datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MultiHumanCarRacing
MultiHumanCarRacing
Documentation: https://github.com/NaOH12/RacingSentimentAnalysis
(meta_data folder is not available since it is being flagged as unsafe. This can be generated/modified with sample_builder.py)
Dataset and code are free to use. Please consider reaching out if you are hiring! 😊
multi_domain_ai_human_text
multi_domain_ai_human_text — Datasheet
Balanced, multi-domain AI-vs-human text detection benchmark with dedicated
out-of-distribution and adversarial evaluation panels. Built by
scripts/build_paper_dataset.py from an 11-corpus unified aggregation.
Splits
Split
AI
Human
Total
Purpose
train
300,000
300,000
600,000
training (balanced, English, clean)
validation
2,996
2,999
5,995
model selection
test
4,991
4,999
9,990
in-distribution test… See the full description on the dataset page: https://huggingface.co/datasets/acmc/multi_domain_ai_human_text.RenderMatte-Human-Multi-Street-2K
RenderMatte Human Multi-Person Street 2K
A synthetic video-matting dataset for the multi-person case. Two or three rigged 3D human characters are
animated and rendered together in one Blender (Cycles) scene, then composited onto real street footage.
Every frame comes with its 16-bit alpha matte.
It is built on top of the single-person VideoMatting pipeline and its source-asset collection, and keeps the
same render and compositing settings wherever possible.
Research use only.… See the full description on the dataset page: https://huggingface.co/datasets/Deanshy1/RenderMatte-Human-Multi-Street-2K.multi-humanevalThis dataset contains a viewer-friendly version of the dataset at mxeval/multi-humaneval with language-specific stop tokens added in. It is made available separately for the convenience of the vllm-code-harness package.
humaneval_multi
humaneval_multi — evaluation data (OpenCompass format)
Bud Ecosystem eval mirror (config humaneval_multi_gen). Source nuprl/MultiPL-E — license MIT, unchanged; all rights remain with the original authors.
Human-Multi-Reference-MT-Benchmark
Human Benchmark Version v3
Parallel sentence benchmarks for the following language pairs:
English–Hindi
English–Telugu
Hindi–Telugu
This repository includes multiple domains, reference translations, and
balanced splits for development and evaluation.
Authors
Vandan Mujadia
Dipti Misra Sharma
Acknowledgment
Developed as part of Himangy, LTRC IIIT H.
Source Data Layout
Raw data is organized by language pair and domain:
English-Hindi/… See the full description on the dataset page: https://huggingface.co/datasets/HimangY/Human-Multi-Reference-MT-Benchmark.ChatGPT-Gemini-Claude-Perplexity-Human-Evaluation-Multi-Aspects-Review-Dataset
ChatGPT Gemini Claude Perplexity Human Evaluation Multi Aspect Review Dataset
Introduction
Human evaluation and reviews with scalar score of AI Services responses are very usefuly in LLM Finetuning, Human Preference Alignment, Few-Shot Learning, Bad Case Shooting, etc, but extremely difficult to collect.
This dataset is collected from DeepNLP AI Service User Review panel (http://www.deepnlp.org/store), which is an open review website for users to give reviews and upload… See the full description on the dataset page: https://huggingface.co/datasets/DeepNLP/ChatGPT-Gemini-Claude-Perplexity-Human-Evaluation-Multi-Aspects-Review-Dataset.human_multi_classifications_500Cabin-multi-modal-recognition-dataset-of-human
XAILab-CyberSpark/Cabin-multi-modal-recognition-dataset-of-human
由XAI Lab 汽车智能座舱数据集生成引擎生成的座舱垂域任务数据集，面向智能座舱舱内识人场景，本次开源demo数据集包含719张图像及标签，涵盖不同性别、年龄、情绪（表情）、行为动作、衣物等的标签，可用于舱内多模态感知模型SFT训练。
The cockpit vertical domain task dataset generated by the XAI Lab vehicle intelligent cockpit dataset generation engine is oriented to the intelligent cockpit human recognition scene. The Open Source demo dataset contains 719 images and tags, labels covering different genders, ages, emotions… See the full description on the dataset page: https://huggingface.co/datasets/XAILab-CyberSpark/Cabin-multi-modal-recognition-dataset-of-human.human_multi_classificationsgpt_4o_mini_classifications_multi_humanspeaker_evaluation_multi_test_v0
Seamless Interaction Pairs
This dataset contains paired query and document audio clips for interaction-based
speaker evaluation. Each row describes a query clip and a related document clip,
with segment metadata and durations for analysis.
Data structure
The dataset uses a single split stored in data.parquet.
Audio files are stored under audio/ and referenced by relative paths in the
parquet file.
Columns
pair_id (string): Pair identifier.
interaction… See the full description on the dataset page: https://huggingface.co/datasets/humanify/speaker_evaluation_multi_test_v0.qwen2_72b_classifications_multi_humangpt_4o_classifications_multi_humangemini_1_5_pro_classifications_multi_humanllama_3_1_8b_classifications_multi_humanmistral_large_classifications_multi_humanllama_3_1_405b_classifications_multi_humancommand_r_plus_classifications_multi_humanllama_3_1_70b_classifications_multi_humanhumanoid-multi-step-task-instructions
Humanoid Multi-Step Task Instructions
A structured dataset containing multi-step task instructions for humanoid robots.
Use Cases
Task planning
Autonomous execution
Robotics simulation
Multi-Head-Recommendation-with-Human-PriorsThis repo contains the datasets for "Don't Waste It: Guiding Generative Recommenders with Structured Human Priors via Multi-head Decoding" (arXiv: https://arxiv.org/abs/2511.10492).
release-dataset.zip contains the user-item interactions, while release-information.zip contains the details of each item.
We have a total of three datasets: Pixel8M.parquet, merrec_2000.parquet, eb_nerd_512.parquet. They are derived from PixelRec (https://epubs.siam.org/doi/pdf/10.1137/1.9781611978032.49), MerRec… See the full description on the dataset page: https://huggingface.co/datasets/zhykoties/Multi-Head-Recommendation-with-Human-Priors.asia-humanitarian-needs-2022-multi-sectoral-needs-assessment-occ
occupied Palestinian territory (oPt) - 2022 Multi-Sectoral Needs Assessment
Publisher: REACH Initiative · Source: HDX · License: cc-by-igo · Updated: 2024-06-07
Abstract
The 2022 Multi-Sector Needs Assessment (MSNA), conducted by the REACH Initiative in close collaboration with the United Nations Office for the Coordination of Humanitarian Affairs (OCHA), aims to identify and assess multi-sectoral and sector-specific needs, circumstances, and vulnerabilities of… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-humanitarian-needs-2022-multi-sectoral-needs-assessment-occ.MULTI_VALUE_mnli_plural_to_singular_human
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MULTI_VALUE_cola_plural_to_singular_human
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humanoid-multi-sensor-perception-dataset
Humanoid Multi-Sensor Perception Dataset
Dataset for training humanoid systems to interpret
multi-sensor inputs including vision, audio, and proximity signals.
Description
Provides synchronized sensor readings for real-time
environment perception and context awareness.
File
multi_sensor_perception_dataset.json
License
MIT
MULTI_VALUE_mrpc_plural_to_singular_human
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MULTI_VALUE_wnli_plural_to_singular_human
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MULTI_VALUE_rte_plural_to_singular_human
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MULTI_VALUE_stsb_plural_to_singular_human
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