from models import InferSent import torch V = 2 MODEL_PATH = 'encoder/infersent%s.pkl' % V params_model = { 'bsize': 64, 'word_emb_dim': 300, 'enc_lstm_dim': 2048, 'pool_type': 'max', 'dpout_model': 0.0, 'version': V } model = InferSent(params_model) model.load_state_dict(torch.load(MODEL_PATH)) W2V_PATH = 'GloVe/glove.840B.300d.txt' model.set_w2v_path(W2V_PATH) model.build_vocab(sentences, tokenize=True) query = "I had pizza and pasta" query_vec = model.encode(query)[0] pprint(query_vec) similarity = [] for sent in sentences: sim = cosine(query_vec, model.encode([sent])[0]) print("Sentence = ", sent, "; similarity = ", sim)