=o(" \u2014 "),Zde=a("a"),dJr=o("TFFlaubertWithLMHeadModel"),cJr=o(" (FlauBERT model)"),fJr=l(),wE=a("li"),jIe=a("strong"),mJr=o("funnel"),gJr=o(" \u2014 "),Yde=a("a"),hJr=o("TFFunnelForPreTraining"),uJr=o(" (Funnel Transformer model)"),pJr=l(),AE=a("li"),OIe=a("strong"),_Jr=o("gpt-sw3"),vJr=o(" \u2014 "),Kde=a("a"),bJr=o("TFGPT2LMHeadModel"),TJr=o(" (GPT-Sw3 model)"),FJr=l(),LE=a("li"),VIe=a("strong"),MJr=o("gpt2"),EJr=o(" \u2014 "),ece=a("a"),CJr=o("TFGPT2LMHeadModel"),wJr=o(" (OpenAI GPT-2 model)"),AJr=l(),yE=a("li"),XIe=a("strong"),LJr=o("layoutlm"),yJr=o(" \u2014 "),oce=a("a"),xJr=o("TFLayoutLMForMaskedLM"),SJr=o(" (LayoutLM model)"),kJr=l(),xE=a("li"),zIe=a("strong"),$Jr=o("lxmert"),PJr=o(" \u2014 "),rce=a("a"),RJr=o("TFLxmertForPreTraining"),IJr=o(" (LXMERT model)"),NJr=l(),SE=a("li"),WIe=a("strong"),BJr=o("mobilebert"),GJr=o(" \u2014 "),tce=a("a"),qJr=o("TFMobileBertForPreTraining"),DJr=o(" (MobileBERT model)"),jJr=l(),kE=a("li"),QIe=a("strong"),OJr=o("mpnet"),VJr=o(" \u2014 "),ace=a("a"),XJr=o("TFMPNetForMaskedLM"),zJr=o(" (MPNet model)"),WJr=l(),$E=a("li"),UIe=a("strong"),QJr=o("openai-gpt"),UJr=o(" \u2014 "),nce=a("a"),HJr=o("TFOpenAIGPTLMHeadModel"),JJr=o(" (OpenAI GPT model)"),ZJr=l(),PE=a("li"),HIe=a("strong"),YJr=o("roberta"),KJr=o(" \u2014 "),sce=a("a"),eZr=o("TFRobertaForMaskedLM"),oZr=o(" (RoBERTa model)"),rZr=l(),RE=a("li"),JIe=a("strong"),tZr=o("roberta-prelayernorm"),aZr=o(" \u2014 "),lce=a("a"),nZr=o("TFRobertaPreLayerNormForMaskedLM"),sZr=o(" (RoBERTa-PreLayerNorm model)"),lZr=l(),IE=a("li"),ZIe=a("strong"),iZr=o("t5"),dZr=o(" \u2014 "),ice=a("a"),cZr=o("TFT5ForConditionalGeneration"),fZr=o(" (T5 model)"),mZr=l(),NE=a("li"),YIe=a("strong"),gZr=o("tapas"),hZr=o(" \u2014 "),dce=a("a"),uZr=o("TFTapasForMaskedLM"),pZr=o(" (TAPAS model)"),_Zr=l(),BE=a("li"),KIe=a("strong"),vZr=o("transfo-xl"),bZr=o(" \u2014 "),cce=a("a"),TZr=o("TFTransfoXLLMHeadModel"),FZr=o(" (Transformer-XL model)"),MZr=l(),GE=a("li"),eNe=a("strong"),EZr=o("vit_mae"),CZr=o(" \u2014 "),fce=a("a"),wZr=o("TFViTMAEForPreTraining"),AZr=o(" (ViTMAE model)"),LZr=l(),qE=a("li"),oNe=a("strong"),yZr=o("xlm"),xZr=o(" \u2014 "),mce=a("a"),SZr=o("TFXLMWithLMHeadModel"),kZr=o(" (XLM model)"),$Zr=l(),DE=a("li"),rNe=a("strong"),PZr=o("xlm-roberta"),RZr=o(" \u2014 "),gce=a("a"),IZr=o("TFXLMRobertaForMaskedLM"),NZr=o(" (XLM-RoBERTa model)"),BZr=l(),jE=a("li"),tNe=a("strong"),GZr=o("xlnet"),qZr=o(" \u2014 "),hce=a("a"),DZr=o("TFXLNetLMHeadModel"),jZr=o(" (XLNet model)"),OZr=l(),b(OE.$$.fragment),Swo=l(),Kc=a("h3"),VE=a("a"),aNe=a("span"),b(xq.$$.fragment),VZr=l(),nNe=a("span"),XZr=o("FlaxAutoModelForPreTraining"),kwo=l(),Uo=a("div"),b(Sq.$$.fragment),zZr=l(),ef=a("p"),WZr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),uce=a("a"),QZr=o("from_pretrained()"),UZr=o(" class method or the "),pce=a("a"),HZr=o("from_config()"),JZr=o(` class
method.`),ZZr=l(),kq=a("p"),YZr=o("This class cannot be instantiated directly using "),sNe=a("code"),KZr=o("__init__()"),eYr=o(" (throws an error)."),oYr=l(),Ot=a("div"),b($q.$$.fragment),rYr=l(),lNe=a("p"),tYr=o("Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),aYr=l(),of=a("p"),nYr=o(`Note:
Loading a model from its configuration file does `),iNe=a("strong"),sYr=o("not"),lYr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),_ce=a("a"),iYr=o("from_pretrained()"),dYr=o(" to load the model weights."),cYr=l(),b(XE.$$.fragment),fYr=l(),rt=a("div"),b(Pq.$$.fragment),mYr=l(),dNe=a("p"),gYr=o("Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),hYr=l(),Nn=a("p"),uYr=o("The model class to instantiate is selected based on the "),cNe=a("code"),pYr=o("model_type"),_Yr=o(` property of the config object (either
passed as an argument or loaded from `),fNe=a("code"),vYr=o("pretrained_model_name_or_path"),bYr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mNe=a("code"),TYr=o("pretrained_model_name_or_path"),FYr=o(":"),MYr=l(),Ae=a("ul"),zE=a("li"),gNe=a("strong"),EYr=o("albert"),CYr=o(" \u2014 "),vce=a("a"),wYr=o("FlaxAlbertForPreTraining"),AYr=o(" (ALBERT model)"),LYr=l(),WE=a("li"),hNe=a("strong"),yYr=o("bart"),xYr=o(" \u2014 "),bce=a("a"),SYr=o("FlaxBartForConditionalGeneration"),kYr=o(" (BART model)"),$Yr=l(),QE=a("li"),uNe=a("strong"),PYr=o("bert"),RYr=o(" \u2014 "),Tce=a("a"),IYr=o("FlaxBertForPreTraining"),NYr=o(" (BERT model)"),BYr=l(),UE=a("li"),pNe=a("strong"),GYr=o("big_bird"),qYr=o(" \u2014 "),Fce=a("a"),DYr=o("FlaxBigBirdForPreTraining"),jYr=o(" (BigBird model)"),OYr=l(),HE=a("li"),_Ne=a("strong"),VYr=o("electra"),XYr=o(" \u2014 "),Mce=a("a"),zYr=o("FlaxElectraForPreTraining"),WYr=o(" (ELECTRA model)"),QYr=l(),JE=a("li"),vNe=a("strong"),UYr=o("longt5"),HYr=o(" \u2014 "),Ece=a("a"),JYr=o("FlaxLongT5ForConditionalGeneration"),ZYr=o(" (LongT5 model)"),YYr=l(),ZE=a("li"),bNe=a("strong"),KYr=o("mbart"),eKr=o(" \u2014 "),Cce=a("a"),oKr=o("FlaxMBartForConditionalGeneration"),rKr=o(" (mBART model)"),tKr=l(),YE=a("li"),TNe=a("strong"),aKr=o("mt5"),nKr=o(" \u2014 "),wce=a("a"),sKr=o("FlaxMT5ForConditionalGeneration"),lKr=o(" (MT5 model)"),iKr=l(),KE=a("li"),FNe=a("strong"),dKr=o("roberta"),cKr=o(" \u2014 "),Ace=a("a"),fKr=o("FlaxRobertaForMaskedLM"),mKr=o(" (RoBERTa model)"),gKr=l(),e3=a("li"),MNe=a("strong"),hKr=o("roberta-prelayernorm"),uKr=o(" \u2014 "),Lce=a("a"),pKr=o("FlaxRobertaPreLayerNormForMaskedLM"),_Kr=o(" (RoBERTa-PreLayerNorm model)"),vKr=l(),o3=a("li"),ENe=a("strong"),bKr=o("roformer"),TKr=o(" \u2014 "),yce=a("a"),FKr=o("FlaxRoFormerForMaskedLM"),MKr=o(" (RoFormer model)"),EKr=l(),r3=a("li"),CNe=a("strong"),CKr=o("t5"),wKr=o(" \u2014 "),xce=a("a"),AKr=o("FlaxT5ForConditionalGeneration"),LKr=o(" (T5 model)"),yKr=l(),t3=a("li"),wNe=a("strong"),xKr=o("wav2vec2"),SKr=o(" \u2014 "),Sce=a("a"),kKr=o("FlaxWav2Vec2ForPreTraining"),$Kr=o(" (Wav2Vec2 model)"),PKr=l(),a3=a("li"),ANe=a("strong"),RKr=o("whisper"),IKr=o(" \u2014 "),kce=a("a"),NKr=o("FlaxWhisperForConditionalGeneration"),BKr=o(" (Whisper model)"),GKr=l(),n3=a("li"),LNe=a("strong"),qKr=o("xlm-roberta"),DKr=o(" \u2014 "),$ce=a("a"),jKr=o("FlaxXLMRobertaForMaskedLM"),OKr=o(" (XLM-RoBERTa model)"),VKr=l(),b(s3.$$.fragment),$wo=l(),rf=a("h2"),l3=a("a"),yNe=a("span"),b(Rq.$$.fragment),XKr=l(),xNe=a("span"),zKr=o("Natural Language Processing"),Pwo=l(),Pce=a("p"),WKr=o("The following auto classes are available for the following natural language processing tasks."),Rwo=l(),tf=a("h3"),i3=a("a"),SNe=a("span"),b(Iq.$$.fragment),QKr=l(),kNe=a("span"),UKr=o("AutoModelForCausalLM"),Iwo=l(),Ho=a("div"),b(Nq.$$.fragment),HKr=l(),af=a("p"),JKr=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 `),Rce=a("a"),ZKr=o("from_pretrained()"),YKr=o(" class method or the "),Ice=a("a"),KKr=o("from_config()"),eet=o(` class
method.`),oet=l(),Bq=a("p"),ret=o("This class cannot be instantiated directly using "),$Ne=a("code"),tet=o("__init__()"),aet=o(" (throws an error)."),net=l(),Vt=a("div"),b(Gq.$$.fragment),set=l(),PNe=a("p"),iet=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),det=l(),nf=a("p"),cet=o(`Note:
Loading a model from its configuration file does `),RNe=a("strong"),fet=o("not"),met=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Nce=a("a"),get=o("from_pretrained()"),het=o(" to load the model weights."),uet=l(),b(d3.$$.fragment),pet=l(),io=a("div"),b(qq.$$.fragment),_et=l(),INe=a("p"),vet=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),bet=l(),Bn=a("p"),Tet=o("The model class to instantiate is selected based on the "),NNe=a("code"),Fet=o("model_type"),Met=o(` property of the config object (either
passed as an argument or loaded from `),BNe=a("code"),Eet=o("pretrained_model_name_or_path"),Cet=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),GNe=a("code"),wet=o("pretrained_model_name_or_path"),Aet=o(":"),Let=l(),H=a("ul"),c3=a("li"),qNe=a("strong"),yet=o("bart"),xet=o(" \u2014 "),Bce=a("a"),Set=o("BartForCausalLM"),ket=o(" (BART model)"),$et=l(),f3=a("li"),DNe=a("strong"),Pet=o("bert"),Ret=o(" \u2014 "),Gce=a("a"),Iet=o("BertLMHeadModel"),Net=o(" (BERT model)"),Bet=l(),m3=a("li"),jNe=a("strong"),Get=o("bert-generation"),qet=o(" \u2014 "),qce=a("a"),Det=o("BertGenerationDecoder"),jet=o(" (Bert Generation model)"),Oet=l(),g3=a("li"),ONe=a("strong"),Vet=o("big_bird"),Xet=o(" \u2014 "),Dce=a("a"),zet=o("BigBirdForCausalLM"),Wet=o(" (BigBird model)"),Qet=l(),h3=a("li"),VNe=a("strong"),Uet=o("bigbird_pegasus"),Het=o(" \u2014 "),jce=a("a"),Jet=o("BigBirdPegasusForCausalLM"),Zet=o(" (BigBird-Pegasus model)"),Yet=l(),u3=a("li"),XNe=a("strong"),Ket=o("biogpt"),eot=o(" \u2014 "),Oce=a("a"),oot=o("BioGptForCausalLM"),rot=o(" (BioGpt model)"),tot=l(),p3=a("li"),zNe=a("strong"),aot=o("blenderbot"),not=o(" \u2014 "),Vce=a("a"),sot=o("BlenderbotForCausalLM"),lot=o(" (Blenderbot model)"),iot=l(),_3=a("li"),WNe=a("strong"),dot=o("blenderbot-small"),cot=o(" \u2014 "),Xce=a("a"),fot=o("BlenderbotSmallForCausalLM"),mot=o(" (BlenderbotSmall model)"),got=l(),v3=a("li"),QNe=a("strong"),hot=o("bloom"),uot=o(" \u2014 "),zce=a("a"),pot=o("BloomForCausalLM"),_ot=o(" (BLOOM model)"),vot=l(),b3=a("li"),UNe=a("strong"),bot=o("camembert"),Tot=o(" \u2014 "),Wce=a("a"),Fot=o("CamembertForCausalLM"),Mot=o(" (CamemBERT model)"),Eot=l(),T3=a("li"),HNe=a("strong"),Cot=o("codegen"),wot=o(" \u2014 "),Qce=a("a"),Aot=o("CodeGenForCausalLM"),Lot=o(" (CodeGen model)"),yot=l(),F3=a("li"),JNe=a("strong"),xot=o("cpmant"),Sot=o(" \u2014 "),Uce=a("a"),kot=o("CpmAntForCausalLM"),$ot=o(" (CPM-Ant model)"),Pot=l(),M3=a("li"),ZNe=a("strong"),Rot=o("ctrl"),Iot=o(" \u2014 "),Hce=a("a"),Not=o("CTRLLMHeadModel"),Bot=o(" (CTRL model)"),Got=l(),E3=a("li"),YNe=a("strong"),qot=o("data2vec-text"),Dot=o(" \u2014 "),Jce=a("a"),jot=o("Data2VecTextForCausalLM"),Oot=o(" (Data2VecText model)"),Vot=l(),C3=a("li"),KNe=a("strong"),Xot=o("electra"),zot=o(" \u2014 "),Zce=a("a"),Wot=o("ElectraForCausalLM"),Qot=o(" (ELECTRA model)"),Uot=l(),w3=a("li"),eBe=a("strong"),Hot=o("ernie"),Jot=o(" \u2014 "),Yce=a("a"),Zot=o("ErnieForCausalLM"),Yot=o(" (ERNIE model)"),Kot=l(),A3=a("li"),oBe=a("strong"),ert=o("git"),ort=o(" \u2014 "),Kce=a("a"),rrt=o("GitForCausalLM"),trt=o(" (GIT model)"),art=l(),L3=a("li"),rBe=a("strong"),nrt=o("gpt-sw3"),srt=o(" \u2014 "),efe=a("a"),lrt=o("GPT2LMHeadModel"),irt=o(" (GPT-Sw3 model)"),drt=l(),y3=a("li"),tBe=a("strong"),crt=o("gpt2"),frt=o(" \u2014 "),ofe=a("a"),mrt=o("GPT2LMHeadModel"),grt=o(" (OpenAI GPT-2 model)"),hrt=l(),x3=a("li"),aBe=a("strong"),urt=o("gpt_bigcode"),prt=o(" \u2014 "),rfe=a("a"),_rt=o("GPTBigCodeForCausalLM"),vrt=o(" (GPTBigCode model)"),brt=l(),S3=a("li"),nBe=a("strong"),Trt=o("gpt_neo"),Frt=o(" \u2014 "),tfe=a("a"),Mrt=o("GPTNeoForCausalLM"),Ert=o(" (GPT Neo model)"),Crt=l(),k3=a("li"),sBe=a("strong"),wrt=o("gpt_neox"),Art=o(" \u2014 "),afe=a("a"),Lrt=o("GPTNeoXForCausalLM"),yrt=o(" (GPT NeoX model)"),xrt=l(),$3=a("li"),lBe=a("strong"),Srt=o("gpt_neox_japanese"),krt=o(" \u2014 "),nfe=a("a"),$rt=o("GPTNeoXJapaneseForCausalLM"),Prt=o(" (GPT NeoX Japanese model)"),Rrt=l(),P3=a("li"),iBe=a("strong"),Irt=o("gptj"),Nrt=o(" \u2014 "),sfe=a("a"),Brt=o("GPTJForCausalLM"),Grt=o(" (GPT-J model)"),qrt=l(),R3=a("li"),dBe=a("strong"),Drt=o("llama"),jrt=o(" \u2014 "),lfe=a("a"),Ort=o("LlamaForCausalLM"),Vrt=o(" (LLaMA model)"),Xrt=l(),I3=a("li"),cBe=a("strong"),zrt=o("marian"),Wrt=o(" \u2014 "),ife=a("a"),Qrt=o("MarianForCausalLM"),Urt=o(" (Marian model)"),Hrt=l(),N3=a("li"),fBe=a("strong"),Jrt=o("mbart"),Zrt=o(" \u2014 "),dfe=a("a"),Yrt=o("MBartForCausalLM"),Krt=o(" (mBART model)"),ett=l(),B3=a("li"),mBe=a("strong"),ott=o("mega"),rtt=o(" \u2014 "),cfe=a("a"),ttt=o("MegaForCausalLM"),att=o(" (MEGA model)"),ntt=l(),G3=a("li"),gBe=a("strong"),stt=o("megatron-bert"),ltt=o(" \u2014 "),ffe=a("a"),itt=o("MegatronBertForCausalLM"),dtt=o(" (Megatron-BERT model)"),ctt=l(),q3=a("li"),hBe=a("strong"),ftt=o("mvp"),mtt=o(" \u2014 "),mfe=a("a"),gtt=o("MvpForCausalLM"),htt=o(" (MVP model)"),utt=l(),D3=a("li"),uBe=a("strong"),ptt=o("open-llama"),_tt=o(" \u2014 "),gfe=a("a"),vtt=o("OpenLlamaForCausalLM"),btt=o(" (OpenLlama model)"),Ttt=l(),j3=a("li"),pBe=a("strong"),Ftt=o("openai-gpt"),Mtt=o(" \u2014 "),hfe=a("a"),Ett=o("OpenAIGPTLMHeadModel"),Ctt=o(" (OpenAI GPT model)"),wtt=l(),O3=a("li"),_Be=a("strong"),Att=o("opt"),Ltt=o(" \u2014 "),ufe=a("a"),ytt=o("OPTForCausalLM"),xtt=o(" (OPT model)"),Stt=l(),V3=a("li"),vBe=a("strong"),ktt=o("pegasus"),$tt=o(" \u2014 "),pfe=a("a"),Ptt=o("PegasusForCausalLM"),Rtt=o(" (Pegasus 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with the `),Fhe=a("a"),R4t=o("from_pretrained()"),I4t=o(" class method or the "),Mhe=a("a"),N4t=o("from_config()"),B4t=o(` class
method.`),G4t=l(),FD=a("p"),q4t=o("This class cannot be instantiated directly using "),_je=a("code"),D4t=o("__init__()"),j4t=o(" (throws an error)."),O4t=l(),Jt=a("div"),b(MD.$$.fragment),V4t=l(),vje=a("p"),X4t=o("Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),z4t=l(),yf=a("p"),W4t=o(`Note:
Loading a model from its configuration file does `),bje=a("strong"),Q4t=o("not"),U4t=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Ehe=a("a"),H4t=o("from_pretrained()"),J4t=o(" to load the model weights."),Z4t=l(),b(o6.$$.fragment),Y4t=l(),lt=a("div"),b(ED.$$.fragment),K4t=l(),Tje=a("p"),e0t=o("Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),o0t=l(),Xn=a("p"),r0t=o("The model class to instantiate is selected based on the "),Fje=a("code"),t0t=o("model_type"),a0t=o(` property of the config object (either
passed as an argument or loaded from `),Mje=a("code"),n0t=o("pretrained_model_name_or_path"),s0t=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Eje=a("code"),l0t=o("pretrained_model_name_or_path"),i0t=o(":"),d0t=l(),Ie=a("ul"),r6=a("li"),Cje=a("strong"),c0t=o("bart"),f0t=o(" \u2014 "),Che=a("a"),m0t=o("TFBartForConditionalGeneration"),g0t=o(" (BART model)"),h0t=l(),t6=a("li"),wje=a("strong"),u0t=o("blenderbot"),p0t=o(" \u2014 "),whe=a("a"),_0t=o("TFBlenderbotForConditionalGeneration"),v0t=o(" (Blenderbot model)"),b0t=l(),a6=a("li"),Aje=a("strong"),T0t=o("blenderbot-small"),F0t=o(" \u2014 "),Ahe=a("a"),M0t=o("TFBlenderbotSmallForConditionalGeneration"),E0t=o(" (BlenderbotSmall model)"),C0t=l(),n6=a("li"),Lje=a("strong"),w0t=o("encoder-decoder"),A0t=o(" \u2014 "),Lhe=a("a"),L0t=o("TFEncoderDecoderModel"),y0t=o(" (Encoder decoder model)"),x0t=l(),s6=a("li"),yje=a("strong"),S0t=o("led"),k0t=o(" \u2014 "),yhe=a("a"),$0t=o("TFLEDForConditionalGeneration"),P0t=o(" (LED model)"),R0t=l(),l6=a("li"),xje=a("strong"),I0t=o("marian"),N0t=o(" \u2014 "),xhe=a("a"),B0t=o("TFMarianMTModel"),G0t=o(" (Marian model)"),q0t=l(),i6=a("li"),Sje=a("strong"),D0t=o("mbart"),j0t=o(" \u2014 "),She=a("a"),O0t=o("TFMBartForConditionalGeneration"),V0t=o(" (mBART model)"),X0t=l(),d6=a("li"),kje=a("strong"),z0t=o("mt5"),W0t=o(" \u2014 "),khe=a("a"),Q0t=o("TFMT5ForConditionalGeneration"),U0t=o(" (MT5 model)"),H0t=l(),c6=a("li"),$je=a("strong"),J0t=o("pegasus"),Z0t=o(" \u2014 "),$he=a("a"),Y0t=o("TFPegasusForConditionalGeneration"),K0t=o(" (Pegasus model)"),ebt=l(),f6=a("li"),Pje=a("strong"),obt=o("t5"),rbt=o(" \u2014 "),Phe=a("a"),tbt=o("TFT5ForConditionalGeneration"),abt=o(" (T5 model)"),nbt=l(),b(m6.$$.fragment),Ywo=l(),xf=a("h3"),g6=a("a"),Rje=a("span"),b(CD.$$.fragment),sbt=l(),Ije=a("span"),lbt=o("FlaxAutoModelForSeq2SeqLM"),Kwo=l(),tr=a("div"),b(wD.$$.fragment),ibt=l(),Sf=a("p"),dbt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),Rhe=a("a"),cbt=o("from_pretrained()"),fbt=o(" class method or the "),Ihe=a("a"),mbt=o("from_config()"),gbt=o(` class
method.`),hbt=l(),AD=a("p"),ubt=o("This class cannot be instantiated directly using "),Nje=a("code"),pbt=o("__init__()"),_bt=o(" (throws an error)."),vbt=l(),Zt=a("div"),b(LD.$$.fragment),bbt=l(),Bje=a("p"),Tbt=o("Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),Fbt=l(),kf=a("p"),Mbt=o(`Note:
Loading a model from its configuration file does `),Gje=a("strong"),Ebt=o("not"),Cbt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Nhe=a("a"),wbt=o("from_pretrained()"),Abt=o(" to load the model weights."),Lbt=l(),b(h6.$$.fragment),ybt=l(),it=a("div"),b(yD.$$.fragment),xbt=l(),qje=a("p"),Sbt=o("Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),kbt=l(),zn=a("p"),$bt=o("The model class to instantiate is selected based on the "),Dje=a("code"),Pbt=o("model_type"),Rbt=o(` property of the config object (either
passed as an argument or loaded from `),jje=a("code"),Ibt=o("pretrained_model_name_or_path"),Nbt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Oje=a("code"),Bbt=o("pretrained_model_name_or_path"),Gbt=o(":"),qbt=l(),Ne=a("ul"),u6=a("li"),Vje=a("strong"),Dbt=o("bart"),jbt=o(" \u2014 "),Bhe=a("a"),Obt=o("FlaxBartForConditionalGeneration"),Vbt=o(" (BART model)"),Xbt=l(),p6=a("li"),Xje=a("strong"),zbt=o("blenderbot"),Wbt=o(" \u2014 "),Ghe=a("a"),Qbt=o("FlaxBlenderbotForConditionalGeneration"),Ubt=o(" (Blenderbot model)"),Hbt=l(),_6=a("li"),zje=a("strong"),Jbt=o("blenderbot-small"),Zbt=o(" \u2014 "),qhe=a("a"),Ybt=o("FlaxBlenderbotSmallForConditionalGeneration"),Kbt=o(" (BlenderbotSmall model)"),e9t=l(),v6=a("li"),Wje=a("strong"),o9t=o("encoder-decoder"),r9t=o(" \u2014 "),Dhe=a("a"),t9t=o("FlaxEncoderDecoderModel"),a9t=o(" (Encoder decoder model)"),n9t=l(),b6=a("li"),Qje=a("strong"),s9t=o("longt5"),l9t=o(" \u2014 "),jhe=a("a"),i9t=o("FlaxLongT5ForConditionalGeneration"),d9t=o(" (LongT5 model)"),c9t=l(),T6=a("li"),Uje=a("strong"),f9t=o("marian"),m9t=o(" \u2014 "),Ohe=a("a"),g9t=o("FlaxMarianMTModel"),h9t=o(" (Marian model)"),u9t=l(),F6=a("li"),Hje=a("strong"),p9t=o("mbart"),_9t=o(" \u2014 "),Vhe=a("a"),v9t=o("FlaxMBartForConditionalGeneration"),b9t=o(" (mBART model)"),T9t=l(),M6=a("li"),Jje=a("strong"),F9t=o("mt5"),M9t=o(" \u2014 "),Xhe=a("a"),E9t=o("FlaxMT5ForConditionalGeneration"),C9t=o(" (MT5 model)"),w9t=l(),E6=a("li"),Zje=a("strong"),A9t=o("pegasus"),L9t=o(" \u2014 "),zhe=a("a"),y9t=o("FlaxPegasusForConditionalGeneration"),x9t=o(" (Pegasus model)"),S9t=l(),C6=a("li"),Yje=a("strong"),k9t=o("t5"),$9t=o(" \u2014 "),Whe=a("a"),P9t=o("FlaxT5ForConditionalGeneration"),R9t=o(" (T5 model)"),I9t=l(),b(w6.$$.fragment),eAo=l(),$f=a("h3"),A6=a("a"),Kje=a("span"),b(xD.$$.fragment),N9t=l(),eOe=a("span"),B9t=o("AutoModelForSequenceClassification"),oAo=l(),ar=a("div"),b(SD.$$.fragment),G9t=l(),Pf=a("p"),q9t=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the `),Qhe=a("a"),D9t=o("from_pretrained()"),j9t=o(" class method or the "),Uhe=a("a"),O9t=o("from_config()"),V9t=o(` class
method.`),X9t=l(),kD=a("p"),z9t=o("This class cannot be instantiated directly using "),oOe=a("code"),W9t=o("__init__()"),Q9t=o(" (throws an error)."),U9t=l(),Yt=a("div"),b($D.$$.fragment),H9t=l(),rOe=a("p"),J9t=o("Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),Z9t=l(),Rf=a("p"),Y9t=o(`Note:
Loading a model from its configuration file does `),tOe=a("strong"),K9t=o("not"),eTt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Hhe=a("a"),oTt=o("from_pretrained()"),rTt=o(" to load the model weights."),tTt=l(),b(L6.$$.fragment),aTt=l(),mo=a("div"),b(PD.$$.fragment),nTt=l(),aOe=a("p"),sTt=o("Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),lTt=l(),Wn=a("p"),iTt=o("The model class to instantiate is selected based on the "),nOe=a("code"),dTt=o("model_type"),cTt=o(` property of the config object (either
passed as an argument or loaded from `),sOe=a("code"),fTt=o("pretrained_model_name_or_path"),mTt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),lOe=a("code"),gTt=o("pretrained_model_name_or_path"),hTt=o(":"),uTt=l(),R=a("ul"),y6=a("li"),iOe=a("strong"),pTt=o("albert"),_Tt=o(" \u2014 "),Jhe=a("a"),vTt=o("AlbertForSequenceClassification"),bTt=o(" (ALBERT model)"),TTt=l(),x6=a("li"),dOe=a("strong"),FTt=o("bart"),MTt=o(" \u2014 "),Zhe=a("a"),ETt=o("BartForSequenceClassification"),CTt=o(" (BART model)"),wTt=l(),S6=a("li"),cOe=a("strong"),ATt=o("bert"),LTt=o(" \u2014 "),Yhe=a("a"),yTt=o("BertForSequenceClassification"),xTt=o(" (BERT model)"),STt=l(),k6=a("li"),fOe=a("strong"),kTt=o("big_bird"),$Tt=o(" \u2014 "),Khe=a("a"),PTt=o("BigBirdForSequenceClassification"),RTt=o(" (BigBird model)"),ITt=l(),$6=a("li"),mOe=a("strong"),NTt=o("bigbird_pegasus"),BTt=o(" \u2014 "),eue=a("a"),GTt=o("BigBirdPegasusForSequenceClassification"),qTt=o(" (BigBird-Pegasus model)"),DTt=l(),P6=a("li"),gOe=a("strong"),jTt=o("biogpt"),OTt=o(" \u2014 "),oue=a("a"),VTt=o("BioGptForSequenceClassification"),XTt=o(" 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passed as an argument or loaded from `),qWe=a("code"),SNt=o("pretrained_model_name_or_path"),kNt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),DWe=a("code"),$Nt=o("pretrained_model_name_or_path"),PNt=o(":"),RNt=l(),uj=a("ul"),pL=a("li"),jWe=a("strong"),INt=o("bert"),NNt=o(" \u2014 "),I2e=a("a"),BNt=o("TFBertForNextSentencePrediction"),GNt=o(" (BERT model)"),qNt=l(),_L=a("li"),OWe=a("strong"),DNt=o("mobilebert"),jNt=o(" \u2014 "),N2e=a("a"),ONt=o("TFMobileBertForNextSentencePrediction"),VNt=o(" (MobileBERT model)"),XNt=l(),b(vL.$$.fragment),pAo=l(),rm=a("h3"),bL=a("a"),VWe=a("span"),b(pj.$$.fragment),zNt=l(),XWe=a("span"),WNt=o("FlaxAutoModelForNextSentencePrediction"),_Ao=l(),mr=a("div"),b(_j.$$.fragment),QNt=l(),tm=a("p"),UNt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
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method.`),KNt=l(),vj=a("p"),eBt=o("This class cannot be instantiated directly using "),zWe=a("code"),oBt=o("__init__()"),rBt=o(" (throws an error)."),tBt=l(),sa=a("div"),b(bj.$$.fragment),aBt=l(),WWe=a("p"),nBt=o("Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),sBt=l(),am=a("p"),lBt=o(`Note:
Loading a model from its configuration file does `),QWe=a("strong"),iBt=o("not"),dBt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),q2e=a("a"),cBt=o("from_pretrained()"),fBt=o(" to load the model weights."),mBt=l(),b(TL.$$.fragment),gBt=l(),ht=a("div"),b(Tj.$$.fragment),hBt=l(),UWe=a("p"),uBt=o("Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),pBt=l(),es=a("p"),_Bt=o("The model class to instantiate is selected based on the "),HWe=a("code"),vBt=o("model_type"),bBt=o(` property of the config object (either
passed as an argument or loaded from `),JWe=a("code"),TBt=o("pretrained_model_name_or_path"),FBt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ZWe=a("code"),MBt=o("pretrained_model_name_or_path"),EBt=o(":"),CBt=l(),YWe=a("ul"),FL=a("li"),KWe=a("strong"),wBt=o("bert"),ABt=o(" \u2014 "),D2e=a("a"),LBt=o("FlaxBertForNextSentencePrediction"),yBt=o(" (BERT model)"),xBt=l(),b(ML.$$.fragment),vAo=l(),nm=a("h3"),EL=a("a"),eQe=a("span"),b(Fj.$$.fragment),SBt=l(),oQe=a("span"),kBt=o("AutoModelForTokenClassification"),bAo=l(),gr=a("div"),b(Mj.$$.fragment),$Bt=l(),sm=a("p"),PBt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
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method.`),GBt=l(),Ej=a("p"),qBt=o("This class cannot be instantiated directly using "),rQe=a("code"),DBt=o("__init__()"),jBt=o(" (throws an error)."),OBt=l(),la=a("div"),b(Cj.$$.fragment),VBt=l(),tQe=a("p"),XBt=o("Instantiates one of the model classes of the library (with a token classification head) from a configuration."),zBt=l(),lm=a("p"),WBt=o(`Note:
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passed as an argument or loaded from `),lQe=a("code"),nGt=o("pretrained_model_name_or_path"),sGt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),iQe=a("code"),lGt=o("pretrained_model_name_or_path"),iGt=o(":"),dGt=l(),Z=a("ul"),wL=a("li"),dQe=a("strong"),cGt=o("albert"),fGt=o(" \u2014 "),X2e=a("a"),mGt=o("AlbertForTokenClassification"),gGt=o(" (ALBERT model)"),hGt=l(),AL=a("li"),cQe=a("strong"),uGt=o("bert"),pGt=o(" \u2014 "),z2e=a("a"),_Gt=o("BertForTokenClassification"),vGt=o(" (BERT model)"),bGt=l(),LL=a("li"),fQe=a("strong"),TGt=o("big_bird"),FGt=o(" \u2014 "),W2e=a("a"),MGt=o("BigBirdForTokenClassification"),EGt=o(" (BigBird model)"),CGt=l(),yL=a("li"),mQe=a("strong"),wGt=o("biogpt"),AGt=o(" \u2014 "),Q2e=a("a"),LGt=o("BioGptForTokenClassification"),yGt=o(" (BioGpt model)"),xGt=l(),xL=a("li"),gQe=a("strong"),SGt=o("bloom"),kGt=o(" \u2014 "),U2e=a("a"),$Gt=o("BloomForTokenClassification"),PGt=o(" (BLOOM model)"),RGt=l(),SL=a("li"),hQe=a("strong"),IGt=o("camembert"),NGt=o(" \u2014 "),H2e=a("a"),BGt=o("CamembertForTokenClassification"),GGt=o(" (CamemBERT 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falling back to using pattern matching on `),pUe=a("code"),kVt=o("pretrained_model_name_or_path"),$Vt=o(":"),PVt=l(),he=a("ul"),Ay=a("li"),_Ue=a("strong"),RVt=o("albert"),IVt=o(" \u2014 "),Xve=a("a"),NVt=o("TFAlbertForTokenClassification"),BVt=o(" (ALBERT model)"),GVt=l(),Ly=a("li"),vUe=a("strong"),qVt=o("bert"),DVt=o(" \u2014 "),zve=a("a"),jVt=o("TFBertForTokenClassification"),OVt=o(" (BERT model)"),VVt=l(),yy=a("li"),bUe=a("strong"),XVt=o("camembert"),zVt=o(" \u2014 "),Wve=a("a"),WVt=o("TFCamembertForTokenClassification"),QVt=o(" (CamemBERT model)"),UVt=l(),xy=a("li"),TUe=a("strong"),HVt=o("convbert"),JVt=o(" \u2014 "),Qve=a("a"),ZVt=o("TFConvBertForTokenClassification"),YVt=o(" (ConvBERT model)"),KVt=l(),Sy=a("li"),FUe=a("strong"),eXt=o("deberta"),oXt=o(" \u2014 "),Uve=a("a"),rXt=o("TFDebertaForTokenClassification"),tXt=o(" (DeBERTa model)"),aXt=l(),ky=a("li"),MUe=a("strong"),nXt=o("deberta-v2"),sXt=o(" \u2014 "),Hve=a("a"),lXt=o("TFDebertaV2ForTokenClassification"),iXt=o(" (DeBERTa-v2 model)"),dXt=l(),$y=a("li"),EUe=a("strong"),cXt=o("distilbert"),fXt=o(" \u2014 "),Jve=a("a"),mXt=o("TFDistilBertForTokenClassification"),gXt=o(" (DistilBERT model)"),hXt=l(),Py=a("li"),CUe=a("strong"),uXt=o("electra"),pXt=o(" \u2014 "),Zve=a("a"),_Xt=o("TFElectraForTokenClassification"),vXt=o(" (ELECTRA model)"),bXt=l(),Ry=a("li"),wUe=a("strong"),TXt=o("esm"),FXt=o(" \u2014 "),Yve=a("a"),MXt=o("TFEsmForTokenClassification"),EXt=o(" (ESM model)"),CXt=l(),Iy=a("li"),AUe=a("strong"),wXt=o("flaubert"),AXt=o(" \u2014 "),Kve=a("a"),LXt=o("TFFlaubertForTokenClassification"),yXt=o(" (FlauBERT model)"),xXt=l(),Ny=a("li"),LUe=a("strong"),SXt=o("funnel"),kXt=o(" \u2014 "),e1e=a("a"),$Xt=o("TFFunnelForTokenClassification"),PXt=o(" (Funnel Transformer model)"),RXt=l(),By=a("li"),yUe=a("strong"),IXt=o("layoutlm"),NXt=o(" \u2014 "),o1e=a("a"),BXt=o("TFLayoutLMForTokenClassification"),GXt=o(" (LayoutLM model)"),qXt=l(),Gy=a("li"),xUe=a("strong"),DXt=o("layoutlmv3"),jXt=o(" \u2014 "),r1e=a("a"),OXt=o("TFLayoutLMv3ForTokenClassification"),VXt=o(" (LayoutLMv3 model)"),XXt=l(),qy=a("li"),SUe=a("strong"),zXt=o("longformer"),WXt=o(" \u2014 "),t1e=a("a"),QXt=o("TFLongformerForTokenClassification"),UXt=o(" (Longformer model)"),HXt=l(),Dy=a("li"),kUe=a("strong"),JXt=o("mobilebert"),ZXt=o(" \u2014 "),a1e=a("a"),YXt=o("TFMobileBertForTokenClassification"),KXt=o(" (MobileBERT model)"),ezt=l(),jy=a("li"),$Ue=a("strong"),ozt=o("mpnet"),rzt=o(" \u2014 "),n1e=a("a"),tzt=o("TFMPNetForTokenClassification"),azt=o(" (MPNet model)"),nzt=l(),Oy=a("li"),PUe=a("strong"),szt=o("rembert"),lzt=o(" \u2014 "),s1e=a("a"),izt=o("TFRemBertForTokenClassification"),dzt=o(" (RemBERT model)"),czt=l(),Vy=a("li"),RUe=a("strong"),fzt=o("roberta"),mzt=o(" \u2014 "),l1e=a("a"),gzt=o("TFRobertaForTokenClassification"),hzt=o(" (RoBERTa model)"),uzt=l(),Xy=a("li"),IUe=a("strong"),pzt=o("roberta-prelayernorm"),_zt=o(" \u2014 "),i1e=a("a"),vzt=o("TFRobertaPreLayerNormForTokenClassification"),bzt=o(" (RoBERTa-PreLayerNorm model)"),Tzt=l(),zy=a("li"),NUe=a("strong"),Fzt=o("roformer"),Mzt=o(" \u2014 "),d1e=a("a"),Ezt=o("TFRoFormerForTokenClassification"),Czt=o(" (RoFormer model)"),wzt=l(),Wy=a("li"),BUe=a("strong"),Azt=o("xlm"),Lzt=o(" \u2014 "),c1e=a("a"),yzt=o("TFXLMForTokenClassification"),xzt=o(" (XLM model)"),Szt=l(),Qy=a("li"),GUe=a("strong"),kzt=o("xlm-roberta"),$zt=o(" \u2014 "),f1e=a("a"),Pzt=o("TFXLMRobertaForTokenClassification"),Rzt=o(" (XLM-RoBERTa model)"),Izt=l(),Uy=a("li"),qUe=a("strong"),Nzt=o("xlnet"),Bzt=o(" \u2014 "),m1e=a("a"),Gzt=o("TFXLNetForTokenClassification"),qzt=o(" (XLNet model)"),Dzt=l(),b(Hy.$$.fragment),MAo=l(),fm=a("h3"),Jy=a("a"),DUe=a("span"),b(kj.$$.fragment),jzt=l(),jUe=a("span"),Ozt=o("FlaxAutoModelForTokenClassification"),EAo=l(),ur=a("div"),b($j.$$.fragment),Vzt=l(),mm=a("p"),Xzt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
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method.`),TQt=l(),Gj=a("p"),FQt=o("This class cannot be instantiated directly using "),sHe=a("code"),MQt=o("__init__()"),EQt=o(" (throws an error)."),CQt=l(),ca=a("div"),b(qj.$$.fragment),wQt=l(),lHe=a("p"),AQt=o("Instantiates one of the model classes of the library (with a question answering head) from a configuration."),LQt=l(),pm=a("p"),yQt=o(`Note:
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model\u2019s configuration. Use `),L1e=a("a"),kQt=o("from_pretrained()"),$Qt=o(" to load the model weights."),PQt=l(),b(dx.$$.fragment),RQt=l(),po=a("div"),b(Dj.$$.fragment),IQt=l(),dHe=a("p"),NQt=o("Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),BQt=l(),as=a("p"),GQt=o("The model class to instantiate is selected based on the "),cHe=a("code"),qQt=o("model_type"),DQt=o(` property of the config object (either
passed as an argument or loaded from `),fHe=a("code"),jQt=o("pretrained_model_name_or_path"),OQt=o(` if possible), or when it\u2019s missing, by
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method.`),uKt=l(),Vj=a("p"),pKt=o("This class cannot be instantiated directly using "),bJe=a("code"),_Kt=o("__init__()"),vKt=o(" (throws an error)."),bKt=l(),fa=a("div"),b(Xj.$$.fragment),TKt=l(),TJe=a("p"),FKt=o("Instantiates one of the model classes of the library (with a question answering head) from a configuration."),MKt=l(),bm=a("p"),EKt=o(`Note:
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falling back to using pattern matching on `),wJe=a("code"),GKt=o("pretrained_model_name_or_path"),qKt=o(":"),DKt=l(),_e=a("ul"),pS=a("li"),AJe=a("strong"),jKt=o("albert"),OKt=o(" \u2014 "),P4e=a("a"),VKt=o("TFAlbertForQuestionAnswering"),XKt=o(" (ALBERT model)"),zKt=l(),_S=a("li"),LJe=a("strong"),WKt=o("bert"),QKt=o(" \u2014 "),R4e=a("a"),UKt=o("TFBertForQuestionAnswering"),HKt=o(" (BERT model)"),JKt=l(),vS=a("li"),yJe=a("strong"),ZKt=o("camembert"),YKt=o(" \u2014 "),I4e=a("a"),KKt=o("TFCamembertForQuestionAnswering"),eea=o(" (CamemBERT model)"),oea=l(),bS=a("li"),xJe=a("strong"),rea=o("convbert"),tea=o(" \u2014 "),N4e=a("a"),aea=o("TFConvBertForQuestionAnswering"),nea=o(" (ConvBERT model)"),sea=l(),TS=a("li"),SJe=a("strong"),lea=o("deberta"),iea=o(" \u2014 "),B4e=a("a"),dea=o("TFDebertaForQuestionAnswering"),cea=o(" (DeBERTa model)"),fea=l(),FS=a("li"),kJe=a("strong"),mea=o("deberta-v2"),gea=o(" \u2014 "),G4e=a("a"),hea=o("TFDebertaV2ForQuestionAnswering"),uea=o(" (DeBERTa-v2 model)"),pea=l(),MS=a("li"),$Je=a("strong"),_ea=o("distilbert"),vea=o(" \u2014 "),q4e=a("a"),bea=o("TFDistilBertForQuestionAnswering"),Tea=o(" (DistilBERT model)"),Fea=l(),ES=a("li"),PJe=a("strong"),Mea=o("electra"),Eea=o(" \u2014 "),D4e=a("a"),Cea=o("TFElectraForQuestionAnswering"),wea=o(" (ELECTRA model)"),Aea=l(),CS=a("li"),RJe=a("strong"),Lea=o("flaubert"),yea=o(" \u2014 "),j4e=a("a"),xea=o("TFFlaubertForQuestionAnsweringSimple"),Sea=o(" (FlauBERT model)"),kea=l(),wS=a("li"),IJe=a("strong"),$ea=o("funnel"),Pea=o(" \u2014 "),O4e=a("a"),Rea=o("TFFunnelForQuestionAnswering"),Iea=o(" (Funnel Transformer model)"),Nea=l(),AS=a("li"),NJe=a("strong"),Bea=o("gptj"),Gea=o(" \u2014 "),V4e=a("a"),qea=o("TFGPTJForQuestionAnswering"),Dea=o(" (GPT-J model)"),jea=l(),LS=a("li"),BJe=a("strong"),Oea=o("layoutlmv3"),Vea=o(" \u2014 "),X4e=a("a"),Xea=o("TFLayoutLMv3ForQuestionAnswering"),zea=o(" (LayoutLMv3 model)"),Wea=l(),yS=a("li"),GJe=a("strong"),Qea=o("longformer"),Uea=o(" \u2014 "),z4e=a("a"),Hea=o("TFLongformerForQuestionAnswering"),Jea=o(" (Longformer model)"),Zea=l(),xS=a("li"),qJe=a("strong"),Yea=o("mobilebert"),Kea=o(" \u2014 "),W4e=a("a"),eoa=o("TFMobileBertForQuestionAnswering"),ooa=o(" (MobileBERT model)"),roa=l(),SS=a("li"),DJe=a("strong"),toa=o("mpnet"),aoa=o(" \u2014 "),Q4e=a("a"),noa=o("TFMPNetForQuestionAnswering"),soa=o(" (MPNet model)"),loa=l(),kS=a("li"),jJe=a("strong"),ioa=o("rembert"),doa=o(" \u2014 "),U4e=a("a"),coa=o("TFRemBertForQuestionAnswering"),foa=o(" (RemBERT model)"),moa=l(),$S=a("li"),OJe=a("strong"),goa=o("roberta"),hoa=o(" \u2014 "),H4e=a("a"),uoa=o("TFRobertaForQuestionAnswering"),poa=o(" (RoBERTa model)"),_oa=l(),PS=a("li"),VJe=a("strong"),voa=o("roberta-prelayernorm"),boa=o(" \u2014 "),J4e=a("a"),Toa=o("TFRobertaPreLayerNormForQuestionAnswering"),Foa=o(" (RoBERTa-PreLayerNorm model)"),Moa=l(),RS=a("li"),XJe=a("strong"),Eoa=o("roformer"),Coa=o(" \u2014 "),Z4e=a("a"),woa=o("TFRoFormerForQuestionAnswering"),Aoa=o(" (RoFormer model)"),Loa=l(),IS=a("li"),zJe=a("strong"),yoa=o("xlm"),xoa=o(" \u2014 "),Y4e=a("a"),Soa=o("TFXLMForQuestionAnsweringSimple"),koa=o(" (XLM model)"),$oa=l(),NS=a("li"),WJe=a("strong"),Poa=o("xlm-roberta"),Roa=o(" \u2014 "),K4e=a("a"),Ioa=o("TFXLMRobertaForQuestionAnswering"),Noa=o(" (XLM-RoBERTa model)"),Boa=l(),BS=a("li"),QJe=a("strong"),Goa=o("xlnet"),qoa=o(" \u2014 "),e0e=a("a"),Doa=o("TFXLNetForQuestionAnsweringSimple"),joa=o(" (XLNet model)"),Ooa=l(),b(GS.$$.fragment),yAo=l(),Tm=a("h3"),qS=a("a"),UJe=a("span"),b(Wj.$$.fragment),Voa=l(),HJe=a("span"),Xoa=o("FlaxAutoModelForQuestionAnswering"),xAo=l(),vr=a("div"),b(Qj.$$.fragment),zoa=l(),Fm=a("p"),Woa=o(`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.`),Zoa=l(),Uj=a("p"),Yoa=o("This class cannot be instantiated directly using "),JJe=a("code"),Koa=o("__init__()"),era=o(" (throws an error)."),ora=l(),ma=a("div"),b(Hj.$$.fragment),rra=l(),ZJe=a("p"),tra=o("Instantiates one of the model classes of the library (with a question answering head) from a configuration."),ara=l(),Mm=a("p"),nra=o(`Note:
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passed as an argument or loaded from `),oZe=a("code"),vra=o("pretrained_model_name_or_path"),bra=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),rZe=a("code"),Tra=o("pretrained_model_name_or_path"),Fra=o(":"),Mra=l(),$e=a("ul"),jS=a("li"),tZe=a("strong"),Era=o("albert"),Cra=o(" \u2014 "),a0e=a("a"),wra=o("FlaxAlbertForQuestionAnswering"),Ara=o(" (ALBERT model)"),Lra=l(),OS=a("li"),aZe=a("strong"),yra=o("bart"),xra=o(" \u2014 "),n0e=a("a"),Sra=o("FlaxBartForQuestionAnswering"),kra=o(" (BART model)"),$ra=l(),VS=a("li"),nZe=a("strong"),Pra=o("bert"),Rra=o(" \u2014 "),s0e=a("a"),Ira=o("FlaxBertForQuestionAnswering"),Nra=o(" (BERT model)"),Bra=l(),XS=a("li"),sZe=a("strong"),Gra=o("big_bird"),qra=o(" \u2014 "),l0e=a("a"),Dra=o("FlaxBigBirdForQuestionAnswering"),jra=o(" (BigBird model)"),Ora=l(),zS=a("li"),lZe=a("strong"),Vra=o("distilbert"),Xra=o(" \u2014 "),i0e=a("a"),zra=o("FlaxDistilBertForQuestionAnswering"),Wra=o(" (DistilBERT model)"),Qra=l(),WS=a("li"),iZe=a("strong"),Ura=o("electra"),Hra=o(" \u2014 "),d0e=a("a"),Jra=o("FlaxElectraForQuestionAnswering"),Zra=o(" (ELECTRA model)"),Yra=l(),QS=a("li"),dZe=a("strong"),Kra=o("mbart"),eta=o(" \u2014 "),c0e=a("a"),ota=o("FlaxMBartForQuestionAnswering"),rta=o(" (mBART model)"),tta=l(),US=a("li"),cZe=a("strong"),ata=o("roberta"),nta=o(" \u2014 "),f0e=a("a"),sta=o("FlaxRobertaForQuestionAnswering"),lta=o(" (RoBERTa model)"),ita=l(),HS=a("li"),fZe=a("strong"),dta=o("roberta-prelayernorm"),cta=o(" \u2014 "),m0e=a("a"),fta=o("FlaxRobertaPreLayerNormForQuestionAnswering"),mta=o(" (RoBERTa-PreLayerNorm model)"),gta=l(),JS=a("li"),mZe=a("strong"),hta=o("roformer"),uta=o(" \u2014 "),g0e=a("a"),pta=o("FlaxRoFormerForQuestionAnswering"),_ta=o(" (RoFormer model)"),vta=l(),ZS=a("li"),gZe=a("strong"),bta=o("xlm-roberta"),Tta=o(" \u2014 "),h0e=a("a"),Fta=o("FlaxXLMRobertaForQuestionAnswering"),Mta=o(" (XLM-RoBERTa model)"),Eta=l(),b(YS.$$.fragment),SAo=l(),Em=a("h2"),KS=a("a"),hZe=a("span"),b(Zj.$$.fragment),Cta=l(),uZe=a("span"),wta=o("Computer vision"),kAo=l(),u0e=a("p"),Ata=o("The following auto classes are available for the following computer vision tasks."),$Ao=l(),Cm=a("h3"),ek=a("a"),pZe=a("span"),b(Yj.$$.fragment),Lta=l(),_Ze=a("span"),yta=o("AutoModelForDepthEstimation"),PAo=l(),br=a("div"),b(Kj.$$.fragment),xta=l(),wm=a("p"),Sta=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a depth estimation head) when created
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passed as an argument or loaded from `),EZe=a("code"),raa=o("pretrained_model_name_or_path"),taa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),CZe=a("code"),aaa=o("pretrained_model_name_or_path"),naa=o(":"),saa=l(),tO=a("ul"),rk=a("li"),wZe=a("strong"),laa=o("dpt"),iaa=o(" \u2014 "),b0e=a("a"),daa=o("DPTForDepthEstimation"),caa=o(" (DPT model)"),faa=l(),tk=a("li"),AZe=a("strong"),maa=o("glpn"),gaa=o(" \u2014 "),T0e=a("a"),haa=o("GLPNForDepthEstimation"),uaa=o(" (GLPN model)"),paa=l(),ak=a("p"),_aa=o("The model is set in evaluation mode by default using "),LZe=a("code"),vaa=o("model.eval()"),baa=o(` (so for instance, dropout modules are
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method.`),Saa=l(),sO=a("p"),kaa=o("This class cannot be instantiated directly using "),kZe=a("code"),$aa=o("__init__()"),Paa=o(" (throws an error)."),Raa=l(),ha=a("div"),b(lO.$$.fragment),Iaa=l(),$Ze=a("p"),Naa=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),Baa=l(),xm=a("p"),Gaa=o(`Note:
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model\u2019s configuration. Use `),E0e=a("a"),jaa=o("from_pretrained()"),Oaa=o(" to load the model weights."),Vaa=l(),b(lk.$$.fragment),Xaa=l(),vo=a("div"),b(iO.$$.fragment),zaa=l(),RZe=a("p"),Waa=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),Qaa=l(),is=a("p"),Uaa=o("The model class to instantiate is selected based on the "),IZe=a("code"),Haa=o("model_type"),Jaa=o(` property of the config object (either
passed as an argument or loaded from `),NZe=a("code"),Zaa=o("pretrained_model_name_or_path"),Yaa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),BZe=a("code"),Kaa=o("pretrained_model_name_or_path"),ena=o(":"),ona=l(),ce=a("ul"),ik=a("li"),GZe=a("strong"),rna=o("beit"),tna=o(" \u2014 "),C0e=a("a"),ana=o("BeitForImageClassification"),nna=o(" (BEiT model)"),sna=l(),dk=a("li"),qZe=a("strong"),lna=o("bit"),ina=o(" \u2014 "),w0e=a("a"),dna=o("BitForImageClassification"),cna=o(" (BiT model)"),fna=l(),ck=a("li"),DZe=a("strong"),mna=o("convnext"),gna=o(" \u2014 "),A0e=a("a"),hna=o("ConvNextForImageClassification"),una=o(" (ConvNeXT model)"),pna=l(),fk=a("li"),jZe=a("strong"),_na=o("convnextv2"),vna=o(" \u2014 "),L0e=a("a"),bna=o("ConvNextV2ForImageClassification"),Tna=o(" (ConvNeXTV2 model)"),Fna=l(),mk=a("li"),OZe=a("strong"),Mna=o("cvt"),Ena=o(" \u2014 "),y0e=a("a"),Cna=o("CvtForImageClassification"),wna=o(" (CvT model)"),Ana=l(),gk=a("li"),VZe=a("strong"),Lna=o("data2vec-vision"),yna=o(" \u2014 "),x0e=a("a"),xna=o("Data2VecVisionForImageClassification"),Sna=o(" (Data2VecVision model)"),kna=l(),Li=a("li"),XZe=a("strong"),$na=o("deit"),Pna=o(" \u2014 "),S0e=a("a"),Rna=o("DeiTForImageClassification"),Ina=o(" or "),k0e=a("a"),Nna=o("DeiTForImageClassificationWithTeacher"),Bna=o(" (DeiT model)"),Gna=l(),hk=a("li"),zZe=a("strong"),qna=o("dinat"),Dna=o(" \u2014 "),$0e=a("a"),jna=o("DinatForImageClassification"),Ona=o(" (DiNAT model)"),Vna=l(),yi=a("li"),WZe=a("strong"),Xna=o("efficientformer"),zna=o(" \u2014 "),P0e=a("a"),Wna=o("EfficientFormerForImageClassification"),Qna=o(" or "),R0e=a("a"),Una=o("EfficientFormerForImageClassificationWithTeacher"),Hna=o(" (EfficientFormer model)"),Jna=l(),uk=a("li"),QZe=a("strong"),Zna=o("efficientnet"),Yna=o(" \u2014 "),I0e=a("a"),Kna=o("EfficientNetForImageClassification"),esa=o(" (EfficientNet model)"),osa=l(),pk=a("li"),UZe=a("strong"),rsa=o("focalnet"),tsa=o(" \u2014 "),N0e=a("a"),asa=o("FocalNetForImageClassification"),nsa=o(" (FocalNet model)"),ssa=l(),_k=a("li"),HZe=a("strong"),lsa=o("imagegpt"),isa=o(" \u2014 "),B0e=a("a"),dsa=o("ImageGPTForImageClassification"),csa=o(" (ImageGPT model)"),fsa=l(),xi=a("li"),JZe=a("strong"),msa=o("levit"),gsa=o(" \u2014 "),G0e=a("a"),hsa=o("LevitForImageClassification"),usa=o(" or "),q0e=a("a"),psa=o("LevitForImageClassificationWithTeacher"),_sa=o(" (LeViT model)"),vsa=l(),vk=a("li"),ZZe=a("strong"),bsa=o("mobilenet_v1"),Tsa=o(" \u2014 "),D0e=a("a"),Fsa=o("MobileNetV1ForImageClassification"),Msa=o(" (MobileNetV1 model)"),Esa=l(),bk=a("li"),YZe=a("strong"),Csa=o("mobilenet_v2"),wsa=o(" \u2014 "),j0e=a("a"),Asa=o("MobileNetV2ForImageClassification"),Lsa=o(" (MobileNetV2 model)"),ysa=l(),Tk=a("li"),KZe=a("strong"),xsa=o("mobilevit"),Ssa=o(" \u2014 "),O0e=a("a"),ksa=o("MobileViTForImageClassification"),$sa=o(" (MobileViT model)"),Psa=l(),Fk=a("li"),eYe=a("strong"),Rsa=o("nat"),Isa=o(" \u2014 "),V0e=a("a"),Nsa=o("NatForImageClassification"),Bsa=o(" (NAT model)"),Gsa=l(),ua=a("li"),oYe=a("strong"),qsa=o("perceiver"),Dsa=o(" \u2014 "),X0e=a("a"),jsa=o("PerceiverForImageClassificationLearned"),Osa=o(" or "),z0e=a("a"),Vsa=o("PerceiverForImageClassificationFourier"),Xsa=o(" or "),W0e=a("a"),zsa=o("PerceiverForImageClassificationConvProcessing"),Wsa=o(" (Perceiver model)"),Qsa=l(),Mk=a("li"),rYe=a("strong"),Usa=o("poolformer"),Hsa=o(" \u2014 "),Q0e=a("a"),Jsa=o("PoolFormerForImageClassification"),Zsa=o(" (PoolFormer model)"),Ysa=l(),Ek=a("li"),tYe=a("strong"),Ksa=o("regnet"),ela=o(" \u2014 "),U0e=a("a"),ola=o("RegNetForImageClassification"),rla=o(" (RegNet model)"),tla=l(),Ck=a("li"),aYe=a("strong"),ala=o("resnet"),nla=o(" \u2014 "),H0e=a("a"),sla=o("ResNetForImageClassification"),lla=o(" (ResNet model)"),ila=l(),wk=a("li"),nYe=a("strong"),dla=o("segformer"),cla=o(" \u2014 "),J0e=a("a"),fla=o("SegformerForImageClassification"),mla=o(" (SegFormer model)"),gla=l(),Ak=a("li"),sYe=a("strong"),hla=o("swin"),ula=o(" \u2014 "),Z0e=a("a"),pla=o("SwinForImageClassification"),_la=o(" (Swin Transformer model)"),vla=l(),Lk=a("li"),lYe=a("strong"),bla=o("swinv2"),Tla=o(" \u2014 "),Y0e=a("a"),Fla=o("Swinv2ForImageClassification"),Mla=o(" (Swin Transformer V2 model)"),Ela=l(),yk=a("li"),iYe=a("strong"),Cla=o("van"),wla=o(" \u2014 "),K0e=a("a"),Ala=o("VanForImageClassification"),Lla=o(" (VAN model)"),yla=l(),xk=a("li"),dYe=a("strong"),xla=o("vit"),Sla=o(" \u2014 "),ebe=a("a"),kla=o("ViTForImageClassification"),$la=o(" (ViT model)"),Pla=l(),Sk=a("li"),cYe=a("strong"),Rla=o("vit_hybrid"),Ila=o(" \u2014 "),obe=a("a"),Nla=o("ViTHybridForImageClassification"),Bla=o(" (ViT Hybrid model)"),Gla=l(),kk=a("li"),fYe=a("strong"),qla=o("vit_msn"),Dla=o(" \u2014 "),rbe=a("a"),jla=o("ViTMSNForImageClassification"),Ola=o(" (ViTMSN model)"),Vla=l(),$k=a("p"),Xla=o("The model is set in evaluation mode by default using "),mYe=a("code"),zla=o("model.eval()"),Wla=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),gYe=a("code"),Qla=o("model.train()"),Ula=l(),b(Pk.$$.fragment),NAo=l(),Sm=a("h3"),Rk=a("a"),hYe=a("span"),b(dO.$$.fragment),Hla=l(),uYe=a("span"),Jla=o("TFAutoModelForImageClassification"),BAo=l(),Fr=a("div"),b(cO.$$.fragment),Zla=l(),km=a("p"),Yla=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),tbe=a("a"),Kla=o("from_pretrained()"),eia=o(" class method or the "),abe=a("a"),oia=o("from_config()"),ria=o(` class
method.`),tia=l(),fO=a("p"),aia=o("This class cannot be instantiated directly using "),pYe=a("code"),nia=o("__init__()"),sia=o(" (throws an error)."),lia=l(),pa=a("div"),b(mO.$$.fragment),iia=l(),_Ye=a("p"),dia=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),cia=l(),$m=a("p"),fia=o(`Note:
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model\u2019s configuration. Use `),nbe=a("a"),hia=o("from_pretrained()"),uia=o(" to load the model weights."),pia=l(),b(Ik.$$.fragment),_ia=l(),bt=a("div"),b(gO.$$.fragment),via=l(),bYe=a("p"),bia=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),Tia=l(),ds=a("p"),Fia=o("The model class to instantiate is selected based on the "),TYe=a("code"),Mia=o("model_type"),Eia=o(` property of the config object (either
passed as an argument or loaded from `),FYe=a("code"),Cia=o("pretrained_model_name_or_path"),wia=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),MYe=a("code"),Aia=o("pretrained_model_name_or_path"),Lia=o(":"),yia=l(),Be=a("ul"),Nk=a("li"),EYe=a("strong"),xia=o("convnext"),Sia=o(" \u2014 "),sbe=a("a"),kia=o("TFConvNextForImageClassification"),$ia=o(" (ConvNeXT model)"),Pia=l(),Bk=a("li"),CYe=a("strong"),Ria=o("cvt"),Iia=o(" \u2014 "),lbe=a("a"),Nia=o("TFCvtForImageClassification"),Bia=o(" (CvT model)"),Gia=l(),Gk=a("li"),wYe=a("strong"),qia=o("data2vec-vision"),Dia=o(" \u2014 "),ibe=a("a"),jia=o("TFData2VecVisionForImageClassification"),Oia=o(" (Data2VecVision model)"),Via=l(),Si=a("li"),AYe=a("strong"),Xia=o("deit"),zia=o(" \u2014 "),dbe=a("a"),Wia=o("TFDeiTForImageClassification"),Qia=o(" or "),cbe=a("a"),Uia=o("TFDeiTForImageClassificationWithTeacher"),Hia=o(" (DeiT model)"),Jia=l(),qk=a("li"),LYe=a("strong"),Zia=o("mobilevit"),Yia=o(" \u2014 "),fbe=a("a"),Kia=o("TFMobileViTForImageClassification"),eda=o(" (MobileViT model)"),oda=l(),Dk=a("li"),yYe=a("strong"),rda=o("regnet"),tda=o(" \u2014 "),mbe=a("a"),ada=o("TFRegNetForImageClassification"),nda=o(" (RegNet model)"),sda=l(),jk=a("li"),xYe=a("strong"),lda=o("resnet"),ida=o(" \u2014 "),gbe=a("a"),dda=o("TFResNetForImageClassification"),cda=o(" (ResNet model)"),fda=l(),Ok=a("li"),SYe=a("strong"),mda=o("segformer"),gda=o(" \u2014 "),hbe=a("a"),hda=o("TFSegformerForImageClassification"),uda=o(" (SegFormer model)"),pda=l(),Vk=a("li"),kYe=a("strong"),_da=o("swin"),vda=o(" \u2014 "),ube=a("a"),bda=o("TFSwinForImageClassification"),Tda=o(" (Swin Transformer model)"),Fda=l(),Xk=a("li"),$Ye=a("strong"),Mda=o("vit"),Eda=o(" \u2014 "),pbe=a("a"),Cda=o("TFViTForImageClassification"),wda=o(" (ViT model)"),Ada=l(),b(zk.$$.fragment),GAo=l(),Pm=a("h3"),Wk=a("a"),PYe=a("span"),b(hO.$$.fragment),Lda=l(),RYe=a("span"),yda=o("FlaxAutoModelForImageClassification"),qAo=l(),Mr=a("div"),b(uO.$$.fragment),xda=l(),Rm=a("p"),Sda=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),_be=a("a"),kda=o("from_pretrained()"),$da=o(" class method or the "),vbe=a("a"),Pda=o("from_config()"),Rda=o(` class
method.`),Ida=l(),pO=a("p"),Nda=o("This class cannot be instantiated directly using "),IYe=a("code"),Bda=o("__init__()"),Gda=o(" (throws an error)."),qda=l(),_a=a("div"),b(_O.$$.fragment),Dda=l(),NYe=a("p"),jda=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),Oda=l(),Im=a("p"),Vda=o(`Note:
Loading a model from its configuration file does `),BYe=a("strong"),Xda=o("not"),zda=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),bbe=a("a"),Wda=o("from_pretrained()"),Qda=o(" to load the model weights."),Uda=l(),b(Qk.$$.fragment),Hda=l(),Tt=a("div"),b(vO.$$.fragment),Jda=l(),GYe=a("p"),Zda=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),Yda=l(),cs=a("p"),Kda=o("The model class to instantiate is selected based on the "),qYe=a("code"),eca=o("model_type"),oca=o(` property of the config object (either
passed as an argument or loaded from `),DYe=a("code"),rca=o("pretrained_model_name_or_path"),tca=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),jYe=a("code"),aca=o("pretrained_model_name_or_path"),nca=o(":"),sca=l(),fs=a("ul"),Uk=a("li"),OYe=a("strong"),lca=o("beit"),ica=o(" \u2014 "),Tbe=a("a"),dca=o("FlaxBeitForImageClassification"),cca=o(" (BEiT model)"),fca=l(),Hk=a("li"),VYe=a("strong"),mca=o("regnet"),gca=o(" \u2014 "),Fbe=a("a"),hca=o("FlaxRegNetForImageClassification"),uca=o(" (RegNet model)"),pca=l(),Jk=a("li"),XYe=a("strong"),_ca=o("resnet"),vca=o(" \u2014 "),Mbe=a("a"),bca=o("FlaxResNetForImageClassification"),Tca=o(" (ResNet model)"),Fca=l(),Zk=a("li"),zYe=a("strong"),Mca=o("vit"),Eca=o(" \u2014 "),Ebe=a("a"),Cca=o("FlaxViTForImageClassification"),wca=o(" (ViT model)"),Aca=l(),b(Yk.$$.fragment),DAo=l(),Nm=a("h3"),Kk=a("a"),WYe=a("span"),b(bO.$$.fragment),Lca=l(),QYe=a("span"),yca=o("AutoModelForVideoClassification"),jAo=l(),Er=a("div"),b(TO.$$.fragment),xca=l(),Bm=a("p"),Sca=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a video classification head) when created
with the `),Cbe=a("a"),kca=o("from_pretrained()"),$ca=o(" class method or the "),wbe=a("a"),Pca=o("from_config()"),Rca=o(` class
method.`),Ica=l(),FO=a("p"),Nca=o("This class cannot be instantiated directly using "),UYe=a("code"),Bca=o("__init__()"),Gca=o(" (throws an error)."),qca=l(),va=a("div"),b(MO.$$.fragment),Dca=l(),HYe=a("p"),jca=o("Instantiates one of the model classes of the library (with a video classification head) from a configuration."),Oca=l(),Gm=a("p"),Vca=o(`Note:
Loading a model from its configuration file does `),JYe=a("strong"),Xca=o("not"),zca=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Abe=a("a"),Wca=o("from_pretrained()"),Qca=o(" to load the model weights."),Uca=l(),b(e$.$$.fragment),Hca=l(),bo=a("div"),b(EO.$$.fragment),Jca=l(),ZYe=a("p"),Zca=o("Instantiate one of the model classes of the library (with a video classification head) from a pretrained model."),Yca=l(),ms=a("p"),Kca=o("The model class to instantiate is selected based on the "),YYe=a("code"),efa=o("model_type"),ofa=o(` property of the config object (either
passed as an argument or loaded from `),KYe=a("code"),rfa=o("pretrained_model_name_or_path"),tfa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),eKe=a("code"),afa=o("pretrained_model_name_or_path"),nfa=o(":"),sfa=l(),CO=a("ul"),o$=a("li"),oKe=a("strong"),lfa=o("timesformer"),ifa=o(" \u2014 "),Lbe=a("a"),dfa=o("TimesformerForVideoClassification"),cfa=o(" (TimeSformer model)"),ffa=l(),r$=a("li"),rKe=a("strong"),mfa=o("videomae"),gfa=o(" \u2014 "),ybe=a("a"),hfa=o("VideoMAEForVideoClassification"),ufa=o(" (VideoMAE model)"),pfa=l(),t$=a("p"),_fa=o("The model is set in evaluation mode by default using "),tKe=a("code"),vfa=o("model.eval()"),bfa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),aKe=a("code"),Tfa=o("model.train()"),Ffa=l(),b(a$.$$.fragment),OAo=l(),qm=a("h3"),n$=a("a"),nKe=a("span"),b(wO.$$.fragment),Mfa=l(),sKe=a("span"),Efa=o("AutoModelForMaskedImageModeling"),VAo=l(),Cr=a("div"),b(AO.$$.fragment),Cfa=l(),Dm=a("p"),wfa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),xbe=a("a"),Afa=o("from_pretrained()"),Lfa=o(" class method or the "),Sbe=a("a"),yfa=o("from_config()"),xfa=o(` class
method.`),Sfa=l(),LO=a("p"),kfa=o("This class cannot be instantiated directly using "),lKe=a("code"),$fa=o("__init__()"),Pfa=o(" (throws an error)."),Rfa=l(),ba=a("div"),b(yO.$$.fragment),Ifa=l(),iKe=a("p"),Nfa=o("Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),Bfa=l(),jm=a("p"),Gfa=o(`Note:
Loading a model from its configuration file does `),dKe=a("strong"),qfa=o("not"),Dfa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),kbe=a("a"),jfa=o("from_pretrained()"),Ofa=o(" to load the model weights."),Vfa=l(),b(s$.$$.fragment),Xfa=l(),To=a("div"),b(xO.$$.fragment),zfa=l(),cKe=a("p"),Wfa=o("Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),Qfa=l(),gs=a("p"),Ufa=o("The model class to instantiate is selected based on the "),fKe=a("code"),Hfa=o("model_type"),Jfa=o(` property of the config object (either
passed as an argument or loaded from `),mKe=a("code"),Zfa=o("pretrained_model_name_or_path"),Yfa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),gKe=a("code"),Kfa=o("pretrained_model_name_or_path"),ema=o(":"),oma=l(),kt=a("ul"),l$=a("li"),hKe=a("strong"),rma=o("deit"),tma=o(" \u2014 "),$be=a("a"),ama=o("DeiTForMaskedImageModeling"),nma=o(" (DeiT model)"),sma=l(),i$=a("li"),uKe=a("strong"),lma=o("focalnet"),ima=o(" \u2014 "),Pbe=a("a"),dma=o("FocalNetForMaskedImageModeling"),cma=o(" (FocalNet model)"),fma=l(),d$=a("li"),pKe=a("strong"),mma=o("swin"),gma=o(" \u2014 "),Rbe=a("a"),hma=o("SwinForMaskedImageModeling"),uma=o(" (Swin Transformer model)"),pma=l(),c$=a("li"),_Ke=a("strong"),_ma=o("swinv2"),vma=o(" \u2014 "),Ibe=a("a"),bma=o("Swinv2ForMaskedImageModeling"),Tma=o(" (Swin Transformer V2 model)"),Fma=l(),f$=a("li"),vKe=a("strong"),Mma=o("vit"),Ema=o(" \u2014 "),Nbe=a("a"),Cma=o("ViTForMaskedImageModeling"),wma=o(" (ViT model)"),Ama=l(),m$=a("p"),Lma=o("The model is set in evaluation mode by default using "),bKe=a("code"),yma=o("model.eval()"),xma=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),TKe=a("code"),Sma=o("model.train()"),kma=l(),b(g$.$$.fragment),XAo=l(),Om=a("h3"),h$=a("a"),FKe=a("span"),b(SO.$$.fragment),$ma=l(),MKe=a("span"),Pma=o("AutoModelForObjectDetection"),zAo=l(),wr=a("div"),b(kO.$$.fragment),Rma=l(),Vm=a("p"),Ima=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created
with the `),Bbe=a("a"),Nma=o("from_pretrained()"),Bma=o(" class method or the "),Gbe=a("a"),Gma=o("from_config()"),qma=o(` class
method.`),Dma=l(),$O=a("p"),jma=o("This class cannot be instantiated directly using "),EKe=a("code"),Oma=o("__init__()"),Vma=o(" (throws an error)."),Xma=l(),Ta=a("div"),b(PO.$$.fragment),zma=l(),CKe=a("p"),Wma=o("Instantiates one of the model classes of the library (with a object detection head) from a configuration."),Qma=l(),Xm=a("p"),Uma=o(`Note:
Loading a model from its configuration file does `),wKe=a("strong"),Hma=o("not"),Jma=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),qbe=a("a"),Zma=o("from_pretrained()"),Yma=o(" to load the model weights."),Kma=l(),b(u$.$$.fragment),ega=l(),Fo=a("div"),b(RO.$$.fragment),oga=l(),AKe=a("p"),rga=o("Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),tga=l(),hs=a("p"),aga=o("The model class to instantiate is selected based on the "),LKe=a("code"),nga=o("model_type"),sga=o(` property of the config object (either
passed as an argument or loaded from `),yKe=a("code"),lga=o("pretrained_model_name_or_path"),iga=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),xKe=a("code"),dga=o("pretrained_model_name_or_path"),cga=o(":"),fga=l(),Ar=a("ul"),p$=a("li"),SKe=a("strong"),mga=o("conditional_detr"),gga=o(" \u2014 "),Dbe=a("a"),hga=o("ConditionalDetrForObjectDetection"),uga=o(" (Conditional DETR model)"),pga=l(),_$=a("li"),kKe=a("strong"),_ga=o("deformable_detr"),vga=o(" \u2014 "),jbe=a("a"),bga=o("DeformableDetrForObjectDetection"),Tga=o(" (Deformable DETR model)"),Fga=l(),v$=a("li"),$Ke=a("strong"),Mga=o("deta"),Ega=o(" \u2014 "),Obe=a("a"),Cga=o("DetaForObjectDetection"),wga=o(" (DETA model)"),Aga=l(),b$=a("li"),PKe=a("strong"),Lga=o("detr"),yga=o(" \u2014 "),Vbe=a("a"),xga=o("DetrForObjectDetection"),Sga=o(" (DETR model)"),kga=l(),T$=a("li"),RKe=a("strong"),$ga=o("table-transformer"),Pga=o(" \u2014 "),Xbe=a("a"),Rga=o("TableTransformerForObjectDetection"),Iga=o(" (Table Transformer model)"),Nga=l(),F$=a("li"),IKe=a("strong"),Bga=o("yolos"),Gga=o(" \u2014 "),zbe=a("a"),qga=o("YolosForObjectDetection"),Dga=o(" (YOLOS model)"),jga=l(),M$=a("p"),Oga=o("The model is set in evaluation mode by default using "),NKe=a("code"),Vga=o("model.eval()"),Xga=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),BKe=a("code"),zga=o("model.train()"),Wga=l(),b(E$.$$.fragment),WAo=l(),zm=a("h3"),C$=a("a"),GKe=a("span"),b(IO.$$.fragment),Qga=l(),qKe=a("span"),Uga=o("AutoModelForImageSegmentation"),QAo=l(),Lr=a("div"),b(NO.$$.fragment),Hga=l(),Wm=a("p"),Jga=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
with the `),Wbe=a("a"),Zga=o("from_pretrained()"),Yga=o(" class method or the "),Qbe=a("a"),Kga=o("from_config()"),eha=o(` class
method.`),oha=l(),BO=a("p"),rha=o("This class cannot be instantiated directly using "),DKe=a("code"),tha=o("__init__()"),aha=o(" (throws an error)."),nha=l(),Fa=a("div"),b(GO.$$.fragment),sha=l(),jKe=a("p"),lha=o("Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),iha=l(),Qm=a("p"),dha=o(`Note:
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model\u2019s configuration. Use `),Ube=a("a"),mha=o("from_pretrained()"),gha=o(" to load the model weights."),hha=l(),b(w$.$$.fragment),uha=l(),Mo=a("div"),b(qO.$$.fragment),pha=l(),VKe=a("p"),_ha=o("Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),vha=l(),us=a("p"),bha=o("The model class to instantiate is selected based on the "),XKe=a("code"),Tha=o("model_type"),Fha=o(` property of the config object (either
passed as an argument or loaded from `),zKe=a("code"),Mha=o("pretrained_model_name_or_path"),Eha=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),WKe=a("code"),Cha=o("pretrained_model_name_or_path"),wha=o(":"),Aha=l(),QKe=a("ul"),A$=a("li"),UKe=a("strong"),Lha=o("detr"),yha=o(" \u2014 "),Hbe=a("a"),xha=o("DetrForSegmentation"),Sha=o(" (DETR model)"),kha=l(),L$=a("p"),$ha=o("The model is set in evaluation mode by default using "),HKe=a("code"),Pha=o("model.eval()"),Rha=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),JKe=a("code"),Iha=o("model.train()"),Nha=l(),b(y$.$$.fragment),UAo=l(),Um=a("h3"),x$=a("a"),ZKe=a("span"),b(DO.$$.fragment),Bha=l(),YKe=a("span"),Gha=o("AutoModelForSemanticSegmentation"),HAo=l(),yr=a("div"),b(jO.$$.fragment),qha=l(),Hm=a("p"),Dha=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
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method.`),zha=l(),OO=a("p"),Wha=o("This class cannot be instantiated directly using "),KKe=a("code"),Qha=o("__init__()"),Uha=o(" (throws an error)."),Hha=l(),Ma=a("div"),b(VO.$$.fragment),Jha=l(),eeo=a("p"),Zha=o("Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),Yha=l(),Jm=a("p"),Kha=o(`Note:
Loading a model from its configuration file does `),oeo=a("strong"),eua=o("not"),oua=o(` load the model weights. It only affects the
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passed as an argument or loaded from `),aeo=a("code"),mua=o("pretrained_model_name_or_path"),gua=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),neo=a("code"),hua=o("pretrained_model_name_or_path"),uua=o(":"),pua=l(),eo=a("ul"),k$=a("li"),seo=a("strong"),_ua=o("beit"),vua=o(" \u2014 "),Kbe=a("a"),bua=o("BeitForSemanticSegmentation"),Tua=o(" (BEiT model)"),Fua=l(),$$=a("li"),leo=a("strong"),Mua=o("data2vec-vision"),Eua=o(" \u2014 "),e9e=a("a"),Cua=o("Data2VecVisionForSemanticSegmentation"),wua=o(" (Data2VecVision model)"),Aua=l(),P$=a("li"),ieo=a("strong"),Lua=o("dpt"),yua=o(" \u2014 "),o9e=a("a"),xua=o("DPTForSemanticSegmentation"),Sua=o(" (DPT model)"),kua=l(),R$=a("li"),deo=a("strong"),$ua=o("mobilenet_v2"),Pua=o(" \u2014 "),r9e=a("a"),Rua=o("MobileNetV2ForSemanticSegmentation"),Iua=o(" (MobileNetV2 model)"),Nua=l(),I$=a("li"),ceo=a("strong"),Bua=o("mobilevit"),Gua=o(" \u2014 "),t9e=a("a"),qua=o("MobileViTForSemanticSegmentation"),Dua=o(" (MobileViT model)"),jua=l(),N$=a("li"),feo=a("strong"),Oua=o("segformer"),Vua=o(" \u2014 "),a9e=a("a"),Xua=o("SegformerForSemanticSegmentation"),zua=o(" (SegFormer model)"),Wua=l(),B$=a("li"),meo=a("strong"),Qua=o("upernet"),Uua=o(" \u2014 "),n9e=a("a"),Hua=o("UperNetForSemanticSegmentation"),Jua=o(" (UPerNet model)"),Zua=l(),G$=a("p"),Yua=o("The model is set in evaluation mode by default using "),geo=a("code"),Kua=o("model.eval()"),epa=o(` (so for instance, dropout modules are
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passed as an argument or loaded from `),Reo=a("code"),L_a=o("pretrained_model_name_or_path"),y_a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ieo=a("code"),x_a=o("pretrained_model_name_or_path"),S_a=o(":"),k_a=l(),Neo=a("ul"),U$=a("li"),Beo=a("strong"),$_a=o("maskformer"),P_a=o(" \u2014 "),u9e=a("a"),R_a=o("MaskFormerForInstanceSegmentation"),I_a=o(" (MaskFormer model)"),N_a=l(),H$=a("p"),B_a=o("The model is set in evaluation mode by default using "),Geo=a("code"),G_a=o("model.eval()"),q_a=o(` (so for instance, dropout modules are
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passed as an argument or loaded from `),Qeo=a("code"),_2a=o("pretrained_model_name_or_path"),v2a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ueo=a("code"),b2a=o("pretrained_model_name_or_path"),T2a=o(":"),F2a=l(),Ts=a("ul"),K$=a("li"),Heo=a("strong"),M2a=o("detr"),E2a=o(" \u2014 "),b9e=a("a"),C2a=o("DetrForSegmentation"),w2a=o(" (DETR model)"),A2a=l(),eP=a("li"),Jeo=a("strong"),L2a=o("mask2former"),y2a=o(" \u2014 "),T9e=a("a"),x2a=o("Mask2FormerForUniversalSegmentation"),S2a=o(" (Mask2Former model)"),k2a=l(),oP=a("li"),Zeo=a("strong"),$2a=o("maskformer"),P2a=o(" \u2014 "),F9e=a("a"),R2a=o("MaskFormerForInstanceSegmentation"),I2a=o(" (MaskFormer model)"),N2a=l(),rP=a("li"),Yeo=a("strong"),B2a=o("oneformer"),G2a=o(" \u2014 "),M9e=a("a"),q2a=o("OneFormerForUniversalSegmentation"),D2a=o(" (OneFormer model)"),j2a=l(),tP=a("p"),O2a=o("The model is set in evaluation mode by default using "),Keo=a("code"),V2a=o("model.eval()"),X2a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),eoo=a("code"),z2a=o("model.train()"),W2a=l(),b(aP.$$.fragment),r7o=l(),lg=a("h3"),nP=a("a"),ooo=a("span"),b(sV.$$.fragment),Q2a=l(),roo=a("span"),U2a=o("AutoModelForZeroShotImageClassification"),t7o=l(),$r=a("div"),b(lV.$$.fragment),H2a=l(),ig=a("p"),J2a=o(`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
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passed as an argument or loaded from `),ioo=a("code"),Mva=o("pretrained_model_name_or_path"),Eva=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),doo=a("code"),Cva=o("pretrained_model_name_or_path"),wva=o(":"),Ava=l(),Pr=a("ul"),lP=a("li"),coo=a("strong"),Lva=o("align"),yva=o(" \u2014 "),A9e=a("a"),xva=o("AlignModel"),Sva=o(" (ALIGN model)"),kva=l(),iP=a("li"),foo=a("strong"),$va=o("altclip"),Pva=o(" \u2014 "),L9e=a("a"),Rva=o("AltCLIPModel"),Iva=o(" (AltCLIP model)"),Nva=l(),dP=a("li"),moo=a("strong"),Bva=o("blip"),Gva=o(" \u2014 "),y9e=a("a"),qva=o("BlipModel"),Dva=o(" (BLIP model)"),jva=l(),cP=a("li"),goo=a("strong"),Ova=o("chinese_clip"),Vva=o(" \u2014 "),x9e=a("a"),Xva=o("ChineseCLIPModel"),zva=o(" (Chinese-CLIP model)"),Wva=l(),fP=a("li"),hoo=a("strong"),Qva=o("clip"),Uva=o(" \u2014 "),S9e=a("a"),Hva=o("CLIPModel"),Jva=o(" (CLIP model)"),Zva=l(),mP=a("li"),uoo=a("strong"),Yva=o("clipseg"),Kva=o(" \u2014 "),k9e=a("a"),e1a=o("CLIPSegModel"),o1a=o(" (CLIPSeg model)"),r1a=l(),gP=a("p"),t1a=o("The model is set in evaluation mode by default using "),poo=a("code"),a1a=o("model.eval()"),n1a=o(` (so for instance, dropout modules are
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passed as an argument or loaded from `),woo=a("code"),B1a=o("pretrained_model_name_or_path"),G1a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Aoo=a("code"),q1a=o("pretrained_model_name_or_path"),D1a=o(":"),j1a=l(),pV=a("ul"),_P=a("li"),Loo=a("strong"),O1a=o("blip"),V1a=o(" \u2014 "),I9e=a("a"),X1a=o("TFBlipModel"),z1a=o(" (BLIP model)"),W1a=l(),vP=a("li"),yoo=a("strong"),Q1a=o("clip"),U1a=o(" \u2014 "),N9e=a("a"),H1a=o("TFCLIPModel"),J1a=o(" (CLIP model)"),Z1a=l(),b(bP.$$.fragment),s7o=l(),gg=a("h3"),TP=a("a"),xoo=a("span"),b(_V.$$.fragment),Y1a=l(),Soo=a("span"),K1a=o("AutoModelForZeroShotObjectDetection"),l7o=l(),Ir=a("div"),b(vV.$$.fragment),e4a=l(),hg=a("p"),o4a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot object detection head) when created
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method.`),K4a=l(),wV=a("p"),e0a=o("This class cannot be instantiated directly using "),Woo=a("code"),o0a=o("__init__()"),r0a=o(" (throws an error)."),t0a=l(),xa=a("div"),b(AV.$$.fragment),a0a=l(),Qoo=a("p"),n0a=o("Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),s0a=l(),bg=a("p"),l0a=o(`Note:
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passed as an argument or loaded from `),Zoo=a("code"),T0a=o("pretrained_model_name_or_path"),F0a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Yoo=a("code"),M0a=o("pretrained_model_name_or_path"),E0a=o(":"),C0a=l(),Pe=a("ul"),yP=a("li"),Koo=a("strong"),w0a=o("audio-spectrogram-transformer"),A0a=o(" \u2014 "),z9e=a("a"),L0a=o("ASTForAudioClassification"),y0a=o(" (Audio Spectrogram Transformer model)"),x0a=l(),xP=a("li"),ero=a("strong"),S0a=o("data2vec-audio"),k0a=o(" \u2014 "),W9e=a("a"),$0a=o("Data2VecAudioForSequenceClassification"),P0a=o(" (Data2VecAudio model)"),R0a=l(),SP=a("li"),oro=a("strong"),I0a=o("hubert"),N0a=o(" \u2014 "),Q9e=a("a"),B0a=o("HubertForSequenceClassification"),G0a=o(" (Hubert model)"),q0a=l(),kP=a("li"),rro=a("strong"),D0a=o("sew"),j0a=o(" \u2014 "),U9e=a("a"),O0a=o("SEWForSequenceClassification"),V0a=o(" (SEW model)"),X0a=l(),$P=a("li"),tro=a("strong"),z0a=o("sew-d"),W0a=o(" \u2014 "),H9e=a("a"),Q0a=o("SEWDForSequenceClassification"),U0a=o(" (SEW-D model)"),H0a=l(),PP=a("li"),aro=a("strong"),J0a=o("unispeech"),Z0a=o(" \u2014 "),J9e=a("a"),Y0a=o("UniSpeechForSequenceClassification"),K0a=o(" (UniSpeech model)"),eba=l(),RP=a("li"),nro=a("strong"),oba=o("unispeech-sat"),rba=o(" \u2014 "),Z9e=a("a"),tba=o("UniSpeechSatForSequenceClassification"),aba=o(" (UniSpeechSat model)"),nba=l(),IP=a("li"),sro=a("strong"),sba=o("wav2vec2"),lba=o(" \u2014 "),Y9e=a("a"),iba=o("Wav2Vec2ForSequenceClassification"),dba=o(" (Wav2Vec2 model)"),cba=l(),NP=a("li"),lro=a("strong"),fba=o("wav2vec2-conformer"),mba=o(" \u2014 "),K9e=a("a"),gba=o("Wav2Vec2ConformerForSequenceClassification"),hba=o(" (Wav2Vec2-Conformer model)"),uba=l(),BP=a("li"),iro=a("strong"),pba=o("wavlm"),_ba=o(" \u2014 "),eTe=a("a"),vba=o("WavLMForSequenceClassification"),bba=o(" (WavLM model)"),Tba=l(),GP=a("li"),dro=a("strong"),Fba=o("whisper"),Mba=o(" \u2014 "),oTe=a("a"),Eba=o("WhisperForAudioClassification"),Cba=o(" (Whisper model)"),wba=l(),qP=a("p"),Aba=o("The model is set in evaluation mode by default using "),cro=a("code"),Lba=o("model.eval()"),yba=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),fro=a("code"),xba=o("model.train()"),Sba=l(),b(DP.$$.fragment),m7o=l(),Tg=a("h3"),jP=a("a"),mro=a("span"),b(yV.$$.fragment),kba=l(),gro=a("span"),$ba=o("AutoModelForAudioFrameClassification"),g7o=l(),Br=a("div"),b(xV.$$.fragment),Pba=l(),Fg=a("p"),Rba=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio frame (token) classification head) when created
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model\u2019s configuration. Use `),aTe=a("a"),Jba=o("from_pretrained()"),Zba=o(" to load the model weights."),Yba=l(),b(OP.$$.fragment),Kba=l(),xo=a("div"),b($V.$$.fragment),e9a=l(),_ro=a("p"),o9a=o("Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),r9a=l(),ws=a("p"),t9a=o("The model class to instantiate is selected based on the "),vro=a("code"),a9a=o("model_type"),n9a=o(` property of the config object (either
passed as an argument or loaded from `),bro=a("code"),s9a=o("pretrained_model_name_or_path"),l9a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Tro=a("code"),i9a=o("pretrained_model_name_or_path"),d9a=o(":"),c9a=l(),$t=a("ul"),VP=a("li"),Fro=a("strong"),f9a=o("data2vec-audio"),m9a=o(" \u2014 "),nTe=a("a"),g9a=o("Data2VecAudioForAudioFrameClassification"),h9a=o(" (Data2VecAudio model)"),u9a=l(),XP=a("li"),Mro=a("strong"),p9a=o("unispeech-sat"),_9a=o(" \u2014 "),sTe=a("a"),v9a=o("UniSpeechSatForAudioFrameClassification"),b9a=o(" (UniSpeechSat model)"),T9a=l(),zP=a("li"),Ero=a("strong"),F9a=o("wav2vec2"),M9a=o(" \u2014 "),lTe=a("a"),E9a=o("Wav2Vec2ForAudioFrameClassification"),C9a=o(" (Wav2Vec2 model)"),w9a=l(),WP=a("li"),Cro=a("strong"),A9a=o("wav2vec2-conformer"),L9a=o(" \u2014 "),iTe=a("a"),y9a=o("Wav2Vec2ConformerForAudioFrameClassification"),x9a=o(" (Wav2Vec2-Conformer model)"),S9a=l(),QP=a("li"),wro=a("strong"),k9a=o("wavlm"),$9a=o(" \u2014 "),dTe=a("a"),P9a=o("WavLMForAudioFrameClassification"),R9a=o(" (WavLM model)"),I9a=l(),UP=a("p"),N9a=o("The model is set in evaluation mode by default using "),Aro=a("code"),B9a=o("model.eval()"),G9a=o(` (so for instance, dropout modules are
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method.`),H9a=l(),IV=a("p"),J9a=o("This class cannot be instantiated directly using "),Sro=a("code"),Z9a=o("__init__()"),Y9a=o(" (throws an error)."),K9a=l(),ka=a("div"),b(NV.$$.fragment),eTa=l(),kro=a("p"),oTa=o("Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),rTa=l(),wg=a("p"),tTa=o(`Note:
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model\u2019s configuration. Use `),mTe=a("a"),sTa=o("from_pretrained()"),lTa=o(" to load the model weights."),iTa=l(),b(ZP.$$.fragment),dTa=l(),So=a("div"),b(BV.$$.fragment),cTa=l(),Pro=a("p"),fTa=o("Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model."),mTa=l(),As=a("p"),gTa=o("The model class to instantiate is selected based on the "),Rro=a("code"),hTa=o("model_type"),uTa=o(` property of the config object (either
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falling back to using pattern matching on `),tto=a("code"),KFa=o("pretrained_model_name_or_path"),eMa=o(":"),oMa=l(),ys=a("ul"),mR=a("li"),ato=a("strong"),rMa=o("speech-encoder-decoder"),tMa=o(" \u2014 "),ATe=a("a"),aMa=o("SpeechEncoderDecoderModel"),nMa=o(" (Speech Encoder decoder model)"),sMa=l(),gR=a("li"),nto=a("strong"),lMa=o("speech_to_text"),iMa=o(" \u2014 "),LTe=a("a"),dMa=o("Speech2TextForConditionalGeneration"),cMa=o(" (Speech2Text model)"),fMa=l(),hR=a("li"),sto=a("strong"),mMa=o("speecht5"),gMa=o(" \u2014 "),yTe=a("a"),hMa=o("SpeechT5ForSpeechToText"),uMa=o(" (SpeechT5 model)"),pMa=l(),uR=a("li"),lto=a("strong"),_Ma=o("whisper"),vMa=o(" \u2014 "),xTe=a("a"),bMa=o("WhisperForConditionalGeneration"),TMa=o(" (Whisper model)"),FMa=l(),pR=a("p"),MMa=o("The model is set in evaluation mode by default using "),ito=a("code"),EMa=o("model.eval()"),CMa=o(` (so for instance, dropout modules are
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`),Xs=n(Ds,"A",{href:!0});var Wno=s(Xs);EG=r(Wno,"AutoTokenizer"),Wno.forEach(t),Ac=r(Ds," will directly create a class of the relevant architecture. For instance"),Ds.forEach(t),ph=i(f),T(An.$$.fragment,f),ro=i(f),Re=n(f,"P",{});var JX=s(Re);jW=r(JX,"will create a model that is an instance of "),Lc=n(JX,"A",{href:!0});var Qno=s(Lc);OW=r(Qno,"BertModel"),Qno.forEach(t),VW=r(JX,"."),JX.forEach(t),Bo=i(f),Ln=n(f,"P",{});var ZX=s(Ln);XW=r(ZX,"There is one class of "),_h=n(ZX,"CODE",{});var Uno=s(_h);zW=r(Uno,"AutoModel"),Uno.forEach(t),Pxo=r(ZX," for each task, and for each backend (PyTorch, TensorFlow, or Flax)."),ZX.forEach(t),owo=i(f),yc=n(f,"H2",{class:!0});var YX=s(yc);vh=n(YX,"A",{id:!0,class:!0,href:!0});var Hno=s(vh);UEe=n(Hno,"SPAN",{});var Jno=s(UEe);T(CG.$$.fragment,Jno),Jno.forEach(t),Hno.forEach(t),Rxo=i(YX),HEe=n(YX,"SPAN",{});var Zno=s(HEe);Ixo=r(Zno,"Extending the Auto Classes"),Zno.forEach(t),YX.forEach(t),rwo=i(f),zs=n(f,"P",{});var fh=s(zs);Nxo=r(fh,`Each of the auto classes has a method to be extended with your custom classes. For instance, if you have defined a
custom class of model `),JEe=n(fh,"CODE",{});var Yno=s(JEe);Bxo=r(Yno,"NewModel"),Yno.forEach(t),Gxo=r(fh,", make sure you have a "),ZEe=n(fh,"CODE",{});var Kno=s(ZEe);qxo=r(Kno,"NewModelConfig"),Kno.forEach(t),Dxo=r(fh,` then you can add those to the auto
classes like this:`),fh.forEach(t),two=i(f),T(wG.$$.fragment,f),awo=i(f),WW=n(f,"P",{});var eso=s(WW);jxo=r(eso,"You will then be able to use the auto classes like you would usually do!"),eso.forEach(t),nwo=i(f),T(bh.$$.fragment,f),swo=i(f),xc=n(f,"H2",{class:!0});var KX=s(xc);Th=n(KX,"A",{id:!0,class:!0,href:!0});var oso=s(Th);YEe=n(oso,"SPAN",{});var rso=s(YEe);T(AG.$$.fragment,rso),rso.forEach(t),oso.forEach(t),Oxo=i(KX),KEe=n(KX,"SPAN",{});var tso=s(KEe);Vxo=r(tso,"AutoConfig"),tso.forEach(t),KX.forEach(t),lwo=i(f),Go=n(f,"DIV",{class:!0});var Rt=s(Go);T(LG.$$.fragment,Rt),Xxo=i(Rt),yG=n(Rt,"P",{});var ez=s(yG);zxo=r(ez,`This is a generic configuration class that will be instantiated as one of the configuration classes of the library
when created with the `),QW=n(ez,"A",{href:!0});var aso=s(QW);Wxo=r(aso,"from_pretrained()"),aso.forEach(t),Qxo=r(ez," class method."),ez.forEach(t),Uxo=i(Rt),xG=n(Rt,"P",{});var oz=s(xG);Hxo=r(oz,"This class cannot be instantiated directly using "),e3e=n(oz,"CODE",{});var nso=s(e3e);Jxo=r(nso,"__init__()"),nso.forEach(t),Zxo=r(oz," (throws an error)."),oz.forEach(t),Yxo=i(Rt),Zr=n(Rt,"DIV",{class:!0});var It=s(Zr);T(SG.$$.fragment,It),Kxo=i(It),o3e=n(It,"P",{});var sso=s(o3e);eSo=r(sso,"Instantiate one of the configuration classes of the library from a pretrained model configuration."),sso.forEach(t),oSo=i(It),Sc=n(It,"P",{});var mh=s(Sc);rSo=r(mh,"The configuration class to instantiate is selected based on the "),r3e=n(mh,"CODE",{});var lso=s(r3e);tSo=r(lso,"model_type"),lso.forEach(t),aSo=r(mh,` property of the config object that
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passed as an argument or loaded from `),VLe=n($Fe,"CODE",{});var wXa=s(VLe);Z4r=r(wXa,"pretrained_model_name_or_path"),wXa.forEach(t),Y4r=r($Fe," if possible):"),$Fe.forEach(t),K4r=i(Wa),oe=n(Wa,"UL",{});var te=s(oe);c4=n(te,"LI",{});var hgo=s(c4);XLe=n(hgo,"STRONG",{});var AXa=s(XLe);e0r=r(AXa,"align"),AXa.forEach(t),o0r=r(hgo," \u2014 "),Gre=n(hgo,"A",{href:!0});var LXa=s(Gre);r0r=r(LXa,"AlignProcessor"),LXa.forEach(t),t0r=r(hgo," (ALIGN model)"),hgo.forEach(t),a0r=i(te),f4=n(te,"LI",{});var ugo=s(f4);zLe=n(ugo,"STRONG",{});var yXa=s(zLe);n0r=r(yXa,"altclip"),yXa.forEach(t),s0r=r(ugo," \u2014 "),qre=n(ugo,"A",{href:!0});var xXa=s(qre);l0r=r(xXa,"AltCLIPProcessor"),xXa.forEach(t),i0r=r(ugo," (AltCLIP model)"),ugo.forEach(t),d0r=i(te),m4=n(te,"LI",{});var pgo=s(m4);WLe=n(pgo,"STRONG",{});var SXa=s(WLe);c0r=r(SXa,"blip"),SXa.forEach(t),f0r=r(pgo," \u2014 "),Dre=n(pgo,"A",{href:!0});var kXa=s(Dre);m0r=r(kXa,"BlipProcessor"),kXa.forEach(t),g0r=r(pgo," (BLIP 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xza=s(mte);R9r=r(xza,"Wav2Vec2Processor"),xza.forEach(t),I9r=r(Ogo," (UniSpeech model)"),Ogo.forEach(t),N9r=i(te),q4=n(te,"LI",{});var Vgo=s(q4);bye=n(Vgo,"STRONG",{});var Sza=s(bye);B9r=r(Sza,"unispeech-sat"),Sza.forEach(t),G9r=r(Vgo," \u2014 "),gte=n(Vgo,"A",{href:!0});var kza=s(gte);q9r=r(kza,"Wav2Vec2Processor"),kza.forEach(t),D9r=r(Vgo," (UniSpeechSat model)"),Vgo.forEach(t),j9r=i(te),D4=n(te,"LI",{});var Xgo=s(D4);Tye=n(Xgo,"STRONG",{});var $za=s(Tye);O9r=r($za,"vilt"),$za.forEach(t),V9r=r(Xgo," \u2014 "),hte=n(Xgo,"A",{href:!0});var Pza=s(hte);X9r=r(Pza,"ViltProcessor"),Pza.forEach(t),z9r=r(Xgo," (ViLT model)"),Xgo.forEach(t),W9r=i(te),j4=n(te,"LI",{});var zgo=s(j4);Fye=n(zgo,"STRONG",{});var Rza=s(Fye);Q9r=r(Rza,"vision-text-dual-encoder"),Rza.forEach(t),U9r=r(zgo," \u2014 "),ute=n(zgo,"A",{href:!0});var Iza=s(ute);H9r=r(Iza,"VisionTextDualEncoderProcessor"),Iza.forEach(t),J9r=r(zgo," (VisionTextDualEncoder model)"),zgo.forEach(t),Z9r=i(te),O4=n(te,"LI",{});var 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Vza=s(bte);uTr=r(Vza,"WhisperProcessor"),Vza.forEach(t),pTr=r(Hgo," (Whisper model)"),Hgo.forEach(t),_Tr=i(te),W4=n(te,"LI",{});var Jgo=s(W4);Aye=n(Jgo,"STRONG",{});var Xza=s(Aye);vTr=r(Xza,"xclip"),Xza.forEach(t),bTr=r(Jgo," \u2014 "),Tte=n(Jgo,"A",{href:!0});var zza=s(Tte);TTr=r(zza,"XCLIPProcessor"),zza.forEach(t),FTr=r(Jgo," (X-CLIP model)"),Jgo.forEach(t),te.forEach(t),MTr=i(Wa),T(Q4.$$.fragment,Wa),ETr=i(Wa),T(U4.$$.fragment,Wa),Wa.forEach(t),CTr=i(Ii),H4=n(Ii,"DIV",{class:!0});var nLo=s(H4);T(oq.$$.fragment,nLo),wTr=i(nLo),Lye=n(nLo,"P",{});var Wza=s(Lye);ATr=r(Wza,"Register a new processor for this class."),Wza.forEach(t),nLo.forEach(t),Ii.forEach(t),pwo=i(f),Nc=n(f,"H2",{class:!0});var sLo=s(Nc);J4=n(sLo,"A",{id:!0,class:!0,href:!0});var Qza=s(J4);yye=n(Qza,"SPAN",{});var Uza=s(yye);T(rq.$$.fragment,Uza),Uza.forEach(t),Qza.forEach(t),LTr=i(sLo),xye=n(sLo,"SPAN",{});var Hza=s(xye);yTr=r(Hza,"Generic model classes"),Hza.forEach(t),sLo.forEach(t),_wo=i(f),Fte=n(f,"P",{});var 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with the `),Mte=n(PFe,"A",{href:!0});var eWa=s(Mte);RTr=r(eWa,"from_pretrained()"),eWa.forEach(t),ITr=r(PFe," class method or the "),Ete=n(PFe,"A",{href:!0});var oWa=s(Ete);NTr=r(oWa,"from_config()"),oWa.forEach(t),BTr=r(PFe,` class
method.`),PFe.forEach(t),GTr=i(Ni),nq=n(Ni,"P",{});var iLo=s(nq);qTr=r(iLo,"This class cannot be instantiated directly using "),$ye=n(iLo,"CODE",{});var rWa=s($ye);DTr=r(rWa,"__init__()"),rWa.forEach(t),jTr=r(iLo," (throws an error)."),iLo.forEach(t),OTr=i(Ni),Bt=n(Ni,"DIV",{class:!0});var nN=s(Bt);T(sq.$$.fragment,nN),VTr=i(nN),Pye=n(nN,"P",{});var tWa=s(Pye);XTr=r(tWa,"Instantiates one of the base model classes of the library from a configuration."),tWa.forEach(t),zTr=i(nN),qc=n(nN,"P",{});var RFe=s(qc);WTr=r(RFe,`Note:
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model\u2019s configuration. Use `),Cte=n(RFe,"A",{href:!0});var nWa=s(Cte);HTr=r(nWa,"from_pretrained()"),nWa.forEach(t),JTr=r(RFe," to load the model weights."),RFe.forEach(t),ZTr=i(nN),T(Y4.$$.fragment,nN),nN.forEach(t),YTr=i(Ni),so=n(Ni,"DIV",{class:!0});var Qa=s(so);T(lq.$$.fragment,Qa),KTr=i(Qa),Iye=n(Qa,"P",{});var sWa=s(Iye);eFr=r(sWa,"Instantiate one of the base model classes of the library from a pretrained model."),sWa.forEach(t),oFr=i(Qa),kn=n(Qa,"P",{});var sN=s(kn);rFr=r(sN,"The model class to instantiate is selected based on the "),Nye=n(sN,"CODE",{});var lWa=s(Nye);tFr=r(lWa,"model_type"),lWa.forEach(t),aFr=r(sN,` property of the config object (either
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uho=s(_0);txe=n(uho,"STRONG",{});var WWa=s(txe);WMr=r(WWa,"camembert"),WWa.forEach(t),QMr=r(uho," \u2014 "),Vte=n(uho,"A",{href:!0});var QWa=s(Vte);UMr=r(QWa,"CamembertModel"),QWa.forEach(t),HMr=r(uho," (CamemBERT model)"),uho.forEach(t),JMr=i(x),v0=n(x,"LI",{});var pho=s(v0);axe=n(pho,"STRONG",{});var UWa=s(axe);ZMr=r(UWa,"canine"),UWa.forEach(t),YMr=r(pho," \u2014 "),Xte=n(pho,"A",{href:!0});var HWa=s(Xte);KMr=r(HWa,"CanineModel"),HWa.forEach(t),eEr=r(pho," (CANINE model)"),pho.forEach(t),oEr=i(x),b0=n(x,"LI",{});var _ho=s(b0);nxe=n(_ho,"STRONG",{});var JWa=s(nxe);rEr=r(JWa,"chinese_clip"),JWa.forEach(t),tEr=r(_ho," \u2014 "),zte=n(_ho,"A",{href:!0});var ZWa=s(zte);aEr=r(ZWa,"ChineseCLIPModel"),ZWa.forEach(t),nEr=r(_ho," (Chinese-CLIP model)"),_ho.forEach(t),sEr=i(x),T0=n(x,"LI",{});var vho=s(T0);sxe=n(vho,"STRONG",{});var YWa=s(sxe);lEr=r(YWa,"clap"),YWa.forEach(t),iEr=r(vho," \u2014 "),Wte=n(vho,"A",{href:!0});var KWa=s(Wte);dEr=r(KWa,"ClapModel"),KWa.forEach(t),cEr=r(vho," (CLAP 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lQa=s(Jte);xEr=r(lQa,"ConditionalDetrModel"),lQa.forEach(t),SEr=r(Mho," (Conditional DETR model)"),Mho.forEach(t),kEr=i(x),w0=n(x,"LI",{});var Eho=s(w0);fxe=n(Eho,"STRONG",{});var iQa=s(fxe);$Er=r(iQa,"convbert"),iQa.forEach(t),PEr=r(Eho," \u2014 "),Zte=n(Eho,"A",{href:!0});var dQa=s(Zte);REr=r(dQa,"ConvBertModel"),dQa.forEach(t),IEr=r(Eho," (ConvBERT model)"),Eho.forEach(t),NEr=i(x),A0=n(x,"LI",{});var Cho=s(A0);mxe=n(Cho,"STRONG",{});var cQa=s(mxe);BEr=r(cQa,"convnext"),cQa.forEach(t),GEr=r(Cho," \u2014 "),Yte=n(Cho,"A",{href:!0});var fQa=s(Yte);qEr=r(fQa,"ConvNextModel"),fQa.forEach(t),DEr=r(Cho," (ConvNeXT model)"),Cho.forEach(t),jEr=i(x),L0=n(x,"LI",{});var who=s(L0);gxe=n(who,"STRONG",{});var mQa=s(gxe);OEr=r(mQa,"convnextv2"),mQa.forEach(t),VEr=r(who," \u2014 "),Kte=n(who,"A",{href:!0});var gQa=s(Kte);XEr=r(gQa,"ConvNextV2Model"),gQa.forEach(t),zEr=r(who," (ConvNeXTV2 model)"),who.forEach(t),WEr=i(x),y0=n(x,"LI",{});var Aho=s(y0);hxe=n(Aho,"STRONG",{});var 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passed as an argument or loaded from `),d$e=n(iN,"CODE",{});var YYa=s(d$e);iPr=r(YYa,"pretrained_model_name_or_path"),YYa.forEach(t),dPr=r(iN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),c$e=n(iN,"CODE",{});var KYa=s(c$e);cPr=r(KYa,"pretrained_model_name_or_path"),KYa.forEach(t),fPr=r(iN,":"),iN.forEach(t),mPr=i(Gi),G=n(Gi,"UL",{});var O=s(G);pT=n(O,"LI",{});var f_o=s(pT);f$e=n(f_o,"STRONG",{});var eKa=s(f$e);gPr=r(eKa,"albert"),eKa.forEach(t),hPr=r(f_o," \u2014 "),Ose=n(f_o,"A",{href:!0});var oKa=s(Ose);uPr=r(oKa,"TFAlbertModel"),oKa.forEach(t),pPr=r(f_o," (ALBERT model)"),f_o.forEach(t),_Pr=i(O),_T=n(O,"LI",{});var m_o=s(_T);m$e=n(m_o,"STRONG",{});var rKa=s(m$e);vPr=r(rKa,"bart"),rKa.forEach(t),bPr=r(m_o," \u2014 "),Vse=n(m_o,"A",{href:!0});var tKa=s(Vse);TPr=r(tKa,"TFBartModel"),tKa.forEach(t),FPr=r(m_o," (BART model)"),m_o.forEach(t),MPr=i(O),vT=n(O,"LI",{});var g_o=s(vT);g$e=n(g_o,"STRONG",{});var aKa=s(g$e);EPr=r(aKa,"bert"),aKa.forEach(t),CPr=r(g_o," \u2014 "),Xse=n(g_o,"A",{href:!0});var nKa=s(Xse);wPr=r(nKa,"TFBertModel"),nKa.forEach(t),APr=r(g_o," (BERT model)"),g_o.forEach(t),LPr=i(O),bT=n(O,"LI",{});var 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method.`),BFe.forEach(t),dqr=i(qi),uq=n(qi,"P",{});var mLo=s(uq);cqr=r(mLo,"This class cannot be instantiated directly using "),FPe=n(mLo,"CODE",{});var Mon=s(FPe);fqr=r(Mon,"__init__()"),Mon.forEach(t),mqr=r(mLo," (throws an error)."),mLo.forEach(t),gqr=i(qi),qt=n(qi,"DIV",{class:!0});var dN=s(qt);T(pq.$$.fragment,dN),hqr=i(dN),MPe=n(dN,"P",{});var Eon=s(MPe);uqr=r(Eon,"Instantiates one of the base model classes of the library from a configuration."),Eon.forEach(t),pqr=i(dN),zc=n(dN,"P",{});var GFe=s(zc);_qr=r(GFe,`Note:
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$rn=s(rRe);gOr=r($rn,"wav2vec2"),$rn.forEach(t),hOr=r(W2o," \u2014 "),yie=n(W2o,"A",{href:!0});var Prn=s(yie);uOr=r(Prn,"FlaxWav2Vec2Model"),Prn.forEach(t),pOr=r(W2o," (Wav2Vec2 model)"),W2o.forEach(t),_Or=i(ie),oM=n(ie,"LI",{});var Q2o=s(oM);tRe=n(Q2o,"STRONG",{});var Rrn=s(tRe);vOr=r(Rrn,"whisper"),Rrn.forEach(t),bOr=r(Q2o," \u2014 "),xie=n(Q2o,"A",{href:!0});var Irn=s(xie);TOr=r(Irn,"FlaxWhisperModel"),Irn.forEach(t),FOr=r(Q2o," (Whisper model)"),Q2o.forEach(t),MOr=i(ie),rM=n(ie,"LI",{});var U2o=s(rM);aRe=n(U2o,"STRONG",{});var Nrn=s(aRe);EOr=r(Nrn,"xglm"),Nrn.forEach(t),COr=r(U2o," \u2014 "),Sie=n(U2o,"A",{href:!0});var Brn=s(Sie);wOr=r(Brn,"FlaxXGLMModel"),Brn.forEach(t),AOr=r(U2o," (XGLM model)"),U2o.forEach(t),LOr=i(ie),tM=n(ie,"LI",{});var H2o=s(tM);nRe=n(H2o,"STRONG",{});var Grn=s(nRe);yOr=r(Grn,"xlm-roberta"),Grn.forEach(t),xOr=r(H2o," \u2014 "),kie=n(H2o,"A",{href:!0});var qrn=s(kie);SOr=r(qrn,"FlaxXLMRobertaModel"),qrn.forEach(t),kOr=r(H2o," (XLM-RoBERTa 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with the `),Pie=n(qFe,"A",{href:!0});var Qrn=s(Pie);DOr=r(Qrn,"from_pretrained()"),Qrn.forEach(t),jOr=r(qFe," class method or the "),Rie=n(qFe,"A",{href:!0});var Urn=s(Rie);OOr=r(Urn,"from_config()"),Urn.forEach(t),VOr=r(qFe,` class
method.`),qFe.forEach(t),XOr=i(ji),Fq=n(ji,"P",{});var uLo=s(Fq);zOr=r(uLo,"This class cannot be instantiated directly using "),cRe=n(uLo,"CODE",{});var Hrn=s(cRe);WOr=r(Hrn,"__init__()"),Hrn.forEach(t),QOr=r(uLo," (throws an error)."),uLo.forEach(t),UOr=i(ji),Dt=n(ji,"DIV",{class:!0});var fN=s(Dt);T(Mq.$$.fragment,fN),HOr=i(fN),fRe=n(fN,"P",{});var Jrn=s(fRe);JOr=r(Jrn,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),Jrn.forEach(t),ZOr=i(fN),Hc=n(fN,"P",{});var DFe=s(Hc);YOr=r(DFe,`Note:
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model\u2019s configuration. Use `),Iie=n(DFe,"A",{href:!0});var Yrn=s(Iie);oVr=r(Yrn,"from_pretrained()"),Yrn.forEach(t),rVr=r(DFe," to load the model weights."),DFe.forEach(t),tVr=i(fN),T(lM.$$.fragment,fN),fN.forEach(t),aVr=i(ji),lo=n(ji,"DIV",{class:!0});var Ua=s(lo);T(Eq.$$.fragment,Ua),nVr=i(Ua),gRe=n(Ua,"P",{});var Krn=s(gRe);sVr=r(Krn,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),Krn.forEach(t),lVr=i(Ua),Rn=n(Ua,"P",{});var mN=s(Rn);iVr=r(mN,"The model class to instantiate is selected based on the "),hRe=n(mN,"CODE",{});var etn=s(hRe);dVr=r(etn,"model_type"),etn.forEach(t),cVr=r(mN,` property of the config object (either
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method.`),jFe.forEach(t),eHr=i(Oi),Aq=n(Oi,"P",{});var _Lo=s(Aq);oHr=r(_Lo,"This class cannot be instantiated directly using "),AIe=n(_Lo,"CODE",{});var Tnn=s(AIe);rHr=r(Tnn,"__init__()"),Tnn.forEach(t),tHr=r(_Lo," (throws an error)."),_Lo.forEach(t),aHr=i(Oi),jt=n(Oi,"DIV",{class:!0});var gN=s(jt);T(Lq.$$.fragment,gN),nHr=i(gN),LIe=n(gN,"P",{});var Fnn=s(LIe);sHr=r(Fnn,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),Fnn.forEach(t),lHr=i(gN),Yc=n(gN,"P",{});var OFe=s(Yc);iHr=r(OFe,`Note:
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model\u2019s configuration. Use `),Vde=n(OFe,"A",{href:!0});var Enn=s(Vde);fHr=r(Enn,"from_pretrained()"),Enn.forEach(t),mHr=r(OFe," to load the model weights."),OFe.forEach(t),gHr=i(gN),T(pE.$$.fragment,gN),gN.forEach(t),hHr=i(Oi),ot=n(Oi,"DIV",{class:!0});var Vi=s(ot);T(yq.$$.fragment,Vi),uHr=i(Vi),xIe=n(Vi,"P",{});var Cnn=s(xIe);pHr=r(Cnn,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),Cnn.forEach(t),_Hr=i(Vi),In=n(Vi,"P",{});var hN=s(In);vHr=r(hN,"The model class to instantiate is selected based on the "),SIe=n(hN,"CODE",{});var wnn=s(SIe);bHr=r(wnn,"model_type"),wnn.forEach(t),THr=r(hN,` property of the config object (either
passed as an argument or loaded from `),kIe=n(hN,"CODE",{});var Ann=s(kIe);FHr=r(Ann,"pretrained_model_name_or_path"),Ann.forEach(t),MHr=r(hN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),$Ie=n(hN,"CODE",{});var Lnn=s($Ie);EHr=r(Lnn,"pretrained_model_name_or_path"),Lnn.forEach(t),CHr=r(hN,":"),hN.forEach(t),wHr=i(Vi),me=n(Vi,"UL",{});var ge=s(me);_E=n(ge,"LI",{});var r1o=s(_E);PIe=n(r1o,"STRONG",{});var ynn=s(PIe);AHr=r(ynn,"albert"),ynn.forEach(t),LHr=r(r1o," \u2014 "),Xde=n(r1o,"A",{href:!0});var xnn=s(Xde);yHr=r(xnn,"TFAlbertForPreTraining"),xnn.forEach(t),xHr=r(r1o," (ALBERT model)"),r1o.forEach(t),SHr=i(ge),vE=n(ge,"LI",{});var t1o=s(vE);RIe=n(t1o,"STRONG",{});var Snn=s(RIe);kHr=r(Snn,"bart"),Snn.forEach(t),$Hr=r(t1o," \u2014 "),zde=n(t1o,"A",{href:!0});var knn=s(zde);PHr=r(knn,"TFBartForConditionalGeneration"),knn.forEach(t),RHr=r(t1o," (BART model)"),t1o.forEach(t),IHr=i(ge),bE=n(ge,"LI",{});var a1o=s(bE);IIe=n(a1o,"STRONG",{});var $nn=s(IIe);NHr=r($nn,"bert"),$nn.forEach(t),BHr=r(a1o," \u2014 "),Wde=n(a1o,"A",{href:!0});var Pnn=s(Wde);GHr=r(Pnn,"TFBertForPreTraining"),Pnn.forEach(t),qHr=r(a1o," (BERT model)"),a1o.forEach(t),DHr=i(ge),TE=n(ge,"LI",{});var n1o=s(TE);NIe=n(n1o,"STRONG",{});var Rnn=s(NIe);jHr=r(Rnn,"camembert"),Rnn.forEach(t),OHr=r(n1o," \u2014 "),Qde=n(n1o,"A",{href:!0});var Inn=s(Qde);VHr=r(Inn,"TFCamembertForMaskedLM"),Inn.forEach(t),XHr=r(n1o," (CamemBERT model)"),n1o.forEach(t),zHr=i(ge),FE=n(ge,"LI",{});var s1o=s(FE);BIe=n(s1o,"STRONG",{});var Nnn=s(BIe);WHr=r(Nnn,"ctrl"),Nnn.forEach(t),QHr=r(s1o," \u2014 "),Ude=n(s1o,"A",{href:!0});var Bnn=s(Ude);UHr=r(Bnn,"TFCTRLLMHeadModel"),Bnn.forEach(t),HHr=r(s1o," (CTRL model)"),s1o.forEach(t),JHr=i(ge),ME=n(ge,"LI",{});var l1o=s(ME);GIe=n(l1o,"STRONG",{});var Gnn=s(GIe);ZHr=r(Gnn,"distilbert"),Gnn.forEach(t),YHr=r(l1o," \u2014 "),Hde=n(l1o,"A",{href:!0});var qnn=s(Hde);KHr=r(qnn,"TFDistilBertForMaskedLM"),qnn.forEach(t),eJr=r(l1o," (DistilBERT model)"),l1o.forEach(t),oJr=i(ge),EE=n(ge,"LI",{});var i1o=s(EE);qIe=n(i1o,"STRONG",{});var Dnn=s(qIe);rJr=r(Dnn,"electra"),Dnn.forEach(t),tJr=r(i1o," \u2014 "),Jde=n(i1o,"A",{href:!0});var jnn=s(Jde);aJr=r(jnn,"TFElectraForPreTraining"),jnn.forEach(t),nJr=r(i1o," (ELECTRA model)"),i1o.forEach(t),sJr=i(ge),CE=n(ge,"LI",{});var d1o=s(CE);DIe=n(d1o,"STRONG",{});var Onn=s(DIe);lJr=r(Onn,"flaubert"),Onn.forEach(t),iJr=r(d1o," \u2014 "),Zde=n(d1o,"A",{href:!0});var Vnn=s(Zde);dJr=r(Vnn,"TFFlaubertWithLMHeadModel"),Vnn.forEach(t),cJr=r(d1o," (FlauBERT model)"),d1o.forEach(t),fJr=i(ge),wE=n(ge,"LI",{});var c1o=s(wE);jIe=n(c1o,"STRONG",{});var Xnn=s(jIe);mJr=r(Xnn,"funnel"),Xnn.forEach(t),gJr=r(c1o," \u2014 "),Yde=n(c1o,"A",{href:!0});var znn=s(Yde);hJr=r(znn,"TFFunnelForPreTraining"),znn.forEach(t),uJr=r(c1o," (Funnel Transformer model)"),c1o.forEach(t),pJr=i(ge),AE=n(ge,"LI",{});var f1o=s(AE);OIe=n(f1o,"STRONG",{});var Wnn=s(OIe);_Jr=r(Wnn,"gpt-sw3"),Wnn.forEach(t),vJr=r(f1o," \u2014 "),Kde=n(f1o,"A",{href:!0});var Qnn=s(Kde);bJr=r(Qnn,"TFGPT2LMHeadModel"),Qnn.forEach(t),TJr=r(f1o," (GPT-Sw3 model)"),f1o.forEach(t),FJr=i(ge),LE=n(ge,"LI",{});var m1o=s(LE);VIe=n(m1o,"STRONG",{});var Unn=s(VIe);MJr=r(Unn,"gpt2"),Unn.forEach(t),EJr=r(m1o," \u2014 "),ece=n(m1o,"A",{href:!0});var Hnn=s(ece);CJr=r(Hnn,"TFGPT2LMHeadModel"),Hnn.forEach(t),wJr=r(m1o," (OpenAI GPT-2 model)"),m1o.forEach(t),AJr=i(ge),yE=n(ge,"LI",{});var g1o=s(yE);XIe=n(g1o,"STRONG",{});var Jnn=s(XIe);LJr=r(Jnn,"layoutlm"),Jnn.forEach(t),yJr=r(g1o," \u2014 "),oce=n(g1o,"A",{href:!0});var Znn=s(oce);xJr=r(Znn,"TFLayoutLMForMaskedLM"),Znn.forEach(t),SJr=r(g1o," (LayoutLM model)"),g1o.forEach(t),kJr=i(ge),xE=n(ge,"LI",{});var h1o=s(xE);zIe=n(h1o,"STRONG",{});var Ynn=s(zIe);$Jr=r(Ynn,"lxmert"),Ynn.forEach(t),PJr=r(h1o," \u2014 "),rce=n(h1o,"A",{href:!0});var Knn=s(rce);RJr=r(Knn,"TFLxmertForPreTraining"),Knn.forEach(t),IJr=r(h1o," (LXMERT model)"),h1o.forEach(t),NJr=i(ge),SE=n(ge,"LI",{});var u1o=s(SE);WIe=n(u1o,"STRONG",{});var esn=s(WIe);BJr=r(esn,"mobilebert"),esn.forEach(t),GJr=r(u1o," \u2014 "),tce=n(u1o,"A",{href:!0});var osn=s(tce);qJr=r(osn,"TFMobileBertForPreTraining"),osn.forEach(t),DJr=r(u1o," (MobileBERT model)"),u1o.forEach(t),jJr=i(ge),kE=n(ge,"LI",{});var p1o=s(kE);QIe=n(p1o,"STRONG",{});var rsn=s(QIe);OJr=r(rsn,"mpnet"),rsn.forEach(t),VJr=r(p1o," \u2014 "),ace=n(p1o,"A",{href:!0});var tsn=s(ace);XJr=r(tsn,"TFMPNetForMaskedLM"),tsn.forEach(t),zJr=r(p1o," (MPNet model)"),p1o.forEach(t),WJr=i(ge),$E=n(ge,"LI",{});var _1o=s($E);UIe=n(_1o,"STRONG",{});var asn=s(UIe);QJr=r(asn,"openai-gpt"),asn.forEach(t),UJr=r(_1o," \u2014 "),nce=n(_1o,"A",{href:!0});var nsn=s(nce);HJr=r(nsn,"TFOpenAIGPTLMHeadModel"),nsn.forEach(t),JJr=r(_1o," (OpenAI GPT model)"),_1o.forEach(t),ZJr=i(ge),PE=n(ge,"LI",{});var v1o=s(PE);HIe=n(v1o,"STRONG",{});var ssn=s(HIe);YJr=r(ssn,"roberta"),ssn.forEach(t),KJr=r(v1o," \u2014 "),sce=n(v1o,"A",{href:!0});var lsn=s(sce);eZr=r(lsn,"TFRobertaForMaskedLM"),lsn.forEach(t),oZr=r(v1o," (RoBERTa model)"),v1o.forEach(t),rZr=i(ge),RE=n(ge,"LI",{});var b1o=s(RE);JIe=n(b1o,"STRONG",{});var isn=s(JIe);tZr=r(isn,"roberta-prelayernorm"),isn.forEach(t),aZr=r(b1o," \u2014 "),lce=n(b1o,"A",{href:!0});var dsn=s(lce);nZr=r(dsn,"TFRobertaPreLayerNormForMaskedLM"),dsn.forEach(t),sZr=r(b1o," (RoBERTa-PreLayerNorm model)"),b1o.forEach(t),lZr=i(ge),IE=n(ge,"LI",{});var T1o=s(IE);ZIe=n(T1o,"STRONG",{});var csn=s(ZIe);iZr=r(csn,"t5"),csn.forEach(t),dZr=r(T1o," \u2014 "),ice=n(T1o,"A",{href:!0});var fsn=s(ice);cZr=r(fsn,"TFT5ForConditionalGeneration"),fsn.forEach(t),fZr=r(T1o," (T5 model)"),T1o.forEach(t),mZr=i(ge),NE=n(ge,"LI",{});var F1o=s(NE);YIe=n(F1o,"STRONG",{});var msn=s(YIe);gZr=r(msn,"tapas"),msn.forEach(t),hZr=r(F1o," \u2014 "),dce=n(F1o,"A",{href:!0});var gsn=s(dce);uZr=r(gsn,"TFTapasForMaskedLM"),gsn.forEach(t),pZr=r(F1o," (TAPAS model)"),F1o.forEach(t),_Zr=i(ge),BE=n(ge,"LI",{});var M1o=s(BE);KIe=n(M1o,"STRONG",{});var hsn=s(KIe);vZr=r(hsn,"transfo-xl"),hsn.forEach(t),bZr=r(M1o," \u2014 "),cce=n(M1o,"A",{href:!0});var usn=s(cce);TZr=r(usn,"TFTransfoXLLMHeadModel"),usn.forEach(t),FZr=r(M1o," (Transformer-XL model)"),M1o.forEach(t),MZr=i(ge),GE=n(ge,"LI",{});var E1o=s(GE);eNe=n(E1o,"STRONG",{});var psn=s(eNe);EZr=r(psn,"vit_mae"),psn.forEach(t),CZr=r(E1o," \u2014 "),fce=n(E1o,"A",{href:!0});var _sn=s(fce);wZr=r(_sn,"TFViTMAEForPreTraining"),_sn.forEach(t),AZr=r(E1o," (ViTMAE model)"),E1o.forEach(t),LZr=i(ge),qE=n(ge,"LI",{});var C1o=s(qE);oNe=n(C1o,"STRONG",{});var vsn=s(oNe);yZr=r(vsn,"xlm"),vsn.forEach(t),xZr=r(C1o," \u2014 "),mce=n(C1o,"A",{href:!0});var bsn=s(mce);SZr=r(bsn,"TFXLMWithLMHeadModel"),bsn.forEach(t),kZr=r(C1o," (XLM model)"),C1o.forEach(t),$Zr=i(ge),DE=n(ge,"LI",{});var w1o=s(DE);rNe=n(w1o,"STRONG",{});var Tsn=s(rNe);PZr=r(Tsn,"xlm-roberta"),Tsn.forEach(t),RZr=r(w1o," \u2014 "),gce=n(w1o,"A",{href:!0});var Fsn=s(gce);IZr=r(Fsn,"TFXLMRobertaForMaskedLM"),Fsn.forEach(t),NZr=r(w1o," (XLM-RoBERTa model)"),w1o.forEach(t),BZr=i(ge),jE=n(ge,"LI",{});var A1o=s(jE);tNe=n(A1o,"STRONG",{});var Msn=s(tNe);GZr=r(Msn,"xlnet"),Msn.forEach(t),qZr=r(A1o," \u2014 "),hce=n(A1o,"A",{href:!0});var Esn=s(hce);DZr=r(Esn,"TFXLNetLMHeadModel"),Esn.forEach(t),jZr=r(A1o," (XLNet model)"),A1o.forEach(t),ge.forEach(t),OZr=i(Vi),T(OE.$$.fragment,Vi),Vi.forEach(t),Oi.forEach(t),Swo=i(f),Kc=n(f,"H3",{class:!0});var vLo=s(Kc);VE=n(vLo,"A",{id:!0,class:!0,href:!0});var Csn=s(VE);aNe=n(Csn,"SPAN",{});var wsn=s(aNe);T(xq.$$.fragment,wsn),wsn.forEach(t),Csn.forEach(t),VZr=i(vLo),nNe=n(vLo,"SPAN",{});var Asn=s(nNe);XZr=r(Asn,"FlaxAutoModelForPreTraining"),Asn.forEach(t),vLo.forEach(t),kwo=i(f),Uo=n(f,"DIV",{class:!0});var Xi=s(Uo);T(Sq.$$.fragment,Xi),zZr=i(Xi),ef=n(Xi,"P",{});var VFe=s(ef);WZr=r(VFe,`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 `),uce=n(VFe,"A",{href:!0});var Lsn=s(uce);QZr=r(Lsn,"from_pretrained()"),Lsn.forEach(t),UZr=r(VFe," class method or the "),pce=n(VFe,"A",{href:!0});var ysn=s(pce);HZr=r(ysn,"from_config()"),ysn.forEach(t),JZr=r(VFe,` class
method.`),VFe.forEach(t),ZZr=i(Xi),kq=n(Xi,"P",{});var bLo=s(kq);YZr=r(bLo,"This class cannot be instantiated directly using "),sNe=n(bLo,"CODE",{});var xsn=s(sNe);KZr=r(xsn,"__init__()"),xsn.forEach(t),eYr=r(bLo," (throws an error)."),bLo.forEach(t),oYr=i(Xi),Ot=n(Xi,"DIV",{class:!0});var uN=s(Ot);T($q.$$.fragment,uN),rYr=i(uN),lNe=n(uN,"P",{});var Ssn=s(lNe);tYr=r(Ssn,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),Ssn.forEach(t),aYr=i(uN),of=n(uN,"P",{});var XFe=s(of);nYr=r(XFe,`Note:
Loading a model from its configuration file does `),iNe=n(XFe,"STRONG",{});var ksn=s(iNe);sYr=r(ksn,"not"),ksn.forEach(t),lYr=r(XFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),_ce=n(XFe,"A",{href:!0});var $sn=s(_ce);iYr=r($sn,"from_pretrained()"),$sn.forEach(t),dYr=r(XFe," to load the model weights."),XFe.forEach(t),cYr=i(uN),T(XE.$$.fragment,uN),uN.forEach(t),fYr=i(Xi),rt=n(Xi,"DIV",{class:!0});var zi=s(rt);T(Pq.$$.fragment,zi),mYr=i(zi),dNe=n(zi,"P",{});var Psn=s(dNe);gYr=r(Psn,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),Psn.forEach(t),hYr=i(zi),Nn=n(zi,"P",{});var pN=s(Nn);uYr=r(pN,"The model class to instantiate is selected based on the "),cNe=n(pN,"CODE",{});var Rsn=s(cNe);pYr=r(Rsn,"model_type"),Rsn.forEach(t),_Yr=r(pN,` property of the config object (either
passed as an argument or loaded from `),fNe=n(pN,"CODE",{});var Isn=s(fNe);vYr=r(Isn,"pretrained_model_name_or_path"),Isn.forEach(t),bYr=r(pN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mNe=n(pN,"CODE",{});var Nsn=s(mNe);TYr=r(Nsn,"pretrained_model_name_or_path"),Nsn.forEach(t),FYr=r(pN,":"),pN.forEach(t),MYr=i(zi),Ae=n(zi,"UL",{});var Le=s(Ae);zE=n(Le,"LI",{});var L1o=s(zE);gNe=n(L1o,"STRONG",{});var Bsn=s(gNe);EYr=r(Bsn,"albert"),Bsn.forEach(t),CYr=r(L1o," \u2014 "),vce=n(L1o,"A",{href:!0});var Gsn=s(vce);wYr=r(Gsn,"FlaxAlbertForPreTraining"),Gsn.forEach(t),AYr=r(L1o," (ALBERT model)"),L1o.forEach(t),LYr=i(Le),WE=n(Le,"LI",{});var y1o=s(WE);hNe=n(y1o,"STRONG",{});var qsn=s(hNe);yYr=r(qsn,"bart"),qsn.forEach(t),xYr=r(y1o," \u2014 "),bce=n(y1o,"A",{href:!0});var Dsn=s(bce);SYr=r(Dsn,"FlaxBartForConditionalGeneration"),Dsn.forEach(t),kYr=r(y1o," (BART model)"),y1o.forEach(t),$Yr=i(Le),QE=n(Le,"LI",{});var x1o=s(QE);uNe=n(x1o,"STRONG",{});var jsn=s(uNe);PYr=r(jsn,"bert"),jsn.forEach(t),RYr=r(x1o," \u2014 "),Tce=n(x1o,"A",{href:!0});var Osn=s(Tce);IYr=r(Osn,"FlaxBertForPreTraining"),Osn.forEach(t),NYr=r(x1o," (BERT model)"),x1o.forEach(t),BYr=i(Le),UE=n(Le,"LI",{});var S1o=s(UE);pNe=n(S1o,"STRONG",{});var Vsn=s(pNe);GYr=r(Vsn,"big_bird"),Vsn.forEach(t),qYr=r(S1o," \u2014 "),Fce=n(S1o,"A",{href:!0});var Xsn=s(Fce);DYr=r(Xsn,"FlaxBigBirdForPreTraining"),Xsn.forEach(t),jYr=r(S1o," (BigBird model)"),S1o.forEach(t),OYr=i(Le),HE=n(Le,"LI",{});var k1o=s(HE);_Ne=n(k1o,"STRONG",{});var zsn=s(_Ne);VYr=r(zsn,"electra"),zsn.forEach(t),XYr=r(k1o," \u2014 "),Mce=n(k1o,"A",{href:!0});var Wsn=s(Mce);zYr=r(Wsn,"FlaxElectraForPreTraining"),Wsn.forEach(t),WYr=r(k1o," (ELECTRA model)"),k1o.forEach(t),QYr=i(Le),JE=n(Le,"LI",{});var $1o=s(JE);vNe=n($1o,"STRONG",{});var Qsn=s(vNe);UYr=r(Qsn,"longt5"),Qsn.forEach(t),HYr=r($1o," \u2014 "),Ece=n($1o,"A",{href:!0});var Usn=s(Ece);JYr=r(Usn,"FlaxLongT5ForConditionalGeneration"),Usn.forEach(t),ZYr=r($1o," (LongT5 model)"),$1o.forEach(t),YYr=i(Le),ZE=n(Le,"LI",{});var P1o=s(ZE);bNe=n(P1o,"STRONG",{});var Hsn=s(bNe);KYr=r(Hsn,"mbart"),Hsn.forEach(t),eKr=r(P1o," \u2014 "),Cce=n(P1o,"A",{href:!0});var Jsn=s(Cce);oKr=r(Jsn,"FlaxMBartForConditionalGeneration"),Jsn.forEach(t),rKr=r(P1o," (mBART model)"),P1o.forEach(t),tKr=i(Le),YE=n(Le,"LI",{});var R1o=s(YE);TNe=n(R1o,"STRONG",{});var Zsn=s(TNe);aKr=r(Zsn,"mt5"),Zsn.forEach(t),nKr=r(R1o," \u2014 "),wce=n(R1o,"A",{href:!0});var Ysn=s(wce);sKr=r(Ysn,"FlaxMT5ForConditionalGeneration"),Ysn.forEach(t),lKr=r(R1o," (MT5 model)"),R1o.forEach(t),iKr=i(Le),KE=n(Le,"LI",{});var I1o=s(KE);FNe=n(I1o,"STRONG",{});var Ksn=s(FNe);dKr=r(Ksn,"roberta"),Ksn.forEach(t),cKr=r(I1o," \u2014 "),Ace=n(I1o,"A",{href:!0});var eln=s(Ace);fKr=r(eln,"FlaxRobertaForMaskedLM"),eln.forEach(t),mKr=r(I1o," (RoBERTa model)"),I1o.forEach(t),gKr=i(Le),e3=n(Le,"LI",{});var N1o=s(e3);MNe=n(N1o,"STRONG",{});var oln=s(MNe);hKr=r(oln,"roberta-prelayernorm"),oln.forEach(t),uKr=r(N1o," \u2014 "),Lce=n(N1o,"A",{href:!0});var rln=s(Lce);pKr=r(rln,"FlaxRobertaPreLayerNormForMaskedLM"),rln.forEach(t),_Kr=r(N1o," (RoBERTa-PreLayerNorm 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tasks."),pln.forEach(t),Rwo=i(f),tf=n(f,"H3",{class:!0});var FLo=s(tf);i3=n(FLo,"A",{id:!0,class:!0,href:!0});var _ln=s(i3);SNe=n(_ln,"SPAN",{});var vln=s(SNe);T(Iq.$$.fragment,vln),vln.forEach(t),_ln.forEach(t),QKr=i(FLo),kNe=n(FLo,"SPAN",{});var bln=s(kNe);UKr=r(bln,"AutoModelForCausalLM"),bln.forEach(t),FLo.forEach(t),Iwo=i(f),Ho=n(f,"DIV",{class:!0});var Wi=s(Ho);T(Nq.$$.fragment,Wi),HKr=i(Wi),af=n(Wi,"P",{});var zFe=s(af);JKr=r(zFe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
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method.`),zFe.forEach(t),oet=i(Wi),Bq=n(Wi,"P",{});var MLo=s(Bq);ret=r(MLo,"This class cannot be instantiated directly using "),$Ne=n(MLo,"CODE",{});var Mln=s($Ne);tet=r(Mln,"__init__()"),Mln.forEach(t),aet=r(MLo," (throws an error)."),MLo.forEach(t),net=i(Wi),Vt=n(Wi,"DIV",{class:!0});var _N=s(Vt);T(Gq.$$.fragment,_N),set=i(_N),PNe=n(_N,"P",{});var Eln=s(PNe);iet=r(Eln,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),Eln.forEach(t),det=i(_N),nf=n(_N,"P",{});var WFe=s(nf);cet=r(WFe,`Note:
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Qin=s(vfe);Vtt=r(Qin,"ProphetNetForCausalLM"),Qin.forEach(t),Xtt=r(E4o," (ProphetNet model)"),E4o.forEach(t),ztt=i(J),W3=n(J,"LI",{});var C4o=s(W3);FBe=n(C4o,"STRONG",{});var Uin=s(FBe);Wtt=r(Uin,"qdqbert"),Uin.forEach(t),Qtt=r(C4o," \u2014 "),bfe=n(C4o,"A",{href:!0});var Hin=s(bfe);Utt=r(Hin,"QDQBertLMHeadModel"),Hin.forEach(t),Htt=r(C4o," (QDQBert model)"),C4o.forEach(t),Jtt=i(J),Q3=n(J,"LI",{});var w4o=s(Q3);MBe=n(w4o,"STRONG",{});var Jin=s(MBe);Ztt=r(Jin,"reformer"),Jin.forEach(t),Ytt=r(w4o," \u2014 "),Tfe=n(w4o,"A",{href:!0});var Zin=s(Tfe);Ktt=r(Zin,"ReformerModelWithLMHead"),Zin.forEach(t),eat=r(w4o," (Reformer model)"),w4o.forEach(t),oat=i(J),U3=n(J,"LI",{});var A4o=s(U3);EBe=n(A4o,"STRONG",{});var Yin=s(EBe);rat=r(Yin,"rembert"),Yin.forEach(t),tat=r(A4o," \u2014 "),Ffe=n(A4o,"A",{href:!0});var Kin=s(Ffe);aat=r(Kin,"RemBertForCausalLM"),Kin.forEach(t),nat=r(A4o," (RemBERT model)"),A4o.forEach(t),sat=i(J),H3=n(J,"LI",{});var L4o=s(H3);CBe=n(L4o,"STRONG",{});var 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method.`),QFe.forEach(t),jnt=i(Qi),Oq=n(Qi,"P",{});var CLo=s(Oq);Ont=r(CLo,"This class cannot be instantiated directly using "),VBe=n(CLo,"CODE",{});var Bdn=s(VBe);Vnt=r(Bdn,"__init__()"),Bdn.forEach(t),Xnt=r(CLo," (throws an error)."),CLo.forEach(t),znt=i(Qi),Xt=n(Qi,"DIV",{class:!0});var bN=s(Xt);T(Vq.$$.fragment,bN),Wnt=i(bN),XBe=n(bN,"P",{});var Gdn=s(XBe);Qnt=r(Gdn,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),Gdn.forEach(t),Unt=i(bN),df=n(bN,"P",{});var UFe=s(df);Hnt=r(UFe,`Note:
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model\u2019s configuration. Use `),qfe=n(UFe,"A",{href:!0});var Ddn=s(qfe);Ynt=r(Ddn,"from_pretrained()"),Ddn.forEach(t),Knt=r(UFe," to load the model weights."),UFe.forEach(t),est=i(bN),T(gC.$$.fragment,bN),bN.forEach(t),ost=i(Qi),tt=n(Qi,"DIV",{class:!0});var Ui=s(tt);T(Xq.$$.fragment,Ui),rst=i(Ui),WBe=n(Ui,"P",{});var jdn=s(WBe);tst=r(jdn,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),jdn.forEach(t),ast=i(Ui),Gn=n(Ui,"P",{});var TN=s(Gn);nst=r(TN,"The model class to instantiate is selected based on the "),QBe=n(TN,"CODE",{});var Odn=s(QBe);sst=r(Odn,"model_type"),Odn.forEach(t),lst=r(TN,` property of the config object (either
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acn=s(Wfe);zst=r(acn,"TFOpenAIGPTLMHeadModel"),acn.forEach(t),Wst=r(H4o," (OpenAI GPT model)"),H4o.forEach(t),Qst=i(we),FC=n(we,"LI",{});var J4o=s(FC);tGe=n(J4o,"STRONG",{});var ncn=s(tGe);Ust=r(ncn,"opt"),ncn.forEach(t),Hst=r(J4o," \u2014 "),Qfe=n(J4o,"A",{href:!0});var scn=s(Qfe);Jst=r(scn,"TFOPTForCausalLM"),scn.forEach(t),Zst=r(J4o," (OPT model)"),J4o.forEach(t),Yst=i(we),MC=n(we,"LI",{});var Z4o=s(MC);aGe=n(Z4o,"STRONG",{});var lcn=s(aGe);Kst=r(lcn,"rembert"),lcn.forEach(t),elt=r(Z4o," \u2014 "),Ufe=n(Z4o,"A",{href:!0});var icn=s(Ufe);olt=r(icn,"TFRemBertForCausalLM"),icn.forEach(t),rlt=r(Z4o," (RemBERT model)"),Z4o.forEach(t),tlt=i(we),EC=n(we,"LI",{});var Y4o=s(EC);nGe=n(Y4o,"STRONG",{});var dcn=s(nGe);alt=r(dcn,"roberta"),dcn.forEach(t),nlt=r(Y4o," \u2014 "),Hfe=n(Y4o,"A",{href:!0});var ccn=s(Hfe);slt=r(ccn,"TFRobertaForCausalLM"),ccn.forEach(t),llt=r(Y4o," (RoBERTa model)"),Y4o.forEach(t),ilt=i(we),CC=n(we,"LI",{});var K4o=s(CC);sGe=n(K4o,"STRONG",{});var fcn=s(sGe);dlt=r(fcn,"roberta-prelayernorm"),fcn.forEach(t),clt=r(K4o," \u2014 "),Jfe=n(K4o,"A",{href:!0});var mcn=s(Jfe);flt=r(mcn,"TFRobertaPreLayerNormForCausalLM"),mcn.forEach(t),mlt=r(K4o," (RoBERTa-PreLayerNorm model)"),K4o.forEach(t),glt=i(we),wC=n(we,"LI",{});var e0o=s(wC);lGe=n(e0o,"STRONG",{});var gcn=s(lGe);hlt=r(gcn,"roformer"),gcn.forEach(t),ult=r(e0o," \u2014 "),Zfe=n(e0o,"A",{href:!0});var hcn=s(Zfe);plt=r(hcn,"TFRoFormerForCausalLM"),hcn.forEach(t),_lt=r(e0o," (RoFormer model)"),e0o.forEach(t),vlt=i(we),AC=n(we,"LI",{});var o0o=s(AC);iGe=n(o0o,"STRONG",{});var ucn=s(iGe);blt=r(ucn,"transfo-xl"),ucn.forEach(t),Tlt=r(o0o," \u2014 "),Yfe=n(o0o,"A",{href:!0});var pcn=s(Yfe);Flt=r(pcn,"TFTransfoXLLMHeadModel"),pcn.forEach(t),Mlt=r(o0o," (Transformer-XL model)"),o0o.forEach(t),Elt=i(we),LC=n(we,"LI",{});var r0o=s(LC);dGe=n(r0o,"STRONG",{});var _cn=s(dGe);Clt=r(_cn,"xglm"),_cn.forEach(t),wlt=r(r0o," \u2014 "),Kfe=n(r0o,"A",{href:!0});var vcn=s(Kfe);Alt=r(vcn,"TFXGLMForCausalLM"),vcn.forEach(t),Llt=r(r0o," (XGLM model)"),r0o.forEach(t),ylt=i(we),yC=n(we,"LI",{});var t0o=s(yC);cGe=n(t0o,"STRONG",{});var bcn=s(cGe);xlt=r(bcn,"xlm"),bcn.forEach(t),Slt=r(t0o," \u2014 "),eme=n(t0o,"A",{href:!0});var Tcn=s(eme);klt=r(Tcn,"TFXLMWithLMHeadModel"),Tcn.forEach(t),$lt=r(t0o," (XLM model)"),t0o.forEach(t),Plt=i(we),xC=n(we,"LI",{});var a0o=s(xC);fGe=n(a0o,"STRONG",{});var Fcn=s(fGe);Rlt=r(Fcn,"xlm-roberta"),Fcn.forEach(t),Ilt=r(a0o," \u2014 "),ome=n(a0o,"A",{href:!0});var Mcn=s(ome);Nlt=r(Mcn,"TFXLMRobertaForCausalLM"),Mcn.forEach(t),Blt=r(a0o," (XLM-RoBERTa model)"),a0o.forEach(t),Glt=i(we),SC=n(we,"LI",{});var n0o=s(SC);mGe=n(n0o,"STRONG",{});var Ecn=s(mGe);qlt=r(Ecn,"xlnet"),Ecn.forEach(t),Dlt=r(n0o," \u2014 "),rme=n(n0o,"A",{href:!0});var Ccn=s(rme);jlt=r(Ccn,"TFXLNetLMHeadModel"),Ccn.forEach(t),Olt=r(n0o," (XLNet model)"),n0o.forEach(t),we.forEach(t),Vlt=i(Ui),T(kC.$$.fragment,Ui),Ui.forEach(t),Qi.forEach(t),Gwo=i(f),cf=n(f,"H3",{class:!0});var wLo=s(cf);$C=n(wLo,"A",{id:!0,class:!0,href:!0});var wcn=s($C);gGe=n(wcn,"SPAN",{});var Acn=s(gGe);T(zq.$$.fragment,Acn),Acn.forEach(t),wcn.forEach(t),Xlt=i(wLo),hGe=n(wLo,"SPAN",{});var Lcn=s(hGe);zlt=r(Lcn,"FlaxAutoModelForCausalLM"),Lcn.forEach(t),wLo.forEach(t),qwo=i(f),Zo=n(f,"DIV",{class:!0});var Hi=s(Zo);T(Wq.$$.fragment,Hi),Wlt=i(Hi),ff=n(Hi,"P",{});var HFe=s(ff);Qlt=r(HFe,`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 `),tme=n(HFe,"A",{href:!0});var ycn=s(tme);Ult=r(ycn,"from_pretrained()"),ycn.forEach(t),Hlt=r(HFe," class method or the "),ame=n(HFe,"A",{href:!0});var xcn=s(ame);Jlt=r(xcn,"from_config()"),xcn.forEach(t),Zlt=r(HFe,` class
method.`),HFe.forEach(t),Ylt=i(Hi),Qq=n(Hi,"P",{});var ALo=s(Qq);Klt=r(ALo,"This class cannot be instantiated directly using "),uGe=n(ALo,"CODE",{});var Scn=s(uGe);eit=r(Scn,"__init__()"),Scn.forEach(t),oit=r(ALo," (throws an error)."),ALo.forEach(t),rit=i(Hi),zt=n(Hi,"DIV",{class:!0});var FN=s(zt);T(Uq.$$.fragment,FN),tit=i(FN),pGe=n(FN,"P",{});var kcn=s(pGe);ait=r(kcn,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),kcn.forEach(t),nit=i(FN),mf=n(FN,"P",{});var JFe=s(mf);sit=r(JFe,`Note:
Loading a model from its configuration file does `),_Ge=n(JFe,"STRONG",{});var $cn=s(_Ge);lit=r($cn,"not"),$cn.forEach(t),iit=r(JFe,` load the model weights. It only affects the
model\u2019s configuration. Use `),nme=n(JFe,"A",{href:!0});var Pcn=s(nme);dit=r(Pcn,"from_pretrained()"),Pcn.forEach(t),cit=r(JFe," to load the model weights."),JFe.forEach(t),fit=i(FN),T(PC.$$.fragment,FN),FN.forEach(t),mit=i(Hi),at=n(Hi,"DIV",{class:!0});var Ji=s(at);T(Hq.$$.fragment,Ji),git=i(Ji),vGe=n(Ji,"P",{});var Rcn=s(vGe);hit=r(Rcn,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),Rcn.forEach(t),uit=i(Ji),qn=n(Ji,"P",{});var MN=s(qn);pit=r(MN,"The model class to instantiate is selected based on the "),bGe=n(MN,"CODE",{});var Icn=s(bGe);_it=r(Icn,"model_type"),Icn.forEach(t),vit=r(MN,` property of the config object (either
passed as an argument or loaded from `),TGe=n(MN,"CODE",{});var Ncn=s(TGe);bit=r(Ncn,"pretrained_model_name_or_path"),Ncn.forEach(t),Tit=r(MN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),FGe=n(MN,"CODE",{});var Bcn=s(FGe);Fit=r(Bcn,"pretrained_model_name_or_path"),Bcn.forEach(t),Mit=r(MN,":"),MN.forEach(t),Eit=i(Ji),ye=n(Ji,"UL",{});var xe=s(ye);RC=n(xe,"LI",{});var s0o=s(RC);MGe=n(s0o,"STRONG",{});var Gcn=s(MGe);Cit=r(Gcn,"bart"),Gcn.forEach(t),wit=r(s0o," \u2014 "),sme=n(s0o,"A",{href:!0});var qcn=s(sme);Ait=r(qcn,"FlaxBartForCausalLM"),qcn.forEach(t),Lit=r(s0o," (BART model)"),s0o.forEach(t),yit=i(xe),IC=n(xe,"LI",{});var l0o=s(IC);EGe=n(l0o,"STRONG",{});var Dcn=s(EGe);xit=r(Dcn,"bert"),Dcn.forEach(t),Sit=r(l0o," \u2014 "),lme=n(l0o,"A",{href:!0});var jcn=s(lme);kit=r(jcn,"FlaxBertForCausalLM"),jcn.forEach(t),$it=r(l0o," (BERT model)"),l0o.forEach(t),Pit=i(xe),NC=n(xe,"LI",{});var i0o=s(NC);CGe=n(i0o,"STRONG",{});var Ocn=s(CGe);Rit=r(Ocn,"big_bird"),Ocn.forEach(t),Iit=r(i0o," \u2014 "),ime=n(i0o,"A",{href:!0});var Vcn=s(ime);Nit=r(Vcn,"FlaxBigBirdForCausalLM"),Vcn.forEach(t),Bit=r(i0o," (BigBird model)"),i0o.forEach(t),Git=i(xe),BC=n(xe,"LI",{});var d0o=s(BC);wGe=n(d0o,"STRONG",{});var Xcn=s(wGe);qit=r(Xcn,"electra"),Xcn.forEach(t),Dit=r(d0o," \u2014 "),dme=n(d0o,"A",{href:!0});var zcn=s(dme);jit=r(zcn,"FlaxElectraForCausalLM"),zcn.forEach(t),Oit=r(d0o," (ELECTRA model)"),d0o.forEach(t),Vit=i(xe),GC=n(xe,"LI",{});var c0o=s(GC);AGe=n(c0o,"STRONG",{});var Wcn=s(AGe);Xit=r(Wcn,"gpt-sw3"),Wcn.forEach(t),zit=r(c0o," \u2014 "),cme=n(c0o,"A",{href:!0});var Qcn=s(cme);Wit=r(Qcn,"FlaxGPT2LMHeadModel"),Qcn.forEach(t),Qit=r(c0o," (GPT-Sw3 model)"),c0o.forEach(t),Uit=i(xe),qC=n(xe,"LI",{});var f0o=s(qC);LGe=n(f0o,"STRONG",{});var Ucn=s(LGe);Hit=r(Ucn,"gpt2"),Ucn.forEach(t),Jit=r(f0o," \u2014 "),fme=n(f0o,"A",{href:!0});var Hcn=s(fme);Zit=r(Hcn,"FlaxGPT2LMHeadModel"),Hcn.forEach(t),Yit=r(f0o," (OpenAI GPT-2 model)"),f0o.forEach(t),Kit=i(xe),DC=n(xe,"LI",{});var m0o=s(DC);yGe=n(m0o,"STRONG",{});var Jcn=s(yGe);edt=r(Jcn,"gpt_neo"),Jcn.forEach(t),odt=r(m0o," \u2014 "),mme=n(m0o,"A",{href:!0});var Zcn=s(mme);rdt=r(Zcn,"FlaxGPTNeoForCausalLM"),Zcn.forEach(t),tdt=r(m0o," (GPT Neo model)"),m0o.forEach(t),adt=i(xe),jC=n(xe,"LI",{});var g0o=s(jC);xGe=n(g0o,"STRONG",{});var Ycn=s(xGe);ndt=r(Ycn,"gptj"),Ycn.forEach(t),sdt=r(g0o," \u2014 "),gme=n(g0o,"A",{href:!0});var Kcn=s(gme);ldt=r(Kcn,"FlaxGPTJForCausalLM"),Kcn.forEach(t),idt=r(g0o," (GPT-J model)"),g0o.forEach(t),ddt=i(xe),OC=n(xe,"LI",{});var h0o=s(OC);SGe=n(h0o,"STRONG",{});var efn=s(SGe);cdt=r(efn,"opt"),efn.forEach(t),fdt=r(h0o," \u2014 "),hme=n(h0o,"A",{href:!0});var ofn=s(hme);mdt=r(ofn,"FlaxOPTForCausalLM"),ofn.forEach(t),gdt=r(h0o," (OPT model)"),h0o.forEach(t),hdt=i(xe),VC=n(xe,"LI",{});var u0o=s(VC);kGe=n(u0o,"STRONG",{});var rfn=s(kGe);udt=r(rfn,"roberta"),rfn.forEach(t),pdt=r(u0o," \u2014 "),ume=n(u0o,"A",{href:!0});var tfn=s(ume);_dt=r(tfn,"FlaxRobertaForCausalLM"),tfn.forEach(t),vdt=r(u0o," (RoBERTa model)"),u0o.forEach(t),bdt=i(xe),XC=n(xe,"LI",{});var p0o=s(XC);$Ge=n(p0o,"STRONG",{});var afn=s($Ge);Tdt=r(afn,"roberta-prelayernorm"),afn.forEach(t),Fdt=r(p0o," \u2014 "),pme=n(p0o,"A",{href:!0});var nfn=s(pme);Mdt=r(nfn,"FlaxRobertaPreLayerNormForCausalLM"),nfn.forEach(t),Edt=r(p0o," (RoBERTa-PreLayerNorm model)"),p0o.forEach(t),Cdt=i(xe),zC=n(xe,"LI",{});var _0o=s(zC);PGe=n(_0o,"STRONG",{});var sfn=s(PGe);wdt=r(sfn,"xglm"),sfn.forEach(t),Adt=r(_0o," \u2014 "),_me=n(_0o,"A",{href:!0});var lfn=s(_me);Ldt=r(lfn,"FlaxXGLMForCausalLM"),lfn.forEach(t),ydt=r(_0o," (XGLM model)"),_0o.forEach(t),xdt=i(xe),WC=n(xe,"LI",{});var v0o=s(WC);RGe=n(v0o,"STRONG",{});var ifn=s(RGe);Sdt=r(ifn,"xlm-roberta"),ifn.forEach(t),kdt=r(v0o," \u2014 "),vme=n(v0o,"A",{href:!0});var dfn=s(vme);$dt=r(dfn,"FlaxXLMRobertaForCausalLM"),dfn.forEach(t),Pdt=r(v0o," (XLM-RoBERTa model)"),v0o.forEach(t),xe.forEach(t),Rdt=i(Ji),T(QC.$$.fragment,Ji),Ji.forEach(t),Hi.forEach(t),Dwo=i(f),gf=n(f,"H3",{class:!0});var LLo=s(gf);UC=n(LLo,"A",{id:!0,class:!0,href:!0});var cfn=s(UC);IGe=n(cfn,"SPAN",{});var ffn=s(IGe);T(Jq.$$.fragment,ffn),ffn.forEach(t),cfn.forEach(t),Idt=i(LLo),NGe=n(LLo,"SPAN",{});var mfn=s(NGe);Ndt=r(mfn,"AutoModelForMaskedLM"),mfn.forEach(t),LLo.forEach(t),jwo=i(f),Yo=n(f,"DIV",{class:!0});var Zi=s(Yo);T(Zq.$$.fragment,Zi),Bdt=i(Zi),hf=n(Zi,"P",{});var ZFe=s(hf);Gdt=r(ZFe,`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 `),bme=n(ZFe,"A",{href:!0});var gfn=s(bme);qdt=r(gfn,"from_pretrained()"),gfn.forEach(t),Ddt=r(ZFe," class method or the "),Tme=n(ZFe,"A",{href:!0});var hfn=s(Tme);jdt=r(hfn,"from_config()"),hfn.forEach(t),Odt=r(ZFe,` class
method.`),ZFe.forEach(t),Vdt=i(Zi),Yq=n(Zi,"P",{});var yLo=s(Yq);Xdt=r(yLo,"This class cannot be instantiated directly using "),BGe=n(yLo,"CODE",{});var ufn=s(BGe);zdt=r(ufn,"__init__()"),ufn.forEach(t),Wdt=r(yLo," (throws an error)."),yLo.forEach(t),Qdt=i(Zi),Wt=n(Zi,"DIV",{class:!0});var EN=s(Wt);T(Kq.$$.fragment,EN),Udt=i(EN),GGe=n(EN,"P",{});var pfn=s(GGe);Hdt=r(pfn,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),pfn.forEach(t),Jdt=i(EN),uf=n(EN,"P",{});var YFe=s(uf);Zdt=r(YFe,`Note:
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model\u2019s configuration. Use `),Fme=n(YFe,"A",{href:!0});var vfn=s(Fme);ect=r(vfn,"from_pretrained()"),vfn.forEach(t),oct=r(YFe," to load the model weights."),YFe.forEach(t),rct=i(EN),T(HC.$$.fragment,EN),EN.forEach(t),tct=i(Zi),co=n(Zi,"DIV",{class:!0});var Ja=s(co);T(eD.$$.fragment,Ja),act=i(Ja),DGe=n(Ja,"P",{});var bfn=s(DGe);nct=r(bfn,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),bfn.forEach(t),sct=i(Ja),Dn=n(Ja,"P",{});var CN=s(Dn);lct=r(CN,"The model class to instantiate is selected based on the "),jGe=n(CN,"CODE",{});var Tfn=s(jGe);ict=r(Tfn,"model_type"),Tfn.forEach(t),dct=r(CN,` property of the config object (either
passed as an argument or loaded from `),OGe=n(CN,"CODE",{});var Ffn=s(OGe);cct=r(Ffn,"pretrained_model_name_or_path"),Ffn.forEach(t),fct=r(CN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),VGe=n(CN,"CODE",{});var Mfn=s(VGe);mct=r(Mfn,"pretrained_model_name_or_path"),Mfn.forEach(t),gct=r(CN,":"),CN.forEach(t),hct=i(Ja),K=n(Ja,"UL",{});var ee=s(K);JC=n(ee,"LI",{});var b0o=s(JC);XGe=n(b0o,"STRONG",{});var Efn=s(XGe);uct=r(Efn,"albert"),Efn.forEach(t),pct=r(b0o," \u2014 "),Mme=n(b0o,"A",{href:!0});var Cfn=s(Mme);_ct=r(Cfn,"AlbertForMaskedLM"),Cfn.forEach(t),vct=r(b0o," (ALBERT model)"),b0o.forEach(t),bct=i(ee),ZC=n(ee,"LI",{});var T0o=s(ZC);zGe=n(T0o,"STRONG",{});var wfn=s(zGe);Tct=r(wfn,"bart"),wfn.forEach(t),Fct=r(T0o," \u2014 "),Eme=n(T0o,"A",{href:!0});var Afn=s(Eme);Mct=r(Afn,"BartForConditionalGeneration"),Afn.forEach(t),Ect=r(T0o," (BART model)"),T0o.forEach(t),Cct=i(ee),YC=n(ee,"LI",{});var F0o=s(YC);WGe=n(F0o,"STRONG",{});var Lfn=s(WGe);wct=r(Lfn,"bert"),Lfn.forEach(t),Act=r(F0o," \u2014 "),Cme=n(F0o,"A",{href:!0});var yfn=s(Cme);Lct=r(yfn,"BertForMaskedLM"),yfn.forEach(t),yct=r(F0o," (BERT 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abo=s(I5);Sqe=n(abo,"STRONG",{});var Qmn=s(Sqe);Ugt=r(Qmn,"xlm"),Qmn.forEach(t),Hgt=r(abo," \u2014 "),sge=n(abo,"A",{href:!0});var Umn=s(sge);Jgt=r(Umn,"XLMWithLMHeadModel"),Umn.forEach(t),Zgt=r(abo," (XLM model)"),abo.forEach(t),Ygt=i(ee),N5=n(ee,"LI",{});var nbo=s(N5);kqe=n(nbo,"STRONG",{});var Hmn=s(kqe);Kgt=r(Hmn,"xlm-roberta"),Hmn.forEach(t),eht=r(nbo," \u2014 "),lge=n(nbo,"A",{href:!0});var Jmn=s(lge);oht=r(Jmn,"XLMRobertaForMaskedLM"),Jmn.forEach(t),rht=r(nbo," (XLM-RoBERTa model)"),nbo.forEach(t),tht=i(ee),B5=n(ee,"LI",{});var sbo=s(B5);$qe=n(sbo,"STRONG",{});var Zmn=s($qe);aht=r(Zmn,"xlm-roberta-xl"),Zmn.forEach(t),nht=r(sbo," \u2014 "),ige=n(sbo,"A",{href:!0});var Ymn=s(ige);sht=r(Ymn,"XLMRobertaXLForMaskedLM"),Ymn.forEach(t),lht=r(sbo," (XLM-RoBERTa-XL model)"),sbo.forEach(t),iht=i(ee),G5=n(ee,"LI",{});var lbo=s(G5);Pqe=n(lbo,"STRONG",{});var Kmn=s(Pqe);dht=r(Kmn,"xmod"),Kmn.forEach(t),cht=r(lbo," \u2014 "),dge=n(lbo,"A",{href:!0});var egn=s(dge);fht=r(egn,"XmodForMaskedLM"),egn.forEach(t),mht=r(lbo," (X-MOD model)"),lbo.forEach(t),ght=i(ee),q5=n(ee,"LI",{});var ibo=s(q5);Rqe=n(ibo,"STRONG",{});var ogn=s(Rqe);hht=r(ogn,"yoso"),ogn.forEach(t),uht=r(ibo," \u2014 "),cge=n(ibo,"A",{href:!0});var rgn=s(cge);pht=r(rgn,"YosoForMaskedLM"),rgn.forEach(t),_ht=r(ibo," (YOSO model)"),ibo.forEach(t),ee.forEach(t),vht=i(Ja),D5=n(Ja,"P",{});var dbo=s(D5);bht=r(dbo,"The model is set in evaluation mode by default using "),Iqe=n(dbo,"CODE",{});var tgn=s(Iqe);Tht=r(tgn,"model.eval()"),tgn.forEach(t),Fht=r(dbo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Nqe=n(dbo,"CODE",{});var agn=s(Nqe);Mht=r(agn,"model.train()"),agn.forEach(t),dbo.forEach(t),Eht=i(Ja),T(j5.$$.fragment,Ja),Ja.forEach(t),Zi.forEach(t),Owo=i(f),pf=n(f,"H3",{class:!0});var xLo=s(pf);O5=n(xLo,"A",{id:!0,class:!0,href:!0});var ngn=s(O5);Bqe=n(ngn,"SPAN",{});var sgn=s(Bqe);T(oD.$$.fragment,sgn),sgn.forEach(t),ngn.forEach(t),Cht=i(xLo),Gqe=n(xLo,"SPAN",{});var lgn=s(Gqe);wht=r(lgn,"TFAutoModelForMaskedLM"),lgn.forEach(t),xLo.forEach(t),Vwo=i(f),Ko=n(f,"DIV",{class:!0});var Yi=s(Ko);T(rD.$$.fragment,Yi),Aht=i(Yi),_f=n(Yi,"P",{});var KFe=s(_f);Lht=r(KFe,`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 `),fge=n(KFe,"A",{href:!0});var ign=s(fge);yht=r(ign,"from_pretrained()"),ign.forEach(t),xht=r(KFe," class method or the "),mge=n(KFe,"A",{href:!0});var dgn=s(mge);Sht=r(dgn,"from_config()"),dgn.forEach(t),kht=r(KFe,` class
method.`),KFe.forEach(t),$ht=i(Yi),tD=n(Yi,"P",{});var SLo=s(tD);Pht=r(SLo,"This class cannot be instantiated directly using "),qqe=n(SLo,"CODE",{});var cgn=s(qqe);Rht=r(cgn,"__init__()"),cgn.forEach(t),Iht=r(SLo," (throws an error)."),SLo.forEach(t),Nht=i(Yi),Qt=n(Yi,"DIV",{class:!0});var wN=s(Qt);T(aD.$$.fragment,wN),Bht=i(wN),Dqe=n(wN,"P",{});var fgn=s(Dqe);Ght=r(fgn,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),fgn.forEach(t),qht=i(wN),vf=n(wN,"P",{});var eMe=s(vf);Dht=r(eMe,`Note:
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model\u2019s configuration. Use `),gge=n(eMe,"A",{href:!0});var ggn=s(gge);Vht=r(ggn,"from_pretrained()"),ggn.forEach(t),Xht=r(eMe," to load the model weights."),eMe.forEach(t),zht=i(wN),T(V5.$$.fragment,wN),wN.forEach(t),Wht=i(Yi),nt=n(Yi,"DIV",{class:!0});var Ki=s(nt);T(nD.$$.fragment,Ki),Qht=i(Ki),Oqe=n(Ki,"P",{});var hgn=s(Oqe);Uht=r(hgn,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),hgn.forEach(t),Hht=i(Ki),jn=n(Ki,"P",{});var AN=s(jn);Jht=r(AN,"The model class to instantiate is selected based on the "),Vqe=n(AN,"CODE",{});var ugn=s(Vqe);Zht=r(ugn,"model_type"),ugn.forEach(t),Yht=r(AN,` property of the config object (either
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falling back to using pattern matching on `),zqe=n(AN,"CODE",{});var _gn=s(zqe);out=r(_gn,"pretrained_model_name_or_path"),_gn.forEach(t),rut=r(AN,":"),AN.forEach(t),tut=i(Ki),ue=n(Ki,"UL",{});var be=s(ue);X5=n(be,"LI",{});var cbo=s(X5);Wqe=n(cbo,"STRONG",{});var vgn=s(Wqe);aut=r(vgn,"albert"),vgn.forEach(t),nut=r(cbo," \u2014 "),hge=n(cbo,"A",{href:!0});var bgn=s(hge);sut=r(bgn,"TFAlbertForMaskedLM"),bgn.forEach(t),lut=r(cbo," (ALBERT model)"),cbo.forEach(t),iut=i(be),z5=n(be,"LI",{});var fbo=s(z5);Qqe=n(fbo,"STRONG",{});var Tgn=s(Qqe);dut=r(Tgn,"bert"),Tgn.forEach(t),cut=r(fbo," \u2014 "),uge=n(fbo,"A",{href:!0});var Fgn=s(uge);fut=r(Fgn,"TFBertForMaskedLM"),Fgn.forEach(t),mut=r(fbo," (BERT model)"),fbo.forEach(t),gut=i(be),W5=n(be,"LI",{});var mbo=s(W5);Uqe=n(mbo,"STRONG",{});var Mgn=s(Uqe);hut=r(Mgn,"camembert"),Mgn.forEach(t),uut=r(mbo," \u2014 "),pge=n(mbo,"A",{href:!0});var Egn=s(pge);put=r(Egn,"TFCamembertForMaskedLM"),Egn.forEach(t),_ut=r(mbo," (CamemBERT 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nhn=s(Rge);Ypt=r(nhn,"TFXLMWithLMHeadModel"),nhn.forEach(t),Kpt=r(Sbo," (XLM model)"),Sbo.forEach(t),e_t=i(be),f8=n(be,"LI",{});var kbo=s(f8);gDe=n(kbo,"STRONG",{});var shn=s(gDe);o_t=r(shn,"xlm-roberta"),shn.forEach(t),r_t=r(kbo," \u2014 "),Ige=n(kbo,"A",{href:!0});var lhn=s(Ige);t_t=r(lhn,"TFXLMRobertaForMaskedLM"),lhn.forEach(t),a_t=r(kbo," (XLM-RoBERTa model)"),kbo.forEach(t),be.forEach(t),n_t=i(Ki),T(m8.$$.fragment,Ki),Ki.forEach(t),Yi.forEach(t),Xwo=i(f),bf=n(f,"H3",{class:!0});var kLo=s(bf);g8=n(kLo,"A",{id:!0,class:!0,href:!0});var ihn=s(g8);hDe=n(ihn,"SPAN",{});var dhn=s(hDe);T(sD.$$.fragment,dhn),dhn.forEach(t),ihn.forEach(t),s_t=i(kLo),uDe=n(kLo,"SPAN",{});var chn=s(uDe);l_t=r(chn,"FlaxAutoModelForMaskedLM"),chn.forEach(t),kLo.forEach(t),zwo=i(f),er=n(f,"DIV",{class:!0});var ed=s(er);T(lD.$$.fragment,ed),i_t=i(ed),Tf=n(ed,"P",{});var oMe=s(Tf);d_t=r(oMe,`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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method.`),oMe.forEach(t),h_t=i(ed),iD=n(ed,"P",{});var $Lo=s(iD);u_t=r($Lo,"This class cannot be instantiated directly using "),pDe=n($Lo,"CODE",{});var ghn=s(pDe);p_t=r(ghn,"__init__()"),ghn.forEach(t),__t=r($Lo," (throws an error)."),$Lo.forEach(t),v_t=i(ed),Ut=n(ed,"DIV",{class:!0});var LN=s(Ut);T(dD.$$.fragment,LN),b_t=i(LN),_De=n(LN,"P",{});var hhn=s(_De);T_t=r(hhn,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),hhn.forEach(t),F_t=i(LN),Ff=n(LN,"P",{});var rMe=s(Ff);M_t=r(rMe,`Note:
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model\u2019s configuration. Use `),Gge=n(rMe,"A",{href:!0});var phn=s(Gge);w_t=r(phn,"from_pretrained()"),phn.forEach(t),A_t=r(rMe," to load the model weights."),rMe.forEach(t),L_t=i(LN),T(h8.$$.fragment,LN),LN.forEach(t),y_t=i(ed),st=n(ed,"DIV",{class:!0});var od=s(st);T(cD.$$.fragment,od),x_t=i(od),bDe=n(od,"P",{});var _hn=s(bDe);S_t=r(_hn,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),_hn.forEach(t),k_t=i(od),On=n(od,"P",{});var yN=s(On);$_t=r(yN,"The model class to instantiate is selected based on the "),TDe=n(yN,"CODE",{});var vhn=s(TDe);P_t=r(vhn,"model_type"),vhn.forEach(t),R_t=r(yN,` property of the config object (either
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falling back to using pattern matching on `),MDe=n(yN,"CODE",{});var Thn=s(MDe);B_t=r(Thn,"pretrained_model_name_or_path"),Thn.forEach(t),G_t=r(yN,":"),yN.forEach(t),q_t=i(od),Se=n(od,"UL",{});var qe=s(Se);u8=n(qe,"LI",{});var $bo=s(u8);EDe=n($bo,"STRONG",{});var Fhn=s(EDe);D_t=r(Fhn,"albert"),Fhn.forEach(t),j_t=r($bo," \u2014 "),qge=n($bo,"A",{href:!0});var Mhn=s(qge);O_t=r(Mhn,"FlaxAlbertForMaskedLM"),Mhn.forEach(t),V_t=r($bo," (ALBERT model)"),$bo.forEach(t),X_t=i(qe),p8=n(qe,"LI",{});var Pbo=s(p8);CDe=n(Pbo,"STRONG",{});var Ehn=s(CDe);z_t=r(Ehn,"bart"),Ehn.forEach(t),W_t=r(Pbo," \u2014 "),Dge=n(Pbo,"A",{href:!0});var Chn=s(Dge);Q_t=r(Chn,"FlaxBartForConditionalGeneration"),Chn.forEach(t),U_t=r(Pbo," (BART model)"),Pbo.forEach(t),H_t=i(qe),_8=n(qe,"LI",{});var Rbo=s(_8);wDe=n(Rbo,"STRONG",{});var whn=s(wDe);J_t=r(whn,"bert"),whn.forEach(t),Z_t=r(Rbo," \u2014 "),jge=n(Rbo,"A",{href:!0});var Ahn=s(jge);Y_t=r(Ahn,"FlaxBertForMaskedLM"),Ahn.forEach(t),K_t=r(Rbo," (BERT model)"),Rbo.forEach(t),e2t=i(qe),v8=n(qe,"LI",{});var Ibo=s(v8);ADe=n(Ibo,"STRONG",{});var Lhn=s(ADe);o2t=r(Lhn,"big_bird"),Lhn.forEach(t),r2t=r(Ibo," \u2014 "),Oge=n(Ibo,"A",{href:!0});var yhn=s(Oge);t2t=r(yhn,"FlaxBigBirdForMaskedLM"),yhn.forEach(t),a2t=r(Ibo," (BigBird model)"),Ibo.forEach(t),n2t=i(qe),b8=n(qe,"LI",{});var Nbo=s(b8);LDe=n(Nbo,"STRONG",{});var xhn=s(LDe);s2t=r(xhn,"distilbert"),xhn.forEach(t),l2t=r(Nbo," \u2014 "),Vge=n(Nbo,"A",{href:!0});var Shn=s(Vge);i2t=r(Shn,"FlaxDistilBertForMaskedLM"),Shn.forEach(t),d2t=r(Nbo," (DistilBERT model)"),Nbo.forEach(t),c2t=i(qe),T8=n(qe,"LI",{});var Bbo=s(T8);yDe=n(Bbo,"STRONG",{});var khn=s(yDe);f2t=r(khn,"electra"),khn.forEach(t),m2t=r(Bbo," \u2014 "),Xge=n(Bbo,"A",{href:!0});var $hn=s(Xge);g2t=r($hn,"FlaxElectraForMaskedLM"),$hn.forEach(t),h2t=r(Bbo," (ELECTRA model)"),Bbo.forEach(t),u2t=i(qe),F8=n(qe,"LI",{});var Gbo=s(F8);xDe=n(Gbo,"STRONG",{});var Phn=s(xDe);p2t=r(Phn,"mbart"),Phn.forEach(t),_2t=r(Gbo," \u2014 "),zge=n(Gbo,"A",{href:!0});var Rhn=s(zge);v2t=r(Rhn,"FlaxMBartForConditionalGeneration"),Rhn.forEach(t),b2t=r(Gbo," (mBART model)"),Gbo.forEach(t),T2t=i(qe),M8=n(qe,"LI",{});var qbo=s(M8);SDe=n(qbo,"STRONG",{});var Ihn=s(SDe);F2t=r(Ihn,"roberta"),Ihn.forEach(t),M2t=r(qbo," \u2014 "),Wge=n(qbo,"A",{href:!0});var Nhn=s(Wge);E2t=r(Nhn,"FlaxRobertaForMaskedLM"),Nhn.forEach(t),C2t=r(qbo," (RoBERTa model)"),qbo.forEach(t),w2t=i(qe),E8=n(qe,"LI",{});var Dbo=s(E8);kDe=n(Dbo,"STRONG",{});var Bhn=s(kDe);A2t=r(Bhn,"roberta-prelayernorm"),Bhn.forEach(t),L2t=r(Dbo," \u2014 "),Qge=n(Dbo,"A",{href:!0});var Ghn=s(Qge);y2t=r(Ghn,"FlaxRobertaPreLayerNormForMaskedLM"),Ghn.forEach(t),x2t=r(Dbo," (RoBERTa-PreLayerNorm model)"),Dbo.forEach(t),S2t=i(qe),C8=n(qe,"LI",{});var jbo=s(C8);$De=n(jbo,"STRONG",{});var qhn=s($De);k2t=r(qhn,"roformer"),qhn.forEach(t),$2t=r(jbo," \u2014 "),Uge=n(jbo,"A",{href:!0});var Dhn=s(Uge);P2t=r(Dhn,"FlaxRoFormerForMaskedLM"),Dhn.forEach(t),R2t=r(jbo," (RoFormer model)"),jbo.forEach(t),I2t=i(qe),w8=n(qe,"LI",{});var Obo=s(w8);PDe=n(Obo,"STRONG",{});var jhn=s(PDe);N2t=r(jhn,"xlm-roberta"),jhn.forEach(t),B2t=r(Obo," \u2014 "),Hge=n(Obo,"A",{href:!0});var Ohn=s(Hge);G2t=r(Ohn,"FlaxXLMRobertaForMaskedLM"),Ohn.forEach(t),q2t=r(Obo," (XLM-RoBERTa model)"),Obo.forEach(t),qe.forEach(t),D2t=i(od),T(A8.$$.fragment,od),od.forEach(t),ed.forEach(t),Wwo=i(f),Mf=n(f,"H3",{class:!0});var PLo=s(Mf);L8=n(PLo,"A",{id:!0,class:!0,href:!0});var Vhn=s(L8);RDe=n(Vhn,"SPAN",{});var Xhn=s(RDe);T(fD.$$.fragment,Xhn),Xhn.forEach(t),Vhn.forEach(t),j2t=i(PLo),IDe=n(PLo,"SPAN",{});var zhn=s(IDe);O2t=r(zhn,"AutoModelForMaskGeneration"),zhn.forEach(t),PLo.forEach(t),Qwo=i(f),mD=n(f,"DIV",{class:!0});var Whn=s(mD);T(gD.$$.fragment,Whn),Whn.forEach(t),Uwo=i(f),Ef=n(f,"H3",{class:!0});var RLo=s(Ef);y8=n(RLo,"A",{id:!0,class:!0,href:!0});var Qhn=s(y8);NDe=n(Qhn,"SPAN",{});var Uhn=s(NDe);T(hD.$$.fragment,Uhn),Uhn.forEach(t),Qhn.forEach(t),V2t=i(RLo),BDe=n(RLo,"SPAN",{});var Hhn=s(BDe);X2t=r(Hhn,"AutoModelForSeq2SeqLM"),Hhn.forEach(t),RLo.forEach(t),Hwo=i(f),or=n(f,"DIV",{class:!0});var rd=s(or);T(uD.$$.fragment,rd),z2t=i(rd),Cf=n(rd,"P",{});var tMe=s(Cf);W2t=r(tMe,`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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Loading a model from its configuration file does `),DDe=n(aMe,"STRONG",{});var eun=s(DDe);svt=r(eun,"not"),eun.forEach(t),lvt=r(aMe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Yge=n(aMe,"A",{href:!0});var oun=s(Yge);ivt=r(oun,"from_pretrained()"),oun.forEach(t),dvt=r(aMe," to load the model weights."),aMe.forEach(t),cvt=i(xN),T(x8.$$.fragment,xN),xN.forEach(t),fvt=i(rd),fo=n(rd,"DIV",{class:!0});var Za=s(fo);T(vD.$$.fragment,Za),mvt=i(Za),jDe=n(Za,"P",{});var run=s(jDe);gvt=r(run,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),run.forEach(t),hvt=i(Za),Vn=n(Za,"P",{});var SN=s(Vn);uvt=r(SN,"The model class to instantiate is selected based on the "),ODe=n(SN,"CODE",{});var tun=s(ODe);pvt=r(tun,"model_type"),tun.forEach(t),_vt=r(SN,` property of the config object (either
passed as an argument or loaded from `),VDe=n(SN,"CODE",{});var aun=s(VDe);vvt=r(aun,"pretrained_model_name_or_path"),aun.forEach(t),bvt=r(SN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),XDe=n(SN,"CODE",{});var nun=s(XDe);Tvt=r(nun,"pretrained_model_name_or_path"),nun.forEach(t),Fvt=r(SN,":"),SN.forEach(t),Mvt=i(Za),pe=n(Za,"UL",{});var Te=s(pe);S8=n(Te,"LI",{});var Vbo=s(S8);zDe=n(Vbo,"STRONG",{});var sun=s(zDe);Evt=r(sun,"bart"),sun.forEach(t),Cvt=r(Vbo," \u2014 "),Kge=n(Vbo,"A",{href:!0});var lun=s(Kge);wvt=r(lun,"BartForConditionalGeneration"),lun.forEach(t),Avt=r(Vbo," (BART model)"),Vbo.forEach(t),Lvt=i(Te),k8=n(Te,"LI",{});var Xbo=s(k8);WDe=n(Xbo,"STRONG",{});var iun=s(WDe);yvt=r(iun,"bigbird_pegasus"),iun.forEach(t),xvt=r(Xbo," \u2014 "),ehe=n(Xbo,"A",{href:!0});var dun=s(ehe);Svt=r(dun,"BigBirdPegasusForConditionalGeneration"),dun.forEach(t),kvt=r(Xbo," (BigBird-Pegasus model)"),Xbo.forEach(t),$vt=i(Te),$8=n(Te,"LI",{});var zbo=s($8);QDe=n(zbo,"STRONG",{});var cun=s(QDe);Pvt=r(cun,"blenderbot"),cun.forEach(t),Rvt=r(zbo," \u2014 "),ohe=n(zbo,"A",{href:!0});var fun=s(ohe);Ivt=r(fun,"BlenderbotForConditionalGeneration"),fun.forEach(t),Nvt=r(zbo," (Blenderbot model)"),zbo.forEach(t),Bvt=i(Te),P8=n(Te,"LI",{});var Wbo=s(P8);UDe=n(Wbo,"STRONG",{});var mun=s(UDe);Gvt=r(mun,"blenderbot-small"),mun.forEach(t),qvt=r(Wbo," \u2014 "),rhe=n(Wbo,"A",{href:!0});var gun=s(rhe);Dvt=r(gun,"BlenderbotSmallForConditionalGeneration"),gun.forEach(t),jvt=r(Wbo," (BlenderbotSmall model)"),Wbo.forEach(t),Ovt=i(Te),R8=n(Te,"LI",{});var Qbo=s(R8);HDe=n(Qbo,"STRONG",{});var hun=s(HDe);Vvt=r(hun,"encoder-decoder"),hun.forEach(t),Xvt=r(Qbo," \u2014 "),the=n(Qbo,"A",{href:!0});var uun=s(the);zvt=r(uun,"EncoderDecoderModel"),uun.forEach(t),Wvt=r(Qbo," (Encoder decoder model)"),Qbo.forEach(t),Qvt=i(Te),I8=n(Te,"LI",{});var Ubo=s(I8);JDe=n(Ubo,"STRONG",{});var pun=s(JDe);Uvt=r(pun,"fsmt"),pun.forEach(t),Hvt=r(Ubo," \u2014 "),ahe=n(Ubo,"A",{href:!0});var _un=s(ahe);Jvt=r(_un,"FSMTForConditionalGeneration"),_un.forEach(t),Zvt=r(Ubo," (FairSeq Machine-Translation model)"),Ubo.forEach(t),Yvt=i(Te),N8=n(Te,"LI",{});var Hbo=s(N8);ZDe=n(Hbo,"STRONG",{});var vun=s(ZDe);Kvt=r(vun,"gptsan-japanese"),vun.forEach(t),e1t=r(Hbo," \u2014 "),nhe=n(Hbo,"A",{href:!0});var bun=s(nhe);o1t=r(bun,"GPTSanJapaneseForConditionalGeneration"),bun.forEach(t),r1t=r(Hbo," (GPTSAN-japanese model)"),Hbo.forEach(t),t1t=i(Te),B8=n(Te,"LI",{});var Jbo=s(B8);YDe=n(Jbo,"STRONG",{});var Tun=s(YDe);a1t=r(Tun,"led"),Tun.forEach(t),n1t=r(Jbo," \u2014 "),she=n(Jbo,"A",{href:!0});var Fun=s(she);s1t=r(Fun,"LEDForConditionalGeneration"),Fun.forEach(t),l1t=r(Jbo," (LED model)"),Jbo.forEach(t),i1t=i(Te),G8=n(Te,"LI",{});var Zbo=s(G8);KDe=n(Zbo,"STRONG",{});var Mun=s(KDe);d1t=r(Mun,"longt5"),Mun.forEach(t),c1t=r(Zbo," \u2014 "),lhe=n(Zbo,"A",{href:!0});var Eun=s(lhe);f1t=r(Eun,"LongT5ForConditionalGeneration"),Eun.forEach(t),m1t=r(Zbo," (LongT5 model)"),Zbo.forEach(t),g1t=i(Te),q8=n(Te,"LI",{});var Ybo=s(q8);eje=n(Ybo,"STRONG",{});var Cun=s(eje);h1t=r(Cun,"m2m_100"),Cun.forEach(t),u1t=r(Ybo," \u2014 "),ihe=n(Ybo,"A",{href:!0});var wun=s(ihe);p1t=r(wun,"M2M100ForConditionalGeneration"),wun.forEach(t),_1t=r(Ybo," (M2M100 model)"),Ybo.forEach(t),v1t=i(Te),D8=n(Te,"LI",{});var Kbo=s(D8);oje=n(Kbo,"STRONG",{});var Aun=s(oje);b1t=r(Aun,"marian"),Aun.forEach(t),T1t=r(Kbo," \u2014 "),dhe=n(Kbo,"A",{href:!0});var Lun=s(dhe);F1t=r(Lun,"MarianMTModel"),Lun.forEach(t),M1t=r(Kbo," (Marian model)"),Kbo.forEach(t),E1t=i(Te),j8=n(Te,"LI",{});var e9o=s(j8);rje=n(e9o,"STRONG",{});var yun=s(rje);C1t=r(yun,"mbart"),yun.forEach(t),w1t=r(e9o," \u2014 "),che=n(e9o,"A",{href:!0});var xun=s(che);A1t=r(xun,"MBartForConditionalGeneration"),xun.forEach(t),L1t=r(e9o," (mBART model)"),e9o.forEach(t),y1t=i(Te),O8=n(Te,"LI",{});var o9o=s(O8);tje=n(o9o,"STRONG",{});var Sun=s(tje);x1t=r(Sun,"mt5"),Sun.forEach(t),S1t=r(o9o," \u2014 "),fhe=n(o9o,"A",{href:!0});var kun=s(fhe);k1t=r(kun,"MT5ForConditionalGeneration"),kun.forEach(t),$1t=r(o9o," (MT5 model)"),o9o.forEach(t),P1t=i(Te),V8=n(Te,"LI",{});var r9o=s(V8);aje=n(r9o,"STRONG",{});var $un=s(aje);R1t=r($un,"mvp"),$un.forEach(t),I1t=r(r9o," \u2014 "),mhe=n(r9o,"A",{href:!0});var Pun=s(mhe);N1t=r(Pun,"MvpForConditionalGeneration"),Pun.forEach(t),B1t=r(r9o," (MVP model)"),r9o.forEach(t),G1t=i(Te),X8=n(Te,"LI",{});var t9o=s(X8);nje=n(t9o,"STRONG",{});var Run=s(nje);q1t=r(Run,"nllb-moe"),Run.forEach(t),D1t=r(t9o," \u2014 "),ghe=n(t9o,"A",{href:!0});var Iun=s(ghe);j1t=r(Iun,"NllbMoeForConditionalGeneration"),Iun.forEach(t),O1t=r(t9o," (NLLB-MOE model)"),t9o.forEach(t),V1t=i(Te),z8=n(Te,"LI",{});var a9o=s(z8);sje=n(a9o,"STRONG",{});var Nun=s(sje);X1t=r(Nun,"pegasus"),Nun.forEach(t),z1t=r(a9o," \u2014 "),hhe=n(a9o,"A",{href:!0});var Bun=s(hhe);W1t=r(Bun,"PegasusForConditionalGeneration"),Bun.forEach(t),Q1t=r(a9o," (Pegasus model)"),a9o.forEach(t),U1t=i(Te),W8=n(Te,"LI",{});var n9o=s(W8);lje=n(n9o,"STRONG",{});var Gun=s(lje);H1t=r(Gun,"pegasus_x"),Gun.forEach(t),J1t=r(n9o," \u2014 "),uhe=n(n9o,"A",{href:!0});var qun=s(uhe);Z1t=r(qun,"PegasusXForConditionalGeneration"),qun.forEach(t),Y1t=r(n9o," (PEGASUS-X model)"),n9o.forEach(t),K1t=i(Te),Q8=n(Te,"LI",{});var s9o=s(Q8);ije=n(s9o,"STRONG",{});var Dun=s(ije);e4t=r(Dun,"plbart"),Dun.forEach(t),o4t=r(s9o," \u2014 "),phe=n(s9o,"A",{href:!0});var jun=s(phe);r4t=r(jun,"PLBartForConditionalGeneration"),jun.forEach(t),t4t=r(s9o," (PLBart model)"),s9o.forEach(t),a4t=i(Te),U8=n(Te,"LI",{});var l9o=s(U8);dje=n(l9o,"STRONG",{});var Oun=s(dje);n4t=r(Oun,"prophetnet"),Oun.forEach(t),s4t=r(l9o," \u2014 "),_he=n(l9o,"A",{href:!0});var Vun=s(_he);l4t=r(Vun,"ProphetNetForConditionalGeneration"),Vun.forEach(t),i4t=r(l9o," (ProphetNet model)"),l9o.forEach(t),d4t=i(Te),H8=n(Te,"LI",{});var i9o=s(H8);cje=n(i9o,"STRONG",{});var Xun=s(cje);c4t=r(Xun,"switch_transformers"),Xun.forEach(t),f4t=r(i9o," \u2014 "),vhe=n(i9o,"A",{href:!0});var zun=s(vhe);m4t=r(zun,"SwitchTransformersForConditionalGeneration"),zun.forEach(t),g4t=r(i9o," (SwitchTransformers model)"),i9o.forEach(t),h4t=i(Te),J8=n(Te,"LI",{});var d9o=s(J8);fje=n(d9o,"STRONG",{});var Wun=s(fje);u4t=r(Wun,"t5"),Wun.forEach(t),p4t=r(d9o," \u2014 "),bhe=n(d9o,"A",{href:!0});var Qun=s(bhe);_4t=r(Qun,"T5ForConditionalGeneration"),Qun.forEach(t),v4t=r(d9o," (T5 model)"),d9o.forEach(t),b4t=i(Te),Z8=n(Te,"LI",{});var c9o=s(Z8);mje=n(c9o,"STRONG",{});var Uun=s(mje);T4t=r(Uun,"xlm-prophetnet"),Uun.forEach(t),F4t=r(c9o," \u2014 "),The=n(c9o,"A",{href:!0});var Hun=s(The);M4t=r(Hun,"XLMProphetNetForConditionalGeneration"),Hun.forEach(t),E4t=r(c9o," (XLM-ProphetNet model)"),c9o.forEach(t),Te.forEach(t),C4t=i(Za),Y8=n(Za,"P",{});var f9o=s(Y8);w4t=r(f9o,"The model is set in evaluation mode by default using "),gje=n(f9o,"CODE",{});var Jun=s(gje);A4t=r(Jun,"model.eval()"),Jun.forEach(t),L4t=r(f9o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),hje=n(f9o,"CODE",{});var Zun=s(hje);y4t=r(Zun,"model.train()"),Zun.forEach(t),f9o.forEach(t),x4t=i(Za),T(K8.$$.fragment,Za),Za.forEach(t),rd.forEach(t),Jwo=i(f),Af=n(f,"H3",{class:!0});var NLo=s(Af);e6=n(NLo,"A",{id:!0,class:!0,href:!0});var Yun=s(e6);uje=n(Yun,"SPAN",{});var Kun=s(uje);T(bD.$$.fragment,Kun),Kun.forEach(t),Yun.forEach(t),S4t=i(NLo),pje=n(NLo,"SPAN",{});var epn=s(pje);k4t=r(epn,"TFAutoModelForSeq2SeqLM"),epn.forEach(t),NLo.forEach(t),Zwo=i(f),rr=n(f,"DIV",{class:!0});var td=s(rr);T(TD.$$.fragment,td),$4t=i(td),Lf=n(td,"P",{});var nMe=s(Lf);P4t=r(nMe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),Fhe=n(nMe,"A",{href:!0});var opn=s(Fhe);R4t=r(opn,"from_pretrained()"),opn.forEach(t),I4t=r(nMe," class method or the "),Mhe=n(nMe,"A",{href:!0});var rpn=s(Mhe);N4t=r(rpn,"from_config()"),rpn.forEach(t),B4t=r(nMe,` class
method.`),nMe.forEach(t),G4t=i(td),FD=n(td,"P",{});var BLo=s(FD);q4t=r(BLo,"This class cannot be instantiated directly using "),_je=n(BLo,"CODE",{});var tpn=s(_je);D4t=r(tpn,"__init__()"),tpn.forEach(t),j4t=r(BLo," (throws an error)."),BLo.forEach(t),O4t=i(td),Jt=n(td,"DIV",{class:!0});var kN=s(Jt);T(MD.$$.fragment,kN),V4t=i(kN),vje=n(kN,"P",{});var apn=s(vje);X4t=r(apn,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),apn.forEach(t),z4t=i(kN),yf=n(kN,"P",{});var sMe=s(yf);W4t=r(sMe,`Note:
Loading a model from its configuration file does `),bje=n(sMe,"STRONG",{});var npn=s(bje);Q4t=r(npn,"not"),npn.forEach(t),U4t=r(sMe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Ehe=n(sMe,"A",{href:!0});var spn=s(Ehe);H4t=r(spn,"from_pretrained()"),spn.forEach(t),J4t=r(sMe," to load the model weights."),sMe.forEach(t),Z4t=i(kN),T(o6.$$.fragment,kN),kN.forEach(t),Y4t=i(td),lt=n(td,"DIV",{class:!0});var ad=s(lt);T(ED.$$.fragment,ad),K4t=i(ad),Tje=n(ad,"P",{});var lpn=s(Tje);e0t=r(lpn,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),lpn.forEach(t),o0t=i(ad),Xn=n(ad,"P",{});var $N=s(Xn);r0t=r($N,"The model class to instantiate is selected based on the "),Fje=n($N,"CODE",{});var ipn=s(Fje);t0t=r(ipn,"model_type"),ipn.forEach(t),a0t=r($N,` property of the config object (either
passed as an argument or loaded from `),Mje=n($N,"CODE",{});var dpn=s(Mje);n0t=r(dpn,"pretrained_model_name_or_path"),dpn.forEach(t),s0t=r($N,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Eje=n($N,"CODE",{});var cpn=s(Eje);l0t=r(cpn,"pretrained_model_name_or_path"),cpn.forEach(t),i0t=r($N,":"),$N.forEach(t),d0t=i(ad),Ie=n(ad,"UL",{});var ze=s(Ie);r6=n(ze,"LI",{});var m9o=s(r6);Cje=n(m9o,"STRONG",{});var fpn=s(Cje);c0t=r(fpn,"bart"),fpn.forEach(t),f0t=r(m9o," \u2014 "),Che=n(m9o,"A",{href:!0});var mpn=s(Che);m0t=r(mpn,"TFBartForConditionalGeneration"),mpn.forEach(t),g0t=r(m9o," (BART model)"),m9o.forEach(t),h0t=i(ze),t6=n(ze,"LI",{});var g9o=s(t6);wje=n(g9o,"STRONG",{});var gpn=s(wje);u0t=r(gpn,"blenderbot"),gpn.forEach(t),p0t=r(g9o," \u2014 "),whe=n(g9o,"A",{href:!0});var hpn=s(whe);_0t=r(hpn,"TFBlenderbotForConditionalGeneration"),hpn.forEach(t),v0t=r(g9o," (Blenderbot model)"),g9o.forEach(t),b0t=i(ze),a6=n(ze,"LI",{});var h9o=s(a6);Aje=n(h9o,"STRONG",{});var upn=s(Aje);T0t=r(upn,"blenderbot-small"),upn.forEach(t),F0t=r(h9o," \u2014 "),Ahe=n(h9o,"A",{href:!0});var ppn=s(Ahe);M0t=r(ppn,"TFBlenderbotSmallForConditionalGeneration"),ppn.forEach(t),E0t=r(h9o," (BlenderbotSmall model)"),h9o.forEach(t),C0t=i(ze),n6=n(ze,"LI",{});var u9o=s(n6);Lje=n(u9o,"STRONG",{});var _pn=s(Lje);w0t=r(_pn,"encoder-decoder"),_pn.forEach(t),A0t=r(u9o," \u2014 "),Lhe=n(u9o,"A",{href:!0});var vpn=s(Lhe);L0t=r(vpn,"TFEncoderDecoderModel"),vpn.forEach(t),y0t=r(u9o," (Encoder decoder model)"),u9o.forEach(t),x0t=i(ze),s6=n(ze,"LI",{});var p9o=s(s6);yje=n(p9o,"STRONG",{});var bpn=s(yje);S0t=r(bpn,"led"),bpn.forEach(t),k0t=r(p9o," \u2014 "),yhe=n(p9o,"A",{href:!0});var Tpn=s(yhe);$0t=r(Tpn,"TFLEDForConditionalGeneration"),Tpn.forEach(t),P0t=r(p9o," (LED model)"),p9o.forEach(t),R0t=i(ze),l6=n(ze,"LI",{});var _9o=s(l6);xje=n(_9o,"STRONG",{});var Fpn=s(xje);I0t=r(Fpn,"marian"),Fpn.forEach(t),N0t=r(_9o," \u2014 "),xhe=n(_9o,"A",{href:!0});var Mpn=s(xhe);B0t=r(Mpn,"TFMarianMTModel"),Mpn.forEach(t),G0t=r(_9o," (Marian model)"),_9o.forEach(t),q0t=i(ze),i6=n(ze,"LI",{});var v9o=s(i6);Sje=n(v9o,"STRONG",{});var Epn=s(Sje);D0t=r(Epn,"mbart"),Epn.forEach(t),j0t=r(v9o," \u2014 "),She=n(v9o,"A",{href:!0});var Cpn=s(She);O0t=r(Cpn,"TFMBartForConditionalGeneration"),Cpn.forEach(t),V0t=r(v9o," (mBART model)"),v9o.forEach(t),X0t=i(ze),d6=n(ze,"LI",{});var b9o=s(d6);kje=n(b9o,"STRONG",{});var wpn=s(kje);z0t=r(wpn,"mt5"),wpn.forEach(t),W0t=r(b9o," \u2014 "),khe=n(b9o,"A",{href:!0});var Apn=s(khe);Q0t=r(Apn,"TFMT5ForConditionalGeneration"),Apn.forEach(t),U0t=r(b9o," (MT5 model)"),b9o.forEach(t),H0t=i(ze),c6=n(ze,"LI",{});var T9o=s(c6);$je=n(T9o,"STRONG",{});var Lpn=s($je);J0t=r(Lpn,"pegasus"),Lpn.forEach(t),Z0t=r(T9o," \u2014 "),$he=n(T9o,"A",{href:!0});var ypn=s($he);Y0t=r(ypn,"TFPegasusForConditionalGeneration"),ypn.forEach(t),K0t=r(T9o," (Pegasus model)"),T9o.forEach(t),ebt=i(ze),f6=n(ze,"LI",{});var F9o=s(f6);Pje=n(F9o,"STRONG",{});var xpn=s(Pje);obt=r(xpn,"t5"),xpn.forEach(t),rbt=r(F9o," \u2014 "),Phe=n(F9o,"A",{href:!0});var Spn=s(Phe);tbt=r(Spn,"TFT5ForConditionalGeneration"),Spn.forEach(t),abt=r(F9o," (T5 model)"),F9o.forEach(t),ze.forEach(t),nbt=i(ad),T(m6.$$.fragment,ad),ad.forEach(t),td.forEach(t),Ywo=i(f),xf=n(f,"H3",{class:!0});var GLo=s(xf);g6=n(GLo,"A",{id:!0,class:!0,href:!0});var kpn=s(g6);Rje=n(kpn,"SPAN",{});var $pn=s(Rje);T(CD.$$.fragment,$pn),$pn.forEach(t),kpn.forEach(t),sbt=i(GLo),Ije=n(GLo,"SPAN",{});var Ppn=s(Ije);lbt=r(Ppn,"FlaxAutoModelForSeq2SeqLM"),Ppn.forEach(t),GLo.forEach(t),Kwo=i(f),tr=n(f,"DIV",{class:!0});var nd=s(tr);T(wD.$$.fragment,nd),ibt=i(nd),Sf=n(nd,"P",{});var lMe=s(Sf);dbt=r(lMe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),Rhe=n(lMe,"A",{href:!0});var Rpn=s(Rhe);cbt=r(Rpn,"from_pretrained()"),Rpn.forEach(t),fbt=r(lMe," class method or the "),Ihe=n(lMe,"A",{href:!0});var Ipn=s(Ihe);mbt=r(Ipn,"from_config()"),Ipn.forEach(t),gbt=r(lMe,` class
method.`),lMe.forEach(t),hbt=i(nd),AD=n(nd,"P",{});var qLo=s(AD);ubt=r(qLo,"This class cannot be instantiated directly using "),Nje=n(qLo,"CODE",{});var Npn=s(Nje);pbt=r(Npn,"__init__()"),Npn.forEach(t),_bt=r(qLo," (throws an error)."),qLo.forEach(t),vbt=i(nd),Zt=n(nd,"DIV",{class:!0});var PN=s(Zt);T(LD.$$.fragment,PN),bbt=i(PN),Bje=n(PN,"P",{});var Bpn=s(Bje);Tbt=r(Bpn,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),Bpn.forEach(t),Fbt=i(PN),kf=n(PN,"P",{});var iMe=s(kf);Mbt=r(iMe,`Note:
Loading a model from its configuration file does `),Gje=n(iMe,"STRONG",{});var Gpn=s(Gje);Ebt=r(Gpn,"not"),Gpn.forEach(t),Cbt=r(iMe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Nhe=n(iMe,"A",{href:!0});var qpn=s(Nhe);wbt=r(qpn,"from_pretrained()"),qpn.forEach(t),Abt=r(iMe," to load the model weights."),iMe.forEach(t),Lbt=i(PN),T(h6.$$.fragment,PN),PN.forEach(t),ybt=i(nd),it=n(nd,"DIV",{class:!0});var sd=s(it);T(yD.$$.fragment,sd),xbt=i(sd),qje=n(sd,"P",{});var Dpn=s(qje);Sbt=r(Dpn,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),Dpn.forEach(t),kbt=i(sd),zn=n(sd,"P",{});var RN=s(zn);$bt=r(RN,"The model class to instantiate is selected based on the "),Dje=n(RN,"CODE",{});var jpn=s(Dje);Pbt=r(jpn,"model_type"),jpn.forEach(t),Rbt=r(RN,` property of the config object (either
passed as an argument or loaded from `),jje=n(RN,"CODE",{});var Opn=s(jje);Ibt=r(Opn,"pretrained_model_name_or_path"),Opn.forEach(t),Nbt=r(RN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Oje=n(RN,"CODE",{});var Vpn=s(Oje);Bbt=r(Vpn,"pretrained_model_name_or_path"),Vpn.forEach(t),Gbt=r(RN,":"),RN.forEach(t),qbt=i(sd),Ne=n(sd,"UL",{});var We=s(Ne);u6=n(We,"LI",{});var M9o=s(u6);Vje=n(M9o,"STRONG",{});var Xpn=s(Vje);Dbt=r(Xpn,"bart"),Xpn.forEach(t),jbt=r(M9o," \u2014 "),Bhe=n(M9o,"A",{href:!0});var zpn=s(Bhe);Obt=r(zpn,"FlaxBartForConditionalGeneration"),zpn.forEach(t),Vbt=r(M9o," (BART model)"),M9o.forEach(t),Xbt=i(We),p6=n(We,"LI",{});var E9o=s(p6);Xje=n(E9o,"STRONG",{});var Wpn=s(Xje);zbt=r(Wpn,"blenderbot"),Wpn.forEach(t),Wbt=r(E9o," \u2014 "),Ghe=n(E9o,"A",{href:!0});var Qpn=s(Ghe);Qbt=r(Qpn,"FlaxBlenderbotForConditionalGeneration"),Qpn.forEach(t),Ubt=r(E9o," (Blenderbot model)"),E9o.forEach(t),Hbt=i(We),_6=n(We,"LI",{});var C9o=s(_6);zje=n(C9o,"STRONG",{});var Upn=s(zje);Jbt=r(Upn,"blenderbot-small"),Upn.forEach(t),Zbt=r(C9o," \u2014 "),qhe=n(C9o,"A",{href:!0});var Hpn=s(qhe);Ybt=r(Hpn,"FlaxBlenderbotSmallForConditionalGeneration"),Hpn.forEach(t),Kbt=r(C9o," (BlenderbotSmall model)"),C9o.forEach(t),e9t=i(We),v6=n(We,"LI",{});var w9o=s(v6);Wje=n(w9o,"STRONG",{});var Jpn=s(Wje);o9t=r(Jpn,"encoder-decoder"),Jpn.forEach(t),r9t=r(w9o," \u2014 "),Dhe=n(w9o,"A",{href:!0});var Zpn=s(Dhe);t9t=r(Zpn,"FlaxEncoderDecoderModel"),Zpn.forEach(t),a9t=r(w9o," (Encoder decoder model)"),w9o.forEach(t),n9t=i(We),b6=n(We,"LI",{});var A9o=s(b6);Qje=n(A9o,"STRONG",{});var Ypn=s(Qje);s9t=r(Ypn,"longt5"),Ypn.forEach(t),l9t=r(A9o," \u2014 "),jhe=n(A9o,"A",{href:!0});var Kpn=s(jhe);i9t=r(Kpn,"FlaxLongT5ForConditionalGeneration"),Kpn.forEach(t),d9t=r(A9o," (LongT5 model)"),A9o.forEach(t),c9t=i(We),T6=n(We,"LI",{});var L9o=s(T6);Uje=n(L9o,"STRONG",{});var e_n=s(Uje);f9t=r(e_n,"marian"),e_n.forEach(t),m9t=r(L9o," \u2014 "),Ohe=n(L9o,"A",{href:!0});var o_n=s(Ohe);g9t=r(o_n,"FlaxMarianMTModel"),o_n.forEach(t),h9t=r(L9o," (Marian model)"),L9o.forEach(t),u9t=i(We),F6=n(We,"LI",{});var y9o=s(F6);Hje=n(y9o,"STRONG",{});var r_n=s(Hje);p9t=r(r_n,"mbart"),r_n.forEach(t),_9t=r(y9o," \u2014 "),Vhe=n(y9o,"A",{href:!0});var t_n=s(Vhe);v9t=r(t_n,"FlaxMBartForConditionalGeneration"),t_n.forEach(t),b9t=r(y9o," (mBART model)"),y9o.forEach(t),T9t=i(We),M6=n(We,"LI",{});var x9o=s(M6);Jje=n(x9o,"STRONG",{});var a_n=s(Jje);F9t=r(a_n,"mt5"),a_n.forEach(t),M9t=r(x9o," \u2014 "),Xhe=n(x9o,"A",{href:!0});var n_n=s(Xhe);E9t=r(n_n,"FlaxMT5ForConditionalGeneration"),n_n.forEach(t),C9t=r(x9o," (MT5 model)"),x9o.forEach(t),w9t=i(We),E6=n(We,"LI",{});var S9o=s(E6);Zje=n(S9o,"STRONG",{});var s_n=s(Zje);A9t=r(s_n,"pegasus"),s_n.forEach(t),L9t=r(S9o," \u2014 "),zhe=n(S9o,"A",{href:!0});var l_n=s(zhe);y9t=r(l_n,"FlaxPegasusForConditionalGeneration"),l_n.forEach(t),x9t=r(S9o," (Pegasus model)"),S9o.forEach(t),S9t=i(We),C6=n(We,"LI",{});var k9o=s(C6);Yje=n(k9o,"STRONG",{});var i_n=s(Yje);k9t=r(i_n,"t5"),i_n.forEach(t),$9t=r(k9o," \u2014 "),Whe=n(k9o,"A",{href:!0});var d_n=s(Whe);P9t=r(d_n,"FlaxT5ForConditionalGeneration"),d_n.forEach(t),R9t=r(k9o," (T5 model)"),k9o.forEach(t),We.forEach(t),I9t=i(sd),T(w6.$$.fragment,sd),sd.forEach(t),nd.forEach(t),eAo=i(f),$f=n(f,"H3",{class:!0});var DLo=s($f);A6=n(DLo,"A",{id:!0,class:!0,href:!0});var c_n=s(A6);Kje=n(c_n,"SPAN",{});var f_n=s(Kje);T(xD.$$.fragment,f_n),f_n.forEach(t),c_n.forEach(t),N9t=i(DLo),eOe=n(DLo,"SPAN",{});var m_n=s(eOe);B9t=r(m_n,"AutoModelForSequenceClassification"),m_n.forEach(t),DLo.forEach(t),oAo=i(f),ar=n(f,"DIV",{class:!0});var ld=s(ar);T(SD.$$.fragment,ld),G9t=i(ld),Pf=n(ld,"P",{});var dMe=s(Pf);q9t=r(dMe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the `),Qhe=n(dMe,"A",{href:!0});var g_n=s(Qhe);D9t=r(g_n,"from_pretrained()"),g_n.forEach(t),j9t=r(dMe," class method or the "),Uhe=n(dMe,"A",{href:!0});var h_n=s(Uhe);O9t=r(h_n,"from_config()"),h_n.forEach(t),V9t=r(dMe,` class
method.`),dMe.forEach(t),X9t=i(ld),kD=n(ld,"P",{});var jLo=s(kD);z9t=r(jLo,"This class cannot be instantiated directly using "),oOe=n(jLo,"CODE",{});var u_n=s(oOe);W9t=r(u_n,"__init__()"),u_n.forEach(t),Q9t=r(jLo," (throws an error)."),jLo.forEach(t),U9t=i(ld),Yt=n(ld,"DIV",{class:!0});var IN=s(Yt);T($D.$$.fragment,IN),H9t=i(IN),rOe=n(IN,"P",{});var p_n=s(rOe);J9t=r(p_n,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),p_n.forEach(t),Z9t=i(IN),Rf=n(IN,"P",{});var cMe=s(Rf);Y9t=r(cMe,`Note:
Loading a model from its configuration file does `),tOe=n(cMe,"STRONG",{});var __n=s(tOe);K9t=r(__n,"not"),__n.forEach(t),eTt=r(cMe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Hhe=n(cMe,"A",{href:!0});var v_n=s(Hhe);oTt=r(v_n,"from_pretrained()"),v_n.forEach(t),rTt=r(cMe," to load the model weights."),cMe.forEach(t),tTt=i(IN),T(L6.$$.fragment,IN),IN.forEach(t),aTt=i(ld),mo=n(ld,"DIV",{class:!0});var Ya=s(mo);T(PD.$$.fragment,Ya),nTt=i(Ya),aOe=n(Ya,"P",{});var b_n=s(aOe);sTt=r(b_n,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),b_n.forEach(t),lTt=i(Ya),Wn=n(Ya,"P",{});var NN=s(Wn);iTt=r(NN,"The model class to instantiate is selected based on the "),nOe=n(NN,"CODE",{});var T_n=s(nOe);dTt=r(T_n,"model_type"),T_n.forEach(t),cTt=r(NN,` property of the config object (either
passed as an argument or loaded from `),sOe=n(NN,"CODE",{});var F_n=s(sOe);fTt=r(F_n,"pretrained_model_name_or_path"),F_n.forEach(t),mTt=r(NN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),lOe=n(NN,"CODE",{});var M_n=s(lOe);gTt=r(M_n,"pretrained_model_name_or_path"),M_n.forEach(t),hTt=r(NN,":"),NN.forEach(t),uTt=i(Ya),R=n(Ya,"UL",{});var I=s(R);y6=n(I,"LI",{});var $9o=s(y6);iOe=n($9o,"STRONG",{});var E_n=s(iOe);pTt=r(E_n,"albert"),E_n.forEach(t),_Tt=r($9o," \u2014 "),Jhe=n($9o,"A",{href:!0});var C_n=s(Jhe);vTt=r(C_n,"AlbertForSequenceClassification"),C_n.forEach(t),bTt=r($9o," (ALBERT model)"),$9o.forEach(t),TTt=i(I),x6=n(I,"LI",{});var P9o=s(x6);dOe=n(P9o,"STRONG",{});var w_n=s(dOe);FTt=r(w_n,"bart"),w_n.forEach(t),MTt=r(P9o," \u2014 "),Zhe=n(P9o,"A",{href:!0});var A_n=s(Zhe);ETt=r(A_n,"BartForSequenceClassification"),A_n.forEach(t),CTt=r(P9o," (BART model)"),P9o.forEach(t),wTt=i(I),S6=n(I,"LI",{});var R9o=s(S6);cOe=n(R9o,"STRONG",{});var L_n=s(cOe);ATt=r(L_n,"bert"),L_n.forEach(t),LTt=r(R9o," \u2014 "),Yhe=n(R9o,"A",{href:!0});var y_n=s(Yhe);yTt=r(y_n,"BertForSequenceClassification"),y_n.forEach(t),xTt=r(R9o," (BERT model)"),R9o.forEach(t),STt=i(I),k6=n(I,"LI",{});var I9o=s(k6);fOe=n(I9o,"STRONG",{});var x_n=s(fOe);kTt=r(x_n,"big_bird"),x_n.forEach(t),$Tt=r(I9o," \u2014 "),Khe=n(I9o,"A",{href:!0});var S_n=s(Khe);PTt=r(S_n,"BigBirdForSequenceClassification"),S_n.forEach(t),RTt=r(I9o," (BigBird model)"),I9o.forEach(t),ITt=i(I),$6=n(I,"LI",{});var N9o=s($6);mOe=n(N9o,"STRONG",{});var k_n=s(mOe);NTt=r(k_n,"bigbird_pegasus"),k_n.forEach(t),BTt=r(N9o," \u2014 "),eue=n(N9o,"A",{href:!0});var $_n=s(eue);GTt=r($_n,"BigBirdPegasusForSequenceClassification"),$_n.forEach(t),qTt=r(N9o," (BigBird-Pegasus model)"),N9o.forEach(t),DTt=i(I),P6=n(I,"LI",{});var B9o=s(P6);gOe=n(B9o,"STRONG",{});var P_n=s(gOe);jTt=r(P_n,"biogpt"),P_n.forEach(t),OTt=r(B9o," \u2014 "),oue=n(B9o,"A",{href:!0});var R_n=s(oue);VTt=r(R_n,"BioGptForSequenceClassification"),R_n.forEach(t),XTt=r(B9o," (BioGpt model)"),B9o.forEach(t),zTt=i(I),R6=n(I,"LI",{});var G9o=s(R6);hOe=n(G9o,"STRONG",{});var I_n=s(hOe);WTt=r(I_n,"bloom"),I_n.forEach(t),QTt=r(G9o," \u2014 "),rue=n(G9o,"A",{href:!0});var N_n=s(rue);UTt=r(N_n,"BloomForSequenceClassification"),N_n.forEach(t),HTt=r(G9o," (BLOOM model)"),G9o.forEach(t),JTt=i(I),I6=n(I,"LI",{});var q9o=s(I6);uOe=n(q9o,"STRONG",{});var B_n=s(uOe);ZTt=r(B_n,"camembert"),B_n.forEach(t),YTt=r(q9o," \u2014 "),tue=n(q9o,"A",{href:!0});var G_n=s(tue);KTt=r(G_n,"CamembertForSequenceClassification"),G_n.forEach(t),eFt=r(q9o," (CamemBERT model)"),q9o.forEach(t),oFt=i(I),N6=n(I,"LI",{});var D9o=s(N6);pOe=n(D9o,"STRONG",{});var q_n=s(pOe);rFt=r(q_n,"canine"),q_n.forEach(t),tFt=r(D9o," \u2014 "),aue=n(D9o,"A",{href:!0});var D_n=s(aue);aFt=r(D_n,"CanineForSequenceClassification"),D_n.forEach(t),nFt=r(D9o," (CANINE model)"),D9o.forEach(t),sFt=i(I),B6=n(I,"LI",{});var j9o=s(B6);_Oe=n(j9o,"STRONG",{});var j_n=s(_Oe);lFt=r(j_n,"convbert"),j_n.forEach(t),iFt=r(j9o," \u2014 "),nue=n(j9o,"A",{href:!0});var O_n=s(nue);dFt=r(O_n,"ConvBertForSequenceClassification"),O_n.forEach(t),cFt=r(j9o," (ConvBERT model)"),j9o.forEach(t),fFt=i(I),G6=n(I,"LI",{});var O9o=s(G6);vOe=n(O9o,"STRONG",{});var V_n=s(vOe);mFt=r(V_n,"ctrl"),V_n.forEach(t),gFt=r(O9o," \u2014 "),sue=n(O9o,"A",{href:!0});var X_n=s(sue);hFt=r(X_n,"CTRLForSequenceClassification"),X_n.forEach(t),uFt=r(O9o," (CTRL model)"),O9o.forEach(t),pFt=i(I),q6=n(I,"LI",{});var V9o=s(q6);bOe=n(V9o,"STRONG",{});var z_n=s(bOe);_Ft=r(z_n,"data2vec-text"),z_n.forEach(t),vFt=r(V9o," \u2014 "),lue=n(V9o,"A",{href:!0});var W_n=s(lue);bFt=r(W_n,"Data2VecTextForSequenceClassification"),W_n.forEach(t),TFt=r(V9o," (Data2VecText model)"),V9o.forEach(t),FFt=i(I),D6=n(I,"LI",{});var X9o=s(D6);TOe=n(X9o,"STRONG",{});var Q_n=s(TOe);MFt=r(Q_n,"deberta"),Q_n.forEach(t),EFt=r(X9o," \u2014 "),iue=n(X9o,"A",{href:!0});var U_n=s(iue);CFt=r(U_n,"DebertaForSequenceClassification"),U_n.forEach(t),wFt=r(X9o," (DeBERTa model)"),X9o.forEach(t),AFt=i(I),j6=n(I,"LI",{});var z9o=s(j6);FOe=n(z9o,"STRONG",{});var H_n=s(FOe);LFt=r(H_n,"deberta-v2"),H_n.forEach(t),yFt=r(z9o," \u2014 "),due=n(z9o,"A",{href:!0});var J_n=s(due);xFt=r(J_n,"DebertaV2ForSequenceClassification"),J_n.forEach(t),SFt=r(z9o," (DeBERTa-v2 model)"),z9o.forEach(t),kFt=i(I),O6=n(I,"LI",{});var W9o=s(O6);MOe=n(W9o,"STRONG",{});var Z_n=s(MOe);$Ft=r(Z_n,"distilbert"),Z_n.forEach(t),PFt=r(W9o," \u2014 "),cue=n(W9o,"A",{href:!0});var Y_n=s(cue);RFt=r(Y_n,"DistilBertForSequenceClassification"),Y_n.forEach(t),IFt=r(W9o," (DistilBERT model)"),W9o.forEach(t),NFt=i(I),V6=n(I,"LI",{});var Q9o=s(V6);EOe=n(Q9o,"STRONG",{});var K_n=s(EOe);BFt=r(K_n,"electra"),K_n.forEach(t),GFt=r(Q9o," \u2014 "),fue=n(Q9o,"A",{href:!0});var e2n=s(fue);qFt=r(e2n,"ElectraForSequenceClassification"),e2n.forEach(t),DFt=r(Q9o," (ELECTRA model)"),Q9o.forEach(t),jFt=i(I),X6=n(I,"LI",{});var U9o=s(X6);COe=n(U9o,"STRONG",{});var o2n=s(COe);OFt=r(o2n,"ernie"),o2n.forEach(t),VFt=r(U9o," \u2014 "),mue=n(U9o,"A",{href:!0});var r2n=s(mue);XFt=r(r2n,"ErnieForSequenceClassification"),r2n.forEach(t),zFt=r(U9o," (ERNIE model)"),U9o.forEach(t),WFt=i(I),z6=n(I,"LI",{});var H9o=s(z6);wOe=n(H9o,"STRONG",{});var t2n=s(wOe);QFt=r(t2n,"ernie_m"),t2n.forEach(t),UFt=r(H9o," \u2014 "),gue=n(H9o,"A",{href:!0});var a2n=s(gue);HFt=r(a2n,"ErnieMForSequenceClassification"),a2n.forEach(t),JFt=r(H9o," (ErnieM model)"),H9o.forEach(t),ZFt=i(I),W6=n(I,"LI",{});var J9o=s(W6);AOe=n(J9o,"STRONG",{});var n2n=s(AOe);YFt=r(n2n,"esm"),n2n.forEach(t),KFt=r(J9o," \u2014 "),hue=n(J9o,"A",{href:!0});var s2n=s(hue);eMt=r(s2n,"EsmForSequenceClassification"),s2n.forEach(t),oMt=r(J9o," (ESM model)"),J9o.forEach(t),rMt=i(I),Q6=n(I,"LI",{});var Z9o=s(Q6);LOe=n(Z9o,"STRONG",{});var l2n=s(LOe);tMt=r(l2n,"flaubert"),l2n.forEach(t),aMt=r(Z9o," \u2014 "),uue=n(Z9o,"A",{href:!0});var i2n=s(uue);nMt=r(i2n,"FlaubertForSequenceClassification"),i2n.forEach(t),sMt=r(Z9o," (FlauBERT model)"),Z9o.forEach(t),lMt=i(I),U6=n(I,"LI",{});var 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model\u2019s configuration. Use `),fpe=n(mMe,"A",{href:!0});var n1n=s(fpe);Z5t=r(n1n,"from_pretrained()"),n1n.forEach(t),Y5t=r(mMe," to load the model weights."),mMe.forEach(t),K5t=i(BN),T(Xw.$$.fragment,BN),BN.forEach(t),e8t=i(id),dt=n(id,"DIV",{class:!0});var dd=s(dt);T(GD.$$.fragment,dd),o8t=i(dd),yVe=n(dd,"P",{});var s1n=s(yVe);r8t=r(s1n,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),s1n.forEach(t),t8t=i(dd),Qn=n(dd,"P",{});var GN=s(Qn);a8t=r(GN,"The model class to instantiate is selected based on the "),xVe=n(GN,"CODE",{});var l1n=s(xVe);n8t=r(l1n,"model_type"),l1n.forEach(t),s8t=r(GN,` property of the config object (either
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falling back to using pattern matching on `),kVe=n(GN,"CODE",{});var d1n=s(kVe);d8t=r(d1n,"pretrained_model_name_or_path"),d1n.forEach(t),c8t=r(GN,":"),GN.forEach(t),f8t=i(dd),le=n(dd,"UL",{});var de=s(le);zw=n(de,"LI",{});var WTo=s(zw);$Ve=n(WTo,"STRONG",{});var c1n=s($Ve);m8t=r(c1n,"albert"),c1n.forEach(t),g8t=r(WTo," \u2014 "),mpe=n(WTo,"A",{href:!0});var f1n=s(mpe);h8t=r(f1n,"TFAlbertForSequenceClassification"),f1n.forEach(t),u8t=r(WTo," (ALBERT model)"),WTo.forEach(t),p8t=i(de),Ww=n(de,"LI",{});var QTo=s(Ww);PVe=n(QTo,"STRONG",{});var m1n=s(PVe);_8t=r(m1n,"bart"),m1n.forEach(t),v8t=r(QTo," \u2014 "),gpe=n(QTo,"A",{href:!0});var g1n=s(gpe);b8t=r(g1n,"TFBartForSequenceClassification"),g1n.forEach(t),T8t=r(QTo," (BART model)"),QTo.forEach(t),F8t=i(de),Qw=n(de,"LI",{});var UTo=s(Qw);RVe=n(UTo,"STRONG",{});var h1n=s(RVe);M8t=r(h1n,"bert"),h1n.forEach(t),E8t=r(UTo," \u2014 "),hpe=n(UTo,"A",{href:!0});var u1n=s(hpe);C8t=r(u1n,"TFBertForSequenceClassification"),u1n.forEach(t),w8t=r(UTo," 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model\u2019s configuration. Use `),zpe=n(hMe,"A",{href:!0});var A4n=s(zpe);CAt=r(A4n,"from_pretrained()"),A4n.forEach(t),wAt=r(hMe," to load the model weights."),hMe.forEach(t),AAt=i(qN),T(CA.$$.fragment,qN),qN.forEach(t),LAt=i(cd),ct=n(cd,"DIV",{class:!0});var fd=s(ct);T(VD.$$.fragment,fd),yAt=i(fd),hXe=n(fd,"P",{});var L4n=s(hXe);xAt=r(L4n,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),L4n.forEach(t),SAt=i(fd),Un=n(fd,"P",{});var DN=s(Un);kAt=r(DN,"The model class to instantiate is selected based on the "),uXe=n(DN,"CODE",{});var y4n=s(uXe);$At=r(y4n,"model_type"),y4n.forEach(t),PAt=r(DN,` property of the config object (either
passed as an argument or loaded from `),pXe=n(DN,"CODE",{});var x4n=s(pXe);RAt=r(x4n,"pretrained_model_name_or_path"),x4n.forEach(t),IAt=r(DN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),_Xe=n(DN,"CODE",{});var S4n=s(_Xe);NAt=r(S4n,"pretrained_model_name_or_path"),S4n.forEach(t),BAt=r(DN,":"),DN.forEach(t),GAt=i(fd),ke=n(fd,"UL",{});var De=s(ke);wA=n(De,"LI",{});var EFo=s(wA);vXe=n(EFo,"STRONG",{});var k4n=s(vXe);qAt=r(k4n,"albert"),k4n.forEach(t),DAt=r(EFo," \u2014 "),Wpe=n(EFo,"A",{href:!0});var $4n=s(Wpe);jAt=r($4n,"FlaxAlbertForSequenceClassification"),$4n.forEach(t),OAt=r(EFo," (ALBERT model)"),EFo.forEach(t),VAt=i(De),AA=n(De,"LI",{});var CFo=s(AA);bXe=n(CFo,"STRONG",{});var P4n=s(bXe);XAt=r(P4n,"bart"),P4n.forEach(t),zAt=r(CFo," \u2014 "),Qpe=n(CFo,"A",{href:!0});var R4n=s(Qpe);WAt=r(R4n,"FlaxBartForSequenceClassification"),R4n.forEach(t),QAt=r(CFo," (BART model)"),CFo.forEach(t),UAt=i(De),LA=n(De,"LI",{});var wFo=s(LA);TXe=n(wFo,"STRONG",{});var I4n=s(TXe);HAt=r(I4n,"bert"),I4n.forEach(t),JAt=r(wFo," \u2014 "),Upe=n(wFo,"A",{href:!0});var N4n=s(Upe);ZAt=r(N4n,"FlaxBertForSequenceClassification"),N4n.forEach(t),YAt=r(wFo," (BERT model)"),wFo.forEach(t),KAt=i(De),yA=n(De,"LI",{});var AFo=s(yA);FXe=n(AFo,"STRONG",{});var B4n=s(FXe);e7t=r(B4n,"big_bird"),B4n.forEach(t),o7t=r(AFo," \u2014 "),Hpe=n(AFo,"A",{href:!0});var G4n=s(Hpe);r7t=r(G4n,"FlaxBigBirdForSequenceClassification"),G4n.forEach(t),t7t=r(AFo," (BigBird model)"),AFo.forEach(t),a7t=i(De),xA=n(De,"LI",{});var LFo=s(xA);MXe=n(LFo,"STRONG",{});var q4n=s(MXe);n7t=r(q4n,"distilbert"),q4n.forEach(t),s7t=r(LFo," \u2014 "),Jpe=n(LFo,"A",{href:!0});var D4n=s(Jpe);l7t=r(D4n,"FlaxDistilBertForSequenceClassification"),D4n.forEach(t),i7t=r(LFo," (DistilBERT model)"),LFo.forEach(t),d7t=i(De),SA=n(De,"LI",{});var yFo=s(SA);EXe=n(yFo,"STRONG",{});var j4n=s(EXe);c7t=r(j4n,"electra"),j4n.forEach(t),f7t=r(yFo," \u2014 "),Zpe=n(yFo,"A",{href:!0});var O4n=s(Zpe);m7t=r(O4n,"FlaxElectraForSequenceClassification"),O4n.forEach(t),g7t=r(yFo," (ELECTRA model)"),yFo.forEach(t),h7t=i(De),kA=n(De,"LI",{});var xFo=s(kA);CXe=n(xFo,"STRONG",{});var V4n=s(CXe);u7t=r(V4n,"mbart"),V4n.forEach(t),p7t=r(xFo," \u2014 "),Ype=n(xFo,"A",{href:!0});var X4n=s(Ype);_7t=r(X4n,"FlaxMBartForSequenceClassification"),X4n.forEach(t),v7t=r(xFo," (mBART model)"),xFo.forEach(t),b7t=i(De),$A=n(De,"LI",{});var SFo=s($A);wXe=n(SFo,"STRONG",{});var z4n=s(wXe);T7t=r(z4n,"roberta"),z4n.forEach(t),F7t=r(SFo," \u2014 "),Kpe=n(SFo,"A",{href:!0});var W4n=s(Kpe);M7t=r(W4n,"FlaxRobertaForSequenceClassification"),W4n.forEach(t),E7t=r(SFo," (RoBERTa model)"),SFo.forEach(t),C7t=i(De),PA=n(De,"LI",{});var kFo=s(PA);AXe=n(kFo,"STRONG",{});var Q4n=s(AXe);w7t=r(Q4n,"roberta-prelayernorm"),Q4n.forEach(t),A7t=r(kFo," \u2014 "),e_e=n(kFo,"A",{href:!0});var U4n=s(e_e);L7t=r(U4n,"FlaxRobertaPreLayerNormForSequenceClassification"),U4n.forEach(t),y7t=r(kFo," (RoBERTa-PreLayerNorm model)"),kFo.forEach(t),x7t=i(De),RA=n(De,"LI",{});var $Fo=s(RA);LXe=n($Fo,"STRONG",{});var H4n=s(LXe);S7t=r(H4n,"roformer"),H4n.forEach(t),k7t=r($Fo," \u2014 "),o_e=n($Fo,"A",{href:!0});var J4n=s(o_e);$7t=r(J4n,"FlaxRoFormerForSequenceClassification"),J4n.forEach(t),P7t=r($Fo," (RoFormer model)"),$Fo.forEach(t),R7t=i(De),IA=n(De,"LI",{});var PFo=s(IA);yXe=n(PFo,"STRONG",{});var Z4n=s(yXe);I7t=r(Z4n,"xlm-roberta"),Z4n.forEach(t),N7t=r(PFo," \u2014 "),r_e=n(PFo,"A",{href:!0});var Y4n=s(r_e);B7t=r(Y4n,"FlaxXLMRobertaForSequenceClassification"),Y4n.forEach(t),G7t=r(PFo," (XLM-RoBERTa model)"),PFo.forEach(t),De.forEach(t),q7t=i(fd),T(NA.$$.fragment,fd),fd.forEach(t),cd.forEach(t),sAo=i(f),jf=n(f,"H3",{class:!0});var WLo=s(jf);BA=n(WLo,"A",{id:!0,class:!0,href:!0});var K4n=s(BA);xXe=n(K4n,"SPAN",{});var e0n=s(xXe);T(XD.$$.fragment,e0n),e0n.forEach(t),K4n.forEach(t),D7t=i(WLo),SXe=n(WLo,"SPAN",{});var o0n=s(SXe);j7t=r(o0n,"AutoModelForMultipleChoice"),o0n.forEach(t),WLo.forEach(t),lAo=i(f),lr=n(f,"DIV",{class:!0});var md=s(lr);T(zD.$$.fragment,md),O7t=i(md),Of=n(md,"P",{});var uMe=s(Of);V7t=r(uMe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
with the `),t_e=n(uMe,"A",{href:!0});var r0n=s(t_e);X7t=r(r0n,"from_pretrained()"),r0n.forEach(t),z7t=r(uMe," class method or the "),a_e=n(uMe,"A",{href:!0});var t0n=s(a_e);W7t=r(t0n,"from_config()"),t0n.forEach(t),Q7t=r(uMe,` class
method.`),uMe.forEach(t),U7t=i(md),WD=n(md,"P",{});var QLo=s(WD);H7t=r(QLo,"This class cannot be instantiated directly using "),kXe=n(QLo,"CODE",{});var a0n=s(kXe);J7t=r(a0n,"__init__()"),a0n.forEach(t),Z7t=r(QLo," (throws an error)."),QLo.forEach(t),Y7t=i(md),oa=n(md,"DIV",{class:!0});var jN=s(oa);T(QD.$$.fragment,jN),K7t=i(jN),$Xe=n(jN,"P",{});var n0n=s($Xe);eLt=r(n0n,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),n0n.forEach(t),oLt=i(jN),Vf=n(jN,"P",{});var pMe=s(Vf);rLt=r(pMe,`Note:
Loading a model from its configuration file does `),PXe=n(pMe,"STRONG",{});var s0n=s(PXe);tLt=r(s0n,"not"),s0n.forEach(t),aLt=r(pMe,` load the model weights. It only affects the
model\u2019s configuration. Use `),n_e=n(pMe,"A",{href:!0});var l0n=s(n_e);nLt=r(l0n,"from_pretrained()"),l0n.forEach(t),sLt=r(pMe," to load the model weights."),pMe.forEach(t),lLt=i(jN),T(GA.$$.fragment,jN),jN.forEach(t),iLt=i(md),go=n(md,"DIV",{class:!0});var Ka=s(go);T(UD.$$.fragment,Ka),dLt=i(Ka),RXe=n(Ka,"P",{});var i0n=s(RXe);cLt=r(i0n,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),i0n.forEach(t),fLt=i(Ka),Hn=n(Ka,"P",{});var ON=s(Hn);mLt=r(ON,"The model class to instantiate is selected based on the "),IXe=n(ON,"CODE",{});var d0n=s(IXe);gLt=r(d0n,"model_type"),d0n.forEach(t),hLt=r(ON,` property of the config object (either
passed as an argument or loaded from `),NXe=n(ON,"CODE",{});var c0n=s(NXe);uLt=r(c0n,"pretrained_model_name_or_path"),c0n.forEach(t),pLt=r(ON,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),BXe=n(ON,"CODE",{});var f0n=s(BXe);_Lt=r(f0n,"pretrained_model_name_or_path"),f0n.forEach(t),vLt=r(ON,":"),ON.forEach(t),bLt=i(Ka),re=n(Ka,"UL",{});var ae=s(re);qA=n(ae,"LI",{});var RFo=s(qA);GXe=n(RFo,"STRONG",{});var m0n=s(GXe);TLt=r(m0n,"albert"),m0n.forEach(t),FLt=r(RFo," \u2014 "),s_e=n(RFo,"A",{href:!0});var g0n=s(s_e);MLt=r(g0n,"AlbertForMultipleChoice"),g0n.forEach(t),ELt=r(RFo," (ALBERT model)"),RFo.forEach(t),CLt=i(ae),DA=n(ae,"LI",{});var IFo=s(DA);qXe=n(IFo,"STRONG",{});var h0n=s(qXe);wLt=r(h0n,"bert"),h0n.forEach(t),ALt=r(IFo," \u2014 "),l_e=n(IFo,"A",{href:!0});var u0n=s(l_e);LLt=r(u0n,"BertForMultipleChoice"),u0n.forEach(t),yLt=r(IFo," (BERT model)"),IFo.forEach(t),xLt=i(ae),jA=n(ae,"LI",{});var NFo=s(jA);DXe=n(NFo,"STRONG",{});var p0n=s(DXe);SLt=r(p0n,"big_bird"),p0n.forEach(t),kLt=r(NFo," \u2014 "),i_e=n(NFo,"A",{href:!0});var _0n=s(i_e);$Lt=r(_0n,"BigBirdForMultipleChoice"),_0n.forEach(t),PLt=r(NFo," (BigBird model)"),NFo.forEach(t),RLt=i(ae),OA=n(ae,"LI",{});var BFo=s(OA);jXe=n(BFo,"STRONG",{});var v0n=s(jXe);ILt=r(v0n,"camembert"),v0n.forEach(t),NLt=r(BFo," \u2014 "),d_e=n(BFo,"A",{href:!0});var b0n=s(d_e);BLt=r(b0n,"CamembertForMultipleChoice"),b0n.forEach(t),GLt=r(BFo," (CamemBERT model)"),BFo.forEach(t),qLt=i(ae),VA=n(ae,"LI",{});var GFo=s(VA);OXe=n(GFo,"STRONG",{});var T0n=s(OXe);DLt=r(T0n,"canine"),T0n.forEach(t),jLt=r(GFo," \u2014 "),c_e=n(GFo,"A",{href:!0});var F0n=s(c_e);OLt=r(F0n,"CanineForMultipleChoice"),F0n.forEach(t),VLt=r(GFo," (CANINE model)"),GFo.forEach(t),XLt=i(ae),XA=n(ae,"LI",{});var qFo=s(XA);VXe=n(qFo,"STRONG",{});var M0n=s(VXe);zLt=r(M0n,"convbert"),M0n.forEach(t),WLt=r(qFo," \u2014 "),f_e=n(qFo,"A",{href:!0});var E0n=s(f_e);QLt=r(E0n,"ConvBertForMultipleChoice"),E0n.forEach(t),ULt=r(qFo," (ConvBERT model)"),qFo.forEach(t),HLt=i(ae),zA=n(ae,"LI",{});var DFo=s(zA);XXe=n(DFo,"STRONG",{});var C0n=s(XXe);JLt=r(C0n,"data2vec-text"),C0n.forEach(t),ZLt=r(DFo," \u2014 "),m_e=n(DFo,"A",{href:!0});var w0n=s(m_e);YLt=r(w0n,"Data2VecTextForMultipleChoice"),w0n.forEach(t),KLt=r(DFo," (Data2VecText model)"),DFo.forEach(t),eyt=i(ae),WA=n(ae,"LI",{});var jFo=s(WA);zXe=n(jFo,"STRONG",{});var A0n=s(zXe);oyt=r(A0n,"deberta-v2"),A0n.forEach(t),ryt=r(jFo," \u2014 "),g_e=n(jFo,"A",{href:!0});var L0n=s(g_e);tyt=r(L0n,"DebertaV2ForMultipleChoice"),L0n.forEach(t),ayt=r(jFo," (DeBERTa-v2 model)"),jFo.forEach(t),nyt=i(ae),QA=n(ae,"LI",{});var OFo=s(QA);WXe=n(OFo,"STRONG",{});var y0n=s(WXe);syt=r(y0n,"distilbert"),y0n.forEach(t),lyt=r(OFo," \u2014 "),h_e=n(OFo,"A",{href:!0});var x0n=s(h_e);iyt=r(x0n,"DistilBertForMultipleChoice"),x0n.forEach(t),dyt=r(OFo," (DistilBERT model)"),OFo.forEach(t),cyt=i(ae),UA=n(ae,"LI",{});var VFo=s(UA);QXe=n(VFo,"STRONG",{});var S0n=s(QXe);fyt=r(S0n,"electra"),S0n.forEach(t),myt=r(VFo," \u2014 "),u_e=n(VFo,"A",{href:!0});var k0n=s(u_e);gyt=r(k0n,"ElectraForMultipleChoice"),k0n.forEach(t),hyt=r(VFo," (ELECTRA model)"),VFo.forEach(t),uyt=i(ae),HA=n(ae,"LI",{});var XFo=s(HA);UXe=n(XFo,"STRONG",{});var $0n=s(UXe);pyt=r($0n,"ernie"),$0n.forEach(t),_yt=r(XFo," \u2014 "),p_e=n(XFo,"A",{href:!0});var P0n=s(p_e);vyt=r(P0n,"ErnieForMultipleChoice"),P0n.forEach(t),byt=r(XFo," (ERNIE model)"),XFo.forEach(t),Tyt=i(ae),JA=n(ae,"LI",{});var zFo=s(JA);HXe=n(zFo,"STRONG",{});var R0n=s(HXe);Fyt=r(R0n,"ernie_m"),R0n.forEach(t),Myt=r(zFo," \u2014 "),__e=n(zFo,"A",{href:!0});var I0n=s(__e);Eyt=r(I0n,"ErnieMForMultipleChoice"),I0n.forEach(t),Cyt=r(zFo," (ErnieM model)"),zFo.forEach(t),wyt=i(ae),ZA=n(ae,"LI",{});var WFo=s(ZA);JXe=n(WFo,"STRONG",{});var N0n=s(JXe);Ayt=r(N0n,"flaubert"),N0n.forEach(t),Lyt=r(WFo," \u2014 "),v_e=n(WFo,"A",{href:!0});var B0n=s(v_e);yyt=r(B0n,"FlaubertForMultipleChoice"),B0n.forEach(t),xyt=r(WFo," (FlauBERT model)"),WFo.forEach(t),Syt=i(ae),YA=n(ae,"LI",{});var QFo=s(YA);ZXe=n(QFo,"STRONG",{});var G0n=s(ZXe);kyt=r(G0n,"fnet"),G0n.forEach(t),$yt=r(QFo," \u2014 "),b_e=n(QFo,"A",{href:!0});var q0n=s(b_e);Pyt=r(q0n,"FNetForMultipleChoice"),q0n.forEach(t),Ryt=r(QFo," (FNet model)"),QFo.forEach(t),Iyt=i(ae),KA=n(ae,"LI",{});var UFo=s(KA);YXe=n(UFo,"STRONG",{});var D0n=s(YXe);Nyt=r(D0n,"funnel"),D0n.forEach(t),Byt=r(UFo," \u2014 "),T_e=n(UFo,"A",{href:!0});var j0n=s(T_e);Gyt=r(j0n,"FunnelForMultipleChoice"),j0n.forEach(t),qyt=r(UFo," (Funnel Transformer model)"),UFo.forEach(t),Dyt=i(ae),e7=n(ae,"LI",{});var HFo=s(e7);KXe=n(HFo,"STRONG",{});var O0n=s(KXe);jyt=r(O0n,"ibert"),O0n.forEach(t),Oyt=r(HFo," \u2014 "),F_e=n(HFo,"A",{href:!0});var V0n=s(F_e);Vyt=r(V0n,"IBertForMultipleChoice"),V0n.forEach(t),Xyt=r(HFo," (I-BERT model)"),HFo.forEach(t),zyt=i(ae),o7=n(ae,"LI",{});var JFo=s(o7);eze=n(JFo,"STRONG",{});var X0n=s(eze);Wyt=r(X0n,"longformer"),X0n.forEach(t),Qyt=r(JFo," \u2014 "),M_e=n(JFo,"A",{href:!0});var z0n=s(M_e);Uyt=r(z0n,"LongformerForMultipleChoice"),z0n.forEach(t),Hyt=r(JFo," (Longformer model)"),JFo.forEach(t),Jyt=i(ae),r7=n(ae,"LI",{});var ZFo=s(r7);oze=n(ZFo,"STRONG",{});var W0n=s(oze);Zyt=r(W0n,"luke"),W0n.forEach(t),Yyt=r(ZFo," \u2014 "),E_e=n(ZFo,"A",{href:!0});var Q0n=s(E_e);Kyt=r(Q0n,"LukeForMultipleChoice"),Q0n.forEach(t),ext=r(ZFo," (LUKE model)"),ZFo.forEach(t),oxt=i(ae),t7=n(ae,"LI",{});var YFo=s(t7);rze=n(YFo,"STRONG",{});var U0n=s(rze);rxt=r(U0n,"mega"),U0n.forEach(t),txt=r(YFo," \u2014 "),C_e=n(YFo,"A",{href:!0});var H0n=s(C_e);axt=r(H0n,"MegaForMultipleChoice"),H0n.forEach(t),nxt=r(YFo," (MEGA model)"),YFo.forEach(t),sxt=i(ae),a7=n(ae,"LI",{});var KFo=s(a7);tze=n(KFo,"STRONG",{});var J0n=s(tze);lxt=r(J0n,"megatron-bert"),J0n.forEach(t),ixt=r(KFo," \u2014 "),w_e=n(KFo,"A",{href:!0});var Z0n=s(w_e);dxt=r(Z0n,"MegatronBertForMultipleChoice"),Z0n.forEach(t),cxt=r(KFo," (Megatron-BERT model)"),KFo.forEach(t),fxt=i(ae),n7=n(ae,"LI",{});var eMo=s(n7);aze=n(eMo,"STRONG",{});var Y0n=s(aze);mxt=r(Y0n,"mobilebert"),Y0n.forEach(t),gxt=r(eMo," \u2014 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with the `),V_e=n(_Me,"A",{href:!0});var Bbn=s(V_e);YSt=r(Bbn,"from_pretrained()"),Bbn.forEach(t),KSt=r(_Me," class method or the "),X_e=n(_Me,"A",{href:!0});var Gbn=s(X_e);ekt=r(Gbn,"from_config()"),Gbn.forEach(t),okt=r(_Me,` class
method.`),_Me.forEach(t),rkt=i(gd),ZD=n(gd,"P",{});var HLo=s(ZD);tkt=r(HLo,"This class cannot be instantiated directly using "),wze=n(HLo,"CODE",{});var qbn=s(wze);akt=r(qbn,"__init__()"),qbn.forEach(t),nkt=r(HLo," (throws an error)."),HLo.forEach(t),skt=i(gd),ra=n(gd,"DIV",{class:!0});var VN=s(ra);T(YD.$$.fragment,VN),lkt=i(VN),Aze=n(VN,"P",{});var Dbn=s(Aze);ikt=r(Dbn,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),Dbn.forEach(t),dkt=i(VN),Wf=n(VN,"P",{});var vMe=s(Wf);ckt=r(vMe,`Note:
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model\u2019s configuration. Use `),z_e=n(vMe,"A",{href:!0});var Obn=s(z_e);gkt=r(Obn,"from_pretrained()"),Obn.forEach(t),hkt=r(vMe," to load the model weights."),vMe.forEach(t),ukt=i(VN),T(w7.$$.fragment,VN),VN.forEach(t),pkt=i(gd),ft=n(gd,"DIV",{class:!0});var hd=s(ft);T(KD.$$.fragment,hd),_kt=i(hd),yze=n(hd,"P",{});var Vbn=s(yze);vkt=r(Vbn,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),Vbn.forEach(t),bkt=i(hd),Jn=n(hd,"P",{});var XN=s(Jn);Tkt=r(XN,"The model class to instantiate is selected based on the "),xze=n(XN,"CODE",{});var Xbn=s(xze);Fkt=r(Xbn,"model_type"),Xbn.forEach(t),Mkt=r(XN,` property of the config object (either
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falling back to using pattern matching on `),kze=n(XN,"CODE",{});var Wbn=s(kze);wkt=r(Wbn,"pretrained_model_name_or_path"),Wbn.forEach(t),Akt=r(XN,":"),XN.forEach(t),Lkt=i(hd),Me=n(hd,"UL",{});var Ce=s(Me);A7=n(Ce,"LI",{});var vMo=s(A7);$ze=n(vMo,"STRONG",{});var Qbn=s($ze);ykt=r(Qbn,"albert"),Qbn.forEach(t),xkt=r(vMo," \u2014 "),W_e=n(vMo,"A",{href:!0});var Ubn=s(W_e);Skt=r(Ubn,"TFAlbertForMultipleChoice"),Ubn.forEach(t),kkt=r(vMo," (ALBERT model)"),vMo.forEach(t),$kt=i(Ce),L7=n(Ce,"LI",{});var bMo=s(L7);Pze=n(bMo,"STRONG",{});var Hbn=s(Pze);Pkt=r(Hbn,"bert"),Hbn.forEach(t),Rkt=r(bMo," \u2014 "),Q_e=n(bMo,"A",{href:!0});var Jbn=s(Q_e);Ikt=r(Jbn,"TFBertForMultipleChoice"),Jbn.forEach(t),Nkt=r(bMo," (BERT model)"),bMo.forEach(t),Bkt=i(Ce),y7=n(Ce,"LI",{});var TMo=s(y7);Rze=n(TMo,"STRONG",{});var Zbn=s(Rze);Gkt=r(Zbn,"camembert"),Zbn.forEach(t),qkt=r(TMo," \u2014 "),U_e=n(TMo,"A",{href:!0});var Ybn=s(U_e);Dkt=r(Ybn,"TFCamembertForMultipleChoice"),Ybn.forEach(t),jkt=r(TMo," (CamemBERT 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method.`),bMe.forEach(t),vPt=i(ud),rj=n(ud,"P",{});var ZLo=s(rj);bPt=r(ZLo,"This class cannot be instantiated directly using "),Yze=n(ZLo,"CODE",{});var R9n=s(Yze);TPt=r(R9n,"__init__()"),R9n.forEach(t),FPt=r(ZLo," (throws an error)."),ZLo.forEach(t),MPt=i(ud),ta=n(ud,"DIV",{class:!0});var zN=s(ta);T(tj.$$.fragment,zN),EPt=i(zN),Kze=n(zN,"P",{});var I9n=s(Kze);CPt=r(I9n,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),I9n.forEach(t),wPt=i(zN),Hf=n(zN,"P",{});var TMe=s(Hf);APt=r(TMe,`Note:
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model\u2019s configuration. Use `),m2e=n(TMe,"A",{href:!0});var B9n=s(m2e);xPt=r(B9n,"from_pretrained()"),B9n.forEach(t),SPt=r(TMe," to load the model weights."),TMe.forEach(t),kPt=i(zN),T(W7.$$.fragment,zN),zN.forEach(t),$Pt=i(ud),mt=n(ud,"DIV",{class:!0});var pd=s(mt);T(aj.$$.fragment,pd),PPt=i(pd),oWe=n(pd,"P",{});var G9n=s(oWe);RPt=r(G9n,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),G9n.forEach(t),IPt=i(pd),Zn=n(pd,"P",{});var WN=s(Zn);NPt=r(WN,"The model class to instantiate is selected based on the "),rWe=n(WN,"CODE",{});var q9n=s(rWe);BPt=r(q9n,"model_type"),q9n.forEach(t),GPt=r(WN,` property of the config object (either
passed as an argument or loaded from `),tWe=n(WN,"CODE",{});var D9n=s(tWe);qPt=r(D9n,"pretrained_model_name_or_path"),D9n.forEach(t),DPt=r(WN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),aWe=n(WN,"CODE",{});var j9n=s(aWe);jPt=r(j9n,"pretrained_model_name_or_path"),j9n.forEach(t),OPt=r(WN,":"),WN.forEach(t),VPt=i(pd),Ve=n(pd,"UL",{});var Ze=s(Ve);Q7=n(Ze,"LI",{});var NMo=s(Q7);nWe=n(NMo,"STRONG",{});var O9n=s(nWe);XPt=r(O9n,"albert"),O9n.forEach(t),zPt=r(NMo," \u2014 "),g2e=n(NMo,"A",{href:!0});var V9n=s(g2e);WPt=r(V9n,"FlaxAlbertForMultipleChoice"),V9n.forEach(t),QPt=r(NMo," (ALBERT model)"),NMo.forEach(t),UPt=i(Ze),U7=n(Ze,"LI",{});var BMo=s(U7);sWe=n(BMo,"STRONG",{});var X9n=s(sWe);HPt=r(X9n,"bert"),X9n.forEach(t),JPt=r(BMo," \u2014 "),h2e=n(BMo,"A",{href:!0});var z9n=s(h2e);ZPt=r(z9n,"FlaxBertForMultipleChoice"),z9n.forEach(t),YPt=r(BMo," (BERT model)"),BMo.forEach(t),KPt=i(Ze),H7=n(Ze,"LI",{});var GMo=s(H7);lWe=n(GMo,"STRONG",{});var W9n=s(lWe);eRt=r(W9n,"big_bird"),W9n.forEach(t),oRt=r(GMo," \u2014 "),u2e=n(GMo,"A",{href:!0});var Q9n=s(u2e);rRt=r(Q9n,"FlaxBigBirdForMultipleChoice"),Q9n.forEach(t),tRt=r(GMo," (BigBird model)"),GMo.forEach(t),aRt=i(Ze),J7=n(Ze,"LI",{});var qMo=s(J7);iWe=n(qMo,"STRONG",{});var U9n=s(iWe);nRt=r(U9n,"distilbert"),U9n.forEach(t),sRt=r(qMo," \u2014 "),p2e=n(qMo,"A",{href:!0});var H9n=s(p2e);lRt=r(H9n,"FlaxDistilBertForMultipleChoice"),H9n.forEach(t),iRt=r(qMo," (DistilBERT model)"),qMo.forEach(t),dRt=i(Ze),Z7=n(Ze,"LI",{});var DMo=s(Z7);dWe=n(DMo,"STRONG",{});var J9n=s(dWe);cRt=r(J9n,"electra"),J9n.forEach(t),fRt=r(DMo," \u2014 "),_2e=n(DMo,"A",{href:!0});var Z9n=s(_2e);mRt=r(Z9n,"FlaxElectraForMultipleChoice"),Z9n.forEach(t),gRt=r(DMo," (ELECTRA model)"),DMo.forEach(t),hRt=i(Ze),Y7=n(Ze,"LI",{});var jMo=s(Y7);cWe=n(jMo,"STRONG",{});var Y9n=s(cWe);uRt=r(Y9n,"roberta"),Y9n.forEach(t),pRt=r(jMo," \u2014 "),v2e=n(jMo,"A",{href:!0});var K9n=s(v2e);_Rt=r(K9n,"FlaxRobertaForMultipleChoice"),K9n.forEach(t),vRt=r(jMo," (RoBERTa model)"),jMo.forEach(t),bRt=i(Ze),K7=n(Ze,"LI",{});var OMo=s(K7);fWe=n(OMo,"STRONG",{});var eTn=s(fWe);TRt=r(eTn,"roberta-prelayernorm"),eTn.forEach(t),FRt=r(OMo," \u2014 "),b2e=n(OMo,"A",{href:!0});var oTn=s(b2e);MRt=r(oTn,"FlaxRobertaPreLayerNormForMultipleChoice"),oTn.forEach(t),ERt=r(OMo," (RoBERTa-PreLayerNorm model)"),OMo.forEach(t),CRt=i(Ze),eL=n(Ze,"LI",{});var VMo=s(eL);mWe=n(VMo,"STRONG",{});var rTn=s(mWe);wRt=r(rTn,"roformer"),rTn.forEach(t),ARt=r(VMo," \u2014 "),T2e=n(VMo,"A",{href:!0});var tTn=s(T2e);LRt=r(tTn,"FlaxRoFormerForMultipleChoice"),tTn.forEach(t),yRt=r(VMo," (RoFormer model)"),VMo.forEach(t),xRt=i(Ze),oL=n(Ze,"LI",{});var XMo=s(oL);gWe=n(XMo,"STRONG",{});var aTn=s(gWe);SRt=r(aTn,"xlm-roberta"),aTn.forEach(t),kRt=r(XMo," \u2014 "),F2e=n(XMo,"A",{href:!0});var nTn=s(F2e);$Rt=r(nTn,"FlaxXLMRobertaForMultipleChoice"),nTn.forEach(t),PRt=r(XMo," (XLM-RoBERTa model)"),XMo.forEach(t),Ze.forEach(t),RRt=i(pd),T(rL.$$.fragment,pd),pd.forEach(t),ud.forEach(t),mAo=i(f),Jf=n(f,"H3",{class:!0});var YLo=s(Jf);tL=n(YLo,"A",{id:!0,class:!0,href:!0});var sTn=s(tL);hWe=n(sTn,"SPAN",{});var lTn=s(hWe);T(nj.$$.fragment,lTn),lTn.forEach(t),sTn.forEach(t),IRt=i(YLo),uWe=n(YLo,"SPAN",{});var iTn=s(uWe);NRt=r(iTn,"AutoModelForNextSentencePrediction"),iTn.forEach(t),YLo.forEach(t),gAo=i(f),cr=n(f,"DIV",{class:!0});var _d=s(cr);T(sj.$$.fragment,_d),BRt=i(_d),Zf=n(_d,"P",{});var FMe=s(Zf);GRt=r(FMe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),M2e=n(FMe,"A",{href:!0});var dTn=s(M2e);qRt=r(dTn,"from_pretrained()"),dTn.forEach(t),DRt=r(FMe," class method or the "),E2e=n(FMe,"A",{href:!0});var cTn=s(E2e);jRt=r(cTn,"from_config()"),cTn.forEach(t),ORt=r(FMe,` class
method.`),FMe.forEach(t),VRt=i(_d),lj=n(_d,"P",{});var KLo=s(lj);XRt=r(KLo,"This class cannot be instantiated directly using "),pWe=n(KLo,"CODE",{});var fTn=s(pWe);zRt=r(fTn,"__init__()"),fTn.forEach(t),WRt=r(KLo," (throws an error)."),KLo.forEach(t),QRt=i(_d),aa=n(_d,"DIV",{class:!0});var QN=s(aa);T(ij.$$.fragment,QN),URt=i(QN),_We=n(QN,"P",{});var mTn=s(_We);HRt=r(mTn,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),mTn.forEach(t),JRt=i(QN),Yf=n(QN,"P",{});var MMe=s(Yf);ZRt=r(MMe,`Note:
Loading a model from its configuration file does `),vWe=n(MMe,"STRONG",{});var gTn=s(vWe);YRt=r(gTn,"not"),gTn.forEach(t),KRt=r(MMe,` load the model weights. It only affects the
model\u2019s configuration. Use `),C2e=n(MMe,"A",{href:!0});var hTn=s(C2e);eIt=r(hTn,"from_pretrained()"),hTn.forEach(t),oIt=r(MMe," to load the model weights."),MMe.forEach(t),rIt=i(QN),T(aL.$$.fragment,QN),QN.forEach(t),tIt=i(_d),ho=n(_d,"DIV",{class:!0});var en=s(ho);T(dj.$$.fragment,en),aIt=i(en),bWe=n(en,"P",{});var uTn=s(bWe);nIt=r(uTn,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),uTn.forEach(t),sIt=i(en),Yn=n(en,"P",{});var UN=s(Yn);lIt=r(UN,"The model class to instantiate is selected based on the "),TWe=n(UN,"CODE",{});var pTn=s(TWe);iIt=r(pTn,"model_type"),pTn.forEach(t),dIt=r(UN,` property of the config object (either
passed as an argument or loaded from `),FWe=n(UN,"CODE",{});var _Tn=s(FWe);cIt=r(_Tn,"pretrained_model_name_or_path"),_Tn.forEach(t),fIt=r(UN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),MWe=n(UN,"CODE",{});var vTn=s(MWe);mIt=r(vTn,"pretrained_model_name_or_path"),vTn.forEach(t),gIt=r(UN,":"),UN.forEach(t),hIt=i(en),Ke=n(en,"UL",{});var xt=s(Ke);nL=n(xt,"LI",{});var zMo=s(nL);EWe=n(zMo,"STRONG",{});var bTn=s(EWe);uIt=r(bTn,"bert"),bTn.forEach(t),pIt=r(zMo," \u2014 "),w2e=n(zMo,"A",{href:!0});var TTn=s(w2e);_It=r(TTn,"BertForNextSentencePrediction"),TTn.forEach(t),vIt=r(zMo," (BERT model)"),zMo.forEach(t),bIt=i(xt),sL=n(xt,"LI",{});var WMo=s(sL);CWe=n(WMo,"STRONG",{});var FTn=s(CWe);TIt=r(FTn,"ernie"),FTn.forEach(t),FIt=r(WMo," \u2014 "),A2e=n(WMo,"A",{href:!0});var MTn=s(A2e);MIt=r(MTn,"ErnieForNextSentencePrediction"),MTn.forEach(t),EIt=r(WMo," (ERNIE model)"),WMo.forEach(t),CIt=i(xt),lL=n(xt,"LI",{});var QMo=s(lL);wWe=n(QMo,"STRONG",{});var ETn=s(wWe);wIt=r(ETn,"fnet"),ETn.forEach(t),AIt=r(QMo," \u2014 "),L2e=n(QMo,"A",{href:!0});var CTn=s(L2e);LIt=r(CTn,"FNetForNextSentencePrediction"),CTn.forEach(t),yIt=r(QMo," (FNet model)"),QMo.forEach(t),xIt=i(xt),iL=n(xt,"LI",{});var UMo=s(iL);AWe=n(UMo,"STRONG",{});var wTn=s(AWe);SIt=r(wTn,"megatron-bert"),wTn.forEach(t),kIt=r(UMo," \u2014 "),y2e=n(UMo,"A",{href:!0});var ATn=s(y2e);$It=r(ATn,"MegatronBertForNextSentencePrediction"),ATn.forEach(t),PIt=r(UMo," (Megatron-BERT model)"),UMo.forEach(t),RIt=i(xt),dL=n(xt,"LI",{});var HMo=s(dL);LWe=n(HMo,"STRONG",{});var LTn=s(LWe);IIt=r(LTn,"mobilebert"),LTn.forEach(t),NIt=r(HMo," \u2014 "),x2e=n(HMo,"A",{href:!0});var yTn=s(x2e);BIt=r(yTn,"MobileBertForNextSentencePrediction"),yTn.forEach(t),GIt=r(HMo," (MobileBERT model)"),HMo.forEach(t),qIt=i(xt),cL=n(xt,"LI",{});var JMo=s(cL);yWe=n(JMo,"STRONG",{});var xTn=s(yWe);DIt=r(xTn,"nezha"),xTn.forEach(t),jIt=r(JMo," \u2014 "),S2e=n(JMo,"A",{href:!0});var STn=s(S2e);OIt=r(STn,"NezhaForNextSentencePrediction"),STn.forEach(t),VIt=r(JMo," (Nezha model)"),JMo.forEach(t),XIt=i(xt),fL=n(xt,"LI",{});var ZMo=s(fL);xWe=n(ZMo,"STRONG",{});var kTn=s(xWe);zIt=r(kTn,"qdqbert"),kTn.forEach(t),WIt=r(ZMo," \u2014 "),k2e=n(ZMo,"A",{href:!0});var $Tn=s(k2e);QIt=r($Tn,"QDQBertForNextSentencePrediction"),$Tn.forEach(t),UIt=r(ZMo," (QDQBert model)"),ZMo.forEach(t),xt.forEach(t),HIt=i(en),mL=n(en,"P",{});var YMo=s(mL);JIt=r(YMo,"The model is set in evaluation mode by default using "),SWe=n(YMo,"CODE",{});var PTn=s(SWe);ZIt=r(PTn,"model.eval()"),PTn.forEach(t),YIt=r(YMo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),kWe=n(YMo,"CODE",{});var RTn=s(kWe);KIt=r(RTn,"model.train()"),RTn.forEach(t),YMo.forEach(t),eNt=i(en),T(gL.$$.fragment,en),en.forEach(t),_d.forEach(t),hAo=i(f),Kf=n(f,"H3",{class:!0});var eyo=s(Kf);hL=n(eyo,"A",{id:!0,class:!0,href:!0});var ITn=s(hL);$We=n(ITn,"SPAN",{});var NTn=s($We);T(cj.$$.fragment,NTn),NTn.forEach(t),ITn.forEach(t),oNt=i(eyo),PWe=n(eyo,"SPAN",{});var BTn=s(PWe);rNt=r(BTn,"TFAutoModelForNextSentencePrediction"),BTn.forEach(t),eyo.forEach(t),uAo=i(f),fr=n(f,"DIV",{class:!0});var vd=s(fr);T(fj.$$.fragment,vd),tNt=i(vd),em=n(vd,"P",{});var EMe=s(em);aNt=r(EMe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),$2e=n(EMe,"A",{href:!0});var GTn=s($2e);nNt=r(GTn,"from_pretrained()"),GTn.forEach(t),sNt=r(EMe," class method or the "),P2e=n(EMe,"A",{href:!0});var qTn=s(P2e);lNt=r(qTn,"from_config()"),qTn.forEach(t),iNt=r(EMe,` class
method.`),EMe.forEach(t),dNt=i(vd),mj=n(vd,"P",{});var oyo=s(mj);cNt=r(oyo,"This class cannot be instantiated directly using "),RWe=n(oyo,"CODE",{});var DTn=s(RWe);fNt=r(DTn,"__init__()"),DTn.forEach(t),mNt=r(oyo," (throws an error)."),oyo.forEach(t),gNt=i(vd),na=n(vd,"DIV",{class:!0});var HN=s(na);T(gj.$$.fragment,HN),hNt=i(HN),IWe=n(HN,"P",{});var jTn=s(IWe);uNt=r(jTn,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),jTn.forEach(t),pNt=i(HN),om=n(HN,"P",{});var CMe=s(om);_Nt=r(CMe,`Note:
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model\u2019s configuration. Use `),R2e=n(CMe,"A",{href:!0});var VTn=s(R2e);TNt=r(VTn,"from_pretrained()"),VTn.forEach(t),FNt=r(CMe," to load the model weights."),CMe.forEach(t),MNt=i(HN),T(uL.$$.fragment,HN),HN.forEach(t),ENt=i(vd),gt=n(vd,"DIV",{class:!0});var bd=s(gt);T(hj.$$.fragment,bd),CNt=i(bd),BWe=n(bd,"P",{});var XTn=s(BWe);wNt=r(XTn,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),XTn.forEach(t),ANt=i(bd),Kn=n(bd,"P",{});var JN=s(Kn);LNt=r(JN,"The model class to instantiate is selected based on the "),GWe=n(JN,"CODE",{});var zTn=s(GWe);yNt=r(zTn,"model_type"),zTn.forEach(t),xNt=r(JN,` property of the config object (either
passed as an argument or loaded from `),qWe=n(JN,"CODE",{});var WTn=s(qWe);SNt=r(WTn,"pretrained_model_name_or_path"),WTn.forEach(t),kNt=r(JN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),DWe=n(JN,"CODE",{});var QTn=s(DWe);$Nt=r(QTn,"pretrained_model_name_or_path"),QTn.forEach(t),PNt=r(JN,":"),JN.forEach(t),RNt=i(bd),uj=n(bd,"UL",{});var ryo=s(uj);pL=n(ryo,"LI",{});var KMo=s(pL);jWe=n(KMo,"STRONG",{});var UTn=s(jWe);INt=r(UTn,"bert"),UTn.forEach(t),NNt=r(KMo," \u2014 "),I2e=n(KMo,"A",{href:!0});var HTn=s(I2e);BNt=r(HTn,"TFBertForNextSentencePrediction"),HTn.forEach(t),GNt=r(KMo," (BERT model)"),KMo.forEach(t),qNt=i(ryo),_L=n(ryo,"LI",{});var eEo=s(_L);OWe=n(eEo,"STRONG",{});var JTn=s(OWe);DNt=r(JTn,"mobilebert"),JTn.forEach(t),jNt=r(eEo," \u2014 "),N2e=n(eEo,"A",{href:!0});var ZTn=s(N2e);ONt=r(ZTn,"TFMobileBertForNextSentencePrediction"),ZTn.forEach(t),VNt=r(eEo," (MobileBERT model)"),eEo.forEach(t),ryo.forEach(t),XNt=i(bd),T(vL.$$.fragment,bd),bd.forEach(t),vd.forEach(t),pAo=i(f),rm=n(f,"H3",{class:!0});var tyo=s(rm);bL=n(tyo,"A",{id:!0,class:!0,href:!0});var YTn=s(bL);VWe=n(YTn,"SPAN",{});var KTn=s(VWe);T(pj.$$.fragment,KTn),KTn.forEach(t),YTn.forEach(t),zNt=i(tyo),XWe=n(tyo,"SPAN",{});var eFn=s(XWe);WNt=r(eFn,"FlaxAutoModelForNextSentencePrediction"),eFn.forEach(t),tyo.forEach(t),_Ao=i(f),mr=n(f,"DIV",{class:!0});var Td=s(mr);T(_j.$$.fragment,Td),QNt=i(Td),tm=n(Td,"P",{});var wMe=s(tm);UNt=r(wMe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),B2e=n(wMe,"A",{href:!0});var oFn=s(B2e);HNt=r(oFn,"from_pretrained()"),oFn.forEach(t),JNt=r(wMe," class method or the "),G2e=n(wMe,"A",{href:!0});var rFn=s(G2e);ZNt=r(rFn,"from_config()"),rFn.forEach(t),YNt=r(wMe,` class
method.`),wMe.forEach(t),KNt=i(Td),vj=n(Td,"P",{});var ayo=s(vj);eBt=r(ayo,"This class cannot be instantiated directly using "),zWe=n(ayo,"CODE",{});var tFn=s(zWe);oBt=r(tFn,"__init__()"),tFn.forEach(t),rBt=r(ayo," (throws an error)."),ayo.forEach(t),tBt=i(Td),sa=n(Td,"DIV",{class:!0});var ZN=s(sa);T(bj.$$.fragment,ZN),aBt=i(ZN),WWe=n(ZN,"P",{});var aFn=s(WWe);nBt=r(aFn,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),aFn.forEach(t),sBt=i(ZN),am=n(ZN,"P",{});var AMe=s(am);lBt=r(AMe,`Note:
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model\u2019s configuration. Use `),q2e=n(AMe,"A",{href:!0});var sFn=s(q2e);cBt=r(sFn,"from_pretrained()"),sFn.forEach(t),fBt=r(AMe," to load the model weights."),AMe.forEach(t),mBt=i(ZN),T(TL.$$.fragment,ZN),ZN.forEach(t),gBt=i(Td),ht=n(Td,"DIV",{class:!0});var Fd=s(ht);T(Tj.$$.fragment,Fd),hBt=i(Fd),UWe=n(Fd,"P",{});var lFn=s(UWe);uBt=r(lFn,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),lFn.forEach(t),pBt=i(Fd),es=n(Fd,"P",{});var YN=s(es);_Bt=r(YN,"The model class to instantiate is selected based on the "),HWe=n(YN,"CODE",{});var iFn=s(HWe);vBt=r(iFn,"model_type"),iFn.forEach(t),bBt=r(YN,` property of the config object (either
passed as an argument or loaded from `),JWe=n(YN,"CODE",{});var dFn=s(JWe);TBt=r(dFn,"pretrained_model_name_or_path"),dFn.forEach(t),FBt=r(YN,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ZWe=n(YN,"CODE",{});var cFn=s(ZWe);MBt=r(cFn,"pretrained_model_name_or_path"),cFn.forEach(t),EBt=r(YN,":"),YN.forEach(t),CBt=i(Fd),YWe=n(Fd,"UL",{});var fFn=s(YWe);FL=n(fFn,"LI",{});var oEo=s(FL);KWe=n(oEo,"STRONG",{});var mFn=s(KWe);wBt=r(mFn,"bert"),mFn.forEach(t),ABt=r(oEo," \u2014 "),D2e=n(oEo,"A",{href:!0});var gFn=s(D2e);LBt=r(gFn,"FlaxBertForNextSentencePrediction"),gFn.forEach(t),yBt=r(oEo," (BERT model)"),oEo.forEach(t),fFn.forEach(t),xBt=i(Fd),T(ML.$$.fragment,Fd),Fd.forEach(t),Td.forEach(t),vAo=i(f),nm=n(f,"H3",{class:!0});var nyo=s(nm);EL=n(nyo,"A",{id:!0,class:!0,href:!0});var hFn=s(EL);eQe=n(hFn,"SPAN",{});var uFn=s(eQe);T(Fj.$$.fragment,uFn),uFn.forEach(t),hFn.forEach(t),SBt=i(nyo),oQe=n(nyo,"SPAN",{});var pFn=s(oQe);kBt=r(pFn,"AutoModelForTokenClassification"),pFn.forEach(t),nyo.forEach(t),bAo=i(f),gr=n(f,"DIV",{class:!0});var Md=s(gr);T(Mj.$$.fragment,Md),$Bt=i(Md),sm=n(Md,"P",{});var LMe=s(sm);PBt=r(LMe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the `),j2e=n(LMe,"A",{href:!0});var _Fn=s(j2e);RBt=r(_Fn,"from_pretrained()"),_Fn.forEach(t),IBt=r(LMe," class method or the "),O2e=n(LMe,"A",{href:!0});var vFn=s(O2e);NBt=r(vFn,"from_config()"),vFn.forEach(t),BBt=r(LMe,` class
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with the `),g1e=n(kMe,"A",{href:!0});var k3n=s(g1e);zzt=r(k3n,"from_pretrained()"),k3n.forEach(t),Wzt=r(kMe," class method or the "),h1e=n(kMe,"A",{href:!0});var $3n=s(h1e);Qzt=r($3n,"from_config()"),$3n.forEach(t),Uzt=r(kMe,` class
method.`),kMe.forEach(t),Hzt=i(wd),Pj=n(wd,"P",{});var cyo=s(Pj);Jzt=r(cyo,"This class cannot be instantiated directly using "),OUe=n(cyo,"CODE",{});var P3n=s(OUe);Zzt=r(P3n,"__init__()"),P3n.forEach(t),Yzt=r(cyo," (throws an error)."),cyo.forEach(t),Kzt=i(wd),da=n(wd,"DIV",{class:!0});var tB=s(da);T(Rj.$$.fragment,tB),eWt=i(tB),VUe=n(tB,"P",{});var R3n=s(VUe);oWt=r(R3n,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),R3n.forEach(t),rWt=i(tB),gm=n(tB,"P",{});var $Me=s(gm);tWt=r($Me,`Note:
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model\u2019s configuration. Use `),u1e=n($Me,"A",{href:!0});var N3n=s(u1e);sWt=r(N3n,"from_pretrained()"),N3n.forEach(t),lWt=r($Me," to load the model weights."),$Me.forEach(t),iWt=i(tB),T(Zy.$$.fragment,tB),tB.forEach(t),dWt=i(wd),pt=n(wd,"DIV",{class:!0});var Ad=s(pt);T(Ij.$$.fragment,Ad),cWt=i(Ad),zUe=n(Ad,"P",{});var B3n=s(zUe);fWt=r(B3n,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),B3n.forEach(t),mWt=i(Ad),ts=n(Ad,"P",{});var aB=s(ts);gWt=r(aB,"The model class to instantiate is selected based on the "),WUe=n(aB,"CODE",{});var G3n=s(WUe);hWt=r(G3n,"model_type"),G3n.forEach(t),uWt=r(aB,` property of the config object (either
passed as an argument or loaded from `),QUe=n(aB,"CODE",{});var q3n=s(QUe);pWt=r(q3n,"pretrained_model_name_or_path"),q3n.forEach(t),_Wt=r(aB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),UUe=n(aB,"CODE",{});var D3n=s(UUe);vWt=r(D3n,"pretrained_model_name_or_path"),D3n.forEach(t),bWt=r(aB,":"),aB.forEach(t),TWt=i(Ad),Xe=n(Ad,"UL",{});var Ye=s(Xe);Yy=n(Ye,"LI",{});var E3o=s(Yy);HUe=n(E3o,"STRONG",{});var j3n=s(HUe);FWt=r(j3n,"albert"),j3n.forEach(t),MWt=r(E3o," \u2014 "),p1e=n(E3o,"A",{href:!0});var O3n=s(p1e);EWt=r(O3n,"FlaxAlbertForTokenClassification"),O3n.forEach(t),CWt=r(E3o," (ALBERT model)"),E3o.forEach(t),wWt=i(Ye),Ky=n(Ye,"LI",{});var C3o=s(Ky);JUe=n(C3o,"STRONG",{});var V3n=s(JUe);AWt=r(V3n,"bert"),V3n.forEach(t),LWt=r(C3o," \u2014 "),_1e=n(C3o,"A",{href:!0});var X3n=s(_1e);yWt=r(X3n,"FlaxBertForTokenClassification"),X3n.forEach(t),xWt=r(C3o," (BERT model)"),C3o.forEach(t),SWt=i(Ye),ex=n(Ye,"LI",{});var w3o=s(ex);ZUe=n(w3o,"STRONG",{});var z3n=s(ZUe);kWt=r(z3n,"big_bird"),z3n.forEach(t),$Wt=r(w3o," \u2014 "),v1e=n(w3o,"A",{href:!0});var W3n=s(v1e);PWt=r(W3n,"FlaxBigBirdForTokenClassification"),W3n.forEach(t),RWt=r(w3o," (BigBird model)"),w3o.forEach(t),IWt=i(Ye),ox=n(Ye,"LI",{});var A3o=s(ox);YUe=n(A3o,"STRONG",{});var Q3n=s(YUe);NWt=r(Q3n,"distilbert"),Q3n.forEach(t),BWt=r(A3o," \u2014 "),b1e=n(A3o,"A",{href:!0});var U3n=s(b1e);GWt=r(U3n,"FlaxDistilBertForTokenClassification"),U3n.forEach(t),qWt=r(A3o," (DistilBERT model)"),A3o.forEach(t),DWt=i(Ye),rx=n(Ye,"LI",{});var L3o=s(rx);KUe=n(L3o,"STRONG",{});var H3n=s(KUe);jWt=r(H3n,"electra"),H3n.forEach(t),OWt=r(L3o," \u2014 "),T1e=n(L3o,"A",{href:!0});var J3n=s(T1e);VWt=r(J3n,"FlaxElectraForTokenClassification"),J3n.forEach(t),XWt=r(L3o," (ELECTRA model)"),L3o.forEach(t),zWt=i(Ye),tx=n(Ye,"LI",{});var y3o=s(tx);eHe=n(y3o,"STRONG",{});var Z3n=s(eHe);WWt=r(Z3n,"roberta"),Z3n.forEach(t),QWt=r(y3o," \u2014 "),F1e=n(y3o,"A",{href:!0});var Y3n=s(F1e);UWt=r(Y3n,"FlaxRobertaForTokenClassification"),Y3n.forEach(t),HWt=r(y3o," (RoBERTa model)"),y3o.forEach(t),JWt=i(Ye),ax=n(Ye,"LI",{});var x3o=s(ax);oHe=n(x3o,"STRONG",{});var K3n=s(oHe);ZWt=r(K3n,"roberta-prelayernorm"),K3n.forEach(t),YWt=r(x3o," \u2014 "),M1e=n(x3o,"A",{href:!0});var eCn=s(M1e);KWt=r(eCn,"FlaxRobertaPreLayerNormForTokenClassification"),eCn.forEach(t),eQt=r(x3o," (RoBERTa-PreLayerNorm model)"),x3o.forEach(t),oQt=i(Ye),nx=n(Ye,"LI",{});var S3o=s(nx);rHe=n(S3o,"STRONG",{});var oCn=s(rHe);rQt=r(oCn,"roformer"),oCn.forEach(t),tQt=r(S3o," \u2014 "),E1e=n(S3o,"A",{href:!0});var rCn=s(E1e);aQt=r(rCn,"FlaxRoFormerForTokenClassification"),rCn.forEach(t),nQt=r(S3o," (RoFormer model)"),S3o.forEach(t),sQt=i(Ye),sx=n(Ye,"LI",{});var k3o=s(sx);tHe=n(k3o,"STRONG",{});var tCn=s(tHe);lQt=r(tCn,"xlm-roberta"),tCn.forEach(t),iQt=r(k3o," \u2014 "),C1e=n(k3o,"A",{href:!0});var aCn=s(C1e);dQt=r(aCn,"FlaxXLMRobertaForTokenClassification"),aCn.forEach(t),cQt=r(k3o," (XLM-RoBERTa model)"),k3o.forEach(t),Ye.forEach(t),fQt=i(Ad),T(lx.$$.fragment,Ad),Ad.forEach(t),wd.forEach(t),CAo=i(f),hm=n(f,"H3",{class:!0});var fyo=s(hm);ix=n(fyo,"A",{id:!0,class:!0,href:!0});var nCn=s(ix);aHe=n(nCn,"SPAN",{});var sCn=s(aHe);T(Nj.$$.fragment,sCn),sCn.forEach(t),nCn.forEach(t),mQt=i(fyo),nHe=n(fyo,"SPAN",{});var lCn=s(nHe);gQt=r(lCn,"AutoModelForQuestionAnswering"),lCn.forEach(t),fyo.forEach(t),wAo=i(f),pr=n(f,"DIV",{class:!0});var Ld=s(pr);T(Bj.$$.fragment,Ld),hQt=i(Ld),um=n(Ld,"P",{});var PMe=s(um);uQt=r(PMe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
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method.`),PMe.forEach(t),TQt=i(Ld),Gj=n(Ld,"P",{});var myo=s(Gj);FQt=r(myo,"This class cannot be instantiated directly using "),sHe=n(myo,"CODE",{});var cCn=s(sHe);MQt=r(cCn,"__init__()"),cCn.forEach(t),EQt=r(myo," (throws an error)."),myo.forEach(t),CQt=i(Ld),ca=n(Ld,"DIV",{class:!0});var nB=s(ca);T(qj.$$.fragment,nB),wQt=i(nB),lHe=n(nB,"P",{});var fCn=s(lHe);AQt=r(fCn,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),fCn.forEach(t),LQt=i(nB),pm=n(nB,"P",{});var RMe=s(pm);yQt=r(RMe,`Note:
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model\u2019s configuration. Use `),L1e=n(RMe,"A",{href:!0});var gCn=s(L1e);kQt=r(gCn,"from_pretrained()"),gCn.forEach(t),$Qt=r(RMe," to load the model weights."),RMe.forEach(t),PQt=i(nB),T(dx.$$.fragment,nB),nB.forEach(t),RQt=i(Ld),po=n(Ld,"DIV",{class:!0});var rn=s(po);T(Dj.$$.fragment,rn),IQt=i(rn),dHe=n(rn,"P",{});var hCn=s(dHe);NQt=r(hCn,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),hCn.forEach(t),BQt=i(rn),as=n(rn,"P",{});var sB=s(as);GQt=r(sB,"The model class to instantiate is selected based on the "),cHe=n(sB,"CODE",{});var uCn=s(cHe);qQt=r(uCn,"model_type"),uCn.forEach(t),DQt=r(sB,` property of the config object (either
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b5n=s(o4e);FJt=r(b5n,"LiltForQuestionAnswering"),b5n.forEach(t),MJt=r(nCo," (LiLT model)"),nCo.forEach(t),EJt=i(U),Gx=n(U,"LI",{});var sCo=s(Gx);jHe=n(sCo,"STRONG",{});var T5n=s(jHe);CJt=r(T5n,"longformer"),T5n.forEach(t),wJt=r(sCo," \u2014 "),r4e=n(sCo,"A",{href:!0});var F5n=s(r4e);AJt=r(F5n,"LongformerForQuestionAnswering"),F5n.forEach(t),LJt=r(sCo," (Longformer model)"),sCo.forEach(t),yJt=i(U),qx=n(U,"LI",{});var lCo=s(qx);OHe=n(lCo,"STRONG",{});var M5n=s(OHe);xJt=r(M5n,"luke"),M5n.forEach(t),SJt=r(lCo," \u2014 "),t4e=n(lCo,"A",{href:!0});var E5n=s(t4e);kJt=r(E5n,"LukeForQuestionAnswering"),E5n.forEach(t),$Jt=r(lCo," (LUKE model)"),lCo.forEach(t),PJt=i(U),Dx=n(U,"LI",{});var iCo=s(Dx);VHe=n(iCo,"STRONG",{});var C5n=s(VHe);RJt=r(C5n,"lxmert"),C5n.forEach(t),IJt=r(iCo," \u2014 "),a4e=n(iCo,"A",{href:!0});var w5n=s(a4e);NJt=r(w5n,"LxmertForQuestionAnswering"),w5n.forEach(t),BJt=r(iCo," (LXMERT model)"),iCo.forEach(t),GJt=i(U),jx=n(U,"LI",{});var dCo=s(jx);XHe=n(dCo,"STRONG",{});var 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deactivated). To train the model, you should first set it back in training mode with `),pJe=n(RCo,"CODE",{});var E8n=s(pJe);nKt=r(E8n,"model.train()"),E8n.forEach(t),RCo.forEach(t),sKt=i(rn),T(gS.$$.fragment,rn),rn.forEach(t),Ld.forEach(t),AAo=i(f),_m=n(f,"H3",{class:!0});var gyo=s(_m);hS=n(gyo,"A",{id:!0,class:!0,href:!0});var C8n=s(hS);_Je=n(C8n,"SPAN",{});var w8n=s(_Je);T(jj.$$.fragment,w8n),w8n.forEach(t),C8n.forEach(t),lKt=i(gyo),vJe=n(gyo,"SPAN",{});var A8n=s(vJe);iKt=r(A8n,"TFAutoModelForQuestionAnswering"),A8n.forEach(t),gyo.forEach(t),LAo=i(f),_r=n(f,"DIV",{class:!0});var yd=s(_r);T(Oj.$$.fragment,yd),dKt=i(yd),vm=n(yd,"P",{});var IMe=s(vm);cKt=r(IMe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
with the `),S4e=n(IMe,"A",{href:!0});var L8n=s(S4e);fKt=r(L8n,"from_pretrained()"),L8n.forEach(t),mKt=r(IMe," class method or the "),k4e=n(IMe,"A",{href:!0});var y8n=s(k4e);gKt=r(y8n,"from_config()"),y8n.forEach(t),hKt=r(IMe,` class
method.`),IMe.forEach(t),uKt=i(yd),Vj=n(yd,"P",{});var hyo=s(Vj);pKt=r(hyo,"This class cannot be instantiated directly using "),bJe=n(hyo,"CODE",{});var x8n=s(bJe);_Kt=r(x8n,"__init__()"),x8n.forEach(t),vKt=r(hyo," (throws an error)."),hyo.forEach(t),bKt=i(yd),fa=n(yd,"DIV",{class:!0});var lB=s(fa);T(Xj.$$.fragment,lB),TKt=i(lB),TJe=n(lB,"P",{});var S8n=s(TJe);FKt=r(S8n,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),S8n.forEach(t),MKt=i(lB),bm=n(lB,"P",{});var NMe=s(bm);EKt=r(NMe,`Note:
Loading a model from its configuration file does `),FJe=n(NMe,"STRONG",{});var k8n=s(FJe);CKt=r(k8n,"not"),k8n.forEach(t),wKt=r(NMe,` load the model weights. It only affects the
model\u2019s configuration. Use `),$4e=n(NMe,"A",{href:!0});var $8n=s($4e);AKt=r($8n,"from_pretrained()"),$8n.forEach(t),LKt=r(NMe," to load the model weights."),NMe.forEach(t),yKt=i(lB),T(uS.$$.fragment,lB),lB.forEach(t),xKt=i(yd),_t=n(yd,"DIV",{class:!0});var xd=s(_t);T(zj.$$.fragment,xd),SKt=i(xd),MJe=n(xd,"P",{});var P8n=s(MJe);kKt=r(P8n,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),P8n.forEach(t),$Kt=i(xd),ns=n(xd,"P",{});var iB=s(ns);PKt=r(iB,"The model class to instantiate is selected based on the "),EJe=n(iB,"CODE",{});var R8n=s(EJe);RKt=r(R8n,"model_type"),R8n.forEach(t),IKt=r(iB,` property of the config object (either
passed as an argument or loaded from `),CJe=n(iB,"CODE",{});var I8n=s(CJe);NKt=r(I8n,"pretrained_model_name_or_path"),I8n.forEach(t),BKt=r(iB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wJe=n(iB,"CODE",{});var N8n=s(wJe);GKt=r(N8n,"pretrained_model_name_or_path"),N8n.forEach(t),qKt=r(iB,":"),iB.forEach(t),DKt=i(xd),_e=n(xd,"UL",{});var Fe=s(_e);pS=n(Fe,"LI",{});var ICo=s(pS);AJe=n(ICo,"STRONG",{});var B8n=s(AJe);jKt=r(B8n,"albert"),B8n.forEach(t),OKt=r(ICo," \u2014 "),P4e=n(ICo,"A",{href:!0});var G8n=s(P4e);VKt=r(G8n,"TFAlbertForQuestionAnswering"),G8n.forEach(t),XKt=r(ICo," (ALBERT model)"),ICo.forEach(t),zKt=i(Fe),_S=n(Fe,"LI",{});var NCo=s(_S);LJe=n(NCo,"STRONG",{});var q8n=s(LJe);WKt=r(q8n,"bert"),q8n.forEach(t),QKt=r(NCo," \u2014 "),R4e=n(NCo,"A",{href:!0});var D8n=s(R4e);UKt=r(D8n,"TFBertForQuestionAnswering"),D8n.forEach(t),HKt=r(NCo," (BERT model)"),NCo.forEach(t),JKt=i(Fe),vS=n(Fe,"LI",{});var BCo=s(vS);yJe=n(BCo,"STRONG",{});var j8n=s(yJe);ZKt=r(j8n,"camembert"),j8n.forEach(t),YKt=r(BCo," \u2014 "),I4e=n(BCo,"A",{href:!0});var O8n=s(I4e);KKt=r(O8n,"TFCamembertForQuestionAnswering"),O8n.forEach(t),eea=r(BCo," (CamemBERT model)"),BCo.forEach(t),oea=i(Fe),bS=n(Fe,"LI",{});var GCo=s(bS);xJe=n(GCo,"STRONG",{});var V8n=s(xJe);rea=r(V8n,"convbert"),V8n.forEach(t),tea=r(GCo," \u2014 "),N4e=n(GCo,"A",{href:!0});var X8n=s(N4e);aea=r(X8n,"TFConvBertForQuestionAnswering"),X8n.forEach(t),nea=r(GCo," (ConvBERT model)"),GCo.forEach(t),sea=i(Fe),TS=n(Fe,"LI",{});var qCo=s(TS);SJe=n(qCo,"STRONG",{});var z8n=s(SJe);lea=r(z8n,"deberta"),z8n.forEach(t),iea=r(qCo," \u2014 "),B4e=n(qCo,"A",{href:!0});var W8n=s(B4e);dea=r(W8n,"TFDebertaForQuestionAnswering"),W8n.forEach(t),cea=r(qCo," (DeBERTa model)"),qCo.forEach(t),fea=i(Fe),FS=n(Fe,"LI",{});var DCo=s(FS);kJe=n(DCo,"STRONG",{});var Q8n=s(kJe);mea=r(Q8n,"deberta-v2"),Q8n.forEach(t),gea=r(DCo," \u2014 "),G4e=n(DCo,"A",{href:!0});var U8n=s(G4e);hea=r(U8n,"TFDebertaV2ForQuestionAnswering"),U8n.forEach(t),uea=r(DCo," (DeBERTa-v2 model)"),DCo.forEach(t),pea=i(Fe),MS=n(Fe,"LI",{});var jCo=s(MS);$Je=n(jCo,"STRONG",{});var H8n=s($Je);_ea=r(H8n,"distilbert"),H8n.forEach(t),vea=r(jCo," \u2014 "),q4e=n(jCo,"A",{href:!0});var J8n=s(q4e);bea=r(J8n,"TFDistilBertForQuestionAnswering"),J8n.forEach(t),Tea=r(jCo," (DistilBERT model)"),jCo.forEach(t),Fea=i(Fe),ES=n(Fe,"LI",{});var OCo=s(ES);PJe=n(OCo,"STRONG",{});var Z8n=s(PJe);Mea=r(Z8n,"electra"),Z8n.forEach(t),Eea=r(OCo," \u2014 "),D4e=n(OCo,"A",{href:!0});var Y8n=s(D4e);Cea=r(Y8n,"TFElectraForQuestionAnswering"),Y8n.forEach(t),wea=r(OCo," (ELECTRA model)"),OCo.forEach(t),Aea=i(Fe),CS=n(Fe,"LI",{});var VCo=s(CS);RJe=n(VCo,"STRONG",{});var K8n=s(RJe);Lea=r(K8n,"flaubert"),K8n.forEach(t),yea=r(VCo," \u2014 "),j4e=n(VCo,"A",{href:!0});var e6n=s(j4e);xea=r(e6n,"TFFlaubertForQuestionAnsweringSimple"),e6n.forEach(t),Sea=r(VCo," (FlauBERT model)"),VCo.forEach(t),kea=i(Fe),wS=n(Fe,"LI",{});var XCo=s(wS);IJe=n(XCo,"STRONG",{});var o6n=s(IJe);$ea=r(o6n,"funnel"),o6n.forEach(t),Pea=r(XCo," \u2014 "),O4e=n(XCo,"A",{href:!0});var r6n=s(O4e);Rea=r(r6n,"TFFunnelForQuestionAnswering"),r6n.forEach(t),Iea=r(XCo," (Funnel Transformer model)"),XCo.forEach(t),Nea=i(Fe),AS=n(Fe,"LI",{});var zCo=s(AS);NJe=n(zCo,"STRONG",{});var t6n=s(NJe);Bea=r(t6n,"gptj"),t6n.forEach(t),Gea=r(zCo," \u2014 "),V4e=n(zCo,"A",{href:!0});var a6n=s(V4e);qea=r(a6n,"TFGPTJForQuestionAnswering"),a6n.forEach(t),Dea=r(zCo," (GPT-J model)"),zCo.forEach(t),jea=i(Fe),LS=n(Fe,"LI",{});var WCo=s(LS);BJe=n(WCo,"STRONG",{});var n6n=s(BJe);Oea=r(n6n,"layoutlmv3"),n6n.forEach(t),Vea=r(WCo," \u2014 "),X4e=n(WCo,"A",{href:!0});var s6n=s(X4e);Xea=r(s6n,"TFLayoutLMv3ForQuestionAnswering"),s6n.forEach(t),zea=r(WCo," (LayoutLMv3 model)"),WCo.forEach(t),Wea=i(Fe),yS=n(Fe,"LI",{});var QCo=s(yS);GJe=n(QCo,"STRONG",{});var l6n=s(GJe);Qea=r(l6n,"longformer"),l6n.forEach(t),Uea=r(QCo," \u2014 "),z4e=n(QCo,"A",{href:!0});var i6n=s(z4e);Hea=r(i6n,"TFLongformerForQuestionAnswering"),i6n.forEach(t),Jea=r(QCo," (Longformer 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"),H4e=n(ZCo,"A",{href:!0});var p6n=s(H4e);uoa=r(p6n,"TFRobertaForQuestionAnswering"),p6n.forEach(t),poa=r(ZCo," (RoBERTa model)"),ZCo.forEach(t),_oa=i(Fe),PS=n(Fe,"LI",{});var YCo=s(PS);VJe=n(YCo,"STRONG",{});var _6n=s(VJe);voa=r(_6n,"roberta-prelayernorm"),_6n.forEach(t),boa=r(YCo," \u2014 "),J4e=n(YCo,"A",{href:!0});var v6n=s(J4e);Toa=r(v6n,"TFRobertaPreLayerNormForQuestionAnswering"),v6n.forEach(t),Foa=r(YCo," (RoBERTa-PreLayerNorm model)"),YCo.forEach(t),Moa=i(Fe),RS=n(Fe,"LI",{});var KCo=s(RS);XJe=n(KCo,"STRONG",{});var b6n=s(XJe);Eoa=r(b6n,"roformer"),b6n.forEach(t),Coa=r(KCo," \u2014 "),Z4e=n(KCo,"A",{href:!0});var T6n=s(Z4e);woa=r(T6n,"TFRoFormerForQuestionAnswering"),T6n.forEach(t),Aoa=r(KCo," (RoFormer model)"),KCo.forEach(t),Loa=i(Fe),IS=n(Fe,"LI",{});var e5o=s(IS);zJe=n(e5o,"STRONG",{});var F6n=s(zJe);yoa=r(F6n,"xlm"),F6n.forEach(t),xoa=r(e5o," \u2014 "),Y4e=n(e5o,"A",{href:!0});var M6n=s(Y4e);Soa=r(M6n,"TFXLMForQuestionAnsweringSimple"),M6n.forEach(t),koa=r(e5o," (XLM 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x6n=s(HJe);Xoa=r(x6n,"FlaxAutoModelForQuestionAnswering"),x6n.forEach(t),uyo.forEach(t),xAo=i(f),vr=n(f,"DIV",{class:!0});var Sd=s(vr);T(Qj.$$.fragment,Sd),zoa=i(Sd),Fm=n(Sd,"P",{});var BMe=s(Fm);Woa=r(BMe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
with the `),o0e=n(BMe,"A",{href:!0});var S6n=s(o0e);Qoa=r(S6n,"from_pretrained()"),S6n.forEach(t),Uoa=r(BMe," class method or the "),r0e=n(BMe,"A",{href:!0});var k6n=s(r0e);Hoa=r(k6n,"from_config()"),k6n.forEach(t),Joa=r(BMe,` class
method.`),BMe.forEach(t),Zoa=i(Sd),Uj=n(Sd,"P",{});var pyo=s(Uj);Yoa=r(pyo,"This class cannot be instantiated directly using "),JJe=n(pyo,"CODE",{});var $6n=s(JJe);Koa=r($6n,"__init__()"),$6n.forEach(t),era=r(pyo," (throws an error)."),pyo.forEach(t),ora=i(Sd),ma=n(Sd,"DIV",{class:!0});var dB=s(ma);T(Hj.$$.fragment,dB),rra=i(dB),ZJe=n(dB,"P",{});var P6n=s(ZJe);tra=r(P6n,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),P6n.forEach(t),ara=i(dB),Mm=n(dB,"P",{});var GMe=s(Mm);nra=r(GMe,`Note:
Loading a model from its configuration file does `),YJe=n(GMe,"STRONG",{});var R6n=s(YJe);sra=r(R6n,"not"),R6n.forEach(t),lra=r(GMe,` load the model weights. It only affects the
model\u2019s configuration. Use `),t0e=n(GMe,"A",{href:!0});var I6n=s(t0e);ira=r(I6n,"from_pretrained()"),I6n.forEach(t),dra=r(GMe," to load the model weights."),GMe.forEach(t),cra=i(dB),T(DS.$$.fragment,dB),dB.forEach(t),fra=i(Sd),vt=n(Sd,"DIV",{class:!0});var kd=s(vt);T(Jj.$$.fragment,kd),mra=i(kd),KJe=n(kd,"P",{});var N6n=s(KJe);gra=r(N6n,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),N6n.forEach(t),hra=i(kd),ss=n(kd,"P",{});var cB=s(ss);ura=r(cB,"The model class to instantiate is selected based on the "),eZe=n(cB,"CODE",{});var B6n=s(eZe);pra=r(B6n,"model_type"),B6n.forEach(t),_ra=r(cB,` property of the config object (either
passed as an argument or loaded from `),oZe=n(cB,"CODE",{});var G6n=s(oZe);vra=r(G6n,"pretrained_model_name_or_path"),G6n.forEach(t),bra=r(cB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),rZe=n(cB,"CODE",{});var q6n=s(rZe);Tra=r(q6n,"pretrained_model_name_or_path"),q6n.forEach(t),Fra=r(cB,":"),cB.forEach(t),Mra=i(kd),$e=n(kd,"UL",{});var je=s($e);jS=n(je,"LI",{});var t5o=s(jS);tZe=n(t5o,"STRONG",{});var D6n=s(tZe);Era=r(D6n,"albert"),D6n.forEach(t),Cra=r(t5o," \u2014 "),a0e=n(t5o,"A",{href:!0});var j6n=s(a0e);wra=r(j6n,"FlaxAlbertForQuestionAnswering"),j6n.forEach(t),Ara=r(t5o," (ALBERT model)"),t5o.forEach(t),Lra=i(je),OS=n(je,"LI",{});var a5o=s(OS);aZe=n(a5o,"STRONG",{});var O6n=s(aZe);yra=r(O6n,"bart"),O6n.forEach(t),xra=r(a5o," \u2014 "),n0e=n(a5o,"A",{href:!0});var V6n=s(n0e);Sra=r(V6n,"FlaxBartForQuestionAnswering"),V6n.forEach(t),kra=r(a5o," (BART model)"),a5o.forEach(t),$ra=i(je),VS=n(je,"LI",{});var n5o=s(VS);nZe=n(n5o,"STRONG",{});var X6n=s(nZe);Pra=r(X6n,"bert"),X6n.forEach(t),Rra=r(n5o," \u2014 "),s0e=n(n5o,"A",{href:!0});var z6n=s(s0e);Ira=r(z6n,"FlaxBertForQuestionAnswering"),z6n.forEach(t),Nra=r(n5o," (BERT model)"),n5o.forEach(t),Bra=i(je),XS=n(je,"LI",{});var s5o=s(XS);sZe=n(s5o,"STRONG",{});var W6n=s(sZe);Gra=r(W6n,"big_bird"),W6n.forEach(t),qra=r(s5o," \u2014 "),l0e=n(s5o,"A",{href:!0});var Q6n=s(l0e);Dra=r(Q6n,"FlaxBigBirdForQuestionAnswering"),Q6n.forEach(t),jra=r(s5o," (BigBird model)"),s5o.forEach(t),Ora=i(je),zS=n(je,"LI",{});var l5o=s(zS);lZe=n(l5o,"STRONG",{});var U6n=s(lZe);Vra=r(U6n,"distilbert"),U6n.forEach(t),Xra=r(l5o," \u2014 "),i0e=n(l5o,"A",{href:!0});var H6n=s(i0e);zra=r(H6n,"FlaxDistilBertForQuestionAnswering"),H6n.forEach(t),Wra=r(l5o," (DistilBERT model)"),l5o.forEach(t),Qra=i(je),WS=n(je,"LI",{});var i5o=s(WS);iZe=n(i5o,"STRONG",{});var J6n=s(iZe);Ura=r(J6n,"electra"),J6n.forEach(t),Hra=r(i5o," \u2014 "),d0e=n(i5o,"A",{href:!0});var Z6n=s(d0e);Jra=r(Z6n,"FlaxElectraForQuestionAnswering"),Z6n.forEach(t),Zra=r(i5o," (ELECTRA model)"),i5o.forEach(t),Yra=i(je),QS=n(je,"LI",{});var d5o=s(QS);dZe=n(d5o,"STRONG",{});var Y6n=s(dZe);Kra=r(Y6n,"mbart"),Y6n.forEach(t),eta=r(d5o," \u2014 "),c0e=n(d5o,"A",{href:!0});var K6n=s(c0e);ota=r(K6n,"FlaxMBartForQuestionAnswering"),K6n.forEach(t),rta=r(d5o," (mBART model)"),d5o.forEach(t),tta=i(je),US=n(je,"LI",{});var c5o=s(US);cZe=n(c5o,"STRONG",{});var ewn=s(cZe);ata=r(ewn,"roberta"),ewn.forEach(t),nta=r(c5o," \u2014 "),f0e=n(c5o,"A",{href:!0});var own=s(f0e);sta=r(own,"FlaxRobertaForQuestionAnswering"),own.forEach(t),lta=r(c5o," (RoBERTa model)"),c5o.forEach(t),ita=i(je),HS=n(je,"LI",{});var f5o=s(HS);fZe=n(f5o,"STRONG",{});var rwn=s(fZe);dta=r(rwn,"roberta-prelayernorm"),rwn.forEach(t),cta=r(f5o," \u2014 "),m0e=n(f5o,"A",{href:!0});var twn=s(m0e);fta=r(twn,"FlaxRobertaPreLayerNormForQuestionAnswering"),twn.forEach(t),mta=r(f5o," (RoBERTa-PreLayerNorm model)"),f5o.forEach(t),gta=i(je),JS=n(je,"LI",{});var m5o=s(JS);mZe=n(m5o,"STRONG",{});var awn=s(mZe);hta=r(awn,"roformer"),awn.forEach(t),uta=r(m5o," \u2014 "),g0e=n(m5o,"A",{href:!0});var nwn=s(g0e);pta=r(nwn,"FlaxRoFormerForQuestionAnswering"),nwn.forEach(t),_ta=r(m5o," (RoFormer model)"),m5o.forEach(t),vta=i(je),ZS=n(je,"LI",{});var g5o=s(ZS);gZe=n(g5o,"STRONG",{});var swn=s(gZe);bta=r(swn,"xlm-roberta"),swn.forEach(t),Tta=r(g5o," \u2014 "),h0e=n(g5o,"A",{href:!0});var lwn=s(h0e);Fta=r(lwn,"FlaxXLMRobertaForQuestionAnswering"),lwn.forEach(t),Mta=r(g5o," (XLM-RoBERTa model)"),g5o.forEach(t),je.forEach(t),Eta=i(kd),T(YS.$$.fragment,kd),kd.forEach(t),Sd.forEach(t),SAo=i(f),Em=n(f,"H2",{class:!0});var _yo=s(Em);KS=n(_yo,"A",{id:!0,class:!0,href:!0});var iwn=s(KS);hZe=n(iwn,"SPAN",{});var dwn=s(hZe);T(Zj.$$.fragment,dwn),dwn.forEach(t),iwn.forEach(t),Cta=i(_yo),uZe=n(_yo,"SPAN",{});var cwn=s(uZe);wta=r(cwn,"Computer vision"),cwn.forEach(t),_yo.forEach(t),kAo=i(f),u0e=n(f,"P",{});var fwn=s(u0e);Ata=r(fwn,"The following auto classes are available for the following computer vision tasks."),fwn.forEach(t),$Ao=i(f),Cm=n(f,"H3",{class:!0});var vyo=s(Cm);ek=n(vyo,"A",{id:!0,class:!0,href:!0});var mwn=s(ek);pZe=n(mwn,"SPAN",{});var gwn=s(pZe);T(Yj.$$.fragment,gwn),gwn.forEach(t),mwn.forEach(t),Lta=i(vyo),_Ze=n(vyo,"SPAN",{});var hwn=s(_Ze);yta=r(hwn,"AutoModelForDepthEstimation"),hwn.forEach(t),vyo.forEach(t),PAo=i(f),br=n(f,"DIV",{class:!0});var $d=s(br);T(Kj.$$.fragment,$d),xta=i($d),wm=n($d,"P",{});var qMe=s(wm);Sta=r(qMe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a depth estimation head) when created
with the `),p0e=n(qMe,"A",{href:!0});var uwn=s(p0e);kta=r(uwn,"from_pretrained()"),uwn.forEach(t),$ta=r(qMe," class method or the "),_0e=n(qMe,"A",{href:!0});var pwn=s(_0e);Pta=r(pwn,"from_config()"),pwn.forEach(t),Rta=r(qMe,` class
method.`),qMe.forEach(t),Ita=i($d),eO=n($d,"P",{});var byo=s(eO);Nta=r(byo,"This class cannot be instantiated directly using "),vZe=n(byo,"CODE",{});var _wn=s(vZe);Bta=r(_wn,"__init__()"),_wn.forEach(t),Gta=r(byo," (throws an error)."),byo.forEach(t),qta=i($d),ga=n($d,"DIV",{class:!0});var fB=s(ga);T(oO.$$.fragment,fB),Dta=i(fB),bZe=n(fB,"P",{});var vwn=s(bZe);jta=r(vwn,"Instantiates one of the model classes of the library (with a depth estimation head) from a configuration."),vwn.forEach(t),Ota=i(fB),Am=n(fB,"P",{});var DMe=s(Am);Vta=r(DMe,`Note:
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model\u2019s configuration. Use `),v0e=n(DMe,"A",{href:!0});var Twn=s(v0e);Wta=r(Twn,"from_pretrained()"),Twn.forEach(t),Qta=r(DMe," to load the model weights."),DMe.forEach(t),Uta=i(fB),T(ok.$$.fragment,fB),fB.forEach(t),Hta=i($d),_o=n($d,"DIV",{class:!0});var tn=s(_o);T(rO.$$.fragment,tn),Jta=i(tn),FZe=n(tn,"P",{});var Fwn=s(FZe);Zta=r(Fwn,"Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model."),Fwn.forEach(t),Yta=i(tn),ls=n(tn,"P",{});var mB=s(ls);Kta=r(mB,"The model class to instantiate is selected based on the "),MZe=n(mB,"CODE",{});var Mwn=s(MZe);eaa=r(Mwn,"model_type"),Mwn.forEach(t),oaa=r(mB,` property of the config object (either
passed as an argument or loaded from `),EZe=n(mB,"CODE",{});var Ewn=s(EZe);raa=r(Ewn,"pretrained_model_name_or_path"),Ewn.forEach(t),taa=r(mB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),CZe=n(mB,"CODE",{});var Cwn=s(CZe);aaa=r(Cwn,"pretrained_model_name_or_path"),Cwn.forEach(t),naa=r(mB,":"),mB.forEach(t),saa=i(tn),tO=n(tn,"UL",{});var Tyo=s(tO);rk=n(Tyo,"LI",{});var h5o=s(rk);wZe=n(h5o,"STRONG",{});var wwn=s(wZe);laa=r(wwn,"dpt"),wwn.forEach(t),iaa=r(h5o," \u2014 "),b0e=n(h5o,"A",{href:!0});var Awn=s(b0e);daa=r(Awn,"DPTForDepthEstimation"),Awn.forEach(t),caa=r(h5o," (DPT model)"),h5o.forEach(t),faa=i(Tyo),tk=n(Tyo,"LI",{});var u5o=s(tk);AZe=n(u5o,"STRONG",{});var Lwn=s(AZe);maa=r(Lwn,"glpn"),Lwn.forEach(t),gaa=r(u5o," \u2014 "),T0e=n(u5o,"A",{href:!0});var ywn=s(T0e);haa=r(ywn,"GLPNForDepthEstimation"),ywn.forEach(t),uaa=r(u5o," (GLPN model)"),u5o.forEach(t),Tyo.forEach(t),paa=i(tn),ak=n(tn,"P",{});var p5o=s(ak);_aa=r(p5o,"The model is set in evaluation mode by default using "),LZe=n(p5o,"CODE",{});var xwn=s(LZe);vaa=r(xwn,"model.eval()"),xwn.forEach(t),baa=r(p5o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),yZe=n(p5o,"CODE",{});var Swn=s(yZe);Taa=r(Swn,"model.train()"),Swn.forEach(t),p5o.forEach(t),Faa=i(tn),T(nk.$$.fragment,tn),tn.forEach(t),$d.forEach(t),RAo=i(f),Lm=n(f,"H3",{class:!0});var Fyo=s(Lm);sk=n(Fyo,"A",{id:!0,class:!0,href:!0});var kwn=s(sk);xZe=n(kwn,"SPAN",{});var $wn=s(xZe);T(aO.$$.fragment,$wn),$wn.forEach(t),kwn.forEach(t),Maa=i(Fyo),SZe=n(Fyo,"SPAN",{});var Pwn=s(SZe);Eaa=r(Pwn,"AutoModelForImageClassification"),Pwn.forEach(t),Fyo.forEach(t),IAo=i(f),Tr=n(f,"DIV",{class:!0});var Pd=s(Tr);T(nO.$$.fragment,Pd),Caa=i(Pd),ym=n(Pd,"P",{});var jMe=s(ym);waa=r(jMe,`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 `),F0e=n(jMe,"A",{href:!0});var Rwn=s(F0e);Aaa=r(Rwn,"from_pretrained()"),Rwn.forEach(t),Laa=r(jMe," class method or the "),M0e=n(jMe,"A",{href:!0});var Iwn=s(M0e);yaa=r(Iwn,"from_config()"),Iwn.forEach(t),xaa=r(jMe,` class
method.`),jMe.forEach(t),Saa=i(Pd),sO=n(Pd,"P",{});var Myo=s(sO);kaa=r(Myo,"This class cannot be instantiated directly using "),kZe=n(Myo,"CODE",{});var Nwn=s(kZe);$aa=r(Nwn,"__init__()"),Nwn.forEach(t),Paa=r(Myo," (throws an error)."),Myo.forEach(t),Raa=i(Pd),ha=n(Pd,"DIV",{class:!0});var gB=s(ha);T(lO.$$.fragment,gB),Iaa=i(gB),$Ze=n(gB,"P",{});var Bwn=s($Ze);Naa=r(Bwn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),Bwn.forEach(t),Baa=i(gB),xm=n(gB,"P",{});var OMe=s(xm);Gaa=r(OMe,`Note:
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model\u2019s configuration. Use `),E0e=n(OMe,"A",{href:!0});var qwn=s(E0e);jaa=r(qwn,"from_pretrained()"),qwn.forEach(t),Oaa=r(OMe," to load the model weights."),OMe.forEach(t),Vaa=i(gB),T(lk.$$.fragment,gB),gB.forEach(t),Xaa=i(Pd),vo=n(Pd,"DIV",{class:!0});var an=s(vo);T(iO.$$.fragment,an),zaa=i(an),RZe=n(an,"P",{});var Dwn=s(RZe);Waa=r(Dwn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),Dwn.forEach(t),Qaa=i(an),is=n(an,"P",{});var hB=s(is);Uaa=r(hB,"The model class to instantiate is selected based on the "),IZe=n(hB,"CODE",{});var jwn=s(IZe);Haa=r(jwn,"model_type"),jwn.forEach(t),Jaa=r(hB,` property of the config object (either
passed as an argument or loaded from `),NZe=n(hB,"CODE",{});var Own=s(NZe);Zaa=r(Own,"pretrained_model_name_or_path"),Own.forEach(t),Yaa=r(hB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),BZe=n(hB,"CODE",{});var Vwn=s(BZe);Kaa=r(Vwn,"pretrained_model_name_or_path"),Vwn.forEach(t),ena=r(hB,":"),hB.forEach(t),ona=i(an),ce=n(an,"UL",{});var fe=s(ce);ik=n(fe,"LI",{});var _5o=s(ik);GZe=n(_5o,"STRONG",{});var Xwn=s(GZe);rna=r(Xwn,"beit"),Xwn.forEach(t),tna=r(_5o," \u2014 "),C0e=n(_5o,"A",{href:!0});var zwn=s(C0e);ana=r(zwn,"BeitForImageClassification"),zwn.forEach(t),nna=r(_5o," (BEiT model)"),_5o.forEach(t),sna=i(fe),dk=n(fe,"LI",{});var v5o=s(dk);qZe=n(v5o,"STRONG",{});var Wwn=s(qZe);lna=r(Wwn,"bit"),Wwn.forEach(t),ina=r(v5o," \u2014 "),w0e=n(v5o,"A",{href:!0});var Qwn=s(w0e);dna=r(Qwn,"BitForImageClassification"),Qwn.forEach(t),cna=r(v5o," (BiT model)"),v5o.forEach(t),fna=i(fe),ck=n(fe,"LI",{});var b5o=s(ck);DZe=n(b5o,"STRONG",{});var Uwn=s(DZe);mna=r(Uwn,"convnext"),Uwn.forEach(t),gna=r(b5o," \u2014 "),A0e=n(b5o,"A",{href:!0});var Hwn=s(A0e);hna=r(Hwn,"ConvNextForImageClassification"),Hwn.forEach(t),una=r(b5o," (ConvNeXT model)"),b5o.forEach(t),pna=i(fe),fk=n(fe,"LI",{});var T5o=s(fk);jZe=n(T5o,"STRONG",{});var Jwn=s(jZe);_na=r(Jwn,"convnextv2"),Jwn.forEach(t),vna=r(T5o," \u2014 "),L0e=n(T5o,"A",{href:!0});var Zwn=s(L0e);bna=r(Zwn,"ConvNextV2ForImageClassification"),Zwn.forEach(t),Tna=r(T5o," (ConvNeXTV2 model)"),T5o.forEach(t),Fna=i(fe),mk=n(fe,"LI",{});var F5o=s(mk);OZe=n(F5o,"STRONG",{});var Ywn=s(OZe);Mna=r(Ywn,"cvt"),Ywn.forEach(t),Ena=r(F5o," \u2014 "),y0e=n(F5o,"A",{href:!0});var Kwn=s(y0e);Cna=r(Kwn,"CvtForImageClassification"),Kwn.forEach(t),wna=r(F5o," (CvT model)"),F5o.forEach(t),Ana=i(fe),gk=n(fe,"LI",{});var M5o=s(gk);VZe=n(M5o,"STRONG",{});var eAn=s(VZe);Lna=r(eAn,"data2vec-vision"),eAn.forEach(t),yna=r(M5o," \u2014 "),x0e=n(M5o,"A",{href:!0});var oAn=s(x0e);xna=r(oAn,"Data2VecVisionForImageClassification"),oAn.forEach(t),Sna=r(M5o," (Data2VecVision model)"),M5o.forEach(t),kna=i(fe),Li=n(fe,"LI",{});var BW=s(Li);XZe=n(BW,"STRONG",{});var rAn=s(XZe);$na=r(rAn,"deit"),rAn.forEach(t),Pna=r(BW," \u2014 "),S0e=n(BW,"A",{href:!0});var tAn=s(S0e);Rna=r(tAn,"DeiTForImageClassification"),tAn.forEach(t),Ina=r(BW," or "),k0e=n(BW,"A",{href:!0});var aAn=s(k0e);Nna=r(aAn,"DeiTForImageClassificationWithTeacher"),aAn.forEach(t),Bna=r(BW," (DeiT model)"),BW.forEach(t),Gna=i(fe),hk=n(fe,"LI",{});var E5o=s(hk);zZe=n(E5o,"STRONG",{});var nAn=s(zZe);qna=r(nAn,"dinat"),nAn.forEach(t),Dna=r(E5o," \u2014 "),$0e=n(E5o,"A",{href:!0});var sAn=s($0e);jna=r(sAn,"DinatForImageClassification"),sAn.forEach(t),Ona=r(E5o," (DiNAT model)"),E5o.forEach(t),Vna=i(fe),yi=n(fe,"LI",{});var GW=s(yi);WZe=n(GW,"STRONG",{});var lAn=s(WZe);Xna=r(lAn,"efficientformer"),lAn.forEach(t),zna=r(GW," \u2014 "),P0e=n(GW,"A",{href:!0});var iAn=s(P0e);Wna=r(iAn,"EfficientFormerForImageClassification"),iAn.forEach(t),Qna=r(GW," or "),R0e=n(GW,"A",{href:!0});var dAn=s(R0e);Una=r(dAn,"EfficientFormerForImageClassificationWithTeacher"),dAn.forEach(t),Hna=r(GW," 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pAn=s(JZe);msa=r(pAn,"levit"),pAn.forEach(t),gsa=r(qW," \u2014 "),G0e=n(qW,"A",{href:!0});var _An=s(G0e);hsa=r(_An,"LevitForImageClassification"),_An.forEach(t),usa=r(qW," or "),q0e=n(qW,"A",{href:!0});var vAn=s(q0e);psa=r(vAn,"LevitForImageClassificationWithTeacher"),vAn.forEach(t),_sa=r(qW," (LeViT model)"),qW.forEach(t),vsa=i(fe),vk=n(fe,"LI",{});var L5o=s(vk);ZZe=n(L5o,"STRONG",{});var bAn=s(ZZe);bsa=r(bAn,"mobilenet_v1"),bAn.forEach(t),Tsa=r(L5o," \u2014 "),D0e=n(L5o,"A",{href:!0});var TAn=s(D0e);Fsa=r(TAn,"MobileNetV1ForImageClassification"),TAn.forEach(t),Msa=r(L5o," (MobileNetV1 model)"),L5o.forEach(t),Esa=i(fe),bk=n(fe,"LI",{});var y5o=s(bk);YZe=n(y5o,"STRONG",{});var FAn=s(YZe);Csa=r(FAn,"mobilenet_v2"),FAn.forEach(t),wsa=r(y5o," \u2014 "),j0e=n(y5o,"A",{href:!0});var MAn=s(j0e);Asa=r(MAn,"MobileNetV2ForImageClassification"),MAn.forEach(t),Lsa=r(y5o," (MobileNetV2 model)"),y5o.forEach(t),ysa=i(fe),Tk=n(fe,"LI",{});var x5o=s(Tk);KZe=n(x5o,"STRONG",{});var 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XAn=s(K0e);Ala=r(XAn,"VanForImageClassification"),XAn.forEach(t),Lla=r(B5o," (VAN model)"),B5o.forEach(t),yla=i(fe),xk=n(fe,"LI",{});var G5o=s(xk);dYe=n(G5o,"STRONG",{});var zAn=s(dYe);xla=r(zAn,"vit"),zAn.forEach(t),Sla=r(G5o," \u2014 "),ebe=n(G5o,"A",{href:!0});var WAn=s(ebe);kla=r(WAn,"ViTForImageClassification"),WAn.forEach(t),$la=r(G5o," (ViT model)"),G5o.forEach(t),Pla=i(fe),Sk=n(fe,"LI",{});var q5o=s(Sk);cYe=n(q5o,"STRONG",{});var QAn=s(cYe);Rla=r(QAn,"vit_hybrid"),QAn.forEach(t),Ila=r(q5o," \u2014 "),obe=n(q5o,"A",{href:!0});var UAn=s(obe);Nla=r(UAn,"ViTHybridForImageClassification"),UAn.forEach(t),Bla=r(q5o," (ViT Hybrid model)"),q5o.forEach(t),Gla=i(fe),kk=n(fe,"LI",{});var D5o=s(kk);fYe=n(D5o,"STRONG",{});var HAn=s(fYe);qla=r(HAn,"vit_msn"),HAn.forEach(t),Dla=r(D5o," \u2014 "),rbe=n(D5o,"A",{href:!0});var JAn=s(rbe);jla=r(JAn,"ViTMSNForImageClassification"),JAn.forEach(t),Ola=r(D5o," (ViTMSN model)"),D5o.forEach(t),fe.forEach(t),Vla=i(an),$k=n(an,"P",{});var 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method.`),VMe.forEach(t),tia=i(Rd),fO=n(Rd,"P",{});var Cyo=s(fO);aia=r(Cyo,"This class cannot be instantiated directly using "),pYe=n(Cyo,"CODE",{});var a7n=s(pYe);nia=r(a7n,"__init__()"),a7n.forEach(t),sia=r(Cyo," (throws an error)."),Cyo.forEach(t),lia=i(Rd),pa=n(Rd,"DIV",{class:!0});var uB=s(pa);T(mO.$$.fragment,uB),iia=i(uB),_Ye=n(uB,"P",{});var n7n=s(_Ye);dia=r(n7n,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),n7n.forEach(t),cia=i(uB),$m=n(uB,"P",{});var XMe=s($m);fia=r(XMe,`Note:
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model\u2019s configuration. Use `),nbe=n(XMe,"A",{href:!0});var l7n=s(nbe);hia=r(l7n,"from_pretrained()"),l7n.forEach(t),uia=r(XMe," to load the model weights."),XMe.forEach(t),pia=i(uB),T(Ik.$$.fragment,uB),uB.forEach(t),_ia=i(Rd),bt=n(Rd,"DIV",{class:!0});var Id=s(bt);T(gO.$$.fragment,Id),via=i(Id),bYe=n(Id,"P",{});var i7n=s(bYe);bia=r(i7n,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),i7n.forEach(t),Tia=i(Id),ds=n(Id,"P",{});var pB=s(ds);Fia=r(pB,"The model class to instantiate is selected based on the "),TYe=n(pB,"CODE",{});var d7n=s(TYe);Mia=r(d7n,"model_type"),d7n.forEach(t),Eia=r(pB,` property of the config object (either
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falling back to using pattern matching on `),MYe=n(pB,"CODE",{});var f7n=s(MYe);Aia=r(f7n,"pretrained_model_name_or_path"),f7n.forEach(t),Lia=r(pB,":"),pB.forEach(t),yia=i(Id),Be=n(Id,"UL",{});var Qe=s(Be);Nk=n(Qe,"LI",{});var O5o=s(Nk);EYe=n(O5o,"STRONG",{});var m7n=s(EYe);xia=r(m7n,"convnext"),m7n.forEach(t),Sia=r(O5o," \u2014 "),sbe=n(O5o,"A",{href:!0});var g7n=s(sbe);kia=r(g7n,"TFConvNextForImageClassification"),g7n.forEach(t),$ia=r(O5o," (ConvNeXT model)"),O5o.forEach(t),Pia=i(Qe),Bk=n(Qe,"LI",{});var V5o=s(Bk);CYe=n(V5o,"STRONG",{});var h7n=s(CYe);Ria=r(h7n,"cvt"),h7n.forEach(t),Iia=r(V5o," \u2014 "),lbe=n(V5o,"A",{href:!0});var u7n=s(lbe);Nia=r(u7n,"TFCvtForImageClassification"),u7n.forEach(t),Bia=r(V5o," (CvT model)"),V5o.forEach(t),Gia=i(Qe),Gk=n(Qe,"LI",{});var X5o=s(Gk);wYe=n(X5o,"STRONG",{});var p7n=s(wYe);qia=r(p7n,"data2vec-vision"),p7n.forEach(t),Dia=r(X5o," \u2014 "),ibe=n(X5o,"A",{href:!0});var _7n=s(ibe);jia=r(_7n,"TFData2VecVisionForImageClassification"),_7n.forEach(t),Oia=r(X5o," (Data2VecVision model)"),X5o.forEach(t),Via=i(Qe),Si=n(Qe,"LI",{});var DW=s(Si);AYe=n(DW,"STRONG",{});var v7n=s(AYe);Xia=r(v7n,"deit"),v7n.forEach(t),zia=r(DW," \u2014 "),dbe=n(DW,"A",{href:!0});var b7n=s(dbe);Wia=r(b7n,"TFDeiTForImageClassification"),b7n.forEach(t),Qia=r(DW," or "),cbe=n(DW,"A",{href:!0});var T7n=s(cbe);Uia=r(T7n,"TFDeiTForImageClassificationWithTeacher"),T7n.forEach(t),Hia=r(DW," (DeiT model)"),DW.forEach(t),Jia=i(Qe),qk=n(Qe,"LI",{});var z5o=s(qk);LYe=n(z5o,"STRONG",{});var F7n=s(LYe);Zia=r(F7n,"mobilevit"),F7n.forEach(t),Yia=r(z5o," \u2014 "),fbe=n(z5o,"A",{href:!0});var M7n=s(fbe);Kia=r(M7n,"TFMobileViTForImageClassification"),M7n.forEach(t),eda=r(z5o," (MobileViT model)"),z5o.forEach(t),oda=i(Qe),Dk=n(Qe,"LI",{});var W5o=s(Dk);yYe=n(W5o,"STRONG",{});var E7n=s(yYe);rda=r(E7n,"regnet"),E7n.forEach(t),tda=r(W5o," \u2014 "),mbe=n(W5o,"A",{href:!0});var C7n=s(mbe);ada=r(C7n,"TFRegNetForImageClassification"),C7n.forEach(t),nda=r(W5o," (RegNet model)"),W5o.forEach(t),sda=i(Qe),jk=n(Qe,"LI",{});var Q5o=s(jk);xYe=n(Q5o,"STRONG",{});var w7n=s(xYe);lda=r(w7n,"resnet"),w7n.forEach(t),ida=r(Q5o," \u2014 "),gbe=n(Q5o,"A",{href:!0});var A7n=s(gbe);dda=r(A7n,"TFResNetForImageClassification"),A7n.forEach(t),cda=r(Q5o," (ResNet model)"),Q5o.forEach(t),fda=i(Qe),Ok=n(Qe,"LI",{});var U5o=s(Ok);SYe=n(U5o,"STRONG",{});var L7n=s(SYe);mda=r(L7n,"segformer"),L7n.forEach(t),gda=r(U5o," \u2014 "),hbe=n(U5o,"A",{href:!0});var y7n=s(hbe);hda=r(y7n,"TFSegformerForImageClassification"),y7n.forEach(t),uda=r(U5o," (SegFormer model)"),U5o.forEach(t),pda=i(Qe),Vk=n(Qe,"LI",{});var H5o=s(Vk);kYe=n(H5o,"STRONG",{});var x7n=s(kYe);_da=r(x7n,"swin"),x7n.forEach(t),vda=r(H5o," \u2014 "),ube=n(H5o,"A",{href:!0});var S7n=s(ube);bda=r(S7n,"TFSwinForImageClassification"),S7n.forEach(t),Tda=r(H5o," (Swin Transformer model)"),H5o.forEach(t),Fda=i(Qe),Xk=n(Qe,"LI",{});var J5o=s(Xk);$Ye=n(J5o,"STRONG",{});var k7n=s($Ye);Mda=r(k7n,"vit"),k7n.forEach(t),Eda=r(J5o," \u2014 "),pbe=n(J5o,"A",{href:!0});var $7n=s(pbe);Cda=r($7n,"TFViTForImageClassification"),$7n.forEach(t),wda=r(J5o," (ViT model)"),J5o.forEach(t),Qe.forEach(t),Ada=i(Id),T(zk.$$.fragment,Id),Id.forEach(t),Rd.forEach(t),GAo=i(f),Pm=n(f,"H3",{class:!0});var wyo=s(Pm);Wk=n(wyo,"A",{id:!0,class:!0,href:!0});var P7n=s(Wk);PYe=n(P7n,"SPAN",{});var R7n=s(PYe);T(hO.$$.fragment,R7n),R7n.forEach(t),P7n.forEach(t),Lda=i(wyo),RYe=n(wyo,"SPAN",{});var I7n=s(RYe);yda=r(I7n,"FlaxAutoModelForImageClassification"),I7n.forEach(t),wyo.forEach(t),qAo=i(f),Mr=n(f,"DIV",{class:!0});var Nd=s(Mr);T(uO.$$.fragment,Nd),xda=i(Nd),Rm=n(Nd,"P",{});var zMe=s(Rm);Sda=r(zMe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
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method.`),zMe.forEach(t),Ida=i(Nd),pO=n(Nd,"P",{});var Ayo=s(pO);Nda=r(Ayo,"This class cannot be instantiated directly using "),IYe=n(Ayo,"CODE",{});var G7n=s(IYe);Bda=r(G7n,"__init__()"),G7n.forEach(t),Gda=r(Ayo," (throws an error)."),Ayo.forEach(t),qda=i(Nd),_a=n(Nd,"DIV",{class:!0});var _B=s(_a);T(_O.$$.fragment,_B),Dda=i(_B),NYe=n(_B,"P",{});var q7n=s(NYe);jda=r(q7n,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),q7n.forEach(t),Oda=i(_B),Im=n(_B,"P",{});var WMe=s(Im);Vda=r(WMe,`Note:
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model\u2019s configuration. Use `),bbe=n(WMe,"A",{href:!0});var j7n=s(bbe);Wda=r(j7n,"from_pretrained()"),j7n.forEach(t),Qda=r(WMe," to load the model weights."),WMe.forEach(t),Uda=i(_B),T(Qk.$$.fragment,_B),_B.forEach(t),Hda=i(Nd),Tt=n(Nd,"DIV",{class:!0});var Bd=s(Tt);T(vO.$$.fragment,Bd),Jda=i(Bd),GYe=n(Bd,"P",{});var O7n=s(GYe);Zda=r(O7n,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),O7n.forEach(t),Yda=i(Bd),cs=n(Bd,"P",{});var vB=s(cs);Kda=r(vB,"The model class to instantiate is selected based on the "),qYe=n(vB,"CODE",{});var V7n=s(qYe);eca=r(V7n,"model_type"),V7n.forEach(t),oca=r(vB,` property of the config object (either
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falling back to using pattern matching on `),jYe=n(vB,"CODE",{});var z7n=s(jYe);aca=r(z7n,"pretrained_model_name_or_path"),z7n.forEach(t),nca=r(vB,":"),vB.forEach(t),sca=i(Bd),fs=n(Bd,"UL",{});var bB=s(fs);Uk=n(bB,"LI",{});var Z5o=s(Uk);OYe=n(Z5o,"STRONG",{});var W7n=s(OYe);lca=r(W7n,"beit"),W7n.forEach(t),ica=r(Z5o," \u2014 "),Tbe=n(Z5o,"A",{href:!0});var Q7n=s(Tbe);dca=r(Q7n,"FlaxBeitForImageClassification"),Q7n.forEach(t),cca=r(Z5o," (BEiT model)"),Z5o.forEach(t),fca=i(bB),Hk=n(bB,"LI",{});var Y5o=s(Hk);VYe=n(Y5o,"STRONG",{});var U7n=s(VYe);mca=r(U7n,"regnet"),U7n.forEach(t),gca=r(Y5o," \u2014 "),Fbe=n(Y5o,"A",{href:!0});var H7n=s(Fbe);hca=r(H7n,"FlaxRegNetForImageClassification"),H7n.forEach(t),uca=r(Y5o," (RegNet model)"),Y5o.forEach(t),pca=i(bB),Jk=n(bB,"LI",{});var K5o=s(Jk);XYe=n(K5o,"STRONG",{});var J7n=s(XYe);_ca=r(J7n,"resnet"),J7n.forEach(t),vca=r(K5o," \u2014 "),Mbe=n(K5o,"A",{href:!0});var Z7n=s(Mbe);bca=r(Z7n,"FlaxResNetForImageClassification"),Z7n.forEach(t),Tca=r(K5o," (ResNet model)"),K5o.forEach(t),Fca=i(bB),Zk=n(bB,"LI",{});var e8o=s(Zk);zYe=n(e8o,"STRONG",{});var Y7n=s(zYe);Mca=r(Y7n,"vit"),Y7n.forEach(t),Eca=r(e8o," \u2014 "),Ebe=n(e8o,"A",{href:!0});var K7n=s(Ebe);Cca=r(K7n,"FlaxViTForImageClassification"),K7n.forEach(t),wca=r(e8o," (ViT model)"),e8o.forEach(t),bB.forEach(t),Aca=i(Bd),T(Yk.$$.fragment,Bd),Bd.forEach(t),Nd.forEach(t),DAo=i(f),Nm=n(f,"H3",{class:!0});var Lyo=s(Nm);Kk=n(Lyo,"A",{id:!0,class:!0,href:!0});var eLn=s(Kk);WYe=n(eLn,"SPAN",{});var oLn=s(WYe);T(bO.$$.fragment,oLn),oLn.forEach(t),eLn.forEach(t),Lca=i(Lyo),QYe=n(Lyo,"SPAN",{});var rLn=s(QYe);yca=r(rLn,"AutoModelForVideoClassification"),rLn.forEach(t),Lyo.forEach(t),jAo=i(f),Er=n(f,"DIV",{class:!0});var Gd=s(Er);T(TO.$$.fragment,Gd),xca=i(Gd),Bm=n(Gd,"P",{});var QMe=s(Bm);Sca=r(QMe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a video classification head) when created
with the `),Cbe=n(QMe,"A",{href:!0});var tLn=s(Cbe);kca=r(tLn,"from_pretrained()"),tLn.forEach(t),$ca=r(QMe," class method or the "),wbe=n(QMe,"A",{href:!0});var aLn=s(wbe);Pca=r(aLn,"from_config()"),aLn.forEach(t),Rca=r(QMe,` class
method.`),QMe.forEach(t),Ica=i(Gd),FO=n(Gd,"P",{});var yyo=s(FO);Nca=r(yyo,"This class cannot be instantiated directly using "),UYe=n(yyo,"CODE",{});var nLn=s(UYe);Bca=r(nLn,"__init__()"),nLn.forEach(t),Gca=r(yyo," (throws an error)."),yyo.forEach(t),qca=i(Gd),va=n(Gd,"DIV",{class:!0});var TB=s(va);T(MO.$$.fragment,TB),Dca=i(TB),HYe=n(TB,"P",{});var sLn=s(HYe);jca=r(sLn,"Instantiates one of the model classes of the library (with a video classification head) from a configuration."),sLn.forEach(t),Oca=i(TB),Gm=n(TB,"P",{});var UMe=s(Gm);Vca=r(UMe,`Note:
Loading a model from its configuration file does `),JYe=n(UMe,"STRONG",{});var lLn=s(JYe);Xca=r(lLn,"not"),lLn.forEach(t),zca=r(UMe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Abe=n(UMe,"A",{href:!0});var iLn=s(Abe);Wca=r(iLn,"from_pretrained()"),iLn.forEach(t),Qca=r(UMe," to load the model weights."),UMe.forEach(t),Uca=i(TB),T(e$.$$.fragment,TB),TB.forEach(t),Hca=i(Gd),bo=n(Gd,"DIV",{class:!0});var nn=s(bo);T(EO.$$.fragment,nn),Jca=i(nn),ZYe=n(nn,"P",{});var dLn=s(ZYe);Zca=r(dLn,"Instantiate one of the model classes of the library (with a video classification head) from a pretrained model."),dLn.forEach(t),Yca=i(nn),ms=n(nn,"P",{});var FB=s(ms);Kca=r(FB,"The model class to instantiate is selected based on the "),YYe=n(FB,"CODE",{});var cLn=s(YYe);efa=r(cLn,"model_type"),cLn.forEach(t),ofa=r(FB,` property of the config object (either
passed as an argument or loaded from `),KYe=n(FB,"CODE",{});var fLn=s(KYe);rfa=r(fLn,"pretrained_model_name_or_path"),fLn.forEach(t),tfa=r(FB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),eKe=n(FB,"CODE",{});var mLn=s(eKe);afa=r(mLn,"pretrained_model_name_or_path"),mLn.forEach(t),nfa=r(FB,":"),FB.forEach(t),sfa=i(nn),CO=n(nn,"UL",{});var xyo=s(CO);o$=n(xyo,"LI",{});var o8o=s(o$);oKe=n(o8o,"STRONG",{});var gLn=s(oKe);lfa=r(gLn,"timesformer"),gLn.forEach(t),ifa=r(o8o," \u2014 "),Lbe=n(o8o,"A",{href:!0});var hLn=s(Lbe);dfa=r(hLn,"TimesformerForVideoClassification"),hLn.forEach(t),cfa=r(o8o," (TimeSformer model)"),o8o.forEach(t),ffa=i(xyo),r$=n(xyo,"LI",{});var r8o=s(r$);rKe=n(r8o,"STRONG",{});var uLn=s(rKe);mfa=r(uLn,"videomae"),uLn.forEach(t),gfa=r(r8o," \u2014 "),ybe=n(r8o,"A",{href:!0});var pLn=s(ybe);hfa=r(pLn,"VideoMAEForVideoClassification"),pLn.forEach(t),ufa=r(r8o," (VideoMAE model)"),r8o.forEach(t),xyo.forEach(t),pfa=i(nn),t$=n(nn,"P",{});var t8o=s(t$);_fa=r(t8o,"The model is set in evaluation mode by default using "),tKe=n(t8o,"CODE",{});var _Ln=s(tKe);vfa=r(_Ln,"model.eval()"),_Ln.forEach(t),bfa=r(t8o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),aKe=n(t8o,"CODE",{});var vLn=s(aKe);Tfa=r(vLn,"model.train()"),vLn.forEach(t),t8o.forEach(t),Ffa=i(nn),T(a$.$$.fragment,nn),nn.forEach(t),Gd.forEach(t),OAo=i(f),qm=n(f,"H3",{class:!0});var Syo=s(qm);n$=n(Syo,"A",{id:!0,class:!0,href:!0});var bLn=s(n$);nKe=n(bLn,"SPAN",{});var TLn=s(nKe);T(wO.$$.fragment,TLn),TLn.forEach(t),bLn.forEach(t),Mfa=i(Syo),sKe=n(Syo,"SPAN",{});var FLn=s(sKe);Efa=r(FLn,"AutoModelForMaskedImageModeling"),FLn.forEach(t),Syo.forEach(t),VAo=i(f),Cr=n(f,"DIV",{class:!0});var qd=s(Cr);T(AO.$$.fragment,qd),Cfa=i(qd),Dm=n(qd,"P",{});var HMe=s(Dm);wfa=r(HMe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),xbe=n(HMe,"A",{href:!0});var MLn=s(xbe);Afa=r(MLn,"from_pretrained()"),MLn.forEach(t),Lfa=r(HMe," class method or the "),Sbe=n(HMe,"A",{href:!0});var ELn=s(Sbe);yfa=r(ELn,"from_config()"),ELn.forEach(t),xfa=r(HMe,` class
method.`),HMe.forEach(t),Sfa=i(qd),LO=n(qd,"P",{});var kyo=s(LO);kfa=r(kyo,"This class cannot be instantiated directly using "),lKe=n(kyo,"CODE",{});var CLn=s(lKe);$fa=r(CLn,"__init__()"),CLn.forEach(t),Pfa=r(kyo," (throws an error)."),kyo.forEach(t),Rfa=i(qd),ba=n(qd,"DIV",{class:!0});var MB=s(ba);T(yO.$$.fragment,MB),Ifa=i(MB),iKe=n(MB,"P",{});var wLn=s(iKe);Nfa=r(wLn,"Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),wLn.forEach(t),Bfa=i(MB),jm=n(MB,"P",{});var JMe=s(jm);Gfa=r(JMe,`Note:
Loading a model from its configuration file does `),dKe=n(JMe,"STRONG",{});var ALn=s(dKe);qfa=r(ALn,"not"),ALn.forEach(t),Dfa=r(JMe,` load the model weights. It only affects the
model\u2019s configuration. Use `),kbe=n(JMe,"A",{href:!0});var LLn=s(kbe);jfa=r(LLn,"from_pretrained()"),LLn.forEach(t),Ofa=r(JMe," to load the model weights."),JMe.forEach(t),Vfa=i(MB),T(s$.$$.fragment,MB),MB.forEach(t),Xfa=i(qd),To=n(qd,"DIV",{class:!0});var sn=s(To);T(xO.$$.fragment,sn),zfa=i(sn),cKe=n(sn,"P",{});var yLn=s(cKe);Wfa=r(yLn,"Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),yLn.forEach(t),Qfa=i(sn),gs=n(sn,"P",{});var EB=s(gs);Ufa=r(EB,"The model class to instantiate is selected based on the "),fKe=n(EB,"CODE",{});var xLn=s(fKe);Hfa=r(xLn,"model_type"),xLn.forEach(t),Jfa=r(EB,` property of the config object (either
passed as an argument or loaded from `),mKe=n(EB,"CODE",{});var SLn=s(mKe);Zfa=r(SLn,"pretrained_model_name_or_path"),SLn.forEach(t),Yfa=r(EB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),gKe=n(EB,"CODE",{});var kLn=s(gKe);Kfa=r(kLn,"pretrained_model_name_or_path"),kLn.forEach(t),ema=r(EB,":"),EB.forEach(t),oma=i(sn),kt=n(sn,"UL",{});var Dd=s(kt);l$=n(Dd,"LI",{});var a8o=s(l$);hKe=n(a8o,"STRONG",{});var $Ln=s(hKe);rma=r($Ln,"deit"),$Ln.forEach(t),tma=r(a8o," \u2014 "),$be=n(a8o,"A",{href:!0});var PLn=s($be);ama=r(PLn,"DeiTForMaskedImageModeling"),PLn.forEach(t),nma=r(a8o," (DeiT model)"),a8o.forEach(t),sma=i(Dd),i$=n(Dd,"LI",{});var n8o=s(i$);uKe=n(n8o,"STRONG",{});var RLn=s(uKe);lma=r(RLn,"focalnet"),RLn.forEach(t),ima=r(n8o," \u2014 "),Pbe=n(n8o,"A",{href:!0});var ILn=s(Pbe);dma=r(ILn,"FocalNetForMaskedImageModeling"),ILn.forEach(t),cma=r(n8o," (FocalNet model)"),n8o.forEach(t),fma=i(Dd),d$=n(Dd,"LI",{});var s8o=s(d$);pKe=n(s8o,"STRONG",{});var NLn=s(pKe);mma=r(NLn,"swin"),NLn.forEach(t),gma=r(s8o," \u2014 "),Rbe=n(s8o,"A",{href:!0});var BLn=s(Rbe);hma=r(BLn,"SwinForMaskedImageModeling"),BLn.forEach(t),uma=r(s8o," (Swin Transformer model)"),s8o.forEach(t),pma=i(Dd),c$=n(Dd,"LI",{});var l8o=s(c$);_Ke=n(l8o,"STRONG",{});var GLn=s(_Ke);_ma=r(GLn,"swinv2"),GLn.forEach(t),vma=r(l8o," \u2014 "),Ibe=n(l8o,"A",{href:!0});var qLn=s(Ibe);bma=r(qLn,"Swinv2ForMaskedImageModeling"),qLn.forEach(t),Tma=r(l8o," (Swin Transformer V2 model)"),l8o.forEach(t),Fma=i(Dd),f$=n(Dd,"LI",{});var i8o=s(f$);vKe=n(i8o,"STRONG",{});var DLn=s(vKe);Mma=r(DLn,"vit"),DLn.forEach(t),Ema=r(i8o," \u2014 "),Nbe=n(i8o,"A",{href:!0});var jLn=s(Nbe);Cma=r(jLn,"ViTForMaskedImageModeling"),jLn.forEach(t),wma=r(i8o," (ViT model)"),i8o.forEach(t),Dd.forEach(t),Ama=i(sn),m$=n(sn,"P",{});var d8o=s(m$);Lma=r(d8o,"The model is set in evaluation mode by default using "),bKe=n(d8o,"CODE",{});var OLn=s(bKe);yma=r(OLn,"model.eval()"),OLn.forEach(t),xma=r(d8o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),TKe=n(d8o,"CODE",{});var VLn=s(TKe);Sma=r(VLn,"model.train()"),VLn.forEach(t),d8o.forEach(t),kma=i(sn),T(g$.$$.fragment,sn),sn.forEach(t),qd.forEach(t),XAo=i(f),Om=n(f,"H3",{class:!0});var $yo=s(Om);h$=n($yo,"A",{id:!0,class:!0,href:!0});var XLn=s(h$);FKe=n(XLn,"SPAN",{});var zLn=s(FKe);T(SO.$$.fragment,zLn),zLn.forEach(t),XLn.forEach(t),$ma=i($yo),MKe=n($yo,"SPAN",{});var WLn=s(MKe);Pma=r(WLn,"AutoModelForObjectDetection"),WLn.forEach(t),$yo.forEach(t),zAo=i(f),wr=n(f,"DIV",{class:!0});var jd=s(wr);T(kO.$$.fragment,jd),Rma=i(jd),Vm=n(jd,"P",{});var ZMe=s(Vm);Ima=r(ZMe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created
with the `),Bbe=n(ZMe,"A",{href:!0});var QLn=s(Bbe);Nma=r(QLn,"from_pretrained()"),QLn.forEach(t),Bma=r(ZMe," class method or the "),Gbe=n(ZMe,"A",{href:!0});var ULn=s(Gbe);Gma=r(ULn,"from_config()"),ULn.forEach(t),qma=r(ZMe,` class
method.`),ZMe.forEach(t),Dma=i(jd),$O=n(jd,"P",{});var Pyo=s($O);jma=r(Pyo,"This class cannot be instantiated directly using "),EKe=n(Pyo,"CODE",{});var HLn=s(EKe);Oma=r(HLn,"__init__()"),HLn.forEach(t),Vma=r(Pyo," (throws an error)."),Pyo.forEach(t),Xma=i(jd),Ta=n(jd,"DIV",{class:!0});var CB=s(Ta);T(PO.$$.fragment,CB),zma=i(CB),CKe=n(CB,"P",{});var JLn=s(CKe);Wma=r(JLn,"Instantiates one of the model classes of the library (with a object detection head) from a configuration."),JLn.forEach(t),Qma=i(CB),Xm=n(CB,"P",{});var YMe=s(Xm);Uma=r(YMe,`Note:
Loading a model from its configuration file does `),wKe=n(YMe,"STRONG",{});var ZLn=s(wKe);Hma=r(ZLn,"not"),ZLn.forEach(t),Jma=r(YMe,` load the model weights. It only affects the
model\u2019s configuration. Use `),qbe=n(YMe,"A",{href:!0});var YLn=s(qbe);Zma=r(YLn,"from_pretrained()"),YLn.forEach(t),Yma=r(YMe," to load the model weights."),YMe.forEach(t),Kma=i(CB),T(u$.$$.fragment,CB),CB.forEach(t),ega=i(jd),Fo=n(jd,"DIV",{class:!0});var ln=s(Fo);T(RO.$$.fragment,ln),oga=i(ln),AKe=n(ln,"P",{});var KLn=s(AKe);rga=r(KLn,"Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),KLn.forEach(t),tga=i(ln),hs=n(ln,"P",{});var wB=s(hs);aga=r(wB,"The model class to instantiate is selected based on the "),LKe=n(wB,"CODE",{});var eyn=s(LKe);nga=r(eyn,"model_type"),eyn.forEach(t),sga=r(wB,` property of the config object (either
passed as an argument or loaded from `),yKe=n(wB,"CODE",{});var oyn=s(yKe);lga=r(oyn,"pretrained_model_name_or_path"),oyn.forEach(t),iga=r(wB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),xKe=n(wB,"CODE",{});var ryn=s(xKe);dga=r(ryn,"pretrained_model_name_or_path"),ryn.forEach(t),cga=r(wB,":"),wB.forEach(t),fga=i(ln),Ar=n(ln,"UL",{});var dn=s(Ar);p$=n(dn,"LI",{});var c8o=s(p$);SKe=n(c8o,"STRONG",{});var tyn=s(SKe);mga=r(tyn,"conditional_detr"),tyn.forEach(t),gga=r(c8o," \u2014 "),Dbe=n(c8o,"A",{href:!0});var ayn=s(Dbe);hga=r(ayn,"ConditionalDetrForObjectDetection"),ayn.forEach(t),uga=r(c8o," (Conditional DETR model)"),c8o.forEach(t),pga=i(dn),_$=n(dn,"LI",{});var f8o=s(_$);kKe=n(f8o,"STRONG",{});var nyn=s(kKe);_ga=r(nyn,"deformable_detr"),nyn.forEach(t),vga=r(f8o," \u2014 "),jbe=n(f8o,"A",{href:!0});var syn=s(jbe);bga=r(syn,"DeformableDetrForObjectDetection"),syn.forEach(t),Tga=r(f8o," (Deformable DETR model)"),f8o.forEach(t),Fga=i(dn),v$=n(dn,"LI",{});var m8o=s(v$);$Ke=n(m8o,"STRONG",{});var lyn=s($Ke);Mga=r(lyn,"deta"),lyn.forEach(t),Ega=r(m8o," \u2014 "),Obe=n(m8o,"A",{href:!0});var iyn=s(Obe);Cga=r(iyn,"DetaForObjectDetection"),iyn.forEach(t),wga=r(m8o," (DETA model)"),m8o.forEach(t),Aga=i(dn),b$=n(dn,"LI",{});var g8o=s(b$);PKe=n(g8o,"STRONG",{});var dyn=s(PKe);Lga=r(dyn,"detr"),dyn.forEach(t),yga=r(g8o," \u2014 "),Vbe=n(g8o,"A",{href:!0});var cyn=s(Vbe);xga=r(cyn,"DetrForObjectDetection"),cyn.forEach(t),Sga=r(g8o," (DETR model)"),g8o.forEach(t),kga=i(dn),T$=n(dn,"LI",{});var h8o=s(T$);RKe=n(h8o,"STRONG",{});var fyn=s(RKe);$ga=r(fyn,"table-transformer"),fyn.forEach(t),Pga=r(h8o," \u2014 "),Xbe=n(h8o,"A",{href:!0});var myn=s(Xbe);Rga=r(myn,"TableTransformerForObjectDetection"),myn.forEach(t),Iga=r(h8o," (Table Transformer model)"),h8o.forEach(t),Nga=i(dn),F$=n(dn,"LI",{});var u8o=s(F$);IKe=n(u8o,"STRONG",{});var gyn=s(IKe);Bga=r(gyn,"yolos"),gyn.forEach(t),Gga=r(u8o," \u2014 "),zbe=n(u8o,"A",{href:!0});var hyn=s(zbe);qga=r(hyn,"YolosForObjectDetection"),hyn.forEach(t),Dga=r(u8o," (YOLOS model)"),u8o.forEach(t),dn.forEach(t),jga=i(ln),M$=n(ln,"P",{});var p8o=s(M$);Oga=r(p8o,"The model is set in evaluation mode by default using "),NKe=n(p8o,"CODE",{});var uyn=s(NKe);Vga=r(uyn,"model.eval()"),uyn.forEach(t),Xga=r(p8o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),BKe=n(p8o,"CODE",{});var pyn=s(BKe);zga=r(pyn,"model.train()"),pyn.forEach(t),p8o.forEach(t),Wga=i(ln),T(E$.$$.fragment,ln),ln.forEach(t),jd.forEach(t),WAo=i(f),zm=n(f,"H3",{class:!0});var Ryo=s(zm);C$=n(Ryo,"A",{id:!0,class:!0,href:!0});var _yn=s(C$);GKe=n(_yn,"SPAN",{});var vyn=s(GKe);T(IO.$$.fragment,vyn),vyn.forEach(t),_yn.forEach(t),Qga=i(Ryo),qKe=n(Ryo,"SPAN",{});var byn=s(qKe);Uga=r(byn,"AutoModelForImageSegmentation"),byn.forEach(t),Ryo.forEach(t),QAo=i(f),Lr=n(f,"DIV",{class:!0});var Od=s(Lr);T(NO.$$.fragment,Od),Hga=i(Od),Wm=n(Od,"P",{});var KMe=s(Wm);Jga=r(KMe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
with the `),Wbe=n(KMe,"A",{href:!0});var Tyn=s(Wbe);Zga=r(Tyn,"from_pretrained()"),Tyn.forEach(t),Yga=r(KMe," class method or the "),Qbe=n(KMe,"A",{href:!0});var Fyn=s(Qbe);Kga=r(Fyn,"from_config()"),Fyn.forEach(t),eha=r(KMe,` class
method.`),KMe.forEach(t),oha=i(Od),BO=n(Od,"P",{});var Iyo=s(BO);rha=r(Iyo,"This class cannot be instantiated directly using "),DKe=n(Iyo,"CODE",{});var Myn=s(DKe);tha=r(Myn,"__init__()"),Myn.forEach(t),aha=r(Iyo," (throws an error)."),Iyo.forEach(t),nha=i(Od),Fa=n(Od,"DIV",{class:!0});var AB=s(Fa);T(GO.$$.fragment,AB),sha=i(AB),jKe=n(AB,"P",{});var Eyn=s(jKe);lha=r(Eyn,"Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),Eyn.forEach(t),iha=i(AB),Qm=n(AB,"P",{});var eEe=s(Qm);dha=r(eEe,`Note:
Loading a model from its configuration file does `),OKe=n(eEe,"STRONG",{});var Cyn=s(OKe);cha=r(Cyn,"not"),Cyn.forEach(t),fha=r(eEe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Ube=n(eEe,"A",{href:!0});var wyn=s(Ube);mha=r(wyn,"from_pretrained()"),wyn.forEach(t),gha=r(eEe," to load the model weights."),eEe.forEach(t),hha=i(AB),T(w$.$$.fragment,AB),AB.forEach(t),uha=i(Od),Mo=n(Od,"DIV",{class:!0});var cn=s(Mo);T(qO.$$.fragment,cn),pha=i(cn),VKe=n(cn,"P",{});var Ayn=s(VKe);_ha=r(Ayn,"Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),Ayn.forEach(t),vha=i(cn),us=n(cn,"P",{});var LB=s(us);bha=r(LB,"The model class to instantiate is selected based on the "),XKe=n(LB,"CODE",{});var Lyn=s(XKe);Tha=r(Lyn,"model_type"),Lyn.forEach(t),Fha=r(LB,` property of the config object (either
passed as an argument or loaded from `),zKe=n(LB,"CODE",{});var yyn=s(zKe);Mha=r(yyn,"pretrained_model_name_or_path"),yyn.forEach(t),Eha=r(LB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),WKe=n(LB,"CODE",{});var xyn=s(WKe);Cha=r(xyn,"pretrained_model_name_or_path"),xyn.forEach(t),wha=r(LB,":"),LB.forEach(t),Aha=i(cn),QKe=n(cn,"UL",{});var Syn=s(QKe);A$=n(Syn,"LI",{});var _8o=s(A$);UKe=n(_8o,"STRONG",{});var kyn=s(UKe);Lha=r(kyn,"detr"),kyn.forEach(t),yha=r(_8o," \u2014 "),Hbe=n(_8o,"A",{href:!0});var $yn=s(Hbe);xha=r($yn,"DetrForSegmentation"),$yn.forEach(t),Sha=r(_8o," (DETR model)"),_8o.forEach(t),Syn.forEach(t),kha=i(cn),L$=n(cn,"P",{});var v8o=s(L$);$ha=r(v8o,"The model is set in evaluation mode by default using "),HKe=n(v8o,"CODE",{});var Pyn=s(HKe);Pha=r(Pyn,"model.eval()"),Pyn.forEach(t),Rha=r(v8o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),JKe=n(v8o,"CODE",{});var Ryn=s(JKe);Iha=r(Ryn,"model.train()"),Ryn.forEach(t),v8o.forEach(t),Nha=i(cn),T(y$.$$.fragment,cn),cn.forEach(t),Od.forEach(t),UAo=i(f),Um=n(f,"H3",{class:!0});var Nyo=s(Um);x$=n(Nyo,"A",{id:!0,class:!0,href:!0});var Iyn=s(x$);ZKe=n(Iyn,"SPAN",{});var Nyn=s(ZKe);T(DO.$$.fragment,Nyn),Nyn.forEach(t),Iyn.forEach(t),Bha=i(Nyo),YKe=n(Nyo,"SPAN",{});var Byn=s(YKe);Gha=r(Byn,"AutoModelForSemanticSegmentation"),Byn.forEach(t),Nyo.forEach(t),HAo=i(f),yr=n(f,"DIV",{class:!0});var Vd=s(yr);T(jO.$$.fragment,Vd),qha=i(Vd),Hm=n(Vd,"P",{});var oEe=s(Hm);Dha=r(oEe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),Jbe=n(oEe,"A",{href:!0});var Gyn=s(Jbe);jha=r(Gyn,"from_pretrained()"),Gyn.forEach(t),Oha=r(oEe," class method or the "),Zbe=n(oEe,"A",{href:!0});var qyn=s(Zbe);Vha=r(qyn,"from_config()"),qyn.forEach(t),Xha=r(oEe,` class
method.`),oEe.forEach(t),zha=i(Vd),OO=n(Vd,"P",{});var Byo=s(OO);Wha=r(Byo,"This class cannot be instantiated directly using "),KKe=n(Byo,"CODE",{});var Dyn=s(KKe);Qha=r(Dyn,"__init__()"),Dyn.forEach(t),Uha=r(Byo," (throws an error)."),Byo.forEach(t),Hha=i(Vd),Ma=n(Vd,"DIV",{class:!0});var yB=s(Ma);T(VO.$$.fragment,yB),Jha=i(yB),eeo=n(yB,"P",{});var jyn=s(eeo);Zha=r(jyn,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),jyn.forEach(t),Yha=i(yB),Jm=n(yB,"P",{});var rEe=s(Jm);Kha=r(rEe,`Note:
Loading a model from its configuration file does `),oeo=n(rEe,"STRONG",{});var Oyn=s(oeo);eua=r(Oyn,"not"),Oyn.forEach(t),oua=r(rEe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Ybe=n(rEe,"A",{href:!0});var Vyn=s(Ybe);rua=r(Vyn,"from_pretrained()"),Vyn.forEach(t),tua=r(rEe," to load the model weights."),rEe.forEach(t),aua=i(yB),T(S$.$$.fragment,yB),yB.forEach(t),nua=i(Vd),Eo=n(Vd,"DIV",{class:!0});var fn=s(Eo);T(XO.$$.fragment,fn),sua=i(fn),reo=n(fn,"P",{});var Xyn=s(reo);lua=r(Xyn,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),Xyn.forEach(t),iua=i(fn),ps=n(fn,"P",{});var xB=s(ps);dua=r(xB,"The model class to instantiate is selected based on the "),teo=n(xB,"CODE",{});var zyn=s(teo);cua=r(zyn,"model_type"),zyn.forEach(t),fua=r(xB,` property of the config object (either
passed as an argument or loaded from `),aeo=n(xB,"CODE",{});var Wyn=s(aeo);mua=r(Wyn,"pretrained_model_name_or_path"),Wyn.forEach(t),gua=r(xB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),neo=n(xB,"CODE",{});var Qyn=s(neo);hua=r(Qyn,"pretrained_model_name_or_path"),Qyn.forEach(t),uua=r(xB,":"),xB.forEach(t),pua=i(fn),eo=n(fn,"UL",{});var St=s(eo);k$=n(St,"LI",{});var b8o=s(k$);seo=n(b8o,"STRONG",{});var Uyn=s(seo);_ua=r(Uyn,"beit"),Uyn.forEach(t),vua=r(b8o," \u2014 "),Kbe=n(b8o,"A",{href:!0});var Hyn=s(Kbe);bua=r(Hyn,"BeitForSemanticSegmentation"),Hyn.forEach(t),Tua=r(b8o," (BEiT model)"),b8o.forEach(t),Fua=i(St),$$=n(St,"LI",{});var T8o=s($$);leo=n(T8o,"STRONG",{});var Jyn=s(leo);Mua=r(Jyn,"data2vec-vision"),Jyn.forEach(t),Eua=r(T8o," \u2014 "),e9e=n(T8o,"A",{href:!0});var Zyn=s(e9e);Cua=r(Zyn,"Data2VecVisionForSemanticSegmentation"),Zyn.forEach(t),wua=r(T8o," (Data2VecVision model)"),T8o.forEach(t),Aua=i(St),P$=n(St,"LI",{});var F8o=s(P$);ieo=n(F8o,"STRONG",{});var Yyn=s(ieo);Lua=r(Yyn,"dpt"),Yyn.forEach(t),yua=r(F8o," \u2014 "),o9e=n(F8o,"A",{href:!0});var Kyn=s(o9e);xua=r(Kyn,"DPTForSemanticSegmentation"),Kyn.forEach(t),Sua=r(F8o," (DPT model)"),F8o.forEach(t),kua=i(St),R$=n(St,"LI",{});var M8o=s(R$);deo=n(M8o,"STRONG",{});var exn=s(deo);$ua=r(exn,"mobilenet_v2"),exn.forEach(t),Pua=r(M8o," \u2014 "),r9e=n(M8o,"A",{href:!0});var oxn=s(r9e);Rua=r(oxn,"MobileNetV2ForSemanticSegmentation"),oxn.forEach(t),Iua=r(M8o," (MobileNetV2 model)"),M8o.forEach(t),Nua=i(St),I$=n(St,"LI",{});var E8o=s(I$);ceo=n(E8o,"STRONG",{});var rxn=s(ceo);Bua=r(rxn,"mobilevit"),rxn.forEach(t),Gua=r(E8o," \u2014 "),t9e=n(E8o,"A",{href:!0});var txn=s(t9e);qua=r(txn,"MobileViTForSemanticSegmentation"),txn.forEach(t),Dua=r(E8o," (MobileViT model)"),E8o.forEach(t),jua=i(St),N$=n(St,"LI",{});var C8o=s(N$);feo=n(C8o,"STRONG",{});var axn=s(feo);Oua=r(axn,"segformer"),axn.forEach(t),Vua=r(C8o," \u2014 "),a9e=n(C8o,"A",{href:!0});var nxn=s(a9e);Xua=r(nxn,"SegformerForSemanticSegmentation"),nxn.forEach(t),zua=r(C8o," (SegFormer model)"),C8o.forEach(t),Wua=i(St),B$=n(St,"LI",{});var w8o=s(B$);meo=n(w8o,"STRONG",{});var sxn=s(meo);Qua=r(sxn,"upernet"),sxn.forEach(t),Uua=r(w8o," \u2014 "),n9e=n(w8o,"A",{href:!0});var lxn=s(n9e);Hua=r(lxn,"UperNetForSemanticSegmentation"),lxn.forEach(t),Jua=r(w8o," (UPerNet model)"),w8o.forEach(t),St.forEach(t),Zua=i(fn),G$=n(fn,"P",{});var A8o=s(G$);Yua=r(A8o,"The model is set in evaluation mode by default using "),geo=n(A8o,"CODE",{});var ixn=s(geo);Kua=r(ixn,"model.eval()"),ixn.forEach(t),epa=r(A8o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),heo=n(A8o,"CODE",{});var dxn=s(heo);opa=r(dxn,"model.train()"),dxn.forEach(t),A8o.forEach(t),rpa=i(fn),T(q$.$$.fragment,fn),fn.forEach(t),Vd.forEach(t),JAo=i(f),Zm=n(f,"H3",{class:!0});var Gyo=s(Zm);D$=n(Gyo,"A",{id:!0,class:!0,href:!0});var cxn=s(D$);ueo=n(cxn,"SPAN",{});var fxn=s(ueo);T(zO.$$.fragment,fxn),fxn.forEach(t),cxn.forEach(t),tpa=i(Gyo),peo=n(Gyo,"SPAN",{});var mxn=s(peo);apa=r(mxn,"TFAutoModelForSemanticSegmentation"),mxn.forEach(t),Gyo.forEach(t),ZAo=i(f),xr=n(f,"DIV",{class:!0});var Xd=s(xr);T(WO.$$.fragment,Xd),npa=i(Xd),Ym=n(Xd,"P",{});var tEe=s(Ym);spa=r(tEe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),s9e=n(tEe,"A",{href:!0});var gxn=s(s9e);lpa=r(gxn,"from_pretrained()"),gxn.forEach(t),ipa=r(tEe," class method or the "),l9e=n(tEe,"A",{href:!0});var hxn=s(l9e);dpa=r(hxn,"from_config()"),hxn.forEach(t),cpa=r(tEe,` class
method.`),tEe.forEach(t),fpa=i(Xd),QO=n(Xd,"P",{});var qyo=s(QO);mpa=r(qyo,"This class cannot be instantiated directly using "),_eo=n(qyo,"CODE",{});var uxn=s(_eo);gpa=r(uxn,"__init__()"),uxn.forEach(t),hpa=r(qyo," (throws an error)."),qyo.forEach(t),upa=i(Xd),Ea=n(Xd,"DIV",{class:!0});var SB=s(Ea);T(UO.$$.fragment,SB),ppa=i(SB),veo=n(SB,"P",{});var pxn=s(veo);_pa=r(pxn,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),pxn.forEach(t),vpa=i(SB),Km=n(SB,"P",{});var aEe=s(Km);bpa=r(aEe,`Note:
Loading a model from its configuration file does `),beo=n(aEe,"STRONG",{});var _xn=s(beo);Tpa=r(_xn,"not"),_xn.forEach(t),Fpa=r(aEe,` load the model weights. It only affects the
model\u2019s configuration. Use `),i9e=n(aEe,"A",{href:!0});var vxn=s(i9e);Mpa=r(vxn,"from_pretrained()"),vxn.forEach(t),Epa=r(aEe," to load the model weights."),aEe.forEach(t),Cpa=i(SB),T(j$.$$.fragment,SB),SB.forEach(t),wpa=i(Xd),Ft=n(Xd,"DIV",{class:!0});var zd=s(Ft);T(HO.$$.fragment,zd),Apa=i(zd),Teo=n(zd,"P",{});var bxn=s(Teo);Lpa=r(bxn,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),bxn.forEach(t),ypa=i(zd),_s=n(zd,"P",{});var kB=s(_s);xpa=r(kB,"The model class to instantiate is selected based on the "),Feo=n(kB,"CODE",{});var Txn=s(Feo);Spa=r(Txn,"model_type"),Txn.forEach(t),kpa=r(kB,` property of the config object (either
passed as an argument or loaded from `),Meo=n(kB,"CODE",{});var Fxn=s(Meo);$pa=r(Fxn,"pretrained_model_name_or_path"),Fxn.forEach(t),Ppa=r(kB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Eeo=n(kB,"CODE",{});var Mxn=s(Eeo);Rpa=r(Mxn,"pretrained_model_name_or_path"),Mxn.forEach(t),Ipa=r(kB,":"),kB.forEach(t),Npa=i(zd),eg=n(zd,"UL",{});var nEe=s(eg);O$=n(nEe,"LI",{});var L8o=s(O$);Ceo=n(L8o,"STRONG",{});var Exn=s(Ceo);Bpa=r(Exn,"data2vec-vision"),Exn.forEach(t),Gpa=r(L8o," \u2014 "),d9e=n(L8o,"A",{href:!0});var Cxn=s(d9e);qpa=r(Cxn,"TFData2VecVisionForSemanticSegmentation"),Cxn.forEach(t),Dpa=r(L8o," (Data2VecVision model)"),L8o.forEach(t),jpa=i(nEe),V$=n(nEe,"LI",{});var y8o=s(V$);weo=n(y8o,"STRONG",{});var wxn=s(weo);Opa=r(wxn,"mobilevit"),wxn.forEach(t),Vpa=r(y8o," \u2014 "),c9e=n(y8o,"A",{href:!0});var Axn=s(c9e);Xpa=r(Axn,"TFMobileViTForSemanticSegmentation"),Axn.forEach(t),zpa=r(y8o," (MobileViT model)"),y8o.forEach(t),Wpa=i(nEe),X$=n(nEe,"LI",{});var x8o=s(X$);Aeo=n(x8o,"STRONG",{});var Lxn=s(Aeo);Qpa=r(Lxn,"segformer"),Lxn.forEach(t),Upa=r(x8o," \u2014 "),f9e=n(x8o,"A",{href:!0});var yxn=s(f9e);Hpa=r(yxn,"TFSegformerForSemanticSegmentation"),yxn.forEach(t),Jpa=r(x8o," (SegFormer model)"),x8o.forEach(t),nEe.forEach(t),Zpa=i(zd),T(z$.$$.fragment,zd),zd.forEach(t),Xd.forEach(t),YAo=i(f),og=n(f,"H3",{class:!0});var Dyo=s(og);W$=n(Dyo,"A",{id:!0,class:!0,href:!0});var xxn=s(W$);Leo=n(xxn,"SPAN",{});var Sxn=s(Leo);T(JO.$$.fragment,Sxn),Sxn.forEach(t),xxn.forEach(t),Ypa=i(Dyo),yeo=n(Dyo,"SPAN",{});var kxn=s(yeo);Kpa=r(kxn,"AutoModelForInstanceSegmentation"),kxn.forEach(t),Dyo.forEach(t),KAo=i(f),Sr=n(f,"DIV",{class:!0});var Wd=s(Sr);T(ZO.$$.fragment,Wd),e_a=i(Wd),rg=n(Wd,"P",{});var sEe=s(rg);o_a=r(sEe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created
with the `),m9e=n(sEe,"A",{href:!0});var $xn=s(m9e);r_a=r($xn,"from_pretrained()"),$xn.forEach(t),t_a=r(sEe," class method or the "),g9e=n(sEe,"A",{href:!0});var Pxn=s(g9e);a_a=r(Pxn,"from_config()"),Pxn.forEach(t),n_a=r(sEe,` class
method.`),sEe.forEach(t),s_a=i(Wd),YO=n(Wd,"P",{});var jyo=s(YO);l_a=r(jyo,"This class cannot be instantiated directly using "),xeo=n(jyo,"CODE",{});var Rxn=s(xeo);i_a=r(Rxn,"__init__()"),Rxn.forEach(t),d_a=r(jyo," (throws an error)."),jyo.forEach(t),c_a=i(Wd),Ca=n(Wd,"DIV",{class:!0});var $B=s(Ca);T(KO.$$.fragment,$B),f_a=i($B),Seo=n($B,"P",{});var Ixn=s(Seo);m_a=r(Ixn,"Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),Ixn.forEach(t),g_a=i($B),tg=n($B,"P",{});var lEe=s(tg);h_a=r(lEe,`Note:
Loading a model from its configuration file does `),keo=n(lEe,"STRONG",{});var Nxn=s(keo);u_a=r(Nxn,"not"),Nxn.forEach(t),p_a=r(lEe,` load the model weights. It only affects the
model\u2019s configuration. Use `),h9e=n(lEe,"A",{href:!0});var Bxn=s(h9e);__a=r(Bxn,"from_pretrained()"),Bxn.forEach(t),v_a=r(lEe," to load the model weights."),lEe.forEach(t),b_a=i($B),T(Q$.$$.fragment,$B),$B.forEach(t),T_a=i(Wd),Co=n(Wd,"DIV",{class:!0});var mn=s(Co);T(eV.$$.fragment,mn),F_a=i(mn),$eo=n(mn,"P",{});var Gxn=s($eo);M_a=r(Gxn,"Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),Gxn.forEach(t),E_a=i(mn),vs=n(mn,"P",{});var PB=s(vs);C_a=r(PB,"The model class to instantiate is selected based on the "),Peo=n(PB,"CODE",{});var qxn=s(Peo);w_a=r(qxn,"model_type"),qxn.forEach(t),A_a=r(PB,` property of the config object (either
passed as an argument or loaded from `),Reo=n(PB,"CODE",{});var Dxn=s(Reo);L_a=r(Dxn,"pretrained_model_name_or_path"),Dxn.forEach(t),y_a=r(PB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ieo=n(PB,"CODE",{});var jxn=s(Ieo);x_a=r(jxn,"pretrained_model_name_or_path"),jxn.forEach(t),S_a=r(PB,":"),PB.forEach(t),k_a=i(mn),Neo=n(mn,"UL",{});var Oxn=s(Neo);U$=n(Oxn,"LI",{});var S8o=s(U$);Beo=n(S8o,"STRONG",{});var Vxn=s(Beo);$_a=r(Vxn,"maskformer"),Vxn.forEach(t),P_a=r(S8o," \u2014 "),u9e=n(S8o,"A",{href:!0});var Xxn=s(u9e);R_a=r(Xxn,"MaskFormerForInstanceSegmentation"),Xxn.forEach(t),I_a=r(S8o," (MaskFormer model)"),S8o.forEach(t),Oxn.forEach(t),N_a=i(mn),H$=n(mn,"P",{});var k8o=s(H$);B_a=r(k8o,"The model is set in evaluation mode by default using "),Geo=n(k8o,"CODE",{});var zxn=s(Geo);G_a=r(zxn,"model.eval()"),zxn.forEach(t),q_a=r(k8o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),qeo=n(k8o,"CODE",{});var Wxn=s(qeo);D_a=r(Wxn,"model.train()"),Wxn.forEach(t),k8o.forEach(t),j_a=i(mn),T(J$.$$.fragment,mn),mn.forEach(t),Wd.forEach(t),e7o=i(f),ag=n(f,"H3",{class:!0});var Oyo=s(ag);Z$=n(Oyo,"A",{id:!0,class:!0,href:!0});var Qxn=s(Z$);Deo=n(Qxn,"SPAN",{});var Uxn=s(Deo);T(oV.$$.fragment,Uxn),Uxn.forEach(t),Qxn.forEach(t),O_a=i(Oyo),jeo=n(Oyo,"SPAN",{});var Hxn=s(jeo);V_a=r(Hxn,"AutoModelForUniversalSegmentation"),Hxn.forEach(t),Oyo.forEach(t),o7o=i(f),kr=n(f,"DIV",{class:!0});var Qd=s(kr);T(rV.$$.fragment,Qd),X_a=i(Qd),ng=n(Qd,"P",{});var iEe=s(ng);z_a=r(iEe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a universal image segmentation head) when created
with the `),p9e=n(iEe,"A",{href:!0});var Jxn=s(p9e);W_a=r(Jxn,"from_pretrained()"),Jxn.forEach(t),Q_a=r(iEe," class method or the "),_9e=n(iEe,"A",{href:!0});var Zxn=s(_9e);U_a=r(Zxn,"from_config()"),Zxn.forEach(t),H_a=r(iEe,` class
method.`),iEe.forEach(t),J_a=i(Qd),tV=n(Qd,"P",{});var Vyo=s(tV);Z_a=r(Vyo,"This class cannot be instantiated directly using "),Oeo=n(Vyo,"CODE",{});var Yxn=s(Oeo);Y_a=r(Yxn,"__init__()"),Yxn.forEach(t),K_a=r(Vyo," (throws an error)."),Vyo.forEach(t),e2a=i(Qd),wa=n(Qd,"DIV",{class:!0});var RB=s(wa);T(aV.$$.fragment,RB),o2a=i(RB),Veo=n(RB,"P",{});var Kxn=s(Veo);r2a=r(Kxn,"Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration."),Kxn.forEach(t),t2a=i(RB),sg=n(RB,"P",{});var dEe=s(sg);a2a=r(dEe,`Note:
Loading a model from its configuration file does `),Xeo=n(dEe,"STRONG",{});var eSn=s(Xeo);n2a=r(eSn,"not"),eSn.forEach(t),s2a=r(dEe,` load the model weights. It only affects the
model\u2019s configuration. Use `),v9e=n(dEe,"A",{href:!0});var oSn=s(v9e);l2a=r(oSn,"from_pretrained()"),oSn.forEach(t),i2a=r(dEe," to load the model weights."),dEe.forEach(t),d2a=i(RB),T(Y$.$$.fragment,RB),RB.forEach(t),c2a=i(Qd),wo=n(Qd,"DIV",{class:!0});var gn=s(wo);T(nV.$$.fragment,gn),f2a=i(gn),zeo=n(gn,"P",{});var rSn=s(zeo);m2a=r(rSn,"Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model."),rSn.forEach(t),g2a=i(gn),bs=n(gn,"P",{});var IB=s(bs);h2a=r(IB,"The model class to instantiate is selected based on the "),Weo=n(IB,"CODE",{});var tSn=s(Weo);u2a=r(tSn,"model_type"),tSn.forEach(t),p2a=r(IB,` property of the config object (either
passed as an argument or loaded from `),Qeo=n(IB,"CODE",{});var aSn=s(Qeo);_2a=r(aSn,"pretrained_model_name_or_path"),aSn.forEach(t),v2a=r(IB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ueo=n(IB,"CODE",{});var nSn=s(Ueo);b2a=r(nSn,"pretrained_model_name_or_path"),nSn.forEach(t),T2a=r(IB,":"),IB.forEach(t),F2a=i(gn),Ts=n(gn,"UL",{});var NB=s(Ts);K$=n(NB,"LI",{});var $8o=s(K$);Heo=n($8o,"STRONG",{});var sSn=s(Heo);M2a=r(sSn,"detr"),sSn.forEach(t),E2a=r($8o," \u2014 "),b9e=n($8o,"A",{href:!0});var lSn=s(b9e);C2a=r(lSn,"DetrForSegmentation"),lSn.forEach(t),w2a=r($8o," (DETR model)"),$8o.forEach(t),A2a=i(NB),eP=n(NB,"LI",{});var P8o=s(eP);Jeo=n(P8o,"STRONG",{});var iSn=s(Jeo);L2a=r(iSn,"mask2former"),iSn.forEach(t),y2a=r(P8o," \u2014 "),T9e=n(P8o,"A",{href:!0});var dSn=s(T9e);x2a=r(dSn,"Mask2FormerForUniversalSegmentation"),dSn.forEach(t),S2a=r(P8o," (Mask2Former model)"),P8o.forEach(t),k2a=i(NB),oP=n(NB,"LI",{});var R8o=s(oP);Zeo=n(R8o,"STRONG",{});var cSn=s(Zeo);$2a=r(cSn,"maskformer"),cSn.forEach(t),P2a=r(R8o," \u2014 "),F9e=n(R8o,"A",{href:!0});var fSn=s(F9e);R2a=r(fSn,"MaskFormerForInstanceSegmentation"),fSn.forEach(t),I2a=r(R8o," (MaskFormer model)"),R8o.forEach(t),N2a=i(NB),rP=n(NB,"LI",{});var I8o=s(rP);Yeo=n(I8o,"STRONG",{});var mSn=s(Yeo);B2a=r(mSn,"oneformer"),mSn.forEach(t),G2a=r(I8o," \u2014 "),M9e=n(I8o,"A",{href:!0});var gSn=s(M9e);q2a=r(gSn,"OneFormerForUniversalSegmentation"),gSn.forEach(t),D2a=r(I8o," (OneFormer model)"),I8o.forEach(t),NB.forEach(t),j2a=i(gn),tP=n(gn,"P",{});var N8o=s(tP);O2a=r(N8o,"The model is set in evaluation mode by default using "),Keo=n(N8o,"CODE",{});var hSn=s(Keo);V2a=r(hSn,"model.eval()"),hSn.forEach(t),X2a=r(N8o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),eoo=n(N8o,"CODE",{});var uSn=s(eoo);z2a=r(uSn,"model.train()"),uSn.forEach(t),N8o.forEach(t),W2a=i(gn),T(aP.$$.fragment,gn),gn.forEach(t),Qd.forEach(t),r7o=i(f),lg=n(f,"H3",{class:!0});var Xyo=s(lg);nP=n(Xyo,"A",{id:!0,class:!0,href:!0});var pSn=s(nP);ooo=n(pSn,"SPAN",{});var _Sn=s(ooo);T(sV.$$.fragment,_Sn),_Sn.forEach(t),pSn.forEach(t),Q2a=i(Xyo),roo=n(Xyo,"SPAN",{});var vSn=s(roo);U2a=r(vSn,"AutoModelForZeroShotImageClassification"),vSn.forEach(t),Xyo.forEach(t),t7o=i(f),$r=n(f,"DIV",{class:!0});var Ud=s($r);T(lV.$$.fragment,Ud),H2a=i(Ud),ig=n(Ud,"P",{});var cEe=s(ig);J2a=r(cEe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created
with the `),E9e=n(cEe,"A",{href:!0});var bSn=s(E9e);Z2a=r(bSn,"from_pretrained()"),bSn.forEach(t),Y2a=r(cEe," class method or the "),C9e=n(cEe,"A",{href:!0});var TSn=s(C9e);K2a=r(TSn,"from_config()"),TSn.forEach(t),eva=r(cEe,` class
method.`),cEe.forEach(t),ova=i(Ud),iV=n(Ud,"P",{});var zyo=s(iV);rva=r(zyo,"This class cannot be instantiated directly using "),too=n(zyo,"CODE",{});var FSn=s(too);tva=r(FSn,"__init__()"),FSn.forEach(t),ava=r(zyo," (throws an error)."),zyo.forEach(t),nva=i(Ud),Aa=n(Ud,"DIV",{class:!0});var BB=s(Aa);T(dV.$$.fragment,BB),sva=i(BB),aoo=n(BB,"P",{});var MSn=s(aoo);lva=r(MSn,"Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),MSn.forEach(t),iva=i(BB),dg=n(BB,"P",{});var fEe=s(dg);dva=r(fEe,`Note:
Loading a model from its configuration file does `),noo=n(fEe,"STRONG",{});var ESn=s(noo);cva=r(ESn,"not"),ESn.forEach(t),fva=r(fEe,` load the model weights. It only affects the
model\u2019s configuration. Use `),w9e=n(fEe,"A",{href:!0});var CSn=s(w9e);mva=r(CSn,"from_pretrained()"),CSn.forEach(t),gva=r(fEe," to load the model weights."),fEe.forEach(t),hva=i(BB),T(sP.$$.fragment,BB),BB.forEach(t),uva=i(Ud),Ao=n(Ud,"DIV",{class:!0});var hn=s(Ao);T(cV.$$.fragment,hn),pva=i(hn),soo=n(hn,"P",{});var wSn=s(soo);_va=r(wSn,"Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),wSn.forEach(t),vva=i(hn),Fs=n(hn,"P",{});var GB=s(Fs);bva=r(GB,"The model class to instantiate is selected based on the "),loo=n(GB,"CODE",{});var ASn=s(loo);Tva=r(ASn,"model_type"),ASn.forEach(t),Fva=r(GB,` property of the config object (either
passed as an argument or loaded from `),ioo=n(GB,"CODE",{});var LSn=s(ioo);Mva=r(LSn,"pretrained_model_name_or_path"),LSn.forEach(t),Eva=r(GB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),doo=n(GB,"CODE",{});var ySn=s(doo);Cva=r(ySn,"pretrained_model_name_or_path"),ySn.forEach(t),wva=r(GB,":"),GB.forEach(t),Ava=i(hn),Pr=n(hn,"UL",{});var un=s(Pr);lP=n(un,"LI",{});var B8o=s(lP);coo=n(B8o,"STRONG",{});var xSn=s(coo);Lva=r(xSn,"align"),xSn.forEach(t),yva=r(B8o," \u2014 "),A9e=n(B8o,"A",{href:!0});var SSn=s(A9e);xva=r(SSn,"AlignModel"),SSn.forEach(t),Sva=r(B8o," (ALIGN model)"),B8o.forEach(t),kva=i(un),iP=n(un,"LI",{});var G8o=s(iP);foo=n(G8o,"STRONG",{});var kSn=s(foo);$va=r(kSn,"altclip"),kSn.forEach(t),Pva=r(G8o," \u2014 "),L9e=n(G8o,"A",{href:!0});var $Sn=s(L9e);Rva=r($Sn,"AltCLIPModel"),$Sn.forEach(t),Iva=r(G8o," (AltCLIP model)"),G8o.forEach(t),Nva=i(un),dP=n(un,"LI",{});var q8o=s(dP);moo=n(q8o,"STRONG",{});var PSn=s(moo);Bva=r(PSn,"blip"),PSn.forEach(t),Gva=r(q8o," \u2014 "),y9e=n(q8o,"A",{href:!0});var RSn=s(y9e);qva=r(RSn,"BlipModel"),RSn.forEach(t),Dva=r(q8o," (BLIP model)"),q8o.forEach(t),jva=i(un),cP=n(un,"LI",{});var D8o=s(cP);goo=n(D8o,"STRONG",{});var ISn=s(goo);Ova=r(ISn,"chinese_clip"),ISn.forEach(t),Vva=r(D8o," \u2014 "),x9e=n(D8o,"A",{href:!0});var NSn=s(x9e);Xva=r(NSn,"ChineseCLIPModel"),NSn.forEach(t),zva=r(D8o," (Chinese-CLIP model)"),D8o.forEach(t),Wva=i(un),fP=n(un,"LI",{});var j8o=s(fP);hoo=n(j8o,"STRONG",{});var BSn=s(hoo);Qva=r(BSn,"clip"),BSn.forEach(t),Uva=r(j8o," \u2014 "),S9e=n(j8o,"A",{href:!0});var GSn=s(S9e);Hva=r(GSn,"CLIPModel"),GSn.forEach(t),Jva=r(j8o," (CLIP model)"),j8o.forEach(t),Zva=i(un),mP=n(un,"LI",{});var O8o=s(mP);uoo=n(O8o,"STRONG",{});var qSn=s(uoo);Yva=r(qSn,"clipseg"),qSn.forEach(t),Kva=r(O8o," \u2014 "),k9e=n(O8o,"A",{href:!0});var DSn=s(k9e);e1a=r(DSn,"CLIPSegModel"),DSn.forEach(t),o1a=r(O8o," (CLIPSeg model)"),O8o.forEach(t),un.forEach(t),r1a=i(hn),gP=n(hn,"P",{});var V8o=s(gP);t1a=r(V8o,"The model is set in evaluation mode by default using "),poo=n(V8o,"CODE",{});var jSn=s(poo);a1a=r(jSn,"model.eval()"),jSn.forEach(t),n1a=r(V8o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),_oo=n(V8o,"CODE",{});var OSn=s(_oo);s1a=r(OSn,"model.train()"),OSn.forEach(t),V8o.forEach(t),l1a=i(hn),T(hP.$$.fragment,hn),hn.forEach(t),Ud.forEach(t),a7o=i(f),cg=n(f,"H3",{class:!0});var Wyo=s(cg);uP=n(Wyo,"A",{id:!0,class:!0,href:!0});var VSn=s(uP);voo=n(VSn,"SPAN",{});var XSn=s(voo);T(fV.$$.fragment,XSn),XSn.forEach(t),VSn.forEach(t),i1a=i(Wyo),boo=n(Wyo,"SPAN",{});var zSn=s(boo);d1a=r(zSn,"TFAutoModelForZeroShotImageClassification"),zSn.forEach(t),Wyo.forEach(t),n7o=i(f),Rr=n(f,"DIV",{class:!0});var Hd=s(Rr);T(mV.$$.fragment,Hd),c1a=i(Hd),fg=n(Hd,"P",{});var mEe=s(fg);f1a=r(mEe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created
with the `),$9e=n(mEe,"A",{href:!0});var WSn=s($9e);m1a=r(WSn,"from_pretrained()"),WSn.forEach(t),g1a=r(mEe," class method or the "),P9e=n(mEe,"A",{href:!0});var QSn=s(P9e);h1a=r(QSn,"from_config()"),QSn.forEach(t),u1a=r(mEe,` class
method.`),mEe.forEach(t),p1a=i(Hd),gV=n(Hd,"P",{});var Qyo=s(gV);_1a=r(Qyo,"This class cannot be instantiated directly using "),Too=n(Qyo,"CODE",{});var USn=s(Too);v1a=r(USn,"__init__()"),USn.forEach(t),b1a=r(Qyo," (throws an error)."),Qyo.forEach(t),T1a=i(Hd),La=n(Hd,"DIV",{class:!0});var qB=s(La);T(hV.$$.fragment,qB),F1a=i(qB),Foo=n(qB,"P",{});var HSn=s(Foo);M1a=r(HSn,"Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),HSn.forEach(t),E1a=i(qB),mg=n(qB,"P",{});var gEe=s(mg);C1a=r(gEe,`Note:
Loading a model from its configuration file does `),Moo=n(gEe,"STRONG",{});var JSn=s(Moo);w1a=r(JSn,"not"),JSn.forEach(t),A1a=r(gEe,` load the model weights. It only affects the
model\u2019s configuration. Use `),R9e=n(gEe,"A",{href:!0});var ZSn=s(R9e);L1a=r(ZSn,"from_pretrained()"),ZSn.forEach(t),y1a=r(gEe," to load the model weights."),gEe.forEach(t),x1a=i(qB),T(pP.$$.fragment,qB),qB.forEach(t),S1a=i(Hd),Mt=n(Hd,"DIV",{class:!0});var Jd=s(Mt);T(uV.$$.fragment,Jd),k1a=i(Jd),Eoo=n(Jd,"P",{});var YSn=s(Eoo);$1a=r(YSn,"Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),YSn.forEach(t),P1a=i(Jd),Ms=n(Jd,"P",{});var DB=s(Ms);R1a=r(DB,"The model class to instantiate is selected based on the "),Coo=n(DB,"CODE",{});var KSn=s(Coo);I1a=r(KSn,"model_type"),KSn.forEach(t),N1a=r(DB,` property of the config object (either
passed as an argument or loaded from `),woo=n(DB,"CODE",{});var ekn=s(woo);B1a=r(ekn,"pretrained_model_name_or_path"),ekn.forEach(t),G1a=r(DB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Aoo=n(DB,"CODE",{});var okn=s(Aoo);q1a=r(okn,"pretrained_model_name_or_path"),okn.forEach(t),D1a=r(DB,":"),DB.forEach(t),j1a=i(Jd),pV=n(Jd,"UL",{});var Uyo=s(pV);_P=n(Uyo,"LI",{});var X8o=s(_P);Loo=n(X8o,"STRONG",{});var rkn=s(Loo);O1a=r(rkn,"blip"),rkn.forEach(t),V1a=r(X8o," \u2014 "),I9e=n(X8o,"A",{href:!0});var tkn=s(I9e);X1a=r(tkn,"TFBlipModel"),tkn.forEach(t),z1a=r(X8o," (BLIP model)"),X8o.forEach(t),W1a=i(Uyo),vP=n(Uyo,"LI",{});var z8o=s(vP);yoo=n(z8o,"STRONG",{});var akn=s(yoo);Q1a=r(akn,"clip"),akn.forEach(t),U1a=r(z8o," \u2014 "),N9e=n(z8o,"A",{href:!0});var nkn=s(N9e);H1a=r(nkn,"TFCLIPModel"),nkn.forEach(t),J1a=r(z8o," (CLIP model)"),z8o.forEach(t),Uyo.forEach(t),Z1a=i(Jd),T(bP.$$.fragment,Jd),Jd.forEach(t),Hd.forEach(t),s7o=i(f),gg=n(f,"H3",{class:!0});var Hyo=s(gg);TP=n(Hyo,"A",{id:!0,class:!0,href:!0});var skn=s(TP);xoo=n(skn,"SPAN",{});var lkn=s(xoo);T(_V.$$.fragment,lkn),lkn.forEach(t),skn.forEach(t),Y1a=i(Hyo),Soo=n(Hyo,"SPAN",{});var ikn=s(Soo);K1a=r(ikn,"AutoModelForZeroShotObjectDetection"),ikn.forEach(t),Hyo.forEach(t),l7o=i(f),Ir=n(f,"DIV",{class:!0});var Zd=s(Ir);T(vV.$$.fragment,Zd),e4a=i(Zd),hg=n(Zd,"P",{});var hEe=s(hg);o4a=r(hEe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot object detection head) when created
with the `),B9e=n(hEe,"A",{href:!0});var dkn=s(B9e);r4a=r(dkn,"from_pretrained()"),dkn.forEach(t),t4a=r(hEe," class method or the "),G9e=n(hEe,"A",{href:!0});var ckn=s(G9e);a4a=r(ckn,"from_config()"),ckn.forEach(t),n4a=r(hEe,` class
method.`),hEe.forEach(t),s4a=i(Zd),bV=n(Zd,"P",{});var Jyo=s(bV);l4a=r(Jyo,"This class cannot be instantiated directly using "),koo=n(Jyo,"CODE",{});var fkn=s(koo);i4a=r(fkn,"__init__()"),fkn.forEach(t),d4a=r(Jyo," (throws an error)."),Jyo.forEach(t),c4a=i(Zd),ya=n(Zd,"DIV",{class:!0});var jB=s(ya);T(TV.$$.fragment,jB),f4a=i(jB),$oo=n(jB,"P",{});var mkn=s($oo);m4a=r(mkn,"Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration."),mkn.forEach(t),g4a=i(jB),ug=n(jB,"P",{});var uEe=s(ug);h4a=r(uEe,`Note:
Loading a model from its configuration file does `),Poo=n(uEe,"STRONG",{});var gkn=s(Poo);u4a=r(gkn,"not"),gkn.forEach(t),p4a=r(uEe,` load the model weights. It only affects the
model\u2019s configuration. Use `),q9e=n(uEe,"A",{href:!0});var hkn=s(q9e);_4a=r(hkn,"from_pretrained()"),hkn.forEach(t),v4a=r(uEe," to load the model weights."),uEe.forEach(t),b4a=i(jB),T(FP.$$.fragment,jB),jB.forEach(t),T4a=i(Zd),Lo=n(Zd,"DIV",{class:!0});var pn=s(Lo);T(FV.$$.fragment,pn),F4a=i(pn),Roo=n(pn,"P",{});var ukn=s(Roo);M4a=r(ukn,"Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model."),ukn.forEach(t),E4a=i(pn),Es=n(pn,"P",{});var OB=s(Es);C4a=r(OB,"The model class to instantiate is selected based on the "),Ioo=n(OB,"CODE",{});var pkn=s(Ioo);w4a=r(pkn,"model_type"),pkn.forEach(t),A4a=r(OB,` property of the config object (either
passed as an argument or loaded from `),Noo=n(OB,"CODE",{});var _kn=s(Noo);L4a=r(_kn,"pretrained_model_name_or_path"),_kn.forEach(t),y4a=r(OB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Boo=n(OB,"CODE",{});var vkn=s(Boo);x4a=r(vkn,"pretrained_model_name_or_path"),vkn.forEach(t),S4a=r(OB,":"),OB.forEach(t),k4a=i(pn),Goo=n(pn,"UL",{});var bkn=s(Goo);MP=n(bkn,"LI",{});var W8o=s(MP);qoo=n(W8o,"STRONG",{});var Tkn=s(qoo);$4a=r(Tkn,"owlvit"),Tkn.forEach(t),P4a=r(W8o," \u2014 "),D9e=n(W8o,"A",{href:!0});var Fkn=s(D9e);R4a=r(Fkn,"OwlViTForObjectDetection"),Fkn.forEach(t),I4a=r(W8o," (OWL-ViT model)"),W8o.forEach(t),bkn.forEach(t),N4a=i(pn),EP=n(pn,"P",{});var Q8o=s(EP);B4a=r(Q8o,"The model is set in evaluation mode by default using "),Doo=n(Q8o,"CODE",{});var Mkn=s(Doo);G4a=r(Mkn,"model.eval()"),Mkn.forEach(t),q4a=r(Q8o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),joo=n(Q8o,"CODE",{});var Ekn=s(joo);D4a=r(Ekn,"model.train()"),Ekn.forEach(t),Q8o.forEach(t),j4a=i(pn),T(CP.$$.fragment,pn),pn.forEach(t),Zd.forEach(t),i7o=i(f),pg=n(f,"H2",{class:!0});var Zyo=s(pg);wP=n(Zyo,"A",{id:!0,class:!0,href:!0});var Ckn=s(wP);Ooo=n(Ckn,"SPAN",{});var wkn=s(Ooo);T(MV.$$.fragment,wkn),wkn.forEach(t),Ckn.forEach(t),O4a=i(Zyo),Voo=n(Zyo,"SPAN",{});var Akn=s(Voo);V4a=r(Akn,"Audio"),Akn.forEach(t),Zyo.forEach(t),d7o=i(f),j9e=n(f,"P",{});var Lkn=s(j9e);X4a=r(Lkn,"The following auto classes are available for the following audio tasks."),Lkn.forEach(t),c7o=i(f),_g=n(f,"H3",{class:!0});var Yyo=s(_g);AP=n(Yyo,"A",{id:!0,class:!0,href:!0});var ykn=s(AP);Xoo=n(ykn,"SPAN",{});var xkn=s(Xoo);T(EV.$$.fragment,xkn),xkn.forEach(t),ykn.forEach(t),z4a=i(Yyo),zoo=n(Yyo,"SPAN",{});var Skn=s(zoo);W4a=r(Skn,"AutoModelForAudioClassification"),Skn.forEach(t),Yyo.forEach(t),f7o=i(f),Nr=n(f,"DIV",{class:!0});var Yd=s(Nr);T(CV.$$.fragment,Yd),Q4a=i(Yd),vg=n(Yd,"P",{});var pEe=s(vg);U4a=r(pEe,`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 `),O9e=n(pEe,"A",{href:!0});var kkn=s(O9e);H4a=r(kkn,"from_pretrained()"),kkn.forEach(t),J4a=r(pEe," class method or the "),V9e=n(pEe,"A",{href:!0});var $kn=s(V9e);Z4a=r($kn,"from_config()"),$kn.forEach(t),Y4a=r(pEe,` class
method.`),pEe.forEach(t),K4a=i(Yd),wV=n(Yd,"P",{});var Kyo=s(wV);e0a=r(Kyo,"This class cannot be instantiated directly using "),Woo=n(Kyo,"CODE",{});var Pkn=s(Woo);o0a=r(Pkn,"__init__()"),Pkn.forEach(t),r0a=r(Kyo," (throws an error)."),Kyo.forEach(t),t0a=i(Yd),xa=n(Yd,"DIV",{class:!0});var VB=s(xa);T(AV.$$.fragment,VB),a0a=i(VB),Qoo=n(VB,"P",{});var Rkn=s(Qoo);n0a=r(Rkn,"Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),Rkn.forEach(t),s0a=i(VB),bg=n(VB,"P",{});var _Ee=s(bg);l0a=r(_Ee,`Note:
Loading a model from its configuration file does `),Uoo=n(_Ee,"STRONG",{});var Ikn=s(Uoo);i0a=r(Ikn,"not"),Ikn.forEach(t),d0a=r(_Ee,` load the model weights. It only affects the
model\u2019s configuration. Use `),X9e=n(_Ee,"A",{href:!0});var Nkn=s(X9e);c0a=r(Nkn,"from_pretrained()"),Nkn.forEach(t),f0a=r(_Ee," to load the model weights."),_Ee.forEach(t),m0a=i(VB),T(LP.$$.fragment,VB),VB.forEach(t),g0a=i(Yd),yo=n(Yd,"DIV",{class:!0});var _n=s(yo);T(LV.$$.fragment,_n),h0a=i(_n),Hoo=n(_n,"P",{});var Bkn=s(Hoo);u0a=r(Bkn,"Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),Bkn.forEach(t),p0a=i(_n),Cs=n(_n,"P",{});var XB=s(Cs);_0a=r(XB,"The model class to instantiate is selected based on the "),Joo=n(XB,"CODE",{});var Gkn=s(Joo);v0a=r(Gkn,"model_type"),Gkn.forEach(t),b0a=r(XB,` property of the config object (either
passed as an argument or loaded from `),Zoo=n(XB,"CODE",{});var qkn=s(Zoo);T0a=r(qkn,"pretrained_model_name_or_path"),qkn.forEach(t),F0a=r(XB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Yoo=n(XB,"CODE",{});var Dkn=s(Yoo);M0a=r(Dkn,"pretrained_model_name_or_path"),Dkn.forEach(t),E0a=r(XB,":"),XB.forEach(t),C0a=i(_n),Pe=n(_n,"UL",{});var Oe=s(Pe);yP=n(Oe,"LI",{});var U8o=s(yP);Koo=n(U8o,"STRONG",{});var jkn=s(Koo);w0a=r(jkn,"audio-spectrogram-transformer"),jkn.forEach(t),A0a=r(U8o," \u2014 "),z9e=n(U8o,"A",{href:!0});var Okn=s(z9e);L0a=r(Okn,"ASTForAudioClassification"),Okn.forEach(t),y0a=r(U8o," (Audio Spectrogram Transformer model)"),U8o.forEach(t),x0a=i(Oe),xP=n(Oe,"LI",{});var H8o=s(xP);ero=n(H8o,"STRONG",{});var Vkn=s(ero);S0a=r(Vkn,"data2vec-audio"),Vkn.forEach(t),k0a=r(H8o," \u2014 "),W9e=n(H8o,"A",{href:!0});var Xkn=s(W9e);$0a=r(Xkn,"Data2VecAudioForSequenceClassification"),Xkn.forEach(t),P0a=r(H8o," (Data2VecAudio model)"),H8o.forEach(t),R0a=i(Oe),SP=n(Oe,"LI",{});var J8o=s(SP);oro=n(J8o,"STRONG",{});var zkn=s(oro);I0a=r(zkn,"hubert"),zkn.forEach(t),N0a=r(J8o," \u2014 "),Q9e=n(J8o,"A",{href:!0});var Wkn=s(Q9e);B0a=r(Wkn,"HubertForSequenceClassification"),Wkn.forEach(t),G0a=r(J8o," (Hubert model)"),J8o.forEach(t),q0a=i(Oe),kP=n(Oe,"LI",{});var Z8o=s(kP);rro=n(Z8o,"STRONG",{});var Qkn=s(rro);D0a=r(Qkn,"sew"),Qkn.forEach(t),j0a=r(Z8o," \u2014 "),U9e=n(Z8o,"A",{href:!0});var Ukn=s(U9e);O0a=r(Ukn,"SEWForSequenceClassification"),Ukn.forEach(t),V0a=r(Z8o," (SEW model)"),Z8o.forEach(t),X0a=i(Oe),$P=n(Oe,"LI",{});var Y8o=s($P);tro=n(Y8o,"STRONG",{});var Hkn=s(tro);z0a=r(Hkn,"sew-d"),Hkn.forEach(t),W0a=r(Y8o," \u2014 "),H9e=n(Y8o,"A",{href:!0});var Jkn=s(H9e);Q0a=r(Jkn,"SEWDForSequenceClassification"),Jkn.forEach(t),U0a=r(Y8o," (SEW-D model)"),Y8o.forEach(t),H0a=i(Oe),PP=n(Oe,"LI",{});var K8o=s(PP);aro=n(K8o,"STRONG",{});var Zkn=s(aro);J0a=r(Zkn,"unispeech"),Zkn.forEach(t),Z0a=r(K8o," \u2014 "),J9e=n(K8o,"A",{href:!0});var Ykn=s(J9e);Y0a=r(Ykn,"UniSpeechForSequenceClassification"),Ykn.forEach(t),K0a=r(K8o," (UniSpeech model)"),K8o.forEach(t),eba=i(Oe),RP=n(Oe,"LI",{});var e6o=s(RP);nro=n(e6o,"STRONG",{});var Kkn=s(nro);oba=r(Kkn,"unispeech-sat"),Kkn.forEach(t),rba=r(e6o," \u2014 "),Z9e=n(e6o,"A",{href:!0});var e$n=s(Z9e);tba=r(e$n,"UniSpeechSatForSequenceClassification"),e$n.forEach(t),aba=r(e6o," (UniSpeechSat model)"),e6o.forEach(t),nba=i(Oe),IP=n(Oe,"LI",{});var o6o=s(IP);sro=n(o6o,"STRONG",{});var o$n=s(sro);sba=r(o$n,"wav2vec2"),o$n.forEach(t),lba=r(o6o," \u2014 "),Y9e=n(o6o,"A",{href:!0});var r$n=s(Y9e);iba=r(r$n,"Wav2Vec2ForSequenceClassification"),r$n.forEach(t),dba=r(o6o," (Wav2Vec2 model)"),o6o.forEach(t),cba=i(Oe),NP=n(Oe,"LI",{});var r6o=s(NP);lro=n(r6o,"STRONG",{});var t$n=s(lro);fba=r(t$n,"wav2vec2-conformer"),t$n.forEach(t),mba=r(r6o," \u2014 "),K9e=n(r6o,"A",{href:!0});var a$n=s(K9e);gba=r(a$n,"Wav2Vec2ConformerForSequenceClassification"),a$n.forEach(t),hba=r(r6o," (Wav2Vec2-Conformer model)"),r6o.forEach(t),uba=i(Oe),BP=n(Oe,"LI",{});var t6o=s(BP);iro=n(t6o,"STRONG",{});var n$n=s(iro);pba=r(n$n,"wavlm"),n$n.forEach(t),_ba=r(t6o," \u2014 "),eTe=n(t6o,"A",{href:!0});var s$n=s(eTe);vba=r(s$n,"WavLMForSequenceClassification"),s$n.forEach(t),bba=r(t6o," (WavLM model)"),t6o.forEach(t),Tba=i(Oe),GP=n(Oe,"LI",{});var a6o=s(GP);dro=n(a6o,"STRONG",{});var l$n=s(dro);Fba=r(l$n,"whisper"),l$n.forEach(t),Mba=r(a6o," \u2014 "),oTe=n(a6o,"A",{href:!0});var i$n=s(oTe);Eba=r(i$n,"WhisperForAudioClassification"),i$n.forEach(t),Cba=r(a6o," (Whisper model)"),a6o.forEach(t),Oe.forEach(t),wba=i(_n),qP=n(_n,"P",{});var n6o=s(qP);Aba=r(n6o,"The model is set in evaluation mode by default using "),cro=n(n6o,"CODE",{});var d$n=s(cro);Lba=r(d$n,"model.eval()"),d$n.forEach(t),yba=r(n6o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),fro=n(n6o,"CODE",{});var c$n=s(fro);xba=r(c$n,"model.train()"),c$n.forEach(t),n6o.forEach(t),Sba=i(_n),T(DP.$$.fragment,_n),_n.forEach(t),Yd.forEach(t),m7o=i(f),Tg=n(f,"H3",{class:!0});var exo=s(Tg);jP=n(exo,"A",{id:!0,class:!0,href:!0});var f$n=s(jP);mro=n(f$n,"SPAN",{});var m$n=s(mro);T(yV.$$.fragment,m$n),m$n.forEach(t),f$n.forEach(t),kba=i(exo),gro=n(exo,"SPAN",{});var g$n=s(gro);$ba=r(g$n,"AutoModelForAudioFrameClassification"),g$n.forEach(t),exo.forEach(t),g7o=i(f),Br=n(f,"DIV",{class:!0});var Kd=s(Br);T(xV.$$.fragment,Kd),Pba=i(Kd),Fg=n(Kd,"P",{});var vEe=s(Fg);Rba=r(vEe,`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 `),rTe=n(vEe,"A",{href:!0});var h$n=s(rTe);Iba=r(h$n,"from_pretrained()"),h$n.forEach(t),Nba=r(vEe," class method or the "),tTe=n(vEe,"A",{href:!0});var u$n=s(tTe);Bba=r(u$n,"from_config()"),u$n.forEach(t),Gba=r(vEe,` class
method.`),vEe.forEach(t),qba=i(Kd),SV=n(Kd,"P",{});var oxo=s(SV);Dba=r(oxo,"This class cannot be instantiated directly using "),hro=n(oxo,"CODE",{});var p$n=s(hro);jba=r(p$n,"__init__()"),p$n.forEach(t),Oba=r(oxo," (throws an error)."),oxo.forEach(t),Vba=i(Kd),Sa=n(Kd,"DIV",{class:!0});var zB=s(Sa);T(kV.$$.fragment,zB),Xba=i(zB),uro=n(zB,"P",{});var _$n=s(uro);zba=r(_$n,"Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),_$n.forEach(t),Wba=i(zB),Mg=n(zB,"P",{});var bEe=s(Mg);Qba=r(bEe,`Note:
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model\u2019s configuration. Use `),aTe=n(bEe,"A",{href:!0});var b$n=s(aTe);Jba=r(b$n,"from_pretrained()"),b$n.forEach(t),Zba=r(bEe," to load the model weights."),bEe.forEach(t),Yba=i(zB),T(OP.$$.fragment,zB),zB.forEach(t),Kba=i(Kd),xo=n(Kd,"DIV",{class:!0});var vn=s(xo);T($V.$$.fragment,vn),e9a=i(vn),_ro=n(vn,"P",{});var T$n=s(_ro);o9a=r(T$n,"Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),T$n.forEach(t),r9a=i(vn),ws=n(vn,"P",{});var WB=s(ws);t9a=r(WB,"The model class to instantiate is selected based on the "),vro=n(WB,"CODE",{});var F$n=s(vro);a9a=r(F$n,"model_type"),F$n.forEach(t),n9a=r(WB,` property of the config object (either
passed as an argument or loaded from `),bro=n(WB,"CODE",{});var M$n=s(bro);s9a=r(M$n,"pretrained_model_name_or_path"),M$n.forEach(t),l9a=r(WB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Tro=n(WB,"CODE",{});var E$n=s(Tro);i9a=r(E$n,"pretrained_model_name_or_path"),E$n.forEach(t),d9a=r(WB,":"),WB.forEach(t),c9a=i(vn),$t=n(vn,"UL",{});var ec=s($t);VP=n(ec,"LI",{});var s6o=s(VP);Fro=n(s6o,"STRONG",{});var C$n=s(Fro);f9a=r(C$n,"data2vec-audio"),C$n.forEach(t),m9a=r(s6o," \u2014 "),nTe=n(s6o,"A",{href:!0});var w$n=s(nTe);g9a=r(w$n,"Data2VecAudioForAudioFrameClassification"),w$n.forEach(t),h9a=r(s6o," (Data2VecAudio model)"),s6o.forEach(t),u9a=i(ec),XP=n(ec,"LI",{});var l6o=s(XP);Mro=n(l6o,"STRONG",{});var A$n=s(Mro);p9a=r(A$n,"unispeech-sat"),A$n.forEach(t),_9a=r(l6o," \u2014 "),sTe=n(l6o,"A",{href:!0});var L$n=s(sTe);v9a=r(L$n,"UniSpeechSatForAudioFrameClassification"),L$n.forEach(t),b9a=r(l6o," (UniSpeechSat model)"),l6o.forEach(t),T9a=i(ec),zP=n(ec,"LI",{});var i6o=s(zP);Ero=n(i6o,"STRONG",{});var y$n=s(Ero);F9a=r(y$n,"wav2vec2"),y$n.forEach(t),M9a=r(i6o," \u2014 "),lTe=n(i6o,"A",{href:!0});var x$n=s(lTe);E9a=r(x$n,"Wav2Vec2ForAudioFrameClassification"),x$n.forEach(t),C9a=r(i6o," (Wav2Vec2 model)"),i6o.forEach(t),w9a=i(ec),WP=n(ec,"LI",{});var d6o=s(WP);Cro=n(d6o,"STRONG",{});var S$n=s(Cro);A9a=r(S$n,"wav2vec2-conformer"),S$n.forEach(t),L9a=r(d6o," \u2014 "),iTe=n(d6o,"A",{href:!0});var k$n=s(iTe);y9a=r(k$n,"Wav2Vec2ConformerForAudioFrameClassification"),k$n.forEach(t),x9a=r(d6o," (Wav2Vec2-Conformer model)"),d6o.forEach(t),S9a=i(ec),QP=n(ec,"LI",{});var c6o=s(QP);wro=n(c6o,"STRONG",{});var $$n=s(wro);k9a=r($$n,"wavlm"),$$n.forEach(t),$9a=r(c6o," \u2014 "),dTe=n(c6o,"A",{href:!0});var P$n=s(dTe);P9a=r(P$n,"WavLMForAudioFrameClassification"),P$n.forEach(t),R9a=r(c6o," (WavLM model)"),c6o.forEach(t),ec.forEach(t),I9a=i(vn),UP=n(vn,"P",{});var f6o=s(UP);N9a=r(f6o,"The model is set in evaluation mode by default using "),Aro=n(f6o,"CODE",{});var R$n=s(Aro);B9a=r(R$n,"model.eval()"),R$n.forEach(t),G9a=r(f6o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Lro=n(f6o,"CODE",{});var I$n=s(Lro);q9a=r(I$n,"model.train()"),I$n.forEach(t),f6o.forEach(t),D9a=i(vn),T(HP.$$.fragment,vn),vn.forEach(t),Kd.forEach(t),h7o=i(f),Eg=n(f,"H3",{class:!0});var rxo=s(Eg);JP=n(rxo,"A",{id:!0,class:!0,href:!0});var N$n=s(JP);yro=n(N$n,"SPAN",{});var B$n=s(yro);T(PV.$$.fragment,B$n),B$n.forEach(t),N$n.forEach(t),j9a=i(rxo),xro=n(rxo,"SPAN",{});var G$n=s(xro);O9a=r(G$n,"AutoModelForCTC"),G$n.forEach(t),rxo.forEach(t),u7o=i(f),Gr=n(f,"DIV",{class:!0});var oc=s(Gr);T(RV.$$.fragment,oc),V9a=i(oc),Cg=n(oc,"P",{});var TEe=s(Cg);X9a=r(TEe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a connectionist temporal classification head) when created
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method.`),TEe.forEach(t),H9a=i(oc),IV=n(oc,"P",{});var txo=s(IV);J9a=r(txo,"This class cannot be instantiated directly using "),Sro=n(txo,"CODE",{});var j$n=s(Sro);Z9a=r(j$n,"__init__()"),j$n.forEach(t),Y9a=r(txo," (throws an error)."),txo.forEach(t),K9a=i(oc),ka=n(oc,"DIV",{class:!0});var QB=s(ka);T(NV.$$.fragment,QB),eTa=i(QB),kro=n(QB,"P",{});var O$n=s(kro);oTa=r(O$n,"Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),O$n.forEach(t),rTa=i(QB),wg=n(QB,"P",{});var FEe=s(wg);tTa=r(FEe,`Note:
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model\u2019s configuration. Use `),mTe=n(FEe,"A",{href:!0});var X$n=s(mTe);sTa=r(X$n,"from_pretrained()"),X$n.forEach(t),lTa=r(FEe," to load the model weights."),FEe.forEach(t),iTa=i(QB),T(ZP.$$.fragment,QB),QB.forEach(t),dTa=i(oc),So=n(oc,"DIV",{class:!0});var bn=s(So);T(BV.$$.fragment,bn),cTa=i(bn),Pro=n(bn,"P",{});var z$n=s(Pro);fTa=r(z$n,"Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model."),z$n.forEach(t),mTa=i(bn),As=n(bn,"P",{});var UB=s(As);gTa=r(UB,"The model class to instantiate is selected based on the "),Rro=n(UB,"CODE",{});var W$n=s(Rro);hTa=r(W$n,"model_type"),W$n.forEach(t),uTa=r(UB,` property of the config object (either
passed as an argument or loaded from `),Iro=n(UB,"CODE",{});var Q$n=s(Iro);pTa=r(Q$n,"pretrained_model_name_or_path"),Q$n.forEach(t),_Ta=r(UB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Nro=n(UB,"CODE",{});var U$n=s(Nro);vTa=r(U$n,"pretrained_model_name_or_path"),U$n.forEach(t),bTa=r(UB,":"),UB.forEach(t),TTa=i(bn),Ge=n(bn,"UL",{});var Ue=s(Ge);YP=n(Ue,"LI",{});var m6o=s(YP);Bro=n(m6o,"STRONG",{});var H$n=s(Bro);FTa=r(H$n,"data2vec-audio"),H$n.forEach(t),MTa=r(m6o," \u2014 "),gTe=n(m6o,"A",{href:!0});var J$n=s(gTe);ETa=r(J$n,"Data2VecAudioForCTC"),J$n.forEach(t),CTa=r(m6o," (Data2VecAudio model)"),m6o.forEach(t),wTa=i(Ue),KP=n(Ue,"LI",{});var g6o=s(KP);Gro=n(g6o,"STRONG",{});var Z$n=s(Gro);ATa=r(Z$n,"hubert"),Z$n.forEach(t),LTa=r(g6o," \u2014 "),hTe=n(g6o,"A",{href:!0});var Y$n=s(hTe);yTa=r(Y$n,"HubertForCTC"),Y$n.forEach(t),xTa=r(g6o," (Hubert model)"),g6o.forEach(t),STa=i(Ue),eR=n(Ue,"LI",{});var h6o=s(eR);qro=n(h6o,"STRONG",{});var K$n=s(qro);kTa=r(K$n,"mctct"),K$n.forEach(t),$Ta=r(h6o," \u2014 "),uTe=n(h6o,"A",{href:!0});var ePn=s(uTe);PTa=r(ePn,"MCTCTForCTC"),ePn.forEach(t),RTa=r(h6o," (M-CTC-T model)"),h6o.forEach(t),ITa=i(Ue),oR=n(Ue,"LI",{});var u6o=s(oR);Dro=n(u6o,"STRONG",{});var oPn=s(Dro);NTa=r(oPn,"sew"),oPn.forEach(t),BTa=r(u6o," \u2014 "),pTe=n(u6o,"A",{href:!0});var rPn=s(pTe);GTa=r(rPn,"SEWForCTC"),rPn.forEach(t),qTa=r(u6o," (SEW model)"),u6o.forEach(t),DTa=i(Ue),rR=n(Ue,"LI",{});var p6o=s(rR);jro=n(p6o,"STRONG",{});var tPn=s(jro);jTa=r(tPn,"sew-d"),tPn.forEach(t),OTa=r(p6o," \u2014 "),_Te=n(p6o,"A",{href:!0});var aPn=s(_Te);VTa=r(aPn,"SEWDForCTC"),aPn.forEach(t),XTa=r(p6o," (SEW-D model)"),p6o.forEach(t),zTa=i(Ue),tR=n(Ue,"LI",{});var _6o=s(tR);Oro=n(_6o,"STRONG",{});var nPn=s(Oro);WTa=r(nPn,"unispeech"),nPn.forEach(t),QTa=r(_6o," \u2014 "),vTe=n(_6o,"A",{href:!0});var sPn=s(vTe);UTa=r(sPn,"UniSpeechForCTC"),sPn.forEach(t),HTa=r(_6o," (UniSpeech model)"),_6o.forEach(t),JTa=i(Ue),aR=n(Ue,"LI",{});var v6o=s(aR);Vro=n(v6o,"STRONG",{});var lPn=s(Vro);ZTa=r(lPn,"unispeech-sat"),lPn.forEach(t),YTa=r(v6o," \u2014 "),bTe=n(v6o,"A",{href:!0});var iPn=s(bTe);KTa=r(iPn,"UniSpeechSatForCTC"),iPn.forEach(t),eFa=r(v6o," (UniSpeechSat model)"),v6o.forEach(t),oFa=i(Ue),nR=n(Ue,"LI",{});var b6o=s(nR);Xro=n(b6o,"STRONG",{});var dPn=s(Xro);rFa=r(dPn,"wav2vec2"),dPn.forEach(t),tFa=r(b6o," \u2014 "),TTe=n(b6o,"A",{href:!0});var cPn=s(TTe);aFa=r(cPn,"Wav2Vec2ForCTC"),cPn.forEach(t),nFa=r(b6o," (Wav2Vec2 model)"),b6o.forEach(t),sFa=i(Ue),sR=n(Ue,"LI",{});var T6o=s(sR);zro=n(T6o,"STRONG",{});var fPn=s(zro);lFa=r(fPn,"wav2vec2-conformer"),fPn.forEach(t),iFa=r(T6o," \u2014 "),FTe=n(T6o,"A",{href:!0});var mPn=s(FTe);dFa=r(mPn,"Wav2Vec2ConformerForCTC"),mPn.forEach(t),cFa=r(T6o," (Wav2Vec2-Conformer model)"),T6o.forEach(t),fFa=i(Ue),lR=n(Ue,"LI",{});var F6o=s(lR);Wro=n(F6o,"STRONG",{});var gPn=s(Wro);mFa=r(gPn,"wavlm"),gPn.forEach(t),gFa=r(F6o," \u2014 "),MTe=n(F6o,"A",{href:!0});var hPn=s(MTe);hFa=r(hPn,"WavLMForCTC"),hPn.forEach(t),uFa=r(F6o," (WavLM model)"),F6o.forEach(t),Ue.forEach(t),pFa=i(bn),iR=n(bn,"P",{});var M6o=s(iR);_Fa=r(M6o,"The model is set in evaluation mode by default using "),Qro=n(M6o,"CODE",{});var uPn=s(Qro);vFa=r(uPn,"model.eval()"),uPn.forEach(t),bFa=r(M6o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Uro=n(M6o,"CODE",{});var pPn=s(Uro);TFa=r(pPn,"model.train()"),pPn.forEach(t),M6o.forEach(t),FFa=i(bn),T(dR.$$.fragment,bn),bn.forEach(t),oc.forEach(t),p7o=i(f),Ag=n(f,"H3",{class:!0});var axo=s(Ag);cR=n(axo,"A",{id:!0,class:!0,href:!0});var _Pn=s(cR);Hro=n(_Pn,"SPAN",{});var vPn=s(Hro);T(GV.$$.fragment,vPn),vPn.forEach(t),_Pn.forEach(t),MFa=i(axo),Jro=n(axo,"SPAN",{});var bPn=s(Jro);EFa=r(bPn,"AutoModelForSpeechSeq2Seq"),bPn.forEach(t),axo.forEach(t),_7o=i(f),qr=n(f,"DIV",{class:!0});var rc=s(qr);T(qV.$$.fragment,rc),CFa=i(rc),Lg=n(rc,"P",{});var MEe=s(Lg);wFa=r(MEe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),ETe=n(MEe,"A",{href:!0});var TPn=s(ETe);AFa=r(TPn,"from_pretrained()"),TPn.forEach(t),LFa=r(MEe," class method or the "),CTe=n(MEe,"A",{href:!0});var FPn=s(CTe);yFa=r(FPn,"from_config()"),FPn.forEach(t),xFa=r(MEe,` class
method.`),MEe.forEach(t),SFa=i(rc),DV=n(rc,"P",{});var nxo=s(DV);kFa=r(nxo,"This class cannot be instantiated directly using "),Zro=n(nxo,"CODE",{});var MPn=s(Zro);$Fa=r(MPn,"__init__()"),MPn.forEach(t),PFa=r(nxo," (throws an error)."),nxo.forEach(t),RFa=i(rc),$a=n(rc,"DIV",{class:!0});var HB=s($a);T(jV.$$.fragment,HB),IFa=i(HB),Yro=n(HB,"P",{});var EPn=s(Yro);NFa=r(EPn,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),EPn.forEach(t),BFa=i(HB),yg=n(HB,"P",{});var EEe=s(yg);GFa=r(EEe,`Note:
Loading a model from its configuration file does `),Kro=n(EEe,"STRONG",{});var CPn=s(Kro);qFa=r(CPn,"not"),CPn.forEach(t),DFa=r(EEe,` load the model weights. It only affects the
model\u2019s configuration. Use `),wTe=n(EEe,"A",{href:!0});var wPn=s(wTe);jFa=r(wPn,"from_pretrained()"),wPn.forEach(t),OFa=r(EEe," to load the model weights."),EEe.forEach(t),VFa=i(HB),T(fR.$$.fragment,HB),HB.forEach(t),XFa=i(rc),ko=n(rc,"DIV",{class:!0});var Tn=s(ko);T(OV.$$.fragment,Tn),zFa=i(Tn),eto=n(Tn,"P",{});var APn=s(eto);WFa=r(APn,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),APn.forEach(t),QFa=i(Tn),Ls=n(Tn,"P",{});var JB=s(Ls);UFa=r(JB,"The model class to instantiate is selected based on the "),oto=n(JB,"CODE",{});var LPn=s(oto);HFa=r(LPn,"model_type"),LPn.forEach(t),JFa=r(JB,` property of the config object (either
passed as an argument or loaded from `),rto=n(JB,"CODE",{});var yPn=s(rto);ZFa=r(yPn,"pretrained_model_name_or_path"),yPn.forEach(t),YFa=r(JB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),tto=n(JB,"CODE",{});var xPn=s(tto);KFa=r(xPn,"pretrained_model_name_or_path"),xPn.forEach(t),eMa=r(JB,":"),JB.forEach(t),oMa=i(Tn),ys=n(Tn,"UL",{});var ZB=s(ys);mR=n(ZB,"LI",{});var E6o=s(mR);ato=n(E6o,"STRONG",{});var SPn=s(ato);rMa=r(SPn,"speech-encoder-decoder"),SPn.forEach(t),tMa=r(E6o," \u2014 "),ATe=n(E6o,"A",{href:!0});var kPn=s(ATe);aMa=r(kPn,"SpeechEncoderDecoderModel"),kPn.forEach(t),nMa=r(E6o," (Speech Encoder decoder model)"),E6o.forEach(t),sMa=i(ZB),gR=n(ZB,"LI",{});var C6o=s(gR);nto=n(C6o,"STRONG",{});var $Pn=s(nto);lMa=r($Pn,"speech_to_text"),$Pn.forEach(t),iMa=r(C6o," \u2014 "),LTe=n(C6o,"A",{href:!0});var PPn=s(LTe);dMa=r(PPn,"Speech2TextForConditionalGeneration"),PPn.forEach(t),cMa=r(C6o," (Speech2Text model)"),C6o.forEach(t),fMa=i(ZB),hR=n(ZB,"LI",{});var w6o=s(hR);sto=n(w6o,"STRONG",{});var RPn=s(sto);mMa=r(RPn,"speecht5"),RPn.forEach(t),gMa=r(w6o," \u2014 "),yTe=n(w6o,"A",{href:!0});var IPn=s(yTe);hMa=r(IPn,"SpeechT5ForSpeechToText"),IPn.forEach(t),uMa=r(w6o," (SpeechT5 model)"),w6o.forEach(t),pMa=i(ZB),uR=n(ZB,"LI",{});var A6o=s(uR);lto=n(A6o,"STRONG",{});var NPn=s(lto);_Ma=r(NPn,"whisper"),NPn.forEach(t),vMa=r(A6o," \u2014 "),xTe=n(A6o,"A",{href:!0});var BPn=s(xTe);bMa=r(BPn,"WhisperForConditionalGeneration"),BPn.forEach(t),TMa=r(A6o," (Whisper model)"),A6o.forEach(t),ZB.forEach(t),FMa=i(Tn),pR=n(Tn,"P",{});var L6o=s(pR);MMa=r(L6o,"The model is set in evaluation mode by default using "),ito=n(L6o,"CODE",{});var GPn=s(ito);EMa=r(GPn,"model.eval()"),GPn.forEach(t),CMa=r(L6o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),dto=n(L6o,"CODE",{});var qPn=s(dto);wMa=r(qPn,"model.train()"),qPn.forEach(t),L6o.forEach(t),AMa=i(Tn),T(_R.$$.fragment,Tn),Tn.forEach(t),rc.forEach(t),v7o=i(f),xg=n(f,"H3",{class:!0});var sxo=s(xg);vR=n(sxo,"A",{id:!0,class:!0,href:!0});var DPn=s(vR);cto=n(DPn,"SPAN",{});var jPn=s(cto);T(VV.$$.fragment,jPn),jPn.forEach(t),DPn.forEach(t),LMa=i(sxo),fto=n(sxo,"SPAN",{});var OPn=s(fto);yMa=r(OPn,"TFAutoModelForSpeechSeq2Seq"),OPn.forEach(t),sxo.forEach(t),b7o=i(f),Dr=n(f,"DIV",{class:!0});var tc=s(Dr);T(XV.$$.fragment,tc),xMa=i(tc),Sg=n(tc,"P",{});var CEe=s(Sg);SMa=r(CEe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),STe=n(CEe,"A",{href:!0});var VPn=s(STe);kMa=r(VPn,"from_pretrained()"),VPn.forEach(t),$Ma=r(CEe," class method or the "),kTe=n(CEe,"A",{href:!0});var XPn=s(kTe);PMa=r(XPn,"from_config()"),XPn.forEach(t),RMa=r(CEe,` class
method.`),CEe.forEach(t),IMa=i(tc),zV=n(tc,"P",{});var lxo=s(zV);NMa=r(lxo,"This class cannot be instantiated directly using "),mto=n(lxo,"CODE",{});var zPn=s(mto);BMa=r(zPn,"__init__()"),zPn.forEach(t),GMa=r(lxo," (throws an error)."),lxo.forEach(t),qMa=i(tc),Pa=n(tc,"DIV",{class:!0});var YB=s(Pa);T(WV.$$.fragment,YB),DMa=i(YB),gto=n(YB,"P",{});var WPn=s(gto);jMa=r(WPn,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),WPn.forEach(t),OMa=i(YB),kg=n(YB,"P",{});var wEe=s(kg);VMa=r(wEe,`Note:
Loading a model from its configuration file does `),hto=n(wEe,"STRONG",{});var QPn=s(hto);XMa=r(QPn,"not"),QPn.forEach(t),zMa=r(wEe,` load the model weights. It only affects the
model\u2019s configuration. Use `),$Te=n(wEe,"A",{href:!0});var UPn=s($Te);WMa=r(UPn,"from_pretrained()"),UPn.forEach(t),QMa=r(wEe," to load the model weights."),wEe.forEach(t),UMa=i(YB),T(bR.$$.fragment,YB),YB.forEach(t),HMa=i(tc),Et=n(tc,"DIV",{class:!0});var ac=s(Et);T(QV.$$.fragment,ac),JMa=i(ac),uto=n(ac,"P",{});var HPn=s(uto);ZMa=r(HPn,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),HPn.forEach(t),YMa=i(ac),xs=n(ac,"P",{});var KB=s(xs);KMa=r(KB,"The model class to instantiate is selected based on the "),pto=n(KB,"CODE",{});var JPn=s(pto);eEa=r(JPn,"model_type"),JPn.forEach(t),oEa=r(KB,` property of the config object (either
passed as an argument or loaded from `),_to=n(KB,"CODE",{});var ZPn=s(_to);rEa=r(ZPn,"pretrained_model_name_or_path"),ZPn.forEach(t),tEa=r(KB,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),vto=n(KB,"CODE",{});var YPn=s(vto);aEa=r(YPn,"pretrained_model_name_or_path"),YPn.forEach(t),nEa=r(KB,":"),KB.forEach(t),sEa=i(ac),UV=n(ac,"UL",{});var ixo=s(UV);TR=n(ixo,"LI",{});var y6o=s(TR);bto=n(y6o,"STRONG",{});var KPn=s(bto);lEa=r(KPn,"speech_to_text"),KPn.forEach(t),iEa=r(y6o," \u2014 "),PTe=n(y6o,"A",{href:!0});var eRn=s(PTe);dEa=r(eRn,"TFSpeech2TextForConditionalGeneration"),eRn.forEach(t),cEa=r(y6o," (Speech2Text model)"),y6o.forEach(t),fEa=i(ixo),FR=n(ixo,"LI",{});var x6o=s(FR);Tto=n(x6o,"STRONG",{});var oRn=s(Tto);mEa=r(oRn,"whisper"),oRn.forEach(t),gEa=r(x6o," \u2014 "),RTe=n(x6o,"A",{href:!0});var rRn=s(RTe);hEa=r(rRn,"TFWhisperForConditionalGeneration"),rRn.forEach(t),uEa=r(x6o," (Whisper model)"),x6o.forEach(t),ixo.forEach(t),pEa=i(ac),T(MR.$$.fragment,ac),ac.forEach(t),tc.forEach(t),T7o=i(f),$g=n(f,"H3",{class:!0});var dxo=s($g);ER=n(dxo,"A",{id:!0,class:!0,href:!0});var tRn=s(ER);Fto=n(tRn,"SPAN",{});var aRn=s(Fto);T(HV.$$.fragment,aRn),aRn.forEach(t),tRn.forEach(t),_Ea=i(dxo),Mto=n(dxo,"SPAN",{});var nRn=s(Mto);vEa=r(nRn,"FlaxAutoModelForSpeechSeq2Seq"),nRn.forEach(t),dxo.forEach(t),F7o=i(f),jr=n(f,"DIV",{class:!0});var nc=s(jr);T(JV.$$.fragment,nc),bEa=i(nc),Pg=n(nc,"P",{});var AEe=s(Pg);TEa=r(AEe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),ITe=n(AEe,"A",{href:!0});var sRn=s(ITe);FEa=r(sRn,"from_pretrained()"),sRn.forEach(t),MEa=r(AEe," class method or the "),NTe=n(AEe,"A",{href:!0});var lRn=s(NTe);EEa=r(lRn,"from_config()"),lRn.forEach(t),CEa=r(AEe,` class
method.`),AEe.forEach(t),wEa=i(nc),ZV=n(nc,"P",{});var cxo=s(ZV);AEa=r(cxo,"This class cannot be instantiated directly using "),Eto=n(cxo,"CODE",{});var iRn=s(Eto);LEa=r(iRn,"__init__()"),iRn.forEach(t),yEa=r(cxo," (throws an error)."),cxo.forEach(t),xEa=i(nc),Ra=n(nc,"DIV",{class:!0});var eG=s(Ra);T(YV.$$.fragment,eG),SEa=i(eG),Cto=n(eG,"P",{});var dRn=s(Cto);kEa=r(dRn,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),dRn.forEach(t),$Ea=i(eG),Rg=n(eG,"P",{});var LEe=s(Rg);PEa=r(LEe,`Note:
Loading a model from its configuration file does `),wto=n(LEe,"STRONG",{});var cRn=s(wto);REa=r(cRn,"not"),cRn.forEach(t),IEa=r(LEe,` load the model weights. It only affects the
model\u2019s configuration. Use `),BTe=n(LEe,"A",{href:!0});var fRn=s(BTe);NEa=r(fRn,"from_pretrained()"),fRn.forEach(t),BEa=r(LEe," to load the model weights."),LEe.forEach(t),GEa=i(eG),T(CR.$$.fragment,eG),eG.forEach(t),qEa=i(nc),Ct=n(nc,"DIV",{class:!0});var sc=s(Ct);T(KV.$$.fragment,sc),DEa=i(sc),Ato=n(sc,"P",{});var mRn=s(Ato);jEa=r(mRn,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),mRn.forEach(t),OEa=i(sc),Ss=n(sc,"P",{});var oG=s(Ss);VEa=r(oG,"The model class to instantiate is selected based on the "),Lto=n(oG,"CODE",{});var gRn=s(Lto);XEa=r(gRn,"model_type"),gRn.forEach(t),zEa=r(oG,` property of the config object (either
passed as an argument or loaded from `),yto=n(oG,"CODE",{});var hRn=s(yto);WEa=r(hRn,"pretrained_model_name_or_path"),hRn.forEach(t),QEa=r(oG,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),xto=n(oG,"CODE",{});var uRn=s(xto);UEa=r(uRn,"pretrained_model_name_or_path"),uRn.forEach(t),HEa=r(oG,":"),oG.forEach(t),JEa=i(sc),eX=n(sc,"UL",{});var fxo=s(eX);wR=n(fxo,"LI",{});var S6o=s(wR);Sto=n(S6o,"STRONG",{});var pRn=s(Sto);ZEa=r(pRn,"speech-encoder-decoder"),pRn.forEach(t),YEa=r(S6o," \u2014 "),GTe=n(S6o,"A",{href:!0});var _Rn=s(GTe);KEa=r(_Rn,"FlaxSpeechEncoderDecoderModel"),_Rn.forEach(t),e3a=r(S6o," (Speech Encoder decoder model)"),S6o.forEach(t),o3a=i(fxo),AR=n(fxo,"LI",{});var k6o=s(AR);kto=n(k6o,"STRONG",{});var vRn=s(kto);r3a=r(vRn,"whisper"),vRn.forEach(t),t3a=r(k6o," \u2014 "),qTe=n(k6o,"A",{href:!0});var bRn=s(qTe);a3a=r(bRn,"FlaxWhisperForConditionalGeneration"),bRn.forEach(t),n3a=r(k6o," (Whisper model)"),k6o.forEach(t),fxo.forEach(t),s3a=i(sc),T(LR.$$.fragment,sc),sc.forEach(t),nc.forEach(t),M7o=i(f),Ig=n(f,"H3",{class:!0});var mxo=s(Ig);yR=n(mxo,"A",{id:!0,class:!0,href:!0});var TRn=s(yR);$to=n(TRn,"SPAN",{});var FRn=s($to);T(oX.$$.fragment,FRn),FRn.forEach(t),TRn.forEach(t),l3a=i(mxo),Pto=n(mxo,"SPAN",{});var MRn=s(Pto);i3a=r(MRn,"AutoModelForAudioXVector"),MRn.forEach(t),mxo.forEach(t),E7o=i(f),Or=n(f,"DIV",{class:!0});var lc=s(Or);T(rX.$$.fragment,lc),d3a=i(lc),Ng=n(lc,"P",{});var yEe=s(Ng);c3a=r(yEe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio retrieval via x-vector head) when created
with the `),DTe=n(yEe,"A",{href:!0});var ERn=s(DTe);f3a=r(ERn,"from_pretrained()"),ERn.forEach(t),m3a=r(yEe," class method or the "),jTe=n(yEe,"A",{href:!0});var CRn=s(jTe);g3a=r(CRn,"from_config()"),CRn.forEach(t),h3a=r(yEe,` class
method.`),yEe.forEach(t),u3a=i(lc),tX=n(lc,"P",{});var gxo=s(tX);p3a=r(gxo,"This class cannot be instantiated directly using "),Rto=n(gxo,"CODE",{});var wRn=s(Rto);_3a=r(wRn,"__init__()"),wRn.forEach(t),v3a=r(gxo," (throws an error)."),gxo.forEach(t),b3a=i(lc),Ia=n(lc,"DIV",{class:!0});var rG=s(Ia);T(aX.$$.fragment,rG),T3a=i(rG),Ito=n(rG,"P",{});var ARn=s(Ito);F3a=r(ARn,"Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration."),ARn.forEach(t),M3a=i(rG),Bg=n(rG,"P",{});var xEe=s(Bg);E3a=r(xEe,`Note:
Loading a model from its configuration file does `),Nto=n(xEe,"STRONG",{});var LRn=s(Nto);C3a=r(LRn,"not"),LRn.forEach(t),w3a=r(xEe,` load the model weights. It only affects the
model\u2019s configuration. Use `),OTe=n(xEe,"A",{href:!0});var yRn=s(OTe);A3a=r(yRn,"from_pretrained()"),yRn.forEach(t),L3a=r(xEe," to load the model weights."),xEe.forEach(t),y3a=i(rG),T(xR.$$.fragment,rG),rG.forEach(t),x3a=i(lc),$o=n(lc,"DIV",{class:!0});var Fn=s($o);T(nX.$$.fragment,Fn),S3a=i(Fn),Bto=n(Fn,"P",{});var xRn=s(Bto);k3a=r(xRn,"Instantiate one of the model classes of the library (with a audio retrieval via x-vector head) from a pretrained model."),xRn.forEach(t),$3a=i(Fn),ks=n(Fn,"P",{});var tG=s(ks);P3a=r(tG,"The model class to instantiate is selected based on the "),Gto=n(tG,"CODE",{});var SRn=s(Gto);R3a=r(SRn,"model_type"),SRn.forEach(t),I3a=r(tG,` property of the config object (either
passed as an argument or loaded from `),qto=n(tG,"CODE",{});var kRn=s(qto);N3a=r(kRn,"pretrained_model_name_or_path"),kRn.forEach(t),B3a=r(tG,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Dto=n(tG,"CODE",{});var $Rn=s(Dto);G3a=r($Rn,"pretrained_model_name_or_path"),$Rn.forEach(t),q3a=r(tG,":"),tG.forEach(t),D3a=i(Fn),Pt=n(Fn,"UL",{});var ic=s(Pt);SR=n(ic,"LI",{});var $6o=s(SR);jto=n($6o,"STRONG",{});var PRn=s(jto);j3a=r(PRn,"data2vec-audio"),PRn.forEach(t),O3a=r($6o," \u2014 "),VTe=n($6o,"A",{href:!0});var RRn=s(VTe);V3a=r(RRn,"Data2VecAudioForXVector"),RRn.forEach(t),X3a=r($6o," (Data2VecAudio model)"),$6o.forEach(t),z3a=i(ic),kR=n(ic,"LI",{});var P6o=s(kR);Oto=n(P6o,"STRONG",{});var IRn=s(Oto);W3a=r(IRn,"unispeech-sat"),IRn.forEach(t),Q3a=r(P6o," \u2014 "),XTe=n(P6o,"A",{href:!0});var NRn=s(XTe);U3a=r(NRn,"UniSpeechSatForXVector"),NRn.forEach(t),H3a=r(P6o," (UniSpeechSat model)"),P6o.forEach(t),J3a=i(ic),$R=n(ic,"LI",{});var R6o=s($R);Vto=n(R6o,"STRONG",{});var BRn=s(Vto);Z3a=r(BRn,"wav2vec2"),BRn.forEach(t),Y3a=r(R6o," \u2014 "),zTe=n(R6o,"A",{href:!0});var GRn=s(zTe);K3a=r(GRn,"Wav2Vec2ForXVector"),GRn.forEach(t),eCa=r(R6o," (Wav2Vec2 model)"),R6o.forEach(t),oCa=i(ic),PR=n(ic,"LI",{});var I6o=s(PR);Xto=n(I6o,"STRONG",{});var qRn=s(Xto);rCa=r(qRn,"wav2vec2-conformer"),qRn.forEach(t),tCa=r(I6o," \u2014 "),WTe=n(I6o,"A",{href:!0});var DRn=s(WTe);aCa=r(DRn,"Wav2Vec2ConformerForXVector"),DRn.forEach(t),nCa=r(I6o," (Wav2Vec2-Conformer model)"),I6o.forEach(t),sCa=i(ic),RR=n(ic,"LI",{});var N6o=s(RR);zto=n(N6o,"STRONG",{});var jRn=s(zto);lCa=r(jRn,"wavlm"),jRn.forEach(t),iCa=r(N6o," \u2014 "),QTe=n(N6o,"A",{href:!0});var ORn=s(QTe);dCa=r(ORn,"WavLMForXVector"),ORn.forEach(t),cCa=r(N6o," (WavLM model)"),N6o.forEach(t),ic.forEach(t),fCa=i(Fn),IR=n(Fn,"P",{});var B6o=s(IR);mCa=r(B6o,"The model is set in evaluation mode by default using "),Wto=n(B6o,"CODE",{});var VRn=s(Wto);gCa=r(VRn,"model.eval()"),VRn.forEach(t),hCa=r(B6o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Qto=n(B6o,"CODE",{});var XRn=s(Qto);uCa=r(XRn,"model.train()"),XRn.forEach(t),B6o.forEach(t),pCa=i(Fn),T(NR.$$.fragment,Fn),Fn.forEach(t),lc.forEach(t),C7o=i(f),Gg=n(f,"H2",{class:!0});var hxo=s(Gg);BR=n(hxo,"A",{id:!0,class:!0,href:!0});var zRn=s(BR);Uto=n(zRn,"SPAN",{});var WRn=s(Uto);T(sX.$$.fragment,WRn),WRn.forEach(t),zRn.forEach(t),_Ca=i(hxo),Hto=n(hxo,"SPAN",{});var QRn=s(Hto);vCa=r(QRn,"Multimodal"),QRn.forEach(t),hxo.forEach(t),w7o=i(f),UTe=n(f,"P",{});var URn=s(UTe);bCa=r(URn,"The following auto classes are available for the following multimodal tasks."),URn.forEach(t),A7o=i(f),qg=n(f,"H3",{class:!0});var uxo=s(qg);GR=n(uxo,"A",{id:!0,class:!0,href:!0});var HRn=s(GR);Jto=n(HRn,"SPAN",{});var JRn=s(Jto);T(lX.$$.fragment,JRn),JRn.forEach(t),HRn.forEach(t),TCa=i(uxo),Zto=n(uxo,"SPAN",{});var ZRn=s(Zto);FCa=r(ZRn,"AutoModelForTableQuestionAnswering"),ZRn.forEach(t),uxo.forEach(t),L7o=i(f),Vr=n(f,"DIV",{class:!0});var dc=s(Vr);T(iX.$$.fragment,dc),MCa=i(dc),Dg=n(dc,"P",{});var SEe=s(Dg);ECa=r(SEe,`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 `),HTe=n(SEe,"A",{href:!0});var YRn=s(HTe);CCa=r(YRn,"from_pretrained()"),YRn.forEach(t),wCa=r(SEe," class method or the "),JTe=n(SEe,"A",{href:!0});var KRn=s(JTe);ACa=r(KRn,"from_config()"),KRn.forEach(t),LCa=r(SEe,` class
method.`),SEe.forEach(t),yCa=i(dc),dX=n(dc,"P",{});var pxo=s(dX);xCa=r(pxo,"This class cannot be instantiated directly using "),Yto=n(pxo,"CODE",{});var eIn=s(Yto);SCa=r(eIn,"__init__()"),eIn.forEach(t),kCa=r(pxo," (throws an error)."),pxo.forEach(t),$Ca=i(dc),Na=n(dc,"DIV",{class:!0});var aG=s(Na);T(cX.$$.fragment,aG),PCa=i(aG),Kto=n(aG,"P",{});var oIn=s(Kto);RCa=r(oIn,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),oIn.forEach(t),ICa=i(aG),jg=n(aG,"P",{});var kEe=s(jg);NCa=r(kEe,`Note:
Loading a model from its configuration file does `),eao=n(kEe,"STRONG",{});var rIn=s(eao);BCa=r(rIn,"not"),rIn.forEach(t),GCa=r(kEe,` load the model weights. It only affects the
model\u2019s configuration. Use `),ZTe=n(kEe,"A",{href:!0});var tIn=s(ZTe);qCa=r(tIn,"from_pretrained()"),tIn.forEach(t),DCa=r(kEe," to load the model weights."),kEe.forEach(t),jCa=i(aG),T(qR.$$.fragment,aG),aG.forEach(t),OCa=i(dc),Po=n(dc,"DIV",{class:!0});var Mn=s(Po);T(fX.$$.fragment,Mn),VCa=i(Mn),oao=n(Mn,"P",{});var aIn=s(oao);XCa=r(aIn,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),aIn.forEach(t),zCa=i(Mn),$s=n(Mn,"P",{});var nG=s($s);WCa=r(nG,"The model class to instantiate is selected based on the "),rao=n(nG,"CODE",{});var nIn=s(rao);QCa=r(nIn,"model_type"),nIn.forEach(t),UCa=r(nG,` property of the config object (either
passed as an argument or loaded from `),tao=n(nG,"CODE",{});var sIn=s(tao);HCa=r(sIn,"pretrained_model_name_or_path"),sIn.forEach(t),JCa=r(nG,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),aao=n(nG,"CODE",{});var lIn=s(aao);ZCa=r(lIn,"pretrained_model_name_or_path"),lIn.forEach(t),YCa=r(nG,":"),nG.forEach(t),KCa=i(Mn),nao=n(Mn,"UL",{});var iIn=s(nao);DR=n(iIn,"LI",{});var G6o=s(DR);sao=n(G6o,"STRONG",{});var dIn=s(sao);e5a=r(dIn,"tapas"),dIn.forEach(t),o5a=r(G6o," \u2014 "),YTe=n(G6o,"A",{href:!0});var cIn=s(YTe);r5a=r(cIn,"TapasForQuestionAnswering"),cIn.forEach(t),t5a=r(G6o," (TAPAS model)"),G6o.forEach(t),iIn.forEach(t),a5a=i(Mn),jR=n(Mn,"P",{});var q6o=s(jR);n5a=r(q6o,"The model is set in evaluation mode by default using "),lao=n(q6o,"CODE",{});var fIn=s(lao);s5a=r(fIn,"model.eval()"),fIn.forEach(t),l5a=r(q6o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),iao=n(q6o,"CODE",{});var mIn=s(iao);i5a=r(mIn,"model.train()"),mIn.forEach(t),q6o.forEach(t),d5a=i(Mn),T(OR.$$.fragment,Mn),Mn.forEach(t),dc.forEach(t),y7o=i(f),Og=n(f,"H3",{class:!0});var _xo=s(Og);VR=n(_xo,"A",{id:!0,class:!0,href:!0});var gIn=s(VR);dao=n(gIn,"SPAN",{});var hIn=s(dao);T(mX.$$.fragment,hIn),hIn.forEach(t),gIn.forEach(t),c5a=i(_xo),cao=n(_xo,"SPAN",{});var uIn=s(cao);f5a=r(uIn,"TFAutoModelForTableQuestionAnswering"),uIn.forEach(t),_xo.forEach(t),x7o=i(f),Xr=n(f,"DIV",{class:!0});var cc=s(Xr);T(gX.$$.fragment,cc),m5a=i(cc),Vg=n(cc,"P",{});var $Ee=s(Vg);g5a=r($Ee,`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 `),KTe=n($Ee,"A",{href:!0});var pIn=s(KTe);h5a=r(pIn,"from_pretrained()"),pIn.forEach(t),u5a=r($Ee," class method or the "),eFe=n($Ee,"A",{href:!0});var _In=s(eFe);p5a=r(_In,"from_config()"),_In.forEach(t),_5a=r($Ee,` class
method.`),$Ee.forEach(t),v5a=i(cc),hX=n(cc,"P",{});var vxo=s(hX);b5a=r(vxo,"This class cannot be instantiated directly using "),fao=n(vxo,"CODE",{});var vIn=s(fao);T5a=r(vIn,"__init__()"),vIn.forEach(t),F5a=r(vxo," (throws an error)."),vxo.forEach(t),M5a=i(cc),Ba=n(cc,"DIV",{class:!0});var sG=s(Ba);T(uX.$$.fragment,sG),E5a=i(sG),mao=n(sG,"P",{});var bIn=s(mao);C5a=r(bIn,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),bIn.forEach(t),w5a=i(sG),Xg=n(sG,"P",{});var PEe=s(Xg);A5a=r(PEe,`Note:
Loading a model from its configuration file does `),gao=n(PEe,"STRONG",{});var TIn=s(gao);L5a=r(TIn,"not"),TIn.forEach(t),y5a=r(PEe,` load the model weights. It only affects the
model\u2019s configuration. Use `),oFe=n(PEe,"A",{href:!0});var FIn=s(oFe);x5a=r(FIn,"from_pretrained()"),FIn.forEach(t),S5a=r(PEe," to load the model weights."),PEe.forEach(t),k5a=i(sG),T(XR.$$.fragment,sG),sG.forEach(t),$5a=i(cc),wt=n(cc,"DIV",{class:!0});var fc=s(wt);T(pX.$$.fragment,fc),P5a=i(fc),hao=n(fc,"P",{});var MIn=s(hao);R5a=r(MIn,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),MIn.forEach(t),I5a=i(fc),Ps=n(fc,"P",{});var lG=s(Ps);N5a=r(lG,"The model class to instantiate is selected based on the "),uao=n(lG,"CODE",{});var EIn=s(uao);B5a=r(EIn,"model_type"),EIn.forEach(t),G5a=r(lG,` property of the config object (either
passed as an argument or loaded from `),pao=n(lG,"CODE",{});var CIn=s(pao);q5a=r(CIn,"pretrained_model_name_or_path"),CIn.forEach(t),D5a=r(lG,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),_ao=n(lG,"CODE",{});var wIn=s(_ao);j5a=r(wIn,"pretrained_model_name_or_path"),wIn.forEach(t),O5a=r(lG,":"),lG.forEach(t),V5a=i(fc),vao=n(fc,"UL",{});var AIn=s(vao);zR=n(AIn,"LI",{});var D6o=s(zR);bao=n(D6o,"STRONG",{});var LIn=s(bao);X5a=r(LIn,"tapas"),LIn.forEach(t),z5a=r(D6o," \u2014 "),rFe=n(D6o,"A",{href:!0});var yIn=s(rFe);W5a=r(yIn,"TFTapasForQuestionAnswering"),yIn.forEach(t),Q5a=r(D6o," (TAPAS model)"),D6o.forEach(t),AIn.forEach(t),U5a=i(fc),T(WR.$$.fragment,fc),fc.forEach(t),cc.forEach(t),S7o=i(f),zg=n(f,"H3",{class:!0});var bxo=s(zg);QR=n(bxo,"A",{id:!0,class:!0,href:!0});var xIn=s(QR);Tao=n(xIn,"SPAN",{});var SIn=s(Tao);T(_X.$$.fragment,SIn),SIn.forEach(t),xIn.forEach(t),H5a=i(bxo),Fao=n(bxo,"SPAN",{});var kIn=s(Fao);J5a=r(kIn,"AutoModelForDocumentQuestionAnswering"),kIn.forEach(t),bxo.forEach(t),k7o=i(f),zr=n(f,"DIV",{class:!0});var mc=s(zr);T(vX.$$.fragment,mc),Z5a=i(mc),Wg=n(mc,"P",{});var REe=s(Wg);Y5a=r(REe,`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 `),tFe=n(REe,"A",{href:!0});var $In=s(tFe);K5a=r($In,"from_pretrained()"),$In.forEach(t),e8a=r(REe," class method or the "),aFe=n(REe,"A",{href:!0});var PIn=s(aFe);o8a=r(PIn,"from_config()"),PIn.forEach(t),r8a=r(REe,` class
method.`),REe.forEach(t),t8a=i(mc),bX=n(mc,"P",{});var Txo=s(bX);a8a=r(Txo,"This class cannot be instantiated directly using "),Mao=n(Txo,"CODE",{});var RIn=s(Mao);n8a=r(RIn,"__init__()"),RIn.forEach(t),s8a=r(Txo," (throws an error)."),Txo.forEach(t),l8a=i(mc),Ga=n(mc,"DIV",{class:!0});var iG=s(Ga);T(TX.$$.fragment,iG),i8a=i(iG),Eao=n(iG,"P",{});var IIn=s(Eao);d8a=r(IIn,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),IIn.forEach(t),c8a=i(iG),Qg=n(iG,"P",{});var IEe=s(Qg);f8a=r(IEe,`Note:
Loading a model from its configuration file does `),Cao=n(IEe,"STRONG",{});var NIn=s(Cao);m8a=r(NIn,"not"),NIn.forEach(t),g8a=r(IEe,` load the model weights. It only affects the
model\u2019s configuration. Use `),nFe=n(IEe,"A",{href:!0});var BIn=s(nFe);h8a=r(BIn,"from_pretrained()"),BIn.forEach(t),u8a=r(IEe," to load the model weights."),IEe.forEach(t),p8a=i(iG),T(UR.$$.fragment,iG),iG.forEach(t),_8a=i(mc),Ro=n(mc,"DIV",{class:!0});var En=s(Ro);T(FX.$$.fragment,En),v8a=i(En),wao=n(En,"P",{});var GIn=s(wao);b8a=r(GIn,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),GIn.forEach(t),T8a=i(En),Rs=n(En,"P",{});var dG=s(Rs);F8a=r(dG,"The model class to instantiate is selected based on the "),Aao=n(dG,"CODE",{});var qIn=s(Aao);M8a=r(qIn,"model_type"),qIn.forEach(t),E8a=r(dG,` property of the config object (either
passed as an argument or loaded from `),Lao=n(dG,"CODE",{});var DIn=s(Lao);C8a=r(DIn,"pretrained_model_name_or_path"),DIn.forEach(t),w8a=r(dG,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),yao=n(dG,"CODE",{});var jIn=s(yao);A8a=r(jIn,"pretrained_model_name_or_path"),jIn.forEach(t),L8a=r(dG,":"),dG.forEach(t),y8a=i(En),Ug=n(En,"UL",{});var NEe=s(Ug);HR=n(NEe,"LI",{});var j6o=s(HR);xao=n(j6o,"STRONG",{});var OIn=s(xao);x8a=r(OIn,"layoutlm"),OIn.forEach(t),S8a=r(j6o," \u2014 "),sFe=n(j6o,"A",{href:!0});var VIn=s(sFe);k8a=r(VIn,"LayoutLMForQuestionAnswering"),VIn.forEach(t),$8a=r(j6o," (LayoutLM model)"),j6o.forEach(t),P8a=i(NEe),JR=n(NEe,"LI",{});var O6o=s(JR);Sao=n(O6o,"STRONG",{});var XIn=s(Sao);R8a=r(XIn,"layoutlmv2"),XIn.forEach(t),I8a=r(O6o," \u2014 "),lFe=n(O6o,"A",{href:!0});var zIn=s(lFe);N8a=r(zIn,"LayoutLMv2ForQuestionAnswering"),zIn.forEach(t),B8a=r(O6o," (LayoutLMv2 model)"),O6o.forEach(t),G8a=i(NEe),ZR=n(NEe,"LI",{});var V6o=s(ZR);kao=n(V6o,"STRONG",{});var WIn=s(kao);q8a=r(WIn,"layoutlmv3"),WIn.forEach(t),D8a=r(V6o," \u2014 "),iFe=n(V6o,"A",{href:!0});var QIn=s(iFe);j8a=r(QIn,"LayoutLMv3ForQuestionAnswering"),QIn.forEach(t),O8a=r(V6o," (LayoutLMv3 model)"),V6o.forEach(t),NEe.forEach(t),V8a=i(En),YR=n(En,"P",{});var X6o=s(YR);X8a=r(X6o,"The model is set in evaluation mode by default using "),$ao=n(X6o,"CODE",{});var UIn=s($ao);z8a=r(UIn,"model.eval()"),UIn.forEach(t),W8a=r(X6o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Pao=n(X6o,"CODE",{});var HIn=s(Pao);Q8a=r(HIn,"model.train()"),HIn.forEach(t),X6o.forEach(t),U8a=i(En),T(KR.$$.fragment,En),En.forEach(t),mc.forEach(t),$7o=i(f),Hg=n(f,"H3",{class:!0});var Fxo=s(Hg);eI=n(Fxo,"A",{id:!0,class:!0,href:!0});var JIn=s(eI);Rao=n(JIn,"SPAN",{});var ZIn=s(Rao);T(MX.$$.fragment,ZIn),ZIn.forEach(t),JIn.forEach(t),H8a=i(Fxo),Iao=n(Fxo,"SPAN",{});var YIn=s(Iao);J8a=r(YIn,"TFAutoModelForDocumentQuestionAnswering"),YIn.forEach(t),Fxo.forEach(t),P7o=i(f),Wr=n(f,"DIV",{class:!0});var gc=s(Wr);T(EX.$$.fragment,gc),Z8a=i(gc),Jg=n(gc,"P",{});var BEe=s(Jg);Y8a=r(BEe,`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 `),dFe=n(BEe,"A",{href:!0});var KIn=s(dFe);K8a=r(KIn,"from_pretrained()"),KIn.forEach(t),e6a=r(BEe," class method or the "),cFe=n(BEe,"A",{href:!0});var eNn=s(cFe);o6a=r(eNn,"from_config()"),eNn.forEach(t),r6a=r(BEe,` class
method.`),BEe.forEach(t),t6a=i(gc),CX=n(gc,"P",{});var Mxo=s(CX);a6a=r(Mxo,"This class cannot be instantiated directly using "),Nao=n(Mxo,"CODE",{});var oNn=s(Nao);n6a=r(oNn,"__init__()"),oNn.forEach(t),s6a=r(Mxo," (throws an error)."),Mxo.forEach(t),l6a=i(gc),qa=n(gc,"DIV",{class:!0});var cG=s(qa);T(wX.$$.fragment,cG),i6a=i(cG),Bao=n(cG,"P",{});var rNn=s(Bao);d6a=r(rNn,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),rNn.forEach(t),c6a=i(cG),Zg=n(cG,"P",{});var GEe=s(Zg);f6a=r(GEe,`Note:
Loading a model from its configuration file does `),Gao=n(GEe,"STRONG",{});var tNn=s(Gao);m6a=r(tNn,"not"),tNn.forEach(t),g6a=r(GEe,` load the model weights. It only affects the
model\u2019s configuration. Use `),fFe=n(GEe,"A",{href:!0});var aNn=s(fFe);h6a=r(aNn,"from_pretrained()"),aNn.forEach(t),u6a=r(GEe," to load the model weights."),GEe.forEach(t),p6a=i(cG),T(oI.$$.fragment,cG),cG.forEach(t),_6a=i(gc),At=n(gc,"DIV",{class:!0});var hc=s(At);T(AX.$$.fragment,hc),v6a=i(hc),qao=n(hc,"P",{});var nNn=s(qao);b6a=r(nNn,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),nNn.forEach(t),T6a=i(hc),Is=n(hc,"P",{});var fG=s(Is);F6a=r(fG,"The model class to instantiate is selected based on the "),Dao=n(fG,"CODE",{});var sNn=s(Dao);M6a=r(sNn,"model_type"),sNn.forEach(t),E6a=r(fG,` property of the config object (either
passed as an argument or loaded from `),jao=n(fG,"CODE",{});var lNn=s(jao);C6a=r(lNn,"pretrained_model_name_or_path"),lNn.forEach(t),w6a=r(fG,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Oao=n(fG,"CODE",{});var iNn=s(Oao);A6a=r(iNn,"pretrained_model_name_or_path"),iNn.forEach(t),L6a=r(fG,":"),fG.forEach(t),y6a=i(hc),Vao=n(hc,"UL",{});var dNn=s(Vao);rI=n(dNn,"LI",{});var z6o=s(rI);Xao=n(z6o,"STRONG",{});var cNn=s(Xao);x6a=r(cNn,"layoutlm"),cNn.forEach(t),S6a=r(z6o," \u2014 "),mFe=n(z6o,"A",{href:!0});var fNn=s(mFe);k6a=r(fNn,"TFLayoutLMForQuestionAnswering"),fNn.forEach(t),$6a=r(z6o," (LayoutLM model)"),z6o.forEach(t),dNn.forEach(t),P6a=i(hc),T(tI.$$.fragment,hc),hc.forEach(t),gc.forEach(t),R7o=i(f),Yg=n(f,"H3",{class:!0});var Exo=s(Yg);aI=n(Exo,"A",{id:!0,class:!0,href:!0});var mNn=s(aI);zao=n(mNn,"SPAN",{});var gNn=s(zao);T(LX.$$.fragment,gNn),gNn.forEach(t),mNn.forEach(t),R6a=i(Exo),Wao=n(Exo,"SPAN",{});var hNn=s(Wao);I6a=r(hNn,"AutoModelForVisualQuestionAnswering"),hNn.forEach(t),Exo.forEach(t),I7o=i(f),Qr=n(f,"DIV",{class:!0});var uc=s(Qr);T(yX.$$.fragment,uc),N6a=i(uc),Kg=n(uc,"P",{});var qEe=s(Kg);B6a=r(qEe,`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 `),gFe=n(qEe,"A",{href:!0});var uNn=s(gFe);G6a=r(uNn,"from_pretrained()"),uNn.forEach(t),q6a=r(qEe," class method or the "),hFe=n(qEe,"A",{href:!0});var pNn=s(hFe);D6a=r(pNn,"from_config()"),pNn.forEach(t),j6a=r(qEe,` class
method.`),qEe.forEach(t),O6a=i(uc),xX=n(uc,"P",{});var Cxo=s(xX);V6a=r(Cxo,"This class cannot be instantiated directly using "),Qao=n(Cxo,"CODE",{});var _Nn=s(Qao);X6a=r(_Nn,"__init__()"),_Nn.forEach(t),z6a=r(Cxo," (throws an error)."),Cxo.forEach(t),W6a=i(uc),Da=n(uc,"DIV",{class:!0});var mG=s(Da);T(SX.$$.fragment,mG),Q6a=i(mG),Uao=n(mG,"P",{});var vNn=s(Uao);U6a=r(vNn,"Instantiates one of the model classes of the library (with a visual question answering head) from a configuration."),vNn.forEach(t),H6a=i(mG),eh=n(mG,"P",{});var DEe=s(eh);J6a=r(DEe,`Note:
Loading a model from its configuration file does `),Hao=n(DEe,"STRONG",{});var bNn=s(Hao);Z6a=r(bNn,"not"),bNn.forEach(t),Y6a=r(DEe,` load the model weights. It only affects the
model\u2019s configuration. Use `),uFe=n(DEe,"A",{href:!0});var TNn=s(uFe);K6a=r(TNn,"from_pretrained()"),TNn.forEach(t),ewa=r(DEe," to load the model weights."),DEe.forEach(t),owa=i(mG),T(nI.$$.fragment,mG),mG.forEach(t),rwa=i(uc),Io=n(uc,"DIV",{class:!0});var Cn=s(Io);T(kX.$$.fragment,Cn),twa=i(Cn),Jao=n(Cn,"P",{});var FNn=s(Jao);awa=r(FNn,"Instantiate one of the model classes of the library (with a visual question answering head) from a pretrained model."),FNn.forEach(t),nwa=i(Cn),Ns=n(Cn,"P",{});var gG=s(Ns);swa=r(gG,"The model class to instantiate is selected based on the "),Zao=n(gG,"CODE",{});var MNn=s(Zao);lwa=r(MNn,"model_type"),MNn.forEach(t),iwa=r(gG,` property of the config object (either
passed as an argument or loaded from `),Yao=n(gG,"CODE",{});var ENn=s(Yao);dwa=r(ENn,"pretrained_model_name_or_path"),ENn.forEach(t),cwa=r(gG,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Kao=n(gG,"CODE",{});var CNn=s(Kao);fwa=r(CNn,"pretrained_model_name_or_path"),CNn.forEach(t),mwa=r(gG,":"),gG.forEach(t),gwa=i(Cn),eno=n(Cn,"UL",{});var wNn=s(eno);sI=n(wNn,"LI",{});var W6o=s(sI);ono=n(W6o,"STRONG",{});var ANn=s(ono);hwa=r(ANn,"vilt"),ANn.forEach(t),uwa=r(W6o," \u2014 "),pFe=n(W6o,"A",{href:!0});var LNn=s(pFe);pwa=r(LNn,"ViltForQuestionAnswering"),LNn.forEach(t),_wa=r(W6o," (ViLT model)"),W6o.forEach(t),wNn.forEach(t),vwa=i(Cn),lI=n(Cn,"P",{});var Q6o=s(lI);bwa=r(Q6o,"The model is set in evaluation mode by default using "),rno=n(Q6o,"CODE",{});var yNn=s(rno);Twa=r(yNn,"model.eval()"),yNn.forEach(t),Fwa=r(Q6o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),tno=n(Q6o,"CODE",{});var xNn=s(tno);Mwa=r(xNn,"model.train()"),xNn.forEach(t),Q6o.forEach(t),Ewa=i(Cn),T(iI.$$.fragment,Cn),Cn.forEach(t),uc.forEach(t),N7o=i(f),oh=n(f,"H3",{class:!0});var wxo=s(oh);dI=n(wxo,"A",{id:!0,class:!0,href:!0});var SNn=s(dI);ano=n(SNn,"SPAN",{});var kNn=s(ano);T($X.$$.fragment,kNn),kNn.forEach(t),SNn.forEach(t),Cwa=i(wxo),nno=n(wxo,"SPAN",{});var $Nn=s(nno);wwa=r($Nn,"AutoModelForVision2Seq"),$Nn.forEach(t),wxo.forEach(t),B7o=i(f),Ur=n(f,"DIV",{class:!0});var pc=s(Ur);T(PX.$$.fragment,pc),Awa=i(pc),rh=n(pc,"P",{});var jEe=s(rh);Lwa=r(jEe,`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 `),_Fe=n(jEe,"A",{href:!0});var PNn=s(_Fe);ywa=r(PNn,"from_pretrained()"),PNn.forEach(t),xwa=r(jEe," class method or the "),vFe=n(jEe,"A",{href:!0});var RNn=s(vFe);Swa=r(RNn,"from_config()"),RNn.forEach(t),kwa=r(jEe,` class
method.`),jEe.forEach(t),$wa=i(pc),RX=n(pc,"P",{});var Axo=s(RX);Pwa=r(Axo,"This class cannot be instantiated directly using "),sno=n(Axo,"CODE",{});var INn=s(sno);Rwa=r(INn,"__init__()"),INn.forEach(t),Iwa=r(Axo," (throws an error)."),Axo.forEach(t),Nwa=i(pc),ja=n(pc,"DIV",{class:!0});var hG=s(ja);T(IX.$$.fragment,hG),Bwa=i(hG),lno=n(hG,"P",{});var NNn=s(lno);Gwa=r(NNn,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),NNn.forEach(t),qwa=i(hG),th=n(hG,"P",{});var OEe=s(th);Dwa=r(OEe,`Note:
Loading a model from its configuration file does `),ino=n(OEe,"STRONG",{});var BNn=s(ino);jwa=r(BNn,"not"),BNn.forEach(t),Owa=r(OEe,` load the model weights. It only affects the
model\u2019s configuration. Use `),bFe=n(OEe,"A",{href:!0});var GNn=s(bFe);Vwa=r(GNn,"from_pretrained()"),GNn.forEach(t),Xwa=r(OEe," to load the model weights."),OEe.forEach(t),zwa=i(hG),T(cI.$$.fragment,hG),hG.forEach(t),Wwa=i(pc),No=n(pc,"DIV",{class:!0});var wn=s(No);T(NX.$$.fragment,wn),Qwa=i(wn),dno=n(wn,"P",{});var qNn=s(dno);Uwa=r(qNn,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),qNn.forEach(t),Hwa=i(wn),Bs=n(wn,"P",{});var uG=s(Bs);Jwa=r(uG,"The model class to instantiate is selected based on the "),cno=n(uG,"CODE",{});var DNn=s(cno);Zwa=r(DNn,"model_type"),DNn.forEach(t),Ywa=r(uG,` property of the config object (either
passed as an argument or loaded from `),fno=n(uG,"CODE",{});var jNn=s(fno);Kwa=r(jNn,"pretrained_model_name_or_path"),jNn.forEach(t),eAa=r(uG,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mno=n(uG,"CODE",{});var ONn=s(mno);oAa=r(ONn,"pretrained_model_name_or_path"),ONn.forEach(t),rAa=r(uG,":"),uG.forEach(t),tAa=i(wn),ah=n(wn,"UL",{});var VEe=s(ah);fI=n(VEe,"LI",{});var U6o=s(fI);gno=n(U6o,"STRONG",{});var VNn=s(gno);aAa=r(VNn,"blip"),VNn.forEach(t),nAa=r(U6o," \u2014 "),TFe=n(U6o,"A",{href:!0});var XNn=s(TFe);sAa=r(XNn,"BlipForConditionalGeneration"),XNn.forEach(t),lAa=r(U6o," (BLIP model)"),U6o.forEach(t),iAa=i(VEe),mI=n(VEe,"LI",{});var H6o=s(mI);hno=n(H6o,"STRONG",{});var zNn=s(hno);dAa=r(zNn,"blip-2"),zNn.forEach(t),cAa=r(H6o," \u2014 "),FFe=n(H6o,"A",{href:!0});var WNn=s(FFe);fAa=r(WNn,"Blip2ForConditionalGeneration"),WNn.forEach(t),mAa=r(H6o," (BLIP-2 model)"),H6o.forEach(t),gAa=i(VEe),gI=n(VEe,"LI",{});var J6o=s(gI);uno=n(J6o,"STRONG",{});var QNn=s(uno);hAa=r(QNn,"vision-encoder-decoder"),QNn.forEach(t),uAa=r(J6o," \u2014 "),MFe=n(J6o,"A",{href:!0});var UNn=s(MFe);pAa=r(UNn,"VisionEncoderDecoderModel"),UNn.forEach(t),_Aa=r(J6o," (Vision Encoder decoder model)"),J6o.forEach(t),VEe.forEach(t),vAa=i(wn),hI=n(wn,"P",{});var Z6o=s(hI);bAa=r(Z6o,"The model is set in evaluation mode by default using "),pno=n(Z6o,"CODE",{});var HNn=s(pno);TAa=r(HNn,"model.eval()"),HNn.forEach(t),FAa=r(Z6o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),_no=n(Z6o,"CODE",{});var JNn=s(_no);MAa=r(JNn,"model.train()"),JNn.forEach(t),Z6o.forEach(t),EAa=i(wn),T(uI.$$.fragment,wn),wn.forEach(t),pc.forEach(t),G7o=i(f),nh=n(f,"H3",{class:!0});var Lxo=s(nh);pI=n(Lxo,"A",{id:!0,class:!0,href:!0});var ZNn=s(pI);vno=n(ZNn,"SPAN",{});var YNn=s(vno);T(BX.$$.fragment,YNn),YNn.forEach(t),ZNn.forEach(t),CAa=i(Lxo),bno=n(Lxo,"SPAN",{});var KNn=s(bno);wAa=r(KNn,"TFAutoModelForVision2Seq"),KNn.forEach(t),Lxo.forEach(t),q7o=i(f),Hr=n(f,"DIV",{class:!0});var _c=s(Hr);T(GX.$$.fragment,_c),AAa=i(_c),sh=n(_c,"P",{});var XEe=s(sh);LAa=r(XEe,`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 `),EFe=n(XEe,"A",{href:!0});var eBn=s(EFe);yAa=r(eBn,"from_pretrained()"),eBn.forEach(t),xAa=r(XEe," class method or the "),CFe=n(XEe,"A",{href:!0});var oBn=s(CFe);SAa=r(oBn,"from_config()"),oBn.forEach(t),kAa=r(XEe,` class
method.`),XEe.forEach(t),$Aa=i(_c),qX=n(_c,"P",{});var yxo=s(qX);PAa=r(yxo,"This class cannot be instantiated directly using "),Tno=n(yxo,"CODE",{});var rBn=s(Tno);RAa=r(rBn,"__init__()"),rBn.forEach(t),IAa=r(yxo," (throws an error)."),yxo.forEach(t),NAa=i(_c),Oa=n(_c,"DIV",{class:!0});var pG=s(Oa);T(DX.$$.fragment,pG),BAa=i(pG),Fno=n(pG,"P",{});var tBn=s(Fno);GAa=r(tBn,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),tBn.forEach(t),qAa=i(pG),lh=n(pG,"P",{});var zEe=s(lh);DAa=r(zEe,`Note:
Loading a model from its configuration file does `),Mno=n(zEe,"STRONG",{});var aBn=s(Mno);jAa=r(aBn,"not"),aBn.forEach(t),OAa=r(zEe,` load the model weights. It only affects the
model\u2019s configuration. Use `),wFe=n(zEe,"A",{href:!0});var nBn=s(wFe);VAa=r(nBn,"from_pretrained()"),nBn.forEach(t),XAa=r(zEe," to load the model weights."),zEe.forEach(t),zAa=i(pG),T(_I.$$.fragment,pG),pG.forEach(t),WAa=i(_c),Lt=n(_c,"DIV",{class:!0});var vc=s(Lt);T(jX.$$.fragment,vc),QAa=i(vc),Eno=n(vc,"P",{});var sBn=s(Eno);UAa=r(sBn,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),sBn.forEach(t),HAa=i(vc),Gs=n(vc,"P",{});var _G=s(Gs);JAa=r(_G,"The model class to instantiate is selected based on the "),Cno=n(_G,"CODE",{});var lBn=s(Cno);ZAa=r(lBn,"model_type"),lBn.forEach(t),YAa=r(_G,` property of the config object (either
passed as an argument or loaded from `),wno=n(_G,"CODE",{});var iBn=s(wno);KAa=r(iBn,"pretrained_model_name_or_path"),iBn.forEach(t),e7a=r(_G,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ano=n(_G,"CODE",{});var dBn=s(Ano);o7a=r(dBn,"pretrained_model_name_or_path"),dBn.forEach(t),r7a=r(_G,":"),_G.forEach(t),t7a=i(vc),OX=n(vc,"UL",{});var xxo=s(OX);vI=n(xxo,"LI",{});var Y6o=s(vI);Lno=n(Y6o,"STRONG",{});var cBn=s(Lno);a7a=r(cBn,"blip"),cBn.forEach(t),n7a=r(Y6o," \u2014 "),AFe=n(Y6o,"A",{href:!0});var fBn=s(AFe);s7a=r(fBn,"TFBlipForConditionalGeneration"),fBn.forEach(t),l7a=r(Y6o," (BLIP model)"),Y6o.forEach(t),i7a=i(xxo),bI=n(xxo,"LI",{});var K6o=s(bI);yno=n(K6o,"STRONG",{});var mBn=s(yno);d7a=r(mBn,"vision-encoder-decoder"),mBn.forEach(t),c7a=r(K6o," \u2014 "),LFe=n(K6o,"A",{href:!0});var gBn=s(LFe);f7a=r(gBn,"TFVisionEncoderDecoderModel"),gBn.forEach(t),m7a=r(K6o," (Vision Encoder decoder model)"),K6o.forEach(t),xxo.forEach(t),g7a=i(vc),T(TI.$$.fragment,vc),vc.forEach(t),_c.forEach(t),D7o=i(f),ih=n(f,"H3",{class:!0});var Sxo=s(ih);FI=n(Sxo,"A",{id:!0,class:!0,href:!0});var hBn=s(FI);xno=n(hBn,"SPAN",{});var uBn=s(xno);T(VX.$$.fragment,uBn),uBn.forEach(t),hBn.forEach(t),h7a=i(Sxo),Sno=n(Sxo,"SPAN",{});var pBn=s(Sno);u7a=r(pBn,"FlaxAutoModelForVision2Seq"),pBn.forEach(t),Sxo.forEach(t),j7o=i(f),Jr=n(f,"DIV",{class:!0});var bc=s(Jr);T(XX.$$.fragment,bc),p7a=i(bc),dh=n(bc,"P",{});var WEe=s(dh);_7a=r(WEe,`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
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