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NAMAA-Space
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GATE-Reranker-V1

Text Ranking
Transformers
Safetensors
sentence-transformers
Arabic
bert
text-classification
reranking
text-embeddings-inference
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@@ -49,11 +49,12 @@ scores = model.predict([(Query, Paragraph1), (Query, Paragraph2)])
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  We evaluate our model on two different datasets using the metrics **MAP**, **MRR** and **NDCG@10**:
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- The purpose of this evaluation is to highlight the performance of our model with regards to: Relevant/Irrelevant labels and
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- 1 - Dataset 1: [NAMAA-Space/Arabic-Reranking-Triplet-5-Eval](https://huggingface.co/datasets/NAMAA-Space/Arabic-Reranking-Triplet-5-Eval)
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- 2 - Dataset 2: [NAMAA-Space/Ar-Reranking-Eval](https://huggingface.co/datasets/NAMAA-Space/Ar-Reranking-Eval)
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- and compare it to other famous models on the hub
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- ![Plot](https://huggingface.co/NAMAA-Space/GATE-Reranker-V1/resolve/main/Dataset1_Evaluation.jpg)
 
 
 
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  We evaluate our model on two different datasets using the metrics **MAP**, **MRR** and **NDCG@10**:
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+ The purpose of this evaluation is to highlight the performance of our model with regards to: Relevant/Irrelevant labels and positive/multiple negatives documents:
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+ Dataset 1: [NAMAA-Space/Ar-Reranking-Eval](https://huggingface.co/datasets/NAMAA-Space/Ar-Reranking-Eval)
 
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+ ![Plot](https://huggingface.co/NAMAA-Space/GATE-Reranker-V1/resolve/main/Dataset1_Evaluation.jpg)
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+ Dataset 2: [NAMAA-Space/Arabic-Reranking-Triplet-5-Eval](https://huggingface.co/datasets/NAMAA-Space/Arabic-Reranking-Triplet-5-Eval)
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+ and compare it to other famous models on the hub