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18 dataset results for Kannada

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.

96 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.

57 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.

55 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.

24 PAPERS • NO BENCHMARKS YET

Belebele

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.

17 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.

14 PAPERS • 3 BENCHMARKS

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.

11 PAPERS • NO BENCHMARKS YET

Kannada-MNIST

The Kannada-MNIST dataset is a drop-in substitute for the standard MNIST dataset for the Kannada language.

7 PAPERS • NO BENCHMARKS YET

NISP (NITK-IISc Multilingual Multi-accent Speaker Profiling)

This dataset contains speech recordings along with speaker physical parameters (height, weight, shoulder size, age ) as well as regional information and linguistic information.

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

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.

3 PAPERS • NO BENCHMARKS YET

KanHope

KanHope (Kannada Hope speech dataset)

KanHope is a code mixed hope speech dataset for equality, diversity, and inclusion in Kannada, an under-resourced Dravidian language. The dataset consists of 6,176 user-generated comments in code mixed Kannada crawled from YouTube and manually labelled as bearing hope speech or not-hope speech.

2 PAPERS • 1 BENCHMARK

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

Kannada Treebank

This dataset was build as a part of development of Treebanks for Indian Languages funded by MeitY, Govt. of India. The Kannada treebank consists of 13.1 K sentences from general, tourism, conversational domains.

1 PAPER • 1 BENCHMARK

NISP- A Multi-lingual Multi-accent Dataset for Speaker Profiling

We announce the release of a new multilingual speaker dataset called NITK-IISc Multilingual Multi-accent Speaker Profiling(NISP) dataset. The dataset contains speech in six different languages -- five Indian languages along with Indian English. The dataset contains speech data from 345 bilingual speakers in India. Each speaker has contributed about 4-5 minutes of data that includes recordings in both English and their mother tongue. The transcript for the text is provided in UTF-8 format. For every speaker, the dataset contains speaker meta-data such as L1, native place, medium of instruction, current residing place etc. In addition the dataset also contains physical parameter information of the speakers such as age, height, shoulder size and weight. We hope that the dataset is useful for a diverse set of research activities including multilingual speaker recognition, language and accent recognition, automatic speech recognition etc.

1 PAPER • NO BENCHMARKS YET

Datasets

18 dataset results for Kannada