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Make latest release note the end of the readme

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Matthew Honnibal 2016-05-05 00:26:16 +10:00
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README.rst
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@ -13,6 +13,46 @@ spaCy is built on the very latest research, but it isn't researchware. It was
designed from day 1 to be used in real products. It's commercial open-source designed from day 1 to be used in real products. It's commercial open-source
software, released under the MIT license. software, released under the MIT license.
Features
--------
* Labelled dependency parsing (91.8% accuracy on OntoNotes 5)
* Named entity recognition (82.6% accuracy on OntoNotes 5)
* Part-of-speech tagging (97.1% accuracy on OntoNotes 5)
* Easy to use word vectors
* All strings mapped to integer IDs
* Export to numpy data arrays
* Alignment maintained to original string, ensuring easy mark up calculation
* Range of easy-to-use orthographic features.
* No pre-processing required. spaCy takes raw text as input, warts and newlines and all.
Top Peformance
--------------
* Fastest in the world: <50ms per document. No faster system has ever been
announced.
* Accuracy within 1% of the current state of the art on all tasks performed
(parsing, named entity recognition, part-of-speech tagging). The only more
accurate systems are an order of magnitude slower or more.
Supports
--------
* CPython 2.6, 2.7, 3.3, 3.4, 3.5 (only 64 bit)
* OSX
* Linux
* Windows (Cygwin, MinGW, Visual Studio)
2016-04-05 v0.100.7: German! 2016-04-05 v0.100.7: German!
---------------------------- ----------------------------
@ -55,42 +95,3 @@ Bugfixes
* Fix bug that led to inconsistent sentence boundaries before and after serialisation. * Fix bug that led to inconsistent sentence boundaries before and after serialisation.
* Fix bug from deserialising untagged documents. * Fix bug from deserialising untagged documents.
Features
--------
* Labelled dependency parsing (91.8% accuracy on OntoNotes 5)
* Named entity recognition (82.6% accuracy on OntoNotes 5)
* Part-of-speech tagging (97.1% accuracy on OntoNotes 5)
* Easy to use word vectors
* All strings mapped to integer IDs
* Export to numpy data arrays
* Alignment maintained to original string, ensuring easy mark up calculation
* Range of easy-to-use orthographic features.
* No pre-processing required. spaCy takes raw text as input, warts and newlines and all.
Top Peformance
--------------
* Fastest in the world: <50ms per document. No faster system has ever been
announced.
* Accuracy within 1% of the current state of the art on all tasks performed
(parsing, named entity recognition, part-of-speech tagging). The only more
accurate systems are an order of magnitude slower or more.
Supports
--------
* CPython 2.6, 2.7, 3.3, 3.4, 3.5 (only 64 bit)
* OSX
* Linux
* Windows (Cygwin, MinGW, Visual Studio)