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: "東北大学で[MASK]の研究をしています。"
---

# BERT base Japanese (IPA dictionary, 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 the WordPiece subword 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 subwords by the WordPiece algorithm.
The vocabulary size is 32000.

## 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.
