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README.md
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<h1 align="center">
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<b>Welcome to Marqo 🙌</b>
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</h1>
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<p align="center">
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<b>Unified
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</p>
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sdk: static
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<img src="marqo.png" alt="Marqo Logo" />
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<h1 align="center">
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<b>Welcome to Marqo 🙌</b>
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</h1>
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<p align="center">
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<b>Your Unified Platform for Embedding Generation and Search</b><br>
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Marqo is an open-source platform designed to streamline embedding generation, search, and ranking, delivering state-of-the-art retrieval across multimodal data. Also available on <a href="https://www.marqo.ai/cloud">Marqo Cloud</a> for seamless integration and scalability.<br>
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</p>
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<p align="center">
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<b>Explore Our Repositories</b><br>
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<ul>
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<li><a href="https://github.com/marqo-ai/marqo">Marqo</a>: The core embedding generation and search engine.</li>
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<li><a href="https://github.com/marqo-ai/GCL">GCL</a>: Generalized Contrastive Learning for multimodal retrieval.</li>
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<li><a href="https://github.com/marqo-ai/marqo-FashionCLIP">FashionCLIP</a>: SOTA image and text embeddings for fashion search.</li>
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</ul>
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</p>
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<p align="center">
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<b>Our Latest Innovations</b><br>
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<ul>
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<li><a href="https://www.marqo.ai/blog/introducing-marqtune">Embedding Fine-Tuning with MarqoTune</a>: Tailor embeddings to your domain for superior search results.</li>
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<li><a href="https://www.marqo.ai/blog/search-model-for-fashion">FashionSigLIP</a>: Our latest model for fashion retrieval, combining cutting-edge techniques for enhanced search accuracy.</li>
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<li><a href="https://www.marqo.ai/blog/generalized-contrastive-learning-for-multi-modal-retrieval-and-ranking?_gl=1*vkeuqm*_gcl_au*MTM0OTc4OTY4Ny4xNzIzNTQ0NTcy">Generalized Contrastive Learning (GCL)</a>: A framework for training robust embedding models for multimodal search. <a href="https://arxiv.org/abs/2404.08535">Read the GCL paper on arXiv.</a></li>
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<li><a href="https://www.marqo.ai/blog/understanding-recall-in-hnsw-search">Understanding Recall in HNSW</a>: Insights into optimizing recall in hierarchical navigable small worlds (HNSW) search structures. <a href="https://arxiv.org/abs/2405.17813">Read the HNSW paper on arXiv.</a></li>
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</ul>
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</p>
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