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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://huggingface.co/zeroentropy/zerank-2#evaluations) 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 (Coming soon!) 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, and on [AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-o7avk66msiukc). |
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## Evaluations |
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NDCG@10 scores between `zerank-2` and competing closed-source proprietary rerankers. Since we are evaluating rerankers, OpenAI's `text-embedding-3-small` is used as an initial retriever for the Top 100 candidate documents. |
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| Domain | OpenAI embeddings | ZeroEntropy zerank-2 | ZeroEntropy zerank-1 | Gemini 2.5 Flash (Listwise) | Cohere rerank-3.5 | |
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|------------------|-------------------|----------------------|----------------------|-----------------------------|-------------------| |
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| Web | 0.3819 | **0.6346** | 0.6069 | 0.5765 | 0.5594 | |
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| Conversational | 0.4305 | **0.6140** | 0.5801 | 0.6021 | 0.5648 | |
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| STEM & Logic | 0.3744 | **0.6521** | 0.6283 | 0.5447 | 0.5418 | |
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| Code | 0.4582 | **0.6528** | 0.6310 | 0.6128 | 0.5364 | |
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| Legal | 0.4101 | **0.6644** | 0.6222 | 0.5565 | 0.5257 | |
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| Biomedical | 0.4783 | **0.7217** | 0.6967 | 0.5371 | 0.6246 | |
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| Finance | 0.6232 | 0.7600 | 0.7539 | **0.7694** | 0.7402 | |
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| **Average** | **0.4509** | **0.6714** | **0.6456** | **0.5999** | **0.5847** | |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/65ec60ccfc59f6e77ecc9ccb/UiDp8LsY4XIdRK5i3CAdD.png" alt="Graph showing the same table" width="1000"/> |
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