parler_hate_speech
| Property | Value |
|---|---|
| Author | OrK7 |
| Architecture | DeBERTa-v3-base |
| Task | Text Classification |
| Language | English |
What is parler_hate_speech?
parler_hate_speech is a state-of-the-art hate speech detection model designed specifically for social media content analysis. It employs an innovative ensemble approach using DeBERTa models, enhanced with pre-training on synthetic data and back-translation techniques. The model demonstrates significant improvements in hate speech detection metrics, reducing RMSE from 0.838 to 0.766 and increasing R-squared from 0.520 to 0.599.
Implementation Details
The model utilizes the DeBERTa-v3-base architecture and implements a unique combination of ensemble methods and test-time augmentation. It processes text inputs with a maximum length of 512 tokens and outputs hate speech scores on a scale of 1-5, where higher scores indicate stronger hate speech content.
- Incorporates back-translation for improved robustness
- Features test-time augmentation for better prediction accuracy
- Implements ensemble methods for enhanced performance
- Includes bias reduction techniques
Core Capabilities
- Accurate hate speech detection in social media posts
- Reduced model bias through ensemble methods
- Support for both Parler and GAB datasets
- Real-time text classification with scores from 1 to 5
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its combination of DeBERTa architecture with ensemble methods and back-translation techniques, resulting in state-of-the-art performance in hate speech detection. The integration of test-time augmentation and bias reduction methods sets it apart from traditional approaches.
Q: What are the recommended use cases?
This model is specifically designed for social media content moderation, helping identify and flag potentially hateful content before it reaches the public. It's particularly effective for platforms seeking automated hate speech detection solutions with high accuracy and reduced bias.
