import{S as sm,i as am,s as im,e as s,k as c,w as T,t as n,M as lm,c as a,d as t,m as p,a as i,x as w,h as r,b as d,N as rm,G as e,g as f,y as R,q as E,o as k,B as $,v as dm,L as Wo}from"../../chunks/vendor-hf-doc-builder.js";import{T as Ht}from"../../chunks/Tip-hf-doc-builder.js";import{D as X}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Oo}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as ze}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as So}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function cm(F){let h,y,_,g,b;return g=new Oo({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DPRConfig, DPRContextEncoder

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a DPR facebook/dpr-ctx_encoder-single-nq-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = DPRConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the facebook/dpr-ctx_encoder-single-nq-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DPRContextEncoder(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`}}),{c(){h=s("p"),y=n("Example:"),_=c(),T(g.$$.fragment)},l(l){h=a(l,"P",{});var u=i(h);y=r(u,"Example:"),u.forEach(t),_=p(l),w(g.$$.fragment,l)},m(l,u){f(l,h,u),e(h,y),f(l,_,u),R(g,l,u),b=!0},p:Wo,i(l){b||(E(g.$$.fragment,l),b=!0)},o(l){k(g.$$.fragment,l),b=!1},d(l){l&&t(h),l&&t(_),$(g,l)}}}function pm(F){let h,y,_,g,b;return g=new Oo({props:{code:"JTVCQ0xTJTVEJTIwJTNDcXVlc3Rpb24lMjB0b2tlbiUyMGlkcyUzRSUyMCU1QlNFUCU1RCUyMCUzQ3RpdGxlcyUyMGlkcyUzRSUyMCU1QlNFUCU1RCUyMCUzQ3RleHRzJTIwaWRzJTNF",highlighted:'[CLS] <span class="hljs-tag">&lt;<span class="hljs-name">question</span> <span class="hljs-attr">token</span> <span class="hljs-attr">ids</span>&gt;</span> [SEP] <span class="hljs-tag">&lt;<span class="hljs-name">titles</span> <span class="hljs-attr">ids</span>&gt;</span> [SEP] <span class="hljs-tag">&lt;<span class="hljs-name">texts</span> <span class="hljs-attr">ids</span>&gt;</span>'}}),{c(){h=s("p"),y=n("with the format:"),_=c(),T(g.$$.fragment)},l(l){h=a(l,"P",{});var u=i(h);y=r(u,"with the format:"),u.forEach(t),_=p(l),w(g.$$.fragment,l)},m(l,u){f(l,h,u),e(h,y),f(l,_,u),R(g,l,u),b=!0},p:Wo,i(l){b||(E(g.$$.fragment,l),b=!0)},o(l){k(g.$$.fragment,l),b=!1},d(l){l&&t(h),l&&t(_),$(g,l)}}}function hm(F){let h,y,_,g,b;return{c(){h=s("p"),y=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s("code"),g=n("Module"),b=n(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){h=a(l,"P",{});var u=i(h);y=r(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a(u,"CODE",{});var z=i(_);g=r(z,"Module"),z.forEach(t),b=r(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(t)},m(l,u){f(l,h,u),e(h,y),e(h,_),e(_,g),e(h,b)},d(l){l&&t(h)}}}function um(F){let h,y,_,g,b;return g=new Oo({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DPRContextEncoder, DPRContextEncoderTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = DPRContextEncoderTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/dpr-ctx_encoder-single-nq-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DPRContextEncoder.from_pretrained(<span class="hljs-string">&quot;facebook/dpr-ctx_encoder-single-nq-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer(<span class="hljs-string">&quot;Hello, is my dog cute ?&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>embeddings = model(input_ids).pooler_output`}}),{c(){h=s("p"),y=n("Examples:"),_=c(),T(g.$$.fragment)},l(l){h=a(l,"P",{});var u=i(h);y=r(u,"Examples:"),u.forEach(t),_=p(l),w(g.$$.fragment,l)},m(l,u){f(l,h,u),e(h,y),f(l,_,u),R(g,l,u),b=!0},p:Wo,i(l){b||(E(g.$$.fragment,l),b=!0)},o(l){k(g.$$.fragment,l),b=!1},d(l){l&&t(h),l&&t(_),$(g,l)}}}function fm(F){let h,y,_,g,b;return{c(){h=s("p"),y=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s("code"),g=n("Module"),b=n(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){h=a(l,"P",{});var u=i(h);y=r(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a(u,"CODE",{});var z=i(_);g=r(z,"Module"),z.forEach(t),b=r(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(t)},m(l,u){f(l,h,u),e(h,y),e(h,_),e(_,g),e(h,b)},d(l){l&&t(h)}}}function mm(F){let h,y,_,g,b;return g=new Oo({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DPRQuestionEncoder, DPRQuestionEncoderTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = DPRQuestionEncoderTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/dpr-question_encoder-single-nq-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DPRQuestionEncoder.from_pretrained(<span class="hljs-string">&quot;facebook/dpr-question_encoder-single-nq-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer(<span class="hljs-string">&quot;Hello, is my dog cute ?&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>embeddings = model(input_ids).pooler_output`}}),{c(){h=s("p"),y=n("Examples:"),_=c(),T(g.$$.fragment)},l(l){h=a(l,"P",{});var u=i(h);y=r(u,"Examples:"),u.forEach(t),_=p(l),w(g.$$.fragment,l)},m(l,u){f(l,h,u),e(h,y),f(l,_,u),R(g,l,u),b=!0},p:Wo,i(l){b||(E(g.$$.fragment,l),b=!0)},o(l){k(g.$$.fragment,l),b=!1},d(l){l&&t(h),l&&t(_),$(g,l)}}}function gm(F){let h,y,_,g,b;return{c(){h=s("p"),y=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s("code"),g=n("Module"),b=n(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){h=a(l,"P",{});var u=i(h);y=r(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a(u,"CODE",{});var z=i(_);g=r(z,"Module"),z.forEach(t),b=r(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(t)},m(l,u){f(l,h,u),e(h,y),e(h,_),e(_,g),e(h,b)},d(l){l&&t(h)}}}function _m(F){let h,y,_,g,b;return g=new Oo({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DPRReader, DPRReaderTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = DPRReaderTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/dpr-reader-single-nq-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DPRReader.from_pretrained(<span class="hljs-string">&quot;facebook/dpr-reader-single-nq-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>encoded_inputs = tokenizer(
<span class="hljs-meta">... </span>    questions=[<span class="hljs-string">&quot;What is love ?&quot;</span>],
<span class="hljs-meta">... </span>    titles=[<span class="hljs-string">&quot;Haddaway&quot;</span>],
<span class="hljs-meta">... </span>    texts=[<span class="hljs-string">&quot;&#x27;What Is Love&#x27; is a song recorded by the artist Haddaway&quot;</span>],
<span class="hljs-meta">... </span>    return_tensors=<span class="hljs-string">&quot;pt&quot;</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**encoded_inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>start_logits = outputs.start_logits
<span class="hljs-meta">&gt;&gt;&gt; </span>end_logits = outputs.end_logits
<span class="hljs-meta">&gt;&gt;&gt; </span>relevance_logits = outputs.relevance_logits`}}),{c(){h=s("p"),y=n("Examples:"),_=c(),T(g.$$.fragment)},l(l){h=a(l,"P",{});var u=i(h);y=r(u,"Examples:"),u.forEach(t),_=p(l),w(g.$$.fragment,l)},m(l,u){f(l,h,u),e(h,y),f(l,_,u),R(g,l,u),b=!0},p:Wo,i(l){b||(E(g.$$.fragment,l),b=!0)},o(l){k(g.$$.fragment,l),b=!1},d(l){l&&t(h),l&&t(_),$(g,l)}}}function vm(F){let h,y,_,g,b,l,u,z,Me,Ee,U,de,H,P,Fe,B,ce,Oe,L,Y,se,S,ie,Ce,W,qe,ke,N,Ie,le,pe,C,I,he,K,$e,ue,O,Je,Pe,M,Ue,Z,je,Ne,A,Ze,Ae,Q,fe,ee,De,te,q,ge,j,Qe,oe;return{c(){h=s("p"),y=n("TensorFlow models and layers in "),_=s("code"),g=n("transformers"),b=n(" accept two formats as input:"),l=c(),u=s("ul"),z=s("li"),Me=n("having all inputs as keyword arguments (like PyTorch models), or"),Ee=c(),U=s("li"),de=n("having all inputs as a list, tuple or dict in the first positional argument."),H=c(),P=s("p"),Fe=n(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),B=s("code"),ce=n("model.fit()"),Oe=n(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),L=s("code"),Y=n("model.fit()"),se=n(` supports! If, however, you want to use the second
format outside of Keras methods like `),S=s("code"),ie=n("fit()"),Ce=n(" and "),W=s("code"),qe=n("predict()"),ke=n(`, such as when creating your own layers or models with
the Keras `),N=s("code"),Ie=n("Functional"),le=n(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),pe=c(),C=s("ul"),I=s("li"),he=n("a single Tensor with "),K=s("code"),$e=n("input_ids"),ue=n(" only and nothing else: "),O=s("code"),Je=n("model(input_ids)"),Pe=c(),M=s("li"),Ue=n(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),Z=s("code"),je=n("model([input_ids, attention_mask])"),Ne=n(" or "),A=s("code"),Ze=n("model([input_ids, attention_mask, token_type_ids])"),Ae=c(),Q=s("li"),fe=n(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
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`),j=s("a"),Qe=n("subclassing"),oe=n(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(v){h=a(v,"P",{});var D=i(h);y=r(D,"TensorFlow models and layers in "),_=a(D,"CODE",{});var ot=i(_);g=r(ot,"transformers"),ot.forEach(t),b=r(D," accept two formats as input:"),D.forEach(t),l=p(v),u=a(v,"UL",{});var ae=i(u);z=a(ae,"LI",{});var nt=i(z);Me=r(nt,"having all inputs as keyword arguments (like PyTorch models), or"),nt.forEach(t),Ee=p(ae),U=a(ae,"LI",{});var rt=i(U);de=r(rt,"having all inputs as a list, tuple or dict in the first positional argument."),rt.forEach(t),ae.forEach(t),H=p(v),P=a(v,"P",{});var x=i(P);Fe=r(x,`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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pass your inputs and labels in any format that `),L=a(x,"CODE",{});var Xe=i(L);Y=r(Xe,"model.fit()"),Xe.forEach(t),se=r(x,` supports! If, however, you want to use the second
format outside of Keras methods like `),S=a(x,"CODE",{});var me=i(S);ie=r(me,"fit()"),me.forEach(t),Ce=r(x," and "),W=a(x,"CODE",{});var _e=i(W);qe=r(_e,"predict()"),_e.forEach(t),ke=r(x,`, such as when creating your own layers or models with
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positional argument:`),x.forEach(t),pe=p(v),C=a(v,"UL",{});var J=i(C);I=a(J,"LI",{});var ne=i(I);he=r(ne,"a single Tensor with "),K=a(ne,"CODE",{});var Ge=i(K);$e=r(Ge,"input_ids"),Ge.forEach(t),ue=r(ne," only and nothing else: "),O=a(ne,"CODE",{});var at=i(O);Je=r(at,"model(input_ids)"),at.forEach(t),ne.forEach(t),Pe=p(J),M=a(J,"LI",{});var G=i(M);Ue=r(G,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
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`),ee=a(Be,"CODE",{});var ve=i(ee);De=r(ve,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),ve.forEach(t),Be.forEach(t),J.forEach(t),te=p(v),q=a(v,"P",{});var re=i(q);ge=r(re,`Note that when creating models and layers with
`),j=a(re,"A",{href:!0,rel:!0});var it=i(j);Qe=r(it,"subclassing"),it.forEach(t),oe=r(re,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),re.forEach(t),this.h()},h(){d(j,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),d(j,"rel","nofollow")},m(v,D){f(v,h,D),e(h,y),e(h,_),e(_,g),e(h,b),f(v,l,D),f(v,u,D),e(u,z),e(z,Me),e(u,Ee),e(u,U),e(U,de),f(v,H,D),f(v,P,D),e(P,Fe),e(P,B),e(B,ce),e(P,Oe),e(P,L),e(L,Y),e(P,se),e(P,S),e(S,ie),e(P,Ce),e(P,W),e(W,qe),e(P,ke),e(P,N),e(N,Ie),e(P,le),f(v,pe,D),f(v,C,D),e(C,I),e(I,he),e(I,K),e(K,$e),e(I,ue),e(I,O),e(O,Je),e(C,Pe),e(C,M),e(M,Ue),e(M,Z),e(Z,je),e(M,Ne),e(M,A),e(A,Ze),e(C,Ae),e(C,Q),e(Q,fe),e(Q,ee),e(ee,De),f(v,te,D),f(v,q,D),e(q,ge),e(q,j),e(j,Qe),e(q,oe)},d(v){v&&t(h),v&&t(l),v&&t(u),v&&t(H),v&&t(P),v&&t(pe),v&&t(C),v&&t(te),v&&t(q)}}}function bm(F){let h,y,_,g,b;return{c(){h=s("p"),y=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s("code"),g=n("Module"),b=n(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){h=a(l,"P",{});var u=i(h);y=r(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a(u,"CODE",{});var z=i(_);g=r(z,"Module"),z.forEach(t),b=r(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(t)},m(l,u){f(l,h,u),e(h,y),e(h,_),e(_,g),e(h,b)},d(l){l&&t(h)}}}function ym(F){let h,y,_,g,b;return g=new Oo({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFDPRContextEncoder, DPRContextEncoderTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = DPRContextEncoderTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/dpr-ctx_encoder-single-nq-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFDPRContextEncoder.from_pretrained(<span class="hljs-string">&quot;facebook/dpr-ctx_encoder-single-nq-base&quot;</span>, from_pt=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer(<span class="hljs-string">&quot;Hello, is my dog cute ?&quot;</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>embeddings = model(input_ids).pooler_output`}}),{c(){h=s("p"),y=n("Examples:"),_=c(),T(g.$$.fragment)},l(l){h=a(l,"P",{});var u=i(h);y=r(u,"Examples:"),u.forEach(t),_=p(l),w(g.$$.fragment,l)},m(l,u){f(l,h,u),e(h,y),f(l,_,u),R(g,l,u),b=!0},p:Wo,i(l){b||(E(g.$$.fragment,l),b=!0)},o(l){k(g.$$.fragment,l),b=!1},d(l){l&&t(h),l&&t(_),$(g,l)}}}function Tm(F){let h,y,_,g,b,l,u,z,Me,Ee,U,de,H,P,Fe,B,ce,Oe,L,Y,se,S,ie,Ce,W,qe,ke,N,Ie,le,pe,C,I,he,K,$e,ue,O,Je,Pe,M,Ue,Z,je,Ne,A,Ze,Ae,Q,fe,ee,De,te,q,ge,j,Qe,oe;return{c(){h=s("p"),y=n("TensorFlow models and layers in "),_=s("code"),g=n("transformers"),b=n(" accept two formats as input:"),l=c(),u=s("ul"),z=s("li"),Me=n("having all inputs as keyword arguments (like PyTorch models), or"),Ee=c(),U=s("li"),de=n("having all inputs as a list, tuple or dict in the first positional argument."),H=c(),P=s("p"),Fe=n(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),B=s("code"),ce=n("model.fit()"),Oe=n(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),L=s("code"),Y=n("model.fit()"),se=n(` supports! If, however, you want to use the second
format outside of Keras methods like `),S=s("code"),ie=n("fit()"),Ce=n(" and "),W=s("code"),qe=n("predict()"),ke=n(`, such as when creating your own layers or models with
the Keras `),N=s("code"),Ie=n("Functional"),le=n(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),pe=c(),C=s("ul"),I=s("li"),he=n("a single Tensor with "),K=s("code"),$e=n("input_ids"),ue=n(" only and nothing else: "),O=s("code"),Je=n("model(input_ids)"),Pe=c(),M=s("li"),Ue=n(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),Z=s("code"),je=n("model([input_ids, attention_mask])"),Ne=n(" or "),A=s("code"),Ze=n("model([input_ids, attention_mask, token_type_ids])"),Ae=c(),Q=s("li"),fe=n(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),ee=s("code"),De=n('model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),te=c(),q=s("p"),ge=n(`Note that when creating models and layers with
`),j=s("a"),Qe=n("subclassing"),oe=n(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(v){h=a(v,"P",{});var D=i(h);y=r(D,"TensorFlow models and layers in "),_=a(D,"CODE",{});var ot=i(_);g=r(ot,"transformers"),ot.forEach(t),b=r(D," accept two formats as input:"),D.forEach(t),l=p(v),u=a(v,"UL",{});var ae=i(u);z=a(ae,"LI",{});var nt=i(z);Me=r(nt,"having all inputs as keyword arguments (like PyTorch models), or"),nt.forEach(t),Ee=p(ae),U=a(ae,"LI",{});var rt=i(U);de=r(rt,"having all inputs as a list, tuple or dict in the first positional argument."),rt.forEach(t),ae.forEach(t),H=p(v),P=a(v,"P",{});var x=i(P);Fe=r(x,`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),B=a(x,"CODE",{});var st=i(B);ce=r(st,"model.fit()"),st.forEach(t),Oe=r(x,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),L=a(x,"CODE",{});var Xe=i(L);Y=r(Xe,"model.fit()"),Xe.forEach(t),se=r(x,` supports! If, however, you want to use the second
format outside of Keras methods like `),S=a(x,"CODE",{});var me=i(S);ie=r(me,"fit()"),me.forEach(t),Ce=r(x," and "),W=a(x,"CODE",{});var _e=i(W);qe=r(_e,"predict()"),_e.forEach(t),ke=r(x,`, such as when creating your own layers or models with
the Keras `),N=a(x,"CODE",{});var Le=i(N);Ie=r(Le,"Functional"),Le.forEach(t),le=r(x,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),x.forEach(t),pe=p(v),C=a(v,"UL",{});var J=i(C);I=a(J,"LI",{});var ne=i(I);he=r(ne,"a single Tensor with "),K=a(ne,"CODE",{});var Ge=i(K);$e=r(Ge,"input_ids"),Ge.forEach(t),ue=r(ne," only and nothing else: "),O=a(ne,"CODE",{});var at=i(O);Je=r(at,"model(input_ids)"),at.forEach(t),ne.forEach(t),Pe=p(J),M=a(J,"LI",{});var G=i(M);Ue=r(G,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),Z=a(G,"CODE",{});var V=i(Z);je=r(V,"model([input_ids, attention_mask])"),V.forEach(t),Ne=r(G," or "),A=a(G,"CODE",{});var xe=i(A);Ze=r(xe,"model([input_ids, attention_mask, token_type_ids])"),xe.forEach(t),G.forEach(t),Ae=p(J),Q=a(J,"LI",{});var Be=i(Q);fe=r(Be,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),ee=a(Be,"CODE",{});var ve=i(ee);De=r(ve,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),ve.forEach(t),Be.forEach(t),J.forEach(t),te=p(v),q=a(v,"P",{});var re=i(q);ge=r(re,`Note that when creating models and layers with
`),j=a(re,"A",{href:!0,rel:!0});var it=i(j);Qe=r(it,"subclassing"),it.forEach(t),oe=r(re,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),re.forEach(t),this.h()},h(){d(j,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),d(j,"rel","nofollow")},m(v,D){f(v,h,D),e(h,y),e(h,_),e(_,g),e(h,b),f(v,l,D),f(v,u,D),e(u,z),e(z,Me),e(u,Ee),e(u,U),e(U,de),f(v,H,D),f(v,P,D),e(P,Fe),e(P,B),e(B,ce),e(P,Oe),e(P,L),e(L,Y),e(P,se),e(P,S),e(S,ie),e(P,Ce),e(P,W),e(W,qe),e(P,ke),e(P,N),e(N,Ie),e(P,le),f(v,pe,D),f(v,C,D),e(C,I),e(I,he),e(I,K),e(K,$e),e(I,ue),e(I,O),e(O,Je),e(C,Pe),e(C,M),e(M,Ue),e(M,Z),e(Z,je),e(M,Ne),e(M,A),e(A,Ze),e(C,Ae),e(C,Q),e(Q,fe),e(Q,ee),e(ee,De),f(v,te,D),f(v,q,D),e(q,ge),e(q,j),e(j,Qe),e(q,oe)},d(v){v&&t(h),v&&t(l),v&&t(u),v&&t(H),v&&t(P),v&&t(pe),v&&t(C),v&&t(te),v&&t(q)}}}function wm(F){let h,y,_,g,b;return{c(){h=s("p"),y=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s("code"),g=n("Module"),b=n(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){h=a(l,"P",{});var u=i(h);y=r(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a(u,"CODE",{});var z=i(_);g=r(z,"Module"),z.forEach(t),b=r(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(t)},m(l,u){f(l,h,u),e(h,y),e(h,_),e(_,g),e(h,b)},d(l){l&&t(h)}}}function Rm(F){let h,y,_,g,b;return g=new Oo({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFDPRQuestionEncoder, DPRQuestionEncoderTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = DPRQuestionEncoderTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/dpr-question_encoder-single-nq-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFDPRQuestionEncoder.from_pretrained(<span class="hljs-string">&quot;facebook/dpr-question_encoder-single-nq-base&quot;</span>, from_pt=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer(<span class="hljs-string">&quot;Hello, is my dog cute ?&quot;</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>embeddings = model(input_ids).pooler_output`}}),{c(){h=s("p"),y=n("Examples:"),_=c(),T(g.$$.fragment)},l(l){h=a(l,"P",{});var u=i(h);y=r(u,"Examples:"),u.forEach(t),_=p(l),w(g.$$.fragment,l)},m(l,u){f(l,h,u),e(h,y),f(l,_,u),R(g,l,u),b=!0},p:Wo,i(l){b||(E(g.$$.fragment,l),b=!0)},o(l){k(g.$$.fragment,l),b=!1},d(l){l&&t(h),l&&t(_),$(g,l)}}}function Em(F){let h,y,_,g,b,l,u,z,Me,Ee,U,de,H,P,Fe,B,ce,Oe,L,Y,se,S,ie,Ce,W,qe,ke,N,Ie,le,pe,C,I,he,K,$e,ue,O,Je,Pe,M,Ue,Z,je,Ne,A,Ze,Ae,Q,fe,ee,De,te,q,ge,j,Qe,oe;return{c(){h=s("p"),y=n("TensorFlow models and layers in "),_=s("code"),g=n("transformers"),b=n(" accept two formats as input:"),l=c(),u=s("ul"),z=s("li"),Me=n("having all inputs as keyword arguments (like PyTorch models), or"),Ee=c(),U=s("li"),de=n("having all inputs as a list, tuple or dict in the first positional argument."),H=c(),P=s("p"),Fe=n(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),B=s("code"),ce=n("model.fit()"),Oe=n(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),L=s("code"),Y=n("model.fit()"),se=n(` supports! If, however, you want to use the second
format outside of Keras methods like `),S=s("code"),ie=n("fit()"),Ce=n(" and "),W=s("code"),qe=n("predict()"),ke=n(`, such as when creating your own layers or models with
the Keras `),N=s("code"),Ie=n("Functional"),le=n(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),pe=c(),C=s("ul"),I=s("li"),he=n("a single Tensor with "),K=s("code"),$e=n("input_ids"),ue=n(" only and nothing else: "),O=s("code"),Je=n("model(input_ids)"),Pe=c(),M=s("li"),Ue=n(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),Z=s("code"),je=n("model([input_ids, attention_mask])"),Ne=n(" or "),A=s("code"),Ze=n("model([input_ids, attention_mask, token_type_ids])"),Ae=c(),Q=s("li"),fe=n(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),ee=s("code"),De=n('model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),te=c(),q=s("p"),ge=n(`Note that when creating models and layers with
`),j=s("a"),Qe=n("subclassing"),oe=n(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(v){h=a(v,"P",{});var D=i(h);y=r(D,"TensorFlow models and layers in "),_=a(D,"CODE",{});var ot=i(_);g=r(ot,"transformers"),ot.forEach(t),b=r(D," accept two formats as input:"),D.forEach(t),l=p(v),u=a(v,"UL",{});var ae=i(u);z=a(ae,"LI",{});var nt=i(z);Me=r(nt,"having all inputs as keyword arguments (like PyTorch models), or"),nt.forEach(t),Ee=p(ae),U=a(ae,"LI",{});var rt=i(U);de=r(rt,"having all inputs as a list, tuple or dict in the first positional argument."),rt.forEach(t),ae.forEach(t),H=p(v),P=a(v,"P",{});var x=i(P);Fe=r(x,`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),B=a(x,"CODE",{});var st=i(B);ce=r(st,"model.fit()"),st.forEach(t),Oe=r(x,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),L=a(x,"CODE",{});var Xe=i(L);Y=r(Xe,"model.fit()"),Xe.forEach(t),se=r(x,` supports! If, however, you want to use the second
format outside of Keras methods like `),S=a(x,"CODE",{});var me=i(S);ie=r(me,"fit()"),me.forEach(t),Ce=r(x," and "),W=a(x,"CODE",{});var _e=i(W);qe=r(_e,"predict()"),_e.forEach(t),ke=r(x,`, such as when creating your own layers or models with
the Keras `),N=a(x,"CODE",{});var Le=i(N);Ie=r(Le,"Functional"),Le.forEach(t),le=r(x,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),x.forEach(t),pe=p(v),C=a(v,"UL",{});var J=i(C);I=a(J,"LI",{});var ne=i(I);he=r(ne,"a single Tensor with "),K=a(ne,"CODE",{});var Ge=i(K);$e=r(Ge,"input_ids"),Ge.forEach(t),ue=r(ne," only and nothing else: "),O=a(ne,"CODE",{});var at=i(O);Je=r(at,"model(input_ids)"),at.forEach(t),ne.forEach(t),Pe=p(J),M=a(J,"LI",{});var G=i(M);Ue=r(G,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),Z=a(G,"CODE",{});var V=i(Z);je=r(V,"model([input_ids, attention_mask])"),V.forEach(t),Ne=r(G," or "),A=a(G,"CODE",{});var xe=i(A);Ze=r(xe,"model([input_ids, attention_mask, token_type_ids])"),xe.forEach(t),G.forEach(t),Ae=p(J),Q=a(J,"LI",{});var Be=i(Q);fe=r(Be,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),ee=a(Be,"CODE",{});var ve=i(ee);De=r(ve,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),ve.forEach(t),Be.forEach(t),J.forEach(t),te=p(v),q=a(v,"P",{});var re=i(q);ge=r(re,`Note that when creating models and layers with
`),j=a(re,"A",{href:!0,rel:!0});var it=i(j);Qe=r(it,"subclassing"),it.forEach(t),oe=r(re,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),re.forEach(t),this.h()},h(){d(j,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),d(j,"rel","nofollow")},m(v,D){f(v,h,D),e(h,y),e(h,_),e(_,g),e(h,b),f(v,l,D),f(v,u,D),e(u,z),e(z,Me),e(u,Ee),e(u,U),e(U,de),f(v,H,D),f(v,P,D),e(P,Fe),e(P,B),e(B,ce),e(P,Oe),e(P,L),e(L,Y),e(P,se),e(P,S),e(S,ie),e(P,Ce),e(P,W),e(W,qe),e(P,ke),e(P,N),e(N,Ie),e(P,le),f(v,pe,D),f(v,C,D),e(C,I),e(I,he),e(I,K),e(K,$e),e(I,ue),e(I,O),e(O,Je),e(C,Pe),e(C,M),e(M,Ue),e(M,Z),e(Z,je),e(M,Ne),e(M,A),e(A,Ze),e(C,Ae),e(C,Q),e(Q,fe),e(Q,ee),e(ee,De),f(v,te,D),f(v,q,D),e(q,ge),e(q,j),e(j,Qe),e(q,oe)},d(v){v&&t(h),v&&t(l),v&&t(u),v&&t(H),v&&t(P),v&&t(pe),v&&t(C),v&&t(te),v&&t(q)}}}function km(F){let h,y,_,g,b;return{c(){h=s("p"),y=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s("code"),g=n("Module"),b=n(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){h=a(l,"P",{});var u=i(h);y=r(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a(u,"CODE",{});var z=i(_);g=r(z,"Module"),z.forEach(t),b=r(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(t)},m(l,u){f(l,h,u),e(h,y),e(h,_),e(_,g),e(h,b)},d(l){l&&t(h)}}}function $m(F){let h,y,_,g,b;return g=new Oo({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFDPRReader, DPRReaderTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = DPRReaderTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/dpr-reader-single-nq-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFDPRReader.from_pretrained(<span class="hljs-string">&quot;facebook/dpr-reader-single-nq-base&quot;</span>, from_pt=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>encoded_inputs = tokenizer(
<span class="hljs-meta">... </span>    questions=[<span class="hljs-string">&quot;What is love ?&quot;</span>],
<span class="hljs-meta">... </span>    titles=[<span class="hljs-string">&quot;Haddaway&quot;</span>],
<span class="hljs-meta">... </span>    texts=[<span class="hljs-string">&quot;&#x27;What Is Love&#x27; is a song recorded by the artist Haddaway&quot;</span>],
<span class="hljs-meta">... </span>    return_tensors=<span class="hljs-string">&quot;tf&quot;</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(encoded_inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>start_logits = outputs.start_logits
<span class="hljs-meta">&gt;&gt;&gt; </span>end_logits = outputs.end_logits
<span class="hljs-meta">&gt;&gt;&gt; </span>relevance_logits = outputs.relevance_logits`}}),{c(){h=s("p"),y=n("Examples:"),_=c(),T(g.$$.fragment)},l(l){h=a(l,"P",{});var u=i(h);y=r(u,"Examples:"),u.forEach(t),_=p(l),w(g.$$.fragment,l)},m(l,u){f(l,h,u),e(h,y),f(l,_,u),R(g,l,u),b=!0},p:Wo,i(l){b||(E(g.$$.fragment,l),b=!0)},o(l){k(g.$$.fragment,l),b=!1},d(l){l&&t(h),l&&t(_),$(g,l)}}}function Pm(F){let h,y,_,g,b,l,u,z,Me,Ee,U,de,H,P,Fe,B,ce,Oe,L,Y,se,S,ie,Ce,W,qe,ke,N,Ie,le,pe,C,I,he,K,$e,ue,O,Je,Pe,M,Ue,Z,je,Ne,A,Ze,Ae,Q,fe,ee,De,te,q,ge,j,Qe,oe,v,D,ot,ae,nt,rt,x,st,Xe,me,_e,Le,J,ne,Ge,at,G,V,xe,Be,ve,re,it,Ai,ls,Qi,Bi,Vi,He,Si,mr,Wi,Oi,gr,Li,Gi,_r,Xi,Hi,Lo,Yi,Ki,el,Go,tl,vr,ol,nl,rl,Yt,Ra,Dt,Kt,ds,Xo,sl,cs,al,Ea,Ye,Ho,il,ps,ll,dl,eo,br,cl,pl,yr,hl,ul,fl,Yo,ml,Tr,gl,_l,ka,xt,to,hs,Ko,vl,us,bl,$a,Ke,en,yl,tn,Tl,fs,wl,Rl,El,oo,wr,kl,$l,Rr,Pl,Dl,xl,on,zl,Er,Ml,Fl,Pa,zt,no,ms,nn,Cl,gs,ql,Da,et,rn,Il,_s,Jl,Ul,ro,kr,jl,Nl,$r,Zl,Al,Ql,sn,Bl,Pr,Vl,Sl,xa,Mt,so,vs,an,Wl,bs,Ol,za,tt,ln,Ll,dn,Gl,ys,Xl,Hl,Yl,ao,Dr,Kl,ed,xr,td,od,nd,cn,rd,zr,sd,ad,Ma,Ft,io,Ts,pn,id,ws,ld,Fa,be,hn,dd,Rs,cd,pd,yt,Mr,hd,ud,Fr,fd,md,Cr,gd,_d,vd,un,bd,qr,yd,Td,wd,Tt,Rd,Es,Ed,kd,ks,$d,Pd,$s,Dd,xd,lo,Ca,Ct,co,Ps,fn,zd,Ds,Md,qa,ye,mn,Fd,gn,Cd,xs,qd,Id,Jd,wt,Ir,Ud,jd,Jr,Nd,Zd,Ur,Ad,Qd,Bd,_n,Vd,jr,Sd,Wd,Od,bt,Ld,zs,Gd,Xd,Ms,Hd,Yd,Fs,Kd,ec,tc,Nr,oc,po,nc,vn,rc,Cs,Ia,qt,ho,qs,bn,sc,Is,ac,Ja,It,yn,ic,Tn,lc,Zr,dc,cc,Ua,Jt,wn,pc,Rn,hc,Ar,uc,fc,ja,Ut,En,mc,kn,gc,Qr,_c,vc,Na,jt,uo,Js,$n,bc,Us,yc,Za,Ve,Pn,Tc,js,wc,Rc,Dn,Ec,Br,kc,$c,Pc,xn,Dc,zn,xc,zc,Mc,lt,Mn,Fc,Nt,Cc,Vr,qc,Ic,Ns,Jc,Uc,jc,fo,Nc,mo,Aa,Zt,go,Zs,Fn,Zc,As,Ac,Qa,Se,Cn,Qc,Qs,Bc,Vc,qn,Sc,Sr,Wc,Oc,Lc,In,Gc,Jn,Xc,Hc,Yc,dt,Un,Kc,At,ep,Wr,tp,op,Bs,np,rp,sp,_o,ap,vo,Ba,Qt,bo,Vs,jn,ip,Ss,lp,Va,We,Nn,dp,Ws,cp,pp,Zn,hp,Or,up,fp,mp,An,gp,Qn,_p,vp,bp,ct,Bn,yp,Bt,Tp,Lr,wp,Rp,Os,Ep,kp,$p,yo,Pp,To,Sa,Vt,wo,Ls,Vn,Dp,Gs,xp,Wa,Te,Sn,zp,Xs,Mp,Fp,Wn,Cp,Gr,qp,Ip,Jp,On,Up,Ln,jp,Np,Zp,Ro,Ap,pt,Gn,Qp,St,Bp,Xr,Vp,Sp,Hs,Wp,Op,Lp,Eo,Gp,ko,Oa,Wt,$o,Ys,Xn,Xp,Ks,Hp,La,we,Hn,Yp,ea,Kp,eh,Yn,th,Hr,oh,nh,rh,Kn,sh,er,ah,ih,lh,Po,dh,ht,tr,ch,Ot,ph,Yr,hh,uh,ta,fh,mh,gh,Do,_h,xo,Ga,Lt,zo,oa,or,vh,na,bh,Xa,Re,nr,yh,ra,Th,wh,rr,Rh,Kr,Eh,kh,$h,sr,Ph,ar,Dh,xh,zh,Mo,Mh,ut,ir,Fh,Gt,Ch,es,qh,Ih,sa,Jh,Uh,jh,Fo,Nh,Co,Ha;return l=new ze({}),ie=new ze({}),J=new ze({}),xe=new X({props:{name:"class transformers.DPRConfig",anchor:"transformers.DPRConfig",parameters:[{name:"vocab_size",val:" = 30522"},{name:"hidden_size",val:" = 768"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 12"},{name:"intermediate_size",val:" = 3072"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout_prob",val:" = 0.1"},{name:"attention_probs_dropout_prob",val:" = 0.1"},{name:"max_position_embeddings",val:" = 512"},{name:"type_vocab_size",val:" = 2"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-12"},{name:"pad_token_id",val:" = 0"},{name:"position_embedding_type",val:" = 'absolute'"},{name:"projection_dim",val:": int = 0"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.DPRConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 30522) &#x2014;
Vocabulary size of the DPR model. Defines the different tokens that can be represented by the <em>inputs_ids</em>
passed to the forward method of <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertModel">BertModel</a>.`,name:"vocab_size"},{anchor:"transformers.DPRConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.DPRConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.DPRConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.DPRConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3072) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.DPRConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;silu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.DPRConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.DPRConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.DPRConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"max_position_embeddings"},{anchor:"transformers.DPRConfig.type_vocab_size",description:`<strong>type_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The vocabulary size of the <em>token_type_ids</em> passed into <a href="/docs/transformers/v4.33.0/en/model_doc/bert#transformers.BertModel">BertModel</a>.`,name:"type_vocab_size"},{anchor:"transformers.DPRConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.DPRConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-12) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.DPRConfig.position_embedding_type",description:`<strong>position_embedding_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;absolute&quot;</code>) &#x2014;
Type of position embedding. Choose one of <code>&quot;absolute&quot;</code>, <code>&quot;relative_key&quot;</code>, <code>&quot;relative_key_query&quot;</code>. For
positional embeddings use <code>&quot;absolute&quot;</code>. For more information on <code>&quot;relative_key&quot;</code>, please refer to
<a href="https://arxiv.org/abs/1803.02155" rel="nofollow">Self-Attention with Relative Position Representations (Shaw et al.)</a>.
For more information on <code>&quot;relative_key_query&quot;</code>, please refer to <em>Method 4</em> in <a href="https://arxiv.org/abs/2009.13658" rel="nofollow">Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)</a>.`,name:"position_embedding_type"},{anchor:"transformers.DPRConfig.projection_dim",description:`<strong>projection_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
Dimension of the projection for the context and question encoders. If it is set to zero (default), then no
projection is done.`,name:"projection_dim"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/configuration_dpr.py#L45"}}),Yt=new So({props:{anchor:"transformers.DPRConfig.example",$$slots:{default:[cm]},$$scope:{ctx:F}}}),Xo=new ze({}),Ho=new X({props:{name:"class transformers.DPRContextEncoderTokenizer",anchor:"transformers.DPRContextEncoderTokenizer",parameters:[{name:"vocab_file",val:""},{name:"do_lower_case",val:" = True"},{name:"do_basic_tokenize",val:" = True"},{name:"never_split",val:" = None"},{name:"unk_token",val:" = '[UNK]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"tokenize_chinese_chars",val:" = True"},{name:"strip_accents",val:" = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/tokenization_dpr.py#L113"}}),Ko=new ze({}),en=new X({props:{name:"class transformers.DPRContextEncoderTokenizerFast",anchor:"transformers.DPRContextEncoderTokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"do_lower_case",val:" = True"},{name:"unk_token",val:" = '[UNK]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"tokenize_chinese_chars",val:" = True"},{name:"strip_accents",val:" = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/tokenization_dpr_fast.py#L114"}}),nn=new ze({}),rn=new X({props:{name:"class transformers.DPRQuestionEncoderTokenizer",anchor:"transformers.DPRQuestionEncoderTokenizer",parameters:[{name:"vocab_file",val:""},{name:"do_lower_case",val:" = True"},{name:"do_basic_tokenize",val:" = True"},{name:"never_split",val:" = None"},{name:"unk_token",val:" = '[UNK]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"tokenize_chinese_chars",val:" = True"},{name:"strip_accents",val:" = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/tokenization_dpr.py#L129"}}),an=new ze({}),ln=new X({props:{name:"class transformers.DPRQuestionEncoderTokenizerFast",anchor:"transformers.DPRQuestionEncoderTokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"do_lower_case",val:" = True"},{name:"unk_token",val:" = '[UNK]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"tokenize_chinese_chars",val:" = True"},{name:"strip_accents",val:" = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/tokenization_dpr_fast.py#L131"}}),pn=new ze({}),hn=new X({props:{name:"class transformers.DPRReaderTokenizer",anchor:"transformers.DPRReaderTokenizer",parameters:[{name:"vocab_file",val:""},{name:"do_lower_case",val:" = True"},{name:"do_basic_tokenize",val:" = True"},{name:"never_split",val:" = None"},{name:"unk_token",val:" = '[UNK]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"tokenize_chinese_chars",val:" = True"},{name:"strip_accents",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.DPRReaderTokenizer.questions",description:`<strong>questions</strong> (<code>str</code> or <code>List[str]</code>) &#x2014;
The questions to be encoded. You can specify one question for many passages. In this case, the question
will be duplicated like <code>[questions] * n_passages</code>. Otherwise you have to specify as many questions as in
<code>titles</code> or <code>texts</code>.`,name:"questions"},{anchor:"transformers.DPRReaderTokenizer.titles",description:`<strong>titles</strong> (<code>str</code> or <code>List[str]</code>) &#x2014;
The passages titles to be encoded. This can be a string or a list of strings if there are several passages.`,name:"titles"},{anchor:"transformers.DPRReaderTokenizer.texts",description:`<strong>texts</strong> (<code>str</code> or <code>List[str]</code>) &#x2014;
The passages texts to be encoded. This can be a string or a list of strings if there are several passages.`,name:"texts"},{anchor:"transformers.DPRReaderTokenizer.padding",description:`<strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/v4.33.0/en/internal/file_utils#transformers.utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>&apos;longest&apos;</code>: Pad to the longest sequence in the batch (or no padding if only a single sequence
if provided).</li>
<li><code>&apos;max_length&apos;</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>&apos;do_not_pad&apos;</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul>`,name:"padding"},{anchor:"transformers.DPRReaderTokenizer.truncation",description:`<strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>&apos;longest_first&apos;</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to
the maximum acceptable input length for the model if that argument is not provided. This will truncate
token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
of pairs) is provided.</li>
<li><code>&apos;only_first&apos;</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the first
sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>&apos;only_second&apos;</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the
second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>&apos;do_not_truncate&apos;</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul>`,name:"truncation"},{anchor:"transformers.DPRReaderTokenizer.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Controls the maximum length to use by one of the truncation/padding parameters.</p>
<p>If left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
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If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>&apos;tf&apos;</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>&apos;pt&apos;</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>&apos;np&apos;</code>: Return Numpy <code>np.ndarray</code> objects.</li>
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<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"return_attention_mask"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/tokenization_dpr.py#L395",returnDescription:`
<p>A dictionary with the following keys:</p>
<ul>
<li><code>input_ids</code>: List of token ids to be fed to a model.</li>
<li><code>attention_mask</code>: List of indices specifying which tokens should be attended to by the model.</li>
</ul>
`,returnType:`
<p><code>Dict[str, List[List[int]]]</code></p>
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The questions to be encoded. You can specify one question for many passages. In this case, the question
will be duplicated like <code>[questions] * n_passages</code>. Otherwise you have to specify as many questions as in
<code>titles</code> or <code>texts</code>.`,name:"questions"},{anchor:"transformers.DPRReaderTokenizerFast.titles",description:`<strong>titles</strong> (<code>str</code> or <code>List[str]</code>) &#x2014;
The passages titles to be encoded. This can be a string or a list of strings if there are several passages.`,name:"titles"},{anchor:"transformers.DPRReaderTokenizerFast.texts",description:`<strong>texts</strong> (<code>str</code> or <code>List[str]</code>) &#x2014;
The passages texts to be encoded. This can be a string or a list of strings if there are several passages.`,name:"texts"},{anchor:"transformers.DPRReaderTokenizerFast.padding",description:`<strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/v4.33.0/en/internal/file_utils#transformers.utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>&apos;longest&apos;</code>: Pad to the longest sequence in the batch (or no padding if only a single sequence
if provided).</li>
<li><code>&apos;max_length&apos;</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>&apos;do_not_pad&apos;</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul>`,name:"padding"},{anchor:"transformers.DPRReaderTokenizerFast.truncation",description:`<strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>&apos;longest_first&apos;</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to
the maximum acceptable input length for the model if that argument is not provided. This will truncate
token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
of pairs) is provided.</li>
<li><code>&apos;only_first&apos;</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the first
sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>&apos;only_second&apos;</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the
second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>&apos;do_not_truncate&apos;</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul>`,name:"truncation"},{anchor:"transformers.DPRReaderTokenizerFast.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Controls the maximum length to use by one of the truncation/padding parameters.</p>
<p>If left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length
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If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>&apos;tf&apos;</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>&apos;pt&apos;</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>&apos;np&apos;</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul>`,name:"return_tensors"},{anchor:"transformers.DPRReaderTokenizerFast.return_attention_mask",description:`<strong>return_attention_mask</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attention mask. If not set, will return the attention mask according to the
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<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"return_attention_mask"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/tokenization_dpr_fast.py#L393",returnDescription:`
<p>A dictionary with the following keys:</p>
<ul>
<li><code>input_ids</code>: List of token ids to be fed to a model.</li>
<li><code>attention_mask</code>: List of indices specifying which tokens should be attended to by the model.</li>
</ul>
`,returnType:`
<p><code>Dict[str, List[List[int]]]</code></p>
`}}),bn=new ze({}),yn=new X({props:{name:"class transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput",anchor:"transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput",parameters:[{name:"pooler_output",val:": FloatTensor"},{name:"hidden_states",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"attentions",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}],parametersDescription:[{anchor:"transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput.pooler_output",description:`<strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, embeddings_size)</code>) &#x2014;
The DPR encoder outputs the <em>pooler_output</em> that corresponds to the context representation. Last layer
hidden-state of the first token of the sequence (classification token) further processed by a Linear layer.
This output is to be used to embed contexts for nearest neighbors queries with questions embeddings.`,name:"pooler_output"},{anchor:"transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput.hidden_states",description:`<strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.`,name:"hidden_states"},{anchor:"transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput.attentions",description:`<strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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The DPR encoder outputs the <em>pooler_output</em> that corresponds to the question representation. Last layer
hidden-state of the first token of the sequence (classification token) further processed by a Linear layer.
This output is to be used to embed questions for nearest neighbors queries with context embeddings.`,name:"pooler_output"},{anchor:"transformers.models.dpr.modeling_dpr.DPRQuestionEncoderOutput.hidden_states",description:`<strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.`,name:"hidden_states"},{anchor:"transformers.models.dpr.modeling_dpr.DPRQuestionEncoderOutput.attentions",description:`<strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.`,name:"attentions"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/modeling_dpr.py#L90"}}),En=new X({props:{name:"class transformers.DPRReaderOutput",anchor:"transformers.DPRReaderOutput",parameters:[{name:"start_logits",val:": FloatTensor"},{name:"end_logits",val:": FloatTensor = None"},{name:"relevance_logits",val:": FloatTensor = None"},{name:"hidden_states",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"attentions",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}],parametersDescription:[{anchor:"transformers.DPRReaderOutput.start_logits",description:`<strong>start_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(n_passages, sequence_length)</code>) &#x2014;
Logits of the start index of the span for each passage.`,name:"start_logits"},{anchor:"transformers.DPRReaderOutput.end_logits",description:`<strong>end_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(n_passages, sequence_length)</code>) &#x2014;
Logits of the end index of the span for each passage.`,name:"end_logits"},{anchor:"transformers.DPRReaderOutput.relevance_logits",description:`<strong>relevance_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(n_passages, )</code>) &#x2014;
Outputs of the QA classifier of the DPRReader that corresponds to the scores of each passage to answer the
question, compared to all the other passages.`,name:"relevance_logits"},{anchor:"transformers.DPRReaderOutput.hidden_states",description:`<strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.`,name:"hidden_states"},{anchor:"transformers.DPRReaderOutput.attentions",description:`<strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.`,name:"attentions"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/modeling_dpr.py#L118"}}),$n=new ze({}),Pn=new X({props:{name:"class transformers.DPRContextEncoder",anchor:"transformers.DPRContextEncoder",parameters:[{name:"config",val:": DPRConfig"}],parametersDescription:[{anchor:"transformers.DPRContextEncoder.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/modeling_dpr.py#L441"}}),Mn=new X({props:{name:"forward",anchor:"transformers.DPRContextEncoder.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.DPRContextEncoder.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. To match pretraining, DPR input sequence should be
formatted with [CLS] and [SEP] tokens as follows:</p>
<p>(a) For sequence pairs (for a pair title+text for example):`,name:"input_ids"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/modeling_dpr.py#L449",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput"
>transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.DPRConfig"
>DPRConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, embeddings_size)</code>) \u2014 The DPR encoder outputs the <em>pooler_output</em> that corresponds to the context representation. Last layer
hidden-state of the first token of the sequence (classification token) further processed by a Linear layer.
This output is to be used to embed contexts for nearest neighbors queries with questions embeddings.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput"
>transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),fo=new Ht({props:{$$slots:{default:[hm]},$$scope:{ctx:F}}}),mo=new So({props:{anchor:"transformers.DPRContextEncoder.forward.example",$$slots:{default:[um]},$$scope:{ctx:F}}}),Fn=new ze({}),Cn=new X({props:{name:"class transformers.DPRQuestionEncoder",anchor:"transformers.DPRQuestionEncoder",parameters:[{name:"config",val:": DPRConfig"}],parametersDescription:[{anchor:"transformers.DPRQuestionEncoder.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/modeling_dpr.py#L522"}}),Un=new X({props:{name:"forward",anchor:"transformers.DPRQuestionEncoder.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.DPRQuestionEncoder.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. To match pretraining, DPR input sequence should be
formatted with [CLS] and [SEP] tokens as follows:</p>
<p>(a) For sequence pairs (for a pair title+text for example):`,name:"input_ids"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/modeling_dpr.py#L530",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.models.dpr.modeling_dpr.DPRQuestionEncoderOutput"
>transformers.models.dpr.modeling_dpr.DPRQuestionEncoderOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.DPRConfig"
>DPRConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, embeddings_size)</code>) \u2014 The DPR encoder outputs the <em>pooler_output</em> that corresponds to the question representation. Last layer
hidden-state of the first token of the sequence (classification token) further processed by a Linear layer.
This output is to be used to embed questions for nearest neighbors queries with context embeddings.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.models.dpr.modeling_dpr.DPRQuestionEncoderOutput"
>transformers.models.dpr.modeling_dpr.DPRQuestionEncoderOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),_o=new Ht({props:{$$slots:{default:[fm]},$$scope:{ctx:F}}}),vo=new So({props:{anchor:"transformers.DPRQuestionEncoder.forward.example",$$slots:{default:[mm]},$$scope:{ctx:F}}}),jn=new ze({}),Nn=new X({props:{name:"class transformers.DPRReader",anchor:"transformers.DPRReader",parameters:[{name:"config",val:": DPRConfig"}],parametersDescription:[{anchor:"transformers.DPRReader.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/modeling_dpr.py#L604"}}),Bn=new X({props:{name:"forward",anchor:"transformers.DPRReader.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.DPRReader.forward.input_ids",description:`<strong>input_ids</strong> (<code>Tuple[torch.LongTensor]</code> of shapes <code>(n_passages, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. It has to be a sequence triplet with 1) the question
and 2) the passages titles and 3) the passages texts To match pretraining, DPR <code>input_ids</code> sequence should
be formatted with [CLS] and [SEP] with the format:</p>
<p><code>[CLS] &lt;question token ids&gt; [SEP] &lt;titles ids&gt; [SEP] &lt;texts ids&gt;</code></p>
<p>DPR is a model with absolute position embeddings so it&#x2019;s usually advised to pad the inputs on the right
rather than the left.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.DPRReaderTokenizer">DPRReaderTokenizer</a>. See this class documentation for more details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.DPRReader.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(n_passages, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.DPRReader.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(n_passages, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DPRReader.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DPRReader.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.DPRReader.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.33.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/modeling_dpr.py#L612",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.DPRReaderOutput"
>transformers.models.dpr.modeling_dpr.DPRReaderOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.DPRConfig"
>DPRConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>start_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(n_passages, sequence_length)</code>) \u2014 Logits of the start index of the span for each passage.</p>
</li>
<li>
<p><strong>end_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(n_passages, sequence_length)</code>) \u2014 Logits of the end index of the span for each passage.</p>
</li>
<li>
<p><strong>relevance_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(n_passages, )</code>) \u2014 Outputs of the QA classifier of the DPRReader that corresponds to the scores of each passage to answer the
question, compared to all the other passages.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.DPRReaderOutput"
>transformers.models.dpr.modeling_dpr.DPRReaderOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),yo=new Ht({props:{$$slots:{default:[gm]},$$scope:{ctx:F}}}),To=new So({props:{anchor:"transformers.DPRReader.forward.example",$$slots:{default:[_m]},$$scope:{ctx:F}}}),Vn=new ze({}),Sn=new X({props:{name:"class transformers.TFDPRContextEncoder",anchor:"transformers.TFDPRContextEncoder",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDPRContextEncoder.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/modeling_tf_dpr.py#L526"}}),Ro=new Ht({props:{$$slots:{default:[vm]},$$scope:{ctx:F}}}),Gn=new X({props:{name:"call",anchor:"transformers.TFDPRContextEncoder.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": tf.Tensor | None = None"},{name:"token_type_ids",val:": tf.Tensor | None = None"},{name:"inputs_embeds",val:": tf.Tensor | None = None"},{name:"output_attentions",val:": bool | None = None"},{name:"output_hidden_states",val:": bool | None = None"},{name:"return_dict",val:": bool | None = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFDPRContextEncoder.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. To match pretraining, DPR input sequence should be
formatted with [CLS] and [SEP] tokens as follows:</p>
<p>(a) For sequence pairs (for a pair title+text for example):`,name:"input_ids"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/modeling_tf_dpr.py#L538",returnDescription:`
<p>A <code>transformers.models.dpr.modeling_tf_dpr.TFDPRContextEncoderOutput</code> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.DPRConfig"
>DPRConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>pooler_output</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, embeddings_size)</code>) \u2014 The DPR encoder outputs the <em>pooler_output</em> that corresponds to the context representation. Last layer
hidden-state of the first token of the sequence (classification token) further processed by a Linear layer.
This output is to be used to embed contexts for nearest neighbors queries with questions embeddings.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.dpr.modeling_tf_dpr.TFDPRContextEncoderOutput</code> or <code>tuple(tf.Tensor)</code></p>
`}}),Eo=new Ht({props:{$$slots:{default:[bm]},$$scope:{ctx:F}}}),ko=new So({props:{anchor:"transformers.TFDPRContextEncoder.call.example",$$slots:{default:[ym]},$$scope:{ctx:F}}}),Xn=new ze({}),Hn=new X({props:{name:"class transformers.TFDPRQuestionEncoder",anchor:"transformers.TFDPRQuestionEncoder",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDPRQuestionEncoder.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/modeling_tf_dpr.py#L607"}}),Po=new Ht({props:{$$slots:{default:[Tm]},$$scope:{ctx:F}}}),tr=new X({props:{name:"call",anchor:"transformers.TFDPRQuestionEncoder.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": tf.Tensor | None = None"},{name:"token_type_ids",val:": tf.Tensor | None = None"},{name:"inputs_embeds",val:": tf.Tensor | None = None"},{name:"output_attentions",val:": bool | None = None"},{name:"output_hidden_states",val:": bool | None = None"},{name:"return_dict",val:": bool | None = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFDPRQuestionEncoder.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. To match pretraining, DPR input sequence should be
formatted with [CLS] and [SEP] tokens as follows:</p>
<p>(a) For sequence pairs (for a pair title+text for example):`,name:"input_ids"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/modeling_tf_dpr.py#L619",returnDescription:`
<p>A <code>transformers.models.dpr.modeling_tf_dpr.TFDPRQuestionEncoderOutput</code> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.DPRConfig"
>DPRConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>pooler_output</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, embeddings_size)</code>) \u2014 The DPR encoder outputs the <em>pooler_output</em> that corresponds to the question representation. Last layer
hidden-state of the first token of the sequence (classification token) further processed by a Linear layer.
This output is to be used to embed questions for nearest neighbors queries with context embeddings.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.dpr.modeling_tf_dpr.TFDPRQuestionEncoderOutput</code> or <code>tuple(tf.Tensor)</code></p>
`}}),Do=new Ht({props:{$$slots:{default:[wm]},$$scope:{ctx:F}}}),xo=new So({props:{anchor:"transformers.TFDPRQuestionEncoder.call.example",$$slots:{default:[Rm]},$$scope:{ctx:F}}}),or=new ze({}),nr=new X({props:{name:"class transformers.TFDPRReader",anchor:"transformers.TFDPRReader",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDPRReader.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.33.0/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/modeling_tf_dpr.py#L687"}}),Mo=new Ht({props:{$$slots:{default:[Em]},$$scope:{ctx:F}}}),ir=new X({props:{name:"call",anchor:"transformers.TFDPRReader.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": tf.Tensor | None = None"},{name:"inputs_embeds",val:": tf.Tensor | None = None"},{name:"output_attentions",val:": bool | None = None"},{name:"output_hidden_states",val:": bool | None = None"},{name:"return_dict",val:": bool | None = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFDPRReader.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shapes <code>(n_passages, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. It has to be a sequence triplet with 1) the question
and 2) the passages titles and 3) the passages texts To match pretraining, DPR <code>input_ids</code> sequence should
be formatted with [CLS] and [SEP] with the format:</p>
<p><code>[CLS] &lt;question token ids&gt; [SEP] &lt;titles ids&gt; [SEP] &lt;texts ids&gt;</code></p>
<p>DPR is a model with absolute position embeddings so it&#x2019;s usually advised to pad the inputs on the right
rather than the left.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.DPRReaderTokenizer">DPRReaderTokenizer</a>. See this class documentation for more details.`,name:"input_ids"},{anchor:"transformers.TFDPRReader.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(n_passages, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFDPRReader.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(n_passages, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFDPRReader.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFDPRReader.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.33.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
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Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/dpr/modeling_tf_dpr.py#L699",returnDescription:`
<p>A <code>transformers.models.dpr.modeling_tf_dpr.TFDPRReaderOutput</code> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.33.0/en/model_doc/dpr#transformers.DPRConfig"
>DPRConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>start_logits</strong> (<code>tf.Tensor</code> of shape <code>(n_passages, sequence_length)</code>) \u2014 Logits of the start index of the span for each passage.</p>
</li>
<li>
<p><strong>end_logits</strong> (<code>tf.Tensor</code> of shape <code>(n_passages, sequence_length)</code>) \u2014 Logits of the end index of the span for each passage.</p>
</li>
<li>
<p><strong>relevance_logits</strong> (<code>tf.Tensor</code> of shape <code>(n_passages, )</code>) \u2014 Outputs of the QA classifier of the DPRReader that corresponds to the scores of each passage to answer the
question, compared to all the other passages.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.dpr.modeling_tf_dpr.TFDPRReaderOutput</code> or <code>tuple(tf.Tensor)</code></p>
`}}),Fo=new Ht({props:{$$slots:{default:[km]},$$scope:{ctx:F}}}),Co=new So({props:{anchor:"transformers.TFDPRReader.call.example",$$slots:{default:[$m]},$$scope:{ctx:F}}}),{c(){h=s("meta"),y=c(),_=s("h1"),g=s("a"),b=s("span"),T(l.$$.fragment),u=c(),z=s("span"),Me=n("DPR"),Ee=c(),U=s("div"),de=s("a"),H=s("img"),Fe=c(),B=s("a"),ce=s("img"),L=c(),Y=s("h2"),se=s("a"),S=s("span"),T(ie.$$.fragment),Ce=c(),W=s("span"),qe=n("Overview"),ke=c(),N=s("p"),Ie=n(`Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. It was
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Vladimir Karpukhin, Barlas O\u011Fuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih.`),I=c(),he=s("p"),K=n("The abstract from the paper is the following:"),$e=c(),ue=s("p"),O=s("em"),Je=n(`Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional
sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can
be practically implemented using dense representations alone, where embeddings are learned from a small number of
questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets,
our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage
retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA
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configuration to that of the DPRContextEncoder
`),Lo=s("a"),Yi=n("facebook/dpr-ctx_encoder-single-nq-base"),Ki=n(`
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sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can
be practically implemented using dense representations alone, where embeddings are learned from a small number of
questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets,
our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage
retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA
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`),_r=a(Et,"A",{href:!0});var ru=i(_r);Xi=r(ru,"DPRReader"),ru.forEach(t),Hi=r(Et,`. It is used to instantiate the components of the DPR model according to the specified arguments,
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splitting and wordpiece.`),ba.forEach(t),fl=p(qo),Yo=a(qo,"P",{});var oi=i(Yo);ml=r(oi,"Refer to superclass "),Tr=a(oi,"A",{href:!0});var uu=i(Tr);gl=r(uu,"BertTokenizer"),uu.forEach(t),_l=r(oi," for usage examples and documentation concerning parameters."),oi.forEach(t),qo.forEach(t),ka=p(o),xt=a(o,"H2",{class:!0});var ni=i(xt);to=a(ni,"A",{id:!0,class:!0,href:!0});var fu=i(to);hs=a(fu,"SPAN",{});var mu=i(hs);w(Ko.$$.fragment,mu),mu.forEach(t),fu.forEach(t),vl=p(ni),us=a(ni,"SPAN",{});var gu=i(us);bl=r(gu,"DPRContextEncoderTokenizerFast"),gu.forEach(t),ni.forEach(t),$a=p(o),Ke=a(o,"DIV",{class:!0});var Io=i(Ke);w(en.$$.fragment,Io),yl=p(Io),tn=a(Io,"P",{});var ri=i(tn);Tl=r(ri,"Construct a \u201Cfast\u201D DPRContextEncoder tokenizer (backed by HuggingFace\u2019s "),fs=a(ri,"EM",{});var _u=i(fs);wl=r(_u,"tokenizers"),_u.forEach(t),Rl=r(ri," library)."),ri.forEach(t),El=p(Io),oo=a(Io,"P",{});var ya=i(oo);wr=a(ya,"A",{href:!0});var vu=i(wr);kl=r(vu,"DPRContextEncoderTokenizerFast"),vu.forEach(t),$l=r(ya," is identical to "),Rr=a(ya,"A",{href:!0});var bu=i(Rr);Pl=r(bu,"BertTokenizerFast"),bu.forEach(t),Dl=r(ya,` and runs end-to-end tokenization:
punctuation splitting and wordpiece.`),ya.forEach(t),xl=p(Io),on=a(Io,"P",{});var si=i(on);zl=r(si,"Refer to superclass "),Er=a(si,"A",{href:!0});var yu=i(Er);Ml=r(yu,"BertTokenizerFast"),yu.forEach(t),Fl=r(si," for usage examples and documentation concerning parameters."),si.forEach(t),Io.forEach(t),Pa=p(o),zt=a(o,"H2",{class:!0});var ai=i(zt);no=a(ai,"A",{id:!0,class:!0,href:!0});var Tu=i(no);ms=a(Tu,"SPAN",{});var wu=i(ms);w(nn.$$.fragment,wu),wu.forEach(t),Tu.forEach(t),Cl=p(ai),gs=a(ai,"SPAN",{});var Ru=i(gs);ql=r(Ru,"DPRQuestionEncoderTokenizer"),Ru.forEach(t),ai.forEach(t),Da=p(o),et=a(o,"DIV",{class:!0});var Jo=i(et);w(rn.$$.fragment,Jo),Il=p(Jo),_s=a(Jo,"P",{});var Eu=i(_s);Jl=r(Eu,"Constructs a DPRQuestionEncoder tokenizer."),Eu.forEach(t),Ul=p(Jo),ro=a(Jo,"P",{});var Ta=i(ro);kr=a(Ta,"A",{href:!0});var ku=i(kr);jl=r(ku,"DPRQuestionEncoderTokenizer"),ku.forEach(t),Nl=r(Ta," is identical to "),$r=a(Ta,"A",{href:!0});var $u=i($r);Zl=r($u,"BertTokenizer"),$u.forEach(t),Al=r(Ta,` and runs end-to-end tokenization: punctuation
splitting and wordpiece.`),Ta.forEach(t),Ql=p(Jo),sn=a(Jo,"P",{});var ii=i(sn);Bl=r(ii,"Refer to superclass "),Pr=a(ii,"A",{href:!0});var Pu=i(Pr);Vl=r(Pu,"BertTokenizer"),Pu.forEach(t),Sl=r(ii," for usage examples and documentation concerning parameters."),ii.forEach(t),Jo.forEach(t),xa=p(o),Mt=a(o,"H2",{class:!0});var li=i(Mt);so=a(li,"A",{id:!0,class:!0,href:!0});var Du=i(so);vs=a(Du,"SPAN",{});var xu=i(vs);w(an.$$.fragment,xu),xu.forEach(t),Du.forEach(t),Wl=p(li),bs=a(li,"SPAN",{});var zu=i(bs);Ol=r(zu,"DPRQuestionEncoderTokenizerFast"),zu.forEach(t),li.forEach(t),za=p(o),tt=a(o,"DIV",{class:!0});var Uo=i(tt);w(ln.$$.fragment,Uo),Ll=p(Uo),dn=a(Uo,"P",{});var di=i(dn);Gl=r(di,"Constructs a \u201Cfast\u201D DPRQuestionEncoder tokenizer (backed by HuggingFace\u2019s "),ys=a(di,"EM",{});var Mu=i(ys);Xl=r(Mu,"tokenizers"),Mu.forEach(t),Hl=r(di," library)."),di.forEach(t),Yl=p(Uo),ao=a(Uo,"P",{});var wa=i(ao);Dr=a(wa,"A",{href:!0});var Fu=i(Dr);Kl=r(Fu,"DPRQuestionEncoderTokenizerFast"),Fu.forEach(t),ed=r(wa," is identical to "),xr=a(wa,"A",{href:!0});var Cu=i(xr);td=r(Cu,"BertTokenizerFast"),Cu.forEach(t),od=r(wa,` and runs end-to-end tokenization:
punctuation splitting and wordpiece.`),wa.forEach(t),nd=p(Uo),cn=a(Uo,"P",{});var ci=i(cn);rd=r(ci,"Refer to superclass "),zr=a(ci,"A",{href:!0});var qu=i(zr);sd=r(qu,"BertTokenizerFast"),qu.forEach(t),ad=r(ci," for usage examples and documentation concerning parameters."),ci.forEach(t),Uo.forEach(t),Ma=p(o),Ft=a(o,"H2",{class:!0});var pi=i(Ft);io=a(pi,"A",{id:!0,class:!0,href:!0});var Iu=i(io);Ts=a(Iu,"SPAN",{});var Ju=i(Ts);w(pn.$$.fragment,Ju),Ju.forEach(t),Iu.forEach(t),id=p(pi),ws=a(pi,"SPAN",{});var Uu=i(ws);ld=r(Uu,"DPRReaderTokenizer"),Uu.forEach(t),pi.forEach(t),Fa=p(o),be=a(o,"DIV",{class:!0});var ft=i(be);w(hn.$$.fragment,ft),dd=p(ft),Rs=a(ft,"P",{});var ju=i(Rs);cd=r(ju,"Construct a DPRReader tokenizer."),ju.forEach(t),pd=p(ft),yt=a(ft,"P",{});var hr=i(yt);Mr=a(hr,"A",{href:!0});var Nu=i(Mr);hd=r(Nu,"DPRReaderTokenizer"),Nu.forEach(t),ud=r(hr," is almost identical to "),Fr=a(hr,"A",{href:!0});var Zu=i(Fr);fd=r(Zu,"BertTokenizer"),Zu.forEach(t),md=r(hr,` and runs end-to-end tokenization: punctuation
splitting and wordpiece. The difference is that is has three inputs strings: question, titles and texts that are
combined to be fed to the `),Cr=a(hr,"A",{href:!0});var Au=i(Cr);gd=r(Au,"DPRReader"),Au.forEach(t),_d=r(hr," model."),hr.forEach(t),vd=p(ft),un=a(ft,"P",{});var hi=i(un);bd=r(hi,"Refer to superclass "),qr=a(hi,"A",{href:!0});var Qu=i(qr);yd=r(Qu,"BertTokenizer"),Qu.forEach(t),Td=r(hi," for usage examples and documentation concerning parameters."),hi.forEach(t),wd=p(ft),Tt=a(ft,"P",{});var ur=i(Tt);Rd=r(ur,"Return a dictionary with the token ids of the input strings and other information to give to "),Es=a(ur,"CODE",{});var Bu=i(Es);Ed=r(Bu,".decode_best_spans"),Bu.forEach(t),kd=r(ur,`.
It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
using the tokenizer and vocabulary. The resulting `),ks=a(ur,"CODE",{});var Vu=i(ks);$d=r(Vu,"input_ids"),Vu.forEach(t),Pd=r(ur," is a matrix of size "),$s=a(ur,"CODE",{});var Su=i($s);Dd=r(Su,"(n_passages, sequence_length)"),Su.forEach(t),ur.forEach(t),xd=p(ft),w(lo.$$.fragment,ft),ft.forEach(t),Ca=p(o),Ct=a(o,"H2",{class:!0});var ui=i(Ct);co=a(ui,"A",{id:!0,class:!0,href:!0});var Wu=i(co);Ps=a(Wu,"SPAN",{});var Ou=i(Ps);w(fn.$$.fragment,Ou),Ou.forEach(t),Wu.forEach(t),zd=p(ui),Ds=a(ui,"SPAN",{});var Lu=i(Ds);Md=r(Lu,"DPRReaderTokenizerFast"),Lu.forEach(t),ui.forEach(t),qa=p(o),ye=a(o,"DIV",{class:!0});var mt=i(ye);w(mn.$$.fragment,mt),Fd=p(mt),gn=a(mt,"P",{});var fi=i(gn);Cd=r(fi,"Constructs a \u201Cfast\u201D DPRReader tokenizer (backed by HuggingFace\u2019s "),xs=a(fi,"EM",{});var Gu=i(xs);qd=r(Gu,"tokenizers"),Gu.forEach(t),Id=r(fi," library)."),fi.forEach(t),Jd=p(mt),wt=a(mt,"P",{});var fr=i(wt);Ir=a(fr,"A",{href:!0});var Xu=i(Ir);Ud=r(Xu,"DPRReaderTokenizerFast"),Xu.forEach(t),jd=r(fr," is almost identical to "),Jr=a(fr,"A",{href:!0});var Hu=i(Jr);Nd=r(Hu,"BertTokenizerFast"),Hu.forEach(t),Zd=r(fr,` and runs end-to-end tokenization:
punctuation splitting and wordpiece. The difference is that is has three inputs strings: question, titles and texts
that are combined to be fed to the `),Ur=a(fr,"A",{href:!0});var Yu=i(Ur);Ad=r(Yu,"DPRReader"),Yu.forEach(t),Qd=r(fr," model."),fr.forEach(t),Bd=p(mt),_n=a(mt,"P",{});var mi=i(_n);Vd=r(mi,"Refer to superclass "),jr=a(mi,"A",{href:!0});var Ku=i(jr);Sd=r(Ku,"BertTokenizerFast"),Ku.forEach(t),Wd=r(mi," for usage examples and documentation concerning parameters."),mi.forEach(t),Od=p(mt),bt=a(mt,"P",{});var jo=i(bt);Ld=r(jo,"Return a dictionary with the token ids of the input strings and other information to give to "),zs=a(jo,"CODE",{});var ef=i(zs);Gd=r(ef,".decode_best_spans"),ef.forEach(t),Xd=r(jo,`.
It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
using the tokenizer and vocabulary. The resulting `),Ms=a(jo,"CODE",{});var tf=i(Ms);Hd=r(tf,"input_ids"),tf.forEach(t),Yd=r(jo," is a matrix of size "),Fs=a(jo,"CODE",{});var of=i(Fs);Kd=r(of,"(n_passages, sequence_length)"),of.forEach(t),ec=r(jo,`
with the format:`),jo.forEach(t),tc=p(mt),Nr=a(mt,"P",{});var Zh=i(Nr);oc=r(Zh,"[CLS] "),po=a(Zh,"QUESTION",{token:!0,ids:!0});var Ah=i(po);nc=r(Ah,"[SEP] "),vn=a(Ah,"TITLES",{ids:!0});var Qh=i(vn);rc=r(Qh,"[SEP] "),Cs=a(Qh,"TEXTS",{ids:!0}),i(Cs).forEach(t),Qh.forEach(t),Ah.forEach(t),Zh.forEach(t),mt.forEach(t),Ia=p(o),qt=a(o,"H2",{class:!0});var gi=i(qt);ho=a(gi,"A",{id:!0,class:!0,href:!0});var nf=i(ho);qs=a(nf,"SPAN",{});var rf=i(qs);w(bn.$$.fragment,rf),rf.forEach(t),nf.forEach(t),sc=p(gi),Is=a(gi,"SPAN",{});var sf=i(Is);ac=r(sf,"DPR specific outputs"),sf.forEach(t),gi.forEach(t),Ja=p(o),It=a(o,"DIV",{class:!0});var _i=i(It);w(yn.$$.fragment,_i),ic=p(_i),Tn=a(_i,"P",{});var vi=i(Tn);lc=r(vi,"Class for outputs of "),Zr=a(vi,"A",{href:!0});var af=i(Zr);dc=r(af,"DPRQuestionEncoder"),af.forEach(t),cc=r(vi,"."),vi.forEach(t),_i.forEach(t),Ua=p(o),Jt=a(o,"DIV",{class:!0});var bi=i(Jt);w(wn.$$.fragment,bi),pc=p(bi),Rn=a(bi,"P",{});var yi=i(Rn);hc=r(yi,"Class for outputs of "),Ar=a(yi,"A",{href:!0});var lf=i(Ar);uc=r(lf,"DPRQuestionEncoder"),lf.forEach(t),fc=r(yi,"."),yi.forEach(t),bi.forEach(t),ja=p(o),Ut=a(o,"DIV",{class:!0});var Ti=i(Ut);w(En.$$.fragment,Ti),mc=p(Ti),kn=a(Ti,"P",{});var wi=i(kn);gc=r(wi,"Class for outputs of "),Qr=a(wi,"A",{href:!0});var df=i(Qr);_c=r(df,"DPRQuestionEncoder"),df.forEach(t),vc=r(wi,"."),wi.forEach(t),Ti.forEach(t),Na=p(o),jt=a(o,"H2",{class:!0});var Ri=i(jt);uo=a(Ri,"A",{id:!0,class:!0,href:!0});var cf=i(uo);Js=a(cf,"SPAN",{});var pf=i(Js);w($n.$$.fragment,pf),pf.forEach(t),cf.forEach(t),bc=p(Ri),Us=a(Ri,"SPAN",{});var hf=i(Us);yc=r(hf,"DPRContextEncoder"),hf.forEach(t),Ri.forEach(t),Za=p(o),Ve=a(o,"DIV",{class:!0});var kt=i(Ve);w(Pn.$$.fragment,kt),Tc=p(kt),js=a(kt,"P",{});var uf=i(js);wc=r(uf,"The bare DPRContextEncoder transformer outputting pooler outputs as context representations."),uf.forEach(t),Rc=p(kt),Dn=a(kt,"P",{});var Ei=i(Dn);Ec=r(Ei,"This model inherits from "),Br=a(Ei,"A",{href:!0});var ff=i(Br);kc=r(ff,"PreTrainedModel"),ff.forEach(t),$c=r(Ei,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Ei.forEach(t),Pc=p(kt),xn=a(kt,"P",{});var ki=i(xn);Dc=r(ki,"This model is also a PyTorch "),zn=a(ki,"A",{href:!0,rel:!0});var mf=i(zn);xc=r(mf,"torch.nn.Module"),mf.forEach(t),zc=r(ki,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),ki.forEach(t),Mc=p(kt),lt=a(kt,"DIV",{class:!0});var No=i(lt);w(Mn.$$.fragment,No),Fc=p(No),Nt=a(No,"P",{});var os=i(Nt);Cc=r(os,"The "),Vr=a(os,"A",{href:!0});var gf=i(Vr);qc=r(gf,"DPRContextEncoder"),gf.forEach(t),Ic=r(os," forward method, overrides the "),Ns=a(os,"CODE",{});var _f=i(Ns);Jc=r(_f,"__call__"),_f.forEach(t),Uc=r(os," special method."),os.forEach(t),jc=p(No),w(fo.$$.fragment,No),Nc=p(No),w(mo.$$.fragment,No),No.forEach(t),kt.forEach(t),Aa=p(o),Zt=a(o,"H2",{class:!0});var $i=i(Zt);go=a($i,"A",{id:!0,class:!0,href:!0});var vf=i(go);Zs=a(vf,"SPAN",{});var bf=i(Zs);w(Fn.$$.fragment,bf),bf.forEach(t),vf.forEach(t),Zc=p($i),As=a($i,"SPAN",{});var yf=i(As);Ac=r(yf,"DPRQuestionEncoder"),yf.forEach(t),$i.forEach(t),Qa=p(o),Se=a(o,"DIV",{class:!0});var $t=i(Se);w(Cn.$$.fragment,$t),Qc=p($t),Qs=a($t,"P",{});var Tf=i(Qs);Bc=r(Tf,"The bare DPRQuestionEncoder transformer outputting pooler outputs as question representations."),Tf.forEach(t),Vc=p($t),qn=a($t,"P",{});var Pi=i(qn);Sc=r(Pi,"This model inherits from "),Sr=a(Pi,"A",{href:!0});var wf=i(Sr);Wc=r(wf,"PreTrainedModel"),wf.forEach(t),Oc=r(Pi,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Pi.forEach(t),Lc=p($t),In=a($t,"P",{});var Di=i(In);Gc=r(Di,"This model is also a PyTorch "),Jn=a(Di,"A",{href:!0,rel:!0});var Rf=i(Jn);Xc=r(Rf,"torch.nn.Module"),Rf.forEach(t),Hc=r(Di,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),Di.forEach(t),Yc=p($t),dt=a($t,"DIV",{class:!0});var Zo=i(dt);w(Un.$$.fragment,Zo),Kc=p(Zo),At=a(Zo,"P",{});var ns=i(At);ep=r(ns,"The "),Wr=a(ns,"A",{href:!0});var Ef=i(Wr);tp=r(Ef,"DPRQuestionEncoder"),Ef.forEach(t),op=r(ns," forward method, overrides the "),Bs=a(ns,"CODE",{});var kf=i(Bs);np=r(kf,"__call__"),kf.forEach(t),rp=r(ns," special method."),ns.forEach(t),sp=p(Zo),w(_o.$$.fragment,Zo),ap=p(Zo),w(vo.$$.fragment,Zo),Zo.forEach(t),$t.forEach(t),Ba=p(o),Qt=a(o,"H2",{class:!0});var xi=i(Qt);bo=a(xi,"A",{id:!0,class:!0,href:!0});var $f=i(bo);Vs=a($f,"SPAN",{});var Pf=i(Vs);w(jn.$$.fragment,Pf),Pf.forEach(t),$f.forEach(t),ip=p(xi),Ss=a(xi,"SPAN",{});var Df=i(Ss);lp=r(Df,"DPRReader"),Df.forEach(t),xi.forEach(t),Va=p(o),We=a(o,"DIV",{class:!0});var Pt=i(We);w(Nn.$$.fragment,Pt),dp=p(Pt),Ws=a(Pt,"P",{});var xf=i(Ws);cp=r(xf,"The bare DPRReader transformer outputting span predictions."),xf.forEach(t),pp=p(Pt),Zn=a(Pt,"P",{});var zi=i(Zn);hp=r(zi,"This model inherits from "),Or=a(zi,"A",{href:!0});var zf=i(Or);up=r(zf,"PreTrainedModel"),zf.forEach(t),fp=r(zi,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),zi.forEach(t),mp=p(Pt),An=a(Pt,"P",{});var Mi=i(An);gp=r(Mi,"This model is also a PyTorch "),Qn=a(Mi,"A",{href:!0,rel:!0});var Mf=i(Qn);_p=r(Mf,"torch.nn.Module"),Mf.forEach(t),vp=r(Mi,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),Mi.forEach(t),bp=p(Pt),ct=a(Pt,"DIV",{class:!0});var Ao=i(ct);w(Bn.$$.fragment,Ao),yp=p(Ao),Bt=a(Ao,"P",{});var rs=i(Bt);Tp=r(rs,"The "),Lr=a(rs,"A",{href:!0});var Ff=i(Lr);wp=r(Ff,"DPRReader"),Ff.forEach(t),Rp=r(rs," forward method, overrides the "),Os=a(rs,"CODE",{});var Cf=i(Os);Ep=r(Cf,"__call__"),Cf.forEach(t),kp=r(rs," special method."),rs.forEach(t),$p=p(Ao),w(yo.$$.fragment,Ao),Pp=p(Ao),w(To.$$.fragment,Ao),Ao.forEach(t),Pt.forEach(t),Sa=p(o),Vt=a(o,"H2",{class:!0});var Fi=i(Vt);wo=a(Fi,"A",{id:!0,class:!0,href:!0});var qf=i(wo);Ls=a(qf,"SPAN",{});var If=i(Ls);w(Vn.$$.fragment,If),If.forEach(t),qf.forEach(t),Dp=p(Fi),Gs=a(Fi,"SPAN",{});var Jf=i(Gs);xp=r(Jf,"TFDPRContextEncoder"),Jf.forEach(t),Fi.forEach(t),Wa=p(o),Te=a(o,"DIV",{class:!0});var gt=i(Te);w(Sn.$$.fragment,gt),zp=p(gt),Xs=a(gt,"P",{});var Uf=i(Xs);Mp=r(Uf,"The bare DPRContextEncoder transformer outputting pooler outputs as context representations."),Uf.forEach(t),Fp=p(gt),Wn=a(gt,"P",{});var Ci=i(Wn);Cp=r(Ci,"This model inherits from "),Gr=a(Ci,"A",{href:!0});var jf=i(Gr);qp=r(jf,"TFPreTrainedModel"),jf.forEach(t),Ip=r(Ci,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Ci.forEach(t),Jp=p(gt),On=a(gt,"P",{});var qi=i(On);Up=r(qi,"This model is also a Tensorflow "),Ln=a(qi,"A",{href:!0,rel:!0});var Nf=i(Ln);jp=r(Nf,"tf.keras.Model"),Nf.forEach(t),Np=r(qi,`
subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to
general usage and behavior.`),qi.forEach(t),Zp=p(gt),w(Ro.$$.fragment,gt),Ap=p(gt),pt=a(gt,"DIV",{class:!0});var Qo=i(pt);w(Gn.$$.fragment,Qo),Qp=p(Qo),St=a(Qo,"P",{});var ss=i(St);Bp=r(ss,"The "),Xr=a(ss,"A",{href:!0});var Zf=i(Xr);Vp=r(Zf,"TFDPRContextEncoder"),Zf.forEach(t),Sp=r(ss," forward method, overrides the "),Hs=a(ss,"CODE",{});var Af=i(Hs);Wp=r(Af,"__call__"),Af.forEach(t),Op=r(ss," special method."),ss.forEach(t),Lp=p(Qo),w(Eo.$$.fragment,Qo),Gp=p(Qo),w(ko.$$.fragment,Qo),Qo.forEach(t),gt.forEach(t),Oa=p(o),Wt=a(o,"H2",{class:!0});var Ii=i(Wt);$o=a(Ii,"A",{id:!0,class:!0,href:!0});var Qf=i($o);Ys=a(Qf,"SPAN",{});var Bf=i(Ys);w(Xn.$$.fragment,Bf),Bf.forEach(t),Qf.forEach(t),Xp=p(Ii),Ks=a(Ii,"SPAN",{});var Vf=i(Ks);Hp=r(Vf,"TFDPRQuestionEncoder"),Vf.forEach(t),Ii.forEach(t),La=p(o),we=a(o,"DIV",{class:!0});var _t=i(we);w(Hn.$$.fragment,_t),Yp=p(_t),ea=a(_t,"P",{});var Sf=i(ea);Kp=r(Sf,"The bare DPRQuestionEncoder transformer outputting pooler outputs as question representations."),Sf.forEach(t),eh=p(_t),Yn=a(_t,"P",{});var Ji=i(Yn);th=r(Ji,"This model inherits from "),Hr=a(Ji,"A",{href:!0});var Wf=i(Hr);oh=r(Wf,"TFPreTrainedModel"),Wf.forEach(t),nh=r(Ji,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),Ji.forEach(t),rh=p(_t),Kn=a(_t,"P",{});var Ui=i(Kn);sh=r(Ui,"This model is also a Tensorflow "),er=a(Ui,"A",{href:!0,rel:!0});var Of=i(er);ah=r(Of,"tf.keras.Model"),Of.forEach(t),ih=r(Ui,`
subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to
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