import{s as Tl,o as bl,n as j}from"../chunks/scheduler.9bc65507.js";import{S as yl,i as wl,g as o,s as r,u as _,A as Rl,h as s,f as t,c as a,j as f,v,y as u,k as h,o as n,a as d,w as T,d as b,t as y,x as w}from"../chunks/index.78c82d43.js";import{T as De}from"../chunks/Tip.87d55b76.js";import{D}from"../chunks/Docstring.4e7352e2.js";import{C as Tt}from"../chunks/CodeBlock.73e038be.js";import{I}from"../chunks/IconCopyLink.bedaa44d.js";import{E as vt}from"../chunks/ExampleCodeBlock.872b014d.js";function $l(P){let i,k="Example:",m,p,$;return p=new Tt({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(){i=o("p"),i.textContent=k,m=r(),_(p.$$.fragment)},l(l){i=s(l,"P",{"data-svelte-h":!0}),u(i)!=="svelte-11lpom8"&&(i.textContent=k),m=a(l),v(p.$$.fragment,l)},m(l,R){d(l,i,R),d(l,m,R),T(p,l,R),$=!0},p:j,i(l){$||(b(p.$$.fragment,l),$=!0)},o(l){y(p.$$.fragment,l),$=!1},d(l){l&&(t(i),t(m)),w(p,l)}}}function kl(P){let i,k="with the format:",m,p,$;return p=new Tt({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(){i=o("p"),i.textContent=k,m=r(),_(p.$$.fragment)},l(l){i=s(l,"P",{"data-svelte-h":!0}),u(i)!=="svelte-1kqkfm0"&&(i.textContent=k),m=a(l),v(p.$$.fragment,l)},m(l,R){d(l,i,R),d(l,m,R),T(p,l,R),$=!0},p:j,i(l){$||(b(p.$$.fragment,l),$=!0)},o(l){y(p.$$.fragment,l),$=!1},d(l){l&&(t(i),t(m)),w(p,l)}}}function xl(P){let i,k=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){i=o("p"),i.innerHTML=k},l(m){i=s(m,"P",{"data-svelte-h":!0}),u(i)!=="svelte-fincs2"&&(i.innerHTML=k)},m(m,p){d(m,i,p)},p:j,d(m){m&&t(i)}}}function Pl(P){let i,k="Examples:",m,p,$;return p=new Tt({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(){i=o("p"),i.textContent=k,m=r(),_(p.$$.fragment)},l(l){i=s(l,"P",{"data-svelte-h":!0}),u(i)!=="svelte-kvfsh7"&&(i.textContent=k),m=a(l),v(p.$$.fragment,l)},m(l,R){d(l,i,R),d(l,m,R),T(p,l,R),$=!0},p:j,i(l){$||(b(p.$$.fragment,l),$=!0)},o(l){y(p.$$.fragment,l),$=!1},d(l){l&&(t(i),t(m)),w(p,l)}}}function Ml(P){let i,k=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){i=o("p"),i.innerHTML=k},l(m){i=s(m,"P",{"data-svelte-h":!0}),u(i)!=="svelte-fincs2"&&(i.innerHTML=k)},m(m,p){d(m,i,p)},p:j,d(m){m&&t(i)}}}function Cl(P){let i,k="Examples:",m,p,$;return p=new Tt({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(){i=o("p"),i.textContent=k,m=r(),_(p.$$.fragment)},l(l){i=s(l,"P",{"data-svelte-h":!0}),u(i)!=="svelte-kvfsh7"&&(i.textContent=k),m=a(l),v(p.$$.fragment,l)},m(l,R){d(l,i,R),d(l,m,R),T(p,l,R),$=!0},p:j,i(l){$||(b(p.$$.fragment,l),$=!0)},o(l){y(p.$$.fragment,l),$=!1},d(l){l&&(t(i),t(m)),w(p,l)}}}function Dl(P){let i,k=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){i=o("p"),i.innerHTML=k},l(m){i=s(m,"P",{"data-svelte-h":!0}),u(i)!=="svelte-fincs2"&&(i.innerHTML=k)},m(m,p){d(m,i,p)},p:j,d(m){m&&t(i)}}}function zl(P){let i,k="Examples:",m,p,$;return p=new Tt({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(){i=o("p"),i.textContent=k,m=r(),_(p.$$.fragment)},l(l){i=s(l,"P",{"data-svelte-h":!0}),u(i)!=="svelte-kvfsh7"&&(i.textContent=k),m=a(l),v(p.$$.fragment,l)},m(l,R){d(l,i,R),d(l,m,R),T(p,l,R),$=!0},p:j,i(l){$||(b(p.$$.fragment,l),$=!0)},o(l){y(p.$$.fragment,l),$=!1},d(l){l&&(t(i),t(m)),w(p,l)}}}function El(P){let i,k="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",m,p,$="<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>",l,R,U=`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:`,E,C,ie=`<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>`,z,M,le=`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(){i=o("p"),i.innerHTML=k,m=r(),p=o("ul"),p.innerHTML=$,l=r(),R=o("p"),R.innerHTML=U,E=r(),C=o("ul"),C.innerHTML=ie,z=r(),M=o("p"),M.innerHTML=le},l(g){i=s(g,"P",{"data-svelte-h":!0}),u(i)!=="svelte-1ajbfxg"&&(i.innerHTML=k),m=a(g),p=s(g,"UL",{"data-svelte-h":!0}),u(p)!=="svelte-qm1t26"&&(p.innerHTML=$),l=a(g),R=s(g,"P",{"data-svelte-h":!0}),u(R)!=="svelte-1v9qsc5"&&(R.innerHTML=U),E=a(g),C=s(g,"UL",{"data-svelte-h":!0}),u(C)!=="svelte-15scerc"&&(C.innerHTML=ie),z=a(g),M=s(g,"P",{"data-svelte-h":!0}),u(M)!=="svelte-1an3odd"&&(M.innerHTML=le)},m(g,x){d(g,i,x),d(g,m,x),d(g,p,x),d(g,l,x),d(g,R,x),d(g,E,x),d(g,C,x),d(g,z,x),d(g,M,x)},p:j,d(g){g&&(t(i),t(m),t(p),t(l),t(R),t(E),t(C),t(z),t(M))}}}function Fl(P){let i,k=`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(){i=o("p"),i.innerHTML=k},l(m){i=s(m,"P",{"data-svelte-h":!0}),u(i)!=="svelte-fincs2"&&(i.innerHTML=k)},m(m,p){d(m,i,p)},p:j,d(m){m&&t(i)}}}function ql(P){let i,k="Examples:",m,p,$;return p=new Tt({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(){i=o("p"),i.textContent=k,m=r(),_(p.$$.fragment)},l(l){i=s(l,"P",{"data-svelte-h":!0}),u(i)!=="svelte-kvfsh7"&&(i.textContent=k),m=a(l),v(p.$$.fragment,l)},m(l,R){d(l,i,R),d(l,m,R),T(p,l,R),$=!0},p:j,i(l){$||(b(p.$$.fragment,l),$=!0)},o(l){y(p.$$.fragment,l),$=!1},d(l){l&&(t(i),t(m)),w(p,l)}}}function Ll(P){let i,k="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",m,p,$="<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>",l,R,U=`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:`,E,C,ie=`<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>`,z,M,le=`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(){i=o("p"),i.innerHTML=k,m=r(),p=o("ul"),p.innerHTML=$,l=r(),R=o("p"),R.innerHTML=U,E=r(),C=o("ul"),C.innerHTML=ie,z=r(),M=o("p"),M.innerHTML=le},l(g){i=s(g,"P",{"data-svelte-h":!0}),u(i)!=="svelte-1ajbfxg"&&(i.innerHTML=k),m=a(g),p=s(g,"UL",{"data-svelte-h":!0}),u(p)!=="svelte-qm1t26"&&(p.innerHTML=$),l=a(g),R=s(g,"P",{"data-svelte-h":!0}),u(R)!=="svelte-1v9qsc5"&&(R.innerHTML=U),E=a(g),C=s(g,"UL",{"data-svelte-h":!0}),u(C)!=="svelte-15scerc"&&(C.innerHTML=ie),z=a(g),M=s(g,"P",{"data-svelte-h":!0}),u(M)!=="svelte-1an3odd"&&(M.innerHTML=le)},m(g,x){d(g,i,x),d(g,m,x),d(g,p,x),d(g,l,x),d(g,R,x),d(g,E,x),d(g,C,x),d(g,z,x),d(g,M,x)},p:j,d(g){g&&(t(i),t(m),t(p),t(l),t(R),t(E),t(C),t(z),t(M))}}}function Jl(P){let i,k=`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(){i=o("p"),i.innerHTML=k},l(m){i=s(m,"P",{"data-svelte-h":!0}),u(i)!=="svelte-fincs2"&&(i.innerHTML=k)},m(m,p){d(m,i,p)},p:j,d(m){m&&t(i)}}}function Hl(P){let i,k="Examples:",m,p,$;return p=new Tt({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(){i=o("p"),i.textContent=k,m=r(),_(p.$$.fragment)},l(l){i=s(l,"P",{"data-svelte-h":!0}),u(i)!=="svelte-kvfsh7"&&(i.textContent=k),m=a(l),v(p.$$.fragment,l)},m(l,R){d(l,i,R),d(l,m,R),T(p,l,R),$=!0},p:j,i(l){$||(b(p.$$.fragment,l),$=!0)},o(l){y(p.$$.fragment,l),$=!1},d(l){l&&(t(i),t(m)),w(p,l)}}}function jl(P){let i,k="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",m,p,$="<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>",l,R,U=`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:`,E,C,ie=`<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>`,z,M,le=`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(){i=o("p"),i.innerHTML=k,m=r(),p=o("ul"),p.innerHTML=$,l=r(),R=o("p"),R.innerHTML=U,E=r(),C=o("ul"),C.innerHTML=ie,z=r(),M=o("p"),M.innerHTML=le},l(g){i=s(g,"P",{"data-svelte-h":!0}),u(i)!=="svelte-1ajbfxg"&&(i.innerHTML=k),m=a(g),p=s(g,"UL",{"data-svelte-h":!0}),u(p)!=="svelte-qm1t26"&&(p.innerHTML=$),l=a(g),R=s(g,"P",{"data-svelte-h":!0}),u(R)!=="svelte-1v9qsc5"&&(R.innerHTML=U),E=a(g),C=s(g,"UL",{"data-svelte-h":!0}),u(C)!=="svelte-15scerc"&&(C.innerHTML=ie),z=a(g),M=s(g,"P",{"data-svelte-h":!0}),u(M)!=="svelte-1an3odd"&&(M.innerHTML=le)},m(g,x){d(g,i,x),d(g,m,x),d(g,p,x),d(g,l,x),d(g,R,x),d(g,E,x),d(g,C,x),d(g,z,x),d(g,M,x)},p:j,d(g){g&&(t(i),t(m),t(p),t(l),t(R),t(E),t(C),t(z),t(M))}}}function Ul(P){let i,k=`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(){i=o("p"),i.innerHTML=k},l(m){i=s(m,"P",{"data-svelte-h":!0}),u(i)!=="svelte-fincs2"&&(i.innerHTML=k)},m(m,p){d(m,i,p)},p:j,d(m){m&&t(i)}}}function Il(P){let i,k="Examples:",m,p,$;return p=new Tt({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(){i=o("p"),i.textContent=k,m=r(),_(p.$$.fragment)},l(l){i=s(l,"P",{"data-svelte-h":!0}),u(i)!=="svelte-kvfsh7"&&(i.textContent=k),m=a(l),v(p.$$.fragment,l)},m(l,R){d(l,i,R),d(l,m,R),T(p,l,R),$=!0},p:j,i(l){$||(b(p.$$.fragment,l),$=!0)},o(l){y(p.$$.fragment,l),$=!1},d(l){l&&(t(i),t(m)),w(p,l)}}}function Nl(P){let i,k,m,p,$,l,R,U,E,C,ie="DPR",z,M,le='<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>',g,x,ze,Mo,bt,Ys,fn,Fa="Overview",es,yt,qa=`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.`,ts,wt,La="The abstract from the paper is the following:",ns,Rt,Ja=`<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>`,os,$t,Ha='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>.',ss,kt,ja="Tips:",rs,xt,Ua="<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>",as,me,Ee,Co,Pt,Ks,gn,Ia="DPRConfig",is,N,Mt,er,_n,Na='<a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> is the configuration class to store the configuration of a <em>DPRModel</em>.',tr,vn,Za=`This is the configuration class to store the configuration of a <a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRContextEncoder">DPRContextEncoder</a>, <a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a>, or a
<a href="/docs/transformers/v4.34.0/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.`,nr,Tn,Va='This class is a subclass of <a href="/docs/transformers/v4.34.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a>. Please check the superclass for the documentation of all kwargs.',or,Fe,ls,he,qe,Do,Ct,sr,bn,Wa="DPRContextEncoderTokenizer",ds,Q,Dt,rr,yn,Qa="Construct a DPRContextEncoder tokenizer.",ar,wn,Sa=`<a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRContextEncoderTokenizer">DPRContextEncoderTokenizer</a> is identical to <a href="/docs/transformers/v4.34.0/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> and runs end-to-end tokenization: punctuation
splitting and wordpiece.`,ir,Rn,Ba='Refer to superclass <a href="/docs/transformers/v4.34.0/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> for usage examples and documentation concerning parameters.',cs,fe,Le,zo,zt,lr,$n,Ga="DPRContextEncoderTokenizerFast",ps,S,Et,dr,kn,Aa="Construct a “fast” DPRContextEncoder tokenizer (backed by HuggingFace’s <em>tokenizers</em> library).",cr,xn,Xa=`<a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRContextEncoderTokenizerFast">DPRContextEncoderTokenizerFast</a> is identical to <a href="/docs/transformers/v4.34.0/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> and runs end-to-end tokenization:
punctuation splitting and wordpiece.`,pr,Pn,Oa='Refer to superclass <a href="/docs/transformers/v4.34.0/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> for usage examples and documentation concerning parameters.',us,ge,Je,Eo,Ft,ur,Mn,Ya="DPRQuestionEncoderTokenizer",ms,B,qt,mr,Cn,Ka="Constructs a DPRQuestionEncoder tokenizer.",hr,Dn,ei=`<a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRQuestionEncoderTokenizer">DPRQuestionEncoderTokenizer</a> is identical to <a href="/docs/transformers/v4.34.0/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> and runs end-to-end tokenization: punctuation
splitting and wordpiece.`,fr,zn,ti='Refer to superclass <a href="/docs/transformers/v4.34.0/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> for usage examples and documentation concerning parameters.',hs,_e,He,Fo,Lt,gr,En,ni="DPRQuestionEncoderTokenizerFast",fs,G,Jt,_r,Fn,oi="Constructs a “fast” DPRQuestionEncoder tokenizer (backed by HuggingFace’s <em>tokenizers</em> library).",vr,qn,si=`<a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRQuestionEncoderTokenizerFast">DPRQuestionEncoderTokenizerFast</a> is identical to <a href="/docs/transformers/v4.34.0/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> and runs end-to-end tokenization:
punctuation splitting and wordpiece.`,Tr,Ln,ri='Refer to superclass <a href="/docs/transformers/v4.34.0/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> for usage examples and documentation concerning parameters.',gs,ve,je,qo,Ht,br,Jn,ai="DPRReaderTokenizer",_s,F,jt,yr,Hn,ii="Construct a DPRReader tokenizer.",wr,jn,li=`<a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRReaderTokenizer">DPRReaderTokenizer</a> is almost identical to <a href="/docs/transformers/v4.34.0/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/v4.34.0/en/model_doc/dpr#transformers.DPRReader">DPRReader</a> model.`,Rr,Un,di='Refer to superclass <a href="/docs/transformers/v4.34.0/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> for usage examples and documentation concerning parameters.',$r,In,ci=`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>`,kr,Ue,vs,Te,Ie,Lo,Ut,xr,Nn,pi="DPRReaderTokenizerFast",Ts,q,It,Pr,Zn,ui="Constructs a “fast” DPRReader tokenizer (backed by HuggingFace’s <em>tokenizers</em> library).",Mr,Vn,mi=`<a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRReaderTokenizerFast">DPRReaderTokenizerFast</a> is almost identical to <a href="/docs/transformers/v4.34.0/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/v4.34.0/en/model_doc/dpr#transformers.DPRReader">DPRReader</a> model.`,Cr,Wn,hi='Refer to superclass <a href="/docs/transformers/v4.34.0/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> for usage examples and documentation concerning parameters.',Dr,Qn,fi=`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:`,zr,Sn,gi="[CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>",bs,be,Ne,Jo,Nt,Er,Bn,_i="DPR specific outputs",ys,ye,Zt,Fr,Gn,vi='Class for outputs of <a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a>.',ws,we,Vt,qr,An,Ti='Class for outputs of <a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a>.',Rs,Re,Wt,Lr,Xn,bi='Class for outputs of <a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a>.',$s,$e,Ze,Ho,Qt,Jr,On,yi="DPRContextEncoder",ks,Z,St,Hr,Yn,wi="The bare DPRContextEncoder transformer outputting pooler outputs as context representations.",jr,Kn,Ri=`This model inherits from <a href="/docs/transformers/v4.34.0/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Ur,eo,$i=`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.`,Ir,X,Bt,Nr,to,ki='The <a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRContextEncoder">DPRContextEncoder</a> forward method, overrides the <code>__call__</code> special method.',Zr,Ve,Vr,We,xs,ke,Qe,jo,Gt,Wr,no,xi="DPRQuestionEncoder",Ps,V,At,Qr,oo,Pi="The bare DPRQuestionEncoder transformer outputting pooler outputs as question representations.",Sr,so,Mi=`This model inherits from <a href="/docs/transformers/v4.34.0/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.)`,Br,ro,Ci=`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.`,Gr,O,Xt,Ar,ao,Di='The <a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a> forward method, overrides the <code>__call__</code> special method.',Xr,Se,Or,Be,Ms,xe,Ge,Uo,Ot,Yr,io,zi="DPRReader",Cs,W,Yt,Kr,lo,Ei="The bare DPRReader transformer outputting span predictions.",ea,co,Fi=`This model inherits from <a href="/docs/transformers/v4.34.0/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.)`,ta,po,qi=`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.`,na,Y,Kt,oa,uo,Li='The <a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRReader">DPRReader</a> forward method, overrides the <code>__call__</code> special method.',sa,Ae,ra,Xe,Ds,Pe,Oe,Io,en,aa,mo,Ji="TFDPRContextEncoder",zs,L,tn,ia,ho,Hi="The bare DPRContextEncoder transformer outputting pooler outputs as context representations.",la,fo,ji=`This model inherits from <a href="/docs/transformers/v4.34.0/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.)`,da,go,Ui=`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.`,ca,Ye,pa,K,nn,ua,_o,Ii='The <a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.TFDPRContextEncoder">TFDPRContextEncoder</a> forward method, overrides the <code>__call__</code> special method.',ma,Ke,ha,et,Es,Me,tt,No,on,fa,vo,Ni="TFDPRQuestionEncoder",Fs,J,sn,ga,To,Zi="The bare DPRQuestionEncoder transformer outputting pooler outputs as question representations.",_a,bo,Vi=`This model inherits from <a href="/docs/transformers/v4.34.0/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.)`,va,yo,Wi=`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.`,Ta,nt,ba,ee,rn,ya,wo,Qi='The <a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.TFDPRQuestionEncoder">TFDPRQuestionEncoder</a> forward method, overrides the <code>__call__</code> special method.',wa,ot,Ra,st,qs,Ce,rt,Zo,an,$a,Ro,Si="TFDPRReader",Ls,H,ln,ka,$o,Bi="The bare DPRReader transformer outputting span predictions.",xa,ko,Gi=`This model inherits from <a href="/docs/transformers/v4.34.0/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.)`,Pa,xo,Ai=`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.`,Ma,at,Ca,te,dn,Da,Po,Xi='The <a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.TFDPRReader">TFDPRReader</a> forward method, overrides the <code>__call__</code> special method.',za,it,Ea,lt,Js,Vo,Hs;return U=new I({}),bt=new I({}),Pt=new I({}),Mt=new D({props:{name:"class transformers.DPRConfig",anchor:"transformers.DPRConfig",parameters:[{name:"vocab_size",val:" = 30522"},{name:"hidden_size",val:" = 768"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 12"},{name:"intermediate_size",val:" = 3072"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout_prob",val:" = 0.1"},{name:"attention_probs_dropout_prob",val:" = 0.1"},{name:"max_position_embeddings",val:" = 512"},{name:"type_vocab_size",val:" = 2"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-12"},{name:"pad_token_id",val:" = 0"},{name:"position_embedding_type",val:" = 'absolute'"},{name:"projection_dim",val:": int = 0"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.DPRConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 30522) &#x2014;
Vocabulary size of the DPR model. Defines the different tokens that can be represented by the <em>inputs_ids</em>
passed to the forward method of <a href="/docs/transformers/v4.34.0/en/model_doc/bert#transformers.BertModel">BertModel</a>.`,name:"vocab_size"},{anchor:"transformers.DPRConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.DPRConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.DPRConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.DPRConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3072) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.DPRConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;silu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.DPRConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.DPRConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.DPRConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"max_position_embeddings"},{anchor:"transformers.DPRConfig.type_vocab_size",description:`<strong>type_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The vocabulary size of the <em>token_type_ids</em> passed into <a href="/docs/transformers/v4.34.0/en/model_doc/bert#transformers.BertModel">BertModel</a>.`,name:"type_vocab_size"},{anchor:"transformers.DPRConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.DPRConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-12) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.DPRConfig.position_embedding_type",description:`<strong>position_embedding_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;absolute&quot;</code>) &#x2014;
Type of position embedding. Choose one of <code>&quot;absolute&quot;</code>, <code>&quot;relative_key&quot;</code>, <code>&quot;relative_key_query&quot;</code>. For
positional embeddings use <code>&quot;absolute&quot;</code>. For more information on <code>&quot;relative_key&quot;</code>, please refer to
<a href="https://arxiv.org/abs/1803.02155" rel="nofollow">Self-Attention with Relative Position Representations (Shaw et al.)</a>.
For more information on <code>&quot;relative_key_query&quot;</code>, please refer to <em>Method 4</em> in <a href="https://arxiv.org/abs/2009.13658" rel="nofollow">Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)</a>.`,name:"position_embedding_type"},{anchor:"transformers.DPRConfig.projection_dim",description:`<strong>projection_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
Dimension of the projection for the context and question encoders. If it is set to zero (default), then no
projection is done.`,name:"projection_dim"}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/dpr/configuration_dpr.py#L45"}}),Fe=new vt({props:{anchor:"transformers.DPRConfig.example",$$slots:{default:[$l]},$$scope:{ctx:P}}}),Ct=new I({}),Dt=new D({props:{name:"class transformers.DPRContextEncoderTokenizer",anchor:"transformers.DPRContextEncoderTokenizer",parameters:[{name:"vocab_file",val:""},{name:"do_lower_case",val:" = True"},{name:"do_basic_tokenize",val:" = True"},{name:"never_split",val:" = None"},{name:"unk_token",val:" = '[UNK]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"tokenize_chinese_chars",val:" = True"},{name:"strip_accents",val:" = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/dpr/tokenization_dpr.py#L113"}}),zt=new I({}),Et=new D({props:{name:"class transformers.DPRContextEncoderTokenizerFast",anchor:"transformers.DPRContextEncoderTokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"do_lower_case",val:" = True"},{name:"unk_token",val:" = '[UNK]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"tokenize_chinese_chars",val:" = True"},{name:"strip_accents",val:" = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/dpr/tokenization_dpr_fast.py#L114"}}),Ft=new I({}),qt=new D({props:{name:"class transformers.DPRQuestionEncoderTokenizer",anchor:"transformers.DPRQuestionEncoderTokenizer",parameters:[{name:"vocab_file",val:""},{name:"do_lower_case",val:" = True"},{name:"do_basic_tokenize",val:" = True"},{name:"never_split",val:" = None"},{name:"unk_token",val:" = '[UNK]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"tokenize_chinese_chars",val:" = True"},{name:"strip_accents",val:" = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/dpr/tokenization_dpr.py#L129"}}),Lt=new I({}),Jt=new D({props:{name:"class transformers.DPRQuestionEncoderTokenizerFast",anchor:"transformers.DPRQuestionEncoderTokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"do_lower_case",val:" = True"},{name:"unk_token",val:" = '[UNK]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"tokenize_chinese_chars",val:" = True"},{name:"strip_accents",val:" = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/dpr/tokenization_dpr_fast.py#L131"}}),Ht=new I({}),jt=new D({props:{name:"class transformers.DPRReaderTokenizer",anchor:"transformers.DPRReaderTokenizer",parameters:[{name:"vocab_file",val:""},{name:"do_lower_case",val:" = True"},{name:"do_basic_tokenize",val:" = True"},{name:"never_split",val:" = None"},{name:"unk_token",val:" = '[UNK]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"tokenize_chinese_chars",val:" = True"},{name:"strip_accents",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.DPRReaderTokenizer.questions",description:`<strong>questions</strong> (<code>str</code> or <code>List[str]</code>) &#x2014;
The questions to be encoded. You can specify one question for many passages. In this case, the question
will be duplicated like <code>[questions] * n_passages</code>. Otherwise you have to specify as many questions as in
<code>titles</code> or <code>texts</code>.`,name:"questions"},{anchor:"transformers.DPRReaderTokenizer.titles",description:`<strong>titles</strong> (<code>str</code> or <code>List[str]</code>) &#x2014;
The passages titles to be encoded. This can be a string or a list of strings if there are several passages.`,name:"titles"},{anchor:"transformers.DPRReaderTokenizer.texts",description:`<strong>texts</strong> (<code>str</code> or <code>List[str]</code>) &#x2014;
The passages texts to be encoded. This can be a string or a list of strings if there are several passages.`,name:"texts"},{anchor:"transformers.DPRReaderTokenizer.padding",description:`<strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/v4.34.0/en/internal/file_utils#transformers.utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>&apos;longest&apos;</code>: Pad to the longest sequence in the batch (or no padding if only a single sequence
if provided).</li>
<li><code>&apos;max_length&apos;</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>&apos;do_not_pad&apos;</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul>`,name:"padding"},{anchor:"transformers.DPRReaderTokenizer.truncation",description:`<strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/v4.34.0/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>&apos;longest_first&apos;</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to
the maximum acceptable input length for the model if that argument is not provided. This will truncate
token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
of pairs) is provided.</li>
<li><code>&apos;only_first&apos;</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the first
sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>&apos;only_second&apos;</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the
second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>&apos;do_not_truncate&apos;</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul>`,name:"truncation"},{anchor:"transformers.DPRReaderTokenizer.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Controls the maximum length to use by one of the truncation/padding parameters.</p>
<p>If left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
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/v4.34.0/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/v4.34.0/src/transformers/models/dpr/tokenization_dpr.py#L395",returnDescription:`
<p>A dictionary with the following keys:</p>
<ul>
<li><code>input_ids</code>: List of token ids to be fed to a model.</li>
<li><code>attention_mask</code>: List of indices specifying which tokens should be attended to by the model.</li>
</ul>
`,returnType:`
<p><code>Dict[str, List[List[int]]]</code></p>
`}}),Ue=new vt({props:{anchor:"transformers.DPRReaderTokenizer.example",$$slots:{default:[kl]},$$scope:{ctx:P}}}),Ut=new I({}),It=new D({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/v4.34.0/en/internal/file_utils#transformers.utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>&apos;longest&apos;</code>: Pad to the longest sequence in the batch (or no padding if only a single sequence
if provided).</li>
<li><code>&apos;max_length&apos;</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>&apos;do_not_pad&apos;</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul>`,name:"padding"},{anchor:"transformers.DPRReaderTokenizerFast.truncation",description:`<strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/v4.34.0/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>&apos;longest_first&apos;</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to
the maximum acceptable input length for the model if that argument is not provided. This will truncate
token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
of pairs) is provided.</li>
<li><code>&apos;only_first&apos;</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the first
sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>&apos;only_second&apos;</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the
second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>&apos;do_not_truncate&apos;</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul>`,name:"truncation"},{anchor:"transformers.DPRReaderTokenizerFast.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Controls the maximum length to use by one of the truncation/padding parameters.</p>
<p>If left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length
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/v4.34.0/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/v4.34.0/src/transformers/models/dpr/tokenization_dpr_fast.py#L393",returnDescription:`
<p>A dictionary with the following keys:</p>
<ul>
<li><code>input_ids</code>: List of token ids to be fed to a model.</li>
<li><code>attention_mask</code>: List of indices specifying which tokens should be attended to by the model.</li>
</ul>
`,returnType:`
<p><code>Dict[str, List[List[int]]]</code></p>
`}}),Nt=new I({}),Zt=new D({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/v4.34.0/src/transformers/models/dpr/modeling_dpr.py#L62"}}),Vt=new D({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/v4.34.0/src/transformers/models/dpr/modeling_dpr.py#L90"}}),Wt=new D({props:{name:"class transformers.DPRReaderOutput",anchor:"transformers.DPRReaderOutput",parameters:[{name:"start_logits",val:": FloatTensor"},{name:"end_logits",val:": FloatTensor = None"},{name:"relevance_logits",val:": FloatTensor = None"},{name:"hidden_states",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"attentions",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}],parametersDescription:[{anchor:"transformers.DPRReaderOutput.start_logits",description:`<strong>start_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(n_passages, sequence_length)</code>) &#x2014;
Logits of the start index of the span for each passage.`,name:"start_logits"},{anchor:"transformers.DPRReaderOutput.end_logits",description:`<strong>end_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(n_passages, sequence_length)</code>) &#x2014;
Logits of the end index of the span for each passage.`,name:"end_logits"},{anchor:"transformers.DPRReaderOutput.relevance_logits",description:`<strong>relevance_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(n_passages, )</code>) &#x2014;
Outputs of the QA classifier of the DPRReader that corresponds to the scores of each passage to answer the
question, compared to all the other passages.`,name:"relevance_logits"},{anchor:"transformers.DPRReaderOutput.hidden_states",description:`<strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.`,name:"hidden_states"},{anchor:"transformers.DPRReaderOutput.attentions",description:`<strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) &#x2014;
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.`,name:"attentions"}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/dpr/modeling_dpr.py#L118"}}),Qt=new I({}),St=new D({props:{name:"class transformers.DPRContextEncoder",anchor:"transformers.DPRContextEncoder",parameters:[{name:"config",val:": DPRConfig"}],parametersDescription:[{anchor:"transformers.DPRContextEncoder.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.34.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/dpr/modeling_dpr.py#L441"}}),Bt=new D({props:{name:"forward",anchor:"transformers.DPRContextEncoder.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.DPRContextEncoder.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. To match pretraining, DPR input sequence should be
formatted with [CLS] and [SEP] tokens as follows:</p>
<p>(a) For sequence pairs (for a pair title+text for example):`,name:"input_ids"}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/dpr/modeling_dpr.py#L449",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput"
>transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRConfig"
>DPRConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, embeddings_size)</code>) — 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:`
<p><a
  href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput"
>transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ve=new De({props:{$$slots:{default:[xl]},$$scope:{ctx:P}}}),We=new vt({props:{anchor:"transformers.DPRContextEncoder.forward.example",$$slots:{default:[Pl]},$$scope:{ctx:P}}}),Gt=new I({}),At=new D({props:{name:"class transformers.DPRQuestionEncoder",anchor:"transformers.DPRQuestionEncoder",parameters:[{name:"config",val:": DPRConfig"}],parametersDescription:[{anchor:"transformers.DPRQuestionEncoder.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.34.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/dpr/modeling_dpr.py#L522"}}),Xt=new D({props:{name:"forward",anchor:"transformers.DPRQuestionEncoder.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.DPRQuestionEncoder.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. To match pretraining, DPR input sequence should be
formatted with [CLS] and [SEP] tokens as follows:</p>
<p>(a) For sequence pairs (for a pair title+text for example):`,name:"input_ids"}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/dpr/modeling_dpr.py#L530",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.models.dpr.modeling_dpr.DPRQuestionEncoderOutput"
>transformers.models.dpr.modeling_dpr.DPRQuestionEncoderOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRConfig"
>DPRConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, embeddings_size)</code>) — 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:`
<p><a
  href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.models.dpr.modeling_dpr.DPRQuestionEncoderOutput"
>transformers.models.dpr.modeling_dpr.DPRQuestionEncoderOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Se=new De({props:{$$slots:{default:[Ml]},$$scope:{ctx:P}}}),Be=new vt({props:{anchor:"transformers.DPRQuestionEncoder.forward.example",$$slots:{default:[Cl]},$$scope:{ctx:P}}}),Ot=new I({}),Yt=new D({props:{name:"class transformers.DPRReader",anchor:"transformers.DPRReader",parameters:[{name:"config",val:": DPRConfig"}],parametersDescription:[{anchor:"transformers.DPRReader.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.34.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/dpr/modeling_dpr.py#L604"}}),Kt=new D({props:{name:"forward",anchor:"transformers.DPRReader.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.DPRReader.forward.input_ids",description:`<strong>input_ids</strong> (<code>Tuple[torch.LongTensor]</code> of shapes <code>(n_passages, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. It has to be a sequence triplet with 1) the question
and 2) the passages titles and 3) the passages texts To match pretraining, DPR <code>input_ids</code> sequence should
be formatted with [CLS] and [SEP] with the format:</p>
<p><code>[CLS] &lt;question token ids&gt; [SEP] &lt;titles ids&gt; [SEP] &lt;texts ids&gt;</code></p>
<p>DPR is a model with absolute position embeddings so it&#x2019;s usually advised to pad the inputs on the right
rather than the left.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRReaderTokenizer">DPRReaderTokenizer</a>. See this class documentation for more details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.DPRReader.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(n_passages, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.DPRReader.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(n_passages, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DPRReader.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DPRReader.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.DPRReader.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.34.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/dpr/modeling_dpr.py#L612",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRReaderOutput"
>transformers.models.dpr.modeling_dpr.DPRReaderOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRConfig"
>DPRConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>start_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(n_passages, sequence_length)</code>) — 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:`
<p><a
  href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRReaderOutput"
>transformers.models.dpr.modeling_dpr.DPRReaderOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ae=new De({props:{$$slots:{default:[Dl]},$$scope:{ctx:P}}}),Xe=new vt({props:{anchor:"transformers.DPRReader.forward.example",$$slots:{default:[zl]},$$scope:{ctx:P}}}),en=new I({}),tn=new D({props:{name:"class transformers.TFDPRContextEncoder",anchor:"transformers.TFDPRContextEncoder",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDPRContextEncoder.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.34.0/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/dpr/modeling_tf_dpr.py#L526"}}),Ye=new De({props:{$$slots:{default:[El]},$$scope:{ctx:P}}}),nn=new D({props:{name:"call",anchor:"transformers.TFDPRContextEncoder.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": tf.Tensor | None = None"},{name:"token_type_ids",val:": tf.Tensor | None = None"},{name:"inputs_embeds",val:": tf.Tensor | None = None"},{name:"output_attentions",val:": bool | None = None"},{name:"output_hidden_states",val:": bool | None = None"},{name:"return_dict",val:": bool | None = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFDPRContextEncoder.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. To match pretraining, DPR input sequence should be
formatted with [CLS] and [SEP] tokens as follows:</p>
<p>(a) For sequence pairs (for a pair title+text for example):`,name:"input_ids"}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/dpr/modeling_tf_dpr.py#L538",returnDescription:`
<p>A <code>transformers.models.dpr.modeling_tf_dpr.TFDPRContextEncoderOutput</code> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRConfig"
>DPRConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>pooler_output</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, embeddings_size)</code>) — 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:`
<p><code>transformers.models.dpr.modeling_tf_dpr.TFDPRContextEncoderOutput</code> or <code>tuple(tf.Tensor)</code></p>
`}}),Ke=new De({props:{$$slots:{default:[Fl]},$$scope:{ctx:P}}}),et=new vt({props:{anchor:"transformers.TFDPRContextEncoder.call.example",$$slots:{default:[ql]},$$scope:{ctx:P}}}),on=new I({}),sn=new D({props:{name:"class transformers.TFDPRQuestionEncoder",anchor:"transformers.TFDPRQuestionEncoder",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDPRQuestionEncoder.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.34.0/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/dpr/modeling_tf_dpr.py#L607"}}),nt=new De({props:{$$slots:{default:[Ll]},$$scope:{ctx:P}}}),rn=new D({props:{name:"call",anchor:"transformers.TFDPRQuestionEncoder.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": tf.Tensor | None = None"},{name:"token_type_ids",val:": tf.Tensor | None = None"},{name:"inputs_embeds",val:": tf.Tensor | None = None"},{name:"output_attentions",val:": bool | None = None"},{name:"output_hidden_states",val:": bool | None = None"},{name:"return_dict",val:": bool | None = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFDPRQuestionEncoder.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. To match pretraining, DPR input sequence should be
formatted with [CLS] and [SEP] tokens as follows:</p>
<p>(a) For sequence pairs (for a pair title+text for example):`,name:"input_ids"}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/dpr/modeling_tf_dpr.py#L619",returnDescription:`
<p>A <code>transformers.models.dpr.modeling_tf_dpr.TFDPRQuestionEncoderOutput</code> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRConfig"
>DPRConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>pooler_output</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, embeddings_size)</code>) — 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:`
<p><code>transformers.models.dpr.modeling_tf_dpr.TFDPRQuestionEncoderOutput</code> or <code>tuple(tf.Tensor)</code></p>
`}}),ot=new De({props:{$$slots:{default:[Jl]},$$scope:{ctx:P}}}),st=new vt({props:{anchor:"transformers.TFDPRQuestionEncoder.call.example",$$slots:{default:[Hl]},$$scope:{ctx:P}}}),an=new I({}),ln=new D({props:{name:"class transformers.TFDPRReader",anchor:"transformers.TFDPRReader",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDPRReader.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.34.0/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/dpr/modeling_tf_dpr.py#L687"}}),at=new De({props:{$$slots:{default:[jl]},$$scope:{ctx:P}}}),dn=new D({props:{name:"call",anchor:"transformers.TFDPRReader.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": tf.Tensor | None = None"},{name:"inputs_embeds",val:": tf.Tensor | None = None"},{name:"output_attentions",val:": bool | None = None"},{name:"output_hidden_states",val:": bool | None = None"},{name:"return_dict",val:": bool | None = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFDPRReader.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shapes <code>(n_passages, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. It has to be a sequence triplet with 1) the question
and 2) the passages titles and 3) the passages texts To match pretraining, DPR <code>input_ids</code> sequence should
be formatted with [CLS] and [SEP] with the format:</p>
<p><code>[CLS] &lt;question token ids&gt; [SEP] &lt;titles ids&gt; [SEP] &lt;texts ids&gt;</code></p>
<p>DPR is a model with absolute position embeddings so it&#x2019;s usually advised to pad the inputs on the right
rather than the left.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRReaderTokenizer">DPRReaderTokenizer</a>. See this class documentation for more details.`,name:"input_ids"},{anchor:"transformers.TFDPRReader.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(n_passages, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFDPRReader.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(n_passages, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFDPRReader.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFDPRReader.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.34.0/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/v4.34.0/src/transformers/models/dpr/modeling_tf_dpr.py#L699",returnDescription:`
<p>A <code>transformers.models.dpr.modeling_tf_dpr.TFDPRReaderOutput</code> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.34.0/en/model_doc/dpr#transformers.DPRConfig"
>DPRConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>start_logits</strong> (<code>tf.Tensor</code> of shape <code>(n_passages, sequence_length)</code>) — 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:`
<p><code>transformers.models.dpr.modeling_tf_dpr.TFDPRReaderOutput</code> or <code>tuple(tf.Tensor)</code></p>
`}}),it=new De({props:{$$slots:{default:[Ul]},$$scope:{ctx:P}}}),lt=new vt({props:{anchor:"transformers.TFDPRReader.call.example",$$slots:{default:[Il]},$$scope:{ctx:P}}}),{c(){i=o("meta"),k=r(),m=o("p"),p=r(),$=o("h1"),l=o("a"),R=o("span"),_(U.$$.fragment),E=r(),C=o("span"),C.textContent=ie,z=r(),M=o("div"),M.innerHTML=le,g=r(),x=o("h2"),ze=o("a"),Mo=o("span"),_(bt.$$.fragment),Ys=r(),fn=o("span"),fn.textContent=Fa,es=r(),yt=o("p"),yt.innerHTML=qa,ts=r(),wt=o("p"),wt.textContent=La,ns=r(),Rt=o("p"),Rt.innerHTML=Ja,os=r(),$t=o("p"),$t.innerHTML=Ha,ss=r(),kt=o("p"),kt.textContent=ja,rs=r(),xt=o("ul"),xt.innerHTML=Ua,as=r(),me=o("h2"),Ee=o("a"),Co=o("span"),_(Pt.$$.fragment),Ks=r(),gn=o("span"),gn.textContent=Ia,is=r(),N=o("div"),_(Mt.$$.fragment),er=r(),_n=o("p"),_n.innerHTML=Na,tr=r(),vn=o("p"),vn.innerHTML=Za,nr=r(),Tn=o("p"),Tn.innerHTML=Va,or=r(),_(Fe.$$.fragment),ls=r(),he=o("h2"),qe=o("a"),Do=o("span"),_(Ct.$$.fragment),sr=r(),bn=o("span"),bn.textContent=Wa,ds=r(),Q=o("div"),_(Dt.$$.fragment),rr=r(),yn=o("p"),yn.textContent=Qa,ar=r(),wn=o("p"),wn.innerHTML=Sa,ir=r(),Rn=o("p"),Rn.innerHTML=Ba,cs=r(),fe=o("h2"),Le=o("a"),zo=o("span"),_(zt.$$.fragment),lr=r(),$n=o("span"),$n.textContent=Ga,ps=r(),S=o("div"),_(Et.$$.fragment),dr=r(),kn=o("p"),kn.innerHTML=Aa,cr=r(),xn=o("p"),xn.innerHTML=Xa,pr=r(),Pn=o("p"),Pn.innerHTML=Oa,us=r(),ge=o("h2"),Je=o("a"),Eo=o("span"),_(Ft.$$.fragment),ur=r(),Mn=o("span"),Mn.textContent=Ya,ms=r(),B=o("div"),_(qt.$$.fragment),mr=r(),Cn=o("p"),Cn.textContent=Ka,hr=r(),Dn=o("p"),Dn.innerHTML=ei,fr=r(),zn=o("p"),zn.innerHTML=ti,hs=r(),_e=o("h2"),He=o("a"),Fo=o("span"),_(Lt.$$.fragment),gr=r(),En=o("span"),En.textContent=ni,fs=r(),G=o("div"),_(Jt.$$.fragment),_r=r(),Fn=o("p"),Fn.innerHTML=oi,vr=r(),qn=o("p"),qn.innerHTML=si,Tr=r(),Ln=o("p"),Ln.innerHTML=ri,gs=r(),ve=o("h2"),je=o("a"),qo=o("span"),_(Ht.$$.fragment),br=r(),Jn=o("span"),Jn.textContent=ai,_s=r(),F=o("div"),_(jt.$$.fragment),yr=r(),Hn=o("p"),Hn.textContent=ii,wr=r(),jn=o("p"),jn.innerHTML=li,Rr=r(),Un=o("p"),Un.innerHTML=di,$r=r(),In=o("p"),In.innerHTML=ci,kr=r(),_(Ue.$$.fragment),vs=r(),Te=o("h2"),Ie=o("a"),Lo=o("span"),_(Ut.$$.fragment),xr=r(),Nn=o("span"),Nn.textContent=pi,Ts=r(),q=o("div"),_(It.$$.fragment),Pr=r(),Zn=o("p"),Zn.innerHTML=ui,Mr=r(),Vn=o("p"),Vn.innerHTML=mi,Cr=r(),Wn=o("p"),Wn.innerHTML=hi,Dr=r(),Qn=o("p"),Qn.innerHTML=fi,zr=r(),Sn=o("p"),Sn.textContent=gi,bs=r(),be=o("h2"),Ne=o("a"),Jo=o("span"),_(Nt.$$.fragment),Er=r(),Bn=o("span"),Bn.textContent=_i,ys=r(),ye=o("div"),_(Zt.$$.fragment),Fr=r(),Gn=o("p"),Gn.innerHTML=vi,ws=r(),we=o("div"),_(Vt.$$.fragment),qr=r(),An=o("p"),An.innerHTML=Ti,Rs=r(),Re=o("div"),_(Wt.$$.fragment),Lr=r(),Xn=o("p"),Xn.innerHTML=bi,$s=r(),$e=o("h2"),Ze=o("a"),Ho=o("span"),_(Qt.$$.fragment),Jr=r(),On=o("span"),On.textContent=yi,ks=r(),Z=o("div"),_(St.$$.fragment),Hr=r(),Yn=o("p"),Yn.textContent=wi,jr=r(),Kn=o("p"),Kn.innerHTML=Ri,Ur=r(),eo=o("p"),eo.innerHTML=$i,Ir=r(),X=o("div"),_(Bt.$$.fragment),Nr=r(),to=o("p"),to.innerHTML=ki,Zr=r(),_(Ve.$$.fragment),Vr=r(),_(We.$$.fragment),xs=r(),ke=o("h2"),Qe=o("a"),jo=o("span"),_(Gt.$$.fragment),Wr=r(),no=o("span"),no.textContent=xi,Ps=r(),V=o("div"),_(At.$$.fragment),Qr=r(),oo=o("p"),oo.textContent=Pi,Sr=r(),so=o("p"),so.innerHTML=Mi,Br=r(),ro=o("p"),ro.innerHTML=Ci,Gr=r(),O=o("div"),_(Xt.$$.fragment),Ar=r(),ao=o("p"),ao.innerHTML=Di,Xr=r(),_(Se.$$.fragment),Or=r(),_(Be.$$.fragment),Ms=r(),xe=o("h2"),Ge=o("a"),Uo=o("span"),_(Ot.$$.fragment),Yr=r(),io=o("span"),io.textContent=zi,Cs=r(),W=o("div"),_(Yt.$$.fragment),Kr=r(),lo=o("p"),lo.textContent=Ei,ea=r(),co=o("p"),co.innerHTML=Fi,ta=r(),po=o("p"),po.innerHTML=qi,na=r(),Y=o("div"),_(Kt.$$.fragment),oa=r(),uo=o("p"),uo.innerHTML=Li,sa=r(),_(Ae.$$.fragment),ra=r(),_(Xe.$$.fragment),Ds=r(),Pe=o("h2"),Oe=o("a"),Io=o("span"),_(en.$$.fragment),aa=r(),mo=o("span"),mo.textContent=Ji,zs=r(),L=o("div"),_(tn.$$.fragment),ia=r(),ho=o("p"),ho.textContent=Hi,la=r(),fo=o("p"),fo.innerHTML=ji,da=r(),go=o("p"),go.innerHTML=Ui,ca=r(),_(Ye.$$.fragment),pa=r(),K=o("div"),_(nn.$$.fragment),ua=r(),_o=o("p"),_o.innerHTML=Ii,ma=r(),_(Ke.$$.fragment),ha=r(),_(et.$$.fragment),Es=r(),Me=o("h2"),tt=o("a"),No=o("span"),_(on.$$.fragment),fa=r(),vo=o("span"),vo.textContent=Ni,Fs=r(),J=o("div"),_(sn.$$.fragment),ga=r(),To=o("p"),To.textContent=Zi,_a=r(),bo=o("p"),bo.innerHTML=Vi,va=r(),yo=o("p"),yo.innerHTML=Wi,Ta=r(),_(nt.$$.fragment),ba=r(),ee=o("div"),_(rn.$$.fragment),ya=r(),wo=o("p"),wo.innerHTML=Qi,wa=r(),_(ot.$$.fragment),Ra=r(),_(st.$$.fragment),qs=r(),Ce=o("h2"),rt=o("a"),Zo=o("span"),_(an.$$.fragment),$a=r(),Ro=o("span"),Ro.textContent=Si,Ls=r(),H=o("div"),_(ln.$$.fragment),ka=r(),$o=o("p"),$o.textContent=Bi,xa=r(),ko=o("p"),ko.innerHTML=Gi,Pa=r(),xo=o("p"),xo.innerHTML=Ai,Ma=r(),_(at.$$.fragment),Ca=r(),te=o("div"),_(dn.$$.fragment),Da=r(),Po=o("p"),Po.innerHTML=Xi,za=r(),_(it.$$.fragment),Ea=r(),_(lt.$$.fragment),Js=r(),Vo=o("p"),this.h()},l(e){const c=Rl("svelte-1phssyn",document.head);i=s(c,"META",{name:!0,content:!0}),c.forEach(t),k=a(e),m=s(e,"P",{}),f(m).forEach(t),p=a(e),$=s(e,"H1",{class:!0});var cn=f($);l=s(cn,"A",{id:!0,class:!0,href:!0});var Wo=f(l);R=s(Wo,"SPAN",{});var Qo=f(R);v(U.$$.fragment,Qo),Qo.forEach(t),Wo.forEach(t),E=a(cn),C=s(cn,"SPAN",{"data-svelte-h":!0}),u(C)!=="svelte-y2q4fl"&&(C.textContent=ie),cn.forEach(t),z=a(e),M=s(e,"DIV",{class:!0,"data-svelte-h":!0}),u(M)!=="svelte-162ccfz"&&(M.innerHTML=le),g=a(e),x=s(e,"H2",{class:!0});var pn=f(x);ze=s(pn,"A",{id:!0,class:!0,href:!0});var So=f(ze);Mo=s(So,"SPAN",{});var Bo=f(Mo);v(bt.$$.fragment,Bo),Bo.forEach(t),So.forEach(t),Ys=a(pn),fn=s(pn,"SPAN",{"data-svelte-h":!0}),u(fn)!=="svelte-1jsw1pg"&&(fn.textContent=Fa),pn.forEach(t),es=a(e),yt=s(e,"P",{"data-svelte-h":!0}),u(yt)!=="svelte-4gki3s"&&(yt.innerHTML=qa),ts=a(e),wt=s(e,"P",{"data-svelte-h":!0}),u(wt)!=="svelte-vfdo9a"&&(wt.textContent=La),ns=a(e),Rt=s(e,"P",{"data-svelte-h":!0}),u(Rt)!=="svelte-1xle5de"&&(Rt.innerHTML=Ja),os=a(e),$t=s(e,"P",{"data-svelte-h":!0}),u($t)!=="svelte-21k9xm"&&($t.innerHTML=Ha),ss=a(e),kt=s(e,"P",{"data-svelte-h":!0}),u(kt)!=="svelte-axv494"&&(kt.textContent=ja),rs=a(e),xt=s(e,"UL",{"data-svelte-h":!0}),u(xt)!=="svelte-zqyul3"&&(xt.innerHTML=Ua),as=a(e),me=s(e,"H2",{class:!0});var un=f(me);Ee=s(un,"A",{id:!0,class:!0,href:!0});var Go=f(Ee);Co=s(Go,"SPAN",{});var Ao=f(Co);v(Pt.$$.fragment,Ao),Ao.forEach(t),Go.forEach(t),Ks=a(un),gn=s(un,"SPAN",{"data-svelte-h":!0}),u(gn)!=="svelte-1lvn27z"&&(gn.textContent=Ia),un.forEach(t),is=a(e),N=s(e,"DIV",{class:!0});var A=f(N);v(Mt.$$.fragment,A),er=a(A),_n=s(A,"P",{"data-svelte-h":!0}),u(_n)!=="svelte-kcf10f"&&(_n.innerHTML=Na),tr=a(A),vn=s(A,"P",{"data-svelte-h":!0}),u(vn)!=="svelte-78z2qc"&&(vn.innerHTML=Za),nr=a(A),Tn=s(A,"P",{"data-svelte-h":!0}),u(Tn)!=="svelte-ugjknn"&&(Tn.innerHTML=Va),or=a(A),v(Fe.$$.fragment,A),A.forEach(t),ls=a(e),he=s(e,"H2",{class:!0});var mn=f(he);qe=s(mn,"A",{id:!0,class:!0,href:!0});var Xo=f(qe);Do=s(Xo,"SPAN",{});var Oo=f(Do);v(Ct.$$.fragment,Oo),Oo.forEach(t),Xo.forEach(t),sr=a(mn),bn=s(mn,"SPAN",{"data-svelte-h":!0}),u(bn)!=="svelte-vmhhfh"&&(bn.textContent=Wa),mn.forEach(t),ds=a(e),Q=s(e,"DIV",{class:!0});var de=f(Q);v(Dt.$$.fragment,de),rr=a(de),yn=s(de,"P",{"data-svelte-h":!0}),u(yn)!=="svelte-1dv8gby"&&(yn.textContent=Qa),ar=a(de),wn=s(de,"P",{"data-svelte-h":!0}),u(wn)!=="svelte-1tnwhzt"&&(wn.innerHTML=Sa),ir=a(de),Rn=s(de,"P",{"data-svelte-h":!0}),u(Rn)!=="svelte-1rv1pma"&&(Rn.innerHTML=Ba),de.forEach(t),cs=a(e),fe=s(e,"H2",{class:!0});var hn=f(fe);Le=s(hn,"A",{id:!0,class:!0,href:!0});var Yo=f(Le);zo=s(Yo,"SPAN",{});var Ko=f(zo);v(zt.$$.fragment,Ko),Ko.forEach(t),Yo.forEach(t),lr=a(hn),$n=s(hn,"SPAN",{"data-svelte-h":!0}),u($n)!=="svelte-a14tbz"&&($n.textContent=Ga),hn.forEach(t),ps=a(e),S=s(e,"DIV",{class:!0});var dt=f(S);v(Et.$$.fragment,dt),dr=a(dt),kn=s(dt,"P",{"data-svelte-h":!0}),u(kn)!=="svelte-1flugr"&&(kn.innerHTML=Aa),cr=a(dt),xn=s(dt,"P",{"data-svelte-h":!0}),u(xn)!=="svelte-1l0wlr9"&&(xn.innerHTML=Xa),pr=a(dt),Pn=s(dt,"P",{"data-svelte-h":!0}),u(Pn)!=="svelte-o46m5a"&&(Pn.innerHTML=Oa),dt.forEach(t),us=a(e),ge=s(e,"H2",{class:!0});var js=f(ge);Je=s(js,"A",{id:!0,class:!0,href:!0});var Oi=f(Je);Eo=s(Oi,"SPAN",{});var Yi=f(Eo);v(Ft.$$.fragment,Yi),Yi.forEach(t),Oi.forEach(t),ur=a(js),Mn=s(js,"SPAN",{"data-svelte-h":!0}),u(Mn)!=="svelte-tvzgbs"&&(Mn.textContent=Ya),js.forEach(t),ms=a(e),B=s(e,"DIV",{class:!0});var ct=f(B);v(qt.$$.fragment,ct),mr=a(ct),Cn=s(ct,"P",{"data-svelte-h":!0}),u(Cn)!=="svelte-1fcy66e"&&(Cn.textContent=Ka),hr=a(ct),Dn=s(ct,"P",{"data-svelte-h":!0}),u(Dn)!=="svelte-9nico1"&&(Dn.innerHTML=ei),fr=a(ct),zn=s(ct,"P",{"data-svelte-h":!0}),u(zn)!=="svelte-1rv1pma"&&(zn.innerHTML=ti),ct.forEach(t),hs=a(e),_e=s(e,"H2",{class:!0});var Us=f(_e);He=s(Us,"A",{id:!0,class:!0,href:!0});var Ki=f(He);Fo=s(Ki,"SPAN",{});var el=f(Fo);v(Lt.$$.fragment,el),el.forEach(t),Ki.forEach(t),gr=a(Us),En=s(Us,"SPAN",{"data-svelte-h":!0}),u(En)!=="svelte-1nfaj3y"&&(En.textContent=ni),Us.forEach(t),fs=a(e),G=s(e,"DIV",{class:!0});var pt=f(G);v(Jt.$$.fragment,pt),_r=a(pt),Fn=s(pt,"P",{"data-svelte-h":!0}),u(Fn)!=="svelte-137cq53"&&(Fn.innerHTML=oi),vr=a(pt),qn=s(pt,"P",{"data-svelte-h":!0}),u(qn)!=="svelte-17bm0zp"&&(qn.innerHTML=si),Tr=a(pt),Ln=s(pt,"P",{"data-svelte-h":!0}),u(Ln)!=="svelte-o46m5a"&&(Ln.innerHTML=ri),pt.forEach(t),gs=a(e),ve=s(e,"H2",{class:!0});var Is=f(ve);je=s(Is,"A",{id:!0,class:!0,href:!0});var tl=f(je);qo=s(tl,"SPAN",{});var nl=f(qo);v(Ht.$$.fragment,nl),nl.forEach(t),tl.forEach(t),br=a(Is),Jn=s(Is,"SPAN",{"data-svelte-h":!0}),u(Jn)!=="svelte-14akejr"&&(Jn.textContent=ai),Is.forEach(t),_s=a(e),F=s(e,"DIV",{class:!0});var ne=f(F);v(jt.$$.fragment,ne),yr=a(ne),Hn=s(ne,"P",{"data-svelte-h":!0}),u(Hn)!=="svelte-7on9jw"&&(Hn.textContent=ii),wr=a(ne),jn=s(ne,"P",{"data-svelte-h":!0}),u(jn)!=="svelte-1d3d748"&&(jn.innerHTML=li),Rr=a(ne),Un=s(ne,"P",{"data-svelte-h":!0}),u(Un)!=="svelte-1rv1pma"&&(Un.innerHTML=di),$r=a(ne),In=s(ne,"P",{"data-svelte-h":!0}),u(In)!=="svelte-q0w208"&&(In.innerHTML=ci),kr=a(ne),v(Ue.$$.fragment,ne),ne.forEach(t),vs=a(e),Te=s(e,"H2",{class:!0});var Ns=f(Te);Ie=s(Ns,"A",{id:!0,class:!0,href:!0});var ol=f(Ie);Lo=s(ol,"SPAN",{});var sl=f(Lo);v(Ut.$$.fragment,sl),sl.forEach(t),ol.forEach(t),xr=a(Ns),Nn=s(Ns,"SPAN",{"data-svelte-h":!0}),u(Nn)!=="svelte-65gd99"&&(Nn.textContent=pi),Ns.forEach(t),Ts=a(e),q=s(e,"DIV",{class:!0});var oe=f(q);v(It.$$.fragment,oe),Pr=a(oe),Zn=s(oe,"P",{"data-svelte-h":!0}),u(Zn)!=="svelte-els1hy"&&(Zn.innerHTML=ui),Mr=a(oe),Vn=s(oe,"P",{"data-svelte-h":!0}),u(Vn)!=="svelte-cev0zk"&&(Vn.innerHTML=mi),Cr=a(oe),Wn=s(oe,"P",{"data-svelte-h":!0}),u(Wn)!=="svelte-o46m5a"&&(Wn.innerHTML=hi),Dr=a(oe),Qn=s(oe,"P",{"data-svelte-h":!0}),u(Qn)!=="svelte-1ov4u9q"&&(Qn.innerHTML=fi),zr=a(oe),Sn=s(oe,"P",{"data-svelte-h":!0}),u(Sn)!=="svelte-kc4w4c"&&(Sn.textContent=gi),oe.forEach(t),bs=a(e),be=s(e,"H2",{class:!0});var Zs=f(be);Ne=s(Zs,"A",{id:!0,class:!0,href:!0});var rl=f(Ne);Jo=s(rl,"SPAN",{});var al=f(Jo);v(Nt.$$.fragment,al),al.forEach(t),rl.forEach(t),Er=a(Zs),Bn=s(Zs,"SPAN",{"data-svelte-h":!0}),u(Bn)!=="svelte-6xibad"&&(Bn.textContent=_i),Zs.forEach(t),ys=a(e),ye=s(e,"DIV",{class:!0});var Vs=f(ye);v(Zt.$$.fragment,Vs),Fr=a(Vs),Gn=s(Vs,"P",{"data-svelte-h":!0}),u(Gn)!=="svelte-1lffm4l"&&(Gn.innerHTML=vi),Vs.forEach(t),ws=a(e),we=s(e,"DIV",{class:!0});var Ws=f(we);v(Vt.$$.fragment,Ws),qr=a(Ws),An=s(Ws,"P",{"data-svelte-h":!0}),u(An)!=="svelte-1lffm4l"&&(An.innerHTML=Ti),Ws.forEach(t),Rs=a(e),Re=s(e,"DIV",{class:!0});var Qs=f(Re);v(Wt.$$.fragment,Qs),Lr=a(Qs),Xn=s(Qs,"P",{"data-svelte-h":!0}),u(Xn)!=="svelte-1lffm4l"&&(Xn.innerHTML=bi),Qs.forEach(t),$s=a(e),$e=s(e,"H2",{class:!0});var Ss=f($e);Ze=s(Ss,"A",{id:!0,class:!0,href:!0});var il=f(Ze);Ho=s(il,"SPAN",{});var ll=f(Ho);v(Qt.$$.fragment,ll),ll.forEach(t),il.forEach(t),Jr=a(Ss),On=s(Ss,"SPAN",{"data-svelte-h":!0}),u(On)!=="svelte-1w6x9dw"&&(On.textContent=yi),Ss.forEach(t),ks=a(e),Z=s(e,"DIV",{class:!0});var ce=f(Z);v(St.$$.fragment,ce),Hr=a(ce),Yn=s(ce,"P",{"data-svelte-h":!0}),u(Yn)!=="svelte-18o6tt0"&&(Yn.textContent=wi),jr=a(ce),Kn=s(ce,"P",{"data-svelte-h":!0}),u(Kn)!=="svelte-hmtw9k"&&(Kn.innerHTML=Ri),Ur=a(ce),eo=s(ce,"P",{"data-svelte-h":!0}),u(eo)!=="svelte-hswkmf"&&(eo.innerHTML=$i),Ir=a(ce),X=s(ce,"DIV",{class:!0});var ut=f(X);v(Bt.$$.fragment,ut),Nr=a(ut),to=s(ut,"P",{"data-svelte-h":!0}),u(to)!=="svelte-6vu5pz"&&(to.innerHTML=ki),Zr=a(ut),v(Ve.$$.fragment,ut),Vr=a(ut),v(We.$$.fragment,ut),ut.forEach(t),ce.forEach(t),xs=a(e),ke=s(e,"H2",{class:!0});var Bs=f(ke);Qe=s(Bs,"A",{id:!0,class:!0,href:!0});var dl=f(Qe);jo=s(dl,"SPAN",{});var cl=f(jo);v(Gt.$$.fragment,cl),cl.forEach(t),dl.forEach(t),Wr=a(Bs),no=s(Bs,"SPAN",{"data-svelte-h":!0}),u(no)!=="svelte-16e3gab"&&(no.textContent=xi),Bs.forEach(t),Ps=a(e),V=s(e,"DIV",{class:!0});var pe=f(V);v(At.$$.fragment,pe),Qr=a(pe),oo=s(pe,"P",{"data-svelte-h":!0}),u(oo)!=="svelte-1trrfka"&&(oo.textContent=Pi),Sr=a(pe),so=s(pe,"P",{"data-svelte-h":!0}),u(so)!=="svelte-hmtw9k"&&(so.innerHTML=Mi),Br=a(pe),ro=s(pe,"P",{"data-svelte-h":!0}),u(ro)!=="svelte-hswkmf"&&(ro.innerHTML=Ci),Gr=a(pe),O=s(pe,"DIV",{class:!0});var mt=f(O);v(Xt.$$.fragment,mt),Ar=a(mt),ao=s(mt,"P",{"data-svelte-h":!0}),u(ao)!=="svelte-1omxt17"&&(ao.innerHTML=Di),Xr=a(mt),v(Se.$$.fragment,mt),Or=a(mt),v(Be.$$.fragment,mt),mt.forEach(t),pe.forEach(t),Ms=a(e),xe=s(e,"H2",{class:!0});var Gs=f(xe);Ge=s(Gs,"A",{id:!0,class:!0,href:!0});var pl=f(Ge);Uo=s(pl,"SPAN",{});var ul=f(Uo);v(Ot.$$.fragment,ul),ul.forEach(t),pl.forEach(t),Yr=a(Gs),io=s(Gs,"SPAN",{"data-svelte-h":!0}),u(io)!=="svelte-84y6y6"&&(io.textContent=zi),Gs.forEach(t),Cs=a(e),W=s(e,"DIV",{class:!0});var ue=f(W);v(Yt.$$.fragment,ue),Kr=a(ue),lo=s(ue,"P",{"data-svelte-h":!0}),u(lo)!=="svelte-sj9qc8"&&(lo.textContent=Ei),ea=a(ue),co=s(ue,"P",{"data-svelte-h":!0}),u(co)!=="svelte-hmtw9k"&&(co.innerHTML=Fi),ta=a(ue),po=s(ue,"P",{"data-svelte-h":!0}),u(po)!=="svelte-hswkmf"&&(po.innerHTML=qi),na=a(ue),Y=s(ue,"DIV",{class:!0});var ht=f(Y);v(Kt.$$.fragment,ht),oa=a(ht),uo=s(ht,"P",{"data-svelte-h":!0}),u(uo)!=="svelte-983aij"&&(uo.innerHTML=Li),sa=a(ht),v(Ae.$$.fragment,ht),ra=a(ht),v(Xe.$$.fragment,ht),ht.forEach(t),ue.forEach(t),Ds=a(e),Pe=s(e,"H2",{class:!0});var As=f(Pe);Oe=s(As,"A",{id:!0,class:!0,href:!0});var ml=f(Oe);Io=s(ml,"SPAN",{});var hl=f(Io);v(en.$$.fragment,hl),hl.forEach(t),ml.forEach(t),aa=a(As),mo=s(As,"SPAN",{"data-svelte-h":!0}),u(mo)!=="svelte-1rs6v56"&&(mo.textContent=Ji),As.forEach(t),zs=a(e),L=s(e,"DIV",{class:!0});var se=f(L);v(tn.$$.fragment,se),ia=a(se),ho=s(se,"P",{"data-svelte-h":!0}),u(ho)!=="svelte-18o6tt0"&&(ho.textContent=Hi),la=a(se),fo=s(se,"P",{"data-svelte-h":!0}),u(fo)!=="svelte-1i0vt4o"&&(fo.innerHTML=ji),da=a(se),go=s(se,"P",{"data-svelte-h":!0}),u(go)!=="svelte-1i1bld9"&&(go.innerHTML=Ui),ca=a(se),v(Ye.$$.fragment,se),pa=a(se),K=s(se,"DIV",{class:!0});var ft=f(K);v(nn.$$.fragment,ft),ua=a(ft),_o=s(ft,"P",{"data-svelte-h":!0}),u(_o)!=="svelte-1wk3xtv"&&(_o.innerHTML=Ii),ma=a(ft),v(Ke.$$.fragment,ft),ha=a(ft),v(et.$$.fragment,ft),ft.forEach(t),se.forEach(t),Es=a(e),Me=s(e,"H2",{class:!0});var Xs=f(Me);tt=s(Xs,"A",{id:!0,class:!0,href:!0});var fl=f(tt);No=s(fl,"SPAN",{});var gl=f(No);v(on.$$.fragment,gl),gl.forEach(t),fl.forEach(t),fa=a(Xs),vo=s(Xs,"SPAN",{"data-svelte-h":!0}),u(vo)!=="svelte-vnhpul"&&(vo.textContent=Ni),Xs.forEach(t),Fs=a(e),J=s(e,"DIV",{class:!0});var re=f(J);v(sn.$$.fragment,re),ga=a(re),To=s(re,"P",{"data-svelte-h":!0}),u(To)!=="svelte-1trrfka"&&(To.textContent=Zi),_a=a(re),bo=s(re,"P",{"data-svelte-h":!0}),u(bo)!=="svelte-1i0vt4o"&&(bo.innerHTML=Vi),va=a(re),yo=s(re,"P",{"data-svelte-h":!0}),u(yo)!=="svelte-1i1bld9"&&(yo.innerHTML=Wi),Ta=a(re),v(nt.$$.fragment,re),ba=a(re),ee=s(re,"DIV",{class:!0});var gt=f(ee);v(rn.$$.fragment,gt),ya=a(gt),wo=s(gt,"P",{"data-svelte-h":!0}),u(wo)!=="svelte-1vx2qnb"&&(wo.innerHTML=Qi),wa=a(gt),v(ot.$$.fragment,gt),Ra=a(gt),v(st.$$.fragment,gt),gt.forEach(t),re.forEach(t),qs=a(e),Ce=s(e,"H2",{class:!0});var Os=f(Ce);rt=s(Os,"A",{id:!0,class:!0,href:!0});var _l=f(rt);Zo=s(_l,"SPAN",{});var vl=f(Zo);v(an.$$.fragment,vl),vl.forEach(t),_l.forEach(t),$a=a(Os),Ro=s(Os,"SPAN",{"data-svelte-h":!0}),u(Ro)!=="svelte-1krv4mo"&&(Ro.textContent=Si),Os.forEach(t),Ls=a(e),H=s(e,"DIV",{class:!0});var ae=f(H);v(ln.$$.fragment,ae),ka=a(ae),$o=s(ae,"P",{"data-svelte-h":!0}),u($o)!=="svelte-sj9qc8"&&($o.textContent=Bi),xa=a(ae),ko=s(ae,"P",{"data-svelte-h":!0}),u(ko)!=="svelte-1i0vt4o"&&(ko.innerHTML=Gi),Pa=a(ae),xo=s(ae,"P",{"data-svelte-h":!0}),u(xo)!=="svelte-1i1bld9"&&(xo.innerHTML=Ai),Ma=a(ae),v(at.$$.fragment,ae),Ca=a(ae),te=s(ae,"DIV",{class:!0});var _t=f(te);v(dn.$$.fragment,_t),Da=a(_t),Po=s(_t,"P",{"data-svelte-h":!0}),u(Po)!=="svelte-1dlce9z"&&(Po.innerHTML=Xi),za=a(_t),v(it.$$.fragment,_t),Ea=a(_t),v(lt.$$.fragment,_t),_t.forEach(t),ae.forEach(t),Js=a(e),Vo=s(e,"P",{}),f(Vo).forEach(t),this.h()},h(){h(i,"name","hf:doc:metadata"),h(i,"content",JSON.stringify(Zl)),h(l,"id","dpr"),h(l,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),h(l,"href","#dpr"),h($,"class","relative group"),h(M,"class","flex flex-wrap space-x-1"),h(ze,"id","overview"),h(ze,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),h(ze,"href","#overview"),h(x,"class","relative group"),h(Ee,"id","transformers.DPRConfig"),h(Ee,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),h(Ee,"href","#transformers.DPRConfig"),h(me,"class","relative group"),h(N,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(qe,"id","transformers.DPRContextEncoderTokenizer"),h(qe,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),h(qe,"href","#transformers.DPRContextEncoderTokenizer"),h(he,"class","relative group"),h(Q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(Le,"id","transformers.DPRContextEncoderTokenizerFast"),h(Le,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),h(Le,"href","#transformers.DPRContextEncoderTokenizerFast"),h(fe,"class","relative group"),h(S,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(Je,"id","transformers.DPRQuestionEncoderTokenizer"),h(Je,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),h(Je,"href","#transformers.DPRQuestionEncoderTokenizer"),h(ge,"class","relative group"),h(B,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(He,"id","transformers.DPRQuestionEncoderTokenizerFast"),h(He,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),h(He,"href","#transformers.DPRQuestionEncoderTokenizerFast"),h(_e,"class","relative group"),h(G,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(je,"id","transformers.DPRReaderTokenizer"),h(je,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),h(je,"href","#transformers.DPRReaderTokenizer"),h(ve,"class","relative group"),h(F,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(Ie,"id","transformers.DPRReaderTokenizerFast"),h(Ie,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),h(Ie,"href","#transformers.DPRReaderTokenizerFast"),h(Te,"class","relative group"),h(q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(Ne,"id","transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput"),h(Ne,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),h(Ne,"href","#transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput"),h(be,"class","relative group"),h(ye,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(we,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(Re,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(Ze,"id","transformers.DPRContextEncoder"),h(Ze,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),h(Ze,"href","#transformers.DPRContextEncoder"),h($e,"class","relative group"),h(X,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(Z,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(Qe,"id","transformers.DPRQuestionEncoder"),h(Qe,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),h(Qe,"href","#transformers.DPRQuestionEncoder"),h(ke,"class","relative group"),h(O,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(V,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(Ge,"id","transformers.DPRReader"),h(Ge,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),h(Ge,"href","#transformers.DPRReader"),h(xe,"class","relative group"),h(Y,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(W,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(Oe,"id","transformers.TFDPRContextEncoder"),h(Oe,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),h(Oe,"href","#transformers.TFDPRContextEncoder"),h(Pe,"class","relative group"),h(K,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(L,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(tt,"id","transformers.TFDPRQuestionEncoder"),h(tt,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),h(tt,"href","#transformers.TFDPRQuestionEncoder"),h(Me,"class","relative group"),h(ee,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(J,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(rt,"id","transformers.TFDPRReader"),h(rt,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),h(rt,"href","#transformers.TFDPRReader"),h(Ce,"class","relative group"),h(te,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(H,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,c){n(document.head,i),d(e,k,c),d(e,m,c),d(e,p,c),d(e,$,c),n($,l),n(l,R),T(U,R,null),n($,E),n($,C),d(e,z,c),d(e,M,c),d(e,g,c),d(e,x,c),n(x,ze),n(ze,Mo),T(bt,Mo,null),n(x,Ys),n(x,fn),d(e,es,c),d(e,yt,c),d(e,ts,c),d(e,wt,c),d(e,ns,c),d(e,Rt,c),d(e,os,c),d(e,$t,c),d(e,ss,c),d(e,kt,c),d(e,rs,c),d(e,xt,c),d(e,as,c),d(e,me,c),n(me,Ee),n(Ee,Co),T(Pt,Co,null),n(me,Ks),n(me,gn),d(e,is,c),d(e,N,c),T(Mt,N,null),n(N,er),n(N,_n),n(N,tr),n(N,vn),n(N,nr),n(N,Tn),n(N,or),T(Fe,N,null),d(e,ls,c),d(e,he,c),n(he,qe),n(qe,Do),T(Ct,Do,null),n(he,sr),n(he,bn),d(e,ds,c),d(e,Q,c),T(Dt,Q,null),n(Q,rr),n(Q,yn),n(Q,ar),n(Q,wn),n(Q,ir),n(Q,Rn),d(e,cs,c),d(e,fe,c),n(fe,Le),n(Le,zo),T(zt,zo,null),n(fe,lr),n(fe,$n),d(e,ps,c),d(e,S,c),T(Et,S,null),n(S,dr),n(S,kn),n(S,cr),n(S,xn),n(S,pr),n(S,Pn),d(e,us,c),d(e,ge,c),n(ge,Je),n(Je,Eo),T(Ft,Eo,null),n(ge,ur),n(ge,Mn),d(e,ms,c),d(e,B,c),T(qt,B,null),n(B,mr),n(B,Cn),n(B,hr),n(B,Dn),n(B,fr),n(B,zn),d(e,hs,c),d(e,_e,c),n(_e,He),n(He,Fo),T(Lt,Fo,null),n(_e,gr),n(_e,En),d(e,fs,c),d(e,G,c),T(Jt,G,null),n(G,_r),n(G,Fn),n(G,vr),n(G,qn),n(G,Tr),n(G,Ln),d(e,gs,c),d(e,ve,c),n(ve,je),n(je,qo),T(Ht,qo,null),n(ve,br),n(ve,Jn),d(e,_s,c),d(e,F,c),T(jt,F,null),n(F,yr),n(F,Hn),n(F,wr),n(F,jn),n(F,Rr),n(F,Un),n(F,$r),n(F,In),n(F,kr),T(Ue,F,null),d(e,vs,c),d(e,Te,c),n(Te,Ie),n(Ie,Lo),T(Ut,Lo,null),n(Te,xr),n(Te,Nn),d(e,Ts,c),d(e,q,c),T(It,q,null),n(q,Pr),n(q,Zn),n(q,Mr),n(q,Vn),n(q,Cr),n(q,Wn),n(q,Dr),n(q,Qn),n(q,zr),n(q,Sn),d(e,bs,c),d(e,be,c),n(be,Ne),n(Ne,Jo),T(Nt,Jo,null),n(be,Er),n(be,Bn),d(e,ys,c),d(e,ye,c),T(Zt,ye,null),n(ye,Fr),n(ye,Gn),d(e,ws,c),d(e,we,c),T(Vt,we,null),n(we,qr),n(we,An),d(e,Rs,c),d(e,Re,c),T(Wt,Re,null),n(Re,Lr),n(Re,Xn),d(e,$s,c),d(e,$e,c),n($e,Ze),n(Ze,Ho),T(Qt,Ho,null),n($e,Jr),n($e,On),d(e,ks,c),d(e,Z,c),T(St,Z,null),n(Z,Hr),n(Z,Yn),n(Z,jr),n(Z,Kn),n(Z,Ur),n(Z,eo),n(Z,Ir),n(Z,X),T(Bt,X,null),n(X,Nr),n(X,to),n(X,Zr),T(Ve,X,null),n(X,Vr),T(We,X,null),d(e,xs,c),d(e,ke,c),n(ke,Qe),n(Qe,jo),T(Gt,jo,null),n(ke,Wr),n(ke,no),d(e,Ps,c),d(e,V,c),T(At,V,null),n(V,Qr),n(V,oo),n(V,Sr),n(V,so),n(V,Br),n(V,ro),n(V,Gr),n(V,O),T(Xt,O,null),n(O,Ar),n(O,ao),n(O,Xr),T(Se,O,null),n(O,Or),T(Be,O,null),d(e,Ms,c),d(e,xe,c),n(xe,Ge),n(Ge,Uo),T(Ot,Uo,null),n(xe,Yr),n(xe,io),d(e,Cs,c),d(e,W,c),T(Yt,W,null),n(W,Kr),n(W,lo),n(W,ea),n(W,co),n(W,ta),n(W,po),n(W,na),n(W,Y),T(Kt,Y,null),n(Y,oa),n(Y,uo),n(Y,sa),T(Ae,Y,null),n(Y,ra),T(Xe,Y,null),d(e,Ds,c),d(e,Pe,c),n(Pe,Oe),n(Oe,Io),T(en,Io,null),n(Pe,aa),n(Pe,mo),d(e,zs,c),d(e,L,c),T(tn,L,null),n(L,ia),n(L,ho),n(L,la),n(L,fo),n(L,da),n(L,go),n(L,ca),T(Ye,L,null),n(L,pa),n(L,K),T(nn,K,null),n(K,ua),n(K,_o),n(K,ma),T(Ke,K,null),n(K,ha),T(et,K,null),d(e,Es,c),d(e,Me,c),n(Me,tt),n(tt,No),T(on,No,null),n(Me,fa),n(Me,vo),d(e,Fs,c),d(e,J,c),T(sn,J,null),n(J,ga),n(J,To),n(J,_a),n(J,bo),n(J,va),n(J,yo),n(J,Ta),T(nt,J,null),n(J,ba),n(J,ee),T(rn,ee,null),n(ee,ya),n(ee,wo),n(ee,wa),T(ot,ee,null),n(ee,Ra),T(st,ee,null),d(e,qs,c),d(e,Ce,c),n(Ce,rt),n(rt,Zo),T(an,Zo,null),n(Ce,$a),n(Ce,Ro),d(e,Ls,c),d(e,H,c),T(ln,H,null),n(H,ka),n(H,$o),n(H,xa),n(H,ko),n(H,Pa),n(H,xo),n(H,Ma),T(at,H,null),n(H,Ca),n(H,te),T(dn,te,null),n(te,Da),n(te,Po),n(te,za),T(it,te,null),n(te,Ea),T(lt,te,null),d(e,Js,c),d(e,Vo,c),Hs=!0},p(e,[c]){const cn={};c&2&&(cn.$$scope={dirty:c,ctx:e}),Fe.$set(cn);const Wo={};c&2&&(Wo.$$scope={dirty:c,ctx:e}),Ue.$set(Wo);const Qo={};c&2&&(Qo.$$scope={dirty:c,ctx:e}),Ve.$set(Qo);const pn={};c&2&&(pn.$$scope={dirty:c,ctx:e}),We.$set(pn);const So={};c&2&&(So.$$scope={dirty:c,ctx:e}),Se.$set(So);const Bo={};c&2&&(Bo.$$scope={dirty:c,ctx:e}),Be.$set(Bo);const un={};c&2&&(un.$$scope={dirty:c,ctx:e}),Ae.$set(un);const Go={};c&2&&(Go.$$scope={dirty:c,ctx:e}),Xe.$set(Go);const Ao={};c&2&&(Ao.$$scope={dirty:c,ctx:e}),Ye.$set(Ao);const A={};c&2&&(A.$$scope={dirty:c,ctx:e}),Ke.$set(A);const mn={};c&2&&(mn.$$scope={dirty:c,ctx:e}),et.$set(mn);const Xo={};c&2&&(Xo.$$scope={dirty:c,ctx:e}),nt.$set(Xo);const Oo={};c&2&&(Oo.$$scope={dirty:c,ctx:e}),ot.$set(Oo);const de={};c&2&&(de.$$scope={dirty:c,ctx:e}),st.$set(de);const hn={};c&2&&(hn.$$scope={dirty:c,ctx:e}),at.$set(hn);const Yo={};c&2&&(Yo.$$scope={dirty:c,ctx:e}),it.$set(Yo);const Ko={};c&2&&(Ko.$$scope={dirty:c,ctx:e}),lt.$set(Ko)},i(e){Hs||(b(U.$$.fragment,e),b(bt.$$.fragment,e),b(Pt.$$.fragment,e),b(Mt.$$.fragment,e),b(Fe.$$.fragment,e),b(Ct.$$.fragment,e),b(Dt.$$.fragment,e),b(zt.$$.fragment,e),b(Et.$$.fragment,e),b(Ft.$$.fragment,e),b(qt.$$.fragment,e),b(Lt.$$.fragment,e),b(Jt.$$.fragment,e),b(Ht.$$.fragment,e),b(jt.$$.fragment,e),b(Ue.$$.fragment,e),b(Ut.$$.fragment,e),b(It.$$.fragment,e),b(Nt.$$.fragment,e),b(Zt.$$.fragment,e),b(Vt.$$.fragment,e),b(Wt.$$.fragment,e),b(Qt.$$.fragment,e),b(St.$$.fragment,e),b(Bt.$$.fragment,e),b(Ve.$$.fragment,e),b(We.$$.fragment,e),b(Gt.$$.fragment,e),b(At.$$.fragment,e),b(Xt.$$.fragment,e),b(Se.$$.fragment,e),b(Be.$$.fragment,e),b(Ot.$$.fragment,e),b(Yt.$$.fragment,e),b(Kt.$$.fragment,e),b(Ae.$$.fragment,e),b(Xe.$$.fragment,e),b(en.$$.fragment,e),b(tn.$$.fragment,e),b(Ye.$$.fragment,e),b(nn.$$.fragment,e),b(Ke.$$.fragment,e),b(et.$$.fragment,e),b(on.$$.fragment,e),b(sn.$$.fragment,e),b(nt.$$.fragment,e),b(rn.$$.fragment,e),b(ot.$$.fragment,e),b(st.$$.fragment,e),b(an.$$.fragment,e),b(ln.$$.fragment,e),b(at.$$.fragment,e),b(dn.$$.fragment,e),b(it.$$.fragment,e),b(lt.$$.fragment,e),Hs=!0)},o(e){y(U.$$.fragment,e),y(bt.$$.fragment,e),y(Pt.$$.fragment,e),y(Mt.$$.fragment,e),y(Fe.$$.fragment,e),y(Ct.$$.fragment,e),y(Dt.$$.fragment,e),y(zt.$$.fragment,e),y(Et.$$.fragment,e),y(Ft.$$.fragment,e),y(qt.$$.fragment,e),y(Lt.$$.fragment,e),y(Jt.$$.fragment,e),y(Ht.$$.fragment,e),y(jt.$$.fragment,e),y(Ue.$$.fragment,e),y(Ut.$$.fragment,e),y(It.$$.fragment,e),y(Nt.$$.fragment,e),y(Zt.$$.fragment,e),y(Vt.$$.fragment,e),y(Wt.$$.fragment,e),y(Qt.$$.fragment,e),y(St.$$.fragment,e),y(Bt.$$.fragment,e),y(Ve.$$.fragment,e),y(We.$$.fragment,e),y(Gt.$$.fragment,e),y(At.$$.fragment,e),y(Xt.$$.fragment,e),y(Se.$$.fragment,e),y(Be.$$.fragment,e),y(Ot.$$.fragment,e),y(Yt.$$.fragment,e),y(Kt.$$.fragment,e),y(Ae.$$.fragment,e),y(Xe.$$.fragment,e),y(en.$$.fragment,e),y(tn.$$.fragment,e),y(Ye.$$.fragment,e),y(nn.$$.fragment,e),y(Ke.$$.fragment,e),y(et.$$.fragment,e),y(on.$$.fragment,e),y(sn.$$.fragment,e),y(nt.$$.fragment,e),y(rn.$$.fragment,e),y(ot.$$.fragment,e),y(st.$$.fragment,e),y(an.$$.fragment,e),y(ln.$$.fragment,e),y(at.$$.fragment,e),y(dn.$$.fragment,e),y(it.$$.fragment,e),y(lt.$$.fragment,e),Hs=!1},d(e){e&&(t(k),t(m),t(p),t($),t(z),t(M),t(g),t(x),t(es),t(yt),t(ts),t(wt),t(ns),t(Rt),t(os),t($t),t(ss),t(kt),t(rs),t(xt),t(as),t(me),t(is),t(N),t(ls),t(he),t(ds),t(Q),t(cs),t(fe),t(ps),t(S),t(us),t(ge),t(ms),t(B),t(hs),t(_e),t(fs),t(G),t(gs),t(ve),t(_s),t(F),t(vs),t(Te),t(Ts),t(q),t(bs),t(be),t(ys),t(ye),t(ws),t(we),t(Rs),t(Re),t($s),t($e),t(ks),t(Z),t(xs),t(ke),t(Ps),t(V),t(Ms),t(xe),t(Cs),t(W),t(Ds),t(Pe),t(zs),t(L),t(Es),t(Me),t(Fs),t(J),t(qs),t(Ce),t(Ls),t(H),t(Js),t(Vo)),t(i),w(U),w(bt),w(Pt),w(Mt),w(Fe),w(Ct),w(Dt),w(zt),w(Et),w(Ft),w(qt),w(Lt),w(Jt),w(Ht),w(jt),w(Ue),w(Ut),w(It),w(Nt),w(Zt),w(Vt),w(Wt),w(Qt),w(St),w(Bt),w(Ve),w(We),w(Gt),w(At),w(Xt),w(Se),w(Be),w(Ot),w(Yt),w(Kt),w(Ae),w(Xe),w(en),w(tn),w(Ye),w(nn),w(Ke),w(et),w(on),w(sn),w(nt),w(rn),w(ot),w(st),w(an),w(ln),w(at),w(dn),w(it),w(lt)}}}const Zl={local:"dpr",sections:[{local:"overview",title:"Overview"},{local:"transformers.DPRConfig",title:"DPRConfig"},{local:"transformers.DPRContextEncoderTokenizer",title:"DPRContextEncoderTokenizer"},{local:"transformers.DPRContextEncoderTokenizerFast",title:"DPRContextEncoderTokenizerFast"},{local:"transformers.DPRQuestionEncoderTokenizer",title:"DPRQuestionEncoderTokenizer"},{local:"transformers.DPRQuestionEncoderTokenizerFast",title:"DPRQuestionEncoderTokenizerFast"},{local:"transformers.DPRReaderTokenizer",title:"DPRReaderTokenizer"},{local:"transformers.DPRReaderTokenizerFast",title:"DPRReaderTokenizerFast"},{local:"transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput",title:"DPR specific outputs"},{local:"transformers.DPRContextEncoder",title:"DPRContextEncoder"},{local:"transformers.DPRQuestionEncoder",title:"DPRQuestionEncoder"},{local:"transformers.DPRReader",title:"DPRReader"},{local:"transformers.TFDPRContextEncoder",title:"TFDPRContextEncoder"},{local:"transformers.TFDPRQuestionEncoder",title:"TFDPRQuestionEncoder"},{local:"transformers.TFDPRReader",title:"TFDPRReader"}],title:"DPR"};function Vl(P){return bl(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Ol extends yl{constructor(i){super(),wl(this,i,Vl,Nl,Tl,{})}}export{Ol as component};
