To ensure that the predicted IUPAC names represent a valid chemical structure, a two-step verification process was implemented. The predicted IUPAC names were reverse-translated back into SMILES notation using OPSIN, and the resulting SMILES strings were then compared to the original input SMILES strings to verify how similar the original and the retranslated structures are using Tanimoto similarity using PubChem fingerprints incorporated in Chemistry Development Kit (CDK) . See Figure for more details. Looking at the results in Figure , the identical predictions and the retranslation accuracy increase with more training data, implying that the model's predicted IUPAC names become more consistent and fluent with more extensive training. Tanimoto 1.0 and Average Tanimoto also improve significantly with more training data, reflecting a higher quality in the model's predictions as it is trained on larger datasets. Overall, this suggests that increasing the training data improves the model's generalisability, and one could get better and more accurate IUPAC names.