chrishuber/kaggle_mnli

# Dataset Card for [Kaggle MNLI]

## Table of Contents
- [Table of Contents](#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://www.kaggle.com/c/multinli-matched-open-evaluation **
- **Repository: chrishuber/roberta-retrained-mlni **
- **Paper: Inference Detection in NLP Using the MultiNLI and SNLI Datasets**
- **Leaderboard: 8**
- **Point of Contact: [e-mail]**

### Dataset Summary

[These are the datasets posted to Kaggle for an inference detection NLP competition. Moving them here to use with Pytorch.]

### Supported Tasks and Leaderboards

Provides train and validation data for sentence pairs with inference labels.
[https://www.kaggle.com/competitions/multinli-matched-open-evaluation/leaderboard]
[https://www.kaggle.com/competitions/multinli-mismatched-open-evaluation/leaderboard]

### Languages

[JSON, Python]

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

[More Information Needed]

### Data Splits

[More Information Needed]

## Dataset Creation

### Curation Rationale

[Reposted from https://www.kaggle.com/c/multinli-matched-open-evaluation and https://www.kaggle.com/c/multinli-mismatched-open-evaluation]

### Source Data

#### Initial Data Collection and Normalization

[Please see the article at https://arxiv.org/abs/1704.05426 which discusses the creation of the MNLI dataset.]

#### Who are the source language producers?

[Please see the article at https://arxiv.org/abs/1704.05426 which discusses the creation of the MNLI dataset.]

### Annotations

#### Annotation process

[Crowdsourcing using MechanicalTurk.]

#### Who are the annotators?

[MechanicalTurk users.]

### Personal and Sensitive Information

[None.]

## 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

[Kaggle]

### Licensing Information

[More Information Needed]

### Citation Information

[https://www.kaggle.com/c/multinli-matched-open-evaluation]
[https://www.kaggle.com/c/multinli-mismatched-open-evaluation]

### Contributions

Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

原始信息汇总

数据集卡片 for [Kaggle MNLI]

数据集描述

数据集概述

这些数据集是为自然语言处理中的推理检测竞赛在Kaggle上发布的。将它们移至此处以便与Pytorch一起使用。

支持的任务和排行榜

提供带有推理标签的句子对的训练和验证数据。

语言

[JSON, Python]

数据集结构

数据实例

[更多信息需补充]

数据字段

[更多信息需补充]

数据分割

[更多信息需补充]

数据集创建

策划理由

从以下链接重新发布：https://www.kaggle.com/c/multinli-matched-open-evaluation 和 https://www.kaggle.com/c/multinli-mismatched-open-evaluation

源数据

初始数据收集和规范化

请参阅文章：https://arxiv.org/abs/1704.05426，该文章讨论了MNLI数据集的创建。

源语言生产者是谁？

请参阅文章：https://arxiv.org/abs/1704.05426，该文章讨论了MNLI数据集的创建。

注释

注释过程

使用MechanicalTurk进行众包。

注释者是谁？

MechanicalTurk用户。

个人和敏感信息

[无]

使用数据时的考虑

数据集的社会影响

[更多信息需补充]

偏见的讨论

[更多信息需补充]

其他已知限制

[更多信息需补充]

附加信息

数据集策展人

[Kaggle]

许可信息

[更多信息需补充]

引用信息

https://www.kaggle.com/c/multinli-matched-open-evaluation https://www.kaggle.com/c/multinli-mismatched-open-evaluation

贡献

感谢@github-username 添加此数据集。