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
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README.md
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* **Backbone:** [LFM2.5-350M](https://huggingface.co/LiquidAI/LFM2.5-350M) (causal → bidirectional)
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* **Task:** Zero-shot Named Entity Recognition (GLiNER span–label matching)
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* **Language(s):** English, French, German, Italian, Spanish
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* **License:** *
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* **Contact:** [VAGO solutions](https://vago-solutions.ai)
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### Architecture
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* **Backbone:** [LFM2.5-350M](https://huggingface.co/LiquidAI/LFM2.5-350M) (causal → bidirectional)
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* **Task:** Zero-shot Named Entity Recognition (GLiNER span–label matching)
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* **Language(s):** English, French, German, Italian, Spanish
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* **License:** *lfm1.0*
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* **Contact:** [VAGO solutions](https://vago-solutions.ai)
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### Architecture
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