Now let us present our language modeling pipeline, from which we obtain benchmarking results. As we experiment on the standardized dataset from Brown et al. (see Section III-A), we do not perform any additional preprocessing except for removal of relatively short compounds (SMILES strings of length less than 5). Next we tokenize our data as discussed in Section III-B without capturing aggregate subcompounds. We noticed that simpler tokenization gives higher overall scores for every model. With the restriction on the minimum number of occurrences equalling 20, our vocabulary (including special tokens) has dimension 42. Having the tokenized corpus, we choose a minibatch sampling method. Our experiments suggest that random sampling generalizes better with recurrent networks trained on bigger datasets. We apply randomized consecutive sampling: set the maximum offset to 2, derive a collection of subsequences from consecutive sampling, and sample 80% of them with replacement.