text: from keras.models import Model
  from keras.layers import Input, Dense
  
  from index_data import DataProcessor
  from encoders import OntoLSTMEncoder
  
  ## Defining constants
  NUM_SENSES = 3
  NUM_HYPS = 5
  ONTO_ATTENTION = True
  SENSE_PRIORS = True
  EMBED_DIM = 50
  BIDIRECTIONAL = False
  TUNE_EMBEDDING = True
  EMBEDDING_FILE = None  # Replace with a gzipped embedding file if needed.
  
  ## Reading text file
  test_file = open('data/test_data.tsv')
  labeled_sentences = [x.strip().split('\t') for x in test_file]
  labels, tagged_sentences = zip(*labeled_sentences)
  
  ## Preparing (indexing) data for classification.
  # word_syn_cutoff is the number of senses per word,
  # and syn_path_cutoff is the number of hypernyms per sense
  data_processor = DataProcessor(word_syn_cutoff=NUM_SENSES, syn_path_cutoff=NUM_HYPS)
  indexed_input = data_processor.prepare_input(tagged_sentences, onto_aware=True)
  one_hot_labels = data_processor.make_one_hot([int(x) for x in labels])
  
  ## Defining Keras model
  input_layer = Input(shape=indexed_input.shape[1:], dtype='int32')
  onto_lstm = OntoLSTMEncoder(num_senses=NUM_SENSES, num_hyps=NUM_HYPS, use_attention=ONTO_ATTENTION,
                              set_sense_priors=SENSE_PRIORS, data_processor=data_processor,
                              embed_dim=EMBED_DIM, return_sequences=False, bidirectional=BIDIRECTIONAL,
                              tune_embedding=TUNE_EMBEDDING)
  encoded_input = onto_lstm.get_encoded_phrase(input_layer, embedding_file=EMBEDDING_FILE)
  softmax_layer = Dense(2, activation='softmax')
  output_predictions = softmax_layer(encoded_input)
  model = Model(input=input_layer, output=output_predictions)
  model.summary()
  model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])
  
  ## Training
  model.fit(indexed_input, one_hot_labels, validation_split=0.2, nb_epoch=1)
  