old_contents: """
  This basic example loads a pre-trained model from the web and uses it to
  generate sentence embeddings for a given list of sentences.
  """
  
  from sentence_transformers import SentenceTransformer, LoggingHandler
  import numpy as np
  import logging
  
  #### Just some code to print debug information to stdout
  np.set_printoptions(threshold=100)
  
  logging.basicConfig(format='%(asctime)s - %(message)s',
                      datefmt='%Y-%m-%d %H:%M:%S',
                      level=logging.INFO,
                      handlers=[LoggingHandler()])
  #### /print debug information to stdout
  
  
  
  # Load Sentence model (based on BERT) from URL
  model = SentenceTransformer('bert-base-nli-mean-tokens')
  
  # Embed a list of sentences
  sentences = ['This framework generates embeddings for each input sentence',
               'Sentences are passed as a list of string.',
               'The quick brown fox jumps over the lazy dog.']
  sentence_embeddings = model.encode(sentences)
  
  # The result is a list of sentence embeddings as numpy arrays
  for sentence, embedding in zip(sentences, sentence_embeddings):
      print("Sentence:", sentence)
      print("Embedding:", embedding)
      print("")
  
new_contents: """
  This basic example loads a pre-trained model from the web and uses it to
  generate sentence embeddings for a given list of sentences.
  """
  
  from sentence_transformers import SentenceTransformer, LoggingHandler
  import numpy as np
  import logging
  
  #### Just some code to print debug information to stdout
  np.set_printoptions(threshold=100)
  
  logging.basicConfig(format='%(asctime)s - %(message)s',
                      datefmt='%Y-%m-%d %H:%M:%S',
                      level=logging.INFO,
                      handlers=[LoggingHandler()])
  #### /print debug information to stdout
  
  
  
  # Load pre-trained Sentence Transformer Model (based on DistilBERT). It will be downloaded automatically
  model = SentenceTransformer('distilbert-base-nli-stsb-mean-tokens')
  
  # Embed a list of sentences
  sentences = ['This framework generates embeddings for each input sentence',
               'Sentences are passed as a list of string.',
               'The quick brown fox jumps over the lazy dog.']
  sentence_embeddings = model.encode(sentences)
  
  # The result is a list of sentence embeddings as numpy arrays
  for sentence, embedding in zip(sentences, sentence_embeddings):
      print("Sentence:", sentence)
      print("Embedding:", embedding)
      print("")
  
  
  
  
subject: change model in example to DistilBERT