Summary of Wikipedia article section on probabilistic tokenization in large language models.

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😎 Summarize this Wikipedia article section: https://en.wikipedia.org/wiki/Large_language_model#Probabilistic_tokenization
🤖 The section on “Probabilistic Tokenization” from the Wikipedia article explains that text must be converted to numbers for machine learning algorithms. This involves deciding on a vocabulary, assigning unique integer indexes to each entry, and associating embeddings with these indexes. Techniques like byte-pair encoding and WordPiece are used. Probabilistic tokenization compresses datasets and deals with varying text lengths by padding shorter texts. It also mentions that tokenization efficiency varies across languages, with some requiring more tokens per word than others. For more detailed information, please visit the [Wikipedia page](https://en.wikipedia.org/wiki/Large_language_model#Probabilistic_tokenization).

Hugging Face Dataset Metrics

All the conversation prompts, responses, and metrics are available to download and explore on Hugging Face dataset reddgr/talking-to-chatbots-chats: