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---
tags:
- flair
- entity-mention-linker
---
## biosyn-sapbert-ncbi-disease
Biomedical Entity Mention Linking for diseases:
- Model: [dmis-lab/biosyn-sapbert-ncbi-disease](https://huggingface.co/dmis-lab/biosyn-sapbert-ncbi-disease)
- Dictionary: [CTD Diseases](https://ctdbase.org/voc.go?type=disease) (See License)
### 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.models import Classifier, EntityMentionLinker
from flair.tokenization import SciSpacyTokenizer
sentence = Sentence(
"The mutation in the ABCD1 gene causes X-linked adrenoleukodystrophy, "
"a neurodegenerative disease, which is exacerbated by exposure to high "
"levels of mercury in dolphin populations.",
use_tokenizer=SciSpacyTokenizer()
)
# load hunflair to detect the entity mentions we want to link.
tagger = Classifier.load("hunflair-disease")
tagger.predict(sentence)
# load the linker and dictionary
linker = EntityMentionLinker.load("hunflair/biosyn-sapbert-ncbi-disease")
dictionary = linker.dictionary
# find then candidates for the mentions
linker.predict(sentence)
# print the results for each entity mention:
for span in sentence.get_spans(tagger.label_type):
print(f"Span: {span.text}")
for candidate_label in span.get_labels(linker.label_type):
candidate = dictionary[candidate_label.value]
print(f"Candidate: {candidate.concept_name}")
```
As an alternative to downloading the already precomputed model (much storage). You can also build the model
and compute the embeddings for the dataset using:
```python
linker = EntityMentionLinker.build("dmis-lab/biosyn-biobert-ncbi-disease", dictionary_name_or_path="ctd-diseases", hybrid_search=True)
```
This will reduce the download requirements, at the cost of computation.
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