Source code and relevant scripts for our ACL 2022 paper: "CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning".
You can install all required Python packages with pip install -r requirements.txt
wget -O episode_data.zip https://cloud.tsinghua.edu.cn/f/8483dc1a34da4a34ab58/?dl=1
wget -O data/few-nerd/inter/train.txt https://cloud.tsinghua.edu.cn/f/45d55face2a14c098a13/?dl=1
wget -O data/few-nerd/intra/train.txt https://cloud.tsinghua.edu.cn/f/b169cfbeb90a48c1bf23/?dl=1
Update (6/13/2022): Looks like the previous links to Few-NERD dataset is expired. Thanks to jiayuemoon for pointing this out. Please follow this issue to get the updated link.
[task-group] [gpu to use] [way (5/10)] [shots (1/5)]
exec_container.sh intra 0 5 5
--target_dir [directory to results.txt] --range [iterations in the script ran]
. Use the directory that contains all the results of the test.inter
and intra
training data, remove any remaining model files in saved_models and run container.py similar to exec_container.sh and use the argument --do_train
. To learn further about all the arguments, please see container.py and exec_container.shIf you use our work, please cite:
@inproceedings{das2022container,
title={CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning},
author={Das, Sarkar Snigdha Sarathi and Katiyar, Arzoo and Passonneau, Rebecca J and Zhang, Rui},
booktitle={ACL},
year={2022}
}