This dataset was collected in roughly 4 hours using the Rapidata Python API, showcasing how quickly large-scale annotations can be performed with the right tooling!
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This dataset was collected in roughly 4 hours using the Rapidata Python API, showcasing how quickly large-scale annotations can be performed with the right tooling!
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All that at less than the cost of a single hour of a typical ML engineer in Zurich!
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All that at less than the cost of a single hour of a typical ML engineer in Zurich!
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The new dataset of ~22,000 human annotations evaluating AI-generated videos based on different dimensions, such as Prompt-Video Alignment, Word for Word Prompt Alignment, Style,  Speed of Time flow and Quality of Physics.
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The new dataset of ~22,000 human annotations evaluating AI-generated videos based on different dimensions, such as Prompt-Video Alignment, Word for Word Prompt Alignment, Style,  Speed of Time flow and Quality of Physics.
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https://huggingface.co/datasets/Rapidata/text-2-video-Rich-Human-Feedback
Rapidata/text-2-video-Rich-Human-Feedback
dataset
resource
https://huggingface.co/datasets/Rapidata/text-2-video-Rich-Human-Feedback
This dataset was collected in roughly 4 hours using the Rapidata Python API, showcasing how quickly large-scale annotations can be performed with the right tooling!

All that at less than the cost of a single hour of a typical ML engineer in Zurich!

The new dataset of ~22,000 human annotations evaluating AI-generated videos based on different dimensions, such as Prompt-Video Alignment, Word for Word Prompt Alignment, Style,  Speed of Time flow and Quality of Physics.

https://huggingface.co/datasets/Rapidata/text-2-video-Rich-Human-Feedback
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