Understanding the output of Doc2Vec from Gensim package

Question

I have some sample sentences that I want to run through a Doc2Vec model. My end goal is a matrix of size (num_sentences, num_features).

I\'m using the Gensim packag

Answer 1

TaggedDocument expects tags to be a list of tags related to document.

In your case,

sentence = TaggedDocument(words=['a', 'b'], tags='400')

gets interpreted as sentence having 3 tags ['4','0','0'], and hence model.docvecs returns vectors corresponding to 10 tags - ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']

Try changing this to

sentence = TaggedDocument(words=['a', 'b'], tags=['400'])