code: # 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.
  #
  
  """
  InferSent models. See https://github.com/facebookresearch/InferSent.
  """
  
  from __future__ import absolute_import, division, unicode_literals
  
  import sys
  import os
  import torch
  import logging
  import sys
  
  print(sys.argv[1:])
  # get models.py from InferSent repo
  from models import InferSent
  
  # Set up logger
  
  logging.basicConfig(format='%(asctime)s : %(message)s', level=logging.DEBUG)
  logging.info("\n\n\nPATH_TO_DATA: " + str(sys.argv[1]) + "\nPATH_TO_W2V: " + str(sys.argv[2]) + "\nMODEL_PATH: " + str(
      sys.argv[3]) + "\n\n")
  
  # Set PATHs
  PATH_SENTEVAL = '../'
  PATH_TO_DATA = sys.argv[1]  # '../data'
  PATH_TO_W2V = sys.argv[2]  # 'fasttext/crawl-300d-2M.vec'# 'glove/glove.840B.300d.txt'  # or crawl-300d-2M.vec for V2
  MODEL_PATH = sys.argv[3]  # 'infersent2.pkl'
  V = int(sys.argv[4])  # 2 # version of InferSent
  
  assert os.path.isfile(MODEL_PATH) and os.path.isfile(PATH_TO_W2V), \
      'Set MODEL and GloVe PATHs'
  
  # define senteval params
  params_senteval = {'task_path': PATH_TO_DATA, 'usepytorch': True, 'kfold': 10}
  
  if (len(sys.argv)>5):
      nhid = int(sys.argv[5])
  else:
      nhid=0
  
  #params_senteval['classifier'] = {'nhid':nhid , 'optim': 'rmsprop', 'batch_size': 128,'tenacity': 3, 'epoch_size': 2}
  params_senteval['classifier'] ={'nhid': 0, 'optim': 'adam','batch_size': 64, 'tenacity': 5,'epoch_size': 4}
  
  
  # import senteval
  sys.path.insert(0, PATH_SENTEVAL)
  import senteval
  
  
  def prepare(params, samples):
      params.infersent.build_vocab([' '.join(s) for s in samples], tokenize=False)
  
  
  def batcher(params, batch):
      sentences = [' '.join(s) for s in batch]
      embeddings = params.infersent.encode(sentences, bsize=params.batch_size, tokenize=False)
      return embeddings
  
  
  """
  Evaluation of trained model on Transfer Tasks (SentEval)
  """
      
  if __name__ == "__main__":
      # Load InferSent model
      params_model = {'bsize': 64, 'word_emb_dim': 300, 'enc_lstm_dim': 2048,
                      'pool_type': 'max', 'dpout_model': 0.0, 'version': V}
      model = InferSent(params_model)
      model.load_state_dict(torch.load(MODEL_PATH))
      model.set_w2v_path(PATH_TO_W2V)
  
      params_senteval['infersent'] = model.cuda()
  
      se = senteval.engine.SE(params_senteval, batcher, prepare)
      transfer_tasks = ['MEDNLI','ClinicalSTS','BIOSSES','ClinicalSTS2']
      results = se.eval(transfer_tasks)
      print(results)
  
apis: torch.load