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Add an model card

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+ ---
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+ language: ja
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+ license: cc-by-sa-3.0
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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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+
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+ # BERT base Japanese (unidic-lite with whole word masking, jawiki-20200831)
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+
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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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+
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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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+
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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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+
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+ ## Model architecture
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+
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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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+
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+ ## Training Data
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+
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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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+
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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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+
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+ ## Tokenization
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+
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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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+
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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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+
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+ ## Training
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+
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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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+
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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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+
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+ ## Licenses
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+
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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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+
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+ ## Acknowledgments
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+
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+ This model is trained with Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.