readme: ---
  language: 
  - zh
  license: "apache-2.0"
  pipeline_tag: "fill-mask"
  ---
  
  **Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
  
  ## Chinese ELECTRA
  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.
  For further accelerating 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](https://github.com/google-research/electra)
  
  You may also 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
  
  
  ## 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",
  }
  ```
  