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fastino
/
gliner2.5-base-v1

Token Classification
GLiNER2
Safetensors
English
extractor
Text classification
Named Entity Recognition
Relation Extraction
Intent classification
Sentiment Analysis
Topic classification
Structured extraction
Json extraction
information-extraction
boundary-extraction
Model card Files Files and versions
xet
Community
3

Instructions to use fastino/gliner2.5-base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • GLiNER2

    How to use fastino/gliner2.5-base-v1 with GLiNER2:

    from gliner2 import GLiNER2
    
    model = GLiNER2.from_pretrained("fastino/gliner2.5-base-v1")
    
    # 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
gliner2.5-base-v1
784 MB
Ctrl+K
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  • 3 contributors
History: 9 commits
urchade's picture
urchade
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thaooonguyennn
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  • encoder_config
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  • GitHub_new.jpg
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  • README.md
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  • config.json
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  • model.safetensors
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  • tokenizer.json
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  • tokenizer_config.json
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