The Universal Dependencies (UD) project seeks to develop cross-linguistically consistent treebank annotation of morphology and syntax for multiple languages. The first version of the dataset was released in 2015 and consisted of 10 treebanks over 10 languages. Version 2.7 released in 2020 consists of 183 treebanks over 104 languages. The annotation consists of UPOS (universal part-of-speech tags), XPOS (language-specific part-of-speech tags), Feats (universal morphological features), Lemmas, dependency heads and universal dependency labels.
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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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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.
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Itihasa is a large-scale corpus for Sanskrit to English translation containing 93,000 pairs of Sanskrit shlokas and their English translations. The shlokas are extracted from two Indian epics viz., The Ramayana and The Mahabharata.
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This dataset contains around 218K sentences, with 1.5 million words, from 30 different books designed for Post-OCR text correction.
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VedantaNY-10M is a curated dataset of over 750 hours of transcripts from public discourses on the Indian philosophy of Advaita Vedanta. Sourced from 612 YouTube lectures by Swami Sarvapriyananda of the Vedanta Society of New York (VSNY), the dataset contains ~10 million tokens. These lectures offer a comprehensive exposition of Advaita Vedanta, making the dataset an invaluable resource for philosophy and linguistics research.
This Sanskrit speech corpus has more than 78 hours of audio data and contains recordings of 45,953 sentences with a sampling rate of 22KHz. The content is mainly readings of texts spanning over various Śāstras of Saṃskṛtam literature and also includes contemporary stories, radio program, extempore discourse, etc.