This model is trained on 180G data, we recommend using this one than the original version.

示例代码

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks


pipeline_ins = pipeline(
		'fill-mask',
		model='dienstag/chinese-electra-180g-small-generator',
        model_revision='v1.0.0'
)

print(pipeline_ins('生活的真谛是[MASK]。'))

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

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",
}