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helpmefindaname/
flair-eml-biobert-ncbi-disease

Name: flair-eml-biobert-ncbi-disease
Availability: InStock

flair

pytorch

entity-mention-linker

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biobert-ncbi-disease

Biomedical Entity Mention Linking for diseases

Demo: How to use in Flair

Requires:

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.models import EntityMentionLinker

sentence = Sentence("Behavioral abnormalities in the Fmr1 KO2 Mouse Model of Fragile X Syndrome")

# load hunflair to detect the entity mentions we want to link.
tagger = Classifier.load("hunflair")
tagger.predict(sentence)

# load the linker and dictionary
linker = EntityMentionLinker.load("helpmefindaname/flair-eml-biobert-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(linker.entity_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:

linker = EntityMentionLinker.build("dmis-lab/biosyn-biobert-ncbi-disease", "diseases", dictionary_name_or_path="ctd-diseases", hybrid_search=False, entity_type="diseases-eml")

This will reduce the download requirements, at the cost of computation.

This EntityMentionLinker uses https://huggingface.co/dmis-lab/biosyn-biobert-ncbi-disease as embeddings for linking mentions to candidates.