
# Dataset Card for "commonsense_qa"

## Table of Contents
- [Table of Contents](#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://www.tau-nlp.org/commonsenseqa
- **Repository:** https://github.com/jonathanherzig/commonsenseqa
- **Paper:** https://arxiv.org/abs/1811.00937
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 4.68 MB
- **Size of the generated dataset:** 2.18 MB
- **Total amount of disk used:** 6.86 MB

### Dataset Summary

CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge
to predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers.
The dataset is provided in two major training/validation/testing set splits: "Random split" which is the main evaluation
split, and "Question token split", see paper for details.

### Supported Tasks and Leaderboards

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

### Languages

The dataset is in English (`en`).

## Dataset Structure

### Data Instances

#### default

- **Size of downloaded dataset files:** 4.68 MB
- **Size of the generated dataset:** 2.18 MB
- **Total amount of disk used:** 6.86 MB

An example of 'train' looks as follows:
```
{'id': '075e483d21c29a511267ef62bedc0461',
 'question': 'The sanctions against the school were a punishing blow, and they seemed to what the efforts the school had made to change?',
 'question_concept': 'punishing',
 'choices': {'label': ['A', 'B', 'C', 'D', 'E'],
  'text': ['ignore', 'enforce', 'authoritarian', 'yell at', 'avoid']},
 'answerKey': 'A'}
```

### Data Fields

The data fields are the same among all splits.

#### default
- `id` (`str`): Unique ID.
- `question`: a `string` feature.
- `question_concept` (`str`): ConceptNet concept associated to the question.
- `choices`: a dictionary feature containing:
  - `label`: a `string` feature.
  - `text`: a `string` feature.
- `answerKey`: a `string` feature.

### Data Splits

| name    | train | validation | test |
|---------|------:|-----------:|-----:|
| default |  9741 |       1221 | 1140 |

## Dataset Creation

### Curation Rationale

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

### 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

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

#### Who are the annotators?

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

### 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

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

### Licensing Information

The dataset is licensed under the MIT License.

See: https://github.com/jonathanherzig/commonsenseqa/issues/5

### Citation Information

```
@inproceedings{talmor-etal-2019-commonsenseqa,
    title = "{C}ommonsense{QA}: A Question Answering Challenge Targeting Commonsense Knowledge",
    author = "Talmor, Alon  and
      Herzig, Jonathan  and
      Lourie, Nicholas  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N19-1421",
    doi = "10.18653/v1/N19-1421",
    pages = "4149--4158",
    archivePrefix = "arXiv",
    eprint        = "1811.00937",
    primaryClass  = "cs",
}
```

### Contributions

Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
task_categories:question-answering task_ids:open-domain-qa annotations_creators:crowdsourced language_creators:crowdsourced multilinguality:monolingual size_categories:1K<n<10K source_datasets:original language:en license:mit arxiv:1811.00937 region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["open-domain-qa"], "paperswithcode_id": "commonsenseqa", "pretty_name": "CommonsenseQA", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_concept", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2207794, "num_examples": 9741}, {"name": "validation", "num_bytes": 273848, "num_examples": 1221}, {"name": "test", "num_bytes": 257842, "num_examples": 1140}], "download_size": 1558570, "dataset_size": 2739484}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}
TAGS
#task_categories-question-answering #task_ids-open-domain-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-mit #arxiv-1811.00937 #region-us 

Dataset Card for "commonsense\_qa"
==================================


Table of Contents
-----------------


* Table of Contents
* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage: URL
* Repository: URL
* Paper: URL
* Point of Contact:
* Size of downloaded dataset files: 4.68 MB
* Size of the generated dataset: 2.18 MB
* Total amount of disk used: 6.86 MB


### Dataset Summary


CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge
to predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers.
The dataset is provided in two major training/validation/testing set splits: "Random split" which is the main evaluation
split, and "Question token split", see paper for details.


### Supported Tasks and Leaderboards


### Languages


The dataset is in English ('en').


Dataset Structure
-----------------


### Data Instances


#### default


* Size of downloaded dataset files: 4.68 MB
* Size of the generated dataset: 2.18 MB
* Total amount of disk used: 6.86 MB


An example of 'train' looks as follows:


### Data Fields


The data fields are the same among all splits.


#### default


* 'id' ('str'): Unique ID.
* 'question': a 'string' feature.
* 'question\_concept' ('str'): ConceptNet concept associated to the question.
* 'choices': a dictionary feature containing:
	+ 'label': a 'string' feature.
	+ 'text': a 'string' feature.
* 'answerKey': a 'string' feature.


### Data Splits



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Licensing Information


The dataset is licensed under the MIT License.


See: URL


### Contributions


Thanks to @thomwolf, @lewtun, @albertvillanova, @patrickvonplaten for adding this dataset.



### Dataset Summary


CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge
to predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers.
The dataset is provided in two major training/validation/testing set splits: "Random split" which is the main evaluation
split, and "Question token split", see paper for details. ### Supported Tasks and Leaderboards ### Languages


The dataset is in English ('en').


Dataset Structure
----------------- ### Data Instances #### default


* Size of downloaded dataset files: 4.68 MB
* Size of the generated dataset: 2.18 MB
* Total amount of disk used: 6.86 MB


An example of 'train' looks as follows: ### Data Fields


The data fields are the same among all splits. #### default


* 'id' ('str'): Unique ID.
* 'question': a 'string' feature.
* 'question\_concept' ('str'): ConceptNet concept associated to the question.
* 'choices': a dictionary feature containing:
	+ 'label': a 'string' feature.
	+ 'text': a 'string' feature.
* 'answerKey': a 'string' feature. ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information


The dataset is licensed under the MIT License.


See: URL ### Contributions


Thanks to @thomwolf, @lewtun, @albertvillanova, @patrickvonplaten for adding this dataset.
TAGS
#task_categories-question-answering #task_ids-open-domain-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-mit #arxiv-1811.00937 #region-us 
 ### Dataset Summary


CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge
to predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers.
The dataset is provided in two major training/validation/testing set splits: "Random split" which is the main evaluation
split, and "Question token split", see paper for details. ### Supported Tasks and Leaderboards ### Languages


The dataset is in English ('en').


Dataset Structure
----------------- ### Data Instances #### default


* Size of downloaded dataset files: 4.68 MB
* Size of the generated dataset: 2.18 MB
* Total amount of disk used: 6.86 MB


An example of 'train' looks as follows: ### Data Fields


The data fields are the same among all splits. #### default


* 'id' ('str'): Unique ID.
* 'question': a 'string' feature.
* 'question\_concept' ('str'): ConceptNet concept associated to the question.
* 'choices': a dictionary feature containing:
	+ 'label': a 'string' feature.
	+ 'text': a 'string' feature.
* 'answerKey': a 'string' feature. ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information


The dataset is licensed under the MIT License.


See: URL ### Contributions


Thanks to @thomwolf, @lewtun, @albertvillanova, @patrickvonplaten for adding this dataset.
passage: TAGS
#task_categories-question-answering #task_ids-open-domain-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-mit #arxiv-1811.00937 #region-us 
### Dataset Summary


CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge
to predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers.
The dataset is provided in two major training/validation/testing set splits: "Random split" which is the main evaluation
split, and "Question token split", see paper for details.### Supported Tasks and Leaderboards### Languages


The dataset is in English ('en').


Dataset Structure
-----------------### Data Instances#### default


* Size of downloaded dataset files: 4.68 MB
* Size of the generated dataset: 2.18 MB
* Total amount of disk used: 6.86 MB


An example of 'train' looks as follows:### Data Fields


The data fields are the same among all splits.#### default


* 'id' ('str'): Unique ID.
* 'question': a 'string' feature.
* 'question\_concept' ('str'): ConceptNet concept associated to the question.
* 'choices': a dictionary feature containing:
	+ 'label': a 'string' feature.
	+ 'text': a 'string' feature.
* 'answerKey': a 'string' feature.### Data Splits



Dataset Creation
----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------### Social Impact of Dataset### Discussion of Biases