HPLT (High Performance Language Technologies)

HPLT (High Performance Language Technologies) is an EU-funded project producing open datasets and models across a broad spread of languages. Its flagship text release, MonoHPLT, is a monolingual corpus, meaning plain text with one language per document rather than aligned translation pairs, covering 75 languages and drawn from a blend of web crawls and the Internet Archive. Importantly, it does not lean solely on Common Crawl, so it gives you a genuinely different pool of text, which is valuable when you want to cut overlap with the corpora everyone else already trains on.

The data comes as line-delimited `JSON`, usually one document per line with the text and its metadata together, so it streams cleanly through a processing pipeline without loading the whole thing into memory. HPLT also publishes its cleaning and deduplication tooling and thorough documentation, so you can see precisely how the corpus was filtered and rerun or retune that processing if your thresholds differ.

For RAG, this is most useful as a multilingual knowledge base or as pretraining and fine-tuning material for non-English work, particularly for the medium- and lower-resource languages that are thin in the usual English-centric datasets. It arrives already cleaned rather than raw, so you start a step ahead of working straight from crawl archives.

On licensing, HPLT releases the data under CC0 wherever possible, which effectively places it in the public domain: commercial use is permitted, with no attribution and no share-alike obligation. Do note the "wherever possible" wording, since some underlying material may carry constraints, so confirm the terms for the specific languages and versions you pull.

Web-crawled text is never flawless: expect some boilerplate, imperfect language detection, and quality that varies from one language to the next, so keep a filtering pass in your pipeline. Alongside Common Crawl derivatives and language-specific corpora like OSCAR, HPLT is a strong, openly licensed option when you want broad multilingual coverage from a source that is transparent about its processing.