## 4 Experiments and Performance Evaluation
### Experimental Setup and Fine Tuning Analysis
The weighted average for both precision and recall are used to calculate the \(F1\)-score, where the emphasis on precision and recall can be adjusted by adjusting the weighting. It provides a single score that represents the model's overall performance, with higher values indicating better performance. The precision, recall, and \(F1\)-score are explained in the following equations:  
\[Precision=\frac{TP_{t}}{TP_{t}+FP_{t}} \tag{5}\]  
\[Recall=\frac{TP_{t}}{TP_{t}+FN_{t}} \tag{6}\]  
\[F1-score=2\times\frac{(Precision\times Recall)}{(Precision+Recall)} \tag{7}\]  
In these equations, \(TP_{t}\) explains the proportion of correctly anticipated positive instances, \(TN_{t}\) explains correctly anticipated negative instances, \(FP_{t}\) explains instances that were predicted as negative but turned out to be positive, and \(FN_{t}\) explains instances that were predicted as negative but turned out to be positive. Based on the previous results, the optimization method that is applied for training the proposed model is called the Adam optimization method. The learning rate explained in Table 3 ranges from \(4\times 10^{-5}\) to \(1\times 10^{-5}\) and batch size = 8 with a batch size = 8. The required time to train the proposed ARABERT4 TWC model can vary depending on the computational resources available and the corpus size being applied and used. The training time can vary from one-to-two hours. The evaluation metrics that are applied to monitor the performance of the model during training can also be reported using the accuracy and \(F1\)-score. The experiments in Table 3 involved testing various learning ratios ranging from \(4\times 10^{-5}\) to \(1\times 10^{-5}\) on different datasets, which required significant time. The results inside this range stated that the suggested model scored better than other models examined on the same datasets. One way to make sure that the differences observed in performance between models are not due to chance is that the result achieved from running the experiments many times, which involves partitioning the dataset into non-overlapping subsets, using one subset for testing and the rest for training, and then repeating the process with different partitions. By applying this method, we can ensure that the model's performance is on new, unseen data and obtain a more reliable estimate of its performance. To train and test the mode on unseen data, a large dataset is used. This large dataset helps in reducing the impact of random initialization. Therefore, the results of the experiments are reproducible and not dependent on the specific random seed used during training. As presented in Figure 8, the accuracy for the conducted datasets is explained where the learning rate from \(4\times 10^{-5}\) (4e-05) to \(1\times 10^{-5}\) (1E-05) is tested with each dataset. As explained, the SANAD dataset achieved the highest accuracy with 98.24% with the learning rate \(1\times 10^{-5}\), while the second-highest result achieved 98.10% with the learning rate \(2\times 10^{-5}\). Using HARD dataset, the highest accuracy recorded 97.49% with the learning rate \(1\times 10^{-5}\), while the learning rate of \(2\times 10^{-5}\) recorded a close result with 97.46%. Using AJGT, the learning rate of \(1\times 10^{-5}\) recorded 94.05% accuracy, while the learning rate of \(2\times 10^{-5}\) recorded 93.50% accuracy. Using the ASTD dataset, the accuracy is decreased to 86.10% accuracy on the learning rate of \(1\times 10^{-5}\), while the lowest accuracy recorded 85.25% on the learning rate \(4\times 10^{-5}\). The ArSenTD-LEV dataset recorded the lowest results on all learning rates with 53.70% on the learning rate \(1\times 10^{-5}\). The \(F1\)-Score presented in Figure 9 showed almost identical results for the same datasets and learning rates.