Instructions to use gowitheflowlab/clip-base-patch16-supervised-mulitilingual-nli 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-nli 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-nli") 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-nli") model = AutoModelForZeroShotImageClassification.from_pretrained("gowitheflowlab/clip-base-patch16-supervised-mulitilingual-nli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0eb55e66a555008777b598001810ecbf1bc0308d9e551ce7d62a38a4194aac6c
- Size of remote file:
- 690 MB
- SHA256:
- a7d3599deb6b9189a482f40f76d3f52ab4abb3a2b3dae3779256e69cb40b1f70
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