bert-base-uncased-emotion
| Property | Value |
|---|---|
| Parameter Count | 109M parameters |
| License | Apache 2.0 |
| Paper | BERT Paper |
| Accuracy | 92.65% |
| F1 Score | 0.926 |
What is bert-base-uncased-emotion?
bert-base-uncased-emotion is a fine-tuned version of BERT specifically optimized for emotion detection in text. This model builds upon the powerful BERT architecture and has been trained to recognize six distinct emotions: sadness, joy, love, anger, fear, and surprise. The model achieves impressive performance metrics with an accuracy of 92.65% on the emotion classification task.
Implementation Details
The model was fine-tuned on the emotion dataset using HuggingFace Trainer with specific parameters including a learning rate of 2e-5, batch size of 64, and training over 8 epochs. It processes uncased text input (meaning it's case-insensitive) and leverages BERT's bidirectional encoder architecture for contextual understanding.
- Built on BERT's transformer-based architecture
- Fine-tuned using emotion-specific dataset
- Processes text with 109M trainable parameters
- Achieves 190.152 test samples per second
Core Capabilities
- Multi-class emotion classification
- Robust performance across different emotional expressions
- Real-time text analysis capabilities
- Production-ready with simple integration via HuggingFace pipeline
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its balanced performance across different emotion categories, achieving high precision and recall scores. It's particularly notable for its practical speed-performance trade-off, processing 190 test samples per second while maintaining high accuracy.
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 well-suited for applications requiring distinction between similar emotions like joy and love.
