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-uniencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use hivetrace/gliner-guard-uniencoder with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("hivetrace/gliner-guard-uniencoder") # 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:
- 08c3e6b25217d0ce50a6fedc3f0a5a4aa601cb0d1e1d5ed874a9ecca3f5a27f9
- Size of remote file:
- 132 kB
- SHA256:
- b2815b0fddf5f9c269fd9bc9ac8c410bb5a4d9f3c44a9089abb0e5cbec443dd6
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