An Amharic News Text classification Dataset

TL;DR

This paper introduces a new Amharic news text classification dataset with over 50,000 articles across six categories, aiming to support NLP research in low-resource languages.

Contribution

It provides the first large-scale labeled Amharic news dataset with baseline performance benchmarks to facilitate future research.

Findings

Dataset contains over 50,000 articles

Categorized into 6 classes

Provides baseline classification results

Abstract

In NLP, text classification is one of the primary problems we try to solve and its uses in language analyses are indisputable. The lack of labeled training data made it harder to do these tasks in low resource languages like Amharic. The task of collecting, labeling, annotating, and making valuable this kind of data will encourage junior researchers, schools, and machine learning practitioners to implement existing classification models in their language. In this short paper, we aim to introduce the Amharic text classification dataset that consists of more than 50k news articles that were categorized into 6 classes. This dataset is made available with easy baseline performances to encourage studies and better performance experiments.