McGill-NLP/bias-bench
ACL 2022: An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models.
Python
Stargazers
- achuthasubhashGUNTUR , INDIA
- agarwalchaitanyaHyderabad, India
- aleidingerAmsterdam
- claudiashi57
- cyc1am3nNC Soft
- daniel-saeedi
- efatmaeWeizenbaum institute
- EliottZemourEPFL | MIT
- fly51flyPRIS
- gkirilNEC Laboratories Europe
- gozdesahinIstanbul, Turkey
- gvanbovenAmsterdam
- haozhe-anUniversity of Maryland, College Park
- inimahIndonesian Institute of Sciences (LIPI)
- Irenehere
- jah05
- jsedoc
- kite99520Peking University
- kr-ramesh
- krangelieHamburg, Germany
- lichengluSalt Lake City
- liyazheng成都
- michaelgira23@Microsoft
- mourga
- pauldebdeep9
- poaboagyeUniversity of Utah
- Praful932prodigal-tech
- PrithivirajDamodaranBangkok
- saroyehun
- sivareddygMcGill University
- squiduuPRML, Korea University, South Korea
- subhobrata
- vaibhavad
- xhluca@McGill-NLP
- xingjian-zhangUniversity of Michigan
- yanyu9385