subword tokenization

Subword tokenization further enhances the model’s language understanding by splitting words into meaningful segments, like breaking “coloring” into “color” and “ing.” This expands the model’s vocabulary and improves its ability to grasp the nuances of language structure and morphology. Understanding that the “ing” part of a word indicates the present tense simplifies how words are represented in different tenses. It no longer requires separate entries for the base form of a word, like “play,” and its present tense form, “playing.” This method increases the number of tokens to represent a piece of text but dramatically reduces the number of tokens in the dictionary.