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import torch
from transformers import T5ForConditionalGeneration,T5Tokenizer
class QuestionGeneration:
def __init__(self):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_squad_v1')
self.tokenizer = T5Tokenizer.from_pretrained('ramsrigouthamg/t5_squad_v1')
self.model = self.model.to(self.device)
def get_question(self, context, answer, model = None, tokenizer = None):
if(model == None):
model = self.model
if(tokenizer == None):
tokenizer = self.tokenizer
text = "context: {} answer: {}".format(context,answer)
encoding = tokenizer.encode_plus(text,max_length=384, pad_to_max_length=False,truncation=True, return_tensors="pt").to(self.device)
input_ids, attention_mask = encoding["input_ids"], encoding["attention_mask"]
outs = model.generate(input_ids=input_ids,
attention_mask=attention_mask,
early_stopping=True,
num_beams=5,
num_return_sequences=1,
no_repeat_ngram_size=2,
max_length=72
)
dec = [tokenizer.decode(ids,skip_special_tokens=True) for ids in outs]
Question = dec[0].replace("question:","")
Question= Question.strip()
return Question
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