## 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