datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
turkey-all-universitiesCertainly! Here’s the dataset description in Markdown format:
All Universities in Turkey Dataset
Description
This dataset contains detailed information about various universities. Each record represents a single university and includes attributes such as the university's name, type, city, website, address, logo URL, and a button for accessing additional details. This data is typically extracted from a web page listing universities.
Fields
1. id… See the full description on the dataset page: https://huggingface.co/datasets/h8st6ptv/turkey-all-universities.RoadmapBench
RoadmapBench
A benchmark for evaluating AI coding agents on multi-target, long-horizon software development tasks derived from open-source project version upgrades.
Overview
RoadmapBench contains 115 tasks spanning 17 open-source repositories across 5 programming languages (Python, TypeScript, Go, Rust, C++). Each task requires an agent to implement multiple interdependent features that correspond to a real version upgrade of the target project.
Quick Start… See the full description on the dataset page: https://huggingface.co/datasets/UnipatAI/RoadmapBench.unified-agent-trajectories
Unified Benchmark Agent Trajectories
Dataset release: v2.1.1 (2026-09-18)Record format: unified-agent-sft-v1
A growing collection of benchmark agent execution trajectories converted into one
transparent, multimodal, tool-aware representation. These are complete recorded benchmark
runs—not ordinary chat transcripts—including benchmark tasks, model reasoning and answers,
tool calls, tool observations, runtime status, and benchmark scores when available. The
directory layout is… See the full description on the dataset page: https://huggingface.co/datasets/ChrisDing1105/unified-agent-trajectories.SPADES-RGBID-VTG
ID-VTG: Image-Disambiguated Video Temporal Grounding
ID-VTG is a benchmark for Image-Disambiguated Video Temporal Grounding.
Each query combines a text description and a reference image. The task
is to localize the temporal segment in which the specific instance
depicted in the reference image performs the action described by the
text query.
Repository Structure
ID-VTG/
├── README.md
├── LICENSE
├── NOTICE.md
├── CITATION.cff
├── CHANGELOG.md
├── IDVTG-Gym/
│… See the full description on the dataset page: https://huggingface.co/datasets/Chloe-UniU-oO/ID-VTG.Rustins_Super_Mega_Awesome_VEDU_Model
Rustin's Super Mega Awesome VEDU Model
A reproducible, heavily-documented pipeline that maps Ventenata dubia ("VEDU", an invasive
winter-annual grass) across Montana from satellite + environmental data.
Science reference: docs/VEDU_48_predictors_detailed.md
Data decisions & gotchas: docs/CONTRADICTIONS.md
Parity with the Earth Engine build: docs/GEE_PARITY.md
Continue-the-build guide: docs/HANDOFF.md
Label inventory: docs/DATA_SOURCES.md
What it produces
57… See the full description on the dataset page: https://huggingface.co/datasets/UniversityOfMontanaSAL/Rustins_Super_Mega_Awesome_VEDU_Model.unit-price-evidence-synthetic
Unit Price Evidence: Synthetic
This dataset contains rendered synthetic shopping pages and evidence-pointer
targets for product-card discovery and unit-price field extraction. It was built
to warm-start small encoder-decoder models without redistributing retailer HTML,
screenshots, product data, account data, or browsing history.
Release
Version: 0.1.0
Source code: erichasinternet/apples-to-apples
Source manifest SHA-256:… See the full description on the dataset page: https://huggingface.co/datasets/hotdogsalesman/unit-price-evidence-synthetic.RxnBench-Doc
RxnBench-Doc: A Benchmark for Multimodal Understanding of Chemistry Reaction Literature
News: This work has been accepted by Journal of Chemical Information and Modeling
📘 Benchmark Summary
RxnBench (FD-QA) is a document-level question answering (DocQA) benchmark comprising 540 multiple-select questions designed to assess PhD-level understanding of organic chemistry reactions in textual and multimodal contexts. All questions underwent multiple rounds of expert… See the full description on the dataset page: https://huggingface.co/datasets/UniParser/RxnBench-Doc.pcbslm-static-v2-unsloth-vlm
PCBSLM static-v2 Unsloth VLM
Portable multimodal Unsloth dataset for PCB layout/document-grounded training.
The JSONL splits use Unsloth/Gemma-style chat messages:
{
"messages": [
{"role": "user", "content": [
{"type": "image", "image": "assets/raw_docs/.../images/page.png"},
{"type": "text", "text": "instruction..."}
]},
{"role": "assistant", "content": [
{"type": "text", "text": "{...json answer...}"}
]}
]
}
Files… See the full description on the dataset page: https://huggingface.co/datasets/henry1477/pcbslm-static-v2-unsloth-vlm.UniSVG
UniSVG Dataset
UniSVG is a comprehensive dataset designed for unified SVG generation (from textual prompts and images) and SVG understanding (color, category, usage, etc.). It comprises 525k data items tailored for Multi-modal Large Language Models (MLLM) training and evaluation.
🔥 Release
[2025/11/27]
🔥 We are glad to announce that our UniSVG benchmark is used by Qwen3-VL！
[2025/09/22]
🔥 Qwen2.5-VL-finetuned released! 🌐 Model Path！… See the full description on the dataset page: https://huggingface.co/datasets/lili24/UniSVG.UniHall
UniHall: Universal Hallucination Fuzzing for MLLMs
UniHall is a systematic benchmark for evaluating hallucination in Multimodal Large Language Models (MLLMs). It integrates a comprehensive benchmark with Self-Adaptive Multimodal Fuzzing (SAMF) to rigorously stress-test models against hallucinations in evolving real-world scenarios.
🚀 Key Features
1. Fine-Grained Benchmark & Taxonomy
UniHall is grounded in a unified taxonomy covering three critical… See the full description on the dataset page: https://huggingface.co/datasets/IntJudge/UniHall.MMMEB-Benchmark
Dataset Card for MMMEB-Benchmark
Dataset Description
MMMEB (Massive Multimodal and Multilingual Embedding Benchmark) is a benchmark for multilingual and multimodal embedding models.
It supports 5 languages: English, French, German, Italian and Spanish.
It is structured into 4 task meta-categories: Image-to-Text Retrieval (I2T), Text-to-Image Retrieval (T2I), Visual Question Answering (VQA), Visual Grounding (VG) and Classification (C).
All datasets that have… See the full description on the dataset page: https://huggingface.co/datasets/swap-uniba/MMMEB-Benchmark.chart-parse-bench
ChartParse-Bench
58 synthetic charts whose exact values were written before the pixels existed.
Every gate in chart extraction measures self-consistency: redraw the extraction, check it
lands on the same ink, call it verified. That passes a series traced confidently off the
wrong axis. Because each chart here ships the array it was drawn from, the question stops
being is this answer plausible and becomes is this answer right.
Contents
family
charts
series… See the full description on the dataset page: https://huggingface.co/datasets/Unsiloed/chart-parse-bench.cross-unlearning-case-400PerMed-MM
PerMed-MM: A Multimodal, Multi-Specialty Persian Medical Benchmark
🤗 Dataset | 📖 Paper | 📄 PDF
Dataset Description
PerMed-MM is a multimodal, multi-specialty benchmark designed to evaluate Vision Language Models (VLMs) on Persian medical question answering.
The dataset consists of 733 multiple-choice questions sourced from the Iranian National Medical Board Exams (years 2021 and 2023). Each question is paired with 1 to 5 clinically relevant images, totaling… See the full description on the dataset page: https://huggingface.co/datasets/universitytehran/PerMed-MM.UniGUI-Bench
UniGUI-Bench
A comprehensive benchmark for evaluating GUI agents across 11 capability dimensions.
Dataset Structure
System Evaluation
trajectory: 1,200 complete GUI task trajectories with step-by-step screenshots
gui_system: 7,108 individual step evaluation samples
Process Evaluation (11 Abilities, 200 samples each)
Ability
Description
element_grounding
Locating UI elements given instructions
action_understanding… See the full description on the dataset page: https://huggingface.co/datasets/AGI-Eval/UniGUI-Bench.Minecraft-Fable-ImageGLB-v1Boat_unity_datasetfigmirror-unified
Unified FigMirror Dataset
Canonical release with 550 samples.
dataset_augmentation: 500
paper_derivative: 50
paper_derivative verified_pass: 50
Data unit:
One row in data/train.jsonl is one task / one data point.
Asset files under assets/ are supporting files, not separate data points.
Semantic task families:
chart_style_augmentation: input is a reference/source chart; output is an augmented chart.
paper_figure_reproduction: input is a paper figure reference; output is a… See the full description on the dataset page: https://huggingface.co/datasets/zcahjl3/figmirror-unified.ogiri-bokete-unsloth-vlm
Japanese Bokete Ogiri — Unsloth VLM format
YANS-official/ogiri-bokete を、UnslothのVision SFTで扱える会話形式に変換した非公開用データセットです。
各JSONLレコードは「1画像 + 1回答」です。
{
"messages": [
{"role": "user", "content": [
{"type": "image", "image": "images/124469.jpg"},
{"type": "text", "text": "この画像のお題に対して、面白い一言を1つ返してください。"}
]},
{"role": "assistant", "content": [
{"type": "text", "text": "..."}
]}
]
}
Files
train.jsonl: 1,678 records / 630 prompts… See the full description on the dataset page: https://huggingface.co/datasets/beezza/ogiri-bokete-unsloth-vlm.testEVWSD-ITAThis is the dataset card for EVWSD-ITA.
This data repo contains two files:
"ds_train.json": contains the train instances
"imgs.zip": contains the images associated to each train instance
Dataset Structure
Data Instances
{
"id": "bn:00022412n",
"hyp_id": "bn:00017670n",
"gloss": "Atto del cuocere",
"lemma": "cucina",
"hyp_lemma": [
"cambiamento di stato"
],
"bns": [
"bn:00018237n", ..., "bn:00049248n"
],
"is_co_hyp": [… See the full description on the dataset page: https://huggingface.co/datasets/swap-uniba/EVWSD-ITA.Boat_unity_exampledogovors-unlimited-ocr
Dogovors Unlimited-OCR Dataset
OCR/document-layout dataset prepared for fine-tuning baidu/Unlimited-OCR.
Files
train.jsonl contains one JSON object per document.
images/ contains the page images referenced by relative path.
JSONL Schema
{
"images": [
"images/doc_001_page_001.jpg",
"images/doc_001_page_002.jpg"
],
"question": "Multi page parsing.",
"answer": "<PAGE><|det|>title [400, 60, 660, 73]<|/det|>...
<PAGE><|det|>text [100… See the full description on the dataset page: https://huggingface.co/datasets/p4ulbr4dl3y/dogovors-unlimited-ocr.Boat_unity_exampleBoat_unity_exampleunion_swag_cocounion_flyte_swag_object_detectionUniLayout-Data
UniLayout-Data
Dataset release for UniLayout layout generation research.
Files
q1.zip: Quadrant 1 product display image content-aware layout data.
q2.zip: Quadrant 2 marketing layout data with text, underlay, logo, and symbol elements.
q3.zip: Quadrant 3 marketing layout data.
q4.zip: Quadrant 4 scientific paper layout data with text, title, list, table, and figure elements.
dpo_reward_data/: reward-scored DPO/SAPO data extracted from the training archive.
The zip files… See the full description on the dataset page: https://huggingface.co/datasets/shuolucs/UniLayout-Data.UniArch