---
title: SynCSE-partial-NLI
canonical_url: "https://www.modelscope.cn/datasets/hkust-nlp/SynCSE-partial-NLI"
md_url: "https://www.modelscope.cn/datasets/hkust-nlp/SynCSE-partial-NLI.md"
repository: hkust-nlp/SynCSE-partial-NLI
last_updated: 2025-02-17
license: "Apache License 2.0"
storage_size: "65 MB"
downloads: 1159
stars: 0
---

# SynCSE-partial-NLI

> SynCSE-partial-NLI - hkust-nlp 在 ModelScope 开源的数据集。Dataset Card for Dataset Name

hkust-nlp/SynCSE-partial-NLI 是 ModelScope 魔搭社区上的数据集，存储大小 65 MB，采用 Apache License 2.0 许可。

- **Repository**: hkust-nlp/SynCSE-partial-NLI
- **License**: Apache License 2.0
- **Storage size**: 65 MB
- **Downloads**: 1159
- **Stars**: 0
- **Last updated**: 2025-02-17

Source: https://www.modelscope.cn/datasets/hkust-nlp/SynCSE-partial-NLI

---

# Dataset Card for Dataset Name

## Dataset Description

- **Repository:** [https://github.com/SJTU-LIT/SynCSE/](https://github.com/SJTU-LIT/SynCSE/)
- **Paper:** [Contrastive Learning of Sentence Embeddings from Scratch](https://arxiv.org/abs/2305.15077)


### Dataset Summary

The SynCSE-scratch-NLI is a Natural Language Inference dataset generated by GPT-3.5-Turbo. You can use it to learn better sentence representation with contrastive learning. More details can be found in [paper](https://arxiv.org/abs/2305.15077) and [code](https://github.com/SJTU-LIT/SynCSE/)

### Supported Tasks and Leaderboards

Natural Language Inference

Contrastive Learning of Sentence Embeddings

### Languages

English

## Dataset Structure

### Data Instances

[More Information Needed]
### Data Fields



### Data Splits

We only provide the training set. Specifically, you can use this dataset to train of model with contrastive learning and evalaute your model on a variey of downstream sentence embedding tasks.


## Dataset Creation

GPT-3.5-turbo

### Curation Rationale

[More Information Needed]

# Citation
```
@article{zhang2023contrastive,
  title={Contrastive Learning of Sentence Embeddings from Scratch},
  author={Zhang, Junlei and Lan, Zhenzhong and He, Junxian},
  journal={arXiv preprint arXiv:2305.15077},
  year={2023}
}
```