| | --- |
| | language: en |
| | license: apache-2.0 |
| | library_name: transformers |
| | tags: |
| | - emotion-classification |
| | - distilbert |
| | - text-classification |
| | - fine-tuned |
| | datasets: |
| | - go_emotions |
| | --- |
| | |
| | # 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 |
| | ```python |
| | 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. |
