TITLE: An Amharic News Text classification Dataset 

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.
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.
An Amharic News Text classification Dataset
http://arxiv.org/abs/2103.05639v1