This repository contains an extensible codebase to measure stereotypical bias on new pretrained models, as well as code to replicate our results. We encourage the community to use this as a springboard for further evaluation of bias in pretrained language models, and to submit attempts to mitigate bias to the leaderboard.


  1. Clone the repository: git clone
  2. Install the requirements: cd stereoset && pip install -r requirements.txt

Reproducing Results

To reproduce our results for the bias in each model:

  1. Run make from the code folder. This step evaluates the biases on each model.
  2. Run the scoring script with respect to each model: python3 --gold-file ../data/dev.json --predictions-dir predictions/.

We have provided our predictions in the predictions/ folder, and the output of the evaluation script in predictions.txt. We have also included code to replicate our numbers on each table in the tables/ folder. Please feel free to file an issue if anything is off; we strongly believe in reproducible research and extensible codebases.


To cite StereoSet:

    title={StereoSet: Measuring stereotypical bias in pretrained language models},
    author={Moin Nadeem and Anna Bethke and Siva Reddy},