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2.4 KiB
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75 lines
2.4 KiB
Plaintext
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
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# BertJapanese
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## Overview
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The BERT models trained on Japanese text.
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There are models with two different tokenization methods:
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- 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/).
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- Tokenize into characters.
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To use *MecabTokenizer*, you should `pip install transformers["ja"]` (or `pip install -e .["ja"]` if you install
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from source) to install dependencies.
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See [details on cl-tohoku repository](https://github.com/cl-tohoku/bert-japanese).
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Example of using a model with MeCab and WordPiece tokenization:
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```python
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>>> import torch
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>>> from transformers import AutoModel, AutoTokenizer
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>>> bertjapanese = AutoModel.from_pretrained("cl-tohoku/bert-base-japanese")
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>>> tokenizer = AutoTokenizer.from_pretrained("cl-tohoku/bert-base-japanese")
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>>> ## Input Japanese Text
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>>> line = "吾輩は猫である。"
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>>> inputs = tokenizer(line, return_tensors="pt")
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>>> print(tokenizer.decode(inputs["input_ids"][0]))
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[CLS] 吾輩 は 猫 で ある 。 [SEP]
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>>> outputs = bertjapanese(**inputs)
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```
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Example of using a model with Character tokenization:
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```python
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>>> bertjapanese = AutoModel.from_pretrained("cl-tohoku/bert-base-japanese-char")
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>>> tokenizer = AutoTokenizer.from_pretrained("cl-tohoku/bert-base-japanese-char")
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>>> ## Input Japanese Text
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>>> line = "吾輩は猫である。"
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>>> inputs = tokenizer(line, return_tensors="pt")
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>>> print(tokenizer.decode(inputs["input_ids"][0]))
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[CLS] 吾 輩 は 猫 で あ る 。 [SEP]
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>>> outputs = bertjapanese(**inputs)
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```
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Tips:
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- This implementation is the same as BERT, except for tokenization method. Refer to the [documentation of BERT](bert) for more usage examples.
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This model was contributed by [cl-tohoku](https://huggingface.co/cl-tohoku).
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## BertJapaneseTokenizer
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[[autodoc]] BertJapaneseTokenizer
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