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jinaai
/
jina-reranker-v2-base-multilingual

Text Classification
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
multilingual
reranker
cross-encoder
custom_code
🇪🇺 Region: EU
Model card Files Community
26
jina-reranker-v2-base-multilingual
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---
pipeline_tag: text-classification
tags:
- transformers
- sentence-transformers
- reranker
- cross-encoder
language:
- multilingual
license: cc-by-nc-4.0
---

<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-v2-base-multilingual


# Usage

1. The easiest way to starting using `jina-reranker-v2-base-multilingual` 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-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:

```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 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.

```python
!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.