text: # Copyright (c) 2017-present, Facebook, Inc.
  # All rights reserved.
  #
  # This source code is licensed under the license found in the
  # LICENSE file in the root directory of this source tree.
  #
  
  from __future__ import absolute_import, division
  
  import os
  import sys
  import logging
  import tensorflow as tf
  import tensorflow_hub as hub
  tf.logging.set_verbosity(0)
  
  # Set PATHs
  PATH_TO_SENTEVAL = '../'
  PATH_TO_DATA = '../data'
  
  # import SentEval
  sys.path.insert(0, PATH_TO_SENTEVAL)
  import src
  
  # tensorflow session
  session = tf.Session()
  os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
  
  # SentEval prepare and batcher
  def prepare(params, samples):
      return
  
  def batcher(params, batch):
      batch = [' '.join(sent) if sent != [] else '.' for sent in batch]
      embeddings = params['google_use'](batch)
      return embeddings
  
  def make_embed_fn(module):
    with tf.Graph().as_default():
      sentences = tf.placeholder(tf.string)
      embed = hub.Module(module)
      embeddings = embed(sentences)
      session = tf.train.MonitoredSession()
    return lambda x: session.run(embeddings, {sentences: x})
  
  # Start TF session and load Google Universal Sentence Encoder
  encoder = make_embed_fn("https://tfhub.dev/google/universal-sentence-encoder-large/2")
  
  # Set params for SentEval
  params_senteval = {'task_path': PATH_TO_DATA, 'usepytorch': True, 'kfold': 5}
  params_senteval['classifier'] = {'nhid': 0, 'optim': 'rmsprop', 'batch_size': 128,
                                   'tenacity': 3, 'epoch_size': 2}
  params_senteval['google_use'] = encoder
  
  # Set up logger
  logging.basicConfig(format='%(asctime)s : %(message)s', level=logging.DEBUG)
  
  if __name__ == "__main__":
      se = src.engine.SE(params_senteval, batcher, prepare)
      transfer_tasks = ['STS12', 'STS13', 'STS14', 'STS15', 'STS16',
                        'MR', 'CR', 'MPQA', 'SUBJ', 'SST2', 'SST5', 'TREC', 'MRPC',
                        'SICKEntailment', 'SICKRelatedness', 'STSBenchmark',
                        'Length', 'WordContent', 'Depth', 'TopConstituents',
                        'BigramShift', 'Tense', 'SubjNumber', 'ObjNumber',
                        'OddManOut', 'CoordinationInversion']
      results = se.eval(transfer_tasks)
      print(results)
  