This is a version of PubmedBERT which has been domain-adapted (via additional pretraining) to a set of PubMed abstracts that likely discuss multiple-drug therapies. This model was the strongest contextualized encoder in the experiments in the paper "A Dataset for N-ary Relation Extraction of Drug Combinations", when used as a component of a larger relation classification model (also hosted here on Huggingface).
If you use this model, cite both
@misc{pubmedbert,
author = {Yu Gu and Robert Tinn and Hao Cheng and Michael Lucas and Naoto Usuyama and Xiaodong Liu and Tristan Naumann and Jianfeng Gao and Hoifung Poon},
title = {Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing},
year = {2020},
eprint = {arXiv:2007.15779},
}
and
@inproceedings{Tiktinsky2022ADF,
title = "A Dataset for N-ary Relation Extraction of Drug Combinations",
author = "Tiktinsky, Aryeh and Viswanathan, Vijay and Niezni, Danna and Meron Azagury, Dana and Shamay, Yosi and Taub-Tabib, Hillel and Hope, Tom and Goldberg, Yoav",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.233",
doi = "10.18653/v1/2022.naacl-main.233",
pages = "3190--3203",
}