This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages. This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository.
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WikiANN, also known as PAN-X, is a multilingual named entity recognition dataset. It consists of Wikipedia articles that have been annotated with LOC (location), PER (person), and ORG (organization) tags in the IOB2 format¹². This dataset serves as a valuable resource for training and evaluating named entity recognition models across various languages.
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OSCAR or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture. The dataset used for training multilingual models such as BART incorporates 138 GB of text.
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The Basic Dataset for Sorani Kurdish Automatic Speech Recognition (BD-4SK-ASR) is a dataset for automatic speech recognition for Sorani Kurdish.
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Bianet is a parallel news corpus in Turkish, Kurdish and English It contains 3,214 Turkish articles with their sentence-aligned Kurdish or English translations from the Bianet online newspaper.
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The GATITOS (Google's Additional Translations Into Tail-languages: Often Short) dataset is a high-quality, multi-way parallel dataset of tokens and short phrases, intended for training and improving machine translation models. This dataset consists in 4,000 English segments (4,500 tokens) that have been translated into each of 26 low-resource languages, as well as three higher-resource pivot languages (es, fr, hi). All translations were made directly from English, with the exception of Aymara, which was translated from the Spanish.
a parallel corpus of Sorani (ckb or Central Kurdish) and Kurmanji (kmr or Northern Kurdish) dialects of Kurdish along with English (eng). The parallel corpus contains three manually-aligned corpus in Sorani-Kurmanji, Sorani-English and Kurmanji-English in various formats, namely Translation Memory eXchange file format (.tmx), parallel annotated text useful for ParaConc and raw parallel texts (.txt). This corpus contains 12,327 translation pairs in the two major dialects of Kurdish, Sorani and Kurmanji. We also provide 1,797 and 650 translation pairs in English-Kurmanji and English-Sorani.
Mega-COV is a billion-scale dataset from Twitter for studying COVID-19. The dataset is diverse (covers 234 countries), longitudinal (goes as back as 2007), multilingual (comes in 65 languages), and has a significant number of location-tagged tweets (~32M tweets).
WEATHub is a dataset containing 24 languages. It contains words organized into groups of (target1, target2, attribute1, attribute2) to measure the association target1:target2 :: attribute1:attribute2. For example target1 can be insects, target2 can be flowers. And we might be trying to measure whether we find insects or flowers pleasant or unpleasant. The measurement of word associations is quantified using the WEAT metric in our paper. It is a metric that calculates an effect size (Cohen's d) and also provides a p-value (to measure statistical significance of the results). In our paper, we use word embeddings from language models to perform these tests and understand biased associations in language models across different languages.
A corpus for two endangered languages of the Zaza-Gorani language family: Zazaki and Gorani.