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17 dataset results for Lithuanian

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.

517 PAPERS • 13 BENCHMARKS

Common Voice

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.

414 PAPERS • 147 BENCHMARKS

Europarl

Europarl (European Parliament Proceedings Parallel Corpus)

A corpus of parallel text in 21 European languages from the proceedings of the European Parliament.

128 PAPERS • NO BENCHMARKS YET

CC100

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.

113 PAPERS • NO BENCHMARKS YET

WikiANN

WikiANN (PAN-X)

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.

69 PAPERS • 4 BENCHMARKS

OSCAR

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.

64 PAPERS • NO BENCHMARKS YET

MultiEURLEX

MultiEURLEX is a multilingual dataset for topic classification of legal documents. The dataset comprises 65k European Union (EU) laws, officially translated in 23 languages, annotated with multiple labels from the EUROVOC taxonomy. The dataset covers 23 official EU languages from 7 language families.

11 PAPERS • NO BENCHMARKS YET

EUR-Lex-Sum

EUR-Lex-Sum is a dataset for cross-lingual summarization. It is based on manually curated document summaries of legal acts from the European Union law platform. Documents and their respective summaries exist as crosslingual paragraph-aligned data in several of the 24 official European languages, enabling access to various cross-lingual and lower-resourced summarization setups. The dataset contains up to 1,500 document/summary pairs per language, including a subset of 375 cross-lingually aligned legal acts with texts available in all 24 languages.

7 PAPERS • NO BENCHMARKS YET

GeoCoV19

GeoCoV19 is a large-scale Twitter dataset containing more than 524 million multilingual tweets. The dataset contains around 378K geotagged tweets and 5.4 million tweets with Place information. The annotations include toponyms from the user location field and tweet content and resolve them to geolocations such as country, state, or city level. In this case, 297 million tweets are annotated with geolocation using the user location field and 452 million tweets using tweet content.

4 PAPERS • NO BENCHMARKS YET

QALD-9-Plus

QALD-9-Plus Dataset Description QALD-9-Plus is the dataset for Knowledge Graph Question Answering (KGQA) based on well-known QALD-9.

3 PAPERS • 1 BENCHMARK

Kaleidoscope

Kaleidoscope (Kaleidoscope: In-language Exams for Massively Multilingual Vision Evaluation)

The evaluation of vision-language models (VLMs) has mainly relied on English-language benchmarks, leaving significant gaps in both multilingual and multicultural coverage. While multilingual benchmarks have expanded, both in size and languages, many rely on translations of English datasets, failing to capture cultural nuances. In this work, we propose Kaleidoscope, as the most comprehensive exam benchmark to date for the multilingual evaluation of vision-language models. Kaleidoscope is a large-scale, in-language multimodal benchmark designed to evaluate VLMs across diverse languages and visual inputs. Kaleidoscope covers 18 languages and 14 different subjects, amounting to a total of 20,911 multiple-choice questions. Built through an open science collaboration with a diverse group of researchers worldwide, Kaleidoscope ensures linguistic and cultural authenticity. We evaluate top-performing multilingual vision-language models and find that they perform poorly on low-resource languages

2 PAPERS • NO BENCHMARKS YET

EUROPA

Dataset Description EUROPA is a dataset designed for training and evaluating multilingual keyphrase generation models in the legal domain. It consists of legal judgments from the Court of Justice of the European Union (EU) and includes instances in all 24 official EU languages.

1 PAPER • NO BENCHMARKS YET

GLAMI-1M (A Multilingual Image-Text Fashion Dataset)

We introduce GLAMI-1M: the largest multilingual image-text classification dataset and benchmark. The dataset contains images of fashion products with item descriptions, each in 1 of 13 languages. Categorization into 191 classes has high-quality annotations: all 100k images in the test set and 75% of the 1M training set were human-labeled. The paper presents baselines for image-text classification showing that the dataset presents a challenging fine-grained classification problem: The best scoring EmbraceNet model using both visual and textual features achieves 69.7% accuracy. Experiments with a modified Imagen model show the dataset is also suitable for image generation conditioned on text.

1 PAPER • 1 BENCHMARK

HALvest

HALvest is a textual dataset comprising 17 billion tokens in 56 languages and 13 domains.

1 PAPER • NO BENCHMARKS YET

INCLUDE-base (44 languages)

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.

1 PAPER • NO BENCHMARKS YET

PolyNews

PolyNews is a multilingual dataset containing news titles in 77 languages and 19 scripts.

1 PAPER • NO BENCHMARKS YET

PolyNewsParallel

PolyNews is a multilingual parallel dataset containing news titles 833 language pairs, spanning in 64 languages and 17 scripts.

1 PAPER • NO BENCHMARKS YET

Datasets

17 dataset results for Lithuanian