metadata
license: apache-2.0
pipeline_tag: text-classification
tags:
- transformers
- sentence-transformers
- text-embeddings-inference
language:
- af
- ar
- az
- be
- bg
- bn
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fr
- gl
- gu
- he
- hi
- hr
- ht
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ky
- lo
- lt
- lv
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- pa
- pl
- pt
- qu
- ro
- ru
- si
- sk
- sl
- so
- sq
- sr
- sv
- sw
- ta
- te
- th
- tl
- tr
- uk
- ur
- vi
- yo
- zh
gte-multilingual-reranker-base
The gte-multilingual-reranker-base model is the first reranker model in the GTE 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: 306M
- Max Input Tokens: 8192
Usage
- It is recommended to install xformers and enable unpadding for acceleration, refer to enable-unpadding-and-xformers.
- How to use it offline: new-impl/discussions/2
Using Huggingface transformers (transformers>=4.36.0)
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name_or_path = "Alibaba-NLP/gte-multilingual-reranker-base"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForSequenceClassification.from_pretrained(
model_name_or_path, trust_remote_code=True,
torch_dtype=torch.float16
)
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)
# tensor([1.2315, 0.5923, 0.3041])
Usage with infinity:
Infinity, a MIT Licensed Inference RestAPI Server.
docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \
michaelf34/infinity:0.0.68 \
v2 --model-id Alibaba-NLP/gte-multilingual-reranker-base --revision "main" --dtype bfloat16 --batch-size 32 --device cuda --engine torch --port 7997
Evaluation
Results of reranking based on multiple text retreival datasets
More detailed experimental results can be found in the paper.
Cloud API Services
In addition to the open-source GTE series models, GTE series models are also available as commercial API services on Alibaba Cloud.
- Embedding Models: Three versions of the text embedding models are available: text-embedding-v1/v2/v3, with v3 being the latest API service.
- ReRank Models: The gte-rerank model service is available.
Note that the models behind the commercial APIs are not entirely identical to the open-source models.
Citation
If you find our paper or models helpful, please consider cite:
@inproceedings{zhang2024mgte,
title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Tang, Jialong and Lin, Huan and Yang, Baosong and Xie, Pengjun and Huang, Fei and others},
booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track},
pages={1393--1412},
year={2024}
}