How to use LegumMagister/MNLP_M3_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LegumMagister/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]
How to use LegumMagister/MNLP_M3_document_encoder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LegumMagister/MNLP_M3_document_encoder") model = AutoModel.from_pretrained("LegumMagister/MNLP_M3_document_encoder", device_map="auto")
