Update landing and feature overview

README.md

@@ -6,11 +6,10 @@ spaCy is a library for advanced Natural Language Processing in Python and
 Cython. It's built on the very latest research, and was designed from day one
 to be used in real products. spaCy comes with
 [pre-trained statistical models](https://spacy.io/models) and word vectors, and
-currently supports tokenization for **45+ languages**. It features the
+currently supports tokenization for **49+ languages**. It features
-**fastest syntactic parser** in the world, convolutional
+state-of-the-art speed, convolutional **neural network models** for tagging,
-**neural network models** for tagging, parsing and **named entity recognition**
+parsing and **named entity recognition** and easy **deep learning** integration.
-and easy **deep learning** integration. It's commercial open-source software,
+It's commercial open-source software, released under the MIT license.
-released under the MIT license.
 
 💫 **Version 2.1 out now!** [Check out the release notes here.](https://github.com/explosion/spaCy/releases)
 
@@ -66,11 +65,11 @@ valuable if it's shared publicly, so that more people can benefit from it.
 
 ## Features
 
--   **Fastest syntactic parser** in the world
--   **Named entity** recognition
 -   Non-destructive **tokenization**
--   Support for **45+ languages**
+-   **Named entity** recognition
+-   Support for **49+ languages**
 -   Pre-trained [statistical models](https://spacy.io/models) and word vectors
+-   State-of-the-art speed
 -   Easy **deep learning** integration
 -   Part-of-speech tagging
 -   Labelled dependency parsing
@@ -80,7 +79,6 @@ valuable if it's shared publicly, so that more people can benefit from it.
 -   Export to numpy data arrays
 -   Efficient binary serialization
 -   Easy **model packaging** and deployment
--   State-of-the-art speed
 -   Robust, rigorously evaluated accuracy
 
 📖 **For more details, see the

website/docs/usage/facts-figures.md

@@ -50,7 +50,7 @@ together.
 
 ## Benchmarks {#benchmarks}
 
-Two peer-reviewed papers in 2015 confirm that spaCy offers the **fastest
+Two peer-reviewed papers in 2015 confirmed that spaCy offers the **fastest
 syntactic parser in the world** and that **its accuracy is within 1% of the
 best** available. The few systems that are more accurate are 20× slower or more.
 

website/src/widgets/landing.js

@@ -75,16 +75,6 @@ const Landing = ({ data }) => {
                 <LandingSubtitle>in Python</LandingSubtitle>
             </LandingHeader>
             <LandingGrid blocks>
-                <LandingCard title="Fastest in the world">
-                    <p>
-                        spaCy excels at large-scale information extraction tasks. It's written from
-                        the ground up in carefully memory-managed Cython. Independent research has
-                        confirmed that spaCy is the fastest in the world. If your application needs
-                        to process entire web dumps, spaCy is the library you want to be using.
-                    </p>
-                    <LandingButton to="/usage/facts-figures">Facts & Figures</LandingButton>
-                </LandingCard>
-
                 <LandingCard title="Get things done">
                     <p>
                         spaCy is designed to help you do real work — to build real products, or
@@ -94,6 +84,15 @@ const Landing = ({ data }) => {
                     </p>
                     <LandingButton to="/usage">Get started</LandingButton>
                 </LandingCard>
+                <LandingCard title="Blazing fast">
+                    <p>
+                        spaCy excels at large-scale information extraction tasks. It's written from
+                        the ground up in carefully memory-managed Cython. Independent research in
+                        2015 found spaCy to be the fastest in the world. If your application needs
+                        to process entire web dumps, spaCy is the library you want to be using.
+                    </p>
+                    <LandingButton to="/usage/facts-figures">Facts & Figures</LandingButton>
+                </LandingCard>
 
                 <LandingCard title="Deep learning">
                     <p>
@@ -129,6 +128,7 @@ const Landing = ({ data }) => {
                         <Li>
                             Pre-trained <strong>word vectors</strong>
                         </Li>
+                        <Li>State-of-the-art speed</Li>
                         <Li>
                             Easy <strong>deep learning</strong> integration
                         </Li>
@@ -144,7 +144,6 @@ const Landing = ({ data }) => {
                         <Li>
                             Easy <strong>model packaging</strong> and deployment
                         </Li>
-                        <Li>State-of-the-art speed</Li>
                         <Li>Robust, rigorously evaluated accuracy</Li>
                     </Ul>
                 </LandingCol>