diff --git a/haystack/retriever/dense.py b/haystack/retriever/dense.py
index 1c65854f8..c2b63db0c 100644
--- a/haystack/retriever/dense.py
+++ b/haystack/retriever/dense.py
@@ -142,7 +142,7 @@ class DensePassageRetriever(BaseRetriever):
         documents = self.document_store.query_by_embedding(query_emb=query_emb[0], top_k=top_k, filters=filters, index=index)
         return documents
 
-    def _get_predictions(self, dicts, tokenizer):
+    def _get_predictions(self, dicts):
         """
         Feed a preprocessed dataset to the model and get the actual predictions (forward pass + formatting).
 
@@ -170,7 +170,7 @@ class DensePassageRetriever(BaseRetriever):
         )
         all_embeddings = {"query": [], "passages": []}
         self.model.eval()
-        for i, batch in enumerate(tqdm(data_loader, desc=f"Inferencing Samples", unit=" Batches", disable=False)):
+        for i, batch in enumerate(tqdm(data_loader, desc=f"Creating Embeddings", unit=" Batches", disable=False)):
             batch = {key: batch[key].to(self.device) for key in batch}
 
             # get logits
@@ -195,7 +195,7 @@ class DensePassageRetriever(BaseRetriever):
         :return: Embeddings, one per input queries
         """
         queries = [{'query': q} for q in texts]
-        result = self._get_predictions(queries, self.query_tokenizer)["query"]
+        result = self._get_predictions(queries)["query"]
         return result
 
     def embed_passages(self, docs: List[Document]) -> List[np.array]:
@@ -211,7 +211,7 @@ class DensePassageRetriever(BaseRetriever):
             "label": d.meta["label"] if d.meta and "label" in d.meta else "positive",
             "external_id": d.id}]
         } for d in docs]
-        embeddings = self._get_predictions(passages, self.passage_tokenizer)["passages"]
+        embeddings = self._get_predictions(passages)["passages"]
 
         return embeddings