def make_scores_lstm(my_rnn, query, answers, emb_size=768, hidden_size=64):
query_embs = bc.encode((clean_punct(cleanhtml(query))).split())
tensor_query_embs = torch.tensor(query_embs)
all_query_hidden, (last_query_hidden, last_query_state) = my_rnn(
tensor_query_embs.unsqueeze(0))
print("all_query_hidden")
#last_answers_hidden = [my_rnn(torch.tensor(bc.encode((clean_punct(cleanhtml(answer))).split())).unsqueeze(0))[1] for answer in answers]
last_answers_hidden = []
for ind, answer in enumerate(answers):
print(ind)
last_answers_hidden.append(
my_rnn(create_tensor(answer).unsqueeze(0))[1][0])
aa = last_query_hidden.detach().numpy()
print("last_answers_hidden")
bb = last_answers_hidden[0].detach().numpy()
#print ("bb shape", bb.shape)
scores = [
cosine_similarity(
last_query_hidden.squeeze(0).detach().numpy(),
last_answer_hidden.squeeze(0).detach().numpy())[0][0]
for last_answer_hidden in last_answers_hidden
]
return scores
def create_tensor(strng):
#print ((clean_punct(cleanhtml(strng))).split())
arr = bc.encode((clean_punct(cleanhtml(strng))).split())
return torch.tensor(np.copy(arr))
