README.md

---
base_model:
- Qwen/Qwen3-4B
language:
- en
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: text-ranking
tags:
- finance
- legal
- code
- stem
- medical
---

<img src="https://i.imgur.com/oxvhvQu.png"/>

# zeroentropy/zerank-1-small

This model, `zeroentropy/zerank-1-small`, is a state-of-the-art open-weight reranker. It was introduced in the paper [zELO: ELO-inspired Training Method for Rerankers and Embedding Models](https://huggingface.co/papers/2509.12541).

## Paper: zELO: ELO-inspired Training Method for Rerankers and Embedding Models

### Abstract
We introduce a novel training methodology named zELO, which optimizes retrieval performance via the analysis that ranking tasks are statically equivalent to a Thurstone model. Based on the zELO method, we use unsupervised data in order train a suite of state-of-the-art open-weight reranker models: zerank-1 and zerank-1-small. These models achieve the highest retrieval scores in multiple domains, including finance, legal, code, and STEM, outperforming closed-source proprietary rerankers on both NDCG@10 and Recall. These models also demonstrate great versatility, maintaining their 0-shot performance on out-of-domain and private customer datasets. The training data included 112,000 queries and 100 documents per query, and was trained end-to-end from unannotated queries and documents in less than 10,000 H100-hours.

## Code
The methodology and benchmarking framework associated with this model can be found in the [zbench GitHub repository](https://github.com/zeroentropy-ai/zbench).

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.

This 1.7B reranker is the smaller version of our flagship model [zeroentropy/zerank-1](https://huggingface.co/zeroentropy/zerank-1). Though the model is over 2x smaller, it maintains nearly the same standard of performance, continuing to outperform other popular rerankers, and displaying massive accuracy gains over traditional vector search.

We release this model under the open-source Apache 2.0 license, in order to support the open-source community and push the frontier of what's possible with open-source models.

## How to Use

```python
from sentence_transformers import CrossEncoder

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

## Evaluations

NDCG@10 scores between `zerank-1-small` 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.

| Task           | Embedding | cohere-rerank-v3.5 | Salesforce/Llama-rank-v1 | **zerank-1-small** | zerank-1 |
|----------------|-----------|--------------------|--------------------------|----------------|----------|
| Code           |    0.678  |       0.724        |           0.694          |      **0.730**     |   0.754  |
| Conversational |    0.250  |       0.571        |           0.484          |      **0.556**     |   0.596  |
| Finance        |    0.839  |       0.824        |           0.828          |      **0.861**     |   0.894  |
| Legal          |    0.703  |       0.804        |           0.767          |      **0.817**     |   0.821  |
| Medical        |    0.619  |       0.750        |           0.719          |      **0.773**     |   0.796  |
| STEM           |    0.401  |       0.510        |           0.595          |      **0.680**     |   0.694  |

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

<img src="https://cdn-uploads.huggingface.co/production/uploads/67776f9dcd9c9435499eafc8/2GPVHFrI39FspnSNklhsM.png" alt="Description" width="400"/> <img src="https://cdn-uploads.huggingface.co/production/uploads/67776f9dcd9c9435499eafc8/dwYo2D7hoL8QiE8u3yqr9.png" alt="Description" width="400"/>