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# Emotion Classification Model
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## Model Description
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This model fine-tunes DistilBERT for emotion classification
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## Training and Evaluation
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- Training Dataset: dair-ai/emotion (16,000 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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## Usage
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‘‘‘python
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from transformers import pipeline
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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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