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Marian Kannwischer PRO
canwiper
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RLHF & Computer Vision
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Rapidata/OpenAI-4o_t2i_human_preference
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At Rapidata, we compared DeepL with LLMs like DeepSeek-R1, Llama, and Mixtral for translation quality using feedback from over 51,000 native speakers. Despite the costs, the performance makes it a valuable investment, especially in critical applications where translation quality is paramount. Now we can say that Europe is more than imposing regulations.
Our dataset, based on these comparisons, is now available on Hugging Face. This might be useful for anyone working on AI translation or language model evaluation.
https://huggingface.co/datasets/Rapidata/Translation-deepseek-llama-mixtral-v-deepl
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Benchmarking Google's Veo2: How Does It Compare?
The results did not meet expectations. Veo2 struggled with style consistency and temporal coherence, falling behind competitors like Runway, Pika, Tencent, and even Alibaba. While the model shows promise, its alignment and quality are not yet there.
Google recently launched Veo2, its latest text-to-video model, through select partners like fal.ai. As part of our ongoing evaluation of state-of-the-art generative video models, we rigorously benchmarked Veo2 against industry leaders.
We generated a large set of Veo2 videos spending hundreds of dollars in the process and systematically evaluated them using our Python-based API for human and automated labeling.
Check out the ranking here: https://www.rapidata.ai/leaderboard/video-models
https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-veo2
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Crowd-sourced Open Preference Dataset for Text-to-Image Generation
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