| --- |
| license: cc0-1.0 |
| language: |
| - zh |
| size_categories: |
| - n<1K |
| task_categories: |
| - text-generation |
| - feature-extraction |
| tags: |
| - islam |
| - travel |
| - mosques |
| - halal |
| - raw-corpus |
| - rag |
| pretty_name: Salaamalykum Islamic Articles |
| configs: |
| - config_name: corpus |
| data_files: |
| - split: train |
| path: data/corpus.parquet |
| - config_name: rag_chunks |
| data_files: |
| - split: train |
| path: data/rag_chunks.parquet |
| --- |
| |
| # Salaamalykum Islamic Articles Corpus (Parquet) |
|
|
| This is a highly structured, machine-readable dataset comprising 34 original articles curated from [salaamalykum.com](https://salaamalykum.com). |
| It has been natively formatted in **Parquet** to support efficient SQL querying in the Hugging Face Dataset Viewer and Data Studio. |
|
|
| ## Strict Metadata Adherence |
| - **Dataset Hygiene**: Contains ONLY informational articles. No fake `ShareGPT` or `Alpaca` tags are used. |
| - **Data Provenance**: All articles are authored by Hasan09, tracking Muslim travel guides, mosque histories, and cross-sectarian observations. |
|
|
| ## Views (Splits) |
| This repository exposes two subsets via Parquet formatting to prevent `TooBigContentError` and enable scalable RAG architectures: |
|
|
| ### 1. `corpus` (Full Articles) |
| Designed for LLM pre-training and long-context analysis. |
| - `id`: Unique article identifier. |
| - `title`: Article title. |
| - `url`: Canonical URL. |
| - `word_count`: Length of the text for easy filtering in SQL. |
| - `tags`: Comma-separated categories. |
| - `preview`: Lightweight summary for UI viewers. |
| - `content`: The complete Markdown text. |
|
|
| ### 2. `rag_chunks` (Retrieval-Augmented Generation) |
| Designed for Embedding models, Semantic Search, and Atlas visualizations. |
| - `chunk_id`: Unique chunk identifier. |
| - `text`: Specific paragraph. |
| - `chunk_index`: Position of the paragraph in the original text. |
|
|
| ## Usage (SQL Console) |
| You can directly query this dataset via the Hugging Face SQL Console: |
| ```sql |
| SELECT title, url FROM "corpus" WHERE word_count > 1000 |
| ``` |
|
|
