Project: Building "Mini-C4" — A Production-Grade LLM Pre-training Pipeline 🏗️

Project: Building "Mini-C4" Pre-training Corpus 🏗️ This project demonstrates how to build a miniaturized version of the C4 (Colossal Clean Crawled Corpus) pipeline. Our mission: transform chaotic, raw web data (Common Crawl) into low-noise, deduplicated, high-quality text ready for LLM pre-training. 👉 GitHub: datascale-ai/data_engineering_book 1. Project Brief Objective: Build a pipeline to process raw Common Crawl data into a clean text corpus. Input: Raw WARC files ( .warc.gz ) containing HTTP headers, HTML source, and binary noise. Output: Categorized JSONL files ( final_data.jsonl ) featuring clean text, language labels, and Perplexity (PPL) scores. Challenges: Extremely Low Signal-to-Noise Ratio: Over 90% of raw web data consists of navbars, ads, SEO spam, and JS code. Fuzzy Deduplication: Identifying semantically similar documents across millions of records is computationally expensive. Quality Quantification: How to distinguish "human-grade prose" from "machine-generated gibberi