datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ms_marco
Dataset Card for "ms_marco"
Dataset Summary
Starting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search.
The first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer.
Since then we released a 1,000,000 question dataset, a natural langauge generation dataset, a passage ranking dataset,
keyphrase extraction dataset, crawling dataset, and a conversational search.… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/ms_marco.orca-math-word-problems-200k
Dataset Card
This dataset contains ~200K grade school math word problems. All the answers in this dataset is generated using Azure GPT4-Turbo. Please refer to Orca-Math: Unlocking the potential of
SLMs in Grade School Math for details about the dataset construction.
Dataset Sources
Repository: microsoft/orca-math-word-problems-200k
Paper: Orca-Math: Unlocking the potential of
SLMs in Grade School Math
Direct Use
This dataset has been designed to… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k.rStar-Coder
rStar-Coder Dataset
Project GitHub | Paper
Dataset Description
rStar-Coder is a large-scale competitive code problem dataset containing 418K programming problems, 580K long-reasoning solutions, and rich test cases of varying difficulty levels. This dataset aims to enhance code reasoning capabilities in large language models, particularly in handling competitive code problems.
Experiments on Qwen models (1.5B-14B) across various code reasoning benchmarks demonstrate… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/rStar-Coder.Updesh_beta
📢 Updesh: Synthetic Multilingual Instruction Tuning Dataset for 13 Indic Languages
NOTE: This is an initial $\beta$-release. We plan to release subsequent versions of Updesh with expanded coverage and enhanced quality control. Future iterations will include larger datasets, improved filtering pipelines.
Updesh is a large-scale synthetic dataset designed to advance post-training of LLMs for Indic languages. It integrates translated reasoning data and synthesized open-domain… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/Updesh_beta.wiki_qa
Dataset Card for "wiki_qa"
Dataset Summary
Wiki Question Answering corpus from Microsoft.
The WikiQA corpus is a publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
default
Size of downloaded dataset files: 7.10 MB
Size… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/wiki_qa.webgym_tasks
WebGym Tasks Dataset
Dataset Description
This dataset contains web navigation tasks for training and evaluating autonomous web agents. Each task consists of a natural language instruction that describes an action to be performed on a specific website, along with evaluation criteria and metadata.
Dataset Summary
Total Training Tasks: 292,092
Total Test Tasks: 1,167
Domains: Multiple domains including Lifestyle & Leisure, Sports & Fitness, and more
Source… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/webgym_tasks.llmail-inject-challenge
Dataset Summary
This dataset contains a large number of attack prompts collected as part of the now closed LLMail-Inject: Adaptive Prompt Injection Challenge.
We first describe the details of the challenge, and then we provide a documentation of the dataset
For the accompanying code, check out: https://github.com/microsoft/llmail-inject-challenge.
Citation
@article{abdelnabi2025,
title = {LLMail-Inject: A Dataset from a Realistic Adaptive Prompt Injection… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/llmail-inject-challenge.IMAGE_UNDERSTANDINGA key question for understanding multimodal performance is analyzing the ability for a model to have basic
vs. detailed understanding of images. These capabilities are needed for models to be used in
real-world tasks, such as an assistant in the physical world. While there are many dataset for object detection
and recognition, there are few that test spatial reasoning and other more targeted task such as visual prompting.
The datasets that do exist are static and publicly available, thus… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/IMAGE_UNDERSTANDING.AVGen-Bench
AVGen-Bench Generated Videos Data Card
Overview
This data card describes the generated audio-video outputs stored directly in the repository root by model directory.
The collection is intended for benchmarking and qualitative/quantitative evaluation of text-to-audio-video (T2AV) systems. It was presented in the paper AVGen-Bench: A Task-Driven Benchmark for Multi-Granular Evaluation of Text-to-Audio-Video Generation. It is not a training dataset. Each item is a… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/AVGen-Bench.SciFormaData-700KSciFormaData-700K: Training Data for Scientific Diagram Generation
SciFormaData-700K is the official training dataset for
SciForma. It contains scientific
methodology-diagram records collected from arXiv papers spanning January
2015–December 2025, structured generation prompts, multi-resolution training
targets, and axis-specific editing triplets.
Features
🧩 Structure-aware prompts. Detailed descriptions organize diagram
components… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/SciFormaData-700K.orca-agentinstruct-1M-v1
Dataset Card
This dataset is a fully synthetic set of instruction pairs where both the prompts and the responses have been synthetically generated, using the AgentInstruct framework.
AgentInstruct is an extensible agentic framework for synthetic data generation.
This dataset contains ~1 million instruction pairs generated by the AgentInstruct, using only raw text content publicly avialble on the Web as seeds. The data covers different capabilities, such as text editing, creative… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1.SCBench
SCBench
[Paper]
[Code]
[Project Page]
SCBench (SharedContextBench) is a comprehensive benchmark to evaluate efficient long-context methods in a KV cache-centric perspective, analyzing their performance across the full KV cache lifecycle (generation, compression, retrieval, and loading) in real-world scenarios where context memory (KV cache) is shared and reused across multiple requests.
🎯 Quick Start
Load Data
You can download and load the SCBench data… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/SCBench.kitab
Overview
🕮 KITAB is a challenging dataset and a dynamic data collection approach for testing abilities of Large Language Models (LLMs) in answering information retrieval queries with constraint filters. A filtering query with constraints can be of the form "List all books written by Toni Morrison that were published between 1970-1980". The dataset was originally contributed by the paper "KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval" Marah I Abdin… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/kitab.Dayhoff
Dataset Card for Dayhoff
Dayhoff is an Atlas of both protein sequence data and generative language models — a centralized resource that brings together 3.34 billion protein sequences across 1.7 billion clusters of metagenomic and natural protein sequences (GigaRef), 46 million structure-derived synthetic sequences (BackboneRef), and 16 million multiple sequence alignments (OpenProteinSet). These models can natively predict zero-shot mutation effects on fitness, scaffold structural… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/Dayhoff.Taskbench
TaskBench: Benchmarking Large Language Models for Task Automation
Introduction
TaskBench is a benchmark for evaluating large language models (LLMs) on task automation. Task automation can be formulated into three critical stages: task decomposition, tool invocation, and parameter prediction. This complexity makes data collection and evaluation more challenging compared to common NLP tasks. To address this challenge, we propose a comprehensive evaluation framework… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/Taskbench.MMLU-CF
MMLU-CF: A Contamination-free Multi-task Language Understanding Benchmark
[📜 Paper] •
[🤗 HF Dataset] •
[🐱 GitHub]
MMLU-CF is a contamination-free and more challenging multiple-choice question benchmark. This dataset contains 10K questions each for the validation set and test set, covering various disciplines.
1. The Motivation of MMLU-CF
The open-source nature of these benchmarks and the broad sources of training data for LLMs have inevitably led to… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/MMLU-CF.RHELM
RHELM: Beyond Static Dialogues
Benchmarking Realistic, Heterogeneous, and Evolving Long-Horizon Memory
RHELM is a benchmark for evaluating long-horizon memory capabilities in AI assistants.
Unlike benchmarks built around static dialogues, RHELM provides realistic,
heterogeneous, and temporally evolving memory sources, together with
challenging questions that require multi-hop reasoning, temporal synthesis, and
hallucination detection.
⚠️ All characters, events, and personal… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/RHELM.ba-calendarOfficeComprehensionBenchmark
OfficeComprehensionBenchmark (OCB)
OCB is a benchmark for evaluating document comprehension and grounded reasoning over Microsoft Office files (Word, Excel, PowerPoint). It comprises two tracks:
File Fidelity Q&A — measures structural and visual perception of document artifacts (text, tables, charts, formulas, formatting, embedded objects).
Domain Q&A — measures expert-level reasoning over real-world business documents across 12 industries.
Companion repository… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/OfficeComprehensionBenchmark.tspOrchard
Orchard Dataset
Overview
Orchard is the trajectory release accompanying the paper "Orchard: An Open-Source Agentic Modeling Framework" (Peng et al., 2026). It bundles two parallel agentic-modeling datasets distilled from strong teacher models, both produced inside the same Orchard Env sandbox infrastructure:
swe — 107,185 multi-turn software-engineering trajectories across 2,788 GitHub repositories, each labeled with whether the agent's final patch passed the… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/Orchard.Taur_CoT_Analysis_Project___microsoft__Phi-3-small-8k-instructhnm-search-data
HnM Search Dataset Created from Recommendations Dataset
This synthetic data-set is created using the recommendations dataset:
https://huggingface.co/datasets/einrafh/hnm-fashion-recommendations-data (Use of this dataset is subject to the terms and conditions set forth on the original distribution page. This dataset is intended for non-commercial and research use.)
https://www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations/data (DATA ACCESS AND USE:… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/hnm-search-data.delegate52
DELEGATE52
Overview
DELEGATE52 is a benchmark dataset for evaluating LLMs on long-horizon delegated document editing across 52 professional document domains (crystallography files, music notation, accounting ledgers, Python source code, etc.). The dataset was developed to study the readiness of AI systems for delegated workflows, a new interaction paradigm where knowledge workers instruct LLMs to edit documents on their behalf over long sessions.
A detailed… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/delegate52.VISION_LANGUAGEA key question for understanding multimodal vs. language capabilities of models is what is
the relative strength of the spatial reasoning and understanding in each modality, as spatial understanding is
expected to be a strength for multimodality? To test this we created a procedurally generatable, synthetic dataset
to testing spatial reasoning, navigation, and counting. These datasets are challenging and by
being procedurally generated new versions can easily be created to combat the effects… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/VISION_LANGUAGE.CUAVerifierBench
CUAVerifierBench: A Human-Annotated Benchmark for Computer-Using-Agent Verifiers
Universal Verifier paper: The Art of Building Verifiers for Computer Use Agents
Dataset Summary
CUAVerifierBench is an evaluation benchmark for verifiers of computer-using agents (CUAs) — i.e. judges that read an agent's trajectory (screenshots + actions + final answer) and decide whether the task was completed correctly. Where benchmarks like WebTailBench measure agents… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/CUAVerifierBench.Do-You-See-Me
DoYouSeeMe
Overview
The DoYouSeeMe benchmark is a comprehensive evaluation framework designed to assess visual perception capabilities in Machine Learning Language Models (MLLMs). This fully automated test suite dynamically generates both visual stimuli and perception-focused questions (VPQA) with incremental difficulty levels, enabling a graded evaluation of MLLM performance across multiple perceptual dimensions. Our benchmark consists of both 2D and 3D… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/Do-You-See-Me.bing_coronavirus_query_set
Dataset Card for BingCoronavirusQuerySet
Dataset Summary
Please note that you can specify the start and end date of the data. You can get start and end dates from here: https://github.com/microsoft/BingCoronavirusQuerySet/tree/master/data/2020
example:
load_dataset("bing_coronavirus_query_set", queries_by="state", start_date="2020-09-01", end_date="2020-09-30")
You can also load the data by country by using queries_by="country".
Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/bing_coronavirus_query_set.XL-DocBench
XL-DocBench
Evidence-grounded reasoning across hundreds or thousands of pages.
Fully verified by 194 human experts.
Hongchen Wei1,†,‡, Yuanzhe Wang2,†,‡,
Bei Liu2,*, Yifan Yang2, Qi Dai2,
Ruichun Ma2, Kai Qiu2, Yunsheng Li2,
Dongdong Chen2, Chong Luo2,
Zhenzhong Chen1, Baining Guo2
1Wuhan University 2Microsoft
†Equal contribution ‡Work done during an internship at MSRA
*Project leader
Project Page ·
Paper ·
Live Leaderboard… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/XL-DocBench.SWE-Sharp-Bench
SWE-Sharp-Bench
SWE-Sharp-Bench is a comprehensive benchmark suite for evaluating software engineering capabilities of AI agents and models on C# and .NET codebases. This benchmark extends the SWE-Bench framework to the C# ecosystem, providing real-world software engineering tasks from popular open-source repositories.
Code - https://github.com/microsoft/prose/tree/main/misc/SWE-Sharp-Bench
Research Paper Draft & Benchmark Analysis: https://aka.ms/swesharparxiv
Contact… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/SWE-Sharp-Bench.