Instructions to use SaulLu/clip-vit-base-patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SaulLu/clip-vit-base-patch32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="SaulLu/clip-vit-base-patch32") 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("SaulLu/clip-vit-base-patch32") model = AutoModelForZeroShotImageClassification.from_pretrained("SaulLu/clip-vit-base-patch32") - Notebooks
- Google Colab
- Kaggle
add new fast version
Browse files- tokenizer.json +2 -5
tokenizer.json
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"pre_tokenizer": {
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"type": "Sequence",
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"pretokenizers": [
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"Regex": "<\\|startoftext\\|>|<\\|endoftext\\|>|'s|'t|'re|'ve|'m|'ll|'d|[\\p{L}]+|[\\p{N}]|[^\\s\\p{L}\\p{N}]+"
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"type": "ByteLevel",
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"pre_tokenizer": {
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"pretokenizers": [
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"type": "Split",
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"Regex": "<\\|startoftext\\|>|<\\|endoftext\\|>|'s|'t|'re|'ve|'m|'ll|'d|[\\p{L}]+|[\\p{N}]|[^\\s\\p{L}\\p{N}]+"
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"behavior": "Removed",
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"invert": true
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"type": "ByteLevel",
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