Instructions to use gowitheflowlab/clip-base-patch16-supervised-mulitilingual-1200 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-1200 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-1200") 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-1200") model = AutoModelForZeroShotImageClassification.from_pretrained("gowitheflowlab/clip-base-patch16-supervised-mulitilingual-1200") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff075d88837f87498046df3c43428406315b2cabef4f3fc6e2b8a6380797b4a5
- Size of remote file:
- 690 MB
- SHA256:
- 21bec720be156bf0ad1cdc6a0c5dc305d7d3ace6d8538d821612358ac8c451a6
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