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This paper presents raceBERT -- a transformer-based model for predicting raceand ethnicity from character sequences in names, and an accompanying pythonpackage. Using a transformer-based model trained on a U.S. Florida voterregistration dataset, the model predicts the likelihood of a name belonging to5 U.S. census race categories (White, Black, Hispanic, Asian & PacificIslander, American Indian & Alaskan Native). I build on Sood and Laohaprapanon(2018) by replacing their LSTM model with transformer-based models (pre-trainedBERT model, and a roBERTa model trained from scratch), and compare the results.To the best of my knowledge, raceBERT achieves state-of-the-art results in raceprediction using names, with an average f1-score of 0.86 -- a 4.1% improvementover the previous state-of-the-art, and improvements between 15-17% fornon-white names.

raceBERT -- A Transformer-based Model for Predicting Race and Ethnicity from Names