Byte Pair Encoding

Byte Pair Encoding (BPE) is a data compression algorithm that iteratively replaces the most frequent pair of bytes in a sequence with a single, unused byte, effectively building a vocabulary of subword units. It is widely used in natural language processing (NLP) for tokenization, breaking down text into subword tokens to handle out-of-vocabulary words and improve model efficiency. BPE helps balance character-level and word-level representations, making it crucial for modern language models like GPT and BERT.

Also known as: BPE, Byte-Pair Encoding, BytePairEncoding, Subword Tokenization, BPE Tokenization

Why learn Byte Pair Encoding?

Developers should learn BPE when working on NLP tasks, especially for tokenization in machine learning models, as it efficiently handles rare or unseen words by splitting them into known subword units. It is essential for training large language models, text preprocessing, and multilingual applications where vocabulary size needs optimization. Use cases include building custom tokenizers, improving model performance on specialized datasets, and reducing memory usage in text-based AI systems.

Learning Resources

• tutorial: Byte Pair Encoding Explained
• docs: Neural Machine Translation of Rare Words with Subword Units
• video: BPE Tokenization in NLP - YouTube Tutorial
• docs: Tokenizers Library Documentation