rinna/japanese-gpt2-medium
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.frompretrained("rinna/japanese-gpt2-medium", usefast=False) tokenizer.dolowercase = 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.
Release date
April 7, 2021 (Updated: August 25, 2021)
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}}
}