Instructions to use AlpachinoNLP/LongCLIP-ViT-B-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlpachinoNLP/LongCLIP-ViT-B-32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="AlpachinoNLP/LongCLIP-ViT-B-32") 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("AlpachinoNLP/LongCLIP-ViT-B-32") model = AutoModelForZeroShotImageClassification.from_pretrained("AlpachinoNLP/LongCLIP-ViT-B-32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f2892774803849dc6ac840db3f015badd2a9ddf44aa90bf145553617b4961806
- Size of remote file:
- 606 MB
- SHA256:
- 2a732f8ee2be5650b32a17373fa546c61a76c36a8267eea233d5dee5cf676754
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