Encodage par paires de bytes (BPE) Tokenisation is a popular text preprocessing technique used primarily in traitement du langage naturel (NLP) to improve the efficiency of text representation. This method involves scanning a given text corpus and identifying the most frequently occurring pairs of characters or subwords. Once identified, these pairs are merged into a single token, thereby reducing the overall vocabulary size and allowing for more efficient encoding of the text.
The process begins by initializing the vocabulary with individual characters. For example, if the text includes the phrase ‘lowercase’, the initial tokens would be: ‘l’, ‘o’, ‘w’, ‘e’, ‘r’, ‘c’, ‘a’, ‘s’, ‘e’. The algorithm then counts the frequency of all adjacent character pairs, such as ‘lo’, ‘ow’, ‘we’, etc. The pair with the highest frequency is replaced by a new token, which is the concatenation of the two characters. This process is repeated iteratively until a predefined vocabulary size is reached or until no more pairs meet a certain frequency threshold.
BPE tokenization is particularly useful in handling out-of-vocabulary words, as it can break down unknown words into smaller, recognizable components. This allows models to process language more flexibly and reduces the chances of encountering rare or unseen tokens during inference. BPE has been widely adopted in various state-of-the-art language models, including those développé par OpenAI and Google, and serves as a foundational technique for many modern NLP applications.