Zero-Shot Classification
GLiNER2
Safetensors
English
Russian
extractor
safety
pii
ai-security
zero-shot
text-classification
span-categorization
token-classification
guardrails
zero-shot generalization
custom policies
Instructions to use hivetrace/gliner-guard-omni with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use hivetrace/gliner-guard-omni with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("hivetrace/gliner-guard-omni") # 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

- Xet hash:
- 717472373ee68d1e0a7c542fb38cd7a0976b0fc467d1715863eb3a4d4fec9216
- Size of remote file:
- 156 kB
- SHA256:
- 5e012fbc1033a9f611df9de49aeef95f3667b091327a4a6b7c607e68d47842fb
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