High Performance: Achieves state-of-the-art (SOTA) results in multilingual retrieval tasks and multi-task representation model evaluations when compared to 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.
Elastic Dense Embedding: Support elastic output dense representation while maintaining the effectiveness of downstream tasks, which significantly reduces storage costs and improves execution efficiency.
Sparse Vectors: In addition to dense representations, it can also generate sparse vectors.

Paper: mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval

Model Information

Model Size: 305M
Embedding Dimension: 768
Max Input Tokens: 8192

Usage Air-Gapped

Tested with vLLM 0.20.0 and above.

Set HF_HOME to a temp directory, eg: /tmp/hf
- OR set HF_MODULES_CACHE to /tmp/hf-modules-cache. (we need this to stage configuration.py and modeling.py from the root dir Reference)
Set env var HF_HUB_OFFLINE=1

bash

vllm serve yankexe/gte-multilingual-base-air-gapped \
  --hf-overrides='{"architectures":["GteNewModel"]}' \
  --runner=pooling \
  --trust-remote-code

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
How to use with TEI: refs/pr/7

Get Dense Embeddings with Transformers

python

# Requires transformers>=4.36.0

import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer

input_texts = [
    "what is the capital of China?",
    "how to implement quick sort in python?",
    "北京",
    "快排算法介绍"
]

model_name_or_path = 'Alibaba-NLP/gte-multilingual-base'
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True)

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt')

outputs = model(**batch_dict)

dimension=768 # The output dimension of the output embedding, should be in [128, 768]
embeddings = outputs.last_hidden_state[:, 0][:dimension]

embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())

# [[0.3016996383666992, 0.7503870129585266, 0.3203084468841553]]

Use with sentence-transformers

python

# Requires sentence-transformers>=3.0.0

from sentence_transformers import SentenceTransformer

input_texts = [
    "what is the capital of China?",
    "how to implement quick sort in python?",
    "北京",
    "快排算法介绍"
]

model_name_or_path="Alibaba-NLP/gte-multilingual-base"
model = SentenceTransformer(model_name_or_path, trust_remote_code=True)
embeddings = model.encode(input_texts, normalize_embeddings=True) # embeddings.shape (4, 768)

# sim scores
scores = model.similarity(embeddings[:1], embeddings[1:])

print(scores.tolist())
# [[0.301699697971344, 0.7503870129585266, 0.32030850648880005]]

Use with infinity

Usage via docker and infinity, MIT Licensed.

docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \
michaelf34/infinity:0.0.69 \
v2 --model-id Alibaba-NLP/gte-multilingual-base --revision "main" --dtype float16 --batch-size 32 --device cuda --engine torch --port 7997

Use with Text Embeddings Inference (TEI)

Usage via Docker and Text Embeddings Inference (TEI):

CPU:

bash

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-base \
  --dtype float16

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-base \
  --dtype float16

Then you can send requests to the deployed API via the OpenAI-compatible v1/embeddings route (more information about the OpenAI Embeddings API):

bash

curl https://0.0.0.0:8080/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{
    "input": [
      "what is the capital of China?",
      "how to implement quick sort in python?",
      "北京",
      "快排算法介绍"
    ],
    "model": "Alibaba-NLP/gte-multilingual-base",
    "encoding_format": "float"
  }'

Use with custom code to get dense embeddings and sparse token weights

python

# You can find the script gte_embedding.py in https://huggingface.co/Alibaba-NLP/gte-multilingual-base/blob/main/scripts/gte_embedding.py

from gte_embedding import GTEEmbeddidng

model_name_or_path = 'Alibaba-NLP/gte-multilingual-base'
model = GTEEmbeddidng(model_name_or_path)
query = "中国的首都在哪儿"

docs = [
    "what is the capital of China?",
    "how to implement quick sort in python?",
    "北京",
    "快排算法介绍"
]

embs = model.encode(docs, return_dense=True,return_sparse=True)
print('dense_embeddings vecs', embs['dense_embeddings'])
print('token_weights', embs['token_weights'])
pairs = [(query, doc) for doc in docs]
dense_scores = model.compute_scores(pairs, dense_weight=1.0, sparse_weight=0.0)
sparse_scores = model.compute_scores(pairs, dense_weight=0.0, sparse_weight=1.0)
hybrid_scores = model.compute_scores(pairs, dense_weight=1.0, sparse_weight=0.3)

print('dense_scores', dense_scores)
print('sparse_scores', sparse_scores)
print('hybrid_scores', hybrid_scores)

# dense_scores [0.85302734375, 0.257568359375, 0.76953125, 0.325439453125]
# sparse_scores [0.0, 0.0, 4.600879669189453, 1.570279598236084]
# hybrid_scores [0.85302734375, 0.257568359375, 2.1497951507568356, 0.7965233325958252]

Evaluation

We validated the performance of the gte-multilingual-base model on multiple downstream tasks, including multilingual retrieval, cross-lingual retrieval, long text retrieval, and general text representation evaluation on the MTEB Leaderboard, among others.

Retrieval Task

Retrieval results on MIRACL and MLDR (multilingual), MKQA (crosslingual), BEIR and LoCo (English).

Detail results on MLDR

Detail results on LoCo

MTEB

Results on MTEB English, Chinese, French, Polish

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}
}