This experiment aimed to investigate the impact of significantly increasing the training dataset size on the performance of a transformer model for IUPAC name translation. Expanding the dataset tenfold provided the model with a substantially larger number of examples, hypothesising that this would lead to improved learning of IUPAC name translation patterns and better overall generalisation.