
# Dataset Card for "cos_e"

## 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:**
- **Repository:** https://github.com/salesforce/cos-e
- **Paper:** [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://arxiv.org/abs/1906.02361)
- **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:** 10.83 MB
- **Size of the generated dataset:** 5.39 MB
- **Total amount of disk used:** 16.22 MB

### Dataset Summary

Common Sense Explanations (CoS-E) allows for training language models to
automatically generate explanations that can be used during training and
inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework.

### 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

#### v1.0

- **Size of downloaded dataset files:** 4.30 MB
- **Size of the generated dataset:** 2.34 MB
- **Total amount of disk used:** 6.64 MB

An example of 'train' looks as follows.
```
{
    "abstractive_explanation": "this is open-ended",
    "answer": "b",
    "choices": ["a", "b", "c"],
    "extractive_explanation": "this is selected train",
    "id": "42",
    "question": "question goes here."
}
```

#### v1.11

- **Size of downloaded dataset files:** 6.53 MB
- **Size of the generated dataset:** 3.05 MB
- **Total amount of disk used:** 9.58 MB

An example of 'train' looks as follows.
```
{
    "abstractive_explanation": "this is open-ended",
    "answer": "b",
    "choices": ["a", "b", "c"],
    "extractive_explanation": "this is selected train",
    "id": "42",
    "question": "question goes here."
}
```

### Data Fields

The data fields are the same among all splits.

#### v1.0
- `id`: a `string` feature.
- `question`: a `string` feature.
- `choices`: a `list` of `string` features.
- `answer`: a `string` feature.
- `abstractive_explanation`: a `string` feature.
- `extractive_explanation`: a `string` feature.

#### v1.11
- `id`: a `string` feature.
- `question`: a `string` feature.
- `choices`: a `list` of `string` features.
- `answer`: a `string` feature.
- `abstractive_explanation`: a `string` feature.
- `extractive_explanation`: a `string` feature.

### Data Splits

|name |train|validation|
|-----|----:|---------:|
|v1.0 | 7610|       950|
|v1.11| 9741|      1221|

## 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

Unknown.

### Citation Information

```
@inproceedings{rajani2019explain,
     title = "Explain Yourself! Leveraging Language models for Commonsense Reasoning",
    author = "Rajani, Nazneen Fatema  and
      McCann, Bryan  and
      Xiong, Caiming  and
      Socher, Richard",
      year="2019",
    booktitle = "Proceedings of the 2019 Conference of the Association for Computational Linguistics (ACL2019)",
    url ="https://arxiv.org/abs/1906.02361"
}
```


### Contributions

Thanks to [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten), [@albertvillanova](https://github.com/albertvillanova), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
task_categories:question-answering task_ids:open-domain-qa annotations_creators:crowdsourced language_creators:crowdsourced multilinguality:monolingual size_categories:10K<n<100K source_datasets:extended|commonsense_qa language:en license:unknown arxiv:1906.02361 region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|commonsense_qa"], "task_categories": ["question-answering"], "task_ids": ["open-domain-qa"], "paperswithcode_id": "cos-e", "pretty_name": "Commonsense Explanations", "dataset_info": [{"config_name": "v1.0", "features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "string"}, {"name": "abstractive_explanation", "dtype": "string"}, {"name": "extractive_explanation", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2067971, "num_examples": 7610}, {"name": "validation", "num_bytes": 260669, "num_examples": 950}], "download_size": 1588340, "dataset_size": 2328640}, {"config_name": "v1.11", "features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "string"}, {"name": "abstractive_explanation", "dtype": "string"}, {"name": "extractive_explanation", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2702777, "num_examples": 9741}, {"name": "validation", "num_bytes": 329897, "num_examples": 1221}], "download_size": 1947552, "dataset_size": 3032674}], "configs": [{"config_name": "v1.0", "data_files": [{"split": "train", "path": "v1.0/train-*"}, {"split": "validation", "path": "v1.0/validation-*"}]}, {"config_name": "v1.11", "data_files": [{"split": "train", "path": "v1.11/train-*"}, {"split": "validation", "path": "v1.11/validation-*"}]}]}
TAGS
#task_categories-question-answering #task_ids-open-domain-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|commonsense_qa #language-English #license-unknown #arxiv-1906.02361 #region-us 

Dataset Card for "cos\_e"
=========================


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:
* Repository: URL
* Paper: Explain Yourself! Leveraging Language Models for Commonsense Reasoning
* Point of Contact:
* Size of downloaded dataset files: 10.83 MB
* Size of the generated dataset: 5.39 MB
* Total amount of disk used: 16.22 MB


### Dataset Summary


Common Sense Explanations (CoS-E) allows for training language models to
automatically generate explanations that can be used during training and
inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework.


### Supported Tasks and Leaderboards


### Languages


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


### Data Instances


#### v1.0


* Size of downloaded dataset files: 4.30 MB
* Size of the generated dataset: 2.34 MB
* Total amount of disk used: 6.64 MB


An example of 'train' looks as follows.


#### v1.11


* Size of downloaded dataset files: 6.53 MB
* Size of the generated dataset: 3.05 MB
* Total amount of disk used: 9.58 MB


An example of 'train' looks as follows.


### Data Fields


The data fields are the same among all splits.


#### v1.0


* 'id': a 'string' feature.
* 'question': a 'string' feature.
* 'choices': a 'list' of 'string' features.
* 'answer': a 'string' feature.
* 'abstractive\_explanation': a 'string' feature.
* 'extractive\_explanation': a 'string' feature.


#### v1.11


* 'id': a 'string' feature.
* 'question': a 'string' feature.
* 'choices': a 'list' of 'string' features.
* 'answer': a 'string' feature.
* 'abstractive\_explanation': a 'string' feature.
* 'extractive\_explanation': 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


Unknown.


### Contributions


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



### Dataset Summary


Common Sense Explanations (CoS-E) allows for training language models to
automatically generate explanations that can be used during training and
inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework. ### Supported Tasks and Leaderboards ### Languages


Dataset Structure
----------------- ### Data Instances #### v1.0


* Size of downloaded dataset files: 4.30 MB
* Size of the generated dataset: 2.34 MB
* Total amount of disk used: 6.64 MB


An example of 'train' looks as follows. #### v1.11


* Size of downloaded dataset files: 6.53 MB
* Size of the generated dataset: 3.05 MB
* Total amount of disk used: 9.58 MB


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


The data fields are the same among all splits. #### v1.0


* 'id': a 'string' feature.
* 'question': a 'string' feature.
* 'choices': a 'list' of 'string' features.
* 'answer': a 'string' feature.
* 'abstractive\_explanation': a 'string' feature.
* 'extractive\_explanation': a 'string' feature. #### v1.11


* 'id': a 'string' feature.
* 'question': a 'string' feature.
* 'choices': a 'list' of 'string' features.
* 'answer': a 'string' feature.
* 'abstractive\_explanation': a 'string' feature.
* 'extractive\_explanation': 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


Unknown. ### Contributions


Thanks to @lewtun, @thomwolf, @mariamabarham, @patrickvonplaten, @albertvillanova, @lhoestq for adding this dataset.
TAGS
#task_categories-question-answering #task_ids-open-domain-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|commonsense_qa #language-English #license-unknown #arxiv-1906.02361 #region-us 
 ### Dataset Summary


Common Sense Explanations (CoS-E) allows for training language models to
automatically generate explanations that can be used during training and
inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework. ### Supported Tasks and Leaderboards ### Languages


Dataset Structure
----------------- ### Data Instances #### v1.0


* Size of downloaded dataset files: 4.30 MB
* Size of the generated dataset: 2.34 MB
* Total amount of disk used: 6.64 MB


An example of 'train' looks as follows. #### v1.11


* Size of downloaded dataset files: 6.53 MB
* Size of the generated dataset: 3.05 MB
* Total amount of disk used: 9.58 MB


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


The data fields are the same among all splits. #### v1.0


* 'id': a 'string' feature.
* 'question': a 'string' feature.
* 'choices': a 'list' of 'string' features.
* 'answer': a 'string' feature.
* 'abstractive\_explanation': a 'string' feature.
* 'extractive\_explanation': a 'string' feature. #### v1.11


* 'id': a 'string' feature.
* 'question': a 'string' feature.
* 'choices': a 'list' of 'string' features.
* 'answer': a 'string' feature.
* 'abstractive\_explanation': a 'string' feature.
* 'extractive\_explanation': 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


Unknown. ### Contributions


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