readme: ---
  license: mit
  tags:
  - flair
  - token-classification
  - sequence-tagger-model
  language: "pt"
  widget:
  - text: "FISIOTERAPIA TRAUMATO - MANHÃ  Henrique Dias, 38 anos. Exercícios metabólicos de extremidades inferiores. Realizo mobilização patelar e leve mobilização de flexão de joelho conforme liberado pelo Dr Marcelo Arocha. Oriento cuidados e posicionamentos."
  ---
  
  ## Portuguese Name Identification
  
  The [NoHarm-Anony - De-Identification of Clinical Notes Using Contextualized Language Models and a Token Classifier](https://link.springer.com/chapter/10.1007/978-3-030-91699-2_3) paper contains Flair-based models for Portuguese Language, initialized with [Flair BBP](https://github.com/jneto04/ner-pt) & trained on clinical notes with names tagged. 
  
  ### Demo: How to use in Flair
  
  Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
  
  ```python
  from flair.data import Sentence
  from flair.models import SequenceTagger
  # load tagger
  tagger = SequenceTagger.load("noharm-ai/anony")
  # make example sentence
  sentence = Sentence("FISIOTERAPIA TRAUMATO - MANHÃ  Henrique Dias, 38 anos. Exercícios metabólicos de extremidades inferiores. Realizo mobilização patelar e leve mobilização de flexão de joelho conforme liberado pelo Dr Marcelo Arocha. Oriento cuidados e posicionamentos.")
  # predict NER tags
  tagger.predict(sentence)
  # print sentence
  print(sentence)
  # print predicted NER spans
  print('The following NER tags are found:')
  # iterate over entities and print
  for entity in sentence.get_spans('ner'):
      print(entity)
  ```
  
  This yields the following output:
  ```
  Span [5,6]: "Henrique Dias"   [− Labels: NOME (0.9735)]
  Span [31,32]: "Marcelo Arocha"   [− Labels: NOME (0.9803)]
  ```
  
  So, the entities "*Henrique Dias*" (labeled as a **nome**) and "*Marcelo Arocha*" (labeled as a **nome**) are found in the sentence. 
  
  
  
  ## More Information
  
  Refer to the original paper, [De-Identification of Clinical Notes Using Contextualized Language Models and a Token Classifier](https://link.springer.com/chapter/10.1007/978-3-030-91699-2_3) for additional details and performance.
  
  ## Acknowledgements
  
  We thank Dr. Ana Helena D. P. S. Ulbrich, who provided the clinical notes dataset from the hospital, for her valuable cooperation. We also thank the volunteers of the Institute of Artificial Intelligence in Healthcare Celso Pereira and Ana Lúcia Dias, for the dataset annotation.
  
  ## Citation
  
  ```
  @inproceedings{santos2021identification,
    title={De-Identification of Clinical Notes Using Contextualized Language Models and a Token Classifier},
    author={Santos, Joaquim and dos Santos, Henrique DP and Tabalipa, F{\'a}bio and Vieira, Renata},
    booktitle={Brazilian Conference on Intelligent Systems},
    pages={33--41},
    year={2021},
    organization={Springer}
  }
  ```