Luke Merrick
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9611734
Add launch blog post link
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README.md
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## News
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07/18/2024: Release of `snowflake-arctic-embed-m-v1.5`, capable of producing highly compressible embedding vectors that preserve quality even when squished as small as 128 bytes per vector.
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05/10/2024: Release of the [technical report on Arctic Embed](https://arxiv.org/abs/2405.05374)
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@@ -7871,4 +7871,4 @@ We thank our modeling engineers, Danmei Xu, Luke Merrick, Gaurav Nuti, and Danie
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We thank our leadership, Himabindu Pucha, Kelvin So, Vivek Raghunathan, and Sridhar Ramaswamy, for supporting this work.
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We also thank the open-source community for producing the great models we could build on top of and making these releases possible.
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Finally, we thank the researchers who created BEIR and MTEB benchmarks.
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It is largely thanks to their tireless work to define what better looks like that we could improve model performance.
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## News
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07/18/2024: Release of `snowflake-arctic-embed-m-v1.5`, capable of producing highly compressible embedding vectors that preserve quality even when squished as small as 128 bytes per vector. Details about the development of this model are available in the [launch post on the Snowflake engineering blog](https://www.snowflake.com/engineering-blog/arctic-embed-m-v1-5-enterprise-retrieval/).
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05/10/2024: Release of the [technical report on Arctic Embed](https://arxiv.org/abs/2405.05374)
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We thank our leadership, Himabindu Pucha, Kelvin So, Vivek Raghunathan, and Sridhar Ramaswamy, for supporting this work.
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We also thank the open-source community for producing the great models we could build on top of and making these releases possible.
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Finally, we thank the researchers who created BEIR and MTEB benchmarks.
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It is largely thanks to their tireless work to define what better looks like that we could improve model performance.
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