Abstract: An example couples a non-standardized tokenizer to an input of a neural network model with attention. The non-standardized tokenizer is to use a non-standardized vocabulary to convert reserved words of a first sequence of actions logged via use of a device by a first entity to first tokens including word-based tokens that describe actions in the first sequence of actions. The example omits a position encoder of the neural network model with attention. The neural network model with attention is trained, with the omitted position encoder, using a training input including the first tokens generated by the non-standardized tokenizer.

Abstract: An example formulates a training input for a neural network model with attention to include action data and descriptive content. The action data includes a first entity identifier (ID) and a first sequence of actions associated with the first entity ID. The descriptive content describes a first entity associated with the first entity ID. An action in the first sequence of actions includes an electronic transmission involving the first entity and a second entity. An example uses the training input, including the first entity ID, and a non-standardized tokenizer, to train the neural network model with attention to generate and output a second sequence of actions.