How to use Matisse6410/MNLP_M2_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Matisse6410/MNLP_M2_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]
How to use Matisse6410/MNLP_M2_document_encoder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Matisse6410/MNLP_M2_document_encoder") model = AutoModel.from_pretrained("Matisse6410/MNLP_M2_document_encoder")
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space.
Files info
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Matisse6410/MNLP_M2_document_encoder") model = AutoModel.from_pretrained("Matisse6410/MNLP_M2_document_encoder")