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AlpachinoNLP
/
LongCLIP-ViT-B-32

Zero-Shot Image Classification
Transformers
Safetensors
clip
vision-text
image-text-retrieval
Model card Files Files and versions
xet
Community

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")
  • Notebooks
  • Google Colab
  • Kaggle
LongCLIP-ViT-B-32
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  • 1 contributor
History: 6 commits
SiyouLi's picture
SiyouLi
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  • .gitattributes
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  • README.md
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  • config.json
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  • merges.txt
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  • model.safetensors
    606 MB
    xet
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  • preprocessor_config.json
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  • special_tokens_map.json
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  • tokenizer.json
    3.64 MB
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  • tokenizer_config.json
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  • vocab.json
    862 kB
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