| | --- |
| | 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 |
| | model_max_length: 32768 |
| | --- |
| | |
| | <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. |
| |
|
| | ## Model Details |
| |
|
| | | Property | Value | |
| | |---|---| |
| | | Parameters | 1.7B | |
| | | Context Length | 32,768 tokens (32k) | |
| | | Base Model | Qwen/Qwen3-4B | |
| | | License | Apache-2.0 | |
| |
|
| | ## 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 | |
| |
|
| | <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"/> |
