datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MME-RealWorld
2024.11.14 🌟 MME-RealWorld now has a lite version (50 samples per task) for inference acceleration, which is also supported by VLMEvalKit and Lmms-eval.
2024.10.27 🌟 LLaVA-OV currently ranks first on our leaderboard, but its overall accuracy remains below 55%, see our leaderboard for the detail.
2024.09.03 🌟 MME-RealWorld is now supported in the VLMEvalKit and Lmms-eval repository, enabling one-click evaluation—give it a try!"
2024.08.20 🌟 We are very proud to launch MME-RealWorld, which… See the full description on the dataset page: https://huggingface.co/datasets/Mapleyuchen/MME-RealWorld.maplestory-worlds-creator-qa
MapleStory Worlds Creator QA
Synthetic question-answer dataset built from the official
MapleStory Worlds Creator Center
documentation. Questions are generated to be self-contained and grounded in the
source docs; answers avoid source/meta references so they read like an expert
explanation. Some QA pairs are composed from multiple related documents
(see combo_sources).
Parallel Korean/English. Intended for instruction tuning, QA, and retrieval.
Composition… See the full description on the dataset page: https://huggingface.co/datasets/msw-ai-tf/maplestory-worlds-creator-qa.MetaRAG_Cross-Issue_OSSQA
MetaRAG Cross-Issue OSSQA
Dataset page: https://huggingface.co/datasets/MapleBi/MetaRAG_Cross-Issue_OSSQA
MetaRAG Cross-Issue OSSQA is an English open-source software issue question-answering and retrieval benchmark. Each example asks a question grounded in one GitHub issue and requires evidence from a related issue. The data contains explicit cross-issue references and a three-document silver evidence path.
Dataset configurations
Configuration
Splits
Rows… See the full description on the dataset page: https://huggingface.co/datasets/MapleBi/MetaRAG_Cross-Issue_OSSQA.maple-personas
MAPLE-Personas: A Benchmark for Evaluating Personalized Conversational AI
A dataset for evaluating how well conversational AI systems learn and apply user preferences from natural dialogue. This benchmark accompanies the MAPLE (Memory-Adaptive Personalized LEarning) framework.
Dataset Description
This dataset tests an AI assistant's ability to implicitly learn user traits from conversation context and apply that knowledge to personalize responses to open-ended… See the full description on the dataset page: https://huggingface.co/datasets/prdeepakbabu/maple-personas.canada-china-trade
Canada-China B2B Trade Dataset
Dataset Description
A curated dataset of Canada-China bilateral trade statistics, commodity breakdowns, provincial data, and B2B sourcing knowledge for use in AI/LLM research and applications.
Maintained by: MapleBridge.io — AI-powered B2B matching platform for Canada-China trade.
Dataset Contents
File
Description
Rows
canada_china_trade_annual.csv
Annual bilateral trade volume 2015-2024 (CAD billions)
10… See the full description on the dataset page: https://huggingface.co/datasets/maplebridge/canada-china-trade.