Common Voice is an audio dataset that consists of a unique MP3 and corresponding text file. There are 9,283 recorded hours in the dataset. The dataset also includes demographic metadata like age, sex, and accent. The dataset consists of 7,335 validated hours in 60 languages.
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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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X-FACT is a large publicly available multilingual dataset for factual verification of naturally existing real-world claims. The dataset contains short statements in 25 languages and is labeled for veracity by expert fact-checkers. The dataset includes a multilingual evaluation benchmark that measures both out-of-domain generalization, and zero-shot capabilities of the multilingual models.
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xMIND is an open, large-scale multilingual news dataset for multi- and cross-lingual news recommendation. xMIND is derived from the English MIND dataset using open-source neural machine translation (i.e., NLLB 3.3B).
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AM2iCo is a wide-coverage and carefully designed cross-lingual and multilingual evaluation set. It aims to assess the ability of state-of-the-art representation models to reason over cross-lingual lexical-level concept alignment in context for 14 language pairs.
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Dataset Summary INCLUDE is a comprehensive knowledge- and reasoning-centric benchmark across 44 languages that evaluates multilingual LLMs for performance in the actual language environments where they would be deployed. It contains 22,637 4-option multiple-choice-questions (MCQ) extracted from academic and professional exams, covering 57 topics, including regional knowledge.
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