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 m,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.f3d04bd2.js";import{C as Tt}from"../chunks/CodeBlock.48618ec2.js";import{I}from"../chunks/IconCopyLink.bedaa44d.js";import{E as vt}from"../chunks/ExampleCodeBlock.872b014d.js";function $l(P){let i,k="Example:",h,p,$;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`}}),{c(){i=o("p"),i.textContent=k,h=r(),_(p.$$.fragment)},l(l){i=s(l,"P",{"data-svelte-h":!0}),u(i)!=="svelte-11lpom8"&&(i.textContent=k),h=a(l),v(p.$$.fragment,l)},m(l,R){d(l,i,R),d(l,h,R),T(p,l,R),$=!0},p:j,i(l){$||(b(p.$$.fragment,l),$=!0)},o(l){y(p.$$.fragment,l),$=!1},d(l){l&&(t(i),t(h)),w(p,l)}}}function kl(P){let i,k="with the format:",h,p,$;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>'}}),{c(){i=o("p"),i.textContent=k,h=r(),_(p.$$.fragment)},l(l){i=s(l,"P",{"data-svelte-h":!0}),u(i)!=="svelte-1kqkfm0"&&(i.textContent=k),h=a(l),v(p.$$.fragment,l)},m(l,R){d(l,i,R),d(l,h,R),T(p,l,R),$=!0},p:j,i(l){$||(b(p.$$.fragment,l),$=!0)},o(l){y(p.$$.fragment,l),$=!1},d(l){l&&(t(i),t(h)),w(p,l)}}}function xl(P){let i,k=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){i=o("p"),i.innerHTML=k},l(h){i=s(h,"P",{"data-svelte-h":!0}),u(i)!=="svelte-fincs2"&&(i.innerHTML=k)},m(h,p){d(h,i,p)},p:j,d(h){h&&t(i)}}}function Pl(P){let i,k="Examples:",h,p,$;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`}}),{c(){i=o("p"),i.textContent=k,h=r(),_(p.$$.fragment)},l(l){i=s(l,"P",{"data-svelte-h":!0}),u(i)!=="svelte-kvfsh7"&&(i.textContent=k),h=a(l),v(p.$$.fragment,l)},m(l,R){d(l,i,R),d(l,h,R),T(p,l,R),$=!0},p:j,i(l){$||(b(p.$$.fragment,l),$=!0)},o(l){y(p.$$.fragment,l),$=!1},d(l){l&&(t(i),t(h)),w(p,l)}}}function Ml(P){let i,k=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){i=o("p"),i.innerHTML=k},l(h){i=s(h,"P",{"data-svelte-h":!0}),u(i)!=="svelte-fincs2"&&(i.innerHTML=k)},m(h,p){d(h,i,p)},p:j,d(h){h&&t(i)}}}function Cl(P){let i,k="Examples:",h,p,$;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`}}),{c(){i=o("p"),i.textContent=k,h=r(),_(p.$$.fragment)},l(l){i=s(l,"P",{"data-svelte-h":!0}),u(i)!=="svelte-kvfsh7"&&(i.textContent=k),h=a(l),v(p.$$.fragment,l)},m(l,R){d(l,i,R),d(l,h,R),T(p,l,R),$=!0},p:j,i(l){$||(b(p.$$.fragment,l),$=!0)},o(l){y(p.$$.fragment,l),$=!1},d(l){l&&(t(i),t(h)),w(p,l)}}}function Dl(P){let i,k=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){i=o("p"),i.innerHTML=k},l(h){i=s(h,"P",{"data-svelte-h":!0}),u(i)!=="svelte-fincs2"&&(i.innerHTML=k)},m(h,p){d(h,i,p)},p:j,d(h){h&&t(i)}}}function zl(P){let i,k="Examples:",h,p,$;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`}}),{c(){i=o("p"),i.textContent=k,h=r(),_(p.$$.fragment)},l(l){i=s(l,"P",{"data-svelte-h":!0}),u(i)!=="svelte-kvfsh7"&&(i.textContent=k),h=a(l),v(p.$$.fragment,l)},m(l,R){d(l,i,R),d(l,h,R),T(p,l,R),$=!0},p:j,i(l){$||(b(p.$$.fragment,l),$=!0)},o(l){y(p.$$.fragment,l),$=!1},d(l){l&&(t(i),t(h)),w(p,l)}}}function El(P){let i,k="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",h,p,$="<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,h=r(),p=o("ul"),p.innerHTML=$,l=r(),R=o("p"),R.innerHTML=U,E=r(),C=o("ul"),C.innerHTML=ie,z=r(),M=o("p"),M.innerHTML=le},l(g){i=s(g,"P",{"data-svelte-h":!0}),u(i)!=="svelte-1ajbfxg"&&(i.innerHTML=k),h=a(g),p=s(g,"UL",{"data-svelte-h":!0}),u(p)!=="svelte-qm1t26"&&(p.innerHTML=$),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,h,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(h),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(h){i=s(h,"P",{"data-svelte-h":!0}),u(i)!=="svelte-fincs2"&&(i.innerHTML=k)},m(h,p){d(h,i,p)},p:j,d(h){h&&t(i)}}}function ql(P){let i,k="Examples:",h,p,$;return p=new Tt({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGRFBSQ29udGV4dEVuY29kZXIlMkMlMjBEUFJDb250ZXh0RW5jb2RlclRva2VuaXplciUwQSUwQXRva2VuaXplciUyMCUzRCUyMERQUkNvbnRleHRFbmNvZGVyVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjJmYWNlYm9vayUyRmRwci1jdHhfZW5jb2Rlci1zaW5nbGUtbnEtYmFzZSUyMiklMEFtb2RlbCUyMCUzRCUyMFRGRFBSQ29udGV4dEVuY29kZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmZhY2Vib29rJTJGZHByLWN0eF9lbmNvZGVyLXNpbmdsZS1ucS1iYXNlJTIyJTJDJTIwZnJvbV9wdCUzRFRydWUpJTBBaW5wdXRfaWRzJTIwJTNEJTIwdG9rZW5pemVyKCUyMkhlbGxvJTJDJTIwaXMlMjBteSUyMGRvZyUyMGN1dGUlMjAlM0YlMjIlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnRmJTIyKSU1QiUyMmlucHV0X2lkcyUyMiU1RCUwQWVtYmVkZGluZ3MlMjAlM0QlMjBtb2RlbChpbnB1dF9pZHMpLnBvb2xlcl9vdXRwdXQ=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFDPRContextEncoder, DPRContextEncoderTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = DPRContextEncoderTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/dpr-ctx_encoder-single-nq-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFDPRContextEncoder.from_pretrained(<span class="hljs-string">&quot;facebook/dpr-ctx_encoder-single-nq-base&quot;</span>, from_pt=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer(<span class="hljs-string">&quot;Hello, is my dog cute ?&quot;</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>embeddings = model(input_ids).pooler_output`}}),{c(){i=o("p"),i.textContent=k,h=r(),_(p.$$.fragment)},l(l){i=s(l,"P",{"data-svelte-h":!0}),u(i)!=="svelte-kvfsh7"&&(i.textContent=k),h=a(l),v(p.$$.fragment,l)},m(l,R){d(l,i,R),d(l,h,R),T(p,l,R),$=!0},p:j,i(l){$||(b(p.$$.fragment,l),$=!0)},o(l){y(p.$$.fragment,l),$=!1},d(l){l&&(t(i),t(h)),w(p,l)}}}function Ll(P){let i,k="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",h,p,$="<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,h=r(),p=o("ul"),p.innerHTML=$,l=r(),R=o("p"),R.innerHTML=U,E=r(),C=o("ul"),C.innerHTML=ie,z=r(),M=o("p"),M.innerHTML=le},l(g){i=s(g,"P",{"data-svelte-h":!0}),u(i)!=="svelte-1ajbfxg"&&(i.innerHTML=k),h=a(g),p=s(g,"UL",{"data-svelte-h":!0}),u(p)!=="svelte-qm1t26"&&(p.innerHTML=$),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,h,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(h),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(h){i=s(h,"P",{"data-svelte-h":!0}),u(i)!=="svelte-fincs2"&&(i.innerHTML=k)},m(h,p){d(h,i,p)},p:j,d(h){h&&t(i)}}}function Hl(P){let i,k="Examples:",h,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`}}),{c(){i=o("p"),i.textContent=k,h=r(),_(p.$$.fragment)},l(l){i=s(l,"P",{"data-svelte-h":!0}),u(i)!=="svelte-kvfsh7"&&(i.textContent=k),h=a(l),v(p.$$.fragment,l)},m(l,R){d(l,i,R),d(l,h,R),T(p,l,R),$=!0},p:j,i(l){$||(b(p.$$.fragment,l),$=!0)},o(l){y(p.$$.fragment,l),$=!1},d(l){l&&(t(i),t(h)),w(p,l)}}}function jl(P){let i,k="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",h,p,$="<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,h=r(),p=o("ul"),p.innerHTML=$,l=r(),R=o("p"),R.innerHTML=U,E=r(),C=o("ul"),C.innerHTML=ie,z=r(),M=o("p"),M.innerHTML=le},l(g){i=s(g,"P",{"data-svelte-h":!0}),u(i)!=="svelte-1ajbfxg"&&(i.innerHTML=k),h=a(g),p=s(g,"UL",{"data-svelte-h":!0}),u(p)!=="svelte-qm1t26"&&(p.innerHTML=$),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,h,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(h),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(h){i=s(h,"P",{"data-svelte-h":!0}),u(i)!=="svelte-fincs2"&&(i.innerHTML=k)},m(h,p){d(h,i,p)},p:j,d(h){h&&t(i)}}}function Il(P){let i,k="Examples:",h,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`}}),{c(){i=o("p"),i.textContent=k,h=r(),_(p.$$.fragment)},l(l){i=s(l,"P",{"data-svelte-h":!0}),u(i)!=="svelte-kvfsh7"&&(i.textContent=k),h=a(l),v(p.$$.fragment,l)},m(l,R){d(l,i,R),d(l,h,R),T(p,l,R),$=!0},p:j,i(l){$||(b(p.$$.fragment,l),$=!0)},o(l){y(p.$$.fragment,l),$=!1},d(l){l&&(t(i),t(h)),w(p,l)}}}function Nl(P){let i,k,h,p,$,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,he,Ee,Co,Pt,Ks,gn,Ia="DPRConfig",is,N,Mt,er,_n,Na='<a href="/docs/transformers/v4.33.3/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/v4.33.3/en/model_doc/dpr#transformers.DPRContextEncoder">DPRContextEncoder</a>, <a href="/docs/transformers/v4.33.3/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a>, or a
<a href="/docs/transformers/v4.33.3/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/v4.33.3/en/model_doc/bert#transformers.BertConfig">BertConfig</a>. Please check the superclass for the documentation of all kwargs.',or,Fe,ls,me,qe,Do,Ct,sr,bn,Wa="DPRContextEncoderTokenizer",ds,Q,Dt,rr,yn,Qa="Construct a DPRContextEncoder tokenizer.",ar,wn,Sa=`<a href="/docs/transformers/v4.33.3/en/model_doc/dpr#transformers.DPRContextEncoderTokenizer">DPRContextEncoderTokenizer</a> is identical to <a href="/docs/transformers/v4.33.3/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/v4.33.3/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/v4.33.3/en/model_doc/dpr#transformers.DPRContextEncoderTokenizerFast">DPRContextEncoderTokenizerFast</a> is identical to <a href="/docs/transformers/v4.33.3/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/v4.33.3/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> for usage examples and documentation concerning parameters.',us,ge,Je,Eo,Ft,ur,Mn,Ya="DPRQuestionEncoderTokenizer",hs,B,qt,hr,Cn,Ka="Constructs a DPRQuestionEncoder tokenizer.",mr,Dn,ei=`<a href="/docs/transformers/v4.33.3/en/model_doc/dpr#transformers.DPRQuestionEncoderTokenizer">DPRQuestionEncoderTokenizer</a> is identical to <a href="/docs/transformers/v4.33.3/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/v4.33.3/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> for usage examples and documentation concerning parameters.',ms,_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/v4.33.3/en/model_doc/dpr#transformers.DPRQuestionEncoderTokenizerFast">DPRQuestionEncoderTokenizerFast</a> is identical to <a href="/docs/transformers/v4.33.3/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/v4.33.3/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/v4.33.3/en/model_doc/dpr#transformers.DPRReaderTokenizer">DPRReaderTokenizer</a> is almost identical to <a href="/docs/transformers/v4.33.3/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/v4.33.3/en/model_doc/dpr#transformers.DPRReader">DPRReader</a> model.`,Rr,Un,di='Refer to superclass <a href="/docs/transformers/v4.33.3/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,hi=`<a href="/docs/transformers/v4.33.3/en/model_doc/dpr#transformers.DPRReaderTokenizerFast">DPRReaderTokenizerFast</a> is almost identical to <a href="/docs/transformers/v4.33.3/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/v4.33.3/en/model_doc/dpr#transformers.DPRReader">DPRReader</a> model.`,Cr,Wn,mi='Refer to superclass <a href="/docs/transformers/v4.33.3/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/v4.33.3/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a>.',ws,we,Vt,qr,An,Ti='Class for outputs of <a href="/docs/transformers/v4.33.3/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a>.',Rs,Re,Wt,Lr,Xn,bi='Class for outputs of <a href="/docs/transformers/v4.33.3/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a>.',$s,$e,Ze,Ho,Qt,Jr,On,yi="DPRContextEncoder",ks,Z,St,Hr,Yn,wi="The bare DPRContextEncoder transformer outputting pooler outputs as context representations.",jr,Kn,Ri=`This model inherits from <a href="/docs/transformers/v4.33.3/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Ur,eo,$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/v4.33.3/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/v4.33.3/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/v4.33.3/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/v4.33.3/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/v4.33.3/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,ho,Ji="TFDPRContextEncoder",zs,L,tn,ia,mo,Hi="The bare DPRContextEncoder transformer outputting pooler outputs as context representations.",la,fo,ji=`This model inherits from <a href="/docs/transformers/v4.33.3/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/v4.33.3/en/model_doc/dpr#transformers.TFDPRContextEncoder">TFDPRContextEncoder</a> forward method, overrides the <code>__call__</code> special method.',ha,Ke,ma,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/v4.33.3/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/v4.33.3/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/v4.33.3/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/v4.33.3/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/v4.33.3/en/model_doc/bert#transformers.BertModel">BertModel</a>.`,name:"vocab_size"},{anchor:"transformers.DPRConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.DPRConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.DPRConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.DPRConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3072) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.DPRConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;silu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.DPRConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.DPRConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.DPRConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"max_position_embeddings"},{anchor:"transformers.DPRConfig.type_vocab_size",description:`<strong>type_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The vocabulary size of the <em>token_type_ids</em> passed into <a href="/docs/transformers/v4.33.3/en/model_doc/bert#transformers.BertModel">BertModel</a>.`,name:"type_vocab_size"},{anchor:"transformers.DPRConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.DPRConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-12) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.DPRConfig.position_embedding_type",description:`<strong>position_embedding_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;absolute&quot;</code>) &#x2014;
Type of position embedding. Choose one of <code>&quot;absolute&quot;</code>, <code>&quot;relative_key&quot;</code>, <code>&quot;relative_key_query&quot;</code>. For
positional embeddings use <code>&quot;absolute&quot;</code>. For more information on <code>&quot;relative_key&quot;</code>, please refer to
<a href="https://arxiv.org/abs/1803.02155" rel="nofollow">Self-Attention with Relative Position Representations (Shaw et al.)</a>.
For more information on <code>&quot;relative_key_query&quot;</code>, please refer to <em>Method 4</em> in <a href="https://arxiv.org/abs/2009.13658" rel="nofollow">Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)</a>.`,name:"position_embedding_type"},{anchor:"transformers.DPRConfig.projection_dim",description:`<strong>projection_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
Dimension of the projection for the context and question encoders. If it is set to zero (default), then no
projection is done.`,name:"projection_dim"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/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/v4.33.3/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/v4.33.3/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/v4.33.3/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/v4.33.3/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/v4.33.3/en/internal/file_utils#transformers.utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>&apos;longest&apos;</code>: Pad to the longest sequence in the batch (or no padding if only a single sequence
if provided).</li>
<li><code>&apos;max_length&apos;</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>&apos;do_not_pad&apos;</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul>`,name:"padding"},{anchor:"transformers.DPRReaderTokenizer.truncation",description:`<strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/v4.33.3/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/v4.33.3/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/v4.33.3/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/v4.33.3/en/internal/file_utils#transformers.utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>&apos;longest&apos;</code>: Pad to the longest sequence in the batch (or no padding if only a single sequence
if provided).</li>
<li><code>&apos;max_length&apos;</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>&apos;do_not_pad&apos;</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul>`,name:"padding"},{anchor:"transformers.DPRReaderTokenizerFast.truncation",description:`<strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/v4.33.3/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/v4.33.3/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/v4.33.3/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/v4.33.3/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/v4.33.3/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/v4.33.3/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/v4.33.3/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.33.3/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/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/v4.33.3/src/transformers/models/dpr/modeling_dpr.py#L449",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/en/model_doc/dpr#transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput"
>transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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/v4.33.3/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.33.3/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/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/v4.33.3/src/transformers/models/dpr/modeling_dpr.py#L530",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/en/model_doc/dpr#transformers.models.dpr.modeling_dpr.DPRQuestionEncoderOutput"
>transformers.models.dpr.modeling_dpr.DPRQuestionEncoderOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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/v4.33.3/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.33.3/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/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/v4.33.3/en/model_doc/dpr#transformers.DPRReaderTokenizer">DPRReaderTokenizer</a>. See this class documentation for more details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.DPRReader.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(n_passages, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.DPRReader.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(n_passages, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DPRReader.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DPRReader.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.DPRReader.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/dpr/modeling_dpr.py#L612",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/en/model_doc/dpr#transformers.DPRReaderOutput"
>transformers.models.dpr.modeling_dpr.DPRReaderOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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/v4.33.3/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.33.3/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/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/v4.33.3/src/transformers/models/dpr/modeling_tf_dpr.py#L538",returnDescription:`
<p>A <code>transformers.models.dpr.modeling_tf_dpr.TFDPRContextEncoderOutput</code> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.33.3/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/v4.33.3/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.33.3/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/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/v4.33.3/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
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configuration (<a
  href="/docs/transformers/v4.33.3/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>
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Initializing with a config file does not load the weights associated with the model, only the
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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>
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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
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Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFDPRReader.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
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<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.33.3/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>
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