Instructions to use Shadowmachete/CLIP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shadowmachete/CLIP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Shadowmachete/CLIP") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("Shadowmachete/CLIP") model = AutoModelForZeroShotImageClassification.from_pretrained("Shadowmachete/CLIP") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e1a425ba6a613bd67c8d1fa4ba59bba1040bad3d875b255ee17baf2998146d5c
- Size of remote file:
- 599 MB
- SHA256:
- 295c21cff30714cc06fcc4fe89550753929be1e781aef621f0b7a9ac89da81e8
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