StereoSet is a dataset that measures stereotype bias in language models. StereoSet consists of 17,000 sentences that measures model preferences across gender, race, religion, and profession.
StereoSet measures racism, sexism, and otherwise discriminatory behavior in a model, while also ensuring that the underlying language model performance remains strong. To perform well in StereoSet, researchers must create a language model that is fair and unbiased, while also having a strong understanding of natural language.Explore StereoSet and model predictionsStereoSet paper (Nadeem et al.)Browse StereoSet on GitHub
We've built a few resources to help you get started with the dataset.
Download a copy of the dataset (distributed under the CC BY-SA 4.0 license):
To evaluate your models, we have also made available the evaluation script we will use for official evaluation, along with a sample prediction file that the script will take as input. To run the evaluation, use
python3 evaluation.py --gold-file <path_to_dev> --predictions-file <path_to_predictions>.
Once you have a built a model that works to your expectations on the dev set, you submit it to get official scores on the dev and a hidden test set. To preserve the integrity of test results, we do not release the test set to the public. Instead, we require you to submit your model so that we can run it on the test set for you. Here's a tutorial walking you through official evaluation of your model:Submission Tutorial
Because StereoSet is an ongoing effort, we expect the dataset to evolve.
To keep up to date with major changes to the dataset, please subscribe:
StereoSet measures model preferences for stereotypical conditions across race, gender, religion, and profession, while also ensuring that debiasing techniques do not affect underlying model performance.
|Rank||Model||LM Score||Stereotype Score||ICAT Score|
|Idealistic LM |
1Apr 21, 2020
2Apr 21, 2020
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