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
model_max_length: 32768
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 (Coming soon!) for more details.
This model is released under a non-commercial license. If you'd like a commercial license, please contact us at contact@zeroentropy.dev.
Model Details
| Property | Value |
|---|---|
| Parameters | 4B |
| Context Length | 32,768 tokens (32k) |
| Base Model | Qwen/Qwen3-4B |
| License | CC-BY-NC-4.0 |
How to Use
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 endpoint, and on AWS Marketplace.
Evaluations
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.
| Domain | OpenAI embeddings | ZeroEntropy zerank-2 | ZeroEntropy zerank-1 | Gemini 2.5 Flash (Listwise) | Cohere rerank-3.5 |
|---|---|---|---|---|---|
| Web | 0.3819 | 0.6346 | 0.6069 | 0.5765 | 0.5594 |
| Conversational | 0.4305 | 0.6140 | 0.5801 | 0.6021 | 0.5648 |
| STEM & Logic | 0.3744 | 0.6521 | 0.6283 | 0.5447 | 0.5418 |
| Code | 0.4582 | 0.6528 | 0.6310 | 0.6128 | 0.5364 |
| Legal | 0.4101 | 0.6644 | 0.6222 | 0.5565 | 0.5257 |
| Biomedical | 0.4783 | 0.7217 | 0.6967 | 0.5371 | 0.6246 |
| Finance | 0.6232 | 0.7600 | 0.7539 | 0.7694 | 0.7402 |
| Average | 0.4509 | 0.6714 | 0.6456 | 0.5999 | 0.5847 |
Graph showing the same table