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:
- d03028ece8da97272a856fd834b5b13e737b52cf810ba5ad2430f913777a1881
- Size of remote file:
- 1.06 kB
- SHA256:
- be843b940cbd307a44ae2d454806a7a3b5ccbefe61b63d347048e72a563c7207
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.