albert-base-v2-emotion
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Paper | ALBERT Paper |
| Accuracy | 93.6% |
| F1 Score | 93.65 |
What is albert-base-v2-emotion?
albert-base-v2-emotion is a fine-tuned version of the ALBERT (A Lite BERT) architecture specifically optimized for emotion classification tasks. This model represents a lighter alternative to traditional BERT models while maintaining robust performance in emotion detection across six categories: sadness, joy, love, anger, fear, and surprise.
Implementation Details
The model was fine-tuned on the emotion dataset using HuggingFace Trainer with specific hyperparameters including a learning rate of 2e-5, batch size of 64, and 8 training epochs. It processes text at 182.794 samples per second during testing, offering efficient emotion classification capabilities.
- Achieves 93.6% accuracy on emotion classification tasks
- Implements the ALBERT architecture for reduced parameter count
- Optimized for Twitter-based emotion analysis
Core Capabilities
- Multi-class emotion classification across 6 categories
- Efficient processing with high throughput
- Easy integration with HuggingFace's transformers pipeline
- Suitable for real-time emotion analysis applications
Frequently Asked Questions
Q: What makes this model unique?
This model combines the efficiency of ALBERT architecture with emotion classification capabilities, offering a lightweight alternative to larger models while maintaining competitive accuracy (93.6%). It's particularly effective for processing social media content and can handle multiple emotion categories simultaneously.
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 emotion detection in text. It's particularly well-suited for processing Twitter-style content and can be easily integrated into existing NLP pipelines.
