Instructions to use BilelDJ/clip-hugging-face-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BilelDJ/clip-hugging-face-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="BilelDJ/clip-hugging-face-finetuned") 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("BilelDJ/clip-hugging-face-finetuned") model = AutoModelForZeroShotImageClassification.from_pretrained("BilelDJ/clip-hugging-face-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3ba8a124aa71ff368f7da349ebd5ec5e358829ba2565c0a2d20ab5bf2289f01
- Size of remote file:
- 605 MB
- SHA256:
- 352172f076d6a58ebf2fc85e11b1cbc041ccf3a96a3ea3e2f8b1e6a9c0fcf049
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