unbiased-toxic-roberta-onnx
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Base Model | unitary/unbiased-toxic-roberta |
| Format | ONNX |
| Language | English |
What is unbiased-toxic-roberta-onnx?
This model is an ONNX-optimized version of the Unitary's unbiased toxic RoBERTa model, specifically designed for detecting toxic content while minimizing unintended bias. It's capable of handling multiple classification tasks derived from three Jigsaw challenges, making it particularly useful for content moderation systems that need to maintain fairness across different demographic groups.
Implementation Details
The model leverages the RoBERTa architecture and has been converted to ONNX format using the Optimum library, enabling faster inference and broader deployment options. It supports multiple classification labels including toxicity, severe toxicity, obscene content, threats, insults, and identity-based attacks.
- Supports both general toxicity detection and identity-aware classification
- Implements comprehensive labeling schema with four severity levels
- Handles nine distinct identity categories for bias assessment
- Optimized for production deployment through ONNX runtime
Core Capabilities
- Multi-label toxic content classification
- Identity-aware bias detection covering major demographic groups
- Granular toxicity assessment from "Not Toxic" to "Very Toxic"
- Integration support through Optimum library and LLM Guard
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its combination of robust toxicity detection while actively addressing demographic biases, all while being optimized for production through ONNX conversion. It's particularly valuable for applications requiring both accuracy and fairness in content moderation.
Q: What are the recommended use cases?
The model is ideal for content moderation systems, social media platforms, and online communities that need to maintain civil discourse while ensuring fair treatment across different demographic groups. It's particularly suited for applications where both speed and bias mitigation are crucial.
