Video Classification
Transformers
PyTorch
Safetensors
English
xclip
feature-extraction
vision
Eval Results (legacy)
Instructions to use microsoft/xclip-base-patch16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/xclip-base-patch16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="microsoft/xclip-base-patch16")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/xclip-base-patch16") model = AutoModel.from_pretrained("microsoft/xclip-base-patch16") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cfca32f8ba508c0857f78c0d1d6dfe3d3283649a75e059bccca22a0aa47b5602
- Size of remote file:
- 780 MB
- SHA256:
- c331b1e49dea973c0034a17278f2c72cc09a8af5a0ddf38ec88307649ad59081
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