# To make the model memory efficient model.init_sims(replace=True) model.train(sentences, total_examples=1, epochs=150) # Saving the model for later use. Can be loaded using Word2Vec.load() model_name = gdrive+"glove1.h5" model.save(model_name) # model.wv.most_similar("dog") # model.wv.wmdistance('hot', 'popular') model = glove numpy.dot(model['spain'], model['france'])/(numpy.linalg.norm(model['spain'])* numpy.linalg.norm(model['france'])) model.accuracy('/tmp/questions-words.txt') D = np.zeros((len(docs), len(docs))) for i in range(len(docs)): for j in range(len(docs)): if i == j: continue # self-distance is 0.0 if i > j: D[i, j] = D[j, i] # re-use earlier calc D[i, j] = model.wmdistance(docs[i], docs[j]) sen =['sen1','sen2','point'] # model.accuracy(SEMEVAL_FOUR) # sentences = readData(SEMEVAL_FOUR) # model = Word2Vec(sentences, size=200) model.__contains__("")