Instructions to use Superlore/clip-vit-large-patch14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Superlore/clip-vit-large-patch14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Superlore/clip-vit-large-patch14") 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("Superlore/clip-vit-large-patch14") model = AutoModelForZeroShotImageClassification.from_pretrained("Superlore/clip-vit-large-patch14") - Notebooks
- Google Colab
- Kaggle
0xnewton-superlore
initial commit - copy https://huggingface.co/openai/clip-vit-large-patch14
93cf9b4 - Xet hash:
- 1cc42f6dca295521183e48a523369c7b44aa54a8386600b7b9b5b0bb3fd99ccc
- Size of remote file:
- 1.71 GB
- SHA256:
- f1a17cdbe0f36fec524f5cafb1c261ea3bbbc13e346e0f74fc9eb0460dedd0d3
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