datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Vchitect_T2V_DataVerse
Vchitect-T2V-Dataverse
Vchitect Team1
1Shanghai Artificial Intelligence Laboratory
Paper |
Project Page |
Data Overview
The Vchitect-T2V-Dataverse is the core dataset used to train our text-to-video diffusion model, Vchitect-2.0: Parallel Transformer for Scaling Up Video Diffusion Models.
It comprises 14 million high-quality videos collected from the Internet, each paired with detailed textual… See the full description on the dataset page: https://huggingface.co/datasets/Vchitect/Vchitect_T2V_DataVerse.wmt_t2t
Dataset Card for "wmt_t2t"
Dataset Summary
The WMT EnDe Translate dataset used by the Tensor2Tensor library.
Translation dataset based on the data from statmt.org.
Versions exist for different years using a combination of data
sources. The base wmt allows you to create a custom dataset by choosing
your own data/language pair. This can be done as follows:
from datasets import inspect_dataset, load_dataset_builder
inspect_dataset("wmt_t2t", "path/to/scripts")
builder =… See the full description on the dataset page: https://huggingface.co/datasets/wmt/wmt_t2t.fine-t2i
Fine-T2I: An Open, Large-Scale, and Diverse Dataset for High-Quality T2I Fine-Tuning [arxiv]
by Xu Ma, Yitian Zhang,
Qihua Dong, Yun Fu
Northeastern Univeristy
Please see our [Dataset Explore] to view detailed samples (loading is slow, be patient).
🆕 What's New
[2026.02.20]: Fine-T2I reaches the #1 spot among Hugging Face Datasets Trending list ⭐️⭐️⭐️
[2026.02.16]: Fine-T2I tops the Hugging Face Datasets Trending list, reaching the #2 spot and #1… See the full description on the dataset page: https://huggingface.co/datasets/ma-xu/fine-t2i.T2I-CoReBench-Images
T2I-CoReBench-Images
📖 Overview
T2I-CoReBench-Images is the companion image dataset of T2I-CoReBench. It contains images generated using 1,080 challenging prompts, covering both composition and reasoning scenarios undere real-world complexities.
This dataset is designed to evaluate how well current Text-to-Image (T2I) models can not only paint (produce visually consistent outputs) but also think (perform reasoning over causal chains, object relations, and logical… See the full description on the dataset page: https://huggingface.co/datasets/lioooox/T2I-CoReBench-Images.t2-ragbench
Dataset Card for T2-RAGBench
Project Page | Paper | Code
IMPORTANT NOTICE:
We deleted VQAonBD from the dataset due to low quality of the question reformulations. If you still want to use it you will find the data in the previous commit history.
Dataset Description
Dataset Summary
T2-RAGBench is a benchmark dataset designed to evaluate Retrieval-Augmented Generation (RAG) on financial documents containing both text and tables. It consists of 23,088… See the full description on the dataset page: https://huggingface.co/datasets/G4KMU/t2-ragbench.T2IScoreScore
Dataset Card for Text-to-Image ScoreScore (T2IScoreScore or TS2)
This dataset exists as part of the T2IScoreScore metaevaluation for assessing the faithfulness and consistency of text-to-image model prompt-image evaluation metrics.
Necessary code for utilizing the resource is present at github.com/michaelsaxon/T2IScoreScore
Dataset Details
Dataset Description
This is a test set of 165 "target prompts" which each have between 5 and 76 generated images of… See the full description on the dataset page: https://huggingface.co/datasets/saxon/T2IScoreScore.t2a-daddy
t2a-daddy
Male-voice ASMR corpus for the text2asmr project.
Previously published as aoxo/audios3.
Companion repos: aoxo/t2a-mommy (female voice),
aoxo/t2a-audios-v1 (the original v1 corpus).
Layout
path
what
<creator>/<title>.m4a
source audio, 48 kHz AAC, one folder per creator
<creator>/<title>.json
word-level Whisper large-v3 alignment ([] = skipped: near-silent or undecodable)
labels/qwen3omni.jsonl
non-speech ontology labels for gap clips… See the full description on the dataset page: https://huggingface.co/datasets/aoxo/t2a-daddy.GPT4O_Image_T2Iatlas-25-sequential-tool-runtime-upgrade
ATLAS report 25: the sequential tool runtime on verl V1
1. Question and links
Read this first. Every stage of the bring-up ran to its evidence; the report is complete for the correctness acceptance of issue 59 and for its performance stack (a second pass: the call parser fixed after an independent judgement, a boundary rollout at a 1024-token cap, one stacked performance ladder whose first tier, a48k, is now the campaign's default) and for its first research use:… See the full description on the dataset page: https://huggingface.co/datasets/t2ance/atlas-25-sequential-tool-runtime-upgrade.EvalCrafter_T2V_Dataset
EvalCrafter Text-to-Video (ECTV) Dataset 🎥📊
Code · Project Page · Huggingface Leaderboard · Paper@ArXiv · Prompt list
Welcome to the ECTV dataset! This repository contains around 10000 videos generated by various methods using the Prompt list. These videos have been evaluated using the innovative EvalCrafter framework, which assesses generative models across visual, content, and motion qualities using 17 objective metrics and subjective user opinions.
Dataset Details 📚… See the full description on the dataset page: https://huggingface.co/datasets/RaphaelLiu/EvalCrafter_T2V_Dataset.T2I-ImageNet-NormalT2Reranking
T2Reranking
An MTEB dataset
Massive Text Embedding Benchmark
T2Ranking: A large-scale Chinese Benchmark for Passage Ranking
Task category
t2t
Domains
None
Reference
https://arxiv.org/abs/2304.03679
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(tasks=["T2Reranking"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To… See the full description on the dataset page: https://huggingface.co/datasets/mteb/T2Reranking.T2AV_TemporalConstraintrecap-t2i-evaluation-sample-2026
Recaptioned T2I Supervision Evaluation Sample
This repository is the small reviewer-inspection companion to the full anonymous caption-metadata release. The full release is hosted separately at https://huggingface.co/datasets/Anonymous1477/recap-t2i-evaluation-metadata-2026; this repository stays under the large-dataset sample threshold and gives reviewers a direct way to inspect redacted caption metadata, join structure, and selected image-conditioned audit packages.… See the full description on the dataset page: https://huggingface.co/datasets/Anonymous1477/recap-t2i-evaluation-sample-2026.T2Retrieval
Dataset Card for "T2Retrieval"
More Information needed
Urbansound8K_t2a
Dataset Card for "Urbansound8K_t2a"
More Information needed
T2Retrieval
T2Retrieval
An MTEB dataset
Massive Text Embedding Benchmark
T2Ranking: A large-scale Chinese Benchmark for Passage Ranking
Task category
t2t
Domains
Medical, Academic, Financial, Government, Non-fiction
Reference
https://arxiv.org/abs/2304.03679
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["T2Retrieval"])
evaluator = mteb.MTEB(task)
model =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/T2Retrieval.BW_illustrations_t2i_512x512MACS_t2a
Dataset Card for "MACS_t2a"
More Information needed
spoken-squad-t2agigaspeech_t2aVchitect_T2V_DataVerse_256p_8fps_wdshttps://huggingface.co/datasets/Vchitect/Vchitect_T2V_DataVerse resampled to 256p. Intended for training https://github.com/NilanEkanayake/TiTok-Video
Runway_Frames_t2i_human_preferences
Rapidata Frames Preference
This T2I dataset contains roughly 400k human responses from over 82k individual annotators, collected in just ~2 Days using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Frames across three categories: preference, coherence, and alignment.
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/Runway_Frames_t2i_human_preferences.atlas-24-frozen-prefix-potential-shaping
ATLAS report 24: frozen-prefix potential shaping
1. Question and links
Read this first. This data root holds the first attempt of report 24 on the campaign's old harness (verl 0.7.1): the shaped training is complete and the unshaped training stopped at step 20 with a known problem (the subsection at the end of this section). The question was rerun on the runtime of report 25 with both trainings at 40 steps; that rerun's trajectories, exports, checkpoints and… See the full description on the dataset page: https://huggingface.co/datasets/t2ance/atlas-24-frozen-prefix-potential-shaping.UnifiedReward-2.0-T2X-score-data
Dataset Summary
UnifiedReward-2.0-T2X-score-data is added for our UnifiedReward-2.0-qwen-[3b/7b/32b/72b] training.
This dataset enables UnifiedReward-2.0 introducing several new capabilities:
Pairwise scoring for image and video generation assessment on Alignment, Coherence, Style dimensions.
Pointwise scoring for image and video generation assessment on Alignment, Coherence/Physics, Style dimensions.
Welcome to try the latest version, and the inference code is available at… See the full description on the dataset page: https://huggingface.co/datasets/CodeGoat24/UnifiedReward-2.0-T2X-score-data.OpenAI-4o_t2i_human_preference
Rapidata OpenAI 4o Preference
This T2I dataset contains over 200'000 human responses from over ~45,000 individual annotators, collected in less than half a day using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating OpenAI 4o (version from 26.3.2025) across three categories: preference, coherence, and alignment.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/OpenAI-4o_t2i_human_preference.xAI_Aurora_t2i_human_preferences
Rapidata Aurora Preference
This T2I dataset contains over 400k human responses from over 86k individual annotators, collected in just ~2 Days using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Aurora across three categories: preference, coherence, and alignment.
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/xAI_Aurora_t2i_human_preferences.ImageNet1K-T2I-QwenVL-QwenImageTalker-T2AV-Data
Talker-T2AV-Data
Joint Talking Audio-Video Generation with Autoregressive Diffusion Modeling
Paper (arXiv 2604.23586) ·
Code (GitHub) ·
Model ·
Samples
Clean training data package for Talker-T2AV. Paths in metadata/train.csv are relative to the dataset root after extracting the shard archives.
Contents
metadata/train.csv: training index used by Talker-T2AV.
shards/*.tar: clean archive shards grouped by modality and dataset source.
audio/, motion/, video/: directories… See the full description on the dataset page: https://huggingface.co/datasets/HKUSTAudio/Talker-T2AV-Data.Imagen4_t2i_human_preference
Rapidata Imagen 4 Preference
This T2I dataset contains over 195k human responses from over 70k individual annotators, collected in just ~1 Day using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Imagen 4 (imagen-4.0-ultra-generate-exp-05-20) across three categories: preference, coherence, and alignment.
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/Imagen4_t2i_human_preference.