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

AfriSenti-SemEval Shared Task 12 of SemEval-2023. The task aims to performmonolingual sentiment classification (sub-task A) for 12 African languages,multilingual sentiment classification (sub-task B), and zero-shot sentimentclassification (task C). For sub-task A, we conducted experiments usingclassical machine learning classifiers, Afro-centric language models, andlanguage-specific models. For task B, we fine-tuned multilingual pre-trainedlanguage models that support many of the languages in the task. For task C, weused we make use of a parameter-efficient Adapter approach that leveragesmonolingual texts in the target language for effective zero-shot transfer. Ourfindings suggest that using pre-trained Afro-centric language models improvesperformance for low-resource African languages. We also ran experiments usingadapters for zero-shot tasks, and the results suggest that we can obtainpromising results by using adapters with a limited amount of resources.

Masakhane-Afrisenti at SemEval-2023 Task 12: Sentiment Analysis using Afro-centric Language Models and Adapters for Low-resource African Languages