Instructions to use OFA-Sys/chinese-clip-vit-base-patch16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OFA-Sys/chinese-clip-vit-base-patch16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="OFA-Sys/chinese-clip-vit-base-patch16") 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("OFA-Sys/chinese-clip-vit-base-patch16") model = AutoModelForZeroShotImageClassification.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16") - Notebooks
- Google Colab
- Kaggle
wsl-debian commited on
Commit ·
f2a455d
1
Parent(s): 36e679e
add safetensors which is transfered via python_model.bin and full tokenizer
Browse files- model.safetensors +3 -0
- tokenizer.json +0 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:c086d02cdb46621f5f2a39038ec3ca8b2831193b31c65a66452af2e41acc21b8
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size 753105988
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tokenizer.json
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