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--- |
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pipeline_tag: text-ranking |
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tags: |
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- mlx |
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- reranker |
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- qwen3 |
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language: |
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- multilingual |
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base_model: jinaai/jina-reranker-v3 |
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base_model_relation: quantized |
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inference: false |
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license: cc-by-nc-4.0 |
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library_name: mlx |
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--- |
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# jina-reranker-v3-mlx |
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MLX port of [jina-reranker-v3](https://huggingface.co/jinaai/jina-reranker-v3), a 0.6B parameter multilingual listwise reranker optimized for Apple Silicon. Features native MLX implementation with 100% matching of rank scores and embeddings to the original implementation. No transformers library required. |
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## Installation |
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```bash |
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pip install -r requirements.txt |
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``` |
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## Usage |
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### Basic Example |
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```python |
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from rerank import MLXReranker |
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# Initialize the reranker |
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reranker = MLXReranker() |
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# Your query and documents |
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query = "What are the health benefits of green tea?" |
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documents = [ |
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"Green tea contains antioxidants called catechins that may help reduce inflammation and protect cells from damage.", |
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"El precio del café ha aumentado un 20% este año debido a problemas en la cadena de suministro.", |
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"Studies show that drinking green tea regularly can improve brain function and boost metabolism.", |
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"Basketball is one of the most popular sports in the United States.", |
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"绿茶富含儿茶素等抗氧化剂，可以降低心脏病风险，还有助于控制体重。", |
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"Le thé vert est riche en antioxydants et peut améliorer la fonction cérébrale.", |
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] |
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# Rerank documents |
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results = reranker.rerank(query, documents) |
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# Results are sorted by relevance score (highest first) |
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for result in results: |
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print(f"Score: {result['relevance_score']:.4f}") |
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print(f"Document: {result['document'][:100]}...") |
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print() |
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``` |
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### API Reference |
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```python |
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reranker.rerank( |
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query: str, # Search query |
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documents: List[str], # Documents to rank |
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top_n: Optional[int] = None, # Return only top N (default: all) |
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return_embeddings: bool = False, # Include doc embeddings (default: False) |
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) |
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``` |
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**Returns:** List of dicts with keys: |
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- `document`: Original document text |
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- `relevance_score`: Float score (higher = more relevant) |
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- `index`: Position in input documents list |
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- `embedding`: Document embedding (if `return_embeddings=True`) |
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### Advanced Usage |
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```python |
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# Get only top 3 results |
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top_results = reranker.rerank(query, documents, top_n=3) |
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# Get embeddings for further processing |
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results_with_embeddings = reranker.rerank(query, documents, return_embeddings=True) |
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for result in results_with_embeddings: |
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embedding = result['embedding'] # numpy array of shape (512,) |
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# Use embedding for downstream tasks... |
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``` |
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### Custom Model Path |
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```python |
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# If model files are in a different location |
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reranker = MLXReranker( |
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model_path="/path/to/model", |
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projector_path="/path/to/projector.safetensors" |
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) |
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``` |
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## Citation |
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If you find `jina-reranker-v3` useful in your research, please cite the [original paper](https://arxiv.org/abs/2509.25085): |
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```bibtex |
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@misc{wang2025jinarerankerv3lateinteractiondocument, |
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title={jina-reranker-v3: Last but Not Late Interaction for Document Reranking}, |
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author={Feng Wang and Yuqing Li and Han Xiao}, |
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year={2025}, |
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eprint={2509.25085}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2509.25085}, |
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} |
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``` |
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## License |
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This MLX implementation follows the same CC BY-NC 4.0 license as the original model. For commercial usage inquiries, please [contact Jina AI](https://jina.ai/contact-sales/). |
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