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import torch
import re
@torch.no_grad()
def question_generation_sampling(
g1_model,
g1_tokenizer,
g2_model,
g2_tokenizer,
context,
num_questions,
device,
):
qa_input_ids = prepare_qa_input(
g1_tokenizer,
context=context,
device=device,
)
max_repeated_sampling = int(num_questions * 1.5) # sometimes generated question+answer is invalid
num_valid_questions = 0
questions = []
for q_ in range(max_repeated_sampling):
# Stage G.1: question+answer generation
outputs = g1_model.generate(
qa_input_ids,
max_new_tokens=128,
do_sample=True,
)
question_answer = g1_tokenizer.decode(outputs[0], skip_special_tokens=False)
question_answer = question_answer.replace(g1_tokenizer.pad_token, "").replace(g1_tokenizer.eos_token, "")
question_answer_split = question_answer.split(g1_tokenizer.sep_token)
if len(question_answer_split) == 2:
# valid Question + Annswer output
num_valid_questions += 1
else:
continue
question = question_answer_split[0].strip()
answer = question_answer_split[1].strip()
# Stage G.2: Distractor Generation
distractor_input_ids = prepare_distractor_input(
g2_tokenizer,
context = context,
question = question,
answer = answer,
device = device,
separator = g2_tokenizer.sep_token,
)
outputs = g2_model.generate(
distractor_input_ids,
max_new_tokens=128,
do_sample=True,
)
distractors = g2_tokenizer.decode(outputs[0], skip_special_tokens=False)
distractors = distractors.replace(g2_tokenizer.pad_token, "").replace(g2_tokenizer.eos_token, "")
distractors = re.sub("<extra\S+>", g2_tokenizer.sep_token, distractors)
distractors = [y.strip() for y in distractors.split(g2_tokenizer.sep_token)]
options = [answer] + distractors
while len(options) < 4:
options.append(options[-1])
question_item = {
'question': question,
'options': options,
}
questions.append(question_item)
if num_valid_questions == num_questions:
break
return questions
def prepare_qa_input(t5_tokenizer, context, device):
"""
input: context
output: question <sep> answer
"""
encoding = t5_tokenizer(
[context],
return_tensors="pt",
)
input_ids = encoding.input_ids.to(device)
return input_ids
def prepare_distractor_input(t5_tokenizer, context, question, answer, device, separator='<sep>'):
"""
input: question <sep> answer <sep> article
output: distractor1 <sep> distractor2 <sep> distractor3
"""
input_text = question + ' ' + separator + ' ' + answer + ' ' + separator + ' ' + context
encoding = t5_tokenizer(
[input_text],
return_tensors="pt",
)
input_ids = encoding.input_ids.to(device)
return input_ids |