How to use Matjac5/MNLP_M2_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Matjac5/MNLP_M2_document_encoder") sentences = [ "His anger reached a boiling point, the therapist said they should take a break and what?", "chicken coop", "cool off", "bark" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
