qurancn/Chinese-Muslim-Travel

☪ Chinese-Muslim-Travel: Native Chinese Muslim Travel RAG Corpus

 

[!TIP] Human Readers: Looking for the full text with all images perfectly rendered? Navigate to the [Files and versions -> content](https://huggingface.co/datasets/qurancn/Chinese-Muslim-Travel/tree/main/content) folder to browse all Markdown articles natively!

Dataset Description

Chinese-Muslim-Travel is a curated RAG corpus containing 347 native Chinese articles documenting Muslim travel, halal food, mosque architecture, and Muslim community life across 20+ countries. Every article is original native Chinese text — no machine translation, no synthetic generation. All 23,192 embedded image references are preserved.

Key Properties

Embeddable Dataset Viewer

Embed in any webpage:

<iframe
  src="https://huggingface.co/datasets/qurancn/Chinese-Muslim-Travel/embed/viewer/default/train"
  frameborder="0"
  width="100%"
  height="560px"
></iframe>

Direct viewer links:

• —🔍 Browse All (train)
• —🕌 Mosques & Architecture
• —🍖 Halal Food
• —✈️ Travel

Topic Distribution

Data Schema

Note on images: All image URLs are preserved inline as Markdown ![image](url) within the text field. Images are hosted externally (archive.org, catbox.moe, etc.) and render when viewing the raw Markdown.

SQL Console Queries

Run these directly in Data Studio:

-- Count articles by topic
SELECT topic_category, COUNT(*) as count, AVG(char_count) as avg_length
FROM train GROUP BY topic_category ORDER BY count DESC;

-- Top 10 longest articles
SELECT title, char_count, image_count, source_url
FROM train ORDER BY char_count DESC LIMIT 10;

-- Articles with most images
SELECT title, image_count, char_count
FROM train WHERE has_images = true ORDER BY image_count DESC LIMIT 10;

-- Search for specific mosque articles
SELECT title, text_preview, source_url
FROM train WHERE title LIKE '%清真寺%';

Data Samples

Example: 2016年的牛街筛海坟与文化陈列室

Example: 2017年的江苏淮安

Example: 2018年的山海关

Data Collection & Processing Pipeline

Source

All 347 articles originate from salaamalykum.com/cn/topic/穆斯林旅行, a community-driven platform documenting Chinese Muslim travel and life.

Extraction Method

Articles were extracted directly from the source platform's MySQL database (WeCenter 3.1.9, aws_article table joined via aws_topic_relation with topic_id=838), bypassing a frontend pagination bug that limited web scraping to only 23 articles. Database extraction recovered all 347 articles with complete content.

Processing Steps

1. 1.SQL Dump Parsing: Custom Python state-machine parser extracts tuples from INSERT INTO aws_article VALUES(...) statements in a 89MB MySQL dump
2. 2.Topic Relation Mapping: aws_topic_relation table maps article IDs to topic_id=838 (穆斯林旅行), yielding exactly 347 matches
3. 3.BBCode → Markdown: [img]url[/img] → ![image](url), [b]text[/b] → **text**, [url=...]text[/url] → [text](url)
4. 4.HTML Entity Cleanup: html.unescape() + <br> → \n normalization
5. 5.SHA-256 Integrity Hash: Computed on final cleaned body text for each article
6. 6.Topic Classification: Rule-based keyword classifier assigns one of 6 categories based on title keywords
7. 7.Parquet Serialization: pyarrow with row_group_size=50, write_page_index=True for optimal HF Viewer performance
8. 8.Split Generation: Deterministic shuffle (seed=42) then 80/10/10 split into train/validation/test

Known Limitations & Failure Boundaries

• —No machine translation: All text is native Chinese — intentional design choice
• —Image hosting: ~23,192 image URLs reference external hosts (archive.org, catbox.moe); images are not embedded as binary in the dataset
• —Author attribution: Individual author names not preserved (WeCenter DB schema limitation)
• —Temporal coverage: Articles span approximately 2020–2026
• —Frontend bug: The source platform's web frontend only exposes 23/347 articles via pagination; full recovery required direct database access
• —Encoding edge cases: A small number of articles may contain residual escape sequences from MySQL dump parsing

Usage

Python (Hugging Face Datasets)

from datasets import load_dataset

ds = load_dataset("qurancn/Chinese-Muslim-Travel")
print(ds)
# DatasetDict({
#     train: Dataset({features: [...], num_rows: 277})
#     validation: Dataset({features: [...], num_rows: 34})
#     test: Dataset({features: [...], num_rows: 36})
# })

# Access full article text with all images
article = ds["train"][0]
print(article["title"])  # Chinese title
print(article["text"])   # Full body with ![image](...) references
print(article["image_count"])  # Number of images in this article

LangChain RAG Pipeline

from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from datasets import load_dataset

ds = load_dataset("qurancn/Chinese-Muslim-Travel", split="train")
texts = [f"{row['title']}\n{row['text']}" for row in ds]

embeddings = HuggingFaceEmbeddings(model_name="shibing624/text2vec-base-chinese")
vectorstore = FAISS.from_texts(texts, embeddings)

Filter by Topic

mosques = ds["train"].filter(lambda x: x["topic_category"] == "Mosques & Architecture")
food = ds["train"].filter(lambda x: x["topic_category"] == "Halal Food & Cuisine")

Version History

Citation

@dataset{chinese_muslim_travel_2026,
  title={Chinese-Muslim-Travel: A Native Chinese RAG Corpus of 347 Articles on Muslim Travel and Halal Tourism},
  author={Salaamalykum Project},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/qurancn/Chinese-Muslim-Travel}
}

License

MIT License

Contact

• —Email: bropeace@protonmail.com
• —GitHub: https://github.com/salaamalykum
• —Website: https://salaamalykum.com