## 4 Results
### Main Use Case and per Class Results  
In this study, we assessed the performance of both a BERT and an FFNN model on a resume multiclass classification task. The optimal results from our extended experiments are presented in Table 2.  
The experiments for both the BERT and FFNN models were conducted as follows:  
* Training with the Indeed_60 and evaluating with the Indeed_40 dataset (experiments three and four). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
The experimental results showcased in Table 2 demonstrated the impact of the different models and training datasets on the accuracy of resume multiclass classification and presented the average performance metrics, offering a comparison between these methods in a per-class manner. In the third experiment, utilizing the FFNN model with the Indeed_60 training dataset, we achieved an accuracy of 84%. The fourth experiment introduced the BERT model to the same training dataset, resulting in an improvement of 85% accuracy. Experiment five maintained the FNN model but employed an augmented version of the training data. Indeed, the augmented dataset improved the accuracy to 85%. The most improved accuracy was observed in the sixth experiment, where the BERT model was combined with the Indeed augmented dataset, resulting in a 92% accuracy. These results underscored the significance of both model selection and the quality of the training data, highlighting the potential of advanced models like BERT when coupled with augmented datasets for enhancing the accuracy of resume classification tasks. In our evaluations, the models trained on ChatGPT data were assessed using the full Indeed dataset, as discussed in experiments from the paper [32], while other models were tested on the Indeed_40 dataset. Figure 2 presents the F1 scores of the FFNN when trained on three different datasets: ChatGPT data, Indeed_60 data and the Indeed augmented dataset. The model seemed to perform best when trained on the augmented dataset for the classes Auto.Mech. and Elec.Eng., with F1 scores reaching as high as 0.95. However, for the classes CSR and Landscape, the model's performance was inconsistent across the datasets. Notably, for the CSR class, the model had its lowest performance when trained on Indeed_60 data. For the Landscape class, the F1 score dipped when trained on Indeed_60, but improved again with the augmented dataset. It is also worth noting that some classes like Dentist, Civ. Eng. Elec. Eng. and Soft. Dev. maintained consistently high F1 scores across all datasets, indicating the model's robust performance for these categories, regardless of the training data source. The tellers class had a significant improvement when trained on the Indeed_60 dataset compared to the ChatGPT dataset. Overall, the results of the FFNN suggested that the Indeed augmented dataset generally enhanced the model's performance, but the effectiveness could vary depending on the specific profession. Figure 3 presents the results of the BERT model trained on the same three datasets: the ChatGPT dataset, Indeed_60 dataset and the Indeed augmented dataset. The results showed that some classes, Dentist, Elec. Eng. and DBA, consistently achieved high scores across all three datasets. This suggested that the BERT model was proficient at correctly classifying these classes. These categories might have had distinctive language patterns or keywords that the model captured effectively, leading to a robust performance. Dataset influence: The variation in F1 scores across the three datasets highlighted the impact of data quality and diversity. The augmentation technique was effective in improving the model's performance. This suggested that the Indeed augmented dataset