text: """
  This example loads the pre-trained SentenceTransformer model 'nli-distilroberta-base-v2' from the server.
  It then fine-tunes this model for some epochs on the STS benchmark dataset.
  
  Note: In this example, you must specify a SentenceTransformer model.
  If you want to fine-tune a huggingface/transformers model like bert-base-uncased, see training_nli.py and training_stsbenchmark.py
  """
  from torch.utils.data import DataLoader
  import math
  from sentence_transformers import SentenceTransformer, LoggingHandler, losses, util, InputExample
  from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
  import logging
  from datetime import datetime
  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
  sts_dataset_path = 'datasets/stsbenchmark.tsv.gz'
  
  if not os.path.exists(sts_dataset_path):
      util.http_get('https://sbert.net/datasets/stsbenchmark.tsv.gz', sts_dataset_path)
  
  
  
  
  # Read the dataset
  model_name = 'nli-distilroberta-base-v2'
  train_batch_size = 16
  num_epochs = 4
  model_save_path = 'output/training_stsbenchmark_continue_training-'+model_name+'-'+datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
  
  
  
  # Load a pre-trained sentence transformer model
  model = SentenceTransformer(model_name)
  
  # Convert the dataset to a DataLoader ready for training
  logging.info("Read STSbenchmark train dataset")
  
  train_samples = []
  dev_samples = []
  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:
          score = float(row['score']) / 5.0  # Normalize score to range 0 ... 1
          inp_example = InputExample(texts=[row['sentence1'], row['sentence2']], label=score)
  
          if row['split'] == 'dev':
              dev_samples.append(inp_example)
          elif row['split'] == 'test':
              test_samples.append(inp_example)
          else:
              train_samples.append(inp_example)
  
  
  
  train_dataloader = DataLoader(train_samples, shuffle=True, batch_size=train_batch_size)
  train_loss = losses.CosineSimilarityLoss(model=model)
  
  
  # Development set: Measure correlation between cosine score and gold labels
  logging.info("Read STSbenchmark dev dataset")
  evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name='sts-dev')
  
  
  # Configure the training. We skip evaluation in this example
  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=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
  #
  ##############################################################################
  
  model = SentenceTransformer(model_save_path)
  test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, name='sts-test')
  test_evaluator(model, output_path=model_save_path)