Datasets

The dataset we use is crawled and filtered from Microsoft Documentation, whose document located at https://github.com/MicrosoftDocs/.

A parallel corpus of KDE4 localization files (v.2). 92 languages, 4,099 bitexts total number of files: 75,535 total number of tokens: 60.75M total number of sentence fragments: 8.89M

The Microsoft Terminology Collection can be used to develop localized versions of applications that integrate with Microsoft products. It can also be used to integrate Microsoft terminology into other terminology collections or serve as a base IT glossary for language development in the nearly 100 languages available. Terminology is provided in ...

Parallel corpora from Web Crawls collected in the ParaCrawl project and further processed for making it a multi-parallel corpus by pivoting via English. Here we only provide the additional language pairs that came out of pivoting. The bitexts for English are available from the ParaCrawl release. 40 languages, 669 bitexts total number of files: 4...

NoReC was created as part of the SANT project (Sentiment Analysis for Norwegian Text), a collaboration between the Language Technology Group (LTG) at the Department of Informatics at the University of Oslo, the Norwegian Broadcasting Corporation (NRK), Schibsted Media Group and Aller Media. This first release of the corpus comprises 35,194 revie...

OPUS-100 is English-centric, meaning that all training pairs include English on either the source or target side. The corpus covers 100 languages (including English).OPUS-100 contains approximately 55M sentence pairs. Of the 99 language pairs, 44 have 1M sentence pairs of training data, 73 have at least 100k, and 95 have at least 10k.

A parallel corpus of GNOME localization files. Source: https://l10n.gnome.org 187 languages, 12,822 bitexts total number of files: 113,344 total number of tokens: 267.27M total number of sentence fragments: 58.12M

Parallel corpora from Web Crawls collected in the ParaCrawl project 40 languages, 41 bitexts total number of files: 20,995 total number of tokens: 21.40G total number of sentence fragments: 1.12G

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...

A freely available paraphrase corpus for 73 languages extracted from the Tatoeba database. Tatoeba is a crowdsourcing project mainly geared towards language learners. Its aim is to provide example sentences and translations for particular linguistic constructions and words. The paraphrase corpus is created by populating a graph with Tatoeba sent...

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 core of WIT3 is the TED Talks corpus, that basically redistributes the original content published by the TED Conference website (http://www.ted.com). Since 2007, the TED Conference, based in California, has been posting all video recordings of its talks together with subtitles in English and their translations in more than 80 languages. Asid...

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...

The Universal Morphology (UniMorph) project is a collaborative effort to improve how NLP handles complex morphology in the world’s languages. The goal of UniMorph is to annotate morphological data in a universal schema that allows an inflected word from any language to be defined by its lexical meaning, typically carried by the lemma, and by a r...

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