|
--- |
|
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) |
|
``` |
