47 lines
2.1 KiB
Markdown
47 lines
2.1 KiB
Markdown
---
|
|
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.
|