diff --git a/website/docs/api/data-formats.md b/website/docs/api/data-formats.md
index 6f166f4b9..210e5d47d 100644
--- a/website/docs/api/data-formats.md
+++ b/website/docs/api/data-formats.md
@@ -95,6 +95,7 @@ annotations. You need to only specify those keys corresponding to the task you
 want to train.
 
 ```python
+### Flat dictionary
 {
     "text": string,                        # Raw text.
     "words": List[string],                 # List of gold tokens.
@@ -161,6 +162,7 @@ the previous section, but there are two main sections `token_annotation` and
 [`Token` attributes](/api/token#attributes) such as "ORTH" and "TAG".
 
 ```python
+### Hierarchical dictionary
 {
     "text": string,                            # Raw text.
     "token_annotation": {
diff --git a/website/docs/api/example.md b/website/docs/api/example.md
index ec4399a8b..e75c84522 100644
--- a/website/docs/api/example.md
+++ b/website/docs/api/example.md
@@ -41,7 +41,7 @@ both documents.
 ## Example.from_dict {#from_dict tag="classmethod"}
 
 Construct an `Example` object from the `predicted` document and the reference
-annotations provided as a dictionary. For more details on the required format,  
+annotations provided as a dictionary. For more details on the required format,
 see the [training format documentation](/api/data-formats#dict-input).
 
 > #### Example
@@ -244,8 +244,9 @@ accuracy of predicted entities against the original gold-standard annotation.
 
 ## Example.to_dict {#to_dict tag="method"}
 
-Return a dictionary representation of the reference annotation contained in this
-`Example`.
+Return a
+[hierarchical dictionary representation](/api/data-formats#dict-hierarch) of the
+reference annotation contained in this `Example`.
 
 > #### Example
 >