datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
circleci-test-resultsCI-VID
📄 CI-VID: A Coherent Interleaved Text-Video Dataset
CI-VID is a large-scale dataset designed to advance coherent multi-clip video generation. Unlike traditional text-to-video (T2V) datasets with isolated clip-caption pairs, CI-VID supports text-and-video-to-video (TV2V) generation by providing over 340,000 interleaved sequences of video clips and rich captions. It enables models to learn both intra-clip content and inter-clip transitions, fostering story-driven generation with… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/CI-VID.grad_cilp0.28_100conll2003-cifloorplans-cityscapes
Dataset Summary
This is a curated collection of floorplan images sourced from across the internet. It is intended for research in architectural AI, layout generation, and urban scene understanding.
Data format: Image files with associated integer labels.
Sources: Publicly available images from various web sources (This dataset is one unified collections).
Purpose: Educational and research use.
Dataset Structure
The dataset follows the standard Hugging Face Image… See the full description on the dataset page: https://huggingface.co/datasets/wheres-my-python/floorplans-cityscapes.squad-cicivil-code-phil
Civilex — Philippine Legal RAG & SFT Dataset
Retrieval corpus and supervised fine-tuning (SFT) data for a retrieval-augmented generation (RAG) pipeline over Philippine law: the Civil Code (Republic Act No. 386) and Supreme Court jurisprudence. Produced by the civilex-thesis research pipeline.
Contents: 11k+ Supreme Court jurisprudence cases spanning 1949–2025, and 2,270 articles from the Civil Code (Republic Act No. 386).
Dataset structure
.
├── README.md
├──… See the full description on the dataset page: https://huggingface.co/datasets/renzzyyy1028/civil-code-phil.cia-world-factbook-snapshotsAusLaw-Citation-BenchmarkThis is the dataset proposed in the paper: Methods for Legal Citation Prediction in the Age of LLMs: An Australian Law Case Study.
CineBoard3D-plus
🎬 CineBoard3D++: Dynamic 3D Story World Dataset
📊 Dataset Summary
CineBoard3D++ is a collection of editable, movie-inspired 3D story worlds built with StoryBlender for narrative-grounded camera planning and world visual attention. It brings together story scripts, animated characters, scene geometry, and shot-level configurations in native Blender projects.
The benchmark covers 50 stories, 457 scenes, 1,585 shots, and 3,197 3D assets (836 plot-related and 2,361… See the full description on the dataset page: https://huggingface.co/datasets/EngineeringAI-LAB/CineBoard3D-plus.parsed_datacinepile_10kObjaversePlusPlus
Objaverse++: Curated 3D Object Dataset with Quality Annotations
Paper
Code
Chendi Lin,
Heshan Liu,
Qunshu Lin,
Zachary Bright,
Shitao Tang,
Yihui He,
Minghao Liu,
Ling Zhu,
Cindy Le
We cleaned the Objaverse dataset so you don't have to. In this work, we meticulously curated a collection of Objaverse objects and developed an effective classifier capable of scoring the entire Objaverse. Our extensive annotation system considers geometric structure and texture… See the full description on the dataset page: https://huggingface.co/datasets/cindyxl/ObjaversePlusPlus.netryx-new-york-city-13km
Nyc-Core-Usethis 13km
Pre-computed MegaLoc index for Netryx Drishti geolocation.
Coverage
Center: 40.713200, -74.002500
Radius: 13.0 km
Panoramas: 663,084
Index entries: 2,652,336
Descriptor model: MegaLoc
Descriptor dim: 1024 (PCA from 8448)
Usage
from netryx_hub import NetryxHub
hub = NetryxHub()
hub.download("nyc-core-usethis-13km", output_dir="./netryx_data/index")
# Now open Netryx and search!
Or download manually and use Import Index in… See the full description on the dataset page: https://huggingface.co/datasets/samsepiol4/netryx-new-york-city-13km.CiteVQA
CiteVQA
English | 简体中文
CiteVQA is a document visual question answering benchmark for faithful evidence attribution. Unlike conventional DocVQA datasets that only score the final answer, CiteVQA requires a model to answer a question with evidence grounded in the source document at the element level. The benchmark is designed to evaluate whether a system can not only answer correctly, but also cite the right supporting region in long, real-world PDFs.
The dataset contains 1,897… See the full description on the dataset page: https://huggingface.co/datasets/opendatalab/CiteVQA.s2orc-citation-pairs-translated-nlThis is a Dutch version of the S2ORC: The Semantic Scholar Open Research Corpus. Which we have auto-translated from English into Dutch using Meta's No Language Left Behind model, specifically the huggingface implementation.
CityCube-BenchTeachArena
TeachArena
TeachArena is a benchmark for evaluating AI tutoring agents across the full teaching
decision chain — from moment-to-moment tutoring dialogue, to pedagogical judgment on
packaged evidence, to multi-step teaching workflows grounded in a learning-management
system. It contains 354 tasks organized into three stages, a mock LMS environment
database, the agent policy documents, and the full scoring logic.
Why three stages
A capable teaching agent must both… See the full description on the dataset page: https://huggingface.co/datasets/CinderD/TeachArena.cia-readingroom-bucket-00wildtrace
WildTrace strict481
WildTrace is a source-internal long-context multi-hop reasoning benchmark
built from natural evidence trails. Unlike reverse-synthetic QA, its tasks are
mined in situ from long source documents before questions are written. The
strict481 release contains 481 locked tasks over 214 public long-form sources,
with full-document, evidence-withheld evaluation. The model under test receives
only the source document and the public question; evidence spans, clue… See the full description on the dataset page: https://huggingface.co/datasets/CinderD/wildtrace.Nemotron-RL-Instruction-Following-Citation-Formatting-v1
Dataset Description:
Teaches the model to cite specific document parts using reference markers like [ref:1], ref:3, etc. Supports single-reference, multi-reference, and inline citations.
This dataset is ready for commercial/non-commercial uses.
Dataset Owner(s):
NVIDIA Corporation
Dataset Creation Date:
Created on: April 10, 2026
Last Modified on: April 10, 2026
Version:
Nemotron-RL-Instruction-Following-CitationFormatting-v1… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-Instruction-Following-Citation-Formatting-v1.cinematic-world-stills
Council of AI — cinematic stills
Cinematic stills produced for Council of AI surfaces. metadata.jsonl gives the
Hub image viewer a caption per file. These are illustrations — they carry no measurement and back no slot.
The live board is the authority
GET https://councilof.ai/api/gspc — quote totals.public_count. This Hub card is a printer of that GET, never a second
engine. If the fetch fails the honest answer is UNCHECKABLE — never a fabricated 0.000.
Status… See the full description on the dataset page: https://huggingface.co/datasets/csoai/cinematic-world-stills.citations
Common Crawl Citations Overview
This dataset contains citations referencing Common Crawl Foundation and its datasets, pulled from Google Scholar.
Please note that these citations are not curated, so they will include some false positives. An annotated subset of these citations with additional fields can be found at cc-citations.
Dr-CiK
Dr-CiK: A Testbed for Foresight-Driven Agents
Dr-CiK is a benchmark for evaluating whether agents can retrieve
forecasting-relevant context from a noisy document corpus, filter out
distractors, distill the retrieved context into forecast-useful evidence, and
produce forecasts grounded in that evidence.
Real-world time-series forecasting often depends not only on historical
observations but also on external context that must be actively discovered
from heterogeneous, noisy… See the full description on the dataset page: https://huggingface.co/datasets/ServiceNow/Dr-CiK.scholar-citation-historyworld-cities-geoDataset containing city, country, region, and continents alongside their longitude and latitude co-ordinates. Cartesian coordinates are provided in x, y, z features.
circoL-CiteEval
L-CITEEVAL: DO LONG-CONTEXT MODELS TRULY LEVERAGE CONTEXT FOR RESPONDING?
Paper Github Zhihu
Benchmark Quickview
L-CiteEval is a multi-task long-context understanding with citation benchmark, covering 5 task categories, including single-document question answering, multi-document question answering, summarization, dialogue understanding, and synthetic tasks, encompassing 11 different long-context tasks. The context lengths for these tasks range from 8K to 48K.… See the full description on the dataset page: https://huggingface.co/datasets/Jonaszky123/L-CiteEval.CIMD
CIMD
[[中文]] | [[English]]
CSGHub Dataset Page | Hugging Face | OpenCSG Community
中文说明
数据集概述
CIMD 是一个面向文档智能任务的跨来源、多语言 JSONL 语料库。当前公开快照包含 111,308 条解析记录，覆盖制度参考、学术与长文档资料、机构分析、企业运营、公共讨论和市场相关材料等来源家族。每条记录都把正文与来源类型、语言、时间、关键词、授权标签和来源字段放在同一个结构里，用户拿到数据后可以直接做检索、抽样、审计和数据治理。
公开数据已转换为统一字段，并按来源家族拆分为可单独加载的子集；它不是原始文件夹的简单打包。用户可以只读取制度参考、学术长文档或公共讨论记录，也可以合并多个子集构建检索库、抽取训练候选样本、构造评测样本池，并按来源、语言和时间字段继续筛选。
CIMD 和通用网页语料的差别在于记录级元数据。它不只提供可索引文本，还提供… See the full description on the dataset page: https://huggingface.co/datasets/opencsg/CIMD.CI-VID-SC-4072-repro
CI-VID SC 4072 Reproduction Subset
Reproduction copy of the exact CI-VID subset used by the UniVideo interleaved
text/video run:
https://wandb.ai/diffusionrl-dyh/univideo-civid/runs/w2292csr
Source and license
Derived from BAAI/CI-VID,
revision 3d6a7b049fd63505a80b751b80d5a0929602ddcf.
CI-VID is provided for non-commercial research use. Access to this subset does
not replace or broaden the upstream license. Users must comply with the
upstream dataset terms.… See the full description on the dataset page: https://huggingface.co/datasets/karrykkk/CI-VID-SC-4072-repro.