#
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, fugashi which is a wrapper around MeCab.
- 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 details on cl-tohoku repository.

Example of using a model with MeCab and WordPiece tokenization:

    >>> 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:

    >>> 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)

This model was contributed by cl-tohoku.

This implementation is the same as BERT, except for tokenization method. Refer to BERT documentation for API reference information.

##
BertJapaneseTokenizer

###
class transformers.BertJapaneseTokenizer

< source >

( 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

< source >

( token_ids_0: List token_ids_1: Optional = 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

< source >

( tokens )

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

####
create_token_type_ids_from_sequences

< source >

( token_ids_0: List token_ids_1: Optional = 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

< source >

( token_ids_0: List token_ids_1: Optional = 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.

< > Update on GitHub