readme: ---
  tags:
  - flair
  - hunflair
  - token-classification
  - sequence-tagger-model
  language: en
  widget:
  - text: "Isolate an enhancer element located between -89 and -50 bp in PAI-1"
  ---
  
  ## HunFlair model for ENHANCER  
  
  [HunFlair](https://github.com/flairNLP/flair/blob/master/resources/docs/HUNFLAIR.md) (biomedical flair) for enhancer entity.
  
  
  Predicts 1 tag:
  
  | **tag**                        | **meaning** |
  |---------------------------------|-----------|
  | Enhancer         | DNA enhancer region | 
  
  ---
  
  ### Cite
  
  Please cite the following paper when using this model.
  
  
  ```
  @article{garda2022regel,
    title={RegEl corpus: identifying DNA regulatory elements in the scientific literature},
    author={Garda, Samuele and Lenihan-Geels, Freyda and Proft, Sebastian and Hochmuth, Stefanie and Sch{\"u}lke, Markus and Seelow, Dominik and Leser, Ulf},
    journal={Database},
    volume={2022},
    year={2022},
    publisher={Oxford Academic}
  }
  ```
  
  ---
  
  ### 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
  # for biomedical-specific tokenization:
  # from flair.tokenization import SciSpacyTokenizer
  
  # load tagger
  tagger = SequenceTagger.load("regel-corpus/hunflair-promoter")
  
  text = "An upstream activator of the mitogen-activated protein (MAP) kinase pathways was used to isolate an enhancer element located between -89 and -50 bp in PAI-1 promoter that was activated by MEKK-1."
  
  # make example sentence
  sentence = Sentence(text)
  
  # for biomedical-specific tokenization:
  # sentence = Sentence(text, use_tokenizer=SciSpacyTokenizer())
  
  # 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 [18,19,20,21,22,23,24,25,26,27,28,29,30]: "enhancer element located between - 89 and - 50 bp in PAI-1 promoter"   [− Labels: Enhancer (0.992)]
  ```
  
  So, the entity "*enhancer element located between - 89 and - 50 bp in PAI-1*" (labeled as a **enhancer**) is found in the sentence. 
  
  Alternatively download all models locally and use the `MultiTagger` class.
  
  ```python
  from flair.models import MultiTagger
  
  tagger = [
  './models/hunflair-promoter/pytorch_model.bin',
  './models/hunflair-enhancer/pytorch_model.bin',
  './models/hunflair-tfbs/pytorch_model.bin',
  ]
  
  tagger = MultiTagger.load(['./models/hunflair-'])
  
  tagger.predict(sentence)
  ```
  
  ---
  
  
  
  