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--- |
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language: en |
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tags: |
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- emotion-classification |
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- text-classification |
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- distilbert |
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datasets: |
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- dair-ai/emotion |
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metrics: |
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- accuracy |
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--- |
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# Emotion Classification Model |
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## Model Description |
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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 |
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## Training and Evaluation |
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- Training Dataset: dair-ai/emotion (16,000 examples) |
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- Validation Dataset: dair-ai/emotion (2,000 examples) |
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- Validation Accuracy: [Your Results] |
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- Test Accuracy: [Your Results] |
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- Training Time: [Your Time] |
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## Hyperparameters |
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- Learning Rate: 5e-5 |
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- Batch Size: 16 |
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- Epochs: 4 |
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- Weight Decay: 0.01 |
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## Usage |
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```python |
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from transformers import pipeline |
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classifier = pipeline("text-classification", |
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model="your-username/emotion-classification-model") |
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text = "I’m so happy today!" |
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result = classifier(text) |
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print(result) |
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``` |
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## Limitations |
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[Discuss any limitations you observed...] |
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