---
license: apache-2.0
---

## gte-multilingual-base

The **gte-multilingual-reranker-base** model is the first reranker model in the [GTE](https://huggingface.co/collections/Alibaba-NLP/gte-models-6680f0b13f885cb431e6d469) family of models, featuring several key attributes:
- **High Performance**: Achieves state-of-the-art (SOTA) results in multilingual retrieval tasks and multi-task representation model evaluations when compared to reranker models of similar size.
- **Training Architecture**: Trained using an encoder-only transformers architecture, resulting in a smaller model size. Unlike previous models based on decode-only LLM architecture (e.g., gte-qwen2-1.5b-instruct), this model has lower hardware requirements for inference, offering a 10x increase in inference speed.
- **Long Context**: Supports text lengths up to **8192** tokens.
- **Multilingual Capability**: Supports over **70** languages.


## Model Information
- Model Size: 304M
- Max Input Tokens: 8192

## Requirements
```
transformers>=4.39.2
flash_attn>=2.5.6
```

### Usage

Using Huggingface transformers
```
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-multilingual-reranker-base')
model = AutoModelForSequenceClassification.from_pretrained('Alibaba-NLP/gte-multilingual-reranker-base', trust_remote_code=True)
model.eval()

pairs = [["中国的首都在哪儿"，"北京"], ["what is the capital of China?", "北京"], ["how to implement quick sort in python?","Introduction of quick sort"]]
with torch.no_grad():
    inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
    scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
    print(scores)
```