diff --git a/tutorials/33_Hybrid_Retrieval.ipynb b/tutorials/33_Hybrid_Retrieval.ipynb
index 9bfc9ed..f489f06 100644
--- a/tutorials/33_Hybrid_Retrieval.ipynb
+++ b/tutorials/33_Hybrid_Retrieval.ipynb
@@ -233,7 +233,7 @@
    "source": [
     "### 2) Rank the Results\n",
     "\n",
-    "Use the [TransformersSimilarityRanker](https://docs.haystack.deepset.ai/docs/transformerssimilarityranker) that scores the relevancy of all retrieved documents for the given search query by using a cross encoder model. In this example, you will use [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) model to rank the retrieved documents but you can replace this model with other cross-encoder models on Hugging Face."
+    "Use the [SentenceTransformersSimilarityRanker](https://docs.haystack.deepset.ai/docs/sentencetransformerssimilarityranker) that scores the relevancy of all retrieved documents for the given search query by using a cross encoder model. In this example, you will use [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) model to rank the retrieved documents but you can replace this model with other cross-encoder models on Hugging Face."
    ]
   },
   {
@@ -244,9 +244,9 @@
    },
    "outputs": [],
    "source": [
-    "from haystack.components.rankers import TransformersSimilarityRanker\n",
+    "from haystack.components.rankers import SentenceTransformersSimilarityRanker\n",
     "\n",
-    "ranker = TransformersSimilarityRanker(model=\"BAAI/bge-reranker-base\")"
+    "ranker = SentenceTransformersSimilarityRanker(model=\"BAAI/bge-reranker-base\")"
    ]
   },
   {
diff --git a/tutorials/44_Creating_Custom_SuperComponents.ipynb b/tutorials/44_Creating_Custom_SuperComponents.ipynb
index 092a0ed..6ac4e47 100644
--- a/tutorials/44_Creating_Custom_SuperComponents.ipynb
+++ b/tutorials/44_Creating_Custom_SuperComponents.ipynb
@@ -245,7 +245,7 @@
     "from haystack import Document, Pipeline, super_component\n",
     "from haystack.components.joiners import DocumentJoiner\n",
     "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
-    "from haystack.components.rankers import TransformersSimilarityRanker\n",
+    "from haystack.components.rankers import SentenceTransformersSimilarityRanker\n",
     "from haystack.components.retrievers import InMemoryBM25Retriever, InMemoryEmbeddingRetriever\n",
     "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
     "\n",
@@ -265,7 +265,7 @@
     "        bm25_retriever = InMemoryBM25Retriever(document_store)\n",
     "        text_embedder = SentenceTransformersTextEmbedder(embedder_model)\n",
     "        document_joiner = DocumentJoiner()\n",
-    "        ranker = TransformersSimilarityRanker(ranker_model)\n",
+    "        ranker = SentenceTransformersSimilarityRanker(ranker_model)\n",
     "\n",
     "        # Create the pipeline\n",
     "        self.pipeline = Pipeline()\n",
@@ -513,7 +513,7 @@
     "\n",
     "The main differences between the two retrievers are:\n",
     "\n",
-    "1. **Added Ranker Component**: The second version includes a `TransformersSimilarityRanker` that re-ranks the documents based on their semantic similarity to the query.\n",
+    "1. **Added Ranker Component**: The second version includes a `SentenceTransformersSimilarityRanker` that re-ranks the documents based on their semantic similarity to the query.\n",
     "2. **Updated Input Mapping**: We added `\"text_embedder.text\"`, `\"bm25_retriever.query\"` and `\"ranker.query\"` to the input mapping to ensure the input query is sent to all three components while simplifying the `retriever.run` method.\n",
     "\n",
     "The ranker can significantly improve the quality of the results by re-ranking the documents based on their semantic similarity to the query, even if they were not ranked highly by the initial retrievers."
@@ -629,7 +629,7 @@
     "from haystack.components.joiners import DocumentJoiner\n",
     "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
     "from haystack.components.retrievers import InMemoryBM25Retriever, InMemoryEmbeddingRetriever\n",
-    "from haystack.components.rankers import TransformersSimilarityRanker\n",
+    "from haystack.components.rankers import SentenceTransformersSimilarityRanker\n",
     "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
     "\n",
     "\n",
@@ -646,7 +646,7 @@
     "        bm25_retriever = InMemoryBM25Retriever(document_store)\n",
     "        text_embedder = SentenceTransformersTextEmbedder(embedder_model)\n",
     "        document_joiner = DocumentJoiner()\n",
-    "        ranker = TransformersSimilarityRanker(ranker_model)\n",
+    "        ranker = SentenceTransformersSimilarityRanker(ranker_model)\n",
     "\n",
     "        # Create the pipeline\n",
     "        self.pipeline = Pipeline()\n",
diff --git a/tutorials/guide_evaluation.ipynb b/tutorials/guide_evaluation.ipynb
index 15eacb0..65f32d6 100644
--- a/tutorials/guide_evaluation.ipynb
+++ b/tutorials/guide_evaluation.ipynb
@@ -110,7 +110,7 @@
     "### Methods to Improve Generation:\n",
     "\n",
     "- **Ranking**: Incorporate a ranking mechanism into your retrieved documents before providing the context to your prompt\n",
-    "    - **Order by similarity**: Reorder your retrieved documents by similarity using cross-encoder models from Hugging Face with [TransformersSimilarityRanker](https://docs.haystack.deepset.ai/docs/transformerssimilarityranker), Rerank models from Cohere with [CohereRanker](https://docs.haystack.deepset.ai/docs/cohereranker), or Rerankers from Jina with [JinaRanker](https://docs.haystack.deepset.ai/docs/jinaranker)\n",
+    "    - **Order by similarity**: Reorder your retrieved documents by similarity using cross-encoder models from Hugging Face with [SentenceTransformersSimilarityRanker](https://docs.haystack.deepset.ai/docs/sentencetransformerssimilarityranker), Rerank models from Cohere with [CohereRanker](https://docs.haystack.deepset.ai/docs/cohereranker), or Rerankers from Jina with [JinaRanker](https://docs.haystack.deepset.ai/docs/jinaranker)\n",
     "    - **Increase diversity by ranking**: Maximize the overall diversity among your context using sentence-transformers models with [SentenceTransformersDiversityRanker](https://docs.haystack.deepset.ai/docs/sentencetransformersdiversityranker) to help increase the semantic answer similarity (SAS) in LFQA applications.\n",
     "    - **Address the \"Lost in the Middle\" problem by reordering**: Position the most relevant documents at the beginning and end of the context using [LostInTheMiddleRanker](https://docs.haystack.deepset.ai/docs/lostinthemiddleranker) to increase faithfulness.\n",
     "- **Different Generators**: Try different large language models and benchmark the results. The full list of model providers is in [Generators](https://docs.haystack.deepset.ai/docs/generators).\n",