datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
TemplateGSM
TemplateMath: Template-based Data Generation (TDG)
This is the official repository for the paper "Training and Evaluating Language Models with Template-based Data Generation", published at the ICLR 2025 DATA-FM Workshop.
Our work introduces Template-based Data Generation (TDG), a scalable paradigm to address the critical data bottleneck in training LLMs for complex reasoning tasks. We use TDG to create TemplateGSM, a massive dataset designed to unlock the next level of… See the full description on the dataset page: https://huggingface.co/datasets/math-ai/TemplateGSM.worldcup2026
⚽ WorldCup Arena
A Leakage-Free Forecasting Benchmark on a Live Tournament
Can a language model forecast a match — when the match had not been played at the moment it was asked?
🌐 Language / 语言 : 中文 ▾
📊 四张表
点开本页顶部的 Data Studio 标签即可浏览，也可以直接按名字加载。
Config
行数
内容
fixtures
104
基准本体 —— 喂给模型的头部信息，以及结算后的 90 分钟赛果，七个盘口全部推导好（outcome_1x2、over_2_5、both_score、odd_total）
dossiers
2,208
简报索引 —— 46 快照 × 48… See the full description on the dataset page: https://huggingface.co/datasets/Social-AI-2026/worldcup2026.thai_exam
Dataset Card for Thai_Exam
ThaiExam is a Thai knowledge benchmarking dataset, consisting of multiple-choice questions from examinations in Thailand. The dataset was originally developed for evaluating Typhoon (Thai LLM). This dataset contains 5 splits corresponding to 5 examinations as follows:
ONET: The Ordinary National Educational Test (ONET) is an examination for students in Thailand. This dataset is based on the grade-12 ONET exam, comprising 4 subjects and each question has 5… See the full description on the dataset page: https://huggingface.co/datasets/typhoon-ai/thai_exam.AIME_2000_2026_Kimi_K3
AIME 2000–2026 — Kimi K3 reasoning traces
🔄 Changelog
2026-08-08 — full re-generation. All reasoning traces were regenerated from scratch and re-verified against the official answer key.
New schema — added gen_attempts_low, gen_attempts_high; renamed gen_parsed_answer → gen_answer_int and answer_note → problem_note; removed gen_effort, gen_pass1.
New generation — only use the bare problem (v1 appended an "ANSWER:" format instruction), so traces are cleaner.… See the full description on the dataset page: https://huggingface.co/datasets/bevangelista/AIME_2000_2026_Kimi_K3.Sujet-Finance-Instruct-177k
Sujet Finance Dataset Overview
The Sujet Finance dataset is a comprehensive collection designed for the fine-tuning of Language Learning Models (LLMs) for specialized tasks in the financial sector. It amalgamates data from 18 distinct datasets hosted on HuggingFace, resulting in a rich repository of 177,597 entries. These entries span across seven key financial LLM tasks, making Sujet Finance a versatile tool for developing and enhancing financial applications of AI.… See the full description on the dataset page: https://huggingface.co/datasets/sujet-ai/Sujet-Finance-Instruct-177k.SocietyBench
🔮 SocietyBench
Forecasting Counterfactual Social-World Evolution
Can a language model forecast how a real social event unfolds — when it cannot tell which event it is?
🌐 Language / 语言 : 中文 ▾
📊 纵览表
点开本页顶部的 Data Studio 标签即可浏览：一行一个事件，五行看完整个榜的规模与构成。
Config
行数
内容
overview
5
一眼看懂这个榜 —— 一行一个事件：领域、时间线节点数、25 个截止点的首尾与跨度、题量与每点均值、真假比、时间事件数与 90 天内占比、A/B/C/D 题型配比、难易分布
from datasets import load_dataset
ov… See the full description on the dataset page: https://huggingface.co/datasets/Social-AI-2026/SocietyBench.aime-1983-2025
AIME Datasets from 1983 to 2025
This dataset contains the AIME datasets from 1983 to 2025.
For AIME 1983 to 2026 use Pandores/aime-1983-2026
Features Description
Feature
Description
Example
year
The year this problem was released. From 1983 to 2025.
2022
index
The index of the problem for a year and part. From 1 to 15.
12
part
The dataset part if this dataset has multiple parts. Can be AIME, AIME I, AIME II or None. Datasets have multiple parts… See the full description on the dataset page: https://huggingface.co/datasets/Pandores/aime-1983-2025.QEvasion
"I Never Said That": A dataset, taxonomy and baselines on response clarity classification.
This repo contains the dataset form the paper: "I Never Said That": A dataset, taxonomy and baselines on response clarity classification. It contains question-answer (QA) pairs extracted from political interviews, with annotations that reflect varying degrees of response clarity or evasion. Below is a brief explanation of each column in the dataset.
Column
Description
title… See the full description on the dataset page: https://huggingface.co/datasets/ailsntua/QEvasion.DecodingTrust
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Overview
This repo contains the source code of DecodingTrust. This research endeavor is designed to help researchers better understand the capabilities, limitations, and potential risks associated with deploying these state-of-the-art Large Language Models (LLMs). See our paper for details.
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Boxin Wang, Weixin Chen, Hengzhi… See the full description on the dataset page: https://huggingface.co/datasets/AI-Secure/DecodingTrust.python-text-copilot-training-instruct-ai-research-2024-02-03
Python Copilot Instructions on How to Code using Alpaca and Yaml
Training and test datasets for building coding multimodal models that understand how to use the open source GitHub projects for the Agora Open Source AI Research Lab:
Agora GitHub Organization
Agora Hugging Face
This dataset is the 2024-02-03 update for the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.
Details… See the full description on the dataset page: https://huggingface.co/datasets/matlok/python-text-copilot-training-instruct-ai-research-2024-02-03.OlymMATH-eval
OlymMATH Evaluation Results
OlymMATH is a dataset we introduced in Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models by Haoxiang Sun, Yingqian Min, Zhipeng Chen, Wayne Xin Zhao, Zheng Liu, Zhongyuan Wang, Lei Fang, and Ji-Rong Wen. You can find more information on GitHub and HuggingFace 🤗.
We have made our evaluation results for the avg@{8, 64} and cons@{8, 64} metrics in this dataset publicly available for academic research… See the full description on the dataset page: https://huggingface.co/datasets/RUC-AIBOX/OlymMATH-eval.EU-MMLU
EU-oriented Massive Multitask Language Understanding (EU-MMLU)
Dataset description
The MMLU benchmarking dataset (https://arxiv.org/pdf/2009.03300) is a widely recognized benchmark of general knowledge attained by AI models. It covers a broad range of topics from 57 different categories, covering elementary-level knowledge up to advanced professional subjects like law, physics, history, and computer science.
The European Commission’s Directorate-General for… See the full description on the dataset page: https://huggingface.co/datasets/EC-DGT-AI/EU-MMLU.AgentJudgeBench
AgentJudgeBench: Evaluating LLM Judge Reliability on Agentic Tool-Calling
A benchmark for systematically evaluating how reliably LLM judges assess
agentic tool-calling workflows across structured, dependency-driven tasks.
Why this benchmark?
AgentJudgeBench measures how reliably LLM judges assess agentic tool-calling outputs. It provides 3,808 benchmark records spanning six DAG topologies and three difficulty… See the full description on the dataset page: https://huggingface.co/datasets/ServiceNow-AI/AgentJudgeBench.smol-worldcup
🏟️ Smol AI WorldCup — SHIFT Benchmark
The world's first 5-axis evaluation framework for small language models.
Not just "how smart?" — but "how honest? how fast? how small? how efficient?"
🏟️ Leaderboard
huggingface.co/spaces/ginigen-ai/smol-worldcup
📊 Dataset
huggingface.co/datasets/ginigen-ai/smol-worldcup
🏅 ALL Bench
huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard
🏆 Official Ranking: WCS (WorldCup Score)
WCS = √( SHIFT × PIR_norm )… See the full description on the dataset page: https://huggingface.co/datasets/ginigen-ai/smol-worldcup.airbnb_embeddings
Overview
This dataset consists of AirBnB listings with property descriptions, reviews, and other metadata.
It also contains text embeddings of the property descriptions as well as image embeddings of the listing image. The text embeddings were created using OpenAI's text-embedding-3-small model and the image embeddings using OpenAI's clip-vit-base-patch32 model available on Hugging Face.
The text embeddings have 1536 dimensions, while the image embeddings have 512 dimensions.… See the full description on the dataset page: https://huggingface.co/datasets/MongoDB/airbnb_embeddings.python-text-training-instruct-ai
Python Copilot Instructions on How to Code using Alpaca and Yaml
Training and test datasets for building coding multimodal models that understand how to use the open source GitHub projects for the Agora Open Source AI Research Lab:
Agora GitHub Organization
Agora Hugging Face
This dataset is the 2024-02-03 update for the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.
Details… See the full description on the dataset page: https://huggingface.co/datasets/DevShubham/python-text-training-instruct-ai.AIME-trajectory
AIME Trajectory Dataset
Model-generated solution trajectories for AIME (American Invitational Mathematics Examination) problems. Each row is one model response to a single problem, including the hidden chain-of-thoughts (when available), and the final response.
Dataset Summary
Split
Rows
Unique Problems
Years
Model(s)
Has reasoning_content
Accuracy
train
1,258
875
1983–2023
deepseek-r1
Yes
100%
test
180
30
2024
Multiple (see below)
No
3.3%… See the full description on the dataset page: https://huggingface.co/datasets/sxiong/AIME-trajectory.EvoAgentBench
EvoAgentBench
EvoAgentBench is a benchmark for evaluating AI agent self-evolution — the ability of agents to improve their performance by learning from past experiences. It provides standardized train/test splits across five diverse task domains, enabling reproducible comparison of skill extraction and experience reuse methods.
Benchmark Overview
Domain
Base Dataset
Train
Test
Task Format
Information Retrieval
BrowseComp-Plus
154
65
Multi-constraint… See the full description on the dataset page: https://huggingface.co/datasets/EverMind-AI/EvoAgentBench.RAIL-HH-10K RAIL-HH-10K: Multi-Dimensional Safety Alignment Dataset
The first large-scale safety dataset with 99.5% multi-dimensional annotation coverage across 8 ethical dimensions.
📖 Read Blog •
📖 Paper (Coming Soon) •
🚀 Quick Start •
🔌 RAIL API •
💻 Examples
🌟 What Makes RAIL-HH-10K Special?
🎯 Near-Complete Coverage
99.5% dimension coverage across all 8 ethical dimensions
Most existing datasets: 40-70% coverage
RAIL-HH-10K: 98-100%… See the full description on the dataset page: https://huggingface.co/datasets/responsible-ai-labs/RAIL-HH-10K.chinese-ai-and-robotics-open-intelligence
🔬 Chinese AI, Humanoid Robotics & Neural Systems Open Intelligence Dataset
Curated open intelligence dataset tracking Chinese frontier developments in Large Language Models (LLMs), Humanoid Dynamic Locomotion, 3D Computer Vision, and Neuromorphic edge processors.
[!IMPORTANT]
Data Completeness & Research Authenticity Notice:
Included in this Hugging Face Open Dataset: English structured abstracts, core quantitative takeaways, author institutional affiliations, and… See the full description on the dataset page: https://huggingface.co/datasets/simpleG2023/chinese-ai-and-robotics-open-intelligence.OR-Clarify
OR-Clarify
📄 Paper: Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization
OR-Clarify is a benchmark for testing whether an agent asks the right questions before formulating an optimization model.
Most optimization benchmarks give an agent a complete problem statement. OR-Clarify instead starts with an incomplete business brief. The agent must identify missing requirements that could change the optimization formulation, ask for the relevant… See the full description on the dataset page: https://huggingface.co/datasets/AIOR-Research/OR-Clarify.python-text-copilot-training-instruct-ai-research
Building an AI Copilot Dataset to help keep up with Leading AI Research
This is a specialized, instruction dataset for training python coding assistants on how to code from leading AI/ML open source repositories (2.3M coding samples).
This dataset is a subset of the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.
Details
This dataset holds the latest coding changes from >1159… See the full description on the dataset page: https://huggingface.co/datasets/matlok/python-text-copilot-training-instruct-ai-research.eu-ai-act
EU AI Act — Structured Chunks (v2026-07-20)
Source-grounded chunks of Regulation (EU) 2024/1689 (the EU AI Act),
parsed from the official EUR-Lex Formex XML and packaged for retrieval-augmented
generation and information-retrieval research.
⚠️ Not legal advice. Not an official EU publication. See the Disclaimer below.
What's in here
2607 rows spanning en, nl, fr.
Chunk-type breakdown:
annex_item: 162
article_full: 339
paragraph: 1566
recital: 540
Per-language… See the full description on the dataset page: https://huggingface.co/datasets/jeroenherczeg/eu-ai-act.singapore-legal-ai-benchmark
Singapore Legal AI Benchmark
Public research release of 102 Singapore legal research questions, model
responses from 6 systems, and overlapping grades on five dimensions.
Headline metrics are overlapping binary flags, not a ranking and not a
partition of 100%.
Interactive explorer
Open the explorer →
— comparison table, category heatmap, per-question comparison, and every answer
with its sources and grades.
(Space page)
Overall (n = 612)… See the full description on the dataset page: https://huggingface.co/datasets/JonathanSu/singapore-legal-ai-benchmark.aim-technical-articles
Analytics India Magazine Technical Articles Dataset 🚀
Dataset Description
This comprehensive dataset contains 25,685 high-quality technical articles from Analytics India Magazine, one of India's leading publications covering artificial intelligence, machine learning, data science, and emerging technologies.
✨ Dataset Highlights
📚 Comprehensive Coverage: Latest AI models, frameworks, and tools
🔬 Technical Depth: Extracted keywords and complexity scoring
🏭… See the full description on the dataset page: https://huggingface.co/datasets/abhilash88/aim-technical-articles.cairo-security-audits
Cairo Security Audits
A source-traceable corpus of public Cairo and Starknet security-audit metadata and normalized finding annotations.
Version 0.3.0 packages every entry in the audit inventory frozen at keep-starknet-strange/starknet-skills@17a76e8. It covers 32 accessible reports from 10 auditing firms and 286 normalized finding annotations. Eleven records are checked against rendered reports and two link to exact vulnerable/fixed commits. The release does not redistribute… See the full description on the dataset page: https://huggingface.co/datasets/starknet-ai/cairo-security-audits.python-text-copilot-training-instruct-ai-research-2024-02-10
Python Copilot Instructions on How to Code using Alpaca and Yaml
Training and test datasets for building coding multimodal models that understand how to use the open source GitHub projects for the multimodal Qwen AI project:
Qwen
Qwen Agent
Qwen VL Chat
Qwen Audio
This dataset is the 2024-02-10 update for the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.
Details
Each row… See the full description on the dataset page: https://huggingface.co/datasets/matlok/python-text-copilot-training-instruct-ai-research-2024-02-10.sob
The Structured Output Benchmark (SOB)
A multi-source benchmark for evaluating structured-output quality in LLMs.
💻 Code ·
📄 Paper ·
✍️ Blog ·
🏆 Leaderboard ·
🏆 Interfaze Leaderboard ·
interfaze-ai
Dataset summary
SOB evaluates how accurately LLMs produce schema-compliant and value-correct JSON from unstructured or semi-structured context — across three source modalities:
Config
Source
Context delivered as
Records
defaultHotpotQA (multi-hop… See the full description on the dataset page: https://huggingface.co/datasets/interfaze-ai/sob.python-text-copilot-training-instruct-ai-research-2024-02-11
Python Copilot Instructions on How to Code using Alpaca and Yaml
Training and test datasets for building coding multimodal models that understand how to use the open source GitHub projects for the Autogen and multimodal Qwen AI project:
Qwen
Qwen Agent
Qwen VL Chat
Qwen Audio
This dataset is the 2024-02-11 update for the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.
Details… See the full description on the dataset page: https://huggingface.co/datasets/matlok/python-text-copilot-training-instruct-ai-research-2024-02-11.theorem-search-dataset
Theorem Search Dataset
The largest open corpus of informal mathematical theorems: 1,341,083 theorem statements with natural-language slogans from 209,777 papers, designed for semantic theorem retrieval.
Paper: Semantic Search over 9 Million Mathematical Theorems
Demo: huggingface.co/spaces/uw-math-ai/theorem-search
Benchmark results
On 110 test queries written by research mathematicians, our best pipeline (Qwen3-Embedding-8B on DeepSeek-V3.1 slogans) outperforms all… See the full description on the dataset page: https://huggingface.co/datasets/uw-math-ai/theorem-search-dataset.