| --- |
| language: en |
| tags: |
| - distilbert |
| - emotion-classification |
| - text-classification |
| datasets: |
| - dair-ai/emotion |
| metrics: |
| - accuracy |
| --- |
| |
| # Emotion Classification Model |
|
|
| ## Model Description |
| This model fine-tunes DistilBERT for multi-class emotion classification on the `dair-ai/emotion` dataset. |
| The model is designed to classify text into one of six emotions: sadness, joy, love, anger, fear, or surprise. |
| It can be used in applications requiring emotional analysis in English text. |
|
|
| ## Training and Evaluation |
| - **Training Dataset**: `dair-ai/emotion` (16,000 examples) |
| - **Training Time**: 8 minutes and 51 seconds |
| - **Training Hyperparameters**: |
| - Learning Rate: `3e-5` |
| - Batch Size: `32` |
| - Epochs: `4` |
| - Weight Decay: `0.01` |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Val. Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------: | |
| | 0.5164 | 1.0 | 500 | 0.1887 | 0.9275 | |
| | 0.1464 | 2.0 | 1000 | 0.1487 | 0.9345 | |
| | 0.0994 | 3.0 | 1500 | 0.1389 | 0.94 | |
| | 0.0701 | 4.0 | 2000 | 0.1479 | 0.94 | |
|
|
| - **Overall Training Loss**: 0.2081 |
| - **Test Accuracy**: 100% accuracy on the 10 examples tested. |
| Confidence scores ranged from 90% to 100%. |
|
|
| ## Usage |
| ```python |
| from transformers import pipeline |
| classifier = pipeline("text-classification", model="Zoopa/emotion-classification-model") |
| |
| text = "I am so happy today!" |
| result = classifier(text) |
| print(result) |
| ``` |
|
|
| ## Limitations |
| - The model only supports English. |
| - The training dataset may contain biases, affecting model predictions on test data. |
| - Edge Cases like mixed emotions might reduce accuracy. |
|
|
