text: from os import sep
  from sentence_transformers import SentenceTransformer, InputExample, losses
  from torch.utils.data import DataLoader
  import argparse
  import pandas as pd
  
  parser = argparse.ArgumentParser()
  
  parser.add_argument(
      '-d',
      '--destPath',
      action='store',
      required=True,
      dest='destPath',
      help='Destination model path'
  )
  parser.add_argument(
      '--modelPath',
      '-m',
      dest='modelPath',
      action='store',
      required=True,
      help='path to folder containing test files'
  )
  parser.add_argument(
      '--trainFile',
      '-t',
      dest='trainFile',
      action='store',
      required=True,
      help='File with all the training text'
  )
  
  args = parser.parse_args()
  
  #Define the model. Either from scratch of by loading a pre-trained model
  model = SentenceTransformer(args.modelPath)
  
  training_file = pd.read_csv(args.trainFile, sep=",", header=None)
  
  train_examples = [ InputExample(texts=[x[0], x[1]], label=x[2]/10) for x in training_file.to_numpy()]
  
  #Define your train dataset, the dataloader and the train loss
  train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=16)
  train_loss = losses.CosineSimilarityLoss(model)
  
  #Tune the model
  model.fit(train_objectives=[(train_dataloader, train_loss)], epochs=100, warmup_steps=100)
  
  model.save(args.destPath)