Awesome
<img src="./.github/logo.svg" width="480">GPT2 for Multiple Languages
- Simplifed GPT2 train scripts（based on Grover, supporting TPUs）
- Ported bert tokenizer, multilingual corpus compatible
- 1.5B GPT2 pretrained Chinese model ( ~15G corpus, 10w steps )
- Batteries-included Colab demo #
- 1.5B GPT2 pretrained Chinese model ( ~30G corpus, 22w steps )
Pretrained Model
| Size | Language | Corpus | Vocab | Link1 | Link2 | SHA256 |
|---|---|---|---|---|---|---|
| 1.5B Params | Chinese | ~30G | CLUE ( 8021 tokens ) | Google Drive | Baidu Pan (ffz6) | e698cc97a7f5f706f84f58bb469d614e<br/>51d3c0ce5f9ab9bf77e01e3fcb41d482 |
| 1.5B Params | Chinese | ~15G | Bert ( 21128 tokens ) | Google Drive | Baidu Pan (q9vr) | 4a6e5124df8db7ac2bdd902e6191b807<br/>a6983a7f5d09fb10ce011f9a073b183e |
Corpus from THUCNews and nlp_chinese_corpus
Using Cloud TPU Pod v3-256 to train 22w steps
Google Colab
With just 2 clicks (not including Colab auth process), the 1.5B pretrained Chinese model demo is ready to go:
<img src="./.github/demo.png" width="640">Train
Disclaimer
The contents in this repository are for academic research purpose, and we do not provide any conclusive remarks.
Citation
@misc{GPT2-ML,
author = {Zhibo Zhang},
title = {GPT2-ML: GPT-2 for Multiple Languages},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/imcaspar/gpt2-ml}},
}
Reference
https://github.com/google-research/bert
https://github.com/rowanz/grover
Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC)