import{s as ot,o as rt,n as at}from"../chunks/scheduler.9bc65507.js";import{S as lt,i as it,g as l,s,r as h,A as pt,h as i,f as n,c as a,j as G,u as g,x as d,k as F,y as r,a as o,v as k,d as b,t as T,w}from"../chunks/index.707bf1b6.js";import{T as ct}from"../chunks/Tip.c2ecdbf4.js";import{D as ie}from"../chunks/Docstring.570ddb64.js";import{C as Qe}from"../chunks/CodeBlock.54a9f38d.js";import{E as mt}from"../chunks/ExampleCodeBlock.bc2be6bc.js";import{H as Ze,E as dt}from"../chunks/EditOnGithub.922df6ba.js";function ut(Y){let p,j='<li>この実装はトークン化方法を除いて BERT と同じです。その他の使用例については、<a href="bert">BERT のドキュメント</a> を参照してください。</li>';return{c(){p=l("ul"),p.innerHTML=j},l(_){p=i(_,"UL",{"data-svelte-h":!0}),d(p)!=="svelte-1wmwtfo"&&(p.innerHTML=j)},m(_,f){o(_,p,f)},p:at,d(_){_&&n(p)}}}function _t(Y){let p,j="pair mask has the following format:",_,f,M;return f=new Qe({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 |`,wrap:!1}}),{c(){p=l("p"),p.textContent=j,_=s(),h(f.$$.fragment)},l(c){p=i(c,"P",{"data-svelte-h":!0}),d(p)!=="svelte-qjgeij"&&(p.textContent=j),_=a(c),g(f.$$.fragment,c)},m(c,v){o(c,p,v),o(c,_,v),k(f,c,v),M=!0},p:at,i(c){M||(b(f.$$.fragment,c),M=!0)},o(c){T(f.$$.fragment,c),M=!1},d(c){c&&(n(p),n(_)),w(f,c)}}}function ft(Y){let p,j,_,f,M,c,v,pe,B,Ve="BERT モデルは日本語テキストでトレーニングされました。",ce,q,Ae="2 つの異なるトークン化方法を備えたモデルがあります。",me,U,Ne='<li>MeCab と WordPiece を使用してトークン化します。これには、<a href="https://taku910.github.io/mecab/" rel="nofollow">MeCab</a> のラッパーである <a href="https://github.com/polm/fugashi" rel="nofollow">fugashi</a> という追加の依存関係が必要です。</li> <li>文字にトークン化します。</li>',de,L,Pe=`<em>MecabTokenizer</em> を使用するには、<code>pip installTransformers[&quot;ja&quot;]</code> (または、インストールする場合は <code>pip install -e .[&quot;ja&quot;]</code>) する必要があります。
ソースから）依存関係をインストールします。`,ue,D,Xe='<a href="https://github.com/cl-tohaku/bert-japanese" rel="nofollow">cl-tohakuリポジトリの詳細</a>を参照してください。',_e,R,Se="MeCab および WordPiece トークン化でモデルを使用する例:",fe,H,he,Z,Oe="文字トークン化を使用したモデルの使用例:",ge,Q,ke,y,be,V,We='このモデルは<a href="https://huggingface.co/cl-tohaku" rel="nofollow">cl-tohaku</a>から提供されました。',Te,A,we,m,N,xe,K,Ge="Construct a BERT tokenizer for Japanese text.",ye,ee,Fe=`This tokenizer inherits from <a href="/docs/transformers/main/ja/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> which contains most of the main methods. Users should refer
to: this superclass for more information regarding those methods.`,ze,J,P,Ce,te,Ye=`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BERT sequence has the following format:`,Ee,ne,Ke="<li>single sequence: <code>[CLS] X [SEP]</code></li> <li>pair of sequences: <code>[CLS] A [SEP] B [SEP]</code></li>",Ie,z,X,Be,se,et="Converts a sequence of tokens (string) in a single string.",qe,$,S,Ue,ae,tt="Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence",Le,C,De,oe,nt="If <code>token_ids_1</code> is <code>None</code>, this method only returns the first portion of the mask (0s).",Re,E,O,He,re,st=`Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer <code>prepare_for_model</code> method.`,Me,W,$e,le,ve;return M=new Ze({props:{title:"BertJapanese",local:"bertjapanese",headingTag:"h1"}}),v=new Ze({props:{title:"Overview",local:"overview",headingTag:"h2"}}),H=new Qe({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwQXV0b01vZGVsJTJDJTIwQXV0b1Rva2VuaXplciUwQSUwQWJlcnRqYXBhbmVzZSUyMCUzRCUyMEF1dG9Nb2RlbC5mcm9tX3ByZXRyYWluZWQoJTIyY2wtdG9ob2t1JTJGYmVydC1iYXNlLWphcGFuZXNlJTIyKSUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmNsLXRvaG9rdSUyRmJlcnQtYmFzZS1qYXBhbmVzZSUyMiklMEElMEElMjMlMjMlMjBJbnB1dCUyMEphcGFuZXNlJTIwVGV4dCUwQWxpbmUlMjAlM0QlMjAlMjIlRTUlOTAlQkUlRTglQkMlQTklRTMlODElQUYlRTclOEMlQUIlRTMlODElQTclRTMlODElODIlRTMlODIlOEIlRTMlODAlODIlMjIlMEElMEFpbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIobGluZSUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIpJTBBJTBBcHJpbnQodG9rZW5pemVyLmRlY29kZShpbnB1dHMlNUIlMjJpbnB1dF9pZHMlMjIlNUQlNUIwJTVEKSklMEElMEFvdXRwdXRzJTIwJTNEJTIwYmVydGphcGFuZXNlKCoqaW5wdXRzKQ==",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> AutoModel, AutoTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>bertjapanese = AutoModel.from_pretrained(<span class="hljs-string">&quot;cl-tohoku/bert-base-japanese&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;cl-tohoku/bert-base-japanese&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment">## Input Japanese Text</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>line = <span class="hljs-string">&quot;吾輩は猫である。&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(line, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(tokenizer.decode(inputs[<span class="hljs-string">&quot;input_ids&quot;</span>][<span class="hljs-number">0</span>]))
[CLS] 吾輩 は 猫 で ある 。 [SEP]

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = bertjapanese(**inputs)`,wrap:!1}}),Q=new Qe({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>bertjapanese = AutoModel.from_pretrained(<span class="hljs-string">&quot;cl-tohoku/bert-base-japanese-char&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;cl-tohoku/bert-base-japanese-char&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment">## Input Japanese Text</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>line = <span class="hljs-string">&quot;吾輩は猫である。&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(line, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(tokenizer.decode(inputs[<span class="hljs-string">&quot;input_ids&quot;</span>][<span class="hljs-number">0</span>]))
[CLS] 吾 輩 は 猫 で あ る 。 [SEP]

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = bertjapanese(**inputs)`,wrap:!1}}),y=new ct({props:{$$slots:{default:[ut]},$$scope:{ctx:Y}}}),A=new Ze({props:{title:"BertJapaneseTokenizer",local:"transformers.BertJapaneseTokenizer",headingTag:"h2"}}),N=new ie({props:{name:"class transformers.BertJapaneseTokenizer",anchor:"transformers.BertJapaneseTokenizer",parameters:[{name:"vocab_file",val:""},{name:"spm_file",val:" = None"},{name:"do_lower_case",val:" = False"},{name:"do_word_tokenize",val:" = True"},{name:"do_subword_tokenize",val:" = True"},{name:"word_tokenizer_type",val:" = 'basic'"},{name:"subword_tokenizer_type",val:" = 'wordpiece'"},{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:"mecab_kwargs",val:" = None"},{name:"sudachi_kwargs",val:" = None"},{name:"jumanpp_kwargs",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BertJapaneseTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
Path to a one-wordpiece-per-line vocabulary file.`,name:"vocab_file"},{anchor:"transformers.BertJapaneseTokenizer.spm_file",description:`<strong>spm_file</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Path to <a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a .spm or .model
extension) that contains the vocabulary.`,name:"spm_file"},{anchor:"transformers.BertJapaneseTokenizer.do_lower_case",description:`<strong>do_lower_case</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to lower case the input. Only has an effect when do_basic_tokenize=True.`,name:"do_lower_case"},{anchor:"transformers.BertJapaneseTokenizer.do_word_tokenize",description:`<strong>do_word_tokenize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to do word tokenization.`,name:"do_word_tokenize"},{anchor:"transformers.BertJapaneseTokenizer.do_subword_tokenize",description:`<strong>do_subword_tokenize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to do subword tokenization.`,name:"do_subword_tokenize"},{anchor:"transformers.BertJapaneseTokenizer.word_tokenizer_type",description:`<strong>word_tokenizer_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;basic&quot;</code>) &#x2014;
Type of word tokenizer. Choose from [&#x201C;basic&#x201D;, &#x201C;mecab&#x201D;, &#x201C;sudachi&#x201D;, &#x201C;jumanpp&#x201D;].`,name:"word_tokenizer_type"},{anchor:"transformers.BertJapaneseTokenizer.subword_tokenizer_type",description:`<strong>subword_tokenizer_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;wordpiece&quot;</code>) &#x2014;
Type of subword tokenizer. Choose from [&#x201C;wordpiece&#x201D;, &#x201C;character&#x201D;, &#x201C;sentencepiece&#x201D;,].`,name:"subword_tokenizer_type"},{anchor:"transformers.BertJapaneseTokenizer.mecab_kwargs",description:`<strong>mecab_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Dictionary passed to the <code>MecabTokenizer</code> constructor.`,name:"mecab_kwargs"},{anchor:"transformers.BertJapaneseTokenizer.sudachi_kwargs",description:`<strong>sudachi_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Dictionary passed to the <code>SudachiTokenizer</code> constructor.`,name:"sudachi_kwargs"},{anchor:"transformers.BertJapaneseTokenizer.jumanpp_kwargs",description:`<strong>jumanpp_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Dictionary passed to the <code>JumanppTokenizer</code> constructor.`,name:"jumanpp_kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert_japanese/tokenization_bert_japanese.py#L62"}}),P=new ie({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.BertJapaneseTokenizer.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.BertJapaneseTokenizer.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.BertJapaneseTokenizer.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/bert_japanese/tokenization_bert_japanese.py#L259",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of <a href="../glossary#input-ids">input IDs</a> with the appropriate special tokens.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),X=new ie({props:{name:"convert_tokens_to_string",anchor:"transformers.BertJapaneseTokenizer.convert_tokens_to_string",parameters:[{name:"tokens",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert_japanese/tokenization_bert_japanese.py#L251"}}),S=new ie({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.BertJapaneseTokenizer.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.BertJapaneseTokenizer.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.BertJapaneseTokenizer.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/bert_japanese/tokenization_bert_japanese.py#L314",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of <a href="../glossary#token-type-ids">token type IDs</a> according to the given sequence(s).</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),C=new mt({props:{anchor:"transformers.BertJapaneseTokenizer.create_token_type_ids_from_sequences.example",$$slots:{default:[_t]},$$scope:{ctx:Y}}}),O=new ie({props:{name:"get_special_tokens_mask",anchor:"transformers.BertJapaneseTokenizer.get_special_tokens_mask",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"},{name:"already_has_special_tokens",val:": bool = False"}],parametersDescription:[{anchor:"transformers.BertJapaneseTokenizer.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.BertJapaneseTokenizer.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.BertJapaneseTokenizer.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/bert_japanese/tokenization_bert_japanese.py#L285",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),W=new dt({props:{source:"https://github.com/huggingface/transformers/blob/main/docs/source/ja/model_doc/bert-japanese.md"}}),{c(){p=l("meta"),j=s(),_=l("p"),f=s(),h(M.$$.fragment),c=s(),h(v.$$.fragment),pe=s(),B=l("p"),B.textContent=Ve,ce=s(),q=l("p"),q.textContent=Ae,me=s(),U=l("ul"),U.innerHTML=Ne,de=s(),L=l("p"),L.innerHTML=Pe,ue=s(),D=l("p"),D.innerHTML=Xe,_e=s(),R=l("p"),R.textContent=Se,fe=s(),h(H.$$.fragment),he=s(),Z=l("p"),Z.textContent=Oe,ge=s(),h(Q.$$.fragment),ke=s(),h(y.$$.fragment),be=s(),V=l("p"),V.innerHTML=We,Te=s(),h(A.$$.fragment),we=s(),m=l("div"),h(N.$$.fragment),xe=s(),K=l("p"),K.textContent=Ge,ye=s(),ee=l("p"),ee.innerHTML=Fe,ze=s(),J=l("div"),h(P.$$.fragment),Ce=s(),te=l("p"),te.textContent=Ye,Ee=s(),ne=l("ul"),ne.innerHTML=Ke,Ie=s(),z=l("div"),h(X.$$.fragment),Be=s(),se=l("p"),se.textContent=et,qe=s(),$=l("div"),h(S.$$.fragment),Ue=s(),ae=l("p"),ae.textContent=tt,Le=s(),h(C.$$.fragment),De=s(),oe=l("p"),oe.innerHTML=nt,Re=s(),E=l("div"),h(O.$$.fragment),He=s(),re=l("p"),re.innerHTML=st,Me=s(),h(W.$$.fragment),$e=s(),le=l("p"),this.h()},l(e){const t=pt("svelte-u9bgzb",document.head);p=i(t,"META",{name:!0,content:!0}),t.forEach(n),j=a(e),_=i(e,"P",{}),G(_).forEach(n),f=a(e),g(M.$$.fragment,e),c=a(e),g(v.$$.fragment,e),pe=a(e),B=i(e,"P",{"data-svelte-h":!0}),d(B)!=="svelte-1h66ss7"&&(B.textContent=Ve),ce=a(e),q=i(e,"P",{"data-svelte-h":!0}),d(q)!=="svelte-1dntg98"&&(q.textContent=Ae),me=a(e),U=i(e,"UL",{"data-svelte-h":!0}),d(U)!=="svelte-1absglq"&&(U.innerHTML=Ne),de=a(e),L=i(e,"P",{"data-svelte-h":!0}),d(L)!=="svelte-1ih4eq4"&&(L.innerHTML=Pe),ue=a(e),D=i(e,"P",{"data-svelte-h":!0}),d(D)!=="svelte-o8cd2v"&&(D.innerHTML=Xe),_e=a(e),R=i(e,"P",{"data-svelte-h":!0}),d(R)!=="svelte-e6qqvy"&&(R.textContent=Se),fe=a(e),g(H.$$.fragment,e),he=a(e),Z=i(e,"P",{"data-svelte-h":!0}),d(Z)!=="svelte-1rz0bcr"&&(Z.textContent=Oe),ge=a(e),g(Q.$$.fragment,e),ke=a(e),g(y.$$.fragment,e),be=a(e),V=i(e,"P",{"data-svelte-h":!0}),d(V)!=="svelte-rqfe8h"&&(V.innerHTML=We),Te=a(e),g(A.$$.fragment,e),we=a(e),m=i(e,"DIV",{class:!0});var u=G(m);g(N.$$.fragment,u),xe=a(u),K=i(u,"P",{"data-svelte-h":!0}),d(K)!=="svelte-1tm7ou1"&&(K.textContent=Ge),ye=a(u),ee=i(u,"P",{"data-svelte-h":!0}),d(ee)!=="svelte-1yon92w"&&(ee.innerHTML=Fe),ze=a(u),J=i(u,"DIV",{class:!0});var x=G(J);g(P.$$.fragment,x),Ce=a(x),te=i(x,"P",{"data-svelte-h":!0}),d(te)!=="svelte-t7qurq"&&(te.textContent=Ye),Ee=a(x),ne=i(x,"UL",{"data-svelte-h":!0}),d(ne)!=="svelte-xi6653"&&(ne.innerHTML=Ke),x.forEach(n),Ie=a(u),z=i(u,"DIV",{class:!0});var je=G(z);g(X.$$.fragment,je),Be=a(je),se=i(je,"P",{"data-svelte-h":!0}),d(se)!=="svelte-b3k2yi"&&(se.textContent=et),je.forEach(n),qe=a(u),$=i(u,"DIV",{class:!0});var I=G($);g(S.$$.fragment,I),Ue=a(I),ae=i(I,"P",{"data-svelte-h":!0}),d(ae)!=="svelte-gn6wi7"&&(ae.textContent=tt),Le=a(I),g(C.$$.fragment,I),De=a(I),oe=i(I,"P",{"data-svelte-h":!0}),d(oe)!=="svelte-owoxgn"&&(oe.innerHTML=nt),I.forEach(n),Re=a(u),E=i(u,"DIV",{class:!0});var Je=G(E);g(O.$$.fragment,Je),He=a(Je),re=i(Je,"P",{"data-svelte-h":!0}),d(re)!=="svelte-1f4f5kp"&&(re.innerHTML=st),Je.forEach(n),u.forEach(n),Me=a(e),g(W.$$.fragment,e),$e=a(e),le=i(e,"P",{}),G(le).forEach(n),this.h()},h(){F(p,"name","hf:doc:metadata"),F(p,"content",ht),F(J,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(z,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F($,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(E,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(m,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,t){r(document.head,p),o(e,j,t),o(e,_,t),o(e,f,t),k(M,e,t),o(e,c,t),k(v,e,t),o(e,pe,t),o(e,B,t),o(e,ce,t),o(e,q,t),o(e,me,t),o(e,U,t),o(e,de,t),o(e,L,t),o(e,ue,t),o(e,D,t),o(e,_e,t),o(e,R,t),o(e,fe,t),k(H,e,t),o(e,he,t),o(e,Z,t),o(e,ge,t),k(Q,e,t),o(e,ke,t),k(y,e,t),o(e,be,t),o(e,V,t),o(e,Te,t),k(A,e,t),o(e,we,t),o(e,m,t),k(N,m,null),r(m,xe),r(m,K),r(m,ye),r(m,ee),r(m,ze),r(m,J),k(P,J,null),r(J,Ce),r(J,te),r(J,Ee),r(J,ne),r(m,Ie),r(m,z),k(X,z,null),r(z,Be),r(z,se),r(m,qe),r(m,$),k(S,$,null),r($,Ue),r($,ae),r($,Le),k(C,$,null),r($,De),r($,oe),r(m,Re),r(m,E),k(O,E,null),r(E,He),r(E,re),o(e,Me,t),k(W,e,t),o(e,$e,t),o(e,le,t),ve=!0},p(e,[t]){const u={};t&2&&(u.$$scope={dirty:t,ctx:e}),y.$set(u);const x={};t&2&&(x.$$scope={dirty:t,ctx:e}),C.$set(x)},i(e){ve||(b(M.$$.fragment,e),b(v.$$.fragment,e),b(H.$$.fragment,e),b(Q.$$.fragment,e),b(y.$$.fragment,e),b(A.$$.fragment,e),b(N.$$.fragment,e),b(P.$$.fragment,e),b(X.$$.fragment,e),b(S.$$.fragment,e),b(C.$$.fragment,e),b(O.$$.fragment,e),b(W.$$.fragment,e),ve=!0)},o(e){T(M.$$.fragment,e),T(v.$$.fragment,e),T(H.$$.fragment,e),T(Q.$$.fragment,e),T(y.$$.fragment,e),T(A.$$.fragment,e),T(N.$$.fragment,e),T(P.$$.fragment,e),T(X.$$.fragment,e),T(S.$$.fragment,e),T(C.$$.fragment,e),T(O.$$.fragment,e),T(W.$$.fragment,e),ve=!1},d(e){e&&(n(j),n(_),n(f),n(c),n(pe),n(B),n(ce),n(q),n(me),n(U),n(de),n(L),n(ue),n(D),n(_e),n(R),n(fe),n(he),n(Z),n(ge),n(ke),n(be),n(V),n(Te),n(we),n(m),n(Me),n($e),n(le)),n(p),w(M,e),w(v,e),w(H,e),w(Q,e),w(y,e),w(A,e),w(N),w(P),w(X),w(S),w(C),w(O),w(W,e)}}}const ht='{"title":"BertJapanese","local":"bertjapanese","sections":[{"title":"Overview","local":"overview","sections":[],"depth":2},{"title":"BertJapaneseTokenizer","local":"transformers.BertJapaneseTokenizer","sections":[],"depth":2}],"depth":1}';function gt(Y){return rt(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class jt extends lt{constructor(p){super(),it(this,p,gt,ft,ot,{})}}export{jt as component};
