zarma
Datasets
All datasets matching “zarma”zarma-collecte-priveezarma_tts_dataenglish-zarma_sentence-pairs_mt560
English-Zarma Parallel Dataset
This dataset contains parallel sentences in English and Zarma (Niger).
Dataset Information
Language Pair: English ↔ Zarma
Language Code: dje
Country: Niger
Original Source: OPUS MT560 Dataset
Dataset Structure
The dataset contains parallel sentences that can be used for:
Machine translation training
Cross-lingual NLP tasks
Language model fine-tuning
Citation
If you use this dataset, please cite the citation guide of… See the full description on the dataset page: https://huggingface.co/datasets/michsethowusu/english-zarma_sentence-pairs_mt560.ZarmaLanguageRules
Description
This repo contains 20 grammar rules for Zarma language. The rules were create as part of the R2T project which is a proof of concept of a paradigm: Principled Learning (PrL).
Citation
If you use this resource, please use this citation.
@article{r2t2025,
title={R2T: A Case Study in Principled Learning for Low-Resource POS Tagging},
author={Author1 and Author2 and Author3},
journal={TBD},
year={2025},
url={TBD}
}
Acknowledgments
We thank… See the full description on the dataset page: https://huggingface.co/datasets/27Group/ZarmaLanguageRules.noisy_zarma
Zarma Noisy Dataset
Overview
The Zarma Noisy Dataset is a collection of Zarma sentences with artificially introduced noise to simulate human-like errors. This dataset is designed for tasks such as grammatical error correction (GEC), text denoising, and robustness testing in natural language processing (NLP) for low-resource languages like Zarma. It is derived from a clean monolingual Zarma dataset (monolingual_zarma.jsonl) by applying various types of noise… See the full description on the dataset page: https://huggingface.co/datasets/Zauberman/noisy_zarma.noisy_zarma
Zarma Noisy Dataset
Overview
The Zarma Noisy Dataset is a collection of Zarma sentences with artificially introduced noise to simulate human-like errors. This dataset is designed for tasks such as grammatical error correction (GEC), text denoising, and robustness testing in natural language processing (NLP) for low-resource languages like Zarma. It is derived from a clean monolingual Zarma dataset (monolingual_zarma.jsonl) by applying various types of noise, including… See the full description on the dataset page: https://huggingface.co/datasets/27Group/noisy_zarma.