Byte Pair Encoding (BPE): Subword Tokenization for NLP & LLMs
Master Byte Pair Encoding (BPE), a key subword tokenization algorithm in NLP and LLMs. Learn how BPE handles OOV words and powers models like GPT and RoBERTa. Step-by-step guide.
Byte Pair Encoding (BPE) is a powerful subword tokenization algorithm widely adopted in Natural Language Processing (NLP) tasks. It's a cornerstone for models like GPT and RoBERTa, offering a significant advantage over traditional word-level tokenization by more effectively managing out-of-vocabulary (OOV) words. This documentation outlines the BPE process step-by-step with a practical example.
Each word is broken down into a sequence of individual characters. We also track the frequency of these character sequences based on the original word frequencies.
We set a desired vocabulary size. This target includes all initial characters and any new subwords created through merging. For this example, let's aim for a vocabulary of 14 tokens.
The core of BPE involves repeatedly merging the most frequent adjacent symbol pairs. A "symbol" can be an individual character or an already formed subword.
Once the BPE vocabulary is built, it's used to tokenize new input text. Each word is broken down into the largest possible subword units that are present in the learned vocabulary. This strategy significantly improves the model's ability to represent rare and compound words, thereby reducing the occurrence of unknown or OOV tokens.
For instance, if the vocabulary contains st and men, the word cost would be tokenized as c, o, st, and best as b, e, st. The word men would be tokenized as men.