datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
aime25
AIME 25
American Invitational Mathematics Examination (AIME) 2025
Citation
If you use the AIME25 dataset in your research, please consider citing it as follows:
@misc{aime25,
title={American Invitational Mathematics Examination (AIME) 2025},
author={Zhang, Yifan and Math-AI, Team},
year={2025},
}
telegram-news-ua-dataset
Aisberg Telegram News UA
A continuously updated, de-identified corpus of Ukrainian Telegram news and the discussion around it, published by the Ukrainian non-profit Aisberg (ГО «АЙЗБЕРГ»). It comes in two layers. The first is the raw monthly stream: every post from a fixed set of public news channels, with its reactions and its comment thread. The second is the analysis behind every report Aisberg publishes: posts from different channels grouped into one event, the manipulation… See the full description on the dataset page: https://huggingface.co/datasets/aisbergpublicorganization/telegram-news-ua-dataset.aime26
AIME 26
American Invitational Mathematics Examination (AIME) 2026
Citation
If you use the AIME26 dataset in your research, please consider citing it as follows:
@misc{aime26,
title={American Invitational Mathematics Examination (AIME) 2026},
author={Zhang, Yifan and Math-AI, Team},
year={2026},
}
AiAppminervamathAiice
Dataset
Aiice benchmark dataset for Arctic sea ice concentration (SIC) forecasting,
based on OSI-SAF satellite products (CC BY 4.0).
Coverage
Period: October 1978 – April 2026
Resolution: 25 km spatial, daily temporal
Grid: 432×432 (Lambert Azimuthal Equal Area, EPSG:6931)
Source products
Product
Source
Period
OSI-450-a
SMMR, SSM/I, SSMIS
1978–2020
OSI-430-a
SSMIS
2021–Jul 2025
OSI-438
AMSR2
Jul 2025–present… See the full description on the dataset page: https://huggingface.co/datasets/ITMO-NSS/Aiice.AIME2025
AIME 2025 Dataset
Dataset Description
This dataset contains problems from the American Invitational Mathematics Examination (AIME) 2025-I & II.
cornstack-python-v1
CoRNStack Python Dataset
The CoRNStack Dataset, accepted to ICLR 2025, is a large-scale high quality training dataset specifically for code retrieval across multiple
programming languages. This dataset comprises of <query, positive, negative> triplets used to train nomic-embed-code,
CodeRankEmbed, and CodeRankLLM.
CoRNStack Dataset Curation
Starting with the deduplicated Stackv2, we create text-code pairs from function docstrings and respective code. We filtered out… See the full description on the dataset page: https://huggingface.co/datasets/nomic-ai/cornstack-python-v1.Aegis-AI-Content-Safety-Dataset-2.0
🛡️ Nemotron Content Safety Dataset V2
The Nemotron Content Safety Dataset V2, formerly known as Aegis AI Content Safety Dataset 2.0, is comprised of 33,416 annotated interactions between humans and LLMs, split into 30,007 training samples, 1,445 validation samples, and 1,964 test samples. This release is an extension of the previously published Nemotron Content Safety Dataset V1.
To curate the dataset, we use the HuggingFace version of human preference data about harmlessness… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0.IndustryBench-MIPU
IndustryBench-MIPU: Benchmarking Multi-Image Attribute Value Extraction for Industrial Products
Multi-Image Industrial Product Understanding Benchmark — evaluating MLLMs on structured attribute extraction from real-world industrial product images.
Industrial product specifications are scattered across multiple heterogeneous images — specification tables, nameplates, technical drawings. IndustryBench-MIPU tests whether MLLMs can reliably recover them through four… See the full description on the dataset page: https://huggingface.co/datasets/alibaba-multimodal-industrial-ai/IndustryBench-MIPU.gaming-500-hours
Gaming Dataset (gaming-1) — 494.7 Hours
Native PC/console gameplay screen-recordings, organized by game. Each workflow
is one play session, trimmed to pure gameplay — login screens, launchers,
desktop, collection-app references, and any watching/streaming are removed.
In-game menus, lobbies, loading, and cutscenes are retained as part of the session.
Workflows: 776
Total gameplay: 494.7 hours
Distinct games: 168
Clip duration (min): median 24.0, p90 90.9, max 457.7
Platforms:… See the full description on the dataset page: https://huggingface.co/datasets/markov-ai/gaming-500-hours.needle-resultsAgentHarm
AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents
Maksym Andriushchenko1,†,*, Alexandra Souly2,*
Mateusz Dziemian1, Derek Duenas1, Maxwell Lin1, Justin Wang1, Dan Hendrycks1,§, Andy Zou1,¶,§, Zico Kolter1,¶, Matt Fredrikson1,¶,*
Eric Winsor2, Jerome Wynne2, Yarin Gal2,♯, Xander Davies2,♯,*
1Gray Swan AI, 2UK AI Safety Institute, *Core Contributor
†EPFL, §Center for AI Safety, ¶Carnegie Mellon University, ♯University of Oxford
Paper: https://arxiv.org/abs/2410.09024… See the full description on the dataset page: https://huggingface.co/datasets/ai-safety-institute/AgentHarm.AIGVDBenchSCPWiki-Cleaned-PDF-Archiveshplt2_edu_scores
HPLT2-Edu-scores
Dataset summary
HPLT2-JQL-Education is a model-annotated language subset of HPLT2, spanning 35 languages.
Our model-annotations allow for a filtering that achieves higher-quality training outcomes without excessively aggressive data reduction.
The original FW2 heuristic filtering method serves as our baseline, providing reference points for both the volume of retained tokens and downstream model performance.
For example, in the Spanish language case… See the full description on the dataset page: https://huggingface.co/datasets/JQL-AI/hplt2_edu_scores.pre-flight-06
Aviation Operations Knowledge LLM Benchmark Dataset
This dataset contains multiple-choice questions designed to evaluate Large Language Models' (LLMs) knowledge of aviation operations, regulations, and technical concepts. It serves as a specialized benchmark for assessing aviation domain expertise.
📄 Paper: Pre-Flight: A Benchmark for Evaluating Large Language Models on Aviation Operational Knowledge (Brooker and Hughes, 2026). The benchmark is runnable via inspect_evals as the… See the full description on the dataset page: https://huggingface.co/datasets/AirsideLabs/pre-flight-06.pii-masking-300k
👉 Looking for the newest release? The current flagship is ai4privacy/pii-masking-openpii-1.5m. 1.6M samples, 30 languages, 19 PII classes, Asia Pacific extension.?** The current flagship is ai4privacy/pii-masking-openpii-1m. 1.4M samples, 23 languages, 19 PII classes.
Purpose and Features
🌍 World's largest open dataset for privacy masking 🌎
The dataset is useful to train and evaluate models to remove personally identifiable and sensitive information from text, especially in… See the full description on the dataset page: https://huggingface.co/datasets/ai4privacy/pii-masking-300k.Evo-BenchEvo-Bench: Can Language Models Improve Agent Harness?
A benchmark for measuring the intrinsic harness-evolving capability of language models.
Overview of the Evo-Bench evaluation pipeline.
✨ Highlights
608 harness-sensitive tasks from five established benchmarks, spanning
Search, Office, and General agent domains with disjoint validation and
evaluation suites.
Harness-guided benchmark construction selects tasks that respond to
harness improvements… See the full description on the dataset page: https://huggingface.co/datasets/RUC-AIBOX/Evo-Bench.fw2_edu_scores
Fineweb2-Edu-scores
Dataset summary
FineWeb2-JQL-Education is a model-annotated language subset of FineWeb2, spanning 36 languages.
Our model-annotations allow for a filtering that achieves higher-quality training outcomes without excessively aggressive data reduction.
The original FW2 heuristic filtering method serves as our baseline, providing reference points for both the volume of retained tokens and downstream model performance.
For example, in the Spanish language… See the full description on the dataset page: https://huggingface.co/datasets/JQL-AI/fw2_edu_scores.wearable-ai
EgoWearBench Dataset (ECCV 2026)
Part of the Wearable AI Workshop at ECCV 2026.
A benchmark of egocentric (first-person, head-mounted wearable camera) videos paired with three complementary video question-answering tasks for evaluating wearable-AI assistants on real-world everyday activity videos.
▶ Baseline code & evaluation scripts: see starter_kit/README.md. The starter kit ships inside this repo, so git clone gives you the code and the data together.
Tasks… See the full description on the dataset page: https://huggingface.co/datasets/facebook/wearable-ai.enemThe ENEM 2022, 2023 and 2024 datasets encompass all multiple-choice questions from the last two editions of the Exame Nacional do Ensino Médio (ENEM), the main standardized entrance examination adopted by Brazilian universities. The datasets have been created to allow the evaluation of both textual-only and textual-visual language models. To evaluate textual-only models, we incorporated into the datasets the textual descriptions of the images that appear in the questions' statements from the… See the full description on the dataset page: https://huggingface.co/datasets/maritaca-ai/enem.pii-masking-200k
👉 Looking for the newest release? The current flagship is ai4privacy/pii-masking-openpii-1.5m. 1.6M samples, 30 languages, 19 PII classes, Asia Pacific extension.?** The current flagship is ai4privacy/pii-masking-openpii-1m. 1.4M samples, 23 languages, 19 PII classes.
Ai4Privacy Community
Join our community at https://discord.gg/FmzWshaaQT to help build open datasets for privacy masking.
Purpose and Features
Previous world's largest open dataset for privacy.… See the full description on the dataset page: https://huggingface.co/datasets/ai4privacy/pii-masking-200k.sounio-code-examples
Sounio Curated Code Examples
Curated compile-clean .sio examples for training and evaluating code models on
Sounio, a self-hosted systems and scientific programming language for epistemic
computing, uncertainty propagation, and algebraic effects.
This directory is the Cx-1 expansion lane for
chiuratto-AIgourakis/sounio-code-examples.
Current batch
Examples: 5,000
Metadata files: 5,000
Compiler gate: bin/souc check pass rate 5,000/5,000
Utility layer: 5,000… See the full description on the dataset page: https://huggingface.co/datasets/chiuratto-AIgourakis/sounio-code-examples.C-VARCThis repository contains all the data associated with the paper "C-VARC: A Large-Scale Chinese Value Rule Corpus for Value Alignment of Large Language Models".
We propose a three-tier value classification framework based on core Chinese values, which includes three dimensions, twelve core values, and fifty derived values. With the assistance of large language models and manual verification, we constructed a large-scale, refined, and high-quality value corpus containing over 250,000 rules. We… See the full description on the dataset page: https://huggingface.co/datasets/Beijing-AISI/C-VARC.draco
DRACO: a Cross-Domain Benchmark for Deep Research Accuracy, Completeness, and Objectivity
The DRACO Benchmark consists of complex, open-ended research tasks with expert-curated rubrics for evaluating deep research systems. Tasks span 10 domains and require drawing on information sources from 40 countries. Each task is paired with a detailed, task-specific rubric featuring an average of ~40 evaluation criteria across four axes: factual accuracy, breadth and depth of analysis… See the full description on the dataset page: https://huggingface.co/datasets/perplexity-ai/draco.MERA
WARNING! This is the deprecated version. The new MERA datasets are now here!
MERA (Multimodal Evaluation for Russian-language Architectures)
Summary
MERA (Multimodal Evaluation for Russian-language Architectures) is a new open benchmark for the Russian language for evaluating fundamental models.
MERA benchmark brings together all industry and academic players in one place to study the capabilities of fundamental models, draw attention to AI problems, develop… See the full description on the dataset page: https://huggingface.co/datasets/ai-forever/MERA.GMAI-VL-5.5M
GMAI-VL-5.5M Dataset
GMAI-VL-5.5M is a comprehensive, large-scale medical General Medical AI Vision-Language (GMAI-VL) dataset built specifically for training multimodal foundation models in the medical domain. It contains an extraordinary scale of high-quality instructions encompassing over 5.5 million multimodal question-answering pairs, carefully constructed based on hundreds of medical classification, segmentation, and detection datasets.
This repository… See the full description on the dataset page: https://huggingface.co/datasets/General-Medical-AI/GMAI-VL-5.5M.africa-synth-aid-flows-medical-multimodal-fracture-all
Africa Synth Aid Flows Medical Multimodal Fracture All | Africa (Electric Sheep Africa metadata inventory)
Size category: 1K<n<10K - Formats: json - Sector: health - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What This Dataset Covers
Health… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-aid-flows-medical-multimodal-fracture-all.H3-Character-Swap-v1
H3 Character Swap v1
A reference-conditioned character-replacement dataset for MiniMax H3 Ref2VA LoRA training with Ostris AI Toolkit. It combines synthetic still-image edits with unchanged real-motion regularization videos.
134 examples: 94 character-swap edits and 40 preservation clips. Training has 76 edits + 32 clips; validation has 18 edits + 8 clips. Prepared resolution is 1344×768 at 24 fps. The companion 1,000-step LoRA are available separately.
Task and… See the full description on the dataset page: https://huggingface.co/datasets/akatz-ai/H3-Character-Swap-v1.