transformers pytorch bert token-classification en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
language:
- en
license: apache-2.0
pipeline_tag: token-classification
---

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](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) fine-tuned on the following datasets:

- [NCBI Disease Corpus](https://www.ncbi.nlm.nih.gov/research/bionlp/Data/disease/) (train and dev sets)
- [PHAEDRA](http://www.nactem.ac.uk/PHAEDRA/) (train, dev, test sets): entity type "Disorder"
- [Corpus for Disease Names and Adverse Effects](https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/corpus-for-disease-names-and-adverse-effects.html) (train, dev, test sets): entity types "DISEASE", "ADVERSE"
- [RareDis corpus](https://github.com/isegura/NLP4RARE-CM-UC3M/tree/main/corpus) (train, dev, test sets): entity types "DISEASE", "RAREDISEASE", "SYMPTOM"
- [CoMAGC](https://github.com/isegura/NLP4RARE-CM-UC3M/tree/main/corpus) (train, dev, test sets): entity type "cancer_term"
- [PGxCorpus](https://www.nature.com/articles/s41597-019-0342-9) (train, dev, test sets):
- [miRNA-Test-Corpus](https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/download-mirna-test-corpus.html) (train, dev, test sets): entity type "Diseases"
- [BC5CDR]() (train and dev sets): entity type "Disease"
- [Mantra](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4986661/pdf/ocv037.pdf) (train, dev, test sets): entity type "DISO"