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computers donamp;#39;t understand words they get numbers they get vectors and matrices the idea is to map every word to a point in space where similar words in meaning are physically closer to each other the space in which they are present is called an embedding space we could pre-train this embedding space to save time or even just use an already pre-trained embedding space this embedding space Maps a word to a vector but the same word in different sentences may have different meanings this is where positional encoders come in itamp;#39;s a vector that has information on distances between words in the sentence the original paper uses a sign and cosine function to generate this Vector but it could be any reasonable function
