from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Matisse6410/MNLP_M3_document_encoder")
sentences = [
"That is a happy person",
"That is a happy dog",
"That is a very happy person",
"Today is a sunny day"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]
Matisse6410/MNLP_M3_document_encoder
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space.
Downloads last month
1
Safetensors
Model size
22.7M params
Tensor type
I64
·
F32
·
Datasets used to train Matisse6410/MNLP_M3_document_encoder
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Matisse6410/MNLP_M3_document_encoder") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]