Upon testing each baseline measure against the test reactions with unseen boronic acids or aryl halides, a modest performance of 19.5 (Random Forest) / 21.6 (Adaboost) and 19.5 (Random Forest) / 21.5 (Adaboost) average MAE, respectively was observed. Yield-BERT, a transformer-based model which acts as a machine translation from the language of reaction SMILES to product yield, was used as an accurate ML benchmark. Yield-BERT has been reported to have excellent performance on both Suzuki and Buchwald-Hartwig yield prediction, even when trained on limited data, making it a suitable benchmark for my framework. Additionally, similar to this pretraining approach, Yield-BERT uses no precomputed data and even outperforms modeling with DFT-based chemical descriptors. A large MPNN, GraphRXN, was similarly chosen as an additional benchmark. GraphRXN had observed notable success on external and internal HTE data. Interestingly, Yield-BERT achieves similar accuracy on my dataset splits to Random Forest models applied to Morgan Fingerprints, yielding 21.9 and 22.0 MAE on unseen boronic acid and aryl halides, respectively. GraphRXN performs worse that Yield-BERT, a surprising outcome given that modeling of GraphRXN on Suzuki, albeit HTE data and not the historical, collated, literature patent data used in this interrogation, was on-par with Yield-BERT. The framework pretrained from CCDC data, dubbed Crystal-Yield, showed a modest performance increase across both the top nucleophile and electrophiles test sets, with a mean MAE of 18.4 for unseen nucleophiles and a mean MAE of 18.5 for unseen electrophiles (Table ).