Trained by Jina AI.
# jina-reranker-v1-turbo-en 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. 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. Here's a breakdown of the reranker models we provide: | Model Name | Layers | Hidden Size | Parameters (Millions) | | ------------------------------------------------------------------------------------ | ------ | ----------- | --------------------- | | [jina-reranker-v1-base-en](https://jina.ai/reranker/) | 12 | 768 | 137.0 | | [jina-reranker-v1-turbo-en](https://huggingface.co/jinaai/jina-reranker-v1-turbo-en) | 6 | 384 | 37.8 | | [jina-reranker-v1-tiny-en](https://huggingface.co/jinaai/jina-reranker-v1-tiny-en) | 4 | 384 | 33.0 | > 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/). 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. # Usage 1. The easiest way to starting using `jina-reranker-v1-turbo-en` is to use Jina AI's [Reranker API](https://jina.ai/reranker/). ```bash curl https://api.jina.ai/v1/rerank \ -H "Content-Type: application/json" \ -H "Authorization: Bearer YOUR_API_KEY" \ -d '{ "model": "jina-reranker-v1-turbo-en", "query": "Organic skincare products for sensitive skin", "documents": [ "Eco-friendly kitchenware for modern homes", "Biodegradable cleaning supplies for eco-conscious consumers", "Organic cotton baby clothes for sensitive skin", "Natural organic skincare range for sensitive skin", "Tech gadgets for smart homes: 2024 edition", "Sustainable gardening tools and compost solutions", "Sensitive skin-friendly facial cleansers and toners", "Organic food wraps and storage solutions", "All-natural pet food for dogs with allergies", "Yoga mats made from recycled materials" ], "top_n": 3 }' ``` 2. Alternatively, you can use the latest version of the `sentence-transformers>=0.27.0` library. You can install it via pip: ```bash pip install -U sentence-transformers ``` Then, you can use the following code to interact with the model: ```python from sentence_transformers import CrossEncoder # Load the model, here we use our turbo sized model model = CrossEncoder("jinaai/jina-reranker-v1-turbo-en", trust_remote_code=True) # Example query and documents query = "Organic skincare products for sensitive skin" documents = [ "Eco-friendly kitchenware for modern homes", "Biodegradable cleaning supplies for eco-conscious consumers", "Organic cotton baby clothes for sensitive skin", "Natural organic skincare range for sensitive skin", "Tech gadgets for smart homes: 2024 edition", "Sustainable gardening tools and compost solutions", "Sensitive skin-friendly facial cleansers and toners", "Organic food wraps and storage solutions", "All-natural pet food for dogs with allergies", "Yoga mats made from recycled materials" ] results = model.rank(query, documents, return_documents=True, top_k=3) ``` 3. You can also use the `transformers` library to interact with the model programmatically. ```python !pip install transformers from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained( 'jinaai/jina-reranker-v1-turbo-en', num_labels=1, trust_remote_code=True ) # Example query and documents query = "Organic skincare products for sensitive skin" documents = [ "Eco-friendly kitchenware for modern homes", "Biodegradable cleaning supplies for eco-conscious consumers", "Organic cotton baby clothes for sensitive skin", "Natural organic skincare range for sensitive skin", "Tech gadgets for smart homes: 2024 edition", "Sustainable gardening tools and compost solutions", "Sensitive skin-friendly facial cleansers and toners", "Organic food wraps and storage solutions", "All-natural pet food for dogs with allergies", "Yoga mats made from recycled materials" ] # construct sentence pairs sentence_pairs = [[query, doc] for doc in documents] scores = model.compute_score(sentence_pairs) ``` 4. You can also use the `transformers.js` library to run the model directly in JavaScript (in-browser, Node.js, Deno, etc.)! If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using: ```bash npm i @xenova/transformers ``` Then, you can use the following code to interact with the model: ```js import { AutoTokenizer, AutoModelForSequenceClassification } from '@xenova/transformers'; const model_id = 'jinaai/jina-reranker-v1-turbo-en'; const model = await AutoModelForSequenceClassification.from_pretrained(model_id, { quantized: false }); const tokenizer = await AutoTokenizer.from_pretrained(model_id); /** * Performs ranking with the CrossEncoder on the given query and documents. Returns a sorted list with the document indices and scores. * @param {string} query A single query * @param {string[]} documents A list of documents * @param {Object} options Options for ranking * @param {number} [options.top_k=undefined] Return the top-k documents. If undefined, all documents are returned. * @param {number} [options.return_documents=false] If true, also returns the documents. If false, only returns the indices and scores. */ async function rank(query, documents, { top_k = undefined, return_documents = false, } = {}) { const inputs = tokenizer( new Array(documents.length).fill(query), { text_pair: documents, padding: true, truncation: true } ) const { logits } = await model(inputs); return logits.sigmoid().tolist() .map(([score], i) => ({ corpus_id: i, score, ...(return_documents ? { text: documents[i] } : {}) })).sort((a, b) => b.score - a.score).slice(0, top_k); } // Example usage: const query = "Organic skincare products for sensitive skin" const documents = [ "Eco-friendly kitchenware for modern homes", "Biodegradable cleaning supplies for eco-conscious consumers", "Organic cotton baby clothes for sensitive skin", "Natural organic skincare range for sensitive skin", "Tech gadgets for smart homes: 2024 edition", "Sustainable gardening tools and compost solutions", "Sensitive skin-friendly facial cleansers and toners", "Organic food wraps and storage solutions", "All-natural pet food for dogs with allergies", "Yoga mats made from recycled materials", ] const results = await rank(query, documents, { return_documents: true, top_k: 3 }); console.log(results); ``` That's it! You can now use the `jina-reranker-v1-turbo-en` model in your projects. # Evaluation We evaluated Jina Reranker on 3 key benchmarks to ensure top-tier performance and search relevance. | Model Name | NDCG@10 (17 BEIR datasets) | NDCG@10 (5 LoCo datasets) | Hit Rate (LlamaIndex RAG) | | ------------------------------------------- | -------------------------- | ------------------------- | ------------------------- | | `jina-reranker-v1-base-en` | **52.45** | **87.31** | **85.53** | | `jina-reranker-v1-turbo-en` (you are here) | **49.60** | **69.21** | **85.13** | | `jina-reranker-v1-tiny-en` | **48.54** | **70.29** | **85.00** | | `mxbai-rerank-base-v1` | 49.19 | - | 82.50 | | `mxbai-rerank-xsmall-v1` | 48.80 | - | 83.69 | | `ms-marco-MiniLM-L-6-v2` | 48.64 | - | 82.63 | | `ms-marco-MiniLM-L-4-v2` | 47.81 | - | 83.82 | | `bge-reranker-base` | 47.89 | - | 83.03 | **Note:** - `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. - The results of LoCo datasets on other models are not available since they **do not support** long documents more than 512 tokens. For more details, please refer to our [benchmarking sheets](https://docs.google.com/spreadsheets/d/1V8pZjENdBBqrKMzZzOWc2aL60wtnR0yrEBY3urfO5P4/edit?usp=sharing). # Contact Join our [Discord community](https://discord.jina.ai/) and chat with other community members about ideas.