* Remove obsolete docs/guide dir

docs/source/guide/install.rst

@@ -1,22 +0,0 @@
-Installation
-============
-
-pip install spacy
------------------
-
-The easiest way to install is from PyPi via pip::
-
-    pip install spacy
-
-git clone http://github.com/honnibal/spaCy.git
-----------------------------------------------
-
-Installation From source via `GitHub <https://github.com/honnibal/spaCy>`_, using virtualenv::
-
-    $ git clone http://github.com/honnibal/spaCy.git
-    $ cd spaCy
-    $ virtualenv .env
-    $ source .env/bin/activate
-    $ pip install -r requirements.txt
-    $ fab make
-    $ fab test

docs/source/guide/overview.rst

@@ -1,70 +0,0 @@
-Overview
-========
-
-What and Why
-------------
-
-spaCy is a lightning-fast, full-cream NLP tokenizer and lexicon.
-
-Most tokenizers give you a sequence of strings.  That's barbaric.
-Giving you strings invites you to compute on every *token*, when what
-you should be doing is computing on every *type*.  Remember
-`Zipf's law <http://en.wikipedia.org/wiki/Zipf's_law>`_: you'll
-see exponentially fewer types than tokens.
-
-Instead of strings, spaCy gives you references to Lexeme objects, from which you
-can access an excellent set of pre-computed orthographic and distributional features:
-
-::
-
-    >>> from spacy import en
-    >>> apples, are, nt, oranges, dots = en.EN.tokenize(u"Apples aren't oranges...")
-    >>> are.prob >= oranges.prob
-    True
-    >>> apples.check_flag(en.IS_TITLE)
-    True
-    >>> apples.check_flag(en.OFT_TITLE)
-    False
-    >>> are.check_flag(en.CAN_NOUN)
-    False
-
-spaCy makes it easy to write efficient NLP applications, because your feature
-functions have to do almost no work: almost every lexical property you'll want
-is pre-computed for you.  See the tutorial for an example POS tagger.
-
-Benchmark
----------
-
-The tokenizer itself is also efficient:
-
-+--------+-------+--------------+--------------+
-| System | Time	 | Words/second | Speed Factor |
-+--------+-------+--------------+--------------+
-| NLTK	 | 6m4s  | 89,000       | 1.00         |
-+--------+-------+--------------+--------------+
-| spaCy	 | 9.5s	 | 3,093,000	| 38.30        |
-+--------+-------+--------------+--------------+
-
-The comparison refers to 30 million words from the English Gigaword, on
-a Maxbook Air.  For context, calling string.split() on the data completes in
-about 5s.
-
-Pros and Cons
--------------
-
-Pros:
-
-- All tokens come with indices into the original string
-- Full unicode support
-- Extendable to other languages
-- Batch operations computed efficiently in Cython
-- Cython API
-- numpy interoperability
-
-Cons:
-
-- It's new (released September 2014)
-- Security concerns, from memory management
-- Higher memory usage (up to 1gb)
-- More conceptually complicated
-- Tokenization rules expressed in code, not as data