datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
pico-robotics-basic
Pico Robotics Dataset · Annotated Edition
Egocentric multimodal capture from a Pico VR headset + custom tracker rig — Annotated tier
Builds on the Advanced edition by adding coarse action segmentation. Every sequence is divided
into labelled temporal segments, so the data can be used directly for action recognition,
temporal segmentation, and behaviour-understanding tasks without an annotation pass of your own.
🔒 This is a gated dataset. Access requests are reviewed manually;… See the full description on the dataset page: https://huggingface.co/datasets/skycn110/pico-robotics-basic.basic-math-problems-with-step-by-step-solutionsSimpleQA
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
basic-skillsja_asr.jsut_basic5000marin-starcoderdata_visual-basicVoidLinuxISOSeditworld_data_basicv0
How to load the image from the parquet file?
import pyarrow.parquet as pq
test_parquet_path = 't2i_branch/group_0.parquet'
table = pq.read_table(test_parquet_path)
df = table.to_pandas()
from PIL import Image
import io
binary_image = df['original_image'][1]
binary_image_t = df['target_image'][1]
image = Image.open(io.BytesIO(binary_image))
image.save("origin.png")
image = Image.open(io.BytesIO(binary_image_t))
image.save("target.png")
exams-basic-and-quantum-cryptography-and-security-latex
Open Problem Exams: Cryptography and Security (LaTeX)
A curated dataset of open-ended exam problems (with solutions) in cryptography and computer security, formatted in LaTeX. The dataset is sourced from university courses at three institutions.
Dataset Overview
Institution
Files
Topics
Questions
Caltech & TU Delft
8
38
145
EPFL
6
19
86
ETH Zurich
1
14
37
MIT
3
33
79
Total
18
104
347
Difficulty Distribution
Institution… See the full description on the dataset page: https://huggingface.co/datasets/natnitaract/exams-basic-and-quantum-cryptography-and-security-latex.JSUT-basic5000
JSUT (Japanese Speech Corpus) - Test Subset
A test subset of the JSUT corpus containing 500 Japanese utterances from the basic5000 dataset (BASIC5000_4501-5000).
Dataset Structure
jsut_ver1.1/
└── basic5000/
├── wav/ # WAV audio files (500 files, 48kHz)
├── transcript_utf8.txt # Transcriptions
└── recording_info.txt # Recording dates
File Formats
transcript_utf8.txt
BASIC5000_4501:だが、エーアイセンター稼動を快く思わない...… See the full description on the dataset page: https://huggingface.co/datasets/FluidInference/JSUT-basic5000.qwen35-math-rl-by-length
수학 RL 학습 데이터 — 응답 예산별 3묶음
폴더 하나만 받으면 됩니다. 학습에 쓸 응답 길이 상한에 맞는 폴더를 고르세요.
각 폴더는 그 자체로 완결이고, 데이터·리워드·루브릭이 모두 들어 있습니다.
폴더
응답 상한
문제 수
이 예산에서 늘어난 분
힌트판
루브릭 보유
to-16k/
16,384
2,429
+2,429
2,069
2,313
to-32k/
32,768
5,542
+3,113
4,245
5,072
to-64k/
65,536
6,455
+913
4,245
5,724
폴더는 누적입니다. to-32k/ 는 to-16k/ 의 문제를 포함합니다.
응답 32,768 로 학습한다면 to-32k/ 하나만 받으면 됩니다.
구간 전용도 따로 있습니다 — bands/
누적이 아니라 그 구간에서 처음 learnable 이 되는 문제만 담았습니다.
세 파일을 합치면 to-64k/ 와 같아집니다.… See the full description on the dataset page: https://huggingface.co/datasets/BASIC-MATH/qwen35-math-rl-by-length.instruct-data-basics-smollm-H4Datasets of basic instructions and answers for SmolLM-Instruct models trainings: it includes answers to greetings and questions such as "Who are you". This dataset was included in training of SmolLM-Instruct v0.2 but we didn't notice that it had an impact on model generations.
We recommend using this generic larger dataset of multi-turn everyday conversations: https://huggingface.co/datasets/HuggingFaceTB/everyday-conversations-llama3.1-2k
cs336-basics-collection
CS336 Assignment 1 — Pre-tokenized Data & BPE Tokenizers
This repository contains preprocessing artifacts produced for Stanford CS336: Language Modeling from Scratch, Spring 2025 — Assignment 1: Basics.
It includes:
pre-tokenized TinyStories train/validation data,
pre-tokenized OpenWebText (OWT sample) train/validation data,
byte-level BPE vocabularies and merge tables for both datasets.
The main purpose of this repository is to avoid repeating the relatively expensive… See the full description on the dataset page: https://huggingface.co/datasets/victorhu493/cs336-basics-collection.BasicSpatialAbility
[ACL'25 Main] Defining and Evaluating Visual Language Models’ Basic Spatial Abilities: A Perspective from Psychometrics
[!IMPORTANT]
You can find the sample testing code on GitHub!
This dataset is a benchmark designed for evaluating Multimodal Large Language Models' Basic Spatial Abilities based on authentic Psychometric theories. It is structured specifically to support both Zero-shot and Few-shot evaluation protocols.
Split Name
Role
Description
test
Query Set… See the full description on the dataset page: https://huggingface.co/datasets/EmbodiedCity/BasicSpatialAbility.part2-basic_foldThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "hand",
"total_episodes": 7536,
"total_frames": 3353104,
"total_tasks": 24,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 30,
"splits": {
"train": "0:7536"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/gsethia08/part2-basic_fold.emilia-basic-snac-with-spk-emblol-basic-matches-challenger-10k
GPTilt: 10K League of Legends Challenger Matches
This dataset is part of the GPTilt open-source initiative, aimed at democratizing access to high-quality LoL data for research and analysis, fostering public exploration, and advancing the community's understanding of League of Legends through data science and AI. It provides detailed data from high-elo matches.
By using this dataset, users accept full responsibility for any consequences arising from its use. GPTilt assumes no… See the full description on the dataset page: https://huggingface.co/datasets/gptilt/lol-basic-matches-challenger-10k.basicreportbasic_3D_shapesbasic_turing_boxes_17This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so101_follower",
"total_episodes": 45,
"total_frames": 10595,
"total_tasks": 1,
"total_videos": 90,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:45"
},
"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/InsaneBrain14/basic_turing_boxes_17.W2H-Basic-Agent-Loop-w-Sandbox
W2H Basic Agent Loop with built in Linux Sandbox
A lightweight home agent that talks, runs code and takes actions in the real world. Access it from anywhere.
This is a vanilla Python agent loop that supports tools, skills, a microVM sandbox, encrypted data-in-transit and the Arduino microcontroller. The web UI includes voice, file uploads and slash commands. Designed for learning and experimentation. Use vibe coding to adapt it for different tasks.
Talk to the agent from… See the full description on the dataset page: https://huggingface.co/datasets/vbookshelf/W2H-Basic-Agent-Loop-w-Sandbox.seed-basic_terminal_tasksqwen35-math-rl-all
qwen35-math-rl-all — 데이터 · 루브릭 · 리워드 한 묶음
대상 모델 Qwen3.5-35B-A3B(thinking)로 후보 풀 63,528문제를 선별해, 그 모델이 풀다 말다 하는
문제만 남긴 GRPO 학습 케이스다. 구성은 BASIC-MATH/t01-q35-bin-v1 과 같다.
응답 상한을 케이스에 박지 않았다. 표본별 생성 토큰 수를 행에 담고 상한별 판정을 함께 넣었으니,
32K 로 학습하든 64K 로 학습하든 같은 키로 걸러 쓴다.
항목
값
데이터
qwen35_math_rl_all.jsonl 6,500행 (train 6,455 / val 45)
선별
롤아웃 K=4~8, temp 0.7 · top-p 0.95. 32K 패스 + 64K 재측정 패스 합산
루브릭
rubrics/rubric.jsonl 5,769행 — 중간값 기준(contains_value, reward, inject=false, rank 9)… See the full description on the dataset page: https://huggingface.co/datasets/BASIC-MATH/qwen35-math-rl-all.basic-math-operations
Mathematical Reasoning Dataset
A comprehensive dataset of mathematical problems with detailed step-by-step reasoning and Python verification, designed for training language models to perform mathematical computations with clear explanations.
Dataset Description
This dataset contains mathematical problems across 20 different operation types, each with detailed step-by-step reasoning that explains the mathematical concepts, handles edge cases, and includes Python code… See the full description on the dataset page: https://huggingface.co/datasets/mkurman/basic-math-operations.SWITCH-Basic-v1-open
SWITCH: Benchmarking Interaction and Verification on Real-World Interfaces in Lifelong Embodied Agents
⚠️ Dataset Note:
This repository hosts the 30% public subset of the full SWITCH-Basic v1 benchmark. It is intended for public exploration, preliminary evaluation, and community feedback.
Overview
SWITCH-Basic covers the collection and annotation of real-world Tangible Computer Interfaces (TCI) interaction data, which we systematically structure into five distinct… See the full description on the dataset page: https://huggingface.co/datasets/BAAI-Agents/SWITCH-Basic-v1-open.Basic-Math-Chinese-1M这是我做数学题的python脚本，做的可能不好，见谅
数学题包含了：
1.基础四则运算
2.一元一次方程
3.实际问题
联系方式：qq：2981447942
bilibili：一髅子Tick
Basic-Math-Chinese-1M-V1.1比较于上一个版本
·1.新增了乘方和开方（二次方根）的题目
·2.新增生成比例：
四则运算45%
一元一次方程30%
实际问题15%
乘方与开方10%
·3.新增四则运算变异：生成时有20%的几率在后面问“这个数（加，减，乘，除）a等于几？”（可堆叠）
联系方式：qq：2981447942
bilibili：一髅子Tick
gstest6basicsvmdtak-transnormer-basic-v1
Dataset Card for DTAK-transnormer-basic (v1.0)
Dataset Details
Dataset Description
DTAK-transnormer-basic is a modified subset of the DTA-Kernkorpus (Deutsches Textarchiv, German Text Archive Core Corpus).
It is a parallel corpus of German texts from the period between 1600 to 1899, that aligns sentences in historical spelling with their normalizations.
A normalization is a modified version of the original text that is adapted to modern spelling conventions.… See the full description on the dataset page: https://huggingface.co/datasets/textplus-bbaw/dtak-transnormer-basic-v1.