Byte-Pair Encoding tokenization - Hugging Face NLP Course

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. BPE training starts by computing the unique set of words used in the corpus (after the normalization and pre-tokenization steps are completed), then building the vocabulary by taking all the symbols used to write those words. As a very simple example, let’s say our corpus uses these five words: The base vocabulary will then be ["b", "g", "h", "n", "p", "s", "u"]. For real-world cases, that base vocabulary will contain all the ASCII characters, at the very least, and probably some Unicode characters as well. If an example you are tokenizing uses a character that is not i

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. BPE training starts by computing the unique set of words used in the corpus (after the normalization and pre-tokenization steps are completed), then building the vocabulary by