Definition

Tokenization is how AI splits up words, sentences, or code into small parts called tokens. These tokens might be words, subwords, or even characters. It helps the model read and work with language step by step.

Example

In the sentence ‘I love pizza,’ tokenization turns it into: [‘I’, ‘love’, ‘pizza’].

How It’s Used in AI

Tokenization is used in every language model. It turns big blocks of text into pieces the AI can understand and process. It’s also used in search, translation, and voice recognition tools. Some models use word-level tokens; others break words into parts to handle more languages.

Brief History

Tokenization has been used in natural language processing for decades. As models got bigger, new tokenization techniques like Byte Pair Encoding (BPE) and SentencePiece became popular for handling complex and multi-language inputs.

Key Tools or Models

Models like GPT-4, BERT, and T5 use tokenization methods such as BPE or WordPiece to manage their inputs. Tools like Hugging Face Tokenizers are commonly used by developers.

Pro Tip

Shorter tokens = more flexibility. But longer tokens = faster processing. Choose the right balance for your use case.