In a language model, the atomic unit that the model is training on and making predictions on. A token is typically one of the following:

a word—for example, the phrase "dogs like cats" consists of three word tokens: "dogs", "like", and "cats".

a character—for example, the phrase "bike fish" consists of nine character tokens. (Note that the blank space counts as one of the tokens.)

subwords—in which a single word can be a single token or multiple tokens. A subword consists of a root word, a prefix, or a suffix. For example, a language model that uses subwords as tokens might view the word "dogs" as two tokens (the root word "dog" and the plural suffix "s"). That same language model might view the single word "taller" as two subwords (the root word "tall" and the suffix "er").

In domains outside of language models, tokens can represent other kinds of atomic units. For example, in computer vision, a token might be a subset of an image.

See Large language models in Machine Learning Crash Course for more information.