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README.md

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+# SurgicBERTa
+SurgicBERTa is a language model based on RoBERTa-base (Liu et al., 2019) architecture. 
+We adapt RoBERTa-base to different **surgical textbooks and academic papers** via continued pretraining. This amount to about 7 million words and 300k surgical sentences.
+We used the full text of the books and papers in training, not just abstracts.
+Specific details of the adaptive pretraining procedure and evaluation tasks can be found in the paper below cited.
+
+# Citation
+If using this model, please cite the following paper:
+<em>
+<br />
+<br />
+@article{bombieri_et_al_surgical_srl_2022, <br />
+title = {Machine understanding surgical actions from intervention procedure textbooks},<br />
+journal = {Computers in Biology and Medicine},<br />
+pages = {106415},<br />
+year = {2022},<br />
+issn = {0010-4825},<br />
+doi = {https://doi.org/10.1016/j.compbiomed.2022.106415},<br />
+url = {https://www.sciencedirect.com/science/article/pii/S0010482522011234},<br />
+author = {Marco Bombieri and Marco Rospocher and Simone Paolo Ponzetto and Paolo Fiorini},<br />
+keywords = {Semantic role labeling, Surgical data science, Procedural knowledge, Information extraction, Natural language processing}<br />
+}
+</em>