fix: fixes implementation of similarity() (#1748)

* fix(#1594): fixes implementation of similarity()

* fix: add similarity to SentenceTransformerWrapper

---------

Co-authored-by: sam021313 <40773225+sam021313@users.noreply.github.com>

mteb/evaluation/evaluators/RetrievalEvaluator.py

@@ -167,12 +167,12 @@ def search(
                     self.corpus_embeddings[request_qid].append(sub_corpus_embeddings)
 
             # Compute similarites using self defined similarity otherwise default to cosine-similarity
-            similarity_scores = cos_sim(query_embeddings, sub_corpus_embeddings)
             if hasattr(self.model, "similarity"):
                 similarity_scores = self.model.similarity(
-                    float(self.model.similarity(e1, e2))
-                    for e1, e2 in zip(query_embeddings, sub_corpus_embeddings)
+                    query_embeddings, sub_corpus_embeddings
                 )
+            else:
+                similarity_scores = cos_sim(query_embeddings, sub_corpus_embeddings)
             is_nan = torch.isnan(similarity_scores)
             if is_nan.sum() > 0:
                 logger.warning(
@@ -376,6 +376,9 @@ def __init__(self, model, **kwargs):
         self.save_corpus_embeddings = kwargs.get("save_corpus_embeddings", False)
         self.corpus_embeddings = {}
 
+        if hasattr(self.model, "similarity") and callable(self.model.similarity):
+            self.similarity = self.model.similarity
+
     def encode_corpus(
         self,
         corpus: list[dict[str, str]],

mteb/models/sentence_transformer_wrapper.py

@@ -59,7 +59,7 @@ def __init__(
         if isinstance(self.model, CrossEncoder):
             self.predict = self._predict
 
-        if hasattr(self.model, "similarity"):
+        if hasattr(self.model, "similarity") and callable(self.model.similarity):
             self.similarity = self.model.similarity
 
     def encode(