Downloads · 30 days
137
0% of all-time downloads
hunflair/biosyn-sapbert-bc5cdr-disease
biosyn-sapbert-bc5cdr-disease is a machine learning model from hunflair. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for flair.
Biomedical Entity Mention Linking for disease:
Downloads · 30 days
137
0% of all-time downloads
All-time downloads
139K
Public
Repo size
2.3 GB
Likes
1
Public
Click a slice to open those files.
.bin1.2 GB · 100%
From the Hugging Face model README
Biomedical Entity Mention Linking for disease:
Requires:
pip install flair or pip 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-disease")
tagger.predict(sentence)
# load the linker and dictionary
linker = EntityMentionLinker.load("disease-linker")
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:
linker = EntityMentionLinker.build("dmis-lab/biosyn-sapbert-bc5cdr-disease", dictionary_name_or_path="ctd-diseases", hybrid_search=True)
This will reduce the download requirements, at the cost of computation.