Instructions to use moka-ai/m3e-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use moka-ai/m3e-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("moka-ai/m3e-base") 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] - Notebooks
- Google Colab
- Kaggle
请问加载模型后产出embedding很慢有什么办法么?
#15
by EEik - opened
使用model.encode处理长度约为几十万的string list,预估速度很慢
- 从 pytorch 实现转为 ONNX,参考这个讨论 [https://huggingface.co/moka-ai/m3e-base/discussions/12#64bf9ebaa0e547106693129c]
- 加机器,加 GPU ,从硬件角度来提升速度