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fastino
/
gliner2.5-multi-v1

Token Classification
GLiNER2
Safetensors
multilingual
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
1

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

  • Libraries
  • GLiNER2

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

    from gliner2 import GLiNER2
    
    model = GLiNER2.from_pretrained("fastino/gliner2.5-multi-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-multi-v1
1.17 GB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 11 commits
m-newhauser's picture
m-newhauser
Update README.md
aaecfe4 verified 8 days ago
  • encoder_config
    Upload folder using huggingface_hub 9 days ago
  • .gitattributes
    1.62 kB
    Add Fastino model card banner. 10 days ago
  • GitHub_new.jpg
    858 kB
    xet
    Add Fastino model card banner. 10 days ago
  • README.md
    21.6 kB
    Update README.md 8 days ago
  • config.json
    3.15 kB
    Upload folder using huggingface_hub 9 days ago
  • model.safetensors
    1.15 GB
    xet
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  • tokenizer.json
    16 MB
    xet
    Upload BoundaryExtractor 16 days ago
  • tokenizer_config.json
    645 Bytes
    Upload BoundaryExtractor 16 days ago