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A Comparative Study of Attention-Based Transformer Networks and Traditional Machine Learning Methods for Toxic Comments Classification
Journal of Social Mathematical & Human Engineering Sciences
| Vol 1, Issue 1
Table 1. Metrics Performance of Attention-Based Transformer andTraditional Machine Learning Methods
| Model | Accuracy | Precision | Recall | F1-Score |
| Logistic Regression | 85.25% | 82.19% | 88.34% | 85.15% |
| Naive Bayes | 78.42% | 75.61% | 82.39% | 78.05% |
| Support Vector Machines (SVM) | 87.62% | 84.28% | 89.46% | 86.14% |
| Decision Trees | 80.92% | 78.12% | 85.92% | 80.89% |
| Random Forests | 86.79% | 83.47% | 88.61% | 85.23% |
| Gradient Boosting | 88.45% | 85.92% | 90.11% | 87.38% |
| Attention-Based Transformer | 92.14% | 89.28% | 94.13% | 91.12% |
| Transformer | 90.68% | 87.83% | 92.04% | 89.19% |