metadata
license: apache-2.0
base_model:
- facebook/bart-large
language:
- en
library_name: pytorch
pipeline_tag: text-generation
tags:
- question-generation
This Question Generation model is a part of the PlainQAFact factuality evaluation framework.
Generating Questions Given Context and Answers
Traditional BART model is not pre-trained on QG tasks. We fine-tuned facebook/bart-large model using 55k human-created question answering pairs with contexts collected by Demszky et al. (2018). The dataset includes SQuAD and QA2D question answering pairs associated with contexts.
How to use
Here is how to use this model in PyTorch:
from transformers import BartForConditionalGeneration, BartTokenizer
import torch
tokenizer = BartTokenizer.from_pretrained('uzw/bart-large-question-generation')
model = BartForConditionalGeneration.from_pretrained('uzw/bart-large-question-generation')
context = "The Thug cult resides at the Pankot Palace."
answer = "The Thug cult"
inputs = tokenizer.encode_plus(
context,
answer,
max_length=512,
padding='max_length',
truncation=True,
return_tensors='pt'
)
with torch.no_grad():
generated_ids = model.generate(
input_ids=inputs['input_ids'],
attention_mask=inputs['attention_mask'],
max_length=64, # Maximum length of generated question
num_return_sequences=3, # Generate multiple questions
do_sample=True, # Enable sampling for diversity
temperature=0.7 # Control randomness of generation
)
generated_questions = tokenizer.batch_decode(
generated_ids,
skip_special_tokens=True
)
for i, question in enumerate(generated_questions, 1):
print(f"Generated Question {i}: {question}")
Adjusting parameter num_return_sequences to generate multiple questions.
Citation
If you use this QG model in your research, please cite with the following BibTex entry:
@misc{you2025plainqafactautomaticfactualityevaluation,
title={PlainQAFact: Automatic Factuality Evaluation Metric for Biomedical Plain Language Summaries Generation},
author={Zhiwen You and Yue Guo},
year={2025},
eprint={2503.08890},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.08890},
}