| --- |
| license: apache-2.0 |
| language: |
| - en |
| - es |
| - sv |
| - de |
| - fr |
| - pt |
| - ar |
| - ru |
| - ch |
| - zh |
| - ja |
| - co |
| - ko |
| - 'no' |
| - rm |
| - fi |
| - ca |
| - bg |
| - be |
| - nl |
| - da |
| - el |
| - ro |
| - ga |
| - it |
| - fa |
| - pl |
| - sl |
| - ka |
| - hi |
| - hr |
| - hu |
| - vi |
| base_model: |
| - Qwen/Qwen3-4B |
| pipeline_tag: text-ranking |
| tags: |
| - finance |
| - legal |
| - code |
| - stem |
| - medical |
| library_name: sentence-transformers |
| --- |
| |
| <img src="https://i.imgur.com/oxvhvQu.png"/> |
|
|
| # Releasing zeroentropy/zerank-1-small |
|
|
| In search enginers, [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"/> |
