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
- AMD Developer Cloud
Fix links that moved after the FlagEmbedding restructure
#155
by abderrahmane11 - opened
The card still points at paths that existed before FlagEmbedding was reorganized, and each one now returns 404. The BGE-M3 code and paper moved under research/, reranker usage lives under FlagEmbedding/inference/reranker, the unified fine-tuning example and the LLM reranker moved under research/, and the MLDR and MKQA pipelines moved under FlagEmbedding/evaluation/. The Milvus hybrid retrieval example moved to examples/hybrid_search.py. Every replacement target returns 200. Only link targets change, no prose or table content.