text: import sys
  import os
  import time
  import load_data
  import torch
  import torch.nn.functional as F
  from torch.autograd import Variable
  import torch.optim as optim
  import numpy as np
  from models.LSTM import LSTMClassifier
  from models.selfAttention import SelfAttention
  
  
  TEXT, vocab_size, word_embeddings, train_iter, valid_iter, test_iter = load_data.load_dataset()
  learning_rate = 2e-5
  batch_size = 32
  output_size = 2
  hidden_size = 256
  embedding_length = 300
  model = SelfAttention(batch_size, output_size, hidden_size, vocab_size, embedding_length, word_embeddings)
  model.load_state_dict(torch.load("model_param.pkl"))
  model.cuda()
  test_sen1 = sys.argv[1]
  test_sen1 = TEXT.preprocess(test_sen1)
  test_sen1 = [[TEXT.vocab.stoi[x] for x in test_sen1]]
  test_sen = np.asarray(test_sen1)
  test_sen = torch.LongTensor(test_sen)
  test_tensor = Variable(test_sen, volatile=True)
  test_tensor = test_tensor.cuda()
  model.eval()
  output = model(test_tensor,1)
  out = F.softmax(output, 1)
  if (torch.argmax(out[0]) == 1):
      print ("Type: Finance")
  else:
      print ("Type: Medical")
  