How to use kev216/sentence-embedding-LaBSE with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kev216/sentence-embedding-LaBSE") 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 kev216/sentence-embedding-LaBSE with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kev216/sentence-embedding-LaBSE") model = AutoModel.from_pretrained("kev216/sentence-embedding-LaBSE", device_map="auto")
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