japanese-pretrained-models

(previously: japanese-gpt2)

This repository provides the code for training Japanese pretrained models. This code has been used for producing japanese-gpt2-medium, japanese-gpt2-small, japanese-gpt2-xsmall, and japanese-roberta-base released on HuggingFace model hub by rinna Co., Ltd.

Currently supported pretrained models include: GPT-2, RoBERTa.

Please open an issue (in English/日本語) if you encounter any problem using the code or using our models via Huggingface.

if you find this work useful, please cite the following paper:

@article{rinna_pretrained2021,
    title={日本語自然言語処理における事前学習モデルの公開},
    author={趙 天雨 and 沢田 慶},
    journal={人工知能学会研究会資料 言語・音声理解と対話処理研究会},
    volume={93},
    pages={169-170},
    year={2021},
    doi={10.11517/jsaislud.93.0_169}
}

Update log

• 2022/01/25 Updated link to rinna/japanese-gpt-1b in the model summary table.

• 2022/01/17 Updated citation information.

• 2021/11/01 Updated corpora links.

• 2021/09/13 Added tips on using position_ids with japanese-roberta-base. Refer to issue 3 for details.

• 2021/08/26 [Important] Updated license from the MIT license to the Apache 2.0 license due to the use of the Wikipedia pre-processing code from cl-tohoku/bert-japanese. See issue 1 for details.

• 2021/08/23 Added Japanese Wikipedia to training corpora. Published code for training rinna/japanese-gpt2-small, rinna/japanese-gpt2-xsmall, and rinna/japanese-roberta-base.

• 2021/08/18 Changed repo name from japanese-gpt2 to japanese-pretrained-models

• 2021/06/15 Fixed best PPL tracking bug when using a checkpoint.

• 2021/05/04 Fixed random seeding bug for Multi-GPU training.

• 2021/04/06 Published code for training rinna/japanese-gpt2-medium.

Use tips

Tips for rinna/japanese-roberta-base

• Use [CLS]: To predict a masked token, be sure to add a [CLS] token before the sentence for the model to correctly encode it, as it is used during the model training.

• Use [MASK] after tokenization: A) Directly typing [MASK] in an input string and B) replacing a token with [MASK] after tokenization will yield different token sequences, and thus different prediction results. It is more appropriate to use [MASK] after tokenization (as it is consistent with how the model was pretrained). However, the Huggingface Inference API only supports typing [MASK] in the input string and produces less robust predictions.

• Provide position_ids as an argument explicitly: When position_ids are not provided for a Roberta* model, Huggingface's transformers will automatically construct it but start from padding_idx instead of 0 (see issue and function create_position_ids_from_input_ids() in Huggingface's implementation), which unfortunately does not work as expected with rinna/japanese-roberta-base since the padding_idx of the corresponding tokenizer is not 0. So please be sure to constrcut the position_ids by yourself and make it start from position id 0.

Use our pretrained models via Huggingface

Model summary

* Training was conducted on a 8x V100 32GB machine.

** Training was conducted using a different codebase and a different computing environment.

Example: use rinna/japanese-roberta-base for predicting masked token

import torch
from transformers import T5Tokenizer, RobertaForMaskedLM

# load tokenizer
tokenizer = T5Tokenizer.from_pretrained("rinna/japanese-roberta-base")
tokenizer.do_lower_case = True  # due to some bug of tokenizer config loading

# load model
model = RobertaForMaskedLM.from_pretrained("rinna/japanese-roberta-base")
model = model.eval()

# original text
text = "4年に1度オリンピックは開かれる。"

# prepend [CLS]
text = "[CLS]" + text

# tokenize
tokens = tokenizer.tokenize(text)
print(tokens)  # output: ['[CLS]', '▁4', '年に', '1', '度', 'オリンピック', 'は', '開かれる', '。']']

# mask a token
masked_idx = 5
tokens[masked_idx] = tokenizer.mask_token
print(tokens)  # output: ['[CLS]', '▁4', '年に', '1', '度', '[MASK]', 'は', '開かれる', '。']

# convert to ids
token_ids = tokenizer.convert_tokens_to_ids(tokens)
print(token_ids)  # output: [4, 1602, 44, 24, 368, 6, 11, 21583, 8]

# convert to tensor
token_tensor = torch.LongTensor([token_ids])

# provide position ids explicitly
position_ids = list(range(0, token_tensor.size(1)))
print(position_ids)  # output: [0, 1, 2, 3, 4, 5, 6, 7, 8]
position_id_tensor = torch.LongTensor([position_ids])

# get the top 10 predictions of the masked token
with torch.no_grad():
    outputs = model(input_ids=token_tensor, position_ids=position_id_tensor)
    predictions = outputs[0][0, masked_idx].topk(10)

for i, index_t in enumerate(predictions.indices):
    index = index_t.item()
    token = tokenizer.convert_ids_to_tokens([index])[0]
    print(i, token)

"""
0 総会
1 サミット
2 ワールドカップ
3 フェスティバル
4 大会
5 オリンピック
6 全国大会
7 党大会
8 イベント
9 世界選手権
"""

Train japanese-gpt2-xsmall from scratch

Install dependencies

Install required packages by running the following command under the repo directory:

pip install -r requirements.txt

Data construction and model training

1. Set up fugashi tokenzier for preprocessing Wikipedia corpus by running:

python -m unidic download

2. Download training corpus Japanese CC-100 and extract the ja.txt file.

3. Move the ja.txt file or modify src/corpus/jp_cc100/config.py to match the filepath of ja.txt with self.raw_data_dir in the config file.

4. Split ja.txt to smaller files by running:

cd src/
python -m corpus.jp_cc100.split_to_small_files

5. First check the versions of Wikipedia dump at Wikipedia cirrussearch and fill in self.download_link (in file src/corpus/jp_wiki/config.py) with the link to your preferred Wikipedia dump version. Then download training corpus Japanese Wikipedia and split it by running:

python -m corpus.jp_wiki.build_pretrain_dataset
python -m corpus.jp_wiki.split_to_small_files

6. Train a xsmall-sized GPT-2 on, for example, 4 V100 GPUs by running:

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m task.pretrain_gpt2.train \
    --n_gpus 4 \
    --save_model True \
    --enable_log True \
    --model_size xsmall \
    --model_config_filepath model/gpt2-ja-xsmall-config.json \
    --batch_size 20 \
    --eval_batch_size 40 \
    --n_training_steps 1600000 \
    --n_accum_steps 3 \
    --init_lr 0.0007

Interact with the trained model

Assume you have run the training script and saved your xsmall-sized GPT-2 to data/model/pretrain_gpt2/gpt2-ja-xsmall-xxx.checkpoint. Run the following command to use it to complete text on one GPU by nucleus sampling with p=0.95 and k=40:

CUDA_VISIBLE_DEVICES=0 python -m task.pretrain_gpt2.interact \
    --checkpoint_path ../data/model/pretrain_gpt2/gpt2-ja-medium-xxx.checkpoint \
    --gen_type top \
    --top_p 0.95 \
    --top_k 40

Prepare files for uploading to Huggingface

1. Make your Huggingface account. Create a model repo. Clone it to your local machine.

2. Create model and config files from a checkpoint by running:

python -m task.pretrain_gpt2.checkpoint2huggingface \
    --checkpoint_path ../data/model/gpt2-medium-xxx.checkpoint \
    --save_dir {huggingface's model repo directory}

3. Validate the created files by running:

python -m task.pretrain_gpt2.check_huggingface \
    --model_dir {huggingface's model repo directory}

4. Add files, commit, and push to your Huggingface repo.

Customize your GPT-2 training

Check available arguments of GPT-2 training script by running:

python -m task.pretrain_gpt2.train --help

Train japanese-roberta-base from scratch

Assume you have finished the data construction process as described above, run the following command to train a base-sized Japanese RoBERTa on, for example, 8 V100 GPUs:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m task.pretrain_roberta.train \
    --n_gpus 8 \
    --save_model True \
    --enable_log True \
    --model_size base \
    --model_config_filepath model/roberta-ja-base-config.json \
    --batch_size 32 \
    --eval_batch_size 32 \
    --n_training_steps 3000000 \
    --n_accum_steps 16 \
    --init_lr 0.0006

License

The Apache 2.0 license