datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
recipes
🦛 Chonkie Recipes 🍳
Chonkie loves to cook up a storm in the kitchen
This repository contains all the recipes that you can use with Chonkie to manage various documents, languages, and more.
Usage
To use the recipes, you need to install chonkie with the hub feature, with the following command:
pip install "chonkie[hub]"
This would enable Hubie which is used internally to get the recipes from this repository. So, you can do things like use the from_recipe… See the full description on the dataset page: https://huggingface.co/datasets/feyninc/recipes.recommendationreclorhttps://whyu.me/reclor/
@inproceedings{yu2020reclor,
author = {Yu, Weihao and Jiang, Zihang and Dong, Yanfei and Feng, Jiashi},
title = {ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning},
booktitle = {International Conference on Learning Representations (ICLR)},
month = {April},
year = {2020}
}
Quranic-Recitation-Data
🌟 Overview
Quranic Recitation Dataset (Word-by-Word Sync) is a highly optimized, production-ready dataset containing high-quality audio recitations of the Holy Quran synchronized at the word-by-word level.
This dataset features 135 world-renowned reciters, with every Surah (114 chapters) mapped precisely to millisecond-accurate word timestamps. It is designed for modern Islamic mobile and web applications — served via a Cloudflare Edge CDN with native… See the full description on the dataset page: https://huggingface.co/datasets/zaibihassan/Quranic-Recitation-Data.RECAP-Libero10-Task0-48succ-DataLLaVA-ReCap-CC12Mcad-recode-v1.5
CAD-Recode: Reverse Engineering CAD Code from Point Clouds
CAD-Recode dataset is provided in form of Python (CadQuery) codes.
Train size is ~1M and validation size is ~1k.
CAD-Recode model and code are released at github https://github.com/filaPro/cad-recode.
And if you like it, give us a github 🌟.
Citation
If you find this work useful for your research, please cite our paper:
@misc{rukhovich2024cadrecode,
title={CAD-Recode: Reverse Engineering CAD Code from Point… See the full description on the dataset page: https://huggingface.co/datasets/filapro/cad-recode-v1.5.recursive-tasktrove-out
AnshKetchum/tasktrove-recursive-task-synthesis
agent-task-recursive-task-synthesis
Apptainer pool for hamishivi/agent-task-recursive-task-synthesis
This repository hosts tmax-compatible SIF images and a unified download manifest. Training data and task archives are in hamishivi/agent-task-recursive-task-synthesis. The manifest includes earlier images hosted under hamishivi and new images hosted under TMaxxx; the downloader selects the correct repository and immutable commit for each image.
Apptainer images
The pool currently contains 29,501 / 29… See the full description on the dataset page: https://huggingface.co/datasets/TMaxxx/agent-task-recursive-task-synthesis.Recap-DataComp-1B
Dataset Card for Recap-DataComp-1B
Recap-DataComp-1B is a large-scale image-text dataset that has been recaptioned using an advanced LLaVA-1.5-LLaMA3-8B model to enhance the alignment and detail of textual descriptions.
Dataset Details
Dataset Description
Our paper aims to bridge this community effort, leveraging the powerful and open-sourced LLaMA-3, a GPT-4 level LLM.
Our recaptioning pipeline is simple: first, we fine-tune a LLaMA-3-8B powered… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/Recap-DataComp-1B.recam-lerobotreclorLogical reasoning is an important ability to examine, analyze, and critically evaluate arguments as they occur in ordinary
language as the definition from LSAC. ReClor is a dataset extracted from logical reasoning questions of standardized graduate
admission examinations. Empirical results show that the state-of-the-art models struggle on ReClor with poor performance
indicating more research is needed to essentially enhance the logical reasoning ability of current models. We hope this
dataset could help push Machine Reading Comprehension (MRC) towards more complicated reasoninvilhyra-recapture-00DNN_Receipt_RecognitionVoRA-Recap-GLDv2-1.4MReClorDataDecide-data-recipes
More than one training run goes into making a large language model, but developers rarely release the small models and datasets they experiment with during the development process. How do they decide what dataset to use for pretraining or which benchmarks to hill climb on? To empower open exploration of these questions, we release DataDecide—a suite of models we pretrain on 25 corpora with differing sources, deduplication, and filtering up to 100B tokens, over 14 different model sizes ranging… See the full description on the dataset page: https://huggingface.co/datasets/allenai/DataDecide-data-recipes.records-aemet
Récords de temperatura por estación (AEMET)
Datos usados en https://records-temperatura.adrimaqueda.com/
JSONs ligeros con los récords de temperatura de las estaciones de AEMET,
derivados de las observaciones climatológicas diarias. Es la capa de datos que
consume directamente una app web (mapa + fichas de estación + agregados por
provincia); no es un dump crudo de observaciones.
Los datos proceden de AEMET (climatologías diarias de
OpenData). Este dataset es una elaboración… See the full description on the dataset page: https://huggingface.co/datasets/adrimaqueda/records-aemet.Imagenet21K_RecaptionThis dataset is the entire 21K ImageNet dataset with about 13 million examples and about 19 thousand classes as strings
(for some reason it only had ~19K classes instead of 21K).
If you want an even larger set of images, I have a recaptioned CC12M and ImageNet dataset: https://huggingface.co/datasets/gmongaras/CC12M_and_Imagenet21K_Recap
The images are in PNG format. They can be decoded like in the following example
import io
from PIL import Image
Image.open(io.BytesIO(row["image"]))
where… See the full description on the dataset page: https://huggingface.co/datasets/gmongaras/Imagenet21K_Recaption.RecreationBench
RecreationBench
GitHub · Website · Leaderboard · Report ↗
RecreationBench is a benchmark of 250 application-recreation tasks across Ubuntu, macOS, Windows, Android, and Web. It is the held-out evaluation suite of RecreationWorld, a framework for scalable and verifiable environments for hybrid computer-use agents.
Given a high-level recreation request and interactive access to a running reference application, an agent explores its behavior, implements a new application, and… See the full description on the dataset page: https://huggingface.co/datasets/Qwen/RecreationBench.B1k_Recovery
B1k_Recovery
Failure-recovery and demonstration data for BEHAVIOR-1K, in LeRobot v3.0
format, organised one subfolder per task.
B1k_Recovery/
└── task5/
└── Demo+Recovery+Success/ setting_mousetraps — human demos + nav + place + success
├── data/ 5,795 Parquet shards (chunk-000 … chunk-005)
├── videos/ 17,385 MP4 files (3 camera keys × 6 chunks)
└── meta/ info.json, tasks.parquet, episodes/, conversion_manifest.jsonl
Further tasks will be… See the full description on the dataset page: https://huggingface.co/datasets/fastwalker1118/B1k_Recovery.cad-recode
CAD-Recode: Reverse Engineering CAD Code from Point Clouds
CAD-Recode dataset is provided in form of Python (CadQuery) codes.
Train size is ~1M and validation size is ~1k.
CAD-Recode model and code are released at github https://github.com/filaPro/cad-recode.
And if you like it, give us a github 🌟.
Citation
If you find this work useful for your research, please cite our paper:
@misc{rukhovich2024cadrecode,
title={CAD-Recode: Reverse Engineering CAD Code from Point… See the full description on the dataset page: https://huggingface.co/datasets/filapro/cad-recode.ReCo-Data
ReCo-Data Dataset Card
Introduction
ReCo-Data is a large-scale, high-quality video editing dataset comprising 500K+ instruction-video pairs. This card provides its statistics, collection pipeline, and dataset format.
1. Dataset Statistics
Statistics
Figure Caption:
(a) Overview of scale
(b) Task distribution showing balanced quantities: Replace (156.6K), Style (130.6K), Remove (121.6K), and Add (115.6K). Human evaluation on 200 randomly… See the full description on the dataset page: https://huggingface.co/datasets/HiDream-ai/ReCo-Data.synthetic-ocr-en-det-rec-120k
Synthetic English OCR Detection and Recognition 240K
📌 Current dataset size: 240,000 paired OCR samples
The current v2.0 release contains exactly 240,000 detector images and
240,000 matching recognition crops.
Each sample ID corresponds to:
one full image for text detection;
one cropped text image for text recognition;
one detector JSONL record;
one recognizer JSONL record.
Therefore, the dataset contains 240,000 aligned OCR pairs and
480,000 JPEG files in… See the full description on the dataset page: https://huggingface.co/datasets/Phitran21/synthetic-ocr-en-det-rec-120k.Tarsier2-Recap-585K
Dataset Card for Tarsier2-Recap-585K
Introduction
✨Tarsier2-Recap-585K✨ consists of 585K distinct video clips, lasting for 1972 hours in total, from open-source datasets (e.g. VATEX, TGIF, LSMDC, etc.) and each one with a detailed video description annotated by Tarsier2-7B, which beats GPT-4o in generating detailed and accurate video descriptions for video clips of 5~20 seconds (See the DREAM-1K Leaderboard). Experiments demonstrate its effectiveness in enhancing the… See the full description on the dataset page: https://huggingface.co/datasets/omni-research/Tarsier2-Recap-585K.epstractor-raw
Epstractor: Epstein Archives Dataset
A comprehensive archive of documents, images, audio, and video files from multiple Epstein-related releases, including estate records and Department of Justice materials obtained through FOIA requests.
Dataset Description
This dataset contains 59,420 files totaling 115.23 GB from three major document releases, plus 2 large videos (40GB) available via a separate config:
Epstein Estate 2025-09: 5 files, 0.09 GB
Epstein Estate 2025-11:… See the full description on the dataset page: https://huggingface.co/datasets/public-records-research/epstractor-raw.JFK-Assassination-Records-2025-Documents-Releasehnm-fashion-recommendations-data
Dataset Rekomendasi Fashion H&M
Dataset ini berisi data transaksi, atribut pelanggan, dan metadata produk yang telah dianonimkan dari H&M Group. Kumpulan data komprehensif ini memungkinkan pemodelan perilaku pembelian pelanggan secara mendalam.
Wawasan yang dihasilkan dapat dimanfaatkan untuk berbagai tujuan bisnis yang strategis, mulai dari meningkatkan personalisasi pengalaman berbelanja, mengoptimalkan manajemen inventaris untuk efisiensi produksi, hingga mendukung inisiatif… See the full description on the dataset page: https://huggingface.co/datasets/einrafh/hnm-fashion-recommendations-data.record-pick-and-place-pos5-so101This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "so101_follower",
"total_episodes": 240,
"total_frames": 119443,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 500,
"fps": 30,
"splits": {
"train": "0:240"
},
"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/hbseong/record-pick-and-place-pos5-so101.funes-handoff-recall-benchmark
handover-vs-recall
A long investigation bloats an agent session until each new turn costs more to carry the context than to
do the work. Switching to a fresh session avoids that — but the findings have to travel somehow, and the
ways of moving them differ in cost. This benchmark measures those ways, as cost per successful task,
on tasks that genuinely require the prior investigation:
arm
channel
A branch-only
switch, carry nothing — the fresh session re-derives the… See the full description on the dataset page: https://huggingface.co/datasets/dacorvo/funes-handoff-recall-benchmark.