What is Tokenizer?
A tokenizer is a tool that breaks a piece of text into smaller parts called tokens-usually words, sub-words, or characters-so a computer can read and process the language. It’s the first step that turns raw sentences into data a model can understand.
Let's break it down
- Tokenizer: a program or function that does the splitting.
- Breaks: separates or cuts up.
- Text: any written words you type or see.
- Smaller parts: the pieces you get after cutting, called tokens.
- Tokens: the individual units (like words or pieces of words) that the computer works with.
- Computer can read: the machine can now handle the information because it’s in a simple, uniform format.
Why does it matter?
Without tokenization, computers would see a whole sentence as one unreadable block, making it impossible to analyze, translate, or generate language. Tokenizers turn messy human language into tidy data that AI models and software can actually use.
Where is it used?
- Search engines: turning queries into tokens to match relevant pages.
- Chatbots and virtual assistants: processing user input to understand intent.
- Machine translation services: breaking sentences into tokens before converting them to another language.
- Sentiment analysis tools: tokenizing reviews to detect positive or negative feelings.
Good things about it
- Simple and fast: turning text into tokens is quick and requires little computing power.
- Enables powerful language models: without tokens, models like GPT couldn’t be trained.
- Flexible: can work with words, sub-words, or characters, adapting to many languages.
- Reduces vocabulary size: sub-word tokenizers keep the list of possible tokens manageable.
Not-so-good things
- May split words incorrectly, especially for rare or compound words.
- Can lose important context when tokens are too small.
- Different languages need different tokenization rules, adding complexity.
- Large token vocabularies can consume more memory and slow down processing.