---
title: SNLI
canonical_url: "https://www.modelscope.cn/datasets/OmniData/SNLI"
md_url: "https://www.modelscope.cn/datasets/OmniData/SNLI.md"
repository: OmniData/SNLI
last_updated: 2024-07-02
license: "[CC BY-SA 4.0]"
storage_size: "90 MB"
domain:
  - publishDate
  - labelTypes
  - paperUrl
  - publishUrl
  - taskTypes
  - displayName
  - publisher
  - mediaTypes
tasks:
  - 2015
  - Classification
  - "https://arxiv.org/pdf/2210.10434.pdf"
  - "https://nlp.stanford.edu/projects/snli/"
  - "Natural Language Inference"
  - SNLI
  - "Stanford University"
  - Text
downloads: 65
stars: 0
---

# SNLI

> SNLI - OmniData 在 ModelScope 开源的数据集。displayName: SNLI labelTypes: English Corpus Classification license: CC BY-SA 4.0 mediaTypes: Text paperUrl: https://arxiv.org/pdf/2210.10434.pdf publishDate: "2015" publishUrl: https://nlp.stanford.edu/projects/snli/…

OmniData/SNLI 是 ModelScope 魔搭社区上的2015、Classification、https://arxiv.org/pdf/2210.10434.pdf数据集，涉及 publishDate、labelTypes、paperUrl 领域，存储大小 90 MB，采用 [CC BY-SA 4.0] 许可。

- **Repository**: OmniData/SNLI
- **License**: [CC BY-SA 4.0]
- **Tasks**: 2015, Classification, https://arxiv.org/pdf/2210.10434.pdf, https://nlp.stanford.edu/projects/snli/, Natural Language Inference, SNLI, Stanford University, Text
- **Domain**: publishDate, labelTypes, paperUrl, publishUrl, taskTypes, displayName, publisher, mediaTypes
- **Storage size**: 90 MB
- **Downloads**: 65
- **Stars**: 0
- **Last updated**: 2024-07-02

Source: https://www.modelscope.cn/datasets/OmniData/SNLI

---

displayName: SNLI
labelTypes:
- English Corpus
- Classification
license:
- CC BY-SA 4.0
mediaTypes:
- Text
paperUrl: https://arxiv.org/pdf/2210.10434.pdf
publishDate: "2015"
publishUrl: https://nlp.stanford.edu/projects/snli/
publisher:
- Stanford University
tags:
- Text
taskTypes:
- Natural Language Inference

---
# 数据集介绍
  ## 简介
  v1.0 语料库是 570k 人工生成的英语句子对的集合，这些句子对被手动标记以实现平衡分类。标签的蕴涵、矛盾和中性通常支持 NLI（自然语言推理）等任务称为 RTE（识别文本蕴涵）。它既可以作为评估文本表示系统的基准，例如由表示学习方法诱导的系统，也可以作为开发各种 NLP（自然语言处理）模型的有用资源。
  ## 类定义
  ```
entailment/neutral/contradiction
```
  ## 引文
  ```
@inproceedings{snli:emnlp2015,
  Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.},
  Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  Publisher = {Association for Computational Linguistics},
  Title = {A large annotated corpus for learning natural language inference},
  Year = {2015}
}
```
  
## Download dataset
:modelscope-code[]{type="git"}