Update README.md

README.md

@@ -1,4 +1,3 @@
-## This is the model  where we enter input in varying forms of natural language and it generates instructions which can be used in later stages of the BPMN, we fine tuned t5 on our own data.
 
 ```
 import torch
@@ -42,9 +41,11 @@ beam_outputs = model.generate(
 )
 
 
-print ("\nApprentice Query ::")
+print ("\
+Apprentice Query ::")
 print (sentence)
-print ("\nAuto Generated Instruction ::")
+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)