* Add quickstart page to docs

docs/source/quickstart.rst

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+Quick Start
+===========
+
+
+Install
+-------
+
+    $ pip install spacy
+    $ python -m spacy.en.download
+
+The download command fetches the parser model, which is too big to host on PyPi
+(about 100mb).  The data is installed within the spacy.en package.
+
+Usage
+-----
+
+The main entry-point is spacy.en.English.__call__, which you use to turn
+a unicode string into a Tokens object:
+
+    >>> from spacy.en import English
+    >>> nlp = English()
+    >>> tokens = nlp(u'A fine, very fine, example sentence')
+
+You shouldn't need to batch up your text or prepare it in any way.
+Processing times are linear in the length of the string, with minimal per-call
+overhead (apart from the first call, when the tagger and parser are lazy-loaded).
+
+Usually, you will only want to create one instance of the pipeline, and pass it
+around.  Each instance maintains its own string-to-id mapping table, so if you
+process a new word, it is likely to be assigned different integer IDs by the
+two different instances.
+
+The Tokens object has a sequences interface, which you can use to get
+individual tokens:
+
+   >>> print tokens[0].lemma
+   'a'
+   >>> for token in tokens:
+   ...   print token.sic, token.pos
+
+For feature extraction, you can select a number of features to export to
+a numpy.ndarray:
+
+    >>> from spacy.en import enums
+    >>> tokens.to_array([enums.LEMMA, enums.SIC])
+
+Another common operation is to export the embeddings vector to a numpy array:
+
+    >>> tokens.to_vec()
+
+Create a bag-of-words representation:
+
+    >>> tokens.count_by(enums.LEMMA)
+
+
+
+(Most of the) API at a glance
+-----------------------------
+
+.. py:class:: spacy.en.English(self, data_dir=join(dirname(__file__), 'data'))
+
+  .. py:method:: __call__(self, text: unicode, tag=True, parse=False) --> Tokens 
+
+.. py:class:: spacy.tokens.Tokens via English.__call__
+
+  .. py:method:: __getitem__(self, i) --> Token
+
+  .. py:method:: __iter__(self) --> Iterator[Token]
+
+  .. py:method:: to_array(self, attr_ids: List[int]) --> numpy.ndarray[ndim=2, dtype=int32]
+
+  .. py:method:: count_by(self, attr_id: int) --> Dict[int, int]
+
+.. py:class:: spacy.tokens.Token via Tokens.__iter__, Tokens.__getitem__
+
+  .. py:method:: __unicode__(self) --> unicode
+
+  .. py:method:: __len__(self) --> int
+
+  .. py:method:: nbor(self, i=1) --> Token
+  
+  .. py:method:: child(self, i=1) --> Token
+  
+  .. py:method:: sibling(self, i=1) --> Token
+
+  .. py:method:: check_flag(self, attr_id: int) --> bool
+  
+  
+
+  .. py:attribute:: cluster: int
+
+  .. py:attribute:: string: unicode
+  
+  .. py:attribute:: string: unicode
+
+  .. py:attribute:: lemma: unicode
+  
+  .. py:attribute:: dep_tag: unicode
+  
+  .. py:attribute:: pos: unicode
+  
+  .. py:attribute:: fine_pos: unicode
+  
+  .. py:attribute:: sic: unicode
+  
+  .. py:attribute:: head: Token
+
+
+