chunk_content: nd labels into the model_inputs.
  Create a separate attention mask for labels and model_inputs.
  Loop through each example in the batch again to pad the input ids, labels, and attention mask to the max_length and convert them to PyTorch tensors.
  
  
  	Copied
  def preprocess_function(examples):
      batch_size = len(examples[text_column])
      inputs = [f"{text_column} : {x} Label : " for x in examples[text_column]]
      targets = [str(x) for x in exampl