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--- |
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language: en |
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license: mit |
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tags: |
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- emotion-classification |
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- text-analysis |
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metrics: |
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- precision |
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- recall |
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- f1-score |
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- accuracy |
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--- |
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# Model Card for uvegesistvan/wildmann_german_proposal_2b |
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## Model Overview |
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This model is a multi-class emotion classifier trained to identify nine distinct emotional states in text. The classes and their corresponding labels are as follows: |
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- **Class 0**: Anger |
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- **Class 1**: Fear |
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- **Class 2**: Disgust |
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- **Class 3**: Sadness |
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- **Class 4**: Joy |
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- **Class 5**: Enthusiasm |
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- **Class 6**: Hope |
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- **Class 7**: Pride |
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- **Class 8**: No emotion |
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### Dataset and Preprocessing |
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The dataset combines original and synthetic data to improve class balance and performance. Below are the evaluation metrics for the model: |
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| Class | Precision | Recall | F1-Score | Support | |
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|------------|-----------|--------|----------|---------| |
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| Anger (0)| 0.54 | 0.60 | 0.57 | 777 | |
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| Fear (1) | 0.85 | 0.76 | 0.80 | 776 | |
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| Disgust (2)| 0.91 | 0.95 | 0.93 | 776 | |
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| Sadness (3)| 0.87 | 0.84 | 0.86 | 775 | |
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| Joy (4) | 0.84 | 0.81 | 0.83 | 777 | |
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| Enthusiasm (5)| 0.64 | 0.62 | 0.63 | 776 | |
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| Hope (6) | 0.53 | 0.55 | 0.54 | 777 | |
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| Pride (7) | 0.75 | 0.81 | 0.78 | 776 | |
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| No emotion (8)| 0.67 | 0.65 | 0.66 | 1553 | |
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### Overall Metrics |
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- **Accuracy**: 0.72 |
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- **Macro Average**: Precision = 0.73, Recall = 0.73, F1-Score = 0.73 |
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- **Weighted Average**: Precision = 0.73, Recall = 0.72, F1-Score = 0.73 |
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### Performance Insights |
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The model achieves strong performance across most classes, particularly for "Sadness" and "Disgust." |
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However, "Enthusiasm" and "Hope" exhibit lower recall and precision, suggesting potential areas for improvement. |
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Future development could include targeted data augmentation or specialized techniques to handle these classes. |
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## Model Usage |
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### Applications |
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- Emotion classification in text-based datasets. |
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- Analyzing emotional tone in social media, reviews, or other text corpora. |
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- Understanding emotional context for human-computer interaction. |
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### Limitations |
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- Performance varies across classes, with some (e.g., "Enthusiasm" and "Hope") showing lower metrics. |
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- The model may not generalize well to domains outside the training data. |
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- Ambiguities in text can lead to misclassification, especially for overlapping emotional states. |
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### Ethical Considerations |
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The model's predictions might not always align with human interpretations of emotions, particularly in ambiguous or context-dependent cases. |
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Misclassification could lead to inappropriate conclusions if used in sensitive applications (e.g., mental health monitoring or automated decision-making). |
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### Future Work |
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- Improving performance on underrepresented classes using advanced augmentation or transfer learning techniques. |
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- Exploring the model's performance in multi-domain datasets. |
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- Adding explainability features to enhance trustworthiness in sensitive applications. |
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