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--- |
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library_name: transformers |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- reranker |
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- cross-encoder |
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--- |
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<br><br> |
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<p align="center"> |
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<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px"> |
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</p> |
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<p align="center"> |
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<b>Trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b> |
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</p> |
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# jina-reranker-v1-turbo-en |
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This model is designed for **blazing-fast** reranking while maintaining **competitive performance**. What's more, it leverages the power of our [JinaBERT](https://arxiv.org/abs/2310.19923) model as its foundation. `JinaBERT` itself is a unique variant of the BERT architecture that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409). This allows `jina-reranker-v1-turbo-en` to process significantly longer sequences of text compared to other reranking models, up to an impressive **8,192** tokens. |
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To achieve the remarkable speed, the `jina-reranker-v1-turbo-en` employ a technique called knowledge distillation. Here, a complex, but slower, model (like our original [jina-reranker-v1-base-en](https://jina.ai/reranker/)) acts as a teacher, condensing its knowledge into a smaller, faster student model. This student retains most of the teacher's knowledge, allowing it to deliver similar accuracy in a fraction of the time. |
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Here's a breakdown of the reranker models we provide: |
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| Model Name | Layers | Hidden Size | Parameters (Millions) | |
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| ------------------------------------------------------------------------------------ | ------ | ----------- | --------------------- | |
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| [jina-reranker-v1-base-en](https://jina.ai/reranker/) | 12 | 768 | 137.0 | |
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| [jina-reranker-v1-turbo-en](https://huggingface.co/jinaai/jina-reranker-v1-turbo-en) | 6 | 384 | 37.8 | |
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| [jina-reranker-v1-tiny-en](https://huggingface.co/jinaai/jina-reranker-v1-tiny-en) | 4 | 384 | 33.0 | |
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> Currently, the `jina-reranker-v1-base-en` model is not available on Hugging Face. You can access it via the [Jina AI Reranker API](https://jina.ai/reranker/). |
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As you can see, the `jina-reranker-v1-turbo-en` offers a balanced approach with **6 layers** and **37.8 million** parameters. This translates to fast search and reranking while preserving a high degree of accuracy. The `jina-reranker-v1-tiny-en` prioritizes speed even further, achieving the fastest inference speeds with its **4-layer**, **33.0 million** parameter architecture. This makes it ideal for scenarios where absolute top accuracy is less crucial. |
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# Usage |
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1. The easiest way to starting using `jina-reranker-v1-turbo-en` is to use Jina AI's [Reranker API](https://jina.ai/reranker/). |
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```bash |
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curl https://api.jina.ai/v1/rerank \ |
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-H "Content-Type: application/json" \ |
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-H "Authorization: Bearer YOUR_API_KEY" \ |
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-d '{ |
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"model": "jina-reranker-v1-turbo-en", |
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"query": "Organic skincare products for sensitive skin", |
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"documents": [ |
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"Eco-friendly kitchenware for modern homes", |
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"Biodegradable cleaning supplies for eco-conscious consumers", |
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"Organic cotton baby clothes for sensitive skin", |
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"Natural organic skincare range for sensitive skin", |
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"Tech gadgets for smart homes: 2024 edition", |
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"Sustainable gardening tools and compost solutions", |
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"Sensitive skin-friendly facial cleansers and toners", |
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"Organic food wraps and storage solutions", |
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"All-natural pet food for dogs with allergies", |
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"Yoga mats made from recycled materials" |
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], |
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"top_n": 3 |
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}' |
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``` |
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2. Alternatively, you can use the latest version of the `sentence-transformers>=0.27.0` library. You can install it via pip: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then, you can use the following code to interact with the model: |
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```python |
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from sentence_transformers import CrossEncoder |
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# Load the model, here we use our turbo sized model |
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model = CrossEncoder("jinaai/jina-reranker-v1-turbo-en", trust_remote_code=True) |
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# Example query and documents |
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query = "Organic skincare products for sensitive skin" |
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documents = [ |
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"Eco-friendly kitchenware for modern homes", |
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"Biodegradable cleaning supplies for eco-conscious consumers", |
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"Organic cotton baby clothes for sensitive skin", |
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"Natural organic skincare range for sensitive skin", |
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"Tech gadgets for smart homes: 2024 edition", |
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"Sustainable gardening tools and compost solutions", |
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"Sensitive skin-friendly facial cleansers and toners", |
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"Organic food wraps and storage solutions", |
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"All-natural pet food for dogs with allergies", |
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"Yoga mats made from recycled materials" |
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] |
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results = model.rank(query, documents, return_documents=True, top_k=3) |
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``` |
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3. You can also use the `transformers` library to interact with the model programmatically. |
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```python |
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!pip install transformers |
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from transformers import AutoModelForSequenceClassification |
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model = AutoModelForSequenceClassification.from_pretrained( |
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'jinaai/jina-reranker-v1-turbo-en', num_labels=1, trust_remote_code=True |
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) |
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# Example query and documents |
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query = "Organic skincare products for sensitive skin" |
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documents = [ |
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"Eco-friendly kitchenware for modern homes", |
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"Biodegradable cleaning supplies for eco-conscious consumers", |
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"Organic cotton baby clothes for sensitive skin", |
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"Natural organic skincare range for sensitive skin", |
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"Tech gadgets for smart homes: 2024 edition", |
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"Sustainable gardening tools and compost solutions", |
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"Sensitive skin-friendly facial cleansers and toners", |
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"Organic food wraps and storage solutions", |
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"All-natural pet food for dogs with allergies", |
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"Yoga mats made from recycled materials" |
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] |
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# construct sentence pairs |
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sentence_pairs = [[query, doc] for doc in documents] |
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scores = model.compute_score(sentence_pairs) |
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``` |
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That's it! You can now use the `jina-reranker-v1-turbo-en` model in your projects. |
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# Evaluation |
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We evaluated Jina Reranker on 3 key benchmarks to ensure top-tier performance and search relevance. |
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| Model Name | NDCG@10 (17 BEIR datasets) | NDCG@10 (5 LoCo datasets) | Hit Rate (LlamaIndex RAG) | |
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| ------------------------------------------- | -------------------------- | ------------------------- | ------------------------- | |
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| `jina-reranker-v1-base-en` | **52.45** | **87.31** | **85.53** | |
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| `jina-reranker-v1-turbo-en` (you are here) | **49.60** | **69.21** | **85.13** | |
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| `jina-reranker-v1-tiny-en` | **48.54** | **70.29** | **85.00** | |
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| `mxbai-rerank-base-v1` | 49.19 | - | 82.50 | |
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| `mxbai-rerank-xsmall-v1` | 48.80 | - | 83.69 | |
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| `ms-marco-MiniLM-L-6-v2` | 48.64 | - | 82.63 | |
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| `ms-marco-MiniLM-L-4-v2` | 47.81 | - | 83.82 | |
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| `bge-reranker-base` | 47.89 | - | 83.03 | |
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**Note:** |
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- `NDCG@10` is a measure of ranking quality, with higher scores indicating better search results. `Hit Rate` measures the percentage of relevant documents that appear in the top 10 search results. |
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- The results of LoCo datasets on other models are not available since they **do not support** long documents more than 512 tokens. |
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For more details, please refer to our [benchmarking sheets](https://docs.google.com/spreadsheets/d/1V8pZjENdBBqrKMzZzOWc2aL60wtnR0yrEBY3urfO5P4/edit?usp=sharing). |
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# Contact |
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Join our [Discord community](https://discord.jina.ai/) and chat with other community members about ideas. |
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