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"], )# pip install -U transformers accelerate # 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
Commit History
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Keeping the vocab to 99.
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Update the tokenizer for 4.17.
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Adding tf weights.
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Removing all merges.
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Going the other way and simplifying the tokenizer instead.
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Revert "Making the vocab_size match the tokenizer vocab_size."
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Making the vocab_size match the tokenizer vocab_size.
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crop_size is after resize ??
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Adding pipeline_tag.
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Making processor + model match.
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initial commit
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