Summarize webpage section on probabilistic tokenization in large language models.
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😎 Summarize this webpage section: https://en.wikipedia.org/wiki/Large_language_model#Probabilistic_tokenization
🤖 The section on “Probabilistic tokenization” from Wikipedia explains that machine learning algorithms require text to be converted into numbers. This is achieved through a process that decides on a vocabulary, assigns integer indexes to each entry, and associates an embedding with these indexes. Techniques such as byte-pair encoding and WordPiece are mentioned. Probabilistic tokenization helps in compressing datasets and requires padding shorter texts to match the length of the longest one. The efficiency of tokenization varies across languages, with some requiring significantly more tokens per word than English.
For more detailed information, visit [Wikipedia](https://en.wikipedia.org/wiki/Large_language_model#Probabilistic_tokenization).
Hugging Face Dataset Metrics
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