pruas/BENT-PubMedBERT-NER-Disease

Named Entity Recognition (NER) model to recognize disease entities.

Please cite our work:

@article{NILNKER2022,
  title = {NILINKER: Attention-based approach to NIL Entity Linking},
  journal = {Journal of Biomedical Informatics},
  volume = {132},
  pages = {104137},
  year = {2022},
  issn = {1532-0464},
  doi = {https://doi.org/10.1016/j.jbi.2022.104137},
  url = {https://www.sciencedirect.com/science/article/pii/S1532046422001526},
  author = {Pedro Ruas and Francisco M. Couto},
}

PubMedBERT fine-tuned on the following datasets:

• NCBI Disease Corpus (train and dev sets)
• PHAEDRA (train, dev, test sets): entity type "Disorder"
• Corpus for Disease Names and Adverse Effects (train, dev, test sets): entity types "DISEASE", "ADVERSE"
• RareDis corpus (train, dev, test sets): entity types "DISEASE", "RAREDISEASE", "SYMPTOM"
• CoMAGC (train, dev, test sets): entity type "cancer_term"
• PGxCorpus (train, dev, test sets):
• miRNA-Test-Corpus (train, dev, test sets): entity type "Diseases"
• BC5CDR (train and dev sets): entity type "Disease"
• Mantra (train, dev, test sets): entity type "DISO"