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agbalu
/
Belaid-31M

Token Classification
Transformers
Safetensors
Kabyle
belaid
kabyle
taqbaylit
berber
amazigh
punctuation-restoration
truecasing
asr-post-processing
low-resource
custom_code
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use agbalu/Belaid-31M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use agbalu/Belaid-31M with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("token-classification", model="agbalu/Belaid-31M", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModelForTokenClassification
    model = AutoModelForTokenClassification.from_pretrained("agbalu/Belaid-31M", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
Belaid-31M
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
ainouche-abderahmane's picture
ainouche-abderahmane
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  • .gitattributes
    1.52 kB
    initial commit 5 days ago
  • README.md
    12.9 kB
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  • __init__.py
    89 Bytes
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  • agbalu-tok-base-16k.model
    260 kB
    xet
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  • config.json
    852 Bytes
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  • configuration_belaid.py
    2.47 kB
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  • export.stats.json
    2.47 kB
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  • model.safetensors
    126 MB
    xet
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  • modeling_belaid.py
    17.9 kB
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  • tokenizer.json
    1.02 MB
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  • tokenizer_config.json
    221 Bytes
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