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VAGOsolutions
/
SauerkrautLM-LFM2.5-GLiNER

Token Classification
GLiNER
PyTorch
ner
named-entity-recognition
zero-shot
pii
privacy
biomedical
multilingual
lfm2.5
bidirectional
sauerkrautlm
vago-solutions
Model card Files Files and versions
xet
Community
4

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
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

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
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