Snipping words into tiny treats.
Tokenization is the process of breaking text into smaller pieces, called tokens, that a language model can understand.
Imagine you have a giant Lego castle. To understand how it's built, you first break it into individual bricks. Tokenization does the same with sentences: it chops them into words or parts of words so the computer can process them one by one.
Every time you chat with an AI or use a search engine, tokenization is the first step that turns your words into numbers the computer can work with. Without it, AI wouldn't understand your questions or generate coherent answers.
Many people think tokenization splits text into whole words, but it often splits into subwords like 'un' + 'believe' + 'able' to handle rare or new words. This means a single word can become multiple tokens, which affects how the AI processes it.
Tokenization is the process of converting a sequence of text into smaller units called tokens, which can be words, subwords, or characters. It is a fundamental preprocessing step in natural language processing (NLP) that maps raw text to a format suitable for machine learning models, typically by using a predefined vocabulary or byte-pair encoding.