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onnx-community
/
clip-vit-base-patch32_hidden-states

Zero-Shot Image Classification
Transformers
ONNX
clip
Model card Files Files and versions
xet
Community

Instructions to use onnx-community/clip-vit-base-patch32_hidden-states with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use onnx-community/clip-vit-base-patch32_hidden-states with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("zero-shot-image-classification", model="onnx-community/clip-vit-base-patch32_hidden-states")
    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("onnx-community/clip-vit-base-patch32_hidden-states")
    model = AutoModelForZeroShotImageClassification.from_pretrained("onnx-community/clip-vit-base-patch32_hidden-states")
  • Notebooks
  • Google Colab
  • Kaggle
clip-vit-base-patch32_hidden-states
609 MB
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  • 1 contributor
History: 5 commits
Xenova's picture
Xenova HF Staff
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68f4eab verified almost 2 years ago
  • onnx
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  • .gitattributes
    1.52 kB
    initial commit almost 2 years ago
  • config.json
    456 Bytes
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  • merges.txt
    525 kB
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  • preprocessor_config.json
    818 Bytes
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  • special_tokens_map.json
    588 Bytes
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  • tokenizer.json
    2.22 MB
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  • tokenizer_config.json
    743 Bytes
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  • vocab.json
    862 kB
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