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An Amharic News Text classification Dataset
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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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Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval
Amharic-specific dense retrieval models beat zero-shot multilingual baselines on a new headline-article benchmark, and a ColBERT variant achieves the top MRR@10 of 0.843.
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