* Add pieces of home page as separate jade files

docs/redesign/installation.jade

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+p With Python 2.7 or Python 3, using Linux or OSX, run:
+
+pre.language-bash: code
+  | $ pip install spacy
+  | $ python -m spacy.en.download</code></pre>
+
+p
+  | The download command fetches and installs about 300mb of data, for
+  | the parser model and word vectors, which it installs within the spacy.en
+  | package directory.
+
+p
+  | If you're stuck using a server with an old version of Python, and you
+  | don't have root access, I've prepared a bootstrap script to help you
+  | compile a local Python install.  Run:
+
+pre.language-bash: code
+  | $ curl https://raw.githubusercontent.com/honnibal/spaCy/master/bootstrap_python_env.sh | bash && source .env/bin/activate
+
+p
+  | The other way to install the package is to clone the github repository,
+  | and build it from source.  This installs an additional dependency,
+  | Cython.  If you're using Python 2, I also recommend installing fabric
+  | and fabtools &ndash; this is how I build the project.
+
+pre.language-bash: code
+  | $ git clone https://github.com/honnibal/spaCy.git
+  | $ cd spaCy
+  | $ virtualenv .env && source .env/bin/activate
+  | $ export PYTHONPATH=`pwd`
+  | $ pip install -r requirements.txt
+  | $ python setup.py build_ext --inplace
+  | $ python -m spacy.en.download
+  | $ pip install pytest
+  | $ py.test tests/
+
+p
+  | Python packaging is awkward at the best of times, and it's particularly tricky
+  | with C extensions, built via Cython, requiring large data files.  So,
+  | please report issues as you encounter them.

docs/redesign/outline.jade

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+- var slogan = "Build Tomorrow's Language Technologies"
+- var tag_line = "spaCy – " + slogan
+
+
+doctype html
+html(lang="en")
+  head
+    meta(charset="utf-8")
+    title!= tag_line
+    meta(name="description" content="")
+    meta(name="author" content="Matthew Honnibal")
+    link(rel="stylesheet" href="css/style.css")
+    <!--[if lt IE 9]>
+    script(src="http://html5shiv.googlecode.com/svn/trunk/html5.js")
+    <![endif]-->
+
+  body(id="home" role="document")
+    header(role="banner")
+      h1(class="logo")!= tag_line
+      div(class="slogan")!= slogan
+
+    nav(role="navigation")
+      ul
+        li: a(href="#") Home
+        li: a(href="#") Docs
+        li: a(href="#") License
+        li: a(href="#") More
+
+    main(id="content" role="main")
+      block intro_block
+
+      block body_block
+ 
+  footer(role="contentinfo")
+
+  script(src="js/prism.js")
+  script(src="js/details_polyfill.js")

docs/redesign/usage_examples.jade

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+mixin example(name)
+  details
+    summary
+      h4= name
+    block
+
+
++example("Load resources and process text")
+  pre.language-python: code
+    | from __future__ import unicode_literals, print_function
+    | from spacy.en import English
+    | nlp = English()
+    | doc = nlp('Hello, world. Here are two sentences.')
+
++example("Get tokens and sentences")
+  pre.language-python: code
+    | token = doc[0]
+    | sentence = doc.sents[0]
+    | assert token[0] is sentence[0]
+
++example("Use integer IDs for any string")
+  pre.language-python: code
+    | hello_id = nlp.vocab.strings['Hello']
+    | hello_str = nlp.vocab.strings[hello_id]
+    | 
+    | assert token.orth == hello_id == 52
+    | assert token.orth_ == hello_str == 'Hello'
+
++example("Get and set string views and flags")
+  pre.language-python: code
+    | assert token.shape_ == 'Xxxx'
+    | for lexeme in nlp.vocab:
+    |     if lexeme.is_alpha:
+    |         lexeme.shape_ = 'W'
+    |     elif lexeme.is_digit:
+    |         lexeme.shape_ = 'D'
+    |     elif lexeme.is_punct:
+    |         lexeme.shape_ = 'P'
+    |     else:
+    |         lexeme.shape_ = 'M'
+    | assert token.shape_ == 'W'
+
++example("Export to numpy arrays")
+  pre.language-python: code
+    | from spacy.en.attrs import ORTH, LIKE_URL, IS_OOV
+    | 
+    | attr_ids = [ORTH, LIKE_URL, IS_OOV]
+    | doc_array = doc.to_array(attr_ids)
+    | assert doc_array.shape == (len(doc), len(attrs)
+    | assert doc[0].orth == doc_array[0, 0]
+    | assert doc[1].orth == doc_array[1, 0]
+    | assert doc[0].like_url == doc_array[0, 1]
+    | assert doc_array[, 1] == [t.like_url for t in doc]
+
++example("Word vectors")
+  pre.language-python: code
+    | doc = nlp("Apples and oranges are similar. Boots and hippos aren't.")
+    | 
+    | apples = doc[0]
+    | oranges = doc[1]
+    | boots = doc[6]
+    | hippos = doc[8]
+    | 
+    | assert apples.similarity(oranges) > boots.similarity(hippos)
+
+
++example("Part-of-speech tags")
+  pre.language-python: code
+    | doc[0].pos
+    | doc[0].tag
+
++example("Syntactic dependencies")
+  pre.language-python: code
+    | for head in tokens:
+    |     for child in head.lefts:
+    |         assert child.head is head
+    |     for child in head.rights:
+    |         assert child.head is head
+    | sent = nlp('The four wheels on the bus turned quickly.')
+    | wheels = sent[2]
+    | bus = sent[5]
+    | assert len(list(wheels.lefts)) == 2
+    | assert len(list(wheels.rights)) == 1
+    | assert len(list(wheels.children)) == 3
+    | assert len(list(bus.lefts)) == 1
+    | assert len(list(bus.rights)) == 0
+    | assert len(list(bus.children)) == 1
+    | 
+    | assert len(list(wheels.subtree)) == 6 
+
++example("Named entities")
+  pre.language-python: code
+    | doc.ents
+    | token.ent_type
+    | token.ent_iob
+
++example("Define custom NER rules")
+  pre.language-python: code
+    | nlp.matcher
+
++example("Calculate inline mark-up on original string")
+  pre.language-python: code
+    | token.string
+    | token.spacy
+    | token.whitespace_
+
++example("Efficient binary serialization")
+  pre.language-python: code
+    |