Un token est la plus petite unité d’entrée ou de sortie traitée par un modèle de langage, comme un mot, un sous-mot, un caractère, un signe de ponctuation ou un symbole spécial. Dans les systèmes multimodaux, les tokens peuvent aussi représenter des unités atomiques de données non textuelles, comme des patchs d’image. Le nombre de tokens sert souvent à mesurer la longueur de contexte, la charge de travail du modèle et l’usage.
A token in a language model is the atomic unit that the model is training and making inferences on, namely words, morphemes, and characters. 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. For more information, see List and count tokens.
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.
The process of splitting a stream of text into separate tokens on detectable boundaries such as white space and hyphens.