One Cut Into Two

# 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

['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