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agbalu
/
Dihya-5M

Text Classification
Transformers
Safetensors
dihya
feature-extraction
berber
amazigh
kabyle
tashelhit
tarifit
tamasheq
tamazight
shawiya
language-identification
conformal-prediction
low-resource
custom_code
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use agbalu/Dihya-5M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use agbalu/Dihya-5M with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="agbalu/Dihya-5M", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("agbalu/Dihya-5M", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
Dihya-5M
83.9 MB
Ctrl+K
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  • 1 contributor
History: 5 commits
ainouche-abderahmane's picture
ainouche-abderahmane
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  • .gitattributes
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  • README.md
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  • __init__.py
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  • best.pt
    63.4 MB
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  • calibration.json
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  • config.json
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  • configuration_dihya.py
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  • export.stats.json
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  • held-out-report.json
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  • model.safetensors
    20.4 MB
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  • modeling_dihya.py
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  • tokenizer.json
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  • tokenizer_config.json
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