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26 dataset results for Gujarati

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 • 3 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

XL-Sum

XL-Sum is a comprehensive and diverse dataset for abstractive summarization comprising 1 million professionally annotated article-summary pairs from BBC, extracted using a set of carefully designed heuristics. The dataset covers 44 languages ranging from low to high-resource, for many of which no public dataset is currently available. XL-Sum is highly abstractive, concise, and of high quality, as indicated by human and intrinsic evaluation.

60 PAPERS • NO BENCHMARKS YET

Samanantar

Samanantar is the largest publicly available parallel corpora collection for Indic languages: Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu. The corpus has 49.6M sentence pairs between English to Indian Languages.

39 PAPERS • NO BENCHMARKS YET

IndicCorp

IndicCorp is a large monolingual corpora with around 9 billion tokens covering 12 of the major Indian languages. It has been developed by discovering and scraping thousands of web sources - primarily news, magazines and books, over a duration of several months.

28 PAPERS • NO BENCHMARKS YET

IndicGLUE

IndicGLUE (Indic General Language Understanding Evaluation Benchmark)

We now introduce IndicGLUE, the Indic General Language Understanding Evaluation Benchmark, which is a collection of various NLP tasks as de- scribed below. The goal is to provide an evaluation benchmark for natural language understanding ca- pabilities of NLP models on diverse tasks and mul- tiple Indian languages.

15 PAPERS • 4 BENCHMARKS

X-Fact

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.

14 PAPERS • 1 BENCHMARK

Dakshina

The Dakshina dataset is a collection of text in both Latin and native scripts for 12 South Asian languages. For each language, the dataset includes a large collection of native script Wikipedia text, a romanization lexicon which consists of words in the native script with attested romanizations, and some full sentence parallel data in both a native script of the language and the basic Latin alphabet.

13 PAPERS • NO BENCHMARKS YET

MuMiN

MuMiN is a misinformation graph dataset containing rich social media data (tweets, replies, users, images, articles, hashtags), spanning 21 million tweets belonging to 26 thousand Twitter threads, each of which have been semantically linked to 13 thousand fact-checked claims across dozens of topics, events and domains, in 41 different languages, spanning more than a decade.

5 PAPERS • 3 BENCHMARKS

Naamapadam

Naamapadam is a Named Entity Recognition (NER) dataset for the 11 major Indian languages from two language families. In each language, it contains more than 400k sentences annotated with a total of at least 100k entities from three standard entity categories (Person, Location and Organization) for 9 out of the 11 languages. The training dataset has been automatically created from the Samanantar parallel corpus by projecting automatically tagged entities from an English sentence to the corresponding Indian language sentence.

5 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

IndicNLP Corpus

The IndicNLP corpus is a large-scale, general-domain corpus containing 2.7 billion words for 10 Indian languages from two language families.

3 PAPERS • NO BENCHMARKS YET

IndicTTS

A special corpus of Indian languages covering 13 major languages of India. It comprises of 10000+ spoken sentences/utterances each of mono and English recorded by both Male and Female native speakers. Speech waveform files are available in .wav format along with the corresponding text. We hope that these recordings will be useful for researchers and speech technologists working on synthesis and recognition. You can request zip archives of the entire database here.

3 PAPERS • 13 BENCHMARKS

Leipzig Corpora

The Leipzig Corpora Collection presents corpora in different languages using the same format and comparable sources. All data are available as plain text files and can be imported into a MySQL database by using the provided import script. They are intended both for scientific use by corpus linguists as well as for applications such as knowledge extraction programs. The corpora are identical in format and similar in size and content. They contain randomly selected sentences in the language of the corpus and are available in sizes from 10,000 sentences up to 1 million sentences. The sources are either newspaper texts or texts randomly collected from the web. The texts are split into sentences. Non-sentences and foreign language material was removed. Because word co-occurrence information is useful for many applications, these data are precomputed and included as well. For each word, the most significant words appearing as immediate left or right neighbor or appearing anywhere within the

2 PAPERS • NO BENCHMARKS YET

OpenSLR

OpenSLR (Open Speech and Language Resources)

OpenSLR is a repository of open speech and language resources, including large-scale transcribed audio corpora and related software. It serves as a central platform for researchers and practitioners to access and share datasets used in speech recognition (ASR), text-to-speech (TTS), and linguistic research.

2 PAPERS • NO BENCHMARKS YET

XAlign

It consists of an extensive collection of a high quality cross-lingual fact-to-text dataset in 11 languages: Assamese (as), Bengali (bn), Gujarati (gu), Hindi (hi), Kannada (kn), Malayalam (ml), Marathi (mr), Oriya (or), Punjabi (pa), Tamil (ta), Telugu (te), and monolingual dataset in English (en). This is the Wikipedia text <--> Wikidata KG aligned corpus used to train the data-to-text generation model. The Train & validation splits are created using distant supervision methods and Test data is generated through human annotations.

2 PAPERS • 1 BENCHMARK

DIGITal (Digitally Generated Numerals)

Digitally Generated Numerals (DIGITal) Description The Digitally Generated Numerals (DIGITal) dataset consists of 100,000 image pairs representing digits from 0 to 9. These image pairs include both low and high-quality versions, with a resolution of 128x128 pixels.

1 PAPER • NO BENCHMARKS YET

IRLCov19

IRLCov19 is a multilingual Twitter dataset related to Covid-19 collected in the period between February 2020 to July 2020 specifically for regional languages in India. It contains more than 13 million tweets.

1 PAPER • NO BENCHMARKS YET

Koo Platform Dataset

Full Koo platform Dataset. See https://zenodo.org/records/10476212

1 PAPER • NO BENCHMARKS YET

M3LS

M3LS (Multi-Lingual Multi-Modal Summarization Dataset)

Significant developments in techniques such as encoder-decoder models have enabled us to represent information comprising multiple modalities. This information can further enhance many downstream tasks in the field of information retrieval and natural language processing; however, improvements in multi-modal techniques and their performance evaluation require large-scale multi-modal data which offers sufficient diversity. Multi-lingual modeling for a variety of tasks like multi-modal summarization, text generation, and translation leverages information derived from high-quality multi-lingual annotated data. In this work, we present the current largest multi-lingual multi-modal summarization dataset (M3LS), and it consists of over a million instances of document-image pairs along with a professionally annotated multi-modal summary for each pair. It is derived from news articles published by British Broadcasting Corporation(BBC) over a decade and spans 20 languages, targeting diversity a

1 PAPER • NO BENCHMARKS YET

MAKED

MAKED (MultiModal MultiLingual Summarization and Keyword Extraction Dataset)

Keyword extraction is an integral task for many downstream problems like clustering, recommendation, search and classification. Development and evaluation of keyword extraction techniques require an exhaustive dataset; however, currently, the community lacks large-scale multi-lingual datasets. In this paper, we present MAKED, a large-scale multi-lingual keyword extraction dataset comprising of 540K+ news articles from British Broadcasting Corporation News (BBC News) spanning 20 languages. It is the first keyword extraction dataset for 11 of these 20 languages. The quality of the dataset is examined by experimentation with several baselines. We believe that the proposed dataset will help advance the field of automatic keyword extraction given its size, diversity in terms of languages used, topics covered and time periods as well as its focus on under-studied languages.

1 PAPER • NO BENCHMARKS YET

MILU

MILU (Multi-task Indic Language Understanding Benchmark)

Overview MILU (Multi-task Indic Language Understanding Benchmark) is a comprehensive evaluation dataset designed to assess the performance of Large Language Models (LLMs) across 11 Indic languages. It spans 8 domains and 42 subjects, reflecting both general and culturally specific knowledge from India.

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

CVIT PIB

We present sentence aligned parallel corpora across 10 Indian Languages - Hindi, Telugu, Tamil, Malayalam, Gujarati, Urdu, Bengali, Oriya, Marathi, Punjabi, and English - many of which are categorized as low resource. The corpora are compiled from online sources which have content shared across languages. The corpora presented significantly extends present resources that are either not large enough or are restricted to a specific domain (such as health). We also provide a separate test corpus compiled from an independent online source that can be independently used for validating the performance in 10 Indian languages. Alongside, we report on the methods of constructing such corpora using tools enabled by recent advances in machine translation and cross-lingual retrieval using deep neural network based methods.

0 PAPER • NO BENCHMARKS YET

Datasets

26 dataset results for Gujarati