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clip/facebook/metaclip-b16-400m/.gitattributes

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clip/facebook/metaclip-b16-400m/README.md

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+---
+license: cc-by-nc-4.0
+tags:
+- vision 
+- metaclip
+widget:
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
+  candidate_labels: playing music, playing sports
+  example_title: Cat & Dog
+---
+
+# MetaCLIP model, base-sized version, patch resolution 16
+
+MetaCLIP model applied to 400 million 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). 
+
+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.
+
+## Model description
+
+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.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/clip_overview.jpg"
+alt="drawing" width="600"/>
+
+<small> CLIP high-level overview. Taken from the <a href="https://arxiv.org/abs/2103.00020">CLIP paper</a>. </small>
+
+## Intended uses & limitations
+
+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.
+
+### How to use
+
+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.
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{xu2023demystifying,
+      title={Demystifying CLIP Data}, 
+      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},
+      year={2023},
+      eprint={2309.16671},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```

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clip/facebook/metaclip-b16-400m/config.json

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+    "model_type": "clip_text_model"
+  },
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+  "transformers_version": "4.34.0",
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+  }
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clip/facebook/metaclip-b16-fullcc2.5b/README.md

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+---
+license: cc-by-nc-4.0
+tags:
+- vision 
+- metaclip
+widget:
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
+  candidate_labels: playing music, playing sports
+  example_title: Cat & Dog
+---
+
+# MetaCLIP model, base-sized version, patch resolution 16
+
+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). 
+
+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.
+
+## Model description
+
+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.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/clip_overview.jpg"
+alt="drawing" width="600"/>
+
+<small> CLIP high-level overview. Taken from the <a href="https://arxiv.org/abs/2103.00020">CLIP paper</a>. </small>
+
+## Intended uses & limitations
+
+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.
+
+### How to use
+
+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.
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{xu2023demystifying,
+      title={Demystifying CLIP Data}, 
+      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},
+      year={2023},
+      eprint={2309.16671},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```

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