In parallel, we implemented deep learning yield-BERT 14 and T5-Chem 51 models. Yield-BERT, based on the BERT architecture was trained on reaction SMILES to predict yields by understanding the sequence-to-sequence relationships within the reaction data. Similarly, T5-Chem, a variant of the T5 transformer model, was also trained to capture contextual information from reaction SMILES strings, enabling it to predict yields by considering the entire reaction context. To do this, we first tokenized the reaction SMILES strings, converting them into a sequence of tokens that represent the individual components of the molecules.