模型:
hfl/chinese-legal-electra-base-discriminator
谷歌和斯坦福大学发布了一个名为ELECTRA的新的预训练模型,与BERT及其变体相比,该模型具有更紧凑的模型大小和相对竞争力的性能。为了进一步加快中文预训练模型的研究进展,哈尔滨工业大学和讯飞研究院联合实验室(HFL)基于ELECTRA的官方代码发布了中文ELECTRA模型。相比BERT及其变体,ELECTRA-small仅使用1/10的参数即可在多个NLP任务上达到相似甚至更高的分数。
此项目基于ELECTRA的官方代码: https://github.com/google-research/electra
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HFL的更多资源: https://github.com/ymcui/HFL-Anthology
如果您认为我们的资源或论文有用,请考虑在您的论文中包含以下引用:
@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", }