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)
