The pre-training set is split into a 90% training/validation and a 10% test set using reaction and solvent splits to evaluate the model's predictive performance on unseen reactions and unseen solvents. For our test splits, 5% reactions and 5% solvents are randomly selected, and all reaction-solvent pairs that include the chosen reactions or chosen solvents are added to the test set and excluded from the training/validation set. Both forward and reverse directions of the selected reactions are included in the test set to prevent data leakage; this procedure is very crucial for evaluating the true performance of a model on unseen reactions since the model can gain the information on the reaction and TS from its own reverse reaction. The importance of the proper reaction split is addressed in the recent studies by Heid and Green and Spiekermann et al. The remaining 90% data are randomly split into a 80% training and 20% validation set. The validation set is used for early stopping to determine the epoch that gives the lowest validation error and prevents over-fitting. Five folds are used to prepare five different training, validation, and test sets from the pre-training set.