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Fix list formatting

This commit is contained in:
Matthew Honnibal 2016-05-05 00:18:25 +10:00
parent 1b8b888a57
commit 886bf55bd9
1 changed files with 16 additions and 2 deletions
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README.rst
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@ -37,26 +37,39 @@ The German model provides tokenization, POS tagging, sentence boundary detection
Bugfixes Bugfixes
-------- --------
* spaCy < 0.100.7 had a bug in the semantics of the Token.__str__ and Token.__unicode__
built-ins: they included a trailing space. * spaCy < 0.100.7 had a bug in the semantics of the Token.__str__ and Token.__unicode__ built-ins: they included a trailing space.
* Improve handling of "infixed" hyphens. Previously the tokenizer struggled with multiple hyphens, such as "well-to-do". * Improve handling of "infixed" hyphens. Previously the tokenizer struggled with multiple hyphens, such as "well-to-do".
* Improve handling of periods after mixed-case tokens * Improve handling of periods after mixed-case tokens
* Improve lemmatization for English special-case tokens * Improve lemmatization for English special-case tokens
* Fix bug that allowed spaces to be treated as heads in the syntactic parse * Fix bug that allowed spaces to be treated as heads in the syntactic parse
* 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 Features
-------- --------
* Labelled dependency parsing (91.8% accuracy on OntoNotes 5) * Labelled dependency parsing (91.8% accuracy on OntoNotes 5)
* Named entity recognition (82.6% accuracy on OntoNotes 5) * Named entity recognition (82.6% accuracy on OntoNotes 5)
* Part-of-speech tagging (97.1% accuracy on OntoNotes 5) * Part-of-speech tagging (97.1% accuracy on OntoNotes 5)
* Easy to use word vectors * Easy to use word vectors
* All strings mapped to integer IDs * All strings mapped to integer IDs
* Export to numpy data arrays * Export to numpy data arrays
* Alignment maintained to original string, ensuring easy mark up calculation * Alignment maintained to original string, ensuring easy mark up calculation
* Range of easy-to-use orthographic features. * Range of easy-to-use orthographic features.
* No pre-processing required. spaCy takes raw text as input, warts and newlines and all. * No pre-processing required. spaCy takes raw text as input, warts and newlines and all.
Top Peformance Top Peformance
@ -64,6 +77,7 @@ Top Peformance
* Fastest in the world: <50ms per document. No faster system has ever been * Fastest in the world: <50ms per document. No faster system has ever been
announced. announced.
* Accuracy within 1% of the current state of the art on all tasks performed * 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 (parsing, named entity recognition, part-of-speech tagging). The only more
accurate systems are an order of magnitude slower or more. accurate systems are an order of magnitude slower or more.