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Sowrabhm
/
fintext-extractor

Token Classification
ONNX
GLiNER2
English
onnxruntime
ner
transaction-extraction
sms-parsing
deberta
on-device
mobile
Model card Files Files and versions
xet
Community

Instructions to use Sowrabhm/fintext-extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • GLiNER2

    How to use Sowrabhm/fintext-extractor with GLiNER2:

    from gliner2 import GLiNER2
    
    model = GLiNER2.from_pretrained("Sowrabhm/fintext-extractor")
    
    # 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
fintext-extractor / tokenizer
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  • 1 contributor
History: 1 commit
Sowrabhm's picture
Sowrabhm
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b0e098b verified 2 months ago
  • added_tokens.json
    26 Bytes
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  • special_tokens_map.json
    301 Bytes
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  • spm.model
    2.46 MB
    xet
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  • tokenizer.json
    8.66 MB
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  • tokenizer_config.json
    1.37 kB
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