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 organism 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:
- [CellFinder](http://cellfinder.org/about/annotation/): entity type "species"
- [CRAFT](https://github.com/UCDenver-ccp/CRAFT/tree/master/concept-annotation): entity type "NCBITaxon"
- [MLEE](http://nactem.ac.uk/MLEE/):entity type "organism"
- [LINNAEUS](http://linnaeus.sourceforge.net/) (train and dev sets):
- [Species-800](https://species.jensenlab.org/)
- [BioNLP11ID](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP11ID-species-IOB): entity type "Organism"
- [BioNLP13CG](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP13CG-species-IOB): entity types "Organism", "Organism subdivision"
- [miRNA-Test-Corpus](https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/download-mirna-test-corpus.html): entity type "species"
- [Mantra](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4986661/pdf/ocv037.pdf):entity type "DISO"