A “Token” in AI represents an individual instance of a sequence. For example, in the context of language processing, a token can represent a word or a character.
If you imagine a sentence is like a bead necklace, each token is a bead. They are individual parts forming a sequence just like beads form a necklace.
The term “Token” in the context of Artificial Intelligence (AI) and Machine Learning (ML), particularly in Natural Language Processing (NLP), refers to the smallest unit of data that a model can understand and manipulate. If the data type is text, tokens are often individual words or characters. For instance, in the sentence “AI is amazing”, “AI”, “is” and “amazing” are considered as individual tokens.
Tokens play a vital role in both traditional statistical NLP methods and modern deep learning-based NLP models. For instance, the bag-of-words (BoW) model, a traditional NLP method, uses word tokens to represent text data, creating frequency histograms of words. On the other hand, modern deep learning models such as transformers need to convert variable-length text data into fixed-length numerical vectors before processing. This conversion is achieved through a process called tokenization, which breaks down the input text into individual tokens.
For deep learning models, each token is typically represented as an embedding vector, which captures the semantic properties of the token. The model learns these embeddings during training and uses them to understand the context and semantic relationships between different tokens in the input. The accurate representation of tokens and their relationships is a critical aspect of the performance of NLP models.
Furthermore, tokenization isn’t limited to word or character level. Subword tokenization methods like Byte Pair Encoding (BPE) and SentencePiece can break words down into frequently used subword units (e.g. syllables or morphemes), which can effectively handle out-of-vocabulary words and learn more efficient representations, especially for agglutinative languages or languages with complex morphology.
Although the concept of tokens is primarily widely used in NLP, it can also apply to other types of sequence data such as time-series, where a token can represent a single time point measurement.