DistilRoBERTa Offensive & Hateful Speech Classifier
| Property | Value |
|---|---|
| License | MIT |
| Accuracy | 94.50% |
| Base Model | DistilRoBERTa |
| Task | Text Multi-classification |
What is distilroberta-base-offensive-hateful-speech-text-multiclassification?
This is a specialized language model fine-tuned on DistilRoBERTa-base for detecting offensive and hateful speech in text. The model performs multi-classification tasks with remarkable accuracy, making it suitable for content moderation and online safety applications.
Implementation Details
Built on the efficient DistilRoBERTa architecture, this model has been fine-tuned on custom datasets specifically curated for hate speech detection. The implementation leverages PyTorch and Transformers libraries, offering both performance and accessibility.
- Fine-tuned on custom hate speech datasets
- Implements multi-classification approach
- Supports inference endpoints for production deployment
- Built with PyTorch and Transformers framework
Core Capabilities
- High-accuracy hate speech detection (94.50%)
- Multi-class classification of offensive content
- Efficient processing with DistilRoBERTa architecture
- Production-ready with inference endpoint support
Frequently Asked Questions
Q: What makes this model unique?
The model combines the efficiency of DistilRoBERTa with specialized training on hate speech datasets, achieving high accuracy while maintaining computational efficiency. Its multi-classification approach allows for more nuanced content analysis.
Q: What are the recommended use cases?
This model is ideal for content moderation systems, social media platforms, and online communities needing to automatically detect and filter offensive or hateful content. It can be integrated into content management systems or used for research purposes.
