Similarly, Figure displays the loss curve for the multi-label model and shows that the loss has stabilized at the end of training. The model has a LRAP score of about 0.81 and a Jaccard score of about 0.68. The higher LRAP score suggests that the ranking of the predictions is often correct, meaning that ground-truth labels are predicted higher than ground-false labels. However, a lower Jaccard score indicates that some predictions are incorrect compared to the ground truth. The label cardinality of the predicted contexts is about 1.0, similar to the label cardinality of the test data. It means that the model reproduces the low label cardinality found in the training data. Thus, the predictions are on average single-label. It is likely that if the label cardinality of the training data had been higher, the label cardinality of the predictions would be higher as well. A model that suggests the three most popular conditions in order of popularity gives a top-1 and top3 of ~0.13 and ~0.30, respectively, again showing the predictive power of the multi-label model. The multi-label model is marginally more performant than the single-label model. Comparing singlelabel and multi-label models is intrinsically non-trivial, as they are not solving the same task. We compare the models by looking at how they rank the ground truth in their predictions in Figure . For the single-label model, it is the top-k accuracy, which means that if the ground-truth is predicted with the k:th highest score it gets rank k. The multi-label model is the rank of the lowest scored groundtruth divided by the number of ground-truths. When there is only one true label, which is the case for most of the data, this calculation will be the same as top-k. More true labels measure how many of the predicted labels, that are ranked equal or higher than all true labels are true labels. The performance is similar between the models, although the multi-label top-1 accuracy is significantly higher. However, it is difficult to conclude whether this difference in accuracy would make a practical difference. The top-3 is above 0.9 for both models, which indicates that it would be easy for a chemist to find a good suggestion. For both models, the specificity is high, whereas the sensitivity is lower (Figure ). This pattern suggests that the model retrieves the true positives less well than true negatives. On average, the specificity decreases by 0.002 when going from single-label to multi-label model, but the sensitivity increases by 0.07. These both changes are significant at the 95% confidence interval, with p-values from a paired ttest being 0.022 for specific and 0.005 for sensitivity. The comparison shows that the single-label and multi-label models perform similarly well and on average both predict one context for a reaction. The multi-label model is better in partially predicting the correct context. To further outline the difference between the multi-label and single-label approaches, we next analyzed the single-label predictions not in the top-3 to see how close the top-1 prediction was to the true context. We assigned a score to each of those predictions, the average number of chemical species that agree when comparing the top-1 prediction to the true context. The average score is 0.47, indicating that the model predicts about half of the context conditions were correct. In Figure , we outline the likelihood of a specific category's correctness. The most properly predicted species is the base, which agrees with a likelihood of 0.79, followed by the ligand as 0.36. The catalyst is most difficult to compare, since this only correctly predicted in 0.14 of cases. That the base is the easiest to get right follows most likely from that there are only three unique bases in the 30 contexts that the model is built upon. In contrast, the other categories have between six and eight unique compounds.