This page lists available pretrained Cross-Encoders. Cross-Encoders require the input of a text pair and output a score 0...1. They do not work for individual sentences and they don't compute embeddings for individual texts.
MS MARCO
MS MARCO Passage Retrieval is a large dataset with real user queries from Bing search engine with annotated relevant text passages.
These models can be used like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name', max_length=512)
scores = model.predict([('Query1', 'Paragraph1'), ('Query1', 'Paragraph2')])
For Example
scores = model.predict([('How many people live in Berlin?', 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.'), 
                        ('How many people live in Berlin?', 'Berlin is well known for its museums.')])
