Once you have SentenceTransformers installed, the usage is simple:
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')
Our sentences we like to encode
sentences = ['This framework generates embeddings for each input sentence',
    'Sentences are passed as a list of string.', 
    'The quick brown fox jumps over the lazy dog.']
Sentences are encoded by calling model.encode()
sentence_embeddings = model.encode(sentences)
Print the embeddings
for sentence, embedding in zip(sentences, sentence_embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding)
    print("")
```
With SentenceTransformer('all-MiniLM-L6-v2') we define which sentence transformer model we like to load. In this example, we load all-MiniLM-L6-v2, which is a MiniLM model fine tuned on a large dataset of over 1 billion training pairs.
