| | --- |
| | 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 | 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 | 0.9415 | |
| | | 0.0701 | 4.0 | 2000 | 0.1479 | 0.94 | |
| |
|
| | - **Overall Training Loss**: 0.2081 |
| | - **Test Accuracy**: 100% accuracy on the provided 10 test examples. |
| |
|
| | ## 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. |
| |
|
