nq-distilbert-base-v1: MRR10: 72.36 on NQ dev set (small)
```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))
```
You can index the passages as shown here.
Note: The NQ model doesn't perform well. Use the above mentioned Multi-QA models to achieve the optimal performance.
More details
DPR-Models
In Dense Passage Retrieval  for Open-Domain Question Answering  Karpukhin et al. trained models based on Google's Natural Questions dataset:
