README.md

---
license: cc-by-sa-4.0
task_categories:
- text-generation
- question-generation
language:
- en
pretty_name: SQuAD-EN-Passage-to-Question
size_categories:
- 10K<n<100K
---

# Dataset Card for SQuAD-EN-Passage-to-Question

## Dataset Summary

**SQuAD-EN-Passage-to-Question** is a reformatted and reorganized version of the **Stanford Question Answering Dataset (SQuAD)**. The dataset is designed for text generation and question generation research tasks.

In the original SQuAD dataset, each context passage is associated with multiple question-answer pairs stored as separate entries. In this modified version, all questions associated with the same context passage are grouped together into a single record.

This restructuring enables research in:

- Multi-question generation
- Context-aware question generation
- Passage-level instruction tuning
- Text generation from contextual inputs
---
## Dataset Structure

Each dataset entry contains:

- `task_id`: Unique identifier for each context entry  
- `context`: A passage of text  
- `question`: A list of questions associated with the context  

### Example

```json
{
  "task_id": 1,
  "context": "Architecturally, the school has a Catholic character...",
  "question": [
    "To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?",
    "What is in front of the Notre Dame Main Building?"
  ]
}
```

---
## Data Splits

The dataset splits are derived from the original SQuAD splits:

| Split | Source |
|-------|--------|
| Train | SQuAD Train Set |
| Validation | Portion of SQuAD Validation Set |
| Test | Portion of SQuAD Validation Set |

The validation split from SQuAD was further divided into validation and test subsets.

---
## Dataset Statistics

* **Language:** English
* **Source dataset:** SQuAD
* **Task type:** Question Generation / Text Generation
* **Data format:** JSONL
* **Questions per context:** Variable (multiple questions grouped together)

---
## Source Dataset

This dataset is derived from:

**Stanford Question Answering Dataset (SQuAD)**

* **Authors:** Rajpurkar et al.
* **Paper:** https://arxiv.org/abs/1606.05250
* **Official Website:** https://rajpurkar.github.io/SQuAD-explorer/
* **Hugging Face Dataset:** https://huggingface.co/datasets/rajpurkar/squad

---
## Modifications from Original Dataset

The following modifications were applied:

1. Grouped multiple questions under a single shared context passage.
2. Removed answer annotations from the original dataset.
3. Reorganized dataset structure into JSONL format.
4. Re-split the validation dataset into validation and test subsets.
5. Added unique `task_id` identifiers for each context entry.

---
## Intended Uses

This dataset is intended for:

* Question generation research
* Instruction tuning
* Text-to-text generation tasks
* Context-based multi-output generation
* Educational NLP experiments

---
## Recommended Usage

### Loading the Dataset

```python
from datasets import load_dataset

dataset = load_dataset("Siam0703/SQuAD-EN-Passage-to-Question")
```

### Accessing Data

```python
dataset["train"][0]
```

### Potential Applications

* Multi-output text generation
* Educational AI systems
* Context-driven question synthesis
* Large language model fine-tuning

---
## Limitations

* Answer annotations from the original SQuAD dataset are not included.
* The dataset inherits biases and limitations from the original SQuAD dataset.
* Questions are human-generated and reflect annotator perspectives.
* Grouped question format may require preprocessing for extractive QA tasks.

---
## Ethical Considerations

The dataset contains human-authored content from publicly available educational and informational text. Users should be aware that:

* The dataset may contain factual or cultural biases present in the original SQuAD dataset.
* Generated outputs trained on this dataset should be evaluated for fairness and accuracy.

---
## Citation

If you use this dataset, please cite the original SQuAD dataset:

```bibtex
@inproceedings{rajpurkar-etal-2016-squad,
    title = "{SQ}u{AD}: 100,000+ Questions for Machine Comprehension of Text",
    author = "Rajpurkar, Pranav  and
      Zhang, Jian  and
      Lopyrev, Konstantin  and
      Liang, Percy",
    editor = "Su, Jian  and
      Duh, Kevin  and
      Carreras, Xavier",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D16-1264",
    doi = "10.18653/v1/D16-1264",
    pages = "2383--2392",
    eprint={1606.05250},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
}
```

---
## Reproducibility Notes

The dataset was generated by programmatically regrouping SQuAD entries based on shared context passages. Users can reconstruct similar datasets using the publicly available SQuAD dataset and grouping logic.