readme: ---
  language: 
    - en
  tags:
  - biomedical
  - bioNLP
  ---
  
  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)).
  
  If you use this model, cite both
  ```latex
  @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
  
  ```latex
  @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",
  }
  ```