flair pytorch entity-mention-linker region:us
---
tags:
- flair
- entity-mention-linker
---

## biobert-bc5cdr-chemical

Biomedical Entity Mention Linking for chemical

### 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

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-bc5cdr-chemical")
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:
```python
linker = EntityMentionLinker.build("dmis-lab/biosyn-biobert-bc5cdr-chemical", "chemical", dictionary_name_or_path="ctd-chemicals", hybrid_search=False, entity_type="chemical-eml")
```
This will reduce the download requirements, at the cost of computation.

This EntityMentionLinker uses [https://huggingface.co/dmis-lab/biosyn-biobert-bc5cdr-chemical](dmis-lab/biosyn-biobert-bc5cdr-chemical) as embeddings for linking mentions to candidates.

