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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
---
<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://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.
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.
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
```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, and on [AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-o7avk66msiukc).
## 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 |