Video Classification
Transformers
PyTorch
Safetensors
English
xclip
feature-extraction
vision
Eval Results (legacy)
Instructions to use microsoft/xclip-base-patch16-zero-shot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/xclip-base-patch16-zero-shot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="microsoft/xclip-base-patch16-zero-shot")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/xclip-base-patch16-zero-shot") model = AutoModel.from_pretrained("microsoft/xclip-base-patch16-zero-shot") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a2cbfbb73e220b35e34e5a1d9a4afdaed875701ef017569bc3c71c0ee5a70f8
- Size of remote file:
- 780 MB
- SHA256:
- 0057077dd2bb6a5852fc9150f6e08f866448f4928f0acd7b267007f22a6adaf7
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