---
title: CoS-E
canonical_url: "https://www.modelscope.cn/datasets/OmniData/CoS-E"
md_url: "https://www.modelscope.cn/datasets/OmniData/CoS-E.md"
repository: OmniData/CoS-E
last_updated: 2024-07-01
license: "[BSD 3-Clause]"
storage_size: "9.2 MB"
domain:
  - publishDate
  - taskTypes
  - displayName
  - paperUrl
  - publishUrl
  - publisher
  - mediaTypes
tasks:
  - 2019
  - "Commonsense Reasoning"
  - CoS-E
  - "https://arxiv.org/pdf/1906.02361.pdf"
  - "https://github.com/salesforce/cos-e"
  - "Salesforce Research"
  - Text
downloads: 181
stars: 0
---

# CoS-E

> CoS-E - OmniData 在 ModelScope 开源的数据集。displayName: CoS-E license: BSD 3-Clause mediaTypes: Text paperUrl: https://arxiv.org/pdf/1906.02361.pdf publishDate: "2019" publishUrl: https://github.com/salesforce/cos-e publisher: Salesforce Research tags: Q&a Text…

OmniData/CoS-E 是 ModelScope 魔搭社区上的2019、Commonsense Reasoning、CoS-E数据集，涉及 publishDate、taskTypes、displayName 领域，存储大小 9.2 MB，采用 [BSD 3-Clause] 许可。

- **Repository**: OmniData/CoS-E
- **License**: [BSD 3-Clause]
- **Tasks**: 2019, Commonsense Reasoning, CoS-E, https://arxiv.org/pdf/1906.02361.pdf, https://github.com/salesforce/cos-e, Salesforce Research, Text
- **Domain**: publishDate, taskTypes, displayName, paperUrl, publishUrl, publisher, mediaTypes
- **Storage size**: 9.2 MB
- **Downloads**: 181
- **Stars**: 0
- **Last updated**: 2024-07-01

Source: https://www.modelscope.cn/datasets/OmniData/CoS-E

---

displayName: CoS-E
license:
- BSD 3-Clause
mediaTypes:
- Text
paperUrl: https://arxiv.org/pdf/1906.02361.pdf
publishDate: "2019"
publishUrl: https://github.com/salesforce/cos-e
publisher:
- Salesforce Research
tags:
- Q&a Text
taskTypes:
- Reading Comprehension
- Visual Question Answering
- Commonsense Reasoning

---
# 数据集介绍
  ## 简介
  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.
  ## 引文
  @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"
}
  
## Download dataset
:modelscope-code[]{type="git"}