datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ReviewRebuttal
Introduction
This dataset is the largest real-world consistency-ensured dataset for peer review, which features the widest range of conferences and the most complete review stages, including initial submissions, reviews, ratings and confidence, aspect ratings, rebuttals, discussions, score changes, meta-reviews, and final decisions.
Paper: https://arxiv.org/abs/2505.07920
If our dataset can help you, please consider include the following citation in your publications:… See the full description on the dataset page: https://huggingface.co/datasets/Daoze/ReviewRebuttal.Qwen3.8-27B-Drafter-SFT
Qwen3.8-27B Drafter SFT Corpus
Supervised fine-tuning data released for training speculative drafters for Qwen/Qwen3.8-27B. All completions were generated with Qwen/Qwen3.8-27B at revision 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0.
The dataset contains 367,535 source conversations and 450,401 train rows, totaling 1,953,218,671 tokens after filtering and evaluation decontamination. Rows contain Qwen3.8-27B-tokenized prompts and target-generated completions, together with loss… See the full description on the dataset page: https://huggingface.co/datasets/DaoCloud/Qwen3.8-27B-Drafter-SFT.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.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.Daoism-Wiki-QA-280K
Daoism-Wiki-QA-280K
規模型道教問答資料集——263,910 條從鼎稔道學館 wiki 與論文庫批量蒸餾、經 critic_score ≥ 93 嚴格篩選後的高品質 QA pairs。
由鼎稔道學館（lius.cc）發布。本資料集與 Daoism-QA-5K（grounded 精選子集）形成「規模 × 精度」雙資產，配合 Daoism-Qwen3.5-9B 開源道教 LLM 完成「模型 + 資料」全棧開源。
規格
項目
值
樣本數
263,910
篩選門檻
critic_score >= 93
來源規模
578,174 raw → 263,910 final（保留率 45.6%）
平均 critic_score
~95.8
滿分 (100) 樣本
41,830（15.9%）
95-99 分樣本
174,006（65.9%）
93-94 分樣本
48,074（18.2%）
語言
繁體中文（zh-Hant）
檔案大小
240 MB
重複去除
4,769 條（依… See the full description on the dataset page: https://huggingface.co/datasets/lius-cc/Daoism-Wiki-QA-280K.Daoism-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.