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 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.
This reranker outperforms other popular rerankers such as cohere-rerank-v3.5 and Salesforce/Llama-rank-v1 across a wide variety of task domains, including on finance, legal, code, STEM, medical, and conversational data. See this post for more details. This model is trained on an innovative multi-stage pipeline that models query-document relevance scores using adjusted Elo-like ratings. See this post and our Technical Report (Coming soon!) for more details.
For this model's smaller twin, see zerank-1-small
How to Use
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)
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!