We further test the fine-tuned model on uniproduct reactions from the ChemRxnExtractor 16 dataset, a set of 123 records with labeled tokens for compound names. All records from this dataset were collected from individual literature passages. These passages can be considered an out-of-distribution challenge to our fine-tuned model: They tend to be defined by general chemical transformations (e.g., "oxidation of A gave B" or "cyclization of A afforded B") instead of specific actions in synthesis procedures, chemical amount information is rarely present, and named entities in these passages are frequently represented by externally referencing tokens. As expected, the fine-tuned model performs poorly on this dataset, with an accuracy of 62.61% and a tendency to include unwanted tokens (Table ). Such a tendency often results from prioritizing chemical entities above referencing tokens. For example, in "by heating tryptophan methyl ester (9) at 140 • C for 3 h" the token "9" is the correct token to extract, while the fine-tuned model only recognizes "tryptophan methyl ester" which is a chemical entity in a more general sense.