| Property | Value |
|---|---|
| Task Type | Binary Classification |
| Accuracy | 76.66% |
| AUC Score | 0.789 |
| F1 Score | 0.653 |
| CO2 Emissions | 1.428g |
| Author | am4nsolanki |
What is autonlp-text-hateful-memes-36789092?
This model is an AutoNLP-trained binary classifier specifically designed to detect hateful content in memes. It demonstrates robust performance with a 76.66% accuracy rate and an impressive AUC score of 0.789, making it particularly effective for content moderation tasks.
Implementation Details
The model was trained using AutoNLP, featuring a balanced approach to classification with precision at 0.691 and recall at 0.619. It's optimized for sequence classification tasks and can be easily integrated using the Hugging Face Transformers library.
- Loss metric: 0.526
- Precision-Recall balance optimized for real-world applications
- Environment-conscious training with only 1.428g CO2 emissions
Core Capabilities
- Binary classification of text content
- High accuracy in detecting hateful content
- Production-ready API integration
- Efficient inference with minimal environmental impact
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its balanced performance metrics and efficient training process, demonstrated by its low CO2 emissions while maintaining high accuracy.
Q: What are the recommended use cases?
This model is ideal for content moderation systems, social media platforms, and any application requiring automated detection of hateful content in meme text.