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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using ⚡ Pytorch Lightning and 🤗 Transformers. For access to our API, please email us at [email protected].
| Date | Stars |
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| 2026-07-24 | 1279 |
| 2026-07-25 | 1279 |
| 2026-07-28 | 1279 |
| 2026-07-30 | 1279 |
| 2026-08-06 | 1279 |
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<div align="center"> # 🙊 Detoxify ## Toxic Comment Classification with ⚡ Pytorch Lightning and 🤗 Transformers [](https://badge.fury.io/py/detoxify)    </div>  ## News & Updates ### 22-10-2021: New improved multilingual model & standardised class names - Updated the `multilingual` model weights used by Detoxify with a model trained on the translated data from the 2nd Jigsaw challenge (as well as the 1st). This model has also been trained to minimise bias and now returns the same categories as the `unbiased` model. New best AUC score on the test set: 92.11 (89.71 before). - All detoxify models now return consistent class names (e.g. "identity_attack" replaces "identity_hate" in the `original` model to match the `unbiased` classes). ### 03-09-2021: New improved unbiased model - Updated the `unbiased` model weights used by Detoxify with a model trained on both datasets from the first 2 Jigsaw challenges. New best score on the test set: 93.74 (93.64 before). ### 15-02-2021: Detoxify featured in Scientific American! - Our opinion piece ["Can AI identify toxic online content?"](https://www.scientificamerican.com/article/can-ai-identify-toxic-online-content/) is now live on Scientific American ### 14-01-2021: Lightweight models - Added smaller models trained with Albert for the `original` and `unbiased` models! Can access these in the same way with detoxify using `original-small` and `unbiased-small` as inputs. The `original-small` achieved a mean AUC score of 98.28 (98.64 before) and the `unbiased-small` achieved a final score of 93.36 (93.64 before). ## Description Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification. Built by [Laura Hanu](https://laurahanu.github.io/) at [Unitary](https://www.unitary.ai/), where we are working to stop harmful content online by interpreting visual content in context. Dependencies: - For inference: - 🤗 Transformers - ⚡ Pytorch lightning - For training will also need: - Kaggle API (to download data) | Challenge | Year | Goal | Original Data Source | Detoxify Model Name | Top Kaggle Leaderboard Score % | Detoxify Score % |-|-|-|-|-|-|-| | [Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge) | 2018 | build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate. | Wikipedia Comments | `original` | 98.86 | 98.64 | [Jigsaw Unintended Bias in Toxicity Classification](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification) | 2019 | build a model that recognizes toxicity and minimizes this type of unintended bias with respect to mentions of identities. You'll be using a dataset labeled for identity mentions and optimizing a metric designed to measure unintended bias. | Civil Comments | `unbiased` | 94.73 | 93.74 | [Jigsaw Multilingual Toxic Comment Classification](https://www.kaggle.com/c/jigsaw-multilingual-toxic-comment-classification) | 2020 | build effective multilingual models | Wikipedia Comments + Civil Comments | `multilingual` | 95.36 | 92.11 It is also noteworthy to mention that the top leadearboard scores have been achieved using model ensembles. The purpose of this library was to build something user-friendly and straightforward to use. ### Multilingual model language breakdown | Language Subgroup | Subgroup size | Subgroup AUC Score % | |:-----------|----------------:|---------------:| 🇮🇹 it | 8494 | 89.18 | 🇫🇷 fr | 10920 | 89.61
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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