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fastino
/
gliner2-large-v1

GLiNER2
Safetensors
English
French
Spanish
extractor
Text classification
Named Entity Recognition
Relation Extraction
Intent classification
Sentiment Analysis
Topic classification
Structured extraction
Json extraction
Model card Files Files and versions
xet
Community
4

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

  • Libraries
  • GLiNER2

    How to use fastino/gliner2-large-v1 with GLiNER2:

    from gliner2 import GLiNER2
    
    model = GLiNER2.from_pretrained("fastino/gliner2-large-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-large-v1
1.96 GB
Ctrl+K
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  • 4 contributors
History: 11 commits
urchade's picture
urchade
thaooonguyennn's picture
thaooonguyennn
Update README.md (#4)
b122b11 2 months ago
  • encoder_config
    Upload GLiNER2 about 1 year ago
  • image
    Upload image/GitHub.png 4 months ago
  • .gitattributes
    1.57 kB
    Upload image/GitHub.png 4 months ago
  • README.md
    8.27 kB
    Update README.md (#4) 2 months ago
  • added_tokens.json
    230 Bytes
    Upload GLiNER2 about 1 year ago
  • config.json
    226 Bytes
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  • model.safetensors
    1.95 GB
    xet
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  • special_tokens_map.json
    2.41 kB
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  • spm.model
    2.46 MB
    xet
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  • tokenizer.json
    8.65 MB
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  • tokenizer_config.json
    3.24 kB
    Upload GLiNER2 about 1 year ago