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What are some alternatives to the doc2vec embedding model? I.e models that convert paragraphs/documents into vectors, not just models that take the mean/sum of the word embeddings of each word in the document.

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Depending on your target task. If you are to classify documents, then e.g. fastText has it's own approach and there are other classification techniques, not strictly generating embeddings, like LSA / LDA (using topic modelling) or word mover distance.

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