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--- |
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license: apache-2.0 |
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base_model: |
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- facebook/bart-large |
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language: |
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- en |
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library_name: pytorch |
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pipeline_tag: text-generation |
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tags: |
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- question-generation |
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--- |
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> This Question Generation model is a part of the [PlainQAFact](https://github.com/zhiwenyou103/PlainQAFact) factuality evaluation framework. |
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## Generating Questions Given Context and Answers |
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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. |
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## How to use |
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Here is how to use this model in PyTorch: |
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```python |
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from transformers import BartForConditionalGeneration, BartTokenizer |
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import torch |
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tokenizer = BartTokenizer.from_pretrained('uzw/bart-large-question-generation') |
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model = BartForConditionalGeneration.from_pretrained('uzw/bart-large-question-generation') |
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context = "The Thug cult resides at the Pankot Palace." |
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answer = "The Thug cult" |
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inputs = tokenizer.encode_plus( |
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context, |
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answer, |
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max_length=512, |
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padding='max_length', |
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truncation=True, |
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return_tensors='pt' |
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) |
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with torch.no_grad(): |
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generated_ids = model.generate( |
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input_ids=inputs['input_ids'], |
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attention_mask=inputs['attention_mask'], |
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max_length=64, # Maximum length of generated question |
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num_return_sequences=3, # Generate multiple questions |
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do_sample=True, # Enable sampling for diversity |
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temperature=0.7 # Control randomness of generation |
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) |
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generated_questions = tokenizer.batch_decode( |
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generated_ids, |
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skip_special_tokens=True |
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) |
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for i, question in enumerate(generated_questions, 1): |
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print(f"Generated Question {i}: {question}") |
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``` |
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Adjusting parameter `num_return_sequences` to generate multiple questions. |
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## Citation |
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If you use this QG model in your research, please cite with the following BibTex entry: |
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``` |
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@misc{you2025plainqafactautomaticfactualityevaluation, |
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title={PlainQAFact: Automatic Factuality Evaluation Metric for Biomedical Plain Language Summaries Generation}, |
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author={Zhiwen You and Yue Guo}, |
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year={2025}, |
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eprint={2503.08890}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2503.08890}, |
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} |
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``` |
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> Code: https://github.com/zhiwenyou103/PlainQAFact |
