27Group/Zarma_NER

ZarmaNER-600 Dataset Dataset Description ZarmaNER-600 is a gold-standard dataset for Named Entity Recognition (NER) in Zarma. This dataset contains 600 manually annotated sentences, making it the first publicly available NER corpus for Zarma. It was created to support research in low-resource NLP, particularly for sequence tagging tasks, as part of the Rule-to-Tag (R2T) framework introduced in our paper, "R2T: A Case Study in Principled Learning for Low-Resource… See the full description on the dataset page: https://huggingface.co/datasets/27Group/Zarma_NER.