This KeyedVectors model is specifically for silicone:dyda_da
*you must download both the .wordvectors and .wordvectors.npy files
from gensim.models.keyedvectors import KeyedVectors
kv=KeyedVectors.load("silicone-dyda_da-utterance-tokens.wordvectors")
print(model.wv.most_similar_cosmul('peter',topn=25))
[('steven', 0.889095664024353),
('alice', 0.8783409595489502),
('li', 0.8624751567840576),
('benjamin', 0.8622595071792603),
('mrs', 0.8615201711654663),
('lin', 0.8603521585464478),
('david', 0.8597986698150635),
('dr', 0.8588740825653076),
('wang', 0.8527941107749939),
('mary', 0.8522424697875977),
('mike', 0.8521847724914551),
('john', 0.8494851589202881),
('michael', 0.84917151927948),
('linda', 0.8488836288452148),
('lucy', 0.8375136256217957),
('jane', 0.8359535336494446),
('monica', 0.834464430809021),
('smith', 0.8331072926521301),
('susan', 0.8329276442527771),
('zhang', 0.8323286771774292),
('professor', 0.8316935896873474),
('ellen', 0.8311569094657898),
('daniel', 0.8285720944404602),
('charles', 0.8285550475120544),
('james', 0.8280013203620911)]