README.md

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
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 ("\nApprentice Query ::")
print (sentence)
print ("\nAuto 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))

Output

Apprentice Query ::
ask user to provide his date of birth

Auto Generated Instruction ::
0: ask for the entity person_date_of_birth
1: ask “What is your date of birth?”


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-