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

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)

Tips:

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

This model was contributed by cl-tohoku.

BertJapaneseTokenizer

autodoc BertJapaneseTokenizer