Feature Extraction
sentence-transformers
PyTorch
Chinese
English
bert
sentence-similarity
mteb
RAG
Eval Results (legacy)
text-embeddings-inference
Instructions to use DMetaSoul/Dmeta-embedding-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DMetaSoul/Dmeta-embedding-zh with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DMetaSoul/Dmeta-embedding-zh") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Inference
- Notebooks
- Google Colab
- Kaggle
请问可以转化成 ONNX 加速推理吗?
#2
by Visitor897 - opened
没有这方面的经验,想知道需要了解哪些知识
当然可以,我们之前写过导出onnx的代码,如果有需要,我们可以整理下放出来。
另外要加速推理的话,onnx本身带来的性能提升相对有限,这个我们之前也对比测试过。对性能加速带来更大提升的方式,一般通过模型蒸馏,不过这种就要训练模型了。
有需要的,能快一点是一点嘛,至于训练模型,咱是没有这个条件了...另外想夸一下,这是我用过的综合效果最好的模型,感谢你们的杰出工作!
optimum-cli export onnx --model ./Dmeta-embedding-zh-small --task sentence-similarity dmeta-embedding-zh-small-onnx