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---
language: en
tags:
- emotion-classification
- text-classification
- distilbert
datasets:
- dair-ai/emotion
metrics:
- accuracy
---
# Emotion Classification Model
## Model Description
This model fine-tunes DistilBERT for a multi-class emotion classification task. The dataset that is used is dair-ai/emotion containing six emotion classes: sadness, joy, love, anger, fear and suprise
## Training and Evaluation
- Training Dataset: dair-ai/emotion (16,000 examples)
- Validation Dataset: dair-ai/emotion (2,000 examples)
- Validation Accuracy: [Your Results]
- Test Accuracy: [Your Results]
- Training Time: [Your Time]
## Hyperparameters
- Learning Rate: 5e-5
- Batch Size: 16
- Epochs: 4
- Weight Decay: 0.01
## Usage
```python
from transformers import pipeline
classifier = pipeline("text-classification",
model="your-username/emotion-classification-model")
text = "I’m so happy today!"
result = classifier(text)
print(result)
```
## Limitations
[Discuss any limitations you observed...]
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