diff --git a/website/docs/usage/v2.jade b/website/docs/usage/v2.jade
index 4a0e6ca2f..a058c5c13 100644
--- a/website/docs/usage/v2.jade
+++ b/website/docs/usage/v2.jade
@@ -8,6 +8,65 @@ p
 
 +h(2, "features") New features
 
++h(3, "features-pipelines") Improved processing pipelines
+
++aside-code("Example").
+    # Modify an existing pipeline
+    nlp = spacy.load('en')
+    nlp.pipeline.append(my_component)
+
+    # Register a factory to create a component
+    spacy.set_factory('my_factory', my_factory)
+    nlp = Language(pipeline=['my_factory', mycomponent])
+
+p
+    |  It's now much easier to customise the pipeline with your own components.
+    |  Components are functions that receive a #[code Doc] object, modify and
+    |  return it. If your component is stateful, you'll want to create a new one
+    |  for each pipeline. You can do that by defining and registering a factory
+    |  which receives the shared #[code Vocab] object and returns a component.
+
+p
+    |  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.
+
++infobox
+    |  #[strong API:] #[+api("language") #[code Language]]
+    |  #[strong Usage:] #[+a("/docs/usage/language-processing-pipeline") Processing text]
+
++h(3, "features-serializer") Saving, loading and serialization
+
++aside-code("Example").
+    nlp = spacy.load('en') # shortcut link
+    nlp = spacy.load('en_core_web_sm') # package
+    nlp = spacy.load('/path/to/en') # unicode path
+    nlp = spacy.load(Path('/path/to/en')) # pathlib Path
+
+    nlp.to_disk('/path/to/nlp')
+    nlp = English().from_disk('/path/to/nlp')
+
+p
+    |  spay's serialization API has been made consistent across classes and
+    |  objects. All container classes and pipeline components now have a
+    |  #[code to_bytes()], #[code from_bytes()], #[code to_disk()] and
+    |  #[code from_disk()] method that supports the Pickle protocol.
+
+p
+    |  The improved #[code spacy.load] makes loading models easier and more
+    |  transparent. You can load a model by supplying its
+    |  #[+a("/docs/usage/models#usage") shortcut link], the name of an installed
+    |  #[+a("/docs/usage/saving-loading#generating") model package] or a path.
+    |  The #[code Language] class to initialise will be determined based on the
+    |  model's settings. For a blank language, you can import the class directly,
+    |  e.g. #[code from spacy.lang.en import English].
+
++infobox
+    |  #[strong API:] #[+api("spacy#load") #[code spacy.load]], #[+api("binder") #[code Binder]]
+    |  #[strong Usage:] #[+a("/docs/usage/saving-loading") Saving and loading]
+
 +h(3, "features-displacy") displaCy visualizer with Jupyter support
 
 +aside-code("Example").
@@ -28,33 +87,6 @@ p
     |  #[strong API:] #[+api("displacy") #[code displacy]]
     |  #[strong Usage:] #[+a("/docs/usage/visualizers") Visualizing spaCy]
 
-+h(3, "features-loading") Loading
-
-+aside-code("Example").
-    nlp = spacy.load('en') # shortcut link
-    nlp = spacy.load('en_core_web_sm') # package
-    nlp = spacy.load('/path/to/en') # unicode path
-    nlp = spacy.load(Path('/path/to/en')) # pathlib Path
-
-p
-    |  The improved #[code spacy.load] makes loading models easier and more
-    |  transparent. You can load a model by supplying its
-    |  #[+a("/docs/usage/models#usage") shortcut link], the name of an installed
-    |  #[+a("/docs/usage/saving-loading#generating") model package], a unicode
-    |  path or a #[code Path]-like object. spaCy will try resolving the load
-    |  argument in this order. The #[code path] keyword argument is now deprecated.
-
-p
-    |  The #[code Language] class to initialise will be determined based on the
-    |  model's settings. If no model is found, spaCy will let you know and won't
-    |  just return an empty #[code Language] object anymore. If you want a blank
-    |  language, you can always import the class directly, e.g.
-    |  #[code from spacy.lang.en import English].
-
-+infobox
-    |  #[strong API:] #[+api("spacy#load") #[code spacy.load]]
-    |  #[strong Usage:] #[+a("/docs/usage/saving-loading") Saving and loading]
-
 +h(3, "features-language") Improved language data and lazy loading
 
 p
@@ -65,46 +97,15 @@ p
     |  complex regular expressions. The language data has also been tidied up
     |  and simplified. It's now also possible to overwrite the functions that
     |  compute lexical attributes like #[code like_num], and supply
-    |  language-specific syntax iterators, e.g. to determine noun chunks.
+    |  language-specific syntax iterators, e.g. to determine noun chunks. spaCy
+    |  now also supports simple lookup-based lemmatization. The data is stored
+    |  in a dictionary mapping a string to its lemma.
 
 +infobox
+    |  #[strong API:] #[+api("language") #[code Language]]
     |  #[strong Code:] #[+src(gh("spaCy", "spacy/lang")) spacy/lang]
     |  #[strong Usage:] #[+a("/docs/usage/adding-languages") Adding languages]
 
-+h(3, "features-pipelines") Improved processing pipelines
-
-+aside-code("Example").
-    from spacy.language import Language
-    nlp = Language(pipeline=['token_vectors', 'tags',
-                             'dependencies'])
-
-+infobox
-    |  #[strong API:] #[+api("language") #[code Language]]
-    |  #[strong Usage:] #[+a("/docs/usage/processing-text") Processing text]
-
-+h(3, "features-lemmatizer") Simple lookup-based lemmatization
-
-+aside-code("Example").
-    LOOKUP = {
-        "aba": "abar",
-        "ababa": "abar",
-        "ababais": "abar",
-        "ababan": "abar",
-        "ababanes": "ababán"
-    }
-
-p
-    |  spaCy now supports simple lookup-based lemmatization. The data is stored
-    |  in a dictionary mapping a string to its lemma. To determine a token's
-    |  lemma, spaCy simply looks it up in the table. The lookup lemmatizer can
-    |  be imported from #[code spacy.lemmatizerlookup]. It's initialised with
-    |  the lookup table, and should be returned by the #[code create_lemmatizer]
-    |  classmethod of the language's defaults.
-
-+infobox
-    |  #[strong API:] #[+api("language") #[code Language]]
-    |  #[strong Usage:] #[+a("/docs/usage/adding-languages") Adding languages]
-
 +h(3, "features-matcher") Revised matcher API
 
 +aside-code("Example").
@@ -129,12 +130,6 @@ p
     |  #[strong API:] #[+api("matcher") #[code Matcher]]
     |  #[strong Usage:] #[+a("/docs/usage/rule-based-matching") Rule-based matching]
 
-+h(3, "features-serializer") Serialization
-
-+infobox
-    |  #[strong API:] #[+api("serializer") #[code Serializer]]
-    |  #[strong Usage:] #[+a("/docs/usage/saving-loading") Saving and loading]
-
 +h(3, "features-models") Neural network models for English, German, French and Spanish
 
 +infobox