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:
- c28c2ee637201fa2566c2292d28cee74aeabf2c0d1f9d34ff2844c48ba53f892
- Size of remote file:
- 14.2 kB
- SHA256:
- 62c07c8744379eb786eff44fab8c913f61ee58d12b31db96d56a74fd477c3546
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