Thanks for providing the tip in #15, where you mentioned that we could use fasttext as an alternative for embedding text, i.e. we could get per word vectors from fasttext pre-trained model. I wonder how we should concat/process these per word vectors to represent the entire product description/name? (i.e. each word vector is 300 dimension and I'd like the final embeddings to be 300 as well...)
Thanks for providing the tip in #15, where you mentioned that we could use fasttext as an alternative for embedding text, i.e. we could get per word vectors from fasttext pre-trained model. I wonder how we should concat/process these per word vectors to represent the entire product description/name? (i.e. each word vector is 300 dimension and I'd like the final embeddings to be 300 as well...)
Thanks in advance!