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* Start writing BERT-Japanese doc * Fix typo, Update toctree * Modify model file to use comment for document, Add examples * Clean bert_japanese by make style * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Split a big code block into two * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Add prefix >>> to all lines in code blocks * Clean bert_japanese by make fixup Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
79 lines
2.8 KiB
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79 lines
2.8 KiB
ReStructuredText
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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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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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BertJapanese
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-----------------------------------------------------------------------------------------------------------------------
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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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
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<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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.. code-block::
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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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Example of using a model with Character tokenization:
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.. code-block::
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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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Tips:
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- This implementation is the same as BERT, except for tokenization method. Refer to the :doc:`documentation of BERT
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<bert>` for more usage examples.
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BertJapaneseTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BertJapaneseTokenizer
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:members:
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