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:
- c86df6f9faa242a648c35e83969738d5db7b26a8b4846d936ad7e444ab74e8a7
- Size of remote file:
- 690 MB
- SHA256:
- 7c31a77678b12fb556328e43f088796f4a81717a9be6020f69dde09b9f6f3bdb
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