readme: 
  ```
  import torch
  from transformers import T5ForConditionalGeneration,T5Tokenizer
  
  
  def set_seed(seed):
      torch.manual_seed(seed)
      if torch.cuda.is_available():
          torch.cuda.manual_seed_all(seed)
      
      set_seed(42)
  
  
  model = T5ForConditionalGeneration.from_pretrained("priyank/Generate_instructions_t5")
  tokenizer = T5Tokenizer.from_pretrained("priyank/Generate_instructions_t5")
  
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  model = model.to(device)
  
  
  sentence = "ask user to provide his date of birth"
  text =  "paraphrase: " + sentence + " </s>"
  
  
  max_len = 256
  
  encoding = tokenizer.encode_plus(text,pad_to_max_length=True, return_tensors="pt")
  input_ids, attention_masks = encoding["input_ids"].to(device), encoding["attention_mask"].to(device)
  
  
  # set top_k = 50 and set top_p = 0.95 and num_return_sequences = 3
  beam_outputs = model.generate(
      input_ids=input_ids, attention_mask=attention_masks,
      do_sample=True,
      max_length=256,
      top_k=120,
      top_p=0.98,
      early_stopping=True,
      num_return_sequences=10
  )
  
  
  print ("\\
  Apprentice Query ::")
  print (sentence)
  print ("\\
  Auto Generated Instruction ::")
  final_outputs =[]
  for beam_output in beam_outputs:
      sent = tokenizer.decode(beam_output, skip_special_tokens=True,clean_up_tokenization_spaces=True)
      if sent.lower() != sentence.lower() and sent not in final_outputs:
          final_outputs.append(sent)
  
  for i, final_output in enumerate(final_outputs):
      print("{}: {}".format(i, final_output))
  
  
  Apprentice Query ::
  if balance is greater than $100, then tell the user he needs more balance
  
  Auto Generated Instruction ::
  0: IF (assert(user.balance > $100)) THEN (say you need more balance)
  
  ```
  
  Reference: https://github.com/ramsrigouthamg/Paraphrase-any-question-with-T5-Text-To-Text-Transfer-Transformer-