Instructions to use hf-internal-testing/tiny-random-clip-zero-shot-image-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-clip-zero-shot-image-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification") 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("hf-internal-testing/tiny-random-clip-zero-shot-image-classification") model = AutoModelForZeroShotImageClassification.from_pretrained("hf-internal-testing/tiny-random-clip-zero-shot-image-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 94b53ab56e3097b667d012c00b91d0e4a2302fa2ec058ee96c8de61259eae89c
- Size of remote file:
- 604 kB
- SHA256:
- 994ae733af6b1b5d0126b2846f8db6f6947848e38ccfdc6804a3db86aa33376c
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