WikiMatrix is a dataset of parallel sentences in the textual content of Wikipedia for all possible language pairs. The mined data consists of:
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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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WikiAnn is a dataset for cross-lingual name tagging and linking based on Wikipedia articles in 295 languages.
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Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the evaluation of mono- and multi-lingual models in high-, medium-, and low-resource languages. Each question has four multiple-choice answers and is linked to a short passage from the FLORES-200 dataset. The human annotation procedure was carefully curated to create questions that discriminate between different levels of generalizable language comprehension and is reinforced by extensive quality checks. While all questions directly relate to the passage, the English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. Belebele opens up new avenues for evaluating and analyzing the multilingual abilities of language models and NLP systems.
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Corpus of Egyptian Arabic-English Code-switching (ArzEn) is a spontaneous conversational speech corpus, obtained through informal interviews held at the German University in Cairo. The participants discussed broad topics, including education, hobbies, work, and life experiences. The corpus currently contains 12 hours of speech, having 6,216 utterances. The recordings were transcribed and translated into monolingual Egyptian Arabic and monolingual English.
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