Multi-label metrics. We evaluate the multi-label model with two metrics. The LRAP score measures how well the model ranks its ground-truths by calculating for each ground-truth how large fraction of the labels with an equal or greater score are also ground-truths. The Jaccard score measures how well the model predicts the ground-truths as positive and ground-falsehoods as not positive. This is calculated by dividing the true positives with the false negatives, true positives, and false positives. The dataset is imbalanced and has limited multilabel character. We first sought analyze the dataset used for training, which consists of reaction data from the in-house ELNs. There are 11,210 recorded Buchwald-Hartwig reactions with a yield greater or equal to 20%. Out of the 1,000 contexts in the dataset, we only keep the 30 most common contexts, as it is likely that these would be of interest to the chemists. It also follows similar cut-offs used previously in the literature. We observe a clear imbalance in the context usage distribution (Figure ), where the most popular context is used for almost 800 reactions and the less common contexts are only used for about 100 reactions or less. The mean of the imbalance ratios per label is approximately 7.0 and the variance approximately 0.6, and according to the criteria of Charte et al. the dataset is considered imbalanced. Similar imbalances have been previously noted in the literature. Label cardinality and density measure the degree of multi-label character of the dataset. The label cardinality is the average number of labels per sample, and the label density is the label cardinality divided by the number of labels. For the current dataset, the label cardinality is 1.01 and the label density 0.033, which shows that most of the data is single-label and the vectors with the true contexts will be sparse. 86% of the reactions have only one variation, i.e., more than one recorded set of conditions, 11% have two variations, and 3% have three or more variations. These statistics underline the sparsity of the dataset used in our study. Temperature is excluded from the model. We next sought to train the models to predict chemical contexts, i.e., a combination of (pre-)catalyst, ligand, base, and solvent. In contrast to earlier approaches, we chose to exclude temperature in the prediction. As discussed above, our data are sparse and only a few variants are present for each reaction. Some of the contexts infrequently appear in the dataset. One further reason to exclude temperature during modelling is the sparsity of temperature data itself. In particular, for 16.0% of reactions the temperature is missing, and for 0.2%, the reported temperature is a range (rather than a single value). In order to incorporate temperatures into modeling, data points would require conversion into a single real number. Previous modeling efforts have replaced missing temperatures with ambient temperature and any corresponding ranges with the midpoint of the range. These results indicate that predicting the conditions as sets (contexts) instead of individually provides higher accuracy for predicting the recorded context. A model that suggests the most popular of our context every time gives top-1 and top-3 of ~0.14 and ~0.18, respectively, which also highlights the single-label model's predictive power.