cos_sim = util.cos_sim(emb1, emb2)
print("Cosine-Similarity:", cos_sim)
```
If you have a list with more sentences, you can use the following code example:
```python
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('all-MiniLM-L6-v2')
sentences = ['A man is eating food.',
          'A man is eating a piece of bread.',
          'The girl is carrying a baby.',
          'A man is riding a horse.',
          'A woman is playing violin.',
          'Two men pushed carts through the woods.',
          'A man is riding a white horse on an enclosed ground.',
          'A monkey is playing drums.',
          'Someone in a gorilla costume is playing a set of drums.'
          ]
Encode all sentences
embeddings = model.encode(sentences)
Compute cosine similarity between all pairs
