Instructions to use ALJIACHI/Mizan-Rerank-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ALJIACHI/Mizan-Rerank-v1 with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("ALJIACHI/Mizan-Rerank-v1") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
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README.md
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## Overview
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Mizan-Rerank-v1 is a leading open-source model based on the
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## Key Features
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If you use Mizan-Rerank-v1 in your research, please cite:
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```bibtex
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@software{
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author = {Ali Aljiachi},
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title = {Mizan-Rerank-v1: A Revolutionary Arabic Text Reranking Model},
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year = {2025},
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publisher = {Hugging Face},
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url = {https://huggingface.co/
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}
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```
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## Overview
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Mizan-Rerank-v1 is a leading open-source model based on the Transformer architecture, specifically designed for reranking search results in Arabic texts. With only 149 million parameters, it offers a perfect balance between performance and efficiency, outperforming larger models while using significantly fewer resources.
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## Key Features
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If you use Mizan-Rerank-v1 in your research, please cite:
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```bibtex
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@software{Mizan_Rerank_v1_2025,
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author = {Ali Aljiachi},
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title = {Mizan-Rerank-v1: A Revolutionary Arabic Text Reranking Model},
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year = {2025},
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publisher = {Hugging Face},
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url = {https://huggingface.co/Aljiachi/Mizan-Rerank-v1}
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}
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```
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