Instructions to use zeroentropy/zerank-2-reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use zeroentropy/zerank-2-reranker with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("zeroentropy/zerank-2-reranker") 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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At ZeroEntropy we've developed an innovative multi-stage pipeline that models query-document relevance scores as adjusted [Elo ratings](https://en.wikipedia.org/wiki/Elo_rating_system). See our Technical Report (Coming soon!) for more details.
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## How to Use
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At ZeroEntropy we've developed an innovative multi-stage pipeline that models query-document relevance scores as adjusted [Elo ratings](https://en.wikipedia.org/wiki/Elo_rating_system). See our Technical Report (Coming soon!) for more details.
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This model is released under a non-commercial license. If you'd like a commercial license, please contact us at contact@zeroentropy.dev.
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## How to Use
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