Training data

Deep learning models are trained on data. During the training process, the training data is passed through the model, and the model is updated to attempt better performance in a task on said training data. For example, for generative NLP models, the training task is (usually) to predict the next word (properly, tokens), given all of the previous words. The goal is the model, at inference time, is able to generalise beyond the information encoded in its training data to unseen test data.