Update website for v2.0

website/_includes/_mixins.jade

@@ -704,7 +704,7 @@ mixin landing-logos(title, logos)
 
 mixin under-construction()
     +infobox("Under construction", "🚧")
-        |  This section is still being written and will be updated for the v2.0
-        |  release. Is there anything that you think should definitely mentioned
-        |  or explained here? Any examples you'd like to see?
+        |  This section is still being written and will be updated as soon as
+        |  possible. Is there anything that you think should definitely
+        |  mentioned or explained here? Any examples you'd like to see?
         |  #[strong Let us know] on the #[+a(gh("spacy") + "/issues") issue tracker]!

website/_includes/_navigation.jade

@@ -6,8 +6,9 @@ nav.c-nav.u-text.js-nav(class=landing ? "c-nav--theme" : null)
     ul.c-nav__menu
         - var current_url = '/' + current.path[0]
         each url, item in NAVIGATION
-            li.c-nav__menu__item(class=(current_url == url) ? "is-active" : null)
-                +a(url)=item
+            - var is_active = (current_url == url)
+            li.c-nav__menu__item(class=is_active ? "is-active" : null)
+                +a(url)(tabindex=is_active ? "-1" : null)=item
 
         li.c-nav__menu__item.u-hidden-xs
             +a(gh("spaCy"))(aria-label="GitHub") #[+icon("github", 20)]

website/_includes/_sidebar.jade

@@ -9,7 +9,7 @@ menu.c-sidebar.js-sidebar.u-text
                 each url, item in items
                     - var is_current = CURRENT == url || (CURRENT == "index" && url == "./")
                     li.c-sidebar__item
-                        +a(url)(class=is_current ? "is-active" : null)=item
+                        +a(url)(class=is_current ? "is-active" : null tabindex=is_current ? "-1" : null)=item
 
                         if is_current
                             if IS_MODELS && CURRENT_MODELS.length

website/api/pipe.jade

@@ -27,8 +27,8 @@ else
 
 p
     |  Initialise a model for the pipe. The model should implement the
-    |  #[code thinc.neural.Model] API. Wrappers are available for
-    |  #[+a("/usage/deep-learning") most major machine learning libraries].
+    |  #[code thinc.neural.Model] API. Wrappers are under development for
+    |  most major machine learning libraries.
 
 +table(["Name", "Type", "Description"])
     +row

website/index.jade

@@ -43,12 +43,11 @@ include _includes/_mixins
         p
             |  spaCy is the best way to prepare text for deep learning.
             |  It interoperates seamlessly with TensorFlow, PyTorch,
-            |  scikit-learn, Gensim and the
-            |  rest of Python's awesome AI ecosystem. spaCy helps you
-            |  connect the statistical models trained by these libraries
-            |  to the rest of your application.
+            |  scikit-learn, Gensim and the rest of Python's awesome AI
+            |  ecosystem. With spaCy, you can easily construct linguistically
+            |  sophisticated statistical models for a variety of NLP problems.
 
-        +button("/usage/deep-learning", true, "primary")
+        +button("/usage/training", true, "primary")
             |  Read more
 
 .o-content

website/usage/_data.json

@@ -11,8 +11,6 @@
             "Linguistic Features": "linguistic-features",
             "Processing Pipelines": "processing-pipelines",
             "Vectors & Similarity": "vectors-similarity",
-            "Text Classification": "text-classification",
-            "Deep Learning": "deep-learning",
             "Training Models": "training",
             "Adding Languages": "adding-languages",
             "Visualizers": "visualizers"
@@ -122,25 +120,6 @@
         }
     },
 
-    "deep-learning": {
-        "title": "Deep Learning",
-        "teaser": "Using spaCy to pre-process text for deep learning, and how to plug in your own machine learning models.",
-        "next": "training",
-        "menu": {
-            "Pre-processing Text": "pre-processing",
-            "spaCy and Thinc": "thinc",
-            "TensorFlow / Keras": "tensorflow-keras",
-            "scikit-learn": "scikit-learn",
-            "PyTorch": "pytorch",
-            "DyNet": "dynet"
-        }
-    },
-
-    "text-classification": {
-        "title": "Text Classification",
-        "next": "training"
-    },
-
     "training": {
         "title": "Training spaCy's Statistical Models",
         "next": "adding-languages",
@@ -181,6 +160,7 @@
     "resources": {
         "title": "Resources",
         "teaser": "Libraries, demos, books, courses and research systems featuring spaCy.",
+        "next": "examples",
         "menu": {
             "Third-party libraries": "libraries",
             "Extensions": "extensions",

website/usage/_facts-figures/_other-libraries.jade

@@ -6,7 +6,7 @@ p
     |  ecosystem of NLP libraries to work with. Here's how we think the pieces
     |  fit together.
 
-+aside("Using spaCy with other libraries")
+//-+aside("Using spaCy with other libraries")
     |  For details on how to use spaCy together with popular machine learning
     |  libraries like TensorFlow, Keras or PyTorch, see the
     |  #[+a("/usage/deep-learning") usage guide on deep learning].

website/usage/text-classification.jade

@@ -1,5 +0,0 @@
-//- 💫 DOCS > USAGE > TEXT CLASSIFICATION
-
-include ../_includes/_mixins
-
-include _training/_textcat