Update README.md
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README.md
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```
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import torch
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from transformers import T5ForConditionalGeneration,T5Tokenizer
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print ("
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Apprentice Query ::")
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print (sentence)
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print ("
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Auto Generated Instruction ::")
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final_outputs =[]
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for beam_output in beam_outputs:
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```
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Apprentice Query ::
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ask user to provide his date of birth
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Auto Generated Instruction ::
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0: ask for the entity person_date_of_birth
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1: ask “What is your date of birth?”
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Reference: https://github.com/ramsrigouthamg/Paraphrase-any-question-with-T5-Text-To-Text-Transfer-Transformer-
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## 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.
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```
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import torch
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from transformers import T5ForConditionalGeneration,T5Tokenizer
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)
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print ("\\
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Apprentice Query ::")
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print (sentence)
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print ("\\
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Auto Generated Instruction ::")
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final_outputs =[]
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for beam_output in beam_outputs:
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```
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## Output
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```
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Apprentice Query ::
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ask user to provide his date of birth
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Auto Generated Instruction ::
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0: ask for the entity person_date_of_birth
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1: ask “What is your date of birth?”
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Apprentice Query ::
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IF (assert(user.balance > $100)) THEN (say you need more balance)
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Auto Generated Instruction ::
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0: IF (assert(user.balance > $100)) THEN (say you need more balance)
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```
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Reference: https://github.com/ramsrigouthamg/Paraphrase-any-question-with-T5-Text-To-Text-Transfer-Transformer-
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