Update global config and landing page

website/_data.json

@@ -3,24 +3,22 @@
         "landing": true,
         "logos": [
             {
-                "quora": [ "https://www.quora.com", 150 ],
+                "airbnb": [ "https://www.airbnb.com", 150, 45],
-                "chartbeat": [ "https://chartbeat.com", 200 ],
+                "quora": [ "https://www.quora.com", 120, 34 ],
-                "duedil": [ "https://www.duedil.com", 150 ],
+                "retriever": [ "https://www.retriever.no", 150, 33 ],
-                "stitchfix": [ "https://www.stitchfix.com", 190 ]
+                "stitchfix": [ "https://www.stitchfix.com", 150, 18 ]
             },
             {
-                "wayblazer": [ "http://wayblazer.com", 200 ],
+                "chartbeat": [ "https://chartbeat.com", 180, 25 ],
-                "indico": [ "https://indico.io", 150 ],
+                "allenai": [ "https://allenai.org", 220, 37 ]
-                "chattermill": [ "https://chattermill.io", 175 ],
+            }
-                "turi": [ "https://turi.com", 150 ],
+        ],
-                "kip": [ "http://kipthis.com", 70 ]
+        "features": [
-            },
             {
-                "socrata": [ "https://www.socrata.com", 150 ],
+                "thoughtworks": ["https://www.thoughtworks.com/radar/tools", 150, 28],
-                "cytora": [ "http://www.cytora.com", 125 ],
+                "wapo": ["https://www.washingtonpost.com/news/wonk/wp/2016/05/18/googles-new-artificial-intelligence-cant-understand-these-sentences-can-you/", 100, 77],
-                "signaln": [ "http://signaln.com", 150 ],
+                "venturebeat": ["https://venturebeat.com/2017/01/27/4-ai-startups-that-analyze-customer-reviews/", 150, 19],
-                "wonderflow": [ "http://www.wonderflow.co", 200 ],
+                "microsoft": ["https://www.microsoft.com/developerblog/2016/09/13/training-a-classifier-for-relation-extraction-from-medical-literature/", 130, 28]
-                "synapsify": [ "http://www.gosynapsify.com", 150 ]
             }
         ]
     },
@@ -34,7 +32,24 @@
         "landing": true
     },
 
-    "announcement" : {
+    "styleguide": {
-        "title": "Important Announcement"
+        "title": "Styleguide",
+        "sidebar": {
+            "Styleguide": { "": "styleguide" },
+            "Resources": {
+                "Website Source": "https://github.com/explosion/spacy/tree/master/website",
+                "Contributing Guide": "https://github.com/explosion/spaCy/blob/master/CONTRIBUTING.md"
+            }
+        },
+        "menu": {
+            "Introduction": "intro",
+            "Logo": "logo",
+            "Colors": "colors",
+            "Typography": "typography",
+            "Elements": "elements",
+            "Components": "components",
+            "Embeds": "embeds",
+            "Markup Reference": "markup"
+        }
     }
 }

website/_harp.json

@@ -11,12 +11,9 @@
         "COMPANY": "Explosion AI",
         "COMPANY_URL": "https://explosion.ai",
         "DEMOS_URL": "https://demos.explosion.ai",
+        "MODELS_REPO": "explosion/spacy-models",
 
-        "SPACY_VERSION": "1.8",
+        "SPACY_VERSION": "2.0",
-        "LATEST_NEWS": {
-            "url": "https://github.com/explosion/spaCy/releases/tag/v2.0.0-alpha",
-            "title": "Test spaCy v2.0.0 alpha!"
-        },
 
         "SOCIAL": {
             "twitter": "spacy_io",
@@ -27,25 +24,23 @@
         },
 
         "NAVIGATION": {
-            "Home": "/",
+            "Usage": "/usage",
-            "Usage": "/docs/usage",
+            "Models": "/models",
-            "Reference": "/docs/api",
+            "API": "/api"
-            "Demos": "/docs/usage/showcase",
-            "Blog": "https://explosion.ai/blog"
         },
 
         "FOOTER": {
             "spaCy": {
-                "Usage": "/docs/usage",
+                "Usage": "/usage",
-                "API Reference": "/docs/api",
+                "Models": "/models",
-                "Tutorials": "/docs/usage/tutorials",
+                "API Reference": "/api",
-                "Showcase": "/docs/usage/showcase"
+                "Resources": "/usage/resources"
             },
             "Support": {
                 "Issue Tracker": "https://github.com/explosion/spaCy/issues",
                 "StackOverflow": "http://stackoverflow.com/questions/tagged/spacy",
-                "Reddit usergroup": "https://www.reddit.com/r/spacynlp/",
+                "Reddit Usergroup": "https://www.reddit.com/r/spacynlp/",
-                "Gitter chat": "https://gitter.im/explosion/spaCy"
+                "Gitter Chat": "https://gitter.im/explosion/spaCy"
             },
             "Connect": {
                 "Twitter": "https://twitter.com/spacy_io",
@@ -74,21 +69,11 @@
                 {"id": "venv", "title": "virtualenv", "help": "Use a virtual environment and install spaCy into a user directory" },
                 {"id": "gpu", "title": "GPU", "help": "Run spaCy on GPU to make it faster. Requires an NVDIA graphics card with CUDA 2+. See section below for more info."}]
             },
-            { "id": "model", "title": "Models", "multiple": true, "options": [
+            { "id": "model", "title": "Models", "multiple": true }
-                { "id": "en", "title": "English", "meta": "50MB" },
-                { "id": "de", "title": "German", "meta": "645MB" },
-                { "id": "fr", "title": "French", "meta": "1.33GB" },
-                { "id": "es", "title": "Spanish", "meta": "377MB"}]
-            }
         ],
 
         "QUICKSTART_MODELS": [
-            { "id": "lang", "title": "Language", "options": [
+            { "id": "lang", "title": "Language"},
-                { "id": "en", "title": "English", "checked": true },
-                { "id": "de", "title": "German" },
-                { "id": "fr", "title": "French" },
-                { "id": "es", "title": "Spanish" }]
-            },
             { "id": "load", "title": "Loading style", "options": [
                 { "id": "spacy", "title": "Use spacy.load()", "checked": true, "help": "Use spaCy's built-in loader to load the model by name." },
                 {  "id": "module", "title": "Import as module", "help": "Import the model explicitly as a Python module." }]
@@ -98,50 +83,15 @@
             }
         ],
 
-        "MODELS": {
-            "en": [
-                { "id": "en_core_web_sm", "lang": "English", "feats": [1, 1, 1, 1], "size": "50 MB", "license": "CC BY-SA", "def": true },
-                { "id": "en_core_web_md", "lang": "English", "feats": [1, 1, 1, 1], "size": "1 GB", "license": "CC BY-SA" },
-                { "id": "en_depent_web_md", "lang": "English", "feats": [1, 1, 1, 0], "size": "328 MB", "license": "CC BY-SA" },
-                { "id": "en_vectors_glove_md", "lang": "English", "feats": [1, 0, 0, 1], "size": "727 MB", "license": "CC BY-SA" }
-            ],
-            "de": [
-                { "id": "de_core_news_md", "lang": "German", "feats": [1, 1, 1, 1], "size": "645 MB", "license": "CC BY-SA" }
-            ],
-            "fr": [
-                { "id": "fr_depvec_web_lg", "lang": "French", "feats": [1, 1, 0, 1], "size": "1.33 GB", "license": "CC BY-NC" }
-            ],
-            "es": [
-                { "id": "es_core_web_md", "lang": "Spanish", "feats": [1, 1, 1, 1], "size": "377 MB", "license": "CC BY-SA"}
-            ]
-        },
-
-        "EXAMPLE_SENTENCES": {
-            "en": "This is a sentence.",
-            "de": "Dies ist ein Satz.",
-            "fr": "C'est une phrase.",
-            "es": "Esto es una frase."
-        },
-
         "ALPHA": true,
-        "V_CSS": "1.6",
+        "V_CSS": "2.0",
-        "V_JS": "1.2",
+        "V_JS": "2.0",
         "DEFAULT_SYNTAX": "python",
         "ANALYTICS": "UA-58931649-1",
         "MAILCHIMP": {
             "user": "spacy.us12",
             "id": "83b0498b1e7fa3c91ce68c3f1",
             "list": "89ad33e698"
-        },
-        "BADGES": {
-            "pipy": {
-                "badge": "https://img.shields.io/pypi/v/spacy.svg?style=flat-square",
-                "link": "https://pypi.python.org/pypi/spacy"
-            },
-            "conda": {
-                "badge": "https://anaconda.org/conda-forge/spacy/badges/version.svg",
-                "link": "https://anaconda.org/conda-forge/spacy"
-            }
         }
     }
 }

website/index.jade

@@ -8,14 +8,12 @@ include _includes/_mixins
         | Natural Language#[br]
         | Processing
 
-    h2.c-landing__title.o-block.u-heading-1
+    h2.c-landing__title.o-block.u-heading-3
-        | in Python
+        span.u-text-label.u-text-label--light in Python
 
-    +landing-badge(gh("spaCy") + "/releases/tag/v2.0.0-alpha", "v2alpha", "Try spaCy v2.0.0 alpha!")
++grid.o-content.c-landing__blocks
-
+    +grid-col("third").c-landing__card.o-card.o-grid.o-grid--space
-    +grid.o-content
+        +h(3) Fastest in the world
-        +grid-col("third").o-card
-            +h(2) Fastest in the world
         p
             |  spaCy excels at large-scale information extraction tasks.
             |  It's written from the ground up in carefully memory-managed
@@ -24,46 +22,35 @@ include _includes/_mixins
             |  process entire web dumps, spaCy is the library you want to
             |  be using.
 
-            +button("/docs/api", true, "primary")
+        +button("/usage/facts-figures", true, "primary")
             |  Facts & figures
 
-        +grid-col("third").o-card
+    +grid-col("third").c-landing__card.o-card.o-grid.o-grid--space
-            +h(2) Get things done
+        +h(3) Get things done
         p
             |  spaCy is designed to help you do real work — to build real
             |  products, or gather real insights. The library respects
             |  your time, and tries to avoid wasting it. It's easy to
-                |  install, and its API is simple and productive. I like to
+            |  install, and its API is simple and productive. We like to
             |  think of spaCy as the Ruby on Rails of Natural Language
             |  Processing.
 
-            +button("/docs/usage", true, "primary")
+        +button("/usage", true, "primary")
             |  Get started
 
-        +grid-col("third").o-card
+    +grid-col("third").c-landing__card.o-card.o-grid.o-grid--space
-            +h(2) Deep learning
+        +h(3) Deep learning
         p
             |  spaCy is the best way to prepare text for deep learning.
-                |  It interoperates seamlessly with
+            |  It interoperates seamlessly with TensorFlow, PyTorch,
-                |  #[+a("https://www.tensorflow.org") TensorFlow],
+            |  scikit-learn, Gensim and the
-                |  #[+a("https://keras.io") Keras],
-                |  #[+a("http://scikit-learn.org") Scikit-Learn],
-                |  #[+a("https://radimrehurek.com/gensim") 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.
 
-            +button("/docs/usage/deep-learning", true, "primary")
+        +button("/usage/deep-learning", true, "primary")
             |  Read more
 
-.o-inline-list.o-block.u-border-bottom.u-text-small.u-text-center.u-padding-small
-    +a(gh("spaCy") + "/releases")
-        strong.u-text-label.u-color-subtle #[+icon("code", 18)] Latest release:
-        |  v#{SPACY_VERSION}
-
-    if LATEST_NEWS
-        +a(LATEST_NEWS.url) #[+tag.o-icon New!] #{LATEST_NEWS.title}
-
 .o-content
     +grid
         +grid-col("two-thirds")
@@ -92,67 +79,77 @@ include _includes/_mixins
             +h(2) Features
             +list
                 +item Non-destructive #[strong tokenization]
-                +item Syntax-driven sentence segmentation
+                +item Support for #[strong #{LANG_COUNT}+ languages]
+                +item #[strong #{MODEL_COUNT} statistical models] for #{MODEL_LANG_COUNT} languages
                 +item Pre-trained #[strong word vectors]
+                +item Easy #[strong deep learning] integration
                 +item Part-of-speech tagging
                 +item #[strong Named entity] recognition
                 +item Labelled dependency parsing
+                +item Syntax-driven sentence segmentation
+                +item Built in #[strong visualizers] for syntax and NER
                 +item Convenient string-to-hash mapping
                 +item Export to numpy data arrays
-                +item GIL-free #[strong multi-threading]
                 +item Efficient binary serialization
-                +item Easy #[strong deep learning] integration
+                +item Easy #[strong model packaging] and deployment
-                +item Statistical models for #[strong English] and #[strong German]
                 +item State-of-the-art speed
                 +item Robust, rigorously evaluated accuracy
 
++landing-banner("Convolutional neural network models", "New in v2.0")
+    p
+        |  spaCy v2.0 features new neural models for #[strong tagging],
+        |  #[strong parsing] and #[strong entity recognition]. The models have
+        |  been designed and implemented from scratch specifically for spaCy, to
+        |  give you an unmatched balance of speed, size and accuracy. A novel
+        |  bloom embedding strategy with subword features is used to support
+        |  huge vocabularies in tiny tables. Convolutional layers with residual
+        |  connections, layer normalization and maxout non-linearity are used,
+        |  giving much better efficiency than the standard BiLSTM solution.
+        |  Finally, the parser and NER use an imitation learning objective to
+        |  deliver accuracy in-line with the latest research systems,
+        |  even when  evaluated from raw text. With these innovations, spaCy
+        |  v2.0's models are #[strong 10× smaller],
+        |  #[strong 20% more accurate], and #[strong just as fast] as the
+        |  previous generation.
+
+    .o-block-small.u-text-right
+        +button("/models", true, "secondary-light") Download models
+
++landing-logos("spaCy is trusted by", logos)
+    +button(gh("spacy") + "/stargazers", false, "secondary", "small")
+        |  and many more
+
++landing-logos("Featured on", features).o-block-small
+
++landing-banner("Prodigy: Radically efficient machine teaching", "From the makers of spaCy")
+    p
+        |  Prodigy is an #[strong annotation tool] so efficient that data scientists can
+        |  do the annotation themselves, enabling a new level of rapid
+        |  iteration. Whether you're working on entity recognition, intent
+        |  detection or image classification, Prodigy can help you
+        |  #[strong train and evaluate] your models faster. Stream in your own examples or
+        |  real-world data from live APIs, update your model in real-time and
+        |  chain models together to build more complex systems.
+
+    .o-block-small.u-text-right
+        +button("https://prodi.gy", true, "secondary-light") Try it out
+
+.o-content
+    +grid
+        +grid-col("half")
+            +h(2) Benchmarks
+
+            p
+                |  In 2015, independent researchers from Emory University and
+                |  Yahoo! Labs showed that spaCy offered the
+                |  #[strong fastest syntactic parser in the world] and that its
+                |  accuracy was #[strong within 1% of the best] available
+                |  (#[+a("https://aclweb.org/anthology/P/P15/P15-1038.pdf") Choi et al., 2015]).
+                |  spaCy v2.0, released in 2017, is more accurate than any of
+                |  the systems Choi et al. evaluated.
+
             .o-inline-list
-                +button("/docs/usage/lightning-tour", true, "secondary")
+                +button("/usage/facts-figures#benchmarks", true, "secondary") See details
-                    | See examples
 
-    .o-block.u-text-center.u-padding
+        +grid-col("half")
-        h3.u-text-label.u-color-subtle.o-block spaCy is trusted by
+            include usage/_facts-figures/_benchmarks-choi-2015
-
-        each row in logos
-            +grid("center").o-inline-list
-                each details, name in row
-                    +a(details[0])
-                        img(src="/assets/img/logos/#{name}.png" alt=name width=(details[1] || 150)).u-padding-small
-
-.u-pattern.u-padding
-    +grid.o-card.o-content
-        +grid-col("quarter")
-            img(src="/assets/img/profile_matt.png" width="280")
-
-        +grid-col("three-quarters")
-            +h(2) What's spaCy all about?
-
-            p
-                |  By 2014, I'd been publishing NLP research for about 10
-                |  years. During that time, I saw a huge gap open between the
-                |  technology that Google-sized companies could take to market,
-                |  and what was available to everyone else. This was especially
-                |  clear when companies started trying to use my research. Like
-                |  most researchers, my work was free to read, but expensive to
-                |  apply. You could run my code, but its requirements were
-                |  narrow. My code's mission in life was to print results
-                |  tables for my papers — it was good at this job, and bad at
-                |  all others.
-
-            p
-                |  spaCy's #[+a("/docs/api/philosophy") mission] is to make
-                |  cutting-edge NLP practical and commonly available.  That's
-                |  why I left academia in 2014, to build a production-quality
-                |  open-source NLP library. It's why
-                |  #[+a("https://twitter.com/_inesmontani") Ines] joined the
-                |  project in 2015, to build visualisations, demos and
-                |  annotation tools that make NLP technologies less abstract
-                |  and easier to use. Together, we've founded
-                |  #[+a(COMPANY_URL, true) Explosion AI], to develop data packs
-                |  you can drop into spaCy to extend its capabilities. If
-                |  you're processing Hindi insurance claims, you need a model
-                |  for that. We can build it for you.
-
-            .o-block
-                +a("https://twitter.com/honnibal")
-                    +svg("graphics", "matt-signature", 60, 45).u-color-theme