Update readme.md

README.md

@@ -24,4 +24,128 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: apache-2.0
+task_categories:
+- feature-extraction
+- sentence-similarity
+language:
+- ar
+size_categories:
+- 1K<n<10K
 ---
+# Arabic With Ranked Hard Negatives
+
+## Dataset Summary
+
+The Arabic Hard Negative Dataset is derived from the Arabic subset of the Mr. TyDi dataset. Using an advanced Arabic embedding model, this dataset restructures the original data to include a query, a positive passage, and the top 4 hard negatives for each query based on similarity scores. These hard negatives are the most semantically similar non-relevant passages to the positive passage, providing a challenging dataset for retrieval and re-ranking tasks.
+This dataset is tailored for applications in retrieval model training, re-ranking, and contrastive learning where the presence of "hard negatives" can significantly improve the performance of machine learning models.
+
+## Dataset Structure
+
+- The dataset contains the following fields:
+- **query**: The user query string.
+- **positive**: The relevant passage for the query.
+- **negative1, negative2, negative3, negative4**: The top 4 semantically similar but non-relevant passages to the positive.
+
+### Example Data
+
+```json
+{
+  "query": "ما هي نظرية الحقل الكمي؟",
+  "positive": {
+    "text": "بدأت نظرية الحقل الكمي بشكل طبيعي بدراسة التفاعلات الكهرومغناطيسية ..."
+  },
+  "negative1": {
+    "text": "تم تطوير النهج مؤخرًا ليشمل نسخة جبرية من الحقل الكمي ..."
+  },
+  "negative2": {
+    "text": "نظرية الحقول الكمومية لها تطبيقات واسعة تشمل العديد من العلوم الفيزيائية ..."
+  },
+  "negative3": {
+    "text": "النظرية الكهرومغناطيسية لها دور محوري في نظرية الحقول الكمومية ..."
+  },
+  "negative4": {
+    "text": "الحقل الكمي يستخدم الآن في الفيزياء النظرية وتطبيقات أخرى ..."
+  },
+  "similarity1": 0.75,
+  "similarity2": 0.72,
+  "similarity3": 0.70,
+  "similarity4": 0.68
+}
+```
+
+## Dataset Statistics
+
+🔸Number of rows: 362,000
+🔸Fields: 6 (query, positive, 4 negatives)
+
+Similarity Ranges:
+🔸`negative1`: Average similarity: ~0.7
+🔸`negative4`: Average similarity: ~0.65
+
+Languages: Arabic (Modern Standard Arabic).
+
+## Dataset Analysis and Insights
+
+1. Average Similarity Across Negatives:
+
+![Gate-sim-results](https://i.ibb.co/7SKdT2F/Gate-sim-results.png)
+   
+🔸The average similarity between the positive passage and the negatives decreases as the rank increases. Below is a bar chart visualizing the average similarity for the top 30 negatives in the original dataset, focusing on the top 4 for this version.
+
+![Gate-sim-results-dis](https://i.ibb.co/gTQD4GH/Gate-sim-result-dis.png)
+
+2. Similarity Distributions:
+
+🔸The similarity scores for each negative passage are distributed differently. Below are the histograms for the similarity distributions of the top 30 negatives, emphasizing the scores for negative1 to negative4.
+
+3. Insights
+   
+The top-ranked negatives (negative1 and negative2) are significantly closer in similarity to the positive passage, making them challenging and ideal for training advanced retrieval models.
+The similarity drops slightly for negative3 and negative4, but they remain "hard negatives," offering diverse yet challenging non-relevant passages for contrastive learning.
+
+## How to Use This Dataset
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset('Omartificial-Intelligence-Space/Arabic-With-Ranked-Hard-Negatives')
+dataset
+```
+
+## Recommended Applications
+▪️ Training Retrieval Models: Use the triplet structure (query, positive, negative) to train retrieval models with loss functions like triplet loss or contrastive loss.
+▪️ Fine-Tuning Re-Ranking Models: Use the ranked negatives to train models to rank positives above hard negatives.
+▪️ Evaluation Benchmarks: Use the dataset as a benchmark to evaluate retrieval models’ ability to handle hard negatives.
+
+## Dataset Creation Process
+
+✔️ Original Data: The Arabic subset of the Mr. TyDi dataset [Mr. TyDi dataset](https://huggingface.co/datasets/castorini/mr-tydi) was used as the foundation.
+✔️ Embedding Model: An Arabic embedding model [GATE](Omartificial-Intelligence-Space/GATE-AraBert-v1) was employed to calculate similarity scores between the positive and all negatives.
+✔️ Ranking Negatives: For each query, the negatives were ranked by descending similarity, and the top 4 were selected as hard negatives.
+✔️ Filtering and Validation: The dataset was validated to ensure the semantic integrity of negatives.
+
+## Limitations and Considerations
+
+▪️ Domain-Specific Bias: The embedding model might favor specific domains, impacting the selection of negatives.
+▪️ Similarity Metric: The dataset relies on the embedding model's similarity scores, which may not perfectly align with human judgment.
+
+### 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{Omartificial-Intelligence-Space,
+      title={Arabic With Ranked Hard Negatives},
+      author={Omer Nacar},
+      year={2024},
+      note={Hugging Face Dataset Repository}
+}
+```