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SQuAD-EN-Passage-to-Question

Tasks:
Text Generation
Modalities:
Text
Formats:
json
Languages:
English
Size:
10K - 100K
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metadata 
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 

{
  "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)

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 

from datasets import load_dataset

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

Accessing Data 

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:

@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.