datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
GUIOdyssey
Dataset Card for GUIOdyssey
Repository: https://github.com/OpenGVLab/GUI-Odyssey
Paper: https://arxiv.org/pdf/2406.08451
News⭐️
Latest version of GUIOdyssey released!🎉
This updated version features a larger dataset with 8,334 episodes, as well as richer semantic annotations. Compared to the previous version, we have added more fine-grained low-level instructions, image descriptions, action intentions, and context review for each step. Additionally, we provide bounding… See the full description on the dataset page: https://huggingface.co/datasets/hflqf88888/GUIOdyssey.GUIGuard-Bench
GUIGuard-Bench (Public Ladder)
GUIGuard-Bench is a cross-platform GUI agent benchmark for studying privacy risks and privacy-preserving execution in multimodal GUI agents.
This public-ladder release contains 121 GUI interaction trajectories (68 Android + 53 PC) for benchmark evaluation, with 26,407 region-level privacy annotations across 2,002 screenshots.
For the anonymous review version of the evaluation toolkit, see GUIGaurd-Bench-CA4F.
Dataset Summary
GUI agents… See the full description on the dataset page: https://huggingface.co/datasets/ShaofantuoshuzhengzhiSha/GUIGuard-Bench.GUI-Odyssey
Dataset Card for GUI Odyssey
News⭐️
A new and improved version of the GUIOdyssey dataset has been released! 🎉🎉
👉 Please use the latest version and refer to the updated README for the most up-to-date information.
We highly recommend using the new version for all training and evaluation!
Repository: https://github.com/OpenGVLab/GUI-Odyssey
Latest Version of Dataset: hflqf88888/GUIOdyssey
Paper: https://arxiv.org/pdf/2406.08451
Introduction
GUI Odyssey is… See the full description on the dataset page: https://huggingface.co/datasets/OpenGVLab/GUI-Odyssey.GUI-Net-1M
Check more details at how to use this dataset at our repo
GUI-Net-1M is the dataset we keep running the pipeline introduced from TongUI paper.
Due to large file size, we have to split image files into parts. To do the extraction of images, please use the following script:
#!/bin/bash
# Directory containing the split files
SPLIT_DIR="/mnt/bofeidisk2/tmp/baidu_experience_full/images/split_parts_baidu_experience"
OUTPUT_DIR="merged_files"
# Create output directory if it doesn't… See the full description on the dataset page: https://huggingface.co/datasets/Bofeee5675/GUI-Net-1M.GUI_BASED_PLATFORMguidelines
🎉 NEW DROP 🎉 PubMed Guidelines
We just added 1627 clinical guidelines found in PubMed and PubMed Central to the dataset on December 23rd, 2023. Merry Christmas!
Clinical Guidelines
The Clinical Guidelines corpus is a new dataset of 47K clinical practice guidelines from 17 high-quality online medical sources. This dataset serves as a crucial component of the original training corpus of the Meditron Large Language Model (LLM). We publicly release a subset of 37K articles… See the full description on the dataset page: https://huggingface.co/datasets/epfl-llm/guidelines.ATO-Australian-Tax-Rulings-and-Guidance
ATO Rulings & Guidance — Australian Tax Law, Structured for AI
67,000+ Australian Taxation Office documents as RAG-ready NDJSON/CSV — Edited Private Advice, public rulings and determinations, ATO Interpretative Decisions, practical compliance guidelines, taxpayer alerts, decision impact statements, practice statements and legislative instruments. Every document parsed into structured, typed fields for legal RAG, LLM fine-tuning, and tax research automation.
Machine-readable… See the full description on the dataset page: https://huggingface.co/datasets/simplelex/ATO-Australian-Tax-Rulings-and-Guidance.GUIOdyssey
Dataset Card for GUIOdyssey
Repository: https://github.com/OpenGVLab/GUI-Odyssey
Paper: https://arxiv.org/pdf/2406.08451
News⭐️
Latest version of GUIOdyssey released!🎉
This updated version features a larger dataset with 8,334 episodes, as well as richer semantic annotations. Compared to the previous version, we have added more fine-grained low-level instructions, image descriptions, action intentions, and context review for each step. Additionally, we provide… See the full description on the dataset page: https://huggingface.co/datasets/hsinv/GUIOdyssey.GUI-Net-1M-relative-annotationsaustralian-tax-guidance-retrieval
Australian Tax Guidance Retrieval 🏦
Australian Tax Guidance Retrieval by Isaacus is a novel, diverse, and challenging legal information retrieval evaluation dataset consisting of 112 real-life Australian tax law questions paired with expert-annotated, relevant Australian Government tax guidance and policies.
Uniquely, this dataset sources its real-life tax questions from the posts of everyday Australian taxpayers on the ATO Community forum, with relevant Australian Government… See the full description on the dataset page: https://huggingface.co/datasets/isaacus/australian-tax-guidance-retrieval.GUI-CC
GUI-CC
GUI-CC is a benchmark for evaluating the contextual consistency of GUI world models when
they are used as agent environments rather than as isolated next-screen predictors.
A GUI world model predicts the next interface given the current screenshot and an action.
When that prediction is fed back as the next state, the rollout must stay coherent: app identity,
navigation history, created entities, selected options, and action affordances all have to remain
mutually… See the full description on the dataset page: https://huggingface.co/datasets/minuzero/GUI-CC.agentnet-success-v1
agentnet-success-v1
GUI state transitions (s, a, s') walked on an Ubuntu desktop by
Qwen3.8-27B, from tasks taken from AgentNet and run inside OSWorld's
Docker environment.
Walks the judge ruled had finished the task. Use these to score an agent: the trajectory is a worked example, and action_target gives the element each step was aiming at, so an answer can be marked right by control rather than by pixel.
This repository is the one-condition pool. Tasks are drawn from the 623… See the full description on the dataset page: https://huggingface.co/datasets/gui-wm/agentnet-success-v1.omniact-gui-trajectories
OmniACT
OmniACT is a GUI trajectory dataset with single-step action traces grounded in screenshots.
Dataset Structure
.
├── README.md
├── .gitattributes
├── data/
│ └── train.jsonl
├── observations/
│ └── OmniACT_pilot_*/000/screenshot.jpg
└── env_meta/
└── OmniACT_pilot_*/000/metadata.json
Each row in data/train.jsonl is one trajectory. The main image path is stored in the top-level image field, and the same relative path is also used inside… See the full description on the dataset page: https://huggingface.co/datasets/Dhscl/omniact-gui-trajectories.GenOmni3D
3D GLB Mesh Dataset
This dataset contains 3D mesh objects in GLB format.
go-swe-bench-v0
go_swe_bench v0 — real Go bug fixes, verified by the Go toolchain
246 tasks from 79 real Go repositories. Each task is a bug-fix commit whose co-committed test is red on the
parent and green on the fix. No LLM anywhere in the build.
Mined on 2026-09-19 from the GuildLM Go mining pipeline by inverting the filter that had thrown the tests
away (the pipeline was built for SFT data; a benchmark needs the opposite). Every task was verified twice
with go test: green at the commit (≥ 1… See the full description on the dataset page: https://huggingface.co/datasets/guildlm/go-swe-bench-v0.GUI-Net-1M-absolute-annotationsguiowl-curated-corpus
GUI-Owl Curated Corpus
This dataset publishes the full curated mobile GUI-agent supervised fine-tuning corpus in a unified norm1000 mobile_use action format. Each row pairs a mobile UI screenshot with an instruction and a normalized target tool call for training GUI agents.
The published files are the curated parquet shards as produced by the source canonicalizers. No parquet shards are merged, re-sharded, or sampled during upload.
Sources
Source
Episodes… See the full description on the dataset page: https://huggingface.co/datasets/KMK040412/guiowl-curated-corpus.GUIOdyssey
Dataset Card for GUIOdyssey
Repository: https://github.com/OpenGVLab/GUI-Odyssey
Paper: https://arxiv.org/pdf/2406.08451
News⭐️
Latest version of GUIOdyssey released!🎉
This updated version features a larger dataset with 8,334 episodes, as well as richer semantic annotations. Compared to the previous version, we have added more fine-grained low-level instructions, image descriptions, action intentions, and context review for each step. Additionally, we provide bounding… See the full description on the dataset page: https://huggingface.co/datasets/jinghang290038/GUIOdyssey.GUIMid
Breaking the Data Barrier – Building GUI Agents Through Task Generalization
🐙 GitHub | 📝 Paper | 🤗 Mid-training Data | 🤗 Post-Training Data
TODO List
Report and release the GUIMid with larger size and more domains (10th May expecetd)
1. Data Overview
AgentBoard is composed of 9 diverse tasks: 7 vision and language tasks and 4 lanuage only tasks.
The performances of different domains as mid-training data are as follows:
Domains… See the full description on the dataset page: https://huggingface.co/datasets/hkust-nlp/GUIMid.Vision_GUI_Assistant
[EMNLP2024] VGA: Vision GUI Assistant - Minimizing Hallucinations through Image-Centric Fine-Tuning
Release
We release our dataset to ensure that everyone can replicate our experimental conclusions.
Directory Description
|-- dataset generate / method(prompts) to generate data
--|-- dataset / data resource
|-- llava training / training code
|-- tuning script / tuing parameters
Setup
Dataset Format
Our dataset follow… See the full description on the dataset page: https://huggingface.co/datasets/zylate/Vision_GUI_Assistant.GEM__bart_base_schema_guided_dialog__1645547915best-agent-sftGuilherme34_uncensor
huihui-ai/Guilherme34_uncensor
This dataset is a copy of Guilherme34/uncensor
This dataset is used for fine-tuning of huihui-ai/gemma-3-1b-it-abliterated,
please refer to GRPO with Unsloth.
Usage
from datasets import Dataset
import json
# Define the system prompt that instructs the model to use a specific format
SYSTEM_PROMPT = """
Respond in the following format:
<reasoning>
...
</reasoning>
<answer>
...
</answer>
"""
def get_harmful_questions(split="train"… See the full description on the dataset page: https://huggingface.co/datasets/huihui-ai/Guilherme34_uncensor.GUIOdyssey
Dataset Card for GUIOdyssey
Repository: https://github.com/OpenGVLab/GUI-Odyssey
Paper: https://arxiv.org/pdf/2406.08451
News⭐️
Latest version of GUIOdyssey released!🎉
This updated version features a larger dataset with 8,334 episodes, as well as richer semantic annotations. Compared to the previous version, we have added more fine-grained low-level instructions, image descriptions, action intentions, and context review for each step. Additionally, we provide bounding… See the full description on the dataset page: https://huggingface.co/datasets/dad3131/GUIOdyssey.japan-math-philosophy-prompts
Japan Math Philosophy Prompts
Microdataset autoral com problemas que combinam matemática e reflexão
filosófica. Há 24 registros: oito instâncias editoriais, cada uma localizada em
pt-BR, en e ja e mantida integralmente no split train.
Todo o conteúdo foi gerado por modelo e permanece sem revisão humana. As
respostas matemáticas funcionam como gabaritos curtos; os critérios filosóficos
indicam qualidades esperadas de uma justificativa, não uma opinião obrigatória.… See the full description on the dataset page: https://huggingface.co/datasets/guicybercode/japan-math-philosophy-prompts.gui-primitives
GUI-Primitives
A controlled minimal-pair diagnostic benchmark for the elementary spatial primitives
that GUI click instructions depend on.
Author(s): Md Abrar Jahin, Md Rizwan Parvez
Accepted at EMNLP 2026 (Main Conference).
VLM-based computer-use agents fail largely at grounding — turning a language
instruction into a click coordinate. Existing spatial-reasoning benchmarks
(What's-Up, BLINK, CV-Bench, VSR) use natural photographs, not screenshots, and none
isolate which… See the full description on the dataset page: https://huggingface.co/datasets/kagnlp/gui-primitives.guiasalud
GuiaSalud
GuiaSalud is an open-answer medical question-answering dataset built from Spanish
medical guidebooks published by the Sistema Nacional de Salud (SNS). It transforms
unstructured clinical guideline text into 919 structured question-answer-evidence
instances, each separating the question asked, the guideline panel's judgement,
and the evidence passage from the guideline that supports it.
The dataset is provided in two languages:
es/: the original Spanish instances.
eu/:… See the full description on the dataset page: https://huggingface.co/datasets/ikergf/guiasalud.ECG-Protocol-Guided-Grounding-CoT
ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation
If you find this project useful, please give us a star🌟.
Jiarui Jin, Haoyu Wang, Xingliang Wu, Xiaocheng Fang, Xiang Lan, Zihan Wang
Deyun Zhang, Bo Liu, Yingying Zhang, Xian Wu, Hongyan Li, Shenda Hong
Introduction
Electrocardiography (ECG) serves as an indispensable diagnostic tool in clinical practice, yet existing multimodal large language models (MLLMs) remain unreliable… See the full description on the dataset page: https://huggingface.co/datasets/PKUDigitalHealth/ECG-Protocol-Guided-Grounding-CoT.GUI-Rise-pseudo-label
Dataset Card for GUI-Rise Pseudo-Labeled GUI Navigation Trajectories
Dataset Description
This dataset contains pseudo-labeled GUI navigation trajectories generated for training and evaluating the GUI-Rise agent, as introduced in the paper "GUI-Rise: Structured Reasoning and History Summarization for GUI Navigation".
Homepage: https://leon022.github.io/GUI-Rise/
Repository: https://github.com/Leon022/GUI-Rise-code
Paper: https://arxiv.org/pdf/2510.27210… See the full description on the dataset page: https://huggingface.co/datasets/Leon022/GUI-Rise-pseudo-label.anthropic-Awareness-interview
anthropic-Awareness-interview
This dataset contains full transcripts of user research interviews where an AI assistant (Claude) interviews people about how they use AI in their work and how they feel about that collaboration.[web:1] Each example includes a long meta-cognitive system prompt plus a complete back-and-forth conversation.
Dataset overview
Domain: Human–AI interaction in professional and day-to-day work.
Format: Multi-turn chat logs with explicit roles.
Scale:… See the full description on the dataset page: https://huggingface.co/datasets/Guilherme34/anthropic-Awareness-interview.