Text-Moderation
| Property | Value |
|---|---|
| Model Type | Multi-class Classification |
| Base Architecture | DeBERTa-v3 |
| Accuracy | 74.9% |
| License | CodeML OpenRAIL-M 0.1 |
| CO2 Emissions | 0.0397g |
What is Text-Moderation?
Text-Moderation is an advanced content filtering model developed by KoalaAI that leverages the DeBERTa-v3 architecture to identify and classify potentially harmful content across 8 distinct categories. The model specializes in detecting sexual content, hate speech, violence, harassment, self-harm, and other concerning textual content, making it particularly valuable for content moderation systems.
Implementation Details
The model employs a sophisticated multi-class classification approach, achieving a 74.9% accuracy rate. It processes English text inputs and returns probability scores across all categories, enabling granular content analysis. The implementation includes both REST API access via cURL and direct Python integration using the Transformers library.
- Trained specifically for English language content
- Provides probability scores for 8 distinct harmful content categories
- Achieves balanced performance metrics with Macro F1: 0.326 and Weighted F1: 0.703
- Implements ethical considerations in its design and deployment
Core Capabilities
- Sexual content detection (including minor-related content)
- Hate speech and threatening content identification
- Violence and graphic content classification
- Harassment detection
- Self-harm content recognition
- Multi-label probability scoring
Frequently Asked Questions
Q: What makes this model unique?
The model's strength lies in its comprehensive coverage of harmful content categories and its ability to provide granular probability scores for each category, making it suitable for nuanced content moderation decisions. Its implementation of the DeBERTa-v3 architecture ensures robust performance while maintaining ethical considerations.
Q: What are the recommended use cases?
The model is ideal for content moderation systems, social media platforms, online communities, and any application requiring automated text analysis for harmful content. It's particularly useful in scenarios requiring real-time content filtering and multi-category classification of potential violations.
