japanese-gpt2-medium

This repository provides a medium-sized Japanese GPT-2 model. The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd.

How to use the model

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt2-medium", use_fast=False)
tokenizer.do_lower_case = True  # due to some bug of tokenizer config loading

model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt2-medium")

Model architecture

A 24-layer, 1024-hidden-size transformer-based language model.

Training

The model was trained on Japanese CC-100 and Japanese Wikipedia to optimize a traditional language modelling objective on 8\*V100 GPUs for around 30 days. It reaches around 18 perplexity on a chosen validation set from the same data.

Tokenization

The model uses a sentencepiece-based tokenizer, the vocabulary was trained on the Japanese Wikipedia using the official sentencepiece training script.

How to cite

@misc{rinna-japanese-gpt2-medium,
    title = {rinna/japanese-gpt2-medium},
    author = {Zhao, Tianyu and Sawada, Kei},
    url = {https://huggingface.co./rinna/japanese-gpt2-medium}
}

@inproceedings{sawada2024release,
    title = {Release of Pre-Trained Models for the {J}apanese Language},
    author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
    booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
    month = {5},
    year = {2024},
    pages = {13898--13905},
    url = {https://aclanthology.org/2024.lrec-main.1213},
    note = {\url{https://arxiv.org/abs/2404.01657}}
}

Licenese

The MIT license