Instructions to use hf-internal-testing/tiny-random-CLIPSegModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-CLIPSegModel 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-CLIPSegModel") 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-CLIPSegModel") model = AutoModelForZeroShotImageClassification.from_pretrained("hf-internal-testing/tiny-random-CLIPSegModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e0246e97c303a8d6909d601e38722d01c0a24f2783573824a6c479ae78934276
- Size of remote file:
- 541 kB
- SHA256:
- 008d81f26639e8d2d492983eec19a03ef739bf4c771f3ec07b5359d613f01d20
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