54 lines
2.6 KiB
Markdown
54 lines
2.6 KiB
Markdown
---
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license: apache-2.0
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datasets:
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- cc100
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- wikipedia
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language:
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- ja
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widget:
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- text: 東北大学で[MASK]の研究をしています。
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---
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# BERT base Japanese (unidic-lite with whole word masking, CC-100 and jawiki-20230102)
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This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
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This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by the WordPiece subword tokenization.
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Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.
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The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/).
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## Model architecture
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The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.
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## Training Data
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The model is trained on the Japanese portion of [CC-100 dataset](https://data.statmt.org/cc-100/) and the Japanese version of Wikipedia.
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For Wikipedia, we generated a text corpus from the [Wikipedia Cirrussearch dump file](https://dumps.wikimedia.org/other/cirrussearch/) as of January 2, 2023.
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The corpus files generated from CC-100 and Wikipedia are 74.3GB and 4.9GB in size and consist of approximately 392M and 34M sentences, respectively.
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For the purpose of splitting texts into sentences, we used [fugashi](https://github.com/polm/fugashi) with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary (v0.0.7).
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## Tokenization
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The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm.
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The vocabulary size is 32768.
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We used [fugashi](https://github.com/polm/fugashi) and [unidic-lite](https://github.com/polm/unidic-lite) packages for the tokenization.
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## Training
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We trained the model first on the CC-100 corpus for 1M steps and then on the Wikipedia corpus for another 1M steps.
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For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.
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For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/).
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## Licenses
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The pretrained models are distributed under the Apache License 2.0.
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## Acknowledgments
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This model is trained with Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/) program.
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