This is a sentence-transformers model trained. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
1from sentence_transformers import SentenceTransformer
23# Download from the 🤗 Hub4model = SentenceTransformer("sentence_transformers_model_id")5# Run inference6sentences =[7'The weather is lovely today.',8"It's so sunny outside!",9'He drove to the stadium.',10]11embeddings = model.encode(sentences)12print(embeddings.shape)13# [3, 1024]1415# Get the similarity scores for the embeddings16similarities = model.similarity(embeddings, embeddings)17print(similarities.shape)18# [3, 3]
