distilbert-base-uncased-finetuned-emotional
| Property | Value |
|---|---|
| Model Type | DistilBERT Fine-tuned |
| Task | Emotion Detection |
| Accuracy | 93.05% |
| F1 Score | 0.9309 |
| Author | TieIncred |
| Framework | PyTorch 2.1.0 |
What is distilbert-base-uncased-finetuned-emotional?
This model is a specialized version of DistilBERT, fine-tuned specifically for emotion detection tasks. Built upon the distilbert-base-uncased architecture, it has been optimized to recognize emotional content in text with high accuracy. The model achieves impressive performance metrics with a 93.05% accuracy rate and an F1 score of 0.9309.
Implementation Details
The model was trained using a carefully tuned configuration with Adam optimizer, utilizing a learning rate of 2e-05 and linear scheduler. The training process consisted of 2 epochs with batch sizes of 64 for both training and evaluation.
- Training Loss: 0.1085 (final epoch)
- Validation Loss: 0.1658
- Optimizer: Adam with betas=(0.9,0.999)
- Learning Rate: 2e-05
Core Capabilities
- Emotion Detection in Text
- High Accuracy Classification
- Efficient Processing (DistilBERT Architecture)
- Uncased Text Processing
Frequently Asked Questions
Q: What makes this model unique?
This model combines the efficiency of DistilBERT with specialized emotion detection capabilities, achieving over 93% accuracy while maintaining computational efficiency through the distilled architecture.
Q: What are the recommended use cases?
The model is particularly suited for emotion analysis in text, sentiment analysis, and emotion-based content classification tasks where high accuracy is required.
