datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
FlashRAG_datasets
⚡FlashRAG: A Python Toolkit for Efficient RAG Research
FlashRAG is a Python toolkit for the reproduction and development of Retrieval Augmented Generation (RAG) research. Our toolkit includes 36 pre-processed benchmark RAG datasets and 16 state-of-the-art RAG algorithms.
With FlashRAG and provided resources, you can effortlessly reproduce existing SOTA works in the RAG domain or implement your custom RAG processes and components.
For more information, please view our GitHub repo… See the full description on the dataset page: https://huggingface.co/datasets/RUC-NLPIR/FlashRAG_datasets.social-sim-bench-gensEvo-BenchEvo-Bench: Can Language Models Improve Agent Harness?
A benchmark for measuring the intrinsic harness-evolving capability of language models.
Overview of the Evo-Bench evaluation pipeline.
✨ Highlights
608 harness-sensitive tasks from five established benchmarks, spanning
Search, Office, and General agent domains with disjoint validation and
evaluation suites.
Harness-guided benchmark construction selects tasks that respond to
harness improvements… See the full description on the dataset page: https://huggingface.co/datasets/RUC-AIBOX/Evo-Bench.rl-run-archive-2026
RL run archive 2026
Archived raw run artifacts (rollout trajectories, rendered frames, policy and optimizer
checkpoints, configs, logs) from simulation reinforcement-learning experiments, published for
long-term preservation and reproducibility.
Layout mirrors the verified backup trees they were copied from:
tilde/20260915-102000/ and taurus/20260915-085631/: batched tar archives. Every archive
carries a per-file SHA-256 manifest inside it; the batch inventories (9998.json.gz… See the full description on the dataset page: https://huggingface.co/datasets/gavinlaw/rl-run-archive-2026.PhD
[CVPR2025 Highlight] PhD: A ChatGPT-Prompted Visual hallucination Evaluation Dataset
preprint
🔥 PhD-webdataset
To enhance usability and integration with evaluation frameworks like lmm-eval, we are pleased to offer a packaged version in webdataset format. This packaged version is designed to facilitate easier deployment and testing. For further details and access, please refer to our repository PhD-webdataset.
Please note that the data in both repositories is completely… See the full description on the dataset page: https://huggingface.co/datasets/AIMClab-RUC/PhD.LongViTU
LongViTU
LongViTU is a large-scale (~121k QA pairs, ~900h videos), automatically generated dataset for long-form video understanding. Please refer to our project page.
ATO-Australian-Tax-Rulings-and-Guidance
ATO Rulings & Guidance — Australian Tax Law, Structured for AI
67,000+ Australian Taxation Office documents as RAG-ready NDJSON/CSV — Edited Private Advice, public rulings and determinations, ATO Interpretative Decisions, practical compliance guidelines, taxpayer alerts, decision impact statements, practice statements and legislative instruments. Every document parsed into structured, typed fields for legal RAG, LLM fine-tuning, and tax research automation.
Machine-readable… See the full description on the dataset page: https://huggingface.co/datasets/simplelex/ATO-Australian-Tax-Rulings-and-Guidance.gdpval_preference_rubricsgaokao-benchrubq-retrievalrussian_dialoguesДатасет русских диалогов собранных с Telegram чатов.
Диалоги имеют разметку по релевантности.
Также были сгенерированы негативные примеры с помощью перемешивания похожих ответов.
Количество диалогов - 2 миллиона
Формат датасета:
{
'question': 'Привет',
'answer': 'Привет, как дела?'
'relevance': 1
}
Программа парсинга: https://github.com/Den4ikAI/telegram_chat_parser
Citation:
@MISC{russian_instructions,
author = {Denis Petrov},
title = {Russian dialogues… See the full description on the dataset page: https://huggingface.co/datasets/Den4ikAI/russian_dialogues.RefWave-Cluster-RunsDataScience-Instruct-500K
DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
Authors: Shaolei Zhang, Ju Fan*, Meihao Fan, Guoliang Li, Xiaoyong Du
DeepAnalyze is the first agentic LLM for autonomous data science. It can autonomously complete a wide range of data-centric tasks without human intervention, supporting:
🛠 Entire data science pipeline: Automatically perform any data science tasks such as data preparation, analysis, modeling, visualization, and report generation.
🔍… See the full description on the dataset page: https://huggingface.co/datasets/RUC-DataLab/DataScience-Instruct-500K.OlymMATH
Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models
This is the official huggingface repository for Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models by Haoxiang Sun, Yingqian Min, Zhipeng Chen, Wayne Xin Zhao, Zheng Liu, Zhongyuan Wang, Lei Fang, and Ji-Rong Wen.
We have also released the OlymMATH-eval dataset on HuggingFace 🤗, together with a data visualization tool OlymMATH-demo… See the full description on the dataset page: https://huggingface.co/datasets/RUC-AIBOX/OlymMATH.agievalOmniPro
OmniPro
A comprehensive benchmark for evaluating proactive video understanding capabilities of omni multimodal large language models (MLLMs). Unlike traditional reactive QA benchmarks where models respond to explicit questions after watching a video, OmniPro evaluates whether models can proactively monitor video streams and respond at the right moment when specific conditions are met.
OmniPro is designed around three core capabilities that define a good omni-proactive model:… See the full description on the dataset page: https://huggingface.co/datasets/RuixiangZhao/OmniPro.varroa_mmdet_yolo_protocol_runsruler-full
RULER Benchmark (Full)
Complete RULER benchmark dataset with all 13 tasks across 6 context lengths (4K to 128K tokens).
Overview
Metric
Value
Total Samples
78,000 (39,000 per variant)
Tasks
13
Context Lengths
4K, 8K, 16K, 32K, 64K, 128K
Samples per Config
500
Variants
memwrap, plain
Tasks
Retrieval (NIAH - Needle in a Haystack)
niah_single_1, niah_single_2, niah_single_3 - Single needle variants
niah_multikey_1… See the full description on the dataset page: https://huggingface.co/datasets/tonychenxyz/ruler-full.ruozhiba_gpt4
ruozhiba_gpt4
本仓库包含使用GPT-4（4T/4o）构建的ruozhiba指令数据[^1]，共计2449条。其中包含以下两个版本，题目相同，仅回答内容不同。
ruozhiba_qa2449_gpt4t.json：利用gpt-4-turbo-20240409 对问题进行了回答。
ruozhiba_qa2449_gpt4o.json：利用gpt-4o-20240514 对问题进行了回答。
注意：指令数据中可能包含冒犯用语。
所属项目
Chinese-LLaMA-Alpaca-3：https://github.com/ymcui/Chinese-LLaMA-Alpaca-3
This repository contains the ruozhiba instruction data[^1] constructed using GPT-4 (4T/4o), totaling 2449 entries. It includes the following two versions with the same questions… See the full description on the dataset page: https://huggingface.co/datasets/hfl/ruozhiba_gpt4.russian_dialogues_2
Den4ikAI/russian_dialogues_2
Датасет русских диалогов для обучения диалоговых моделей.
Количество диалогов - 1.6 миллиона
Формат датасета:
{
'sample': ['Привет', 'Привет', 'Как дела?']
}
Citation:
@MISC{russian_instructions,
author = {Denis Petrov},
title = {Russian context dialogues dataset for conversational agents},
url = {https://huggingface.co/datasets/Den4ikAI/russian_dialogues_2},
year = 2023
}
BenchMAX_Rule-based
Dataset Sources
Paper: BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models
Link: https://huggingface.co/papers/2502.07346
Repository: https://github.com/CONE-MT/BenchMAX
Dataset Description
BenchMAX_Rule-based is a dataset of BenchMAX, sourcing from IFEval, which is a rule-based benchmark for evaluating the instruction following capabilities in multilingual scenarios.
We extend the original dataset to 16 non-English languages by first… See the full description on the dataset page: https://huggingface.co/datasets/LLaMAX/BenchMAX_Rule-based.russian_instructions_2June 10:
Почищены криво переведенные примеры кода
Добавлено >50000 человеческих примеров QA и инструкций
Обновленная версия русского датасета инструкций и QA.
Улучшения:
1. Увеличен размер с 40 мегабайт до 130 (60к сэмплов - 200к)
2. Улучшено качество перевода.
Структура датасета:
{
"sample":[
"Как я могу улучшить свою связь между телом и разумом?",
"Начните с разработки регулярной практики осознанности. 2. Обязательно практикуйте баланс на нескольких уровнях: физическом… See the full description on the dataset page: https://huggingface.co/datasets/Den4ikAI/russian_instructions_2.kernelbench-hard-runs
KernelBench-Hard — Agent Runs
84 full agent transcripts (12 frontier models × 7 problems) from the KernelBench-Hard sweep on a single Blackwell GPU (RTX PRO 6000, sm_120, CUDA 13.2). Each run contains the model's full reasoning trace, every tool call, the final solution.py, and the eval result.
Companion datasets:
Infatoshi/kernelbench-hard-problems — the 7 problem definitions
Live site: https://kernelbench.com/hard
100 themed transcript viewers (HTML): https://kernelbench.com/runs… See the full description on the dataset page: https://huggingface.co/datasets/Infatoshi/kernelbench-hard-runs.swe-agent-tool-rubrics-860
SWE Agent 逐 turn 工具调用评判数据集（860 个决策点）
本数据集来自 2026-08-06 的一次实验：**从真实 SWE agent 轨迹中归纳"怎么判断一次工具调用的好坏"**。
包含两个文件：
文件
行数
大小
内容
cases.jsonl
860
5.0 MB
决策点原始数据（题目、历史、两个候选命令、执行结果、现役判官打分）
map_io.jsonl
860
9.6 MB
每个决策点喂给 GPT-5.6 的完整 prompt 原文与完整回复
两个文件通过 case_id 一一对应。
背景：为什么是"按动作分类"而不是"按工具分类"
轨迹来自 slime 的 minimal harness，该 harness 只暴露一个工具 bash
（slime/agent/harness/minimal.py 里的 BASH_TOOL），全部 328,270 次调用的工具名都是 bash。
所以"不同工具用不同 rubric"无法按工具名实现，只能按命令在干什么分类。… See the full description on the dataset page: https://huggingface.co/datasets/MasterVito/swe-agent-tool-rubrics-860.bbhREBEL-RUrummlu
ruMMLU
Task Description
Russian Massive Multitask Language Understanding (ruMMLU) is a dataset designed to measure model professional knowledge acquired during pretraining in various fields . The task covers 57 subjects (subdomains) across different topics (domains): HUMANITIES; SOCIAL SCIENCE; SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATICS (STEM); OTHER. The dataset was created based on the English MMLU dataset proposed in the original paper and follows its… See the full description on the dataset page: https://huggingface.co/datasets/gametwix/rummlu.Rustins_Super_Mega_Awesome_VEDU_Model
Rustin's Super Mega Awesome VEDU Model
A reproducible, heavily-documented pipeline that maps Ventenata dubia ("VEDU", an invasive
winter-annual grass) across Montana from satellite + environmental data.
Science reference: docs/VEDU_48_predictors_detailed.md
Data decisions & gotchas: docs/CONTRADICTIONS.md
Parity with the Earth Engine build: docs/GEE_PARITY.md
Continue-the-build guide: docs/HANDOFF.md
Label inventory: docs/DATA_SOURCES.md
What it produces
57… See the full description on the dataset page: https://huggingface.co/datasets/UniversityOfMontanaSAL/Rustins_Super_Mega_Awesome_VEDU_Model.hf-coding-tools-traces-run-april12
HuggingFace AI Coding Tools — Agent Traces
This dataset rehydrates the benchmark results from
davidkling/hf-coding-tools-dashboard
into the JSONL session format consumed by the
Hugging Face Agent Trace Viewer.
What's inside
31 sessions, one per (tool, model, effort, thinking) configuration
8,875 query → response turns total (≈17,750 events)
Tools covered: claude_code, codex, copilot, cursor
Models: claude-opus-4-6, claude-sonnet-4-6, claude-sonnet-4.6, composer-2… See the full description on the dataset page: https://huggingface.co/datasets/davidkling/hf-coding-tools-traces-run-april12.common_corpus_pre1939_RusssianДемонстрация низкого качества OCR/HTR, поражающего обучающие датасеты на русском 19 века и ранее.
Использовать для тестирования спеллчекеров и корректоров.
TODO:
common_corpus_pre1939_Russian -как результат коррекции, вместо нынешнего некачественного
common_corpus_pre1939_Russsian
Лицензия кау у исходного PleIAs/common_corpus
Мотивация:
https://huggingface.co/datasets/PleIAs/common_corpus/discussions/2#67adb8aba035b45ce76b33dd
Feb 13, 2025
A language column was added in Common Corpus 2.0, so… See the full description on the dataset page: https://huggingface.co/datasets/foss22/common_corpus_pre1939_Russsian.