How to use Cutting3dg3/MMiniLM_TID_d with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Cutting3dg3/MMiniLM_TID_d") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3]
How to use Cutting3dg3/MMiniLM_TID_d with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Cutting3dg3/MMiniLM_TID_d") model = AutoModel.from_pretrained("Cutting3dg3/MMiniLM_TID_d", device_map="auto")
