## 4 Results  
This section presents the performance evaluation of the XLM-Roberta Large and mBART models across various languages and their translations. The models were assessed on their accuracy and F1 scores, comparing original language data against translated counterparts. To calculate the metrics, we first evaluated the original language and calculated  
Figure 1: Fine-Tuning Models on XNLI Dataset. Figure 2: Evaluating Models on XNLI Data. the metrics on the basis of the number of correct target predictions by the models, and similarly, we calculated the metrics for the translated language dataset. The evaluations cover diverse language pairs, highlighting the impact of translation on model performance. Table 2 details the results for the XLM-Roberta Large model, while Table 3 outlines the outcomes for the mBART model. Both models were trained and evaluated under specific hyperparameters and dataset configurations to ensure consistent and reliable comparisons. The calculation of the F1 score was performed on the basis of Equations (1)-(3), the predictions of the target class by the model, and the ground truth value of the labels. \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{1}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[\text{F1}=2*\frac{\text{Precision}*\text{Recall}}{\text{Precision}+\text{ Recall}} \tag{3}\]  
In the experimental settings for the baselines, the dataset consisted of 150,000 examples, with 10,000 examples per language. The maximum sequence length was set to 100, and the training batch size per GPU (Graphical Processing Unit) was 54, while the evaluation batch size was 32. The learning rate was set to \(1\times 10^{-5}\), with a weight decay of 0.5 and an Adam epsilon value of \(1\times 10^{-8}\). The maximum gradient norm was set to 1.0, and the number of training epochs was set to 1.0 due to the large size of the training dataset. The XLM-Roberta Large model has different parameters compared to its baseline version. Specifically, the \"per_gpu_train_batch_size\" and \"per_gpu_eval_batch_size\" for this model are set to 16. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Language** & **Translated Language** & **Original Accuracy** & **Original F1** & **Translated F1** & **Translated Accuracy** \\ \hline Urdu & Hindi & 65.26\% & 0.65220 & 0.62188 & 62.53\% \\ Turkish & Hindi & 68.86\% & 0.68756 & 0.64380 & 64.57\% \\ Thai & Hindi & 67.34\% & 0.67321 & 0.61364 & 61.65\% \\ Urdu & French & 65.26\% & 0.65220 & 0.59863 & 60.67\% \\ Turkish & French & 68.86\% & 0.68756 & 0.65220 & 65.26\% \\ Thai & French & 67.34\% & 0.67321 & 0.62240 & 62.77\% \\ Hindi & French & 65.72\% & 0.65608 & 0.622511 & 62.63\% \\ Swahili & French & 62.29\% & 0.61920 & 0.620873 & 62.71\% \\ Arabic & English & 67.50\% & 0.67415 & 0.66890 & 67.06\% \\ Bulgaria & English & 71.85\% & 0.67415 & 0.674153 & 67.50\% \\ German & English & 71.85\% & 0.71869 & 0.70858 & 70.95\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the Evaluation of the XLM Roberta Model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Language** & **Translated Language** & **Original Accuracy** & **Original F1** & **Translated F1** & **Translated Accuracy** \\ \hline English & Arabic & 83.49\% & 0.83492 & 0.73392 & 73.47\% \\ English & French & 83.49\% & 0.83492 & 0.78212 & 78.22\% \\ English & Spanish & 83.49\% & 0.83492 & 0.78511 & 78.56\% \\ French & Urdu & 78.96\% & 0.78944 & 0.52871 & 53.11\% \\ French & Thai & 78.96\% & 0.78944 & 0.47269 & 47.52\% \\ Hindi & Urdu & 70.45\% & 0.70368 & 0.52529 & 52.61\% \\ Hindi & English & 70.45\% & 0.70368 & 0.68986 & 69.38\% \\ Swahili & Arabic & 49.74\% & 0.49189 & 0.59687 & 60.29\% \\ Turkish & Urdu & 74.45\% & 0.74374 & 0.53902 & 54.07\% \\ Turkish & Hindi & 74.45\% & 0.74374 & 0.70586 & 70.71\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the Evaluation of the Multilingual Bart Model.