68 lines
2.4 KiB
Markdown
68 lines
2.4 KiB
Markdown
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---
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language: ja
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thumbnail: https://github.com/rinnakk/japanese-gpt2/blob/master/rinna.png
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tags:
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- gpt2
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- text-generation
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- lm
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- nlp
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license: mit
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datasets:
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- cc100
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- wikipedia
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widget:
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- text: "生命、宇宙、そして万物についての究極の疑問の答えは"
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---
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# japanese-gpt2-xsmall
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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/)
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# How to use the model
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~~~~
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt2-xsmall", use_fast=False)
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tokenizer.do_lower_case = True # due to some bug of tokenizer config loading
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model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt2-xsmall")
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~~~~
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# Model architecture
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A 6-layer, 512-hidden-size transformer-based language model.
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# Training
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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.
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# Tokenization
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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.
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# Release date
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August 25, 2021
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# How to cite
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```bibtex
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@misc{rinna-japanese-gpt2-xsmall,
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title = {rinna/japanese-gpt2-xsmall},
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author = {Zhao, Tianyu and Sawada, Kei},
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url = {https://huggingface.co/rinna/japanese-gpt2-xsmall}
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}
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@inproceedings{sawada2024release,
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title = {Release of Pre-Trained Models for the {J}apanese Language},
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author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
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booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
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month = {5},
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year = {2024},
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pages = {13898--13905},
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url = {https://aclanthology.org/2024.lrec-main.1213},
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note = {\url{https://arxiv.org/abs/2404.01657}}
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}
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```
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# Licenese
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[The MIT license](https://opensource.org/licenses/MIT)
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