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

- Xet hash:
- 3b8eed8c78bb1abd8e47e44f5327bb3303e8705d96bc61cd8eec724f138233bc
- Size of remote file:
- 129 kB
- SHA256:
- 1050e9a3841dff8291de5a4e35e819de1259c9e42c0864d3ae2463b910988737
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