# Build the LSTM model
def getModel():
    units1, units2 = int (nWords/4), int (nWords/8)
    model = keras.models.Sequential()
    model.add(keras.layers.embeddings.Embedding(input_dim = len(kTokenizer.word_index)+1,output_dim=units1,input_length=sequenceLength, trainable=True))               # Line 5
    model.add(keras.layers.LSTM(units = units2, return_sequences =False))                               # Line 6
    model.add(keras.layers.Dense(len(labelToName), activation ='softmax'))                           # Line 7
    model.compile(optimizer='adam', loss = 'categorical_crossentropy', metrics=['acc'])
    return model