Instructions to use OFA-Sys/chinese-clip-vit-large-patch14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OFA-Sys/chinese-clip-vit-large-patch14 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-large-patch14") 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("OFA-Sys/chinese-clip-vit-large-patch14") model = AutoModelForZeroShotImageClassification.from_pretrained("OFA-Sys/chinese-clip-vit-large-patch14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# Chinese-CLIP-
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## Introduction
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This is the large-version of the Chinese CLIP, with ViT-L/14 as the image encoder and RoBERTa-wwm-base as the text encoder. Chinese CLIP is a simple implementation of CLIP on a large-scale dataset of around 200 million Chinese image-text pairs. For more details, please refer to our technical report https://arxiv.org/abs/2211.01335 and our official github repo https://github.com/OFA-Sys/Chinese-CLIP
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# Chinese-CLIP-ViT-Large-Patch14
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## Introduction
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This is the large-version of the Chinese CLIP, with ViT-L/14 as the image encoder and RoBERTa-wwm-base as the text encoder. Chinese CLIP is a simple implementation of CLIP on a large-scale dataset of around 200 million Chinese image-text pairs. For more details, please refer to our technical report https://arxiv.org/abs/2211.01335 and our official github repo https://github.com/OFA-Sys/Chinese-CLIP
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