Replace dpr with embeddingretriever tut5 (#2274)

* ipynb: EmbeddingRetriever made more prominent than DPR

* ipynb: EmbeddingRetriever more prominent than DPR

* Update Documentation & Code Style

* indentation fix

* Update Documentation & Code Style

* py: EmbeddingRetriever more prominent than DPR

* indentation fix

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

docs/_src/tutorials/tutorials/5.md

@@ -131,24 +131,25 @@ from haystack.nodes import ElasticsearchRetriever
 
 retriever = ElasticsearchRetriever(document_store=document_store)
 
-# Alternative: Evaluate dense retrievers (DensePassageRetriever or EmbeddingRetriever)
-# DensePassageRetriever uses two separate transformer based encoders for query and document.
-# In contrast, EmbeddingRetriever uses a single encoder for both.
+# Alternative: Evaluate dense retrievers (EmbeddingRetriever or DensePassageRetriever)
+# The EmbeddingRetriever uses a single transformer based encoder model for query and document.
+# In contrast, DensePassageRetriever uses two separate encoders for both.
+
 # Please make sure the "embedding_dim" parameter in the DocumentStore above matches the output dimension of your models!
 # Please also take care that the PreProcessor splits your files into chunks that can be completely converted with
 #        the max_seq_len limitations of Transformers
-# The SentenceTransformer model "all-mpnet-base-v2" generally works well with the EmbeddingRetriever on any kind of English text.
-# For more information check out the documentation at: https://www.sbert.net/docs/pretrained_models.html
+# The SentenceTransformer model "sentence-transformers/multi-qa-mpnet-base-dot-v1" generally works well with the EmbeddingRetriever on any kind of English text.
+# For more information and suggestions on different models check out the documentation at: https://www.sbert.net/docs/pretrained_models.html
 
-# from haystack.retriever import DensePassageRetriever, EmbeddingRetriever
+# from haystack.retriever import EmbeddingRetriever, DensePassageRetriever
+# retriever = EmbeddingRetriever(document_store=document_store, model_format="sentence_transformers",
+# embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1")
 # retriever = DensePassageRetriever(document_store=document_store,
 #                                   query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
 #                                   passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
 #                                   use_gpu=True,
 #                                   max_seq_len_passage=256,
 #                                   embed_title=True)
-# retriever = EmbeddingRetriever(document_store=document_store, model_format="sentence_transformers",
-#                                embedding_model="all-mpnet-base-v2")
 # document_store.update_embeddings(retriever, index=doc_index)
 ```