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Update website source

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This commit is contained in:
Ines Montani 2016-09-30 20:29:03 +02:00
parent 7245c26f92
commit 970ec145d9
60 changed files with 2597 additions and 431 deletions
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14
website/_data.json
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@ -3,6 +3,10 @@
"landing": true
},
"consulting": {
"landing": true
},
"feed": {
"layout": false
},
@ -16,16 +20,6 @@
"asides": false
},
"team": {
"title": "Team"
},
"legal": {
"title": "Legal & Imprint",
"sidebar": true,
"asides": true
},
"styleguide": {
"title" : "Styleguide",
"standalone" : true,

94
website/_fabfile.py
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@ -1,94 +0,0 @@
from __future__ import print_function
from fabric.api import local
import os
import hashlib
import mimetypes
import shutil
import boto.s3.connection
mimetypes.init()
buckets = {
'staging': 'staging.spacy.io',
'production': 'spacy.io',
}
def compile():
shutil.rmtree('www')
local('NODE_ENV=s3 harp compile')
def publish(env='staging', site_path='www'):
os.environ['S3_USE_SIGV4'] = 'True'
conn = boto.s3.connection.S3Connection(host='s3.eu-central-1.amazonaws.com',
calling_format=boto.s3.connection.OrdinaryCallingFormat())
bucket = conn.get_bucket(buckets[env], validate=False)
keys = {k.name: k for k in bucket.list()}
keys_left = set(keys)
for root, dirnames, filenames in os.walk(site_path):
for dirname in dirnames:
target = os.path.relpath(os.path.join(root, dirname), site_path)
source = os.path.join(target, 'index.html')
if os.path.exists(os.path.join(root, dirname, 'index.html')):
redirect = '//%s/%s' % (bucket.name, target)
key = bucket.lookup(source)
if not key:
key = bucket.new_key(source)
key.set_redirect(redirect)
print('setting redirect for %s' % target)
elif key.get_redirect() != redirect:
key.set_redirect(redirect)
print('setting redirect for %s' % target)
if source in keys_left:
keys_left.remove(source)
for filename in filenames:
source = os.path.join(root, filename)
if filename == 'index.html':
target = os.path.normpath(os.path.relpath(root, site_path))
if target == '.':
target = filename
else:
target = os.path.normpath(os.path.join(os.path.relpath(root, site_path), filename))
if target.endswith('.html'):
target = target[:-len('.html')]
content_type = mimetypes.guess_type(source)[0]
cache_control = 'no-transform,public,max-age=300,s-maxage=300'
checksum = hashlib.md5(open(source).read()).hexdigest()
if (target not in keys
or keys[target].etag.replace('"', '') != checksum):
key = bucket.new_key(target)
if content_type:
key.content_type = content_type
key.set_contents_from_filename(source,
headers={'Cache-Control': cache_control})
print('uploading %s' % target)
elif content_type:
key = bucket.lookup(target)
if (key
and (key.content_type != content_type
or key.cache_control != cache_control)):
key.copy(key.bucket, key.name, preserve_acl=True,
metadata={'Content-Type': content_type,
'Cache-Control': cache_control})
print('update headers %s' % target)
if target in keys_left:
keys_left.remove(target)
for key_name in keys_left:
print('deleting %s' % key_name)
bucket.delete_key(key_name)

46
website/_harp.json
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@ -6,10 +6,9 @@
"description": "spaCy is a free open-source library featuring state-of-the-art speed and accuracy and a powerful Python API.",
"url": "https://spacy.io",
"email": "contact@spacy.io",
"company": "spaCy GmbH",
"team_members": [ "henning", "matt", "wolfgang", "elmar", "ines" ],
"company": "spaCy.io",
"navigation": { "Docs": "docs", "Demos": "demos", "Team": "team", "Blog": "blog" },
"navigation": { "Docs": "docs", "Demos": "demos", "Blog": "blog" },
"profiles": { "twitter": "spacy_io", "github": "spacy-io", "reddit": "spacynlp", "medium": "spacy" },
"google_analytics": "UA-58931649-1",
@ -19,66 +18,37 @@
"image_sizes" : { "small" : "640", "medium": "1440", "large": "2000" },
"default_syntax" : "python",
"spacy_version": "0.100.6",
"spacy_stars": "1500",
"spacy_version": "0.101.0",
"spacy_stars": "2100",
"github_settings": { "user": "spacy-io", "repo": "spacy" },
"apis": {
"displacy": "https://displacy.spacy.io/",
"sense2vec": "https://sense2vec.spacy.io/api/similarity/reddit/"
},
"authors" : {
"matt" : {
"name" : "Matthew Honnibal",
"title": "CTO",
"description" : "is co-founder and CTO of spaCy. He studied linguistics as an undergrad, and never thought he'd be a programmer. By 2009 he had a PhD in computer science, and in 2014 he left academia to write spaCy. He's from Sydney and lives in Berlin.",
"description" : "studied linguistics as an undergrad, and never thought he'd be a programmer. By 2009 he had a PhD in computer science, and in 2014 he left academia to write spaCy. He's from Sydney and lives in Berlin.",
"links": {
"twitter": [ "https://twitter.com/honnibal", "Twitter" ],
"website": [ "https://www.semanticscholar.org/search?q=Matthew%20Honnibal", "Semantic Scholar" ]
}
},
"henning": {
"name": "Henning Peters",
"title": "CEO",
"description": "is co-founder and CEO of spaCy. He holds a MSc in computer science and has been co-founder and CTO of Skoobe and Absolventa. His passions are uncommon languages and backcountry skiing.",
"links": {
"twitter": [ "https://twitter.com/henningpeters", "Twitter"],
"linkedin": [ "https://de.linkedin.com/in/hepeters", "LinkedIn"],
"github": [ "https://github.com/henningpeters", "GitHub"]
}
},
"ines": {
"name": "Ines Montani",
"title": "Front-End",
"description": "As Head of Front-End, Ines is in charge of showing people what spaCy can do. She develops, designs and implements our interactive demos and the spacy.io website. Ines has a degree in media, linguistics and communications, and over ten years experience in web development.",
"description": "has developed, designed and implemented our interactive demos and the spacy.io website. She has a degree in media, linguistics and communications, and over ten years experience in web development.",
"links": {
"twitter": [ "https://twitter.com/_inesmontani", "Twitter" ],
"codepen": [ "https://codepen.io/inesmontani", "Codepen"],
"github": [ "https://github.com/inesmontani", "GitHub"],
"codepen": [ "https://codepen.io/ines", "Codepen"],
"github": [ "https://github.com/ines", "GitHub"],
"website": [ "http://ines.io", "Blog" ]
}
},
"wolfgang": {
"name": "Wolfgang Seeker",
"title": "NLP Engineer",
"description": "is a computational linguist from Germany. He is fascinated with the complexity and variety of human language, and spent his PhD looking for ways to make NLP work well with any kind of language in the world. He joined spaCy to build effective and truly multilingual NLP software.",
"links": {
"website": [ "https://www.semanticscholar.org/search?q=Wolfgang%20Seeker", "Semantic Scholar" ]
}
},
"elmar": {
"name": "Elmar Haußmann",
"title": "NLP Engineer",
"description": "is an NLP engineer at spaCy, passionate about deep learning. He has a background in both, academic research, with a PhD in computer science, and industry, as a former consultant and software engineer at IBM. Originally from Stuttgart, the avid snowboarder and mountain biker doesn't only ride powder and trails but also covers distances via plane between the spaCy office in Berlin and his new home in Beijing.",
"links": {
"github": [ "https://github.com/elmar-haussmann", "GitHub"],
"twitter": [ "https://twitter.com/elhaussmann", "Twitter" ]
}
}
}
}

3
website/_includes/_article.jade
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@ -9,7 +9,7 @@ article.article(id=current.source)
+h2.article-title=title
.article-meta
if author
| by #[a.link(href=(authors[author].url || url) target='_blank')=authors[author].name] on
| by #[a.link(href=(authors[author].url || url) target='_blank')=authors[author].name] on  
| #[+date(date)]
.article-body!=yield
@ -17,6 +17,7 @@ article.article(id=current.source)
footer.article-footer
+grid('padding', 'align-right', 'valign-center')
if hide_social != true
+tweet(title)
if links

1
website/_includes/_footer.jade
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@ -5,7 +5,6 @@ include _mixins
footer.footer
span © #{new Date().getFullYear()} #{company}
a(href='/legal') Legal / Imprint
a(href='https://twitter.com/' + profiles.twitter target='_blank' aria-label="Twitter")
+icon('twitter', 'secondary')

2
website/_includes/_functions.jade
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@ -93,7 +93,7 @@
- function getMetaImage() {
- if(current.path[0] == 'blog' && image && image.file) {
- return url + '/blog/img/' + image.file;
- return url + '/blog/img/' + (image.file_small || image.file);
- }
- else {
- return url + '/assets/img/social.png';

6
website/_includes/_head.jade
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@ -1,8 +1,5 @@
include _mixins
- var is_blog = (_section == 'blog')
//- Head
//- ============================================================================
@ -12,6 +9,7 @@ head
meta(charset='utf-8')
meta(name="viewport" content="width=device-width, initial-scale=1.0")
meta(name='referrer' content='always')
meta(name='description' content=description)
meta(property='og:type' content='website')
meta(property='og:site_name' content=sitename)
@ -27,5 +25,3 @@ head
meta(name='twitter:image' content=getMetaImage())
link(rel='icon' type='image/x-icon' href='/assets/img/favicon.ico')
link(href='/assets/css/' + ((is_blog) ? stylesheets.blog : stylesheets.default) + '.css' rel='stylesheet')
link(href='/' + feed rel='alternate' type='application/rss+xml' title='RSS')

5
website/_includes/_mixins.jade
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@ -169,9 +169,9 @@ mixin icon(type, ...style)
alt - [string] descriptive alt text (optional)
caption - [string] image caption (optional)
mixin image(file, alt, caption)
mixin image(file, alt, caption, size)
figure.image-container&attributes(attributes)
img(src='img/' + file alt=alt)
img(src='img/' + file alt=alt class=(size) ? 'image--' + size : '')
if caption
figcaption.text-caption=caption
@ -306,6 +306,7 @@ mixin infobox(label)
mixin alert(button)
.alert&attributes(attributes)
span
block
if button

3
website/_layout.jade
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@ -3,6 +3,7 @@ include _includes/_mixins
- var _section = current.path[0]
- var _site = current.source
- var is_blog = (_section == 'blog')
- var is_article = ( (_section == 'blog' && _site != 'index') || template == 'article')
- var has_asides = (is_article || (_section == 'docs' && asides != false) || asides)
@ -14,6 +15,8 @@ doctype html
html(lang="en")
!=partial("_includes/_head", { _section: _section })
link(href='/assets/css/' + ((is_blog) ? stylesheets.blog : stylesheets.default) + '.css' rel='stylesheet')
link(href='/' + feed rel='alternate' type='application/rss+xml' title='RSS')
body.body
!=partial('_includes/_nav', { _section: _section, _site: _site })

4
website/assets/css/_components/_alerts.sass
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@ -2,10 +2,11 @@
// ============================================================================
$alert-background : color(white)
$alert-border : 1px solid
$alert-border : 2px solid
$alert-close-size : 2.25rem
$alert-color : color($theme)
$alert-padding : 1.5rem 2rem
$alert-shadow : 0 0 10px rgba(color(black), 0.25)
// Style
@ -20,6 +21,7 @@ $alert-padding : 1.5rem 2rem
align-items: center
background: $alert-background
border-top: $alert-border
box-shadow: $alert-shadow
color: $alert-color
display: flex
justify-content: space-between

18
website/assets/css/_components/_images.sass
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@ -5,6 +5,9 @@ $image-background : color(grey, light)
$image-profile-margin : 1rem 3rem
$image-profile-width : $width-profile
$image-ratio : $image-ratio
$image-width-l : 100%
$image-width-m : 75%
$image-width-s : 50%
// Images - Style
@ -29,6 +32,21 @@ $image-ratio : $image-ratio
shape-outside: circle()
// Image sizes
// .image--small - small width
// .image--medium - medium width
// .image--large - large width
.image--small
width: $image-width-s
.image--medium
width: $image-width-m
.image--large
width: $image-width-l
// Global image ratio
.image-ratio

5
website/assets/css/_layout/_footer.sass
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@ -3,7 +3,7 @@
$footer-background : color($theme) pattern($theme)
$footer-color : color(white)
$footer-padding : 2.75rem
$footer-padding : 2.75rem 0 7.5rem 0
$footer-text-align : center
@ -16,5 +16,6 @@ $footer-text-align : center
color: $footer-color
overflow: auto
padding: $footer-padding
position: relative
text-align: $footer-text-align
z-index: 100
z-index: 200

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94
website/blog/_data.json
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@ -4,6 +4,81 @@
"title" : "Blog"
},
"announcement" : {
"title": "Important Announcement",
"date": "2016-08-09",
"description": "Dear spaCy users, Unfortunately, we (Henning Peters and Matthew Honnibal) are parting ways. Breaking up is never easy, and it's taken us a while to get our stuff together. Hopefully, you didn't notice anything was up — if you did, we hope you haven't been inconvenienced.",
"image": {
"file": "introducing-spacy.jpg",
"file_small": "introducing-spacy_small.jpg",
"file_large": "introducing-spacy_large.jpg"
},
"links": false,
"hide_social": true
},
"syntaxnet-in-context": {
"title": "SyntaxNet in context: Understanding Google's new TensorFlow NLP model",
"date": "2016-05-13",
"author": "matt",
"description": "Yesterday, Google open sourced their Tensorflow-based dependency parsing library, SyntaxNet. The library gives access to a line of neural network parsing models published by Google researchers over the last two years. I've been following this work closely since it was published, and have been looking forward to the software being published. This post tries to provide some context around the release — what's new here, and how important is it?",
"image": {
"file": "syntaxnet.jpg",
"file_small": "syntaxnet_small.jpg",
"file_large": "syntaxnet_large.jpg",
"credit": "Kemal Sanli",
"url": "https://dribbble.com/kemal"
},
"links": false
},
"german-model": {
"title": "spaCy now speaks German",
"date": "2016-05-09",
"author": "wolfgang",
"description": "Many people have asked us to make spaCy available for their language. Being based in Berlin, German was an obvious choice for our first second language. Now spaCy can do all the cool things you use for processing English on German text too. But more importantly, teaching spaCy to speak German required us to drop some comfortable but English-specific assumptions about how language works and made spaCy fit to learn more languages in the future.",
"image": {
"file": "german.jpg",
"file_small": "german_small.jpg",
"file_large": "german_large.jpg",
"credit": "Kemal Sanli",
"url": "https://dribbble.com/kemal"
},
"links": {
"HackerNews": "https://news.ycombinator.com/item?id=11690212"
}
},
"multithreading-with-cython": {
"title": "Multi-threading spaCy's parser and named entity recogniser",
"date": "2016-05-11",
"author": "matt",
"description": "In v0.100.3, we quietly rolled out support for GIL-free multi-threading for spaCy's syntactic dependency parsing and named entity recognition models. Because these models take up a lot of memory, we've wanted to release the global interpretter lock (GIL) around them for a long time. When we finally did, it seemed a little too good to be true, so we delayed celebration — and then quickly moved on to other things. It's now past time for a write-up.",
"image" : {
"file": "cython.jpg",
"file_small": "cython_small.jpg",
"file_large": "cython_large.jpg",
"credit": "Kemal Sanli",
"url": "https://dribbble.com/kemal"
},
"links": false
},
"eli5-computers-learn-reading": {
"title": "Statistical NLP in the Ten Hundred Most Common English Words",
"date": "2016-04-04",
"author": "matt",
"description": "When I was little, my favorite TV shows all had talking computers. Now I’m big and there are still no talking computers, so I’m trying to make some myself. Well, we can make computers say things. But when we say things back, they don’t really understand. Why not?",
"image" : {
"file": "basic-english.jpg",
"file_small": "basic-english_small.jpg",
"file_large": "basic-english_large.jpg",
"credit": "Kemal Sanli",
"url": "https://dribbble.com/kemal"
},
"links": false
},
"modular-markup": {
"title": "Rebuilding a Website with Modular Markup Components",
"date": "2016-03-31",
@ -71,24 +146,9 @@
}
},
"eli5-computers-learn-reading": {
"title": "Statistical NLP in Basic English",
"date": "2015-08-24",
"author": "matt",
"description": "When I was little, my favorite TV shows all had talking computers. Now I’m big and there are still no talking computers, so I’m trying to make some myself. Well, we can make computers say things. But when we say things back, they don’t really understand. Why not?",
"image" : {
"file": "basic-english.jpg",
"file_small": "basic-english_small.jpg",
"file_large": "basic-english_large.jpg",
"credit": "Kemal Sanli",
"url": "https://dribbble.com/kemal"
},
"links": false
},
"displacy": {
"displacy-dependency-visualizer": {
"featured": true,
"title": "Displaying Linguistic Structure With CSS",
"title": "Displaying Linguistic Structure with CSS",
"date": "2015-08-19",
"author": "matt",
"description": "One of the features of the relaunch I'm most excited about is the displaCy visualizer and annotation tool. This solves two problems I've thought about a lot: first, how can I help people understand what information spaCy gives them access to? Without a good visualization, the ideas are very abstract. Second, how can we make dependency trees easy for humans to create?",

9
website/blog/announcement.jade Normal file
View File

@ -0,0 +1,9 @@
include ../_includes/_mixins
+lead Dear spaCy users,
p Unfortunately, we (Henning Peters and Matthew Honnibal) are parting ways. Breaking up is never easy, and it's taken us a while to get our stuff together. Hopefully, you didn't notice anything was up — if you did, we hope you haven't been inconvenienced.
p Here's how this is going to work: Matt will continue to develop and maintain spaCy and all related projects under his name. Nothing will change for you. Henning will take over our legal structure and start a new business under a new name.
p Sincerely,#[br] Henning Peters and Matthew Honnibal

0
website/blog/displacy.jade → website/blog/displacy-dependency-visualizer.jade
View File

16
website/blog/eli5-computers-learn-reading.jade
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@ -1,12 +1,12 @@
include ../_includes/_mixins
+lead As told with the #[a(href="http://splasho.com/upgoer5/" target="_blank") ten hundred most common words] that I speak.
+lead Because we've been doing the same thing for a long time, sometimes we get very used to talking about our work in words that most people don't use. So, here's another take on it, in the style of #[a(href="https://xkcd.com/thing-explainer/" target="_blank") thing explainer].
p When I was little, my favorite TV shows all had talking computers. Now I'm big and there are still no talking computers. At least, not really talking. We can make them, like, say things — but I want them to tell us things. And I want them to listen, and to read. Why is this so hard?
p When I was little, my favorite TV shows all had talking computers. Now I'm big and there are still no talking computers. At least, not really talking. We can make them, like, #[em say] things — but I want them to #[em tell us] things. And I want them to listen, and to read. Why is this so hard?
p It turns out that almost anything we say could mean many many different things, but we don't notice because almost all of those meanings would be weird or stupid or just not possible. If I say:
+example: a(href="http://spacy.io/displacy/?full=I%20saw%20a%20movie%20in%20a%20dress" target="_blank") I saw a movie in a dress
+example: a(href="http://spacy.io/demos/displacy?full=I%20saw%20a%20movie%20in%20a%20dress" target="_blank") I saw a movie in a dress
p Would you ever ask me,
@ -14,16 +14,22 @@ p Would you ever ask me,
p It's weird to even think of that. But a computer just might, because there are other cases like:
+example: a(href="http://spacy.io/displacy/?full=The%20TV%20showed%20a%20girl%20in%20a%20dress" target="_blank") The TV showed a girl in a dress
+example: a(href="http://spacy.io/demos/displacy?full=The%20TV%20showed%20a%20girl%20in%20a%20dress" target="_blank") The TV showed a girl in a dress
p Where the words hang together in the other way. People used to think that the answer was to tell the computer lots and lots of facts. But then you wake up one day and you're writing facts like #[em movies do not wear dresses], and you wonder where it all went wrong. Actually it's even worse than that. Not only are there too many facts, most of them are not even really facts! #[a(href="https://en.wikipedia.org/wiki/Cyc" target="_blank") People really tried this]. We've found that the world is made up of #[em if]s and #[em but]s.
+aside('Unconstrained Vocabulary').
If you have a fixed constraint like #[em People wear dresses], and #[em Movies are not people], how does the system cope when someone talks about #[em dressing a script]? Even if nobody has ever said this before, someone might in future. Language is creative, and exceptions are the rule.
p These days we just show the computer lots and lots and lots of words. We gave up trying to get it to understand what a “dress” is. We let #[em dress] be just some letters. But if it is seen it around #[em girl] enough times (which is just some other letters, which are seen around some #[strong other] other letters), it can make good guesses.
p It doesn't always guess right, but we can tell how often it does, and we can think of ways t help it learn better. We have a number, and we can slowly make it bigger, a little bit by a little bit.
p It doesn't always guess right, but we can tell how often it does, and we can think of ways it help it learn better. We have a number, and we can slowly make it bigger, a little bit by a little bit.
p (One thing I've learned is, people are great at making a number bigger, if you pay a lot of them to try. The key is to pick numbers where, if they make the number bigger, they can't help but have done something actually good. This is harder than it sounds. Some say no numbers are like this. I ask them to show me much good being done another way, but they never can.)
+aside("Unconstrained Vocabulary").
The potential problem with focusing on a benchmark task is #[a(href="https://en.wikipedia.org/wiki/Goodhart%27s_law") Goodhart's Law]. The AI community is conscious of the problem and has done well at averting it.
+pullquote("Instead of telling the computer facts, what we needed to do was tell it how to learn.")
p The ideas we come up with for getting the computer to talk, listen or read a little better can be used to get it to see or plan a little better, and the other way around. Once we stopped telling it things like “#[em movies do not wear dresses]”, things really took off.

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include ../_includes/_mixins
+lead Many people have asked us to make spaCy available for their language. Being based in Berlin, German was an obvious choice for our first second language. Now SpaCy can do all the cool things you use for processing English on German text too. But more importantly, teaching spaCy to speak German required us to drop some comfortable but English-specific assumptions about how language works and made spaCy fit to learn more languages in the future.
p The current release features high-accuracy syntactic dependency parsing, named entity recognition, part-of-speech tagging, token and sentence segmentation, and noun phrase chunking. It also comes with word vectors representations, produced from word2vec. As you'll see below, #[a(href="#run-spacy") installation and usage] work much the same for both German and English. However, there are some small differences, that follow from the two languages' differing linguistic structure.
+h2("german-like-english") German is like English but different
p On the evolutionary tree of languages, German and English are close cousins, on the Germanic branch of the Indo-European family. They share a relatively recent common ancestor, so they're structurally similar. And where they differ, it's mostly English that's weird, not German. The algorithmic changes needed to process German are an important step towards processing many other languages.
p English has very simple rules for word formation (aka #[b morphology]), and very strict rules for #[b word order]. This means that an English-only NLP system can get away with some very useful simplifying assumptions. German is the perfect language to unwind these. German word order and morphology are still relatively restricted, so we can make the necessary algorithmic changes without being overwhelmed by the additional complexity.
+aside("Word order and morphology") While the division between word order and morphology is useful for thinking about the problem, it is an artificial one. In reality, both phenomena are two sides of the same coin and interact heavily with each other.
+h2("word-order") Word order
p When Germans learn English in school, one of the first things they are taught to memorize is #[em Subject-Verb-Object] or SVO. In English, the subject comes first, then the verb, then the object. If you change that order, the meaning of the sentence changes. #[em The dog bites the man] means something different from #[em The man bites the dog] even though both sentences use the exact same words. In German — as in many other languages — this is not the case. German allows for any order of subject and object in a sentence and only restricts the position of the verb. In German, you can say #[em Der Hund beißt den Mann] and #[em Den Mann beißt der Hund] and both sentences mean #[em The dog bites the man].
+aside("Subject and Object") In a prototypical sentence, e.g., #[em John hits the ball], the #[a(href="https://en.wikipedia.org/wiki/Subject_(grammar)" target="_blank") grammatical subject] maps to the person/thing that acts (#[em John]) whereas the #[a(href="https://en.wikipedia.org/wiki/Object_(grammar)" target="_blank") grammatical object] maps to the person/thing that is acted upon (#[em the ball]). All languages have mechanisms to change this mapping though.
+quote("Sherlock Holmes, in <em>A Scandal in Bohemia</em>")
| Do you note the peculiar construction of the sentence &mdash; 'This account of you we have from all quarters received.'
br
br
| A Frenchman or Russian could not have written that. It is the German who is so uncourteous to his verbs.
p One of the more difficult things for people who learn German as a second language is to figure out where to put the verb. German verbs are usually at the end of a sentence, under certain circumstances, the verb or a part of it moves to the first or second position. For instance, compare the English sentence in the example below to its German counterpart. While all the parts of the English verb stay together, the German verb is distributed over the sentence. The main part (the one carrying the meaning) is at the end of the sentence and the other part (the auxiliary verb) comes in second position after the subject.
+image("german_verb_align.svg", "", "In German, verbs are put at the end of a sentence.", "small").text-center
p The fact that German verbs come at the end of the sentence, or are split as in the example above, has some implications for language understanding technologies. So far, the syntactic structures that spaCy predicted for English sentences were always #[i projective], which means that you could draw them without ever having to cross two arcs. In order to accommodate languages with less restrictive word order than English &mdash; for example German &mdash; the parser now also predicts non-projective structures, i.e., structures where arcs may cross.
+aside("Non-projectivity") Formally, an arc from word #[i h] to word #[i d] is called non-projective if there is at least one word #[i k] between #[i k] and #[i d] which is not a direct or indirect descendant of #[i h]. A tree is non-projective if it has at least one non-projective arc.
p To illustrate the difference, consider the example below. We want the syntactic structure to represent the fact that it is the flight that was booked the day before, hence we want the parser to predict an arc between #[em flight] and #[em booked]. And of course we want the parser to predict the same arc also for the German counterpart, in this case between #[em Flug] and #[em gebucht habe]. However, because the German verb comes last, there can be crossing arcs in the German structure. This is not the only type of German construction that leaves us with a non-projective parse, but it is a frequent one. Moreover, unlike some cases where you could change your linguistic theory to avoid the crossing arcs, this one is well motivated by data and very difficult to avoid without losing information.
+image("german_english_proj.svg", "", "Syntactic structures for English are usually projective.", "small").text-center
+image("german_german_nonproj.svg", "", "Syntactic structure for German. Non-projective arcs marked in blue.", "small").text-center
p To summarize the above, we need non-projective trees to represent the information that we are interested in when parsing natural language. Okay, so we want crossing arcs. What's the problem? The problem is that crossing arcs force us to give up a very useful constraint. The set of possible non-projective trees is considerably larger than the set of possible projective trees. What's more, the algorithm spaCy uses to search for a projective tree is both simpler and more efficient than the equivalent non-projective algorithms, so restricting spaCy to projective dependency parsing has given us a win on two fronts: we've been able to do less work computationally, while also encoding important prior knowledge about the problem space into the system.
p Unfortunately, this &lsquo;prior knowledge&rsquo; was never quite true. It's a simplifying assumption. In the same way that a physicist might assume a frictionless surface or a #[a(href="https://en.wikipedia.org/wiki/Spherical_cow") spherical cow], sometimes it's useful for computational linguists to assume projective trees and context-free grammars. For English, projective trees are a good-value simplification &mdash; the cow of English is not quite a perfect sphere, but it's close. The cow of German is considerably less round, and we can make our model more accurate by taking this into account.
+h2("pseudoproj-parsing") Pseudo-projective parsing
p Luckily for us, the problem of predicting non-projective structures has received a lot of attention over the last decade. One observation that was made early on is that these non-projective arcs are rare. Usually, only a few percent of the arcs in #[a(href="https://en.wikipedia.org/wiki/Treebank" target="_blank") linguistic treebanks] are non-projective, even for languages with unrestrictive word order. This means that we can afford to use approaches with a higher worst-case complexity because the worst case basically never occurs and therefore has virtually no impact on the efficiency of our NLP systems.
p Several methods have been proposed for dealing with non-projective arcs. Most change the parsing algorithm to search through the full space of possible structures directly, or at least a large part of it. In spaCy, we opted for a more indirect approach. #[a(href="http://www.aclweb.org/anthology/P05-1013" target="_blank") Nivre and Nilsson (2005)] propose a simple procedure they call #[b pseudo-projective parsing]. The parser is trained on projective structures that are produced from non-projective structures by reattaching the non-projective arcs higher in the tree until they are projective. The original attachment site is encoded in the label of the respective arc. The parser thus learns to predict projective structures with specially decorated arc labels. The output of the parser is then post-processed to reattach decorated arcs to their proper syntactic head according to their arc label, thereby re-introducing non-projective arcs.
+aside("Decoration schemes") #[a(href="http://www.aclweb.org/anthology/P05-1013" target="_blank") Nivre and Nilsson (2005)] test three different ways of decorating labels. SpaCy currently uses the #[em head] decoration scheme because it is a good compromise between the amount of encoded information and increase in the number of arc labels.
+image("german_pseudoproj.svg", "", "Pseudo-projective parsing. Training data is projectivized and decorated before training the model. Decorated arcs in parser output are re-attached to their non-projective head in a post-processing step.", "large").text-center
p Using pseudo-projective parsing allows spaCy to produce non-projective structures without having to sacrifice the efficient parsing algorithm, which is restricted to projective structures. And because non-projective arcs are rare, the post-processing step only ever has to reattach one or two arcs in every other sentence, which makes its impact on the overall parsing speed negligable even though its worst case complexity is higher than the parser's. In fact, we didn't notice any difference in speed when parsing German with this approach. And when we know that our training data is projective, we just switch it off.
+h3("accuracy") Evaluation of pseudo-projective parsing
p Pseudo-projective parsing makes a big difference in German because the parser can recover arcs that a purely projective model cannot. The numbers in the table show the percentage of arcs that were correctly attached by the parser (unlabeled attachment score (UAS) ignores the label, labeled attachment score (LAS) takes it into account). We train and evaluate the German model on the TiGer treebank (see #[a(href="#Data-sources") below]).
+table(["System", "UAS", "LAS"])
+row
+cell German, forcing projective structures
+cell 90.86%
+cell 88.60%
+row
+cell German, allowing non-projective structures
+cell 92.22%
+cell 90.14%
//- +row
//- +cell English
//- +cell 91.15%
//- +cell 89.13%
+h2("morphology") Morphology
p One other important difference between English and German is the richer morphology of German words. German words can change their form depending on their grammatical function in a sentence. English words do this too, for example by appending an #[i s] to a noun to mark plural (#[i ticket] &rarr; #[i tickets]). However, in most languages word forms of the same word show much more variety than in English, and this is also the case in German. German is also famous for its capacity to form #[a(href="https://en.wikipedia.org/wiki/Rinderkennzeichnungs-_und_Rindfleischetikettierungsüberwachungsaufgabenübertragungsgesetz" target="_blank") really long words], another process that is driven by the morphological system.
p While German is clearly a language with rich morphology, it isn't the most crucial aspect for natural language processing of German (depending on the task, of course &#128521;). While processing languages like Hungarian, Turkish, or Czech is hopeless without a proper treatment of morphological processes, German can be processed reasonably well without. We therefore released the German model without a morphological component &mdash; for now. We're working on adding such a component to spaCy, not just for improving the German model but also to make the next step towards learning more languages.
+h2("run-spacy") Showtime
p As for English, spaCy now provides a pretrained model for processing German. This model currently provides functionality for tokenization, part-of-speech tagging, syntactic parsing, and named entity recognition. In addition, #[code spacy.de] also comes with pre-trained word representations, in the form of word vectors and hierarchical cluster IDs.
p Installing the German model on your machine is as easy as for English:
+code('bash','Install German model').
pip install spacy
python -m spacy.de.download
p Once installed you can use it from Python like the English model. If you've been loading spaCy using the #[code English()] class directly, now's a good time to switch over to the newer #[code spacy.load()] function:
+code('python','Parse German').
from __future__ import print_function, unicode_literals
import spacy
nlp = spacy.load('de')
doc = nlp(u'Ich bin ein Berliner.')
# show universal pos tags
print(' '.join('{word}/{tag}'.format(word=t.orth_, tag=t.pos_) for t in doc))
# output: Ich/PRON bin/AUX ein/DET Berliner/NOUN ./PUNCT
# show German specific pos tags (STTS)
print(' '.join('{word}/{tag}'.format(word.orth_, tag.tag_) for t in doc))
# output: Ich/PPER bin/VAFIN ein/ART Berliner/NN ./$.
# show dependency arcs
print('\n'.join('{child:&lt;8} &lt;{label:-^7} {head}'.format(child=t.orth_, label=t.dep_, head=t.head.orth_) for t in doc))
# output: (sb: subject, nk: noun kernel, pd: predicate)
# Ich &lt;--sb--- bin
# bin &lt;-ROOT-- bin
# ein &lt;--nk--- Berliner
# Berliner &lt;--pd--- bin
# . &lt;-punct- bin
p As for English, German provides named entities and a noun chunk iterator to extract basic information from the data. The NER model can currently distinguish persons, locations, and organizations. We are currently looking into ways of extending this to more classes.
+code('python','Named entity recognition').
# show named entities
for ent in doc.ents:
print(ent.text)
# output:
# Berliner
p The noun chunk iterator provides easy access to base noun phrases in the form of an iterator. The iterator requires the dependency structure to be present and returns all noun phrases that the parser recognized.
+code('python','Noun chunks').
# show noun chunks
for chunk in doc.noun_chunks:
print(chunk.text)
# output:
# ein Berliner
# noun chunks include so-called measure constructions ...
doc = de(u'Ich möchte gern zum Essen eine Tasse Kaffee bestellen.')
print [ chunk for chunk in doc.noun_chunks ]
# output:
# [Essen, eine Tasse Kaffee]
# ... and close appositions
doc = de(u'Der Senator vermeidet das Thema Flughafen.')
print [ chunk for chunk in doc.noun_chunks ]
# output:
# [Der Senator, das Thema Flughafen]
p The German model comes with word vectors trained on a mix of text from Wikipedia and the Open Subtitles corpus. The vectors were produced using the skip-gram with negative sampling word2vec algorithm using #[a(href="https://radimrehurek.com/gensim/" target="_blank") Gensim], with a context window of 2.
p You can use the vector representation with the #[code .vector] attribute and the #[code .similarity()] method on spaCy's #[code Lexeme], #[code Token], #[code Span] and #[code Doc] objects.
+code('python','Word vectors').
# Use word vectors
de = spacy.load('de')
doc = de(u'Der Apfel und die Orange sind ähnlich')
assert len(doc.vector) == len(doc[0].vector))
der_apfel = doc[:2]
die_orange = doc[3:5]
der_apfel.similarity(die_orange)
# output:
# 0.63665210991205579
der, apfel = der_apfel
der.similarity(apfel)
# output:
# 0.24995991403916812
p While we try to always provide good defaults in spaCy, the word2vec family of algorithms give you a lot of knobs to twiddle, so you might benefit from custom trained vectors. You can get expert help on this by #[a(href="mailto:" + email) contacting us] about consulting.
//- The developers of Gensim, #[a(href="https://rare-technologies.com/" target="_blank") RaRe Technologies], also offer excellent services to help you put word2vec into production.
//- +h2("Performance") Performance
//- +table(["", "POS acc", "Dep (UAS/LAS)", "NER (Prec/Rec/F1)"])
//- +row
//- +cell spaCy German
//- +cell 97.56
//- +cell 92.22/90.14
//- +cell 82.95/73.76/78.08
+h2("Caveats") Caveats
p With the German parser potentially returning non-projective structures, some assumptions about syntactic structures that would hold for the English parser don't hold for the German one. For example, the subtree of a particular token doesn't necessarily span a consecutive substring of the input sentence anymore. Furthermore, a token may have no direct left dependents but can still have a left edge (the left-most descendant of the token) that is further left of the token.
+code('python','Caveats with non-projectivity').
doc = nlp(u'Den Berliner hat der Hund nicht gebissen.')
# heads array: [1, 6, 2, 4, 2, 6, 2, 2] (second token is attached with a non-projective arc)
# most subtrees cover a consecutive span of the input
print [ (t.i, t.orth_) for t in doc[4].subtree ]
# output:
# [(3, u'der'), (4, u'Hund')]
# but some subtrees have gaps
print [ (t.i, t.orth_) for t in doc[6].subtree ]
# output:
# [(0, u'Den'), (1, u'Berliner'), (5, u'nicht'), (6, u'gebissen')]
# the root has no left dependents:
print doc[2].n_lefts
# output:
# 0
# but the root's left-most descendant is not the root itself but a token further left
print (doc[2].left_edge.i, doc[2].left_edge.orth_)
# output:
# (0, u'Den')
+h2("Data-sources") Data sources
p The German model is trained on the German #[a(href="http://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/tiger.html" target="_blank") TiGer treebank] converted to dependencies. The language-specific part-of-speech tags use the #[a(href="http://www.sfs.uni-tuebingen.de/resources/stts-1999.pdf" target="_blank") Stuttgart-Tübingen Tag Set (STTS)] (document in German). The model for named entity recognition is trained on the #[a(href="https://www.lt.tu-darmstadt.de/de/data/german-named-entity-recognition/" target="_blank") German Named Entity Recognition Data] from the TU Darmstadt. For estimating word probabilities we rely on data provided by the #[a(href="http://hpsg.fu-berlin.de/cow/" target="_blank") COW project]. Word vectors and Brown clusters are computed on a combination of the German Wikipedia and the German part of #[a(href="http://opus.lingfil.uu.se/OpenSubtitles2016.php" target="_blank") OpenSubtitles2016] which is based on data from #[a(href="http://www.opensubtitles.org/" target="_blank") opensubtitles.org]. Buy these people a beer and a cookie when you meet them :).
+h2("call") Want spaCy to speak your language?
p There is still a lot to do for German, but that doesn't mean spaCy can't start learning another language in the meantime. You can advocate for what languages should be added next on the #[a(href="https://reddit.com/r/" + profiles.reddit) spaCy subreddit], or #[a(href="mailto:" + email) get in touch] about sponsoring development.

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@ -0,0 +1,157 @@
include ../_includes/_mixins
+lead In v0.100.3, we quietly rolled out support for GIL-free multi-threading for spaCy's syntactic dependency parsing and named entity recognition models. Because these models take up a lot of memory, we've wanted to release the global interpretter lock (GIL) around them for a long time. When we finally did, it seemed a little too good to be true, so we delayed celebration &mdash; and then quickly moved on to other things. It's now past time for a write-up.
p This is mostly an implementation post, but to me, implementation is the pain and the product is the pleasure. So, let's start with the pay-off. The pay-off is the #[code .pipe()] method, which adds #[em data-streaming] capabilities to spaCy:
+code('python', 'Stream Parsing').
import spacy
nlp = spacy.load('de')
for doc in nlp.pipe(texts, n_threads=16, batch_size=10000):
analyse_text(doc)
p The #[code .pipe()] method accepts an iterator (above, #[code texts]), and produces an iterator. Internally, a buffer is accumulated (given by the #[code batch_size] argument, and multiple threads are allowed to work on the batch simultaneously. Once the batch is complete, the processed documents are yielded from the iterator.
+aside("Iterators") #[a(href="http://rare-technologies.com/data-streaming-in-python-generators-iterators-iterables/") My favourite post] on the Zen of Python iterators was written by Radim, the creator of #[a(href="http://radimrehurek.com/gensim/") Gensim]. I was on board with generators before, but I hadn't really thought about the simplicity of minibatching.
p Each document is processed independently, so if your batch size is large enough, and OpenMP is enabled, you should be able to work all your cores with only one copy of the spaCy models in memory. spaCy is designed for web-scale data processing &mdash; we want you to be able to perform sophisticated linguistic analysis on whole dumps of the Common Crawl. With effective shared memory parallelism, those jobs are many times cheaper.
+table(["Method", "Number threads", "Seconds (lower is better)"])
+row
+cell Loop
+cell 1
+cell 691s
+row
+cell Pipe
+cell 1
+cell 678s
+row
+cell Pipe
+cell 2
+cell 432s
+row
+cell Pipe
+cell 4
+cell 312s
caption.
Seconds to parse 20,000 documents, with 1, 2 or 4 threads. The #[em loop] condition uses the #[code doc = nlp(text)] function, instead of the #[code .pipe] method, to show the overhead incurred from the minibatched stream processing (within measurement error).
+h3("implementation") Python, Cython and the Global Interpretter Lock
p Endless ink has been spilled about the CPython #[a(href="https://wiki.python.org/moin/GlobalInterpreterLock") Global Interpretter Lock (GIL)]. It isn't a problem for most code, but for spaCy, it really is. Computers may be fast and getting faster, but the internet is big and getting bigger. We have a lot fo text to process, and we'd like to use our machines efficiently.
p Python maintains reference counts in a global data structure. When you create or delete a Python object, its reference count has to change. However, the data structure holding the reference counts is not thread-safe. To change the reference counts, you therefore need to acquire the global interpretter lock.
p One way around the GIL is therefore to avoid the need for Python variables. This is what I've done with spaCy. More specifically, spaCy is a Python library, but it's not actually written in Python. It's implemented in Cython, and transpiled into a C++ extension module.
p In ordinary Python code, you can have a list of numbers like this:
+code('my_list.py', 'Python list').
my_list = [0, 1, 2]
p In Cython, you can write exactly the same code, but the code is not interpreted by Python directly. Instead, it's transpiled into C or C++ code, which calls the Python C-API. Here's some of the resulting code:
+code('my_list.pyx', 'Transpiled C').
__pyx_t_1 = PyList_New(3); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_1);
__Pyx_INCREF(__pyx_int_0);
__Pyx_GIVEREF(__pyx_int_0);
PyList_SET_ITEM(__pyx_t_1, 0, __pyx_int_0);
__Pyx_INCREF(__pyx_int_1);
__Pyx_GIVEREF(__pyx_int_1);
PyList_SET_ITEM(__pyx_t_1, 1, __pyx_int_1);
__Pyx_INCREF(__pyx_int_2);
__Pyx_GIVEREF(__pyx_int_2);
PyList_SET_ITEM(__pyx_t_1, 2, __pyx_int_2)
p You can't call any of those functions if you're not holding the GIL. But you can call plain old C and C++ functions, such as #[code malloc()] and #[code free]:
+code('my_array.pyx', 'C in Cython').
from libc.stlib cimport malloc, free
my_arr = &lt;int*&gt;malloc(sizeof(int) * 3)
my_arr[0] = 1
my_arr[1] = 2
my_arr[2] = 3
do_stuff(my_arr)
free(my_arr)
p The Cython #[code nogil] keyword allows you to declare that a function is safe to call even if you're not already holding the GIL. You can read more about releasing the GIL with Cython #[a(href="http://docs.cython.org/src/userguide/parallelism.html") here].
p The disadvantages of writing with #[code nogil] semantics are obvious &mdash; you're limited to writing #[a(href="https://spacy.io/blog/writing-c-in-cython") C with (arguably) nicer syntax]. If you've never tried it, I think it's an interesting exercise to do without the Python semantics. It does make you appreciate what the language is providing. Probably the thing I miss most are the exceptions and the lists. The Python unicode object is also very useful.
+h3("parser-pipe") Implementation of the Parser.pipe method
p Here's the implementation of the Parser.pipe method in spaCy. This method does the following:
+list
+item Buffers the texts into temporary work arrays
+item Releases the GIL
+item Iterates over the work arrays in an OpenMP #[code prange] loop
+item Calls the #[code Parser.parseC()] method for each unit of work (each document)
+code('python', 'Parser.pipe').
def pipe(self, stream, int batch_size=1000, int n_threads=2):
cdef Pool mem = Pool()
cdef TokenC** doc_ptr = &lt;TokenC**&gt;mem.alloc(batch_size, sizeof(TokenC*))
cdef int* lengths = &lt;int*&gt;mem.alloc(batch_size, sizeof(int))
cdef Doc doc
cdef int i
cdef int nr_class = self.moves.n_moves
cdef int nr_feat = self.model.nr_feat
cdef int status
queue = []
for doc in stream:
doc_ptr[len(queue)] = doc.c
lengths[len(queue)] = doc.length
queue.append(doc)
if len(queue) == batch_size:
with nogil:
for i in cython.parallel.prange(batch_size, num_threads=n_threads):
status = self.parseC(doc_ptr[i], lengths[i], nr_feat, nr_class)
if status != 0:
with gil:
sent_str = queue[i].text
raise ValueError("Error parsing doc: %s" % sent_str)
PyErr_CheckSignals()
for doc in queue:
self.moves.finalize_doc(doc)
yield doc
queue = []
batch_size = len(queue)
with nogil:
for i in cython.parallel.prange(batch_size, num_threads=n_threads):
status = self.parseC(doc_ptr[i], lengths[i], nr_feat, nr_class)
if status != 0:
with gil:
sent_str = queue[i].text
raise ValueError("Error parsing doc: %s" % sent_str)
PyErr_CheckSignals()
for doc in queue:
self.moves.finalize_doc(doc)
yield doc
p The actual mechanics of the multi-threading are super simple, because NLP is (often) embarrassingly parallel &mdash; every document is parsed independently, so we just need to make a #[code prange] loop over a stream of texts. The #[code prange] function is an auto-magical work-sharing loop, that manages the OpenMP semantics for you. You still need to reason about false-sharing, thread safety, etc &mdash; all the parts that make writing multi-threaded code fundamentally challenging. But, at least the calling syntax is clean, and a few incidental details are taken care of for you.
+h3('the-hard-part') The hard part
p I couldn't tell you that multi-threading the parser was easy. At least, not with a straight face. I've never written a significant Java program, but I imagine writing multi-threaded Java is significantly easier. Using Cython, the task was at least possible. But it definitely wasn't easy.
p If you count my time in academia, I've been writing statistical parsers in Cython for a five or six years now, and I've always wanted to release the GIL around the parsing loop. By late 2015 I had the machine learning, hash table, outer parsing loop, and most of the feature extraction as #[code nogil] functions. But the #[a(href="https://github.com/spacy-io/spaCy/blob/0.100.2/spacy/syntax/stateclass.pxd#L10") state object] had a complicated interface, and was implemented as a #[code cdef class]. I couldn't create this object or store it in a container without acquiring the GIL.
p The break-through came when I figured out an undocumented way to write a C++ class in Cython. This allowed me to hollow out the existing #[code cdef class] that controlled the parser state. I proxied its interface to the inner C++ class, method by method. This way I could keep the code working, and make sure I didn't introduce any subtle bugs into the feature calculation.
p You can see the inner class #[a(href="https://github.com/spacy-io/spaCy/blob/master/spacy/syntax/_state.pxd") here]. If you navigate around the git history of this file, you can see the patches where I implemented the #[code .pipe] method.
+h3('conclusion') Conclusion
p Natural language processing (NLP) programs have some peculiar performance characterstics. The algorithms and data structures involved are often rather complicated, and the numeric calculations being performed are often quite trivial.
p spaCy's parser and named entity recognition system have to make two or three predictions on each word of the input document, using a linear model. All of the complexity is in the feature extraction and state management code, and the computational bottle-neck ends up being #[a(href="https://github.com/spacy-io/thinc/blob/master/thinc/linear/avgtron.pyx#L128") retrieval of the weights data] from main memory.
p When we finally switch over to a neural network model, the considerations will be a little bit different. It's possible to implement the parser such that you're stepping forward through multiple sentences at once. However, that has its own challenges. I think it's both easier and more effective to parallelise the outer loop, so I expect the work put into the current implementation will serve us well.

2
website/blog/sense2vec-with-spacy.jade
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@ -37,7 +37,7 @@ p Here's how the current pre-processing function looks, at the time of writing.
# Merge them into single tokens
ent.merge(ent.root.tag_, ent.text, ent.label_)
token_strings = []
for token in doc:
for token in tokens:
text = token.text.replace(' ', '_')
tag = token.ent_type_ or token.pos_
token_strings.append('%s|%s' % (text, tag))

55
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@ -0,0 +1,55 @@
include ../_includes/_mixins
+lead Yesterday, Google open sourced their Tensorflow-based dependency parsing library, SyntaxNet. The library gives access to a line of neural network parsing models published by Google researchers over the last two years. I've been following this work closely since it was published, and have been looking forward to the software being published. This post tries to provide some context around the release &mdash; what's new here, and how important is it?
p #[a(href="http://googleresearch.blogspot.de/2016/05/announcing-syntaxnet-worlds-most.html") SyntaxNet] provides an important module in a #[a(href="https://en.wikipedia.org/wiki/Natural_language_processing") natural language processing (NLP)] pipeline such as #[a(href="https://spacy.io" target="_blank") spaCy]. If you zoom out of NLP a little, the technology trend you see is all about extending the range of work computers can take on. Until recently, you couldn't write software to control a car, and you couldn't write software to tweak #[a(href="https://foxtype.com") the tone of your emails], analyse #[a(href="https://wonderflo.co") customer feedback] or monitor global news for #[a(href="http://cytora.com/") significant business risks]. Admittedly, none of those capabilities are a self-driving car. But just wait. Language is at the heart of human endeavour. Our total mastery of moving stuff around has long been inevitable, but the potential upside of NLP technologies is so good that it's hard to predict.
+aside("NLP's killer app") Google search is an NLP application, so in some ways its weird to talk about #[em waiting] for NLP to change the world. It already did. But I definitely think there's still much more to come.
p Within this larger value chain, SyntaxNet is a fairly low-level technology. It's like an improved drill bit. By itself, it doesn't give you any oil &mdash; and oil, by itself, doesn't give you any energy or plastics, and energy and plastics by themselves don't give you any work or any products. But if the bottle-neck in that whole value chain was in the efficiency of oil extraction, and your drill bit improves that substantially, the low-level technology can prove #[a(href="https://en.wikipedia.org/wiki/Hughes_Tool_Company") pretty important].
p I think that syntactic parsing is a bottle-neck technology in NLP, and that the last 4 or 5 years of improvements to this technology will have outsize impacts. Now, you could argue that I think that because this is the problem I've been working on for all my adult life, and the technology that I left academia to #[a(href="https://spacy.io/blog/introducing-spacy") take commercial]. All I can say is that this reverses causation: it's my belief in the problem's importance that's caused my investment in it, not the other way around.
p Okay, so I think the #[em problem] is important. But how big a step forward is SyntaxNet? If you've been using the neural network model in #[a(href="http://stanfordnlp.github.io/CoreNLP/") Stanford CoreNLP], you're using an algorithm that's almost identical in design, but not in detail. The #[a(href="https://aclweb.org/anthology/D/D15/D15-1162.pdf") parsing model] used by spaCy is also similar. Conceptually, the contribution of the SyntaxNet work is pretty subtle. It's mostly about careful experimentation, tuning, and refinement. However, if Google didn't do this work, it's possible that nobody else would have. The neural network models that make SyntaxNet tick have also opened up a rich landscape of sexier ideas, and researchers are busily exploring them. There's a bias towards ideas that make researchers look (and feel!) clever. Probably, we would have ended up with parsing models that were just as accurate, but with incorrect assumptions about which aspects of the system design were important to accuracy, leading to slower progress in the future.
+aside("Research notes") #[a(href="http://www.petrovi.de/data/acl15.pdf") The first SyntaxNet paper] was published about six months after the description of #[a(href="http://cs.stanford.edu/people/danqi/papers/emnlp2014.pdf") the CoreNLP model]. Mostly, they used a larger network, a better activation function, and a different optimisation method. They also applied a somewhat hacky approach to beam-search, which #[a(href="http://arxiv.org/pdf/1603.06042v1.pdf") their more recent work] replaced with a more principled approach. A parallel line of work that achieves equivalent accuracy using #[a(href="http://www.cs.cmu.edu/~lingwang/papers/acl2015.pdf") LSTM] models instead of feed-forward networks was published at the same time as the first SyntaxNet paper.
+h2("dependency-parsing") What SyntaxNet does
p A #[a(href="http://googleresearch.blogspot.de/2013/05/syntactic-ngrams-over-time.html" target="_blank") syntactic parser] describes a sentence’s grammatical structure, to help another application reason about it. Natural languages introduce many unexpected ambiguities, which our world-knowledge immediately filters out. A favourite example:
+example They ate the pizza with anchovies
+image("anchovies.png", "Eat-with pizza-with ambiguity")
p A correct parse would link “with” to “pizza”, while an incorrect parse would link “with” to “eat”:
+displacy("pizza-with-anchovies-bad")
+displacy("pizza-with-anchovies-good")
p You can explore the technology visually with our #[a(href="https://spacy.io/demos/displacy/") displaCy demo], or see a terse example of a #[a(href="https://spacy.io/docs/tutorials/syntax-search") rule-based approach] to computing with the parse tree. The tree of word-word relationships can also be used to recognise simple phrases, which makes it easy to extend "bag-of-words" technologies such as word2vec. For instance, we parsed every comment posted to Reddit in 2015, and used word2vec on the phrases, entities and words. This produces a nice #[a(href="https://spacy.io/demos/sense2vec") conceptual map] that's more useful than a model strictly limited to whitespace-delimited words.
p SyntaxNet is a library for training and running syntactic dependency parsing models. One model that it provides offers a particularly good speed/accuracy trade-off. In keeping with the fashion of the moment, they've called this model Parsey McParseface. Hopefully, they can maintain this sort of meme-based naming system. I think it'll be a good way to keep the timeline clear. Memes get old fast, and so does NLP technology.
+h2("advance") How big is the advance?
p Despite the "most accurate in the world" billing, Parsey McParseface is really only inches ahead of #[a(href="http://arxiv.org/pdf/1603.04351v1.pdf") equivalent recent research], that uses a more complicated neural network architecture, but with more limited parameter tuning. So, similar technologies are out there in academia. On the other hand, if what you care about is actually #[em doing] things, those technologies haven't been available yet.
+aside("Why doesn't spaCy use a neural network?") I've been working on a neural network model for spaCy on and off since the first SyntaxNet paper was published last year. However, we want to keep spaCy easy to install, we want it to be fast on a CPU, and we want it to stay multi-threaded. This has turned out to be a tricky mix of requirements to deliver.
p On the time-honoured benchmark for this task, Parsey McParseface achieves over 94% accuracy, at around 600 words per second. On the same task, spaCy achieves 92.4%, at around 15,000 words per second. The extra accuracy might not sound like much, but for applications, it's likely to be pretty significant.
p For any predictive system, often the important consideration is the difference to a baseline predictor, rather than the absolute accuracy. A model that predicts that the weather will be the same as yesterday will often be accurate, but it's not adding any value. The thing about dependency parsing is that about 80% of the dependencies are very easy and unambiguous, which means that a system that only predicts those dependencies correctly is injecting very little extra information, that wasn't trivially available by just looking at each word and its neighbours.
p In summary, I think Parsey McParseface is a very nice milestone on a larger trend. The thing that's really significant is how quickly the speed and accuracy of natural language processing technologies is advancing. I think there are lots of ideas that didn't work yesterday, that are suddenly becoming very viable.
+h2("whats-next") What's next?
p One of the things I'm most excited about is that there's a very clear way forward, given the design of the Parsey McParseface model. It's one of those things where you can say, "Okay, if this works, that's great news". This type of parser, pioneered by #[a(href="https://www.semanticscholar.org/author/Joakim-Nivre/1720988") Joakim Nivre] in 2004, reads the sentence one word at a time, and maintains only a handful of competing representations. You can slot in any state representation, any set of actions, and any probability model into this architecture. For instance, if you're parsing the output of a speech recognition system, you could have the parser refine the speech recogniser's guess at the words, based on the syntactic context. If you're populating a knowledge base, you can extend the state representation to include your target semantics, and learn it jointly with the syntax.
p Joint models and semi-supervised learning have always been the "motherhood and apple pie" of natural language understanding research. There was never any doubt that these things were good &mdash; but without a concrete proposal, they're just platitudes. It was always clear that chopping the task of understanding a sentence up into lots of subproblems, and having a pipeline of distinct models, was an unsatisfying solution. It was also obvious that a natural language understanding system should be able to make use of the vast quantities of unannotated text available. I think a transition-based neural network model gives a convincing answer to both questions. You can learn any structure you like this way, and the more text you see, the more you learn, without adding any new parameters to the model.
p Obviously, we want to build a bridge between Parsey McParseface and spaCy, so that you can use the more accurate model with the sweeter spaCy API. However, for any individual use-case, there's always a lot of tuning to do to make these technologies really perform. In particular, every application sees a different type of text. Accuracy goes up substantially if the statistical model is tuned to the domain. For instance, in well edited text such as a financial report, you want the model to consider capitalisation as a decisive indicator &mdash; but if you're parsing tweets, capitalisation is almost meaningless.
p Our plan is to solve this problem by providing a range of pre-trained models, adapted to different languages and genres. We also have some ideas we're really excited about, to help each user train their own custom model, as painlessly as possible. We think that in NLP, the algorithms are racing ahead, while the data is lagging behind. We want to fix that. If you can't wait for the result, I hope you'll #[a(href="mailto:contact@spacy.io") get in touch].

22
website/demos/_data.json
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@ -1,6 +1,26 @@
{
"index": {
"title": "Demos",
"demos": ["displacy", "sense2vec"]
"demos": {
"displacy": {
"title": "displaCy: Dependency Parse Tree Visualization with CSS and JavaScript",
"description": "displaCy lets you peek inside spaCy's syntactic parser, as it reads a sentence word-by-word. By repeatedly choosing from a small set of actions, it links the words together according to their syntactic structure.",
"image": {
"file": "displacy.jpg"
}
},
"sense2vec": {
"title": "Semantic Analysis of the Reddit Hivemind",
"description": "Our neural network read every comment posted to Reddit from 2015. We combined word2vec with sophisticated natural language processing, to give you a little look into the Reddit hivemind. Type a phrase, and see what Reddit associates it with.",
"image": {
"file": "sense2vec.jpg"
},
"links": {
"blogpost": "https://spacy.io/blog/sense2vec-with-spacy",
"github": "https://github.com/spacy-io/sense2vec"
}
}
}
}
}

15
website/demos/index.jade
View File

@ -4,12 +4,15 @@ include ../_includes/_mixins
//- ============================================================================
+grid('padding')
each demo in demos
- var teaser = public.demos[demo]._data.index
- teaser.target = '_blank'
- teaser.showmeta = false
each demo, slug in demos
+grid-col('half', 'space-between')
!=partial('../_includes/_teaser', { teaser: teaser, showmeta: false, slug: demo, _root: '/demos/' })
.teaser
a(href='https://' + slug + '.spacy.io' target='_blank')
.image-ratio: img(src='/demos/img/' + demo.image.file)
+h2
a.block(href='https://' + slug + '.spacy.io' target='_blank')=demo.title
p=demo.description
!=partial('../_includes/_newsletter', { divider: 'top' })

6
website/docs/_annotation-specs.jade
View File

@ -23,7 +23,7 @@
+code.
from spacy.en import English
nlp = English(parse=False)
nlp = English(parser=False)
tokens = nlp('Some\nspaces and\ttab characters')
print([t.orth_ for t in tokens])
@ -102,8 +102,8 @@
+cell Nationalities or religious or political groups.
+row
+cell FACILITY
+cell Buildings, airports, highways, bridges, etc.
+cell FAC
+cell Facilities, such as buildings, airports, highways, bridges, etc.
+row
+cell ORG

4
website/docs/_api-doc.jade
View File

@ -108,7 +108,7 @@
use (e.g. tokenizer, parser, entity recognizer).
+row
+cell orth_and_spaces
+cell orths_and_spaces
+cell.
A list of tokens in the document as a sequence of
#[code (orth_id, has_space)] tuples, where #[code orth_id]
@ -186,7 +186,7 @@
assert ents[0].label == 346
assert ents[0].label_ == 'PERSON'
assert ents[0].orth_ == 'Best'
assert ents[0].string == ents[0].string
assert ents[0].text == 'Mr. Best'
+h3('doc-spans-nounchunks')
| #[+label('tag') property] Doc.noun_chunks

4
website/docs/_api-english.jade
View File

@ -27,8 +27,8 @@
class English(Language):
lang = 'en'
# class German(Language): &lt;-- Coming soon
# lang = 'de'
class German(Language):
lang = 'de'
+section('english-init')
+h3('english-init')

4
website/docs/_api-token.jade
View File

@ -267,6 +267,10 @@
| Navigating the Parse Tree
+table(['Name', 'Description'], 'params')
+row
+cell dep / dep_
+cell.
The syntactic relation type, aka the dependency label, connecting the word to its head.
+row
+cell head
+cell.

2
website/docs/_data.json
View File

@ -4,7 +4,7 @@
"sidebar": {
"Quickstart": [
["Install", "#install", "install"],
["Getting started", "#getting-started", "getting-started"],
["Usage Examples", "#examples", "examples"]
],
"API": [

18
website/docs/_quickstart-examples.jade
View File

@ -8,9 +8,10 @@
+h3('examples-resources') Load resources and process text
+code.
from spacy.en import English
nlp = English()
doc = nlp(u'Hello, world. Here are two sentences.')
import spacy
en_nlp = spacy.load('en')
en_doc = en_nlp(u'Hello, world. Here are two sentences.')
de_doc = de_nlp(u'ich bin ein Berliner.')
+h3('multi-threaded') Multi-threaded generator (using OpenMP. No GIL!)
@ -46,13 +47,13 @@
assert token.shape_ == 'Xxxxx'
for lexeme in nlp.vocab:
if lexeme.is_alpha:
lexeme.shape_ = u'W'
lexeme.shape_ = 'W'
elif lexeme.is_digit:
lexeme.shape_ = u'D'
lexeme.shape_ = 'D'
elif lexeme.is_punct:
lexeme.shape_ = u'P'
lexeme.shape_ = 'P'
else:
lexeme.shape_ = u'M'
lexeme.shape_ = 'M'
assert token.shape_ == 'W'
+h3('examples-numpy-arrays') Export to numpy arrays
@ -162,12 +163,13 @@
+h3('examples-binary') Efficient binary serialization
+code.
import spacy
from spacy.tokens.doc import Doc
byte_string = doc.to_bytes()
open('moby_dick.bin', 'wb').write(byte_string)
nlp = spacy.en.English()
nlp = spacy.load('en')
for byte_string in Doc.read_bytes(open('moby_dick.bin', 'rb')):
doc = Doc(nlp.vocab)
doc.from_bytes(byte_string)

256
website/docs/_quickstart-install.jade
View File

@ -1,119 +1,177 @@
//- Docs > Quickstart > Install
//- Docs > Quickstart > Getting started
//- ============================================================================
+section('install')
+h2('install')
| Install spaCy v#{spacy_version}
+section('getting-started')
+h2('getting-started')
| Getting started
+section('install-pip-virtualenv')
+h3('install-pip-virtualenv')
| pip and virtualenv
+section('install-spacy')
+h3('install-spacy')
| Install spaCy
p.
With Python 2.7 or Python 3, using Linux or OSX, ensure that you have
the packages #[code build-essential] and #[code python-dev] installed.
Then run:
spaCy is compatible with 64-bit CPython 2.6+/3.3+ and runs on Unix/Linux,
OS X and Windows. Source and binary packages are available via
#[a(href='https://pypi.python.org/pypi/spacy' target='_blank') pip] and
#[a(href='https://anaconda.org/spacy/spacy' target='_blank') conda]. If there are
no binary packages for your platform available please make sure that you have
a working build enviroment set up. See
notes on #[a(href="/docs#install-source-ubuntu") Ubuntu],
#[a(href="/docs#install-source-osx") OS X] and
#[a(href="/docs#install-source-windows") Windows] for details.
+code('bash').
pip install spacy
python -m spacy.en.download
+code('bash', 'conda').
conda config --add channels spacy # only needed once
conda install spacy
p.
The download command fetches and installs about 500mb of data, for the
parser model and word vectors, which it installs within the spacy
package directory. Usually you'll want to install spaCy within a
#[a(href='https://virtualenv.readthedocs.org/en/latest/' target='_blank') virtualenv],
When using pip it is generally recommended to install packages in a
#[a(href='https://virtualenv.readthedocs.org/en/latest/' target='_blank') virtualenv]
to avoid modifying system state:
+code('bash').
virtualenv my_env_dir
source my_env_dir/bin/activate
+code('bash', 'pip').
# make sure you are using a recent pip/virtualenv version
python -m pip install -U pip virtualenv
+section('install-conda')
+h3('install-conda')
| conda
virtualenv .env
source .env/bin/activate
+code('bash').
conda config --add channels spacy
conda install spacy
python -m spacy.en.download
p.
Sometimes conda is not up to date with the latest release. If you
can't get the latest version on conda, you can always fall back to the
pip install.
+section('install-windows')
+h3('install-windows')
| Windows (64 bit)
p.
We've been working on Windows support. Our tests now succeed on 64 bit
builds of Windows. Installation from pip should work if you have a C++
compiler installed. Please see the README-MSVC.txt file for instructions
on compiling from source.
+section('install-update')
+h3('install-update')
| Updating your installation
p.
To update your installation:
+code('bash').
pip install --upgrade spacy
python -m spacy.en.download
p.
Most updates ship a new model, so you will usually have to redownload
the data.
+section('install-obsolete-python')
+h3('install-obsolete-python')
| Workaround for obsolete system Python
p.
If you're stuck using a server with an old version of Python, and you
don't have root access, we've prepared a bootstrap script to help you
compile a local Python install. Run:
+code('bash')
curl https://raw.githubusercontent.com/spacy-io/gist/master/bootstrap_python_env.sh | bash && source .env/bin/activate
+section('install-compile')
+h3('install-compile')
| Compile from source
p.
The other way to install the package is to clone the github repository,
and build it from source. This installs an additional dependency, Cython.
If you're using Python 2, we also recommend installing fabric and
fabtools – this is how we build the project. Ensure that you have the
packages #[code build-essential], #[code python-dev], #[code git] and
#[code python-virtualenv] installed.
+code('bash')
git clone https://github.com/spacy-io/spaCy.git
cd spaCy
virtualenv .env && source .env/bin/activate
pip install -r requirements.txt
pip install -e .
python -m spacy.en.download
pip install pytest
python -m pytest spacy
pip install spacy
p.
Python packaging is awkward at the best of times, and it's particularly
tricky with C extensions, built via Cython, requiring large data files.
So, please report issues as you encounter them.
+section('install-pypy')
+h3('install-pypy')
| pypy (unsupported)
+section('install-model')
+h3('install-model')
| Install model
p.
If PyPy support is a priority for you, please get in touch. We could
likely fix the remaining issues, if necessary. However, the library
is likely to be much slower on PyPy, as it's written in Cython, which
produces code tuned for the performance of CPython.
After installation you need to download a language model.
Currently only models for English and German, named #[code en] and #[code de], are available. Please get in touch with us if you need support for a particular language.
+code('bash').
sputnik --name spacy --repository-url http://index.spacy.io install en==1.1.0
p.
Then check whether the model was successfully installed:
+code('bash').
python -c "import spacy; spacy.load('en'); print('OK')"
p.
The download command fetches and installs about 500 MB of data which it installs
within the #[code spacy] package directory.
+section('install-upgrade')
+h3('install-upgrade')
| Upgrading spaCy
p.
To upgrade spaCy to the latest release:
+code('bash', 'conda').
conda update spacy
+code('bash', 'pip').
pip install -U spacy
p.
Sometimes new releases require a new language model. Then you will have to upgrade to
a new model, too. You can also force re-downloading and installing a new language model:
+code('bash').
python -m spacy.en.download --force
+section('install-source')
+h3('install-source')
| Compile from source
p.
The other way to install spaCy is to clone its
#[a(href="https://github.com/spacy-io/spaCy") GitHub repository] and
build it from source. That is the common way if you want to make changes
to the code base.
p.
You'll need to make sure that you have a development enviroment consisting
of a Python distribution including header files, a compiler, pip,
virtualenv and git installed. The compiler
part is the trickiest. How to do that depends on your system. See
notes on #[a(href="/docs#install-source-ubuntu") Ubuntu],
#[a(href="/docs#install-source-osx") OS X] and
#[a(href="/docs#install-source-windows") Windows] for details.
+code('bash').
# make sure you are using recent pip/virtualenv versions
python -m pip install -U pip virtualenv
# find git install instructions at https://git-scm.com/downloads
git clone https://github.com/spacy-io/spaCy.git
cd spaCy
virtualenv .env && source .env/bin/activate
pip install -r requirements.txt
pip install -e .
p.
Compared to regular install via #[code pip] and #[code conda]
#[a(href="https://github.com/spacy-io/spaCy/blob/master/requirements.txt") requirements.txt]
additionally installs developer dependencies such as #[code cython].
+h4('install-source-ubuntu')
| Ubuntu
p.
Install system-level dependencies via #[code apt-get]:
+code('bash').
sudo apt-get install build-essential python-dev git
+h4('install-source-osx')
| OS X
p.
Install a recent version of XCode, including the so-called "Command Line Tools". OS X
ships with Python and git preinstalled.
+h4('install-source-windows')
| Windows
p.
Install a version of Visual Studio Express or higher that matches the version that was
used to compile your Python interpreter. For official distributions
these are VS 2008 (Python 2.7), VS 2010 (Python 3.4) and VS 2015 (Python 3.5).
+section('install-obsolete-python')
+h3('install-obsolete-python')
| Workaround for obsolete system Python
p.
If you're stuck using a system with an old version of Python, and you
don't have root access, we've prepared a bootstrap script to help you
compile a local Python install. Run:
+code('bash').
curl https://raw.githubusercontent.com/spacy-io/gist/master/bootstrap_python_env.sh | bash && source .env/bin/activate
+section('run-tests')
+h3('run-tests')
| Run tests
p.
spaCy comes with an extensive test suite. First, find out where spaCy is installed:
+code('bash').
python -c "import os; import spacy; print(os.path.dirname(spacy.__file__))"
p.
Then run #[code pytest] on that directory. The flags #[code --vectors],
#[code --slow] and #[code --model] are optional and enable additional tests:
+code('bash').
# make sure you are using recent pytest version
python -m pip install -U pytest
python -m pytest &lt;spacy-directory&gt; --vectors --model --slow

14
website/docs/index.jade
View File

@ -8,13 +8,13 @@ include ../_includes/_mixins
//- Docs
//- ============================================================================
+infobox('Update March, 2016').
We know change can be jarring, especially when you've got a deadline. So to
ease the transition to the new documentation style, we've kept
#[a(href='/docs/legacy' target='_blank') the old docs] online too.
If you have any problems, you can let us know on the
#[a(href='https://github.com/' + profiles.github + '/spaCy' target='_blank') spaCy issue tracker]
or the #[a(href='https://reddit.com/r/' + profiles.reddit target='_blank') Reddit user group].
//+infobox('Update March, 2016').
// We know change can be jarring, especially when you've got a deadline. So to
// ease the transition to the new documentation style, we've kept
// #[a(href='/docs/legacy' target='_blank') the old docs] online too.
// If you have any problems, you can let us know on the
// #[a(href='https://github.com/' + profiles.github + '/spaCy' target='_blank') spaCy issue tracker]
// or the #[a(href='https://reddit.com/r/' + profiles.reddit target='_blank') Reddit user group].
include _quickstart-install
include _quickstart-examples

7
website/docs/tutorials/_data.json
View File

@ -6,6 +6,13 @@
"description": "Word vectors allow simple similarity queries, and drive many NLP applications. This tutorial explains how to load custom word vectors into spaCy, to make use of task or data-specific representations."
},
"byo-annotations": {
"template": "article",
"title": "Using Pre-existing Tokenization, Tags, and Other Annotations",
"date": "2016-04-15",
"description": "spaCy assumes by default that your data is raw text. However, sometimes your data is partially annotated, e.g. with pre-existing tokenization, part-of-speech tags, etc. This tutorial explains how to use these annotations in spaCy."
},
"mark-adverbs": {
"template": "article",
"title": "Mark all adverbs, particularly for verbs of speech",

118
website/docs/tutorials/byo-annotations.jade Normal file
View File

@ -0,0 +1,118 @@
include ../../_includes/_mixins
+lead spaCy assumes by default that your data is raw text. However, sometimes your data is partially annotated, e.g. with pre-existing tokenization, part-of-speech tags, etc. This tutorial explains how to use these annotations in spaCy.
+h2 Quick Reference
+table(['Description', 'Usage'], 'code')
+row
+cell Use pre-existing tokenization
+cell #[code.lang-python doc = Doc(nlp.vocab, [('A', True), ('token', False), ('!', False)])]
+row
+cell Use pre-existing tokenization (deprecated)
+cell #[code.lang-python doc = nlp.tokenizer.tokens_from_list([u'A', u'token', u'!'])]
+row
+cell Assign pre-existing tags
+cell #[code.lang-python nlp.tagger.tag_from_strings(doc, ['DT', 'NN'])]
+row
+cell Assign named entity annotations from an array
+cell #[code.lang-python doc.from_array([ENT_TYPE, ENT_IOB], values)]
+row
+cell Assign dependency parse annotations from an array
+cell #[code.lang-python doc.from_array([HEAD, DEP], values)]
+h2 Examples
+code('python', 'Tokenization').
import spacy
nlp = spacy.load('en')
tokens = [u'A', u'list', u'of', u'strings', u'.']
doc = nlp.tokenizer.tokens_from_list(tokens)
assert len(doc) == len(tokens)
# With this method, we don't get to specify how the corresponding string
# would be spaced, so we have to assume a space before every token.
assert doc.text == u'A list of strings .'
+code('python', 'Tokenization').
import spacy
from spacy.tokens import Doc
nlp = spacy.load('en')
tokens = [u'A', u'list', u'of', u'strings', u'.']
has_space = [True, True, True, False, False]
doc = Doc(nlp.vocab, orth_and_spaces=zip(tokens, has_space))
assert len(doc) == len(tokens)
# Spacing is correct, given by boolean values above.
assert doc.text == u'A list of strings.'
# Here's how it would look with different boolean values.
tokens = [u'A', u'list', u'of', u'strings', u'.']
has_space = [False, True, True, True, False]
doc = Doc(nlp.vocab, orth_and_spaces=zip(tokens, has_space))
assert doc.text == u'Alist of strings .'
+code('python', 'POS Tags').
import spacy
nlp = spacy.load('en')
# Tokenize a string into a Doc, but don't apply the whole pipeline ---
# that is, don't predict the part-of-speech tags, syntactic parse, named
# entities, etc.
doc = nlp.tokenizer(u'A unicode string, untokenized.')
nlp.tagger.tag_from_strings([u'DT', u'JJ', u'NN', u',', u'VBN', u'.'])
# Now predict dependency parse and named entities. Note that if you assign
# tags in a way that's very unlike the behaviour of the POS tagger model,
# the subsequent models may perform worse. These models use the POS tags
# as features, so if you give them unexpected tags, you may be giving them
# run-time conditions that don't resemble the training data.
nlp.parser(doc)
nlp.entity(doc)
+code('python', 'Dependency Parse').
import spacy
from spacy.attrs import HEAD, DEP
from spacy.symbols import det, nmod, root, punct
from numpy import ndarray
nlp = spacy.load('en')
# Get the Doc object, and apply the pipeline except the dependency parser
doc = nlp(u'A unicode string.', parse=False)
columns = [HEAD, DEP]
values = ndarray(shape=(len(columns), len(doc)), dtype='int32')
# Syntactic parse specified as head offsets
heads = [2, 1, 0, -1]
# Integer IDs for the dependency labels. See the parse in the displaCy
# demo at spacy.io/demos/displacy
labels = [det, nmod, root, punct]
values[0] = heads
values[1] = labels
doc.from_array(columns, values)
+code('python', 'Named Entities').
import spacy
from spacy.attrs import ENT_TYPE, ENT_IOB
from spacy.symbols import PERSON, ORG
from numpy import ndarray
nlp = spacy.load('en')
# Get the Doc object, and apply the pipeline except the entity recognizer
doc = nlp(u'My name is Matt.', entity=False)
columns = [ENT_TYPE, ENT_IOB]
values = ndarray(shape=(len(columns), len(doc)), dtype='int32')
# IOB values are 0=missing, 1=I, 2=O, 3=B
values[0] = [1, 1, 1, 3, 1]
values[1] = [0, 0, 0, PERSON, 0]
doc.from_array(columns, values)

2
website/feed.xml.jade
View File

@ -12,7 +12,7 @@ rss(version='2.0', xmlns:atom='http://www.w3.org/2005/Atom')
description= description
atom:link(href='#{ url }/feed.xml', rel='self', type='application/rss+xml')
for post, slug in public.blog._data
if post.published != false
if post.published != false && slug != 'index'
item
title=post.title
description

10
website/index.jade
View File

@ -1,6 +1,6 @@
include _includes/_mixins
- var logos = [ ['chartbeat', 'https://chartbeat.com'], ['socrata', 'https://www.socrata.com'], ['keyreply', 'https://keyreply.com/'], [ 'kip', 'http://kipthis.com'], ['cytora', 'http://www.cytora.com'], ['signaln', 'http://signaln.com'] ]
- var logos = [ ['chartbeat', 'https://chartbeat.com'], ['socrata', 'https://www.socrata.com'], ['chattermill', 'https://chattermill.io'], ['cytora', 'http://www.cytora.com'], ['signaln', 'http://signaln.com'], ['duedil', 'https://www.duedil.com/'], ['spyjack', 'https://spyjack.io'], ['keyreply', 'https://keyreply.com/'], ['dato', 'https://dato.com'], [ 'kip', 'http://kipthis.com'], ['wonderflow', 'http://www.wonderflow.co'], ['foxtype', 'https://foxtype.com'], ['synapsify', 'http://www.gosynapsify.com'], ['stitchfix', 'https://www.stitchfix.com/'], ['wayblazer', 'http://wayblazer.com'] ]
//- Landing Page
@ -20,7 +20,7 @@ header.header
| We help you get their work out of papers and#[br]
| into production.#[br]
+button('secondary')(href='/docs/#install') Install spaCy
+button('secondary')(href='/docs/#getting-started') Install spaCy
+divider('bar')
| #[a(href='https://github.com/' + profiles.github + '/spaCy/releases' target='_blank'): #[strong Latest Release:] v#{spacy_version}]
@ -96,7 +96,9 @@ main.main
+h2.text-center
+label('strong') spaCy is trusted by
+grid('space-around', 'valign-center', 'padding')
+grid('align-center')
+grid-col('two-thirds')
+grid('align-center', 'valign-center', 'padding')
each logo in logos
a(href=logo[1] target='_blank')
img(src='assets/img/logos/' + logo[0] + '.png').logo--small
@ -130,7 +132,7 @@ main.main
<blockquote class="twitter-tweet" data-cards="hidden" data-lang="en"><p lang="en" dir="ltr">spaCy seems pretty exciting to me - and it is clear that NLTK has not kept up with <a href="https://twitter.com/hashtag/NLP?src=hash">#NLP</a>. <a href="http://t.co/mUPFUMLrbo">http://t.co/mUPFUMLrbo</a> <a href="https://twitter.com/hashtag/python?src=hash">#python</a> <a href="https://twitter.com/hashtag/datascience?src=hash">#datascience</a></p>&mdash; Alex Engler (@AlexCEngler) <a href="https://twitter.com/AlexCEngler/status/648537133544833025">September 28, 2015</a></blockquote>
+grid-col('third', 'valign-center')
<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Explore what NLP can do these days with Sense2vec and Spacy <a href="https://t.co/MHZEP2yLo4">https://t.co/MHZEP2yLo4</a></p>&mdash; Chattermill.io (@chatter_mill) <a href="https://twitter.com/chatter_mill/status/699660272907059200">February 16, 2016</a></blockquote>
<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">I wish I&#39;d known about nlp.pipe about two weeks ago. Nice feature in <a href="https://twitter.com/Spacy">@spacy</a> to parallelize your NLP pipeline.</p>&mdash; Matti Lyra (@mattilyra) <a href="https://twitter.com/mattilyra/status/704753660329369600">March 1, 2016</a></blockquote>
+divider

77
website/legal.jade
View File

@ -1,77 +0,0 @@
include _includes/_mixins
//- Legal / Imprint
//- ============================================================================
+h2 Imprint
+label('strong') Editorial responsibility
p spaCy GmbH
+label('strong') Information according to § 5 TMG (Telekommunikationsgesetz)
p.
Buchholzer Str. 2#[br]
10437 Berlin#[br]
Germany
p.
#[strong Authorized representative:]
Henning Peters#[br]
+label('strong') Contact
p #[a(href='mailto:' + email)=email]
p.
#[strong Registergericht/district court:] Amtsgericht Berlin-Charlottenburg#[br]
#[strong Handelsregister/Commercial Register:] HRB 168190 B#[br]
#[strong Umsatzsteuer-Identifikationsnummer/ VAT–ID No.:] DE304746312#[br]
p.
#[strong Responsible for the content according to § 55 RStV (Rundfunkstaatsvertrag):]
Henning Peters
+h2 Credits
p.
#[strong Illustrations:]
#[a(href='https://dribbble.com/kemal' target='_blank') Kemal Sanli]#[br]
#[strong Photography:]
#[a(href='http://johannawarda.com' target='_blank') Johanna Warda]
+h2 Disclaimer
+label('strong') Legal notice
p.
spaCy constantly checks and updates the information on its web pages. Despite
the utmost care taken, we cannot rule out that some of the data may since have
become outdated. Therefore, we cannot accept liability for the currency,
accuracy and completeness of the information displayed. The same applies to
all other web pages that can be reached via hyperlink. spaCy is not responsible
for the content of websites which can be reached via such links. Furthermore,
spaCy reserves the right to make changes or additions to the information
displayed. Content and structure of the spaCy website is protected by
copyright. The reproduction of information or data, in particular the use of
texts, text excerpts or illustrative material, requires our prior approval.
We wish you an informative stay on our pages and would welcome any suggestions,
requests or criticism you may have.
+label('strong') Web Analytics
p.
This website uses Google Analytics, a web analytics service provided by Google,
Inc. Google Analytics uses “cookies”, which are text files placed on your
computer, to help the website analyze how users use the site. The information
generated by the cookie about your use of the website (including your IP
address) will be transmitted to and stored by Google on servers in the United
States . Google will use this information for the purpose of evaluating your
use of the website, compiling reports on website activity for website operators
and providing other services relating to website activity and internet usage.
Google may also transfer this information to third parties where required to
do so by law, or where such third parties process the information on Google’s
behalf. Google will not associate your IP address with any other data held by
Google. You may refuse the use of cookies by selecting the appropriate settings
on your browser, however please note that if you do this you may not be able
to use the full functionality of this website. By using this website, you
consent to the processing of data about you by Google in the manner and for
the purposes set out above.

19
website/team.jade
View File

@ -1,19 +0,0 @@
include _includes/_mixins
//- Team
//- ============================================================================
+grid('align-center', 'padding')
each member in team_members
+grid-col('fifth', 'valign-center')
.image-container
img(src='/assets/img/profile_' + member + '.png')
!=authors[member].name
+label=(authors[member].title || 'job title')
if authors[member].links
.block
each link, index in authors[member].links
a(href=link[0] target='_blank')
+icon(index)(data-tooltip=(link[1] || index))