What Is Tokenization in AI? Understanding Tokenization for Large Language Models

Описание: In this quick tutorial, we explore the concept of tokenization, a critical process in large language models. Learn how words are broken into tokens, why this is essential for AI efficiency, and how different tokenization techniques influence outcomes. This video provides practical examples using OpenAI’s official tokenizer tool and sets the stage for upcoming lessons focused on full-word analysis.

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Chapters:
00:00 Introduction to Tokenization
00:30 How Words Are Broken into Tokens
01:05 The Efficiency of Tokenization
01:41 Examples of Tokenization in Practice
02:15 Tokenization Techniques Explained
02:46 Rule of Thumb for Tokenization
03:20 Focus on Full Words in This Course
03:48 Conclusion and Additional Resources

From this video, you will learn:
What Tokenization Is: An introduction to the concept of tokenization and how it is used in large language models like GPT-4.
How Words Are Broken Into Tokens: Examples of how words, special characters, and spaces are split into tokens for efficient text processing.
*Why Tokenization Matters: *The role tokenization plays in balancing efficiency and accuracy in AI language models.
Different Tokenization Techniques: An overview of techniques such as byte-pair encoding, word-piece, and character-level tokenization.
Practical Application: A demonstration using OpenAI’s tokenizer tool to see how text is tokenized in real-time.
Helpful Rules of Thumb: Insights like how one token corresponds to approximately four characters in common English text.
Background Knowledge for Future Learning: Understanding tokenization as a foundation for more advanced concepts in natural language processing (NLP) and AI development.

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