Pretrained Japanese BERT models

This is a repository of pretrained Japanese BERT models. The models are available in Transformers by Hugging Face.

Model hub: https://huggingface.co/cl-tohoku

For information on the previous versions of our pretrained models, see the v1.0 tag of this repository.

Model Architecture

The architecture of our models are the same as the original BERT models proposed by Google.

BERT-base models consist of 12 layers, 768 dimensions of hidden states, and 12 attention heads.
BERT-large models consist of 24 layers, 1024 dimensions of hidden states, and 16 attention heads.

Training Data

The models are trained on the Japanese version of Wikipedia. The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020.

The generated corpus files are 4.0GB in total, consisting of approximately 30M sentences. We used the MeCab morphological parser with mecab-ipadic-NEologd dictionary to split texts into sentences.

$WORK_DIR/corpus/jawiki-20200831/corpus_sampled.txt">

$ WORK_DIR="$HOME/work/bert-japanese"

$ python make_corpus_wiki.py \
--input_file jawiki-20200831-cirrussearch-content.json.gz \
--output_file $WORK_DIR/corpus/jawiki-20200831/corpus.txt \
--min_text_length 10 \
--max_text_length 200 \
--mecab_option "-r $HOME/local/etc/mecabrc -d $HOME/local/lib/mecab/dic/mecab-ipadic-neologd-v0.0.7"

# Split corpus files for parallel preprocessing of the files
$ python merge_split_corpora.py \
--input_files $WORK_DIR/corpus/jawiki-20200831/corpus.txt \
--output_dir $WORK_DIR/corpus/jawiki-20200831 \
--num_files 8

# Sample some lines for training tokenizers
$ cat $WORK_DIR/corpus/jawiki-20200831/corpus.txt|grep -v '^$'|shuf|head -n 1000000 \
> $WORK_DIR/corpus/jawiki-20200831/corpus_sampled.txt

Tokenization

For each of BERT-base and BERT-large, we provide two models with different tokenization methods.

For wordpiece models, 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.
For character models, the texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into characters. The vocabulary size is 6144.

We used fugashi and unidic-lite packages for the tokenization.

$WORK_DIR/tokenizers/jawiki-20200831/character/vocab.txt">

$ WORK_DIR="$HOME/work/bert-japanese"

# WordPiece (unidic_lite)
$ TOKENIZERS_PARALLELISM=false python train_tokenizer.py \
--input_files $WORK_DIR/corpus/jawiki-20200831/corpus_sampled.txt \
--output_dir $WORK_DIR/tokenizers/jawiki-20200831/wordpiece_unidic_lite \
--tokenizer_type wordpiece \
--mecab_dic_type unidic_lite \
--vocab_size 32768 \
--limit_alphabet 6129 \
--num_unused_tokens 10

# Character
$ head -n 6144 $WORK_DIR/tokenizers/jawiki-20200831/wordpiece_unidic_lite/vocab.txt \
> $WORK_DIR/tokenizers/jawiki-20200831/character/vocab.txt

Training

The models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training 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 TensorFlow Research Cloud program. The training took about 5 days and 14 days for BERT-base and BERT-large models, respectively.

Creation of the pretraining data

$ WORK_DIR="$HOME/work/bert-japanese"

# WordPiece (unidic_lite)
$ mkdir -p $WORK_DIR/bert/jawiki-20200831/wordpiece_unidic_lite/pretraining_data
# It takes 3h and 420GB RAM, producing 43M instances
$ seq -f %02g 1 8|xargs -L 1 -I {} -P 8 python create_pretraining_data.py \
--input_file $WORK_DIR/corpus/jawiki-20200831/corpus_{}.txt \
--output_file $WORK_DIR/bert/jawiki-20200831/wordpiece_unidic_lite/pretraining_data/pretraining_data_{}.tfrecord.gz \
--vocab_file $WORK_DIR/tokenizers/jawiki-20200831/wordpiece_unidic_lite/vocab.txt \
--tokenizer_type wordpiece \
--mecab_dic_type unidic_lite \
--do_whole_word_mask \
--gzip_compress \
--max_seq_length 512 \
--max_predictions_per_seq 80 \
--dupe_factor 10

# Character
$ mkdir $WORK_DIR/bert/jawiki-20200831/character/pretraining_data
# It takes 4h10m and 615GB RAM, producing 55M instances
$ seq -f %02g 1 8|xargs -L 1 -I {} -P 8 python create_pretraining_data.py \
--input_file $WORK_DIR/corpus/jawiki-20200831/corpus_{}.txt \
--output_file $WORK_DIR/bert/jawiki-20200831/character/pretraining_data/pretraining_data_{}.tfrecord.gz \
--vocab_file $WORK_DIR/tokenizers/jawiki-20200831/character/vocab.txt \
--tokenizer_type character \
--mecab_dic_type unidic_lite \
--do_whole_word_mask \
--gzip_compress \
--max_seq_length 512 \
--max_predictions_per_seq 80 \
--dupe_factor 10

Training of the models

Note: all the necessary files need to be stored in a Google Cloud Storage (GCS) bucket.

# BERT-base, WordPiece (unidic_lite)
$ ctpu up -name tpu01 -tpu-size v3-8 -tf-version 2.3
$ cd /usr/share/models
$ sudo pip3 install -r official/requirements.txt
$ tmux
$ export PYTHONPATH="$PYTHONPATH:/usr/share/tpu/models"
$ WORK_DIR="gs://
   
    /bert-japanese
    "
   
$ python3 official/nlp/bert/run_pretraining.py \
--input_files="$WORK_DIR/bert/jawiki-20200831/wordpiece_unidic_lite/pretraining_data/pretraining_data_*.tfrecord" \
--model_dir="$WORK_DIR/bert/jawiki-20200831/wordpiece_unidic_lite/bert-base" \
--bert_config_file="$WORK_DIR/bert/jawiki-20200831/wordpiece_unidic_lite/bert-base/config.json" \
--max_seq_length=512 \
--max_predictions_per_seq=80 \
--train_batch_size=256 \
--learning_rate=1e-4 \
--num_train_epochs=100 \
--num_steps_per_epoch=10000 \
--optimizer_type=adamw \
--warmup_steps=10000 \
--distribution_strategy=tpu \
--tpu=tpu01

# BERT-base, Character
$ ctpu up -name tpu02 -tpu-size v3-8 -tf-version 2.3
$ cd /usr/share/models
$ sudo pip3 install -r official/requirements.txt
$ tmux
$ export PYTHONPATH="$PYTHONPATH:/usr/share/tpu/models"
$ WORK_DIR="gs://
   
    /bert-japanese
    "
   
$ python3 official/nlp/bert/run_pretraining.py \
--input_files="$WORK_DIR/bert/jawiki-20200831/character/pretraining_data/pretraining_data_*.tfrecord" \
--model_dir="$WORK_DIR/bert/jawiki-20200831/character/bert-base" \
--bert_config_file="$WORK_DIR/bert/jawiki-20200831/character/bert-base/config.json" \
--max_seq_length=512 \
--max_predictions_per_seq=80 \
--train_batch_size=256 \
--learning_rate=1e-4 \
--num_train_epochs=100 \
--num_steps_per_epoch=10000 \
--optimizer_type=adamw \
--warmup_steps=10000 \
--distribution_strategy=tpu \
--tpu=tpu02

# BERT-large, WordPiece (unidic_lite)
$ ctpu up -name tpu03 -tpu-size v3-8 -tf-version 2.3
$ cd /usr/share/models
$ sudo pip3 install -r official/requirements.txt
$ tmux
$ export PYTHONPATH="$PYTHONPATH:/usr/share/tpu/models"
$ WORK_DIR="gs://
   
    /bert-japanese
    "
   
$ python3 official/nlp/bert/run_pretraining.py \
--input_files="$WORK_DIR/bert/jawiki-20200831/wordpiece_unidic_lite/pretraining_data/pretraining_data_*.tfrecord" \
--model_dir="$WORK_DIR/bert/jawiki-20200831/wordpiece_unidic_lite/bert-large" \
--bert_config_file="$WORK_DIR/bert/jawiki-20200831/wordpiece_unidic_lite/bert-large/config.json" \
--max_seq_length=512 \
--max_predictions_per_seq=80 \
--train_batch_size=256 \
--learning_rate=5e-5 \
--num_train_epochs=100 \
--num_steps_per_epoch=10000 \
--optimizer_type=adamw \
--warmup_steps=10000 \
--distribution_strategy=tpu \
--tpu=tpu03

# BERT-large, Character
$ ctpu up -name tpu04 -tpu-size v3-8 -tf-version 2.3
$ cd /usr/share/models
$ sudo pip3 install -r official/requirements.txt
$ tmux
$ export PYTHONPATH="$PYTHONPATH:/usr/share/tpu/models"
$ WORK_DIR="gs://
   
    /bert-japanese
    "
   
$ python3 official/nlp/bert/run_pretraining.py \
--input_files="$WORK_DIR/bert/jawiki-20200831/character/pretraining_data/pretraining_data_*.tfrecord" \
--model_dir="$WORK_DIR/bert/jawiki-20200831/character/bert-large" \
--bert_config_file="$WORK_DIR/bert/jawiki-20200831/character/bert-large/config.json" \
--max_seq_length=512 \
--max_predictions_per_seq=80 \
--train_batch_size=256 \
--learning_rate=5e-5 \
--num_train_epochs=100 \
--num_steps_per_epoch=10000 \
--optimizer_type=adamw \
--warmup_steps=10000 \
--distribution_strategy=tpu \
--tpu=tpu04

Licenses

The pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.

The codes in this repository are distributed under the Apache License 2.0.

Related Work

Original BERT model by Google Research Team
- https://github.com/google-research/bert
- https://github.com/tensorflow/models/tree/master/official/nlp/bert (for TensorFlow 2.0)
Juman-tokenized Japanese BERT model
- Author: Kurohashi-Kawahara Laboratory, Kyoto University
- http://nlp.ist.i.kyoto-u.ac.jp/index.php?BERT日本語Pretrainedモデル
MeCab-Jumandic-tokenized Japanese BERT model (trained with a large mini-batch size)
- Author: National Institute of Information and Communications Technology (NICT)
- https://alaginrc.nict.go.jp/nict-bert/index.html
Sentencepiece Japanese BERT model
- Author: Yohei Kikuta
- https://github.com/yoheikikuta/bert-japanese
Sentencepiece Japanese BERT model, trained on SNS corpus
- Author: Hottolink, Inc.
- https://github.com/hottolink/hottoSNS-bert

Acknowledgments

The models are trained with Cloud TPUs provided by TensorFlow Research Cloud program.

Pretrained Japanese BERT models

Related tags

Overview

Pretrained Japanese BERT models

Model Architecture

Training Data

Tokenization

Training

Creation of the pretraining data

Training of the models

Licenses

Related Work

Acknowledgments

Owner

Inui Laboratory

Tools, wrappers, etc... for data science with a concentration on text processing

Code for EMNLP'21 paper "Types of Out-of-Distribution Texts and How to Detect Them"

Unsupervised text tokenizer for Neural Network-based text generation.

PyTorch implementation of the NIPS-17 paper "Poincaré Embeddings for Learning Hierarchical Representations"

Code for the ACL 2021 paper "Structural Guidance for Transformer Language Models"

Non-Autoregressive Predictive Coding

Command Line Text-To-Speech using Google TTS

glow-speak is a fast, local, neural text to speech system that uses eSpeak-ng as a text/phoneme front-end.

Protein Language Model

AllenNLP integration for Shiba: Japanese CANINE model

Entity Disambiguation as text extraction (ACL 2022)

Code for Findings of ACL 2022 Paper "Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors"

👄 The most accurate natural language detection library for Python, suitable for long and short text alike

NLP project that works with news (NER, context generation, news trend analytics)

Linking data between GBIF, Biodiverse, and Open Tree of Life

Findings of ACL 2021

NLP tool to extract emotional phrase from tweets 🤩

Full Spectrum Bioinformatics - a free online text designed to introduce key topics in Bioinformatics using the Python

Package for controllable summarization

This repository has a implementations of data augmentation for NLP for Japanese.