task_categories:text2text-generation annotations_creators:crowdsourced language_creators:crowdsourced multilinguality:monolingual size_categories:10K<n<100K source_datasets:extended|squad language:en license:cc-by-4.0 questiongeneration question-generation text2text-generation arxiv:1606.05250 arxiv:2005.01107 region:us
---
dataset_info:
  features:
  - name: context
    dtype: string
  - name: questions
    dtype: string
  splits:
  - name: train
    num_bytes: 20293805
    num_examples: 18896
  - name: validation
    num_bytes: 2376313
    num_examples: 2067
  download_size: 12600387
  dataset_size: 22670118
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Question Generation for T5 based on Squad V1.1
size_categories:
- 10K<n<100K
source_datasets:
- extended|squad
tags:
- questiongeneration
- question-generation
- text2text-generation
task_categories:
- text2text-generation
task_ids: []
---


# Dataset Card for "squad-v1.1-t5-question-generation"
## Dataset Description

- **Homepage:** [https://rajpurkar.github.io/SQuAD-explorer/](https://rajpurkar.github.io/SQuAD-explorer/)
- **Paper:** [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://arxiv.org/abs/1606.05250)

### Dataset Summary

This is a modified Stanford Question Answering Dataset (SQuAD) to suit question generation with All Questions in One Line (AQOL) just like in [Transformer-based End-to-End Question Generation](https://arxiv.org/pdf/2005.01107v1.pdf)
specifically for the T5 family of models. The prefix is `generate questions: ` so that the task can be unique to a trained model.

Check out the generation notebook [here](https://nbviewer.org/urls/huggingface.co/datasets/derek-thomas/squad-v1.1-t5-question-generation/resolve/main/Squad_V1_Question_Generation.ipynb).

### Supported Tasks and Leaderboards

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Languages

## Dataset Structure

### Data Instances

#### plain_text

An example of 'train' looks as follows.
```
{
    "context": "generate questions: This is a test context.",
    "question": "Is this a test? {sep_token} Is this another Test {sep_token}"
}
```

### Data Fields

The data fields are the same among all splits.

#### plain_text
- `context`: a `string` feature.
- `question`: a `string` feature.

### Data Splits
|   name   |train|validation|
|----------|----:|---------:|
|plain_text|18896|      2067|

### Citation Information

```
@article{2016arXiv160605250R,
       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
                 Konstantin and {Liang}, Percy},
        title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
      journal = {arXiv e-prints},
         year = 2016,
          eid = {arXiv:1606.05250},
        pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
       eprint = {1606.05250},
}

```

### Contributions

Thanks to [Derek Thomas](https://huggingface.co/derek-thomas) and [Thomas Simonini](https://huggingface.co/ThomasSimonini) for adding this to the hub

Check out: [How to contribute more](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Visitors
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