Toxic Comment Model
| Property | Value |
|---|---|
| Author | martin-ha |
| Downloads | 905,252 |
| Framework | PyTorch |
| Base Architecture | DistilBERT |
| Accuracy | 94% |
What is toxic-comment-model?
The toxic-comment-model is a specialized implementation of DistilBERT, fine-tuned specifically for detecting and classifying toxic comments in online content. This model achieves an impressive 94% accuracy and 0.59 F1-score on test data, making it a reliable tool for content moderation and online safety applications.
Implementation Details
Built on the DistilBERT architecture, this model is implemented using PyTorch and the Transformers library. It was trained on a subset of the Jigsaw Unintended Bias in Toxicity Classification competition dataset, utilizing 10% of the training data with a 3-hour training process on a P-100 GPU.
- Easy integration using the Transformers library
- Supports batch processing of text inputs
- Pre-trained tokenization handling
- Optimized for production deployment
Core Capabilities
- Toxic comment detection with 94% accuracy
- Identity-aware classification across different demographic groups
- Real-time text analysis capabilities
- Production-ready implementation with inference endpoints
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on toxic comment detection while maintaining awareness of potential biases across different identity groups. It provides detailed performance metrics for various demographic subgroups, making it particularly valuable for balanced content moderation.
Q: What are the recommended use cases?
The model is ideal for content moderation systems, online platforms, and social media applications requiring automated toxic content detection. However, users should be aware of its varying performance across different identity groups and implement appropriate safeguards.
