At publication, the model card reports nearly one million downloads and more than 3,500 likes for Unitary’s unbiased-toxic-roberta. The 125-million-parameter English classifier addresses a common moderation failure: neutral identity terms can receive high toxicity scores because those terms correlate with abuse in training data. Developers get an open, locally runnable baseline with category-level outputs and an explicit bias-reduction objective.

The model is the unbiased checkpoint from the Detoxify repository, which provides models for the original, unintended-bias, and multilingual Jigsaw Toxic Comment challenges. Laura Hanu developed the project at Unitary using PyTorch Lightning and Hugging Face Transformers. The repository and model are available under the Apache-2.0 license.

Seven harm scores from one pass

The checkpoint emits independent, probability-like scores between 0 and 1 for seven moderation categories. Applications can inspect every score, apply category-specific thresholds, or combine the outputs with other moderation signals.