To evaluate the generated models, various test datasets were used. First, we used 10-fold cross-validation with 80%/10%/10% training/validation/test splits to quantify indomain accuracy. In k-fold cross-validation, k non-overlapping test sets are generated, and models are trained on the remaining 90% of data. Validation sets are also non-overlapping and are used to prevent overfitting.