social_i_qa

# Dataset Card for "social_i_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://leaderboard.allenai.org/socialiqa/submissions/get-started](https://leaderboard.allenai.org/socialiqa/submissions/get-started)
- **Repository:** https://maartensap.com/social-iqa/
- **Paper:** https://www.aclweb.org/anthology/D19-1454
- **Point of Contact:** [Maarten Sap](http://www.maartensap.com)
- **Size of downloaded dataset files:** 2.20 MB
- **Size of the generated dataset:** 6.76 MB
- **Total amount of disk used:** 8.97 MB

### Dataset Summary

We introduce Social IQa: Social Interaction QA, a new question-answering benchmark for testing social commonsense intelligence. Contrary to many prior benchmarks that focus on physical or taxonomic knowledge, Social IQa focuses on reasoning about people’s actions and their social implications. For example, given an action like "Jesse saw a concert" and a question like "Why did Jesse do this?", humans can easily infer that Jesse wanted "to see their favorite performer" or "to enjoy the music", and not "to see what's happening inside" or "to see if it works". The actions in Social IQa span a wide variety of social situations, and answer candidates contain both human-curated answers and adversarially-filtered machine-generated candidates. Social IQa contains over 37,000 QA pairs for evaluating models’ abilities to reason about the social implications of everyday events and situations. (Less)

### 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:** 2.20 MB
- **Size of the generated dataset:** 6.76 MB
- **Total amount of disk used:** 8.97 MB

An example of 'validation' looks as follows.
```
{
"answerA": "sympathetic",
"answerB": "like a person who was unable to help",
"answerC": "incredulous",
"context": "Sydney walked past a homeless woman asking for change but did not have any money they could give to her. Sydney felt bad afterwards.",
"label": "1",
"question": "How would you describe Sydney?"
}
```

### Data Fields

The data fields are the same among all splits.

#### default
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answerA`: a `string` feature.
- `answerB`: a `string` feature.
- `answerC`: a `string` feature.
- `label`: a `string` feature.

### Data Splits

| name |train|validation|
|-------|----:|---------:|
|default|33410| 1954|

## 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

CC-BY-4.0

### Citation Information

```
@inproceedings{sap2019socialIQa,
title={Social IQa: Commonsense Reasoning about Social Interactions},
author={Sap, Maarten and Rashkin, Hannah and Chen, Derek and LeBras, Ronan and Choi, Yejin},
year={2019},
booktitle={EMNLP},
url={https://www.aclweb.org/anthology/D19-1454}
}
```

### Contributions

Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.

# "social_i_qa" 数据集卡片

## 目录
- [数据集描述](#dataset-description)
- [数据集摘要](#dataset-summary)
- [支持任务与排行榜](#supported-tasks-and-leaderboards)
- [语言](#languages)
- [数据集结构](#dataset-structure)
- [数据实例](#data-instances)
- [数据字段](#data-fields)
- [数据拆分](#data-splits)
- [数据集构建](#dataset-creation)
- [构建依据](#curation-rationale)
- [源数据](#source-data)
- [标注流程](#annotations)
- [个人与敏感信息](#personal-and-sensitive-information)
- [数据集使用注意事项](#considerations-for-using-the-data)
- [数据集的社会影响](#social-impact-of-dataset)
- [偏差讨论](#discussion-of-biases)
- [其他已知局限性](#other-known-limitations)
- [附加信息](#additional-information)
- [数据集维护者](#dataset-curators)
- [许可信息](#licensing-information)
- [引用信息](#citation-information)
- [贡献致谢](#contributions)

## 数据集描述

- **主页：** [https://leaderboard.allenai.org/socialiqa/submissions/get-started](https://leaderboard.allenai.org/socialiqa/submissions/get-started)
- **代码仓库：** https://maartensap.com/social-iqa/
- **相关论文：** https://www.aclweb.org/anthology/D19-1454
- **联系人：** [Maarten Sap](http://www.maartensap.com)
- **下载数据集文件大小：** 2.20 MB
- **生成数据集大小：** 6.76 MB
- **总磁盘占用量：** 8.97 MB

### 数据集摘要

我们推出了社交交互问答（Social IQa）基准数据集，一款用于测试社会常识智能的新型问答基准。与众多聚焦于物理或分类学知识的现有基准数据集不同，社交交互问答聚焦于对人类行为及其社会影响的推理。例如，给定"杰西观看了一场音乐会"这一行为，以及"杰西为何这么做？"这一问题，人类可轻松推断出杰西的目的是"观看自己喜爱的表演者"或"享受音乐"，而非"查看场内正在发生的事"或"查看其运作原理"。社交交互问答中的行为涵盖了多样的社交场景，候选答案既包含人工精心编写的回答，也包含经过对抗过滤处理的机器生成候选内容。社交交互问答包含超过37000个问答对，用于评估模型对日常事件与场景的社会影响进行推理的能力。(Less)

### 支持任务与排行榜

[更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### 语言

[更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## 数据集结构

### 数据实例

#### 默认

- **下载数据集文件大小：** 2.20 MB
- **生成数据集大小：** 6.76 MB
- **总磁盘占用量：** 8.97 MB

验证集的一个示例如下：

{
"answerA": "sympathetic",
"answerB": "like a person who was unable to help",
"answerC": "incredulous",
"context": "Sydney walked past a homeless woman asking for change but did not have any money they could give to her. Sydney felt bad afterwards.",
"label": "1",
"question": "How would you describe Sydney?"
}

### 数据字段

所有数据拆分下的字段均保持一致。

#### 默认
- `context`：字符串类型特征。
- `question`：字符串类型特征。
- `answerA`：字符串类型特征。
- `answerB`：字符串类型特征。
- `answerC`：字符串类型特征。
- `label`：字符串类型特征。

### 数据拆分

| 拆分名称 | 训练集 | 验证集 |
|---------|-------:|--------:|
| default | 33410 | 1954 |

## 数据集构建

### 构建依据

[更多信息待补充](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

[更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### 源语言生产者是谁？

[更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### 标注流程

#### 标注过程

[更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### 标注者是谁？

[更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### 个人与敏感信息

[更多信息待补充](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

[更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### 偏差讨论

[更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### 其他已知局限性

[更多信息待补充](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

[更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### 许可信息

CC BY 4.0

### 引用信息

@inproceedings{sap2019socialIQa,
title={Social IQa: Commonsense Reasoning about Social Interactions},
author={Sap, Maarten and Rashkin, Hannah and Chen, Derek and LeBras, Ronan and Choi, Yejin},
year={2019},
booktitle={EMNLP},
url={https://www.aclweb.org/anthology/D19-1454}
}

### 贡献致谢

感谢 [@bhavitvyamalik](https://github.com/bhavitvyamalik)、[@thomwolf](https://github.com/thomwolf)、[@patrickvonplaten](https://github.com/patrickvonplaten)、[@lewtun](https://github.com/lewtun) 为本数据集的添加工作。

搜集汇总

背景与挑战

背景概述

Social IQa是一个用于评估社交常识推理能力的问答基准数据集，专注于分析人们日常行为及其社交含义。它包含超过37,000个问答对，覆盖多种社交情境，旨在测试模型对互动社会影响的理解。