roberta_toxicity_classifier
| Property | Value |
|---|---|
| Base Model | FacebookAI/roberta-large |
| License | OpenRAIL++ |
| Paper | RoBERTa Paper |
| Performance | AUC-ROC: 0.98, F1-score: 0.76 |
What is roberta_toxicity_classifier?
The roberta_toxicity_classifier is a specialized model designed for detecting toxic content in text. Built upon RoBERTa architecture, it has been fine-tuned on approximately 2 million examples from Jigsaw's toxic comment datasets (2018-2020). This model represents a significant advancement in content moderation technology, achieving impressive performance metrics on toxic content detection.
Implementation Details
The model utilizes the RoBERTa architecture and can be easily implemented using the Transformers library. It processes text input and outputs binary classifications (toxic/neutral), making it particularly useful for content moderation systems.
- Built on RoBERTa-large architecture
- Trained on merged Jigsaw datasets
- Implements binary classification (neutral/toxic)
- Easily integrable using Hugging Face Transformers
Core Capabilities
- High-accuracy toxicity detection (0.98 AUC-ROC)
- Robust performance across various toxic content types
- Production-ready implementation
- Efficient processing of English text
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its extensive training on a comprehensive dataset of toxic comments, achieving state-of-the-art performance metrics (0.98 AUC-ROC, 0.76 F1-score) while maintaining practical usability through the Transformers library.
Q: What are the recommended use cases?
The model is ideal for content moderation systems, social media platforms, online forums, and any application requiring automatic detection of toxic content. It's particularly suitable for production environments requiring reliable toxicity detection.
