How to use azizdh00/MNLP_M3_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("azizdh00/MNLP_M3_document_encoder") sentences = [ "A hash function $h$ is collision-resistant if\\dots", "6471::[9216:9728]", "13251::[1536:2048]", "5817::[2688:3200]" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
