Update readme.md

README.md

@@ -1,3 +1,84 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- feature-extraction
+- sentence-similarity
+language:
+- ar
+size_categories:
+- 10K<n<100K
+---
+
+# Arabic Triplet with Multi Negatives
+
+## Dataset Summary
+
+This dataset is a modified version of the Arabic subset of the [Mr. TyDi dataset](https://huggingface.co/datasets/castorini/mr-tydi), tailored for retrieval and re-ranking tasks. The original dataset has been restructured by splitting the negative passages into separate fields (`negative1`, `negative2`, ..., `negativeN`) for each query. This modification allows more flexibility for training and evaluating retrieval and re-ranking models.
+
+The dataset retains the original intent of Mr. Tydi, focusing on monolingual retrieval for the Arabic language while offering a new structure for ease of use in ranking-based learning tasks.
+
+## Dataset Structure
+
+The dataset includes train split only where each query is paired with a set of positive passages and multiple individually enumerated negative passages (up to 30).
+
+### Example Data
+
+#### Train Set
+
+```json
+{
+  "query_id": "1", 
+  "query": "متى تم تطوير نظرية الحقل الكمي؟", 
+  "positive_passages": [
+    {
+      "text": "بدأت نظرية الحقل الكمي بشكل طبيعي بدراسة التفاعلات الكهرومغناطيسية ..."
+    }
+  ],
+  "negative1": {
+      "text": "تم تنفيذ النهج مؤخرًا ليشمل نسخة جبرية من الحقل الكمي ..."
+  },
+  "negative2": {
+      "text": "تتناول هذه المقالة الخلفية التاريخية لتطوير نظرية الحقل ..."
+  },
+  ...
+}
+```
+
+### 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/Arabic-Triplet-With-Multi-Negatives')
+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={Arabic Triplet With Multi Negatives},
+      author={Omer Nacar},
+      year={2024},
+      note={Hugging Face Dataset Repository}
+}
+```
+