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---
language: en
tags:
- distilbert
- emotion-classification
- text-classification
datasets:
- dair-ai/emotion
metrics:
- accuracy
---
# Emotion Classification Model
## Model Description
This model fine-tunes DistilBERT for multi-class emotion classification on the `dair-ai/emotion` dataset.
The model is designed to classify text into one of six emotions: sadness, joy, love, anger, fear, or surprise.
It can be used in applications requiring emotional analysis in English text.
## Training and Evaluation
- **Training Dataset**: `dair-ai/emotion` (16,000 examples)
- **Training Time**: 8 minutes and 51 seconds
- **Training Hyperparameters**:
- Learning Rate: `3e-5`
- Batch Size: `32`
- Epochs: `4`
- Weight Decay: `0.01`
### Training results
| Training Loss | Epoch | Step | Validation Loss | Val. Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------: |
| 0.5164 | 1.0 | 500 | 0.1887 | 0.9275 |
| 0.1464 | 2.0 | 1000 | 0.1487 | 0.9345 |
| 0.0994 | 3.0 | 1500 | 0.1389 | 0.94 |
| 0.0701 | 4.0 | 2000 | 0.1479 | 0.94 |
- **Overall Training Loss**: 0.2081
- **Test Accuracy**: 100% accuracy on the 10 examples tested.
Confidence scores ranged from 90% to 100%.
## Usage
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="Zoopa/emotion-classification-model")
text = "I am so happy today!"
result = classifier(text)
print(result)
```
## Limitations
- The model only supports English.
- The training dataset may contain biases, affecting model predictions on test data.
- Edge Cases like mixed emotions might reduce accuracy.
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