# FewRel

**Repository Path**: bobtuan/FewRel

## Basic Information

- **Project Name**: FewRel
- **Description**: A Large-Scale Few-Shot Relation Extraction Dataset
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-01-21
- **Last Updated**: 2022-01-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# FewRel Dataset, Toolkits and Baseline Models

FewRel is a large-scale few-shot relation extraction dataset, which contains 70000 natural language sentences expressing 100 different relations. This dataset is presented in the our EMNLP 2018 paper [FewRel: A Large-Scale Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://github.com/ProKil/FewRel/blob/master/paper/fewrel.pdf).

More info at http://zhuhao.me/fewrel.html .

## Citing
If you used our data, toolkits or baseline models, please kindly cite our paper:
```
@inproceedings{han2018fewrel,
               title={FewRel:A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation},
               author={Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong},
               booktitle={EMNLP},
               year={2018}
}
```


If you have questions about any part of the paper, submission, leaderboard, codes, data, please e-mail zhuhao@cmu.edu.

## Contributions

Hao Zhu first proposed this problem and proposed the way to build the dataset and the baseline system; Ziyuan Wang built and maintained the crowdsourcing website; Yuan Yao helped download the original data and conducted preprocess; 
Xu Han, Hao Zhu, Pengfei Yu and Ziyun Wang implemented baselines and wrote the paper together; Zhiyuan Liu provided thoughtful advice and funds through the whole project. The order of the first four authors are determined by dice rolling. 

## Dataset and Word Embedding

The dataset has already be contained in the github repo. However, due to the large size, glove files (pre-trained word embeddings) are not included. Please download `glove.6B.50d.json` from [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/b14bf0d3c9e04ead9c0a/?dl=1) or [Google Drive](https://drive.google.com/open?id=1UnncRYzDpezPkwIqhgkVW6BacIqz6EaB) and put it under `data/` folder.

## Usage

To run our baseline models, use command

```bash
python train_demo.py {MODEL_NAME}
```

replace `{MODEL_NAME}` with `proto`, `metanet`, `gnn` or `snail`.