Instructions to use microsoft/xclip-base-patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/xclip-base-patch32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="microsoft/xclip-base-patch32")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("microsoft/xclip-base-patch32") model = AutoModel.from_pretrained("microsoft/xclip-base-patch32") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e018f861e74e9f263cf364b74de11859488be6e04cee8e60d84e83939dab7e4
- Size of remote file:
- 787 MB
- SHA256:
- d2840b05bd4ed269688ff76a239e703dd930db4a160726c5b79b9ef26f173452
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