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@ Programmers - InfoSphere
2024-10-23 18:21:36
Discovering the World of Tokenization: Unlocking Language Processing Secrets
In the world of natural language processing (NLP), tokenization is a crucial step in preparing text for machine learning models. This process breaks down raw text into smaller units called tokens, allowing LLMs to understand and process language effectively.
There are several approaches to tokenization, including character-level, word-level, and subword tokenization methods like BPE and WordPiece. Each method has its strengths and limitations, with modern LLMs often using subword tokenization to find a balance between preserving semantic meaning and handling out-of-vocabulary words.
In this post, we explored the concept of tokenization, highlighting the importance of vocabulary size and the need for strategies to handle unknown tokens. We also provided an example of implementing a simple BPE tokenizer, demonstrating the underlying logic and functionality.
Whether you're a developer or just curious about NLP, understanding tokenization is essential for unlocking the secrets of language processing.
Source: https://dev.to/stacklok/llm-basics-tokenization-37ef