asdf6675/tab_fact

---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
paperswithcode_id: tabfact
pretty_name: TabFact
dataset_info:
- config_name: tab_fact
features:
- name: id
dtype: int32
- name: table_id
dtype: string
- name: table_text
dtype: string
- name: table_caption
dtype: string
- name: statement
dtype: string
- name: label
dtype:
class_label:
names:
'0': refuted
'1': entailed
splits:
- name: train
num_bytes: 99852664
num_examples: 92283
- name: validation
num_bytes: 13846872
num_examples: 12792
- name: test
num_bytes: 13493391
num_examples: 12779
download_size: 196508436
dataset_size: 127192927
- config_name: blind_test
features:
- name: id
dtype: int32
- name: table_id
dtype: string
- name: table_text
dtype: string
- name: table_caption
dtype: string
- name: statement
dtype: string
- name: test_id
dtype: string
splits:
- name: test
num_bytes: 10954442
num_examples: 9750
download_size: 196508436
dataset_size: 10954442
---

# Dataset Card for TabFact

## 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:** [TabFact](https://tabfact.github.io/index.html)
- **Repository:** [GitHub](https://github.com/wenhuchen/Table-Fact-Checking)
- **Paper:** [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://arxiv.org/abs/1909.02164)
- **Leaderboard:** [Leaderboard](https://competitions.codalab.org/competitions/21611)
- **Point of Contact:** [Wenhu Chen]([e-mail])

### Dataset Summary

The problem of verifying whether a textual hypothesis holds the truth based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation. However, existing studies are restricted to dealing with unstructured textual evidence (e.g., sentences and passages, a pool of passages), while verification using structured forms of evidence, such as tables, graphs, and databases, remains unexplored. TABFACT is large scale dataset with 16k Wikipedia tables as evidence for 118k human annotated statements designed for fact verification with semi-structured evidence. The statements are labeled as either ENTAILED or REFUTED. TABFACT is challenging since it involves both soft linguistic reasoning and hard symbolic reasoning.

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

[More Information Needed]

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

[More Information Needed]

### Data Splits

[More Information Needed]
## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

[More Information Needed]

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

[More Information Needed]

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

```
@inproceedings{2019TabFactA,
title={TabFact : A Large-scale Dataset for Table-based Fact Verification},
author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},
booktitle = {International Conference on Learning Representations (ICLR)},
address = {Addis Ababa, Ethiopia},
month = {April},
year = {2020}
}
```

### Contributions

Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

---
annotations_creators:
- 众包（crowdsourced）
language_creators:
- 众包（crowdsourced）
language:
- 英语（en）
license:
- 知识共享署名4.0许可（CC BY 4.0）
multilinguality:
- 单语言（monolingual）
size_categories:
- 10万<样本量<100万
source_datasets:
- 原始数据集
task_categories:
- 文本分类（text-classification）
task_ids:
- 事实核查（fact-checking）
paperswithcode_id: tabfact
pretty_name: TabFact
dataset_info:
- config_name: tab_fact
features:
- name: 样本ID
dtype: 32位整数
- name: 表格ID
dtype: 字符串
- name: 表格文本
dtype: 字符串
- name: 表格标题
dtype: 字符串
- name: 陈述语句
dtype: 字符串
- name: 标签
dtype:
类标签:
命名:
'0': 被驳斥（refuted）
'1': 被蕴含（entailed）
splits:
- name: 训练集
num_bytes: 99852664
num_examples: 92283
- name: 验证集
num_bytes: 13846872
num_examples: 12792
- name: 测试集
num_bytes: 13493391
num_examples: 12779
download_size: 196508436
dataset_size: 127192927
- config_name: 盲测集
features:
- name: 样本ID
dtype: 32位整数
- name: 表格ID
dtype: 字符串
- name: 表格文本
dtype: 字符串
- name: 表格标题
dtype: 字符串
- name: 陈述语句
dtype: 字符串
- name: 测试ID
dtype: 字符串
splits:
- name: 测试集
num_bytes: 10954442
num_examples: 9750
download_size: 196508436
dataset_size: 10954442
---

## TabFact 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)
- [支持任务与排行榜](#支持任务与排行榜)
- [语言](#语言)
- [数据集结构](#数据集结构)
- [数据实例](#数据实例)
- [数据字段](#数据字段)
- [数据划分](#数据划分)
- [数据集创建](#数据集创建)
- [数据集构建初衷](#数据集构建初衷)
- [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:** [TabFact](https://tabfact.github.io/index.html)
- **代码仓库:** [GitHub](https://github.com/wenhuchen/Table-Fact-Checking)
- **相关论文:** [TabFact: 面向表格事实核查的大规模数据集](https://arxiv.org/abs/1909.02164)
- **排行榜:** [排行榜](https://competitions.codalab.org/competitions/21611)
- **联系方式:** [Wenhu Chen]([e-mail])

### 数据集概述

基于给定证据验证文本假设是否属实的任务，即事实核查（fact verification），在自然语言理解（natural language understanding）与语义表征（semantic representation）研究中具有重要意义。然而，现有研究多局限于处理非结构化文本证据（如语句、段落或段落池），而针对表格、图谱、数据库等结构化证据的核查研究仍有待探索。TabFact 是一个大规模数据集，以1.6万个维基百科表格作为证据，对应11.8万条人工标注的陈述语句，专为半结构化证据（semi-structured evidence）下的事实核查任务设计。这些陈述被标注为「被蕴含（entailed）」或「被驳斥（refuted）」两类。TabFact 兼具挑战性，因其同时涉及软性语言推理（soft linguistic reasoning）与硬性符号推理（hard symbolic reasoning）。

### 支持任务与排行榜

[更多信息待补充]

### 语言

[更多信息待补充]

## 数据集结构

### 数据实例

[更多信息待补充]

### 数据字段

[更多信息待补充]

### 数据划分

[更多信息待补充]

## 数据集创建

### 数据集构建初衷

[更多信息待补充]

### 源数据

[更多信息待补充]

#### 初始数据收集与标准化

[更多信息待补充]

#### 源语言生产者是谁？

[更多信息待补充]

### 标注信息

[更多信息待补充]

#### 标注流程

[更多信息待补充]

#### 标注人员是谁？

[更多信息待补充]

### 个人与敏感信息

[更多信息待补充]

## 数据集使用注意事项

### 数据集的社会影响

[更多信息待补充]

### 偏差讨论

[更多信息待补充]

### 其他已知局限性

[更多信息待补充]

## 附加信息

### 数据集维护者

[更多信息待补充]

### 许可信息

[更多信息待补充]

### 引用信息

@inproceedings{2019TabFactA,
title={TabFact : A Large-scale Dataset for Table-based Fact Verification},
author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},
booktitle = {International Conference on Learning Representations (ICLR)},
address = {埃塞俄比亚亚的斯亚贝巴},
month = {四月},
year = {2020}
}

### 贡献致谢

感谢 [@patil-suraj](https://github.com/patil-suraj) 为本数据集的收录提供了支持。