dataset_info:
features:
- name: query
dtype: string
- name: positive
dtype: string
- name: negative
dtype: string
splits:
- name: train
num_bytes: 719317257
num_examples: 362146
download_size: 184143892
dataset_size: 719317257
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
task_categories:
- sentence-similarity
- feature-extraction
language:
- ar
size_categories:
- 100K<n<1M
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}
}