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
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.
@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}
}
---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
license:
- unknown
multilinguality:
- monolingual
pretty_name: Commonsense Explanations
size_categories:
- 10K<n<100K
source_datasets:
- extended|commonsense_qa
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: cos-e
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: 2077517
    num_examples: 7610
  - name: validation
    num_bytes: 261887
    num_examples: 950
  download_size: 4295320
  dataset_size: 2339404
- 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: 2717420
    num_examples: 9741
  - name: validation
    num_bytes: 331760
    num_examples: 1221
  download_size: 6535534
  dataset_size: 3049180
---

# 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.