| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| base_model: |
| - Qwen/Qwen3-4B |
| pipeline_tag: text-ranking |
| tags: |
| - finance |
| - legal |
| - code |
| - stem |
| - medical |
| library_name: sentence-transformers |
| --- |
| |
| <img src="https://i.imgur.com/oxvhvQu.png"/> |
|
|
| # Releasing zeroentropy/zerank-2 |
|
|
| 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. |
|
|
| 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. |
|
|
| 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. |
|
|
| 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 |
| ) for more details. |
|
|
| 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. |
|
|
| ## How to Use |
|
|
| ```python |
| from sentence_transformers import CrossEncoder |
| |
| model = CrossEncoder("zeroentropy/zerank-2", trust_remote_code=True) |
| |
| query_documents = [ |
| ("What is 2+2?", "4"), |
| ("What is 2+2?", "The answer is definitely 1 million"), |
| ] |
| |
| scores = model.predict(query_documents) |
| |
| print(scores) |
| ``` |
|
|
| The model can also be inferenced using ZeroEntropy's [/models/rerank](https://docs.zeroentropy.dev/api-reference/models/rerank) endpoint. |
|
|
