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 AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("microsoft/xclip-base-patch16-zero-shot") model = AutoModel.from_pretrained("microsoft/xclip-base-patch16-zero-shot") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bd3e7519804d4861e31797c64a47225aa2c92204781981fe6cce085f3808af41
- Size of remote file:
- 780 MB
- SHA256:
- 6f57805daf0db792cc4a09e99b72c97feb2b7e7a0a99c219718d28186d852fe4
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