What is a token in the context of model data processing?

A token in the context of model data processing refers to an atomic unit of data that a model interacts with during computation or learning processes. In natural language processing (NLP), for example, tokens can represent words, characters, or even subwords, depending on how the text is being processed. This means that breaking down data into tokens allows the model to analyze and understand the structure and meaning of the input more effectively.

By treating each token as an individual unit, machine learning models can better perform tasks such as language generation, sentiment analysis, and text classification, among others. When constructing these models, it is essential to tokenize the input data appropriately to ensure accurate and efficient processing. This foundational concept is critical for training and deploying many AI-powered applications.