def predict(input_text, net_trained, candidate_num=3, output_print=False):
TEXT = pickle_load(PKL_FILE)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
tokenizer_bert = BertTokenizer(vocab_file=VOCAB_FILE, do_lower_case=False)
text = preprocessing_text(input_text)
text = tokenizer_bert.tokenize(text)
text.insert(0, "[CLS]")
text.append("[SEP]")
token_ids = torch.ones((max_length)).to(torch.int64)
ids_list = list(map(lambda x: TEXT.vocab.stoi[x], text))
for i, index in enumerate(ids_list):
token_ids[i] = index
ids_list = token_ids.unsqueeze_(0)
input = ids_list.to(device)
input_mask = (input != 1)
outputs, attention_probs = net_trained(input,
token_type_ids=None,
attention_mask=None,
output_all_encoded_layers=False,
attention_show_flg=True)
offset_tensor = torch.tensor(offset, device=device)
outputs -= offset_tensor
if output_print == True: print(outputs)
_, preds = torch.topk(outputs, candidate_num)
return preds
def tokenizer_with_preprocessing(text):
tokenizer_bert = BertTokenizer(vocab_file=VOCAB_FILE, do_lower_case=False)
text = preprocessing_text(text)
ret = tokenizer_bert.tokenize(text)
return ret
