In summary, the latent spaces of the models were found to exhibit ordering. We quantified the ordering with the Silhouette score. Surprisingly, we found that AMP correlations are not always present in the first or second PC pairs and as such it is important to investigate downstream PCs before ruling out potential correlations in the model latent space. Once a correlation has been identified, the variance explained per PC can serve as an approximate weight factor indicating the importance attributed to this ordering by the model. For the 128-dimensional AAE, summing the variance of the two top PCs, this is about 10%; for the 128-dimensional Transformer, it is about 4%.