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HMC83
/
name-detector-32m

Token Classification
Transformers
ONNX
Safetensors
English
modernbert
pii
redaction
person-names
ner
ettin
distillation
Model card Files Files and versions
xet

Instructions to use HMC83/name-detector-32m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use HMC83/name-detector-32m with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("token-classification", model="HMC83/name-detector-32m")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForTokenClassification
    
    tokenizer = AutoTokenizer.from_pretrained("HMC83/name-detector-32m")
    model = AutoModelForTokenClassification.from_pretrained("HMC83/name-detector-32m", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
name-detector-32m
260 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
HMC83's picture
HMC83
fix teacher attribution
04e2338 verified 3 days ago
  • code
    Add the model, its run-time code and the model card 3 days ago
  • .gitattributes
    1.52 kB
    initial commit 3 days ago
  • ATTRIBUTION.md
    3.41 kB
    fix teacher attribution 3 days ago
  • LICENSE
    1.27 kB
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  • README.md
    6.89 kB
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  • SHA256SUMS
    1.21 kB
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  • config.json
    1.37 kB
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  • example.py
    855 Bytes
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  • model.onnx
    128 MB
    xet
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  • model.safetensors
    128 MB
    xet
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  • namecfg.json
    179 Bytes
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  • requirements.txt
    41 Bytes
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  • special_tokens_map.json
    694 Bytes
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  • tokenizer.json
    3.58 MB
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  • tokenizer_config.json
    20.8 kB
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