From 02d0fe242c6abf67648cac3385b96892c7c03021 Mon Sep 17 00:00:00 2001 From: Matthew Honnibal Date: Thu, 5 May 2016 00:26:16 +1000 Subject: [PATCH] Make latest release note the end of the readme --- README.rst | 79 +++++++++++++++++++++++++++--------------------------- 1 file changed, 40 insertions(+), 39 deletions(-) diff --git a/README.rst b/README.rst index 34b108aec..7c869bf9e 100644 --- a/README.rst +++ b/README.rst @@ -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 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! ---------------------------- @@ -55,42 +95,3 @@ Bugfixes * Fix bug that led to inconsistent sentence boundaries before and after serialisation. * 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)