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---
language:
- en
tags:
- question-answering
- cloze
- text
- knowledge-based
- QA
license: cc-by-sa-4.0
task_categories:
- question-answering
- text-classification
---
# Cloze QA Dataset (WikiText-2)
## Dataset Description
The **Cloze QA Dataset** is automatically generated from the [WikiText-2](https://huggingface.co/datasets/mindchain/wikitext2) corpus. It contains **fill-in-the-blank (cloze) style questions** derived directly from sentences in Wikipedia articles. This dataset is particularly useful for evaluating **local recall, reading comprehension, and contextual understanding**.
Each document produces **exactly three unique QA pairs**, preserving document structure and sentence alignment while avoiding redundancy. QA pairs are stored in **JSONL format**, with each entry tied to a specific sentence.
---
## Dataset Structure
| Split | Documents | QA Pairs | Description |
|------------|-----------|----------|-----------------------------------------------|
| Train | 5,135 | 15,405 | Used for model training and evaluation |
| Validation | 502 | 1,506 | Used for hyperparameter tuning |
| Test | 569 | 1,707 | Used for final performance benchmarking |
---
## Example Record
```json
{
"doc_id": 0,
"sent_id": 8,
"title": "Robert Boulter",
"question": "He appeared on a 2006 episode of the television series , ____ ,",
"answer": "Doctors"
}
````
---
## File Structure
```
cloze_qa_dataset/
├── train/
│ └── qa.jsonl
├── val/
│ └── qa.jsonl
└── test/
└── qa.jsonl
```
---
## Usage
This dataset is suitable for:
* Training QA models (extractive or generative)
* Benchmarking trivia-style QA tasks
* Knowledge-based reasoning research
---
## Source
Collected and curated by **WIDELab – Web Information & Data Engineering Laboratory, Department of Computer Science and Information Engineering, Chang Gung University, Taiwan**.
For more information, visit [CGU-Widelab](https://widelab.cgu.edu.tw/).
---
## License
© 2025 Chaithra Lokasara Mahadevaswamy et al., CGU-WIDELab.
Released under **CC BY-SA 4.0**, respecting Mistral AI and Hugging Face model terms.
---
## Citation
```
@misc{CGU-Widelab/Cloze_QA_Dataset_Wikitext2,
title={Cloze_QA_Dataset_Wikitext2},
author={WIDELab – Web Information & Data Engineering Laboratory, Chang Gung University},
year={2025},
howpublished={\url{https://huggingface.co/datasets/CGU-Widelab/Cloze_QA_Dataset_Wikitext2}},
note={Accessed: 2025-10-25}
}
@inproceedings{chaithra2025optimizingrag,
title={Optimizing Retrieval in RAG Systems with Reinforcement Learning: A Trade-off Between Quality and Cost},
author={Mahadevaswamy, Chaithra Lokasara and Nguyen, Khoa and Singh, Mayank and Chang, Hsien-Tsung},
booktitle={Proceedings of the 9th International Conference on Natural Language Processing and Information Retrieval (NLPIR 2025)},
year={2025},
address={Fukuoka, Japan}
}
```
```
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