Tokenization

Breaking text into smaller pieces that AI models can understand

Overview

Tokenization is the process of converting text into smaller units called tokens, which serve as the basic building blocks for natural language processing tasks. These tokens can be words, subwords, or characters, depending on the chosen tokenization strategy. This fundamental step in text processing enables machines to analyze and understand human language by converting raw text into a format that can be effectively processed by neural networks and other machine learning models.

Why Do We Need Tokenization?

Imagine teaching a computer to read - it needs text broken down into manageable pieces. Tokenization:

  • Converts text into a format AI can process
  • Helps handle different languages consistently
  • Reduces vocabulary size while maintaining meaning
  • Makes processing more efficient

How It Works

Tokenization can work in different ways:

  • Word-based: "I love cats" → ["I", "love", "cats"]
  • Subword-based: "playing" → ["play", "ing"]
  • Character-based: "cat" → ["c", "a", "t"]
  • Special handling for:
    • Punctuation
    • Numbers
    • Special characters
    • Different languages

Common Applications

Best Practices

  • Choose the right tokenization method
  • Consider your language needs
  • Handle special cases carefully
  • Test with diverse text
  • Monitor token usage