anli

# Dataset Card for "anli"

## 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/facebookresearch/anli/](https://github.com/facebookresearch/anli/)
- **Paper:** [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://arxiv.org/abs/1910.14599)
- **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:** 18.62 MB
- **Size of the generated dataset:** 77.12 MB
- **Total amount of disk used:** 95.75 MB

### Dataset Summary

The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset,
The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure.
ANLI is much more difficult than its predecessors including SNLI and MNLI.
It contains three rounds. Each round has train/dev/test splits.

### Supported Tasks and Leaderboards

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Languages

English

## Dataset Structure

### Data Instances

#### plain_text

- **Size of downloaded dataset files:** 18.62 MB
- **Size of the generated dataset:** 77.12 MB
- **Total amount of disk used:** 95.75 MB

An example of 'train_r2' looks as follows.
```
This example was too long and was cropped:

{
"hypothesis": "Idris Sultan was born in the first month of the year preceding 1994.",
"label": 0,
"premise": "\"Idris Sultan (born January 1993) is a Tanzanian Actor and comedian, actor and radio host who won the Big Brother Africa-Hotshot...",
"reason": "",
"uid": "ed5c37ab-77c5-4dbc-ba75-8fd617b19712"
}
```

### Data Fields

The data fields are the same among all splits.

#### plain_text
- `uid`: a `string` feature.
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `reason`: a `string` feature.

### Data Splits

| name |train_r1|dev_r1|train_r2|dev_r2|train_r3|dev_r3|test_r1|test_r2|test_r3|
|----------|-------:|-----:|-------:|-----:|-------:|-----:|------:|------:|------:|
|plain_text| 16946| 1000| 45460| 1000| 100459| 1200| 1000| 1000| 1200|

## 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-4 Attribution-NonCommercial](https://github.com/facebookresearch/anli/blob/main/LICENSE)

### Citation Information

```
@InProceedings{nie2019adversarial,
title={Adversarial NLI: A New Benchmark for Natural Language Understanding},
author={Nie, Yixin
and Williams, Adina
and Dinan, Emily
and Bansal, Mohit
and Weston, Jason
and Kiela, Douwe},
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
year = "2020",
publisher = "Association for Computational Linguistics",
}

```

### Contributions

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

# 「对抗式自然语言推理（Adversarial Natural Language Inference，简称ANLI）」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)
- [数据划分](#data-splits)
- [数据集构建](#dataset-creation)
- [采集依据](#curation-rationale)
- [源数据](#source-data)
- [标注信息](#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：**
- **代码仓库：** [https://github.com/facebookresearch/anli/](https://github.com/facebookresearch/anli/)
- **论文：** [《对抗式自然语言推理：自然语言理解的全新基准》](https://arxiv.org/abs/1910.14599)
- **联系方式：** [需补充更多信息](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：** 18.62 MB
- **生成后数据集大小：** 77.12 MB
- **总磁盘占用：** 95.75 MB

### 数据集摘要

对抗式自然语言推理（Adversarial Natural Language Inference，简称ANLI）是一款全新的大规模自然语言推理（Natural Language Inference，简称NLI）基准数据集。该数据集通过迭代式的人机协同对抗采集流程构建完成。ANLI的难度远高于其前代数据集，包括斯坦福自然语言推理数据集（Stanford Natural Language Inference，简称SNLI）和多领域自然语言推理数据集（Multi-Genre Natural Language Inference，简称MNLI）。该数据集包含三个轮次，每个轮次均设有训练集、开发集与测试集划分。

### 支持任务与排行榜

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

### 语言

英语

## 数据集结构

### 数据实例

#### 纯文本格式

- **下载数据集文件大小：** 18.62 MB
- **生成后数据集大小：** 77.12 MB
- **总磁盘占用：** 95.75 MB

`train_r2`的一个示例如下所示：

该示例过长，已被截断：

{
"hypothesis": "Idris Sultan was born in the first month of the year preceding 1994.",
"label": 0,
"premise": ""Idris Sultan (born January 1993) is a Tanzanian Actor and comedian, actor and radio host who won the Big Brother Africa-Hotshot...",
"reason": "",
"uid": "ed5c37ab-77c5-4dbc-ba75-8fd617b19712"
}

### 数据字段

所有划分的数据字段格式保持一致。

#### 纯文本格式
- `uid`：字符串类型特征。
- `premise`：字符串类型特征。
- `hypothesis`：字符串类型特征。
- `label`：分类标签，可选值包括`entailment`（蕴含，0）、`neutral`（中立，1）、`contradiction`（矛盾，2）。
- `reason`：字符串类型特征。

### 数据划分

| 名称 | train_r1 | dev_r1 | train_r2 | dev_r2 | train_r3 | dev_r3 | test_r1 | test_r2 | test_r3 |
|----------|---------:|-------:|---------:|-------:|---------:|-------:|--------:|--------:|--------:|
| 纯文本格式| 16946| 1000| 45460| 1000| 100459| 1200| 1000| 1000| 1200|

## 数据集构建

### 采集依据

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

### 源数据

#### 初始数据采集与标准化

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

#### 源语言生成者是谁？

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

### 标注信息

#### 标注流程

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

#### 标注人员是谁？

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

### 个人与敏感信息

[需补充更多信息](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

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

### 偏差讨论

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

### 其他已知局限性

[需补充更多信息](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

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

### 许可信息

[知识共享署名-非商业性使用许可（CC BY-NC 4.0）](https://github.com/facebookresearch/anli/blob/main/LICENSE)

### 引用信息

@InProceedings{nie2019adversarial,
title={Adversarial NLI: A New Benchmark for Natural Language Understanding},
author={Nie, Yixin
and Williams, Adina
and Dinan, Emily
and Bansal, Mohit
and Weston, Jason
and Kiela, Douwe},
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
year = "2020",
publisher = "Association for Computational Linguistics",
}

### 贡献致谢

感谢[@thomwolf](https://github.com/thomwolf)、[@easonnie](https://github.com/easonnie)、[@lhoestq](https://github.com/lhoestq)、[@patrickvonplaten](https://github.com/patrickvonplaten) 为本数据集的添加提供支持。