Datasets

A colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org". This is the processed version of Google's mC4 dataset by AllenAI.

A parallel corpus of Ubuntu localization files. Source: https://translations.launchpad.net 244 languages, 23,988 bitexts total number of files: 30,959 total number of tokens: 29.84M total number of sentence fragments: 7.73M

The QCRI Educational Domain Corpus (formerly QCRI AMARA Corpus) is an open multilingual collection of subtitles for educational videos and lectures collaboratively transcribed and translated over the AMARA web-based platform. Developed by: Qatar Computing Research Institute, Arabic Language Technologies Group The QED Corpus is made public for RE...

This is a collection of translated sentences from Tatoeba 359 languages, 3,403 bitexts total number of files: 750 total number of tokens: 65.54M total number of sentence fragments: 8.96M

The Universal Declaration of Human Rights (UDHR) is a milestone document in the history of human rights. Drafted by representatives with different legal and cultural backgrounds from all regions of the world, it set out, for the first time, fundamental human rights to be universally protected. The Declaration was adopted by the UN General Assemb...

WikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles annotated with LOC (location), PER (person), and ORG (organisation) tags in the IOB2 format. This version corresponds to the balanced train, dev, and test splits of Rahimi et al. (2019), which supports 176 of the 282 languages fro...

It is a benchmark dataset for language identification and contains 235000 paragraphs of 235 languages

A sampling-enabled version of mC4, the colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org". This is a version of the processed version of Google's mC4 dataset by AllenAI, in which sampling methods are implemented to perform on the fly.