Many reaction condition prediction models have focused on indirect prediction of conditions by learning to predict a measure of reaction performance such as yield . The downside of these models are that they are usually only scoped to a single class of reactions due to the lack of reliable yield measurements in benchmarks based on the USPTO dataset . However, Gao et al. built a model for reaction condition prediction agnostic of reaction class for sequential prediction of agents and temperature using approximately ten million reactions mined from a closed-source dataset, Reaxys . We train this model with minor modifications on our new open-source condition prediction benchmark.