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voidful
/
mhubert-base

Feature Extraction
Transformers
PyTorch
Safetensors
hubert
Model card Files Files and versions
xet
Community
2

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

  • Libraries
  • Transformers

    How to use voidful/mhubert-base with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="voidful/mhubert-base")
    # Load model directly
    from transformers import AutoProcessor, AutoModel
    
    processor = AutoProcessor.from_pretrained("voidful/mhubert-base")
    model = AutoModel.from_pretrained("voidful/mhubert-base")
  • Notebooks
  • Google Colab
  • Kaggle
mhubert-base
758 MB
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  • 2 contributors
History: 5 commits
SFconvertbot's picture
SFconvertbot
Adding `safetensors` variant of this model
57426f9 about 3 years ago
  • .gitattributes
    1.23 kB
    Adding `safetensors` variant of this model about 3 years ago
  • README.md
    3.08 kB
    Create README.md almost 4 years ago
  • config.json
    1.38 kB
    init commit almost 4 years ago
  • mhubert_base_vp_en_es_fr_it3_L11_km1000.bin

    Detected Pickle imports (4)

    • "numpy.dtype",
    • "sklearn.cluster._kmeans.MiniBatchKMeans",
    • "joblib.numpy_pickle.NumpyArrayWrapper",
    • "numpy.ndarray"

    How to fix it?

    3.08 MB
    xet
    init commit almost 4 years ago
  • model.safetensors
    378 MB
    xet
    Adding `safetensors` variant of this model about 3 years ago
  • preprocessor_config.json
    212 Bytes
    add preprocessor_config.json almost 4 years ago
  • pytorch_model.bin
    378 MB
    xet
    init commit almost 4 years ago