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  language:
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  - en
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  pipeline_tag: token-classification
 
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  ---
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  Named Entity Recognition (NER) model to recognize disease entities.
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  [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) fine-tuned on the following datasets:
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  - [NCBI Disease Corpus](https://www.ncbi.nlm.nih.gov/research/bionlp/Data/disease/) (train and dev sets)
 
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  language:
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  - en
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  pipeline_tag: token-classification
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+ license: apache-2.0
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  ---
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  Named Entity Recognition (NER) model to recognize disease entities.
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+ Please cite our work:
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+
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+ ```
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+ @article{NILNKER2022,
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+ title = {NILINKER: Attention-based approach to NIL Entity Linking},
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+ journal = {Journal of Biomedical Informatics},
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+ volume = {132},
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+ pages = {104137},
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+ year = {2022},
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+ issn = {1532-0464},
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+ doi = {https://doi.org/10.1016/j.jbi.2022.104137},
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+ url = {https://www.sciencedirect.com/science/article/pii/S1532046422001526},
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+ author = {Pedro Ruas and Francisco M. Couto},
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+ }
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+ ```
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+
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  [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) fine-tuned on the following datasets:
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  - [NCBI Disease Corpus](https://www.ncbi.nlm.nih.gov/research/bionlp/Data/disease/) (train and dev sets)