import{s as nt,o as st,n as tt}from"../chunks/scheduler.bdbef820.js";import{S as at,i as ot,g as i,s,r as g,A as rt,h as p,f as n,c as a,j as W,u as h,x as u,k as G,y as r,a as o,v as k,d as b,t as T,w}from"../chunks/index.33f81d56.js";import{T as lt}from"../chunks/Tip.34194030.js";import{D as le}from"../chunks/Docstring.64554317.js";import{C as He}from"../chunks/CodeBlock.362b34a4.js";import{E as it}from"../chunks/ExampleCodeBlock.4f2252c6.js";import{H as Re,E as pt}from"../chunks/EditOnGithub.a9246e21.js";function ct(F){let l,v=`이는 토큰화 방법을 제외하고는 BERT와 동일합니다. API 참조 정보는 <a href="https://huggingface.co/docs/transformers/main/en/model_doc/bert" rel="nofollow">BERT 문서</a>를 참조하세요.
이 모델은 <a href="https://huggingface.co/cl-tohoku" rel="nofollow">cl-tohoku</a>께서 기여하였습니다.`;return{c(){l=i("p"),l.innerHTML=v},l(_){l=p(_,"P",{"data-svelte-h":!0}),u(l)!=="svelte-1sfw8c9"&&(l.innerHTML=v)},m(_,f){o(_,l,f)},p:tt,d(_){_&&n(l)}}}function mt(F){let l,v="pair mask has the following format:",_,f,M;return f=new He({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(){l=i("p"),l.textContent=v,_=s(),g(f.$$.fragment)},l(c){l=p(c,"P",{"data-svelte-h":!0}),u(l)!=="svelte-qjgeij"&&(l.textContent=v),_=a(c),h(f.$$.fragment,c)},m(c,j){o(c,l,j),o(c,_,j),k(f,c,j),M=!0},p:tt,i(c){M||(b(f.$$.fragment,c),M=!0)},o(c){T(f.$$.fragment,c),M=!1},d(c){c&&(n(l),n(_)),w(f,c)}}}function dt(F){let l,v,_,f,M,c,j,ie,B,Ze="일본어 문장에 학습된 BERT 모델 입니다.",pe,q,Ae="각각 서로 다른 토큰화 방법을 사용하는 두 모델:",ce,U,Qe='<li>MeCab와 WordPiece를 사용하여 토큰화합니다. 이를 위해 추가 의존성 <a href="https://github.com/polm/fugashi" rel="nofollow">fugashi</a>이 필요합니다. (이는 <a href="https://taku910.github.io/mecab/" rel="nofollow">MeCab</a>의 래퍼입니다.)</li> <li>문자 단위로 토큰화합니다.</li>',me,L,Ve="<em>MecabTokenizer</em>를 사용하려면, 의존성을 설치하기 위해 <code>pip install transformers[&quot;ja&quot;]</code> (또는 소스에서 설치하는 경우 <code>pip install -e .[&quot;ja&quot;]</code>) 명령을 실행해야 합니다.",de,D,Ne='자세한 내용은 <a href="https://github.com/cl-tohoku/bert-japanese" rel="nofollow">cl-tohoku 리포지토리</a>에서 확인하세요.',ue,R,Pe="MeCab과 WordPiece 토큰화를 사용하는 모델 예시:",_e,H,fe,Z,Xe="문자 토큰화를 사용하는 모델 예시:",ge,A,he,x,ke,Q,be,m,V,ve,Y,Se="Construct a BERT tokenizer for Japanese text.",Je,K,Oe=`This tokenizer inherits from <code>PreTrainedTokenizer</code> which contains most of the main methods. Users should refer
to: this superclass for more information regarding those methods.`,ye,J,N,xe,ee,We=`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:`,ze,te,Ge="<li>single sequence: <code>[CLS] X [SEP]</code></li> <li>pair of sequences: <code>[CLS] A [SEP] B [SEP]</code></li>",Ce,z,P,Ee,ne,Fe="Converts a sequence of tokens (string) in a single string.",Ie,$,X,Be,se,Ye="Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence",qe,C,Ue,ae,Ke="If <code>token_ids_1</code> is <code>None</code>, this method only returns the first portion of the mask (0s).",Le,E,S,De,oe,et=`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.`,Te,O,we,re,Me;return M=new Re({props:{title:"일본어 BERT (BertJapanese)",local:"bertjapanese",headingTag:"h1"}}),j=new Re({props:{title:"개요",local:"overview",headingTag:"h2"}}),H=new He({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}}),A=new He({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}}),x=new lt({props:{$$slots:{default:[ct]},$$scope:{ctx:F}}}),Q=new Re({props:{title:"BertJapaneseTokenizer",local:"transformers.BertJapaneseTokenizer",headingTag:"h2"}}),V=new le({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#L61"}}),N=new le({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/main/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>
`}}),P=new le({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#L250"}}),X=new le({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/main/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 it({props:{anchor:"transformers.BertJapaneseTokenizer.create_token_type_ids_from_sequences.example",$$slots:{default:[mt]},$$scope:{ctx:F}}}),S=new le({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/main/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>
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