🎉 I am excited to share news of a project my brother, Umar Butler, and I have been working on for what feels like an eternity now.
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🎉 I am excited to share news of a project my brother, Umar Butler, and I have been working on for what feels like an eternity now.
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𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐌𝐋𝐄𝐁 — 𝐭𝐡𝐞 𝐌𝐚𝐬𝐬𝐢𝐯𝐞 𝐋𝐞𝐠𝐚𝐥 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤.
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𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐌𝐋𝐄𝐁 — 𝐭𝐡𝐞 𝐌𝐚𝐬𝐬𝐢𝐯𝐞 𝐋𝐞𝐠𝐚𝐥 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤.
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A suite of 10 high-quality English legal IR datasets, designed by legal experts to set a new standard for comparing embedding models. 
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A suite of 10 high-quality English legal IR datasets, designed by legal experts to set a new standard for comparing embedding models. 
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Whether you’re exploring legal RAG on your home computer, or running enterprise-scale retrieval, apples-to-apples evaluation is crucial. That’s why we’ve open-sourced everything - including our 7 brand-new, hand-crafted retrieval datasets. All of these datasets are now live on Hugging Face.
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Whether you’re exploring legal RAG on your home computer, or running enterprise-scale retrieval, apples-to-apples evaluation is crucial. That’s why we’ve open-sourced everything - including our 7 brand-new, hand-crafted retrieval datasets. All of these datasets are now live on Hugging Face.
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Any guesses which embedding model leads on legal retrieval?
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Any guesses which embedding model leads on legal retrieval?
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𝐇𝐢𝐧𝐭: it’s not OpenAI or Google - they place 7th and 9th on our leaderboard.
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𝐇𝐢𝐧𝐭: it’s not OpenAI or Google - they place 7th and 9th on our leaderboard.
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To do well on MLEB, embedding models must demonstrate both extensive legal domain knowledge and strong legal reasoning skills.
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To do well on MLEB, embedding models must demonstrate both extensive legal domain knowledge and strong legal reasoning skills.
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https://huggingface.co/blog/isaacus/introducing-mleb
https://huggingface.co/blog/isaacus/introducing-mleb
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🎉 I am excited to share news of a project my brother, Umar Butler, and I have been working on for what feels like an eternity now.

𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐌𝐋𝐄𝐁 — 𝐭𝐡𝐞 𝐌𝐚𝐬𝐬𝐢𝐯𝐞 𝐋𝐞𝐠𝐚𝐥 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤.

A suite of 10 high-quality English legal IR datasets, designed by legal experts to set a new standard for comparing embedding models. 

Whether you’re exploring legal RAG on your home computer, or running enterprise-scale retrieval, apples-to-apples evaluation is crucial. That’s why we’ve open-sourced everything - including our 7 brand-new, hand-crafted retrieval datasets. All of these datasets are now live on Hugging Face.

Any guesses which embedding model leads on legal retrieval?

𝐇𝐢𝐧𝐭: it’s not OpenAI or Google - they place 7th and 9th on our leaderboard.

To do well on MLEB, embedding models must demonstrate both extensive legal domain knowledge and strong legal reasoning skills.

https://huggingface.co/blog/isaacus/introducing-mleb
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