Update tokenizer and doc init example (#3939)

* Fix Doc.to_json hyperlink

* Update tokenizer and doc init examples

* Change "matchin rules" to "punctuation rules"

* Auto-format

spacy/tokens/doc.pyx

@@ -85,13 +85,14 @@ cdef class Doc:
     Python-level `Token` and `Span` objects are views of this array, i.e.
     they don't own the data themselves.
 
-    EXAMPLE: Construction 1
+    EXAMPLE:
+        Construction 1
         >>> doc = nlp(u'Some text')
 
         Construction 2
         >>> from spacy.tokens import Doc
         >>> doc = Doc(nlp.vocab, words=[u'hello', u'world', u'!'],
-                      spaces=[True, False, False])
+        >>>           spaces=[True, False, False])
 
     DOCS: https://spacy.io/api/doc
     """

website/docs/api/doc.md

@@ -264,7 +264,7 @@ ancestor is found, e.g. if span excludes a necessary ancestor.
 | ----------- | -------------------------------------- | ----------------------------------------------- |
 | **RETURNS** | `numpy.ndarray[ndim=2, dtype='int32']` | The lowest common ancestor matrix of the `Doc`. |
 
-## Doc.to_json {#to_json, tag="method" new="2.1"}
+## Doc.to_json {#to_json tag="method" new="2.1"}
 
 Convert a Doc to JSON. The format it produces will be the new format for the
 [`spacy train`](/api/cli#train) command (not implemented yet). If custom

website/docs/api/tokenizer.md

@@ -9,7 +9,10 @@ Segment text, and create `Doc` objects with the discovered segment boundaries.
 
 ## Tokenizer.\_\_init\_\_ {#init tag="method"}
 
-Create a `Tokenizer`, to create `Doc` objects given unicode text.
+Create a `Tokenizer`, to create `Doc` objects given unicode text. For examples
+of how to construct a custom tokenizer with different tokenization rules, see
+the
+[usage documentation](https://spacy.io/usage/linguistic-features#native-tokenizers).
 
 > #### Example
 >
@@ -18,11 +21,14 @@ Create a `Tokenizer`, to create `Doc` objects given unicode text.
 > from spacy.tokenizer import Tokenizer
 > from spacy.lang.en import English
 > nlp = English()
+> # Create a blank Tokenizer with just the English vocab
 > tokenizer = Tokenizer(nlp.vocab)
 >
 > # Construction 2
 > from spacy.lang.en import English
 > nlp = English()
+> # Create a Tokenizer with the default settings for English
+> # including punctuation rules and exceptions
 > tokenizer = nlp.Defaults.create_tokenizer(nlp)
 > ```