Alibaba
Datasets
All datasets matching “Alibaba”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.OmniThoughtV_Raw_1.8M
Dataset Introduction
OmniThoughtV is a large-scale multimodal long-chain-of-thought dataset distilled from the FineVision dataset using Alibaba Cloud's AI platform (PAI) distillation toolkit, EasyDistill. This dataset establishes a transparent and reproducible data distillation pipeline, enabling efficient construction of multimodal reasoning chains of thought. Fine-tuning smaller models with this dataset effectively endows them with stronger reasoning capabilities and enhances… See the full description on the dataset page: https://huggingface.co/datasets/alibaba-pai/OmniThoughtV_Raw_1.8M.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.OmniThoughtV_Filter_0.5M
Dataset Introduction
OmniThoughtV is a large-scale multimodal long-chain-of-thought dataset distilled from the FineVision dataset using Alibaba Cloud's AI platform (PAI) distillation toolkit, EasyDistill. This dataset establishes a transparent and reproducible data distillation pipeline, enabling efficient construction of multimodal reasoning chains of thought. Fine-tuning smaller models with this dataset effectively endows them with stronger reasoning capabilities and enhances… See the full description on the dataset page: https://huggingface.co/datasets/alibaba-pai/OmniThoughtV_Filter_0.5M.alibaba-ctf
Alibaba CTF Benchmark
Alibaba CTF Benchmark is a CTF benchmark designed to measure the frontier of agent work on Capture The Flag security challenges. It consists of 87 high-quality tasks curated from the 2023–2026 AlibabaCTF (formerly AliyunCTF) competition series, covering five core categories: Web (25), Pwn (19), Misc (14), Reverse (16), and Crypto (13). During the curation process, LLM-based challenges were excluded due to their additional credential requirements and test… See the full description on the dataset page: https://huggingface.co/datasets/Alibaba-AAIG/alibaba-ctf.terminal-bench-pro
Terminal-Bench Pro
Overview
Terminal-Bench Pro is a systematic extension of the original Terminal-Bench, designed to address key limitations in existing terminal-agent benchmarks.
400 tasks (200 public + 200 private) across 8 domains: data processing, games, debugging, system admin, scientific computing, software engineering, ML, and security
Expert-designed tasks derived from real-world scenarios and GitHub issues
High test coverage with ~28.3 test cases per… See the full description on the dataset page: https://huggingface.co/datasets/alibabagroup/terminal-bench-pro.