# Detecting Personally Identifiable Information Through Natural Language Processing: A Step Forward
## 4 Results
### Experimental Results Obtained from Model Training
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Epoch** & \begin{tabular}{c} **Training** \\ **Loss** \\ \end{tabular} & \begin{tabular}{c} **Training** \\ **Time** \\ \end{tabular} & \
\begin{tabular}{c} **Allocated** \\ **Memory** \\ \end{tabular} &
\begin{tabular}{c} **Test** \\ **Accuracy** \\ \end{tabular} \\ \hline \multirow{3}{*}{K-fold 1} & 1 & 1.44\% & \multirow{3}{*}{31.39 min} & \multirow{3}{*}{1688.71 MB} & \multirow{3}{*}{99.92\%} \\  & 2 & 0.28\% & & & \\  & 3 & 0.20\% & & & \\ \hline \multirow{3}{*}{K-fold 2} & 1 & 1.42\% & \multirow{3}{*}{30.78 min} & \multirow{3}{*}{1689.42 MB} & \multirow{3}{*}{99.86\%} \\  & 2 & 0.25\% & & & \\  & 3 & 0.19\% & & & \\ \hline \multirow{3}{*}{K-fold 3} & 1 & 1.45\% & \multirow{3}{*}{30.55 min} & \multirow{3}{*}{1689.58 MB} & \multirow{3}{*}{98.91\%} \\  & 3 & 0.25\% & & & \\ \hline \multirow{3}{*}{K-fold 4} & 1 & 1.43\% & \multirow{3}{*}{30.75 min} & \multirow{3}{*}{1690.58 MB} & \multirow{3}{*}{99.84\%} \\  & 2 & 0.30\% & & & \\ \cline{1-1}  & 3 & 0.31\% & & & \\ \hline \multirow{3}{*}{K-fold 5} & 1 & 1.33\% & \multirow{3}{*}{30.70 min} & \multirow{3}{*}{1690.91 MB} & \multirow{3}{*}{99.87\%} \\  & 3 & 0.23\% & & & \\ \hline \multirow{3}{*}{Stratified k-fold 1} & 1 & 1.62\% & \multirow{3}{*}{30.59 min} & \multirow{3}{*}{1688.71 MB} & \multirow{3}{*}{99.62\%} \\  & 2 & 0.21\% & & & \\ \cline{1-1}  & 3 & 0.22\% & & & \\ \hline \multirow{3}{*}{Stratified k-fold 2} & 1 & 1.29\% & \multirow{3}{*}{30.63 min} & \multirow{3}{*}{1689.42 MB} & \multirow{3}{*}{99.93\%} \\  & 2 & 0.32\% & & & \\ \cline{1-1}  & 3 & 0.17\% & & & \\ \hline \multirow{3}{*}{Stratified k-fold 3} & 1 & 1.50\% & \multirow{3}{*}{30.54 min} & \multirow{3}{*}{1689.58 MB} & \multirow{3}{*}{99.78\%} \\  & 2 & 0.20\% & & & \\ \cline{1-1}  & 3 & 0.29\% & & & \\ \hline \multirow{3}{*}{Stratified k-fold 4} & 1 & 1.42\% & \multirow{3}{*}{30.60 min} & \multirow{3}{*}{1690.58 MB} & \multirow{3}{*}{99.84\%} \\  & 2 & 0.28\% & & & \\ \cline{1-1}  & 3 & 0.28\% & & & \\ \hline \multirow{3}{*}{Stratified k-fold 5} & 1 & 1.45\% & \multirow{3}{*}{30.52 min} & \multirow{3}{*}{1690.91 MB} & \multirow{3}{*}{99.91\%} \\  & 2 & 0.21\% & & & \\ \cline{1-1}  & 3 & 0.25\% & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results obtained during the training phase for cross-validation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Epoch** & \begin{tabular}{c} **Training** \\ **Loss** \\ \end{tabular} & \begin{tabular}{c} **Validation** \\ **Accuracy** \\ \end{tabular} & \begin{tabular}{c} **Training** \\ **Time** \\ \end{tabular} & \
\begin{tabular}{c} **Allocated** \\ **Memory** \\ \end{tabular} &
\begin{tabular}{c} **Test** \\ **Accuracy** \\ \end{tabular} \\ \hline \multirow{3}{*}{Training-Validation-Test} & 1 & 1.66\% & 99.93\% & \multirow{3}{*}{31.92 min} & \multirow{3}{*}{1689.06 MB} & \multirow{3}{*}{99.86\%} \\  & 2 & 0.26\% & 99.92\% & & \\ \cline{1-1}  & 3 & 0.20\% & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results obtained during the training phase for training-validation-test. Figure 7 provides a comprehensive graphical comparison of the results, summarizing the performance differences between all trained models. This comparative analysis helps to highlight the strengths and weaknesses of each approach and allows for a more informed decision when selecting the optimal training method. By analyzing the data collected during the experimentation, we found that the maximum variation in performance across the models was 1.02313%. Despite this relatively small variation, the average test accuracy across all models trained using the k-fold cross-validation technique was 99.68%. For the models trained using the stratified k-fold technique, the average test accuracy was slightly higher, 99.82%. Figure 6: Evolution of the Training Loss with increasing epochs: (**a**) training-validation-test and k-fold cross-validation; (**b**) stratified k-fold cross-validation.