mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-25 17:36:30 +03:00
Merge branch 'develop' of https://github.com/explosion/spaCy into develop
This commit is contained in:
commit
b3a7082bf8
4
.gitignore
vendored
4
.gitignore
vendored
|
@ -1,14 +1,12 @@
|
|||
# spaCy
|
||||
spacy/data/
|
||||
corpora/
|
||||
models/
|
||||
/models/
|
||||
keys/
|
||||
|
||||
# Website
|
||||
website/www/
|
||||
website/_deploy.sh
|
||||
website/package.json
|
||||
website/announcement.jade
|
||||
website/.gitignore
|
||||
|
||||
# Cython / C extensions
|
||||
|
|
|
@ -11,7 +11,7 @@ __email__ = 'contact@explosion.ai'
|
|||
__license__ = 'MIT'
|
||||
__release__ = False
|
||||
|
||||
__docs_models__ = 'https://spacy.io/docs/usage/models'
|
||||
__docs_models__ = 'https://alpha.spacy.io/usage/models'
|
||||
__download_url__ = 'https://github.com/explosion/spacy-models/releases/download'
|
||||
__compatibility__ = 'https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json'
|
||||
__shortcuts__ = 'https://raw.githubusercontent.com/explosion/spacy-models/master/shortcuts.json'
|
||||
|
|
|
@ -8,4 +8,5 @@ include _includes/_mixins
|
|||
| does not exist!
|
||||
|
||||
h2.c-landing__title.u-heading-3.u-padding-small
|
||||
a(href="javascript:history.go(-1)") Click here to go back.
|
||||
+button(false, true, "secondary-light")(href="javascript:history.go(-1)")
|
||||
| Click here to go back
|
||||
|
|
|
@ -3,24 +3,22 @@
|
|||
"landing": true,
|
||||
"logos": [
|
||||
{
|
||||
"quora": [ "https://www.quora.com", 150 ],
|
||||
"chartbeat": [ "https://chartbeat.com", 200 ],
|
||||
"duedil": [ "https://www.duedil.com", 150 ],
|
||||
"stitchfix": [ "https://www.stitchfix.com", 190 ]
|
||||
"airbnb": [ "https://www.airbnb.com", 150, 45],
|
||||
"quora": [ "https://www.quora.com", 120, 34 ],
|
||||
"retriever": [ "https://www.retriever.no", 150, 33 ],
|
||||
"stitchfix": [ "https://www.stitchfix.com", 150, 18 ]
|
||||
},
|
||||
{
|
||||
"wayblazer": [ "http://wayblazer.com", 200 ],
|
||||
"indico": [ "https://indico.io", 150 ],
|
||||
"chattermill": [ "https://chattermill.io", 175 ],
|
||||
"turi": [ "https://turi.com", 150 ],
|
||||
"kip": [ "http://kipthis.com", 70 ]
|
||||
},
|
||||
"chartbeat": [ "https://chartbeat.com", 180, 25 ],
|
||||
"allenai": [ "https://allenai.org", 220, 37 ]
|
||||
}
|
||||
],
|
||||
"features": [
|
||||
{
|
||||
"socrata": [ "https://www.socrata.com", 150 ],
|
||||
"cytora": [ "http://www.cytora.com", 125 ],
|
||||
"signaln": [ "http://signaln.com", 150 ],
|
||||
"wonderflow": [ "http://www.wonderflow.co", 200 ],
|
||||
"synapsify": [ "http://www.gosynapsify.com", 150 ]
|
||||
"thoughtworks": ["https://www.thoughtworks.com/radar/tools", 150, 28],
|
||||
"wapo": ["https://www.washingtonpost.com/news/wonk/wp/2016/05/18/googles-new-artificial-intelligence-cant-understand-these-sentences-can-you/", 100, 77],
|
||||
"venturebeat": ["https://venturebeat.com/2017/01/27/4-ai-startups-that-analyze-customer-reviews/", 150, 19],
|
||||
"microsoft": ["https://www.microsoft.com/developerblog/2016/09/13/training-a-classifier-for-relation-extraction-from-medical-literature/", 130, 28]
|
||||
}
|
||||
]
|
||||
},
|
||||
|
@ -34,7 +32,24 @@
|
|||
"landing": true
|
||||
},
|
||||
|
||||
"announcement" : {
|
||||
"title": "Important Announcement"
|
||||
"styleguide": {
|
||||
"title": "Styleguide",
|
||||
"sidebar": {
|
||||
"Styleguide": { "": "styleguide" },
|
||||
"Resources": {
|
||||
"Website Source": "https://github.com/explosion/spacy/tree/master/website",
|
||||
"Contributing Guide": "https://github.com/explosion/spaCy/blob/master/CONTRIBUTING.md"
|
||||
}
|
||||
},
|
||||
"menu": {
|
||||
"Introduction": "intro",
|
||||
"Logo": "logo",
|
||||
"Colors": "colors",
|
||||
"Typography": "typography",
|
||||
"Elements": "elements",
|
||||
"Components": "components",
|
||||
"Embeds": "embeds",
|
||||
"Markup Reference": "markup"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
@ -11,12 +11,9 @@
|
|||
"COMPANY": "Explosion AI",
|
||||
"COMPANY_URL": "https://explosion.ai",
|
||||
"DEMOS_URL": "https://demos.explosion.ai",
|
||||
"MODELS_REPO": "explosion/spacy-models",
|
||||
|
||||
"SPACY_VERSION": "1.8",
|
||||
"LATEST_NEWS": {
|
||||
"url": "https://github.com/explosion/spaCy/releases/tag/v2.0.0-alpha",
|
||||
"title": "Test spaCy v2.0.0 alpha!"
|
||||
},
|
||||
"SPACY_VERSION": "2.0",
|
||||
|
||||
"SOCIAL": {
|
||||
"twitter": "spacy_io",
|
||||
|
@ -27,25 +24,23 @@
|
|||
},
|
||||
|
||||
"NAVIGATION": {
|
||||
"Home": "/",
|
||||
"Usage": "/docs/usage",
|
||||
"Reference": "/docs/api",
|
||||
"Demos": "/docs/usage/showcase",
|
||||
"Blog": "https://explosion.ai/blog"
|
||||
"Usage": "/usage",
|
||||
"Models": "/models",
|
||||
"API": "/api"
|
||||
},
|
||||
|
||||
"FOOTER": {
|
||||
"spaCy": {
|
||||
"Usage": "/docs/usage",
|
||||
"API Reference": "/docs/api",
|
||||
"Tutorials": "/docs/usage/tutorials",
|
||||
"Showcase": "/docs/usage/showcase"
|
||||
"Usage": "/usage",
|
||||
"Models": "/models",
|
||||
"API Reference": "/api",
|
||||
"Resources": "/usage/resources"
|
||||
},
|
||||
"Support": {
|
||||
"Issue Tracker": "https://github.com/explosion/spaCy/issues",
|
||||
"StackOverflow": "http://stackoverflow.com/questions/tagged/spacy",
|
||||
"Reddit usergroup": "https://www.reddit.com/r/spacynlp/",
|
||||
"Gitter chat": "https://gitter.im/explosion/spaCy"
|
||||
"Reddit Usergroup": "https://www.reddit.com/r/spacynlp/",
|
||||
"Gitter Chat": "https://gitter.im/explosion/spaCy"
|
||||
},
|
||||
"Connect": {
|
||||
"Twitter": "https://twitter.com/spacy_io",
|
||||
|
@ -74,21 +69,11 @@
|
|||
{"id": "venv", "title": "virtualenv", "help": "Use a virtual environment and install spaCy into a user directory" },
|
||||
{"id": "gpu", "title": "GPU", "help": "Run spaCy on GPU to make it faster. Requires an NVDIA graphics card with CUDA 2+. See section below for more info."}]
|
||||
},
|
||||
{ "id": "model", "title": "Models", "multiple": true, "options": [
|
||||
{ "id": "en", "title": "English", "meta": "50MB" },
|
||||
{ "id": "de", "title": "German", "meta": "645MB" },
|
||||
{ "id": "fr", "title": "French", "meta": "1.33GB" },
|
||||
{ "id": "es", "title": "Spanish", "meta": "377MB"}]
|
||||
}
|
||||
{ "id": "model", "title": "Models", "multiple": true }
|
||||
],
|
||||
|
||||
"QUICKSTART_MODELS": [
|
||||
{ "id": "lang", "title": "Language", "options": [
|
||||
{ "id": "en", "title": "English", "checked": true },
|
||||
{ "id": "de", "title": "German" },
|
||||
{ "id": "fr", "title": "French" },
|
||||
{ "id": "es", "title": "Spanish" }]
|
||||
},
|
||||
{ "id": "lang", "title": "Language"},
|
||||
{ "id": "load", "title": "Loading style", "options": [
|
||||
{ "id": "spacy", "title": "Use spacy.load()", "checked": true, "help": "Use spaCy's built-in loader to load the model by name." },
|
||||
{ "id": "module", "title": "Import as module", "help": "Import the model explicitly as a Python module." }]
|
||||
|
@ -98,50 +83,15 @@
|
|||
}
|
||||
],
|
||||
|
||||
"MODELS": {
|
||||
"en": [
|
||||
{ "id": "en_core_web_sm", "lang": "English", "feats": [1, 1, 1, 1], "size": "50 MB", "license": "CC BY-SA", "def": true },
|
||||
{ "id": "en_core_web_md", "lang": "English", "feats": [1, 1, 1, 1], "size": "1 GB", "license": "CC BY-SA" },
|
||||
{ "id": "en_depent_web_md", "lang": "English", "feats": [1, 1, 1, 0], "size": "328 MB", "license": "CC BY-SA" },
|
||||
{ "id": "en_vectors_glove_md", "lang": "English", "feats": [1, 0, 0, 1], "size": "727 MB", "license": "CC BY-SA" }
|
||||
],
|
||||
"de": [
|
||||
{ "id": "de_core_news_md", "lang": "German", "feats": [1, 1, 1, 1], "size": "645 MB", "license": "CC BY-SA" }
|
||||
],
|
||||
"fr": [
|
||||
{ "id": "fr_depvec_web_lg", "lang": "French", "feats": [1, 1, 0, 1], "size": "1.33 GB", "license": "CC BY-NC" }
|
||||
],
|
||||
"es": [
|
||||
{ "id": "es_core_web_md", "lang": "Spanish", "feats": [1, 1, 1, 1], "size": "377 MB", "license": "CC BY-SA"}
|
||||
]
|
||||
},
|
||||
|
||||
"EXAMPLE_SENTENCES": {
|
||||
"en": "This is a sentence.",
|
||||
"de": "Dies ist ein Satz.",
|
||||
"fr": "C'est une phrase.",
|
||||
"es": "Esto es una frase."
|
||||
},
|
||||
|
||||
"ALPHA": true,
|
||||
"V_CSS": "1.6",
|
||||
"V_JS": "1.2",
|
||||
"V_CSS": "2.0",
|
||||
"V_JS": "2.0",
|
||||
"DEFAULT_SYNTAX": "python",
|
||||
"ANALYTICS": "UA-58931649-1",
|
||||
"MAILCHIMP": {
|
||||
"user": "spacy.us12",
|
||||
"id": "83b0498b1e7fa3c91ce68c3f1",
|
||||
"list": "89ad33e698"
|
||||
},
|
||||
"BADGES": {
|
||||
"pipy": {
|
||||
"badge": "https://img.shields.io/pypi/v/spacy.svg?style=flat-square",
|
||||
"link": "https://pypi.python.org/pypi/spacy"
|
||||
},
|
||||
"conda": {
|
||||
"badge": "https://anaconda.org/conda-forge/spacy/badges/version.svg",
|
||||
"link": "https://anaconda.org/conda-forge/spacy"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
@ -1,8 +1,6 @@
|
|||
//- 💫 INCLUDES > FOOTER
|
||||
|
||||
include _mixins
|
||||
|
||||
footer.o-footer.u-text.u-border-dotted
|
||||
footer.o-footer.u-text
|
||||
+grid.o-content
|
||||
each group, label in FOOTER
|
||||
+grid-col("quarter")
|
||||
|
@ -13,18 +11,18 @@ footer.o-footer.u-text.u-border-dotted
|
|||
li
|
||||
+a(url)=item
|
||||
|
||||
if SECTION != "docs"
|
||||
if SECTION == "index"
|
||||
+grid-col("quarter")
|
||||
include _newsletter
|
||||
|
||||
if SECTION == "docs"
|
||||
if SECTION != "index"
|
||||
.o-content.o-block.u-border-dotted
|
||||
include _newsletter
|
||||
|
||||
.o-inline-list.u-text-center.u-text-tiny.u-color-subtle
|
||||
span © 2016-#{new Date().getFullYear()} #[+a(COMPANY_URL, true)=COMPANY]
|
||||
|
||||
+a(COMPANY_URL, true)
|
||||
+svg("graphics", "explosion", 45).o-icon.u-color-theme.u-grayscale
|
||||
+a(COMPANY_URL, true)(aria-label="Explosion AI")
|
||||
+icon("explosion", 45).o-icon.u-color-theme.u-grayscale
|
||||
|
||||
+a(COMPANY_URL + "/legal", true) Legal / Imprint
|
||||
|
|
|
@ -1,35 +1,71 @@
|
|||
//- 💫 INCLUDES > FUNCTIONS
|
||||
|
||||
//- More descriptive variables for current.path and current.source
|
||||
//- Descriptive variables, available in the global scope
|
||||
|
||||
- CURRENT = current.source
|
||||
- SECTION = current.path[0]
|
||||
- SUBSECTION = current.path[1]
|
||||
- LANGUAGES = public.models._data.LANGUAGES
|
||||
- MODELS = public.models._data.MODELS
|
||||
- CURRENT_MODELS = MODELS[current.source] || []
|
||||
|
||||
- MODEL_COUNT = Object.keys(MODELS).map(m => Object.keys(MODELS[m]).length).reduce((a, b) => a + b)
|
||||
- MODEL_LANG_COUNT = Object.keys(MODELS).length
|
||||
- LANG_COUNT = Object.keys(LANGUAGES).length
|
||||
|
||||
- MODEL_META = public.models._data.MODEL_META
|
||||
- MODEL_LICENSES = public.models._data.MODEL_LICENSES
|
||||
- MODEL_ACCURACY = public.models._data.MODEL_ACCURACY
|
||||
- EXAMPLE_SENTENCES = public.models._data.EXAMPLE_SENTENCES
|
||||
|
||||
- IS_PAGE = (SECTION != "index") && !landing
|
||||
- IS_MODELS = (SECTION == "models" && LANGUAGES[current.source])
|
||||
- HAS_MODELS = IS_MODELS && CURRENT_MODELS.length
|
||||
|
||||
|
||||
//- Add prefixes to items of an array (for modifier CSS classes)
|
||||
array - [array] list of class names or options, e.g. ["foot"]
|
||||
prefix - [string] prefix to add to each class, e.g. "c-table__row"
|
||||
RETURNS - [array] list of modified class names
|
||||
|
||||
- function prefixArgs(array, prefix) {
|
||||
- return array.map(function(arg) {
|
||||
- return prefix + '--' + arg;
|
||||
- }).join(' ');
|
||||
- return array.map(arg => prefix + '--' + arg).join(' ');
|
||||
- }
|
||||
|
||||
|
||||
//- Convert API paths (semi-temporary fix for renamed sections)
|
||||
path - [string] link path supplied to +api mixin
|
||||
RETURNS - [string] new link path to correct location
|
||||
|
||||
- function convertAPIPath(path) {
|
||||
- if (path.startsWith('spacy#') || path.startsWith('displacy#') || path.startsWith('util#')) {
|
||||
- var comps = path.split('#');
|
||||
- return "top-level#" + comps[0] + '.' + comps[1];
|
||||
- }
|
||||
- else if (path.startsWith('cli#')) {
|
||||
- return "top-level#" + path.split('#')[1];
|
||||
- }
|
||||
- return path;
|
||||
- }
|
||||
|
||||
|
||||
//- Get model components from ID. Components can then be looked up in LANGUAGES
|
||||
and MODEL_META respectively, to get their human-readable form.
|
||||
id - [string] model ID, e.g. "en_core_web_sm"
|
||||
RETURNS - [object] object keyed by components lang, type, genre and size
|
||||
|
||||
- function getModelComponents(id) {
|
||||
- var comps = id.split('_');
|
||||
- return {'lang': comps[0], 'type': comps[1], 'genre': comps[2], 'size': comps[3]}
|
||||
- }
|
||||
|
||||
|
||||
//- Generate GitHub links
|
||||
repo - [string] name of repo owned by explosion
|
||||
filepath - [string] logical path to file relative to repository root
|
||||
branch - [string] optional branch, defaults to "master"
|
||||
RETURNS - [string] the correct link to the file on GitHub
|
||||
|
||||
- function gh(repo, filepath, branch) {
|
||||
- var branch = ALPHA ? 'develop' : branch
|
||||
- return 'https://github.com/' + SOCIAL.github + '/' + repo + (filepath ? '/blob/' + (branch || 'master') + '/' + filepath : '' );
|
||||
- }
|
||||
|
||||
|
||||
//- Get social images
|
||||
|
||||
- function getSocialImg() {
|
||||
- var base = SITE_URL + '/assets/img/social/preview_'
|
||||
- var image = ALPHA ? 'alpha' : 'default'
|
||||
- if (preview) image = preview
|
||||
- else if (SECTION == 'docs' && !ALPHA) image = 'docs'
|
||||
- return base + image + '.jpg'
|
||||
- return 'https://github.com/' + SOCIAL.github + '/' + (repo || '') + (filepath ? '/blob/' + (branch || 'master') + '/' + filepath : '' );
|
||||
- }
|
||||
|
|
|
@ -1,5 +1,13 @@
|
|||
//- 💫 MIXINS > BASE
|
||||
|
||||
//- Section
|
||||
id - [string] anchor assigned to section (used for breadcrumb navigation)
|
||||
|
||||
mixin section(id)
|
||||
section.o-section(id="section-" + id data-section=id)
|
||||
block
|
||||
|
||||
|
||||
//- Aside wrapper
|
||||
label - [string] aside label
|
||||
|
||||
|
@ -11,34 +19,26 @@ mixin aside-wrapper(label)
|
|||
|
||||
block
|
||||
|
||||
//- Date
|
||||
input - [string] date in the format YYYY-MM-DD
|
||||
|
||||
mixin date(input)
|
||||
- var date = new Date(input)
|
||||
- var months = [ 'January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December' ]
|
||||
|
||||
time(datetime=JSON.parse(JSON.stringify(date)))&attributes(attributes)=months[date.getMonth()] + ' ' + date.getDate() + ', ' + date.getFullYear()
|
||||
|
||||
|
||||
//- SVG from map
|
||||
file - [string] SVG file name in /assets/img/
|
||||
//- SVG from map (uses embedded SVG sprite)
|
||||
name - [string] SVG symbol id
|
||||
width - [integer] width in px
|
||||
height - [integer] height in px (default: same as width)
|
||||
|
||||
mixin svg(file, name, width, height)
|
||||
mixin svg(name, width, height)
|
||||
svg(aria-hidden="true" viewBox="0 0 #{width} #{height || width}" width=width height=(height || width))&attributes(attributes)
|
||||
use(xlink:href="/assets/img/#{file}.svg##{name}")
|
||||
use(xlink:href="#svg_#{name}")
|
||||
|
||||
|
||||
//- Icon
|
||||
name - [string] icon name, should be SVG symbol ID
|
||||
size - [integer] icon width and height (default: 20)
|
||||
name - [string] icon name (will be used as symbol id: #svg_{name})
|
||||
width - [integer] icon width (default: 20)
|
||||
height - [integer] icon height (defaults to width)
|
||||
|
||||
mixin icon(name, size)
|
||||
- var size = size || 20
|
||||
+svg("icons", name, size).o-icon(style="min-width: #{size}px")&attributes(attributes)
|
||||
mixin icon(name, width, height)
|
||||
- var width = width || 20
|
||||
- var height = height || width
|
||||
+svg(name, width, height).o-icon(style="min-width: #{width}px")&attributes(attributes)
|
||||
|
||||
|
||||
//- Pro/Con/Neutral icon
|
||||
|
@ -46,8 +46,8 @@ mixin icon(name, size)
|
|||
size - [integer] icon size (optional)
|
||||
|
||||
mixin procon(icon, size)
|
||||
- colors = { pro: "green", con: "red", neutral: "yellow" }
|
||||
+icon(icon, size)(class="u-color-#{colors[icon] || 'subtle'}" aria-label=icon)&attributes(attributes)
|
||||
- colors = { pro: "green", con: "red", neutral: "subtle" }
|
||||
+icon("circle", size || 16)(class="u-color-#{colors[icon] || 'subtle'}" aria-label=icon)&attributes(attributes)
|
||||
|
||||
|
||||
//- Headlines Helper Mixin
|
||||
|
@ -80,8 +80,7 @@ mixin headline(level)
|
|||
|
||||
mixin permalink(id)
|
||||
if id
|
||||
a.u-permalink(id=id href="##{id}")
|
||||
+icon("anchor").u-permalink__icon
|
||||
a.u-permalink(href="##{id}")
|
||||
block
|
||||
|
||||
else
|
||||
|
@ -109,7 +108,7 @@ mixin quickstart(groups, headline, description, hide_results)
|
|||
.c-quickstart__fields
|
||||
for option in group.options
|
||||
input.c-quickstart__input(class="c-quickstart__input--" + (group.input_style ? group.input_style : group.multiple ? "check" : "radio") type=group.multiple ? "checkbox" : "radio" name=group.id id="qs-#{option.id}" value=option.id checked=option.checked)
|
||||
label.c-quickstart__label(for="qs-#{option.id}")!=option.title
|
||||
label.c-quickstart__label.u-text-tiny(for="qs-#{option.id}")!=option.title
|
||||
if option.meta
|
||||
| #[span.c-quickstart__label__meta (#{option.meta})]
|
||||
if option.help
|
||||
|
@ -122,12 +121,10 @@ mixin quickstart(groups, headline, description, hide_results)
|
|||
code.c-code-block__content.c-quickstart__code(data-qs-results="")
|
||||
block
|
||||
|
||||
.c-quickstart__info.u-text-tiny.o-block.u-text-right
|
||||
| Like this widget? Check out #[+a("https://github.com/ines/quickstart").u-link quickstart.js]!
|
||||
|
||||
|
||||
//- Quickstart code item
|
||||
data [object] - Rendering conditions (keyed by option group ID, value: option)
|
||||
data - [object] Rendering conditions (keyed by option group ID, value: option)
|
||||
style - [string] modifier ID for line style
|
||||
|
||||
mixin qs(data, style)
|
||||
- args = {}
|
||||
|
@ -148,6 +145,13 @@ mixin terminal(label)
|
|||
+code.x-terminal__code
|
||||
block
|
||||
|
||||
//- Chart.js
|
||||
id - [string] chart ID, will be assigned as #chart_{id}
|
||||
|
||||
mixin chart(id)
|
||||
figure.o-block&attributes(attributes)
|
||||
canvas(id="chart_#{id}" width="800" height="400" style="max-width: 100%")
|
||||
|
||||
|
||||
//- Gitter chat button and widget
|
||||
button - [string] text shown on button
|
||||
|
@ -156,26 +160,24 @@ mixin terminal(label)
|
|||
mixin gitter(button, label)
|
||||
aside.js-gitter.c-chat.is-collapsed(data-title=(label || button))
|
||||
|
||||
button.js-gitter-button.c-chat__button.u-text-small
|
||||
+icon("chat").o-icon--inline
|
||||
button.js-gitter-button.c-chat__button.u-text-tag
|
||||
+icon("chat", 16).o-icon--inline
|
||||
!=button
|
||||
|
||||
|
||||
//- Badge
|
||||
name - [string] "pipy" or "conda"
|
||||
image - [string] path to badge image
|
||||
url - [string] badge link
|
||||
|
||||
mixin badge(name)
|
||||
- site = BADGES[name]
|
||||
|
||||
if site
|
||||
+a(site.link).u-padding-small
|
||||
img(src=site.badge alt="{name} version" height="20")
|
||||
mixin badge(image, url)
|
||||
+a(url).u-padding-small.u-hide-link&attributes(attributes)
|
||||
img.o-badge(src=image alt=url height="20")
|
||||
|
||||
|
||||
//- Logo
|
||||
//- spaCy logo
|
||||
|
||||
mixin logo()
|
||||
+svg("graphics", "spacy", 675, 215).o-logo&attributes(attributes)
|
||||
+svg("spacy", 675, 215).o-logo&attributes(attributes)
|
||||
|
||||
|
||||
//- Landing
|
||||
|
@ -186,18 +188,56 @@ mixin landing-header()
|
|||
.c-landing__content
|
||||
block
|
||||
|
||||
mixin landing-banner(headline, label)
|
||||
.c-landing__banner.u-padding.o-block.u-color-light
|
||||
+grid.c-landing__banner__content.o-no-block
|
||||
+grid-col("third")
|
||||
h3.u-heading.u-heading-1
|
||||
if label
|
||||
div
|
||||
span.u-text-label.u-text-label--light=label
|
||||
!=headline
|
||||
|
||||
mixin landing-badge(url, graphic, alt, size)
|
||||
+a(url)(aria-label=alt title=alt).c-landing__badge
|
||||
+svg("graphics", graphic, size || 225)
|
||||
+grid-col("two-thirds").c-landing__banner__text
|
||||
block
|
||||
|
||||
|
||||
mixin landing-logos(title, logos)
|
||||
.o-content.u-text-center&attributes(attributes)
|
||||
h3.u-heading.u-text-label.u-color-dark=title
|
||||
|
||||
each row, i in logos
|
||||
- var is_last = i == logos.length - 1
|
||||
+grid("center").o-inline-list.o-no-block(class=is_last ? "o-no-block" : null)
|
||||
each details, name in row
|
||||
+a(details[0]).u-padding-medium
|
||||
+icon(name, details[1], details[2])
|
||||
|
||||
if is_last
|
||||
block
|
||||
|
||||
|
||||
//- Under construction (temporary)
|
||||
Marks sections that still need to be completed for the v2.0 release.
|
||||
|
||||
mixin under-construction()
|
||||
+infobox("🚧 Under construction")
|
||||
+infobox("Under construction", "🚧")
|
||||
| This section is still being written and will be updated for the v2.0
|
||||
| release. Is there anything that you think should definitely mentioned or
|
||||
| explained here? Any examples you'd like to see? #[strong Let us know]
|
||||
| on the #[+a(gh("spacy") + "/issues/1105") v2.0 alpha thread] on GitHub!
|
||||
|
||||
|
||||
//- Alpha infobox (temporary)
|
||||
Added in the templates to notify user that they're visiting the alpha site.
|
||||
|
||||
mixin alpha-info()
|
||||
+infobox("You are viewing the spaCy v2.0.0 alpha docs", "⚠️")
|
||||
strong This page is part of the alpha documentation for spaCy v2.0.
|
||||
| It does not reflect the state of the latest stable release.
|
||||
| Because v2.0 is still under development, the implementation
|
||||
| may differ from the intended state described here. See the
|
||||
| #[+a(gh("spaCy") + "/releases/tag/v2.0.0-alpha") release notes]
|
||||
| for details on how to install and test the new version. To
|
||||
| read the official docs for spaCy v1.x,
|
||||
| #[+a("https://spacy.io/docs") go here].
|
||||
|
|
|
@ -8,11 +8,15 @@ include _mixins-base
|
|||
level - [integer] headline level, corresponds to h1, h2, h3 etc.
|
||||
id - [string] unique identifier, creates permalink (optional)
|
||||
|
||||
mixin h(level, id)
|
||||
+headline(level).u-heading&attributes(attributes)
|
||||
mixin h(level, id, source)
|
||||
+headline(level).u-heading(id=id)&attributes(attributes)
|
||||
+permalink(id)
|
||||
block
|
||||
|
||||
if source
|
||||
+button(gh("spacy", source), false, "secondary", "small").u-nowrap.u-float-right
|
||||
span Source #[+icon("code", 14).o-icon--inline]
|
||||
|
||||
|
||||
//- External links
|
||||
url - [string] link href
|
||||
|
@ -38,21 +42,23 @@ mixin src(url)
|
|||
|
||||
|
||||
//- API link (with added tag and automatically generated path)
|
||||
path - [string] path to API docs page relative to /docs/api/
|
||||
path - [string] path to API docs page relative to /api/
|
||||
|
||||
mixin api(path)
|
||||
+a("/docs/api/" + path, true)(target="_self").u-no-border.u-inline-block.u-nowrap
|
||||
- path = convertAPIPath(path)
|
||||
+a("/api/" + path, true)(target="_self").u-no-border.u-inline-block.u-nowrap
|
||||
block
|
||||
|
||||
| #[+icon("book", 18).o-icon--inline.u-color-theme]
|
||||
| #[+icon("book", 16).o-icon--inline.u-color-theme]
|
||||
|
||||
|
||||
//- Help icon with tooltip
|
||||
tooltip - [string] Tooltip text
|
||||
tooltip - [string] Tooltip text
|
||||
icon_size - [integer] Optional size of help icon in px.
|
||||
|
||||
mixin help(tooltip)
|
||||
mixin help(tooltip, icon_size)
|
||||
span(data-tooltip=tooltip)&attributes(attributes)
|
||||
+icon("help", 16).i-icon--inline
|
||||
+icon("help", icon_size || 16).o-icon--inline
|
||||
|
||||
|
||||
//- Aside for text
|
||||
|
@ -68,24 +74,43 @@ mixin aside(label)
|
|||
label - [string] aside title (optional or false for no label)
|
||||
language - [string] language for syntax highlighting (default: "python")
|
||||
supports basic relevant languages available for PrismJS
|
||||
prompt - [string] prompt displayed before first line, e.g. "$"
|
||||
|
||||
mixin aside-code(label, language)
|
||||
mixin aside-code(label, language, prompt)
|
||||
+aside-wrapper(label)
|
||||
+code(false, language).o-no-block
|
||||
+code(false, language, prompt).o-no-block
|
||||
block
|
||||
|
||||
|
||||
//- Infobox
|
||||
label - [string] infobox title (optional or false for no title)
|
||||
emoji - [string] optional emoji displayed before the title, necessary as
|
||||
argument to be able to wrap it for spacing
|
||||
|
||||
mixin infobox(label)
|
||||
mixin infobox(label, emoji)
|
||||
aside.o-box.o-block.u-text-small
|
||||
if label
|
||||
h3.u-text-label.u-color-theme=label
|
||||
h3.u-heading.u-text-label.u-color-theme
|
||||
if emoji
|
||||
span.o-emoji=emoji
|
||||
| #{label}
|
||||
|
||||
block
|
||||
|
||||
|
||||
//- Logos displayed in the top corner of some infoboxes
|
||||
logos - [array] List of icon ID, width, height and link.
|
||||
|
||||
mixin infobox-logos(...logos)
|
||||
.o-box__logos.u-text-right.u-float-right
|
||||
for logo in logos
|
||||
if logo[3]
|
||||
| #[+a(logo[3]).u-inline-block.u-hide-link.u-padding-small #[+icon(logo[0], logo[1], logo[2]).u-color-dark]]
|
||||
else
|
||||
| #[+icon(logo[0], logo[1], logo[2]).u-color-dark]
|
||||
|
||||
|
||||
|
||||
//- Link button
|
||||
url - [string] link href
|
||||
trusted - [boolean] if not set / false, rel="noopener nofollow" is added
|
||||
|
@ -94,7 +119,7 @@ mixin infobox(label)
|
|||
see assets/css/_components/_buttons.sass
|
||||
|
||||
mixin button(url, trusted, ...style)
|
||||
- external = url.includes("http")
|
||||
- external = url && url.includes("http")
|
||||
a.c-button.u-text-label(href=url class=prefixArgs(style, "c-button") role="button" target=external ? "_blank" : null rel=external && !trusted ? "noopener nofollow" : null)&attributes(attributes)
|
||||
block
|
||||
|
||||
|
@ -103,31 +128,33 @@ mixin button(url, trusted, ...style)
|
|||
label - [string] aside title (optional or false for no label)
|
||||
language - [string] language for syntax highlighting (default: "python")
|
||||
supports basic relevant languages available for PrismJS
|
||||
prompt - [string] prompt or icon to display next to code block, (mostly used for old/new)
|
||||
prompt - [string] prompt displayed before first line, e.g. "$"
|
||||
height - [integer] optional height to clip code block to
|
||||
icon - [string] icon displayed next to code block (e.g. "accept" for new code)
|
||||
wrap - [boolean] wrap text and disable horizontal scrolling
|
||||
|
||||
mixin code(label, language, prompt, height)
|
||||
mixin code(label, language, prompt, height, icon, wrap)
|
||||
pre.c-code-block.o-block(class="lang-#{(language || DEFAULT_SYNTAX)}" class=icon ? "c-code-block--has-icon" : null style=height ? "height: #{height}px" : null)&attributes(attributes)
|
||||
if label
|
||||
h4.u-text-label.u-text-label--dark=label
|
||||
- var icon = (prompt == 'accept' || prompt == 'reject')
|
||||
- var icon = icon || (prompt == 'accept' || prompt == 'reject')
|
||||
if icon
|
||||
- var classes = {'accept': 'u-color-green', 'reject': 'u-color-red'}
|
||||
.c-code-block__icon(class=classes[icon] || null class=classes[icon] ? "c-code-block__icon--border" : null)
|
||||
+icon(icon, 18)
|
||||
|
||||
code.c-code-block__content(data-prompt=icon ? null : prompt)
|
||||
code.c-code-block__content(class=wrap ? "u-wrap" : null data-prompt=icon ? null : prompt)
|
||||
block
|
||||
|
||||
|
||||
//- Code blocks to display old/new versions
|
||||
|
||||
mixin code-old()
|
||||
+code(false, false, "reject").o-block-small
|
||||
+code(false, false, false, false, "reject").o-block-small
|
||||
block
|
||||
|
||||
mixin code-new()
|
||||
+code(false, false, "accept").o-block-small
|
||||
+code(false, false, false, false, "accept").o-block-small
|
||||
block
|
||||
|
||||
|
||||
|
@ -138,12 +165,33 @@ mixin code-new()
|
|||
|
||||
mixin codepen(slug, height, default_tab)
|
||||
figure.o-block(style="min-height: #{height}px")&attributes(attributes)
|
||||
.codepen(data-height=height data-theme-id="26467" data-slug-hash=slug data-default-tab=(default_tab || "result") data-embed-version="2" data-user=SOCIAL.codepen)
|
||||
.codepen(data-height=height data-theme-id="31335" data-slug-hash=slug data-default-tab=(default_tab || "result") data-embed-version="2" data-user=SOCIAL.codepen)
|
||||
+a("https://codepen.io/" + SOCIAL.codepen + "/" + slug) View on CodePen
|
||||
|
||||
script(async src="https://assets.codepen.io/assets/embed/ei.js")
|
||||
|
||||
|
||||
//- GitHub embed
|
||||
repo - [string] repository owned by explosion organization
|
||||
file - [string] logical path to file, relative to repository root
|
||||
alt_file - [string] alternative file path used in footer and link button
|
||||
height - [integer] height of code preview in px
|
||||
|
||||
mixin github(repo, file, alt_file, height)
|
||||
- var branch = ALPHA ? "develop" : "master"
|
||||
- var height = height || 250
|
||||
|
||||
figure.o-block
|
||||
pre.c-code-block.o-block-small(class="lang-#{(language || DEFAULT_SYNTAX)}" style="height: #{height}px; min-height: #{height}px")
|
||||
code.c-code-block__content(data-gh-embed="#{repo}/#{branch}/#{file}")
|
||||
|
||||
footer.o-grid.u-text
|
||||
.o-block-small.u-flex-full #[+icon("github")] #[code=repo + '/' + (alt_file || file)]
|
||||
div
|
||||
+button(gh(repo, alt_file || file), false, "primary", "small") View on GitHub
|
||||
|
||||
|
||||
|
||||
//- Images / figures
|
||||
url - [string] url or path to image
|
||||
width - [integer] image width in px, for better rendering (default: 500)
|
||||
|
@ -168,10 +216,26 @@ mixin image-caption()
|
|||
block
|
||||
|
||||
|
||||
//- Label
|
||||
//- Graphic or illustration with button
|
||||
original - [string] Path to original image
|
||||
|
||||
mixin graphic(original)
|
||||
+image
|
||||
block
|
||||
if original
|
||||
.u-text-right
|
||||
+button(original, false, "secondary", "small") View large graphic
|
||||
|
||||
|
||||
//- Labels
|
||||
|
||||
mixin label()
|
||||
.u-text-label.u-color-subtle&attributes(attributes)
|
||||
.u-text-label.u-color-dark&attributes(attributes)
|
||||
block
|
||||
|
||||
|
||||
mixin label-inline()
|
||||
strong.u-text-label.u-color-dark&attributes(attributes)
|
||||
block
|
||||
|
||||
|
||||
|
@ -188,8 +252,10 @@ mixin tag()
|
|||
mixin tag-model(...capabs)
|
||||
- var intro = "To use this functionality, spaCy needs a model to be installed"
|
||||
- var ext = capabs.length ? " that supports the following capabilities: " + capabs.join(', ') : ""
|
||||
+tag Requires model
|
||||
+help(intro + ext + ".").u-color-theme
|
||||
|
||||
span.u-nowrap
|
||||
+tag Needs model
|
||||
+help(intro + ext + ".").u-color-theme
|
||||
|
||||
|
||||
//- "New" tag to label features new in a specific version
|
||||
|
@ -219,15 +285,9 @@ mixin list(type, start)
|
|||
|
||||
//- List item (only used within +list)
|
||||
|
||||
mixin item(procon)
|
||||
if procon
|
||||
li&attributes(attributes)
|
||||
+procon(procon).c-list__icon
|
||||
block
|
||||
|
||||
else
|
||||
li.c-list__item&attributes(attributes)
|
||||
block
|
||||
mixin item()
|
||||
li.c-list__item&attributes(attributes)
|
||||
block
|
||||
|
||||
|
||||
//- Table
|
||||
|
@ -237,9 +297,9 @@ mixin table(head)
|
|||
table.c-table.o-block&attributes(attributes)
|
||||
|
||||
if head
|
||||
+row
|
||||
+row("head")
|
||||
each column in head
|
||||
th.c-table__head-cell.u-text-label=column
|
||||
+head-cell=column
|
||||
|
||||
block
|
||||
|
||||
|
@ -251,10 +311,11 @@ mixin row(...style)
|
|||
block
|
||||
|
||||
|
||||
//- Footer table row (only ued within +table)
|
||||
|
||||
mixin footrow()
|
||||
tr.c-table__row.c-table__row--foot&attributes(attributes)
|
||||
//- Header table cell (only used within +row)
|
||||
|
||||
mixin head-cell()
|
||||
th.c-table__head-cell.u-text-label&attributes(attributes)
|
||||
block
|
||||
|
||||
|
||||
|
@ -284,71 +345,58 @@ mixin grid-col(width)
|
|||
|
||||
|
||||
//- Card (only used within +grid)
|
||||
title - [string] card title
|
||||
details - [object] url, image, author, description, tags etc.
|
||||
(see /docs/usage/_data.json)
|
||||
title - [string] card title
|
||||
url - [string] link for card
|
||||
author - [string] optional author, displayed as byline at the bottom
|
||||
icon - [string] optional ID of icon displayed with card
|
||||
width - [string] optional width of grid column, defaults to "half"
|
||||
|
||||
mixin card(title, details)
|
||||
+grid-col("half").o-card.u-text&attributes(attributes)
|
||||
if details.image
|
||||
+a(details.url).o-block-small
|
||||
img(src=details.image alt=title width="300" role="presentation")
|
||||
|
||||
if title
|
||||
+a(details.url)
|
||||
+h(3)=title
|
||||
|
||||
if details.author
|
||||
.u-text-small.u-color-subtle by #{details.author}
|
||||
|
||||
if details.description || details.tags
|
||||
ul
|
||||
if details.description
|
||||
li=details.description
|
||||
|
||||
if details.tags
|
||||
li
|
||||
each tag in details.tags
|
||||
span.u-text-tag #{tag}
|
||||
|
|
||||
|
||||
block
|
||||
mixin card(title, url, author, icon, width)
|
||||
+grid-col(width || "half").o-box.o-grid.o-grid--space.u-text&attributes(attributes)
|
||||
+a(url)
|
||||
h4.u-heading.u-text-label
|
||||
if icon
|
||||
+icon(icon, 25).u-float-right
|
||||
if title
|
||||
span.u-color-dark=title
|
||||
.o-block-small.u-text-small
|
||||
block
|
||||
if author
|
||||
.u-color-subtle.u-text-tiny by #{author}
|
||||
|
||||
|
||||
//- Simpler card list item (only used within +list)
|
||||
title - [string] card title
|
||||
details - [object] url, image, author, description, tags etc.
|
||||
(see /docs/usage/_data.json)
|
||||
//- Table of contents, to be used with +item mixins for links
|
||||
col - [string] width of column (see +grid-col)
|
||||
|
||||
mixin card-item(title, details)
|
||||
+item&attributes(attributes)
|
||||
+a(details.url)=title
|
||||
|
||||
if details.description
|
||||
br
|
||||
span=details.description
|
||||
|
||||
if details.author
|
||||
br
|
||||
span.u-text-small.u-color-subtle by #{details.author}
|
||||
mixin table-of-contents(col)
|
||||
+grid-col(col || "half")
|
||||
+infobox
|
||||
+label.o-block-small Table of contents
|
||||
+list("numbers").u-text-small.o-no-block
|
||||
block
|
||||
|
||||
|
||||
//- Table row for models table
|
||||
//- Bibliography
|
||||
id - [string] ID of bibliography component, for anchor links. Can be used if
|
||||
there's more than one bibliography on one page.
|
||||
|
||||
mixin model-row(name, lang, procon, size, license, default_model, divider)
|
||||
- var licenses = { "CC BY-SA": "https://creativecommons.org/licenses/by-sa/3.0/", "CC BY-NC": "https://creativecommons.org/licenses/by-nc/3.0/" }
|
||||
mixin bibliography(id)
|
||||
section(id=id || "bibliography")
|
||||
+infobox
|
||||
+label.o-block-small Bibliography
|
||||
+list("numbers").u-text-small.o-no-block
|
||||
block
|
||||
|
||||
+row(divider ? "divider": null)
|
||||
+cell #[code=name]
|
||||
if default_model
|
||||
| #[span.u-color-theme(title="default model") #[+icon("star", 16)]]
|
||||
+cell=lang
|
||||
each icon in procon
|
||||
+cell.u-text-center #[+procon(icon ? "pro" : "con")]
|
||||
+cell.u-text-right=size
|
||||
+cell
|
||||
if license in licenses
|
||||
+a(licenses[license])=license
|
||||
|
||||
//- Footnote
|
||||
id - [string / integer] ID of footnote.
|
||||
bib_id - [string] ID of bibliography component, defaults to "bibliography".
|
||||
tooltip - [string] optional text displayed as tooltip
|
||||
|
||||
mixin fn(id, bib_id, tooltip)
|
||||
sup.u-padding-small(id="bib" + id data-tooltip=tooltip)
|
||||
span.u-text-tag
|
||||
+a("#" + (bib_id || "bibliography")).u-hide-link #{id}
|
||||
|
||||
|
||||
//- Table rows for annotation specs
|
||||
|
@ -383,14 +431,3 @@ mixin annotation-row(annots, style)
|
|||
else
|
||||
+cell=cell
|
||||
block
|
||||
|
||||
|
||||
//- Table of contents, to be used with +item mixins for links
|
||||
col - [string] width of column (see +grid-col)
|
||||
|
||||
mixin table-of-contents(col)
|
||||
+grid-col(col || "half")
|
||||
+infobox
|
||||
+label.o-block-small Table of contents
|
||||
+list("numbers").u-text-small.o-no-block
|
||||
block
|
||||
|
|
|
@ -1,19 +1,15 @@
|
|||
//- 💫 INCLUDES > TOP NAVIGATION
|
||||
|
||||
include _mixins
|
||||
|
||||
nav.c-nav.u-text.js-nav(class=landing ? "c-nav--theme" : null)
|
||||
a(href='/') #[+logo]
|
||||
|
||||
if SUBSECTION != "index"
|
||||
.u-text-label.u-padding-small.u-hidden-xs=SUBSECTION
|
||||
a(href="/" aria-label=SITENAME) #[+logo]
|
||||
|
||||
ul.c-nav__menu
|
||||
- var NAV = ALPHA ? { "Usage": "/docs/usage", "Reference": "/docs/api" } : NAVIGATION
|
||||
|
||||
each url, item in NAV
|
||||
li.c-nav__menu__item(class=(url == "/") ? "u-hidden-xs" : null)
|
||||
- var current_url = '/' + current.path[0]
|
||||
each url, item in NAVIGATION
|
||||
li.c-nav__menu__item(class=(current_url == url) ? "is-active" : null)
|
||||
+a(url)=item
|
||||
|
||||
li.c-nav__menu__item
|
||||
+a(gh("spaCy"))(aria-label="GitHub").u-hidden-xs #[+icon("github", 20)]
|
||||
li.c-nav__menu__item.u-hidden-xs
|
||||
+a(gh("spaCy"))(aria-label="GitHub") #[+icon("github", 20)]
|
||||
|
||||
progress.c-progress.js-progress(value="0" max="1")
|
||||
|
|
|
@ -1,6 +1,6 @@
|
|||
//- 💫 INCLUDES > NEWSLETTER
|
||||
|
||||
ul.o-block
|
||||
ul.o-block-small
|
||||
li.u-text-label.u-color-subtle Stay in the loop!
|
||||
li Receive updates about new releases, tutorials and more.
|
||||
|
||||
|
@ -10,7 +10,6 @@ form.o-grid#mc-embedded-subscribe-form(action="//#{MAILCHIMP.user}.list-manage.c
|
|||
div(style="position: absolute; left: -5000px;" aria-hidden="true")
|
||||
input(type="text" name="b_#{MAILCHIMP.id}_#{MAILCHIMP.list}" tabindex="-1" value="")
|
||||
|
||||
.o-grid-col.u-border.u-padding-small
|
||||
input#mce-EMAIL.u-text(type="email" name="EMAIL" placeholder="Your email")
|
||||
|
||||
button#mc-embedded-subscribe.u-text-label.u-color-theme(type="submit" name="subscribe") Sign up
|
||||
.o-grid-col.o-grid.o-grid--nowrap.o-field.u-padding-small
|
||||
input#mce-EMAIL.o-field__input.u-text(type="email" name="EMAIL" placeholder="Your email" aria-label="Your email")
|
||||
button#mc-embedded-subscribe.o-field__button.u-text-label.u-color-theme.u-nowrap(type="submit" name="subscribe") Sign up
|
||||
|
|
|
@ -1,47 +1,56 @@
|
|||
//- 💫 INCLUDES > DOCS PAGE TEMPLATE
|
||||
|
||||
- sidebar_content = (SUBSECTION != "index") ? public.docs[SUBSECTION]._data.sidebar : public.docs._data.sidebar || FOOTER
|
||||
- sidebar_content = (public[SECTION] ? public[SECTION]._data.sidebar : public._data[SECTION] ? public._data[SECTION].sidebar : false) || FOOTER
|
||||
|
||||
include _sidebar
|
||||
|
||||
main.o-main.o-main--sidebar.o-main--aside
|
||||
article.o-content
|
||||
+grid.o-no-block
|
||||
+grid-col(source ? "two-thirds" : "full")
|
||||
+h(1)=title
|
||||
if tag
|
||||
+tag=tag
|
||||
+h(1).u-heading--title=title.replace("'", "’")
|
||||
if tag
|
||||
+tag=tag
|
||||
if tag_new
|
||||
+tag-new(tag_new)
|
||||
|
||||
if teaser
|
||||
.u-heading__teaser.u-text-small.u-color-dark=teaser
|
||||
else if IS_MODELS
|
||||
.u-heading__teaser.u-text-small.u-color-dark
|
||||
| Available statistical models for
|
||||
| #[code=current.source] (#{LANGUAGES[current.source]}).
|
||||
|
||||
if source
|
||||
+grid-col("third").u-text-right
|
||||
.o-inline-list
|
||||
+button(gh("spacy", source), false, "secondary").u-text-tag Source #[+icon("code", 14)]
|
||||
.o-block.u-text-right
|
||||
+button(gh("spacy", source), false, "secondary", "small").u-nowrap
|
||||
| Source #[+icon("code", 14)]
|
||||
|
||||
//-if ALPHA
|
||||
//- +alpha-info
|
||||
|
||||
if ALPHA
|
||||
+infobox("⚠️ You are viewing the spaCy v2.0.0 alpha docs")
|
||||
strong This page is part of the alpha documentation for spaCy v2.0.
|
||||
| It does not reflect the state of the latest stable release.
|
||||
| Because v2.0 is still under development, the implementation
|
||||
| may differ from the intended state described here. See the
|
||||
| #[+a(gh("spaCy") + "/releases/tag/v2.0.0-alpha") release notes]
|
||||
| for details on how to install and test the new version. To
|
||||
| read the official docs for spaCy v1.x,
|
||||
| #[+a("https://spacy.io/docs") go here].
|
||||
|
||||
!=yield
|
||||
if IS_MODELS
|
||||
include _page_models
|
||||
else
|
||||
!=yield
|
||||
|
||||
+grid.o-content.u-text
|
||||
+grid-col("half")
|
||||
if next && public.docs[SUBSECTION]._data[next]
|
||||
- data = public.docs[SUBSECTION]._data[next]
|
||||
|
||||
if !IS_MODELS
|
||||
.o-inline-list
|
||||
span #[strong.u-text-label Read next:] #[+a(next).u-link=data.title]
|
||||
+button(gh("spacy", "website/" + current.path.join('/') + ".jade"), false, "secondary", "small")
|
||||
| #[span.o-icon Suggest edits] #[+icon("code", 14)]
|
||||
|
||||
+grid-col("half").u-text-right
|
||||
.o-inline-list
|
||||
+button(gh("spacy", "website/" + current.path.join('/') + ".jade"), false, "secondary").u-text-tag Suggest edits #[+icon("code", 14)]
|
||||
if next && public[SECTION]._data[next]
|
||||
- data = public[SECTION]._data[next]
|
||||
|
||||
+grid("vcenter")
|
||||
+a(next).u-text-small.u-flex-full
|
||||
h4.u-text-label.u-color-dark Read next
|
||||
| #{data.title}
|
||||
|
||||
+a(next).c-icon-button.c-icon-button--right(aria-hidden="true")
|
||||
+icon("arrow-right", 24)
|
||||
|
||||
+gitter("spaCy chat")
|
||||
|
||||
|
|
77
website/_includes/_page_models.jade
Normal file
77
website/_includes/_page_models.jade
Normal file
|
@ -0,0 +1,77 @@
|
|||
//- 💫 INCLUDES > MODELS PAGE TEMPLATE
|
||||
|
||||
for id in CURRENT_MODELS
|
||||
+section(id)
|
||||
+grid("vcenter").o-no-block(id=id)
|
||||
+grid-col("two-thirds")
|
||||
+h(2)
|
||||
+a("#" + id).u-permalink=id
|
||||
|
||||
+grid-col("third").u-text-right
|
||||
.u-color-subtle.u-text-tiny
|
||||
+button(gh("spacy-models") + "/releases", true, "secondary", "small")(data-tpl=id data-tpl-key="download")
|
||||
| Release details
|
||||
.u-padding-small Latest: #[code(data-tpl=id data-tpl-key="version") n/a]
|
||||
|
||||
+aside-code("Installation", "bash", "$").
|
||||
spacy download #{id}
|
||||
|
||||
- var comps = getModelComponents(id)
|
||||
|
||||
p(data-tpl=id data-tpl-key="description")
|
||||
|
||||
div(data-tpl=id data-tpl-key="error" style="display: none")
|
||||
+infobox
|
||||
| Unable to load model details from GitHub. To find out more
|
||||
| about this model, see the overview of the
|
||||
| #[+a(gh("spacy-models") + "/releases") latest model releases].
|
||||
|
||||
+table(data-tpl=id data-tpl-key="table")
|
||||
+row
|
||||
+cell #[+label Language]
|
||||
+cell #[+tag=comps.lang] #{LANGUAGES[comps.lang]}
|
||||
for comp, label in {"Type": comps.type, "Genre": comps.genre}
|
||||
+row
|
||||
+cell #[+label=label]
|
||||
+cell #[+tag=comp] #{MODEL_META[comp]}
|
||||
+row
|
||||
+cell #[+label Size]
|
||||
+cell #[+tag=comps.size] #[span(data-tpl=id data-tpl-key="size") #[em n/a]]
|
||||
|
||||
each label in ["Pipeline", "Sources", "Author", "License"]
|
||||
- var field = label.toLowerCase()
|
||||
+row
|
||||
+cell.u-nowrap
|
||||
+label=label
|
||||
if MODEL_META[field]
|
||||
| #[+help(MODEL_META[field]).u-color-subtle]
|
||||
+cell
|
||||
span(data-tpl=id data-tpl-key=field) #[em n/a]
|
||||
|
||||
+row(data-tpl=id data-tpl-key="compat-wrapper" style="display: none")
|
||||
+cell
|
||||
+label Compat #[+help("Latest compatible model version for your spaCy installation").u-color-subtle]
|
||||
+cell
|
||||
.o-field.u-float-left
|
||||
select.o-field__select.u-text-small(data-tpl=id data-tpl-key="compat")
|
||||
.o-empty(data-tpl=id data-tpl-key="compat-versions")
|
||||
|
||||
section(data-tpl=id data-tpl-key="accuracy-wrapper" style="display: none")
|
||||
+grid.o-no-block
|
||||
+grid-col("third")
|
||||
+h(4) Accuracy
|
||||
+table.o-block-small
|
||||
for label, field in MODEL_ACCURACY
|
||||
+row(style="display: none")
|
||||
+cell.u-nowrap
|
||||
+label=label
|
||||
if MODEL_META[field]
|
||||
| #[+help(MODEL_META[field]).u-color-subtle]
|
||||
+cell.u-text-right(data-tpl=id data-tpl-key=field)
|
||||
| n/a
|
||||
|
||||
+grid-col("two-thirds")
|
||||
+h(4) Comparison
|
||||
+chart(id).u-padding-small
|
||||
|
||||
p.u-text-small.u-color-dark(data-tpl=id data-tpl-key="notes")
|
|
@ -1,27 +1,46 @@
|
|||
//- 💫 INCLUDES > SCRIPTS
|
||||
|
||||
script(src="/assets/js/main.js?v#{V_JS}")
|
||||
script(src="/assets/js/prism.js")
|
||||
if quickstart
|
||||
script(src="/assets/js/quickstart.min.js")
|
||||
|
||||
if SECTION == "docs"
|
||||
if quickstart
|
||||
script(src="/assets/js/quickstart.js")
|
||||
script var qs = new Quickstart("#qs")
|
||||
if IS_PAGE
|
||||
script(src="/assets/js/in-view.min.js")
|
||||
|
||||
script.
|
||||
((window.gitter = {}).chat = {}).options = {
|
||||
useStyles: false,
|
||||
activationElement: '.js-gitter-button',
|
||||
targetElement: '.js-gitter',
|
||||
room: '!{SOCIAL.gitter}'
|
||||
};
|
||||
|
||||
script(src="https://sidecar.gitter.im/dist/sidecar.v1.js" async defer)
|
||||
if HAS_MODELS
|
||||
script(src="/assets/js/chart.min.js")
|
||||
|
||||
if environment == "deploy"
|
||||
script
|
||||
script(async src="https://www.google-analytics.com/analytics.js")
|
||||
|
||||
script(src="/assets/js/prism.min.js")
|
||||
script(src="/assets/js/main.js?v#{V_JS}")
|
||||
|
||||
script
|
||||
| new ProgressBar('.js-progress');
|
||||
|
||||
if changelog
|
||||
| new Changelog('!{SOCIAL.github}', 'spacy');
|
||||
|
||||
if quickstart
|
||||
| new Quickstart("#qs");
|
||||
|
||||
if IS_PAGE
|
||||
| new SectionHighlighter('data-section', 'data-nav');
|
||||
| new GitHubEmbed('!{SOCIAL.github}', 'data-gh-embed');
|
||||
| ((window.gitter = {}).chat = {}).options = {
|
||||
| useStyles: false,
|
||||
| activationElement: '.js-gitter-button',
|
||||
| targetElement: '.js-gitter',
|
||||
| room: '!{SOCIAL.gitter}'
|
||||
| };
|
||||
|
||||
if HAS_MODELS
|
||||
| new ModelLoader('!{MODELS_REPO}', !{JSON.stringify(CURRENT_MODELS)}, !{JSON.stringify(MODEL_LICENSES)}, !{JSON.stringify(MODEL_ACCURACY)});
|
||||
|
||||
if environment == "deploy"
|
||||
| window.ga=window.ga||function(){
|
||||
| (ga.q=ga.q||[]).push(arguments)}; ga.l=+new Date;
|
||||
| ga('create', '#{ANALYTICS}', 'auto'); ga('send', 'pageview');
|
||||
|
||||
script(async src="https://www.google-analytics.com/analytics.js")
|
||||
if IS_PAGE
|
||||
script(src="https://sidecar.gitter.im/dist/sidecar.v1.js" async defer)
|
||||
|
|
|
@ -1,13 +1,23 @@
|
|||
//- 💫 INCLUDES > SIDEBAR
|
||||
|
||||
include _mixins
|
||||
|
||||
menu.c-sidebar.js-sidebar.u-text
|
||||
if sidebar_content
|
||||
each items, menu in sidebar_content
|
||||
ul.c-sidebar__section.o-block
|
||||
li.u-text-label.u-color-subtle=menu
|
||||
each items, sectiontitle in sidebar_content
|
||||
ul.c-sidebar__section.o-block-small
|
||||
li.u-text-label.u-color-dark=sectiontitle
|
||||
|
||||
each url, item in items
|
||||
li(class=(CURRENT == url || (CURRENT == "index" && url == "./")) ? "is-active" : null)
|
||||
+a(url)=item
|
||||
- var is_current = CURRENT == url || (CURRENT == "index" && url == "./")
|
||||
li.c-sidebar__item
|
||||
+a(url)(class=is_current ? "is-active" : null)=item
|
||||
|
||||
if is_current
|
||||
if IS_MODELS && CURRENT_MODELS.length
|
||||
- menu = Object.assign({}, ...CURRENT_MODELS.map(id => ({ [id]: id })))
|
||||
if menu
|
||||
ul.c-sidebar__crumb.u-hidden-sm
|
||||
- var counter = 0
|
||||
for id, title in menu
|
||||
- counter++
|
||||
li.c-sidebar__crumb__item(data-nav=id class=(counter == 1) ? "is-active" : null)
|
||||
+a("#section-" + id)=title
|
||||
|
|
157
website/_includes/_svg.jade
Normal file
157
website/_includes/_svg.jade
Normal file
File diff suppressed because one or more lines are too long
|
@ -2,11 +2,16 @@
|
|||
|
||||
include _includes/_mixins
|
||||
|
||||
- title = IS_MODELS ? LANGUAGES[current.source] || title : title
|
||||
- social_title = (SECTION == "index") ? SITENAME + " - " + SLOGAN : title + " - " + SITENAME
|
||||
- social_img = SITE_URL + "/assets/img/social/preview_" + (preview || ALPHA ? "alpha" : "default") + ".jpg"
|
||||
|
||||
doctype html
|
||||
html(lang="en")
|
||||
title
|
||||
if SECTION == "docs" && SUBSECTION && SUBSECTION != "index"
|
||||
| #{title} | #{SITENAME} #{SUBSECTION == "api" ? "API" : "Usage"} Documentation
|
||||
if SECTION == "api" || SECTION == "usage" || SECTION == "models"
|
||||
- var title_section = (SECTION == "api") ? "API" : SECTION.charAt(0).toUpperCase() + SECTION.slice(1)
|
||||
| #{title} | #{SITENAME} #{title_section} Documentation
|
||||
|
||||
else if SECTION != "index"
|
||||
| #{title} | #{SITENAME}
|
||||
|
@ -22,32 +27,30 @@ html(lang="en")
|
|||
meta(property="og:type" content="website")
|
||||
meta(property="og:site_name" content=sitename)
|
||||
meta(property="og:url" content="#{SITE_URL}/#{current.path.join('/')}")
|
||||
meta(property="og:title" content="#{title} - spaCy")
|
||||
meta(property="og:title" content=social_title)
|
||||
meta(property="og:description" content=description)
|
||||
meta(property="og:image" content=getSocialImg())
|
||||
meta(property="og:image" content=social_img)
|
||||
|
||||
meta(name="twitter:card" content="summary_large_image")
|
||||
meta(name="twitter:site" content="@" + SOCIAL.twitter)
|
||||
meta(name="twitter:title" content="#{title} - spaCy")
|
||||
meta(name="twitter:title" content=social_title)
|
||||
meta(name="twitter:description" content=description)
|
||||
meta(name="twitter:image" content=getSocialImg())
|
||||
meta(name="twitter:image" content=social_img)
|
||||
|
||||
link(rel="shortcut icon" href="/assets/img/favicon.ico")
|
||||
link(rel="icon" type="image/x-icon" href="/assets/img/favicon.ico")
|
||||
|
||||
if ALPHA && SECTION == "docs"
|
||||
if SECTION == "api"
|
||||
link(href="/assets/css/style_green.css?v#{V_CSS}" rel="stylesheet")
|
||||
|
||||
else if SUBSECTION == "usage"
|
||||
link(href="/assets/css/style_red.css?v#{V_CSS}" rel="stylesheet")
|
||||
|
||||
else
|
||||
link(href="/assets/css/style.css?v#{V_CSS}" rel="stylesheet")
|
||||
|
||||
body
|
||||
include _includes/_svg
|
||||
include _includes/_navigation
|
||||
|
||||
if SECTION == "docs"
|
||||
if !landing
|
||||
include _includes/_page-docs
|
||||
|
||||
else
|
||||
|
|
43
website/api/_annotation/_biluo.jade
Normal file
43
website/api/_annotation/_biluo.jade
Normal file
|
@ -0,0 +1,43 @@
|
|||
//- 💫 DOCS > API > ANNOTATION > BILUO
|
||||
|
||||
+table([ "Tag", "Description" ])
|
||||
+row
|
||||
+cell #[code #[span.u-color-theme B] EGIN]
|
||||
+cell The first token of a multi-token entity.
|
||||
|
||||
+row
|
||||
+cell #[code #[span.u-color-theme I] N]
|
||||
+cell An inner token of a multi-token entity.
|
||||
|
||||
+row
|
||||
+cell #[code #[span.u-color-theme L] AST]
|
||||
+cell The final token of a multi-token entity.
|
||||
|
||||
+row
|
||||
+cell #[code #[span.u-color-theme U] NIT]
|
||||
+cell A single-token entity.
|
||||
|
||||
+row
|
||||
+cell #[code #[span.u-color-theme O] UT]
|
||||
+cell A non-entity token.
|
||||
|
||||
+aside("Why BILUO, not IOB?")
|
||||
| There are several coding schemes for encoding entity annotations as
|
||||
| token tags. These coding schemes are equally expressive, but not
|
||||
| necessarily equally learnable.
|
||||
| #[+a("http://www.aclweb.org/anthology/W09-1119") Ratinov and Roth]
|
||||
| showed that the minimal #[strong Begin], #[strong In], #[strong Out]
|
||||
| scheme was more difficult to learn than the #[strong BILUO] scheme that
|
||||
| we use, which explicitly marks boundary tokens.
|
||||
|
||||
p
|
||||
| spaCy translates the character offsets into this scheme, in order to
|
||||
| decide the cost of each action given the current state of the entity
|
||||
| recogniser. The costs are then used to calculate the gradient of the
|
||||
| loss, to train the model. The exact algorithm is a pastiche of
|
||||
| well-known methods, and is not currently described in any single
|
||||
| publication. The model is a greedy transition-based parser guided by a
|
||||
| linear model whose weights are learned using the averaged perceptron
|
||||
| loss, via the #[+a("http://www.aclweb.org/anthology/C12-1059") dynamic oracle]
|
||||
| imitation learning strategy. The transition system is equivalent to the
|
||||
| BILOU tagging scheme.
|
115
website/api/_architecture/_cython.jade
Normal file
115
website/api/_architecture/_cython.jade
Normal file
|
@ -0,0 +1,115 @@
|
|||
//- 💫 DOCS > API > ARCHITECTURE > CYTHON
|
||||
|
||||
+aside("What's Cython?")
|
||||
| #[+a("http://cython.org/") Cython] is a language for writing
|
||||
| C extensions for Python. Most Python code is also valid Cython, but
|
||||
| you can add type declarations to get efficient memory-managed code
|
||||
| just like C or C++.
|
||||
|
||||
p
|
||||
| spaCy's core data structures are implemented as
|
||||
| #[+a("http://cython.org/") Cython] #[code cdef] classes. Memory is
|
||||
| managed through the #[+a(gh("cymem")) #[code cymem]]
|
||||
| #[code cymem.Pool] class, which allows you
|
||||
| to allocate memory which will be freed when the #[code Pool] object
|
||||
| is garbage collected. This means you usually don't have to worry
|
||||
| about freeing memory. You just have to decide which Python object
|
||||
| owns the memory, and make it own the #[code Pool]. When that object
|
||||
| goes out of scope, the memory will be freed. You do have to take
|
||||
| care that no pointers outlive the object that owns them — but this
|
||||
| is generally quite easy.
|
||||
|
||||
p
|
||||
| All Cython modules should have the #[code # cython: infer_types=True]
|
||||
| compiler directive at the top of the file. This makes the code much
|
||||
| cleaner, as it avoids the need for many type declarations. If
|
||||
| possible, you should prefer to declare your functions #[code nogil],
|
||||
| even if you don't especially care about multi-threading. The reason
|
||||
| is that #[code nogil] functions help the Cython compiler reason about
|
||||
| your code quite a lot — you're telling the compiler that no Python
|
||||
| dynamics are possible. This lets many errors be raised, and ensures
|
||||
| your function will run at C speed.
|
||||
|
||||
|
||||
p
|
||||
| Cython gives you many choices of sequences: you could have a Python
|
||||
| list, a numpy array, a memory view, a C++ vector, or a pointer.
|
||||
| Pointers are preferred, because they are fastest, have the most
|
||||
| explicit semantics, and let the compiler check your code more
|
||||
| strictly. C++ vectors are also great — but you should only use them
|
||||
| internally in functions. It's less friendly to accept a vector as an
|
||||
| argument, because that asks the user to do much more work. Here's
|
||||
| how to get a pointer from a numpy array, memory view or vector:
|
||||
|
||||
+code.
|
||||
cdef void get_pointers(np.ndarray[int, mode='c'] numpy_array, vector[int] cpp_vector, int[::1] memory_view) nogil:
|
||||
pointer1 = <int*>numpy_array.data
|
||||
pointer2 = cpp_vector.data()
|
||||
pointer3 = &memory_view[0]
|
||||
|
||||
p
|
||||
| Both C arrays and C++ vectors reassure the compiler that no Python
|
||||
| operations are possible on your variable. This is a big advantage:
|
||||
| it lets the Cython compiler raise many more errors for you.
|
||||
|
||||
p
|
||||
| When getting a pointer from a numpy array or memoryview, take care
|
||||
| that the data is actually stored in C-contiguous order — otherwise
|
||||
| you'll get a pointer to nonsense. The type-declarations in the code
|
||||
| above should generate runtime errors if buffers with incorrect
|
||||
| memory layouts are passed in. To iterate over the array, the
|
||||
| following style is preferred:
|
||||
|
||||
+code.
|
||||
cdef int c_total(const int* int_array, int length) nogil:
|
||||
total = 0
|
||||
for item in int_array[:length]:
|
||||
total += item
|
||||
return total
|
||||
|
||||
p
|
||||
| If this is confusing, consider that the compiler couldn't deal with
|
||||
| #[code for item in int_array:] — there's no length attached to a raw
|
||||
| pointer, so how could we figure out where to stop? The length is
|
||||
| provided in the slice notation as a solution to this. Note that we
|
||||
| don't have to declare the type of #[code item] in the code above —
|
||||
| the compiler can easily infer it. This gives us tidy code that looks
|
||||
| quite like Python, but is exactly as fast as C — because we've made
|
||||
| sure the compilation to C is trivial.
|
||||
|
||||
p
|
||||
| Your functions cannot be declared #[code nogil] if they need to
|
||||
| create Python objects or call Python functions. This is perfectly
|
||||
| okay — you shouldn't torture your code just to get #[code nogil]
|
||||
| functions. However, if your function isn't #[code nogil], you should
|
||||
| compile your module with #[code cython -a --cplus my_module.pyx] and
|
||||
| open the resulting #[code my_module.html] file in a browser. This
|
||||
| will let you see how Cython is compiling your code. Calls into the
|
||||
| Python run-time will be in bright yellow. This lets you easily see
|
||||
| whether Cython is able to correctly type your code, or whether there
|
||||
| are unexpected problems.
|
||||
|
||||
p
|
||||
| Working in Cython is very rewarding once you're over the initial
|
||||
| learning curve. As with C and C++, the first way you write something
|
||||
| in Cython will often be the performance-optimal approach. In
|
||||
| contrast, Python optimisation generally requires a lot of
|
||||
| experimentation. Is it faster to have an #[code if item in my_dict]
|
||||
| check, or to use #[code .get()]? What about
|
||||
| #[code try]/#[code except]? Does this numpy operation create a copy?
|
||||
| There's no way to guess the answers to these questions, and you'll
|
||||
| usually be dissatisfied with your results — so there's no way to
|
||||
| know when to stop this process. In the worst case, you'll make a
|
||||
| mess that invites the next reader to try their luck too. This is
|
||||
| like one of those
|
||||
| #[+a("http://www.wemjournal.org/article/S1080-6032%2809%2970088-2/abstract") volcanic gas-traps],
|
||||
| where the rescuers keep passing out from low oxygen, causing
|
||||
| another rescuer to follow — only to succumb themselves. In short,
|
||||
| just say no to optimizing your Python. If it's not fast enough the
|
||||
| first time, just switch to Cython.
|
||||
|
||||
+infobox("Resources")
|
||||
+list.o-no-block
|
||||
+item #[+a("http://docs.cython.org/en/latest/") Official Cython documentation] (cython.org)
|
||||
+item #[+a("https://explosion.ai/blog/writing-c-in-cython", true) Writing C in Cython] (explosion.ai)
|
||||
+item #[+a("https://explosion.ai/blog/multithreading-with-cython") Multi-threading spaCy’s parser and named entity recogniser] (explosion.ai)
|
141
website/api/_architecture/_nn-model.jade
Normal file
141
website/api/_architecture/_nn-model.jade
Normal file
|
@ -0,0 +1,141 @@
|
|||
//- 💫 DOCS > API > ARCHITECTURE > NN MODEL ARCHITECTURE
|
||||
|
||||
p
|
||||
| The parsing model is a blend of recent results. The two recent
|
||||
| inspirations have been the work of Eli Klipperwasser and Yoav Goldberg at
|
||||
| Bar Ilan#[+fn(1)], and the SyntaxNet team from Google. The foundation of
|
||||
| the parser is still based on the work of Joakim Nivre#[+fn(2)], who
|
||||
| introduced the transition-based framework#[+fn(3)], the arc-eager
|
||||
| transition system, and the imitation learning objective. The model is
|
||||
| implemented using #[+a(gh("thinc")) Thinc], spaCy's machine learning
|
||||
| library. We first predict context-sensitive vectors for each word in the
|
||||
| input:
|
||||
|
||||
+code.
|
||||
(embed_lower | embed_prefix | embed_suffix | embed_shape)
|
||||
>> Maxout(token_width)
|
||||
>> convolution ** 4
|
||||
|
||||
p
|
||||
| This convolutional layer is shared between the tagger, parser and NER,
|
||||
| and will also be shared by the future neural lemmatizer. Because the
|
||||
| parser shares these layers with the tagger, the parser does not require
|
||||
| tag features. I got this trick from David Weiss's "Stack Combination"
|
||||
| paper#[+fn(4)].
|
||||
|
||||
p
|
||||
| To boost the representation, the tagger actually predicts a "super tag"
|
||||
| with POS, morphology and dependency label#[+fn(5)]. The tagger predicts
|
||||
| these supertags by adding a softmax layer onto the convolutional layer –
|
||||
| so, we're teaching the convolutional layer to give us a representation
|
||||
| that's one affine transform from this informative lexical information.
|
||||
| This is obviously good for the parser (which backprops to the
|
||||
| convolutions too). The parser model makes a state vector by concatenating
|
||||
| the vector representations for its context tokens. The current context
|
||||
| tokens:
|
||||
|
||||
+table
|
||||
+row
|
||||
+cell #[code S0], #[code S1], #[code S2]
|
||||
+cell Top three words on the stack.
|
||||
|
||||
+row
|
||||
+cell #[code B0], #[code B1]
|
||||
+cell First two words of the buffer.
|
||||
|
||||
+row
|
||||
+cell.u-nowrap
|
||||
| #[code S0L1], #[code S1L1], #[code S2L1], #[code B0L1],
|
||||
| #[code B1L1]#[br]
|
||||
| #[code S0L2], #[code S1L2], #[code S2L2], #[code B0L2],
|
||||
| #[code B1L2]
|
||||
+cell
|
||||
| Leftmost and second leftmost children of #[code S0], #[code S1],
|
||||
| #[code S2], #[code B0] and #[code B1].
|
||||
|
||||
+row
|
||||
+cell.u-nowrap
|
||||
| #[code S0R1], #[code S1R1], #[code S2R1], #[code B0R1],
|
||||
| #[code B1R1]#[br]
|
||||
| #[code S0R2], #[code S1R2], #[code S2R2], #[code B0R2],
|
||||
| #[code B1R2]
|
||||
+cell
|
||||
| Rightmost and second rightmost children of #[code S0], #[code S1],
|
||||
| #[code S2], #[code B0] and #[code B1].
|
||||
|
||||
p
|
||||
| This makes the state vector quite long: #[code 13*T], where #[code T] is
|
||||
| the token vector width (128 is working well). Fortunately, there's a way
|
||||
| to structure the computation to save some expense (and make it more
|
||||
| GPU-friendly).
|
||||
|
||||
p
|
||||
| The parser typically visits #[code 2*N] states for a sentence of length
|
||||
| #[code N] (although it may visit more, if it back-tracks with a
|
||||
| non-monotonic transition#[+fn(4)]). A naive implementation would require
|
||||
| #[code 2*N (B, 13*T) @ (13*T, H)] matrix multiplications for a batch of
|
||||
| size #[code B]. We can instead perform one #[code (B*N, T) @ (T, 13*H)]
|
||||
| multiplication, to pre-compute the hidden weights for each positional
|
||||
| feature with respect to the words in the batch. (Note that our token
|
||||
| vectors come from the CNN — so we can't play this trick over the
|
||||
| vocabulary. That's how Stanford's NN parser#[+fn(3)] works — and why its
|
||||
| model is so big.)
|
||||
|
||||
p
|
||||
| This pre-computation strategy allows a nice compromise between
|
||||
| GPU-friendliness and implementation simplicity. The CNN and the wide
|
||||
| lower layer are computed on the GPU, and then the precomputed hidden
|
||||
| weights are moved to the CPU, before we start the transition-based
|
||||
| parsing process. This makes a lot of things much easier. We don't have to
|
||||
| worry about variable-length batch sizes, and we don't have to implement
|
||||
| the dynamic oracle in CUDA to train.
|
||||
|
||||
p
|
||||
| Currently the parser's loss function is multilabel log loss#[+fn(6)], as
|
||||
| the dynamic oracle allows multiple states to be 0 cost. This is defined
|
||||
| as follows, where #[code gZ] is the sum of the scores assigned to gold
|
||||
| classes:
|
||||
|
||||
+code.
|
||||
(exp(score) / Z) - (exp(score) / gZ)
|
||||
|
||||
+bibliography
|
||||
+item
|
||||
| #[+a("https://www.semanticscholar.org/paper/Simple-and-Accurate-Dependency-Parsing-Using-Bidir-Kiperwasser-Goldberg/3cf31ecb2724b5088783d7c96a5fc0d5604cbf41") Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations]
|
||||
br
|
||||
| Eliyahu Kiperwasser, Yoav Goldberg. (2016)
|
||||
|
||||
+item
|
||||
| #[+a("https://www.semanticscholar.org/paper/A-Dynamic-Oracle-for-Arc-Eager-Dependency-Parsing-Goldberg-Nivre/22697256ec19ecc3e14fcfc63624a44cf9c22df4") A Dynamic Oracle for Arc-Eager Dependency Parsing]
|
||||
br
|
||||
| Yoav Goldberg, Joakim Nivre (2012)
|
||||
|
||||
+item
|
||||
| #[+a("https://explosion.ai/blog/parsing-english-in-python") Parsing English in 500 Lines of Python]
|
||||
br
|
||||
| Matthew Honnibal (2013)
|
||||
|
||||
+item
|
||||
| #[+a("https://www.semanticscholar.org/paper/Stack-propagation-Improved-Representation-Learning-Zhang-Weiss/0c133f79b23e8c680891d2e49a66f0e3d37f1466") Stack-propagation: Improved Representation Learning for Syntax]
|
||||
br
|
||||
| Yuan Zhang, David Weiss (2016)
|
||||
|
||||
+item
|
||||
| #[+a("https://www.semanticscholar.org/paper/Deep-multi-task-learning-with-low-level-tasks-supe-S%C3%B8gaard-Goldberg/03ad06583c9721855ccd82c3d969a01360218d86") Deep multi-task learning with low level tasks supervised at lower layers]
|
||||
br
|
||||
| Anders Søgaard, Yoav Goldberg (2016)
|
||||
|
||||
+item
|
||||
| #[+a("https://www.semanticscholar.org/paper/An-Improved-Non-monotonic-Transition-System-for-De-Honnibal-Johnson/4094cee47ade13b77b5ab4d2e6cb9dd2b8a2917c") An Improved Non-monotonic Transition System for Dependency Parsing]
|
||||
br
|
||||
| Matthew Honnibal, Mark Johnson (2015)
|
||||
|
||||
+item
|
||||
| #[+a("http://cs.stanford.edu/people/danqi/papers/emnlp2014.pdf") A Fast and Accurate Dependency Parser using Neural Networks]
|
||||
br
|
||||
| Danqi Cheng, Christopher D. Manning (2014)
|
||||
|
||||
+item
|
||||
| #[+a("https://www.semanticscholar.org/paper/Parsing-the-Wall-Street-Journal-using-a-Lexical-Fu-Riezler-King/0ad07862a91cd59b7eb5de38267e47725a62b8b2") Parsing the Wall Street Journal using a Lexical-Functional Grammar and Discriminative Estimation Techniques]
|
||||
br
|
||||
| Stefan Riezler et al. (2002)
|
|
@ -1,29 +1,32 @@
|
|||
{
|
||||
"sidebar": {
|
||||
"Introduction": {
|
||||
"Facts & Figures": "./",
|
||||
"Languages": "language-models",
|
||||
"Annotation Specs": "annotation"
|
||||
"Overview": {
|
||||
"Architecture": "./",
|
||||
"Annotation Specs": "annotation",
|
||||
"Functions": "top-level"
|
||||
},
|
||||
"Top-level": {
|
||||
"spacy": "spacy",
|
||||
"displacy": "displacy",
|
||||
"Utility Functions": "util",
|
||||
"Command line": "cli"
|
||||
},
|
||||
"Classes": {
|
||||
"Containers": {
|
||||
"Doc": "doc",
|
||||
"Token": "token",
|
||||
"Span": "span",
|
||||
"Lexeme": "lexeme"
|
||||
},
|
||||
|
||||
"Pipeline": {
|
||||
"Language": "language",
|
||||
"Tokenizer": "tokenizer",
|
||||
"Pipe": "pipe",
|
||||
"Tensorizer": "tensorizer",
|
||||
"Tagger": "tagger",
|
||||
"DependencyParser": "dependencyparser",
|
||||
"EntityRecognizer": "entityrecognizer",
|
||||
"TextCategorizer": "textcategorizer",
|
||||
"Tokenizer": "tokenizer",
|
||||
"Lemmatizer": "lemmatizer",
|
||||
"Matcher": "matcher",
|
||||
"Lexeme": "lexeme",
|
||||
"PhraseMatcher": "phrasematcher"
|
||||
},
|
||||
|
||||
"Other": {
|
||||
"Vocab": "vocab",
|
||||
"StringStore": "stringstore",
|
||||
"Vectors": "vectors",
|
||||
|
@ -34,52 +37,37 @@
|
|||
},
|
||||
|
||||
"index": {
|
||||
"title": "Facts & Figures",
|
||||
"next": "language-models"
|
||||
"title": "Architecture",
|
||||
"next": "annotation",
|
||||
"menu": {
|
||||
"Basics": "basics",
|
||||
"Neural Network Model": "nn-model",
|
||||
"Cython Conventions": "cython"
|
||||
}
|
||||
},
|
||||
|
||||
"language-models": {
|
||||
"title": "Languages",
|
||||
"next": "philosophy"
|
||||
},
|
||||
|
||||
"philosophy": {
|
||||
"title": "Philosophy"
|
||||
},
|
||||
|
||||
"spacy": {
|
||||
"title": "spaCy top-level functions",
|
||||
"source": "spacy/__init__.py",
|
||||
"next": "displacy"
|
||||
},
|
||||
|
||||
"displacy": {
|
||||
"title": "displaCy",
|
||||
"tag": "module",
|
||||
"source": "spacy/displacy",
|
||||
"next": "util"
|
||||
},
|
||||
|
||||
"util": {
|
||||
"title": "Utility Functions",
|
||||
"source": "spacy/util.py",
|
||||
"next": "cli"
|
||||
},
|
||||
|
||||
"cli": {
|
||||
"title": "Command Line Interface",
|
||||
"source": "spacy/cli"
|
||||
"top-level": {
|
||||
"title": "Top-level Functions",
|
||||
"menu": {
|
||||
"spacy": "spacy",
|
||||
"displacy": "displacy",
|
||||
"Utility Functions": "util",
|
||||
"Compatibility": "compat",
|
||||
"Command Line": "cli"
|
||||
}
|
||||
},
|
||||
|
||||
"language": {
|
||||
"title": "Language",
|
||||
"tag": "class",
|
||||
"teaser": "A text-processing pipeline.",
|
||||
"source": "spacy/language.py"
|
||||
},
|
||||
|
||||
"doc": {
|
||||
"title": "Doc",
|
||||
"tag": "class",
|
||||
"teaser": "A container for accessing linguistic annotations.",
|
||||
"source": "spacy/tokens/doc.pyx"
|
||||
},
|
||||
|
||||
|
@ -103,6 +91,7 @@
|
|||
|
||||
"vocab": {
|
||||
"title": "Vocab",
|
||||
"teaser": "A storage class for vocabulary and other data shared across a language.",
|
||||
"tag": "class",
|
||||
"source": "spacy/vocab.pyx"
|
||||
},
|
||||
|
@ -115,10 +104,27 @@
|
|||
|
||||
"matcher": {
|
||||
"title": "Matcher",
|
||||
"teaser": "Match sequences of tokens, based on pattern rules.",
|
||||
"tag": "class",
|
||||
"source": "spacy/matcher.pyx"
|
||||
},
|
||||
|
||||
"phrasematcher": {
|
||||
"title": "PhraseMatcher",
|
||||
"teaser": "Match sequences of tokens, based on documents.",
|
||||
"tag": "class",
|
||||
"tag_new": 2,
|
||||
"source": "spacy/matcher.pyx"
|
||||
},
|
||||
|
||||
"pipe": {
|
||||
"title": "Pipe",
|
||||
"teaser": "Abstract base class defining the API for pipeline components.",
|
||||
"tag": "class",
|
||||
"tag_new": 2,
|
||||
"source": "spacy/pipeline.pyx"
|
||||
},
|
||||
|
||||
"dependenyparser": {
|
||||
"title": "DependencyParser",
|
||||
"tag": "class",
|
||||
|
@ -127,18 +133,22 @@
|
|||
|
||||
"entityrecognizer": {
|
||||
"title": "EntityRecognizer",
|
||||
"teaser": "Annotate named entities on documents.",
|
||||
"tag": "class",
|
||||
"source": "spacy/pipeline.pyx"
|
||||
},
|
||||
|
||||
"textcategorizer": {
|
||||
"title": "TextCategorizer",
|
||||
"teaser": "Add text categorization models to spaCy pipelines.",
|
||||
"tag": "class",
|
||||
"tag_new": 2,
|
||||
"source": "spacy/pipeline.pyx"
|
||||
},
|
||||
|
||||
"dependencyparser": {
|
||||
"title": "DependencyParser",
|
||||
"teaser": "Annotate syntactic dependencies on documents.",
|
||||
"tag": "class",
|
||||
"source": "spacy/pipeline.pyx"
|
||||
},
|
||||
|
@ -149,15 +159,23 @@
|
|||
"source": "spacy/tokenizer.pyx"
|
||||
},
|
||||
|
||||
"lemmatizer": {
|
||||
"title": "Lemmatizer",
|
||||
"tag": "class"
|
||||
},
|
||||
|
||||
"tagger": {
|
||||
"title": "Tagger",
|
||||
"teaser": "Annotate part-of-speech tags on documents.",
|
||||
"tag": "class",
|
||||
"source": "spacy/pipeline.pyx"
|
||||
},
|
||||
|
||||
"tensorizer": {
|
||||
"title": "Tensorizer",
|
||||
"teaser": "Add a tensor with position-sensitive meaning representations to a document.",
|
||||
"tag": "class",
|
||||
"tag_new": 2,
|
||||
"source": "spacy/pipeline.pyx"
|
||||
},
|
||||
|
||||
|
@ -169,23 +187,38 @@
|
|||
|
||||
"goldcorpus": {
|
||||
"title": "GoldCorpus",
|
||||
"teaser": "An annotated corpus, using the JSON file format.",
|
||||
"tag": "class",
|
||||
"tag_new": 2,
|
||||
"source": "spacy/gold.pyx"
|
||||
},
|
||||
|
||||
"binder": {
|
||||
"title": "Binder",
|
||||
"tag": "class",
|
||||
"tag_new": 2,
|
||||
"source": "spacy/tokens/binder.pyx"
|
||||
},
|
||||
|
||||
"vectors": {
|
||||
"title": "Vectors",
|
||||
"teaser": "Store, save and load word vectors.",
|
||||
"tag": "class",
|
||||
"tag_new": 2,
|
||||
"source": "spacy/vectors.pyx"
|
||||
},
|
||||
|
||||
"annotation": {
|
||||
"title": "Annotation Specifications"
|
||||
"title": "Annotation Specifications",
|
||||
"teaser": "Schemes used for labels, tags and training data.",
|
||||
"menu": {
|
||||
"Tokenization": "tokenization",
|
||||
"Sentence Boundaries": "sbd",
|
||||
"POS Tagging": "pos-tagging",
|
||||
"Lemmatization": "lemmatization",
|
||||
"Dependencies": "dependency-parsing",
|
||||
"Named Entities": "named-entities",
|
||||
"Training Data": "training"
|
||||
}
|
||||
}
|
||||
}
|
|
@ -1,26 +1,17 @@
|
|||
//- 💫 DOCS > USAGE > COMMAND LINE INTERFACE
|
||||
|
||||
include ../../_includes/_mixins
|
||||
//- 💫 DOCS > API > TOP-LEVEL > COMMAND LINE INTERFACE
|
||||
|
||||
p
|
||||
| As of v1.7.0, spaCy comes with new command line helpers to download and
|
||||
| link models and show useful debugging information. For a list of available
|
||||
| commands, type #[code spacy --help].
|
||||
|
||||
+infobox("⚠️ Deprecation note")
|
||||
| As of spaCy 2.0, the #[code model] command to initialise a model data
|
||||
| directory is deprecated. The command was only necessary because previous
|
||||
| versions of spaCy expected a model directory to already be set up. This
|
||||
| has since been changed, so you can use the #[+api("cli#train") #[code train]]
|
||||
| command straight away.
|
||||
|
||||
+h(2, "download") Download
|
||||
+h(3, "download") Download
|
||||
|
||||
p
|
||||
| Download #[+a("/docs/usage/models") models] for spaCy. The downloader finds the
|
||||
| Download #[+a("/usage/models") models] for spaCy. The downloader finds the
|
||||
| best-matching compatible version, uses pip to download the model as a
|
||||
| package and automatically creates a
|
||||
| #[+a("/docs/usage/models#usage") shortcut link] to load the model by name.
|
||||
| #[+a("/usage/models#usage") shortcut link] to load the model by name.
|
||||
| Direct downloads don't perform any compatibility checks and require the
|
||||
| model name to be specified with its version (e.g., #[code en_core_web_sm-1.2.0]).
|
||||
|
||||
|
@ -49,15 +40,15 @@ p
|
|||
| detailed messages in case things go wrong. It's #[strong not recommended]
|
||||
| to use this command as part of an automated process. If you know which
|
||||
| model your project needs, you should consider a
|
||||
| #[+a("/docs/usage/models#download-pip") direct download via pip], or
|
||||
| #[+a("/usage/models#download-pip") direct download via pip], or
|
||||
| uploading the model to a local PyPi installation and fetching it straight
|
||||
| from there. This will also allow you to add it as a versioned package
|
||||
| dependency to your project.
|
||||
|
||||
+h(2, "link") Link
|
||||
+h(3, "link") Link
|
||||
|
||||
p
|
||||
| Create a #[+a("/docs/usage/models#usage") shortcut link] for a model,
|
||||
| Create a #[+a("/usage/models#usage") shortcut link] for a model,
|
||||
| either a Python package or a local directory. This will let you load
|
||||
| models from any location using a custom name via
|
||||
| #[+api("spacy#load") #[code spacy.load()]].
|
||||
|
@ -95,7 +86,7 @@ p
|
|||
+cell flag
|
||||
+cell Show help message and available arguments.
|
||||
|
||||
+h(2, "info") Info
|
||||
+h(3, "info") Info
|
||||
|
||||
p
|
||||
| Print information about your spaCy installation, models and local setup,
|
||||
|
@ -122,15 +113,15 @@ p
|
|||
+cell flag
|
||||
+cell Show help message and available arguments.
|
||||
|
||||
+h(2, "convert") Convert
|
||||
+h(3, "convert") Convert
|
||||
|
||||
p
|
||||
| Convert files into spaCy's #[+a("/docs/api/annotation#json-input") JSON format]
|
||||
| Convert files into spaCy's #[+a("/api/annotation#json-input") JSON format]
|
||||
| for use with the #[code train] command and other experiment management
|
||||
| functions. The right converter is chosen based on the file extension of
|
||||
| the input file. Currently only supports #[code .conllu].
|
||||
|
||||
+code(false, "bash", "$").
|
||||
+code(false, "bash", "$", false, false, true).
|
||||
spacy convert [input_file] [output_dir] [--n-sents] [--morphology]
|
||||
|
||||
+table(["Argument", "Type", "Description"])
|
||||
|
@ -159,14 +150,18 @@ p
|
|||
+cell flag
|
||||
+cell Show help message and available arguments.
|
||||
|
||||
+h(2, "train") Train
|
||||
+h(3, "train") Train
|
||||
|
||||
p
|
||||
| Train a model. Expects data in spaCy's
|
||||
| #[+a("/docs/api/annotation#json-input") JSON format].
|
||||
| #[+a("/api/annotation#json-input") JSON format]. On each epoch, a model
|
||||
| will be saved out to the directory. Accuracy scores and model details
|
||||
| will be added to a #[+a("/usage/training#models-generating") #[code meta.json]]
|
||||
| to allow packaging the model using the
|
||||
| #[+api("cli#package") #[code package]] command.
|
||||
|
||||
+code(false, "bash", "$").
|
||||
spacy train [lang] [output_dir] [train_data] [dev_data] [--n-iter] [--n-sents] [--use-gpu] [--no-tagger] [--no-parser] [--no-entities]
|
||||
+code(false, "bash", "$", false, false, true).
|
||||
spacy train [lang] [output_dir] [train_data] [dev_data] [--n-iter] [--n-sents] [--use-gpu] [--meta-path] [--vectors] [--no-tagger] [--no-parser] [--no-entities] [--gold-preproc]
|
||||
|
||||
+table(["Argument", "Type", "Description"])
|
||||
+row
|
||||
|
@ -204,6 +199,27 @@ p
|
|||
+cell option
|
||||
+cell Use GPU.
|
||||
|
||||
+row
|
||||
+cell #[code --vectors], #[code -v]
|
||||
+cell option
|
||||
+cell Model to load vectors from.
|
||||
|
||||
+row
|
||||
+cell #[code --meta-path], #[code -m]
|
||||
+cell option
|
||||
+cell
|
||||
| #[+tag-new(2)] Optional path to model
|
||||
| #[+a("/usage/training#models-generating") #[code meta.json]].
|
||||
| All relevant properties like #[code lang], #[code pipeline] and
|
||||
| #[code spacy_version] will be overwritten.
|
||||
|
||||
+row
|
||||
+cell #[code --version], #[code -V]
|
||||
+cell option
|
||||
+cell
|
||||
| Model version. Will be written out to the model's
|
||||
| #[code meta.json] after training.
|
||||
|
||||
+row
|
||||
+cell #[code --no-tagger], #[code -T]
|
||||
+cell flag
|
||||
|
@ -219,12 +235,18 @@ p
|
|||
+cell flag
|
||||
+cell Don't train NER.
|
||||
|
||||
+row
|
||||
+cell #[code --gold-preproc], #[code -G]
|
||||
+cell flag
|
||||
+cell Use gold preprocessing.
|
||||
|
||||
+row
|
||||
+cell #[code --help], #[code -h]
|
||||
+cell flag
|
||||
+cell Show help message and available arguments.
|
||||
|
||||
+h(3, "train-hyperparams") Environment variables for hyperparameters
|
||||
+h(4, "train-hyperparams") Environment variables for hyperparameters
|
||||
+tag-new(2)
|
||||
|
||||
p
|
||||
| spaCy lets you set hyperparameters for training via environment variables.
|
||||
|
@ -236,98 +258,149 @@ p
|
|||
+code(false, "bash").
|
||||
parser_hidden_depth=2 parser_maxout_pieces=1 train-parser
|
||||
|
||||
+under-construction
|
||||
|
||||
+table(["Name", "Description", "Default"])
|
||||
+row
|
||||
+cell #[code dropout_from]
|
||||
+cell
|
||||
+cell Initial dropout rate.
|
||||
+cell #[code 0.2]
|
||||
|
||||
+row
|
||||
+cell #[code dropout_to]
|
||||
+cell
|
||||
+cell Final dropout rate.
|
||||
+cell #[code 0.2]
|
||||
|
||||
+row
|
||||
+cell #[code dropout_decay]
|
||||
+cell
|
||||
+cell Rate of dropout change.
|
||||
+cell #[code 0.0]
|
||||
|
||||
+row
|
||||
+cell #[code batch_from]
|
||||
+cell
|
||||
+cell Initial batch size.
|
||||
+cell #[code 1]
|
||||
|
||||
+row
|
||||
+cell #[code batch_to]
|
||||
+cell
|
||||
+cell Final batch size.
|
||||
+cell #[code 64]
|
||||
|
||||
+row
|
||||
+cell #[code batch_compound]
|
||||
+cell
|
||||
+cell Rate of batch size acceleration.
|
||||
+cell #[code 1.001]
|
||||
|
||||
+row
|
||||
+cell #[code token_vector_width]
|
||||
+cell
|
||||
+cell Width of embedding tables and convolutional layers.
|
||||
+cell #[code 128]
|
||||
|
||||
+row
|
||||
+cell #[code embed_size]
|
||||
+cell
|
||||
+cell Number of rows in embedding tables.
|
||||
+cell #[code 7500]
|
||||
|
||||
+row
|
||||
+cell #[code parser_maxout_pieces]
|
||||
+cell
|
||||
+cell Number of pieces in the parser's and NER's first maxout layer.
|
||||
+cell #[code 2]
|
||||
|
||||
+row
|
||||
+cell #[code parser_hidden_depth]
|
||||
+cell
|
||||
+cell Number of hidden layers in the parser and NER.
|
||||
+cell #[code 1]
|
||||
|
||||
+row
|
||||
+cell #[code hidden_width]
|
||||
+cell
|
||||
+cell Size of the parser's and NER's hidden layers.
|
||||
+cell #[code 128]
|
||||
|
||||
+row
|
||||
+cell #[code learn_rate]
|
||||
+cell
|
||||
+cell Learning rate.
|
||||
+cell #[code 0.001]
|
||||
|
||||
+row
|
||||
+cell #[code optimizer_B1]
|
||||
+cell
|
||||
+cell Momentum for the Adam solver.
|
||||
+cell #[code 0.9]
|
||||
|
||||
+row
|
||||
+cell #[code optimizer_B2]
|
||||
+cell
|
||||
+cell Adagrad-momentum for the Adam solver.
|
||||
+cell #[code 0.999]
|
||||
|
||||
+row
|
||||
+cell #[code optimizer_eps]
|
||||
+cell
|
||||
+cell Epsylon value for the Adam solver.
|
||||
+cell #[code 1e-08]
|
||||
|
||||
+row
|
||||
+cell #[code L2_penalty]
|
||||
+cell
|
||||
+cell L2 regularisation penalty.
|
||||
+cell #[code 1e-06]
|
||||
|
||||
+row
|
||||
+cell #[code grad_norm_clip]
|
||||
+cell
|
||||
+cell Gradient L2 norm constraint.
|
||||
+cell #[code 1.0]
|
||||
|
||||
+h(2, "package") Package
|
||||
+h(3, "evaluate") Evaluate
|
||||
+tag-new(2)
|
||||
|
||||
p
|
||||
| Generate a #[+a("/docs/usage/saving-loading#generating") model Python package]
|
||||
| Evaluate a model's accuracy and speed on JSON-formatted annotated data.
|
||||
| Will print the results and optionally export
|
||||
| #[+a("/usage/visualizers") displaCy visualizations] of a sample set of
|
||||
| parses to #[code .html] files. Visualizations for the dependency parse
|
||||
| and NER will be exported as separate files if the respective component
|
||||
| is present in the model's pipeline.
|
||||
|
||||
+code(false, "bash", "$", false, false, true).
|
||||
spacy evaluate [model] [data_path] [--displacy-path] [--displacy-limit] [--gpu-id] [--gold-preproc]
|
||||
|
||||
+table(["Argument", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code model]
|
||||
+cell positional
|
||||
+cell
|
||||
| Model to evaluate. Can be a package or shortcut link name, or a
|
||||
| path to a model data directory.
|
||||
|
||||
+row
|
||||
+cell #[code data_path]
|
||||
+cell positional
|
||||
+cell Location of JSON-formatted evaluation data.
|
||||
|
||||
+row
|
||||
+cell #[code --displacy-path], #[code -dp]
|
||||
+cell option
|
||||
+cell
|
||||
| Directory to output rendered parses as HTML. If not set, no
|
||||
| visualizations will be generated.
|
||||
|
||||
+row
|
||||
+cell #[code --displacy-limit], #[code -dl]
|
||||
+cell option
|
||||
+cell
|
||||
| Number of parses to generate per file. Defaults to #[code 25].
|
||||
| Keep in mind that a significantly higher number might cause the
|
||||
| #[code .html] files to render slowly.
|
||||
|
||||
+row
|
||||
+cell #[code --gpu-id], #[code -g]
|
||||
+cell option
|
||||
+cell GPU to use, if any. Defaults to #[code -1] for CPU.
|
||||
|
||||
+row
|
||||
+cell #[code --gold-preproc], #[code -G]
|
||||
+cell flag
|
||||
+cell Use gold preprocessing.
|
||||
|
||||
|
||||
+h(3, "package") Package
|
||||
|
||||
p
|
||||
| Generate a #[+a("/usage/training#models-generating") model Python package]
|
||||
| from an existing model data directory. All data files are copied over.
|
||||
| If the path to a meta.json is supplied, or a meta.json is found in the
|
||||
| input directory, this file is used. Otherwise, the data can be entered
|
||||
|
@ -336,8 +409,8 @@ p
|
|||
| sure you're always using the latest versions. This means you need to be
|
||||
| connected to the internet to use this command.
|
||||
|
||||
+code(false, "bash", "$").
|
||||
spacy package [input_dir] [output_dir] [--meta] [--force]
|
||||
+code(false, "bash", "$", false, false, true).
|
||||
spacy package [input_dir] [output_dir] [--meta-path] [--create-meta] [--force]
|
||||
|
||||
+table(["Argument", "Type", "Description"])
|
||||
+row
|
||||
|
@ -353,14 +426,14 @@ p
|
|||
+row
|
||||
+cell #[code --meta-path], #[code -m]
|
||||
+cell option
|
||||
+cell Path to meta.json file (optional).
|
||||
+cell #[+tag-new(2)] Path to meta.json file (optional).
|
||||
|
||||
+row
|
||||
+cell #[code --create-meta], #[code -c]
|
||||
+cell flag
|
||||
+cell
|
||||
| Create a meta.json file on the command line, even if one already
|
||||
| exists in the directory.
|
||||
| #[+tag-new(2)] Create a meta.json file on the command line, even
|
||||
| if one already exists in the directory.
|
||||
|
||||
+row
|
||||
+cell #[code --force], #[code -f]
|
91
website/api/_top-level/_compat.jade
Normal file
91
website/api/_top-level/_compat.jade
Normal file
|
@ -0,0 +1,91 @@
|
|||
//- 💫 DOCS > API > TOP-LEVEL > COMPATIBILITY
|
||||
|
||||
p
|
||||
| All Python code is written in an
|
||||
| #[strong intersection of Python 2 and Python 3]. This is easy in Cython,
|
||||
| but somewhat ugly in Python. Logic that deals with Python or platform
|
||||
| compatibility only lives in #[code spacy.compat]. To distinguish them from
|
||||
| the builtin functions, replacement functions are suffixed with an
|
||||
| undersocre, e.e #[code unicode_]. For specific checks, spaCy uses the
|
||||
| #[code six] and #[code ftfy] packages.
|
||||
|
||||
+aside-code("Example").
|
||||
from spacy.compat import unicode_, json_dumps
|
||||
|
||||
compatible_unicode = unicode_('hello world')
|
||||
compatible_json = json_dumps({'key': 'value'})
|
||||
|
||||
+table(["Name", "Python 2", "Python 3"])
|
||||
+row
|
||||
+cell #[code compat.bytes_]
|
||||
+cell #[code str]
|
||||
+cell #[code bytes]
|
||||
|
||||
+row
|
||||
+cell #[code compat.unicode_]
|
||||
+cell #[code unicode]
|
||||
+cell #[code str]
|
||||
|
||||
+row
|
||||
+cell #[code compat.basestring_]
|
||||
+cell #[code basestring]
|
||||
+cell #[code str]
|
||||
|
||||
+row
|
||||
+cell #[code compat.input_]
|
||||
+cell #[code raw_input]
|
||||
+cell #[code input]
|
||||
|
||||
+row
|
||||
+cell #[code compat.json_dumps]
|
||||
+cell #[code ujson.dumps] with #[code .decode('utf8')]
|
||||
+cell #[code ujson.dumps]
|
||||
|
||||
+row
|
||||
+cell #[code compat.path2str]
|
||||
+cell #[code str(path)] with #[code .decode('utf8')]
|
||||
+cell #[code str(path)]
|
||||
|
||||
+h(3, "is_config") compat.is_config
|
||||
+tag function
|
||||
|
||||
p
|
||||
| Check if a specific configuration of Python version and operating system
|
||||
| matches the user's setup. Mostly used to display targeted error messages.
|
||||
|
||||
+aside-code("Example").
|
||||
from spacy.compat import is_config
|
||||
|
||||
if is_config(python2=True, windows=True):
|
||||
print("You are using Python 2 on Windows.")
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code python2]
|
||||
+cell bool
|
||||
+cell spaCy is executed with Python 2.x.
|
||||
|
||||
+row
|
||||
+cell #[code python3]
|
||||
+cell bool
|
||||
+cell spaCy is executed with Python 3.x.
|
||||
|
||||
+row
|
||||
+cell #[code windows]
|
||||
+cell bool
|
||||
+cell spaCy is executed on Windows.
|
||||
|
||||
+row
|
||||
+cell #[code linux]
|
||||
+cell bool
|
||||
+cell spaCy is executed on Linux.
|
||||
|
||||
+row
|
||||
+cell #[code osx]
|
||||
+cell bool
|
||||
+cell spaCy is executed on OS X or macOS.
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bool
|
||||
+cell Whether the specified configuration matches the user's platform.
|
|
@ -1,14 +1,12 @@
|
|||
//- 💫 DOCS > API > DISPLACY
|
||||
|
||||
include ../../_includes/_mixins
|
||||
//- 💫 DOCS > API > TOP-LEVEL > DISPLACY
|
||||
|
||||
p
|
||||
| As of v2.0, spaCy comes with a built-in visualization suite. For more
|
||||
| info and examples, see the usage guide on
|
||||
| #[+a("/docs/usage/visualizers") visualizing spaCy].
|
||||
| #[+a("/usage/visualizers") visualizing spaCy].
|
||||
|
||||
|
||||
+h(2, "serve") displacy.serve
|
||||
+h(3, "displacy.serve") displacy.serve
|
||||
+tag method
|
||||
+tag-new(2)
|
||||
|
||||
|
@ -60,7 +58,7 @@ p
|
|||
+cell bool
|
||||
+cell
|
||||
| Don't parse #[code Doc] and instead, expect a dict or list of
|
||||
| dicts. #[+a("/docs/usage/visualizers#manual-usage") See here]
|
||||
| dicts. #[+a("/usage/visualizers#manual-usage") See here]
|
||||
| for formats and examples.
|
||||
+cell #[code False]
|
||||
|
||||
|
@ -70,7 +68,7 @@ p
|
|||
+cell Port to serve visualization.
|
||||
+cell #[code 5000]
|
||||
|
||||
+h(2, "render") displacy.render
|
||||
+h(3, "displacy.render") displacy.render
|
||||
+tag method
|
||||
+tag-new(2)
|
||||
|
||||
|
@ -127,24 +125,24 @@ p Render a dependency parse tree or named entity visualization.
|
|||
+cell bool
|
||||
+cell
|
||||
| Don't parse #[code Doc] and instead, expect a dict or list of
|
||||
| dicts. #[+a("/docs/usage/visualizers#manual-usage") See here]
|
||||
| dicts. #[+a("/usage/visualizers#manual-usage") See here]
|
||||
| for formats and examples.
|
||||
+cell #[code False]
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell unicode
|
||||
+cell Rendered HTML markup.
|
||||
+cell
|
||||
|
||||
+h(2, "options") Visualizer options
|
||||
+h(3, "displacy_options") Visualizer options
|
||||
|
||||
p
|
||||
| The #[code options] argument lets you specify additional settings for
|
||||
| each visualizer. If a setting is not present in the options, the default
|
||||
| value will be used.
|
||||
|
||||
+h(3, "options-dep") Dependency Visualizer options
|
||||
+h(4, "options-dep") Dependency Visualizer options
|
||||
|
||||
+aside-code("Example").
|
||||
options = {'compact': True, 'color': 'blue'}
|
||||
|
@ -219,7 +217,7 @@ p
|
|||
+cell Distance between words in px.
|
||||
+cell #[code 175] / #[code 85] (compact)
|
||||
|
||||
+h(3, "options-ent") Named Entity Visualizer options
|
||||
+h(4, "displacy_options-ent") Named Entity Visualizer options
|
||||
|
||||
+aside-code("Example").
|
||||
options = {'ents': ['PERSON', 'ORG', 'PRODUCT'],
|
||||
|
@ -244,6 +242,6 @@ p
|
|||
|
||||
p
|
||||
| By default, displaCy comes with colours for all
|
||||
| #[+a("/docs/api/annotation#named-entities") entity types supported by spaCy].
|
||||
| #[+a("/api/annotation#named-entities") entity types supported by spaCy].
|
||||
| If you're using custom entity types, you can use the #[code colors]
|
||||
| setting to add your own colours for them.
|
|
@ -1,15 +1,13 @@
|
|||
//- 💫 DOCS > API > SPACY
|
||||
//- 💫 DOCS > API > TOP-LEVEL > SPACY
|
||||
|
||||
include ../../_includes/_mixins
|
||||
|
||||
+h(2, "load") spacy.load
|
||||
+h(3, "spacy.load") spacy.load
|
||||
+tag function
|
||||
+tag-model
|
||||
|
||||
p
|
||||
| Load a model via its #[+a("/docs/usage/models#usage") shortcut link],
|
||||
| Load a model via its #[+a("/usage/models#usage") shortcut link],
|
||||
| the name of an installed
|
||||
| #[+a("/docs/usage/saving-loading#generating") model package], a unicode
|
||||
| #[+a("/usage/training#models-generating") model package], a unicode
|
||||
| path or a #[code Path]-like object. spaCy will try resolving the load
|
||||
| argument in this order. If a model is loaded from a shortcut link or
|
||||
| package name, spaCy will assume it's a Python package and import it and
|
||||
|
@ -38,25 +36,57 @@ p
|
|||
+cell list
|
||||
+cell
|
||||
| Names of pipeline components to
|
||||
| #[+a("/docs/usage/language-processing-pipeline#disabling") disable].
|
||||
| #[+a("/usage/processing-pipelines#disabling") disable].
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Language]
|
||||
+cell A #[code Language] object with the loaded model.
|
||||
|
||||
+infobox("⚠️ Deprecation note")
|
||||
+infobox("Deprecation note", "⚠️")
|
||||
.o-block
|
||||
| As of spaCy 2.0, the #[code path] keyword argument is deprecated. spaCy
|
||||
| will also raise an error if no model could be loaded and never just
|
||||
| return an empty #[code Language] object. If you need a blank language,
|
||||
| you need to import it explicitly (#[code from spacy.lang.en import English])
|
||||
| or use #[+api("util#get_lang_class") #[code util.get_lang_class]].
|
||||
| you can use the new function #[+api("spacy#blank") #[code spacy.blank()]]
|
||||
| or import the class explicitly, e.g.
|
||||
| #[code from spacy.lang.en import English].
|
||||
|
||||
+code-new nlp = spacy.load('/model')
|
||||
+code-old nlp = spacy.load('en', path='/model')
|
||||
|
||||
+h(2, "info") spacy.info
|
||||
+h(3, "spacy.blank") spacy.blank
|
||||
+tag function
|
||||
+tag-new(2)
|
||||
|
||||
p
|
||||
| Create a blank model of a given language class. This function is the
|
||||
| twin of #[code spacy.load()].
|
||||
|
||||
+aside-code("Example").
|
||||
nlp_en = spacy.blank('en')
|
||||
nlp_de = spacy.blank('de')
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code name]
|
||||
+cell unicode
|
||||
+cell ISO code of the language class to load.
|
||||
|
||||
+row
|
||||
+cell #[code disable]
|
||||
+cell list
|
||||
+cell
|
||||
| Names of pipeline components to
|
||||
| #[+a("/usage/processing-pipelines#disabling") disable].
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Language]
|
||||
+cell An empty #[code Language] object of the appropriate subclass.
|
||||
|
||||
|
||||
+h(4, "spacy.info") spacy.info
|
||||
+tag function
|
||||
|
||||
p
|
||||
|
@ -83,13 +113,13 @@ p
|
|||
+cell Print information as Markdown.
|
||||
|
||||
|
||||
+h(2, "explain") spacy.explain
|
||||
+h(3, "spacy.explain") spacy.explain
|
||||
+tag function
|
||||
|
||||
p
|
||||
| Get a description for a given POS tag, dependency label or entity type.
|
||||
| For a list of available terms, see
|
||||
| #[+src(gh("spacy", "spacy/glossary.py")) glossary.py].
|
||||
| #[+src(gh("spacy", "spacy/glossary.py")) #[code glossary.py]].
|
||||
|
||||
+aside-code("Example").
|
||||
spacy.explain('NORP')
|
||||
|
@ -107,18 +137,18 @@ p
|
|||
+cell unicode
|
||||
+cell Term to explain.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell unicode
|
||||
+cell The explanation, or #[code None] if not found in the glossary.
|
||||
|
||||
+h(2, "set_factory") spacy.set_factory
|
||||
+h(3, "spacy.set_factory") spacy.set_factory
|
||||
+tag function
|
||||
+tag-new(2)
|
||||
|
||||
p
|
||||
| Set a factory that returns a custom
|
||||
| #[+a("/docs/usage/language-processing-pipeline") processing pipeline]
|
||||
| #[+a("/usage/processing-pipelines") processing pipeline]
|
||||
| component. Factories are useful for creating stateful components, especially ones which depend on shared data.
|
||||
|
||||
+aside-code("Example").
|
|
@ -1,10 +1,8 @@
|
|||
//- 💫 DOCS > API > UTIL
|
||||
|
||||
include ../../_includes/_mixins
|
||||
//- 💫 DOCS > API > TOP-LEVEL > UTIL
|
||||
|
||||
p
|
||||
| spaCy comes with a small collection of utility functions located in
|
||||
| #[+src(gh("spaCy", "spacy/util.py")) spacy/util.py].
|
||||
| #[+src(gh("spaCy", "spacy/util.py")) #[code spacy/util.py]].
|
||||
| Because utility functions are mostly intended for
|
||||
| #[strong internal use within spaCy], their behaviour may change with
|
||||
| future releases. The functions documented on this page should be safe
|
||||
|
@ -12,7 +10,7 @@ p
|
|||
| recommend having additional tests in place if your application depends on
|
||||
| any of spaCy's utilities.
|
||||
|
||||
+h(2, "get_data_path") util.get_data_path
|
||||
+h(3, "util.get_data_path") util.get_data_path
|
||||
+tag function
|
||||
|
||||
p
|
||||
|
@ -25,12 +23,12 @@ p
|
|||
+cell bool
|
||||
+cell Only return path if it exists, otherwise return #[code None].
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Path] / #[code None]
|
||||
+cell Data path or #[code None].
|
||||
|
||||
+h(2, "set_data_path") util.set_data_path
|
||||
+h(3, "util.set_data_path") util.set_data_path
|
||||
+tag function
|
||||
|
||||
p
|
||||
|
@ -47,12 +45,12 @@ p
|
|||
+cell unicode or #[code Path]
|
||||
+cell Path to new data directory.
|
||||
|
||||
+h(2, "get_lang_class") util.get_lang_class
|
||||
+h(3, "util.get_lang_class") util.get_lang_class
|
||||
+tag function
|
||||
|
||||
p
|
||||
| Import and load a #[code Language] class. Allows lazy-loading
|
||||
| #[+a("/docs/usage/adding-languages") language data] and importing
|
||||
| #[+a("/usage/adding-languages") language data] and importing
|
||||
| languages using the two-letter language code.
|
||||
|
||||
+aside-code("Example").
|
||||
|
@ -67,12 +65,12 @@ p
|
|||
+cell unicode
|
||||
+cell Two-letter language code, e.g. #[code 'en'].
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Language]
|
||||
+cell Language class.
|
||||
|
||||
+h(2, "load_model") util.load_model
|
||||
+h(3, "util.load_model") util.load_model
|
||||
+tag function
|
||||
+tag-new(2)
|
||||
|
||||
|
@ -101,12 +99,12 @@ p
|
|||
+cell -
|
||||
+cell Specific overrides, like pipeline components to disable.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Language]
|
||||
+cell #[code Language] class with the loaded model.
|
||||
|
||||
+h(2, "load_model_from_path") util.load_model_from_path
|
||||
+h(3, "util.load_model_from_path") util.load_model_from_path
|
||||
+tag function
|
||||
+tag-new(2)
|
||||
|
||||
|
@ -139,18 +137,18 @@ p
|
|||
+cell -
|
||||
+cell Specific overrides, like pipeline components to disable.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Language]
|
||||
+cell #[code Language] class with the loaded model.
|
||||
|
||||
+h(2, "load_model_from_init_py") util.load_model_from_init_py
|
||||
+h(3, "util.load_model_from_init_py") util.load_model_from_init_py
|
||||
+tag function
|
||||
+tag-new(2)
|
||||
|
||||
p
|
||||
| A helper function to use in the #[code load()] method of a model package's
|
||||
| #[+src(gh("spacy-dev-resources", "templates/model/en_model_name/__init__.py")) __init__.py].
|
||||
| #[+src(gh("spacy-dev-resources", "templates/model/en_model_name/__init__.py")) #[code __init__.py]].
|
||||
|
||||
+aside-code("Example").
|
||||
from spacy.util import load_model_from_init_py
|
||||
|
@ -169,12 +167,12 @@ p
|
|||
+cell -
|
||||
+cell Specific overrides, like pipeline components to disable.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Language]
|
||||
+cell #[code Language] class with the loaded model.
|
||||
|
||||
+h(2, "get_model_meta") util.get_model_meta
|
||||
+h(3, "util.get_model_meta") util.get_model_meta
|
||||
+tag function
|
||||
+tag-new(2)
|
||||
|
||||
|
@ -190,17 +188,17 @@ p
|
|||
+cell unicode or #[code Path]
|
||||
+cell Path to model directory.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell dict
|
||||
+cell The model's meta data.
|
||||
|
||||
+h(2, "is_package") util.is_package
|
||||
+h(3, "util.is_package") util.is_package
|
||||
+tag function
|
||||
|
||||
p
|
||||
| Check if string maps to a package installed via pip. Mainly used to
|
||||
| validate #[+a("/docs/usage/models") model packages].
|
||||
| validate #[+a("/usage/models") model packages].
|
||||
|
||||
+aside-code("Example").
|
||||
util.is_package('en_core_web_sm') # True
|
||||
|
@ -212,18 +210,18 @@ p
|
|||
+cell unicode
|
||||
+cell Name of package.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code bool]
|
||||
+cell #[code True] if installed package, #[code False] if not.
|
||||
|
||||
+h(2, "get_package_path") util.get_package_path
|
||||
+h(3, "util.get_package_path") util.get_package_path
|
||||
+tag function
|
||||
+tag-new(2)
|
||||
|
||||
p
|
||||
| Get path to an installed package. Mainly used to resolve the location of
|
||||
| #[+a("/docs/usage/models") model packages]. Currently imports the package
|
||||
| #[+a("/usage/models") model packages]. Currently imports the package
|
||||
| to find its path.
|
||||
|
||||
+aside-code("Example").
|
||||
|
@ -236,12 +234,12 @@ p
|
|||
+cell unicode
|
||||
+cell Name of installed package.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Path]
|
||||
+cell Path to model package directory.
|
||||
|
||||
+h(2, "is_in_jupyter") util.is_in_jupyter
|
||||
+h(3, "util.is_in_jupyter") util.is_in_jupyter
|
||||
+tag function
|
||||
+tag-new(2)
|
||||
|
||||
|
@ -257,17 +255,17 @@ p
|
|||
return display(HTML(html))
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bool
|
||||
+cell #[code True] if in Jupyter, #[code False] if not.
|
||||
|
||||
+h(2, "update_exc") util.update_exc
|
||||
+h(3, "util.update_exc") util.update_exc
|
||||
+tag function
|
||||
|
||||
p
|
||||
| Update, validate and overwrite
|
||||
| #[+a("/docs/usage/adding-languages#tokenizer-exceptions") tokenizer exceptions].
|
||||
| #[+a("/usage/adding-languages#tokenizer-exceptions") tokenizer exceptions].
|
||||
| Used to combine global exceptions with custom, language-specific
|
||||
| exceptions. Will raise an error if key doesn't match #[code ORTH] values.
|
||||
|
||||
|
@ -288,20 +286,20 @@ p
|
|||
+cell dicts
|
||||
+cell Exception dictionaries to add to the base exceptions, in order.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell dict
|
||||
+cell Combined tokenizer exceptions.
|
||||
|
||||
|
||||
+h(2, "prints") util.prints
|
||||
+h(3, "util.prints") util.prints
|
||||
+tag function
|
||||
+tag-new(2)
|
||||
|
||||
p
|
||||
| Print a formatted, text-wrapped message with optional title. If a text
|
||||
| argument is a #[code Path], it's converted to a string. Should only
|
||||
| be used for interactive components like the #[+api("cli") cli].
|
||||
| be used for interactive components like the command-line interface.
|
||||
|
||||
+aside-code("Example").
|
||||
data_path = Path('/some/path')
|
131
website/api/annotation.jade
Normal file
131
website/api/annotation.jade
Normal file
|
@ -0,0 +1,131 @@
|
|||
//- 💫 DOCS > API > ANNOTATION SPECS
|
||||
|
||||
include ../_includes/_mixins
|
||||
|
||||
p This document describes the target annotations spaCy is trained to predict.
|
||||
|
||||
|
||||
+section("tokenization")
|
||||
+h(2, "tokenization") Tokenization
|
||||
|
||||
p
|
||||
| Tokenization standards are based on the
|
||||
| #[+a("https://catalog.ldc.upenn.edu/LDC2013T19") OntoNotes 5] corpus.
|
||||
| The tokenizer differs from most by including tokens for significant
|
||||
| whitespace. Any sequence of whitespace characters beyond a single space
|
||||
| (#[code ' ']) is included as a token.
|
||||
|
||||
+aside-code("Example").
|
||||
from spacy.lang.en import English
|
||||
nlp = English()
|
||||
tokens = nlp('Some\nspaces and\ttab characters')
|
||||
tokens_text = [t.text for t in tokens]
|
||||
assert tokens_text == ['Some', '\n', 'spaces', ' ', 'and',
|
||||
'\t', 'tab', 'characters']
|
||||
|
||||
p
|
||||
| The whitespace tokens are useful for much the same reason punctuation is
|
||||
| – it's often an important delimiter in the text. By preserving it in the
|
||||
| token output, we are able to maintain a simple alignment between the
|
||||
| tokens and the original string, and we ensure that no information is
|
||||
| lost during processing.
|
||||
|
||||
+section("sbd")
|
||||
+h(2, "sentence-boundary") Sentence boundary detection
|
||||
|
||||
p
|
||||
| Sentence boundaries are calculated from the syntactic parse tree, so
|
||||
| features such as punctuation and capitalisation play an important but
|
||||
| non-decisive role in determining the sentence boundaries. Usually this
|
||||
| means that the sentence boundaries will at least coincide with clause
|
||||
| boundaries, even given poorly punctuated text.
|
||||
|
||||
+section("pos-tagging")
|
||||
+h(2, "pos-tagging") Part-of-speech Tagging
|
||||
|
||||
+aside("Tip: Understanding tags")
|
||||
| You can also use #[code spacy.explain()] to get the description for the
|
||||
| string representation of a tag. For example,
|
||||
| #[code spacy.explain("RB")] will return "adverb".
|
||||
|
||||
include _annotation/_pos-tags
|
||||
|
||||
+section("lemmatization")
|
||||
+h(2, "lemmatization") Lemmatization
|
||||
|
||||
p A "lemma" is the uninflected form of a word. In English, this means:
|
||||
|
||||
+list
|
||||
+item #[strong Adjectives]: The form like "happy", not "happier" or "happiest"
|
||||
+item #[strong Adverbs]: The form like "badly", not "worse" or "worst"
|
||||
+item #[strong Nouns]: The form like "dog", not "dogs"; like "child", not "children"
|
||||
+item #[strong Verbs]: The form like "write", not "writes", "writing", "wrote" or "written"
|
||||
|
||||
p
|
||||
| The lemmatization data is taken from
|
||||
| #[+a("https://wordnet.princeton.edu") WordNet]. However, we also add a
|
||||
| special case for pronouns: all pronouns are lemmatized to the special
|
||||
| token #[code -PRON-].
|
||||
|
||||
+infobox("About spaCy's custom pronoun lemma")
|
||||
| Unlike verbs and common nouns, there's no clear base form of a personal
|
||||
| pronoun. Should the lemma of "me" be "I", or should we normalize person
|
||||
| as well, giving "it" — or maybe "he"? spaCy's solution is to introduce a
|
||||
| novel symbol, #[code -PRON-], which is used as the lemma for
|
||||
| all personal pronouns.
|
||||
|
||||
+section("dependency-parsing")
|
||||
+h(2, "dependency-parsing") Syntactic Dependency Parsing
|
||||
|
||||
+aside("Tip: Understanding labels")
|
||||
| You can also use #[code spacy.explain()] to get the description for the
|
||||
| string representation of a label. For example,
|
||||
| #[code spacy.explain("prt")] will return "particle".
|
||||
|
||||
include _annotation/_dep-labels
|
||||
|
||||
+section("named-entities")
|
||||
+h(2, "named-entities") Named Entity Recognition
|
||||
|
||||
+aside("Tip: Understanding entity types")
|
||||
| You can also use #[code spacy.explain()] to get the description for the
|
||||
| string representation of an entity label. For example,
|
||||
| #[code spacy.explain("LANGUAGE")] will return "any named language".
|
||||
|
||||
include _annotation/_named-entities
|
||||
|
||||
+h(3, "biluo") BILUO Scheme
|
||||
|
||||
include _annotation/_biluo
|
||||
|
||||
+section("training")
|
||||
+h(2, "json-input") JSON input format for training
|
||||
|
||||
+under-construction
|
||||
|
||||
p spaCy takes training data in the following format:
|
||||
|
||||
+code("Example structure").
|
||||
doc: {
|
||||
id: string,
|
||||
paragraphs: [{
|
||||
raw: string,
|
||||
sents: [int],
|
||||
tokens: [{
|
||||
start: int,
|
||||
tag: string,
|
||||
head: int,
|
||||
dep: string
|
||||
}],
|
||||
ner: [{
|
||||
start: int,
|
||||
end: int,
|
||||
label: string
|
||||
}],
|
||||
brackets: [{
|
||||
start: int,
|
||||
end: int,
|
||||
label: string
|
||||
}]
|
||||
}]
|
||||
}
|
|
@ -1,6 +1,6 @@
|
|||
//- 💫 DOCS > API > BINDER
|
||||
|
||||
include ../../_includes/_mixins
|
||||
include ../_includes/_mixins
|
||||
|
||||
p A container class for serializing collections of #[code Doc] objects.
|
||||
|
5
website/api/dependencyparser.jade
Normal file
5
website/api/dependencyparser.jade
Normal file
|
@ -0,0 +1,5 @@
|
|||
//- 💫 DOCS > API > DEPENDENCYPARSER
|
||||
|
||||
include ../_includes/_mixins
|
||||
|
||||
!=partial("pipe", { subclass: "DependencyParser", short: "parser", pipeline_id: "parser" })
|
|
@ -1,8 +1,6 @@
|
|||
//- 💫 DOCS > API > DOC
|
||||
|
||||
include ../../_includes/_mixins
|
||||
|
||||
p A container for accessing linguistic annotations.
|
||||
include ../_includes/_mixins
|
||||
|
||||
p
|
||||
| A #[code Doc] is a sequence of #[+api("token") #[code Token]] objects.
|
||||
|
@ -47,7 +45,7 @@ p
|
|||
| subsequent space. Must have the same length as #[code words], if
|
||||
| specified. Defaults to a sequence of #[code True].
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Doc]
|
||||
+cell The newly constructed object.
|
||||
|
@ -73,7 +71,7 @@ p
|
|||
+cell int
|
||||
+cell The index of the token.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Token]
|
||||
+cell The token at #[code doc[i]].
|
||||
|
@ -96,7 +94,7 @@ p
|
|||
+cell tuple
|
||||
+cell The slice of the document to get.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Span]
|
||||
+cell The span at #[code doc[start : end]].
|
||||
|
@ -120,7 +118,7 @@ p
|
|||
| from Cython.
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell #[code Token]
|
||||
+cell A #[code Token] object.
|
||||
|
@ -135,7 +133,7 @@ p Get the number of tokens in the document.
|
|||
assert len(doc) == 7
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell int
|
||||
+cell The number of tokens in the document.
|
||||
|
@ -172,7 +170,7 @@ p Create a #[code Span] object from the slice #[code doc.text[start : end]].
|
|||
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
|
||||
+cell A meaning representation of the span.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Span]
|
||||
+cell The newly constructed object.
|
||||
|
@ -200,7 +198,7 @@ p
|
|||
| The object to compare with. By default, accepts #[code Doc],
|
||||
| #[code Span], #[code Token] and #[code Lexeme] objects.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell float
|
||||
+cell A scalar similarity score. Higher is more similar.
|
||||
|
@ -226,7 +224,7 @@ p
|
|||
+cell int
|
||||
+cell The attribute ID
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell dict
|
||||
+cell A dictionary mapping attributes to integer counts.
|
||||
|
@ -251,7 +249,7 @@ p
|
|||
+cell list
|
||||
+cell A list of attribute ID ints.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code.u-break numpy.ndarray[ndim=2, dtype='int32']]
|
||||
+cell
|
||||
|
@ -285,7 +283,7 @@ p
|
|||
+cell #[code.u-break numpy.ndarray[ndim=2, dtype='int32']]
|
||||
+cell The attribute values to load.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Doc]
|
||||
+cell Itself.
|
||||
|
@ -326,7 +324,7 @@ p Loads state from a directory. Modifies the object in place and returns it.
|
|||
| A path to a directory. Paths may be either strings or
|
||||
| #[code Path]-like objects.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Doc]
|
||||
+cell The modified #[code Doc] object.
|
||||
|
@ -341,7 +339,7 @@ p Serialize, i.e. export the document contents to a binary string.
|
|||
doc_bytes = doc.to_bytes()
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bytes
|
||||
+cell
|
||||
|
@ -367,7 +365,7 @@ p Deserialize, i.e. import the document contents from a binary string.
|
|||
+cell bytes
|
||||
+cell The string to load from.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Doc]
|
||||
+cell The #[code Doc] object.
|
||||
|
@ -378,7 +376,7 @@ p Deserialize, i.e. import the document contents from a binary string.
|
|||
p
|
||||
| Retokenize the document, such that the span at
|
||||
| #[code doc.text[start_idx : end_idx]] is merged into a single token. If
|
||||
| #[code start_idx] and #[end_idx] do not mark start and end token
|
||||
| #[code start_idx] and #[code end_idx] do not mark start and end token
|
||||
| boundaries, the document remains unchanged.
|
||||
|
||||
+aside-code("Example").
|
||||
|
@ -405,7 +403,7 @@ p
|
|||
| attributes are inherited from the syntactic root token of
|
||||
| the span.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Token]
|
||||
+cell
|
||||
|
@ -440,7 +438,7 @@ p
|
|||
+cell bool
|
||||
+cell Don't include arcs or modifiers.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell dict
|
||||
+cell Parse tree as dict.
|
||||
|
@ -462,7 +460,7 @@ p
|
|||
assert ents[0].text == 'Mr. Best'
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell #[code Span]
|
||||
+cell Entities in the document.
|
||||
|
@ -485,7 +483,7 @@ p
|
|||
assert chunks[1].text == "another phrase"
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell #[code Span]
|
||||
+cell Noun chunks in the document.
|
||||
|
@ -507,7 +505,7 @@ p
|
|||
assert [s.root.text for s in sents] == ["is", "'s"]
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell #[code Span]
|
||||
+cell Sentences in the document.
|
||||
|
@ -525,7 +523,7 @@ p
|
|||
assert doc.has_vector
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bool
|
||||
+cell Whether the document has a vector data attached.
|
||||
|
@ -544,7 +542,7 @@ p
|
|||
assert doc.vector.shape == (300,)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
|
||||
+cell A 1D numpy array representing the document's semantics.
|
||||
|
@ -564,7 +562,7 @@ p
|
|||
assert doc1.vector_norm != doc2.vector_norm
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell float
|
||||
+cell The L2 norm of the vector representation.
|
5
website/api/entityrecognizer.jade
Normal file
5
website/api/entityrecognizer.jade
Normal file
|
@ -0,0 +1,5 @@
|
|||
//- 💫 DOCS > API > ENTITYRECOGNIZER
|
||||
|
||||
include ../_includes/_mixins
|
||||
|
||||
!=partial("pipe", { subclass: "EntityRecognizer", short: "ner", pipeline_id: "ner" })
|
|
@ -1,14 +1,12 @@
|
|||
//- 💫 DOCS > API > GOLDCORPUS
|
||||
|
||||
include ../../_includes/_mixins
|
||||
include ../_includes/_mixins
|
||||
|
||||
p
|
||||
| An annotated corpus, using the JSON file format. Manages annotations for
|
||||
| tagging, dependency parsing and NER.
|
||||
| This class manages annotations for tagging, dependency parsing and NER.
|
||||
|
||||
+h(2, "init") GoldCorpus.__init__
|
||||
+tag method
|
||||
+tag-new(2)
|
||||
|
||||
p Create a #[code GoldCorpus].
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
//- 💫 DOCS > API > GOLDPARSE
|
||||
|
||||
include ../../_includes/_mixins
|
||||
include ../_includes/_mixins
|
||||
|
||||
p Collection for training annotations.
|
||||
|
||||
|
@ -40,7 +40,7 @@ p Create a #[code GoldParse].
|
|||
+cell iterable
|
||||
+cell A sequence of named entity annotations, either as BILUO tag strings, or as #[code (start_char, end_char, label)] tuples, representing the entity positions.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code GoldParse]
|
||||
+cell The newly constructed object.
|
||||
|
@ -51,7 +51,7 @@ p Create a #[code GoldParse].
|
|||
p Get the number of gold-standard tokens.
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell int
|
||||
+cell The number of gold-standard tokens.
|
||||
|
@ -64,7 +64,7 @@ p
|
|||
| tree.
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bool
|
||||
+cell Whether annotations form projective tree.
|
||||
|
@ -119,7 +119,7 @@ p
|
|||
|
||||
p
|
||||
| Encode labelled spans into per-token tags, using the
|
||||
| #[+a("/docs/api/annotation#biluo") BILUO scheme] (Begin/In/Last/Unit/Out).
|
||||
| #[+a("/api/annotation#biluo") BILUO scheme] (Begin/In/Last/Unit/Out).
|
||||
|
||||
p
|
||||
| Returns a list of unicode strings, describing the tags. Each tag string
|
||||
|
@ -157,11 +157,11 @@ p
|
|||
| and #[code end] should be character-offset integers denoting the
|
||||
| slice into the original string.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell list
|
||||
+cell
|
||||
| Unicode strings, describing the
|
||||
| #[+a("/docs/api/annotation#biluo") BILUO] tags.
|
||||
| #[+a("/api/annotation#biluo") BILUO] tags.
|
||||
|
||||
|
14
website/api/index.jade
Normal file
14
website/api/index.jade
Normal file
|
@ -0,0 +1,14 @@
|
|||
//- 💫 DOCS > API > ARCHITECTURE
|
||||
|
||||
include ../_includes/_mixins
|
||||
|
||||
+section("basics")
|
||||
include ../usage/_spacy-101/_architecture
|
||||
|
||||
+section("nn-model")
|
||||
+h(2, "nn-model") Neural network model architecture
|
||||
include _architecture/_nn-model
|
||||
|
||||
+section("cython")
|
||||
+h(2, "cython") Cython conventions
|
||||
include _architecture/_cython
|
|
@ -1,10 +1,10 @@
|
|||
//- 💫 DOCS > API > LANGUAGE
|
||||
|
||||
include ../../_includes/_mixins
|
||||
include ../_includes/_mixins
|
||||
|
||||
p
|
||||
| A text-processing pipeline. Usually you'll load this once per process,
|
||||
| and pass the instance around your application.
|
||||
| Usually you'll load this once per process as #[code nlp] and pass the
|
||||
| instance around your application.
|
||||
|
||||
+h(2, "init") Language.__init__
|
||||
+tag method
|
||||
|
@ -49,7 +49,7 @@ p Initialise a #[code Language] object.
|
|||
| Custom meta data for the #[code Language] class. Is written to by
|
||||
| models to add model meta data.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Language]
|
||||
+cell The newly constructed object.
|
||||
|
@ -77,14 +77,14 @@ p
|
|||
+cell list
|
||||
+cell
|
||||
| Names of pipeline components to
|
||||
| #[+a("/docs/usage/language-processing-pipeline#disabling") disable].
|
||||
| #[+a("/usage/processing-pipelines#disabling") disable].
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Doc]
|
||||
+cell A container for accessing the annotations.
|
||||
|
||||
+infobox("⚠️ Deprecation note")
|
||||
+infobox("Deprecation note", "⚠️")
|
||||
.o-block
|
||||
| Pipeline components to prevent from being loaded can now be added as
|
||||
| a list to #[code disable], instead of specifying one keyword argument
|
||||
|
@ -136,9 +136,9 @@ p
|
|||
+cell list
|
||||
+cell
|
||||
| Names of pipeline components to
|
||||
| #[+a("/docs/usage/language-processing-pipeline#disabling") disable].
|
||||
| #[+a("/usage/processing-pipelines#disabling") disable].
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell #[code Doc]
|
||||
+cell Documents in the order of the original text.
|
||||
|
@ -175,7 +175,7 @@ p Update the models in the pipeline.
|
|||
+cell callable
|
||||
+cell An optimizer.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell dict
|
||||
+cell Results from the update.
|
||||
|
@ -200,7 +200,7 @@ p
|
|||
+cell -
|
||||
+cell Config parameters.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell tuple
|
||||
+cell An optimizer.
|
||||
|
@ -242,7 +242,7 @@ p
|
|||
+cell iterable
|
||||
+cell Tuples of #[code Doc] and #[code GoldParse] objects.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell tuple
|
||||
+cell Tuples of #[code Doc] and #[code GoldParse] objects.
|
||||
|
@ -271,7 +271,7 @@ p
|
|||
+cell list
|
||||
+cell
|
||||
| Names of pipeline components to
|
||||
| #[+a("/docs/usage/language-processing-pipeline#disabling") disable]
|
||||
| #[+a("/usage/processing-pipelines#disabling") disable]
|
||||
| and prevent from being saved.
|
||||
|
||||
+h(2, "from_disk") Language.from_disk
|
||||
|
@ -300,14 +300,14 @@ p
|
|||
+cell list
|
||||
+cell
|
||||
| Names of pipeline components to
|
||||
| #[+a("/docs/usage/language-processing-pipeline#disabling") disable].
|
||||
| #[+a("/usage/processing-pipelines#disabling") disable].
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Language]
|
||||
+cell The modified #[code Language] object.
|
||||
|
||||
+infobox("⚠️ Deprecation note")
|
||||
+infobox("Deprecation note", "⚠️")
|
||||
.o-block
|
||||
| As of spaCy v2.0, the #[code save_to_directory] method has been
|
||||
| renamed to #[code to_disk], to improve consistency across classes.
|
||||
|
@ -332,10 +332,10 @@ p Serialize the current state to a binary string.
|
|||
+cell list
|
||||
+cell
|
||||
| Names of pipeline components to
|
||||
| #[+a("/docs/usage/language-processing-pipeline#disabling") disable]
|
||||
| #[+a("/usage/processing-pipelines#disabling") disable]
|
||||
| and prevent from being serialized.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bytes
|
||||
+cell The serialized form of the #[code Language] object.
|
||||
|
@ -362,14 +362,14 @@ p Load state from a binary string.
|
|||
+cell list
|
||||
+cell
|
||||
| Names of pipeline components to
|
||||
| #[+a("/docs/usage/language-processing-pipeline#disabling") disable].
|
||||
| #[+a("/usage/processing-pipelines#disabling") disable].
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Language]
|
||||
+cell The #[code Language] object.
|
||||
|
||||
+infobox("⚠️ Deprecation note")
|
||||
+infobox("Deprecation note", "⚠️")
|
||||
.o-block
|
||||
| Pipeline components to prevent from being loaded can now be added as
|
||||
| a list to #[code disable], instead of specifying one keyword argument
|
5
website/api/lemmatizer.jade
Normal file
5
website/api/lemmatizer.jade
Normal file
|
@ -0,0 +1,5 @@
|
|||
//- 💫 DOCS > API > LEMMATIZER
|
||||
|
||||
include ../_includes/_mixins
|
||||
|
||||
+under-construction
|
|
@ -1,6 +1,6 @@
|
|||
//- 💫 DOCS > API > LEXEME
|
||||
|
||||
include ../../_includes/_mixins
|
||||
include ../_includes/_mixins
|
||||
|
||||
p
|
||||
| An entry in the vocabulary. A #[code Lexeme] has no string context – it's
|
||||
|
@ -24,7 +24,7 @@ p Create a #[code Lexeme] object.
|
|||
+cell int
|
||||
+cell The orth id of the lexeme.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Lexeme]
|
||||
+cell The newly constructed object.
|
||||
|
@ -65,7 +65,7 @@ p Check the value of a boolean flag.
|
|||
+cell int
|
||||
+cell The attribute ID of the flag to query.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bool
|
||||
+cell The value of the flag.
|
||||
|
@ -91,7 +91,7 @@ p Compute a semantic similarity estimate. Defaults to cosine over vectors.
|
|||
| The object to compare with. By default, accepts #[code Doc],
|
||||
| #[code Span], #[code Token] and #[code Lexeme] objects.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell float
|
||||
+cell A scalar similarity score. Higher is more similar.
|
||||
|
@ -110,7 +110,7 @@ p
|
|||
assert apple.has_vector
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bool
|
||||
+cell Whether the lexeme has a vector data attached.
|
||||
|
@ -127,7 +127,7 @@ p A real-valued meaning representation.
|
|||
assert apple.vector.shape == (300,)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
|
||||
+cell A 1D numpy array representing the lexeme's semantics.
|
||||
|
@ -146,7 +146,7 @@ p The L2 norm of the lexeme's vector representation.
|
|||
assert apple.vector_norm != pasta.vector_norm
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell float
|
||||
+cell The L2 norm of the vector representation.
|
|
@ -1,10 +1,8 @@
|
|||
//- 💫 DOCS > API > MATCHER
|
||||
|
||||
include ../../_includes/_mixins
|
||||
include ../_includes/_mixins
|
||||
|
||||
p Match sequences of tokens, based on pattern rules.
|
||||
|
||||
+infobox("⚠️ Deprecation note")
|
||||
+infobox("Deprecation note", "⚠️")
|
||||
| As of spaCy 2.0, #[code Matcher.add_pattern] and #[code Matcher.add_entity]
|
||||
| are deprecated and have been replaced with a simpler
|
||||
| #[+api("matcher#add") #[code Matcher.add]] that lets you add a list of
|
||||
|
@ -39,7 +37,7 @@ p Create the rule-based #[code Matcher].
|
|||
+cell dict
|
||||
+cell Patterns to add to the matcher, keyed by ID.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Matcher]
|
||||
+cell The newly constructed object.
|
||||
|
@ -64,7 +62,7 @@ p Find all token sequences matching the supplied patterns on the #[code Doc].
|
|||
+cell #[code Doc]
|
||||
+cell The document to match over.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell list
|
||||
+cell
|
||||
|
@ -81,7 +79,7 @@ p Find all token sequences matching the supplied patterns on the #[code Doc].
|
|||
| actions per pattern within the same matcher. For example, you might only
|
||||
| want to merge some entity types, and set custom flags for other matched
|
||||
| patterns. For more details and examples, see the usage guide on
|
||||
| #[+a("/docs/usage/rule-based-matching") rule-based matching].
|
||||
| #[+a("/usage/linguistic-features#rule-based-matching") rule-based matching].
|
||||
|
||||
+h(2, "pipe") Matcher.pipe
|
||||
+tag method
|
||||
|
@ -113,7 +111,7 @@ p Match a stream of documents, yielding them in turn.
|
|||
| parallel, if the #[code Matcher] implementation supports
|
||||
| multi-threading.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell #[code Doc]
|
||||
+cell Documents, in order.
|
||||
|
@ -134,7 +132,7 @@ p
|
|||
assert len(matcher) == 1
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell int
|
||||
+cell The number of rules.
|
||||
|
@ -156,7 +154,8 @@ p Check whether the matcher contains rules for a match ID.
|
|||
+cell #[code key]
|
||||
+cell unicode
|
||||
+cell The match ID.
|
||||
+footrow
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell int
|
||||
+cell Whether the matcher contains rules for this match ID.
|
||||
|
@ -203,7 +202,7 @@ p
|
|||
| Match pattern. A pattern consists of a list of dicts, where each
|
||||
| dict describes a token.
|
||||
|
||||
+infobox("⚠️ Deprecation note")
|
||||
+infobox("Deprecation note", "⚠️")
|
||||
.o-block
|
||||
| As of spaCy 2.0, #[code Matcher.add_pattern] and #[code Matcher.add_entity]
|
||||
| are deprecated and have been replaced with a simpler
|
||||
|
@ -257,7 +256,7 @@ p
|
|||
+cell unicode
|
||||
+cell The ID of the match rule.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell tuple
|
||||
+cell The rule, as an #[code (on_match, patterns)] tuple.
|
181
website/api/phrasematcher.jade
Normal file
181
website/api/phrasematcher.jade
Normal file
|
@ -0,0 +1,181 @@
|
|||
//- 💫 DOCS > API > PHRASEMATCHER
|
||||
|
||||
include ../_includes/_mixins
|
||||
|
||||
p
|
||||
| The #[code PhraseMatcher] lets you efficiently match large terminology
|
||||
| lists. While the #[+api("matcher") #[code Matcher]] lets you match
|
||||
| squences based on lists of token descriptions, the #[code PhraseMatcher]
|
||||
| accepts match patterns in the form of #[code Doc] objects.
|
||||
|
||||
+h(2, "init") PhraseMatcher.__init__
|
||||
+tag method
|
||||
|
||||
p Create the rule-based #[code PhraseMatcher].
|
||||
|
||||
+aside-code("Example").
|
||||
from spacy.matcher import PhraseMatcher
|
||||
matcher = Matcher(nlp.vocab, max_length=6)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code vocab]
|
||||
+cell #[code Vocab]
|
||||
+cell
|
||||
| The vocabulary object, which must be shared with the documents
|
||||
| the matcher will operate on.
|
||||
|
||||
+row
|
||||
+cell #[code max_length]
|
||||
+cell int
|
||||
+cell Mamimum length of a phrase pattern to add.
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code PhraseMatcher]
|
||||
+cell The newly constructed object.
|
||||
|
||||
+h(2, "call") PhraseMatcher.__call__
|
||||
+tag method
|
||||
|
||||
p Find all token sequences matching the supplied patterns on the #[code Doc].
|
||||
|
||||
+aside-code("Example").
|
||||
from spacy.matcher import Matcher
|
||||
|
||||
matcher = Matcher(nlp.vocab)
|
||||
matcher.add('OBAMA', None, nlp(u"Barack Obama"))
|
||||
doc = nlp(u"Barack Obama lifts America one last time in emotional farewell")
|
||||
matches = matcher(doc)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code doc]
|
||||
+cell #[code Doc]
|
||||
+cell The document to match over.
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell list
|
||||
+cell
|
||||
| A list of #[code (match_id, start, end)] tuples, describing the
|
||||
| matches. A match tuple describes a span #[code doc[start:end]].
|
||||
| The #[code match_id] is the ID of the added match pattern.
|
||||
|
||||
+h(2, "pipe") PhraseMatcher.pipe
|
||||
+tag method
|
||||
|
||||
p Match a stream of documents, yielding them in turn.
|
||||
|
||||
+aside-code("Example").
|
||||
from spacy.matcher import PhraseMatcher
|
||||
matcher = PhraseMatcher(nlp.vocab)
|
||||
for doc in matcher.pipe(texts, batch_size=50, n_threads=4):
|
||||
pass
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code docs]
|
||||
+cell iterable
|
||||
+cell A stream of documents.
|
||||
|
||||
+row
|
||||
+cell #[code batch_size]
|
||||
+cell int
|
||||
+cell The number of documents to accumulate into a working set.
|
||||
|
||||
+row
|
||||
+cell #[code n_threads]
|
||||
+cell int
|
||||
+cell
|
||||
| The number of threads with which to work on the buffer in
|
||||
| parallel, if the #[code PhraseMatcher] implementation supports
|
||||
| multi-threading.
|
||||
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell #[code Doc]
|
||||
+cell Documents, in order.
|
||||
|
||||
+h(2, "len") PhraseMatcher.__len__
|
||||
+tag method
|
||||
|
||||
p
|
||||
| Get the number of rules added to the matcher. Note that this only returns
|
||||
| the number of rules (identical with the number of IDs), not the number
|
||||
| of individual patterns.
|
||||
|
||||
+aside-code("Example").
|
||||
matcher = PhraseMatcher(nlp.vocab)
|
||||
assert len(matcher) == 0
|
||||
matcher.add('OBAMA', None, nlp(u"Barack Obama"))
|
||||
assert len(matcher) == 1
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell int
|
||||
+cell The number of rules.
|
||||
|
||||
+h(2, "contains") PhraseMatcher.__contains__
|
||||
+tag method
|
||||
|
||||
p Check whether the matcher contains rules for a match ID.
|
||||
|
||||
+aside-code("Example").
|
||||
matcher = PhraseMatcher(nlp.vocab)
|
||||
assert len(matcher) == 0
|
||||
matcher.add('OBAMA', None, nlp(u"Barack Obama"))
|
||||
assert len(matcher) == 1
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code key]
|
||||
+cell unicode
|
||||
+cell The match ID.
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell int
|
||||
+cell Whether the matcher contains rules for this match ID.
|
||||
|
||||
+h(2, "add") PhraseMatcher.add
|
||||
+tag method
|
||||
|
||||
p
|
||||
| Add a rule to the matcher, consisting of an ID key, one or more patterns, and
|
||||
| a callback function to act on the matches. The callback function will
|
||||
| receive the arguments #[code matcher], #[code doc], #[code i] and
|
||||
| #[code matches]. If a pattern already exists for the given ID, the
|
||||
| patterns will be extended. An #[code on_match] callback will be
|
||||
| overwritten.
|
||||
|
||||
+aside-code("Example").
|
||||
def on_match(matcher, doc, id, matches):
|
||||
print('Matched!', matches)
|
||||
|
||||
matcher = PhraseMatcher(nlp.vocab)
|
||||
matcher.add('OBAMA', on_match, nlp(u"Barack Obama"))
|
||||
matcher.add('HEALTH', on_match, nlp(u"health care reform"),
|
||||
nlp(u"healthcare reform"))
|
||||
doc = nlp(u"Barack Obama urges Congress to find courage to defend his healthcare reforms")
|
||||
matches = matcher(doc)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code match_id]
|
||||
+cell unicode
|
||||
+cell An ID for the thing you're matching.
|
||||
|
||||
+row
|
||||
+cell #[code on_match]
|
||||
+cell callable or #[code None]
|
||||
+cell
|
||||
| Callback function to act on matches. Takes the arguments
|
||||
| #[code matcher], #[code doc], #[code i] and #[code matches].
|
||||
|
||||
+row
|
||||
+cell #[code *docs]
|
||||
+cell list
|
||||
+cell
|
||||
| #[code Doc] objects of the phrases to match.
|
390
website/api/pipe.jade
Normal file
390
website/api/pipe.jade
Normal file
|
@ -0,0 +1,390 @@
|
|||
//- 💫 DOCS > API > PIPE
|
||||
|
||||
include ../_includes/_mixins
|
||||
|
||||
//- This page can be used as a template for all other classes that inherit
|
||||
//- from `Pipe`.
|
||||
|
||||
if subclass
|
||||
+infobox
|
||||
| This class is a subclass of #[+api("pipe") #[code Pipe]] and
|
||||
| follows the same API. The pipeline component is available in the
|
||||
| #[+a("/usage/processing-pipelines") processing pipeline] via the ID
|
||||
| #[code "#{pipeline_id}"].
|
||||
|
||||
else
|
||||
p
|
||||
| This class is not instantiated directly. Components inherit from it,
|
||||
| and it defines the interface that components should follow to
|
||||
| function as components in a spaCy analysis pipeline.
|
||||
|
||||
- CLASSNAME = subclass || 'Pipe'
|
||||
- VARNAME = short || CLASSNAME.toLowerCase()
|
||||
|
||||
|
||||
+h(2, "model") #{CLASSNAME}.Model
|
||||
+tag classmethod
|
||||
|
||||
p
|
||||
| Initialise a model for the pipe. The model should implement the
|
||||
| #[code thinc.neural.Model] API. Wrappers are available for
|
||||
| #[+a("/usage/deep-learning") most major machine learning libraries].
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code **kwargs]
|
||||
+cell -
|
||||
+cell Parameters for initialising the model
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell object
|
||||
+cell The initialised model.
|
||||
|
||||
+h(2, "init") #{CLASSNAME}.__init__
|
||||
+tag method
|
||||
|
||||
p Create a new pipeline instance.
|
||||
|
||||
+aside-code("Example").
|
||||
from spacy.pipeline import #{CLASSNAME}
|
||||
#{VARNAME} = #{CLASSNAME}(nlp.vocab)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code vocab]
|
||||
+cell #[code Vocab]
|
||||
+cell The shared vocabulary.
|
||||
|
||||
+row
|
||||
+cell #[code model]
|
||||
+cell #[code thinc.neural.Model] or #[code True]
|
||||
+cell
|
||||
| The model powering the pipeline component. If no model is
|
||||
| supplied, the model is created when you call
|
||||
| #[code begin_training], #[code from_disk] or #[code from_bytes].
|
||||
|
||||
+row
|
||||
+cell #[code **cfg]
|
||||
+cell -
|
||||
+cell Configuration parameters.
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code=CLASSNAME]
|
||||
+cell The newly constructed object.
|
||||
|
||||
+h(2, "call") #{CLASSNAME}.__call__
|
||||
+tag method
|
||||
|
||||
p
|
||||
| Apply the pipe to one document. The document is modified in place, and
|
||||
| returned. Both #[code #{CLASSNAME}.__call__] and
|
||||
| #[code #{CLASSNAME}.pipe] should delegate to the
|
||||
| #[code #{CLASSNAME}.predict] and #[code #{CLASSNAME}.set_annotations]
|
||||
| methods.
|
||||
|
||||
+aside-code("Example").
|
||||
#{VARNAME} = #{CLASSNAME}(nlp.vocab)
|
||||
doc = nlp(u"This is a sentence.")
|
||||
processed = #{VARNAME}(doc)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code doc]
|
||||
+cell #[code Doc]
|
||||
+cell The document to process.
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Doc]
|
||||
+cell The processed document.
|
||||
|
||||
+h(2, "pipe") #{CLASSNAME}.pipe
|
||||
+tag method
|
||||
|
||||
p
|
||||
| Apply the pipe to a stream of documents. Both
|
||||
| #[code #{CLASSNAME}.__call__] and #[code #{CLASSNAME}.pipe] should
|
||||
| delegate to the #[code #{CLASSNAME}.predict] and
|
||||
| #[code #{CLASSNAME}.set_annotations] methods.
|
||||
|
||||
+aside-code("Example").
|
||||
texts = [u'One doc', u'...', u'Lots of docs']
|
||||
#{VARNAME} = #{CLASSNAME}(nlp.vocab)
|
||||
for doc in #{VARNAME}.pipe(texts, batch_size=50):
|
||||
pass
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code stream]
|
||||
+cell iterable
|
||||
+cell A stream of documents.
|
||||
|
||||
+row
|
||||
+cell #[code batch_size]
|
||||
+cell int
|
||||
+cell The number of texts to buffer. Defaults to #[code 128].
|
||||
|
||||
+row
|
||||
+cell #[code n_threads]
|
||||
+cell int
|
||||
+cell
|
||||
| The number of worker threads to use. If #[code -1], OpenMP will
|
||||
| decide how many to use at run time. Default is #[code -1].
|
||||
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell #[code Doc]
|
||||
+cell Processed documents in the order of the original text.
|
||||
|
||||
+h(2, "predict") #{CLASSNAME}.predict
|
||||
+tag method
|
||||
|
||||
p
|
||||
| Apply the pipeline's model to a batch of docs, without modifying them.
|
||||
|
||||
+aside-code("Example").
|
||||
#{VARNAME} = #{CLASSNAME}(nlp.vocab)
|
||||
scores = #{VARNAME}.predict([doc1, doc2])
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code docs]
|
||||
+cell iterable
|
||||
+cell The documents to predict.
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell -
|
||||
+cell Scores from the model.
|
||||
|
||||
+h(2, "set_annotations") #{CLASSNAME}.set_annotations
|
||||
+tag method
|
||||
|
||||
p
|
||||
| Modify a batch of documents, using pre-computed scores.
|
||||
|
||||
+aside-code("Example").
|
||||
#{VARNAME} = #{CLASSNAME}(nlp.vocab)
|
||||
scores = #{VARNAME}.predict([doc1, doc2])
|
||||
#{VARNAME}.set_annotations([doc1, doc2], scores)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code docs]
|
||||
+cell iterable
|
||||
+cell The documents to modify.
|
||||
|
||||
+row
|
||||
+cell #[code scores]
|
||||
+cell -
|
||||
+cell The scores to set, produced by #[code #{CLASSNAME}.predict].
|
||||
|
||||
+h(2, "update") #{CLASSNAME}.update
|
||||
+tag method
|
||||
|
||||
p
|
||||
| Learn from a batch of documents and gold-standard information, updating
|
||||
| the pipe's model. Delegates to #[code #{CLASSNAME}.predict] and
|
||||
| #[code #{CLASSNAME}.get_loss].
|
||||
|
||||
+aside-code("Example").
|
||||
#{VARNAME} = #{CLASSNAME}(nlp.vocab)
|
||||
losses = {}
|
||||
optimizer = nlp.begin_training()
|
||||
#{VARNAME}.update([doc1, doc2], [gold1, gold2], losses=losses, sgd=optimizer)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code docs]
|
||||
+cell iterable
|
||||
+cell A batch of documents to learn from.
|
||||
|
||||
+row
|
||||
+cell #[code golds]
|
||||
+cell iterable
|
||||
+cell The gold-standard data. Must have the same length as #[code docs].
|
||||
|
||||
+row
|
||||
+cell #[code drop]
|
||||
+cell int
|
||||
+cell The dropout rate.
|
||||
|
||||
+row
|
||||
+cell #[code sgd]
|
||||
+cell callable
|
||||
+cell
|
||||
| The optimizer. Should take two arguments #[code weights] and
|
||||
| #[code gradient], and an optional ID.
|
||||
|
||||
+row
|
||||
+cell #[code losses]
|
||||
+cell dict
|
||||
+cell
|
||||
| Optional record of the loss during training. The value keyed by
|
||||
| the model's name is updated.
|
||||
|
||||
+h(2, "get_loss") #{CLASSNAME}.get_loss
|
||||
+tag method
|
||||
|
||||
p
|
||||
| Find the loss and gradient of loss for the batch of documents and their
|
||||
| predicted scores.
|
||||
|
||||
+aside-code("Example").
|
||||
#{VARNAME} = #{CLASSNAME}(nlp.vocab)
|
||||
scores = #{VARNAME}.predict([doc1, doc2])
|
||||
loss, d_loss = #{VARNAME}.get_loss([doc1, doc2], [gold1, gold2], scores)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code docs]
|
||||
+cell iterable
|
||||
+cell The batch of documents.
|
||||
|
||||
+row
|
||||
+cell #[code golds]
|
||||
+cell iterable
|
||||
+cell The gold-standard data. Must have the same length as #[code docs].
|
||||
|
||||
+row
|
||||
+cell #[code scores]
|
||||
+cell -
|
||||
+cell Scores representing the model's predictions.
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell tuple
|
||||
+cell The loss and the gradient, i.e. #[code (loss, gradient)].
|
||||
|
||||
+h(2, "begin_training") #{CLASSNAME}.begin_training
|
||||
+tag method
|
||||
|
||||
p
|
||||
| Initialize the pipe for training, using data exampes if available. If no
|
||||
| model has been initialized yet, the model is added.
|
||||
|
||||
+aside-code("Example").
|
||||
#{VARNAME} = #{CLASSNAME}(nlp.vocab)
|
||||
nlp.pipeline.append(#{VARNAME})
|
||||
#{VARNAME}.begin_training(pipeline=nlp.pipeline)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code gold_tuples]
|
||||
+cell iterable
|
||||
+cell
|
||||
| Optional gold-standard annotations from which to construct
|
||||
| #[+api("goldparse") #[code GoldParse]] objects.
|
||||
|
||||
+row
|
||||
+cell #[code pipeline]
|
||||
+cell list
|
||||
+cell
|
||||
| Optional list of #[+api("pipe") #[code Pipe]] components that
|
||||
| this component is part of.
|
||||
|
||||
+h(2, "use_params") #{CLASSNAME}.use_params
|
||||
+tag method
|
||||
+tag contextmanager
|
||||
|
||||
p Modify the pipe's model, to use the given parameter values.
|
||||
|
||||
+aside-code("Example").
|
||||
#{VARNAME} = #{CLASSNAME}(nlp.vocab)
|
||||
with #{VARNAME}.use_params():
|
||||
#{VARNAME}.to_disk('/best_model')
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code params]
|
||||
+cell -
|
||||
+cell
|
||||
| The parameter values to use in the model. At the end of the
|
||||
| context, the original parameters are restored.
|
||||
|
||||
+h(2, "to_disk") #{CLASSNAME}.to_disk
|
||||
+tag method
|
||||
|
||||
p Serialize the pipe to disk.
|
||||
|
||||
+aside-code("Example").
|
||||
#{VARNAME} = #{CLASSNAME}(nlp.vocab)
|
||||
#{VARNAME}.to_disk('/path/to/#{VARNAME}')
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code path]
|
||||
+cell unicode or #[code Path]
|
||||
+cell
|
||||
| A path to a directory, which will be created if it doesn't exist.
|
||||
| Paths may be either strings or #[code Path]-like objects.
|
||||
|
||||
+h(2, "from_disk") #{CLASSNAME}.from_disk
|
||||
+tag method
|
||||
|
||||
p Load the pipe from disk. Modifies the object in place and returns it.
|
||||
|
||||
+aside-code("Example").
|
||||
#{VARNAME} = #{CLASSNAME}(nlp.vocab)
|
||||
#{VARNAME}.from_disk('/path/to/#{VARNAME}')
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code path]
|
||||
+cell unicode or #[code Path]
|
||||
+cell
|
||||
| A path to a directory. Paths may be either strings or
|
||||
| #[code Path]-like objects.
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code=CLASSNAME]
|
||||
+cell The modified #[code=CLASSNAME] object.
|
||||
|
||||
+h(2, "to_bytes") #{CLASSNAME}.to_bytes
|
||||
+tag method
|
||||
|
||||
+aside-code("example").
|
||||
#{VARNAME} = #{CLASSNAME}(nlp.vocab)
|
||||
#{VARNAME}_bytes = #{VARNAME}.to_bytes()
|
||||
|
||||
p Serialize the pipe to a bytestring.
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code **exclude]
|
||||
+cell -
|
||||
+cell Named attributes to prevent from being serialized.
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bytes
|
||||
+cell The serialized form of the #[code=CLASSNAME] object.
|
||||
|
||||
+h(2, "from_bytes") #{CLASSNAME}.from_bytes
|
||||
+tag method
|
||||
|
||||
p Load the pipe from a bytestring. Modifies the object in place and returns it.
|
||||
|
||||
+aside-code("Example").
|
||||
#{VARNAME}_bytes = #{VARNAME}.to_bytes()
|
||||
#{VARNAME} = #{CLASSNAME}(nlp.vocab)
|
||||
#{VARNAME}.from_bytes(#{VARNAME}_bytes)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code bytes_data]
|
||||
+cell bytes
|
||||
+cell The data to load from.
|
||||
|
||||
+row
|
||||
+cell #[code **exclude]
|
||||
+cell -
|
||||
+cell Named attributes to prevent from being loaded.
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code=CLASSNAME]
|
||||
+cell The #[code=CLASSNAME] object.
|
|
@ -1,6 +1,6 @@
|
|||
//- 💫 DOCS > API > SPAN
|
||||
|
||||
include ../../_includes/_mixins
|
||||
include ../_includes/_mixins
|
||||
|
||||
p A slice from a #[+api("doc") #[code Doc]] object.
|
||||
|
||||
|
@ -40,7 +40,7 @@ p Create a Span object from the #[code slice doc[start : end]].
|
|||
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
|
||||
+cell A meaning representation of the span.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Span]
|
||||
+cell The newly constructed object.
|
||||
|
@ -61,7 +61,7 @@ p Get a #[code Token] object.
|
|||
+cell int
|
||||
+cell The index of the token within the span.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Token]
|
||||
+cell The token at #[code span[i]].
|
||||
|
@ -79,7 +79,7 @@ p Get a #[code Span] object.
|
|||
+cell tuple
|
||||
+cell The slice of the span to get.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Span]
|
||||
+cell The span at #[code span[start : end]].
|
||||
|
@ -95,7 +95,7 @@ p Iterate over #[code Token] objects.
|
|||
assert [t.text for t in span] == ['it', 'back', '!']
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell #[code Token]
|
||||
+cell A #[code Token] object.
|
||||
|
@ -111,7 +111,7 @@ p Get the number of tokens in the span.
|
|||
assert len(span) == 3
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell int
|
||||
+cell The number of tokens in the span.
|
||||
|
@ -140,7 +140,7 @@ p
|
|||
| The object to compare with. By default, accepts #[code Doc],
|
||||
| #[code Span], #[code Token] and #[code Lexeme] objects.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell float
|
||||
+cell A scalar similarity score. Higher is more similar.
|
||||
|
@ -167,7 +167,7 @@ p
|
|||
+cell list
|
||||
+cell A list of attribute ID ints.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code.u-break numpy.ndarray[long, ndim=2]]
|
||||
+cell
|
||||
|
@ -194,7 +194,7 @@ p Retokenize the document, such that the span is merged into a single token.
|
|||
| Attributes to assign to the merged token. By default, attributes
|
||||
| are inherited from the syntactic root token of the span.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Token]
|
||||
+cell The newly merged token.
|
||||
|
@ -216,7 +216,7 @@ p
|
|||
assert new_york.root.text == 'York'
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Token]
|
||||
+cell The root token.
|
||||
|
@ -233,7 +233,7 @@ p Tokens that are to the left of the span, whose head is within the span.
|
|||
assert lefts == [u'New']
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell #[code Token]
|
||||
+cell A left-child of a token of the span.
|
||||
|
@ -250,7 +250,7 @@ p Tokens that are to the right of the span, whose head is within the span.
|
|||
assert rights == [u'in']
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell #[code Token]
|
||||
+cell A right-child of a token of the span.
|
||||
|
@ -267,7 +267,7 @@ p Tokens that descend from tokens in the span, but fall outside it.
|
|||
assert subtree == [u'Give', u'it', u'back', u'!']
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell #[code Token]
|
||||
+cell A descendant of a token within the span.
|
||||
|
@ -285,7 +285,7 @@ p
|
|||
assert doc[1:].has_vector
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bool
|
||||
+cell Whether the span has a vector data attached.
|
||||
|
@ -304,7 +304,7 @@ p
|
|||
assert doc[1:].vector.shape == (300,)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
|
||||
+cell A 1D numpy array representing the span's semantics.
|
||||
|
@ -323,7 +323,7 @@ p
|
|||
assert doc[1:].vector_norm != doc[2:].vector_norm
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell float
|
||||
+cell The L2 norm of the vector representation.
|
|
@ -1,6 +1,6 @@
|
|||
//- 💫 DOCS > API > STRINGSTORE
|
||||
|
||||
include ../../_includes/_mixins
|
||||
include ../_includes/_mixins
|
||||
|
||||
p
|
||||
| Look up strings by 64-bit hashes. As of v2.0, spaCy uses hash values
|
||||
|
@ -23,7 +23,7 @@ p
|
|||
+cell iterable
|
||||
+cell A sequence of unicode strings to add to the store.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code StringStore]
|
||||
+cell The newly constructed object.
|
||||
|
@ -38,7 +38,7 @@ p Get the number of strings in the store.
|
|||
assert len(stringstore) == 2
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell int
|
||||
+cell The number of strings in the store.
|
||||
|
@ -60,7 +60,7 @@ p Retrieve a string from a given hash, or vice versa.
|
|||
+cell bytes, unicode or uint64
|
||||
+cell The value to encode.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell unicode or int
|
||||
+cell The value to be retrieved.
|
||||
|
@ -81,7 +81,7 @@ p Check whether a string is in the store.
|
|||
+cell unicode
|
||||
+cell The string to check.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bool
|
||||
+cell Whether the store contains the string.
|
||||
|
@ -100,7 +100,7 @@ p
|
|||
assert all_strings == [u'apple', u'orange']
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell unicode
|
||||
+cell A string in the store.
|
||||
|
@ -125,7 +125,7 @@ p Add a string to the #[code StringStore].
|
|||
+cell unicode
|
||||
+cell The string to add.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell uint64
|
||||
+cell The string's hash value.
|
||||
|
@ -166,7 +166,7 @@ p Loads state from a directory. Modifies the object in place and returns it.
|
|||
| A path to a directory. Paths may be either strings or
|
||||
| #[code Path]-like objects.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code StringStore]
|
||||
+cell The modified #[code StringStore] object.
|
||||
|
@ -185,7 +185,7 @@ p Serialize the current state to a binary string.
|
|||
+cell -
|
||||
+cell Named attributes to prevent from being serialized.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bytes
|
||||
+cell The serialized form of the #[code StringStore] object.
|
||||
|
@ -211,7 +211,7 @@ p Load state from a binary string.
|
|||
+cell -
|
||||
+cell Named attributes to prevent from being loaded.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code StringStore]
|
||||
+cell The #[code StringStore] object.
|
||||
|
@ -233,7 +233,7 @@ p Get a 64-bit hash for a given string.
|
|||
+cell unicode
|
||||
+cell The string to hash.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell uint64
|
||||
+cell The hash.
|
5
website/api/tagger.jade
Normal file
5
website/api/tagger.jade
Normal file
|
@ -0,0 +1,5 @@
|
|||
//- 💫 DOCS > API > TAGGER
|
||||
|
||||
include ../_includes/_mixins
|
||||
|
||||
!=partial("pipe", { subclass: "Tagger", pipeline_id: "tagger" })
|
5
website/api/tensorizer.jade
Normal file
5
website/api/tensorizer.jade
Normal file
|
@ -0,0 +1,5 @@
|
|||
//- 💫 DOCS > API > TENSORIZER
|
||||
|
||||
include ../_includes/_mixins
|
||||
|
||||
!=partial("pipe", { subclass: "Tensorizer", pipeline_id: "tensorizer" })
|
19
website/api/textcategorizer.jade
Normal file
19
website/api/textcategorizer.jade
Normal file
|
@ -0,0 +1,19 @@
|
|||
//- 💫 DOCS > API > TEXTCATEGORIZER
|
||||
|
||||
include ../_includes/_mixins
|
||||
|
||||
p
|
||||
| The model supports classification with multiple, non-mutually exclusive
|
||||
| labels. You can change the model architecture rather easily, but by
|
||||
| default, the #[code TextCategorizer] class uses a convolutional
|
||||
| neural network to assign position-sensitive vectors to each word in the
|
||||
| document. This step is similar to the #[+api("tensorizer") #[code Tensorizer]]
|
||||
| component, but the #[code TextCategorizer] uses its own CNN model, to
|
||||
| avoid sharing weights with the other pipeline components. The document
|
||||
| tensor is then
|
||||
| summarized by concatenating max and mean pooling, and a multilayer
|
||||
| perceptron is used to predict an output vector of length #[code nr_class],
|
||||
| before a logistic activation is applied elementwise. The value of each
|
||||
| output neuron is the probability that some class is present.
|
||||
|
||||
!=partial("pipe", { subclass: "TextCategorizer", short: "textcat", pipeline_id: "textcat" })
|
|
@ -1,6 +1,6 @@
|
|||
//- 💫 DOCS > API > TOKEN
|
||||
|
||||
include ../../_includes/_mixins
|
||||
include ../_includes/_mixins
|
||||
|
||||
p An individual token — i.e. a word, punctuation symbol, whitespace, etc.
|
||||
|
||||
|
@ -30,7 +30,7 @@ p Construct a #[code Token] object.
|
|||
+cell int
|
||||
+cell The index of the token within the document.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Token]
|
||||
+cell The newly constructed object.
|
||||
|
@ -46,7 +46,7 @@ p The number of unicode characters in the token, i.e. #[code token.text].
|
|||
assert len(token) == 4
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell int
|
||||
+cell The number of unicode characters in the token.
|
||||
|
@ -68,7 +68,7 @@ p Check the value of a boolean flag.
|
|||
+cell int
|
||||
+cell The attribute ID of the flag to check.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bool
|
||||
+cell Whether the flag is set.
|
||||
|
@ -93,7 +93,7 @@ p Compute a semantic similarity estimate. Defaults to cosine over vectors.
|
|||
| The object to compare with. By default, accepts #[code Doc],
|
||||
| #[code Span], #[code Token] and #[code Lexeme] objects.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell float
|
||||
+cell A scalar similarity score. Higher is more similar.
|
||||
|
@ -114,7 +114,7 @@ p Get a neighboring token.
|
|||
+cell int
|
||||
+cell The relative position of the token to get. Defaults to #[code 1].
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Token]
|
||||
+cell The token at position #[code self.doc[self.i+i]].
|
||||
|
@ -139,7 +139,7 @@ p
|
|||
+cell #[code Token]
|
||||
+cell Another token.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bool
|
||||
+cell Whether this token is the ancestor of the descendant.
|
||||
|
@ -158,7 +158,7 @@ p The rightmost token of this token's syntactic descendants.
|
|||
assert [t.text for t in he_ancestors] == [u'pleaded']
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell #[code Token]
|
||||
+cell
|
||||
|
@ -177,7 +177,7 @@ p A sequence of coordinated tokens, including the token itself.
|
|||
assert [t.text for t in apples_conjuncts] == [u'oranges']
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell #[code Token]
|
||||
+cell A coordinated token.
|
||||
|
@ -194,7 +194,7 @@ p A sequence of the token's immediate syntactic children.
|
|||
assert [t.text for t in give_children] == [u'it', u'back', u'!']
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell #[code Token]
|
||||
+cell A child token such that #[code child.head==self].
|
||||
|
@ -211,7 +211,7 @@ p A sequence of all the token's syntactic descendents.
|
|||
assert [t.text for t in give_subtree] == [u'Give', u'it', u'back', u'!']
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell #[code Token]
|
||||
+cell A descendant token such that #[code self.is_ancestor(descendant)].
|
||||
|
@ -230,7 +230,7 @@ p
|
|||
assert apples.has_vector
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bool
|
||||
+cell Whether the token has a vector data attached.
|
||||
|
@ -248,7 +248,7 @@ p A real-valued meaning representation.
|
|||
assert apples.vector.shape == (300,)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
|
||||
+cell A 1D numpy array representing the token's semantics.
|
||||
|
@ -268,7 +268,7 @@ p The L2 norm of the token's vector representation.
|
|||
assert apples.vector_norm != pasta.vector_norm
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell float
|
||||
+cell The L2 norm of the vector representation.
|
||||
|
@ -280,20 +280,29 @@ p The L2 norm of the token's vector representation.
|
|||
+cell #[code text]
|
||||
+cell unicode
|
||||
+cell Verbatim text content.
|
||||
|
||||
+row
|
||||
+cell #[code text_with_ws]
|
||||
+cell unicode
|
||||
+cell Text content, with trailing space character if present.
|
||||
|
||||
+row
|
||||
+cell #[code whitespace]
|
||||
+cell int
|
||||
+cell Trailing space character if present.
|
||||
+row
|
||||
+cell #[code whitespace_]
|
||||
+cell unicode
|
||||
+cell Trailing space character if present.
|
||||
|
||||
+row
|
||||
+cell #[code orth]
|
||||
+cell int
|
||||
+cell ID of the verbatim text content.
|
||||
|
||||
+row
|
||||
+cell #[code orth_]
|
||||
+cell unicode
|
||||
+cell
|
||||
| Verbatim text content (identical to #[code Token.text]). Existst
|
||||
| mostly for consistency with the other attributes.
|
||||
|
||||
+row
|
||||
+cell #[code vocab]
|
||||
+cell #[code Vocab]
|
|
@ -1,6 +1,6 @@
|
|||
//- 💫 DOCS > API > TOKENIZER
|
||||
|
||||
include ../../_includes/_mixins
|
||||
include ../_includes/_mixins
|
||||
|
||||
p
|
||||
| Segment text, and create #[code Doc] objects with the discovered segment
|
||||
|
@ -57,7 +57,7 @@ p Create a #[code Tokenizer], to create #[code Doc] objects given unicode text.
|
|||
+cell callable
|
||||
+cell A boolean function matching strings to be recognised as tokens.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Tokenizer]
|
||||
+cell The newly constructed object.
|
||||
|
@ -77,7 +77,7 @@ p Tokenize a string.
|
|||
+cell unicode
|
||||
+cell The string to tokenize.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Doc]
|
||||
+cell A container for linguistic annotations.
|
||||
|
@ -110,7 +110,7 @@ p Tokenize a stream of texts.
|
|||
| The number of threads to use, if the implementation supports
|
||||
| multi-threading. The default tokenizer is single-threaded.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell #[code Doc]
|
||||
+cell A sequence of Doc objects, in order.
|
||||
|
@ -126,7 +126,7 @@ p Find internal split points of the string.
|
|||
+cell unicode
|
||||
+cell The string to split.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell list
|
||||
+cell
|
||||
|
@ -147,7 +147,7 @@ p
|
|||
+cell unicode
|
||||
+cell The string to segment.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell int
|
||||
+cell The length of the prefix if present, otherwise #[code None].
|
||||
|
@ -165,7 +165,7 @@ p
|
|||
+cell unicode
|
||||
+cell The string to segment.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell int / #[code None]
|
||||
+cell The length of the suffix if present, otherwise #[code None].
|
||||
|
@ -176,7 +176,7 @@ p
|
|||
p
|
||||
| Add a special-case tokenization rule. This mechanism is also used to add
|
||||
| custom tokenizer exceptions to the language data. See the usage guide
|
||||
| on #[+a("/docs/usage/adding-languages#tokenizer-exceptions") adding languages]
|
||||
| on #[+a("/usage/adding-languages#tokenizer-exceptions") adding languages]
|
||||
| for more details and examples.
|
||||
|
||||
+aside-code("Example").
|
24
website/api/top-level.jade
Normal file
24
website/api/top-level.jade
Normal file
|
@ -0,0 +1,24 @@
|
|||
//- 💫 DOCS > API > TOP-LEVEL
|
||||
|
||||
include ../_includes/_mixins
|
||||
|
||||
+section("spacy")
|
||||
//-+h(2, "spacy") spaCy
|
||||
//- spacy/__init__.py
|
||||
include _top-level/_spacy
|
||||
|
||||
+section("displacy")
|
||||
+h(2, "displacy", "spacy/displacy") displaCy
|
||||
include _top-level/_displacy
|
||||
|
||||
+section("util")
|
||||
+h(2, "util", "spacy/util.py") Utility functions
|
||||
include _top-level/_util
|
||||
|
||||
+section("compat")
|
||||
+h(2, "compat", "spacy/compaty.py") Compatibility functions
|
||||
include _top-level/_compat
|
||||
|
||||
+section("cli", "spacy/cli")
|
||||
+h(2, "cli") Command line
|
||||
include _top-level/_cli
|
333
website/api/vectors.jade
Normal file
333
website/api/vectors.jade
Normal file
|
@ -0,0 +1,333 @@
|
|||
//- 💫 DOCS > API > VECTORS
|
||||
|
||||
include ../_includes/_mixins
|
||||
|
||||
p
|
||||
| Vectors data is kept in the #[code Vectors.data] attribute, which should
|
||||
| be an instance of #[code numpy.ndarray] (for CPU vectors) or
|
||||
| #[code cupy.ndarray] (for GPU vectors).
|
||||
|
||||
+h(2, "init") Vectors.__init__
|
||||
+tag method
|
||||
|
||||
p
|
||||
| Create a new vector store. To keep the vector table empty, pass
|
||||
| #[code data_or_width=0]. You can also create the vector table and add
|
||||
| vectors one by one, or set the vector values directly on initialisation.
|
||||
|
||||
+aside-code("Example").
|
||||
from spacy.vectors import Vectors
|
||||
from spacy.strings import StringStore
|
||||
|
||||
empty_vectors = Vectors(StringStore())
|
||||
|
||||
vectors = Vectors([u'cat'], 300)
|
||||
vectors[u'cat'] = numpy.random.uniform(-1, 1, (300,))
|
||||
|
||||
vector_table = numpy.zeros((3, 300), dtype='f')
|
||||
vectors = Vectors(StringStore(), vector_table)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code strings]
|
||||
+cell #[code StringStore] or list
|
||||
+cell
|
||||
| List of strings, or a #[+api("stringstore") #[code StringStore]]
|
||||
| that maps strings to hash values, and vice versa.
|
||||
|
||||
+row
|
||||
+cell #[code data_or_width]
|
||||
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']] or int
|
||||
+cell Vector data or number of dimensions.
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Vectors]
|
||||
+cell The newly created object.
|
||||
|
||||
+h(2, "getitem") Vectors.__getitem__
|
||||
+tag method
|
||||
|
||||
p
|
||||
| Get a vector by key. If key is a string, it is hashed to an integer ID
|
||||
| using the #[code Vectors.strings] table. If the integer key is not found
|
||||
| in the table, a #[code KeyError] is raised.
|
||||
|
||||
+aside-code("Example").
|
||||
vectors = Vectors(StringStore(), 300)
|
||||
vectors.add(u'cat', numpy.random.uniform(-1, 1, (300,)))
|
||||
cat_vector = vectors[u'cat']
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code key]
|
||||
+cell unicode / int
|
||||
+cell The key to get the vector for.
|
||||
|
||||
+row
|
||||
+cell returns
|
||||
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
|
||||
+cell The vector for the key.
|
||||
|
||||
+h(2, "setitem") Vectors.__setitem__
|
||||
+tag method
|
||||
|
||||
p
|
||||
| Set a vector for the given key. If key is a string, it is hashed to an
|
||||
| integer ID using the #[code Vectors.strings] table.
|
||||
|
||||
+aside-code("Example").
|
||||
vectors = Vectors(StringStore(), 300)
|
||||
vectors[u'cat'] = numpy.random.uniform(-1, 1, (300,))
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code key]
|
||||
+cell unicode / int
|
||||
+cell The key to set the vector for.
|
||||
|
||||
+row
|
||||
+cell #[code vector]
|
||||
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
|
||||
+cell The vector to set.
|
||||
|
||||
+h(2, "iter") Vectors.__iter__
|
||||
+tag method
|
||||
|
||||
p Yield vectors from the table.
|
||||
|
||||
+aside-code("Example").
|
||||
vector_table = numpy.zeros((3, 300), dtype='f')
|
||||
vectors = Vectors(StringStore(), vector_table)
|
||||
for vector in vectors:
|
||||
print(vector)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
|
||||
+cell A vector from the table.
|
||||
|
||||
+h(2, "len") Vectors.__len__
|
||||
+tag method
|
||||
|
||||
p Return the number of vectors that have been assigned.
|
||||
|
||||
+aside-code("Example").
|
||||
vector_table = numpy.zeros((3, 300), dtype='f')
|
||||
vectors = Vectors(StringStore(), vector_table)
|
||||
assert len(vectors) == 3
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell int
|
||||
+cell The number of vectors in the data.
|
||||
|
||||
+h(2, "contains") Vectors.__contains__
|
||||
+tag method
|
||||
|
||||
p
|
||||
| Check whether a key has a vector entry in the table. If key is a string,
|
||||
| it is hashed to an integer ID using the #[code Vectors.strings] table.
|
||||
|
||||
+aside-code("Example").
|
||||
vectors = Vectors(StringStore(), 300)
|
||||
vectors.add(u'cat', numpy.random.uniform(-1, 1, (300,)))
|
||||
assert u'cat' in vectors
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code key]
|
||||
+cell unicode / int
|
||||
+cell The key to check.
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bool
|
||||
+cell Whether the key has a vector entry.
|
||||
|
||||
+h(2, "add") Vectors.add
|
||||
+tag method
|
||||
|
||||
p
|
||||
| Add a key to the table, optionally setting a vector value as well. If
|
||||
| key is a string, it is hashed to an integer ID using the
|
||||
| #[code Vectors.strings] table.
|
||||
|
||||
+aside-code("Example").
|
||||
vectors = Vectors(StringStore(), 300)
|
||||
vectors.add(u'cat', numpy.random.uniform(-1, 1, (300,)))
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code key]
|
||||
+cell unicode / int
|
||||
+cell The key to add.
|
||||
|
||||
+row
|
||||
+cell #[code vector]
|
||||
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
|
||||
+cell An optional vector to add.
|
||||
|
||||
+h(2, "items") Vectors.items
|
||||
+tag method
|
||||
|
||||
p Iterate over #[code (string key, vector)] pairs, in order.
|
||||
|
||||
+aside-code("Example").
|
||||
vectors = Vectors(StringStore(), 300)
|
||||
vectors.add(u'cat', numpy.random.uniform(-1, 1, (300,)))
|
||||
for key, vector in vectors.items():
|
||||
print(key, vector)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell tuple
|
||||
+cell #[code (string key, vector)] pairs, in order.
|
||||
|
||||
+h(2, "shape") Vectors.shape
|
||||
+tag property
|
||||
|
||||
p
|
||||
| Get #[code (rows, dims)] tuples of number of rows and number of
|
||||
| dimensions in the vector table.
|
||||
|
||||
+aside-code("Example").
|
||||
vectors = Vectors(StringStore(), 300)
|
||||
vectors.add(u'cat', numpy.random.uniform(-1, 1, (300,)))
|
||||
rows, dims = vectors.shape
|
||||
assert rows == 1
|
||||
assert dims == 300
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell tuple
|
||||
+cell #[code (rows, dims)] pairs.
|
||||
|
||||
+h(2, "from_glove") Vectors.from_glove
|
||||
+tag method
|
||||
|
||||
p
|
||||
| Load #[+a("https://nlp.stanford.edu/projects/glove/") GloVe] vectors from
|
||||
| a directory. Assumes binary format, that the vocab is in a
|
||||
| #[code vocab.txt], and that vectors are named
|
||||
| #[code vectors.{size}.[fd].bin], e.g. #[code vectors.128.f.bin] for 128d
|
||||
| float32 vectors, #[code vectors.300.d.bin] for 300d float64 (double)
|
||||
| vectors, etc. By default GloVe outputs 64-bit vectors.
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code path]
|
||||
+cell unicode / #[code Path]
|
||||
+cell The path to load the GloVe vectors from.
|
||||
|
||||
+h(2, "to_disk") Vectors.to_disk
|
||||
+tag method
|
||||
|
||||
p Save the current state to a directory.
|
||||
|
||||
+aside-code("Example").
|
||||
vectors.to_disk('/path/to/vectors')
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code path]
|
||||
+cell unicode or #[code Path]
|
||||
+cell
|
||||
| A path to a directory, which will be created if it doesn't exist.
|
||||
| Paths may be either strings or #[code Path]-like objects.
|
||||
|
||||
+h(2, "from_disk") Vectors.from_disk
|
||||
+tag method
|
||||
|
||||
p Loads state from a directory. Modifies the object in place and returns it.
|
||||
|
||||
+aside-code("Example").
|
||||
vectors = Vectors(StringStore())
|
||||
vectors.from_disk('/path/to/vectors')
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code path]
|
||||
+cell unicode or #[code Path]
|
||||
+cell
|
||||
| A path to a directory. Paths may be either strings or
|
||||
| #[code Path]-like objects.
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Vectors]
|
||||
+cell The modified #[code Vectors] object.
|
||||
|
||||
+h(2, "to_bytes") Vectors.to_bytes
|
||||
+tag method
|
||||
|
||||
p Serialize the current state to a binary string.
|
||||
|
||||
+aside-code("Example").
|
||||
vectors_bytes = vectors.to_bytes()
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code **exclude]
|
||||
+cell -
|
||||
+cell Named attributes to prevent from being serialized.
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bytes
|
||||
+cell The serialized form of the #[code Vectors] object.
|
||||
|
||||
+h(2, "from_bytes") Vectors.from_bytes
|
||||
+tag method
|
||||
|
||||
p Load state from a binary string.
|
||||
|
||||
+aside-code("Example").
|
||||
fron spacy.vectors import Vectors
|
||||
vectors_bytes = vectors.to_bytes()
|
||||
new_vectors = Vectors(StringStore())
|
||||
new_vectors.from_bytes(vectors_bytes)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code bytes_data]
|
||||
+cell bytes
|
||||
+cell The data to load from.
|
||||
|
||||
+row
|
||||
+cell #[code **exclude]
|
||||
+cell -
|
||||
+cell Named attributes to prevent from being loaded.
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Vectors]
|
||||
+cell The #[code Vectors] object.
|
||||
|
||||
+h(2, "attributes") Attributes
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code data]
|
||||
+cell #[code numpy.ndarray] / #[code cupy.ndarray]
|
||||
+cell
|
||||
| Stored vectors data. #[code numpy] is used for CPU vectors,
|
||||
| #[code cupy] for GPU vectors.
|
||||
|
||||
+row
|
||||
+cell #[code key2row]
|
||||
+cell dict
|
||||
+cell
|
||||
| Dictionary mapping word hashes to rows in the
|
||||
| #[code Vectors.data] table.
|
||||
|
||||
+row
|
||||
+cell #[code keys]
|
||||
+cell #[code numpy.ndarray]
|
||||
+cell
|
||||
| Array keeping the keys in order, such that
|
||||
| #[code keys[vectors.key2row[key]] == key]
|
|
@ -1,17 +1,22 @@
|
|||
//- 💫 DOCS > API > VOCAB
|
||||
|
||||
include ../../_includes/_mixins
|
||||
include ../_includes/_mixins
|
||||
|
||||
p
|
||||
| A lookup table that allows you to access #[code Lexeme] objects. The
|
||||
| #[code Vocab] instance also provides access to the #[code StringStore],
|
||||
| and owns underlying C-data that is shared between #[code Doc] objects.
|
||||
| The #[code Vocab] object provides a lookup table that allows you to
|
||||
| access #[+api("lexeme") #[code Lexeme]] objects, as well as the
|
||||
| #[+api("stringstore") #[code StringStore]]. It also owns underlying
|
||||
| C-data that is shared between #[code Doc] objects.
|
||||
|
||||
+h(2, "init") Vocab.__init__
|
||||
+tag method
|
||||
|
||||
p Create the vocabulary.
|
||||
|
||||
+aside-code("Example").
|
||||
from spacy.vocab import Vocab
|
||||
vocab = Vocab(strings=[u'hello', u'world'])
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code lex_attr_getters]
|
||||
|
@ -39,7 +44,7 @@ p Create the vocabulary.
|
|||
| A #[+api("stringstore") #[code StringStore]] that maps
|
||||
| strings to hash values, and vice versa, or a list of strings.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Vocab]
|
||||
+cell The newly constructed object.
|
||||
|
@ -54,7 +59,7 @@ p Get the current number of lexemes in the vocabulary.
|
|||
assert len(nlp.vocab) > 0
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell int
|
||||
+cell The number of lexems in the vocabulary.
|
||||
|
@ -76,7 +81,7 @@ p
|
|||
+cell int / unicode
|
||||
+cell The hash value of a word, or its unicode string.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Lexeme]
|
||||
+cell The lexeme indicated by the given ID.
|
||||
|
@ -90,7 +95,7 @@ p Iterate over the lexemes in the vocabulary.
|
|||
stop_words = (lex for lex in nlp.vocab if lex.is_stop)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell yields
|
||||
+cell #[code Lexeme]
|
||||
+cell An entry in the vocabulary.
|
||||
|
@ -115,7 +120,7 @@ p
|
|||
+cell unicode
|
||||
+cell The ID string.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bool
|
||||
+cell Whether the string has an entry in the vocabulary.
|
||||
|
@ -152,11 +157,100 @@ p
|
|||
| which the flag will be stored. If #[code -1], the lowest
|
||||
| available bit will be chosen.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell int
|
||||
+cell The integer ID by which the flag value can be checked.
|
||||
|
||||
+h(2, "add_flag") Vocab.clear_vectors
|
||||
+tag method
|
||||
+tag-new(2)
|
||||
|
||||
p
|
||||
| Drop the current vector table. Because all vectors must be the same
|
||||
| width, you have to call this to change the size of the vectors.
|
||||
|
||||
+aside-code("Example").
|
||||
nlp.vocab.clear_vectors(new_dim=300)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code new_dim]
|
||||
+cell int
|
||||
+cell
|
||||
| Number of dimensions of the new vectors. If #[code None], size
|
||||
| is not changed.
|
||||
|
||||
+h(2, "add_flag") Vocab.get_vector
|
||||
+tag method
|
||||
+tag-new(2)
|
||||
|
||||
p
|
||||
| Retrieve a vector for a word in the vocabulary. Words can be looked up
|
||||
| by string or hash value. If no vectors data is loaded, a
|
||||
| #[code ValueError] is raised.
|
||||
|
||||
+aside-code("Example").
|
||||
nlp.vocab.get_vector(u'apple')
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code orth]
|
||||
+cell int / unicode
|
||||
+cell The hash value of a word, or its unicode string.
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
|
||||
+cell
|
||||
| A word vector. Size and shape are determined by the
|
||||
| #[code Vocab.vectors] instance.
|
||||
|
||||
+h(2, "add_flag") Vocab.set_vector
|
||||
+tag method
|
||||
+tag-new(2)
|
||||
|
||||
p
|
||||
| Set a vector for a word in the vocabulary. Words can be referenced by
|
||||
| by string or hash value.
|
||||
|
||||
+aside-code("Example").
|
||||
nlp.vocab.set_vector(u'apple', array([...]))
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code orth]
|
||||
+cell int / unicode
|
||||
+cell The hash value of a word, or its unicode string.
|
||||
|
||||
+row
|
||||
+cell #[code vector]
|
||||
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
|
||||
+cell The vector to set.
|
||||
|
||||
+h(2, "add_flag") Vocab.has_vector
|
||||
+tag method
|
||||
+tag-new(2)
|
||||
|
||||
p
|
||||
| Check whether a word has a vector. Returns #[code False] if no vectors
|
||||
| are loaded. Words can be looked up by string or hash value.
|
||||
|
||||
+aside-code("Example").
|
||||
if nlp.vocab.has_vector(u'apple'):
|
||||
vector = nlp.vocab.get_vector(u'apple')
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code orth]
|
||||
+cell int / unicode
|
||||
+cell The hash value of a word, or its unicode string.
|
||||
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bool
|
||||
+cell Whether the word has a vector.
|
||||
|
||||
+h(2, "to_disk") Vocab.to_disk
|
||||
+tag method
|
||||
+tag-new(2)
|
||||
|
@ -192,7 +286,7 @@ p Loads state from a directory. Modifies the object in place and returns it.
|
|||
| A path to a directory. Paths may be either strings or
|
||||
| #[code Path]-like objects.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Vocab]
|
||||
+cell The modified #[code Vocab] object.
|
||||
|
@ -211,7 +305,7 @@ p Serialize the current state to a binary string.
|
|||
+cell -
|
||||
+cell Named attributes to prevent from being serialized.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell bytes
|
||||
+cell The serialized form of the #[code Vocab] object.
|
||||
|
@ -238,7 +332,7 @@ p Load state from a binary string.
|
|||
+cell -
|
||||
+cell Named attributes to prevent from being loaded.
|
||||
|
||||
+footrow
|
||||
+row("foot")
|
||||
+cell returns
|
||||
+cell #[code Vocab]
|
||||
+cell The #[code Vocab] object.
|
||||
|
@ -256,3 +350,14 @@ p Load state from a binary string.
|
|||
+cell #[code strings]
|
||||
+cell #[code StringStore]
|
||||
+cell A table managing the string-to-int mapping.
|
||||
|
||||
+row
|
||||
+cell #[code vectors]
|
||||
+tag-new(2)
|
||||
+cell #[code Vectors]
|
||||
+cell A table associating word IDs to word vectors.
|
||||
|
||||
+row
|
||||
+cell #[code vectors_length]
|
||||
+cell int
|
||||
+cell Number of dimensions for each word vector.
|
|
@ -19,3 +19,10 @@
|
|||
|
||||
to
|
||||
transform: translate3d(0, 0, 0)
|
||||
|
||||
|
||||
//- Element rotates
|
||||
|
||||
@keyframes rotate
|
||||
to
|
||||
transform: rotate(360deg)
|
||||
|
|
|
@ -1,41 +1,27 @@
|
|||
//- 💫 CSS > BASE > FONTS
|
||||
|
||||
// Source Sans Pro
|
||||
// HK Grotesk
|
||||
|
||||
@font-face
|
||||
font-family: "Source Sans Pro"
|
||||
font-family: "HK Grotesk"
|
||||
font-style: normal
|
||||
font-weight: 400
|
||||
src: url("/assets/fonts/sourcesanspro-regular.eot")
|
||||
src: url("/assets/fonts/sourcesanspro-regular.eot?#iefix") format("embedded-opentype"), url("/assets/fonts/sourcesanspro-regular.woff2") format("woff2"), url("/assets/fonts/sourcesanspro-regular.woff") format("woff"), url("/assets/fonts/sourcesanspro-regular.ttf") format("truetype"), url("/assets/fonts/sourcesanspro-regular.svg#source_sans_proregular") format("svg")
|
||||
font-weight: 500
|
||||
src: url("/assets/fonts/hkgrotesk-semibold.woff2") format("woff2"), url("/assets/fonts/hkgrotesk-semibold.woff") format("woff")
|
||||
|
||||
@font-face
|
||||
font-family: "Source Sans Pro"
|
||||
font-family: "HK Grotesk"
|
||||
font-style: italic
|
||||
font-weight: 400
|
||||
src: url("/assets/fonts/sourcesanspro-italic.eot")
|
||||
src: url("/assets/fonts/sourcesanspro-italic.eot?#iefix") format("embedded-opentype"), url("/assets/fonts/sourcesanspro-italic.woff2") format("woff2"), url("/assets/fonts/sourcesanspro-italic.woff") format("woff"), url("/assets/fonts/sourcesanspro-italic.ttf") format("truetype"), url("/assets/fonts/sourcesanspro-italic.svg#source_sans_proitalic") format("svg")
|
||||
font-weight: 500
|
||||
src: url("/assets/fonts/hkgrotesk-semibolditalic.woff2") format("woff2"), url("/assets/fonts/hkgrotesk-semibolditalic.woff") format("woff")
|
||||
|
||||
@font-face
|
||||
font-family: "Source Sans Pro"
|
||||
font-style: normal
|
||||
font-weight: 700
|
||||
src: url("/assets/fonts/sourcesanspro-bold.eot")
|
||||
src: url("/assets/fonts/sourcesanspro-bold.eot?#iefix") format("embedded-opentype"), url("/assets/fonts/sourcesanspro-bold.woff2") format("woff2"), url("/assets/fonts/sourcesanspro-bold.woff") format("woff"), url("/assets/fonts/sourcesanspro-bold.ttf") format("truetype"), url("/assets/fonts/sourcesanspro-bold.svg#source_sans_probold") format("svg")
|
||||
|
||||
@font-face
|
||||
font-family: "Source Sans Pro"
|
||||
font-style: italic
|
||||
font-weight: 700
|
||||
src: url("/assets/fonts/sourcesanspro-bolditalic.eot")
|
||||
src: url("/assets/fonts/sourcesanspro-bolditalic.eot?#iefix") format("embedded-opentype"), url("/assets/fonts/sourcesanspro-bolditalic.woff2") format("woff2"), url("/assets/fonts/sourcesanspro-bolditalic.woff") format("woff"), url("/assets/fonts/sourcesanspro-bolditalic.ttf") format("truetype"), url("/assets/fonts/sourcesanspro-bolditalic.svg#source_sans_probold_italic") format("svg")
|
||||
|
||||
|
||||
// Source Code Pro
|
||||
|
||||
@font-face
|
||||
font-family: "Source Code Pro"
|
||||
font-family: "HK Grotesk"
|
||||
font-style: normal
|
||||
font-weight: 600
|
||||
src: url("/assets/fonts/sourcecodepro-semibold.eot")
|
||||
src: url("/assets/fonts/sourcecodepro-semibold.eot?#iefix") format("embedded-opentype"), url("/assets/fonts/sourcecodepro-semibold.woff") format("woff"), url("/assets/fonts/sourcecodepro-semibold.ttf") format("truetype"), url("/assets/fonts/sourcecodepro-semibold.svg#sourcecodepro_semibold") format("svg")
|
||||
src: url("/assets/fonts/hkgrotesk-bold.woff2") format("woff2"), url("/assets/fonts/hkgrotesk-bold.woff") format("woff")
|
||||
|
||||
@font-face
|
||||
font-family: "HK Grotesk"
|
||||
font-style: italic
|
||||
font-weight: 600
|
||||
src: url("/assets/fonts/hkgrotesk-bolditalic.woff2") format("woff2"), url("/assets/fonts/hkgrotesk-bolditalic.woff") format("woff")
|
||||
|
|
|
@ -15,6 +15,15 @@
|
|||
align-items: center
|
||||
justify-content: center
|
||||
|
||||
&.o-grid--vcenter
|
||||
align-items: center
|
||||
|
||||
&.o-grid--space
|
||||
justify-content: space-between
|
||||
|
||||
&.o-grid--nowrap
|
||||
flex-wrap: nowrap
|
||||
|
||||
|
||||
//- Grid column
|
||||
|
||||
|
@ -22,7 +31,6 @@
|
|||
$grid-gutter: 2rem
|
||||
|
||||
margin-top: $grid-gutter
|
||||
overflow: hidden
|
||||
|
||||
@include breakpoint(min, lg)
|
||||
display: flex
|
||||
|
|
|
@ -12,6 +12,7 @@ body
|
|||
animation: fadeIn 0.25s ease
|
||||
background: $color-back
|
||||
color: $color-front
|
||||
//scroll-behavior: smooth
|
||||
|
||||
|
||||
//- Paragraphs
|
||||
|
@ -19,6 +20,9 @@ body
|
|||
p
|
||||
@extend .o-block, .u-text
|
||||
|
||||
p:empty
|
||||
margin-bottom: 0
|
||||
|
||||
|
||||
//- Links
|
||||
|
||||
|
|
|
@ -43,12 +43,25 @@
|
|||
position: relative
|
||||
padding: 2.5rem 0
|
||||
overflow: auto
|
||||
background: $color-subtle-light
|
||||
|
||||
.o-main &
|
||||
border-top-left-radius: $border-radius
|
||||
|
||||
|
||||
//- Blocks
|
||||
|
||||
.o-section
|
||||
width: 100%
|
||||
max-width: 100%
|
||||
|
||||
&:not(:last-child)
|
||||
margin-bottom: 7rem
|
||||
padding-bottom: 4rem
|
||||
border-bottom: 1px dotted $color-subtle
|
||||
|
||||
.o-block
|
||||
margin-bottom: 3rem
|
||||
margin-bottom: 4rem
|
||||
|
||||
.o-block-small
|
||||
margin-bottom: 2rem
|
||||
|
@ -58,17 +71,18 @@
|
|||
|
||||
.o-card
|
||||
background: $color-back
|
||||
border-radius: 2px
|
||||
border: 1px solid $color-subtle
|
||||
padding: 3rem 2.5%
|
||||
|
||||
border-radius: $border-radius
|
||||
box-shadow: $box-shadow
|
||||
|
||||
//- Box
|
||||
|
||||
.o-box
|
||||
background: $color-theme-light
|
||||
background: $color-subtle-light
|
||||
padding: 2rem
|
||||
border-left: 4px solid $color-theme
|
||||
border-radius: $border-radius
|
||||
|
||||
.o-box__logos
|
||||
padding-bottom: 1rem
|
||||
|
||||
|
||||
//- Icons
|
||||
|
@ -77,7 +91,14 @@
|
|||
vertical-align: middle
|
||||
|
||||
&.o-icon--inline
|
||||
margin: 0 0.5rem 0 0.25rem
|
||||
margin: 0 0.5rem 0 0.1rem
|
||||
|
||||
.o-emoji
|
||||
margin-right: 0.75rem
|
||||
vertical-align: text-bottom
|
||||
|
||||
.o-badge
|
||||
border-radius: 1em
|
||||
|
||||
|
||||
//- SVG
|
||||
|
@ -102,3 +123,45 @@
|
|||
fill: currentColor
|
||||
vertical-align: middle
|
||||
margin: 0 0.5rem
|
||||
|
||||
|
||||
//- Embeds
|
||||
|
||||
.o-chart
|
||||
max-width: 100%
|
||||
|
||||
.cp_embed_iframe
|
||||
border: 1px solid $color-subtle
|
||||
border-radius: $border-radius
|
||||
|
||||
|
||||
//- Form fields
|
||||
|
||||
.o-field
|
||||
background: $color-back
|
||||
padding: 0 0.25em
|
||||
border-radius: 2em
|
||||
border: 1px solid $color-subtle
|
||||
margin-bottom: 0.25rem
|
||||
|
||||
.o-field__input,
|
||||
.o-field__button
|
||||
padding: 0 0.35em
|
||||
|
||||
.o-field__input
|
||||
width: 100%
|
||||
|
||||
.o-field__select
|
||||
background: transparent
|
||||
color: $color-dark
|
||||
height: 1.4em
|
||||
border: none
|
||||
text-align-last: center
|
||||
|
||||
.o-empty:empty:before
|
||||
@include size(1em)
|
||||
border-radius: 50%
|
||||
content: ""
|
||||
display: inline-block
|
||||
background: $color-red
|
||||
vertical-align: middle
|
||||
|
|
|
@ -1,6 +1,6 @@
|
|||
//- 💫 CSS > BASE > RESET
|
||||
|
||||
*
|
||||
*, *:before, *:after
|
||||
box-sizing: border-box
|
||||
padding: 0
|
||||
margin: 0
|
||||
|
@ -94,7 +94,10 @@ ul, ol
|
|||
|
||||
input, button
|
||||
appearance: none
|
||||
background: transparent
|
||||
|
||||
button
|
||||
background: transparent
|
||||
cursor: pointer
|
||||
|
||||
progress
|
||||
appearance: none
|
||||
|
|
|
@ -2,38 +2,53 @@
|
|||
|
||||
//- Text
|
||||
|
||||
.u-text,
|
||||
.u-text-small,
|
||||
.u-text-tiny
|
||||
font-family: $font-primary
|
||||
|
||||
.u-text
|
||||
font: 1.5rem/#{1.55} $font-primary
|
||||
font-size: 1.35rem
|
||||
line-height: 1.5
|
||||
|
||||
.u-text-small
|
||||
font: 1.4rem/#{1.375} $font-primary
|
||||
font-size: 1.3rem
|
||||
line-height: 1.375
|
||||
|
||||
.u-text-tiny
|
||||
font: 1.1rem/#{1.375} $font-primary
|
||||
|
||||
font-size: 1.1rem
|
||||
line-height: 1.375
|
||||
|
||||
//- Labels & Tags
|
||||
|
||||
.u-text-label
|
||||
font: normal 600 1.4rem/#{1.5} $font-code
|
||||
font: normal 600 1.4rem/#{1.5} $font-secondary
|
||||
text-transform: uppercase
|
||||
|
||||
&.u-text-label--light,
|
||||
&.u-text-label--dark
|
||||
display: inline-block
|
||||
border-radius: 1em
|
||||
padding: 0 1rem 0.15rem
|
||||
|
||||
&.u-text-label--dark
|
||||
background: $color-dark
|
||||
box-shadow: inset 1px 1px 1px rgba($color-front, 0.25)
|
||||
color: $color-back
|
||||
padding: 0 0.75rem
|
||||
margin: 1.5rem 0 0 2rem
|
||||
border-radius: 2px
|
||||
|
||||
&.u-text-label--light
|
||||
background: $color-back
|
||||
color: $color-theme
|
||||
margin-bottom: 1rem
|
||||
|
||||
.u-text-tag
|
||||
display: inline-block
|
||||
font: 600 1.1rem/#{1} $font-code
|
||||
font: 600 1.1rem/#{1} $font-secondary
|
||||
background: $color-theme
|
||||
color: $color-back
|
||||
padding: 0.15em 0.25em
|
||||
border-radius: 2px
|
||||
padding: 0.15em 0.5em 0.35em
|
||||
border-radius: 1em
|
||||
text-transform: uppercase
|
||||
vertical-align: middle
|
||||
|
||||
|
@ -45,7 +60,7 @@
|
|||
//- Headings
|
||||
|
||||
.u-heading
|
||||
margin-bottom: 2rem
|
||||
margin-bottom: 1em
|
||||
|
||||
@include breakpoint(max, md)
|
||||
word-wrap: break-word
|
||||
|
@ -53,12 +68,29 @@
|
|||
&:not(:first-child)
|
||||
padding-top: 3.5rem
|
||||
|
||||
&.u-heading--title:after
|
||||
content: ""
|
||||
display: block
|
||||
width: 10%
|
||||
min-width: 6rem
|
||||
height: 6px
|
||||
background: $color-theme
|
||||
margin-top: 3rem
|
||||
|
||||
.u-heading-0
|
||||
font: normal bold 7rem/#{1} $font-primary
|
||||
font: normal 600 7rem/#{1} $font-secondary
|
||||
|
||||
@include breakpoint(max, sm)
|
||||
font-size: 6rem
|
||||
|
||||
|
||||
@each $level, $size in $headings
|
||||
.u-heading-#{$level}
|
||||
font: normal bold #{$size}rem/#{1.25} $font-primary
|
||||
font: normal 500 #{$size}rem/#{1.1} $font-secondary
|
||||
|
||||
.u-heading__teaser
|
||||
margin-top: 2rem
|
||||
font-weight: normal
|
||||
|
||||
|
||||
//- Links
|
||||
|
@ -66,31 +98,59 @@
|
|||
.u-link
|
||||
color: $color-theme
|
||||
border-bottom: 1px solid
|
||||
transition: color 0.2s ease
|
||||
|
||||
&:hover
|
||||
color: $color-theme-dark
|
||||
|
||||
.u-hide-link.u-hide-link
|
||||
border: none
|
||||
color: inherit
|
||||
|
||||
&:hover
|
||||
color: inherit
|
||||
|
||||
.u-permalink
|
||||
position: relative
|
||||
|
||||
&:before
|
||||
content: "\00b6"
|
||||
font-size: 0.9em
|
||||
font-weight: normal
|
||||
color: $color-subtle
|
||||
@include position(absolute, top, left, 0.15em, -2.85rem)
|
||||
opacity: 0
|
||||
transition: opacity 0.2s ease
|
||||
|
||||
&:hover:before
|
||||
opacity: 1
|
||||
|
||||
&:active:before
|
||||
color: $color-theme
|
||||
|
||||
&:target
|
||||
display: inline-block
|
||||
padding-top: $nav-height * 1.25
|
||||
|
||||
& + *
|
||||
margin-top: $nav-height * 1.25
|
||||
&:before
|
||||
bottom: 0.15em
|
||||
top: initial
|
||||
|
||||
.u-permalink__icon
|
||||
@include position(absolute, bottom, left, 0.35em, -2.75rem)
|
||||
@include size(1.5rem)
|
||||
color: $color-subtle
|
||||
|
||||
.u-permalink:hover &
|
||||
color: $color-subtle-dark
|
||||
[id]:target
|
||||
padding-top: $nav-height * 1.25
|
||||
|
||||
.u-permalink:active &
|
||||
color: $color-theme
|
||||
|
||||
|
||||
//- Layout
|
||||
|
||||
.u-float-left
|
||||
float: left
|
||||
margin-right: 1rem
|
||||
|
||||
.u-float-right
|
||||
float: right
|
||||
margin-left: 1rem
|
||||
|
||||
.u-text-center
|
||||
text-align: center
|
||||
|
||||
|
@ -104,14 +164,20 @@
|
|||
padding: 0.5em 0.75em
|
||||
|
||||
.u-padding-medium
|
||||
padding: 2.5rem
|
||||
padding: 1.8rem
|
||||
|
||||
.u-inline-block
|
||||
display: inline-block
|
||||
|
||||
.u-flex-full
|
||||
flex: 1
|
||||
|
||||
.u-nowrap
|
||||
white-space: nowrap
|
||||
|
||||
.u-wrap
|
||||
white-space: pre-wrap
|
||||
|
||||
.u-break.u-break
|
||||
word-wrap: break-word
|
||||
white-space: initial
|
||||
|
@ -123,13 +189,10 @@
|
|||
border: 1px solid $color-subtle
|
||||
border-radius: 2px
|
||||
|
||||
.u-border-bottom
|
||||
border: 1px solid $color-subtle
|
||||
|
||||
.u-border-dotted
|
||||
border-top: 1px dotted $color-subtle
|
||||
border-bottom: 1px dotted $color-subtle
|
||||
|
||||
@each $name, $color in (theme: $color-theme, subtle: $color-subtle-dark, light: $color-back, red: $color-red, green: $color-green, yellow: $color-yellow)
|
||||
@each $name, $color in (theme: $color-theme, dark: $color-dark, subtle: $color-subtle-dark, light: $color-back, red: $color-red, green: $color-green, yellow: $color-yellow)
|
||||
.u-color-#{$name}
|
||||
color: $color
|
||||
|
||||
|
@ -145,6 +208,32 @@
|
|||
background: $pattern
|
||||
|
||||
|
||||
//- Loaders
|
||||
|
||||
.u-loading,
|
||||
[data-loading]
|
||||
$spinner-size: 75px
|
||||
$spinner-bar: 8px
|
||||
|
||||
position: relative
|
||||
|
||||
& > *
|
||||
opacity: 0.35
|
||||
|
||||
&:before
|
||||
@include position(absolute, top, left, 0, 0)
|
||||
@include size($spinner-size)
|
||||
right: 0
|
||||
bottom: 0
|
||||
margin: auto
|
||||
content: ""
|
||||
border: $spinner-bar solid $color-subtle
|
||||
border-right: $spinner-bar solid $color-theme
|
||||
border-radius: 50%
|
||||
animation: rotate 1s linear infinite
|
||||
z-index: 10
|
||||
|
||||
|
||||
//- Hidden elements
|
||||
|
||||
.u-hidden
|
||||
|
|
|
@ -10,6 +10,8 @@
|
|||
|
||||
.c-aside__content
|
||||
background: $color-front
|
||||
border-top-left-radius: $border-radius
|
||||
border-bottom-left-radius: $border-radius
|
||||
z-index: 10
|
||||
|
||||
@include breakpoint(min, md)
|
||||
|
@ -21,12 +23,12 @@
|
|||
&:after
|
||||
$triangle-size: 2rem
|
||||
|
||||
@include position(absolute, bottom, left, -$triangle-size / 2, 0)
|
||||
@include position(absolute, bottom, left, -$triangle-size / 2, $border-radius / 2)
|
||||
@include size(0)
|
||||
border-color: transparent
|
||||
border-style: solid
|
||||
border-top-color: $color-dark
|
||||
border-width: $triangle-size / 2 0 0 $triangle-size
|
||||
border-width: $triangle-size / 2 0 0 calc(#{$triangle-size} - #{$border-radius / 2})
|
||||
content: ""
|
||||
|
||||
@include breakpoint(max, sm)
|
||||
|
|
|
@ -3,23 +3,50 @@
|
|||
.c-button
|
||||
display: inline-block
|
||||
font-weight: bold
|
||||
padding: 0.75em 1em
|
||||
padding: 0.8em 1.1em 1em
|
||||
margin-bottom: 1px
|
||||
border: 2px solid
|
||||
border-radius: 2px
|
||||
border: 2px solid $color-theme
|
||||
border-radius: 2em
|
||||
text-align: center
|
||||
transition: background 0.25s ease
|
||||
transition: background-color, color 0.25s ease
|
||||
|
||||
&:hover
|
||||
border-color: $color-theme-dark
|
||||
|
||||
&.c-button--small
|
||||
font-size: 1.1rem
|
||||
padding: 0.65rem 1.1rem 0.825rem
|
||||
|
||||
&.c-button--primary
|
||||
background: $color-theme
|
||||
color: $color-back
|
||||
border-color: $color-theme
|
||||
|
||||
&:hover
|
||||
background: $color-theme-dark
|
||||
border-color: $color-theme-dark
|
||||
|
||||
&.c-button--secondary
|
||||
background: $color-back
|
||||
color: $color-theme
|
||||
border-color: $color-theme
|
||||
|
||||
&:hover
|
||||
color: $color-theme-dark
|
||||
|
||||
&.c-button--secondary-light
|
||||
background: transparent
|
||||
color: $color-back
|
||||
border-color: $color-back
|
||||
|
||||
.c-icon-button
|
||||
@include size(35px)
|
||||
background: $color-subtle-light
|
||||
color: $color-subtle-dark
|
||||
border-radius: 50%
|
||||
padding: 0.5rem
|
||||
transition: color 0.2s ease
|
||||
|
||||
&:hover
|
||||
color: $color-theme
|
||||
|
||||
&.c-icon-button--right
|
||||
float: right
|
||||
margin-left: 3rem
|
||||
|
|
|
@ -24,9 +24,9 @@
|
|||
transform: translateX(110%)
|
||||
|
||||
&:before
|
||||
@include position(absolute, top, left, 1rem, 2rem)
|
||||
@include position(absolute, top, left, 1.25rem, 2rem)
|
||||
content: attr(data-title)
|
||||
font: bold 1.4rem $font-code
|
||||
font: bold 1.4rem $font-secondary
|
||||
text-transform: uppercase
|
||||
color: $color-back
|
||||
|
||||
|
@ -88,13 +88,18 @@
|
|||
background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0Ij48cGF0aCBmaWxsPSIjZmZmIiBkPSJNMTguOTg0IDYuNDIybC01LjU3OCA1LjU3OCA1LjU3OCA1LjU3OC0xLjQwNiAxLjQwNi01LjU3OC01LjU3OC01LjU3OCA1LjU3OC0xLjQwNi0xLjQwNiA1LjU3OC01LjU3OC01LjU3OC01LjU3OCAxLjQwNi0xLjQwNiA1LjU3OCA1LjU3OCA1LjU3OC01LjU3OHoiPjwvcGF0aD48L3N2Zz4=)
|
||||
|
||||
.c-chat__button
|
||||
@include position(fixed, bottom, right, 0, 2rem)
|
||||
padding: 1rem 1.5rem
|
||||
background: $color-front
|
||||
@include position(fixed, bottom, right, 1.5rem, 1.5rem)
|
||||
z-index: 5
|
||||
color: $color-back
|
||||
border-top-left-radius: 4px
|
||||
border-top-right-radius: 4px
|
||||
z-index: 20
|
||||
border-color: $color-theme
|
||||
border-style: solid
|
||||
border-width: 1px 1px 0 1px
|
||||
background: $color-front
|
||||
border-radius: 1em
|
||||
padding: 0.5rem 1.15rem 0.35rem
|
||||
opacity: 0.7
|
||||
transition: opacity 0.2s ease
|
||||
|
||||
&:hover
|
||||
opacity: 1
|
||||
|
||||
|
||||
.gitter-open-chat-button
|
||||
display: none
|
||||
|
|
|
@ -4,9 +4,9 @@
|
|||
|
||||
.c-code-block
|
||||
background: $color-front
|
||||
color: $color-back
|
||||
color: darken($color-back, 20)
|
||||
padding: 0.75em 0
|
||||
border-radius: 2px
|
||||
border-radius: $border-radius
|
||||
overflow: auto
|
||||
width: 100%
|
||||
max-width: 100%
|
||||
|
@ -16,6 +16,8 @@
|
|||
&.c-code-block--has-icon
|
||||
padding: 0
|
||||
display: flex
|
||||
border-top-left-radius: 0
|
||||
border-bottom-left-radius: 0
|
||||
|
||||
.c-code-block__icon
|
||||
padding: 0 0 0 1rem
|
||||
|
@ -43,17 +45,26 @@
|
|||
opacity: 0.5
|
||||
|
||||
|
||||
//- Code
|
||||
|
||||
code
|
||||
-webkit-font-smoothing: subpixel-antialiased
|
||||
-moz-osx-font-smoothing: auto
|
||||
|
||||
|
||||
//- Inline code
|
||||
|
||||
*:not(a):not(.c-code-block) > code
|
||||
color: $color-dark
|
||||
|
||||
*:not(.c-code-block) > code
|
||||
font: normal 600 0.8em/#{1} $font-code
|
||||
background: darken($color-theme-light, 5)
|
||||
box-shadow: 1px 1px 0 rgba($color-front, 0.05)
|
||||
text-shadow: 1px 1px 0 rgba($color-back, 0.5)
|
||||
color: $color-front
|
||||
padding: 0.1em 0.5em
|
||||
font-size: 90%
|
||||
background-color: $color-subtle-light
|
||||
padding: 0.2rem 0.4rem
|
||||
border-radius: 0.25rem
|
||||
font-family: $font-code
|
||||
white-space: nowrap
|
||||
margin: 0
|
||||
border-radius: 1px
|
||||
box-decoration-break: clone
|
||||
white-space: nowrap
|
||||
|
||||
|
|
|
@ -2,12 +2,11 @@
|
|||
|
||||
.c-landing
|
||||
background: $color-theme
|
||||
padding-top: 5rem
|
||||
padding-top: $nav-height * 1.5
|
||||
width: 100%
|
||||
|
||||
.c-landing__wrapper
|
||||
background: $pattern
|
||||
padding-bottom: 6rem
|
||||
width: 100%
|
||||
|
||||
.c-landing__content
|
||||
|
@ -15,9 +14,45 @@
|
|||
width: 100%
|
||||
min-height: 573px
|
||||
|
||||
.c-landing__headlines
|
||||
position: relative
|
||||
top: -1.5rem
|
||||
left: 1rem
|
||||
|
||||
.c-landing__title
|
||||
color: $color-back
|
||||
text-align: center
|
||||
margin-bottom: 0.75rem
|
||||
|
||||
.c-landing__blocks
|
||||
@include breakpoint(min, sm)
|
||||
position: relative
|
||||
top: -25rem
|
||||
margin-bottom: -25rem
|
||||
|
||||
.c-landing__card
|
||||
padding: 3rem 2.5rem
|
||||
|
||||
.c-landing__banner
|
||||
background: $color-theme
|
||||
|
||||
.c-landing__banner__content
|
||||
@include breakpoint(min, md)
|
||||
border: 4px solid
|
||||
padding: 1rem 6.5rem 2rem 4rem
|
||||
|
||||
|
||||
.c-landing__banner__text
|
||||
font-weight: 500
|
||||
|
||||
strong
|
||||
font-weight: 800
|
||||
|
||||
p
|
||||
font-size: 1.5rem
|
||||
|
||||
@include breakpoint(min, md)
|
||||
padding-top: 7rem
|
||||
|
||||
.c-landing__badge
|
||||
transform: rotate(7deg)
|
||||
|
|
|
@ -9,6 +9,8 @@
|
|||
|
||||
.c-list__item:before
|
||||
content: counter(li, #{$counter}) '.'
|
||||
font-size: 1em
|
||||
padding-right: 1rem
|
||||
|
||||
|
||||
//- List Item
|
||||
|
@ -21,13 +23,14 @@
|
|||
&:before
|
||||
content: '\25CF'
|
||||
display: inline-block
|
||||
font-size: 1em
|
||||
font-size: 0.6em
|
||||
font-weight: bold
|
||||
padding-right: 1.25rem
|
||||
margin-left: -3.75rem
|
||||
text-align: right
|
||||
width: 2.5rem
|
||||
counter-increment: li
|
||||
box-sizing: content-box
|
||||
|
||||
|
||||
//- List icon
|
||||
|
|
|
@ -3,9 +3,8 @@
|
|||
.x-terminal
|
||||
background: $color-subtle-light
|
||||
color: $color-front
|
||||
padding: 4px
|
||||
border: 1px dotted $color-subtle
|
||||
border-radius: 5px
|
||||
padding: $border-radius
|
||||
border-radius: 1em
|
||||
width: 100%
|
||||
|
||||
.x-terminal__icons
|
||||
|
|
|
@ -1,22 +1,21 @@
|
|||
//- 💫 CSS > COMPONENTS > NAVIGATION
|
||||
|
||||
.c-nav
|
||||
@include position(absolute, top, left, 0, 0)
|
||||
@include position(fixed, top, left, 0, 0)
|
||||
@include size(100%, $nav-height)
|
||||
background: $color-back
|
||||
color: $color-theme
|
||||
align-items: center
|
||||
display: flex
|
||||
justify-content: space-between
|
||||
flex-flow: row wrap
|
||||
padding: 0 2rem 0 1rem
|
||||
z-index: 20
|
||||
z-index: 30
|
||||
width: 100%
|
||||
border-bottom: 1px solid $color-subtle
|
||||
box-shadow: $box-shadow
|
||||
|
||||
&.c-nav--theme
|
||||
background: $color-theme
|
||||
color: $color-back
|
||||
border-bottom: none
|
||||
//@include breakpoint(min, md)
|
||||
// position: fixed
|
||||
|
||||
&.is-fixed
|
||||
animation: slideInDown 0.5s ease-in-out
|
||||
|
@ -28,12 +27,21 @@
|
|||
justify-content: flex-end
|
||||
flex-flow: row nowrap
|
||||
border-color: inherit
|
||||
flex: 1
|
||||
|
||||
.c-nav__menu__item
|
||||
display: flex
|
||||
align-items: center
|
||||
height: 100%
|
||||
text-transform: uppercase
|
||||
font-family: $font-secondary
|
||||
font-size: 1.6rem
|
||||
font-weight: bold
|
||||
color: $color-theme
|
||||
|
||||
&:not(:last-child)
|
||||
margin-right: 1em
|
||||
&:not(:first-child)
|
||||
margin-left: 2em
|
||||
|
||||
&.is-active
|
||||
color: $color-dark
|
||||
pointer-events: none
|
||||
|
|
24
website/assets/css/_components/_progress.sass
Normal file
24
website/assets/css/_components/_progress.sass
Normal file
|
@ -0,0 +1,24 @@
|
|||
//- 💫 CSS > COMPONENTS > PROGRESS
|
||||
|
||||
.c-progress
|
||||
display: block
|
||||
flex: 105%
|
||||
width: 105%
|
||||
height: 3px
|
||||
color: $color-theme
|
||||
background: transparent
|
||||
border: none
|
||||
position: absolute
|
||||
bottom: 0
|
||||
left: -2.5%
|
||||
|
||||
&::-webkit-progress-bar
|
||||
background: $color-back
|
||||
border-radius: none
|
||||
|
||||
&::-webkit-progress-value
|
||||
background: $color-theme
|
||||
border-radius: none
|
||||
|
||||
&::-moz-progress-bar
|
||||
background: $color-theme
|
|
@ -1,14 +1,17 @@
|
|||
//- 💫 CSS > COMPONENTS > QUICKSTART
|
||||
|
||||
.c-quickstart
|
||||
border: 1px solid $color-subtle
|
||||
border-radius: 2px
|
||||
border-radius: $border-radius
|
||||
display: none
|
||||
background: $color-subtle-light
|
||||
|
||||
&:not([style]) + .c-quickstart__info
|
||||
display: none
|
||||
|
||||
.c-code-block
|
||||
border-top-left-radius: 0
|
||||
border-top-right-radius: 0
|
||||
|
||||
.c-quickstart__content
|
||||
padding: 2rem 3rem
|
||||
|
||||
|
@ -72,7 +75,6 @@
|
|||
flex: 100%
|
||||
|
||||
.c-quickstart__legend
|
||||
color: $color-subtle-dark
|
||||
margin-right: 2rem
|
||||
padding-top: 0.75rem
|
||||
flex: 1 1 35%
|
||||
|
@ -95,4 +97,4 @@
|
|||
padding: 1.5rem 0
|
||||
|
||||
.c-quickstart__code
|
||||
font-size: 1.6rem
|
||||
font-size: 1.4rem
|
||||
|
|
|
@ -3,16 +3,15 @@
|
|||
//- Sidebar container
|
||||
|
||||
.c-sidebar
|
||||
background: $color-subtle-light
|
||||
overflow-y: auto
|
||||
|
||||
@include breakpoint(min, md)
|
||||
@include position(fixed, top, left, 0, 0)
|
||||
@include size($sidebar-width, 100vh)
|
||||
@include size($sidebar-width, calc(100vh - 3px))
|
||||
@include scroll-shadow($color-back, $color-front, $nav-height)
|
||||
flex: 0 0 $sidebar-width
|
||||
padding: calc(#{$nav-height} + 1.5rem) 0 0
|
||||
z-index: 10
|
||||
border-right: 1px solid $color-subtle
|
||||
|
||||
@include breakpoint(max, sm)
|
||||
flex: 100%
|
||||
|
@ -27,7 +26,7 @@
|
|||
|
||||
.c-sidebar__section
|
||||
& > *
|
||||
padding: 0 2rem
|
||||
padding: 0 2rem 0.35rem
|
||||
|
||||
@include breakpoint(max, sm)
|
||||
flex: 1 1 0
|
||||
|
@ -38,7 +37,59 @@
|
|||
&:not(:last-child)
|
||||
border-right: 1px solid $color-subtle
|
||||
|
||||
.is-active
|
||||
.c-sidebar__item
|
||||
color: $color-theme
|
||||
|
||||
&:hover
|
||||
color: $color-theme-dark
|
||||
|
||||
& > .is-active
|
||||
font-weight: bold
|
||||
color: $color-theme
|
||||
background: rgba($color-subtle, 0.4)
|
||||
color: $color-dark
|
||||
margin-top: 1rem
|
||||
|
||||
|
||||
//- Sidebar subsections
|
||||
|
||||
$crumb-bullet: 14px
|
||||
$crumb-bar: 2px
|
||||
|
||||
.c-sidebar__crumb
|
||||
display: block
|
||||
padding-top: 1rem
|
||||
padding-left: 1rem
|
||||
position: relative
|
||||
|
||||
.c-sidebar__crumb__item
|
||||
margin-bottom: $crumb-bullet / 2
|
||||
position: relative
|
||||
padding-left: 2rem
|
||||
color: $color-theme
|
||||
font-size: 1.2rem
|
||||
|
||||
&:hover
|
||||
color: $color-theme-dark
|
||||
|
||||
&:after
|
||||
@include size($crumb-bullet)
|
||||
@include position(absolute, top, left, $crumb-bullet / 4, 0)
|
||||
content: ""
|
||||
border-radius: 50%
|
||||
background: $color-theme
|
||||
z-index: 10
|
||||
|
||||
&:not(:last-child):before
|
||||
@include size($crumb-bar, 100%)
|
||||
@include position(absolute, top, left, $crumb-bullet, ($crumb-bullet - $crumb-bar) / 2)
|
||||
content: ""
|
||||
background: $color-subtle
|
||||
|
||||
&:first-child:before
|
||||
height: calc(100% + #{$crumb-bullet * 2})
|
||||
top: -$crumb-bullet / 2
|
||||
|
||||
&.is-active
|
||||
color: $color-dark
|
||||
|
||||
&:after
|
||||
background: $color-dark
|
||||
|
|
|
@ -9,7 +9,7 @@
|
|||
//- Table row
|
||||
|
||||
.c-table__row
|
||||
&:nth-child(odd)
|
||||
&:nth-child(odd):not(.c-table__row--head)
|
||||
background: rgba($color-subtle-light, 0.35)
|
||||
|
||||
&.c-table__row--foot
|
||||
|
@ -38,7 +38,6 @@
|
|||
.c-table__head-cell
|
||||
font-weight: bold
|
||||
color: $color-theme
|
||||
background: $color-back
|
||||
padding: 1rem 0.5rem
|
||||
border-bottom: 2px solid $color-theme
|
||||
|
||||
|
|
|
@ -4,24 +4,34 @@
|
|||
position: relative
|
||||
|
||||
@include breakpoint(min, sm)
|
||||
&[data-tooltip-style="code"]:before
|
||||
-webkit-font-smoothing: subpixel-antialiased
|
||||
-moz-osx-font-smoothing: auto
|
||||
padding: 0.35em 0.85em 0.45em
|
||||
font: normal 1rem/#{1.25} $font-code
|
||||
white-space: nowrap
|
||||
min-width: auto
|
||||
|
||||
&:before
|
||||
@include position(absolute, top, left, 125%, 50%)
|
||||
display: inline-block
|
||||
content: attr(data-tooltip)
|
||||
background: $color-front
|
||||
border-radius: 2px
|
||||
border-radius: $border-radius
|
||||
border: 1px solid rgba($color-subtle-dark, 0.5)
|
||||
color: $color-back
|
||||
font: normal 1.3rem/#{1.25} $font-primary
|
||||
font: normal 1.2rem/#{1.25} $font-primary
|
||||
text-transform: none
|
||||
text-align: left
|
||||
opacity: 0
|
||||
padding: 0.5em 0.75em
|
||||
transform: translateX(-50%) translateY(-2px)
|
||||
transition: opacity 0.1s ease-out, transform 0.1s ease-out
|
||||
visibility: hidden
|
||||
min-width: 200px
|
||||
max-width: 300px
|
||||
min-width: 200px
|
||||
padding: 0.75em 1em 1em
|
||||
z-index: 200
|
||||
white-space: pre-wrap
|
||||
|
||||
&:hover:before
|
||||
opacity: 1
|
||||
|
|
|
@ -42,8 +42,8 @@
|
|||
// $scroll-shadow-side - side to cover shadow (left or right)
|
||||
// $scroll-shadow-background - original background color to match
|
||||
|
||||
@mixin scroll-shadow-base($scroll-shadow-color)
|
||||
background: radial-gradient(left, ellipse, rgba(0,0,0, .2) 0%, rgba(0,0,0, 0) 75%) 0 center, radial-gradient(right, ellipse, rgba(0,0,0, .2) 0%, rgba(0,0,0, 0) 75%) 100% center
|
||||
@mixin scroll-shadow-base($scroll-shadow-color, $scroll-shadow-intensity: 0.2)
|
||||
background: radial-gradient(ellipse at 0 50%, rgba($scroll-shadow-color, $scroll-shadow-intensity) 0%, rgba(0,0,0,0) 75%) 0 center, radial-gradient(ellipse at 100% 50%, rgba($scroll-shadow-color, $scroll-shadow-intensity) 0%, transparent 75%) 100% center
|
||||
background-attachment: scroll, scroll
|
||||
background-repeat: no-repeat
|
||||
background-size: 10px 100%, 10px 100%
|
||||
|
@ -58,3 +58,16 @@
|
|||
background-image: linear-gradient(to #{$scroll-gradient-direction}, rgba($scroll-shadow-background, 1) 50%, rgba($scroll-shadow-background, 0) 100%)
|
||||
background-repeat: no-repeat
|
||||
background-size: 20px 100%
|
||||
|
||||
|
||||
// Full vertical scroll shadows
|
||||
// adapted from: https://codepen.io/laustdeleuran/pen/DBaAu
|
||||
|
||||
@mixin scroll-shadow($background-color, $shadow-color, $shadow-offset: 0, $shadow-intensity: 0.4, $cover-size: 40px, $shadow-size: 15px)
|
||||
background: linear-gradient($background-color 30%, rgba($background-color,0)) 0 $shadow-offset, linear-gradient(rgba($background-color,0), $background-color 70%) 0 100%, radial-gradient(50% 0, farthest-side, rgba($shadow-color,$shadow-intensity), rgba($shadow-color,0)) 0 $shadow-offset, radial-gradient(50% 100%,farthest-side, rgba($shadow-color,$shadow-intensity), rgba($shadow-color,0)) 0 100%
|
||||
|
||||
background: linear-gradient($background-color 30%, rgba($background-color,0)) 0 $shadow-offset, linear-gradient(rgba($background-color,0), $background-color 70%) 0 100%, radial-gradient(farthest-side at 50% 0, rgba($shadow-color,$shadow-intensity), rgba($shadow-color,0)) -20px $shadow-offset, radial-gradient(farthest-side at 50% 100%, rgba($shadow-color, $shadow-intensity), rgba($shadow-color,0)) 0 100%
|
||||
background-repeat: no-repeat
|
||||
background-color: $background-color
|
||||
background-size: 100% $cover-size, 100% $cover-size, 100% $shadow-size, 100% $shadow-size
|
||||
background-attachment: local, local, scroll, scroll
|
||||
|
|
|
@ -4,47 +4,48 @@
|
|||
|
||||
$type-base: 11px
|
||||
|
||||
$nav-height: 45px
|
||||
$nav-height: 55px
|
||||
$content-width: 1250px
|
||||
$sidebar-width: 200px
|
||||
$aside-width: 30vw
|
||||
$sidebar-width: 235px
|
||||
$aside-width: 27.5vw
|
||||
$aside-padding: 25px
|
||||
$border-radius: 6px
|
||||
|
||||
$logo-width: 85px
|
||||
$logo-height: 27px
|
||||
|
||||
$grid: ( quarter: 4, third: 3, half: 2, two-thirds: 1.5, three-quarters: 1.33 )
|
||||
$breakpoints: ( sm: 768px, md: 992px, lg: 1200px )
|
||||
$headings: (1: 3, 2: 2.6, 3: 2, 4: 1.8, 5: 1.5)
|
||||
|
||||
$headings: (1: 4.4, 2: 3.4, 3: 2.6, 4: 2.2, 5: 1.8)
|
||||
|
||||
// Fonts
|
||||
|
||||
$font-primary: "Source Sans Pro", Tahoma, Geneva, sans-serif !default
|
||||
$font-code: 'Source Code Pro', Consolas, 'Andale Mono', Menlo, Monaco, Courier, monospace !default
|
||||
|
||||
$font-primary: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol" !default
|
||||
$font-secondary: "HK Grotesk", Roboto, Helvetica, Arial, sans-serif !default
|
||||
$font-code: Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace !default
|
||||
|
||||
// Colors
|
||||
|
||||
$colors: ( blue: #09a3d5, red: #d9515d, green: #08c35e )
|
||||
$colors: ( blue: #09a3d5, green: #05b083 )
|
||||
|
||||
$color-back: #fff !default
|
||||
$color-front: #1a1e23 !default
|
||||
$color-dark: lighten($color-front, 20) !default
|
||||
|
||||
$color-theme: map-get($colors, $theme)
|
||||
$color-theme-dark: darken(map-get($colors, $theme), 5)
|
||||
$color-theme-dark: darken(map-get($colors, $theme), 10)
|
||||
$color-theme-light: rgba($color-theme, 0.05)
|
||||
|
||||
$color-subtle: #ddd !default
|
||||
$color-subtle-light: #f6f6f6 !default
|
||||
$color-subtle-dark: #949e9b !default
|
||||
|
||||
$color-red: #d9515d
|
||||
$color-green: #3ec930
|
||||
$color-red: #ef476f
|
||||
$color-green: #7ddf64
|
||||
$color-yellow: #f4c025
|
||||
|
||||
$syntax-highlighting: ( comment: #949e9b, tag: #b084eb, number: #b084eb, selector: #ffb86c, operator: #ff2c6d, function: #35b3dc, keyword: #ff2c6d, regex: #f4c025 )
|
||||
|
||||
$pattern: $color-theme url("/assets/img/pattern_#{$theme}.jpg") center top repeat
|
||||
$pattern-overlay: transparent url("/assets/img/pattern_landing.jpg") center -138px no-repeat
|
||||
$box-shadow: 0 1px 5px rgba(0, 0, 0, 0.2)
|
||||
|
|
|
@ -30,6 +30,7 @@ $theme: blue !default
|
|||
@import _components/lists
|
||||
@import _components/misc
|
||||
@import _components/navigation
|
||||
@import _components/progress
|
||||
@import _components/sidebar
|
||||
@import _components/tables
|
||||
@import _components/quickstart
|
||||
|
|
|
@ -1,4 +0,0 @@
|
|||
//- 💫 STYLESHEET (RED)
|
||||
|
||||
$theme: red
|
||||
@import style
|
BIN
website/assets/fonts/hkgrotesk-bold.woff
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website/assets/fonts/hkgrotesk-bold.woff2
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website/assets/fonts/hkgrotesk-semibold.woff2
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website/assets/fonts/hkgrotesk-semibolditalic.woff
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website/assets/fonts/hkgrotesk-semibolditalic.woff
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website/assets/fonts/hkgrotesk-semibolditalic.woff2
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|
@ -1,244 +0,0 @@
|
|||
<?xml version="1.0" standalone="no"?>
|
||||
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
|
||||
<svg xmlns="http://www.w3.org/2000/svg">
|
||||
<metadata></metadata>
|
||||
<defs>
|
||||
<font id="source_code_prosemibold" horiz-adv-x="1228" >
|
||||
<font-face units-per-em="2048" ascent="1536" descent="-512" />
|
||||
<missing-glyph horiz-adv-x="500" />
|
||||
<glyph horiz-adv-x="0" />
|
||||
<glyph horiz-adv-x="682" />
|
||||
<glyph horiz-adv-x="0" />
|
||||
<glyph horiz-adv-x="0" />
|
||||
<glyph unicode="
" />
|
||||
<glyph unicode=" " />
|
||||
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|
||||
<glyph unicode=""" d="M219 1405h287l-6 -262l-62 -473h-151l-62 473zM725 1405h287l-6 -262l-62 -473h-151l-62 473z" />
|
||||
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|
||||
<glyph unicode="$" d="M152 233l106 162q88 -59 178 -96t197 -37q98 0 148 37t50 98q0 53 -46 88t-115.5 65t-149.5 59.5t-149.5 72.5t-115.5 103.5t-46 152.5q0 133 90 217t244 105v268h164v-266q109 -12 185.5 -55.5t139.5 -106.5l-119 -137q-72 53 -136 81.5t-159 28.5q-86 0 -134 -34.5 t-48 -96.5q0 -47 46 -77.5t116 -58.5t150.5 -57.5t150.5 -72.5t116 -105.5t46 -156.5q0 -131 -93.5 -222.5t-260.5 -115.5v-301h-164v297q-106 10 -211 54t-180 107z" />
|
||||
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|
||||
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||||
<glyph unicode="'" d="M471 1405h287l-6 -262l-62 -473h-151l-62 473z" />
|
||||
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||||
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||||
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|
||||
<glyph unicode="-" d="M160 590v172h909v-172h-909z" />
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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Reference in New Issue
Block a user