Token Classification
GLiNER2
Safetensors
GLiNER
French
extractor
medical
healthcare
french
finemed
ner
medical-entity-recognition
Instructions to use doctolib-lab/finemed-entity-extractor-fr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use doctolib-lab/finemed-entity-extractor-fr with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("doctolib-lab/finemed-entity-extractor-fr") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - GLiNER
How to use doctolib-lab/finemed-entity-extractor-fr with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("doctolib-lab/finemed-entity-extractor-fr") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba75cafa615417b5fc120088385494f01665c2c4de9b297e7ef6857700dbc0ba
- Size of remote file:
- 16.3 MB
- SHA256:
- a1c7ccb287623cccb7c03150953b6d2a09dd95122933393c9151c3a60095c97e
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