update readme.md

README.md

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 ## Dataset Structure
 
 - The dataset contains the following fields:
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 - **query**: The user query string.
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 - **positive**: The relevant passage for the query.
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 - **negative1, negative2, negative3, negative4**: The top 4 semantically similar but non-relevant passages to the positive.
 
 ### Example Data
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 ## Dataset Statistics
 
 🔸Number of rows: 362,000
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 🔸Fields: 6 (query, positive, 4 negatives)
 
 Similarity Ranges:
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 🔸`negative1`: Average similarity: ~0.7
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 🔸`negative4`: Average similarity: ~0.65
 
 Languages: Arabic (Modern Standard Arabic).
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 ## 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.
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 ▪️ Fine-Tuning Re-Ranking Models: Use the ranked negatives to train models to rank positives above hard negatives.
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 ▪️ 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.
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 ✔️ 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.
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 ✔️ Ranking Negatives: For each query, the negatives were ranked by descending similarity, and the top 4 were selected as hard negatives.
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 ✔️ 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.
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 ▪️ Similarity Metric: The dataset relies on the embedding model's similarity scores, which may not perfectly align with human judgment.
 
 ### Citation Information