datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
LongBench-v2
LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks
🌐 Project Page: https://longbench2.github.io
💻 Github Repo: https://github.com/THUDM/LongBench
📚 Arxiv Paper: https://arxiv.org/abs/2412.15204
LongBench v2 is designed to assess the ability of LLMs to handle long-context problems requiring deep understanding and reasoning across real-world multitasks. LongBench v2 has the following features: (1) Length: Context length ranging from 8k to… See the full description on the dataset page: https://huggingface.co/datasets/zai-org/LongBench-v2.UltraData-SFT-2605
UltraData-SFT-2605
📦 UltraData Collection |
🌐 UltraData |
🤗 MiniCPM5 Series
English |
中文
📚 Introduction
UltraData-SFT-2605 is the full set of core-domain SFT data used in the post-training of MiniCPM5-1B-SFT within the MiniCPM5-1B series, and a key representative of L3 refined data in the UltraData L0-L4 tiered data management framework. It covers math, code, knowledge, instruction following, and other core domains, containing over 15 million Deep… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/UltraData-SFT-2605.UltraData-SFT-Agent-2609
UltraData-SFT-Agent-2609
📦 UltraData Collection |
🌐 UltraData |
🤗 MiniCPM5 Series
English |
中文
📚 Introduction
UltraData-SFT-Agent-2609 is the L3 refined data for Agent instruction-tuning within UltraData's L0-L4 tiered data management framework. Built for the post-training of MiniCPM5-2B, it complements UltraData-SFT-2605 (core-domain SFT) with executable Agent trajectories. The release contains approximately 500,000 samples spanning tool use… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/UltraData-SFT-Agent-2609.AIME2025
AIME 2025 Dataset
Dataset Description
This dataset contains problems from the American Invitational Mathematics Examination (AIME) 2025-I & II.
python-codes-25k
License
MIT
This is a Cleaned Python Dataset Covering 25,000 Instructional Tasks
Overview
The dataset has 4 key features (fields): instruction, input, output, and text.It's a rich source for Python codes, tasks, and extends into behavioral aspects.
Dataset Statistics
Total Entries: 24,813
Unique Instructions: 24,580
Unique Inputs: 3,666
Unique Outputs: 24,581
Unique Texts: 24,813
Average Tokens per example: 508
Features… See the full description on the dataset page: https://huggingface.co/datasets/flytech/python-codes-25k.sql-create-context
Overview
This dataset builds from WikiSQL and Spider.
There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-sql LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-sql datasets. The CREATE TABLE statement can often be copy and pasted from different DBMS and provides table names, column… See the full description on the dataset page: https://huggingface.co/datasets/b-mc2/sql-create-context.t2-ragbench
Dataset Card for T2-RAGBench
Project Page | Paper | Code
IMPORTANT NOTICE:
We deleted VQAonBD from the dataset due to low quality of the question reformulations. If you still want to use it you will find the data in the previous commit history.
Dataset Description
Dataset Summary
T2-RAGBench is a benchmark dataset designed to evaluate Retrieval-Augmented Generation (RAG) on financial documents containing both text and tables. It consists of 23,088… See the full description on the dataset page: https://huggingface.co/datasets/G4KMU/t2-ragbench.docinsights-2026-shared-task-data
DocInsights 2026 Shared Task: DocSem
Document-grounded quantitative reasoning with evidence attribution
DocSem is the shared task of DocInsights 2026, the Workshop on Document Intelligence and Understanding co-located with EMNLP 2026 in Budapest, Hungary. The workshop theme is Beyond Plain Text: Bridging NLP and Document AI.
Workshop shared task | Source repository | Submission portal | Participant guide
Participants receive a PDF document and a paraphrased user_query. Systems… See the full description on the dataset page: https://huggingface.co/datasets/amitbcp/docinsights-2026-shared-task-data.TempPerturb-Eval-data
TempPerturb-Eval-data
Summary
TempPerturb-Eval-data is the released output dataset for TempPerturb-Eval, a benchmark for analyzing the robustness of Retrieval-Augmented Generation (RAG) systems under both internal variation and external perturbation.
This is an evaluation-artifact dataset: it stores model outputs and experiment metadata for controlled robustness analysis, rather than a new QA training corpus.
The release covers:
5 models
11 temperatures from 0.0 to 2.0
4… See the full description on the dataset page: https://huggingface.co/datasets/yongxin2020/TempPerturb-Eval-data.pii-masking-200k
👉 Looking for the newest release? The current flagship is ai4privacy/pii-masking-openpii-1.5m. 1.6M samples, 30 languages, 19 PII classes, Asia Pacific extension.?** The current flagship is ai4privacy/pii-masking-openpii-1m. 1.4M samples, 23 languages, 19 PII classes.
Ai4Privacy Community
Join our community at https://discord.gg/FmzWshaaQT to help build open datasets for privacy masking.
Purpose and Features
Previous world's largest open dataset for privacy.… See the full description on the dataset page: https://huggingface.co/datasets/ai4privacy/pii-masking-200k.spreadsheet-bench-v2-modified
SpreadsheetBench V2 Modified: Multi-Document QA
1,060 questions and reference answers grounded in 127 Excel workbooks, 35 PDFs and 9 DOCX files. This independent derivative of SpreadsheetBench 2 shifts the task from editing spreadsheets and producing workbook deliverables toward finding, interpreting and combining information in business documents.
An independent project built entirely from publicly available source material and newly authored QA annotations. No private company… See the full description on the dataset page: https://huggingface.co/datasets/hashmortar/spreadsheet-bench-v2-modified.Data-Prep-Bench
Data-Prep-Bench
This repository contains the data presented in DataPrep-Bench: Benchmarking LLMs as Training Data Preparators.
Code: https://github.com/OpenDCAI/Data-Preparation-Bench
Dataset Overview
This dataset is a comprehensive resource built for Supervised Fine-Tuning (SFT) and evaluation of Large Language Models (LLMs), covering six domains: Finance, Medicine, Law, Mathematics, Science, and General.
A key feature of this dataset is that we employed 12… See the full description on the dataset page: https://huggingface.co/datasets/lhpku20010120/Data-Prep-Bench.JMedBench
Maintainers
Junfeng Jiang@Aizawa Lab: jiangjf (at) is.s.u-tokyo.ac.jp
Jiahao Huang@Aizawa Lab: jiahao-huang (at) g.ecc.u-tokyo.ac.jp
If you find any error in this benchmark or want to contribute to this benchmark, please feel free to contact us.
Introduction
This is a dataset collection of JMedBench, which is a benchmark for evaluating Japanese biomedical large language models (LLMs).
Details can be found in this paper. We also provide an evaluation framework, med-eval… See the full description on the dataset page: https://huggingface.co/datasets/Coldog2333/JMedBench.DeepSWEGym2
Dataset Description
This dataset is a filtered and deduplicated version of a merge containing many high quality SWE datasets, it aims to improve benchmark results on DeepSWE-style problems, benchmarks, and general coding skills.
It is specifically filtered for rows with complex/long code problems in the original datasets, having an average row size of 214.19kb, a total uncompressed size of 17.56GB, and a total of 85974 examples.
Dataset Details
Curated by:… See the full description on the dataset page: https://huggingface.co/datasets/MoreThought/DeepSWEGym2.sorry-bench-202503
Dataset Card for SORRY-Bench Dataset (2025/03)
🏠Website
📑Paper
📚Dataset
💻Github
🧑⚖️Human Judgment Dataset
🤖Judge LLM
🪧UPDATE: In this iteration, we removed the category "Impersonation" due to its ambiguous definition, and that most models more or less fulfill such requests.This dataset contains 9.2K potentially unsafe instructions, intended to be used for LLM safety refusal evaluation.
Particularly, our base dataset consists of 440 unsafe… See the full description on the dataset page: https://huggingface.co/datasets/sorry-bench/sorry-bench-202503.commonsense_qa_2.0https://github.com/allenai/csqa2
@article{talmor2022commonsenseqa,
title={CommonsenseQA 2.0: Exposing the limits of AI through gamification},
author={Talmor, Alon and Yoran, Ori and Bras, Ronan Le and Bhagavatula, Chandra and Goldberg, Yoav and Choi, Yejin and Berant, Jonathan},
journal={arXiv preprint arXiv:2201.05320},
year={2022}
}
bird_sql_dev_20251106
BIRD-SQL Dev
🆕 Update 2025-11-06
We would like to express our sincere gratitude to the community for their continuous support and constructive feedback on the BIRD-SQL Dev dataset. Over the past year, we have received valuable suggestions through GitHub discussions, emails, and user reports. Based on these insights, we organized a quality review program led by a team of five PhD researchers in Data Science and AI, supported by a globally distributed group of industry… See the full description on the dataset page: https://huggingface.co/datasets/birdsql/bird_sql_dev_20251106.DeepSWEGym2-Ultra
Dataset Description
This dataset is an EXTREMELY filtered and deduplicated version of DeepSWE-Gym2-Edu, it aims to improve benchmark results on DeepSWE-style problems, benchmarks, and general coding skills.
It is specifically filtered for rows with complex/long code problems in the original datasets, having an average row size of 8.27MB, a total uncompressed size of 8.27GB, and a total of 1000 examples.
Dataset Details
Curated by: MoreThought
Funded by:… See the full description on the dataset page: https://huggingface.co/datasets/MoreThought/DeepSWEGym2-Ultra.SHP-2
🚢 Stanford Human Preferences Dataset v2 (SHP-2)
Summary
SHP-2 is a dataset of 4.8M collective human preferences over responses to questions/instructions in 129 different subject areas, from cooking to legal advice. It is an extended version of the original 385K SHP dataset.
The preferences are meant to reflect the helpfulness of one response over another, and are intended to be used for training RLHF reward models and NLG evaluation models (e.g., SteamSHP).
Each example… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/SHP-2.DeepSWEGym2-Edu
Dataset Description
This dataset is a filtered and deduplicated version of a merge containing many high quality SWE datasets, it aims to improve benchmark results on DeepSWE-style problems, benchmarks, and general coding skills.
It is specifically filtered for rows with complex/long code problems in the original datasets, having an average row size of 291.74kb, a total uncompressed size of 14.93GB, and a total of 53649 examples.
Dataset Details
Curated by:… See the full description on the dataset page: https://huggingface.co/datasets/MoreThought/DeepSWEGym2-Edu.bird23-train-filtered
BIRD-SQL Train (Filtered)
A high-quality subset of the original BIRD train split for text-to-SQL finetuning.
Overview
Over the past year the community has shared many observations about data quality in BIRD. We performed a rigorous data quality check process to retain examples that are consistent with schema and faithfully answer the question. The resulting set keeps 6,601 instances out of 9,428 (≈70%), and serves as a drop-in replacement for training.
Original Train: 9… See the full description on the dataset page: https://huggingface.co/datasets/birdsql/bird23-train-filtered.sorry-bench-202406
Dataset Card for SORRY-Bench Dataset (2024/06)
🏠Website
📑Paper
📚Dataset
💻Github
🧑⚖️Human Judgment Dataset
🤖Judge LLM
This dataset contains 9.5K potentially unsafe instructions, intended to be used for LLM safety refusal evaluation.
Particularly, our base dataset consists of 450 unsafe instructions in total, spanning across 45 finegrained safety categories (10 data points per category).
The dataset we present here equally captures risks from… See the full description on the dataset page: https://huggingface.co/datasets/sorry-bench/sorry-bench-202406.Omni-Edu
Omni-Edu — Core V6 SFT mixture
69,999 supervised instruction examples (~158M characters) covering K-12 subject
competence, curriculum grounding, diagnostic reasoning, pedagogical action and
general-purpose instruction. 12,146 rows (17.4%) are multimodal; every image
referenced by the JSONL ships in this repository under images/.
This is the system-prompted assembly of the v6 core mixture: every row carries
an explicit system message, and the non-system turns are byte-identical… See the full description on the dataset page: https://huggingface.co/datasets/lhpku20010120/Omni-Edu.ArtiMuse-10K
ArtiMuse:
Fine-Grained Image Aesthetics Assessment with Joint Scoring and Expert-Level Understanding
[🌐 Project Page]
[🚀 Online Demo]
[💻 Code]
[📄 Paper]
[[🧩 Checkpoints: 🤗 Hugging Face | 🤖 ModelScope]]
🌟 Building upon on ArtiMuse, we introduce UniPercept, a comprehensive follow-up work that provides a meticulous study on perceptual-level image understanding. It spans Image Aesthetics Assessment (IAA), Image Quality Assessment (IQA), and Image Structure & Texture… See the full description on the dataset page: https://huggingface.co/datasets/Thunderbolt215215/ArtiMuse-10K.DeepSWEGym2-Full
Dataset Description
This dataset is a merge containing many high quality SWE datasets, it aims to improve benchmark results on DeepSWE-style problems, benchmarks, and general coding skills.
It is NOT specifically filtered for rows with complex/long code problems in the original datasets, despite still having an average row size of 169.08kb, a total uncompressed size of 19.80GB, and a total of 122791 examples.
Dataset Details
Curated by: MoreThought
Funded by:… See the full description on the dataset page: https://huggingface.co/datasets/MoreThought/DeepSWEGym2-Full.advbench
AdvBench
This repository hosts a copy of the widely used AdvBench dataset,
a benchmark for evaluating the adversarial robustness and safety alignment of Large Language Models (LLMs).
AdvBench consists of adversarial prompts designed to elicit unsafe, harmful, or policy-violating responses from LLMs.
It is used in many LLM safety and jailbreak research papers as a standard evaluation dataset.
Contents
advbench.jsonl (or your actual filename): the standard set of… See the full description on the dataset page: https://huggingface.co/datasets/carl213/advbench.Think-Bench
THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Models
Official repository for "THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Models".
For more details, please refer to the project page with dataset exploration and visualization tools.
[Paper] [Github] [ModelScope Dataset] [Visualization]
👀 About Think-Bench
Reasoning models have made remarkable progress in complex tasks… See the full description on the dataset page: https://huggingface.co/datasets/zhiyuan218/Think-Bench.long-context-qa-curated-20
Dataset Card / 数据集卡
Dataset Description / 数据集简介
This public release contains 20 curated samples selected from a 10,000-record long-context QA collection. It targets retrieval over long documents, cross-section evidence synthesis, numerical reasoning, timeline reconstruction, and structured answer evaluation. The public subset contains 15 short-answer questions and 5 multiple-choice questions, balanced across Chinese and English.
本公开版本从 10,000 条长上下文问答数据中精选 20… See the full description on the dataset page: https://huggingface.co/datasets/LianeMarilin/long-context-qa-curated-20.Huatuo26M-Lite
Huatuo26M-Lite 📚
Table of Contents 🗂
Dataset Description 📝
Dataset Information ℹ️
Data Distribution 📊
Usage 🔧
Citation 📖
Dataset Description 📝
Huatuo26M-Lite is a refined and optimized dataset based on the Huatuo26M dataset, which has undergone multiple purification processes and rewrites. It has more data dimensions and higher data quality. We welcome you to try using it.
Dataset Information ℹ️
Dataset Name: Huatuo26M-Lite
Version:… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/Huatuo26M-Lite.Kimi-K2.5-Reasoning-1M-Cleaned
🪐 Kimi-K2.5-Reasoning-1M-Cleaned
Kimi-K2.5-Reasoning-1M-Cleaned is a cleaned derivative of ianncity/KIMI-K2.5-1000000x. It preserves the original four-config layout from the source dataset and rewrites each record into a unified reasoning-SFT schema with id, conversations, input, output, domain, and meta.
Summary
Source dataset: ianncity/KIMI-K2.5-1000000x
Source author: ianncity
Teacher model recorded in meta.teacher_model: KIMI-K2.5
Token lengths computed with… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned.