Figure shows the confusion matrix of reaction role assignment from the fine-tuned model for all compounds in ReactionInput from the test dataset. The classification accuracy decreases from REACTANT to SOLVENT to CATALYST, with a tendency to mislabel SOLVENT or CATALYST as REACTANT, as expected based on class populations. Compared to extracting compounds of other roles (2.58% for REACTANT, 1.37% for SOLVENT), the model failed more frequently (4.2%) when extracting catalysts. Figure shows the results from the popularity baseline with similar accuracies for SOLVENT and CATALYST, and lower accuracy for REACTANT compared to the fine-tuned model. A macro-average F1 score of 86.06% is calculated for the fine-tuned model, while the popularity baseline gives 63.51%. For compounds whose reaction role in the dataset varies from reaction to reaction, the difference between the fine-tuned model (Figure ) and the popularity baseline (Figure ) becomes more pronounced: the former exhibits better performance for both REACTANT and CATALYST. These results suggest through fine-tuning the model learned to make role classifications based on reaction context. Percentage values were normalized using the number of true instances. In addition to three reaction role classes, prediction results can also be labeled as "MISSING" -when the corresponding compound is absent in the extracted ORD record, and "ER-ROR" -when the name of the extracted compound is incorrect.