As a smart chemical NER tool, the fine-tuned model learned to recognize cross-referencing tokens and to ignore unwanted chemical entities. This is reflected in the comparison (Table ) be-tween the fine-tuned model and ChemDataExtractor (version 2.1.0), a toolkit for extracting chemical information mainly from scientific literature. While ChemDataExtractor is capable of recognizing many chemical entities, it frequently fails to identify referencing tokens, such as "desired product" or "compound 322" (the "Removal" column). It also captures excess chemical entities, such as "1H" from NMR reports ("the "Addition" column). These errors are at least partially attributable to the distribution shift in how procedures are described in our source text paragraphs.