Tokenization in Natural Language Processing

Understanding how machines break down and process text

Overview

Tokenization is a fundamental concept in Natural Language Processing (NLP) that involves breaking down text into smaller units called tokens. This module covers various tokenization approaches, from basic techniques to advanced methods used in modern language models, with practical implementations using popular frameworks.

1. Understanding Tokenization Fundamentals

Tokenization serves as the foundation for text processing in NLP, converting raw text into machine-processable tokens. This section explores basic tokenization concepts, different token types, and their applications in text processing.

Learning Materials

• 📄 Medium Article: Introduction to Tokenization
  • Comprehensive guide to tokenization basics, types, and theoretical perspective
• 🟠 Colab Notebook: Tokenization Techniques
  • Hands-on implementation of Simple Tokenizers
• ▶️ YouTube Video: Let’s build the GPT Tokenizer by Andrej Karpathy
• Practical implementation of GPT tokenization approach
• 🟠 Colab Notebook: Let’s build the GPT Tokenizer by Andrej Karpathy
  • Implementing and analyzing GPT tokenization approach
• 📄 Medium Article: Understanding BPE Tokenization
  • Deep dive into BPE algorithm, its advantages, and applications
• 🟠 Colab Notebook: Build and Push a Tokenizer
  • Building different kinds of tokenizers and pushing them to Hugging Face Hub
• 🟠 Colab Notebook: Tokenizer Comparison
  • Comparing different tokenization models

2. Fast Tokenizers

Explore the powerful Hugging Face Tokenizers library, which provides fast and efficient tokenization for modern transformer models.

Learning Materials

• 📖 Documents: Hugging Face Tokenizers
  • Complete guide to Hugging Face tokenization ecosystem
• 🟠 Colab Notebook: Hugging Face Tokenizers
• 🟠 Colab Notebook: New Tokenizer Training
• 📄 Medium Article: Fast Tokenizers: How Rust is Turbocharging NLP

Additional Resources

Interactive Playgrounds:

Documentation & Tools: