adapt when model output label num is 2

Signed-off-by: ChengZi <chen.zhang@zilliz.com>

rerank.py

@@ -10,13 +10,18 @@ class ReRank(NNOperator):
     def __init__(self, model_name: str = 'cross-encoder/ms-marco-MiniLM-L-6-v2', threshold: float = 0.6, device: str = None):
         super().__init__()
         self._model_name = model_name
-        self._model = CrossEncoder(self._model_name, device=device, default_activation_function=nn.Sigmoid())
+        self._model = CrossEncoder(self._model_name, device=device)
+        if self._model.config.num_labels == 1:
+            self._model.default_activation_function = nn.Sigmoid()
         self._threshold = threshold
 
     def __call__(self, query: str, docs: List):
         if len(docs) == 0:
             return [], []
-        scores = self._model.predict([(query, doc) for doc in docs])
+        if self._model.config.num_labels > 1:
+            scores = self._model.predict([(query, doc) for doc in docs], apply_softmax=True)[:, 1]
+        else:
+            scores = self._model.predict([(query, doc) for doc in docs])
         re_ids = sorted(range(len(scores)), key=lambda k: scores[k], reverse=True)
         if self._threshold is None:
             re_docs = [docs[i] for i in re_ids]