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BertJapanese

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Last updated 1 year ago

BertJapanese

Overview

The BERT models trained on Japanese text.

There are models with two different tokenization methods:

  • Tokenize with MeCab and WordPiece. This requires some extra dependencies, which is a wrapper around .

  • Tokenize into characters.

To use MecabTokenizer, you should pip install transformers["ja"] (or pip install -e .["ja"] if you install from source) to install dependencies.

See .

Example of using a model with MeCab and WordPiece tokenization:

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>>> 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)

Example of using a model with Character tokenization:

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>>> 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)

Tips:

BertJapaneseTokenizer

class transformers.BertJapaneseTokenizer

( vocab_filespm_file = Nonedo_lower_case = Falsedo_word_tokenize = Truedo_subword_tokenize = Trueword_tokenizer_type = 'basic'subword_tokenizer_type = 'wordpiece'never_split = Noneunk_token = '[UNK]'sep_token = '[SEP]'pad_token = '[PAD]'cls_token = '[CLS]'mask_token = '[MASK]'mecab_kwargs = Nonesudachi_kwargs = Nonejumanpp_kwargs = None**kwargs )

Parameters

  • vocab_file (str) — Path to a one-wordpiece-per-line vocabulary file.

  • 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.

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]

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]

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:

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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]] = Nonealready_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.

This implementation is the same as BERT, except for tokenization method. Refer to the for more usage examples.

This model was contributed by .

spm_file (str, optional) — Path to file (generally has a .spm or .model extension) that contains the vocabulary.

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

List of with the appropriate special tokens.

List of according to the given sequence(s).

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fugashi
MeCab
details on cl-tohoku repository
documentation of BERT
cl-tohoku
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SentencePiece
PreTrainedTokenizer
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input IDs
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token type IDs
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