Token Classification
GLiNER2
Safetensors
GLiNER
French
extractor
ner
biomedical
french
clinical
multi-task
Instructions to use rntc/gliner2-fr-biomed-v3c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use rntc/gliner2-fr-biomed-v3c with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("rntc/gliner2-fr-biomed-v3c") # 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-v3c with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("rntc/gliner2-fr-biomed-v3c") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e3308bec09321bfbab0a079d86b1f990f864053a6143739175e0b30ed566358
- Size of remote file:
- 641 MB
- SHA256:
- fdbc2d774a3f5bb39421b6968e0ef71864d5af2122a7ef3fc82240116de797ab
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