pytorch tf jax bert masked-lm ja dataset:wikipedia transformers license:cc-by-sa-3.0 fill-mask
.gitattributes README.md config.json flax_model.msgpack pytorch_model.bin tf_model.h5 tokenizer_config.json vocab.txt
---
language: ja
license: cc-by-sa-3.0
datasets:
- wikipedia
widget:
- text: "仙台は「[MASK]の都」と呼ばれている。"
---

# BERT base Japanese (character tokenization, whole word masking enabled)

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.
Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.

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.

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.

## 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.
