# Rerank QA Content
## Description
The Rerank operator is used to reorder the list of relevant documents for a query. It uses the [MS MARCO Cross-Encoders](https://www.sbert.net/docs/pretrained_cross-encoders.html#ms-marco) model to get the relevant scores and then reorders the documents.
## Code Example
- Run with ops
```Python
from towhee import ops
op = ops.rerank(threshold=0)
res = op('What is Towhee?',
['Towhee is Towhee is a cutting-edge framework to deal with unstructure data.', 'I do not know about towhee', 'Towhee has many powerful operators.', 'The weather is good' ])
```
- Run a pipeline
```python
from towhee import ops, pipe, DataCollection
p = (pipe.input('query', 'doc')
.map(('query', 'doc'), ('doc', 'score'), ops.rerank(threshold=0))
.flat_map(('doc', 'score'), ('doc', 'score'), lambda x, y: [(i, j) for i, j in zip(x, y)])
.output('query', 'doc', 'score')
)
DataCollection(p('What is Towhee?',
['Towhee is Towhee is a cutting-edge framework to deal with unstructure data.', 'I do not know about towhee', 'Towhee has many powerful operators.', 'The weather is good' ])
).show()
```
## Factory Constructor
Create the operator via the following factory method
***towhee.rerank(model_name: str = 'cross-encoder/ms-marco-MiniLM-L-12-v2')***
**Parameters:**
***model_name***: str
The model name of CrossEncoder, you can set it according to the [Model List](https://www.sbert.net/docs/pretrained-models/ce-msmarco.html#models-performance).
***threshold***: float
The threshold for filtering with score
***device***: str
## Interface
This operator is used to sort the documents of the query content and return the score, and can also set a threshold to filter the results.
**Parameters:**
***query***: str
The query content.
***docs***: list
A list of sentences to check the correlation with the query content.
**Return**: List[str], List[float]
The list of documents after rerank and the list of corresponding scores.