NAMAA-Space/Ara-TyDi-Triplet

Arabic Mr. TyDi in Triplet Format

Dataset Summary

This dataset is a transformed version of the Arabic subset of the Mr. TyDi dataset, designed specifically for training retrieval and re-ranking models. Each query is paired with a positive passage and one of the multiple negative passages in a triplet format: (query, positive, negative). This restructuring resulted in a total of 362,000 rows, making it ideal for pairwise ranking tasks and contrastive learning approaches.

The dataset maintains the original purpose of Mr. TyDi for monolingual retrieval, while offering a simplified and scalable format for learning-to-rank tasks.

Dataset Structure

The dataset includes a train split, presented in the triplet format with the following fields:

• query: The query string.
• positive: The relevant passage for the query.
• negative: A non-relevant passage for the query.

Example Data

Triplet Format

{
  "query": "متى تم تطوير نظرية الحقل الكمي؟", 
  "positive": {
    "text": "بدأت نظرية الحقل الكمي بشكل طبيعي بدراسة التفاعلات الكهرومغناطيسية ..."
  },
  "negative": {
    "text": "تم تنفيذ النهج مؤخرًا ليشمل نسخة جبرية من الحقل الكمي ..."
  }
}

Language Coverage

The dataset focuses exclusively on the Arabic subset of Mr. TyDi.

Loading the Dataset

You can load the dataset using the datasets library from Hugging Face:

from datasets import load_dataset

dataset = load_dataset('NAMAA-Space/Ara-TyDi-Triplet')
dataset 

Dataset Usage

The new format facilitates training retrieval and re-ranking models by providing explicit negative passage fields. This structure simplifies the handling of negative examples during model training and evaluation.

Citation Information

If you use this dataset in your research, please cite the original Mr. TyDi paper and this dataset as follows:

@article{mrtydi,
      title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval}, 
      author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
      year={2021},
      journal={arXiv:2108.08787},
}

@dataset{Namaa,
      title={Ara TyDi Triplet},
      author={Omer Nacar},
      year={2024},
      note={Hugging Face Dataset Repository}
}