zeroentropy / zerank-1

Releasing zeroentropy/zerank-1

In search enginers, 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 Salesforce/LlamaRank-v1 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 (Coming soon!) 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 [email protected] and we'll get you a license ASAP.

For this model's smaller twin, see zerank-1-small , which we've fully open-sourced under an Apache 2.0 License.

How to Use

from sentence_transformers import CrossEncoder

model = CrossEncoder("zeroentropy/zerank-1", 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 endpoint.

Evaluations

NDCG@10 scores between zerank-1 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.

Comparing BM25 and Hybrid Search without and with zerank-1: