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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen3-4B |
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pipeline_tag: text-ranking |
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tags: |
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- finance |
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- legal |
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- code |
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- stem |
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- medical |
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library_name: sentence-transformers |
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--- |
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<img src="https://i.imgur.com/oxvhvQu.png"/> |
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# Releasing zeroentropy/zerank-2 |
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In search engines, [rerankers are crucial](https://www.zeroentropy.dev/blog/what-is-a-reranker-and-do-i-need-one) for improving the accuracy of your retrieval system. |
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However, SOTA rerankers are closed-source and proprietary. At ZeroEntropy, we've trained a SOTA reranker outperforming closed-source competitors, and we're launching our model here on HuggingFace. |
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This reranker [outperforms proprietary rerankers](https://www.zeroentropy.dev/articles/zerank-2-advanced-instruction-following-multimodal-reranker) such as `cohere-rerank-v3.5` and `gemini-2.5-flash` across a wide variety of domains, including finance, legal, code, STEM, medical, and conversational data. |
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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 (https://arxiv.org/abs/2509.12541 |
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) for more details. |
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Since we're a small company, this model is only released under a non-commercial license. If you'd like a commercial license, please contact us at founders@zeroentropy.dev and we'll get you a license ASAP. |
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## How to Use |
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```python |
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from sentence_transformers import CrossEncoder |
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model = CrossEncoder("zeroentropy/zerank-2", trust_remote_code=True) |
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query_documents = [ |
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("What is 2+2?", "4"), |
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("What is 2+2?", "The answer is definitely 1 million"), |
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] |
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scores = model.predict(query_documents) |
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print(scores) |
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``` |
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The model can also be inferenced using ZeroEntropy's [/models/rerank](https://docs.zeroentropy.dev/api-reference/models/rerank) endpoint. |
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