Users praised the Surge AI team for releasing a strong evaluation dataset, thanking them for enabling better benchmarks on document parsing advances like ReductoAI's.
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@echen @reductoai 🫡 Great eval dataset, thanks to the @HelloSurgeAI team for releasing it
Great to see others build on top of our benchmarks. Surge’s GDP.pdf eval tests whether AI can understand the documents that run the world. @reductoai took our public set, and tested if adding their document parse would lift model quality. Models gained +9pp on average while cutting reasoning tokens by 13%. Dense engineering tasks (wiring diagrams, cross-referenced tables, etc) had the biggest gain: 7% to 23%. If you're working on any part of the document AI stack, our public set is ready for you to build on. Try it and reach out! https://huggingface.co/datasets/surgeai/GDP.pdf
Great to see others build on top of our benchmarks. Surge’s GDP.pdf eval tests whether AI can understand the documents that run the world. @reducto took our public set, and tested if adding their document parse would lift model quality. Models gained +9pp on average while cutting reasoning tokens by 13%. Dense engineering tasks (wiring diagrams, cross-referenced tables, etc) had the biggest gain: 7% to 23%. If you're working on any part of the document AI stack, our public set is ready for you to build on. Try it and reach out! https://huggingface.co/datasets/surgeai/GDP.pdf
@echen @reductoai 🫡 Great eval dataset, thanks to the @HelloSurgeAI team for releasing it
Great to see others build on top of our benchmarks. Surge’s GDP.pdf eval tests whether AI can understand the documents that run the world. @reductoai took our public set, and tested if adding their document parse would lift model quality. Models gained +9pp on average while cutting reasoning tokens by 13%. Dense engineering tasks (wiring diagrams, cross-referenced tables, etc) had the biggest gain: 7% to 23%. If you're working on any part of the document AI stack, our public set is ready for you to build on. Try it and reach out! https://huggingface.co/datasets/surgeai/GDP.pdf
Great to see others build on top of our benchmarks. Surge’s GDP.pdf eval tests whether AI can understand the documents that run the world. @reducto took our public set, and tested if adding their document parse would lift model quality. Models gained +9pp on average while cutting reasoning tokens by 13%. Dense engineering tasks (wiring diagrams, cross-referenced tables, etc) had the biggest gain: 7% to 23%. If you're working on any part of the document AI stack, our public set is ready for you to build on. Try it and reach out! https://huggingface.co/datasets/surgeai/GDP.pdf
Users praised the Surge AI team for releasing a strong evaluation dataset, thanking them for enabling better benchmarks on document parsing advances like ReductoAI's.
Based on 1 visible X reactions from 2 accounts; directional sample.
Ask a question below.
Published answers will appear here.