# An Amharic News Text classification Dataset

arXiv: https://arxiv.org/abs/2103.05639 · HTML: https://mlsanity.com/paper/2103.05639/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 10 Mar 2021.

## Builds on (most cited first, 1 of 1)

- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)

## Built on by (most cited first, 10 of 10)

- [Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://mlsanity.com/paper/2204.06487/) (2022)
- [SERENGETI: Massively Multilingual Language Models for Africa](https://mlsanity.com/paper/2212.10785/) (2022)
- [MasakhaNEWS: News Topic Classification for African languages](https://mlsanity.com/paper/2304.09972/) (2023)
- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [Natural Language Processing in Ethiopian Languages: Current State, Challenges, and Opportunities](https://mlsanity.com/paper/2303.14406/) (2023)
- [EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation](https://mlsanity.com/paper/2403.13737/) (2024)
- [LR-Sum: Summarization for Less-Resourced Languages](https://mlsanity.com/paper/2212.09674/) (2022)
- [Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval](https://mlsanity.com/paper/2505.19356/) (2025)
- [AmharicIR+Instr: A Two-Dataset Resource for Neural Retrieval and Instruction Tuning](https://mlsanity.com/paper/2602.09914/) (2026)
- [The Multilingual Curse at the Retrieval Layer: Evidence from Amharic](https://mlsanity.com/paper/2605.24556/) (2026)

## Abstract (arXiv)

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.