bert-base-uncased-emotion
| Property | Value |
|---|---|
| Parameter Count | 109M |
| License | Apache 2.0 |
| Paper | BERT Paper |
| Accuracy | 92.65% |
| F1 Score | 0.8821 |
What is bert-base-uncased-emotion?
bert-base-uncased-emotion is a fine-tuned version of BERT specifically optimized for emotion detection in text. Built on the foundation of BERT's bidirectional encoder architecture, this model has been trained to classify text into six distinct emotional categories: sadness, joy, love, anger, fear, and surprise. The model achieves impressive performance metrics with 92.65% accuracy on the emotion dataset.
Implementation Details
The model was fine-tuned using the HuggingFace Trainer with specific parameters including a learning rate of 2e-5, batch size of 64, and training over 8 epochs. It maintains BERT's original architecture while specializing in emotion detection tasks.
- Based on BERT's bidirectional transformer architecture
- Fine-tuned on Twitter emotion dataset
- Implements modern transformer-based classification techniques
- Supports real-time emotion analysis with high throughput
Core Capabilities
- Multi-class emotion classification across 6 categories
- High-precision emotion detection (88.59% precision macro)
- Efficient processing at 190.152 samples per second
- Production-ready with simple implementation via HuggingFace pipeline
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its balanced performance across multiple emotion categories, offering better accuracy (92.65%) compared to similar models while maintaining reasonable inference speed. It's particularly well-suited for production environments requiring reliable emotion detection.
Q: What are the recommended use cases?
The model is ideal for sentiment analysis in social media monitoring, customer feedback analysis, and any application requiring nuanced emotion detection in text. It's particularly effective for English language content and can be easily integrated into existing NLP pipelines.
