import{s as Bt,o as qt,n as Lt}from"../chunks/scheduler.9bc65507.js";import{S as Ut,i as Dt,g as s,s as l,u as b,A as At,h as a,f as t,c as i,j as u,v,y as c,k as m,o,a as r,w,d as T,t as M,x as y}from"../chunks/index.78c82d43.js";import{D as be}from"../chunks/Docstring.4e7352e2.js";import{C as ot}from"../chunks/CodeBlock.73e038be.js";import{I as at}from"../chunks/IconCopyLink.bedaa44d.js";import{E as Rt}from"../chunks/ExampleCodeBlock.872b014d.js";function Nt(ve){let h,U="pair mask has the following format:",j,g,f;return g=new ot({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(){h=s("p"),h.textContent=U,j=l(),b(g.$$.fragment)},l(p){h=a(p,"P",{"data-svelte-h":!0}),c(h)!=="svelte-qjgeij"&&(h.textContent=U),j=i(p),v(g.$$.fragment,p)},m(p,J){r(p,h,J),r(p,j,J),w(g,p,J),f=!0},p:Lt,i(p){f||(T(g.$$.fragment,p),f=!0)},o(p){M(g.$$.fragment,p),f=!1},d(p){p&&(t(h),t(j)),y(g,p)}}}function Pt(ve){let h,U,j,g,f,p,J,D,He,ae,rt="BertJapanese",we,$,C,_e,A,Se,oe,lt="Overview",Te,R,it="The BERT models trained on Japanese text.",Me,N,pt="There are models with two different tokenization methods:",ye,P,ct='<li>Tokenize with MeCab and WordPiece. This requires some extra dependencies, <a href="https://github.com/polm/fugashi" rel="nofollow">fugashi</a> which is a wrapper around <a href="https://taku910.github.io/mecab/" rel="nofollow">MeCab</a>.</li> <li>Tokenize into characters.</li>',je,H,dt=`To use <em>MecabTokenizer</em>, you should <code>pip install transformers[&quot;ja&quot;]</code> (or <code>pip install -e .[&quot;ja&quot;]</code> if you install
from source) to install dependencies.`,xe,S,mt='See <a href="https://github.com/cl-tohoku/bert-japanese" rel="nofollow">details on cl-tohoku repository</a>.',Je,Z,ht="Example of using a model with MeCab and WordPiece tokenization:",$e,Q,ze,V,ut="Example of using a model with Character tokenization:",Ce,X,Ee,O,ft="Tips:",Ie,W,_t='<li>This implementation is the same as BERT, except for tokenization method. Refer to the <a href="bert">documentation of BERT</a> for more usage examples.</li>',Be,G,gt='This model was contributed by <a href="https://huggingface.co/cl-tohoku" rel="nofollow">cl-tohoku</a>.',qe,z,E,ge,F,Ze,re,kt="BertJapaneseTokenizer",Le,d,Y,Qe,le,bt="Construct a BERT tokenizer for Japanese text.",Ve,ie,vt=`This tokenizer inherits from <a href="/docs/transformers/v4.34.0/en/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.`,Xe,x,K,Oe,pe,wt=`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:`,We,ce,Tt="<li>single sequence: <code>[CLS] X [SEP]</code></li> <li>pair of sequences: <code>[CLS] A [SEP] B [SEP]</code></li>",Ge,I,ee,Fe,de,Mt="Converts a sequence of tokens (string) in a single string.",Ye,k,te,Ke,me,yt="Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence",et,B,tt,he,jt="If <code>token_ids_1</code> is <code>None</code>, this method only returns the first portion of the mask (0s).",nt,q,ne,st,ue,xt=`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.`,Ue,ke,De;return D=new at({}),A=new at({}),Q=new ot({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}}),X=new ot({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}}),F=new at({}),Y=new be({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/v4.34.0/src/transformers/models/bert_japanese/tokenization_bert_japanese.py#L107"}}),K=new be({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.BertJapaneseTokenizer.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.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/v4.34.0/src/transformers/models/bert_japanese/tokenization_bert_japanese.py#L307",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>
`}}),ee=new be({props:{name:"convert_tokens_to_string",anchor:"transformers.BertJapaneseTokenizer.convert_tokens_to_string",parameters:[{name:"tokens",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/bert_japanese/tokenization_bert_japanese.py#L299"}}),te=new be({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.BertJapaneseTokenizer.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.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/v4.34.0/src/transformers/models/bert_japanese/tokenization_bert_japanese.py#L362",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>
`}}),B=new Rt({props:{anchor:"transformers.BertJapaneseTokenizer.create_token_type_ids_from_sequences.example",$$slots:{default:[Nt]},$$scope:{ctx:ve}}}),ne=new be({props:{name:"get_special_tokens_mask",anchor:"transformers.BertJapaneseTokenizer.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.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/v4.34.0/src/transformers/models/bert_japanese/tokenization_bert_japanese.py#L333",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>
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