Fix typos, wording and formatting

website/docs/usage/_spacy-101/_similarity.jade

@@ -5,7 +5,7 @@ p
     |  #[strong how similar they are]. Predicting similarity is useful for
     |  building recommendation systems or flagging duplicates. For example, you
     |  can suggest a user content that's similar to what they're currently
-    |  looking at, or label a support ticket as a duplicate, if it's very
+    |  looking at, or label a support ticket as a duplicate if it's very
     |  similar to an already existing one.
 
 p

website/docs/usage/language-processing-pipeline.jade

@@ -144,7 +144,7 @@ p
 +table(["Argument", "Type", "Description"])
     +row
         +cell #[code vocab]
-        +cell #[coce Vocab]
+        +cell #[code Vocab]
         +cell
             |  Shared data between components, including strings, morphology,
             |  vectors etc.

website/docs/usage/spacy-101.jade

@@ -65,7 +65,7 @@ p
     |  spaCy provides a variety of linguistic annotations to give you insights
     |  into a text's grammatical structure. This includes the word types,
     |  i.e. the parts of speech, and how the words are related to each other.
-    |  For example, if you're analysing text, it makes a #[em huge] difference
+    |  For example, if you're analysing text, it makes a huge difference
     |  whether a noun is the subject of a sentence, or the object – or whether
     |  "google" is used as a verb, or refers to the website or company in a
     |  specific context.
@@ -119,9 +119,11 @@ include _spacy-101/_named-entities
 
 +infobox
     |  To learn more about entity recognition in spaCy, how to
-    |  #[strong add your own entities] to a document and how to train and update
+    |  #[strong add your own entities] to a document and how to
-    |  the entity predictions of a model, see the usage guide on
+    |  #[strong train and update] the entity predictions of a model, see the
-    |  #[+a("/docs/usage/entity-recognition") named entity recognition].
+    |  usage guides on
+    |  #[+a("/docs/usage/entity-recognition") named entity recognition] and
+    |  #[+a("/docs/usage/training-ner") training the named entity recognizer].
 
 +h(2, "vectors-similarity") Word vectors and similarity
     +tag-model("vectors")

website/docs/usage/v2.jade

@@ -20,19 +20,18 @@ p
     nlp = Language(pipeline=['my_factory', mycomponent])
 
 p
-    |  It's now much easier to customise the pipeline with your own components.
+    |  It's now much easier to #[strong customise the pipeline] with your own
-    |  Components are functions that receive a #[code Doc] object, modify and
+    |  components, functions that receive a #[code Doc] object, modify and
-    |  return it. If your component is stateful, you'll want to create a new one
+    |  return it. If your component is stateful, you can define and register a
-    |  for each pipeline. You can do that by defining and registering a factory
+    |  factory which receives the shared #[code Vocab] object and returns a
-    |  which receives the shared #[code Vocab] object and returns a component.
+    |  component. spaCy's default components can be added to your pipeline by
-
+    |  using their string IDs. This way, you won't have to worry about finding
-p
+    |  and implementing them – simply add #[code "tagger"] to the pipeline,
-    |  spaCy's default components – the vectorizer, tagger, parser and entity
-    |  recognizer, can be added to your pipeline by using their string IDs.
-    |  This way, you won't have to worry about finding and implementing them –
-    |  to use the default tagger, simply add #[code "tagger"] to the pipeline,
     |  and spaCy will know what to do.
 
++image
+    include ../../assets/img/docs/pipeline.svg
+
 +infobox
     |  #[strong API:] #[+api("language") #[code Language]]
     |  #[strong Usage:] #[+a("/docs/usage/language-processing-pipeline") Processing text]
@@ -96,11 +95,10 @@ p
     |  #[code Language] class, or load a model that initialises one. This allows
     |  languages to contain more custom data, e.g. lemmatizer lookup tables, or
     |  complex regular expressions. The language data has also been tidied up
-    |  and simplified. It's now also possible to overwrite the functions that
+    |  and simplified. spaCy now also supports simple lookup-based lemmatization.
-    |  compute lexical attributes like #[code like_num], and supply
+
-    |  language-specific syntax iterators, e.g. to determine noun chunks. spaCy
++image
-    |  now also supports simple lookup-based lemmatization. The data is stored
+    include ../../assets/img/docs/language_data.svg
-    |  in a dictionary mapping a string to its lemma.
 
 +infobox
     |  #[strong API:] #[+api("language") #[code Language]]
@@ -111,13 +109,10 @@ p
 
 +aside-code("Example").
     from spacy.matcher import Matcher
-    from spacy.attrs import LOWER, IS_PUNCT
     matcher = Matcher(nlp.vocab)
-    matcher.add('HelloWorld', None,
+    matcher.add('HEARTS', None, [{'ORTH': '❤️', 'OP': '+'}])
-                [{LOWER: 'hello'}, {IS_PUNCT: True}, {LOWER: 'world'}],
-                [{LOWER: 'hello'}, {LOWER: 'world'}])
     assert len(matcher) == 1
-    assert 'HelloWorld' in matcher
+    assert 'HEARTS' in matcher
 
 p
     |  Patterns can now be added to the matcher by calling
@@ -157,28 +152,8 @@ p
         +cell #[+api("language#to_disk") #[code Language.to_disk]]
 
     +row
-        +cell #[code Tokenizer.load]
+        +cell #[code Language.create_make_doc]
-        +cell
+        +cell #[+api("language#attributes") #[code Language.tokenizer]]
-            |  #[+api("tokenizer#from_disk") #[code Tokenizer.from_disk]]
-            |  #[+api("tokenizer#from_bytes") #[code Tokenizer.from_bytes]]
-
-    +row
-        +cell #[code Tagger.load]
-        +cell
-            |  #[+api("tagger#from_disk") #[code Tagger.from_disk]]
-            |  #[+api("tagger#from_bytes") #[code Tagger.from_bytes]]
-
-    +row
-        +cell #[code DependencyParser.load]
-        +cell
-            |  #[+api("dependencyparser#from_disk") #[code DependencyParser.from_disk]]
-            |  #[+api("dependencyparser#from_bytes") #[code DependencyParser.from_bytes]]
-
-    +row
-        +cell #[code EntityRecognizer.load]
-        +cell
-            |  #[+api("entityrecognizer#from_disk") #[code EntityRecognizer.from_disk]]
-            |  #[+api("entityrecognizer#from_bytes") #[code EntityRecognizer.from_bytes]]
 
     +row
         +cell
@@ -212,6 +187,28 @@ p
             |  #[+api("stringstore#to_disk") #[code StringStore.to_disk]]
             |  #[+api("stringstore#to_bytes") #[code StringStore.to_bytes]]
 
+    +row
+        +cell #[code Tokenizer.load]
+        +cell -
+
+    +row
+        +cell #[code Tagger.load]
+        +cell
+            |  #[+api("tagger#from_disk") #[code Tagger.from_disk]]
+            |  #[+api("tagger#from_bytes") #[code Tagger.from_bytes]]
+
+    +row
+        +cell #[code DependencyParser.load]
+        +cell
+            |  #[+api("dependencyparser#from_disk") #[code DependencyParser.from_disk]]
+            |  #[+api("dependencyparser#from_bytes") #[code DependencyParser.from_bytes]]
+
+    +row
+        +cell #[code EntityRecognizer.load]
+        +cell
+            |  #[+api("entityrecognizer#from_disk") #[code EntityRecognizer.from_disk]]
+            |  #[+api("entityrecognizer#from_bytes") #[code EntityRecognizer.from_bytes]]
+
     +row
         +cell #[code Matcher.load]
         +cell -
@@ -232,7 +229,7 @@ p
 
     +row
         +cell #[code Doc.read_bytes]
-        +cell
+        +cell #[+api("binder") #[code Binder]]
 
     +row
         +cell #[code Token.is_ancestor_of]