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fastino
/
gliner2-base-v1

GLiNER2
Safetensors
English
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
12

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

  • Libraries
  • GLiNER2

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

    from gliner2 import AutoExtractor
    
    extractor = AutoExtractor.from_pretrained("fastino/gliner2-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-base-v1
851 MB
Ctrl+K
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  • 6 contributors
History: 25 commits
urchade's picture
urchade
Point the fine-tune banner to agent.fastino.ai
f963421 verified 8 days ago
  • encoder_config
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  • .gitattributes
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  • GitHub_new.jpg
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  • README.md
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  • SKILL.md
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  • added_tokens.json
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  • config.json
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  • model.safetensors
    834 MB
    xet
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  • special_tokens_map.json
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  • spm.model
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  • tokenizer.json
    8.65 MB
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  • tokenizer_config.json
    3.24 kB
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