license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-4B
pipeline_tag: text-ranking
tags:
- finance
- legal
- code
- stem
- medical
library_name: sentence-transformers
Releasing zeroentropy/zerank-1-small
In search enginers, rerankers are crucial for improving the accuracy of your retrieval system.
This 1.7B reranker is the smaller version of our flagship model 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
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 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:
