def setup_uncased(model_config):
  """Setup the uncased bert model.

  Args:
    model_config: The model configuration to be loaded.

  Returns:
    tokenizer: The tokenizer to be used to convert between tokens and ids.
    model: The model that has been initialized.
    device: The device to be used in this run.
    embedding_map: Holding all token embeddings.
  """
  # Load pre-trained model tokenizer (vocabulary)
  tokenizer = tokenization.BertTokenizer.from_pretrained(model_config)
  # Load pre-trained model (weights)
  model = modeling.BertModel.from_pretrained(model_config)
  _ = model.eval()
  # Set up the device in use
  device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
  print('device : ', device)
  model = model.to(device)
  # Initialize the embedding map
  embedding_map = embeddings_helper.EmbeddingMap(device, model)
  return tokenizer, model, device, embedding_map