metadata
license: cc-by-sa-4.0
# RexTRO111/articles
Welcome to the **RexTRO111/articles** dataset. This repository contains a curated, multi-source text dataset specifically preprocessed and structured for large language model (LLM) pretraining, fine-tuning, and natural language processing pipelines. 🌐
The dataset features a clean, flat architecture optimized for high-performance streaming and distributed data loading. 📊
## 📂 Dataset Structure
The dataset is saved in the Apache Parquet format. Each shard contains a flat schema with the following fields:
* `text` (string): The raw extracted text content, split exactly into **one line per paragraph** to preserve natural semantic context and chunking boundaries.
* `url` (string): The exact source URL from which the text was extracted to maintain strict provenance and citation history.
### Repository Layout
```bash
.
├── README.md
├── articles-0000.parquet
└── ...
🛠️ Data Sources & Curation
This dataset aggregates high-quality, text-dense knowledge bases from across the web:
- Wikipedia: Extracted main body content covering diverse domains, providing broad foundational knowledge. 📖
- arXiv: Academic abstracts and structured paper content spanning machine learning, computer science, and related fields. 🔬
- Other Text Repositories: Integrated text sequences designed to improve the general knowledge and reasoning capabilities of language models. 🧠
🚀 Quick Start / How to Use
You can easily stream or load this dataset using pandas or the Hugging Face datasets library.
Using Pandas 🐼
import pandas as pd
# Load a specific shard
df = pd.read_parquet("articles-0000.parquet")
print(df.head())
Using Hugging Face Datasets 🤗
from datasets import load_dataset
dataset = load_dataset("RexTRO111/articles")
print(dataset["train"][0])
⚖️ Licensing & Terms
This dataset is licensed under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.
- Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made.
- ShareAlike: If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- Disclaimer: Content included in this dataset is gathered from public web sources. Users are responsible for ensuring downstream compliance regarding fair use and source-specific terms (such as individual author licenses on arXiv).