MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using Bing search engine. The provided models can be used for semantic search, i.e., given keywords / a search phrase / a question, the model will find passages that are relevant for the search query.
The training data consists of over 500k examples, while the complete  corpus consist of over 8.8 Million passages.
Usage
```python
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('msmarco-distilroberta-base-v2')
query_embedding = model.encode('How big is London')
passage_embedding = model.encode('London has 9,787,426 inhabitants at the 2011 census')
print("Similarity:", util.pytorch_cos_sim(query_embedding, passage_embedding))
```
For more details on the usage, see Applications - Information Retrieval
