unbiased-toxic-roberta
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Architecture | RoBERTa |
| Task | Toxic Comment Classification |
| Framework | PyTorch |
What is unbiased-toxic-roberta?
unbiased-toxic-roberta is a specialized model designed to detect toxic content in text while minimizing unintended bias against mentions of identities. Built on RoBERTa architecture, it achieved a remarkable 0.93639 score on the Jigsaw Unintended Bias in Toxicity Classification challenge, approaching the performance of ensemble models that topped the leaderboard at 0.94734.
Implementation Details
The model is implemented using PyTorch Lightning and Hugging Face Transformers, built upon the roberta-base architecture. It can detect multiple types of toxic content including toxicity, severe toxicity, obscene content, threats, insults, identity attacks, and sexual explicit content.
- Trained on the Civil Comments dataset from the Jigsaw Unintended Bias challenge
- Supports both single string and batch prediction
- Implements sophisticated bias mitigation techniques
- Provides probability scores for multiple toxicity categories
Core Capabilities
- Multi-label toxic content classification
- Bias-aware predictions for nine identity categories including gender, religion, and ethnicity
- Production-ready with simple API interface
- Suitable for content moderation systems
Frequently Asked Questions
Q: What makes this model unique?
This model specifically addresses the challenge of unintended bias in toxicity detection, making it more reliable when analyzing content mentioning different identity groups. It achieves this while maintaining high accuracy in toxic content detection.
Q: What are the recommended use cases?
The model is best suited for research purposes, content moderation systems, and applications where avoiding demographic biases is crucial. It's particularly valuable for platforms that need to maintain civil discourse while ensuring fair treatment across different identity groups.
