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