| Property | Value |
|---|---|
| Parameter Count | 67M |
| License | Apache 2.0 |
| Paper | DistilBERT Paper |
| Accuracy | 92.7% |
| F1 Score | 92.69% |
What is distilbert-base-uncased-emotion?
This is a specialized emotion classification model built on DistilBERT architecture, designed to identify emotions in text with high accuracy while maintaining computational efficiency. It's a knowledge-distilled version of BERT that retains 97% of the language understanding capabilities while being 40% smaller.
Implementation Details
The model was fine-tuned on an emotion dataset using HuggingFace Trainer with carefully selected hyperparameters (learning rate 2e-5, batch size 64, 8 training epochs). It can process 398.69 test samples per second, making it significantly faster than BERT-based alternatives.
- Achieves 92.7% accuracy on emotion classification
- Supports 6 emotion categories: sadness, joy, love, anger, fear, and surprise
- Optimized for production deployment with F32 tensor type
Core Capabilities
- Fast inference with 398.69 samples per second processing speed
- Efficient memory footprint with 67M parameters
- High precision (88.8%) and recall (87.9%) across emotion categories
- Simple integration with HuggingFace's pipeline architecture
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimal balance between performance and efficiency, offering comparable accuracy to larger models while being significantly faster and lighter. It processes text twice as fast as BERT-based alternatives while maintaining 93%+ accuracy.
Q: What are the recommended use cases?
The model is ideal for real-time emotion analysis in customer feedback, social media monitoring, chatbot responses, and any application requiring efficient emotion classification. It's particularly suitable for deployment in resource-constrained environments.