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:
- e7588a9f420769fc015f1531c1ae1c8f086c5e62f59646dab7bba3facc239f64
- Size of remote file:
- 3.7 kB
- SHA256:
- 02ed0e9b97fa67e073ac83bfb1b1a4896371275444d60fd5fdd3ef9e92951f39
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