configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: darija_arabizi
dtype: string
- name: darija_arabic
dtype: string
splits:
- name: train
num_bytes: 1745286
num_examples: 67186
download_size: 1173353
dataset_size: 1745286
Overview
This dataset is a modified version of the Darija Open Dataset (DODa), tailored specifically for the purpose of learning a transliteration mapping from Arabizi Darija text to Arabic letters Darija.
The Arabizi-To-Arabic-Mapping (ATAM) Transliteration Dataset serves as a valuable resource for training models to accurately transliterate Arabizi Darija text into Arabic letters Darija, facilitating natural language processing tasks in the Moroccan dialect.
Key Features:
Adapted Structure: The dataset has been meticulously adapted from the original DODa format to focus on the transliteration task, ensuring relevance and effectiveness in training transliteration models.
Diverse Text Samples: It encompasses a diverse range of Arabizi Darija text samples, covering various linguistic nuances and expressions commonly found in informal communications.
Annotated Transliterations: Each Arabizi Darija text entry is accompanied by its corresponding transliteration into Arabic letters Darija, enabling supervised learning for transliteration mapping.
N.B: We have a One-To-Many relationship as each word in the Arabic letter format can be associated (written) to many others in the Arabizi format.
Usage:
Researchers and developers can leverage this dataset to train and evaluate machine learning models aimed at automating the transliteration process from Arabizi Darija to Arabic letters Darija.
Acknowledgments:
We extend our gratitude to the creators and contributors of the Darija Open Dataset (DODa) for their pioneering work, which serves as the foundation for this transliteration dataset adaptation.
Contact:
For inquiries or contributions related to the ATAM Transliteration Dataset, feel free to reach out.