Create README.md

README.md

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+---
+language:
+- en
+tags:
+- biomedical
+- bionlp
+- entity linking
+- embedding
+- bert
+---
+The GEBERT model pre-trained with GAT graph encoder.
+
+The model was published at [CLEF 2023 conference](https://clef2023.clef-initiative.eu/). The source code is available at [github](https://github.com/Andoree/GEBERT).
+
+
+Pretraining data: biomedical concept graph and concept names from the UMLS (2020AB release).
+
+Base model: [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext).
+
+
+
+```bibtex
+@inproceedings{sakhovskiy2023gebert,
+author="Sakhovskiy, Andrey
+and Semenova, Natalia
+and Kadurin, Artur
+and Tutubalina, Elena",
+title="Graph-Enriched Biomedical Entity Representation Transformer",
+booktitle="Experimental IR Meets Multilinguality, Multimodality, and Interaction",
+year="2023",
+publisher="Springer Nature Switzerland",
+address="Cham",
+pages="109--120",
+isbn="978-3-031-42448-9"
+}
+```