File size: 2,967 Bytes
c37cd6e e00a879 5814397 c37cd6e 9aea531 c37cd6e 9aea531 c37cd6e 51238fa c37cd6e 9aea531 c37cd6e 3993464 9aea531 264e7b1 9aea531 c37cd6e 5814397 c37cd6e 8a3b707 811b39e 8a3b707 811b39e 8a3b707 9aea531 c37cd6e 9aea531 c37cd6e 5814397 48fac2a 5814397 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | ---
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-1
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-1#evaluations) 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](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.
For this model's smaller twin, see [zerank-1-small](https://huggingface.co/zeroentropy/zerank-1-small), which we've fully open-sourced under an Apache 2.0 License.
## 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-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](https://docs.zeroentropy.dev/api-reference/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.
| 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: Description Description |