datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
PMC
Data collected from PMC
Only CC-BY, CC-BY-SA licenses are included.
For all records, check the jsonl files in the data folder
hlebabilong-1k-samples
BABILong (1000 samples) : a long-context needle-in-a-haystack benchmark for LLMs
Preprint is on arXiv and code for LLM evaluation is available on GitHub.
BABILong Leaderboard with top-performing long-context models.
bAbI + Books = BABILong
BABILong is a novel generative benchmark for evaluating the performance of NLP models in
processing arbitrarily long documents with distributed facts.
It contains 9 configs, corresponding to different sequence lengths in tokens: 0k… See the full description on the dataset page: https://huggingface.co/datasets/RMT-team/babilong-1k-samples.GameQA-140K
[ICLR 2026] Game-RL: Synthesizing Multimodal Verifiable Game Data to Boost VLMs' General Reasoning
🎊 News
[2026/07] 🔥Peking University and Kuaishou Kling Team evaluate their agentic visual reasoning method Beacon on our GameQA benchmark. Beacon learns when tools are truly needed (Mode Adaptiveness) and how tool use extends capability on hard problems (Tool Effect), and achieves the highest accuracy on GameQA among open-source models of the same scale… See the full description on the dataset page: https://huggingface.co/datasets/OpenMOSS-Team/GameQA-140K.rendered-wikipedia-english
Dataset Card for Team-PIXEL/rendered-wikipedia-english
Dataset Summary
This dataset contains the full English Wikipedia from February 1, 2018, rendered into images of 16x8464 resolution.
The original text dataset was built from a Wikipedia dump. Each example in the original text dataset contained the content of one full Wikipedia article with cleaning to strip markdown and unwanted sections (references, etc.). Each rendered example contains a subset of one full article.… See the full description on the dataset page: https://huggingface.co/datasets/Team-PIXEL/rendered-wikipedia-english.hle-extractteam_ozaki_submit1hle_labeled-v1.0
HLE Labeled Dataset
このデータセットは「Humanity’s Last Exam」ベンチマーク用のデータセットの
categoryにsubcategoryを追加したものです。subcategoryの分類ラベルはqwen/qwen3-235b-a22bで生成しています。
モデルのカテゴリ別の評価に利用するのが目的です。
データ構造
変更点はもともとのHLEにsubcategoryフィールド追加したのみです。
id: レコードのユニークID
question: 問題文（文字列）
answer: 正解
answer_type: "exactMatch" などの解答形式
rationale: 解答手順・根拠
category: 大分類（例: "Math"）
subcategory: 小分類のリスト（例: ["Math/Number Theory","Math/Discrete Mathematics"]）
image, image_preview, rationale_image:… See the full description on the dataset page: https://huggingface.co/datasets/LLMcompe-Team-Watanabe/hle_labeled-v1.0.RMC-AIDA-L_pour_tea
RMC-AIDA-L_pour_tea
📋 Overview
This dataset uses an extended format based on LeRobot and is fully compatible with LeRobot.
Robot Type: realman_rmc_aidal
| Codebase Version: v2.1
End-Effector Type: two_finger_gripper
🏠 Scene Types
This dataset covers the following scene types:
home
restaurant
🤖 Atomic Actions
This dataset includes the following atomic actions:
grasp
place
pick
pour
📊 Dataset Statistics… See the full description on the dataset page: https://huggingface.co/datasets/RoboCOIN/RMC-AIDA-L_pour_tea.Agilex_Cobot_Magic_pour_tea
Split_aloha_pour_tea
📋 Overview
This dataset uses an extended format based on LeRobot and is fully compatible with LeRobot.
Robot Type: agilex_cobot_decoupled_magic
| Codebase Version: v2.1
End-Effector Type: two_finger_gripper
🏠 Scene Types
This dataset covers the following scene types:
home
office
🤖 Atomic Actions
This dataset includes the following atomic actions:
grasp
place
pick
pour
📊 Dataset Statistics… See the full description on the dataset page: https://huggingface.co/datasets/RoboCOIN/Agilex_Cobot_Magic_pour_tea.oercommons-v1-optimized
OERCommons v1 Optimized
Authors: Junjie Wang and Yuhan SunHosted by: PIN TeamDataset: pin-team/oercommons-v1-optimized
OERCommons v1 Optimized is a provenance-preserving multimodal pretraining corpus built on the OERCommons subset of The Common Pile v0.1, which serves as its upstream data and licensing baseline. We extend it with full-page recovery, canonical Markdown, ordered image/PDF/link metadata, conservative corrections, and integrity evidence.
At a glance… See the full description on the dataset page: https://huggingface.co/datasets/pin-team/oercommons-v1-optimized.R1_Lite_make_tea
R1_Lite_make_tea
📋 Overview
This dataset uses an extended format based on LeRobot and is fully compatible with LeRobot.
Robot Type: galaxea_r1_lite
| Codebase Version: v2.1
End-Effector Type: two_finger_gripper
🏠 Scene Types
This dataset covers the following scene types:
home
restaurant
🤖 Atomic Actions
This dataset includes the following atomic actions:
grasp
pick
place
📊 Dataset Statistics
Metric
Value… See the full description on the dataset page: https://huggingface.co/datasets/RoboCOIN/R1_Lite_make_tea.gsm8k-v0.4.1The dataset is generated based on verl 0.4.1 with command:
python3 examples/data_preprocess/gsm8k.py
LibriTTS
Usage
from datasets import load_dataset
dataset = load_dataset('TeamSODA/LibriTTS', streaming=True)
ndlj_tosho_1
国会図書館に収蔵される著作権切れのデータです
FINSABER-V2-Data
FINSABER V2 Data
FINSABER V2 Data is the parquet dataset used by the upgraded FINSABER-2 backtesting framework. It contains S&P 500 daily market data, financial news items, and SEC filing text organized by year for reproducible financial strategy research.
Dataset Structure
The dataset is partitioned by modality and year:
price_daily/year=<YYYY>/part-000.parquet
news_items/year=<YYYY>/part-000.parquet
filingk/year=<YYYY>/part-000.parquet… See the full description on the dataset page: https://huggingface.co/datasets/finsaber-team/FINSABER-V2-Data.Scale-SWE-Distilled
Immersion in the GitHub Universe: Scaling Coding Agents to Mastery
🔥 Highlights
Source from 6M+ pull requests and 23000+ repositories.
Cover 5200 Repositories.
100k high-quality instances.
71k trajectories from DeepSeek v3.2 with 3.5B token.
Strong performance: 64% in SWE-bench-Verified trained from Qwen3-30A3B-Instruct.
📣 News
2026-02-26 🚀 We released a portion of our data on Hugging Face. This release includes 20,000 SWE task… See the full description on the dataset page: https://huggingface.co/datasets/AweAI-Team/Scale-SWE-Distilled.R1_Lite_tea_service_table_setting
R1_Lite_tea_service_table_setting
📋 Overview
This dataset uses an extended format based on LeRobot and is fully compatible with LeRobot.
Robot Type: galaxea_r1_lite
| Codebase Version: v2.1
End-Effector Type: two_finger_gripper
🏠 Scene Types
This dataset covers the following scene types:
home
🤖 Atomic Actions
This dataset includes the following atomic actions:
grasp
pick
place
📊 Dataset Statistics
Metric… See the full description on the dataset page: https://huggingface.co/datasets/RoboCOIN/R1_Lite_tea_service_table_setting.teacher-embedding-corpusVideoThinkBench
[CVPR 2026] Thinking with Video: Video Generation as a Promising Multimodal Reasoning Paradigm
🎊 News
[2026.02] 🔥🔥Our work has been accepted by CVPR 2026! 🎉🎉🎉
[2025.11] Our paper "Thinking with Video: Video Generation as a Promising Multimodal Reasoning Paradigm" has been released on arXiv! 📄 [Paper] On HuggingFace, it has achieved "#1 Paper of the Day"!
[2025.11] 🔥We release "minitest" of our VideoThinkBench, including 500… See the full description on the dataset page: https://huggingface.co/datasets/OpenMOSS-Team/VideoThinkBench.adapter-based-multimodal-fusion
Falcon-Audio Training Dataset
Training-ready Parquet shards for Falcon-Audio. Rows contain Gemma-tokenized inputs/labels and fp16 Whisper encoder features encoded as raw bytes.
brain-teasersYuLan-Mini-Datasets-Phasae-26The tokenized datasets for YuLan-Mini phase 26, where each line has been packed to 28K tokens.
Usage
dataset = []
dataset_path = "/path/to/YuLan-Mini-Datasets-Phasae-26"
seed = 42
for data_name in sorted(os.listdir(dataset_path)):
d = load_dataset(
os.path.join(dataset_path, data_name),
split="train",
num_proc=8,
)
dataset.append(d)
print(f"Num subsets: {len(dataset)}")
dataset = concatenate_datasets(dataset).shuffle(seed=seed)… See the full description on the dataset page: https://huggingface.co/datasets/yulan-team/YuLan-Mini-Datasets-Phasae-26.rendered-bookcorpus-bigramsrendered-bookcorpus
Dataset Card for Team-PIXEL/rendered-bookcorpus
Dataset Summary
This dataset is a version of the BookCorpus available at https://huggingface.co/datasets/bookcorpusopen with examples rendered as images with resolution 16x8464 pixels.
The original BookCorpus was introduced by Zhu et al. (2015) in Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books and contains 17868 books of various genres. The rendered BookCorpus was used… See the full description on the dataset page: https://huggingface.co/datasets/Team-PIXEL/rendered-bookcorpus.so101_tea2This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so101",
"total_episodes": 101,
"total_frames": 78833,
"total_tasks": 1,
"total_videos": 202,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:101"
},
"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/Cornito/so101_tea2.PIXELSum_en_wiki_for_TAgreek_legal_code
Dataset Card for Greek Legal Code
Dataset Summary
Greek_Legal_Code (GLC) is a dataset consisting of approx. 47k legal resources from Greek legislation. The origin of GLC is “Permanent Greek Legislation Code - Raptarchis”, a collection of Greek legislative documents classified into multi-level (from broader to more specialized) categories.
Topics
GLC consists of 47 legislative volumes and each volume corresponds to a main thematic topic. Each volume is divided into… See the full description on the dataset page: https://huggingface.co/datasets/AI-team-UoA/greek_legal_code.team2-guess_who_so100_lightThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so100",
"total_episodes": 96,
"total_frames": 19853,
"total_tasks": 1,
"total_videos": 96,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:96"
},
"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/roboticshack/team2-guess_who_so100_light.japanese2010
日本語ウェブコーパス2010
こちらのデータをhuggingfaceにアップロードしたものです｡
2009 年度における著作権法の改正（平成21年通常国会 著作権法改正等について | 文化庁）に基づき，情報解析研究への利用に限って利用可能です｡
形態素解析を用いて､自動で句点をつけました｡
変換コード
変換スクリプト
形態素解析など