Byte-Pair Encoding tokenization

No code, maths, or mention of Keys, Queries and Values Since their introduction in 2017, transformers have emerged as a prominent force in the field of Machine Learning, revolutionizing the capabilities of major translation and autocomplete services. Recently, the popularity of transformers has soared even higher with the advent of large language models like OpenAI’s ChatGPT, , and Meta’s LLama.

Byte-Pair Encoding (BPE) was initially developed as an algorithm to compress texts, and then used by OpenAI for tokenization when pretraining the GPT model. It’s used by a lot of Transformer models, including GPT, GPT-2, RoBERTa, BART, and DeBERTa. 💡 This section covers BPE in depth, going as far as showing a full implementation. You can skip to the end if you just want a general overview of the tokenization algorithm.