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This video tutorial from Cohere AI explains word and sentence embeddings, which are essential for large language models. The purpose of language models is to enable computers to understand and process human language, which consists of words, while computers use numerical data. Word embeddings convert words into numerical lists, and sentence embeddings do the same for sentences, ensuring that the numerical representation makes contextual sense. This process is automated, typically using a neural network, which analyzes the context in which words and sentences appear. By doing so, it brings similar words and sentences closer together in their numerical representation, leading to impressive outcomes in language understanding.
