text: from keras.preprocessing.sequence import pad_sequences
  from nltk.tokenize import TweetTokenizer
  
  def parser_string(text, ner_train):
      
      tmp_text_1 = TweetTokenizer().tokenize(text)
      tmp_text_2 = []
      
      for x in tmp_text_1:
          try:
              token_idx = ner_train.word2idx[x] 
          except:
              token_idx = ner_train.word2idx['UNK'] 
              
          tmp_text_2.append(token_idx)
  
              
      return pad_sequences(maxlen=ner_train.max_len, 
                           sequences=[ np.asarray(tmp_text_2)],
                           padding="post", 
                           value=0)
  
  def cnn_predict_news_ent():
  
  	tweet = "I say burn it all down I and my frind Matthews (metaphorically of course). Abolish Venezuela, China, \
  	America, Canada, Australia, Russia, Mexico, every single country on the planet. \
  	They’re all illegitimate and only exist because we allow them to"
  
  	tokens_idx = parser_string(tweet)
  	tokens_idx_char = get_X_char(tweet, ner_train.max_len, max_len_char)
  
  
  
  	preds = model.predict([tokens_idx, tokens_idx_char])
  	preds = np.argmax(preds, axis=-1)
  	y_pred, _ = ner_train.parser2categorical(preds, preds)
  
  
  	print("{:10}   {:5}".format("Word", "Pred"))
  	print("=" * 50)
  
  	for tok, tag in zip([ner_train.idx2word[x] for x in list(tokens_idx[0])], y_pred[0]):
  	    if tag != 'PAD':
  	        print("{:10}   {:5}".format(tok, tag))
  	        
  