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
bge-M3和baai_general_embedding是什么关系
#20
by biaodiluer - opened
为啥微调bge-M3的链接点进去是baai_general_embedding的例子,baai_general_embedding属于bge-M3么
我知道bge是baai_general_embedding的缩写,但FlagEmbedding/BGE_M3和FlagEmbedding/baai_general_embedding的README中,分别是BGEM3FlagModel和FlagModel,这俩是啥区别
Bge-m3(BGEM3FlagModel) support more retrieval modes, while bge(FlagModel) only support dense retrieval.
You only can fine-tune the dense embedding of bge-m3 via baai_general_embedding. If you want to fine-tune all embedding functions (including dense embedding, sparse embedding, and colbert), you should use m3 fine-tune example