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:
- e973fdcbadc844673fa3eb13a57e626d6914ed64199695da882963422ccc3f80
- Size of remote file:
- 786 MB
- SHA256:
- abf286e8cdd0612761c3e42d3a55eca998382dfa67a04a0f3fdcdfa4f150cdbb
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