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regel-corpus
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hunflair-enhancer

Token Classification
Flair
PyTorch
English
hunflair
sequence-tagger-model
Model card Files Community
wbi-sg commited on
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1 Parent(s): fefe953

First model version

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Files changed (4) hide show
  1. README.md +93 -3
  2. loss.tsv +101 -0
  3. pytorch_model.bin +3 -0
  4. training.log +0 -0
README.md CHANGED
@@ -1,3 +1,93 @@
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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ tags:
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+ - flair
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+ - hunflair
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+ - sequence-tagger-model
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+ language: en
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+ widget:
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+ - text: "Isolate an enhancer element located between -89 and -50 bp in PAI-1"
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+ ---
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+
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+ ## HunFlair model for ENHANCER
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+
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+ [HunFlair](https://github.com/flairNLP/flair/blob/master/resources/docs/HUNFLAIR.md) (biomedical flair) for enhancer entity.
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+
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+
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+ Predicts 1 tag:
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+
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+ | **tag** | **meaning** |
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+ |---------------------------------|-----------|
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+ | Enhancer | DNA enhancer region |
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+
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+ ---
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+
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+ ### Demo: How to use in Flair
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+
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+ Requires:
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+ - **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
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+
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+ ```python
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+ from flair.data import Sentence
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+ from flair.models import SequenceTagger
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+ # for biomedical-specific tokenization:
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+ # from flair.tokenization import SciSpacyTokenizer
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+
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+ # load tagger
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+ tagger = SequenceTagger.load("regel-corpus/hunflair-promoter")
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+
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+ 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."
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+
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+ # make example sentence
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+ sentence = Sentence(text)
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+
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+ # for biomedical-specific tokenization:
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+ # sentence = Sentence(text, use_tokenizer=SciSpacyTokenizer())
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+
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+ # predict NER tags
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+ tagger.predict(sentence)
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+
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+ # print sentence
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+ print(sentence)
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+
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+ # print predicted NER spans
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+ print('The following NER tags are found:')
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+ # iterate over entities and print
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+ for entity in sentence.get_spans('ner'):
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+ print(entity)
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+
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+ ```
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+
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+ This yields the following output:
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+ ```
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+ 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)]
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+ ```
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+
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+ So, the entity "*enhancer element located between - 89 and - 50 bp in PAI-1*" (labeled as a **enhancer**) is found in the sentence.
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+
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+ Alternatively download all models locally and use the `MultiTagger` class.
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+
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+ ```python
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+ from flair.models import MultiTagger
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+
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+ tagger = [
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+ './models/hunflair-promoter/pytorch_model.bin',
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+ './models/hunflair-enhancer/pytorch_model.bin',
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+ './models/hunflair-tfbs/pytorch_model.bin',
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+ ]
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+
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+ tagger = MultiTagger.load(['./models/hunflair-'])
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+
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+ tagger.predict(sentence)
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+ ```
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+
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+ ---
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+
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+ ### Cite
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+
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+ Please cite the following paper when using this model.
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+
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+ ```
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+ TODO
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+ ```
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+
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+
loss.tsv ADDED
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+ EPOCH TIMESTAMP BAD_EPOCHS LEARNING_RATE TRAIN_LOSS
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pytorch_model.bin ADDED
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+ oid sha256:5ced0b96e5269c14364dd434ba56563401ede7054a14088c080cfe55b7843307
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+ size 1104819835
training.log ADDED
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