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lm_eval/tasks/crowspairsde.py

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 success across many NLP tasks. However, there is ample evidence that they use the cultural biases that are 
 undoubtedly present in the corpora they are trained on, implicitly creating harm with biased representations. To 
 measure some forms of social bias in language models against protected demographic groups in the US, we introduce the 
-Crowdsourced Stereotype Pairs benchmark (CrowS-Pairs). CrowS-Pairs has 1508 examples that cover stereotypes dealing 
+Crowdsources Stereotype Pairs benchmark (CrowS-Pairs). CrowS-Pairs has 1508 examples that cover stereotypes dealing 
 with nine types of bias, like race, religion, and age. In CrowS-Pairs a model is presented with two sentences: one 
 that is more stereotyping and another that is less stereotyping. The data focuses on stereotypes about historically 
 disadvantaged groups and contrasts them with advantaged groups. We find that all three of the widely-used MLMs we