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

The extensive rise in consumption of online social media (OSMs) by a largenumber of people poses a critical problem of curbing the spread of hatefulcontent on these platforms. With the growing usage of OSMs in multiplelanguages, the task of detecting and characterizing hate becomes more complex.The subtle variations of code-mixed texts along with switching scripts only addto the complexity. This paper presents a solution for the HASOC 2021Multilingual Twitter Hate-Speech Detection challenge by team PreCog IIITHyderabad. We adopt a multilingual transformer based approach and describe ourarchitecture for all 6 subtasks as part of the challenge. Out of the 6 teamsthat participated in all the subtasks, our submissions rank 3rd overall.

Battling Hateful Content in Indic Languages HASOC '21