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
Update README.md
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README.md
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Mizan-Rerank-v1 was trained on a diverse corpus of **741,159,981 tokens** from:
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- Authentic Arabic open-source
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- Manually processed text
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This comprehensive training approach enables deep understanding of Arabic linguistic contexts.
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Mizan-Rerank-v1 was trained on a diverse corpus of **741,159,981 tokens** from:
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| 68 |
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- Authentic Arabic open-source datasets
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- Manually crafted and processed text
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- Purpose-generated synthetic data
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This comprehensive training approach enables deep understanding of Arabic linguistic contexts.
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