text: """
  The system trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) on the SNLI + MultiNLI (AllNLI) dataset
  with softmax loss function. At every 1000 training steps, the model is evaluated on the
  STS benchmark dataset
  
  Usage:
  python training_nli.py
  
  OR
  python training_nli.py pretrained_transformer_model_name
  """
  from torch.utils.data import DataLoader
  import math
  from sentence_transformers import models, losses
  from sentence_transformers import LoggingHandler, SentenceTransformer, util, InputExample
  from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
  import logging
  from datetime import datetime
  import sys
  import os
  import gzip
  import csv
  
  #### Just some code to print debug information to stdout
  logging.basicConfig(format='%(asctime)s - %(message)s',
                      datefmt='%Y-%m-%d %H:%M:%S',
                      level=logging.INFO,
                      handlers=[LoggingHandler()])
  #### /print debug information to stdout
  
  #Check if dataset exsist. If not, download and extract  it
  nli_dataset_path = 'data/AllNLI.tsv.gz'
  sts_dataset_path = 'data/stsbenchmark.tsv.gz'
  
  if not os.path.exists(nli_dataset_path):
      util.http_get('https://sbert.net/datasets/AllNLI.tsv.gz', nli_dataset_path)
  
  if not os.path.exists(sts_dataset_path):
      util.http_get('https://sbert.net/datasets/stsbenchmark.tsv.gz', sts_dataset_path)
  
  
  #You can specify any huggingface/transformers pre-trained model here, for example, bert-base-uncased, roberta-base, xlm-roberta-base
  model_name = sys.argv[1] if len(sys.argv) > 1 else 'bert-base-uncased'
  
  # Read the dataset
  train_batch_size = 16
  
  
  model_save_path = 'output/training_nli_'+model_name.replace("/", "-")+'-'+datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
  
  
  # Use Huggingface/transformers model (like BERT, RoBERTa, XLNet, XLM-R) for mapping tokens to embeddings
  word_embedding_model = models.Transformer(model_name)
  
  # Apply mean pooling to get one fixed sized sentence vector
  pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(),
                                 pooling_mode_mean_tokens=True,
                                 pooling_mode_cls_token=False,
                                 pooling_mode_max_tokens=False)
  
  model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
  
  
  # Read the AllNLI.tsv.gz file and create the training dataset
  logging.info("Read AllNLI train dataset")
  
  label2int = {"contradiction": 0, "entailment": 1, "neutral": 2}
  train_samples = []
  with gzip.open(nli_dataset_path, 'rt', encoding='utf8') as fIn:
      reader = csv.DictReader(fIn, delimiter='\t', quoting=csv.QUOTE_NONE)
      for row in reader:
          if row['split'] == 'train':
              label_id = label2int[row['label']]
              train_samples.append(InputExample(texts=[row['sentence1'], row['sentence2']], label=label_id))
  
  
  
  train_dataloader = DataLoader(train_samples, shuffle=True, batch_size=train_batch_size)
  train_loss = losses.SoftmaxLoss(model=model, sentence_embedding_dimension=model.get_sentence_embedding_dimension(), num_labels=len(label2int))
  
  
  #Read STSbenchmark dataset and use it as development set
  logging.info("Read STSbenchmark dev dataset")
  dev_samples = []
  with gzip.open(sts_dataset_path, 'rt', encoding='utf8') as fIn:
      reader = csv.DictReader(fIn, delimiter='\t', quoting=csv.QUOTE_NONE)
      for row in reader:
          if row['split'] == 'dev':
              score = float(row['score']) / 5.0 #Normalize score to range 0 ... 1
              dev_samples.append(InputExample(texts=[row['sentence1'], row['sentence2']], label=score))
  
  dev_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, batch_size=train_batch_size, name='sts-dev')
  
  # Configure the training
  num_epochs = 1
  
  warmup_steps = math.ceil(len(train_dataloader) * num_epochs * 0.1) #10% of train data for warm-up
  logging.info("Warmup-steps: {}".format(warmup_steps))
  
  
  
  # Train the model
  model.fit(train_objectives=[(train_dataloader, train_loss)],
            evaluator=dev_evaluator,
            epochs=num_epochs,
            evaluation_steps=1000,
            warmup_steps=warmup_steps,
            output_path=model_save_path
            )
  
  
  
  ##############################################################################
  #
  # Load the stored model and evaluate its performance on STS benchmark dataset
  #
  ##############################################################################
  
  test_samples = []
  with gzip.open(sts_dataset_path, 'rt', encoding='utf8') as fIn:
      reader = csv.DictReader(fIn, delimiter='\t', quoting=csv.QUOTE_NONE)
      for row in reader:
          if row['split'] == 'test':
              score = float(row['score']) / 5.0 #Normalize score to range 0 ... 1
              test_samples.append(InputExample(texts=[row['sentence1'], row['sentence2']], label=score))
  
  model = SentenceTransformer(model_save_path)
  test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, batch_size=train_batch_size, name='sts-test')
  test_evaluator(model, output_path=model_save_path)
  