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

Prasanna Parasurama

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

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arxiv 2112.03807 v3 pith:3T63CMBX submitted 2021-12-07 cs.CL cs.LG

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

Prasanna Parasurama This is my paper

classification cs.CL cs.LG

keywords modelnamesracetransformer-basedracebertethnicitypredictingresults

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This paper presents raceBERT -- a transformer-based model for predicting race and ethnicity from character sequences in names, and an accompanying python package. Using a transformer-based model trained on a U.S. Florida voter registration dataset, the model predicts the likelihood of a name belonging to 5 U.S. census race categories (White, Black, Hispanic, Asian & Pacific Islander, American Indian & Alaskan Native). I build on Sood and Laohaprapanon (2018) by replacing their LSTM model with transformer-based models (pre-trained BERT 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 race prediction using names, with an average f1-score of 0.86 -- a 4.1% improvement over the previous state-of-the-art, and improvements between 15-17% for non-white names.

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Cited by 2 Pith papers

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Using Embedding Models to Improve Probabilistic Race Prediction
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Embedding models trained on Census surname, first-name, and voter file data improve probabilistic race prediction for uncommon surnames, with full-name embeddings delivering the largest gains especially for Hispanic a...
NameBERT: Scaling Name-Based Nationality Classification with LLM-Augmented Open Academic Data
cs.CL 2026-04 unverdicted novelty 7.0

NameBERT models trained on LLM-augmented academic name data outperform state-of-the-art baselines in nationality classification from names, with augmentation providing gains especially on tail countries.