# 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](https://github.com/polm/fugashi) which is a wrapper around [MeCab](https://taku910.github.io/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](https://github.com/cl-tohoku/bert-japanese).

Example of using a model with MeCab and WordPiece tokenization:

```python
>>> 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:

```python
>>> 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](https://huggingface.co/cl-tohoku).

This implementation is the same as BERT, except for tokenization method. Refer to [BERT documentation](bert) for
API reference information.

## BertJapaneseTokenizer[[transformers.BertJapaneseTokenizer]]

#### transformers.BertJapaneseTokenizer[[transformers.BertJapaneseTokenizer]]

[Source](https://github.com/huggingface/transformers/blob/v4.57.1/src/transformers/models/bert_japanese/tokenization_bert_japanese.py#L61)

Construct a BERT tokenizer for Japanese text.

This tokenizer inherits from [PreTrainedTokenizer](/docs/transformers/v4.57.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizer) which contains most of the main methods. Users should refer
to: this superclass for more information regarding those methods.

build_inputs_with_special_tokenstransformers.BertJapaneseTokenizer.build_inputs_with_special_tokenshttps://github.com/huggingface/transformers/blob/v4.57.1/src/transformers/models/bert_japanese/tokenization_bert_japanese.py#L258[{"name": "token_ids_0", "val": ": list"}, {"name": "token_ids_1", "val": ": typing.Optional[list[int]] = None"}]- **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.0`List[int]`List of [input IDs](../glossary#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]`

**Parameters:**

vocab_file (`str`) : Path to a one-wordpiece-per-line vocabulary file.

spm_file (`str`, *optional*) : Path to [SentencePiece](https://github.com/google/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.

**Returns:**

``List[int]``

List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
#### convert_tokens_to_string[[transformers.BertJapaneseTokenizer.convert_tokens_to_string]]

[Source](https://github.com/huggingface/transformers/blob/v4.57.1/src/transformers/models/bert_japanese/tokenization_bert_japanese.py#L250)

Converts a sequence of tokens (string) in a single string.
#### get_special_tokens_mask[[transformers.BertJapaneseTokenizer.get_special_tokens_mask]]

[Source](https://github.com/huggingface/transformers/blob/v4.57.1/src/transformers/models/bert_japanese/tokenization_bert_japanese.py#L284)

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.

**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.