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Co-authored-by: Alex Fang <[email protected]>

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  2. LICENSE +88 -0
  3. README.md +31 -0
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  9. tokenizer_config.json +34 -0
  10. vocab.json +0 -0
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LICENSE ADDED
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+ Disclaimer: IMPORTANT: This Apple Machine Learning Research Model is
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+ specifically developed and released by Apple Inc. ("Apple") for the sole purpose
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+ of scientific research of artificial intelligence and machine-learning
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+ technology. “Apple Machine Learning Research Model” means the model, including
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+ but not limited to algorithms, formulas, trained model weights, parameters,
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+ configurations, checkpoints, and any related materials (including
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+ documentation).
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+ This Apple Machine Learning Research Model is provided to You by
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+ Apple in consideration of your agreement to the following terms, and your use,
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+ modification, creation of Model Derivatives, and or redistribution of the Apple
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+ Machine Learning Research Model constitutes acceptance of this Agreement. If You
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+ * Term and Termination: The term of this Agreement will begin upon your
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README.md ADDED
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+ ---
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+ license: apple-amlr
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+ license_name: apple-sample-code-license
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+ license_link: LICENSE
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+ ---
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+
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+ A CLIP (Contrastive Language-Image Pre-training) ViT-B/32 model trained on Conceptual Captions 12M, Conceptual Captions 3M, and Shutterstock 15M.
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+ Data Filtering Networks (DFNs) are small networks used to automatically filter large pools of uncurated data.
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+ This model is a DFN trained on publicly available data.
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+
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+ This model has been converted to PyTorch from the original JAX checkpoints from Axlearn (https://github.com/apple/axlearn).
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+
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+
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+ ## Model Details
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+
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+ - **Model Type:** Contrastive Image-Text, Zero-Shot Image Classification.
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+ - **Dataset:** CC12M + CC3M + SS15M
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+ - **Papers:**
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+ - Data Filtering Networks: https://arxiv.org/abs/2309.17425
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+ - **Examples Seen:** 1.28B
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+
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+ ## Citation
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+ ```bibtex
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+ @article{fang2023data,
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+ title={Data Filtering Networks},
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+ author={Fang, Alex and Jose, Albin Madappally and Jain, Amit and Schmidt, Ludwig and Toshev, Alexander and Shankar, Vaishaal},
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+ journal={arXiv preprint arXiv:2309.17425},
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+ year={2023}
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+ }
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+
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+ ```
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