datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
gneissweb-annotation-url-testing-v1
GneissWeb Annotations
GneissWeb Annotations, powered by IBM Research's GneissWeb methodology, is a dataset of quality and category annotations applied to the Common Crawl corpus.
This dataset enables precise filtering of web content across medical, educational, technology, and scientific domains, making it easier to build high-quality corpora for research projects, language models, and specialized applications.
Learn more about the annotation process and methodology in our… See the full description on the dataset page: https://huggingface.co/datasets/commoncrawl/gneissweb-annotation-url-testing-v1.propella-annotations
This dataset contains document annotations produced with propella-1-4b, a small multilingual LLM that annotates text documents across six categories: core content, classification, quality & value, audience & purpose, safety & compliance, and geographic relevance. The annotations can be used to filter, select, and curate LLM training data at scale.
Properties
Each document is annotated across 18 properties organized into six categories:
Category
Property
Description… See the full description on the dataset page: https://huggingface.co/datasets/openeurollm/propella-annotations.Truebones-ZOO-Annotations
Truebones ZOO Annotations
Text prompts, per-clip metadata, rest-pose renders and the exact build pipeline for
Truebones ZOO — 1,097 animal motion clips across 74 skeletons: mammals, birds,
reptiles, insects, marine and prehistoric creatures. 1.02 hours, 111,158 frames, uniformly
30 fps. Rigs range from 9 to 143 joints; clips from 0.3 to 18.5 seconds.
The motion files themselves are not in this repository. Truebones ZOO is a commercial
library by Truebones Motions Animation… See the full description on the dataset page: https://huggingface.co/datasets/tanish434/Truebones-ZOO-Annotations.audiosnippets_small_with_detailed_annotationaudiosnippets_small_with_detailed_annotation2beat2-additional-annotations
BEAT2 Official Release + Additional Annotations
This is a fork of H-Liu1997/BEAT2
that adds annotations contributed by the
RAG-Gesture (CVPR 2025)
and MIBURI (CVPR 2026) projects.
The base BEAT2-English data (motion, audio, TextGrids, semantic labels,
pretrained motion-autoencoder weights) is inherited verbatim from upstream;
the additional annotations from RAG-Gesture and MIBURI are pushed on top.
Citations
If you use only the original BEAT2 dataset, please cite… See the full description on the dataset page: https://huggingface.co/datasets/m-hamza-mughal/beat2-additional-annotations.gsd-humaneval-annotationsTruebones-ZOO-Annotations
Truebones ZOO Annotations
Text prompts, per-clip metadata, rest-pose renders and the exact build pipeline for
Truebones ZOO — 1,097 animal motion clips across 74 skeletons: mammals, birds,
reptiles, insects, marine and prehistoric creatures. 1.02 hours, 111,158 frames, uniformly
30 fps. Rigs range from 9 to 143 joints; clips from 0.3 to 18.5 seconds.
The motion files themselves are not in this repository. Truebones ZOO is a commercial
library by Truebones Motions Animation… See the full description on the dataset page: https://huggingface.co/datasets/Linzhan/Truebones-ZOO-Annotations.hpltv2-llama33-edu-annotation
HPLT version 2.0 educational annotations
This dataset contains annotations derived from HPLT v2 cleaned samples.
There are 500,000 annotations for each language if the source contains at least 500,000 samples.
We prompt Llama-3.3-70B-Instruct to score web pages based on their educational value following FineWeb-Edu classifier.
Note 1: The dataset contains the prompt (using the first 1500 characters of the text sample), the scores, and the full Llama 3 generation. The column "idx"… See the full description on the dataset page: https://huggingface.co/datasets/LumiOpen/hpltv2-llama33-edu-annotation.ProcVQA-20M-annotations
ProcVQA-20M Annotations
Project Page |
arXiv |
Code |
Model |
Media
This repository contains the text annotations for the ProcVQA-20M dataset. The full image files are hosted separately on ProcVQA-20M-media.
Overview
This dataset is constructed from over 26 embodied datasets, comprising:
20M QA pairs for training
330K original trajectories
50M annotated frames from ~5,000 hours of manipulation data
200+ different tasks
Dataset Structure
The… See the full description on the dataset page: https://huggingface.co/datasets/ce-amtic/ProcVQA-20M-annotations.Soofi-sft-annotation-filteredJQL-LLM-Edu-Annotations
📚 JQL Educational Quality Annotations from LLMs
This dataset provides 17,186,606 documents with high-quality LLM annotations for evaluating the educational value of web documents, and serves as a benchmark for training and evaluating multilingual LLM annotators as described in the JQL paper.
📝 Dataset Summary
Multilingual document-level quality annotations scored on a 0–5 educational value scale by three state-of-the-art LLMs:
Gemma-3-27B-it, Mistral-3.1-24B-it… See the full description on the dataset page: https://huggingface.co/datasets/JQL-AI/JQL-LLM-Edu-Annotations.Emilia-with-Emotion-Annotations4seamless-interaction-jefferson-annotations
Seamless Interaction Jefferson-Style Annotations
An automatic, turn-oriented annotation layer for the
Meta Seamless Interaction Dataset.
It compares the dataset's traditional transcript with an ASR-derived
Jefferson-style condition and supplies speech-act, communicative-purpose,
interactional-signal, alignment, and quality fields.
This is a derived noncommercial research dataset. It does not redistribute
the source audio. Every record retains the original interaction ID, split… See the full description on the dataset page: https://huggingface.co/datasets/kennethli319/seamless-interaction-jefferson-annotations.Drone-Orthomosaic-Vehicles-Yolo-annotation
Dataset Tailings Mining Vehicles & Instruments (High-Res Drone Imagery)
Dataset Summary
This dataset contains high-resolution aerial imagery focused on vehicle detection and geotechnical monitoring instruments within active mining environments (tailings dams). The data was acquired using a DJI Zenmuse P1 sensor at 120m altitude.
Photogrammetric Context
The images originate from large-scale georeferenced orthomosaics generated from bi-daily… See the full description on the dataset page: https://huggingface.co/datasets/titoruizh/Drone-Orthomosaic-Vehicles-Yolo-annotation.python-edu-annotations
Annotations for 📚 Python-Edu classifier
This dataset contains the annotations used for training Python-Edu educational quality classifier. We prompt Llama-3-70B-Instruct to score python programs from StarCoderData based on their educational value.
Note: the dataset contains the Python program, the prompt (using the first 1000 characters of the program) and the scores but it doesn't contain the full Llama 3 generation.
Emilia-with-Emotion-Annotations5GUI-Net-1M-relative-annotationsvast27m_annotations
VAST-27M Annotations Dataset
This dataset contains annotations from the VAST-27M dataset, originally created for the paper "VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset" by Chen et al. (2024).
Original Source
This dataset is derived from the VAST-27M dataset, which was created by researchers at the University of Chinese Academy of Sciences and the Institute of Automation, Chinese Academy of Science. The original dataset and more… See the full description on the dataset page: https://huggingface.co/datasets/it-just-works/vast27m_annotations.gneissweb-annotation-host-testing-v1
GneissWeb Annotations
GneissWeb Annotations, powered by IBM Research's GneissWeb methodology, is a dataset of quality and category annotations applied to the Common Crawl corpus.
This dataset enables precise filtering of web content across medical, educational, technology, and scientific domains, making it easier to build high-quality corpora for research projects, language models, and specialized applications.
Learn more about the annotation process and methodology in our… See the full description on the dataset page: https://huggingface.co/datasets/commoncrawl/gneissweb-annotation-host-testing-v1.od-syn-page-annotations-com
📦 Dhivehi Synthetic Document Layout + Textline Dataset
This dataset contains synthetically generated image-document pairs with detailed layout annotations and ground-truth Dhivehi text extractions.It’s designed for document layout analysis, visual document understanding, OCR fine-tuning, and related tasks specifically for Dhivehi script.
Note: this version image are compressed.
Raw version 📁 Repository: Hugging Face Datasets
📋 Dataset Summary
Total Examples: ~58… See the full description on the dataset page: https://huggingface.co/datasets/alakxender/od-syn-page-annotations-com.fineweb-edu-llama3-annotations
Annotations for 📚 FineWeb-Edu classifier
This dataset contains the annotations used for training 📚 FineWeb-Edu educational quality classifier. We prompt Llama-3-70B-Instruct to score web pages from 🍷 FineWeb based on their educational value.
Note: the dataset contains the FineWeb text sample, the prompt (using the first 1000 characters of the text sample) and the scores but it doesn't contain the full Llama 3 generation.
highlevel_thinking_with_grounding_annotation_split1000_v3VR-egodex-annotation-converted-v6.0
VR-egodex-annotation-converted-v6.0
EgoDex converted from LeRobot v2.1 into the Layer-1 v0.6.0 annotation schema, with
per-clip narration included as language sidecars.
314,839 clips · 78,282,306 frames · 724.8 hours @ 30 fps · 129 tasks
100% narration coverage (1 sidecar per clip)
71 GB annotations + 2.3 GB narratives
Videos are NOT included. This release contains annotations and narration only. Source
video lives in griffinlabs/EgoDex-LeRobot-v3.0;
orig_id in the manifest… See the full description on the dataset page: https://huggingface.co/datasets/VR-VLA/VR-egodex-annotation-converted-v6.0.SWE-bench_Verified_With_Annotationsod-syn-page-annotations
📦 Dhivehi Synthetic Document Layout + Textline Dataset
This dataset contains synthetically generated image-document pairs with detailed layout annotations and ground-truth Dhivehi text extractions.It’s designed for document layout analysis , visual document understanding , OCR fine-tuning, and related tasks specifically for Dhivehi script.
📋 Dataset Summary
Total Examples: ~58,738
Image Content: Synthetic Dhivehi documents generated to simulate real-world layouts… See the full description on the dataset page: https://huggingface.co/datasets/alakxender/od-syn-page-annotations.Emilia-with-Emotion-Annotations3LocateAnything-Data-ShareGPT-Annotationhighlevel_thinking_with_grounding_annotation_split1000_v3_merged_promptsmerge_annotations_self_and_swegym