diff --git a/mteb/evaluation/evaluators/RetrievalEvaluator.py b/mteb/evaluation/evaluators/RetrievalEvaluator.py
index 4b2596c4d5..20a29b3ad5 100644
--- a/mteb/evaluation/evaluators/RetrievalEvaluator.py
+++ b/mteb/evaluation/evaluators/RetrievalEvaluator.py
@@ -268,7 +268,7 @@ def search_cross_encoder(
         for qid in queries.keys():
             if self.previous_results is None:
                 # try to use all of them
-                logging.logging(
+                logging.info(
                     f"previous_results is None. Using all the documents to rerank: {len(corpus)}"
                 )
                 q_results = {doc_id: 0.0 for doc_id in corpus.keys()}
@@ -318,7 +318,9 @@ def search_cross_encoder(
                 len(queries_in_pair) == len(corpus_in_pair) == len(instructions_in_pair)
             )
 
-            if isinstance(self.model.model, CrossEncoder):
+            if hasattr(self.model, "model") and isinstance(
+                self.model.model, CrossEncoder
+            ):
                 # can't take instructions, so add them here
                 queries_in_pair = [
                     f"{q} {i}".strip()
@@ -428,7 +430,7 @@ def encode(
 
 
 def is_cross_encoder_compatible(model) -> bool:
-    op = getattr(model.model, "predict", None)
+    op = getattr(model, "predict", None)
     return callable(op)
 
 
diff --git a/mteb/models/sentence_transformer_wrapper.py b/mteb/models/sentence_transformer_wrapper.py
index 5cc824fa82..13d39e4031 100644
--- a/mteb/models/sentence_transformer_wrapper.py
+++ b/mteb/models/sentence_transformer_wrapper.py
@@ -53,6 +53,9 @@ def __init__(
             self.model.prompts = model_prompts
         self.model_prompts = self.validate_task_to_prompt_name(model_prompts)
 
+        if isinstance(self.model, CrossEncoder):
+            self.predict = self._predict
+
     def encode(
         self,
         sentences: Sequence[str],
@@ -106,7 +109,7 @@ def encode(
             embeddings = embeddings.cpu().detach().float().numpy()
         return embeddings
 
-    def predict(
+    def _predict(
         self,
         sentences: Sequence[str],
         **kwargs: Any,