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So my question is how Supervised FastText works for the most part. I understood in the original paper they use bag of n-grams for features, but then they released a paper with enriching the word vectors using character-n-grams. Using the FastText package released by FAIR, when learning a supervised model you incorporate both a character-n-gram size and word-n-gram size, so I am a bit confused on what they do for the implmentation?

Do they use the character-n-gram to learn representation as its own objective and then use the n-gram BOW ontop of the learned representation?

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