diff --git a/README.rst b/README.rst
index 31531cd55..d5f231c1e 100644
--- a/README.rst
+++ b/README.rst
@@ -133,9 +133,10 @@ ElasticsearchRetriever
 Scoring text similarity via sparse Bag-of-words representations are strong and well-established baselines in Information Retrieval.
 The default :code:`ElasticsearchRetriever` uses Elasticsearch's native scoring (BM25), but can be extended easily with custom queries or filtering.
 
-Example::
+Example
 
 .. code-block:: python
+
     retriever = ElasticsearchRetriever(document_store=document_store, custom_query=None)
     retriever.retrieve(query="Why did the revenue increase?", filters={"years": ["2019"], "company": ["Q1", "Q2"]})
     # returns: [Document, Document]
@@ -145,9 +146,10 @@ EmbeddingRetriever
 Using dense embeddings (i.e. vector representations) of texts is a powerful alternative to score similarity of texts.
 This retriever allows you to transform your query into an embedding using a model (e.g. Sentence-BERT) and find similar texts by using cosine similarity.
 
-Example::
+Example
 
 .. code-block:: python
+
     retriever = EmbeddingRetriever(document_store=document_store,
                                    embedding_model="deepset/sentence-bert",
                                    model_format="farm")
@@ -168,9 +170,10 @@ Both readers can load either a local model or any public model from  `Hugging Fa
 FARMReader
 ^^^^^^^^^^
 Implementing various QA models via the `FARM <https://github.com/deepset-ai/FARM>`_ Framework.
-Example::
+Example
 
 .. code-block:: python
+
     reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2",
                     use_gpu=False, no_ans_boost=-10, context_window_size=500,
                     top_k_per_candidate=3, top_k_per_sample=1,
@@ -194,9 +197,10 @@ TransformersReader
 ^^^^^^^^^^^^^^^^^^
 Implementing various QA models via the :code:`pipeline` class of `Transformers <https://github.com/huggingface/transformers>`_ Framework.
 
-Example::
+Example
 
 .. code-block:: python
+
     reader = TransformersReader(model="distilbert-base-uncased-distilled-squad",
                                 tokenizer="distilbert-base-uncased",
                                 context_window_size=500,