Chinese Electra Small Generator

🚀 Chinese ELECTRA

This project releases Chinese ELECTRA models, which have a compact size and competitive performance, accelerating the research on Chinese pre - trained models.

🚀 Quick Start

Please use ElectraForPreTraining for discriminator and ElectraForMaskedLM for generator if you are re - training these models.

Google and Stanford University released a new pre - trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants. To further accelerate the research of the Chinese pre - trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA. ELECTRA - small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.

This project is based on the official code of ELECTRA: https://github.com/google-research/electra

You may also be interested in:

• Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
• Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
• Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
• Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer

More resources by HFL: https://github.com/ymcui/HFL-Anthology

📄 License

This project is under the Apache - 2.0 license.

📚 Documentation

Citation

If you find our resource or paper is useful, please consider including the following citation in your paper.

• https://arxiv.org/abs/2004.13922

@inproceedings{cui-etal-2020-revisiting,
    title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
    author = "Cui, Yiming  and
      Che, Wanxiang  and
      Liu, Ting  and
      Qin, Bing  and
      Wang, Shijin  and
      Hu, Guoping",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
    pages = "657--668",
}