Token Classification
GLiNER
PyTorch
ner
named-entity-recognition
zero-shot
pii
privacy
biomedical
multilingual
lfm2.5
bidirectional
sauerkrautlm
vago-solutions
Instructions to use VAGOsolutions/SauerkrautLM-LFM2.5-GLiNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use VAGOsolutions/SauerkrautLM-LFM2.5-GLiNER with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("VAGOsolutions/SauerkrautLM-LFM2.5-GLiNER") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) - Notebooks
- Google Colab
- Kaggle
File size: 135 Bytes
c69956b | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:cf4554350bc88f4b77f961e82614c98f1d919ea0ac07e1d5c7ab0c607c42c52f
size 1644195154
|