Add brief description and BibTex to readme

README.md

@@ -4,4 +4,33 @@ language:
 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).
+
+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",
+}
+```