Once you have  sentence embeddings computed, you usually want to compare them to each other. Here, I show you how you can compute the cosine similarity between embeddings, for example, to measure the semantic similarity of two texts.
```python
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('all-MiniLM-L6-v2')
Two lists of sentences
sentences1 = ['The cat sits outside',
             'A man is playing guitar',
             'The new movie is awesome']
sentences2 = ['The dog plays in the garden',
              'A woman watches TV',
              'The new movie is so great']
Compute embedding for both lists
embeddings1 = model.encode(sentences1, convert_to_tensor=True)
embeddings2 = model.encode(sentences2, convert_to_tensor=True)
Compute cosine-similarits
