How to use tessimago/bge-large-repmus-cross_entropy with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tessimago/bge-large-repmus-cross_entropy") sentences = [ "After rescue, survivors may require hospital treatment. This must be provided as quickly as possible. The SMC should consider having ambulance and hospital facilities ready.", "What should the SMC consider having ready after a rescue?", "What is critical for mass rescue operations?", "What can computer programs do to relieve the search planner of computational burden?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
