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Today, we're exploring Google's universal sentence encoder, a deep learning model that can be downloaded and used for Natural Language Processing. The encoder is considered better than traditional bag of words or word Tyvek methods. Bag of words encodes individual words, while word Tyvek produces embeddings by predicting words in context. Similar context words are mapped to similar vectors in the vector space, providing semantic meaningfulness. Models like the universal sentence encoder predict the next set of words given prior words, with variations like Bert also offering encoding beyond individual words.
