metadata
license: cc-by-nc-4.0
language:
- en
base_model:
- Qwen/Qwen3-4B
pipeline_tag: text-ranking
tags:
- finance
- legal
- code
- stem
- medical
zerank-1: ZeroEntropy Inc.'s SoTA reranker
This model is the smaller version of zeroentropy/zerank-1. This model is over 2x smaller, but maintains nearly the same standard of performance, continuing to outperform other popular rerankers.
It is an open-weights reranker model meant to be integrated into RAG applications to rerank results from preliminary search methods such as embeddings, BM25, and hybrid search.
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)
Evaluations
Comparing NDCG@10 starting from top 100 documents by embedding (using text-3-embedding-small):
| 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:
Citation
BibTeX:
Coming soon!
APA:
Coming soon!