# Introduction In this project, you will learn how to implement a subword tokenizer, which is a crucial step in natural language processing tasks. Tokenization is the process of breaking down a string of text into smaller units, called tokens, which can be individual words, characters, or subwords. This project focuses on subword-level tokenization, which is commonly used in English and other Latin-based languages. ## 👀 Preview ```python ['I', 'studied', 'in', 'LabEx', 'for', '1', '0', 'days', 'and', 'completed', 'the', '[UNK]', '[UNK]', 'course', '.'] ``` ## 🎯 Tasks In this project, you will learn: - How to implement a subword tokenizer function that performs character-level tokenization using the greedy longest-match-first algorithm - How to test the subword tokenizer with a provided example and analyze the output - How to understand the tokenization algorithm and its implementation ## 🏆 Achievements After completing this project, you will be able to: - Understand the importance of tokenization in natural language processing tasks - Implement a core component of a natural language processing pipeline - Differentiate between character-level and subword-level tokenization - Apply the greedy longest-match-first algorithm to tokenize text into subwords
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