The different subsets were collated into a total dataset with 304,001 peptides with lengths between 2 and 50 amino acids. This total dataset was split into a 80-20, train-test split, resulting in a training set of 243,201 training sequences and a test set of 60,800 sequences. The model hyperparameters were set to be identical for all 15 models and their three respective latent dimensions. We fixed the parameters to maintain consistency between the different models and allow authentic comparison of the fully trained networks. The RNN, RNN-Attn and Transformer feature a Kullback-Leibler divergence annealing term β (see Table . 1). β starts small and increases in weight as training progresses to avoid posterior collapse. Batch Sizes of 200 were found to be ideal in that they fit on the NVIDIA Tesla P100 graphics cards used for training and results in good performance. 300 epochs was found to be an appropriate number by a posteriori inspection of the loss curves. 3 Results