Code for the paper Extend, don't rebuild: Phrasing conditional graph modification as autoregressive sequence labelling, EMNLP 2021
pip install -r requirements.txtFor GPU acceleration, please install a suitable version of PyTorch.
python -m extend.train dataset=scene dataset.name=crowdsourced model.num_epochs=100
$ ls outputs/2021-11-01/17-12-02/
'epoch=19-step=8719.ckpt' train.log wandbpython -m extend.eval.predict_extend dataset=bionlp_completion checkpoint="outputs/2021-11-01/17-12-02/epoch\=19-step\=8719.ckpt"python -m extend.eval.predict_extend input="~/.cache/extend/crowdsourced_data/test checkpoint="outputs/2021-11-01/17-12-02/epoch\=19-step\=8719.ckpt"All datasets can be automatically downloaded by extend. Alternatively, the PC13 biomedical event graph modification dataset can be found here.
@inproceedings{weber-etal-2021-extend,
title = "Extend, don't rebuild: Phrasing conditional graph modification as autoregressive sequence labelling",
author = "Leon Weber and Samuele Garda and Jannes Münchmeyer and Ulf Leser",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
year = "2021",
publisher = "Association for Computational Linguistics"
}The MIT License (MIT) Copyright © 2021 Leon Weber
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