transformers/docs/source/en/model_doc/bert-japanese.md

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# BertJapanese

<div class="flex flex-wrap space-x-1">
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">
</div>

## 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).

<Tip> 

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

</Tip>


## BertJapaneseTokenizer

[[autodoc]] BertJapaneseTokenizer