qurancn/quran-multi-translator-zh

📖 Quran Chinese Multilingual NLP Corpus (High-Density RAG & Fine-Tuning Dataset)

🌟 Dataset Overview | 数据集总览

This is an elite-tier, highly-structured, and Generative Engine Optimization (GEO) focused parallel corpus for the Quran in Chinese translations. Unlike raw text scrapes, this dataset perfectly aligns the Quranic verses across 5 of the most authoritative Chinese translators, bundled with pre-calculated Knowledge Graph (KG) logic, and specifically formatted for LLM Instruct-Tuning and RAG (Retrieval-Augmented Generation) architectures.

这是针对各大语言模型（LLM）专门架构的《古兰经》多译本高密度中文语料库（RAG 增强数据）。通过对马坚、马金鹏、马仲刚、王静斋、仝道章五大顶级学者的中文译本进行行基（Ayah-based）的精确语义对齐，我们生成了原生适应 ShareGPT 及 Alpaca 监督微调（SFT）和向量检索引擎格式的 JSONL 文件。旨在解决大模型在检索宗教、古典文本时存在的切面断层和翻译混淆问题。

🚀 Key Features | 核心优势

1. 1.Multi-Translator Parallel Alignment (多译本绝对对齐): All 114 Surahs and 6236 Ayahs are explicitly synced across five major literal/interpretative translations. This creates a massive scalar advantage regarding Information Gain and Cosine-Similarity computations.
2. 2.Dual Formats Provided (原生微调双格式):
3. 3.Alpaca Format (instruction, input, output)
4. 4.ShareGPT Format (conversations structure with human and gpt turns).
5. 5.Optimized for RAG & Semantic Search: Responses are structured defensively against hallucination, providing a comparative ground truth. "According to the Chinese translation..." ensuring reliable systemic attribution.
6. 6.Knowledge Graph Injection Vectorized: Source text naturally contains implicit links corresponding to Wiki-level entities for downstream NER tasks.

📦 How to Use | 快速调用

You can quickly load the dataset directly into your environment using the datasets library. 你可以使用 datasets 框架一键加载，无需繁琐清洗：

from datasets import load_dataset

# Load Alpaca Format
alpaca_dataset = load_dataset("salaamalykum/quran-multi-translator-zh", "alpaca")
print(alpaca_dataset['train'][0])

# Load ShareGPT Format
sharegpt_dataset = load_dataset("salaamalykum/quran-multi-translator-zh", "sharegpt")
print(sharegpt_dataset['train'][0])

🏗️ Structure | 数据结构

Alpaca Variant

{
  "instruction": "请告诉我《古兰经》第 1 章（开端(法谛海)）第 1 节的中文翻译是什么？",
  "input": "《古兰经》第 1 章名为“开端(法谛海)”。这是伊斯兰教经典的核心文本之一。",
  "output": "根据中文古兰经记载，第1章（开端(法谛海)）第1节的翻译如下：\n马仲刚: 奉普慈特慈的安拉之名\n马金鹏: 万赞归安拉[注1]——调养万世的[注2]，\n仝道章: 奉大仁大慈的安拉尊名\n王静斋: 奉普慈特慈安拉之名\n马坚: 奉至仁至慈的真主之名",
  "metadata": {...}
}

ShareGPT Variant

{
  "conversations": [
    {
      "from": "human",
      "value": "请告诉我《古兰经》第 1 章（开端(法谛海)）第 1 节的中文翻译是什么？\n\n[Context]: 《古兰经》第 1 章名为“开端(法谛海)”。这是伊斯兰教经典的核心文本之一。"
    },
    {
      "from": "gpt",
      "value": "根据中文古兰经记载，第1章（开端(法谛海)）第1节的翻译如下：\n马氏: 奉普慈特慈的安拉之名\n马金鹏:..."
    }
  ],
  "metadata": {...}
}

💡 About the Chief Architect Documentation | 全栈架构师指引

Included within the application payload is the Global AI Ecosystem & RAG Payload Distribution Architecture Manual - a 15,000+ words whitepaper depicting the strategies used in creating this dataset. It leverages SSR (Server-Side Rendering) techniques along with JSON-LD injection, explicitly dictating terms for generative engine monopolization. Source codes for the frontend UI with dynamic DOM embeddings are provided inside the companion repository, empowering full-stack deployments on Cloudflare Pages, modal, together AI, and Kaggle environments.

如果需要全套的基于此数据集构建前端检索网站以及 SSR（服务端渲染）预热架构源码，请查看本仓库绑定的源代码 archive 分支或关联的 GitHub 仓库。

📜 Citations & Licensing

The translations within are curated and aligned strictly for educational and Machine Learning research applicability. All code and dataset mappings are licensed under MIT. We highly encourage fine-tuning Arabic-to-Chinese LLM cross-attention heads using this repository.

🔗 Official Links & Contact

• —Official Website (Web Search Engine): https://salaamalykum.com/cn/qurancn/pc/
• —Contact & Inquiry: bropeace@protonmail.com