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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 100b0595ea5cd3503f74f3e63cd79367ef680065c58b3d35ba2e96551c63c496
- Size of remote file:
- 1.64 GB
- SHA256:
- cf4554350bc88f4b77f961e82614c98f1d919ea0ac07e1d5c7ab0c607c42c52f
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