Semantic Search
The following models have been specifically trained for Semantic Search: Given a question / search query, these models are able to find relevant text passages. For more details, see Usage - Semantic Search.
```python
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('multi-qa-MiniLM-L6-cos-v1')
query_embedding = model.encode('How big is London')
passage_embedding = model.encode(['London has 9,787,426 inhabitants at the 2011 census',
                                  'London is known for its finacial district'])
print("Similarity:", util.dot_score(query_embedding, passage_embedding))
```
Multi-QA Models
The following models have been trained on 215M question-answer pairs from various sources and domains, including StackExchange, Yahoo Answers, Google & Bing search queries and many more. These model perform well across many search tasks and domains.
