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--- |
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language: |
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- en |
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tags: |
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- biomedical |
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- bioNLP |
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--- |
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This is a version of [PubmedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext?text=%5BMASK%5D+is+a+tumor+suppressor+gene.) 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"](https://arxiv.org/abs/2205.02289), when used as a component of a larger relation classification model (also hosted [here on Huggingface](https://huggingface.co/allenai/drug-combo-classifier-pubmedbert-dapt)). |
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If you use this model, cite both |
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```latex |
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@misc{pubmedbert, |
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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}, |
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title = {Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing}, |
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year = {2020}, |
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eprint = {arXiv:2007.15779}, |
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} |
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``` |
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and |
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```latex |
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@inproceedings{Tiktinsky2022ADF, |
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title = "A Dataset for N-ary Relation Extraction of Drug Combinations", |
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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", |
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booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", |
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month = jul, |
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year = "2022", |
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address = "Seattle, United States", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2022.naacl-main.233", |
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doi = "10.18653/v1/2022.naacl-main.233", |
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pages = "3190--3203", |
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} |
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``` |
