datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Multi-SWE-bench
SWE-bench-Java: A GitHub Issue Resolving Benchmark for Java
📰 News
[Aug. 27, 2024]:We’ve released the JAVA version of SWE-bench! Check it out on Hugging Face. For more details, see our paper!
📄 Abstract
GitHub issue resolving is a critical task in software engineering, recently gaining significant attention in both industry and academia. Within this task, SWE-bench has been released to evaluate issue resolving capabilities of large language models (LLMs)… See the full description on the dataset page: https://huggingface.co/datasets/Daoguang/Multi-SWE-bench.CodeM-Multilinugal-Data
CodeM: Can Programming Languages Boost Each Other via Instruction Tuning?
Paper GitHub
Abstract
When human programmers have mastered a programming language, it would be easier when they learn a new programming language. In this report, we focus on exploring whether programming languages can boost each other during the instruction fine-tuning phase of code large language models. We conduct extensive experiments of 8 popular programming languages (Python, JavaScript… See the full description on the dataset page: https://huggingface.co/datasets/Daoguang/CodeM-Multilinugal-Data.Muse-Glimmer-OPB-100K
Muse Glimmer OPB 100K
On-policy OpenPerfectBlend training data used for DaoCloud/Muse-Glimmer-30B-DSpark.
Prompts are sampled from mlabonne/open-perfectblend, and assistant turns are regenerated on-policy with Muse Glimmer 30B.
The dataset contains 99,984 successfully generated conversations and 148,900 train-turn rows. Responses were regenerated with Muse Glimmer 30B at four reasoning strengths.
Reasoning strength
Conversations
Train-turn rows
low
64,997
96,765… See the full description on the dataset page: https://huggingface.co/datasets/DaoCloud/Muse-Glimmer-OPB-100K.MM-Bench-E-CommerceThis is the HuggingFace repository of the paper named MOON: Generative MLLM-based Multimodal Representation Learning for E-commerce Product Understanding in WSDM 2026 (oral).
In this paper, we argue that generative Multimodal Large Language Models (MLLMs) hold significant potential for improving product representation learning.
We propose the first generative MLLM-based model named MOON for product representation learning.
Furthermore, we contruct and publish a large-scale real-world… See the full description on the dataset page: https://huggingface.co/datasets/Daoze/MM-Bench-E-Commerce.CondAmbigQA-2K
CondAmbigQA-2K Dataset
Dataset Description
This is an expanded version of the CondAmbigQA dataset, growing from the original 200 entries to 2000 entries.
Dataset Summary
CondAmbigQA-2K contains 2000 question-answering pairs with conditional contexts and ground truth answers. Each entry includes:
Question: The ambiguous question
Properties: Contains condition, groundtruth, and citations
Context (ctxs): Retrieved relevant passages with scores
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Apocalypse-AGI-DAO/CondAmbigQA-2K.daoism-knowledge-rag
Daoism Knowledge RAG
鼎稔道學館（Dingren Daoxue Lab）館藏知識庫——專為 RAG（Retrieval-Augmented Generation）優化的道教知識結構化資料集，95,919 條目。
Overview
This dataset is the curated knowledge base behind Dingren Daoxue Lab (鼎稔道學館), released under Apache 2.0 to enable open-source RAG with the Daoism-Qwen3.5-9B model.
It covers eight categories of Daoist knowledge:
Type
Count
Content
concept
~32,700
神學概念、宇宙論、修煉名相
scripture
~23,000
經文、戒律、科儀文獻
deity
~12,000
神祇、仙真、神格體系
ritual
~11,000… See the full description on the dataset page: https://huggingface.co/datasets/lius-cc/daoism-knowledge-rag.han-dao-proposal-records-v1
Humanoid DAO Proposal Records
This dataset contains governance proposals
submitted within the Humanoid Network DAO.
It enables humanoid agents to analyze,
simulate, and vote on protocol decisions.
Contents
Proposal description
Category
Impact scope
Voting outcome
Use Cases
Governance analysis
DAO participation
Decision modeling
Part of
Humanoid Network (HAN)
License
MIT
Daoism-QA-Eval-v1
Daoism-QA-Eval-v1
Benchmark Evaluation Set for Daoism-Qwen3.5-9B and other LLMs on Daoist knowledge tasks.
由鼎稔道學館（lius.cc）發布。本 eval set 是 Daoism-QA-5K v0.1 中經 stratified sampling 抽出的 120 題 hold-out 集，永久切出不再用於任何 SFT 訓練。
完整評測方法論見本 repo 的 methodology.md 與 evaluator_prompt_v1.md。
規格
項目
值
樣本數
120
抽樣方式
Stratified（5 task_type × 24 題）
分層
每類依 groundedness_score 取 top/mid/bottom 1/3 各 8 題
來源
Daoism-QA-5K v0.1（249 條 pilot）
切出狀態
Hold-out，永久不再用於 SFT 訓練
語言
繁體中文… See the full description on the dataset page: https://huggingface.co/datasets/lius-cc/Daoism-QA-Eval-v1.qa-daotao-sft
Data SFT - Dữ liệu QA miền đào tạo Ver 2
Thư mục này chứa bộ dữ liệu QA miền đào tạo Ver 2 dùng cho fine-tune SFT chung. Bài toán chính của bộ dữ liệu là hỏi đáp theo ngữ cảnh, trong đó mỗi mẫu gồm câu hỏi, câu trả lời và văn bản tham chiếu.
Cấu trúc thư mục
data_sft/
├── train.jsonl
├── validation.jsonl
└── test.jsonl
Mỗi file ở định dạng JSONL, mỗi dòng là một mẫu dữ liệu độc lập.
Thống kê
Split
Số mẫu
train.jsonl
11,431
validation.jsonl
615… See the full description on the dataset page: https://huggingface.co/datasets/vnu-llm2023-ftdata/qa-daotao-sft.daoism-rag-sft-v1
Daoism RAG-Aware SFT Dataset v1
每個 pair 都帶 retrieved context（2 份道教文獻），訓練 LLM 學會「看上下文回答」。
格式
{
"instruction": "以下是相關的道教文獻：\n【文件 A：...】\n... \n【文件 B：...】\n...\n\n問題：請說明...",
"input": "",
"output": "..."
}
規模
Train: 344455
Val: 18129
總計: 362584
用途
訓練 LLM 走 RAG-SaaS 場景。部署到「客戶丟自己道教資料庫 + 用戶問題」這種 production 工作流時，比純 SFT model 準確率高 30%+。
License
CC-BY-NC-SA 4.0
qa-daotao-cho-rlDaoism-QA-5K
Daoism-QA-5K
首個結構化、帶證據引用的道教問答資料集（v0.1 pilot preview — 249 條；v1.0 計畫 5,000 條）
由鼎稔道學館（lius.cc）發布，配合全球首個開源道教 LLM Daoism-Qwen3.5-9B 同步建設道教 NLP 標準語料庫。
概要
每條樣本是一組「問題 + 結構化回答 + 引用證據鏈」，所有可驗證主張都對應到 retrieval 來源的 evidence_id。資料來自鼎稔道學館內部 wiki（117,830 條條目、102,303 條 embedding）以及學術論文庫。
生成模型：OpenAI gpt-5.5（透過 codex-oauth proxy）
Retrieval：PostgreSQL FTS（NodeSearch.haystack gin_trgm_ops 索引）
驗證：Pydantic v2 schema + 6 個品質指標 hard validators
語言：繁體中文（zh-Hant）
規格
項目
值… See the full description on the dataset page: https://huggingface.co/datasets/lius-cc/Daoism-QA-5K.llama3-msc-daojuadl1daozhenlangchain_docsvintern_data1