--- language: ja thumbnail: https://github.com/rinnakk/japanese-gpt2/blob/master/rinna.png tags: - gpt2 - text-generation - lm - nlp license: mit datasets: - cc100 - wikipedia widget: - text: "生命、宇宙、そして万物についての究極の疑問の答えは" --- # japanese-gpt2-xsmall ![rinna-icon](./rinna.png) This repository provides an extra-small-sized Japanese GPT-2 model. The model was trained using code from Github repository [rinnakk/japanese-pretrained-models](https://github.com/rinnakk/japanese-pretrained-models) by [rinna Co., Ltd.](https://corp.rinna.co.jp/) # How to use the model ~~~~ from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt2-xsmall", use_fast=False) tokenizer.do_lower_case = True # due to some bug of tokenizer config loading model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt2-xsmall") ~~~~ # Model architecture A 6-layer, 512-hidden-size transformer-based language model. # Training The model was trained on [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz) and [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) to optimize a traditional language modelling objective on 8\\*V100 GPUs for around 4 days. It reaches around 28 perplexity on a chosen validation set from CC-100. # Tokenization The model uses a [sentencepiece](https://github.com/google/sentencepiece)-based tokenizer, the vocabulary was trained on the Japanese Wikipedia using the official sentencepiece training script. # How to cite ```bibtex @misc{rinna-japanese-gpt2-xsmall, title = {rinna/japanese-gpt2-xsmall}, author = {Zhao, Tianyu and Sawada, Kei}, url = {https://huggingface.co/rinna/japanese-gpt2-xsmall} } @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](https://opensource.org/licenses/MIT)