Joint Laboratory of HIT and iFLYTEK Research (HFL) commited on
Commit
2438a49
•
1 Parent(s): eff9855

Create README.md

Browse files
Files changed (1) hide show
  1. README.md +45 -0
README.md ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language:
3
+ - zh
4
+ license: "apache-2.0"
5
+ ---
6
+
7
+ # This model is specifically designed for legal domain.
8
+
9
+ ## Chinese ELECTRA
10
+ 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.
11
+ 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.
12
+ ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
13
+
14
+ This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
15
+
16
+ You may also interested in,
17
+ - Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
18
+ - Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
19
+ - Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
20
+ - Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
21
+
22
+ More resources by HFL: https://github.com/ymcui/HFL-Anthology
23
+
24
+
25
+ ## Citation
26
+ If you find our resource or paper is useful, please consider including the following citation in your paper.
27
+ - https://arxiv.org/abs/2004.13922
28
+ ```
29
+ @inproceedings{cui-etal-2020-revisiting,
30
+ title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
31
+ author = "Cui, Yiming and
32
+ Che, Wanxiang and
33
+ Liu, Ting and
34
+ Qin, Bing and
35
+ Wang, Shijin and
36
+ Hu, Guoping",
37
+ booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
38
+ month = nov,
39
+ year = "2020",
40
+ address = "Online",
41
+ publisher = "Association for Computational Linguistics",
42
+ url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
43
+ pages = "657--668",
44
+ }
45
+ ```