English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this work, we introduce L3Cube-IndicNews, a multilingual textclassification corpus aimed at curating a high-quality dataset for Indianregional languages, with a specific focus on news headlines and articles. Wehave centered our work on 10 prominent Indic languages, including Hindi,Bengali, Marathi, Telugu, Tamil, Gujarati, Kannada, Odia, Malayalam, andPunjabi. Each of these news datasets comprises 10 or more classes of newsarticles. L3Cube-IndicNews offers 3 distinct datasets tailored to handledifferent document lengths that are classified as: Short HeadlinesClassification (SHC) dataset containing the news headline and news category,Long Document Classification (LDC) dataset containing the whole news articleand the news category, and Long Paragraph Classification (LPC) containingsub-articles of the news and the news category. We maintain consistent labelingacross all 3 datasets for in-depth length-based analysis. We evaluate each ofthese Indic language datasets using 4 different models including monolingualBERT, multilingual Indic Sentence BERT (IndicSBERT), and IndicBERT. Thisresearch contributes significantly to expanding the pool of available textclassification datasets and also makes it possible to develop topicclassification models for Indian regional languages. This also serves as anexcellent resource for cross-lingual analysis owing to the high overlap oflabels among languages. The datasets and models are shared publicly athttps://github.com/l3cube-pune/indic-nlp

L3Cube-IndicNews: News-based Short Text and Long Document Classification Datasets in Indic Languages