Byte-Pair Encoding (BPE) Explained: Tokenization for Large Language Models

This video explains Byte-Pair Encoding (BPE), a data compression technique used for tokenization in large language models (LLMs). The video covers: What BPE is and why it's used instead of character-level or word-level tokenization. The difference between character-based, word-based, and BPE tokenization. The steps involved in BPE, including initializing a vocabulary, splitting the corpus, counting pair frequencies, and merging the most frequent pairs. An example of how BPE works, using the sentence "I love cats." How BPE is implemented in Python using the Hugging Face Transformers library. How BPE is used in models like GPT-2. The video also includes a demonstration of BPE using Google Colab. Code: https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter6/section5.ipynb