datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
aime24_nofiguresThe 30 problems from AIME 2024 only with the ASY code for figures when it is necessary to solve the problem. Figure code that is not core to the problem was excluded.
Citation Information
@misc{muennighoff2025s1simpletesttimescaling,
title={s1: Simple test-time scaling},
author={Niklas Muennighoff and Zitong Yang and Weijia Shi and Xiang Lisa Li and Li Fei-Fei and Hannaneh Hajishirzi and Luke Zettlemoyer and Percy Liang and Emmanuel Candès and Tatsunori Hashimoto}… See the full description on the dataset page: https://huggingface.co/datasets/simplescaling/aime24_nofigures.Chinese-SimpleQA
Overview
🌐 Website • 🤗 Hugging Face • ⏬ Data • 📃 Paper • 📊 Leaderboard
Chinese SimpleQA is the first comprehensive Chinese benchmark to evaluate the factuality ability of language models to answer short questions, and Chinese SimpleQA mainly has five properties (i.e., Chinese, Diverse, High-quality, Static, Easy-to-evaluate). Specifically, our benchmark covers 6 major topics with 99 diverse subtopics.
Please visit our website or check our paper for more details.… See the full description on the dataset page: https://huggingface.co/datasets/OpenStellarTeam/Chinese-SimpleQA.SimpleToM
SimpleToM Dataset and Evaluation data
The SimpleToM dataset of stories with associated questions are described in the paper
"SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMs"
Associated evaluation data for the models analyzed in the paper can be found in the
separate dataset: SimpleToM-eval-data.
Question sets
There are three question sets in the SimpleToM dataset:
mental-state-qa questions about information awareness… See the full description on the dataset page: https://huggingface.co/datasets/allenai/SimpleToM.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.aime25_nofiguressf-index-history
SimpleFunctions Index History
Time series of the SF Index: a four-number summary of prediction-market consensus — disagreement (0-100), geo-risk (0-100), breadth (-1..+1), and activity (0-100) — computed every 15 minutes from ~50K markets. Flat JSONL for easy charting / analysis.
License and Use
This dataset is released under Creative Commons Attribution 4.0 International
(CC-BY-4.0; https://creativecommons.org/licenses/by/4.0/). You may use it
freely for personal… See the full description on the dataset page: https://huggingface.co/datasets/SimpleFunctions/sf-index-history.chinese-materials-science-open-intelligence
🔬 Chinese Materials Science & Metallurgy Open Intelligence Dataset
Curated open intelligence dataset providing English research briefs, authoritative DOIs, executive summaries, and high-resolution micrographs of breakthrough Chinese scientific research in Materials Science, Metallurgy, Advanced Alloys, and Mining Engineering.
[!IMPORTANT]
Data Completeness & Research Authenticity Notice:
Included in this Hugging Face Open Dataset: English structured abstracts, core… See the full description on the dataset page: https://huggingface.co/datasets/simpleG2023/chinese-materials-science-open-intelligence.aime_nofiguresThe 90 problems from AIME 2022, 2023, 2024 only with the ASY code for figures when it is necessary to solve the problem. Figure code that is not core to the problem was excluded.
Citation Information
@misc{muennighoff2025s1simpletesttimescaling,
title={s1: Simple test-time scaling},
author={Niklas Muennighoff and Zitong Yang and Weijia Shi and Xiang Lisa Li and Li Fei-Fei and Hannaneh Hajishirzi and Luke Zettlemoyer and Percy Liang and Emmanuel Candès and Tatsunori… See the full description on the dataset page: https://huggingface.co/datasets/simplescaling/aime_nofigures.chinese-clean-energy-battery-open-intelligence
🔬 Chinese Clean Energy, Battery Chemistry & Smart Grid Open Intelligence Dataset
Curated open intelligence dataset tracking authentic Chinese scientific breakthroughs in Solid-State Battery chemistry, Perovskite Solar cells, Ultra-High Voltage (UHV) power grids, and industrial decarbonization.
[!IMPORTANT]
Data Completeness & Research Authenticity Notice:
Included in this Hugging Face Open Dataset: English structured abstracts, core quantitative takeaways, author… See the full description on the dataset page: https://huggingface.co/datasets/simpleG2023/chinese-clean-energy-battery-open-intelligence.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.chinese-biomedicine-and-genomics-open-intelligence
🔬 Chinese Biomedicine, Cell Therapy & Genomics Open Intelligence Dataset
Curated open intelligence dataset providing English briefs, clinical trial benchmarks, verified abstracts, and DOIs of frontier Chinese research in Cellular Therapeutics, Gene Editing, ADCs, and NMPA Clinical Approvals.
[!IMPORTANT]
Data Completeness & Research Authenticity Notice:
Included in this Hugging Face Open Dataset: English structured abstracts, core quantitative takeaways, author… See the full description on the dataset page: https://huggingface.co/datasets/simpleG2023/chinese-biomedicine-and-genomics-open-intelligence.simple-evalsSimpleQA-Bench
SimpleQA-Bench
Tags: factuality, EN, ZH, short-form-answer, human-label
Copyright: © 2024 alibaba-pai
Source.OpenAI's SimpleQA: Blog & Paper / Data & simple-evals ProjectOpenStellarTeam's Chinese-SimpleQA: Blog & Paper, Data@HF
Factuality is a complicated topic because it is hard to measure—evaluating the factuality of any given arbitrary claim is challenging, and language models can generate long completions that contain dozens of factual claims. In SimpleQA, we will focus on… See the full description on the dataset page: https://huggingface.co/datasets/alibaba-pai/SimpleQA-Bench.openaimathThe 500 problems from MATH that were used in "Let's verify step-by-step" and "s1: Simple test-time scaling" are the test set; rest is regular train set.
Citation Information
@misc{muennighoff2025s1simpletesttimescaling,
title={s1: Simple test-time scaling},
author={Niklas Muennighoff and Zitong Yang and Weijia Shi and Xiang Lisa Li and Li Fei-Fei and Hannaneh Hajishirzi and Luke Zettlemoyer and Percy Liang and Emmanuel Candès and Tatsunori Hashimoto},
year={2025}… See the full description on the dataset page: https://huggingface.co/datasets/simplescaling/openaimath.simple-zundamon
シンプルずんだもんデータセット
はじめに
ずんだもんの設定が詰まったシンプルなデータセットです。
作者がインターネットで調べたり、運営の人からもらったデータから作成しました。
キャラクターLLMを作るための動作確認にお使いください。
ただし、可能な限り動作確認でもライセンスをよく読んでください。
他の用途はライセンスをよく読んでください。
各種フォーマット
ChatGPT: zmn.jsonl
axolotlでの設定例
以下のようにデータセット周りを設定してください。
# データセットの設定
datasets:
- path: alfredplpl/simple-zundamon # 使用するデータセット（Hugging Face上のデータセット名）
type: chat_template # 会話形式のデータセットを使用
field_messages: messages #… See the full description on the dataset page: https://huggingface.co/datasets/alfredplpl/simple-zundamon.Australian-Tax-Legislation-and-Amendment-History
Australian Tax Legislation & Amendment History
The full text of every section of the 22 principal Acts the Australian Taxation Office administers — Income Tax Assessment Act 1997, Income Tax Assessment Act 1936, the GST Act, FBTAA, the Taxation Administration Act 1953, the superannuation and fuel tax Acts and more — each section joined to its complete amendment history: which Act changed it, which schedule item, and when it commenced. Sourced from the Federal Register of… See the full description on the dataset page: https://huggingface.co/datasets/simplelex/Australian-Tax-Legislation-and-Amendment-History.aime24_figuresThe 30 problems from AIME 2024 with all ASY code for figures.
Citation Information
@misc{muennighoff2025s1simpletesttimescaling,
title={s1: Simple test-time scaling},
author={Niklas Muennighoff and Zitong Yang and Weijia Shi and Xiang Lisa Li and Li Fei-Fei and Hannaneh Hajishirzi and Luke Zettlemoyer and Percy Liang and Emmanuel Candès and Tatsunori Hashimoto},
year={2025},
eprint={2501.19393},
archivePrefix={arXiv},
primaryClass={cs.CL}… See the full description on the dataset page: https://huggingface.co/datasets/simplescaling/aime24_figures.simple-wiki
Dataset Card for "simple-wiki"
Dataset Summary
This dataset contains pairs of equivalent sentences obtained from Wikipedia.
Supported Tasks
Sentence Transformers training; useful for semantic search and sentence similarity.
Languages
English.
Dataset Structure
Each example in the dataset contains pairs of equivalent sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value".
{"set":… See the full description on the dataset page: https://huggingface.co/datasets/embedding-data/simple-wiki.SimpleS2
SimpleS2
To load the data:
import json
import pickle
# Read cube
with open('cubo1_pickle', 'rb') as file:
data = pickle.load(file).to_dataset(dim='band')
# Read metadata
with open('cubo1.json') as f:
meta = json.load(f)
Citation
This dataset is related to the paper: arXiv:2506.19656aime_figuresThe 90 problems from AIME 2022, 2023, 2024 with all ASY code for figures.
Citation Information
@misc{muennighoff2025s1simpletesttimescaling,
title={s1: Simple test-time scaling},
author={Niklas Muennighoff and Zitong Yang and Weijia Shi and Xiang Lisa Li and Li Fei-Fei and Hannaneh Hajishirzi and Luke Zettlemoyer and Percy Liang and Emmanuel Candès and Tatsunori Hashimoto},
year={2025},
eprint={2501.19393},
archivePrefix={arXiv}… See the full description on the dataset page: https://huggingface.co/datasets/simplescaling/aime_figures.simplemath-cot
🧮 SimpleMath-100k CoT
A chain-of-thought (CoT) extension of the
ProCreations/SimpleMath
dataset. Every one of the 100 000 algebra / arithmetic problems is paired with a
short, numbered reasoning trace (Step 1: … Step 2: …) that walks a language
model from the problem statement to the known-correct answer.
The traces in the Jupyter notebook are generated by
Qwen3.8-27B and then post-processed to strip formatting noise,
enforce sequential step numbering, and cap output at 1 000… See the full description on the dataset page: https://huggingface.co/datasets/alexfromapex/simplemath-cot.simple-llm-sft
Simple LLM SFT Dataset
This synthetic dataset contains 1,000 English prompt-response pairs for
supervised fine-tuning. It was created to fine-tune
Qwen/Qwen3.5-4B to give clear,
direct, and technically correct answers in simple English.
The writing guidance is inspired by ASD-STE100 Simplified Technical English.
The dataset does not claim official ASD-STE100 compliance or certification.
Dataset structure
The default configuration contains:
Split
Examples… See the full description on the dataset page: https://huggingface.co/datasets/thisisandreeeee/simple-llm-sft.SimpleQuestions-3000LiteResearcher-Corpussimple-facts
Simple Facts
A dataset of simple, no BS, human collected, ethicly sourced facts.
About 1000 examples.
This dataset is growing, and every day I plan to add a few more facts.
SimpleMCQ
SimpleMCQ
Dataset Summary
SimpleMCQ is a collection of multiple-choice question sets in the "fill-in-the-blank" format.
Each item supplies a question sentence that contains a single blank ({}), a list of discrete answer options, and the index of the correct choice.
The dataset is organized into four subsets—KR-200m, KR-200s, P-100, and P-20—and does not contain predefined splits such as train, validation, or test.
Original paper is "Applying Relation Extraction and Graph… See the full description on the dataset page: https://huggingface.co/datasets/naos-ku/SimpleMCQ.0.8k-data-SimpleDeepSearcherVidChain-Datanvidia_openmathinstruct-2-simple-processed元データ
https://huggingface.co/datasets/nvidia/OpenMathInstruct-2
VeriReason-RTL-Coder_7b_reasoning_tb_simple
Verireason-RTL-Coder_7b_reasoning_tb_simple
For implementation details, visit our GitHub repository: VeriReason and our page
Check out our paper: VeriReason: Reinforcement Learning with Testbench Feedback for Reasoning-Enhanced Verilog Generation
Update Log
2025.05.17: Initial release of Nellyw888/Verireason-RTL-Coder_7b_reasoning_tb_simple
Project Description
This study introduces VeriReason, a novel approach utilizing reinforcement learning with… See the full description on the dataset page: https://huggingface.co/datasets/Nellyw888/VeriReason-RTL-Coder_7b_reasoning_tb_simple.