README.md

---
pretty_name: SQuAD2.0
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
languages:
- en
licenses:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
- question-generation
task_ids:
- open-domain-qa
- extractive-qa
paperswithcode_id: squad
---

# Dataset Card for "squad_v2"

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [https://rajpurkar.github.io/SQuAD-explorer/](https://rajpurkar.github.io/SQuAD-explorer/)
- **Repository:** [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)
- **Point of Contact:** [Google Group](https://groups.google.com/g/squad-stanford-qa) or [robinjia](robinjia@stanford.edu)
- **Size of downloaded dataset files:** 44.34 MB
- **Size of the generated dataset:** 122.57 MB
- **Total amount of disk used:** 166.91 MB

### Dataset Summary

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers
to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but
also determine when no answer is supported by the paragraph and abstain from answering.

### Supported Tasks and Leaderboards

SQuAD2.0 tests the ability of a system to not only answer reading comprehension questions, but also abstain when presented with a question that cannot be answered based on the provided paragraph. Leaderboard is present on the [Homepage](https://rajpurkar.github.io/SQuAD-explorer/).

### Languages

English (en)

## Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

### Data Instances

#### squad_v2

- **Size of downloaded dataset files:** 146.21 MB
- **Size of the generated dataset:** 122.70 MB
- **Total amount of disk used:** 268.90 MB

An example of 'validation' looks as follows.
```JSON
This example was too long and was cropped:

{
    "gem_id": "gem-squad_v2-validation-1",
    "id": "56ddde6b9a695914005b9629",
    "answers": {
        "answer_start": [94, 87, 94, 94],
        "text": ["10th and 11th centuries", "in the 10th and 11th centuries", "10th and 11th centuries", "10th and 11th centuries"]
    },
    "context": "\"The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave thei...",
    "question": "When were the Normans in Normandy?",
    "title": "Normans"
}
```

### Data Fields

The data fields are the same among all splits.

#### squad_v2
- `id`: a `string` feature.
- `gem_id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
  - `text`: a `string` feature.
  - `answer_start`: a `int32` feature.

### Data Splits

The original SQuAD2.0 dataset has only training and dev (validation) splits. The train split is further divided into test split and added as part of the GEM datasets.

| name       |  train | validation | test  |
| ---------  | -----: | ---------: | ----: |
| squad_v2   | 90403  |      11873 | 39916 |


## Dataset Creation

### Curation Rationale

The dataset is curated in three stages: 
- Curating passages, 
- Crowdsourcing question-answers on those passages,
- Obtaining additional answers

As part of SQuAD1.1, 10000 high-quality articles from English Wikipedia is extracted using Project Nayuki’s Wikipedia’s internal
PageRanks, from which 536 articles are sampled uniformly at random. From each of these articles, individual paragraphs are extracted, stripping away images, figures, tables, and discarding paragraphs shorter than 500 characters. 

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. 

### Source Data

#### Initial Data Collection and Normalization

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

#### Who are the source language producers?

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

### Annotations

#### Annotation process

The Daemo platform (Gaikwad et al., 2015), with Amazon Mechanical Turk as its backend is used for annotation. 

- On each paragraph, crowdworkers were tasked with asking and answering up to 5 questions on the content of that paragraph and were asked spend 4 minutes on every paragraph. Questions need to be entered in a text box and answers need to be highlighted in the paragraph.
- To get an indication of human performance on SQuAD and to make the  evaluation more robust, at least 2 additional answers for each question is obtained in the development and test sets. 
- In the secondary answer generation task, each crowdworker was shown only the questions along with the paragraphs of an article,
and asked to select the shortest span in the paragraph that answered the question. If a question was not answerable by a span in the paragraph, workers were asked to submit the question without marking an answer

#### Who are the annotators?

Crowdworkers from the United States or Canada with a 97% HIT acceptance rate, a minimum of 1000 HITs, were employed to create questions. 

### Personal and Sensitive Information

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

## Considerations for Using the Data

### Social Impact of Dataset

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

### Discussion of Biases

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

### Other Known Limitations

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

## Additional Information

### Dataset Curators

The authors of SQuAD dataset would like to thank Durim Morina and Professor Michael Bernstein for their help in crowdsourcing the collection of the dataset, both in terms of funding and technical support of the Daemo platform.

### Licensing Information

The dataset is distributed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode) license.

### 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 [@AbinayaM02](https://github.com/AbinayaM02) for adding this dataset to GEM. All the details are obtained from the cited paper.