datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Multitask-National-Speech-Corpus-v1Multitask-National-Speech-Corpus (MNSC v1) is derived from IMDA's NSC Corpus.
MNSC is a multitask speech understanding dataset derived and further annotated from IMDA NSC Corpus. It focuses on the knowledge of Singapore's local accent, localised terms, and code-switching.
ASR: Automatic Speech Recognition
SQA: Speech Question Answering
SDS: Spoken Dialogue Summarization
PQA: Paralinguistic Question Answering
from datasets import load_dataset
data =… See the full description on the dataset page: https://huggingface.co/datasets/MERaLiON/Multitask-National-Speech-Corpus-v1.Multi-SWE-smith-tasksMultitask-National-Speech-Corpus-v1-extendmulti_task_multi_modal_knowledge_retrieval_benchmark_M2KR
PreFLMR M2KR Dataset Card
Dataset details
Dataset type:
M2KR is a benchmark dataset for multimodal knowledge retrieval. It contains a collection of tasks and datasets for training and evaluating multimodal knowledge retrieval models.
We pre-process the datasets into a uniform format and write several task-specific prompting instructions for each dataset. The details of the instruction can be found in the paper. The M2KR benchmark contains three types of tasks:… See the full description on the dataset page: https://huggingface.co/datasets/BByrneLab/multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR.multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR_CN
PreFLMR M2KR Dataset Card
Dataset details
Dataset type:
M2KR is a benchmark dataset for multimodal knowledge retrieval. It contains a collection of tasks and datasets for training and evaluating multimodal knowledge retrieval models.
We pre-process the datasets into a uniform format and write several task-specific prompting instructions for each dataset. The details of the instruction can be found in the paper. The M2KR benchmark contains three types of tasks:… See the full description on the dataset page: https://huggingface.co/datasets/BByrneLab/multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR_CN.multitask_german_examples_32kmultitask_vqa_benchmarkThis dataset is a part of .
🍈 MMT-47: Multimodal Multi-Task Benchmark
47 Tasks · 7 Categories · 3 Modalities (Image, Video, Text)
Cite our ICML-2026 paper for this dataset
@article{kowsher2026lime,
title={LiME: Lightweight Mixture of Experts for Efficient Multimodal Multi-task Learning},
author={Kowsher, Md and Mansoor, Haris and Prottasha, Nusrat Jahan and Garibay, Ozlem and Zhu, Victor and Ji, Zhengping and Chen, Chen},
journal={arXiv preprint… See the full description on the dataset page: https://huggingface.co/datasets/Kowsher/multitask_vqa_benchmark.multi_task_multi_modal_knowledge_retrieval_benchmark_M2KRVQA-MultiTasktask1577_amazon_reviews_multi_japanese_language_classification
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1577_amazon_reviews_multi_japanese_language_classification
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1577_amazon_reviews_multi_japanese_language_classification.coastal-multitask-380
Coastal & Rural Bangladesh — Multi-Task Visual Dataset
379 field photographs (JPEG, native resolution as shot — see classification/metadata.csv for per-image width/height) collected on foot along the Bakkhali river embankment and surrounding villages/farmland near Cox's Bazar, Bangladesh, structured into three ML-task "levels": classification, semantic segmentation, and change detection.
Source: huggingface data 06 (Golam Rob / Tawhid Enterprise photo collection).… See the full description on the dataset page: https://huggingface.co/datasets/golamrob/coastal-multitask-380.GPRadar-Defect-MultiTask
GPRadar-Defect-MultiTask 数据集
本仓库包含用于微调PaLI-GEMMA多模态模型的地质雷达(GPR)缺陷检测数据集。该数据集专注于地下结构中的空洞和裂缝检测与分析。
数据集结构
数据集组织如下：
dataset/
├── annotations/ - 包含JSON和JSONL格式的标注文件
│ ├── _annotations.train.jsonl - 训练集标注
│ ├── _annotations.valid.jsonl - 验证集标注
│ ├── _annotations.test.jsonl - 测试集标注
│ ├── p-1.v1i.paligemma/ - 主数据集元数据
│ └── p-1.v1i.paligemma-multimodal/ - 多模态数据集元数据
├── images/ - 包含所有图像文件
特点
包含874张带注释的地质雷达扫描图像
图像预处理为640x640像素大小
支持多种任务类型：缺陷检测、位置定位和描述生成… See the full description on the dataset page: https://huggingface.co/datasets/xingqiang/GPRadar-Defect-MultiTask.Sampling-Multitask-National-Speech-Corpus-v1
Sampling Multitask-National-Speech-Corpus-v1
Original dataset from https://huggingface.co/datasets/MERaLiON/Multitask-National-Speech-Corpus-v1, we only take Part 3 and do sampling.
how to prepare the dataset
huggingface-cli download \
mesolitica/Sampling-Multitask-National-Speech-Corpus-v1 \
--include "*.zip" \
--repo-type "dataset" \
--local-dir './'
wget… See the full description on the dataset page: https://huggingface.co/datasets/mesolitica/Sampling-Multitask-National-Speech-Corpus-v1.task638_multi_woz_classification
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task638_multi_woz_classification
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task638_multi_woz_classification.dynasample_multitasks_cleanGPRadar-Defect-MultiTask
GPRadar-Defect-MultiTask 数据集
本仓库包含用于微调PaLI-GEMMA多模态模型的地质雷达(GPR)缺陷检测数据集。该数据集专注于地下结构中的空洞和裂缝检测与分析。
数据集结构
数据集组织如下：
dataset/
├── annotations/ - 包含JSON和JSONL格式的标注文件
│ ├── _annotations.train.jsonl - 训练集标注
│ ├── _annotations.valid.jsonl - 验证集标注
│ ├── _annotations.test.jsonl - 测试集标注
│ ├── p-1.v1i.paligemma/ - 主数据集元数据
│ └── p-1.v1i.paligemma-multimodal/ - 多模态数据集元数据
├── images/ - 包含所有图像文件
特点
包含874张带注释的地质雷达扫描图像
图像预处理为640x640像素大小
支持多种任务类型：缺陷检测、位置定位和描述生成… See the full description on the dataset page: https://huggingface.co/datasets/LiZHENGzai/GPRadar-Defect-MultiTask.multi-strategy-algorithmic-tasks
Multi-Strategy Algorithmic Tasks
A synthetic benchmark of parseable algorithmic problems with multiple valid
solution strategies for each task. Each example contains a problem,a strategy-specific
solution trace, and the strategy used to generate that trace.
The benchmark accompanies
Uncovering Latent Reasoning Strategies in Language Models,
which studies the problem of recovering mixtures of strategies implicitly represented in language models.
The benchmark provides a… See the full description on the dataset page: https://huggingface.co/datasets/awni00/multi-strategy-algorithmic-tasks.customer_service_client_agent_conversations_40k_multi_task
Dataset Card for "customer_service_client_agent_conversations_40k_multi_task"
More Information needed
configurable-system-prompt-multitask
Configurable System Prompt Multi-task Dataset 🛞
We release the synthetic dataset for the multi-task experiments from the paper "Configurable Safety Tuning of Language Models with Synthetic Preference Data", https://huggingface.co/papers/2404.00495. This dataset has two sources for the examples:
Self-critique on a safety task from Harmful Behaviours, using the SOLAR-Instruct model. It employs two system prompts to learn the different behaviors:
You are a helpful yet harmless… See the full description on the dataset page: https://huggingface.co/datasets/vicgalle/configurable-system-prompt-multitask.lunamax-multitask-programming-1000
LunaMax Multitask Programming 1000
A 1,000-record synthetic multitask programming dataset generated with
ChatGPT LunaMax.
The recovered dataset combines code review, implementation, bug and severity
classification, and strict output-contract tasks across multiple programming
languages.
The historical source shards were reviewed with ChatGPT 5.6 Sol High according
to dataset creator confirmation. During Hugging Face publication preparation,
all 1,000 records received a new… See the full description on the dataset page: https://huggingface.co/datasets/TaskPuppyAI/lunamax-multitask-programming-1000.multilingual-multitask-refusal
Multilingual Multitask Refusal
A multilingual, multitask prompt dataset for analysing refusal behaviour across language, task wrapper, and harmful / harmless labels.
English seed spans and labels come from the previous Multitask Multilingual Refusal dataset. Non-English content is produced with Google Sheets GOOGLETRANSLATE. Task instructions are language-localized via templates_localized.json.
Rows
211,320
English seeds
1,761
Languages
15
Tasks
8
Product
1… See the full description on the dataset page: https://huggingface.co/datasets/kaustubhg73/multilingual-multitask-refusal.multi-task-dataset
Multi-Task Dataset
Description
A large-scale multi-task dataset designed for training and evaluating AI models across reasoning, mathematics, code, research, verification, data analysis, and general problem solving.
Content
100,000,001 examples
20+ task families
English + French
Train / Validation / Test splits
Structured reasoning and verification signals
Multiple difficulty levels
OOD and generalization-oriented examples
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Lelonthecodeur/multi-task-dataset.UR7e_CaP_MultiTask_300epi_10fps
UR7e CaP MultiTask 300epi 10fps
This is a public LeRobot v3.0 multi-task dataset for UR7e Code-as-Policies manipulation. It merges three 100-episode 10fps datasets into one 300-episode training corpus while preserving the original numeric observations, actions, and task labels. The final uploaded copy stores all videos as 10fps H.264 MP4 files.
Dataset Summary
Robot: ur7e
Format: LeRobot v3.0
FPS: 10
Episodes: 300
Frames: 152,926
Cameras:… See the full description on the dataset page: https://huggingface.co/datasets/Cache-SCA/UR7e_CaP_MultiTask_300epi_10fps.MultiTasks-v2
MultiTasks-v2
MultiTasks-v2 is a collection of twelve multimodal benchmark subsets normalized
into a shared image-question-answer format. Each subset provides a train split
and a test split.
Dataset Structure
Each example contains:
id: a unique sample identifier in the form {Dataset}_{split}_{index}.
images: a list containing one image. Images are stored as JPEG bytes.
problem: the prompt shown to the model.
answer: the target answer.
RefAdv is the only subset… See the full description on the dataset page: https://huggingface.co/datasets/LoserLi/MultiTasks-v2.Multitask_Preplay_Craftax_models
Multitask Preplay — craftax model data
Data for the paper "Multitask Preplay" (PNAS).
Analysis code: https://github.com/wcarvalho/multitask_preplay (branch pnas).
Splits: qlearning, usfa, dyna, preplay, her, greedy_euclidean, memory_based_euclidean.
Each split is also available as a top-level parquet file.
multi-task-amharic-prompt-settmp-multitask-en-clinical
Dataset Card for "tmp-multitask-en-clinical"
More Information needed
Multitask_Preplay_JaxMaze_models
Multitask Preplay — jaxmaze model data
Data for the paper "Multitask Preplay" (PNAS).
Analysis code: https://github.com/wcarvalho/multitask_preplay (branch pnas).
Splits: qlearning, usfa, dyna, preplay, her, bfs, dfs, greedy_euclidean, memory_based_euclidean.
Each split is also available as a top-level parquet file.
sanskrit-multitask-devanagariSR-MultiTask