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smdesai
/
GLiNER25-Multi-FP16-CoreAI

Token Classification
GLiNER2
multilingual
English
named-entity-recognition
relation-extraction
text-classification
information-extraction
coreai
apple
ios
on-device
fp16
Model card Files Files and versions
xet
Community

Instructions to use smdesai/GLiNER25-Multi-FP16-CoreAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • GLiNER2

    How to use smdesai/GLiNER25-Multi-FP16-CoreAI with GLiNER2:

    from gliner2 import GLiNER2
    
    model = GLiNER2.from_pretrained("smdesai/GLiNER25-Multi-FP16-CoreAI")
    
    # 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
GLiNER25-Multi-FP16-CoreAI
593 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
smdesai's picture
smdesai
Add a 1024-token bucket (a_t1024..d_t1024)
ce68e45 verified 6 days ago
  • GLiNER25-Multi-FP16.aimodel
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  • .gitattributes
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  • LICENSE
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  • README.md
    8.33 kB
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  • constants.json
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  • functions.json
    12.3 kB
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  • model-card.md
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  • tokenizer.json
    16 MB
    xet
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  • tokenizer_config.json
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