You need to use infer_vector to get a document vector of the new text – which does not alter the underlying model. Here is how you do it: tokens = “a new sentence to match”.split() new_vector = model.infer_vector(tokens) sims = model.docvecs.most_similar([new_vector]) #gives you top 10 document tags and their cosine similarity Edit: Here is an … Read more
Note that the “DBOW” (dm=0) training mode doesn’t require or even create word-vectors as part of the training. It merely learns document vectors that are good at predicting each word in turn (much like the word2vec skip-gram training mode). (Before gensim 0.12.0, there was the parameter train_words mentioned in another comment, which some documentation suggested … Read more
Old question, but an answer would be useful for future visitors. So here are some of my thoughts. There are some problems in the tensorflow implementation: window is 1-side size, so window=5 would be 5*2+1 = 11 words. Note that with PV-DM version of doc2vec, the batch_size would be the number of documents. So train_word_dataset … Read more