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--- |
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language: ja |
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license: cc-by-sa-4.0 |
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datasets: |
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- wikipedia |
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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 large Japanese (character-level tokenization with whole word masking, jawiki-20200831) |
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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 character-level 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/tree/v2.0). |
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## Model architecture |
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The model architecture is the same as the original BERT large model; 24 layers, 1024 dimensions of hidden states, and 16 attention heads. |
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## Training Data |
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The models are trained on the Japanese version of Wikipedia. |
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The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020. |
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The generated corpus files are 4.0GB in total, containing approximately 30M sentences. |
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We used the [MeCab](https://taku910.github.io/mecab/) morphological parser with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary to split texts into sentences. |
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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 characters. |
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The vocabulary size is 6144. |
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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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The models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training 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 [TensorFlow Research Cloud program](https://www.tensorflow.org/tfrc/). |
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The training took about 5 days to finish. |
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## Licenses |
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The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). |
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## Acknowledgments |
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This model is trained with Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program. |
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