readme: ---
  language: ja
  license: cc-by-sa-4.0
  datasets:
  - wikipedia
  widget:
  - text: 仙台は「[MASK]の都」と呼ばれている。
  ---
  
  # BERT base Japanese (character tokenization)
  
  This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
  
  This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level tokenization.
  
  The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v1.0).
  
  ## Model architecture
  
  The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.
  
  ## Training Data
  
  The model is trained on Japanese Wikipedia as of September 1, 2019.
  To generate the training corpus, [WikiExtractor](https://github.com/attardi/wikiextractor) is used to extract plain texts from a dump file of Wikipedia articles.
  The text files used for the training are 2.6GB in size, consisting of approximately 17M sentences.
  
  ## Tokenization
  
  The texts are first tokenized by [MeCab](https://taku910.github.io/mecab/) morphological parser with the IPA dictionary and then split into characters.
  The vocabulary size is 4000.
  
  ## Training
  
  The model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.
  
  ## Licenses
  
  The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/).
  
  ## Acknowledgments
  
  For training models, we used Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.
  