Rapidata/multilingual-llm-jokes-4o-claude-gemini
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Mads Kuhlmann-Joergensen
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Rapidata/multilingual-llm-jokes-4o-claude-gemini:Update README.md
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Update README.md
#4 opened 5 days ago
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daniilpyatko
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Minor pre-release fixes
#3 opened 5 days ago
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Create README.md
#1 opened 6 days ago
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Imagine you could have an Image Arena score equivalent at each checkpoint during training. We released the first version of just that:
Crowd-Eval
Add one line of code to your training loop and you will have a new real human loss curve in your W&B dashboard.
Thousands of real humans from around the world rating your model in real time at the cost of a few dollars per checkpoint is a game changer.
Check it out here: https://github.com/RapidataAI/crowd-eval
First 5 people to put it in their loop get 100'000 human responses for free! (ping me)
Crowd-Eval
Add one line of code to your training loop and you will have a new real human loss curve in your W&B dashboard.
Thousands of real humans from around the world rating your model in real time at the cost of a few dollars per checkpoint is a game changer.
Check it out here: https://github.com/RapidataAI/crowd-eval
First 5 people to put it in their loop get 100'000 human responses for free! (ping me)
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Benchmark Update:
@google
Veo3 (Text-to-Video)
Two months ago, we benchmarked @google ’s Veo2 model. It fell short, struggling with style consistency and temporal coherence, trailing behind Runway, Pika, @tencent , and even @alibaba-pai .
That’s changed.
We just wrapped up benchmarking Veo3, and the improvements are substantial. It outperformed every other model by a wide margin across all key metrics. Not just better, dominating across style, coherence, and prompt adherence. It's rare to see such a clear lead in today’s hyper-competitive T2V landscape.
Dataset coming soon. Stay tuned.
Two months ago, we benchmarked @google ’s Veo2 model. It fell short, struggling with style consistency and temporal coherence, trailing behind Runway, Pika, @tencent , and even @alibaba-pai .
That’s changed.
We just wrapped up benchmarking Veo3, and the improvements are substantial. It outperformed every other model by a wide margin across all key metrics. Not just better, dominating across style, coherence, and prompt adherence. It's rare to see such a clear lead in today’s hyper-competitive T2V landscape.
Dataset coming soon. Stay tuned.
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🔥 Hidream I1 is online! 🔥
We just added Hidream I1 to our T2I leaderboard (https://www.rapidata.ai/leaderboard/image-models) benchmarked using 195k+ human responses from 38k+ annotators, all collected in under 24 hours.
It landed #3 overall, right behind:
- @openai 4o
- @black-forest-labs Flux 1 Pro
...and just ahead of @black-forest-labs Flux 1.1 Pro, @xai-org Aurora and @google Imagen3.
Want to dig into the data? Check out our dataset here:
Rapidata/Hidream_t2i_human_preference
What model should we benchmark next?
We just added Hidream I1 to our T2I leaderboard (https://www.rapidata.ai/leaderboard/image-models) benchmarked using 195k+ human responses from 38k+ annotators, all collected in under 24 hours.
It landed #3 overall, right behind:
- @openai 4o
- @black-forest-labs Flux 1 Pro
...and just ahead of @black-forest-labs Flux 1.1 Pro, @xai-org Aurora and @google Imagen3.
Want to dig into the data? Check out our dataset here:
Rapidata/Hidream_t2i_human_preference
What model should we benchmark next?
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Thank you! If you also like the dataset itself that will make a big impact!
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🚀 We tried something new!
We just published a dataset using a new (for us) preference modality: direct ranking based on aesthetic preference. We ranked a couple of thousand images from most to least preferred, all sampled from the Open Image Preferences v1 dataset by the amazing @data-is-better-together team.
📊 Check it out here:
Rapidata/2k-ranked-images-open-image-preferences-v1
We're really curious to hear your thoughts!
Is this kind of ranking interesting or useful to you? Let us know! 💬
If it is, please consider leaving a ❤️ and if we hit 30 ❤️s, we’ll go ahead and rank the full 17k image dataset!
We just published a dataset using a new (for us) preference modality: direct ranking based on aesthetic preference. We ranked a couple of thousand images from most to least preferred, all sampled from the Open Image Preferences v1 dataset by the amazing @data-is-better-together team.
📊 Check it out here:
Rapidata/2k-ranked-images-open-image-preferences-v1
We're really curious to hear your thoughts!
Is this kind of ranking interesting or useful to you? Let us know! 💬
If it is, please consider leaving a ❤️ and if we hit 30 ❤️s, we’ll go ahead and rank the full 17k image dataset!
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