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readme.md

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.

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 or Google Drive and put it under data/ folder.

Usage

To run our baseline models, use command

python train_demo.py {MODEL_NAME}

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

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A Large-Scale Few-Shot Relation Extraction Dataset

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