

More information can be found in our github repo and paper. Please cite the paper, if you use the model.

https://github.com/Aitslab/EasyNER


@article{ahmed2023easyner,
  title={EasyNER: A Customizable Easy-to-Use Pipeline for Deep Learning- and Dictionary-based Named Entity Recognition from Medical Text},
  author={Rafsan Ahmed and Petter Berntsson and Alexander Skafte and Salma Kazemi Rashed and Marcus Klang and Adam Barvesten and Ola Olde and William Lindholm and Antton Lamarca Arrizabalaga and Pierre Nugues and Sonja Aits},
  year={2023},
  eprint={2304.07805},
  archivePrefix={arXiv},
  primaryClass={q-bio.QM}
}



{"license": "apache-2.0"}
transformers pytorch bert token-classification arxiv:2304.07805 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #arxiv-2304.07805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 



More information can be found in our github repo and paper. Please cite the paper, if you use the model.

URL


@article{ahmed2023easyner,
  title={EasyNER: A Customizable Easy-to-Use Pipeline for Deep Learning- and Dictionary-based Named Entity Recognition from Medical Text},
  author={Rafsan Ahmed and Petter Berntsson and Alexander Skafte and Salma Kazemi Rashed and Marcus Klang and Adam Barvesten and Ola Olde and William Lindholm and Antton Lamarca Arrizabalaga and Pierre Nugues and Sonja Aits},
  year={2023},
  eprint={2304.07805},
  archivePrefix={arXiv},
  primaryClass={q-bio.QM}
}



TAGS
#transformers #pytorch #bert #token-classification #arxiv-2304.07805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #bert #token-classification #arxiv-2304.07805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
