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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
3

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
SauerkrautLM-LFM2.5-GLiNER
1.65 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
DavidGF's picture
DavidGF
Update README.md
d231d87 verified 9 days ago
  • code_modification
    Initial content (migrated from Liquid_Gliner, fresh history) 9 days ago
  • imgs
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  • .gitattributes
    1.65 kB
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  • README.md
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  • chat_template.jinja
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  • gliner_config.json
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  • pytorch_model.bin

    Detected Pickle imports (3)

    • "torch.FloatStorage",
    • "collections.OrderedDict",
    • "torch._utils._rebuild_tensor_v2"

    What is a pickle import?

    1.64 GB
    xet
    Initial content (migrated from Liquid_Gliner, fresh history) 9 days ago
  • tokenizer.json
    4.73 MB
    Initial content (migrated from Liquid_Gliner, fresh history) 9 days ago
  • tokenizer_config.json
    574 Bytes
    Initial content (migrated from Liquid_Gliner, fresh history) 9 days ago