Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing

  • Log In
  • Sign Up

rapadilla
/
xclip-base-patch32

Video Classification
Transformers
PyTorch
English
xclip
vision
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use rapadilla/xclip-base-patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use rapadilla/xclip-base-patch32 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("video-classification", model="rapadilla/xclip-base-patch32")
    # Load model directly
    from transformers import AutoProcessor, AutoModel
    
    processor = AutoProcessor.from_pretrained("rapadilla/xclip-base-patch32")
    model = AutoModel.from_pretrained("rapadilla/xclip-base-patch32")
  • Notebooks
  • Google Colab
  • Kaggle
xclip-base-patch32
790 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
rapadilla's picture
rapadilla
copying
fae568a over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    2.73 kB
    copying over 2 years ago
  • config.json
    4.72 kB
    copying over 2 years ago
  • merges.txt
    525 kB
    copying over 2 years ago
  • preprocessor_config.json
    309 Bytes
    copying over 2 years ago
  • pytorch_model.bin

    Detected Pickle imports (4)

    • "collections.OrderedDict",
    • "torch._utils._rebuild_tensor_v2",
    • "torch.FloatStorage",
    • "torch.LongStorage"

    What is a pickle import?

    787 MB
    xet
    copying over 2 years ago
  • special_tokens_map.json
    472 Bytes
    copying over 2 years ago
  • tokenizer.json
    2.22 MB
    copying over 2 years ago
  • tokenizer_config.json
    965 Bytes
    copying over 2 years ago
  • vocab.json
    862 kB
    copying over 2 years ago