Instructions to use flaviagiammarino/pubmed-clip-vit-base-patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flaviagiammarino/pubmed-clip-vit-base-patch32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="flaviagiammarino/pubmed-clip-vit-base-patch32") 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("flaviagiammarino/pubmed-clip-vit-base-patch32") model = AutoModelForZeroShotImageClassification.from_pretrained("flaviagiammarino/pubmed-clip-vit-base-patch32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download scripts/output.jpeg from flaviagiammarino/pubmed-clip-vit-base-patch32: direct link, hf CLI and curl.
- Browser
- Download file 37.5 kB
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https://huggingface.co/flaviagiammarino/pubmed-clip-vit-base-patch32/resolve/main/scripts/output.jpeg
- Command line
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hf download hf://flaviagiammarino/pubmed-clip-vit-base-patch32/scripts/output.jpeg
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curl -L -o output.jpeg https://huggingface.co/flaviagiammarino/pubmed-clip-vit-base-patch32/resolve/main/scripts/output.jpeg
37.5 kB
