bioasyn-sapbert-bc2gn-gene-no-ab3p
Biomedical Entity Mention Linking for gene:
- Model: dmis-lab/biosyn-sapbert-bc2gn
- Dictionary: NCBI Gene (Homo_sapiens.gene_info.gz)
NOTE: This model variant does not perform abbreviation resolution via A3bP
Demo: How to use in Flair
Requires:
- Flair>=0.14.0 (
pip install flair
orpip install git+https://github.com/flairNLP/flair.git
)
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-gene")
tagger.predict(sentence)
# load the linker and dictionary
linker = EntityMentionLinker.load("gene-linker-no-abbres")
linker.predict(sentence)
# print the results for each entity mention:
for span in sentence.get_spans(tagger.label_type):
for link in span.get_labels(linker.label_type):
print(f"{span.text} -> {link.value}")
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:
from flair.models.entity_mention_linking import BioSynEntityPreprocessor
linker = EntityMentionLinker.build("dmis-lab/biosyn-sapbert-bc2gn", dictionary_name_or_path="ncbi-gene", preprocessor=BioSynEntityPreprocessor(), hybrid_search=False)
This will reduce the download requirements, at the cost of computation.
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