Template-free methods, such as graph-and sequence-based methods, offer a means to directly circumvent several of these issues. The former uses a graph representation of the reactants and completes a series of graph edits that lead to the product. Sequence-based models take a text representation of the reactants and translates the string from the language of reactants and reagents to the language of products. These kinds of text-based schemes have recently been applied to prediction of chemical reaction yields, enzymatic reactions, and regio-and stereoselective reactions on carbohydrates. Outside of chemistry, transformer neural networks have become increasingly popular for natural language processing (NLP) tasks, from the transfer learning method ULMFiT 10 to BERT, Google's NLP pre-training neural network technique.