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 as M}from"../chunks/index.707bf1b6.js";import{T as ct}from"../chunks/Tip.c2ecdbf4.js";import{D as ie}from"../chunks/Docstring.86474e80.js";import{C as Qe}from"../chunks/CodeBlock.54a9f38d.js";import{E as mt}from"../chunks/ExampleCodeBlock.034f3a73.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,w;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),w=!0},p:at,i(c){w||(b(f.$$.fragment,c),w=!0)},o(c){T(f.$$.fragment,c),w=!1},d(c){c&&(n(p),n(_)),M(f,c)}}}function ft(Y){let p,j,_,f,w,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,Me,m,N,xe,K,Ge="Construct a BERT tokenizer for Japanese text.",ye,ee,Fe=`This tokenizer inherits from <a href="/docs/transformers/pr_30356/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,$,S,Ue,ae,tt="Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence",Le,C,De,oe,nt="If <code>token_ids_1</code> is <code>None</code>, this method only returns the first portion of the mask (0s).",Re,E,O,He,re,st=`Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer <code>prepare_for_model</code> method.`,we,W,$e,le,ve;return w=new Ze({props:{title:"BertJapanese",local:"bertjapanese",headingTag:"h1"}}),v=new Ze({props:{title:"Overview",local:"overview",headingTag:"h2"}}),H=new Qe({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> 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/vr_30356/src/transformers/models/bert_japanese/tokenization_bert_japanese.py#L61"}}),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/vr_30356/src/transformers/models/bert_japanese/tokenization_bert_japanese.py#L258",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/vr_30356/src/transformers/models/bert_japanese/tokenization_bert_japanese.py#L250"}}),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/vr_30356/src/transformers/models/bert_japanese/tokenization_bert_japanese.py#L313",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/vr_30356/src/transformers/models/bert_japanese/tokenization_bert_japanese.py#L284",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(w.$$.fragment),c=s(),h(v.$$.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),Me=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,we=s(),h(W.$$.fragment),$e=s(),le=l("p"),this.h()},l(e){const 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