Papers › An Amharic News Text classification Dataset
An Amharic News Text classification Dataset
Israel Abebe Azime, Nebil Mohammed
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text Classification | An Amharic News Text classification Dataset | Naive Bayes using Tf-idf features | Accuracy | 62.3 | #1 of 2 | Archive leaderboard | report |
| Text Classification | An Amharic News Text classification Dataset | Naive Bayes using count vectorizer features | Accuracy | 62.2 | #2 of 2 | Archive leaderboard | report |
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