jina-reranker-tiny-en

This model is designed for blazing-fast reranking while maintaining competitive performance. What's more, it leverages the power of our JinaBERT model as its foundation. JinaBERT itself is a unique variant of the BERT architecture that supports the symmetric bidirectional variant of ALiBi. This allows jina-reranker-v1-tiny-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-tiny-en employ a technique called knowledge distillation. Here, a complex, but slower, model (like our original jina-reranker-v1-base-en) 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:

Currently, the jina-reranker-v1-base-en model is not available on Hugging Face. You can access it via the Jina AI Reranker API.

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-tiny-en is to use Jina AI's Reranker API.

curl https://api.jina.ai/v1/rerank \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
  "model": "jina-reranker-v1-tiny-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:

pip install -U sentence-transformers

Then, you can use the following code to interact with the model:

from sentence_transformers import CrossEncoder

# Load the model, here we use our tiny sized model
model = CrossEncoder("jinaai/jina-reranker-v1-tiny-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.

!pip install transformers
from transformers import AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained(
    'jinaai/jina-reranker-v1-tiny-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 JavaScript library from NPM using:

npm i @xenova/transformers

Then, you can use the following code to interact with the model:

import { AutoTokenizer, AutoModelForSequence