---
tags:
  - flair
  - hunflair
  - token-classification
  - sequence-tagger-model
language: en
widget:
  - text: Two putative extended promoters consensus sequences (p1 and p2).
---

## HunFlair2 model for PROMOTER

[HunFlair](https://github.com/flairNLP/flair/blob/master/resources/docs/HUNFLAIR2.md) (biomedical flair) for enhancer entity:

- pre-trained language model: michiyasunaga/BioLinkBERT-base
- fine-tuned on RegEl corpus for `Promoter` entity type

Predicts 1 tag:

| **tag**  | **meaning**         |
| -------- | ------------------- |
| Promoter | DNA promoter region |

______________________________________________________________________

## Info

### Demo: How to use in Flair

Requires:

- **[Flair](https://github.com/flairNLP/flair/)>=0.14.0** (`pip install flair` or `pip install git+https://github.com/flairNLP/flair.git`)

```python
from flair.data import Sentence
from flair.nn import Classifier
from flair.tokenization import SciSpacyTokenizer

text = "The upstream region of the glnA gene contained two putative extended promoter consensus sequences (p1 and p2)."
sentence = Sentence(text, use_tokenizer=SciSpacyTokenizer())

tagger = Classifier.load("regel-corpus/hunflair2-regel-promoter")
tagger.predict(sentence)

print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
    print(entity)
```