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---
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](https://huggingface.co/datasets/castorini/mr-tydi), 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
```json
{
"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:
```python
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}
}
```
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