README.md

metadata

pipeline_tag: text-classification
tags:
  - transformers
  - sentence-transformers
  - reranker
  - cross-encoder
language:
  - multilingual
license: cc-by-nc-4.0

Trained by Jina AI.

jina-reranker-v2-base-multilingual

Usage

1. The easiest way to starting using jina-reranker-v2-base-multilingual 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-v2-base-multilingual",
  "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 base sized model
model = CrossEncoder("jinaai/jina-reranker-v2-base-multilingual", 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"
]

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-v2-base-multilingual', 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)

That's it! You can now use the jina-reranker-v2-base-multilingual model in your projects.