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---
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](https://github.com/zhiwenyou103/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)](https://arxiv.org/abs/1809.02922). The dataset includes SQuAD and QA2D question answering pairs associated with contexts.
## How to use
Here is how to use this model in PyTorch:
```python
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},
}
```
> Code: https://github.com/zhiwenyou103/PlainQAFact |