Our results raise a very important question: how can a natural language model with no prior training in chemistry outperform dedicated machine-learning models? To our knowledge, this fundamental question has no rigorous answer. The fact that we get good results independent of the chemical representation illustrates that these language models are very apt at extracting correlations from any text. For example, we found promising results using both conventional chemical names and entirely hypothetical representations. In both cases, the model could quantitatively correlate the pattern of repeating units correctly to different kinds of properties.