The content discusses the three main types of tokenizers used in Transformers: Byte-Pair Encoding (BPE), WordPiece, and SentencePiece. BPE is used to handle rare words by splitting text into subword units that iteratively merge the most frequent pairs of characters or subwords. WordPiece tokenization is used in models like BERT to handle out-of-vocabulary words by building a vocabulary of subword units. SentencePiece is an unsupervised text tokenizer and detokenizer that processes text as a sequence of Unicode characters, making it suitable for various NLP tasks.
The article provides step-by-step examples of how BPE works, starting with pre-tokenization and creating a base vocabulary to finding the most frequent pair and merging them to form new tokens. Similarly, WordPiece and SentencePiece tokenization processes are explained with examples and decoding of tokenized text.
Additionally, the content includes code comparisons of sentences tokenized using all three tokenizers: BPE, WordPiece, and SentencePiece, showing the tokenized sequences and their corresponding decoded tokens. The examples demonstrate how each tokenizer processes the input text and converts it into tokenized sequences for further NLP tasks.
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Source link: https://medium.com/@0192.mayuri/summarizing-techniques-in-tokenizers-69126001997d?source=rss——large_language_models-5