Update README

Signed-off-by: shiyu22 <shiyu.chen@zilliz.com>

README.md

@@ -1,2 +1,86 @@
-# rerank
+# 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.
+
+<br />
+
+
+
+## Code Example
+
+- Run with ops
+
+```Python
+from towhee import ops
+
+op = ops.rerank()
+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' ],
+         0)
+```
+
+- Run a pipeline
+
+```python
+from towhee import ops, pipe, DataCollection
+
+p = (pipe.input('query', 'doc', 'threshold')
+         .map(('query', 'doc', 'threshold'), ('doc', 'score'), ops.rerank())
+         .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' ],
+                 0)
+              ).show()
+```
+
+<img src="./result.png" height="100px"/>
+
+<br />
+
+
+
+## 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).
+
+<br />
+
+
+
+## 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.
+
+​	***threshold***: float
+
+​    The threshold for filtering with score, defaults to none, i.e., no filtering.
+
+
+<br />
+
+**Return**: List[str], List[float]
+
+The list of documents after rerank and the list of corresponding scores.

rerank.py

@@ -6,7 +6,7 @@ from towhee.operator import NNOperator
 
 
 class ReRank(NNOperator):
-    def __init__(self, model_name: str = 'cross-encoder/ms-marco-MiniLM-L-6-v2'):
+    def __init__(self, model_name: str = 'cross-encoder/ms-marco-MiniLM-L-12-v2'):
         super().__init__()
         self._model_name = model_name
         self._model = CrossEncoder(self._model_name, max_length=1000)
@@ -20,4 +20,4 @@ class ReRank(NNOperator):
         else:
             re_docs = [docs[i] for i in re_ids if scores[i] >= threshold]
             re_scores = [scores[i] for i in re_ids if scores[i] >= threshold]
-        return re_docs, re_scores
+        return re_docs, re_scores