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:
- d3fa589213d136f451fa562144cf417a8bbd21588e0b103f7cc74fd8a505d36d
- Size of remote file:
- 1.21 GB
- SHA256:
- f3cc96b5a320c1fd197287169bd1be9d3b418529ebc8506e7f419e461cebb462
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.