Instructions to use zeroentropy/zerank-1-small-reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use zeroentropy/zerank-1-small-reranker with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("zeroentropy/zerank-1-small-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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# zerank-1:
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<!-- Provide a quick summary of what the model is/does. -->
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This model is the smaller version of [zeroentropy/zerank-1](https://huggingface.co/zeroentropy/zerank-1).
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It is an open-weights reranker model meant to be integrated into RAG applications to rerank results from preliminary search methods such as embeddings, BM25, and hybrid search.
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# zerank-1-small: Smaller, faster version of zerank-1
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This model is the smaller version of [zeroentropy/zerank-1](https://huggingface.co/zeroentropy/zerank-1). Though the model is over 2x smaller, it maintains nearly the same standard of performance, continuing to outperform other popular rerankers.
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It is an open-weights reranker model meant to be integrated into RAG applications to rerank results from preliminary search methods such as embeddings, BM25, and hybrid search.
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