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") - Notebooks
- Google Colab
- Kaggle
Easy evaluation of the zero-shot capabilities of pubmedclip
#3 opened about 2 years ago
by
fhvilshoj
Adding `safetensors` variant of this model
#2 opened about 2 years ago
by
SFconvertbot
Adding `safetensors` variant of this model
#1 opened over 2 years ago
by
SFconvertbot