## 4 Result and Discussion
### Answer-Grading Process after Balancing Dataset
The lowest accuracy score, 0.30488, comes from Exp 5, with the implementation of the BalMohler-4 dataset and the bert-base-uncased model. Figure 11 displays the final evaluation results based on the Pearson correlation value. It is also clear that the balanced dataset from the implementation of GPT data augmentation improves performance results. Slightly different from the previous evaluation, the best result was obtained from Exp 3 and the all-distilroberta-v1 model but with the implementation of BalMohler-3.5. The highest Pearson correlation score is 0.95855. The lowest score, 0.28748, was obtained from Exp 4 with the implementation of the BalMohler-4 dataset and the bert-base-uncased model. Based on the experimental results so far, on average, the best implemented model is the all-distilroberta-v1 model, along with the use of BalMohler-3.5. The all-distilroberta-v1 model has a size of around 290 MB and can achieve good results for all the evaluation metrics conducted. As mentioned in Section 2, there have been other studies that have succeeded in achieving satisfactory evaluation metrics values, but those studies typically use larger models than those we recommend. For instance, [PERSON] and [PERSON] used the all-roberta-large model [12], which is nearly 400% larger than our recommended model, and [PERSON] used T5-XL [20] which is almost eight times larger than our recommended model. Figure 10: Comparing accuracy for all models and dataset. We also conducted additional experiments using larger models to observe their performance. Table 6 displays our overall best experimental results, as well as the results from previous research. Based on this summary, it can also be seen that data augmentation implementation to balance the dataset helps improve the performance for smaller-sized SentenceTransformers models. A smaller size means a smaller number of parameters and also results in a faster processing time [14; 27]. Our best results from the experiment are marked with green in the respective column. Results labeled in blue indicate additional experiments using larger models. The results obtained for F1-score, accuracy, and Pearson correlation are indeed better than those from experiments with smaller models, but the difference in performance is not significant compared to the average running time. In Figure 12 below, it can be seen that the average running time of the all-roberta-large model is many times longer than the other models. Given the disparity in performance, it is not proportional to the computational cost required. Balancing the dataset is also recommended in research by [PERSON] et al. [13] and [PERSON] [20]. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model Name** & **Dataset** & **Pre-Processing** & **Gradient** & **Split** & **Batch** & **RMSE** & **F1-Score** & **Accuracy** & **Elation** \\ \hline all-distikroberts-v1 & & & & & & & & & \\ multi-qa-mprenet-base-dot-v1 & & & & & & & & & \\ multi-qa-mprenet-base-dot-v1 & & & & & & & & & \\ multi-qa-mprenet-base-dot-v1 & & & & & & & & & \\ multi-qa-mprenet-base-dot-v1 & & & & & & & & & \\ multi-qa-mprenet-base-dot-v1 & & & & & & & & & \\ \hline all-distikroberts-v1 & & & & & & & & & \\ multi-qa-mprenet-base-dot-v1 & & & & & & & & & \\ multi-qa-mprenet-base-dot-v1 & & & & & & & & & \\ \hline RoBERTa-large & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\ \hline RoBERTa-large [12] & & & & & & & & & \\ T5-XL [20] & & & & & & & & & \\ paraphrase-albert-small-v2 [13] & & & & & & & & & \\  & & & & & & & & & \\ Ridge-LR [21] & & & & & & & & & \\  & & & & & & & & \\ Ridge-LR [21] & & & & & & & & & \\  & & & & & & & & \\ Ridge-LR [21] & & & & & & & & & \\  & & & & & & & & \\ \hline \hline \end{tabular} \({}^{*}\) Exp 2, \({}^{*}\) Exp 3, \({}^{*}\) Exp 5.  
\end{table} 
Table 6: Comparison of all experimental results with previous research. Figure 11: Comparing Pearson correlation for all models and datasets. et al. [21].