datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
HiFi-UMI-2K
HiFi-UMI-2K: High-Fidelity Robot-Free Manipulation Data
2,000 hours released · 6 synchronized camera views · 480+ scenes · 3 mm pose accuracy · <40 µs synchronization
🌐 Project Website |
📦 Dataset |
📄 Paper: arXiv:2607.25895
Examples from the HiFi-UMI corpus. Click the image to play the video.
📚 Introduction
HiFi-UMI is a portable, high-fidelity bimanual capture system for collecting robot-free manipulation demonstrations.… See the full description on the dataset page: https://huggingface.co/datasets/simple-world-lab/HiFi-UMI-2K.simplipy-assets
simplipy assets
Rule sets and engine configurations for simplipy,
a fast, contract-sound simplification engine for symbolic mathematical expressions.
The engine and these rule sets are described in: Saegert & Köthe 2026, Breaking the
Simplification Bottleneck in Amortized Neural Symbolic Regression (ICML 2026),
arXiv:2602.08885.
Install an engine by name (downloads this repository's files on first use):
pip install simplipy
import simplipy as sp
engine =… See the full description on the dataset page: https://huggingface.co/datasets/psaegert/simplipy-assets.s1K-1.1
Dataset Card for s1K
Dataset Summary
s1K-1.1 consists of the same 1,000 questions as in s1K but with traces instead generated by DeepSeek r1. We find that these traces lead to much better performance.
Usage
# pip install -q datasets
from datasets import load_dataset
ds = load_dataset("simplescaling/s1K-1.1")["train"]
ds[0]
Dataset Structure
Data Instances
An example looks as follows:
{
'solution': '1. **Rewrite the function using… See the full description on the dataset page: https://huggingface.co/datasets/simplescaling/s1K-1.1.s1K
Dataset Card for s1K
Dataset Summary
s1K is a dataset of 1,000 examples of diverse, high-quality & difficult questions with distilled reasoning traces & solutions from Gemini Thining. Refer to the s1 paper for more details.
Usage
# pip install -q datasets
from datasets import load_dataset
ds = load_dataset("simplescaling/s1K")["train"]
ds[0]
Dataset Structure
Data Instances
An example looks as follows:
{
'solution': '1. **Rewrite… See the full description on the dataset page: https://huggingface.co/datasets/simplescaling/s1K.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.SimpleRL-Zoo-Datagpt-edit-simplerSIMPLEyou data output is here
quantum-representations
Epsilon-Transformers Belief Analysis Dataset
This dataset contains trained neural network models and their corresponding belief state regression analysis from the Epsilon-Transformers project. The models were trained on four different stochastic processes and analyzed for their ability to learn and represent belief states.
See https://github.com/adamimos/epsilon-transformers/tree/quantum-public for codebase which generated this data.
Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/SimplexAI/quantum-representations.HC3Human ChatGPT Comparison Corpus (HC3)SimpleQA
SimpleQA
A factuality benchmark called SimpleQA that measures the ability for language models to answer short, fact-seeking questions.
Sources
openai/simple-evals
Introducing SimpleQA
Measuring short-form factuality in large language models
SimpleSafetyTestssimple_robotwinSimpleStories
📘📕 SimpleStories 📙📗
SimpleStories is a dataset of >2 million model-generated short stories. It was made to train small, interpretable language models on it. The generation process is open-source: To see how the dataset was generated, or to generate some stories yourself, head over to this repository.
If you'd like to commission other languages or story formats, feel free to send mail.
When using SimpleStories in your work, please cite the SimpleStories paper:… See the full description on the dataset page: https://huggingface.co/datasets/SimpleStories/SimpleStories.simpleqa-verified
SimpleQA Verified
A 1,000-prompt factuality benchmark from Google DeepMind and Google Research, designed to reliably evaluate LLM parametric knowledge.
▶ SimpleQA Verified Leaderboard on Kaggle▶ Technical Report▶ Evaluation Starter Code
Benchmark
SimpleQA Verified is a 1,000-prompt benchmark for reliably evaluating Large Language Models (LLMs) on short-form factuality
and parametric knowledge. The authors from Google DeepMind and Google Research… See the full description on the dataset page: https://huggingface.co/datasets/google/simpleqa-verified.SimpleDeco-Runssimplified_grooveThis is a copy of the Magenta Groove dataset
The script ´simplify_midi_pretty.py` reads the midi data and simplifies it, by removing any midi values that aren't kicks or snares, and quantizing the notes.
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.simple-wikipedia
simple-wikipedia
Processed, text-only dump of the Simple Wikipedia (English). Contains 23,886,673 words.
details_grimjim__Llama-3-Instruct-8B-SimPO-SPPO-Iter3-merge
Dataset Card for Evaluation run of grimjim/Llama-3-Instruct-8B-SimPO-SPPO-Iter3-merge
Dataset automatically created during the evaluation run of model grimjim/Llama-3-Instruct-8B-SimPO-SPPO-Iter3-merge.
The dataset is composed of 136 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is… See the full description on the dataset page: https://huggingface.co/datasets/OALL/details_grimjim__Llama-3-Instruct-8B-SimPO-SPPO-Iter3-merge.simpleqaSimpleQA-VerifiedSimpleQA Verified is a 1,000-prompt benchmark for reliably evaluating Large Language Models (LLMs) on short-form factuality and parametric knowledge. The authors from Google DeepMind and Google Research address various limitations of SimpleQA, originally designed by Wei et al. (2024) at OpenAI, including noisy and incorrect labels, topical biases, and question redundancy. SimpleQA Verified was created to provide the research community with a more precise instrument to track genuine progress in… See the full description on the dataset page: https://huggingface.co/datasets/codelion/SimpleQA-Verified.omnifall
OmniFall: A Unified Benchmark for Staged-to-Wild Fall Detection
OmniFall is a comprehensive fall detection benchmark with dense temporal segment annotations across three components: OF-Staged (8 public lab datasets), OF-In-the-Wild (genuine accidents from OOPS), and OF-Synthetic (12,000 diffusion-generated videos with demographic diversity). All components share a sixteen-class activity taxonomy.
[Paper] [Project Page]
Quickstart… See the full description on the dataset page: https://huggingface.co/datasets/simplexsigil2/omnifall.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.sim-posttrain
HUMANUAL Posttraining Data
Posttraining data for user simulation, derived from the train splits of the
HUMANUAL benchmark datasets.
Datasets
HUMANUAL (posttraining)
Config
Rows
Description
news
48,618
News article comment responses
politics
45,429
Political discussion responses
opinion
37,791
Reddit AITA / opinion thread responses
book
34,170
Book review responses
chat
23,141
Casual chat responses
email
6,377
Email reply responses… See the full description on the dataset page: https://huggingface.co/datasets/Xuhui/sim-posttrain.simplificationHC3-ChineseHuman ChatGPT Comparison Corpus (HC3) Chinese Versionworld-state-daily
World State Daily
Daily end-of-day world state snapshots from Kalshi + Polymarket. Each JSON file captures consensus probabilities across tens of thousands of prediction markets, the SF Index (disagreement, geo-risk, breadth, activity), top edges, divergences, and a markdown summary.
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… See the full description on the dataset page: https://huggingface.co/datasets/SimpleFunctions/world-state-daily.simple_wikipedia
simple wikipedia
the 'simple' split of Wikipedia, from Sept 1 2023. The train split contains about 65M tokens,
Pulled via:
dataset = load_dataset(
"wikipedia", language="simple", date="20230901", beam_runner="DirectRunner"
)
stats
train split
general info
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 226242 entries, 0 to 226241
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 id… See the full description on the dataset page: https://huggingface.co/datasets/pszemraj/simple_wikipedia.