Google's Natural Questions dataset constists of about 100k real search queries from Google with the respective, relevant passage from Wikipedia. Models trained on this dataset work well for question-answer retrieval.
Usage
```python
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('nq-distilbert-base-v1')
query_embedding = model.encode('How many people live in London?')
The passages are encoded as [ [title1, text1], [title2, text2], ...]
passage_embedding = model.encode([['London', 'London has 9,787,426 inhabitants at the 2011 census.']])
print("Similarity:", util.cos_sim(query_embedding, passage_embedding))
```
Note: For the passage, we have to encode the Wikipedia article title together with a text paragraph from that article.
Performance
The models are evaluated on the Natural Questions development dataset using MRR@10.
