In the field of predictive synthesis, it is widely understood that publicly and commercially available reaction datasets, such as those from CAS, Reaxys, Pistachio, or the USPTO, often contain limitations in quality. As a result, relying solely on these datasets may not be adequate for developing highly effective machine learning models for yield prediction. These data sets suffer, among many challenges, from significant bias towards successful reaction examples, high yield variance where the reaction yields are determined inconsistently using different methods and protocols, with often omitted or mislabelled reaction components or missing information about conditions. Due to the known issues with the data quality for these reaction collections, we set on evaluating our ML models using only the publicly available HTE data for Buchwald-Hartwig and Suzuki-Miyaura couplings. The filtered and standardised reactions obtained from the patent-based Pistachio data set were used solely for the purpose of model pre-training through MLM on full reaction schemes representing variety of chemistries, without the information about reaction conditions (e.g. temperature, time, pressure) or yields.