How to use sugiv/embeddinggemma-300m-mortgage with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sugiv/embeddinggemma-300m-mortgage") sentences = [ "For a conventional conforming loan, what are the common down payment amounts?", "fannie_mae_selling_guide_chunk_001", "fannie_mae_selling_guide", "Standard down payment options for a conventional conforming loan range from 3 to 20 of the purchase price." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
