According to Table , the performance of SMILES models improves with increasing dataset size, while the increment with unique tokens and maximum length has little effect on performance. This trend can be seen with increasing dataset size, as illustrated in Figure . When examining IUPAC names tokenised by rules versus tokenided by character, the character-level tokenisation demonstrates better performance in models trained with more than 1 million data points. Overall, the IUPAC split by characters performs better when used as output using SMILES as input.