Instructions to use Bingsu/clip-vit-base-patch32-ko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bingsu/clip-vit-base-patch32-ko with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Bingsu/clip-vit-base-patch32-ko") 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("Bingsu/clip-vit-base-patch32-ko") model = AutoModelForZeroShotImageClassification.from_pretrained("Bingsu/clip-vit-base-patch32-ko") - Notebooks
- Google Colab
- Kaggle
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candidate_labels: 고양이, 강아지
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example_title: cat and
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language: ko
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license: mit
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
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candidate_labels: 기타치는 고양이, 피아노 치는 강아지
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example_title: Guitar, cat and dog
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language: ko
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license: mit
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