| ## Mini Encoder | |
| from sentence_transformers import SentenceTransformer | |
| ## Model 1: mini-encoder | |
| model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') | |
| def get_embeddings(sentences): | |
| embeddings = model.encode(sentences) | |
| return embeddings | |
| ## Model 2: intfloat/multilingual-e5-large | |
| from sentence_transformers import SentenceTransformer | |
| modelbig = SentenceTransformer("sentence-transformers/gtr-t5-large") | |
| """ | |
| sentences = [ | |
| "That is a happy person", | |
| "That is a happy dog", | |
| "That is a very happy person", | |
| "Today is a sunny day" | |
| ] | |
| similarities = model.similarity(embeddings, embeddings) | |
| print(similarities.shape) | |
| # [4, 4] | |
| """ | |
| def get_embeddings_big(sentences): | |
| embeddings = modelbig.encode(sentences) | |
| return embeddings | |