
# Dataset Card for "cosmos_qa"

## 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://wilburone.github.io/cosmos/](https://wilburone.github.io/cosmos/)
- **Repository:** https://github.com/wilburOne/cosmosqa/
- **Paper:** [Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning](https://arxiv.org/abs/1909.00277)
- **Point of Contact:** [Lifu Huang](mailto:warrior.fu@gmail.com)
- **Size of downloaded dataset files:** 24.40 MB
- **Size of the generated dataset:** 24.51 MB
- **Total amount of disk used:** 48.91 MB

### Dataset Summary

Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context

### Supported Tasks and Leaderboards

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

### Languages

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

## Dataset Structure

### Data Instances

#### default

- **Size of downloaded dataset files:** 24.40 MB
- **Size of the generated dataset:** 24.51 MB
- **Total amount of disk used:** 48.91 MB

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

{
    "answer0": "If he gets married in the church he wo nt have to get a divorce .",
    "answer1": "He wants to get married to a different person .",
    "answer2": "He wants to know if he does nt like this girl can he divorce her ?",
    "answer3": "None of the above choices .",
    "context": "\"Do i need to go for a legal divorce ? I wanted to marry a woman but she is not in the same religion , so i am not concern of th...",
    "id": "3BFF0DJK8XA7YNK4QYIGCOG1A95STE##3180JW2OT5AF02OISBX66RFOCTG5J7##A2LTOS0AZ3B28A##Blog_56156##q1_a1##378G7J1SJNCDAAIN46FM2P7T6KZEW2",
    "label": 1,
    "question": "Why is this person asking about divorce ?"
}
```

### Data Fields

The data fields are the same among all splits.

#### default
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answer0`: a `string` feature.
- `answer1`: a `string` feature.
- `answer2`: a `string` feature.
- `answer3`: a `string` feature.
- `label`: a `int32` feature.

### Data Splits

| name  |train|validation|test|
|-------|----:|---------:|---:|
|default|25262|      2985|6963|

## 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

As reported via email by Yejin Choi, the dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.

### Citation Information

```
@inproceedings{huang-etal-2019-cosmos,
    title = "Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning",
    author = "Huang, Lifu  and
      Le Bras, Ronan  and
      Bhagavatula, Chandra  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1243",
    doi = "10.18653/v1/D19-1243",
    pages = "2391--2401",
}
```


### Contributions

Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
task_categories:multiple-choice task_ids:multiple-choice-qa annotations_creators:crowdsourced language_creators:found multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:en license:cc-by-4.0 arxiv:1909.00277 region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["multiple-choice"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "cosmosqa", "pretty_name": "CosmosQA", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer0", "dtype": "string"}, {"name": "answer1", "dtype": "string"}, {"name": "answer2", "dtype": "string"}, {"name": "answer3", "dtype": "string"}, {"name": "label", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 17159918, "num_examples": 25262}, {"name": "test", "num_bytes": 5121479, "num_examples": 6963}, {"name": "validation", "num_bytes": 2186987, "num_examples": 2985}], "download_size": 24399475, "dataset_size": 24468384}}
TAGS
#task_categories-multiple-choice #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-1909.00277 #region-us 

Dataset Card for "cosmos\_qa"
=============================


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: Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning
* Point of Contact: Lifu Huang
* Size of downloaded dataset files: 24.40 MB
* Size of the generated dataset: 24.51 MB
* Total amount of disk used: 48.91 MB


### Dataset Summary


Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context


### Supported Tasks and Leaderboards


### Languages


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


### Data Instances


#### default


* Size of downloaded dataset files: 24.40 MB
* Size of the generated dataset: 24.51 MB
* Total amount of disk used: 48.91 MB


An example of 'validation' looks as follows.


### Data Fields


The data fields are the same among all splits.


#### default


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answer0': a 'string' feature.
* 'answer1': a 'string' feature.
* 'answer2': a 'string' feature.
* 'answer3': a 'string' feature.
* 'label': a 'int32' 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


As reported via email by Yejin Choi, the dataset is licensed under CC BY 4.0 license.


### Contributions


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



### Dataset Summary


Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context ### Supported Tasks and Leaderboards ### Languages


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


* Size of downloaded dataset files: 24.40 MB
* Size of the generated dataset: 24.51 MB
* Total amount of disk used: 48.91 MB


An example of 'validation' looks as follows. ### Data Fields


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


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answer0': a 'string' feature.
* 'answer1': a 'string' feature.
* 'answer2': a 'string' feature.
* 'answer3': a 'string' feature.
* 'label': a 'int32' 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


As reported via email by Yejin Choi, the dataset is licensed under CC BY 4.0 license. ### Contributions


Thanks to @patrickvonplaten, @lewtun, @albertvillanova, @thomwolf for adding this dataset.
TAGS
#task_categories-multiple-choice #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-1909.00277 #region-us 
 ### Dataset Summary


Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context ### Supported Tasks and Leaderboards ### Languages


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


* Size of downloaded dataset files: 24.40 MB
* Size of the generated dataset: 24.51 MB
* Total amount of disk used: 48.91 MB


An example of 'validation' looks as follows. ### Data Fields


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


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answer0': a 'string' feature.
* 'answer1': a 'string' feature.
* 'answer2': a 'string' feature.
* 'answer3': a 'string' feature.
* 'label': a 'int32' 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


As reported via email by Yejin Choi, the dataset is licensed under CC BY 4.0 license. ### Contributions


Thanks to @patrickvonplaten, @lewtun, @albertvillanova, @thomwolf for adding this dataset.
passage: TAGS
#task_categories-multiple-choice #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-1909.00277 #region-us 
### Dataset Summary


Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context### Supported Tasks and Leaderboards### Languages


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


* Size of downloaded dataset files: 24.40 MB
* Size of the generated dataset: 24.51 MB
* Total amount of disk used: 48.91 MB


An example of 'validation' looks as follows.### Data Fields


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


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answer0': a 'string' feature.
* 'answer1': a 'string' feature.
* 'answer2': a 'string' feature.
* 'answer3': a 'string' feature.
* 'label': a 'int32' 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