Pith. sign in

REVIEW 1 cited by

A New Massive Multilingual Dataset for High-Performance Language Technologies

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2403.14009 v1 pith:BLDT2RNQ submitted 2024-03-20 cs.CL

Ona de Gibert , Graeme Nail , Nikolay Arefyev , Marta Bañón , Jelmer van der Linde , Shaoxiong Ji , Jaume Zaragoza-Bernabeu , Mikko Aulamo
show 5 more authors
Gema Ramírez-Sánchez Andrey Kutuzov Sampo Pyysalo Stephan Oepen Jörg Tiedemann
This is my paper
classification cs.CL
keywords languagecorporamonolingualcoversdatasethigh-performancehpltlanguages
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive. We describe our methods for data acquisition, management and processing of large corpora, which rely on open-source software tools and high-performance computing. Our monolingual collection focuses on low- to medium-resourced languages and covers 75 languages and a total of ~5.6 trillion word tokens de-duplicated on the document level. Our English-centric parallel corpus is derived from its monolingual counterpart and covers 18 language pairs and more than 96 million aligned sentence pairs with roughly 1.4 billion English tokens. The HPLT language resources are one of the largest open text corpora ever released, providing a great resource for language modeling and machine translation training. We publicly release the corpora, the software, and the tools used in this work.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language

    cs.CL 2025-06 conditional novelty 6.0 of 10

    An adaptive, per-language data filtering and deduplication pipeline produces multilingual LLM pre-training corpora that beat prior public datasets on 11 of 14 evaluated languages, and a 20TB, 1,868 language-script dat...

Pith tools