datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.beat2-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.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/Linzhan/Truebones-ZOO-Annotations.annotation_app_data
Dataset Card for Systematic Review of Acceptability Judgments data
A curated dataset of research articles used in a systematic review of judgment tasks in linguistics. Each entry records article-level metadata and experiment-level methodological features, supporting structured comparison and analysis across studies.
Dataset Description
This annotation dataset comprises systematically coded observations from a corpus of published studies employing judgment tasks in… See the full description on the dataset page: https://huggingface.co/datasets/jasongraf1/annotation_app_data.Crypto-Address-Annotation-10K
Codatta Crypto Address Annotations (Sample)
Overview
This dataset is a 10,000-row sample of the comprehensive Codatta Crypto Address Annotations database. The full database serves as a massive repository of over 500 million labeled address pairs across multiple blockchains.
The data provides critical metadata aimed at solving the problem of fragmented and siloed blockchain information. It includes entity names, functional categories (e.g., Exchanges, DeFi, Scam)… See the full description on the dataset page: https://huggingface.co/datasets/Humanbased-AI/Crypto-Address-Annotation-10K.emboss-roof-annotations
Emboss 3D Roof Reference Annotations
Manual 3D reference meshes and editable annotations for Swiss and Brazilian buildings, prepared for the evaluation and parameter tuning of Emboss. The annotations describe building and roof geometry, including roof superstructures.
Emboss source code
3dlabel annotation tool
Emboss segmentation model
Example reference annotation in 3dlabel: annotated mesh and LiDAR points (Figure D.1(a) in the paper).
youtube_annotations_text
Youtube Annotations Text
YouTube 注释(YouTube Annotations)是 YouTube 在 2008 年推出的一项功能,
允许视频创作者在视频上添加文本、链接和互动元素, 以增强观众的观看体验.
YouTube 已在 2019 年删除了此功能.
您可以在这里找到由 omarroth 创建的存档 YouTube Annotations,
本数据集从13亿条存档中提取出了文本.
如果您需要 x_id 与 videoId 的映射, 请使用 utilities/video_text_mapping_indexed.sqlite3 数据库.
steam-reviews-constructiveness-binary-label-annotations-1.5k
1.5K Steam Reviews Binary Labeled for Constructiveness
Dataset Summary
This dataset contains 1,461 Steam reviews from 10 of the most reviewed games. Each game has about the same amount of reviews. Each review is annotated with a binary label indicating whether the review is constructive or not. The dataset is designed to support tasks related to text classification, particularly constructiveness detection tasks in the gaming domain.
Also available as… See the full description on the dataset page: https://huggingface.co/datasets/abullard1/steam-reviews-constructiveness-binary-label-annotations-1.5k.SWE-bench_Verified_With_AnnotationsCounselBench-Adv-human-annotationINVOICE_ANNOTATION_V2lidc-idri-text-annotations
🩺 LIDC-IDRI Text-Annotated
We release a text-annotated version of the LIDC-IDRI dataset, where each annotation is carefully curated from structured metadata provided by radiologists (e.g., malignancy, size, shape, margin, texture, spiculation, etc.).
This enables new research directions in:
Multi-modal learning (image + text)
Text-guided medical image segmentation
Includes
Radiologist Nodule annotations (radiologist contours, malignancy scores)
Natural language… See the full description on the dataset page: https://huggingface.co/datasets/siddharthdhara17/lidc-idri-text-annotations.samuelbullard_steam-reviews-constructiveness-annotations-1-5k
steam-reviews-constructiveness-annotations-1.5k
Mirror of the Kaggle dataset samuelbullard/steam-reviews-constructiveness-annotations-1-5k by Samuel Bullard, released under MIT. All credit goes to the original author; please cite and link the Kaggle page when using this data.
1,461 Steam game-reviews annotated for constructiveness.
License
MIT License, Copyright (c) Samuel Bullard. The full license text is in LICENSE and applies to all files in this repository.… See the full description on the dataset page: https://huggingface.co/datasets/haoxianc/samuelbullard_steam-reviews-constructiveness-annotations-1-5k.tweet-annotation-sensitivity-2
Tweet Annotation Sensitivity Experiment 2: Annotations in Five Experimental Conditions
Attention: This repository contains cases that might be offensive or upsetting. We do not support the views expressed in these hateful posts.
Description
The dataset contains tweet data annotations of hate speech (HS) and offensive language (OL) in five experimental conditions. The tweet data was sampled from the corpus created by Davidson et al. (2017). We selected 3,000 Tweets for our… See the full description on the dataset page: https://huggingface.co/datasets/soda-lmu/tweet-annotation-sensitivity-2.llm-delusion-response-annotations
LLM Delusion-Like Belief Reinforcement Annotations
This dataset contains human annotations of responses generated by conversational large language models (LLMs) to prompts expressing potentially delusion-like or reality-distorted beliefs.
The purpose of the dataset is to support evaluation of whether conversational LLM responses may unintentionally reinforce or strengthen delusion-like beliefs.
Dataset Files
Consensus Dataset… See the full description on the dataset page: https://huggingface.co/datasets/vennu95/llm-delusion-response-annotations.annotation-pack-a
Annotation pack A — does a response genuinely follow an instruction?
50 rows. Each row: a user prompt, one instruction from it, and a model response that an automatic checker
marks as satisfying that instruction. Annotators judge whether it is satisfied genuinely.
For annotators / 标注人:
Read RUBRIC_HUMAN.md (English + 中文).
Annotator A downloads annotator_A.csv; annotator B downloads annotator_B.csv (same items).
Fill label (GENUINE / LOOPHOLE / GARBLED), helpfulness (1–5)… See the full description on the dataset page: https://huggingface.co/datasets/LawrenceYin/annotation-pack-a.GTSinger-EN-Annotations
These customized annotation files are based on, and curated for, the English partition of the GTSinger dataset.
The annotation files were created for singing-oriented forced alignment experiments in our SynthGT project.
The original GTSinger annotations have been processed by removing stress markers, lowerchasing phonemes, and substituting all instances of AP with SP, to share the same vocabulary as our SynthGT dataset.
The annotations are distributed in train/valid/test splits as .csv… See the full description on the dataset page: https://huggingface.co/datasets/Silasimo/GTSinger-EN-Annotations.gemba_annotation
GEMBA Annotation Dataset
Description
This dataset contains human and machine-generated Multidimensional Quality Metrics (MQM) annotations for machine-translated Thai text. It is intended for evaluating and comparing MT system outputs and error annotation quality across different automated models. The dataset features annotations from three large language models (LLMs): Claude 3.7 Sonnet, Gemini 2.0 Flash, and 4o Mini, alongside human annotations, providing a comprehensive… See the full description on the dataset page: https://huggingface.co/datasets/wasanx/gemba_annotation.legal-reference-annotationsIn this dataset, we present a dataset of 2944 legal references in German law that are manually annotated by law experts. This dataset has 21 properties for each law reference in the dataset, such as Buch, Teil, Titel, Untertitel, etc. It also provides the complete text of each law reference in the dataset, along with specific paragraph text mentioned in the law reference.
Paper: A Dataset of German Legal Reference Annotations
Please reference our work when using this dataset:… See the full description on the dataset page: https://huggingface.co/datasets/PaDaS-Lab/legal-reference-annotations.annotation-pack-b
Annotation pack B — are any words forced into the text?
100 short texts (60 web-style excerpts, 40 one-sentence news summaries) written by small language models.
Annotators judge whether any word looks forced in.
For annotators / 标注人:
Read RUBRIC_HUMAN.md.
Annotator A downloads annotator_A.csv; annotator B downloads annotator_B.csv (same items).
Fill label (GENUINE / LOOPHOLE / GARBLED), wrong_sense (Y/N), fluency (1–5), flagged_words, optional notes. Work alone.
Send the… See the full description on the dataset page: https://huggingface.co/datasets/LawrenceYin/annotation-pack-b.INVOICE_ANNOTATION_V1gsl-multimodal-annotation
GSL Multimodal Annotation Dataset
Multimodal annotation of 20 signs from Ghanaian Sign Language (GSL),
capturing manual and non-manual phonological features across 14 columns
including handshape, location, movement, facial expression, mouth
pattern, and head movement.
Dataset description
This dataset accompanies a pilot Linked Data representation of GSL
(DOI: 10.5281/zenodo.20961293). It documents the annotation decisions,
uncertainties, and limitations… See the full description on the dataset page: https://huggingface.co/datasets/LINGUISTEUNICE/gsl-multimodal-annotation.abortion_definitions_annotations
Dataset of plausibility and stance annotations of the generated definitions.
The dataset was produced as part of the annotation study described in the paper:
Stance-aware Definition Generation for Argumentative Texts.
The dataset can be used for studies in the plausibility and stance evaluation of the generated output.
This dataset contains only arguments and definitions on the topic of abortion.
The dataset contains an original argument, the stance of the original argument, the… See the full description on the dataset page: https://huggingface.co/datasets/LT3/abortion_definitions_annotations.llm-delusion-response-annotations
LLM Delusion-Like Belief Reinforcement Annotations
This dataset contains human annotations of responses generated by conversational large language models (LLMs) to prompts expressing potentially delusion-like or reality-distorted beliefs.
The purpose of the dataset is to support evaluation of whether conversational LLM responses may unintentionally reinforce or strengthen delusion-like beliefs.
Dataset Files
Consensus Dataset… See the full description on the dataset page: https://huggingface.co/datasets/ManjuKrish/llm-delusion-response-annotations.WAMA-West-African-Marketing-Annotation-Dataset
license: cc-by-4.0
task_categories:
text-classification
text-generation
language:
en
pcm
tags:
marketing
west-africa
nigeria
ghana
consumer-psychology
trust-signals
nigerian-english
ghanaian-english
nigerian-pidgin
cultural-bias
ai-alignment
annotation
fintech
brand-strategy
underrepresented
africa
size_categories:
n<1K
WAMA West African Marketing Annotation Dataset
Version: 1.0Entries: 300Countries: Nigeria (165 entries), Ghana (135 entries)Created by: Deborah John Digital… See the full description on the dataset page: https://huggingface.co/datasets/Debbyjaye001/WAMA-West-African-Marketing-Annotation-Dataset.allaimovies-annotations
allaimovies overview annotations
The raw per-film codings behind the allaimovies dataset: 3,092 science-fiction films
(1911-2026) whose TMDB plot overview was read by gpt-5.4-mini against a fixed rubric (below) with
schema-enforced JSON output. 2,069 were coded as having an AI present. This table is the
model's output as collected, one row per film, before it was joined with reception, credits and
character data; use it if you want to re-check, re-code or compare against another… See the full description on the dataset page: https://huggingface.co/datasets/prateek-0-gupta/allaimovies-annotations.EDUMATH_annotations
EDUMATH Annotation Dataset
The EDUMATH Annotation Dataset contains 3,012 math word problems annotated by teachers and Gemma 3 27B IT as reported in EDUMATH: Generating Standards-aligned Educational Math Word Problems. The dataset contains the final labels from human annotators in the solvability, accuracy, appropriateness, and standards_alignment columns along with the final label for Meets all Criteria (MaC), which was assigned as described in the paper. The model_labels and… See the full description on the dataset page: https://huggingface.co/datasets/bryanchrist/EDUMATH_annotations.FER2013-VAD-annotationThis dataset involves train-20240123-14902.csv, publictest-20240508.csv and privatetest-20240506-yh.csv, which could be used for public and private test respectively. 14902, 1298 and 3589 samples are for train, public and private dataset at present.
Data_AnnotationSMS_ANNOTATION_V1