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---
library_name: transformers
license: apache-2.0
language:
- en
tags:
- reranker
- cross-encoder
---
<br><br>
<p align="center">
<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">
</p>
<p align="center">
<b>Trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
# 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
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
}'
```
Alternatively, you can 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)
```
# 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-rereanker-v1-base-en` | **52.45** | **87.31** | **85.53** |
| `jina-rereanker-v1-turbo-en` (you are here) | **49.60** | **69.21** | **85.13** |
| `jina-rereanker-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.
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