Token Classification
GLiNER2
Safetensors
GLiNER
French
extractor
ner
biomedical
french
clinical
multi-task
Instructions to use rntc/gliner2-fr-biomed-v3e-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use rntc/gliner2-fr-biomed-v3e-large with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("rntc/gliner2-fr-biomed-v3e-large") # 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 rntc/gliner2-fr-biomed-v3e-large with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("rntc/gliner2-fr-biomed-v3e-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c9e8a63bf05596b4dae0e70b9e23cdb4f6ddb6b269480194b5d63c7432ae703
- Size of remote file:
- 1.68 GB
- SHA256:
- ec12ee5789523158d255a03500deedf50da7c95d3e21d4d2000c271aa1059327
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