我在文档看到如下描述：
You also can use sentence-transformers and huggingface transformers to generate dense embeddings. Refer to baai_general_embedding for details.

以及具体文档有这样的Using HuggingFace Transformers示例：
Using HuggingFace Transformers
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.

from transformers import AutoTokenizer, AutoModel
import torch

Sentences we want sentence embeddings for

sentences = ["样例数据-1", "样例数据-2"]

Load model from HuggingFace Hub

tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
model.eval()

Tokenize sentences

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)

encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')

Compute token embeddings

with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = model_output[0][:, 0]

normalize embeddings

sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)

请问是否目前用Transformers调用bge-m3能否有方法输出稀疏向量呢？thx~