Datasets:

Modalities:
Text
Formats:
parquet
Languages:
Arabic
DOI:
Libraries:
Datasets
pandas
License:

You need to agree to share your contact information to access this dataset

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Log in or Sign Up to review the conditions and access this dataset content.

Dataset Card for Sawalni-AI/Darija-Arabic-Classification

This dataset provides a starting point to develop a classifier for Moroccan Darija in Arabic writing.

Dataset Details

This dataset contains 1.5k classified records, 85% in Moroccan Darija and 15% in Modern Standard Arabic. Classification has been performed using internal tools by the Sawalni AI project, and is based on the Goud/Goud-sum dataset.

  • Curated by: Omar Kamali
  • Funded by: Omar Kamali
  • Shared by: Omar Kamali
  • Language(s) (NLP): Moroccan Darija, Modern Standard Arabic
  • License: Exclusively licensed for use during the ThinkAI.ma 2024 hackathon.

Uses

This dataset is intended to develop classification tooling or approaches to distinguish between Moroccan Darija and MSA (Modern Standard Arabic).

Citation Information

You can cite this work as follows:

@dataset{sawalni-darija-arabic-classification,
author={ Omar Kamali },
title={ Darija Arabic Classification },
year={ 2024 },
month={ may },
url={ https://huggingface.co/datasets/sawalni-ai/darija-arabic-classification },
doi={ 10.57967/hf/2240 },
publisher={ Hugging face }
}

References

Thanks to the contributors of the Goud/Goud-sum dataset for making their work available.

@inproceedings{issam2022goudma,
title={Goud.ma: a News Article Dataset for Summarization in Moroccan Darija},
author={Abderrahmane Issam and Khalil Mrini},
booktitle={3rd Workshop on African Natural Language Processing},
year={2022},
url={https://openreview.net/forum?id=BMVq5MELb9}
}

Dataset Card Contact

Contact the Sawalni AI team here.

Learn more at

License

This dataset is intended and licensed for exclusive use during the ThinkAI.ma hackathon, edition 2024, except with written permission from the authors. Derivative models are allowed with attribution and citation, provided the models do not include significant portions of the dataset verbatim. Derivative datasets are not allowed. Given sufficient demand a public version can be made available after the hackathon upon request.

Downloads last month
36