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  license: cc-by-nc-4.0
 
 
 
 
 
 
 
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  license: cc-by-nc-4.0
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+ tags:
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+ - vision
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+ - metaclip
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+ widget:
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+ - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
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+ candidate_labels: playing music, playing sports
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+ example_title: Cat & Dog
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  ---
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+
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+ # MetaCLIP model, huge-sized version, patch resolution 14
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+
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+ MetaCLIP model applied to 2.5 billion data points of CommonCrawl (CC). It was introduced in the paper [Demystifying CLIP Data](https://arxiv.org/abs/2309.16671) by Xu et al. and first released in [this repository](https://github.com/facebookresearch/MetaCLIP).
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+ Disclaimer: The team releasing MetaCLIP did not write a model card for this model so this model card has been written by the Hugging Face team.
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+
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+ ## Model description
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+ The [Demystifying CLIP Data](https://arxiv.org/abs/2309.16671) paper aims to reveal CLIP’s method around training data curation. OpenAI never open-sourced code regarding their data preparation pipeline.
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+ <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/clip_overview.jpg"
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+ alt="drawing" width="600"/>
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+
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+ <small> CLIP high-level overview. Taken from the <a href="https://arxiv.org/abs/2103.00020">CLIP paper</a>. </small>
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+
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+ ## Intended uses & limitations
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+ You can use the raw model for linking images with text in a shared embedding space. This enables things like zero-shot image classification, text-based image retrieval, image-based text retrieval, etc.
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+ ### How to use
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+
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+ We refer to the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/clip#usage). Just replace the names of the models on the hub.
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+
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+ ### BibTeX entry and citation info
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+
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+ ```bibtex
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+ @misc{xu2023demystifying,
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+ title={Demystifying CLIP Data},
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+ author={Hu Xu and Saining Xie and Xiaoqing Ellen Tan and Po-Yao Huang and Russell Howes and Vasu Sharma and Shang-Wen Li and Gargi Ghosh and Luke Zettlemoyer and Christoph Feichtenhofer},
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+ year={2023},
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+ eprint={2309.16671},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CV}
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+ }
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+ ```