text: from keras.models import Sequential
  from keras.layers import Embedding,LSTM,Dense
  
  model = Sequential()
  model.add(word_vectors.get_keras_embedding(train_embeddings=True))
  model.add(LSTM(args.value,
                 activation='tanh', # activation function used
                 recurrent_activation='hard_sigmoid', # activation function for recurrent step
                 use_bias=True, # whether the layer uses a bias vector
                 kernel_initializer='glorot_uniform', # initialiser for the weights matrix
                 recurrent_initializer='orthogonal', # initialiser for the recurrent kernal's weights
                 bias_initializer='zeros', # initialiser for the bias vector
                 unit_forget_bias=True, # add 1 to the bias of the forget gate at initialization
                 kernel_regularizer=None, # regularizer function applied to kernal
                 recurrent_regularizer=None, # regularizer function applied to recurrent kernal
                 bias_regularizer=None, # regularizer function applied to bias vector
                 activity_regularizer=None, # regularizer function applied to output of the layer
                 kernel_constraint=None, # constraint function applied to the kernal
                 recurrent_constraint=None, # constraint function applied to the recurrent kernal
                 bias_constraint=None, # constraint function applied to the bias vector
                 dropout=0.0, # fraction of units to drop for the linear transformation of the inputs
                 recurrent_dropout=0.0, # fraction of units to drop for the linear transformation of the recurrent state
                 implementation=1, # implementation mode, either 1 or 2.
                 return_sequences=False,
                 return_state=False,
                 go_backwards=False,
                 stateful=False, # If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.
                 unroll=False)) # whether the network will be unrolled, otherwise a symbolic loop will be used.
  model.add(Dense(2,
                  activation='softmax',
                  use_bias=True,
                  kernel_initializer='glorot_uniform',
                  bias_initializer='zeros',
                  kernel_regularizer=None,
                  bias_regularizer=None,
                  activity_regularizer=None,
                  kernel_constraint=None,
                  bias_constraint=None))
  model.compile(optimizer='Adadelta',
                loss='binary_crossentropy',
                metrics=['acc'],
                sample_weight_mode=None,
                weighted_metrics=None,
                target_tensors=None)
  