Releasing zeroentropy/zerank 2 In search engines, rerankers are crucial 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 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. See our Technical Report (https://arxiv.org/abs/2509.12541 ) for more details. This model is released under the Apache License 2.0. Model Details Property Value Parameters 4B Context Length 32,768 tokens (32k) Base Model Qwen/Qwen3 4B License Apache 2.0 How to Use Breaking change (May 2026): model.predict() now returns raw "Yes" logits instead of sigmoid'd probabilities in [0, 1] . Rankings are unchanged. To recover the previous 0 1 score, apply (scores / 5).sigmoid() — see the example below. Loading no longer requires trust remote cod…
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