emotion-classifier-distilbert

Emotion Classification with DistilBERT

This model is a fine-tuned version of distilbert-base-uncased for emotion classification. It classifies text into 6 emotions:

• 0: admiration
• 1: amusement
• 2: anger
• 3: annoyance
• 4: approval
• 5: caring

Training Data

The model was fine-tuned on the Go Emotions dataset, filtered to these 6 emotion categories.

Performance

• Accuracy: 78.3%
• F1 Score: 77.9%
• Training Loss: 0.45 (from 0.93)

Usage

from transformers import pipeline

classifier = pipeline('text-classification', model='your-username/emotion-classifier-distilbert')
result = classifier('I love this amazing product!')
print(f"Emotion: {result[0]['label']}, Confidence: {result[0]['score']:.3f}")

Example Predictions

• 'I love this so much!' → admiration (confidence: ~0.85)
• 'This is so frustrating!' → anger (confidence: ~0.82)
• 'That's hilarious!' → amusement (confidence: ~0.88)
• 'This is annoying me' → annoyance (confidence: ~0.79)
• 'Great job on this!' → approval (confidence: ~0.81)
• 'I'm here to support you' → caring (confidence: ~0.83)

Training Details

• Base Model: distilbert-base-uncased
• Epochs: 3
• Batch Size: 16
• Learning Rate: 2e-5
• Dataset: Go Emotions (filtered)

Intended Use

This model is suitable for emotion analysis in text, customer feedback analysis, sentiment-aware chatbots, and social media monitoring.