Web crawl processing converts massive raw HTML archives into clean text suitable for language model training. The processor handles the full pipeline from WARC file ingestion through content extraction, language identification, quality filtering, and structured output.
Content extraction uses a combination of readability algorithms and DOM analysis to separate main content from boilerplate elements like navigation menus, advertisements, cookie banners, and footer links. The extractor preserves document structure including headings, lists, and tables.
Language identification classifies each document using a fast n-gram based detector that supports over 200 languages. Documents are routed to language-specific processing pipelines that apply appropriate tokenization, normalization, and quality heuristics.
Quality filtering removes machine-generated spam, SEO content farms, cookie-cutter templates, and other low-value pages. The filter combines rule-based heuristics with a trained quality classifier that scores content on informativeness, coherence, and writing quality.
The processor outputs clean text in JSONL format with rich metadata including source URL, crawl timestamp, language, quality score, and content category.