gte-multilingual-reranker-base
This is the Transformers version of Alibaba-NLP/gte-multilingual-reranker-base. The weights are unchanged, only config.json is converted so that the model loads natively in Transformers without trust_remote_code.
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
Using Huggingface transformers (from source: pip install git+https://github.com/huggingface/transformers)
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name_or_path = "alibaba-nlp-community/gte-multilingual-reranker-base"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForSequenceClassification.from_pretrained(
model_name_or_path,
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
Usage with Text Embeddings Inference (TEI):
- CPU:
docker run --platform linux/amd64 \
-p 8080:80 \
-v $PWD/data:/data \
--pull always \
ghcr.io/huggingface/text-embeddings-inference:cpu-1.7 \
--model-id Alibaba-NLP/gte-multilingual-reranker-base
- GPU:
docker run --gpus all \
-p 8080:80 \
-v $PWD/data:/data \
--pull always \
ghcr.io/huggingface/text-embeddings-inference:1.7 \
--model-id Alibaba-NLP/gte-multilingual-reranker-base
Then you can send requests to the deployed API via the /rerank route (see the Text Embeddings Inference OpenAPI Specification for more details):
curl https://0.0.0.0:8080/rerank \
-H "Content-Type: application/json" \
-d '{
"query": "中国的首都在哪儿",
"raw_scores": false,
"return_text": false,
"texts": [ "北京" ],
"truncate": true,
"truncation_direction": "right"
}'
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}
}