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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 87f5f83fde80b3fe8a8a7b5c5a1f90580a4ebea66f2edb69abf33d04c826605c
- Size of remote file:
- 459 kB
- SHA256:
- da1b6551bcd916ba5f28203ab4aed21360442f4c9dd554654c2cc0adc6e6895a
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