cos_e

# 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.

# 「CoS-E」数据集卡片

## 目录
- [数据集描述](#数据集描述)
- [数据集概述](#数据集概述)
- [支持任务与评测榜单](#支持任务与评测榜单)
- [语言覆盖](#语言覆盖)
- [数据集结构](#数据集结构)
- [数据样例](#数据样例)
- [数据字段](#数据字段)
- [数据划分](#数据划分)
- [数据集构建](#数据集构建)
- [构建初衷](#构建初衷)
- [源数据](#源数据)
- [标注信息](#标注信息)
- [个人与敏感信息](#个人与敏感信息)
- [数据集使用注意事项](#数据集使用注意事项)
- [数据集的社会影响](#数据集的社会影响)
- [偏差讨论](#偏差讨论)
- [其他已知局限性](#其他已知局限性)
- [附加信息](#附加信息)
- [数据集维护者](#数据集维护者)
- [许可信息](#许可信息)
- [引用信息](#引用信息)
- [贡献致谢](#贡献致谢)

## 数据集描述
- **主页：**
- **代码仓库：** https://github.com/salesforce/cos-e
- **相关论文：** [《自我解释！借助大语言模型完成常识推理》](https://arxiv.org/abs/1906.02361)
- **联系人：** [更多信息请参阅](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **下载数据集文件大小：** 10.83 MB
- **生成数据集大小：** 5.39 MB
- **总占用磁盘空间：** 16.22 MB

### 数据集概述
常识解释数据集（Common Sense Explanations，简称CoS-E）可用于训练大语言模型，使其能够自动生成解释文本，该类解释可在全新的常识自动生成解释（Commonsense Auto-Generated Explanation，简称CAGE）框架的训练与推理阶段中投入使用。

### 支持任务与评测榜单
[更多信息请参阅](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### 语言覆盖
[更多信息请参阅](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## 数据集结构

### 数据样例

#### v1.0
- **下载数据集文件大小：** 4.30 MB
- **生成数据集大小：** 2.34 MB
- **总占用磁盘空间：** 6.64 MB

训练集的一条样例如所示：

{
"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
- **下载数据集文件大小：** 6.53 MB
- **生成数据集大小：** 3.05 MB
- **总占用磁盘空间：** 9.58 MB

训练集的一条样例如所示：

{
"abstractive_explanation": "this is open-ended",
"answer": "b",
"choices": ["a", "b", "c"],
"extractive_explanation": "this is selected train",
"id": "42",
"question": "question goes here."
}

### 数据字段
所有划分下的数据字段均保持一致。

#### v1.0
- `id`：字符串类型特征。
- `question`：字符串类型特征。
- `choices`：字符串特征列表。
- `answer`：字符串类型特征。
- `abstractive_explanation`：字符串类型特征。
- `extractive_explanation`：字符串类型特征。

#### v1.11
- `id`：字符串类型特征。
- `question`：字符串类型特征。
- `choices`：字符串特征列表。
- `answer`：字符串类型特征。
- `abstractive_explanation`：字符串类型特征。
- `extractive_explanation`：字符串类型特征。

### 数据划分
| 数据集版本 | 训练集样本数 | 验证集样本数 |
| :-------- | ----------: | -----------: |
| v1.0 | 7610 | 950 |
| v1.11 | 9741 | 1221 |

## 数据集构建

### 构建初衷
[更多信息请参阅](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### 源数据

#### 初始数据收集与标准化
[更多信息请参阅](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### 源语言生产者是谁？
[更多信息请参阅](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### 标注信息

#### 标注流程
[更多信息请参阅](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### 标注人员是谁？
[更多信息请参阅](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### 个人与敏感信息
[更多信息请参阅](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## 数据集使用注意事项

### 数据集的社会影响
[更多信息请参阅](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### 偏差讨论
[更多信息请参阅](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### 其他已知局限性
[更多信息请参阅](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## 附加信息

### 数据集维护者
[更多信息请参阅](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### 许可信息
未知。

### 引用信息

@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"
}

### 贡献致谢
感谢[@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)以及[@lhoestq](https://github.com/lhoestq) 为本数据集的收录工作提供支持。