Model Card for t5-english-qg

Model Description

t5-english-qg is a T5 model, fine-tuned from T5-base in English using the original English FairytaleQA dataset. The task of fine-tuning is Question Generation. You can check our paper, accepted in ECTEL 2024.

Training Data

FairytaleQA is an open-source dataset designed to enhance comprehension of narratives, aimed at students from kindergarten to eighth grade. The dataset is meticulously annotated by education experts following an evidence-based theoretical framework. It comprises 10,580 explicit and implicit questions derived from 278 child-friendly stories, covering seven types of narrative elements or relations.

Implementation Details

The encoder concatenates the answer and text, and the decoder generates the question. We use special labels to differentiate the components. Our maximum token input is set to 512, while the maximum token output is set to 128. During training, the models undergo a maximum of 20 epochs and incorporate early stopping with a patience of 2. A batch size of 16 is employed. During inference, we utilize beam search with a beam width of 5.

Evaluation - Question Generation

Load Model and Tokenizer

>>> from transformers import T5ForConditionalGeneration, T5Tokenizer
>>> model = T5ForConditionalGeneration.from_pretrained("benjleite/t5-english-qg")
>>> tokenizer = T5Tokenizer.from_pretrained("t5-base", model_max_length=512)

Important Note: Special tokens need to be added and model tokens must be resized:

>>> tokenizer.add_tokens(['<attribute>', '<question>','<answer>','<answertype>','<text>'], special_tokens=True)
>>> model.resize_token_embeddings(len(tokenizer))

Inference Example (same parameters as used in paper experiments)

Note: See our repository for additional code details.

input_text = '<answer>' + 'A Bear.' + '<text>' + 'Once upon a time, a bear was walking in the forest....'

source_encoding = tokenizer(
 input_text,
 max_length=512,
 padding='max_length',
 truncation = 'only_second',
 return_attention_mask=True,
 add_special_tokens=True,
 return_tensors='pt'
)
 
input_ids = source_encoding['input_ids']
attention_mask = source_encoding['attention_mask']

generated_ids = model.generate(
 input_ids=input_ids,
 attention_mask=attention_mask,
 num_return_sequences=1,
 num_beams=5,
 max_length=512,
 repetition_penalty=1.0,
 length_penalty=1.0,
 early_stopping=True,
 use_cache=True
)

prediction = {
 tokenizer.decode(generated_id, skip_special_tokens=False, clean_up_tokenization_spaces=True)
 for generated_id in generated_ids
}

generated_str = ''.join(preds)

print(generated_str)

Licensing Information

This fine-tuned model is released under the Apache-2.0 License.

Citation Information

Our paper at ECTEL 2024:

@InProceedings{leite_fairytaleqa_translated_2024,
 author="Leite, Bernardo
 and Os{\'o}rio, Tom{\'a}s Freitas
 and Cardoso, Henrique Lopes",
 editor="Ferreira Mello, Rafael
 and Rummel, Nikol
 and Jivet, Ioana
 and Pishtari, Gerti
 and Ruip{\'e}rez Valiente, Jos{\'e} A.",
 title="FairytaleQA Translated: Enabling Educational Question and Answer Generation in Less-Resourced Languages",
 booktitle="Technology Enhanced Learning for Inclusive and Equitable Quality Education",
 year="2024",
 publisher="Springer Nature Switzerland",
 address="Cham",
 pages="222--236",
 isbn="978-3-031-72315-5"
}

Original FairytaleQA paper:

@inproceedings{xu-etal-2022-fantastic,
 title = "Fantastic Questions and Where to Find Them: {F}airytale{QA} {--} An Authentic Dataset for Narrative Comprehension",
 author = "Xu, Ying and
 Wang, Dakuo and
 Yu, Mo and
 Ritchie, Daniel and
 Yao, Bingsheng and
 Wu, Tongshuang and
 Zhang, Zheng and
 Li, Toby and
 Bradford, Nora and
 Sun, Branda and
 Hoang, Tran and
 Sang, Yisi and
 Hou, Yufang and
 Ma, Xiaojuan and
 Yang, Diyi and
 Peng, Nanyun and
 Yu, Zhou and
 Warschauer, Mark",
 editor = "Muresan, Smaranda and
 Nakov, Preslav and
 Villavicencio, Aline",
 booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
 month = may,
 year = "2022",
 address = "Dublin, Ireland",
 publisher = "Association for Computational Linguistics",
 url = "https://aclanthology.org/2022.acl-long.34",
 doi = "10.18653/v1/2022.acl-long.34",
 pages = "447--460",
 abstract = "Question answering (QA) is a fundamental means to facilitate assessment and training of narrative comprehension skills for both machines and young children, yet there is scarcity of high-quality QA datasets carefully designed to serve this purpose. In particular, existing datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements. Drawing on the reading education research, we introduce FairytaleQA, a dataset focusing on narrative comprehension of kindergarten to eighth-grade students. Generated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. Our dataset is valuable in two folds: First, we ran existing QA models on our dataset and confirmed that this annotation helps assess models{'} fine-grained learning skills. Second, the dataset supports question generation (QG) task in the education domain. Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.",
}

T5 model:

@article{raffel_2020_t5,
 author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
 title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
 journal = {Journal of Machine Learning Research},
 year = {2020},
 volume = {21},
 number = {140},
 pages = {1-67},
 url = {http://jmlr.org/papers/v21/20-074.html},
 note={Model URL: \url{huggingface.co/google-t5/t5-base}}
}

10 items • Updated Jul 16, 2025