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fastino
/
GLiNER2-Guardrails-PII-Multi

Token Classification
GLiNER2
Safetensors
GLiNER
extractor
pii
ner
privacy
redaction
safety
moderation
guardrails
information-extraction
span-extraction
text-classification
multi-label-classification
jailbreak-detection
toxicity-classification
Model card Files Files and versions
xet
Community
3

Instructions to use fastino/GLiNER2-Guardrails-PII-Multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • GLiNER2

    How to use fastino/GLiNER2-Guardrails-PII-Multi with GLiNER2:

    from gliner2 import GLiNER2
    
    model = GLiNER2.from_pretrained("fastino/GLiNER2-Guardrails-PII-Multi")
    
    # Extract entities
    text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday."
    result = extractor.extract_entities(text, ["company", "person", "product", "location"])
    
    print(result)
  • GLiNER

    How to use fastino/GLiNER2-Guardrails-PII-Multi with GLiNER:

    from gliner import GLiNER
    
    model = GLiNER.from_pretrained("fastino/GLiNER2-Guardrails-PII-Multi")
  • Notebooks
  • Google Colab
  • Kaggle
GLiNER2-Guardrails-PII-Multi
1.52 kB
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  • 3 contributors
History: 1 commit
urchade's picture
urchade
initial commit
c6076d6 verified 26 days ago
  • .gitattributes
    1.52 kB
    initial commit 26 days ago