The models were assessed from the test dataset of the two sources to determine the ratios of correct prediction of the exact hidden molecules, at both sides of reaction, as shown in Table . From the test dataset, the model' ability to predict short token length (token length=1), middle token length (1<token length≤10) and long token length (token length>10) hidden molecules were assessed, and the comparisons between exemplified predicted and ground truth hidden molecules at different lengths are shown in Table . The ChemMLM models could predict almost 100% the ground truth hidden molecules at the short token length, while remain very high correct rate at middle token lengths. However, the models were unable to predict long token length molecules, with predictions not only semantically incorrect, but also invalid in SMILES syntax. For example, the last predicted molecule example shown in Table has more right parentheses than left. This is because with longer token lengths in the hidden molecules, it becomes harder for the ChemMLM model to pick up neighbour semantic context of the reaction SMILES strings, since the very neighbour tokens are also within the hidden molecule and uncertain.