datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
text-2-video-human-preferences
Rapidata Video Generation Preference Dataset
This dataset was collected in ~12 hours using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
The data collected in this dataset informs our text-2-video model benchmark. We just started so currently only two models are represented in this set:
Sora
Hunyouan
Pika 2.0
Runway ML Alpha
Luma Ray 2
Explore our latest model rankings on our website.
If you get value from this dataset and would… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences.text-2-video-human-preferences-wan2.1
Rapidata Video Generation Alibaba Wan2.1 Human Preference
If you get value from this dataset and would like to see more in the future, please consider liking it.
This dataset was collected in ~1 hour total using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Overview
In this dataset, ~45'000 human annotations were collected to evaluate Alibaba Wan 2.1 video generation model on our benchmark. The up to date benchmark… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-wan2.1.text-2-video-human-preferences-seedance-1-pro
Rapidata Video Generation Seedance 1 Pro Human Preference
In this dataset, ~60k human responses from ~20k human annotators were collected to evaluate Seedance 1 Pro video generation model on our benchmark. This dataset was collected in roughtly 30 min using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-seedance-1-pro.text-2-video-human-preferences-moonvalley-marey
Rapidata Video Generation Marey Pro Human Preference
In this dataset, ~75k human responses from ~15k human annotators were collected to evaluate Marey video generation model on our benchmark. This dataset was collected in roughtly 30 min using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-moonvalley-marey.text-2-video-human-preferences-veo3
Rapidata Video Generation Veo 3 Human Preference
In this dataset, ~46k human responses from ~20k human annotators were collected to evaluate Veo3 video generation model on our benchmark. This dataset was collected in roughly 35 minutes using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider liking… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-veo3.text2video-energy-benchmark-cross-modeltext-2-video-human-preferences-veo2
Rapidata Video Generation Google DeepMind Veo2 Human Preference
If you get value from this dataset and would like to see more in the future, please consider liking it.
This dataset was collected in ~1 hour total using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Overview
In this dataset, ~45'000 human annotations were collected to evaluate Google DeepMind Veo2 video generation model on our benchmark. The up to… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-veo2.text-2-video-human-preferences-veo3.1
Rapidata Video Generation Veo 3.1 Human Preference
In this dataset, ~74k human responses from ~23k human annotators were collected to evaluate the Veo 3.1 video generation model on our benchmark. This dataset was collected using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider liking it ❤️… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-veo3.1.awesome-text2video-prompts
Rapidata Video Generation Preference Dataset
If you get value from this dataset and would like to see more in the future, please consider liking it.
This dataset contains prompts for video generation for 14 different categories. They were collected with a combination of manual prompting and ChatGPT 4o. We provide one example sora video generation for each video.
Overview
Categories and Comments
Object Interactions Scenes: Basic scenes with… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/awesome-text2video-prompts.text-2-video-human-preferences-genmo-mochi-1
Rapidata Video Generation Genmo Mochi-1 Human Preference
In this dataset, ~60k human responses from ~20k human annotators were collected to evaluate mochi-1 video generation model on our benchmark. This dataset was collected in roughtly 30 min using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-genmo-mochi-1.code-video-text
Code Video Text Data Notes
Dataset summary
A documented Code data-preparation workflow for Video Text records. The bundled rows demonstrate the schema and validation path rather than pretending to be a full training corpus.
Included material
dataloader.py — loading, cleaning, and split preparation code.
dataset_infos.json — schema and split metadata.
metadata_sample.jsonl — small, human-readable records for checking the schema.
README.md — data… See the full description on the dataset page: https://huggingface.co/datasets/divyasi2/code-video-text.text-2-video-human-preferences-pika2.2
Rapidata Video Generation Pika 2.2 Human Preference
In this dataset, ~756k human responses from ~29k human annotators were collected to evaluate Pika 2.2 video generation model on our benchmark. This dataset was collected in ~1 day total using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-pika2.2.art-video-text-clean2
Art Video Text Data Notes
Dataset summary
A documented Art data-preparation workflow for Video Text records. The bundled rows demonstrate the schema and validation path rather than pretending to be a full training corpus.
Included material
dataloader.py — loading, cleaning, and split preparation code.
dataset_infos.json — schema and split metadata.
metadata_sample.jsonl — small, human-readable records for checking the schema.
README.md — data card… See the full description on the dataset page: https://huggingface.co/datasets/bin-owang/art-video-text-clean2.text2video-energy-benchmark-generated-videostext-2-video-human-preferences-sora-2
Rapidata Video Generation Sora 2 Human Preference
In this dataset, ~75k human responses from ~15k human annotators were collected to evaluate the Sora 2 video generation model on our benchmark. This dataset was collected in roughtly 30 min using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-sora-2.geology-video-text-v2
Geology Video Text Data Notes
Dataset summary
This data card accompanies a lightweight Geology loader for Video Text metadata. It is meant for pipeline inspection, source adaptation, and reproducible split preparation.
Included material
load_data.py — loading, cleaning, and split preparation code.
dataset_infos.json — schema and split metadata.
metadata_sample.jsonl — small, human-readable records for checking the schema.
README.md — data card and… See the full description on the dataset page: https://huggingface.co/datasets/aaravpatel/geology-video-text-v2.text-2-video-human-preferences-kling-v2.1-master
Rapidata Video Generation Kling v2.1 Master Human Preference
In this dataset, ~60k human responses from ~20k human annotators were collected to evaluate Kling v2.1 Master video generation model on our benchmark. This dataset was collected in roughtly 30 min using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-kling-v2.1-master.agriculture-video-text-v2
Agriculture Video Text Data Notes
Dataset summary
This data card accompanies a lightweight Agriculture loader for Video Text metadata. It is meant for pipeline inspection, source adaptation, and reproducible split preparation.
Included material
clean.py — loading, cleaning, and split preparation code.
dataset_infos.json — schema and split metadata.
metadata_sample.jsonl — small, human-readable records for checking the schema.
README.md — data card… See the full description on the dataset page: https://huggingface.co/datasets/anika-kumar/agriculture-video-text-v2.text-2-video-human-preferences-luma-ray2
Rapidata Video Generation Luma Ray2 Human Preference
If you get value from this dataset and would like to see more in the future, please consider liking it.
This dataset was collected in ~1 hour total using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Overview
In this dataset, ~45'000 human annotations were collected to evaluate Luma's Ray 2 video generation model on our benchmark. The up to date benchmark can be… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-luma-ray2.text-2-video-human-preferences-sora-2-pro
Rapidata Video Generation Sora 2 Pro Human Preference
In this dataset, ~75k human responses from ~15k human annotators were collected to evaluate the Sora 2 Pro video generation model on our benchmark. This dataset was collected in roughtly 30 min using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-sora-2-pro.text-2-video-human-preferences-runway-alpha
Rapidata Video Generation Runway Alpha Human Preference
If you get value from this dataset and would like to see more in the future, please consider liking it.
This dataset was collected in ~1 hour total using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Overview
In this dataset, ~30'000 human annotations were collected to evaluate Runway's Alpha video generation model on our benchmark. The up to date benchmark can… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-runway-alpha.text-2-video-Rich-Human-Feedback
Rapidata Video Generation Rich Human Feedback Dataset
If you get value from this dataset and would like to see more in the future, please consider liking it.
This dataset was collected in ~4 hours total using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Overview
In this dataset, ~22'000 human annotations were collected to evaluate AI-generated videos (using Sora) in 5 different categories.
Prompt - Video… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-Rich-Human-Feedback.text2video-test-3nlp-summarization-video-text-v2
NLP Summarization Video Text Data Notes
Dataset summary
This data card accompanies a lightweight NLP Summarization loader for Video Text metadata. It is meant for pipeline inspection, source adaptation, and reproducible split preparation.
Included material
prepare.py — loading, cleaning, and split preparation code.
dataset_infos.json — schema and split metadata.
metadata_sample.jsonl — small, human-readable records for checking the schema.… See the full description on the dataset page: https://huggingface.co/datasets/daniel-ramos/nlp-summarization-video-text-v2.text-2-video-ranking-human-preferences
T2V Ranking Human Preferences
~91,000 human ranking labels across 18 text-to-video models on 3 quality dimensions, collected from real annotators via Datapoint AI.
This is the first public ranking-based (not pairwise) human preference dataset for text-to-video generation. Each datapoint contains 5 videos generated from the same prompt by different models, ranked 1st through 5th by 15 annotators on each dimension.
Why This Dataset
Existing video preference… See the full description on the dataset page: https://huggingface.co/datasets/datapointai/text-2-video-ranking-human-preferences.text-2-video-human-preferences-motion
Human Preferences for AI-Generated Video: Motion Quality
29,283 pairwise human preference labels comparing 4 frontier video generation models on human motion across 3 quality dimensions, collected from 4,349 real annotators via Datapoint AI.
This is the largest publicly available human preference dataset focused specifically on human motion in AI-generated video.
Why This Dataset
Video generation models are improving fast, but evaluating human motion remains… See the full description on the dataset page: https://huggingface.co/datasets/nusdufv/text-2-video-human-preferences-motion.text-2-video-human-preferences-motion
Human Preferences for AI-Generated Video: Motion Quality
29,283 pairwise human preference labels comparing 4 frontier video generation models on human motion across 3 quality dimensions, collected from 4,349 real annotators via Datapoint AI.
This is the largest publicly available human preference dataset focused specifically on human motion in AI-generated video.
Why This Dataset
Video generation models are improving fast, but evaluating human motion remains… See the full description on the dataset page: https://huggingface.co/datasets/datapointai/text-2-video-human-preferences-motion.movie-posters-video-text-v2
Movie Posters Video Text Data Notes
Dataset summary
A documented Movie Posters data-preparation workflow for Video Text records. The bundled rows demonstrate the schema and validation path rather than pretending to be a full training corpus.
Included material
dataset.py — loading, cleaning, and split preparation code.
dataset_infos.json — schema and split metadata.
metadata_sample.jsonl — small, human-readable records for checking the schema.… See the full description on the dataset page: https://huggingface.co/datasets/wuchunhung/movie-posters-video-text-v2.ecommerce-video-text-v2
Ecommerce Video Text Data Notes
Dataset summary
A documented Ecommerce data-preparation workflow for Video Text records. The bundled rows demonstrate the schema and validation path rather than pretending to be a full training corpus.
Included material
loader.py — loading, cleaning, and split preparation code.
dataset_infos.json — schema and split metadata.
metadata_sample.jsonl — small, human-readable records for checking the schema.
README.md —… See the full description on the dataset page: https://huggingface.co/datasets/Maria-popov/ecommerce-video-text-v2.geology-video-text-v2-2023
Geology Video Text Data Notes
Dataset summary
This data card accompanies a lightweight Geology loader for Video Text metadata. It is meant for pipeline inspection, source adaptation, and reproducible split preparation.
Included material
load_data.py — loading, cleaning, and split preparation code.
dataset_infos.json — schema and split metadata.
metadata_sample.jsonl — small, human-readable records for checking the schema.
README.md — data card and… See the full description on the dataset page: https://huggingface.co/datasets/Vasilisaivanov/geology-video-text-v2-2023.