Token Classification
GLiNER
ONNX
German
English
extractor
ner
zero-shot
pii-detection
privacy
multilingual
quantized
edge
Instructions to use patronus-studio/gliner2-multi-edge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use patronus-studio/gliner2-multi-edge with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("patronus-studio/gliner2-multi-edge") 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
Commit History
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docs: link patronus.studio pages (blog EN/DE, product), Ark is open source, replace dashes
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tags: curated discovery keywords (remove competitor names, dedupe, add search terms)
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docs: reference Patronus Ark (library not yet public, no link)
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docs: condense redistribution note
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docs: model card with Apache-2.0 license, upstream attribution, and changes notice
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license: add Apache-2.0 license text
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docs: add model card with Apache-2.0 license and upstream attribution
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Publish source-faithful fully quantized GLiNER2 edge bundle
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Clean release of quantized (INT8+INT4) edge model fragments at root level
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Upload FP32 and Quantized (INT8+INT4) ONNX model fragments for Rust native gliner2-rs support
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Initial commit of gliner2-multi-edge model (INT8 layers + INT4 Embedding)
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initial commit
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