import{S as Mm,i as $m,s as Em,e as n,k as d,w as v,t as i,M as zm,c as r,d as t,m as c,a,x as y,h as l,b as p,G as e,g as _,y as w,q as T,o as R,B as M,v as xm,L as rt}from"../../chunks/vendor-hf-doc-builder.js";import{T as Mr}from"../../chunks/Tip-hf-doc-builder.js";import{D as z}from"../../chunks/Docstring-hf-doc-builder.js";import{C as at}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Q}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as nt}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function jm($){let m,k,u,f,b;return f=new at({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> RealmConfig, RealmEmbedder

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a REALM realm-cc-news-pretrained-* style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = RealmConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the google/realm-cc-news-pretrained-embedder style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RealmEmbedder(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(){m=n("p"),k=i("Example:"),u=d(),v(f.$$.fragment)},l(s){m=r(s,"P",{});var h=a(m);k=l(h,"Example:"),h.forEach(t),u=c(s),y(f.$$.fragment,s)},m(s,h){_(s,m,h),e(m,k),_(s,u,h),w(f,s,h),b=!0},p:rt,i(s){b||(T(f.$$.fragment,s),b=!0)},o(s){R(f.$$.fragment,s),b=!1},d(s){s&&t(m),s&&t(u),M(f,s)}}}function Jm($){let m,k,u,f,b;return f=new at({props:{code:"MCUyMDAlMjAwJTIwMCUyMDAlMjAwJTIwMCUyMDAlMjAwJTIwMCUyMDAlMjAxJTIwMSUyMDElMjAxJTIwMSUyMDElMjAxJTIwMSUyMDElMEElN0MlMjBmaXJzdCUyMHNlcXVlbmNlJTIwJTIwJTIwJTIwJTdDJTIwc2Vjb25kJTIwc2VxdWVuY2UlMjAlN0M=",highlighted:`0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1 1
| first sequence    | second sequence |`}}),{c(){m=n("p"),k=i("pair mask has the following format:"),u=d(),v(f.$$.fragment)},l(s){m=r(s,"P",{});var h=a(m);k=l(h,"pair mask has the following format:"),h.forEach(t),u=c(s),y(f.$$.fragment,s)},m(s,h){_(s,m,h),e(m,k),_(s,u,h),w(f,s,h),b=!0},p:rt,i(s){b||(T(f.$$.fragment,s),b=!0)},o(s){R(f.$$.fragment,s),b=!1},d(s){s&&t(m),s&&t(u),M(f,s)}}}function Am($){let m,k,u,f,b;return f=new at({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> RealmTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># batch_size = 2, num_candidates = 2</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>text = [[<span class="hljs-string">&quot;Hello world!&quot;</span>, <span class="hljs-string">&quot;Nice to meet you!&quot;</span>], [<span class="hljs-string">&quot;The cute cat.&quot;</span>, <span class="hljs-string">&quot;The adorable dog.&quot;</span>]]

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RealmTokenizer.from_pretrained(<span class="hljs-string">&quot;google/realm-cc-news-pretrained-encoder&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenized_text = tokenizer.batch_encode_candidates(text, max_length=<span class="hljs-number">10</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)`}}),{c(){m=n("p"),k=i("Example:"),u=d(),v(f.$$.fragment)},l(s){m=r(s,"P",{});var h=a(m);k=l(h,"Example:"),h.forEach(t),u=c(s),y(f.$$.fragment,s)},m(s,h){_(s,m,h),e(m,k),_(s,u,h),w(f,s,h),b=!0},p:rt,i(s){b||(T(f.$$.fragment,s),b=!0)},o(s){R(f.$$.fragment,s),b=!1},d(s){s&&t(m),s&&t(u),M(f,s)}}}function qm($){let m,k,u,f,b;return f=new at({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> RealmTokenizerFast

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># batch_size = 2, num_candidates = 2</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>text = [[<span class="hljs-string">&quot;Hello world!&quot;</span>, <span class="hljs-string">&quot;Nice to meet you!&quot;</span>], [<span class="hljs-string">&quot;The cute cat.&quot;</span>, <span class="hljs-string">&quot;The adorable dog.&quot;</span>]]

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = RealmTokenizerFast.from_pretrained(<span class="hljs-string">&quot;google/realm-cc-news-pretrained-encoder&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenized_text = tokenizer.batch_encode_candidates(text, max_length=<span class="hljs-number">10</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)`}}),{c(){m=n("p"),k=i("Example:"),u=d(),v(f.$$.fragment)},l(s){m=r(s,"P",{});var h=a(m);k=l(h,"Example:"),h.forEach(t),u=c(s),y(f.$$.fragment,s)},m(s,h){_(s,m,h),e(m,k),_(s,u,h),w(f,s,h),b=!0},p:rt,i(s){b||(T(f.$$.fragment,s),b=!0)},o(s){R(f.$$.fragment,s),b=!1},d(s){s&&t(m),s&&t(u),M(f,s)}}}function Um($){let m,k,u,f,b;return{c(){m=n("p"),k=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=n("code"),f=i("Module"),b=i(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(s){m=r(s,"P",{});var h=a(m);k=l(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r(h,"CODE",{});var x=a(u);f=l(x,"Module"),x.forEach(t),b=l(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(t)},m(s,h){_(s,m,h),e(m,k),e(m,u),e(u,f),e(m,b)},d(s){s&&t(m)}}}function Im($){let m,k,u,f,b;return f=new at({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> AutoTokenizer, RealmEmbedder
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/realm-cc-news-pretrained-embedder&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RealmEmbedder.from_pretrained(<span class="hljs-string">&quot;google/realm-cc-news-pretrained-embedder&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>projected_score = outputs.projected_score`}}),{c(){m=n("p"),k=i("Example:"),u=d(),v(f.$$.fragment)},l(s){m=r(s,"P",{});var h=a(m);k=l(h,"Example:"),h.forEach(t),u=c(s),y(f.$$.fragment,s)},m(s,h){_(s,m,h),e(m,k),_(s,u,h),w(f,s,h),b=!0},p:rt,i(s){b||(T(f.$$.fragment,s),b=!0)},o(s){R(f.$$.fragment,s),b=!1},d(s){s&&t(m),s&&t(u),M(f,s)}}}function Fm($){let m,k,u,f,b;return{c(){m=n("p"),k=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=n("code"),f=i("Module"),b=i(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(s){m=r(s,"P",{});var h=a(m);k=l(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r(h,"CODE",{});var x=a(u);f=l(x,"Module"),x.forEach(t),b=l(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(t)},m(s,h){_(s,m,h),e(m,k),e(m,u),e(u,f),e(m,b)},d(s){s&&t(m)}}}function Zm($){let m,k,u,f,b;return f=new at({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, RealmScorer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/realm-cc-news-pretrained-scorer&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RealmScorer.from_pretrained(<span class="hljs-string">&quot;google/realm-cc-news-pretrained-scorer&quot;</span>, num_candidates=<span class="hljs-number">2</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># batch_size = 2, num_candidates = 2</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>input_texts = [<span class="hljs-string">&quot;How are you?&quot;</span>, <span class="hljs-string">&quot;What is the item in the picture?&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>candidates_texts = [[<span class="hljs-string">&quot;Hello world!&quot;</span>, <span class="hljs-string">&quot;Nice to meet you!&quot;</span>], [<span class="hljs-string">&quot;A cute cat.&quot;</span>, <span class="hljs-string">&quot;An adorable dog.&quot;</span>]]

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(input_texts, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>candidates_inputs = tokenizer.batch_encode_candidates(candidates_texts, max_length=<span class="hljs-number">10</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(
<span class="hljs-meta">... </span>    **inputs,
<span class="hljs-meta">... </span>    candidate_input_ids=candidates_inputs.input_ids,
<span class="hljs-meta">... </span>    candidate_attention_mask=candidates_inputs.attention_mask,
<span class="hljs-meta">... </span>    candidate_token_type_ids=candidates_inputs.token_type_ids,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>relevance_score = outputs.relevance_score`}}),{c(){m=n("p"),k=i("Example:"),u=d(),v(f.$$.fragment)},l(s){m=r(s,"P",{});var h=a(m);k=l(h,"Example:"),h.forEach(t),u=c(s),y(f.$$.fragment,s)},m(s,h){_(s,m,h),e(m,k),_(s,u,h),w(f,s,h),b=!0},p:rt,i(s){b||(T(f.$$.fragment,s),b=!0)},o(s){R(f.$$.fragment,s),b=!1},d(s){s&&t(m),s&&t(u),M(f,s)}}}function Wm($){let m,k,u,f,b;return{c(){m=n("p"),k=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=n("code"),f=i("Module"),b=i(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(s){m=r(s,"P",{});var h=a(m);k=l(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r(h,"CODE",{});var x=a(u);f=l(x,"Module"),x.forEach(t),b=l(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(t)},m(s,h){_(s,m,h),e(m,k),e(m,u),e(u,f),e(m,b)},d(s){s&&t(m)}}}function Cm($){let m,k,u,f,b;return f=new at({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, RealmKnowledgeAugEncoder

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/realm-cc-news-pretrained-encoder&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RealmKnowledgeAugEncoder.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;google/realm-cc-news-pretrained-encoder&quot;</span>, num_candidates=<span class="hljs-number">2</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># batch_size = 2, num_candidates = 2</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>text = [[<span class="hljs-string">&quot;Hello world!&quot;</span>, <span class="hljs-string">&quot;Nice to meet you!&quot;</span>], [<span class="hljs-string">&quot;The cute cat.&quot;</span>, <span class="hljs-string">&quot;The adorable dog.&quot;</span>]]

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer.batch_encode_candidates(text, max_length=<span class="hljs-number">10</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){m=n("p"),k=i("Example:"),u=d(),v(f.$$.fragment)},l(s){m=r(s,"P",{});var h=a(m);k=l(h,"Example:"),h.forEach(t),u=c(s),y(f.$$.fragment,s)},m(s,h){_(s,m,h),e(m,k),_(s,u,h),w(f,s,h),b=!0},p:rt,i(s){b||(T(f.$$.fragment,s),b=!0)},o(s){R(f.$$.fragment,s),b=!1},d(s){s&&t(m),s&&t(u),M(f,s)}}}function Lm($){let m,k,u,f,b;return{c(){m=n("p"),k=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=n("code"),f=i("Module"),b=i(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(s){m=r(s,"P",{});var h=a(m);k=l(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r(h,"CODE",{});var x=a(u);f=l(x,"Module"),x.forEach(t),b=l(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(t)},m(s,h){_(s,m,h),e(m,k),e(m,u),e(u,f),e(m,b)},d(s){s&&t(m)}}}function Sm($){let m,k,u,f,b;return{c(){m=n("p"),k=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=n("code"),f=i("Module"),b=i(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(s){m=r(s,"P",{});var h=a(m);k=l(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r(h,"CODE",{});var x=a(u);f=l(x,"Module"),x.forEach(t),b=l(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(t)},m(s,h){_(s,m,h),e(m,k),e(m,u),e(u,f),e(m,b)},d(s){s&&t(m)}}}function Nm($){let m,k,u,f,b;return f=new at({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwUmVhbG1Gb3JPcGVuUUElMkMlMjBSZWFsbVJldHJpZXZlciUyQyUyMEF1dG9Ub2tlbml6ZXIlMEElMEFyZXRyaWV2ZXIlMjAlM0QlMjBSZWFsbVJldHJpZXZlci5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlJTJGcmVhbG0tb3JxYS1ucS1vcGVucWElMjIpJTBBdG9rZW5pemVyJTIwJTNEJTIwQXV0b1Rva2VuaXplci5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlJTJGcmVhbG0tb3JxYS1ucS1vcGVucWElMjIpJTBBbW9kZWwlMjAlM0QlMjBSZWFsbUZvck9wZW5RQS5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlJTJGcmVhbG0tb3JxYS1ucS1vcGVucWElMjIlMkMlMjByZXRyaWV2ZXIlM0RyZXRyaWV2ZXIpJTBBJTBBcXVlc3Rpb24lMjAlM0QlMjAlMjJXaG8lMjBpcyUyMHRoZSUyMHBpb25lZXIlMjBpbiUyMG1vZGVybiUyMGNvbXB1dGVyJTIwc2NpZW5jZSUzRiUyMiUwQXF1ZXN0aW9uX2lkcyUyMCUzRCUyMHRva2VuaXplciglNUJxdWVzdGlvbiU1RCUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIpJTBBYW5zd2VyX2lkcyUyMCUzRCUyMHRva2VuaXplciglMEElMjAlMjAlMjAlMjAlNUIlMjJhbGFuJTIwbWF0aGlzb24lMjB0dXJpbmclMjIlNUQlMkMlMEElMjAlMjAlMjAlMjBhZGRfc3BlY2lhbF90b2tlbnMlM0RGYWxzZSUyQyUwQSUyMCUyMCUyMCUyMHJldHVybl90b2tlbl90eXBlX2lkcyUzREZhbHNlJTJDJTBBJTIwJTIwJTIwJTIwcmV0dXJuX2F0dGVudGlvbl9tYXNrJTNERmFsc2UlMkMlMEEpLmlucHV0X2lkcyUwQSUwQXJlYWRlcl9vdXRwdXQlMkMlMjBwcmVkaWN0ZWRfYW5zd2VyX2lkcyUyMCUzRCUyMG1vZGVsKCoqcXVlc3Rpb25faWRzJTJDJTIwYW5zd2VyX2lkcyUzRGFuc3dlcl9pZHMlMkMlMjByZXR1cm5fZGljdCUzREZhbHNlKSUwQXByZWRpY3RlZF9hbnN3ZXIlMjAlM0QlMjB0b2tlbml6ZXIuZGVjb2RlKHByZWRpY3RlZF9hbnN3ZXJfaWRzKSUwQWxvc3MlMjAlM0QlMjByZWFkZXJfb3V0cHV0Lmxvc3M=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> RealmForOpenQA, RealmRetriever, AutoTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>retriever = RealmRetriever.from_pretrained(<span class="hljs-string">&quot;google/realm-orqa-nq-openqa&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/realm-orqa-nq-openqa&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RealmForOpenQA.from_pretrained(<span class="hljs-string">&quot;google/realm-orqa-nq-openqa&quot;</span>, retriever=retriever)

<span class="hljs-meta">&gt;&gt;&gt; </span>question = <span class="hljs-string">&quot;Who is the pioneer in modern computer science?&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>question_ids = tokenizer([question], return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>answer_ids = tokenizer(
<span class="hljs-meta">... </span>    [<span class="hljs-string">&quot;alan mathison turing&quot;</span>],
<span class="hljs-meta">... </span>    add_special_tokens=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>    return_token_type_ids=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>    return_attention_mask=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>).input_ids

<span class="hljs-meta">&gt;&gt;&gt; </span>reader_output, predicted_answer_ids = model(**question_ids, answer_ids=answer_ids, return_dict=<span class="hljs-literal">False</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_answer = tokenizer.decode(predicted_answer_ids)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = reader_output.loss`}}),{c(){m=n("p"),k=i("Example:"),u=d(),v(f.$$.fragment)},l(s){m=r(s,"P",{});var h=a(m);k=l(h,"Example:"),h.forEach(t),u=c(s),y(f.$$.fragment,s)},m(s,h){_(s,m,h),e(m,k),_(s,u,h),w(f,s,h),b=!0},p:rt,i(s){b||(T(f.$$.fragment,s),b=!0)},o(s){R(f.$$.fragment,s),b=!1},d(s){s&&t(m),s&&t(u),M(f,s)}}}function Pm($){let m,k,u,f,b,s,h,x,Ea,$r,oe,ye,ln,st,za,dn,xa,Er,we,ja,it,Ja,Aa,zr,go,qa,xr,_o,cn,Ua,jr,D,Ia,lt,Fa,Za,dt,Wa,Ca,Jr,ne,Te,mn,ct,La,pn,Sa,Ar,J,mt,Na,hn,Pa,Va,A,fn,bo,Ga,Ba,un,ko,Xa,Oa,gn,vo,Qa,Da,_n,yo,Ha,Ya,bn,wo,Ka,es,kn,To,ts,os,pt,ns,ht,rs,as,ss,re,is,Ro,ls,ds,Mo,cs,ms,ps,Re,qr,ae,Me,vn,ft,hs,yn,fs,Ur,E,ut,us,wn,gs,_s,$e,$o,bs,ks,Eo,vs,ys,ws,gt,Ts,zo,Rs,Ms,$s,H,_t,Es,Tn,zs,xs,bt,xo,js,Rn,Js,As,jo,qs,Mn,Us,Is,Ee,kt,Fs,vt,Zs,$n,Ws,Cs,Ls,Z,yt,Ss,En,Ns,Ps,ze,Vs,se,Gs,zn,Bs,Xs,xn,Os,Qs,Ds,Jo,wt,Hs,U,Tt,Ys,Rt,Ks,jn,ei,ti,oi,ie,Jn,ni,ri,Mt,ai,An,si,ii,li,$t,di,qn,ci,mi,pi,Et,Ao,hi,Un,fi,ui,qo,gi,In,_i,bi,xe,Ir,le,je,Fn,zt,ki,Zn,vi,Fr,q,xt,yi,jt,wi,Wn,Ti,Ri,Mi,Je,Uo,$i,Ei,Io,zi,xi,ji,Jt,Ji,Fo,Ai,qi,Ui,I,At,Ii,qt,Fi,Cn,Zi,Wi,Ci,de,Ln,Li,Si,Ut,Ni,Sn,Pi,Vi,Gi,It,Bi,Nn,Xi,Oi,Qi,Ft,Zo,Di,Pn,Hi,Yi,Wo,Ki,Vn,el,tl,Ae,Zr,ce,qe,Gn,Zt,ol,Bn,nl,Wr,V,Wt,rl,Xn,al,sl,Ue,Ct,il,On,ll,Cr,me,Ie,Qn,Lt,dl,Dn,cl,Lr,G,St,ml,Nt,pl,Pt,hl,fl,ul,W,Vt,gl,pe,_l,Co,bl,kl,Hn,vl,yl,wl,Fe,Tl,Ze,Sr,he,We,Yn,Gt,Rl,Kn,Ml,Nr,B,Bt,$l,Xt,El,Ot,zl,xl,jl,C,Qt,Jl,fe,Al,Lo,ql,Ul,er,Il,Fl,Zl,Ce,Wl,Le,Pr,ue,Se,tr,Dt,Cl,or,Ll,Vr,X,Ht,Sl,Yt,Nl,Kt,Pl,Vl,Gl,L,eo,Bl,ge,Xl,So,Ol,Ql,nr,Dl,Hl,Yl,Ne,Kl,Pe,Gr,_e,Ve,rr,to,ed,ar,td,Br,O,oo,od,no,nd,ro,rd,ad,sd,Y,ao,id,be,ld,No,dd,cd,sr,md,pd,hd,Ge,Xr,ke,Be,ir,so,fd,lr,ud,Or,F,io,gd,Xe,dr,_d,bd,lo,kd,vd,yd,Oe,co,wd,mo,Td,cr,Rd,Md,$d,S,po,Ed,ve,zd,Po,xd,jd,mr,Jd,Ad,qd,Qe,Ud,De,Qr;return s=new Q({}),st=new Q({}),ct=new Q({}),mt=new z({props:{name:"class transformers.RealmConfig",anchor:"transformers.RealmConfig",parameters:[{name:"vocab_size",val:" = 30522"},{name:"hidden_size",val:" = 768"},{name:"retriever_proj_size",val:" = 128"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 12"},{name:"num_candidates",val:" = 8"},{name:"intermediate_size",val:" = 3072"},{name:"hidden_act",val:" = 'gelu_new'"},{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:"span_hidden_size",val:" = 256"},{name:"max_span_width",val:" = 10"},{name:"reader_layer_norm_eps",val:" = 0.001"},{name:"reader_beam_size",val:" = 5"},{name:"reader_seq_len",val:" = 320"},{name:"num_block_records",val:" = 13353718"},{name:"searcher_beam_size",val:" = 5000"},{name:"pad_token_id",val:" = 1"},{name:"bos_token_id",val:" = 0"},{name:"eos_token_id",val:" = 2"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.RealmConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 30522) &#x2014;
Vocabulary size of the REALM model. Defines the number of different tokens that can be represented by the
<code>inputs_ids</code> passed when calling <a href="/docs/transformers/main/en/model_doc/realm#transformers.RealmEmbedder">RealmEmbedder</a>, <a href="/docs/transformers/main/en/model_doc/realm#transformers.RealmScorer">RealmScorer</a>, <a href="/docs/transformers/main/en/model_doc/realm#transformers.RealmKnowledgeAugEncoder">RealmKnowledgeAugEncoder</a>, or
<a href="/docs/transformers/main/en/model_doc/realm#transformers.RealmReader">RealmReader</a>.`,name:"vocab_size"},{anchor:"transformers.RealmConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimension of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.RealmConfig.retriever_proj_size",description:`<strong>retriever_proj_size</strong> (<code>int</code>, <em>optional</em>, defaults to 128) &#x2014;
Dimension of the retriever(embedder) projection.`,name:"retriever_proj_size"},{anchor:"transformers.RealmConfig.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.RealmConfig.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.RealmConfig.num_candidates",description:`<strong>num_candidates</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
Number of candidates inputted to the RealmScorer or RealmKnowledgeAugEncoder.`,name:"num_candidates"},{anchor:"transformers.RealmConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3072) &#x2014;
Dimension of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.RealmConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu_new&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;selu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.RealmConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.RealmConfig.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.RealmConfig.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.RealmConfig.type_vocab_size",description:`<strong>type_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The vocabulary size of the <code>token_type_ids</code> passed when calling <a href="/docs/transformers/main/en/model_doc/realm#transformers.RealmEmbedder">RealmEmbedder</a>, <a href="/docs/transformers/main/en/model_doc/realm#transformers.RealmScorer">RealmScorer</a>,
<a href="/docs/transformers/main/en/model_doc/realm#transformers.RealmKnowledgeAugEncoder">RealmKnowledgeAugEncoder</a>, or <a href="/docs/transformers/main/en/model_doc/realm#transformers.RealmReader">RealmReader</a>.`,name:"type_vocab_size"},{anchor:"transformers.RealmConfig.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.RealmConfig.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.RealmConfig.span_hidden_size",description:`<strong>span_hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 256) &#x2014;
Dimension of the reader&#x2019;s spans.`,name:"span_hidden_size"},{anchor:"transformers.RealmConfig.max_span_width",description:`<strong>max_span_width</strong> (<code>int</code>, <em>optional</em>, defaults to 10) &#x2014;
Max span width of the reader.`,name:"max_span_width"},{anchor:"transformers.RealmConfig.reader_layer_norm_eps",description:`<strong>reader_layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-3) &#x2014;
The epsilon used by the reader&#x2019;s layer normalization layers.`,name:"reader_layer_norm_eps"},{anchor:"transformers.RealmConfig.reader_beam_size",description:`<strong>reader_beam_size</strong> (<code>int</code>, <em>optional</em>, defaults to 5) &#x2014;
Beam size of the reader.`,name:"reader_beam_size"},{anchor:"transformers.RealmConfig.reader_seq_len",description:`<strong>reader_seq_len</strong> (<code>int</code>, <em>optional</em>, defaults to 288+32) &#x2014;
Maximum sequence length of the reader.`,name:"reader_seq_len"},{anchor:"transformers.RealmConfig.num_block_records",description:`<strong>num_block_records</strong> (<code>int</code>, <em>optional</em>, defaults to 13353718) &#x2014;
Number of block records.`,name:"num_block_records"},{anchor:"transformers.RealmConfig.searcher_beam_size",description:`<strong>searcher_beam_size</strong> (<code>int</code>, <em>optional</em>, defaults to 5000) &#x2014;
Beam size of the searcher. Note that when eval mode is enabled, <em>searcher_beam_size</em> will be the same as
<em>reader_beam_size</em>.`,name:"searcher_beam_size"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/configuration_realm.py#L44"}}),Re=new nt({props:{anchor:"transformers.RealmConfig.example",$$slots:{default:[jm]},$$scope:{ctx:$}}}),ft=new Q({}),ut=new z({props:{name:"class transformers.RealmTokenizer",anchor:"transformers.RealmTokenizer",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.RealmTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
File containing the vocabulary.`,name:"vocab_file"},{anchor:"transformers.RealmTokenizer.do_lower_case",description:`<strong>do_lower_case</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to lowercase the input when tokenizing.`,name:"do_lower_case"},{anchor:"transformers.RealmTokenizer.do_basic_tokenize",description:`<strong>do_basic_tokenize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to do basic tokenization before WordPiece.`,name:"do_basic_tokenize"},{anchor:"transformers.RealmTokenizer.never_split",description:`<strong>never_split</strong> (<code>Iterable</code>, <em>optional</em>) &#x2014;
Collection of tokens which will never be split during tokenization. Only has an effect when
<code>do_basic_tokenize=True</code>`,name:"never_split"},{anchor:"transformers.RealmTokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[UNK]&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.RealmTokenizer.sep_token",description:`<strong>sep_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[SEP]&quot;</code>) &#x2014;
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.`,name:"sep_token"},{anchor:"transformers.RealmTokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[PAD]&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.RealmTokenizer.cls_token",description:`<strong>cls_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[CLS]&quot;</code>) &#x2014;
The classifier token which is used when doing sequence classification (classification of the whole sequence
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The token used for masking values. This is the token used when training this model with masked language
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Whether or not to tokenize Chinese characters.</p>
<p>This should likely be deactivated for Japanese (see this
<a href="https://github.com/huggingface/transformers/issues/328" rel="nofollow">issue</a>).`,name:"tokenize_chinese_chars"},{anchor:"transformers.RealmTokenizer.strip_accents",description:`<strong>strip_accents</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for <code>lowercase</code> (as in the original BERT).`,name:"strip_accents"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/tokenization_realm.py#L95"}}),_t=new z({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.RealmTokenizer.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.RealmTokenizer.build_inputs_with_special_tokens.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs to which the special tokens will be added.`,name:"token_ids_0"},{anchor:"transformers.RealmTokenizer.build_inputs_with_special_tokens.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/tokenization_realm.py#L301",returnDescription:`
<p>List of <a href="../glossary#input-ids">input IDs</a> with the appropriate special tokens.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),kt=new z({props:{name:"get_special_tokens_mask",anchor:"transformers.RealmTokenizer.get_special_tokens_mask",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"},{name:"already_has_special_tokens",val:": bool = False"}],parametersDescription:[{anchor:"transformers.RealmTokenizer.get_special_tokens_mask.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.RealmTokenizer.get_special_tokens_mask.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"},{anchor:"transformers.RealmTokenizer.get_special_tokens_mask.already_has_special_tokens",description:`<strong>already_has_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the token list is already formatted with special tokens for the model.`,name:"already_has_special_tokens"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/tokenization_realm.py#L326",returnDescription:`
<p>A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),yt=new z({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.RealmTokenizer.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.RealmTokenizer.create_token_type_ids_from_sequences.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.RealmTokenizer.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/tokenization_realm.py#L354",returnDescription:`
<p>List of <a href="../glossary#token-type-ids">token type IDs</a> according to the given sequence(s).</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),ze=new nt({props:{anchor:"transformers.RealmTokenizer.create_token_type_ids_from_sequences.example",$$slots:{default:[Jm]},$$scope:{ctx:$}}}),wt=new z({props:{name:"save_vocabulary",anchor:"transformers.RealmTokenizer.save_vocabulary",parameters:[{name:"save_directory",val:": str"},{name:"filename_prefix",val:": typing.Optional[str] = None"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/tokenization_realm.py#L383"}}),Tt=new z({props:{name:"batch_encode_candidates",anchor:"transformers.RealmTokenizer.batch_encode_candidates",parameters:[{name:"text",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.RealmTokenizer.batch_encode_candidates.text",description:`<strong>text</strong> (<code>List[List[str]]</code>) &#x2014;
The batch of sequences to be encoded. Each sequence must be in this format: (batch_size,
num_candidates, text).`,name:"text"},{anchor:"transformers.RealmTokenizer.batch_encode_candidates.text_pair",description:`<strong>text_pair</strong> (<code>List[List[str]]</code>, <em>optional</em>) &#x2014;
The batch of sequences to be encoded. Each sequence must be in this format: (batch_size,
num_candidates, text).
**kwargs &#x2014;
Keyword arguments of the <strong>call</strong> method.`,name:"text_pair"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/tokenization_realm.py#L228",returnDescription:`
<p>Encoded text or text pair.</p>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></p>
`}}),xe=new nt({props:{anchor:"transformers.RealmTokenizer.batch_encode_candidates.example",$$slots:{default:[Am]},$$scope:{ctx:$}}}),zt=new Q({}),xt=new z({props:{name:"class transformers.RealmTokenizerFast",anchor:"transformers.RealmTokenizerFast",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.RealmTokenizerFast.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
File containing the vocabulary.`,name:"vocab_file"},{anchor:"transformers.RealmTokenizerFast.do_lower_case",description:`<strong>do_lower_case</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to lowercase the input when tokenizing.`,name:"do_lower_case"},{anchor:"transformers.RealmTokenizerFast.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[UNK]&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.RealmTokenizerFast.sep_token",description:`<strong>sep_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[SEP]&quot;</code>) &#x2014;
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.`,name:"sep_token"},{anchor:"transformers.RealmTokenizerFast.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[PAD]&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.RealmTokenizerFast.cls_token",description:`<strong>cls_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[CLS]&quot;</code>) &#x2014;
The classifier token which is used when doing sequence classification (classification of the whole sequence
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The token used for masking values. This is the token used when training this model with masked language
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Whether or not to clean the text before tokenization by removing any control characters and replacing all
whitespaces by the classic one.`,name:"clean_text"},{anchor:"transformers.RealmTokenizerFast.tokenize_chinese_chars",description:`<strong>tokenize_chinese_chars</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see <a href="https://github.com/huggingface/transformers/issues/328" rel="nofollow">this
issue</a>).`,name:"tokenize_chinese_chars"},{anchor:"transformers.RealmTokenizerFast.strip_accents",description:`<strong>strip_accents</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for <code>lowercase</code> (as in the original BERT).`,name:"strip_accents"},{anchor:"transformers.RealmTokenizerFast.wordpieces_prefix",description:`<strong>wordpieces_prefix</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;##&quot;</code>) &#x2014;
The prefix for subwords.`,name:"wordpieces_prefix"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/tokenization_realm_fast.py#L102"}}),At=new z({props:{name:"batch_encode_candidates",anchor:"transformers.RealmTokenizerFast.batch_encode_candidates",parameters:[{name:"text",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.RealmTokenizerFast.batch_encode_candidates.text",description:`<strong>text</strong> (<code>List[List[str]]</code>) &#x2014;
The batch of sequences to be encoded. Each sequence must be in this format: (batch_size,
num_candidates, text).`,name:"text"},{anchor:"transformers.RealmTokenizerFast.batch_encode_candidates.text_pair",description:`<strong>text_pair</strong> (<code>List[List[str]]</code>, <em>optional</em>) &#x2014;
The batch of sequences to be encoded. Each sequence must be in this format: (batch_size,
num_candidates, text).
**kwargs &#x2014;
Keyword arguments of the <strong>call</strong> method.`,name:"text_pair"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/tokenization_realm_fast.py#L193",returnDescription:`
<p>Encoded text or text pair.</p>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></p>
`}}),Ae=new nt({props:{anchor:"transformers.RealmTokenizerFast.batch_encode_candidates.example",$$slots:{default:[qm]},$$scope:{ctx:$}}}),Zt=new Q({}),Wt=new z({props:{name:"class transformers.RealmRetriever",anchor:"transformers.RealmRetriever",parameters:[{name:"block_records",val:""},{name:"tokenizer",val:""}],parametersDescription:[{anchor:"transformers.RealmRetriever.block_records",description:`<strong>block_records</strong> (<code>np.ndarray</code>) &#x2014;
A numpy array which cantains evidence texts.`,name:"block_records"},{anchor:"transformers.RealmRetriever.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/main/en/model_doc/realm#transformers.RealmTokenizer">RealmTokenizer</a>) &#x2014;
The tokenizer to encode retrieved texts.`,name:"tokenizer"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/retrieval_realm.py#L72"}}),Ct=new z({props:{name:"block_has_answer",anchor:"transformers.RealmRetriever.block_has_answer",parameters:[{name:"concat_inputs",val:""},{name:"answer_ids",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/retrieval_realm.py#L129"}}),Lt=new Q({}),St=new z({props:{name:"class transformers.RealmEmbedder",anchor:"transformers.RealmEmbedder",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RealmEmbedder.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/realm#transformers.RealmConfig">RealmConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/modeling_realm.py#L1151"}}),Vt=new z({props:{name:"forward",anchor:"transformers.RealmEmbedder.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = 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.RealmEmbedder.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.RealmEmbedder.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 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.RealmEmbedder.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RealmEmbedder.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RealmEmbedder.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.RealmEmbedder.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 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 <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RealmEmbedder.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.RealmEmbedder.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.RealmEmbedder.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/modeling_realm.py#L1167",returnDescription:`
<p>A <code>transformers.models.realm.modeling_realm.RealmEmbedderOutput</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/en/model_doc/realm#transformers.RealmConfig"
>RealmConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>projected_score</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.retriever_proj_size)</code>) \u2014 Projected score.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.realm.modeling_realm.RealmEmbedderOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Fe=new Mr({props:{$$slots:{default:[Um]},$$scope:{ctx:$}}}),Ze=new nt({props:{anchor:"transformers.RealmEmbedder.forward.example",$$slots:{default:[Im]},$$scope:{ctx:$}}}),Gt=new Q({}),Bt=new z({props:{name:"class transformers.RealmScorer",anchor:"transformers.RealmScorer",parameters:[{name:"config",val:""},{name:"query_embedder",val:" = None"}],parametersDescription:[{anchor:"transformers.RealmScorer.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/realm#transformers.RealmConfig">RealmConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.RealmScorer.query_embedder",description:`<strong>query_embedder</strong> (<a href="/docs/transformers/main/en/model_doc/realm#transformers.RealmEmbedder">RealmEmbedder</a>) &#x2014;
Embedder for input sequences. If not specified, it will use the same embedder as candidate sequences.`,name:"query_embedder"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/modeling_realm.py#L1233"}}),Qt=new z({props:{name:"forward",anchor:"transformers.RealmScorer.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"candidate_input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"candidate_attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"candidate_token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"candidate_inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = 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.RealmScorer.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.RealmScorer.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 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.RealmScorer.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RealmScorer.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RealmScorer.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.RealmScorer.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 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 <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RealmScorer.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.RealmScorer.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.RealmScorer.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RealmScorer.forward.candidate_input_ids",description:`<strong>candidate_input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_candidates, sequence_length)</code>) &#x2014;
Indices of candidate input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"candidate_input_ids"},{anchor:"transformers.RealmScorer.forward.candidate_attention_mask",description:`<strong>candidate_attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_candidates, 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:"candidate_attention_mask"},{anchor:"transformers.RealmScorer.forward.candidate_token_type_ids",description:`<strong>candidate_token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_candidates, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"candidate_token_type_ids"},{anchor:"transformers.RealmScorer.forward.candidate_inputs_embeds",description:`<strong>candidate_inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_candidates, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>candidate_input_ids</code> you can choose to directly pass an embedded
representation. This is useful if you want more control over how to convert <em>candidate_input_ids</em> indices
into associated vectors than the model&#x2019;s internal embedding lookup matrix.`,name:"candidate_inputs_embeds"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/modeling_realm.py#L1249",returnDescription:`
<p>A <code>transformers.models.realm.modeling_realm.RealmScorerOutput</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/en/model_doc/realm#transformers.RealmConfig"
>RealmConfig</a>) and inputs.</p>
<ul>
<li><strong>relevance_score</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_candidates)</code>) \u2014 The relevance score of document candidates (before softmax).</li>
<li><strong>query_score</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.retriever_proj_size)</code>) \u2014 Query score derived from the query embedder.</li>
<li><strong>candidate_score</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_candidates, config.retriever_proj_size)</code>) \u2014 Candidate score derived from the embedder.</li>
</ul>
`,returnType:`
<p><code>transformers.models.realm.modeling_realm.RealmScorerOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ce=new Mr({props:{$$slots:{default:[Fm]},$$scope:{ctx:$}}}),Le=new nt({props:{anchor:"transformers.RealmScorer.forward.example",$$slots:{default:[Zm]},$$scope:{ctx:$}}}),Dt=new Q({}),Ht=new z({props:{name:"class transformers.RealmKnowledgeAugEncoder",anchor:"transformers.RealmKnowledgeAugEncoder",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RealmKnowledgeAugEncoder.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/realm#transformers.RealmConfig">RealmConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/modeling_realm.py#L1381"}}),eo=new z({props:{name:"forward",anchor:"transformers.RealmKnowledgeAugEncoder.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"relevance_score",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"mlm_mask",val:": typing.Optional[torch.LongTensor] = 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.RealmKnowledgeAugEncoder.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_candidates, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.RealmKnowledgeAugEncoder.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_candidates, 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.RealmKnowledgeAugEncoder.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_candidates, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RealmKnowledgeAugEncoder.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_candidates, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RealmKnowledgeAugEncoder.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.RealmKnowledgeAugEncoder.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_candidates, 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 <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RealmKnowledgeAugEncoder.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.RealmKnowledgeAugEncoder.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.RealmKnowledgeAugEncoder.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RealmKnowledgeAugEncoder.forward.relevance_score",description:`<strong>relevance_score</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_candidates)</code>, <em>optional</em>) &#x2014;
Relevance score derived from RealmScorer, must be specified if you want to compute the masked language
modeling loss.`,name:"relevance_score"},{anchor:"transformers.RealmKnowledgeAugEncoder.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should be in <code>[-100, 0, ..., config.vocab_size]</code> (see <code>input_ids</code> docstring) Tokens with indices set to <code>-100</code> are ignored (masked), the
loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>`,name:"labels"},{anchor:"transformers.RealmKnowledgeAugEncoder.forward.mlm_mask",description:`<strong>mlm_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid calculating joint loss on certain positions. If not specified, the loss will not be masked.
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>`,name:"mlm_mask"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/modeling_realm.py#L1402",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/en/model_doc/realm#transformers.RealmConfig"
>RealmConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Masked language modeling (MLM) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
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 optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ne=new Mr({props:{$$slots:{default:[Wm]},$$scope:{ctx:$}}}),Pe=new nt({props:{anchor:"transformers.RealmKnowledgeAugEncoder.forward.example",$$slots:{default:[Cm]},$$scope:{ctx:$}}}),to=new Q({}),oo=new z({props:{name:"class transformers.RealmReader",anchor:"transformers.RealmReader",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RealmReader.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/realm#transformers.RealmConfig">RealmConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/modeling_realm.py#L1531"}}),ao=new z({props:{name:"forward",anchor:"transformers.RealmReader.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"relevance_score",val:": typing.Optional[torch.FloatTensor] = None"},{name:"block_mask",val:": typing.Optional[torch.BoolTensor] = None"},{name:"start_positions",val:": typing.Optional[torch.LongTensor] = None"},{name:"end_positions",val:": typing.Optional[torch.LongTensor] = None"},{name:"has_answers",val:": typing.Optional[torch.BoolTensor] = 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.RealmReader.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(reader_beam_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.RealmReader.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(reader_beam_size, 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.RealmReader.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(reader_beam_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RealmReader.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(reader_beam_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RealmReader.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.RealmReader.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(reader_beam_size, 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 <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RealmReader.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.RealmReader.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.RealmReader.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RealmReader.forward.relevance_score",description:`<strong>relevance_score</strong> (<code>torch.FloatTensor</code> of shape <code>(searcher_beam_size,)</code>, <em>optional</em>) &#x2014;
Relevance score, which must be specified if you want to compute the logits and marginal log loss.`,name:"relevance_score"},{anchor:"transformers.RealmReader.forward.block_mask",description:`<strong>block_mask</strong> (<code>torch.BoolTensor</code> of shape <code>(searcher_beam_size, sequence_length)</code>, <em>optional</em>) &#x2014;
The mask of the evidence block, which must be specified if you want to compute the logits and marginal log
loss.`,name:"block_mask"},{anchor:"transformers.RealmReader.forward.start_positions",description:`<strong>start_positions</strong> (<code>torch.LongTensor</code> of shape <code>(searcher_beam_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"start_positions"},{anchor:"transformers.RealmReader.forward.end_positions",description:`<strong>end_positions</strong> (<code>torch.LongTensor</code> of shape <code>(searcher_beam_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"end_positions"},{anchor:"transformers.RealmReader.forward.has_answers",description:`<strong>has_answers</strong> (<code>torch.BoolTensor</code> of shape <code>(searcher_beam_size,)</code>, <em>optional</em>) &#x2014;
Whether or not the evidence block has answer(s).`,name:"has_answers"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/modeling_realm.py#L1542",returnDescription:`
<p>A <code>transformers.models.realm.modeling_realm.RealmReaderOutput</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/en/model_doc/realm#transformers.RealmConfig"
>RealmConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>start_positions</code>, <code>end_positions</code>, <code>has_answers</code> are provided) \u2014 Total loss.</p>
</li>
<li>
<p><strong>retriever_loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>start_positions</code>, <code>end_positions</code>, <code>has_answers</code> are provided) \u2014 Retriever loss.</p>
</li>
<li>
<p><strong>reader_loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>start_positions</code>, <code>end_positions</code>, <code>has_answers</code> are provided) \u2014 Reader loss.</p>
</li>
<li>
<p><strong>retriever_correct</strong> (<code>torch.BoolTensor</code> of shape <code>(config.searcher_beam_size,)</code>, <em>optional</em>) \u2014 Whether or not an evidence block contains answer.</p>
</li>
<li>
<p><strong>reader_correct</strong> (<code>torch.BoolTensor</code> of shape <code>(config.reader_beam_size, num_candidates)</code>, <em>optional</em>) \u2014 Whether or not a span candidate contains answer.</p>
</li>
<li>
<p><strong>block_idx</strong> (<code>torch.LongTensor</code> of shape <code>()</code>) \u2014 The index of the retrieved evidence block in which the predicted answer is most likely.</p>
</li>
<li>
<p><strong>candidate</strong> (<code>torch.LongTensor</code> of shape <code>()</code>) \u2014 The index of the retrieved span candidates in which the predicted answer is most likely.</p>
</li>
<li>
<p><strong>start_pos</strong> (<code>torch.IntTensor</code> of shape <code>()</code>) \u2014 Predicted answer starting position in <em>RealmReader</em>\u2019s inputs.</p>
</li>
<li>
<p><strong>end_pos</strong> (<code>torch.IntTensor</code> of shape <code>()</code>) \u2014 Predicted answer ending position in <em>RealmReader</em>\u2019s inputs.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.realm.modeling_realm.RealmReaderOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ge=new Mr({props:{$$slots:{default:[Lm]},$$scope:{ctx:$}}}),so=new Q({}),io=new z({props:{name:"class transformers.RealmForOpenQA",anchor:"transformers.RealmForOpenQA",parameters:[{name:"config",val:""},{name:"retriever",val:" = None"}],parametersDescription:[{anchor:"transformers.RealmForOpenQA.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/realm#transformers.RealmConfig">RealmConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/modeling_realm.py#L1735"}}),co=new z({props:{name:"block_embedding_to",anchor:"transformers.RealmForOpenQA.block_embedding_to",parameters:[{name:"device",val:""}],parametersDescription:[{anchor:"transformers.RealmForOpenQA.block_embedding_to.device",description:`<strong>device</strong> (<code>str</code> or <code>torch.device</code>) &#x2014;
The device to which <code>self.block_emb</code> will be sent.`,name:"device"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/modeling_realm.py#L1758"}}),po=new z({props:{name:"forward",anchor:"transformers.RealmForOpenQA.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor]"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"answer_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.RealmForOpenQA.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(1, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.RealmForOpenQA.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(1, 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.RealmForOpenQA.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(1, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token (should not be used in this model by design).</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RealmForOpenQA.forward.answer_ids",description:`<strong>answer_ids</strong> (<code>list</code> of shape <code>(num_answers, answer_length)</code>, <em>optional</em>) &#x2014;
Answer ids for computing the marginal log-likelihood loss. Indices should be in <code>[-1, 0, ..., config.vocab_size]</code> (see <code>input_ids</code> docstring) Tokens with indices set to <code>-1</code> are ignored (masked), the
loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>`,name:"answer_ids"},{anchor:"transformers.RealmForOpenQA.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/realm/modeling_realm.py#L1768",returnDescription:`
<p>A <code>transformers.models.realm.modeling_realm.RealmForOpenQAOutput</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/en/model_doc/realm#transformers.RealmConfig"
>RealmConfig</a>) and inputs.</p>
<ul>
<li><strong>reader_output</strong> (<code>dict</code>) \u2014 Reader output.</li>
<li><strong>predicted_answer_ids</strong> (<code>torch.LongTensor</code> of shape <code>(answer_sequence_length)</code>) \u2014 Predicted answer ids.</li>
</ul>
`,returnType:`
<p><code>transformers.models.realm.modeling_realm.RealmForOpenQAOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Qe=new Mr({props:{$$slots:{default:[Sm]},$$scope:{ctx:$}}}),De=new nt({props:{anchor:"transformers.RealmForOpenQA.forward.example",$$slots:{default:[Nm]},$$scope:{ctx:$}}}),{c(){m=n("meta"),k=d(),u=n("h1"),f=n("a"),b=n("span"),v(s.$$.fragment),h=d(),x=n("span"),Ea=i("REALM"),$r=d(),oe=n("h2"),ye=n("a"),ln=n("span"),v(st.$$.fragment),za=d(),dn=n("span"),xa=i("Overview"),Er=d(),we=n("p"),ja=i("The REALM model was proposed in "),it=n("a"),Ja=i("REALM: Retrieval-Augmented Language Model Pre-Training"),Aa=i(` by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang. It\u2019s a
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such as question answering. However, this knowledge is stored implicitly in the parameters of a neural network,
requiring ever-larger networks to cover more facts. To capture knowledge in a more modular and interpretable way, we
augment language model pre-training with a latent knowledge retriever, which allows the model to retrieve and attend
over documents from a large corpus such as Wikipedia, used during pre-training, fine-tuning and inference. For the
first time, we show how to pre-train such a knowledge retriever in an unsupervised manner, using masked language
modeling as the learning signal and backpropagating through a retrieval step that considers millions of documents. We
demonstrate the effectiveness of Retrieval-Augmented Language Model pre-training (REALM) by fine-tuning on the
challenging task of Open-domain Question Answering (Open-QA). We compare against state-of-the-art models for both
explicit and implicit knowledge storage on three popular Open-QA benchmarks, and find that we outperform all previous
methods by a significant margin (4-16% absolute accuracy), while also providing qualitative benefits such as
interpretability and modularity.`),jr=d(),D=n("p"),Ia=i("This model was contributed by "),lt=n("a"),Fa=i("qqaatw"),Za=i(`. The original code can be found
`),dt=n("a"),Wa=i("here"),Ca=i("."),Jr=d(),ne=n("h2"),Te=n("a"),mn=n("span"),v(ct.$$.fragment),La=d(),pn=n("span"),Sa=i("RealmConfig"),Ar=d(),J=n("div"),v(mt.$$.fragment),Na=d(),hn=n("p"),Pa=i("This is the configuration class to store the configuration of"),Va=d(),A=n("ol"),fn=n("li"),bo=n("a"),Ga=i("RealmEmbedder"),Ba=d(),un=n("li"),ko=n("a"),Xa=i("RealmScorer"),Oa=d(),gn=n("li"),vo=n("a"),Qa=i("RealmKnowledgeAugEncoder"),Da=d(),_n=n("li"),yo=n("a"),Ha=i("RealmRetriever"),Ya=d(),bn=n("li"),wo=n("a"),Ka=i("RealmReader"),es=d(),kn=n("li"),To=n("a"),ts=i("RealmForOpenQA"),os=d(),pt=n("p"),ns=i(`It is used to instantiate an REALM model according to the specified arguments, defining the model architecture.
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`),ht=n("a"),rs=i("google/realm-cc-news-pretrained-embedder"),as=i(`
architecture.`),ss=d(),re=n("p"),is=i("Configuration objects inherit from "),Ro=n("a"),ls=i("PretrainedConfig"),ds=i(` and can be used to control the model outputs. Read the
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splitting and wordpiece.`),ji=d(),Jt=n("p"),Ji=i("This tokenizer inherits from "),Fo=n("a"),Ai=i("PreTrainedTokenizerFast"),qi=i(` which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.`),Ui=d(),I=n("div"),v(At.$$.fragment),Ii=d(),qt=n("p"),Fi=i("Encode a batch of text or text pair. This method is similar to regular "),Cn=n("strong"),Zi=i("call"),Wi=i(` method but has the following
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positions.\u201D`),sl=d(),Ue=n("div"),v(Ct.$$.fragment),il=d(),On=n("p"),ll=i("check if retrieved_blocks has answers."),Cr=d(),me=n("h2"),Ie=n("a"),Qn=n("span"),v(Lt.$$.fragment),dl=d(),Dn=n("span"),cl=i("RealmEmbedder"),Lr=d(),G=n("div"),v(St.$$.fragment),ml=d(),Nt=n("p"),pl=i(`The embedder of REALM outputting projected score that will be used to calculate relevance score.
This model is a PyTorch `),Pt=n("a"),hl=i("torch.nn.Module"),fl=i(` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),ul=d(),W=n("div"),v(Vt.$$.fragment),gl=d(),pe=n("p"),_l=i("The "),Co=n("a"),bl=i("RealmEmbedder"),kl=i(" forward method, overrides the "),Hn=n("code"),vl=i("__call__"),yl=i(" special method."),wl=d(),v(Fe.$$.fragment),Tl=d(),v(Ze.$$.fragment),Sr=d(),he=n("h2"),We=n("a"),Yn=n("span"),v(Gt.$$.fragment),Rl=d(),Kn=n("span"),Ml=i("RealmScorer"),Nr=d(),B=n("div"),v(Bt.$$.fragment),$l=d(),Xt=n("p"),El=i(`The scorer of REALM outputting relevance scores representing the score of document candidates (before softmax).
This model is a PyTorch `),Ot=n("a"),zl=i("torch.nn.Module"),xl=i(` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),jl=d(),C=n("div"),v(Qt.$$.fragment),Jl=d(),fe=n("p"),Al=i("The "),Lo=n("a"),ql=i("RealmScorer"),Ul=i(" forward method, overrides the "),er=n("code"),Il=i("__call__"),Fl=i(" special method."),Zl=d(),v(Ce.$$.fragment),Wl=d(),v(Le.$$.fragment),Pr=d(),ue=n("h2"),Se=n("a"),tr=n("span"),v(Dt.$$.fragment),Cl=d(),or=n("span"),Ll=i("RealmKnowledgeAugEncoder"),Vr=d(),X=n("div"),v(Ht.$$.fragment),Sl=d(),Yt=n("p"),Nl=i(`The knowledge-augmented encoder of REALM outputting masked language model logits and marginal log-likelihood loss.
This model is a PyTorch `),Kt=n("a"),Pl=i("torch.nn.Module"),Vl=i(` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Gl=d(),L=n("div"),v(eo.$$.fragment),Bl=d(),ge=n("p"),Xl=i("The "),So=n("a"),Ol=i("RealmKnowledgeAugEncoder"),Ql=i(" forward method, overrides the "),nr=n("code"),Dl=i("__call__"),Hl=i(" special method."),Yl=d(),v(Ne.$$.fragment),Kl=d(),v(Pe.$$.fragment),Gr=d(),_e=n("h2"),Ve=n("a"),rr=n("span"),v(to.$$.fragment),ed=d(),ar=n("span"),td=i("RealmReader"),Br=d(),O=n("div"),v(oo.$$.fragment),od=d(),no=n("p"),nd=i(`The reader of REALM.
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it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),sd=d(),Y=n("div"),v(ao.$$.fragment),id=d(),be=n("p"),ld=i("The "),No=n("a"),dd=i("RealmReader"),cd=i(" forward method, overrides the "),sr=n("code"),md=i("__call__"),pd=i(" special method."),hd=d(),v(Ge.$$.fragment),Xr=d(),ke=n("h2"),Be=n("a"),ir=n("span"),v(so.$$.fragment),fd=d(),lr=n("span"),ud=i("RealmForOpenQA"),Or=d(),F=n("div"),v(io.$$.fragment),gd=d(),Xe=n("p"),dr=n("code"),_d=i("RealmForOpenQA"),bd=i(` for end-to-end open domain question answering.
This model is a PyTorch `),lo=n("a"),kd=i("torch.nn.Module"),vd=i(` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),yd=d(),Oe=n("div"),v(co.$$.fragment),wd=d(),mo=n("p"),Td=i("Send "),cr=n("code"),Rd=i("self.block_emb"),Md=i(" to a specific device."),$d=d(),S=n("div"),v(po.$$.fragment),Ed=d(),ve=n("p"),zd=i("The "),Po=n("a"),xd=i("RealmForOpenQA"),jd=i(" forward method, overrides the "),mr=n("code"),Jd=i("__call__"),Ad=i(" special method."),qd=d(),v(Qe.$$.fragment),Ud=d(),v(De.$$.fragment),this.h()},l(o){const g=zm('[data-svelte="svelte-1phssyn"]',document.head);m=r(g,"META",{name:!0,content:!0}),g.forEach(t),k=c(o),u=r(o,"H1",{class:!0});var ho=a(u);f=r(ho,"A",{id:!0,class:!0,href:!0});var pr=a(f);b=r(pr,"SPAN",{});var hr=a(b);y(s.$$.fragment,hr),hr.forEach(t),pr.forEach(t),h=c(ho),x=r(ho,"SPAN",{});var fr=a(x);Ea=l(fr,"REALM"),fr.forEach(t),ho.forEach(t),$r=c(o),oe=r(o,"H2",{class:!0});var fo=a(oe);ye=r(fo,"A",{id:!0,class:!0,href:!0});var ur=a(ye);ln=r(ur,"SPAN",{});var gr=a(ln);y(st.$$.fragment,gr),gr.forEach(t),ur.forEach(t),za=c(fo),dn=r(fo,"SPAN",{});var _r=a(dn);xa=l(_r,"Overview"),_r.forEach(t),fo.forEach(t),Er=c(o),we=r(o,"P",{});var uo=a(we);ja=l(uo,"The REALM model was proposed in "),it=r(uo,"A",{href:!0,rel:!0});var br=a(it);Ja=l(br,"REALM: Retrieval-Augmented Language Model Pre-Training"),br.forEach(t),Aa=l(uo,` by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang. It\u2019s a
retrieval-augmented language model that firstly retrieves documents from a textual knowledge corpus and then
utilizes retrieved documents to process question answering tasks.`),uo.forEach(t),zr=c(o),go=r(o,"P",{});var kr=a(go);qa=l(kr,"The abstract from the paper is the following:"),kr.forEach(t),xr=c(o),_o=r(o,"P",{});var vr=a(_o);cn=r(vr,"EM",{});var yr=a(cn);Ua=l(yr,`Language model pre-training has been shown to capture a surprising amount of world knowledge, crucial for NLP tasks
such as question answering. However, this knowledge is stored implicitly in the parameters of a neural network,
requiring ever-larger networks to cover more facts. To capture knowledge in a more modular and interpretable way, we
augment language model pre-training with a latent knowledge retriever, which allows the model to retrieve and attend
over documents from a large corpus such as Wikipedia, used during pre-training, fine-tuning and inference. For the
first time, we show how to pre-train such a knowledge retriever in an unsupervised manner, using masked language
modeling as the learning signal and backpropagating through a retrieval step that considers millions of documents. We
demonstrate the effectiveness of Retrieval-Augmented Language Model pre-training (REALM) by fine-tuning on the
challenging task of Open-domain Question Answering (Open-QA). We compare against state-of-the-art models for both
explicit and implicit knowledge storage on three popular Open-QA benchmarks, and find that we outperform all previous
methods by a significant margin (4-16% absolute accuracy), while also providing qualitative benefits such as
interpretability and modularity.`),yr.forEach(t),vr.forEach(t),jr=c(o),D=r(o,"P",{});var Vo=a(D);Ia=l(Vo,"This model was contributed by "),lt=r(Vo,"A",{href:!0,rel:!0});var Sd=a(lt);Fa=l(Sd,"qqaatw"),Sd.forEach(t),Za=l(Vo,`. The original code can be found
`),dt=r(Vo,"A",{href:!0,rel:!0});var Nd=a(dt);Wa=l(Nd,"here"),Nd.forEach(t),Ca=l(Vo,"."),Vo.forEach(t),Jr=c(o),ne=r(o,"H2",{class:!0});var Dr=a(ne);Te=r(Dr,"A",{id:!0,class:!0,href:!0});var Pd=a(Te);mn=r(Pd,"SPAN",{});var Vd=a(mn);y(ct.$$.fragment,Vd),Vd.forEach(t),Pd.forEach(t),La=c(Dr),pn=r(Dr,"SPAN",{});var Gd=a(pn);Sa=l(Gd,"RealmConfig"),Gd.forEach(t),Dr.forEach(t),Ar=c(o),J=r(o,"DIV",{class:!0});var N=a(J);y(mt.$$.fragment,N),Na=c(N),hn=r(N,"P",{});var Bd=a(hn);Pa=l(Bd,"This is the configuration class to store the configuration of"),Bd.forEach(t),Va=c(N),A=r(N,"OL",{});var P=a(A);fn=r(P,"LI",{});var Xd=a(fn);bo=r(Xd,"A",{href:!0});var Od=a(bo);Ga=l(Od,"RealmEmbedder"),Od.forEach(t),Xd.forEach(t),Ba=c(P),un=r(P,"LI",{});var Qd=a(un);ko=r(Qd,"A",{href:!0});var Dd=a(ko);Xa=l(Dd,"RealmScorer"),Dd.forEach(t),Qd.forEach(t),Oa=c(P),gn=r(P,"LI",{});var Hd=a(gn);vo=r(Hd,"A",{href:!0});var Yd=a(vo);Qa=l(Yd,"RealmKnowledgeAugEncoder"),Yd.forEach(t),Hd.forEach(t),Da=c(P),_n=r(P,"LI",{});var Kd=a(_n);yo=r(Kd,"A",{href:!0});var ec=a(yo);Ha=l(ec,"RealmRetriever"),ec.forEach(t),Kd.forEach(t),Ya=c(P),bn=r(P,"LI",{});var tc=a(bn);wo=r(tc,"A",{href:!0});var oc=a(wo);Ka=l(oc,"RealmReader"),oc.forEach(t),tc.forEach(t),es=c(P),kn=r(P,"LI",{});var nc=a(kn);To=r(nc,"A",{href:!0});var rc=a(To);ts=l(rc,"RealmForOpenQA"),rc.forEach(t),nc.forEach(t),P.forEach(t),os=c(N),pt=r(N,"P",{});var Hr=a(pt);ns=l(Hr,`It is used to instantiate an REALM model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the REALM
`),ht=r(Hr,"A",{href:!0,rel:!0});var ac=a(ht);rs=l(ac,"google/realm-cc-news-pretrained-embedder"),ac.forEach(t),as=l(Hr,`
architecture.`),Hr.forEach(t),ss=c(N),re=r(N,"P",{});var Go=a(re);is=l(Go,"Configuration objects inherit from "),Ro=r(Go,"A",{href:!0});var sc=a(Ro);ls=l(sc,"PretrainedConfig"),sc.forEach(t),ds=l(Go,` and can be used to control the model outputs. Read the
documentation from `),Mo=r(Go,"A",{href:!0});var ic=a(Mo);cs=l(ic,"PretrainedConfig"),ic.forEach(t),ms=l(Go," for more information."),Go.forEach(t),ps=c(N),y(Re.$$.fragment,N),N.forEach(t),qr=c(o),ae=r(o,"H2",{class:!0});var Yr=a(ae);Me=r(Yr,"A",{id:!0,class:!0,href:!0});var lc=a(Me);vn=r(lc,"SPAN",{});var dc=a(vn);y(ft.$$.fragment,dc),dc.forEach(t),lc.forEach(t),hs=c(Yr),yn=r(Yr,"SPAN",{});var cc=a(yn);fs=l(cc,"RealmTokenizer"),cc.forEach(t),Yr.forEach(t),Ur=c(o),E=r(o,"DIV",{class:!0});var j=a(E);y(ut.$$.fragment,j),us=c(j),wn=r(j,"P",{});var mc=a(wn);gs=l(mc,"Construct a REALM tokenizer."),mc.forEach(t),_s=c(j),$e=r(j,"P",{});var wr=a($e);$o=r(wr,"A",{href:!0});var pc=a($o);bs=l(pc,"RealmTokenizer"),pc.forEach(t),ks=l(wr," is identical to "),Eo=r(wr,"A",{href:!0});var hc=a(Eo);vs=l(hc,"BertTokenizer"),hc.forEach(t),ys=l(wr,` and runs end-to-end tokenization: punctuation splitting and
wordpiece.`),wr.forEach(t),ws=c(j),gt=r(j,"P",{});var Kr=a(gt);Ts=l(Kr,"This tokenizer inherits from "),zo=r(Kr,"A",{href:!0});var fc=a(zo);Rs=l(fc,"PreTrainedTokenizer"),fc.forEach(t),Ms=l(Kr,` which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.`),Kr.forEach(t),$s=c(j),H=r(j,"DIV",{class:!0});var Bo=a(H);y(_t.$$.fragment,Bo),Es=c(Bo),Tn=r(Bo,"P",{});var uc=a(Tn);zs=l(uc,`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A REALM sequence has the following format:`),uc.forEach(t),xs=c(Bo),bt=r(Bo,"UL",{});var ea=a(bt);xo=r(ea,"LI",{});var Id=a(xo);js=l(Id,"single sequence: "),Rn=r(Id,"CODE",{});var gc=a(Rn);Js=l(gc,"[CLS] X [SEP]"),gc.forEach(t),Id.forEach(t),As=c(ea),jo=r(ea,"LI",{});var Fd=a(jo);qs=l(Fd,"pair of sequences: "),Mn=r(Fd,"CODE",{});var _c=a(Mn);Us=l(_c,"[CLS] A [SEP] B [SEP]"),_c.forEach(t),Fd.forEach(t),ea.forEach(t),Bo.forEach(t),Is=c(j),Ee=r(j,"DIV",{class:!0});var ta=a(Ee);y(kt.$$.fragment,ta),Fs=c(ta),vt=r(ta,"P",{});var oa=a(vt);Zs=l(oa,`Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `),$n=r(oa,"CODE",{});var bc=a($n);Ws=l(bc,"prepare_for_model"),bc.forEach(t),Cs=l(oa," method."),oa.forEach(t),ta.forEach(t),Ls=c(j),Z=r(j,"DIV",{class:!0});var He=a(Z);y(yt.$$.fragment,He),Ss=c(He),En=r(He,"P",{});var kc=a(En);Ns=l(kc,"Create a mask from the two sequences passed to be used in a sequence-pair classification task. A REALM sequence"),kc.forEach(t),Ps=c(He),y(ze.$$.fragment,He),Vs=c(He),se=r(He,"P",{});var Xo=a(se);Gs=l(Xo,"If "),zn=r(Xo,"CODE",{});var vc=a(zn);Bs=l(vc,"token_ids_1"),vc.forEach(t),Xs=l(Xo," is "),xn=r(Xo,"CODE",{});var yc=a(xn);Os=l(yc,"None"),yc.forEach(t),Qs=l(Xo,", this method only returns the first portion of the mask (0s)."),Xo.forEach(t),He.forEach(t),Ds=c(j),Jo=r(j,"DIV",{class:!0});var wc=a(Jo);y(wt.$$.fragment,wc),wc.forEach(t),Hs=c(j),U=r(j,"DIV",{class:!0});var K=a(U);y(Tt.$$.fragment,K),Ys=c(K),Rt=r(K,"P",{});var na=a(Rt);Ks=l(na,"Encode a batch of text or text pair. This method is similar to regular "),jn=r(na,"STRONG",{});var Tc=a(jn);ei=l(Tc,"call"),Tc.forEach(t),ti=l(na,` method but has the following
differences:`),na.forEach(t),oi=c(K),ie=r(K,"OL",{});var Oo=a(ie);Jn=r(Oo,"LI",{});var Rc=a(Jn);ni=l(Rc,"Handle additional num_candidate axis. (batch_size, num_candidates, text)"),Rc.forEach(t),ri=c(Oo),Mt=r(Oo,"LI",{});var ra=a(Mt);ai=l(ra,"Always pad the sequences to "),An=r(ra,"EM",{});var Mc=a(An);si=l(Mc,"max_length"),Mc.forEach(t),ii=l(ra,"."),ra.forEach(t),li=c(Oo),$t=r(Oo,"LI",{});var aa=a($t);di=l(aa,"Must specify "),qn=r(aa,"EM",{});var $c=a(qn);ci=l($c,"max_length"),$c.forEach(t),mi=l(aa," in order to stack packs of candidates into a batch."),aa.forEach(t),Oo.forEach(t),pi=c(K),Et=r(K,"UL",{});var sa=a(Et);Ao=r(sa,"LI",{});var Zd=a(Ao);hi=l(Zd,"single sequence: "),Un=r(Zd,"CODE",{});var Ec=a(Un);fi=l(Ec,"[CLS] X [SEP]"),Ec.forEach(t),Zd.forEach(t),ui=c(sa),qo=r(sa,"LI",{});var Wd=a(qo);gi=l(Wd,"pair of sequences: "),In=r(Wd,"CODE",{});var zc=a(In);_i=l(zc,"[CLS] A [SEP] B [SEP]"),zc.forEach(t),Wd.forEach(t),sa.forEach(t),bi=c(K),y(xe.$$.fragment,K),K.forEach(t),j.forEach(t),Ir=c(o),le=r(o,"H2",{class:!0});var ia=a(le);je=r(ia,"A",{id:!0,class:!0,href:!0});var xc=a(je);Fn=r(xc,"SPAN",{});var jc=a(Fn);y(zt.$$.fragment,jc),jc.forEach(t),xc.forEach(t),ki=c(ia),Zn=r(ia,"SPAN",{});var Jc=a(Zn);vi=l(Jc,"RealmTokenizerFast"),Jc.forEach(t),ia.forEach(t),Fr=c(o),q=r(o,"DIV",{class:!0});var ee=a(q);y(xt.$$.fragment,ee),yi=c(ee),jt=r(ee,"P",{});var la=a(jt);wi=l(la,"Construct a \u201Cfast\u201D REALM tokenizer (backed by HuggingFace\u2019s "),Wn=r(la,"EM",{});var Ac=a(Wn);Ti=l(Ac,"tokenizers"),Ac.forEach(t),Ri=l(la," library). Based on WordPiece."),la.forEach(t),Mi=c(ee),Je=r(ee,"P",{});var Tr=a(Je);Uo=r(Tr,"A",{href:!0});var qc=a(Uo);$i=l(qc,"RealmTokenizerFast"),qc.forEach(t),Ei=l(Tr," is identical to "),Io=r(Tr,"A",{href:!0});var Uc=a(Io);zi=l(Uc,"BertTokenizerFast"),Uc.forEach(t),xi=l(Tr,` and runs end-to-end tokenization: punctuation
splitting and wordpiece.`),Tr.forEach(t),ji=c(ee),Jt=r(ee,"P",{});var da=a(Jt);Ji=l(da,"This tokenizer inherits from "),Fo=r(da,"A",{href:!0});var Ic=a(Fo);Ai=l(Ic,"PreTrainedTokenizerFast"),Ic.forEach(t),qi=l(da,` which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.`),da.forEach(t),Ui=c(ee),I=r(ee,"DIV",{class:!0});var te=a(I);y(At.$$.fragment,te),Ii=c(te),qt=r(te,"P",{});var ca=a(qt);Fi=l(ca,"Encode a batch of text or text pair. This method is similar to regular "),Cn=r(ca,"STRONG",{});var Fc=a(Cn);Zi=l(Fc,"call"),Fc.forEach(t),Wi=l(ca,` method but has the following
differences:`),ca.forEach(t),Ci=c(te),de=r(te,"OL",{});var Qo=a(de);Ln=r(Qo,"LI",{});var Zc=a(Ln);Li=l(Zc,"Handle additional num_candidate axis. (batch_size, num_candidates, text)"),Zc.forEach(t),Si=c(Qo),Ut=r(Qo,"LI",{});var ma=a(Ut);Ni=l(ma,"Always pad the sequences to "),Sn=r(ma,"EM",{});var Wc=a(Sn);Pi=l(Wc,"max_length"),Wc.forEach(t),Vi=l(ma,"."),ma.forEach(t),Gi=c(Qo),It=r(Qo,"LI",{});var pa=a(It);Bi=l(pa,"Must specify "),Nn=r(pa,"EM",{});var Cc=a(Nn);Xi=l(Cc,"max_length"),Cc.forEach(t),Oi=l(pa," in order to stack packs of candidates into a batch."),pa.forEach(t),Qo.forEach(t),Qi=c(te),Ft=r(te,"UL",{});var ha=a(Ft);Zo=r(ha,"LI",{});var Cd=a(Zo);Di=l(Cd,"single sequence: "),Pn=r(Cd,"CODE",{});var Lc=a(Pn);Hi=l(Lc,"[CLS] X [SEP]"),Lc.forEach(t),Cd.forEach(t),Yi=c(ha),Wo=r(ha,"LI",{});var Ld=a(Wo);Ki=l(Ld,"pair of sequences: "),Vn=r(Ld,"CODE",{});var Sc=a(Vn);el=l(Sc,"[CLS] A [SEP] B [SEP]"),Sc.forEach(t),Ld.forEach(t),ha.forEach(t),tl=c(te),y(Ae.$$.fragment,te),te.forEach(t),ee.forEach(t),Zr=c(o),ce=r(o,"H2",{class:!0});var fa=a(ce);qe=r(fa,"A",{id:!0,class:!0,href:!0});var Nc=a(qe);Gn=r(Nc,"SPAN",{});var Pc=a(Gn);y(Zt.$$.fragment,Pc),Pc.forEach(t),Nc.forEach(t),ol=c(fa),Bn=r(fa,"SPAN",{});var Vc=a(Bn);nl=l(Vc,"RealmRetriever"),Vc.forEach(t),fa.forEach(t),Wr=c(o),V=r(o,"DIV",{class:!0});var Do=a(V);y(Wt.$$.fragment,Do),rl=c(Do),Xn=r(Do,"P",{});var Gc=a(Xn);al=l(Gc,`The retriever of REALM outputting the retrieved evidence block and whether the block has answers as well as answer
positions.\u201D`),Gc.forEach(t),sl=c(Do),Ue=r(Do,"DIV",{class:!0});var ua=a(Ue);y(Ct.$$.fragment,ua),il=c(ua),On=r(ua,"P",{});var Bc=a(On);ll=l(Bc,"check if retrieved_blocks has answers."),Bc.forEach(t),ua.forEach(t),Do.forEach(t),Cr=c(o),me=r(o,"H2",{class:!0});var ga=a(me);Ie=r(ga,"A",{id:!0,class:!0,href:!0});var Xc=a(Ie);Qn=r(Xc,"SPAN",{});var Oc=a(Qn);y(Lt.$$.fragment,Oc),Oc.forEach(t),Xc.forEach(t),dl=c(ga),Dn=r(ga,"SPAN",{});var Qc=a(Dn);cl=l(Qc,"RealmEmbedder"),Qc.forEach(t),ga.forEach(t),Lr=c(o),G=r(o,"DIV",{class:!0});var Ho=a(G);y(St.$$.fragment,Ho),ml=c(Ho),Nt=r(Ho,"P",{});var _a=a(Nt);pl=l(_a,`The embedder of REALM outputting projected score that will be used to calculate relevance score.
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it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),_a.forEach(t),ul=c(Ho),W=r(Ho,"DIV",{class:!0});var Ye=a(W);y(Vt.$$.fragment,Ye),gl=c(Ye),pe=r(Ye,"P",{});var Yo=a(pe);_l=l(Yo,"The "),Co=r(Yo,"A",{href:!0});var Hc=a(Co);bl=l(Hc,"RealmEmbedder"),Hc.forEach(t),kl=l(Yo," forward method, overrides the "),Hn=r(Yo,"CODE",{});var Yc=a(Hn);vl=l(Yc,"__call__"),Yc.forEach(t),yl=l(Yo," special method."),Yo.forEach(t),wl=c(Ye),y(Fe.$$.fragment,Ye),Tl=c(Ye),y(Ze.$$.fragment,Ye),Ye.forEach(t),Ho.forEach(t),Sr=c(o),he=r(o,"H2",{class:!0});var ba=a(he);We=r(ba,"A",{id:!0,class:!0,href:!0});var Kc=a(We);Yn=r(Kc,"SPAN",{});var em=a(Yn);y(Gt.$$.fragment,em),em.forEach(t),Kc.forEach(t),Rl=c(ba),Kn=r(ba,"SPAN",{});var tm=a(Kn);Ml=l(tm,"RealmScorer"),tm.forEach(t),ba.forEach(t),Nr=c(o),B=r(o,"DIV",{class:!0});var Ko=a(B);y(Bt.$$.fragment,Ko),$l=c(Ko),Xt=r(Ko,"P",{});var ka=a(Xt);El=l(ka,`The scorer of REALM outputting relevance scores representing the score of document candidates (before softmax).
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it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),ka.forEach(t),jl=c(Ko),C=r(Ko,"DIV",{class:!0});var Ke=a(C);y(Qt.$$.fragment,Ke),Jl=c(Ke),fe=r(Ke,"P",{});var en=a(fe);Al=l(en,"The "),Lo=r(en,"A",{href:!0});var nm=a(Lo);ql=l(nm,"RealmScorer"),nm.forEach(t),Ul=l(en," forward method, overrides the "),er=r(en,"CODE",{});var rm=a(er);Il=l(rm,"__call__"),rm.forEach(t),Fl=l(en," special method."),en.forEach(t),Zl=c(Ke),y(Ce.$$.fragment,Ke),Wl=c(Ke),y(Le.$$.fragment,Ke),Ke.forEach(t),Ko.forEach(t),Pr=c(o),ue=r(o,"H2",{class:!0});var va=a(ue);Se=r(va,"A",{id:!0,class:!0,href:!0});var am=a(Se);tr=r(am,"SPAN",{});var sm=a(tr);y(Dt.$$.fragment,sm),sm.forEach(t),am.forEach(t),Cl=c(va),or=r(va,"SPAN",{});var im=a(or);Ll=l(im,"RealmKnowledgeAugEncoder"),im.forEach(t),va.forEach(t),Vr=c(o),X=r(o,"DIV",{class:!0});var tn=a(X);y(Ht.$$.fragment,tn),Sl=c(tn),Yt=r(tn,"P",{});var ya=a(Yt);Nl=l(ya,`The knowledge-augmented encoder of REALM outputting masked language model logits and marginal log-likelihood loss.
This model is a PyTorch `),Kt=r(ya,"A",{href:!0,rel:!0});var lm=a(Kt);Pl=l(lm,"torch.nn.Module"),lm.forEach(t),Vl=l(ya,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),ya.forEach(t),Gl=c(tn),L=r(tn,"DIV",{class:!0});var et=a(L);y(eo.$$.fragment,et),Bl=c(et),ge=r(et,"P",{});var on=a(ge);Xl=l(on,"The "),So=r(on,"A",{href:!0});var dm=a(So);Ol=l(dm,"RealmKnowledgeAugEncoder"),dm.forEach(t),Ql=l(on," forward method, overrides the "),nr=r(on,"CODE",{});var cm=a(nr);Dl=l(cm,"__call__"),cm.forEach(t),Hl=l(on," special method."),on.forEach(t),Yl=c(et),y(Ne.$$.fragment,et),Kl=c(et),y(Pe.$$.fragment,et),et.forEach(t),tn.forEach(t),Gr=c(o),_e=r(o,"H2",{class:!0});var wa=a(_e);Ve=r(wa,"A",{id:!0,class:!0,href:!0});var mm=a(Ve);rr=r(mm,"SPAN",{});var pm=a(rr);y(to.$$.fragment,pm),pm.forEach(t),mm.forEach(t),ed=c(wa),ar=r(wa,"SPAN",{});var hm=a(ar);td=l(hm,"RealmReader"),hm.forEach(t),wa.forEach(t),Br=c(o),O=r(o,"DIV",{class:!0});var nn=a(O);y(oo.$$.fragment,nn),od=c(nn),no=r(nn,"P",{});var Ta=a(no);nd=l(Ta,`The reader of REALM.
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