I have a word2vec model for every user, so I understand what two words look like on different models. Is there a more optimized way to compare the trained models than this?userAvec = Word2Vec.load(userAvec.w2v)  userBvec = Word2Vec.load(userBvec.w2v)  #for word in vocab, perform dot product:cosine_similarity = np.dot(userAvec['president'], userBvec['president'])/(np.linalg.norm(userAvec['president'])* np.linalg.norm(userBvec['president']))Is this the best way to compare two models? Is there a stronger way to see how two models compare rather than word by word? Picture 1000 users/models, each with similar number of words in the vocab.
Optimizing word2vec model comparisons
machine learning;natural language;nlp;models;wordvector