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+

README.md

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

pytorch_model.bin

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