## 3 Results
### Experimental Results  
Table 4 shows the results in the Reddit classification dataset for the base models with sequence lengths of 128 and 256 as well the results of the non-pretrained model for comparison. The results are shown in each distinct class. Furthermore, the performance of the deep CNN model presented in [48] and the MentalBert model is also documented for comparative analysis. As can be observed, the pretrained BERT models surpass the performance of their counterparts, with no significant differences between the 128 and 256 max-length models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Metric** & **CNN** & **MentalBert** & \multicolumn{3}{c}{**BERT (Ours)**} \\  & & & **Base\({}_{128}\)** & **Base\({}_{256}\)** & **LORA** \\ \hline r/BPD & P & 0.88 & 0.77 & 0.74 & 0.79 & 0.78 \\  & R & 0.46 & 0.61 & 0.62 & 0.60 & 0.59 \\  & F & 0.60 & 0.68 & 0.67 & 0.69 & 0.67 \\ r/bipolar & P & 0.77 & 0.72 & 0.80 & 0.75 & 0.79 \\  & R & 0.60 & 0.74 & 0.70 & 0.73 & 0.69 \\  & F & 0.67 & 0.73 & 0.74 & 0.74 & 0.74 \\ r/schizophrenia & P & 0.75 & 0.77 & 0.71 & 0.74 & 0.62 \\  & R & 0.48 & 0.56 & 0.58 & 0.57 & 0.57 \\  & F & 0.58 & 0.65 & 0.64 & 0.64 & 0.64 \\ r/Anxiety & P & 0.83 & 0.75 & 0.79 & 0.79 & 0.80 \\  & R & 0.75 & 0.82 & 0.78 & 0.79 & 0.77 \\  & F & 0.79 & 0.78 & 0.79 & 0.79 & 0.79 \\ r/depression & P & 0.70 & 0.73 & 0.70 & 0.72 & 0.71 \\  & R & 0.77 & 0.79 & 0.80 & 0.82 & 0.83 \\  & F & 0.73 & 0.76 & 0.75 & 0.77 & 0.77 \\ r/selfharm & P & 0.70 & 0.79 & 0.82 & 0.82 & 0.79 \\  & R & 0.58 & 0.78 & 0.80 & 0.76 & 0.77 \\  & F & 0.64 & 0.78 & 0.81 & 0.79 & 0.78 \\ r/suicidewatch & P & 0.62 & 0.65 & 0.65 & 0.64 & 0.65 \\  & R & 0.59 & 0.55 & 0.58 & 0.56 & 0.56 \\  & F & 0.61 & 0.59 & 0.61 & 0.60 & 0.60 \\ r/addiction & P & 0.72 & 0.70 & 0.68 & 0.71 & 0.69 \\  & R & 0.41 & 0.53 & 0.54 & 0.53 & 0.48 \\  & F & 0.52 & 0.60 & 0.60 & 0.60 & 0.57 \\ r/cripplingalcoholism & P & 0.68 & 0.87 & 0.87 & 0.86 & 0.84 \\  & R & 0.76 & 0.74 & 0.82 & 0.77 & 0.74 \\  & F & 0.72 & 0.80 & 0.84 & 0.81 & 0.79 \\ r/Opiates & P & 0.76 & 0.92 & 0.92 & 0.94 & 0.92 \\  & R & 0.86 & 0.93 & 0.92 & 0.92 & 0.92 \\  & F & 0.80 & 0.93 & 0.92 & 0.83 & 0.92 \\ r/autism & P & 0.84 & 0.88 & 0.86 & 0.90 & 0.90 \\  & R & 0.71 & 0.78 & 0.78 & 0.78 & 0.75 \\  & F & 0.77 & 0.83 & 0.82 & 0.83 & 0.82 \\ \hline Macro Averaged & P & 0.75 & 0.78 & 0.78 & 0.79 & 0.78 \\  & R & 0.63 & 0.71 & 0.72 & 0.71 & 0.70 \\  & F & 0.67 & 0.74 & 0.75 & 0.75 & 0.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: We compared results from the Reddit test dataset using the deep model presented in [48] and our pretrained BERT models. We also included a non-pretrained model for reference. Coming very close, the MentalBert model also presents highly competitive results in this dataset. In addition, we compared the results with the fine-tuned model using LoRA adapters. Our best pretrained model achieved a macro-averaged F1-score of 0.754. The LoRA-based model attains a macro-averaged F1-score of 0.73. Although it is smaller, it should be noted that the LoRA adapter trains only a staggering 2.08% of the original BERT weights, which considerably reduces training requirements in terms of memory, making it a very appealing option. Based on this, we chose the best performing model--in this case, our pretrained BERT model with a maximum length of 256--and fine-tuned it on our extended dataset to obtain a model that can classify a post into any of the main diagnostic categories of the DSM-V. Given the performance advantages of the LoRA adapter approach, we opted to train it with LoRA adapters. The results are summarized in Table 5. As can be seen, the results are very promising, with an average accuracy of 0.71. The results also highlight the difficulty of training with this more comprehensive dataset. This is expected given the increased complexity of the task, with a larger number of classes and a considerable imbalance in the number of examples, especially in the most rare classes.