  Hate speech is a growing problem on social media. It can seriously impact
society, especially in countries like Ethiopia, where it can trigger conflicts
among diverse ethnic and religious groups. While hate speech detection in
resource rich languages are progressing, for low resource languages such as
Amharic are lacking. To address this gap, we develop Amharic hate speech data
and SBi-LSTM deep learning model that can detect and classify text into four
categories of hate speech: racial, religious, gender, and non-hate speech. We
have annotated 5k Amharic social media post and comment data into four
categories. The data is annotated using a custom annotation tool by a total of
100 native Amharic speakers. The model achieves a 94.8 F1-score performance.
Future improvements will include expanding the dataset and develop state-of-the
art models.
  Keywords: Amharic hate speech detection, classification, Amharic dataset,
Deep Learning, SBi-LSTM
