metadata
tags:
- mteb
- sentence-transformers
- transformers
- multilingual
- sentence-similarity
license: apache-2.0
gte-multilingual-base
The gte-multilingual-base model is the latest in the GTE (General Text Embedding) 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 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.
Model Information
- Model Size: 304M
- Embedding Dimension: 768
- Max Input Tokens: 8192
Requirements
transformers>=4.39.2
flash_attn>=2.5.6
Usage
Get Dense Embeddings with Transformers
# 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_path = 'Alibaba-NLP/gte-multilingual-base'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModel.from_pretrained(model_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())
Use with sentence-transformers
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
input_texts = [
"what is the capital of China?",
"how to implement quick sort in python?",
"北京",
"快排算法介绍"
]
model = SentenceTransformer('Alibaba-NLP/gte-multilingual-base', trust_remote_code=True)
embeddings = model.encode(input_texts)
Use with custom code to get dense embeddigns and sparse token weights
# You can find the gte_embeddings.py in https://huggingface.co/Alibaba-NLP/gte-multilingual-base/blob/main/scripts/gte_embedding.py
from gte_embeddings import GTEEmbeddidng
model_path = 'Alibaba-NLP/gte-multilingual-base'
model = GTEEmbeddidng(model_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)
hybird_scores = model.compute_scores(pairs, dense_weight=1.0, sparse_weight=0.3)
print('dense_scores', dense_scores)
print('sparse_scores', sparse_scores)
print('hybird_scores', hybird_scores)