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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: "仙台は「[MASK]の都」と呼ばれている。" |
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--- |
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# BERT base Japanese (character tokenization, whole word masking enabled) |
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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 IPA dictionary, 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/v1.0). |
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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 Japanese Wikipedia as of September 1, 2019. |
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To generate the training corpus, [WikiExtractor](https://github.com/attardi/wikiextractor) is used to extract plain texts from a dump file of Wikipedia articles. |
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The text files used for the training are 2.6GB in size, consisting of approximately 17M sentences. |
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## Tokenization |
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The texts are first tokenized by [MeCab](https://taku910.github.io/mecab/) morphological parser with the IPA dictionary and then split into characters. |
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The vocabulary size is 4000. |
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## Training |
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The model is 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 the training of the MLM (masked language modeling) objective, we introduced the **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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## 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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For training models, we used Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program. |
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