Instructions to use gowitheflowlab/clip-base-patch16-supervised-mulitilingual-1920 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gowitheflowlab/clip-base-patch16-supervised-mulitilingual-1920 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="gowitheflowlab/clip-base-patch16-supervised-mulitilingual-1920") 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("gowitheflowlab/clip-base-patch16-supervised-mulitilingual-1920") model = AutoModelForZeroShotImageClassification.from_pretrained("gowitheflowlab/clip-base-patch16-supervised-mulitilingual-1920") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5951404cf1330eb148fe38dd658bafc92f4e95887bde5fa265371fbab7c92b01
- Size of remote file:
- 599 MB
- SHA256:
- 095e5d88b98105c5037f68d1fa924a050ef09e7a989acea5c24e015a02922089
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