|
--- |
|
license: apache-2.0 |
|
datasets: |
|
- cc100 |
|
- wikipedia |
|
language: |
|
- ja |
|
widget: |
|
- text: 東北大学で[MASK]の研究をしています。 |
|
--- |
|
|
|
# BERT base Japanese (unidic-lite with whole word masking, CC-100 and jawiki-20230102) |
|
|
|
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 Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), 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/). |
|
|
|
## 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 the Japanese portion of [CC-100 dataset](https://data.statmt.org/cc-100/) and the Japanese version of Wikipedia. |
|
For Wikipedia, we generated a text corpus from the [Wikipedia Cirrussearch dump file](https://dumps.wikimedia.org/other/cirrussearch/) as of January 2, 2023. |
|
The corpus files generated from CC-100 and Wikipedia are 74.3GB and 4.9GB in size and consist of approximately 392M and 34M sentences, respectively. |
|
|
|
For the purpose of splitting texts into sentences, we used [fugashi](https://github.com/polm/fugashi) with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary (v0.0.7). |
|
|
|
## Tokenization |
|
|
|
The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm. |
|
The vocabulary size is 32768. |
|
|
|
We used [fugashi](https://github.com/polm/fugashi) and [unidic-lite](https://github.com/polm/unidic-lite) packages for the tokenization. |
|
|
|
## Training |
|
|
|
We trained the model first on the CC-100 corpus for 1M steps and then on the Wikipedia corpus for another 1M steps. |
|
For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. |
|
|
|
For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/). |
|
|
|
## Licenses |
|
|
|
The pretrained models are distributed under the Apache License 2.0. |
|
|
|
## Acknowledgments |
|
|
|
This model is trained with Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/) program. |
