new_contents: # Copyright 2020 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """Text classification demo code of model customization for TFLite."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from absl import app
  from absl import flags
  from absl import logging
  
  import tensorflow.compat.v2 as tf
  from tensorflow_examples.lite.model_maker.core.data_util.text_dataloader import QuestionAnswerDataLoader
  from tensorflow_examples.lite.model_maker.core.task import model_spec
  from tensorflow_examples.lite.model_maker.core.task import question_answer
  
  FLAGS = flags.FLAGS
  
  
  def define_flags():
    flags.DEFINE_string('export_dir', None,
                        'The directory to save exported files.')
    flags.mark_flag_as_required('export_dir')
  
  
  def download_demo_data(**kwargs):
    """Downloads demo data, and returns directory path."""
    train_data_path = tf.keras.utils.get_file(
        fname='train-v1.1.json',
        origin='https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json',
        **kwargs)
    validation_data_path = tf.keras.utils.get_file(
        fname='dev-v1.1.json',
        origin='https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json',
        **kwargs)
    return train_data_path, validation_data_path
  
  
  def run(train_data_path,
          validation_data_path,
          export_dir,
          spec='bert_qa',
          **kwargs):
    """Runs demo."""
    # Chooses model specification that represents model.
    spec = model_spec.get(spec)
  
    # Gets training data and validation data.
    train_data = QuestionAnswerDataLoader.from_squad(
        train_data_path, spec, is_training=True)
    validation_data = QuestionAnswerDataLoader.from_squad(
        validation_data_path, spec, is_training=False)
  
    # Fine-tunes the model.
    model = question_answer.create(train_data, model_spec=spec, **kwargs)
  
    # Gets evaluation results.
    metric = model.evaluate(validation_data)
    tf.compat.v1.logging.info('Eval F1 score:%f' % metric['final_f1'])
  
    # Exports to TFLite format.
    model.export(export_dir)
  
  
  def main(_):
    logging.set_verbosity(logging.INFO)
  
    train_data_path, validation_data_path = download_demo_data()
    run(train_data_path, validation_data_path, FLAGS.export_dir)
  
  
  if __name__ == '__main__':
    define_flags()
    app.run(main)
  
subject: Add question answering demo for model maker.