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embedding models

Embedding models are used to transform high-dimensional data into lower-dimensional vectors. These vectors, known as embeddings, capture the essential features of the data. For example, in natural language processing, words are converted into vectors where similar meaning words have closer vectors. This is crucial for tasks like text classification or recommendation systems as it simplifies handling complex data.

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