diff --git a/README b/README
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--- a/README
+++ /dev/null
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-PySpamSum v1.0
-==============
-
-spamsum is a fuzzy hash specifically designed for hashing email messages
-to detect if they are SPAM. The spamsum utility includes the ability to
-generate the spamsum hash and check a new message against a existing set
-of hashes to find a match.
-
-pyspamsum is a Python wrapper for the core API of spamsum.
-
-The original spamsum code has been licensed under the terms of the
-the Perl Artistic License. It has been slightly modified
-
-The original code is Copyright Andrew Tridgell <tridge@samba.org> 2002.
-It forms part of Andrew's junkcode, and is available here:
-
-    http://www.samba.org/junkcode/#spamsum
-
-The spamsum code in this project is derived from an updated version that
-was published at Linux.conf.au 2004:
-
-    http://linux.anu.edu.au/linux.conf.au/2004/papers/junkcode/spamsum
-
-For details on spamsum itself, please see the spamsum README:
-
-    http://samba.org/ftp/unpacked/junkcode/spamsum/README
-
-This Python wrapper is released under the new BSD license, and is
-Copyright Russell Keith-Magee <russell@keith-magee.com> 2009.
-
-Installation
-------------
-
-At a prompt, run:
-
-$ python setup.py install
-
-Usage
------
-
-# Import spamsum and set up some strings
->>> import spamsum
->>> s1 = "I am the very model of a modern Major-General, I've information animal and vegetable and mineral"
->>> s2 = "I am the very model of a modern Brigadier, I've information animal and vegetable and something else"
->>> s3 = "Huh? Gilbert and Who?"
-
-# Evaluate the edit distance between two strings
->>> spamsum.edit_distance(s1, s2)
-28
-
-# Evaluate the spamsum of some strings
->>> sum1 = spamsum.spamsum(s1)
->>> sum2 = spamsum.spamsum(s2)
->>> sum3 = spamsum.spamsum(s2)
->>> print sum1
-3:kEvyc/sFIKwYclQY4MKLFE4IgunfELzIKygn:kE6Ai3KQ/MKOgWf/KZn
-
-# Compare two spamsums. 0 = no match, 100 = perfect match.
->>> spamsum.match(sum1, sum1)
-100
->>> spamsum.match(sum1, sum2)
-66
->>> spamsum.match(sum1, sum3)
-0
-
diff --git a/README.rst b/README.rst
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+PySpamSum
+=========
+
+.. image:: https://img.shields.io/pypi/pyversions/pyspamsum.svg
+    :target: https://pypi.python.org/pypi/pyspamsum
+
+.. image:: https://img.shields.io/pypi/v/pyspamsum.svg
+    :target: https://pypi.python.org/pypi/pyspamsum
+
+.. image:: https://img.shields.io/pypi/status/pyspamsum.svg
+    :target: https://pypi.python.org/pypi/pyspamsum
+
+.. image:: https://img.shields.io/pypi/l/pyspamsum.svg
+    :target: https://github.com/pybee/pyspamsum/blob/master/LICENSE
+
+.. image:: https://travis-ci.org/pybee/pyspamsum.svg?branch=master
+    :target: https://travis-ci.org/pybee/pyspamsum
+
+.. image:: https://badges.gitter.im/pybee/general.svg
+    :target: https://gitter.im/pybee/general
+
+spamsum is a fuzzy hash specifically designed for hashing email messages
+to detect if they are SPAM. The spamsum utility includes the ability to
+generate the spamsum hash and check a new message against a existing set
+of hashes to find a match.
+
+pyspamsum is a Python wrapper for the core API of spamsum.
+
+The original spamsum code has been licensed under the terms of the
+the Perl Artistic License. It has been slightly modified
+
+The original code is Copyright Andrew Tridgell <tridge@samba.org> 2002.
+It forms part of Andrew's junkcode, and is available here:
+
+    http://www.samba.org/junkcode/#spamsum
+
+The spamsum code in this project is derived from an updated version that
+was published at Linux.conf.au 2004:
+
+    http://linux.anu.edu.au/linux.conf.au/2004/papers/junkcode/spamsum
+
+For details on spamsum itself, please see the spamsum README:
+
+    http://samba.org/ftp/unpacked/junkcode/spamsum/README
+
+Installation
+------------
+
+At a prompt, run::
+
+    $ python setup.py install
+
+Usage
+-----
+
+Once installed, here are some examples of usage::
+
+    # Import spamsum and set up some strings
+    >>> import spamsum
+    >>> s1 = "I am the very model of a modern Major-General, I've information animal and vegetable and mineral"
+    >>> s2 = "I am the very model of a modern Brigadier, I've information animal and vegetable and something else"
+    >>> s3 = "Huh? Gilbert and Who?"
+
+    # Evaluate the edit distance between two strings
+    >>> spamsum.edit_distance(s1, s2)
+    27
+
+    # Evaluate the spamsum of some strings
+    >>> sum1 = spamsum.spamsum(s1)
+    >>> sum2 = spamsum.spamsum(s2)
+    >>> sum3 = spamsum.spamsum(s2)
+    >>> print sum1
+    3:kEvyc/sFIKwYclQY4MKLFE4Igu0uLzIKygn:kE6Ai3KQ/MKOgDKZn
+
+    # Compare two spamsums. 0 = no match, 100 = perfect match.
+    >>> spamsum.match(sum1, sum1)
+    100
+    >>> spamsum.match(sum1, sum2)
+    72
+    >>> spamsum.match(sum1, sum3)
+    0