The rapid and accurate prediction of organic chemistry reactions is an important area of research at the interface of theoretical chemistry and artificial intelligence. High-throughput quantum chemistry calculations can predict reaction outcomes, but the computational cost is prohibitively high. Data-driven approaches offer an alternative method for reaction prediction. Two prominent types of machine learning models have emerged for the task of organic reaction prediction: template-based methods and template-free methods. Template-based methods rely on extensive databases of rules to guide all possible transformations for a chemical system. A disadvantage of this methodology is the arduous task of hand-curating templates, which becomes increasingly intractable as more reactions are reported in the literature. Methods to automate template generation with machine learning have been successful, and offer an alternative to methods that utilize underlying data bases.