File size: 1,798 Bytes
1a0b801 4abfb06 1a0b801 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
---
tags:
- flair
- entity-mention-linker
---
## biosyn-sapbert-bc5cdr-disease
Biomedical Entity Mention Linking for disease:
- Model: [dmis-lab/biosyn-sapbert-bc5cdr-disease](https://huggingface.co/dmis-lab/biosyn-sapbert-bc5cdr-disease)
- Dictionary: [CTD Diseases](https://ctdbase.org/help/diseaseDetailHelp.jsp) (See [License](https://ctdbase.org/about/legal.jsp))
### 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("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:
```python
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.
|