Instructions to use Bingsu/clip-vit-large-patch14-ko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bingsu/clip-vit-large-patch14-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-large-patch14-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-large-patch14-ko") model = AutoModelForZeroShotImageClassification.from_pretrained("Bingsu/clip-vit-large-patch14-ko", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dfe2b724e9ba2b8bdc79c16f3f9911d2fa0bead304ef07d5f8e9e44bfa0505aa
- Size of remote file:
- 1.71 GB
- SHA256:
- ed4098b0f7105aa76c024b35aa027048a353e10c053ca991fc43b01fa14764d3
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