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fastino
/
GLiNER2.5-multi-Decide

Token Classification
GLiNER2
Safetensors
multilingual
English
extractor
Text classification
Intent classification
Sentiment Analysis
Topic classification
Named Entity Recognition
Model card Files Files and versions
xet
Community
5

Instructions to use fastino/GLiNER2.5-multi-Decide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • GLiNER2

    How to use fastino/GLiNER2.5-multi-Decide with GLiNER2:

    from gliner2 import AutoExtractor
    
    extractor = AutoExtractor.from_pretrained("fastino/GLiNER2.5-multi-Decide")
    
    # 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
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Supported languages

👀 1
#5 opened 8 days ago by
jglowa

Demo for this model on Spaces

🔥 1
#4 opened 11 days ago by
multimodalart

Install with the local extra; tag as text-classification

#3 opened 11 days ago by
bkinge
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