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
Demo for this model on Spaces
❤️👍 2
2
#4 opened 11 days ago
by
multimodalart
Ported to CrispEmbed (pure C/C++ ggml inference)
❤️ 2
#3 opened about 1 month ago
by
cstr
Zero entities with the documented setup (gliner 0.2.26/0.2.27 + transformers 5.1.0) — repro included
1
#2 opened about 1 month ago
by
baconnier
PII Masking Dataset Clarification
#1 opened about 1 month ago
by
MikeDoes