datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
eMCR
eMCR: A Benchmark for Multi-Condition Product Retrieval in Chinese E-Commerce
This is the official dataset and evaluation code for the paper "eMCR: A Benchmark for Multi-Condition Product Retrieval in Chinese E-Commerce".
Overview
Product search increasingly involves queries that combine multiple requirements — product attributes, brands, prices, exclusions, and visual descriptions. Existing retrieval benchmarks provide limited support for diagnosing which… See the full description on the dataset page: https://huggingface.co/datasets/alibabagroup/eMCR.IndustryBench-MIPU
IndustryBench-MIPU: Benchmarking Multi-Image Attribute Value Extraction for Industrial Products
Multi-Image Industrial Product Understanding Benchmark — evaluating MLLMs on structured attribute extraction from real-world industrial product images.
Industrial product specifications are scattered across multiple heterogeneous images — specification tables, nameplates, technical drawings. IndustryBench-MIPU tests whether MLLMs can reliably recover them through four… See the full description on the dataset page: https://huggingface.co/datasets/alibaba-multimodal-industrial-ai/IndustryBench-MIPU.Superior-Reasoning-SFT-gpt-oss-120b
Superior-Reasoning-SFT-gpt-oss-120b
📣 News
Our dataset ranked #1 on the Hugging Face Datasets Trending leaderboard from January 20 to January 30.
🚀 Overview
The Superior-Reasoning-SFT-gpt-oss-120b dataset is a high-quality, open-source collection containing 435K samples designed to democratize the training of high-performance Long Chain-of-Thought (Long-CoT) models. Unlike standard distilled datasets that rely on random sampling or… See the full description on the dataset page: https://huggingface.co/datasets/Alibaba-Apsara/Superior-Reasoning-SFT-gpt-oss-120b.RynnBrain-Bench
RynnBrain-Bench
Introduction
We introduce RynnBrain-Bench, a high-dimensional evaluation suite designed to holistically benchmark the cognition and localization capabilities of embodied understanding models in complex household environments.
Advancing beyond existing benchmarks, RynnBrain-Bench features a unique emphasis on fine-grained understanding and precise spatiotemporal localization within episodic video sequences.
RynnBrain-Bench systematically… See the full description on the dataset page: https://huggingface.co/datasets/Alibaba-DAMO-Academy/RynnBrain-Bench.aacr-bench
Dataset for Running AACR-Bench
English | 简体中文
This is a test set designed for automated code review reflection models, primarily aiming to evaluate the extent to which a model can intercept low-quality review comments. The dataset contains 2,145 code review comments, consisting of 1,505 expert-verified correct comments and 640 incorrect comments.
This data is part of the AACR-Bench project and is provided by the Alibaba Aone team.
Data Sample
Each sample in the… See the full description on the dataset page: https://huggingface.co/datasets/Alibaba-Aone/aacr-bench.WebShaper
WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization
Github: https://github.com/Alibaba-NLP/WebAgent
Paper: https://arxiv.org/pdf/2507.15061
TLTR
WebShaper is a synthesized training dataset for information-seeking (IS) task. It is based on our proposed task formalization of IS, and synthesized by our Expander Agent. WebShaper would cover a broader range of task forms, reasoning structure, and diversified knowledge.
Description… See the full description on the dataset page: https://huggingface.co/datasets/Alibaba-NLP/WebShaper.SimpleQA-Bench
SimpleQA-Bench
Tags: factuality, EN, ZH, short-form-answer, human-label
Copyright: © 2024 alibaba-pai
Source.OpenAI's SimpleQA: Blog & Paper / Data & simple-evals ProjectOpenStellarTeam's Chinese-SimpleQA: Blog & Paper, Data@HF
Factuality is a complicated topic because it is hard to measure—evaluating the factuality of any given arbitrary claim is challenging, and language models can generate long completions that contain dozens of factual claims. In SimpleQA, we will focus on… See the full description on the dataset page: https://huggingface.co/datasets/alibaba-pai/SimpleQA-Bench.ClinHallu
CLINHALLU Benchmark
CLINHALLU is a benchmark for diagnosing stage-wise hallucinations in medical MLLM reasoning.
Paper: CLINHALLU: A Benchmark for Diagnosing Stage-Wise Hallucinations in Medical MLLM ReasoningGitHub: alibaba-damo-academy/ClinHallu
Benchmark Results
Accuracy and stage-wise hallucination rates on CLINHALLU. We report answer accuracy (Acc) and hallucination rates for visual recognition (H^V), knowledge recall (H^K), and reasoning integration (H^R).… See the full description on the dataset page: https://huggingface.co/datasets/Alibaba-DAMO-Academy/ClinHallu.SynSearch-Data
SynSearch-Data
SynSearch-Data is an environment-aligned Search Agent training dataset produced through task synthesis and Solver-in-the-Loop verification. This first release contains 5,000 high-quality multi-hop ReAct trajectories covering 4,182 audited tasks.
Data format
Each JSONL row contains:
messages: the complete ReAct conversation, including tool calls and tool responses;
metadata.task_id / run_idx / trajectory_rank: trajectory provenance;
metadata.seed:… See the full description on the dataset page: https://huggingface.co/datasets/alibaba-pai/SynSearch-Data.SKYLENAGE-ReasoningMath
SKYLENAGE-ReasoningMath
I. 基准介绍
SKYLENAGE-ReasoningMath（推理数学评测集）覆盖从基础算术到高等数学的多层次推理任务，涵盖逻辑推导、代数变换、几何分析、概率统计等多个维度，旨在评估模型在结构化数学问题中的理解、推理与解答能力。数据集设计注重问题难度分级与认知层次划分，支持细粒度的能力评估。
数据集介绍：
ReasoningMath.json为数据文件，总计开源100道题，包括了序号，难度，学科分类，题目，最终解答。
Selected Problem Appendix.xlsx为部分题目解答附录，总计开源30道题，包括了序号，思维链分析标准，解题分析标准。
SKYLENAGE Technical Report.pdf为技术报告。
II. 基准特性… See the full description on the dataset page: https://huggingface.co/datasets/alibabagroup/SKYLENAGE-ReasoningMath.SKYLENAGE-GameCodeGym
V-GameGym: Visual Game Generation for Code Large Language Models
Abstract
Code large language models have demonstrated remarkable capabilities in programming tasks, yet current benchmarks primarily focus on single modality rather than visual game development. Most existing code-related benchmarks evaluate syntax correctness and execution accuracy, overlooking critical game-specific metrics such as playability, visual aesthetics, and user engagement that are… See the full description on the dataset page: https://huggingface.co/datasets/alibabagroup/SKYLENAGE-GameCodeGym.OmniThought-0528
OmniThought-0528
A High-Quality Chain-of-Thought (CoT) Dataset for Enhanced Model Distillation
Overview
OmniThought-0528 is an advanced version of the OmniThought dataset, designed to enhance reasoning capabilities in language models through high-quality Chain-of-Thought (CoT) distillation. It consists of 365,000 reasoning chains across diverse domains, including mathematics, coding, and science, generated and rigorously validated using state-of-the-art teacher models.… See the full description on the dataset page: https://huggingface.co/datasets/alibaba-pai/OmniThought-0528.E2Rank_ranking_datasetsAlibaba-NLP__gte-Qwen2-7B-instruct-details
Dataset Card for Evaluation run of Alibaba-NLP/gte-Qwen2-7B-instruct
Dataset automatically created during the evaluation run of model Alibaba-NLP/gte-Qwen2-7B-instruct
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/Alibaba-NLP__gte-Qwen2-7B-instruct-details.SimulationTuning_google_datasetxvbench
XVBench
XVBench is a benchmark for evaluating multimodal retrieval-augmented generation systems on cross-video understanding. It is introduced alongside the paper VimRAG: Navigating Massive Visual Context in Retrieval-Augmented Generation via Multimodal Memory Graph.
The questions in XVBench are created based on videos from HowTo100M, a large-scale corpus of narrated instructional videos. The benchmark focuses on questions that require models or agents to retrieve and reason over… See the full description on the dataset page: https://huggingface.co/datasets/Alibaba-NLP/xvbench.SimulationTuning_wiki_dataset