This repository provides the code for training Japanese GPT-2 models. This code has been used for producing japanese-gpt2-medium released on HuggingFace model hub by rinna.

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

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

2. 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.

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

cd src/
python -m corpus.jp_cc100.split_to_small_files

4. Train a medium-sized GPT-2 on 4 GPUs by running:

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m task.pretrain.train --n_gpus 4 --save_model True --enable_log True

Assume you have run the training script and saved your medium-sized GPT-2 to data/model/gpt2-medium-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.interact --checkpoint_path ../data/model/gpt2-medium-xxx.checkpoint --gen_type top --top_p 0.95 --top_k 40

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.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.check_huggingface --model_dir {huggingface's model repo directory}

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

Check available arguments by running:

python -m task.pretrain.train --help

The MIT license