"""

This basic example loads a pre-trained model from the web and uses it to

generate sentence embeddings for a given list of sentences.

"""

import logging

import numpy as np

from sentence_transformers import LoggingHandler, SentenceTransformer

#### Just some code to print debug information to stdout

np.set_printoptions(threshold=100)

logging.basicConfig(

format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO, handlers=[LoggingHandler()]

)

#### /print debug information to stdout

# Load pre-trained Sentence Transformer Model. It will be downloaded automatically

model = SentenceTransformer("all-MiniLM-L6-v2")

# Embed a list of sentences

sentences = [

]

sentence_embeddings = model.encode(sentences)

# The result is a list of sentence embeddings as numpy arrays

for sentence, embedding in zip(sentences, sentence_embeddings):

print("Sentence:", sentence)

print("Embedding:", embedding)

print("")