Sentence Similarity
sentence-transformers
PyTorch
ONNX
xlm-roberta
feature-extraction
Eval Results
text-embeddings-inference
Instructions to use BAAI/bge-m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-m3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/bge-m3") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
关于多阶段训练
#21
by xxxcliu - opened
看paper有点困惑,是先进行了RetroMAE的预训练,然后用无监督数据进行了dense retrieval的训练,然后又用self蒸馏进行了三种方式的训练吗?
Yes. RetroMAE->dense retrieval->unified fine-tuning
Yes. RetroMAE->dense retrieval->unified fine-tuning
Thanks for your reply! Do both bge and bge-m3 adopt a bi-encoder architecture? If so, is there actually a single roberta encoder or two seperate models?
bge and bge-m3 both are bi-encoder model, and query and passage share the same encoder.