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
| { | |
| "counting_layer": "count_lstm_v2", | |
| "max_width": 8, | |
| "model_name": "/lustre/fsn1/projects/rech/rua/uvb79kr/almanach--ModernCamemBERT-bio-large", | |
| "model_type": "extractor", | |
| "token_pooling": "first", | |
| "transformers_version": "4.57.6" | |
| } | |