Understanding Tokenization: A Beginner's Guide

Ever wondered how machines understand sentences like we do? The answer is tokenization. It’s the process of breaking down text → sentences into words, and words into characters. Machines only understand numbers (0s and 1s), so tokenization converts these words and characters into numerical tokens that a machine can process.

A tokenizer acts like a language “chopper” for machines. While humans read full sentences to understand meaning, machines work better with smaller pieces of text called tokens. Tokens can be words, characters, or parts of words, depending on the type of tokenizer.

There are several types of tokenizers:

• Whitespace Tokenizer – Splits text based on spaces.

• Sentence Tokenizer – Splits sentences from paragraphs.

• Word Tokenizer – Splits words from sentences.

• Character Tokenizer – Splits words into individual characters.

• Subword Tokenizer – Splits words into smaller units (useful for rare words).

Tokenization plays an integral part in NLP and LLMs because it defines how text is converted into numbers that machines can understand.