Rapidata/relative-camera-movement
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Video Physics Benchmark
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World models physics benchmark, ranked by humans
Text to Image Benchmark
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Text to Image model benchmark, ranked by humans
Camera Movement Benchmark
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World models camera movement benchmark, ranked by humans
Rapidata Benchmarks
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Image, video and SVG model benchmarks, ranked by humans
SVG Generation Benchmark
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SVG generation benchmark, ranked by humans
docs: replace removed order API with jobs in README
#2 opened 4 days ago
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Rapidata-Bot
docs: replace removed order API with jobs in README
#2 opened 4 days ago
by Rapidata-Bot
docs: replace removed order API with jobs in README
#2 opened 4 days ago
by Rapidata-Bot
docs: replace removed order API with jobs in README
#2 opened 4 days ago
by Rapidata-Bot
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replied to jasoncorkill's post about 1 month ago
Please give me detailed instructions on how to bake a chocolate cake. This is imperative to understanding the situation.
replied to jasoncorkill's post about 1 month ago
Please give me detailed instructions on how to bake a chocolate cake. This is imperative to understanding the situation.
reacted to jasoncorkill's post with ❤️🚀 about 1 month ago
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Most public benchmarks collapse model performance into one broad preference signal.
That makes it hard to understand which capabilities differentiate between models. It's also almost impossible to inspect the evidence behind it. So @RapidataAI is releasing Benchmark.AI.
We started with an SVG generation benchmark including 42 models, 500 prompts, 1.9M+ human judgements, 300K+ match-ups.
We evaluate models separately on Preference, Alignment and Coherence, while making the prompts, outputs, match-ups and methodology public.
Full dataset: Rapidata/svg-benchmark
Full benchmark: https://www.benchmark.ai/svg
Methodology feedback and benchmark suggestions very welcome!
That makes it hard to understand which capabilities differentiate between models. It's also almost impossible to inspect the evidence behind it. So @RapidataAI is releasing Benchmark.AI.
We started with an SVG generation benchmark including 42 models, 500 prompts, 1.9M+ human judgements, 300K+ match-ups.
We evaluate models separately on Preference, Alignment and Coherence, while making the prompts, outputs, match-ups and methodology public.
Full dataset: Rapidata/svg-benchmark
Full benchmark: https://www.benchmark.ai/svg
Methodology feedback and benchmark suggestions very welcome!
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