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.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.SimpleRL-Zoo-Datagpt-edit-simplerSimpleStories
📘📕 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.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.simpleqaomnifall
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.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.SimpleVQA
SimpleVQA
SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Models
Dataset: https://huggingface.co/datasets/m-a-p/SimpleVQA
Abstract
The increasing application of multi-modal large language models (MLLMs) across various sectors have spotlighted the essence of their output reliability and accuracy, particularly their ability to produce content grounded in factual information (e.g. common and domain-specific knowledge). In this work, we… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/SimpleVQA.wikipedia_chunked
Dataset Card for "wikipedia_chunked"
More Information needed
simplerl-minerva-mathsimplerl-OlympiadBenchSimpleQA
SimpleQA
SimpleQA is a factuality benchmark developed by OpenAI to evaluate the factual accuracy of language models when answering concise, fact-seeking questions. The dataset comprises 4,326 questions spanning diverse topics including science, technology, entertainment, and more.
Dataset Description
SimpleQA measures the ability for language models to answer short, fact-seeking questions. Each question is designed to have a single, indisputable answer, ensuring… See the full description on the dataset page: https://huggingface.co/datasets/OpenEvals/SimpleQA.glm-simple-evals-dataset
glm-simple-evals-dataset
This repository is dedicated to storing various evaluation data required for the glm-simple-evals evaluation project, to enable industry researchers and developers to reproduce the performance of the GLM-4.5 series models on reported benchmarks.
Currently, this repository covers the data required for the following evaluation tasks:
AIME
GPQA
HLE
LiveCodeBench
MATH 500
SciCode
MMLU Pro
Usage Instructions
To use these evaluation datasets… See the full description on the dataset page: https://huggingface.co/datasets/zai-org/glm-simple-evals-dataset.simple-arithmetic-problemssimpsons_script_lines_parsedsynthetic-mapping-simpleA synthetic dataset to test injection of new knowledge into an LLM. This is created using the following script:
import argparse
import random
import pandas as pd
from datasets import Dataset
kv_templates = [
"What is the value of {key}?",
"{key} = ?",
"What number is assigned to {key}?",
"Find the value of {key}.",
"In this dataset, what does {key} represent?",
"What is the value associated with {key}?",
"Give me the value of {key}.",
"What is the number… See the full description on the dataset page: https://huggingface.co/datasets/rbiswasfc/synthetic-mapping-simple.conceptnet_en_simplesimple-math
Simple Math: 2+2=4 -1=3 (LoLo: Learning Only Logical Operations)
Just like my teacher gave me homework, i thought maybe we can also add some of these basics on the trainings of our models.
It was created with very simple code that is in the repo, if you add more complex operations and so.. please share the code :D thank you
Current Code Version: 20240127.fblgit (A modification over @win10 for progressive and DPO operation)
Does it Works?
34BEAGLES… See the full description on the dataset page: https://huggingface.co/datasets/fblgit/simple-math.simpsons_prompt_lines
Dataset Card for "simpsons_prompt_lines"
I used the Simpsons Kaggle dataset (simpsons_episodes.csv and simpsons_script_lines.csv)
I got the idea and part of the code from this blog post from Replicate.
This can be used to fine-tune a Chat LLM model, to speak like one of the characters of the show !
Example
{
"previous": "Marge Simpson: Homer, get up! Up, up, up!\nMarge Simpson: Oh no!\nHomer Simpson: Whuzzit... My juice box!\nMarge Simpson: Sorry, Homie, but you… See the full description on the dataset page: https://huggingface.co/datasets/PlenitudeAI/simpsons_prompt_lines.simpleqaso100_cutlery_handling_simpleThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 50,
"total_frames": 29853,
"total_tasks": 1,
"total_videos": 50,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:50"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/masato-ka/so100_cutlery_handling_simple.single_pickplace
pick_and_place-300 — Unitree G1 + Dex3, "pick octopus and place inside brown basket"
LeRobot v2.1 dataset. Teleoperated bimanual G1 with Dex3 hands; lower body under a GR00T
whole-body-control policy, upper body teleoperated.
Episodes
349 (322 positive demos + 27 negative samples)
Frames
161,440 (2.24 h @ 20 fps)
FPS
20
Cameras
3 × h264 640×480 yuv420p
State / action
43-dim whole body (float64)
Task string
pick octopus and place inside brown basket
Size… See the full description on the dataset page: https://huggingface.co/datasets/simpk/single_pickplace.data_ablation_full59K
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},
url={https://arxiv.org/abs/2501.19393},
}
simpsonssimpleqa-verified-multilingual
SimpleQA Verified Multilingual
Multilingual translations of SimpleQA Verified, a 1,000-prompt factuality benchmark from Google DeepMind that evaluates short-form parametric knowledge (facts stored in model weights).
Source: google/simpleqa-verified (eval split, 1,000 examples)
Languages
Config
Language
Examples
ces
Czech
100
dan
Danish
100
deu
German
1,000
fra
French
100
ita
Italian
100
nld
Dutch
100
pol
Polish
100
spa
Spanish
100
More to… See the full description on the dataset page: https://huggingface.co/datasets/ellamind/simpleqa-verified-multilingual.hepha_act_100_simple_drawer_5
tmeynier/hepha_act_100_simple_drawer_5
LeRobot-style behavior-cloning dataset generated from the Hepha MuJoCo simulation.
Summary
Robot type: hepha_mujoco
Codebase version: v3.0
Episodes: 100
Frames: 200000
FPS: 30
Joint normalization: min_max_0_1
Features
timestamp: float32 [1]
frame_index: int64 [1]
episode_index: int64 [1]
index: int64 [1]
task_index: int64 [1]
episode.drawer_index: int64 [1]
episode.cube_position: float32 [3]… See the full description on the dataset page: https://huggingface.co/datasets/tmeynier/hepha_act_100_simple_drawer_5.