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BertJapanese

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BertJapanese

Overview

BERT モデルは日本語テキストでトレーニングされました。

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。

  • MeCab と WordPiece を使用してトークン化します。これには、MeCab のラッパーである fugashi という追加の依存関係が必要です。
  • 文字にトークン化します。

MecabTokenizer を使用するには、pip installTransformers["ja"] (または、インストールする場合は pip install -e .["ja"]) 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。

cl-tohakuリポジトリの詳細を参照してください。

MeCab および WordPiece トークン化でモデルを使用する例:

>>> import torch
>>> from transformers import AutoModel, AutoTokenizer

>>> bertjapanese = AutoModel.from_pretrained("cl-tohoku/bert-base-japanese")
>>> tokenizer = AutoTokenizer.from_pretrained("cl-tohoku/bert-base-japanese")

>>> ## Input Japanese Text
>>> line = "吾輩は猫である。"

>>> inputs = tokenizer(line, return_tensors="pt")

>>> print(tokenizer.decode(inputs["input_ids"][0]))
[CLS] 吾輩 は 猫 で ある 。 [SEP]

>>> outputs = bertjapanese(**inputs)

文字トークン化を使用したモデルの使用例:

>>> bertjapanese = AutoModel.from_pretrained("cl-tohoku/bert-base-japanese-char")
>>> tokenizer = AutoTokenizer.from_pretrained("cl-tohoku/bert-base-japanese-char")

>>> ## Input Japanese Text
>>> line = "吾輩は猫である。"

>>> inputs = tokenizer(line, return_tensors="pt")

>>> print(tokenizer.decode(inputs["input_ids"][0]))
[CLS] 吾 輩 は 猫 で あ る 。 [SEP]

>>> outputs = bertjapanese(**inputs)
  • 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、BERT のドキュメント を参照してください。

このモデルはcl-tohakuから提供されました。

BertJapaneseTokenizer

class transformers.BertJapaneseTokenizer

< >

( vocab_file spm_file = None do_lower_case = False do_word_tokenize = True do_subword_tokenize = True word_tokenizer_type = 'basic' subword_tokenizer_type = 'wordpiece' never_split = None unk_token = '[UNK]' sep_token = '[SEP]' pad_token = '[PAD]' cls_token = '[CLS]' mask_token = '[MASK]' mecab_kwargs = None sudachi_kwargs = None jumanpp_kwargs = None **kwargs )

Parameters

  • vocab_file (str) — Path to a one-wordpiece-per-line vocabulary file.
  • spm_file (str, optional) — Path to SentencePiece file (generally has a .spm or .model extension) that contains the vocabulary.
  • do_lower_case (bool, optional, defaults to True) — Whether to lower case the input. Only has an effect when do_basic_tokenize=True.
  • do_word_tokenize (bool, optional, defaults to True) — Whether to do word tokenization.
  • do_subword_tokenize (bool, optional, defaults to True) — Whether to do subword tokenization.
  • word_tokenizer_type (str, optional, defaults to "basic") — Type of word tokenizer. Choose from [“basic”, “mecab”, “sudachi”, “jumanpp”].
  • subword_tokenizer_type (str, optional, defaults to "wordpiece") — Type of subword tokenizer. Choose from [“wordpiece”, “character”, “sentencepiece”,].
  • mecab_kwargs (dict, optional) — Dictionary passed to the MecabTokenizer constructor.
  • sudachi_kwargs (dict, optional) — Dictionary passed to the SudachiTokenizer constructor.
  • jumanpp_kwargs (dict, optional) — Dictionary passed to the JumanppTokenizer constructor.

Construct a BERT tokenizer for Japanese text.

This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to: this superclass for more information regarding those methods.

build_inputs_with_special_tokens

< >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) → List[int]

Parameters

  • token_ids_0 (List[int]) — List of IDs to which the special tokens will be added.
  • token_ids_1 (List[int], optional) — Optional second list of IDs for sequence pairs.

Returns

List[int]

List of input IDs with the appropriate special tokens.

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:

  • single sequence: [CLS] X [SEP]
  • pair of sequences: [CLS] A [SEP] B [SEP]

convert_tokens_to_string

< >

( tokens )

Converts a sequence of tokens (string) in a single string.

create_token_type_ids_from_sequences

< >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) → List[int]

Parameters

  • token_ids_0 (List[int]) — List of IDs.
  • token_ids_1 (List[int], optional) — Optional second list of IDs for sequence pairs.

Returns

List[int]

List of token type IDs according to the given sequence(s).

Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence

pair mask has the following format:

0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence    | second sequence |

If token_ids_1 is None, this method only returns the first portion of the mask (0s).

get_special_tokens_mask

< >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None already_has_special_tokens: bool = False ) → List[int]

Parameters

  • token_ids_0 (List[int]) — List of IDs.
  • token_ids_1 (List[int], optional) — Optional second list of IDs for sequence pairs.
  • already_has_special_tokens (bool, optional, defaults to False) — Whether or not the token list is already formatted with special tokens for the model.

Returns

List[int]

A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.

Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer prepare_for_model method.

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