Zero-Shot Classification
GLiNER2
Safetensors
English
Russian
extractor
safety
pii
ai-security
zero-shot
text-classification
span-categorization
token-classification
guardrails
Instructions to use hivetrace/gliner-guard-biencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use hivetrace/gliner-guard-biencoder with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("hivetrace/gliner-guard-biencoder") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - Notebooks
- Google Colab
- Kaggle
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```
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@misc{minko2026glinerguardunifiedencoder,
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title={GLiNER Guard: Unified Encoder Family for Production LLM Safety and Privacy},
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</details>
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# Citation
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```
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@misc{minko2026glinerguardunifiedencoder,
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title={GLiNER Guard: Unified Encoder Family for Production LLM Safety and Privacy},
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