from model2vec import StaticModel
# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("WiseIntelligence/static-bge-code-v1-st")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])
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BAAI/bge-code-v1
from sentence_transformers import SentenceTransformer model = SentenceTransformer("WiseIntelligence/static-bge-code-v1-st") 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]