import{s as jr,o as Ur,n as U}from"../chunks/scheduler.9bc65507.js";import{S as Hr,i as Ir,g as l,s,r as g,A as Zr,h as c,f as n,c as r,j as D,u as _,x as u,k as M,y as a,a as d,v as T,d as b,t as v,w as y}from"../chunks/index.707bf1b6.js";import{T as Re}from"../chunks/Tip.c2ecdbf4.js";import{D as z}from"../chunks/Docstring.17db21ae.js";import{C as We}from"../chunks/CodeBlock.54a9f38d.js";import{E as Ve}from"../chunks/ExampleCodeBlock.4f515aa9.js";import{H}from"../chunks/Heading.342b1fa6.js";function Nr(w){let t,$="Example:",m,p,R;return p=new We({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`,wrap:!1}}),{c(){t=l("p"),t.textContent=$,m=s(),g(p.$$.fragment)},l(o){t=c(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=$),m=r(o),_(p.$$.fragment,o)},m(o,h){d(o,t,h),d(o,m,h),T(p,o,h),R=!0},p:U,i(o){R||(b(p.$$.fragment,o),R=!0)},o(o){v(p.$$.fragment,o),R=!1},d(o){o&&(n(t),n(m)),y(p,o)}}}function Vr(w){let t,$="with the format:",m,p,R;return p=new We({props:{code:"JTVCQ0xTJTVEJTIwJTNDcXVlc3Rpb24lMjB0b2tlbiUyMGlkcyUzRSUyMCU1QlNFUCU1RCUyMCUzQ3RpdGxlcyUyMGlkcyUzRSUyMCU1QlNFUCU1RCUyMCUzQ3RleHRzJTIwaWRzJTNF",highlighted:'[CLS] <span class="hljs-tag">&lt;<span class="hljs-name">question</span> <span class="hljs-attr">token</span> <span class="hljs-attr">ids</span>&gt;</span> [SEP] <span class="hljs-tag">&lt;<span class="hljs-name">titles</span> <span class="hljs-attr">ids</span>&gt;</span> [SEP] <span class="hljs-tag">&lt;<span class="hljs-name">texts</span> <span class="hljs-attr">ids</span>&gt;</span>',wrap:!1}}),{c(){t=l("p"),t.textContent=$,m=s(),g(p.$$.fragment)},l(o){t=c(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-1kqkfm0"&&(t.textContent=$),m=r(o),_(p.$$.fragment,o)},m(o,h){d(o,t,h),d(o,m,h),T(p,o,h),R=!0},p:U,i(o){R||(b(p.$$.fragment,o),R=!0)},o(o){v(p.$$.fragment,o),R=!1},d(o){o&&(n(t),n(m)),y(p,o)}}}function Wr(w){let t,$=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){t=l("p"),t.innerHTML=$},l(m){t=c(m,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=$)},m(m,p){d(m,t,p)},p:U,d(m){m&&n(t)}}}function Br(w){let t,$="Examples:",m,p,R;return p=new We({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`,wrap:!1}}),{c(){t=l("p"),t.textContent=$,m=s(),g(p.$$.fragment)},l(o){t=c(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-kvfsh7"&&(t.textContent=$),m=r(o),_(p.$$.fragment,o)},m(o,h){d(o,t,h),d(o,m,h),T(p,o,h),R=!0},p:U,i(o){R||(b(p.$$.fragment,o),R=!0)},o(o){v(p.$$.fragment,o),R=!1},d(o){o&&(n(t),n(m)),y(p,o)}}}function Qr(w){let t,$=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){t=l("p"),t.innerHTML=$},l(m){t=c(m,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=$)},m(m,p){d(m,t,p)},p:U,d(m){m&&n(t)}}}function Gr(w){let t,$="Examples:",m,p,R;return p=new We({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`,wrap:!1}}),{c(){t=l("p"),t.textContent=$,m=s(),g(p.$$.fragment)},l(o){t=c(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-kvfsh7"&&(t.textContent=$),m=r(o),_(p.$$.fragment,o)},m(o,h){d(o,t,h),d(o,m,h),T(p,o,h),R=!0},p:U,i(o){R||(b(p.$$.fragment,o),R=!0)},o(o){v(p.$$.fragment,o),R=!1},d(o){o&&(n(t),n(m)),y(p,o)}}}function Sr(w){let t,$=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){t=l("p"),t.innerHTML=$},l(m){t=c(m,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=$)},m(m,p){d(m,t,p)},p:U,d(m){m&&n(t)}}}function Xr(w){let t,$="Examples:",m,p,R;return p=new We({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`,wrap:!1}}),{c(){t=l("p"),t.textContent=$,m=s(),g(p.$$.fragment)},l(o){t=c(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-kvfsh7"&&(t.textContent=$),m=r(o),_(p.$$.fragment,o)},m(o,h){d(o,t,h),d(o,m,h),T(p,o,h),R=!0},p:U,i(o){R||(b(p.$$.fragment,o),R=!0)},o(o){v(p.$$.fragment,o),R=!1},d(o){o&&(n(t),n(m)),y(p,o)}}}function Or(w){let t,$="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",m,p,R="<li>having all inputs as keyword arguments (like PyTorch models), or</li> <li>having all inputs as a list, tuple or dict in the first positional argument.</li>",o,h,pe=`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like <code>model.fit()</code> things should “just work” for you - just
pass your inputs and labels in any format that <code>model.fit()</code> supports! If, however, you want to use the second
format outside of Keras methods like <code>fit()</code> and <code>predict()</code>, such as when creating your own layers or models with
the Keras <code>Functional</code> API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`,F,x,G=`<li>a single Tensor with <code>input_ids</code> only and nothing else: <code>model(input_ids)</code></li> <li>a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
<code>model([input_ids, attention_mask])</code> or <code>model([input_ids, attention_mask, token_type_ids])</code></li> <li>a dictionary with one or several input Tensors associated to the input names given in the docstring:
<code>model({&quot;input_ids&quot;: input_ids, &quot;token_type_ids&quot;: token_type_ids})</code></li>`,C,P,S=`Note that when creating models and layers with
<a href="https://keras.io/guides/making_new_layers_and_models_via_subclassing/" rel="nofollow">subclassing</a> then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`;return{c(){t=l("p"),t.innerHTML=$,m=s(),p=l("ul"),p.innerHTML=R,o=s(),h=l("p"),h.innerHTML=pe,F=s(),x=l("ul"),x.innerHTML=G,C=s(),P=l("p"),P.innerHTML=S},l(f){t=c(f,"P",{"data-svelte-h":!0}),u(t)!=="svelte-1ajbfxg"&&(t.innerHTML=$),m=r(f),p=c(f,"UL",{"data-svelte-h":!0}),u(p)!=="svelte-qm1t26"&&(p.innerHTML=R),o=r(f),h=c(f,"P",{"data-svelte-h":!0}),u(h)!=="svelte-1v9qsc5"&&(h.innerHTML=pe),F=r(f),x=c(f,"UL",{"data-svelte-h":!0}),u(x)!=="svelte-15scerc"&&(x.innerHTML=G),C=r(f),P=c(f,"P",{"data-svelte-h":!0}),u(P)!=="svelte-1an3odd"&&(P.innerHTML=S)},m(f,k){d(f,t,k),d(f,m,k),d(f,p,k),d(f,o,k),d(f,h,k),d(f,F,k),d(f,x,k),d(f,C,k),d(f,P,k)},p:U,d(f){f&&(n(t),n(m),n(p),n(o),n(h),n(F),n(x),n(C),n(P))}}}function Ar(w){let t,$=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){t=l("p"),t.innerHTML=$},l(m){t=c(m,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=$)},m(m,p){d(m,t,p)},p:U,d(m){m&&n(t)}}}function Yr(w){let t,$="Examples:",m,p,R;return p=new We({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`,wrap:!1}}),{c(){t=l("p"),t.textContent=$,m=s(),g(p.$$.fragment)},l(o){t=c(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-kvfsh7"&&(t.textContent=$),m=r(o),_(p.$$.fragment,o)},m(o,h){d(o,t,h),d(o,m,h),T(p,o,h),R=!0},p:U,i(o){R||(b(p.$$.fragment,o),R=!0)},o(o){v(p.$$.fragment,o),R=!1},d(o){o&&(n(t),n(m)),y(p,o)}}}function Kr(w){let t,$="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",m,p,R="<li>having all inputs as keyword arguments (like PyTorch models), or</li> <li>having all inputs as a list, tuple or dict in the first positional argument.</li>",o,h,pe=`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like <code>model.fit()</code> things should “just work” for you - just
pass your inputs and labels in any format that <code>model.fit()</code> supports! If, however, you want to use the second
format outside of Keras methods like <code>fit()</code> and <code>predict()</code>, such as when creating your own layers or models with
the Keras <code>Functional</code> API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`,F,x,G=`<li>a single Tensor with <code>input_ids</code> only and nothing else: <code>model(input_ids)</code></li> <li>a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
<code>model([input_ids, attention_mask])</code> or <code>model([input_ids, attention_mask, token_type_ids])</code></li> <li>a dictionary with one or several input Tensors associated to the input names given in the docstring:
<code>model({&quot;input_ids&quot;: input_ids, &quot;token_type_ids&quot;: token_type_ids})</code></li>`,C,P,S=`Note that when creating models and layers with
<a href="https://keras.io/guides/making_new_layers_and_models_via_subclassing/" rel="nofollow">subclassing</a> then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`;return{c(){t=l("p"),t.innerHTML=$,m=s(),p=l("ul"),p.innerHTML=R,o=s(),h=l("p"),h.innerHTML=pe,F=s(),x=l("ul"),x.innerHTML=G,C=s(),P=l("p"),P.innerHTML=S},l(f){t=c(f,"P",{"data-svelte-h":!0}),u(t)!=="svelte-1ajbfxg"&&(t.innerHTML=$),m=r(f),p=c(f,"UL",{"data-svelte-h":!0}),u(p)!=="svelte-qm1t26"&&(p.innerHTML=R),o=r(f),h=c(f,"P",{"data-svelte-h":!0}),u(h)!=="svelte-1v9qsc5"&&(h.innerHTML=pe),F=r(f),x=c(f,"UL",{"data-svelte-h":!0}),u(x)!=="svelte-15scerc"&&(x.innerHTML=G),C=r(f),P=c(f,"P",{"data-svelte-h":!0}),u(P)!=="svelte-1an3odd"&&(P.innerHTML=S)},m(f,k){d(f,t,k),d(f,m,k),d(f,p,k),d(f,o,k),d(f,h,k),d(f,F,k),d(f,x,k),d(f,C,k),d(f,P,k)},p:U,d(f){f&&(n(t),n(m),n(p),n(o),n(h),n(F),n(x),n(C),n(P))}}}function ea(w){let t,$=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){t=l("p"),t.innerHTML=$},l(m){t=c(m,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=$)},m(m,p){d(m,t,p)},p:U,d(m){m&&n(t)}}}function ta(w){let t,$="Examples:",m,p,R;return p=new We({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`,wrap:!1}}),{c(){t=l("p"),t.textContent=$,m=s(),g(p.$$.fragment)},l(o){t=c(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-kvfsh7"&&(t.textContent=$),m=r(o),_(p.$$.fragment,o)},m(o,h){d(o,t,h),d(o,m,h),T(p,o,h),R=!0},p:U,i(o){R||(b(p.$$.fragment,o),R=!0)},o(o){v(p.$$.fragment,o),R=!1},d(o){o&&(n(t),n(m)),y(p,o)}}}function na(w){let t,$="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",m,p,R="<li>having all inputs as keyword arguments (like PyTorch models), or</li> <li>having all inputs as a list, tuple or dict in the first positional argument.</li>",o,h,pe=`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like <code>model.fit()</code> things should “just work” for you - just
pass your inputs and labels in any format that <code>model.fit()</code> supports! If, however, you want to use the second
format outside of Keras methods like <code>fit()</code> and <code>predict()</code>, such as when creating your own layers or models with
the Keras <code>Functional</code> API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`,F,x,G=`<li>a single Tensor with <code>input_ids</code> only and nothing else: <code>model(input_ids)</code></li> <li>a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
<code>model([input_ids, attention_mask])</code> or <code>model([input_ids, attention_mask, token_type_ids])</code></li> <li>a dictionary with one or several input Tensors associated to the input names given in the docstring:
<code>model({&quot;input_ids&quot;: input_ids, &quot;token_type_ids&quot;: token_type_ids})</code></li>`,C,P,S=`Note that when creating models and layers with
<a href="https://keras.io/guides/making_new_layers_and_models_via_subclassing/" rel="nofollow">subclassing</a> then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`;return{c(){t=l("p"),t.innerHTML=$,m=s(),p=l("ul"),p.innerHTML=R,o=s(),h=l("p"),h.innerHTML=pe,F=s(),x=l("ul"),x.innerHTML=G,C=s(),P=l("p"),P.innerHTML=S},l(f){t=c(f,"P",{"data-svelte-h":!0}),u(t)!=="svelte-1ajbfxg"&&(t.innerHTML=$),m=r(f),p=c(f,"UL",{"data-svelte-h":!0}),u(p)!=="svelte-qm1t26"&&(p.innerHTML=R),o=r(f),h=c(f,"P",{"data-svelte-h":!0}),u(h)!=="svelte-1v9qsc5"&&(h.innerHTML=pe),F=r(f),x=c(f,"UL",{"data-svelte-h":!0}),u(x)!=="svelte-15scerc"&&(x.innerHTML=G),C=r(f),P=c(f,"P",{"data-svelte-h":!0}),u(P)!=="svelte-1an3odd"&&(P.innerHTML=S)},m(f,k){d(f,t,k),d(f,m,k),d(f,p,k),d(f,o,k),d(f,h,k),d(f,F,k),d(f,x,k),d(f,C,k),d(f,P,k)},p:U,d(f){f&&(n(t),n(m),n(p),n(o),n(h),n(F),n(x),n(C),n(P))}}}function oa(w){let t,$=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){t=l("p"),t.innerHTML=$},l(m){t=c(m,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=$)},m(m,p){d(m,t,p)},p:U,d(m){m&&n(t)}}}function sa(w){let t,$="Examples:",m,p,R;return p=new We({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`,wrap:!1}}),{c(){t=l("p"),t.textContent=$,m=s(),g(p.$$.fragment)},l(o){t=c(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-kvfsh7"&&(t.textContent=$),m=r(o),_(p.$$.fragment,o)},m(o,h){d(o,t,h),d(o,m,h),T(p,o,h),R=!0},p:U,i(o){R||(b(p.$$.fragment,o),R=!0)},o(o){v(p.$$.fragment,o),R=!1},d(o){o&&(n(t),n(m)),y(p,o)}}}function ra(w){let t,$,m,p,R,o,h,pe='<a href="https://huggingface.co/models?filter=dpr"><img alt="Models" src="https://img.shields.io/badge/All_model_pages-dpr-blueviolet"/></a> <a href="https://huggingface.co/spaces/docs-demos/dpr-question_encoder-bert-base-multilingual"><img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a>',F,x,G,C,P=`Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&amp;A research. It was
introduced in <a href="https://arxiv.org/abs/2004.04906" rel="nofollow">Dense Passage Retrieval for Open-Domain Question Answering</a> by
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih.`,S,f,k="The abstract from the paper is the following:",qn,Be,Js=`<em>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
benchmarks.</em>`,En,Qe,js='This model was contributed by <a href="https://huggingface.co/lhoestq" rel="nofollow">lhoestq</a>. The original code can be found <a href="https://github.com/facebookresearch/DPR" rel="nofollow">here</a>.',Ln,Ge,Us="Tips:",Jn,Se,Hs="<li><p>DPR consists in three models:</p> <ul><li>Question encoder: encode questions as vectors</li> <li>Context encoder: encode contexts as vectors</li> <li>Reader: extract the answer of the questions inside retrieved contexts, along with a relevance score (high if the inferred span actually answers the question).</li></ul></li>",jn,Xe,Un,I,Oe,go,Et,Is='<a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> is the configuration class to store the configuration of a <em>DPRModel</em>.',_o,Lt,Zs=`This is the configuration class to store the configuration of a <a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRContextEncoder">DPRContextEncoder</a>, <a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a>, or a
<a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRReader">DPRReader</a>. It is used to instantiate the components of the DPR model according to the specified arguments,
defining the model component architectures. Instantiating a configuration with the defaults will yield a similar
configuration to that of the DPRContextEncoder
<a href="https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base" rel="nofollow">facebook/dpr-ctx_encoder-single-nq-base</a>
architecture.`,To,Jt,Ns='This class is a subclass of <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a>. Please check the superclass for the documentation of all kwargs.',bo,$e,Hn,Ae,In,X,Ye,vo,jt,Vs="Construct a DPRContextEncoder tokenizer.",yo,Ut,Ws=`<a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRContextEncoderTokenizer">DPRContextEncoderTokenizer</a> is identical to <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> and runs end-to-end tokenization: punctuation
splitting and wordpiece.`,Ro,Ht,Bs='Refer to superclass <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> for usage examples and documentation concerning parameters.',Zn,Ke,Nn,O,et,$o,It,Qs="Construct a “fast” DPRContextEncoder tokenizer (backed by HuggingFace’s <em>tokenizers</em> library).",wo,Zt,Gs=`<a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRContextEncoderTokenizerFast">DPRContextEncoderTokenizerFast</a> is identical to <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> and runs end-to-end tokenization:
punctuation splitting and wordpiece.`,ko,Nt,Ss='Refer to superclass <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> for usage examples and documentation concerning parameters.',Vn,tt,Wn,A,nt,xo,Vt,Xs="Constructs a DPRQuestionEncoder tokenizer.",Po,Wt,Os=`<a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRQuestionEncoderTokenizer">DPRQuestionEncoderTokenizer</a> is identical to <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> and runs end-to-end tokenization: punctuation
splitting and wordpiece.`,Mo,Bt,As='Refer to superclass <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> for usage examples and documentation concerning parameters.',Bn,ot,Qn,Y,st,Do,Qt,Ys="Constructs a “fast” DPRQuestionEncoder tokenizer (backed by HuggingFace’s <em>tokenizers</em> library).",Co,Gt,Ks=`<a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRQuestionEncoderTokenizerFast">DPRQuestionEncoderTokenizerFast</a> is identical to <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> and runs end-to-end tokenization:
punctuation splitting and wordpiece.`,zo,St,er='Refer to superclass <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> for usage examples and documentation concerning parameters.',Gn,rt,Sn,q,at,Fo,Xt,tr="Construct a DPRReader tokenizer.",qo,Ot,nr=`<a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRReaderTokenizer">DPRReaderTokenizer</a> is almost identical to <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> 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 <a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRReader">DPRReader</a> model.`,Eo,At,or='Refer to superclass <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> for usage examples and documentation concerning parameters.',Lo,Yt,sr=`Return a dictionary with the token ids of the input strings and other information to give to <code>.decode_best_spans</code>.
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 <code>input_ids</code> is a matrix of size <code>(n_passages, sequence_length)</code>`,Jo,we,Xn,it,On,E,dt,jo,Kt,rr="Constructs a “fast” DPRReader tokenizer (backed by HuggingFace’s <em>tokenizers</em> library).",Uo,en,ar=`<a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRReaderTokenizerFast">DPRReaderTokenizerFast</a> is almost identical to <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> 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 <a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRReader">DPRReader</a> model.`,Ho,tn,ir='Refer to superclass <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> for usage examples and documentation concerning parameters.',Io,nn,dr=`Return a dictionary with the token ids of the input strings and other information to give to <code>.decode_best_spans</code>.
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 <code>input_ids</code> is a matrix of size <code>(n_passages, sequence_length)</code>
with the format:`,Zo,on,lr="[CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>",An,lt,Yn,be,ct,No,sn,cr='Class for outputs of <a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a>.',Kn,ve,pt,Vo,rn,pr='Class for outputs of <a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a>.',eo,ye,mt,Wo,an,mr='Class for outputs of <a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a>.',to,ut,no,Z,ft,Bo,dn,ur="The bare DPRContextEncoder transformer outputting pooler outputs as context representations.",Qo,ln,fr=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. 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.)`,Go,cn,hr=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,So,oe,ht,Xo,pn,gr='The <a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRContextEncoder">DPRContextEncoder</a> forward method, overrides the <code>__call__</code> special method.',Oo,ke,Ao,xe,oo,gt,so,N,_t,Yo,mn,_r="The bare DPRQuestionEncoder transformer outputting pooler outputs as question representations.",Ko,un,Tr=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. 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.)`,es,fn,br=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,ts,se,Tt,ns,hn,vr='The <a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a> forward method, overrides the <code>__call__</code> special method.',os,Pe,ss,Me,ro,bt,ao,V,vt,rs,gn,yr="The bare DPRReader transformer outputting span predictions.",as,_n,Rr=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. 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.)`,is,Tn,$r=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,ds,re,yt,ls,bn,wr='The <a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRReader">DPRReader</a> forward method, overrides the <code>__call__</code> special method.',cs,De,ps,Ce,io,Rt,lo,L,$t,ms,vn,kr="The bare DPRContextEncoder transformer outputting pooler outputs as context representations.",us,yn,xr=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,fs,Rn,Pr=`This model is also a Tensorflow <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">tf.keras.Model</a>
subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to
general usage and behavior.`,hs,ze,gs,ae,wt,_s,$n,Mr='The <a href="/docs/transformers/main/en/model_doc/dpr#transformers.TFDPRContextEncoder">TFDPRContextEncoder</a> forward method, overrides the <code>__call__</code> special method.',Ts,Fe,bs,qe,co,kt,po,J,xt,vs,wn,Dr="The bare DPRQuestionEncoder transformer outputting pooler outputs as question representations.",ys,kn,Cr=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>. 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.)`,Rs,xn,zr=`This model is also a Tensorflow <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">tf.keras.Model</a>
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.`,$s,Ee,ws,ie,Pt,ks,Pn,Fr='The <a href="/docs/transformers/main/en/model_doc/dpr#transformers.TFDPRQuestionEncoder">TFDPRQuestionEncoder</a> forward method, overrides the <code>__call__</code> special method.',xs,Le,Ps,Je,mo,Mt,uo,j,Dt,Ms,Mn,qr="The bare DPRReader transformer outputting span predictions.",Ds,Dn,Er=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>. 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.)`,Cs,Cn,Lr=`This model is also a Tensorflow <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">tf.keras.Model</a>
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.`,zs,je,Fs,de,Ct,qs,zn,Jr='The <a href="/docs/transformers/main/en/model_doc/dpr#transformers.TFDPRReader">TFDPRReader</a> forward method, overrides the <code>__call__</code> special method.',Es,Ue,Ls,He,fo,Fn,ho;return R=new H({props:{title:"DPR",local:"dpr",headingTag:"h1"}}),x=new H({props:{title:"Overview",local:"overview",headingTag:"h2"}}),Xe=new H({props:{title:"DPRConfig",local:"transformers.DPRConfig",headingTag:"h2"}}),Oe=new z({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/main/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/main/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/main/src/transformers/models/dpr/configuration_dpr.py#L45"}}),$e=new Ve({props:{anchor:"transformers.DPRConfig.example",$$slots:{default:[Nr]},$$scope:{ctx:w}}}),Ae=new H({props:{title:"DPRContextEncoderTokenizer",local:"transformers.DPRContextEncoderTokenizer",headingTag:"h2"}}),Ye=new z({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/main/src/transformers/models/dpr/tokenization_dpr.py#L113"}}),Ke=new H({props:{title:"DPRContextEncoderTokenizerFast",local:"transformers.DPRContextEncoderTokenizerFast",headingTag:"h2"}}),et=new z({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/main/src/transformers/models/dpr/tokenization_dpr_fast.py#L114"}}),tt=new H({props:{title:"DPRQuestionEncoderTokenizer",local:"transformers.DPRQuestionEncoderTokenizer",headingTag:"h2"}}),nt=new z({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/main/src/transformers/models/dpr/tokenization_dpr.py#L129"}}),ot=new H({props:{title:"DPRQuestionEncoderTokenizerFast",local:"transformers.DPRQuestionEncoderTokenizerFast",headingTag:"h2"}}),st=new z({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/main/src/transformers/models/dpr/tokenization_dpr_fast.py#L131"}}),rt=new H({props:{title:"DPRReaderTokenizer",local:"transformers.DPRReaderTokenizer",headingTag:"h2"}}),at=new z({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/main/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/main/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
length (like XLNet) truncation/padding to a maximum length will be deactivated.`,name:"max_length"},{anchor:"transformers.DPRReaderTokenizer.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/main/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) &#x2014;
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.DPRReaderTokenizer.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
specific tokenizer&#x2019;s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"return_attention_mask"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/dpr/tokenization_dpr.py#L395",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<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:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Dict[str, List[List[int]]]</code></p>
`}}),we=new Ve({props:{anchor:"transformers.DPRReaderTokenizer.example",$$slots:{default:[Vr]},$$scope:{ctx:w}}}),it=new H({props:{title:"DPRReaderTokenizerFast",local:"transformers.DPRReaderTokenizerFast",headingTag:"h2"}}),dt=new z({props:{name:"class transformers.DPRReaderTokenizerFast",anchor:"transformers.DPRReaderTokenizerFast",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:""}],parametersDescription:[{anchor:"transformers.DPRReaderTokenizerFast.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.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/main/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/main/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
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.`,name:"max_length"},{anchor:"transformers.DPRReaderTokenizerFast.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/main/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) &#x2014;
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
specific tokenizer&#x2019;s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"return_attention_mask"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/dpr/tokenization_dpr_fast.py#L393",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<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:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Dict[str, List[List[int]]]</code></p>
`}}),lt=new H({props:{title:"DPR specific outputs",local:"transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput",headingTag:"h2"}}),ct=new z({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
heads.`,name:"attentions"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/dpr/modeling_dpr.py#L62"}}),pt=new z({props:{name:"class transformers.models.dpr.modeling_dpr.DPRQuestionEncoderOutput",anchor:"transformers.models.dpr.modeling_dpr.DPRQuestionEncoderOutput",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.DPRQuestionEncoderOutput.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 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/main/src/transformers/models/dpr/modeling_dpr.py#L90"}}),mt=new z({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/main/src/transformers/models/dpr/modeling_dpr.py#L118"}}),ut=new H({props:{title:"DPRContextEncoder",local:"transformers.DPRContextEncoder",headingTag:"h2"}}),ft=new z({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/main/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/main/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/main/src/transformers/models/dpr/modeling_dpr.py#L441"}}),ht=new z({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/main/src/transformers/models/dpr/modeling_dpr.py#L449",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/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/main/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>) — 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>) — 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>) — 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:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/en/model_doc/dpr#transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput"
>transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ke=new Re({props:{$$slots:{default:[Wr]},$$scope:{ctx:w}}}),xe=new Ve({props:{anchor:"transformers.DPRContextEncoder.forward.example",$$slots:{default:[Br]},$$scope:{ctx:w}}}),gt=new H({props:{title:"DPRQuestionEncoder",local:"transformers.DPRQuestionEncoder",headingTag:"h2"}}),_t=new z({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/main/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/main/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/main/src/transformers/models/dpr/modeling_dpr.py#L522"}}),Tt=new z({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/main/src/transformers/models/dpr/modeling_dpr.py#L530",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/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/main/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>) — 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>) — 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>) — 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:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/en/model_doc/dpr#transformers.models.dpr.modeling_dpr.DPRQuestionEncoderOutput"
>transformers.models.dpr.modeling_dpr.DPRQuestionEncoderOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Pe=new Re({props:{$$slots:{default:[Qr]},$$scope:{ctx:w}}}),Me=new Ve({props:{anchor:"transformers.DPRQuestionEncoder.forward.example",$$slots:{default:[Gr]},$$scope:{ctx:w}}}),bt=new H({props:{title:"DPRReader",local:"transformers.DPRReader",headingTag:"h2"}}),vt=new z({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/main/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/main/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/main/src/transformers/models/dpr/modeling_dpr.py#L604"}}),yt=new z({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/main/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/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/dpr/modeling_dpr.py#L612",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/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/main/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>) — 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>) — 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>) — 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>) — 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>) — 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:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRReaderOutput"
>transformers.models.dpr.modeling_dpr.DPRReaderOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),De=new Re({props:{$$slots:{default:[Sr]},$$scope:{ctx:w}}}),Ce=new Ve({props:{anchor:"transformers.DPRReader.forward.example",$$slots:{default:[Xr]},$$scope:{ctx:w}}}),Rt=new H({props:{title:"TFDPRContextEncoder",local:"transformers.TFDPRContextEncoder",headingTag:"h2"}}),$t=new z({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/main/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/main/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/main/src/transformers/models/dpr/modeling_tf_dpr.py#L526"}}),ze=new Re({props:{$$slots:{default:[Or]},$$scope:{ctx:w}}}),wt=new z({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/main/src/transformers/models/dpr/modeling_tf_dpr.py#L538",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<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/main/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>) — 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>) — 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>) — 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:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.models.dpr.modeling_tf_dpr.TFDPRContextEncoderOutput</code> or <code>tuple(tf.Tensor)</code></p>
`}}),Fe=new Re({props:{$$slots:{default:[Ar]},$$scope:{ctx:w}}}),qe=new Ve({props:{anchor:"transformers.TFDPRContextEncoder.call.example",$$slots:{default:[Yr]},$$scope:{ctx:w}}}),kt=new H({props:{title:"TFDPRQuestionEncoder",local:"transformers.TFDPRQuestionEncoder",headingTag:"h2"}}),xt=new z({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/main/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/main/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/main/src/transformers/models/dpr/modeling_tf_dpr.py#L607"}}),Ee=new Re({props:{$$slots:{default:[Kr]},$$scope:{ctx:w}}}),Pt=new z({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/main/src/transformers/models/dpr/modeling_tf_dpr.py#L619",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<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/main/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>) — 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>) — 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>) — 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:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.models.dpr.modeling_tf_dpr.TFDPRQuestionEncoderOutput</code> or <code>tuple(tf.Tensor)</code></p>
`}}),Le=new Re({props:{$$slots:{default:[ea]},$$scope:{ctx:w}}}),Je=new Ve({props:{anchor:"transformers.TFDPRQuestionEncoder.call.example",$$slots:{default:[ta]},$$scope:{ctx:w}}}),Mt=new H({props:{title:"TFDPRReader",local:"transformers.TFDPRReader",headingTag:"h2"}}),Dt=new z({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/main/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/main/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/main/src/transformers/models/dpr/modeling_tf_dpr.py#L687"}}),je=new Re({props:{$$slots:{default:[na]},$$scope:{ctx:w}}}),Ct=new z({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/main/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/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFDPRReader.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
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/main/src/transformers/models/dpr/modeling_tf_dpr.py#L699",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<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/main/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>) — 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>) — 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>) — 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>) — 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>) — 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:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.models.dpr.modeling_tf_dpr.TFDPRReaderOutput</code> or <code>tuple(tf.Tensor)</code></p>
`}}),Ue=new Re({props:{$$slots:{default:[oa]},$$scope:{ctx:w}}}),He=new 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