Instructions to use VAGOsolutions/SauerkrautLM-GLiNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use VAGOsolutions/SauerkrautLM-GLiNER with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("VAGOsolutions/SauerkrautLM-GLiNER") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -60,9 +60,7 @@ We compare **SauerkrautLM-GLiNER** against two baselines:
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- **urchade/gliner_multi-v2.1** – General multilingual NER model
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- **urchade/gliner_multi_pii-v1** – Specialized PII detection model
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#### CrossNER + Multilingual Domain Benchmarks (Threshold: 0.8 for SauerkrautLM-GLiNER, 0.65 for gliner_multi-v2.1)
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| Dataset | Samples | Entities | **SauerkrautLM-GLiNER**<br/>P / R / F1 | **gliner_multi-v2.1**<br/>P / R / F1 |
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- **urchade/gliner_multi-v2.1** – General multilingual NER model
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- **urchade/gliner_multi_pii-v1** – Specialized PII detection model
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#### CrossNER + Multilingual Domain Benchmarks (Threshold: 0.8 for SauerkrautLM-GLiNER, 0.65 for gliner_multi-v2.1)
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| Dataset | Samples | Entities | **SauerkrautLM-GLiNER**<br/>P / R / F1 | **gliner_multi-v2.1**<br/>P / R / F1 |
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