Now that molecular representations can be used to interpret chemical structures, the training process can begin with the model learning from the input data provided. The model itself can vary significantly, in terms of the architecture and the statistical approach behind the strategy. As such, a brief introduction to the key architectures for TS generation will be presented below, along with a critical evaluation of their strengths and weaknesses and references to literature that can provide further information.