and tags
- 'nowiki': {
- pattern: /<(nowiki|pre|source)\b[\w\W]*?>[\w\W]*?<\/\1>/i,
- inside: {
- 'tag': {
- pattern: /<(?:nowiki|pre|source)\b[\w\W]*?>|<\/(?:nowiki|pre|source)>/i,
- inside: Prism.languages.markup['tag'].inside
- }
- }
- }
-});
-;
diff --git a/website/docs/legacy/resources/js/prism.min.js b/website/docs/legacy/resources/js/prism.min.js
deleted file mode 100644
index cc3c68d6a..000000000
--- a/website/docs/legacy/resources/js/prism.min.js
+++ /dev/null
@@ -1,2 +0,0 @@
-var _self="undefined"!=typeof window?window:"undefined"!=typeof WorkerGlobalScope&&self instanceof WorkerGlobalScope?self:{},Prism=function(){var e=/\blang(?:uage)?-(?!\*)(\w+)\b/i,t=_self.Prism={util:{encode:function(e){return e instanceof n?new n(e.type,t.util.encode(e.content),e.alias):"Array"===t.util.type(e)?e.map(t.util.encode):e.replace(/&/g,"&").replace(/e.length)break e;if(!(h instanceof i)){p.lastIndex=0;var f=p.exec(h);if(f){d&&(g=f[1].length);var b=f.index-1+g,f=f[0].slice(g),T=f.length,S=b+T,P=h.slice(0,b+1),k=h.slice(S+1),I=[E,1];P&&I.push(P);var A=new i(o,c?t.tokenize(f,c):f,m);I.push(A),k&&I.push(k),Array.prototype.splice.apply(r,I)}}}}}return r},hooks:{all:{},add:function(e,n){var a=t.hooks.all;a[e]=a[e]||[],a[e].push(n)},run:function(e,n){var a=t.hooks.all[e];if(a&&a.length)for(var i,r=0;i=a[r++];)i(n)}}},n=t.Token=function(e,t,n){this.type=e,this.content=t,this.alias=n};if(n.stringify=function(e,a,i){if("string"==typeof e)return e;if("Array"===t.util.type(e))return e.map(function(t){return n.stringify(t,a,e)}).join("");var r={type:e.type,content:n.stringify(e.content,a,i),tag:"span",classes:["token",e.type],attributes:{},language:a,parent:i};if("comment"==r.type&&(r.attributes.spellcheck="true"),e.alias){var s="Array"===t.util.type(e.alias)?e.alias:[e.alias];Array.prototype.push.apply(r.classes,s)}t.hooks.run("wrap",r);var o="";for(var l in r.attributes)o+=l+'="'+(r.attributes[l]||"")+'"';return"<"+r.tag+' class="'+r.classes.join(" ")+'" '+o+">"+r.content+""},!_self.document)return _self.addEventListener?(_self.addEventListener("message",function(e){var n=JSON.parse(e.data),a=n.language,i=n.code;_self.postMessage(JSON.stringify(t.util.encode(t.tokenize(i,t.languages[a])))),_self.close()},!1),_self.Prism):_self.Prism;var a=document.getElementsByTagName("script");return a=a[a.length-1],a&&(t.filename=a.src,document.addEventListener&&!a.hasAttribute("data-manual")&&document.addEventListener("DOMContentLoaded",t.highlightAll)),_self.Prism}();"undefined"!=typeof module&&module.exports&&(module.exports=Prism),Prism.languages.markup={comment://,prolog:/<\?[\w\W]+?\?>/,doctype://,cdata://i,tag:{pattern:/<\/?[^\s>\/]+(?:\s+[^\s>\/=]+(?:=(?:("|')(?:\\\1|\\?(?!\1)[\w\W])*\1|[^\s'">=]+))?)*\s*\/?>/i,inside:{tag:{pattern:/^<\/?[^\s>\/]+/i,inside:{punctuation:/^<\/?/,namespace:/^[^\s>\/:]+:/}},"attr-value":{pattern:/=(?:('|")[\w\W]*?(\1)|[^\s>]+)/i,inside:{punctuation:/[=>"']/}},punctuation:/\/?>/,"attr-name":{pattern:/[^\s>\/]+/,inside:{namespace:/^[^\s>\/:]+:/}}}},entity:/&#?[\da-z]{1,8};/i},Prism.hooks.add("wrap",function(e){"entity"===e.type&&(e.attributes.title=e.content.replace(/&/,"&"))}),Prism.languages.css={comment:/\/\*[\w\W]*?\*\//,atrule:{pattern:/@[\w-]+?.*?(;|(?=\s*\{))/i,inside:{rule:/@[\w-]+/}},url:/url\((?:(["'])(\\(?:\r\n|[\w\W])|(?!\1)[^\\\r\n])*\1|.*?)\)/i,selector:/[^\{\}\s][^\{\};]*?(?=\s*\{)/,string:/("|')(\\(?:\r\n|[\w\W])|(?!\1)[^\\\r\n])*\1/,property:/(\b|\B)[\w-]+(?=\s*:)/i,important:/\B!important\b/i,"function":/[-a-z0-9]+(?=\()/i,punctuation:/[(){};:]/},Prism.languages.css.atrule.inside.rest=Prism.util.clone(Prism.languages.css),Prism.languages.markup&&(Prism.languages.insertBefore("markup","tag",{style:{pattern:/[\w\W]*?<\/style>/i,inside:{tag:{pattern:/|<\/style>/i,inside:Prism.languages.markup.tag.inside},rest:Prism.languages.css},alias:"language-css"}}),Prism.languages.insertBefore("inside","attr-value",{"style-attr":{pattern:/\s*style=("|').*?\1/i,inside:{"attr-name":{pattern:/^\s*style/i,inside:Prism.languages.markup.tag.inside},punctuation:/^\s*=\s*['"]|['"]\s*$/,"attr-value":{pattern:/.+/i,inside:Prism.languages.css}},alias:"language-css"}},Prism.languages.markup.tag)),Prism.languages.clike={comment:[{pattern:/(^|[^\\])\/\*[\w\W]*?\*\//,lookbehind:!0},{pattern:/(^|[^\\:])\/\/.*/,lookbehind:!0}],string:/("|')(\\(?:\r\n|[\s\S])|(?!\1)[^\\\r\n])*\1/,"class-name":{pattern:/((?:(?:class|interface|extends|implements|trait|instanceof|new)\s+)|(?:catch\s+\())[a-z0-9_\.\\]+/i,lookbehind:!0,inside:{punctuation:/(\.|\\)/}},keyword:/\b(if|else|while|do|for|return|in|instanceof|function|new|try|throw|catch|finally|null|break|continue)\b/,"boolean":/\b(true|false)\b/,"function":/[a-z0-9_]+(?=\()/i,number:/\b-?(0x[\dA-Fa-f]+|\d*\.?\d+([Ee]-?\d+)?)\b/,operator:/[-+]{1,2}|!|<=?|>=?|={1,3}|&{1,2}|\|?\||\?|\*|\/|~|\^|%/,punctuation:/[{}[\];(),.:]/},Prism.languages.javascript=Prism.languages.extend("clike",{keyword:/\b(as|async|await|break|case|catch|class|const|continue|debugger|default|delete|do|else|enum|export|extends|false|finally|for|from|function|get|if|implements|import|in|instanceof|interface|let|new|null|of|package|private|protected|public|return|set|static|super|switch|this|throw|true|try|typeof|var|void|while|with|yield)\b/,number:/\b-?(0x[\dA-Fa-f]+|0b[01]+|0o[0-7]+|\d*\.?\d+([Ee][+-]?\d+)?|NaN|Infinity)\b/,"function":/(?!\d)[a-z0-9_$]+(?=\()/i}),Prism.languages.insertBefore("javascript","keyword",{regex:{pattern:/(^|[^/])\/(?!\/)(\[.+?]|\\.|[^/\\\r\n])+\/[gimyu]{0,5}(?=\s*($|[\r\n,.;})]))/,lookbehind:!0}}),Prism.languages.insertBefore("javascript","class-name",{"template-string":{pattern:/`(?:\\`|\\?[^`])*`/,inside:{interpolation:{pattern:/\$\{[^}]+\}/,inside:{"interpolation-punctuation":{pattern:/^\$\{|\}$/,alias:"punctuation"},rest:Prism.languages.javascript}},string:/[\s\S]+/}}}),Prism.languages.markup&&Prism.languages.insertBefore("markup","tag",{script:{pattern:/[\w\W]*?<\/script>/i,inside:{tag:{pattern:/|<\/script>/i,inside:Prism.languages.markup.tag.inside},rest:Prism.languages.javascript},alias:"language-javascript"}}),Prism.languages.bash=Prism.languages.extend("clike",{comment:{pattern:/(^|[^"{\\])#.*/,lookbehind:!0},string:{pattern:/("|')(\\?[\s\S])*?\1/,inside:{property:/\$([a-zA-Z0-9_#\?\-\*!@]+|\{[^\}]+\})/}},number:{pattern:/([^\w\.])-?(0x[\dA-Fa-f]+|\d*\.?\d+([Ee]-?\d+)?)\b/,lookbehind:!0},"function":/\b(?:alias|apropos|apt-get|aptitude|aspell|awk|basename|bash|bc|bg|builtin|bzip2|cal|cat|cd|cfdisk|chgrp|chmod|chown|chroot|chkconfig|cksum|clear|cmp|comm|command|cp|cron|crontab|csplit|cut|date|dc|dd|ddrescue|df|diff|diff3|dig|dir|dircolors|dirname|dirs|dmesg|du|egrep|eject|enable|env|ethtool|eval|exec|expand|expect|export|expr|fdformat|fdisk|fg|fgrep|file|find|fmt|fold|format|free|fsck|ftp|fuser|gawk|getopts|git|grep|groupadd|groupdel|groupmod|groups|gzip|hash|head|help|hg|history|hostname|htop|iconv|id|ifconfig|ifdown|ifup|import|install|jobs|join|kill|killall|less|link|ln|locate|logname|logout|look|lpc|lpr|lprint|lprintd|lprintq|lprm|ls|lsof|make|man|mkdir|mkfifo|mkisofs|mknod|more|most|mount|mtools|mtr|mv|mmv|nano|netstat|nice|nl|nohup|notify-send|nslookup|open|op|passwd|paste|pathchk|ping|pkill|popd|pr|printcap|printenv|printf|ps|pushd|pv|pwd|quota|quotacheck|quotactl|ram|rar|rcp|read|readarray|readonly|reboot|rename|renice|remsync|rev|rm|rmdir|rsync|screen|scp|sdiff|sed|seq|service|sftp|shift|shopt|shutdown|sleep|slocate|sort|source|split|ssh|stat|strace|su|sudo|sum|suspend|sync|tail|tar|tee|test|time|timeout|times|touch|top|traceroute|trap|tr|tsort|tty|type|ulimit|umask|umount|unalias|uname|unexpand|uniq|units|unrar|unshar|uptime|useradd|userdel|usermod|users|uuencode|uudecode|v|vdir|vi|vmstat|wait|watch|wc|wget|whereis|which|who|whoami|write|xargs|xdg-open|yes|zip)\b/,keyword:/\b(if|then|else|elif|fi|for|break|continue|while|in|case|function|select|do|done|until|echo|exit|return|set|declare)\b/}),Prism.languages.insertBefore("bash","keyword",{property:/\$([a-zA-Z0-9_#\?\-\*!@]+|\{[^}]+\})/}),Prism.languages.insertBefore("bash","comment",{important:/^#!\s*\/bin\/bash|^#!\s*\/bin\/sh/}),Prism.languages.c=Prism.languages.extend("clike",{keyword:/\b(asm|typeof|inline|auto|break|case|char|const|continue|default|do|double|else|enum|extern|float|for|goto|if|int|long|register|return|short|signed|sizeof|static|struct|switch|typedef|union|unsigned|void|volatile|while)\b/,operator:/\-[>-]?|\+\+?|!=?|<>?=?|==?|&&?|\|?\||[~^%?*\/]/}),Prism.languages.insertBefore("c","string",{macro:{pattern:/(^\s*)#\s*[a-z]+([^\r\n\\]|\\.|\\(?:\r\n?|\n))*/im,lookbehind:!0,alias:"property",inside:{string:{pattern:/(#\s*include\s*)(<.+?>|("|')(\\?.)+?\3)/,lookbehind:!0}}}}),delete Prism.languages.c["class-name"],delete Prism.languages.c["boolean"],Prism.languages.cpp=Prism.languages.extend("c",{keyword:/\b(alignas|alignof|asm|auto|bool|break|case|catch|char|char16_t|char32_t|class|compl|const|constexpr|const_cast|continue|decltype|default|delete|do|double|dynamic_cast|else|enum|explicit|export|extern|float|for|friend|goto|if|inline|int|long|mutable|namespace|new|noexcept|nullptr|operator|private|protected|public|register|reinterpret_cast|return|short|signed|sizeof|static|static_assert|static_cast|struct|switch|template|this|thread_local|throw|try|typedef|typeid|typename|union|unsigned|using|virtual|void|volatile|wchar_t|while)\b/,"boolean":/\b(true|false)\b/,operator:/[-+]{1,2}|!=?|<{1,2}=?|>{1,2}=?|\->|:{1,2}|={1,2}|\^|~|%|&{1,2}|\|?\||\?|\*|\/|\b(and|and_eq|bitand|bitor|not|not_eq|or|or_eq|xor|xor_eq)\b/}),Prism.languages.insertBefore("cpp","keyword",{"class-name":{pattern:/(class\s+)[a-z0-9_]+/i,lookbehind:!0}}),Prism.languages.css.selector={pattern:/[^\{\}\s][^\{\}]*(?=\s*\{)/,inside:{"pseudo-element":/:(?:after|before|first-letter|first-line|selection)|::[-\w]+/,"pseudo-class":/:[-\w]+(?:\(.*\))?/,"class":/\.[-:\.\w]+/,id:/#[-:\.\w]+/}},Prism.languages.insertBefore("css","function",{hexcode:/#[\da-f]{3,6}/i,entity:/\\[\da-f]{1,8}/i,number:/[\d%\.]+/}),Prism.languages.git={comment:/^#.*$/m,string:/("|')(\\?.)*?\1/m,command:{pattern:/^.*\$ git .*$/m,inside:{parameter:/\s(--|-)\w+/m}},coord:/^@@.*@@$/m,deleted:/^-(?!-).+$/m,inserted:/^\+(?!\+).+$/m,commit_sha1:/^commit \w{40}$/m},function(e){e.languages.haml={"multiline-comment":[{pattern:/((?:^|\n)([\t ]*))\/.*(\n\2[\t ]+.+)*/,lookbehind:!0,alias:"comment"},{pattern:/((?:^|\n)([\t ]*))-#.*(\n\2[\t ]+.+)*/,lookbehind:!0,alias:"comment"}],"multiline-code":[{pattern:/((?:^|\n)([\t ]*)(?:[~-]|[&!]?=)).*,[\t ]*(\n\2[\t ]+.*,[\t ]*)*(\n\2[\t ]+.+)/,lookbehind:!0,inside:{rest:e.languages.ruby}},{pattern:/((?:^|\n)([\t ]*)(?:[~-]|[&!]?=)).*\|[\t ]*(\n\2[\t ]+.*\|[\t ]*)*/,lookbehind:!0,inside:{rest:e.languages.ruby}}],filter:{pattern:/((?:^|\n)([\t ]*)):[\w-]+(\n(?:\2[\t ]+.+|\s*?(?=\n)))+/,lookbehind:!0,inside:{"filter-name":{pattern:/^:[\w-]+/,alias:"variable"}}},markup:{pattern:/((?:^|\n)[\t ]*)<.+/,lookbehind:!0,inside:{rest:e.languages.markup}},doctype:{pattern:/((?:^|\n)[\t ]*)!!!(?: .+)?/,lookbehind:!0},tag:{pattern:/((?:^|\n)[\t ]*)[%.#][\w\-#.]*[\w\-](?:\([^)]+\)|\{(?:\{[^}]+\}|[^}])+\}|\[[^\]]+\])*[\/<>]*/,lookbehind:!0,inside:{attributes:[{pattern:/(^|[^#])\{(?:\{[^}]+\}|[^}])+\}/,lookbehind:!0,inside:{rest:e.languages.ruby}},{pattern:/\([^)]+\)/,inside:{"attr-value":{pattern:/(=\s*)(?:"(?:\\?.)*?"|[^)\s]+)/,lookbehind:!0},"attr-name":/[\w:-]+(?=\s*!?=|\s*[,)])/,punctuation:/[=(),]/}},{pattern:/\[[^\]]+\]/,inside:{rest:e.languages.ruby}}],punctuation:/[<>]/}},code:{pattern:/((?:^|\n)[\t ]*(?:[~-]|[&!]?=)).+/,lookbehind:!0,inside:{rest:e.languages.ruby}},interpolation:{pattern:/#\{[^}]+\}/,inside:{delimiter:{pattern:/^#\{|\}$/,alias:"punctuation"},rest:e.languages.ruby}},punctuation:{pattern:/((?:^|\n)[\t ]*)[~=\-&!]/,lookbehind:!0}};for(var t="((?:^|\\n)([\\t ]*)):{{filter_name}}(\\n(?:\\2[\\t ]+.+|\\s*?(?=\\n)))+",n=["css",{filter:"coffee",language:"coffeescript"},"erb","javascript","less","markdown","ruby","scss","textile"],a={},i=0,r=n.length;r>i;i++){var s=n[i];s="string"==typeof s?{filter:s,language:s}:s,e.languages[s.language]&&(a["filter-"+s.filter]={pattern:RegExp(t.replace("{{filter_name}}",s.filter)),lookbehind:!0,inside:{"filter-name":{pattern:/^:[\w-]+/,alias:"variable"},rest:e.languages[s.language]}})}e.languages.insertBefore("haml","filter",a)}(Prism),function(e){e.languages.jade={"multiline-comment":{pattern:/((?:^|\n)([\t ]*))\/\/.*(\n\2[\t ]+.+)*/,lookbehind:!0,alias:"comment"},"multiline-script":{pattern:/((?:^|\n)([\t ]*)script\b.*\.[\t ]*)(\n(?:\2[\t ]+.+|\s*?(?=\n)))+/,lookbehind:!0,inside:{rest:e.languages.javascript}},filter:{pattern:/((?:^|\n)([\t ]*)):.+(\n(?:\2[\t ]+.+|\s*?(?=\n)))+/,lookbehind:!0,inside:{"filter-name":{pattern:/^:[\w-]+/,alias:"variable"}}},"multiline-plain-text":{pattern:/((?:^|\n)([\t ]*)[\w\-#.]+\.[\t ]*)(\n(?:\2[\t ]+.+|\s*?(?=\n)))+/,lookbehind:!0},markup:{pattern:/((?:^|\n)[\t ]*)<.+/,lookbehind:!0,inside:{rest:e.languages.markup}},comment:{pattern:/((?:^|\n)[\t ]*)\/\/.+/,lookbehind:!0},doctype:{pattern:/((?:^|\n)[\t ]*)doctype(?: .+)?/,lookbehind:!0},"flow-control":{pattern:/((?:^|\n)[\t ]*)(?:if|unless|else|case|when|default|each|while)(?: .+)?/,lookbehind:!0,inside:{each:{pattern:/((?:^|\n)[\t ]*)each .+? in\b/,lookbehind:!0,inside:{keyword:/\b(?:each|in)\b/,punctuation:/,/}},branch:{pattern:/((?:^|\n)[\t ]*)(?:if|unless|else|case|when|default|while)/,lookbehind:!0,alias:"keyword"},rest:e.languages.javascript}},keyword:{pattern:/((?:^|\n)[\t ]*)(?:block|extends|include|append|prepend)\b.+/,lookbehind:!0},mixin:[{pattern:/((?:^|\n)[\t ]*)mixin .+/,lookbehind:!0,inside:{keyword:/^mixin/,"function":/\w+(?=\s*\(|\s*$)/,punctuation:/[(),.]/}},{pattern:/((?:^|\n)[\t ]*)\+.+/,lookbehind:!0,inside:{name:{pattern:/^\+\w+/,alias:"function"},rest:e.languages.javascript}}],script:{pattern:/((?:^|\n)[\t ]*script(?:(?:&[^(]+)?\([^)]+\))*) .+/,lookbehind:!0,inside:{rest:e.languages.javascript}},"plain-text":{pattern:/((?:^|\n)[\t ]*(?!-)[\w\-#.]*[\w\-](?:(?:&[^(]+)?\([^)]+\))*\/?[\t ]+).+/,lookbehind:!0},tag:{pattern:/((?:^|\n)[\t ]*)(?!-)[\w\-#.]*[\w\-](?:(?:&[^(]+)?\([^)]+\))*\/?:?/,lookbehind:!0,inside:{attributes:[{pattern:/&[^(]+\([^)]+\)/,inside:{rest:e.languages.javascript}},{pattern:/\([^)]+\)/,inside:{"attr-value":{pattern:/(=\s*)(?:\{[^}]*\}|[^,)\n]+)/,lookbehind:!0,inside:{rest:e.languages.javascript}},"attr-name":/[\w-]+(?=\s*!?=|\s*[,)])/,punctuation:/[!=(),]/}}],punctuation:/[:]/}},code:[{pattern:/((?:^|\n)[\t ]*(?:-|!?=)).+/,lookbehind:!0,inside:{rest:e.languages.javascript}}],punctuation:/[.\-!=|]/};for(var t="((?:^|\\n)([\\t ]*)):{{filter_name}}(\\n(?:\\2[\\t ]+.+|\\s*?(?=\\n)))+",n=[{filter:"atpl",language:"twig"},{filter:"coffee",language:"coffeescript"},"ejs","handlebars","hogan","less","livescript","markdown","mustache","plates",{filter:"sass",language:"scss"},"stylus","swig"],a={},i=0,r=n.length;r>i;i++){var s=n[i];s="string"==typeof s?{filter:s,language:s}:s,e.languages[s.language]&&(a["filter-"+s.filter]={pattern:RegExp(t.replace("{{filter_name}}",s.filter)),lookbehind:!0,inside:{"filter-name":{pattern:/^:[\w-]+/,alias:"variable"},rest:e.languages[s.language]}})}e.languages.insertBefore("jade","filter",a)}(Prism),Prism.languages.latex={comment:/%.*?(\r?\n|$)$/m,string:/(\$)(\\?.)*?\1/,punctuation:/[{}]/,selector:/\\[a-z;,:\.]*/i},Prism.languages.makefile={comment:{pattern:/(^|[^\\])#(?:\\[\s\S]|.)*/,lookbehind:!0},string:/(["'])(?:\\[\s\S]|(?!\1)[^\\\r\n])*\1/,builtin:/\.[A-Z][^:#=\s]+(?=\s*:(?!=))/,symbol:{pattern:/^[^:=\r\n]+(?=\s*:(?!=))/m,inside:{variable:/\$+(?:[^(){}:#=\s]+|(?=[({]))/}},variable:/\$+(?:[^(){}:#=\s]+|\([@*%<^+?][DF]\)|(?=[({]))/,keyword:[/\b(?:define|else|endef|endif|export|ifn?def|ifn?eq|-?include|override|private|sinclude|undefine|unexport|vpath)\b/,{pattern:/(\()(?:addsuffix|abspath|and|basename|call|dir|error|eval|file|filter(?:-out)?|findstring|firstword|flavor|foreach|guile|if|info|join|lastword|load|notdir|or|origin|patsubst|realpath|shell|sort|strip|subst|suffix|value|warning|wildcard|word(?:s|list)?)(?=[ 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diff --git a/website/docs/tutorials/_data.json b/website/docs/tutorials/_data.json
index 0e4f18364..9bdd75ce3 100644
--- a/website/docs/tutorials/_data.json
+++ b/website/docs/tutorials/_data.json
@@ -6,6 +6,13 @@
"description": "Word vectors allow simple similarity queries, and drive many NLP applications. This tutorial explains how to load custom word vectors into spaCy, to make use of task or data-specific representations."
},
+ "byo-annotations": {
+ "template": "article",
+ "title": "Using Pre-existing Tokenization, Tags, and Other Annotations",
+ "date": "2016-04-15",
+ "description": "spaCy assumes by default that your data is raw text. However, sometimes your data is partially annotated, e.g. with pre-existing tokenization, part-of-speech tags, etc. This tutorial explains how to use these annotations in spaCy."
+ },
+
"mark-adverbs": {
"template": "article",
"title": "Mark all adverbs, particularly for verbs of speech",
diff --git a/website/docs/tutorials/byo-annotations.jade b/website/docs/tutorials/byo-annotations.jade
new file mode 100644
index 000000000..05a8548ca
--- /dev/null
+++ b/website/docs/tutorials/byo-annotations.jade
@@ -0,0 +1,117 @@
+include ../../_includes/_mixins
+
+p.u-text-large spaCy assumes by default that your data is raw text. However, sometimes your data is partially annotated, e.g. with pre-existing tokenization, part-of-speech tags, etc. This tutorial explains how to use these annotations in spaCy.
+
++h(2, "quick-reference") Quick Reference
+
++table(['Description', 'Usage'], 'code')
+ +row
+ +cell Use pre-existing tokenization
+ +cell #[code.lang-python doc = Doc(nlp.vocab, [('A', True), ('token', False), ('!', False)])]
+ +row
+ +cell Use pre-existing tokenization (deprecated)
+ +cell #[code.lang-python doc = nlp.tokenizer.tokens_from_list([u'A', u'token', u'!'])]
+
+ +row
+ +cell Assign pre-existing tags
+ +cell #[code.lang-python nlp.tagger.tag_from_strings(doc, ['DT', 'NN'])]
+ +row
+ +cell Assign named entity annotations from an array
+ +cell #[code.lang-python doc.from_array([ENT_TYPE, ENT_IOB], values)]
+ +row
+ +cell Assign dependency parse annotations from an array
+ +cell #[code.lang-python doc.from_array([HEAD, DEP], values)]
+
++h(2, "examples") Examples
+
++code('python', 'Tokenization').
+ import spacy
+
+ nlp = spacy.load('en')
+
+ tokens = [u'A', u'list', u'of', u'strings', u'.']
+
+ doc = nlp.tokenizer.tokens_from_list(tokens)
+
+ assert len(doc) == len(tokens)
+ # With this method, we don't get to specify how the corresponding string
+ # would be spaced, so we have to assume a space before every token.
+ assert doc.text == u'A list of strings .'
+
++code('python', 'Tokenization').
+ import spacy
+ from spacy.tokens import Doc
+
+ nlp = spacy.load('en')
+
+ tokens = [u'A', u'list', u'of', u'strings', u'.']
+ has_space = [True, True, True, False, False]
+
+ doc = Doc(nlp.vocab, orth_and_spaces=zip(tokens, has_space))
+
+ assert len(doc) == len(tokens)
+ # Spacing is correct, given by boolean values above.
+ assert doc.text == u'A list of strings.'
+ # Here's how it would look with different boolean values.
+ tokens = [u'A', u'list', u'of', u'strings', u'.']
+ has_space = [False, True, True, True, False]
+ doc = Doc(nlp.vocab, orth_and_spaces=zip(tokens, has_space))
+ assert doc.text == u'Alist of strings .'
+
++code('python', 'POS Tags').
+ import spacy
+
+ nlp = spacy.load('en')
+
+ # Tokenize a string into a Doc, but don't apply the whole pipeline ---
+ # that is, don't predict the part-of-speech tags, syntactic parse, named
+ # entities, etc.
+ doc = nlp.tokenizer(u'A unicode string, untokenized.')
+ nlp.tagger.tag_from_strings([u'DT', u'JJ', u'NN', u',', u'VBN', u'.'])
+ # Now predict dependency parse and named entities. Note that if you assign
+ # tags in a way that's very unlike the behaviour of the POS tagger model,
+ # the subsequent models may perform worse. These models use the POS tags
+ # as features, so if you give them unexpected tags, you may be giving them
+ # run-time conditions that don't resemble the training data.
+ nlp.parser(doc)
+ nlp.entity(doc)
+
++code('python', 'Dependency Parse').
+ import spacy
+ from spacy.attrs import HEAD, DEP
+ from spacy.symbols import det, nmod, root, punct
+ from numpy import ndarray
+
+ nlp = spacy.load('en')
+
+ # Get the Doc object, and apply the pipeline except the dependency parser
+ doc = nlp(u'A unicode string.', parse=False)
+
+ columns = [HEAD, DEP]
+ values = ndarray(shape=(len(columns), len(doc)), dtype='int32')
+ # Syntactic parse specified as head offsets
+ heads = [2, 1, 0, -1]
+ # Integer IDs for the dependency labels. See the parse in the displaCy
+ # demo at spacy.io/demos/displacy
+ labels = [det, nmod, root, punct]
+ values[0] = heads
+ values[1] = labels
+ doc.from_array(columns, values)
+
++code('python', 'Named Entities').
+ import spacy
+ from spacy.attrs import ENT_TYPE, ENT_IOB
+ from spacy.symbols import PERSON, ORG
+ from numpy import ndarray
+
+ nlp = spacy.load('en')
+
+ # Get the Doc object, and apply the pipeline except the entity recognizer
+ doc = nlp(u'My name is Matt.', entity=False)
+
+ columns = [ENT_TYPE, ENT_IOB]
+ values = ndarray(shape=(len(columns), len(doc)), dtype='int32')
+ # IOB values are 0=missing, 1=I, 2=O, 3=B
+ values[0] = [1, 1, 1, 3, 1]
+ values[1] = [0, 0, 0, PERSON, 0]
+ doc.from_array(columns, values)
diff --git a/website/docs/tutorials/load-new-word-vectors.jade b/website/docs/tutorials/load-new-word-vectors.jade
index 84ba4a641..47d016793 100644
--- a/website/docs/tutorials/load-new-word-vectors.jade
+++ b/website/docs/tutorials/load-new-word-vectors.jade
@@ -2,7 +2,7 @@ include ../../_includes/_mixins
p By default spaCy loads a #[code data/vocab/vec.bin] file, where the #[em data] directory is within the #[code spacy.en] module directory. This file can be replaced, to customize the word vectors that spaCy loads. You can also replace the word vectors at run-time.
-+h3("replacing-vec-bin") Replacing vec.bin
++h(2, "replacing-vec-bin") Replacing vec.bin
p The function #[code spacy.vocab.write_binary_vectors] creates a word vectors file in spaCy's binary data format. It expects a #[code bz2] file in the following format:
@@ -28,7 +28,7 @@ p The following example script will replace the #[code vec.bin] file with vector
if __name__ == '__main__':
plac.call(main)
-+h3("replace-vectors-archive") Replace the Vectors at Run-Time, From an Archive
++h(2, "replace-vectors-archive") Replace the Vectors at Run-Time, From an Archive
p Since v0.93, instances of #[code Vocab] allow new vectors to be loaded from #[code bz2] archive files. This allows vectors to be loaded as follows:
@@ -39,7 +39,7 @@ p Since v0.93, instances of #[code Vocab] allow new vectors to be loaded from #[
>>> n_dimensions
300
-+h3("replace-vectors-per-word") Replace Vectors at Run-Time, Per Word
++h(2, "replace-vectors-per-word") Replace Vectors at Run-Time, Per Word
p Since v0.93, you can assign to the #[code .vector] attribute of #[code Lexeme] instances. Tokens of that lexical type will then inherit the updated vector. For instance:
diff --git a/website/docs/tutorials/mark-adverbs.jade b/website/docs/tutorials/mark-adverbs.jade
index 3707bf415..ca3061f81 100644
--- a/website/docs/tutorials/mark-adverbs.jade
+++ b/website/docs/tutorials/mark-adverbs.jade
@@ -1,6 +1,6 @@
include ../../_includes/_mixins
-+lead Let's say you're developing a proofreading tool, or possibly an IDE for writers. You're convinced by Stephen King's advice that #[a(href="http://www.brainpickings.org/2013/03/13/stephen-king-on-adverbs" target="_blank") adverbs are not your friend] so you want to #[strong highlight all adverbs]. We'll use one of the examples he finds particularly egregious:
+p.u-text-large Let's say you're developing a proofreading tool, or possibly an IDE for writers. You're convinced by Stephen King's advice that #[a(href="http://www.brainpickings.org/2013/03/13/stephen-king-on-adverbs" target="_blank") adverbs are not your friend] so you want to #[strong highlight all adverbs]. We'll use one of the examples he finds particularly egregious:
+code.
>>> import spacy.en
@@ -11,7 +11,7 @@ include ../../_includes/_mixins
>>> print u''.join(tok.string.upper() if tok.pos == ADV else tok.string for tok in tokens)
u‘Give it BACK,’ he pleaded ABJECTLY, ‘it’s mine.’
-p Easy enough --- but the problem is that we've also highlighted "back". While "back" is undoubtedly an adverb, we probably don't want to highlight it. If what we're trying to do is flag dubious stylistic choices, we'll need to refine our logic. It turns out only a certain type of adverb is of interest to us.
+p Easy enough --- but the problem is that we've also highlighted "back". While "back" is undoubtedly an adverb, we probably don't want to highlight it. If what we're trying to do is flag dubious stylistic choices, we'll need to refine our logic. It turns out only a certain type of adverb is of interest to us.
p There are lots of ways we might do this, depending on just what words we want to flag. The simplest way to exclude adverbs like "back" and "not" is by word frequency: these words are much more common than the prototypical manner adverbs that the style guides are worried about.
@@ -25,7 +25,7 @@ p The #[code Lexeme.prob] and #[code Token.prob] attribute gives a log probabili
>>> nlp.vocab[u'quietly'].prob
-11.07155704498291
-p (The probability estimate is based on counts from a 3 billion word corpus, smoothed using the `Simple Good-Turing`_ method.)
+p (The probability estimate is based on counts from a 3 billion word corpus, smoothed using the `Simple Good-Turing`_ method.)
p So we can easily exclude the N most frequent words in English from our adverb marker. Let's try N=1000 for now:
@@ -41,7 +41,7 @@ p So we can easily exclude the N most frequent words in English from our adverb
>>> print u''.join(tok.string.upper() if is_adverb(tok) else tok.string for tok in tokens)
‘Give it back,’ he pleaded ABJECTLY, ‘it’s mine.’
-p There are lots of other ways we could refine the logic, depending on just what words we want to flag. Let's say we wanted to only flag adverbs that modified words similar to "pleaded". This is easy to do, as spaCy loads a vector-space representation for every word (by default, the vectors produced by `Levy and Goldberg (2014)`_). Naturally, the vector is provided as a numpy array:
+p There are lots of other ways we could refine the logic, depending on just what words we want to flag. Let's say we wanted to only flag adverbs that modified words similar to "pleaded". This is easy to do, as spaCy loads a vector-space representation for every word (by default, the vectors produced by Levy and Goldberg (2014)). Naturally, the vector is provided as a numpy array:
+code.
>>> pleaded = tokens[7]
diff --git a/website/docs/tutorials/syntax-search.jade b/website/docs/tutorials/syntax-search.jade
index ff85e7b55..1a508a7d8 100644
--- a/website/docs/tutorials/syntax-search.jade
+++ b/website/docs/tutorials/syntax-search.jade
@@ -1,65 +1,65 @@
include ../../_includes/_mixins
-+lead Example use of the #[a(href="/docs") spaCy NLP tools] for data exploration. Here we will look for Reddit comments that describe Google doing something, i.e. discuss the company's actions. This is difficult, because other senses of "Google" now dominate usage of the word in conversation, particularly references to using Google products.
+p.u-text-large Example use of the #[a(href="/docs") spaCy NLP tools] for data exploration. Here we will look for Reddit comments that describe Google doing something, i.e. discuss the company's actions. This is difficult, because other senses of "Google" now dominate usage of the word in conversation, particularly references to using Google products.
p The heuristics used are quick and dirty – about 5 minutes work.
-
-+h3("imports") Imports
+
++h(2, "imports") Imports
+code.
from __future__ import unicode_literals
from __future__ import print_function
import sys
-
+
import plac
import bz2
import ujson
import spacy.en
-
-+h3("load-model-and-iterate") Load the Model and Iterate Over the Data
+
++h(2, "load-model-and-iterate") Load the Model and Iterate Over the Data
+code.
def main(input_loc):
nlp = spacy.en.English() # Load the model takes 10-20 seconds.
- for line in bz2.BZ2File(input_loc): # Iterate over the Reddit comments from the dump.
- comment_str = ujson.loads(line)['body'] # Parse the json object, and extract the 'body' attribute.
-
-+h3("apply-spacy-pipeline") Apply the spaCy NLP Pipeline, and Look For the Cases We Want
+ for line in bz2.BZ2File(input_loc): # Iterate over the Reddit comments from the dump.
+ comment_str = ujson.loads(line)['body'] # Parse the json object, and extract the 'body' attribute.
+
++h(2, "apply-spacy-pipeline") Apply the spaCy NLP Pipeline, and Look For the Cases We Want
+code.
- comment_parse = nlp(comment_str)
- for word in comment_parse:
+ comment_parse = nlp(comment_str)
+ for word in comment_parse:
if google_doing_something(word):
# Print the clause
print(''.join(w.string for w in word.head.subtree).strip())
-+h3("define-filter-function") Define the Filter Function
++h(2, "define-filter-function") Define the Filter Function
+code.
def google_doing_something(w):
if w.lower_ != 'google':
return False
# Is it the subject of a verb?
- elif w.dep_ != 'nsubj':
+ elif w.dep_ != 'nsubj':
return False
# And not 'is'
- elif w.head.lemma_ == 'be' and w.head.dep_ != 'aux':
+ elif w.head.lemma_ == 'be' and w.head.dep_ != 'aux':
return False
# Exclude e.g. "Google says..."
- elif w.head.lemma_ in ('say', 'show'):
+ elif w.head.lemma_ in ('say', 'show'):
return False
else:
return True
-+h3("call-main") Call main
++h(2, "call-main") Call main
+code.
if __name__ == '__main__':
plac.call(main)
-+h3("example-output") Example output
++h(2, "example-output") Example output
-p Many false positives remain. Some are from incorrect interpretations of the sentence by spaCy, some are flaws in our filtering logic. But the results are vastly better than a string-based search, which returns almost no examples of the pattern we're looking for.
+p Many false positives remain. Some are from incorrect interpretations of the sentence by spaCy, some are flaws in our filtering logic. But the results are vastly better than a string-based search, which returns almost no examples of the pattern we're looking for.
+code("markup").
Google dropped support for Android < 4.0 already
@@ -67,7 +67,7 @@ p Many false positives remain. Some are from incorrect interpretations of the se
Google to enforce a little more uniformity in its hardware so that we can see a better 3rd party market for things like mounts, cases, etc
When Google responds
Google translate cyka pasterino.
- A quick google looks like Synology does have a sync'ing feature which does support block level so that should work
+ A quick google looks like Synology does have a sync'ing feature which does support block level so that should work
(google came up with some weird One Piece/FairyTail crossover stuff), and is their knowledge universally infallible?
Until you have the gear, google some videos on best farming runs on each planet, you can get a lot REAL fast with the right loop.
Google offers something like this already, but it is truly terrible.
diff --git a/website/docs/tutorials/twitter-filter.jade b/website/docs/tutorials/twitter-filter.jade
index 87777664c..986fd537f 100644
--- a/website/docs/tutorials/twitter-filter.jade
+++ b/website/docs/tutorials/twitter-filter.jade
@@ -1,16 +1,16 @@
include ../../_includes/_mixins
-+lead #[a(href=url) spaCy] is great for data exploration. Poking, prodding and sifting is fundamental to good data science. In this tutorial, we'll do a broad keword search of Twitter, and then sift through the live stream of tweets, zooming in on some topics and excluding others.
+p.u-text-large #[+a(href=SITE_URL, true) spaCy] is great for data exploration. Poking, prodding and sifting is fundamental to good data science. In this tutorial, we'll do a broad keword search of Twitter, and then sift through the live stream of tweets, zooming in on some topics and excluding others.
p An example filter-function:
+code.
accept = map(get_vector, 'Jeb Cheney Republican 9/11h'.split())
reject = map(get_vector, 'garden Reggie hairy'.split())
-
+
y = sum(max(cos(w1, w2), 0) for w1 in tweet for w2 in accept)
n = sum(max(cos(w1, w2), 0) for w1 in tweet for w2 in reject)
-
+
if (y / (y + n)) >= 0.5 or True:
print(text)
@@ -23,13 +23,13 @@ p We'll build the filter as we go, simply editing the script in place. However,
import codecs
import pathlib
import random
-
+
import twython
import spacy.en
-
+
import _handler
-
-
+
+
class Connection(twython.TwythonStreamer):
def __init__(self, keys_dir, nlp, query):
keys_dir = pathlib.Path(keys_dir)
@@ -38,40 +38,40 @@ p We'll build the filter as we go, simply editing the script in place. However,
twython.TwythonStreamer.__init__(self, *api_key)
self.nlp = nlp
self.query = query
-
+
def on_success(self, data):
_handler.handle_tweet(self.nlp, data, self.query)
if random.random() >= 0.1:
reload(_handler)
-
-
+
+
def main(keys_dir, term):
nlp = spacy.en.English()
twitter = Connection(keys_dir, nlp, term)
twitter.statuses.filter(track=term, language='en')
-
-
+
+
if __name__ == '__main__':
plac.call(main)
+code("python", "handler.py (complete script)").
from __future__ import unicode_literals, print_function
-
+
from math import sqrt
from numpy import dot
from numpy.linalg import norm
-
-
+
+
def handle_tweet(spacy, tweet_data, query):
text = tweet_data.get('text', '')
match_tweet(spacy, text.decode('utf8'), query)
-
-
+
+
def match_tweet(spacy, text, query):
def get_vector(word):
return spacy.vocab[word].repvec
-
+
tweet = spacy(text)
tweet = [w.repvec for w in tweet if w.is_alpha and w.lower_ != query]
if tweet:
@@ -79,20 +79,20 @@ p We'll build the filter as we go, simply editing the script in place. However,
reject = map(get_vector, 'garden Reggie hairy'.split())
y = sum(max(cos(w1, w2), 0) for w1 in tweet for w2 in accept)
n = sum(max(cos(w1, w2), 0) for w1 in tweet for w2 in reject)
-
+
if (y / (y + n)) >= 0.5 or True:
print(text)
-
-
+
+
def cos(v1, v2):
return dot(v1, v2) / (norm(v1) * norm(v2))
p Below, I go through the script in execution-order.
-+h3("command-line-interface-main") Command-line Interface and main()
-
++h(2, "command-line-interface-main") Command-line Interface and main()
+
p I always use the plac library for command-line arguments, as I think it "scales down" the best. This just wraps the main functon, so that the two arguments of main are positional arguments on the command-line.
-
+
p We accept a directory that stores the twitter keys, and a search term, which we live-filter twitter for. We build a spaCy instance, and pass it to the connection. From here, #[code Connection.on_success] is called with each tweet – so that's where the rest of our logic lives.
+code.
@@ -100,12 +100,12 @@ p We accept a directory that stores the twitter keys, and a search term, which w
nlp = spacy.en.English()
twitter = Connection(keys_dir, nlp, term)
twitter.statuses.filter(track=term, language='en')
-
-
+
+
if __name__ == '__main__':
plac.call(main)
-+h3("event-loop") The Event Loop
++h(2, "event-loop") The Event Loop
p We read our key files off disk, and connect to Twitter. In the #[code on_success()] method, which is called with each tweet, we reload the handler 10% of the time. This way we can live-edit the handler, without constantly reconnecting to Twitter.
@@ -118,14 +118,14 @@ p We read our key files off disk, and connect to Twitter. In the #[code on_succe
twython.TwythonStreamer.__init__(self, *api_key)
self.nlp = nlp
self.query = query
-
+
def on_success(self, data):
_handler.handle_tweet(self.nlp, data, self.query)
if random.random() >= 0.1:
reload(_handler)
-+h3("the-part-you-live-edit") The Part You Live Edit
++h(2, "the-part-you-live-edit") The Part You Live Edit
p We can now use spaCy on the live Twitter feed. In the example below I sketch out a simple word-vector based tweet filter. The idea here is to be a bit more flexible than simple keyword filters, as we can turn the strictness of the filter up or down with the constant given, and we can specify exclusion terms to filter out distractor topics as they arise.
@@ -135,20 +135,20 @@ p Instead of averaging the vectors in the tweet, I've set it to sum the values a
def handle_tweet(spacy, resp, query):
def get_vector(word):
return spacy.vocab[word].repvec
-
+
text = resp.get('text', '').decode('utf8')
tweet = spacy(text)
tweet = [w.repvec for w in tweet if w.is_alpha and w.lower_ != query]
if tweet:
accept = map(get_vector, 'Jeb Cheney Republican 9/11h'.split())
reject = map(get_vector, 'garden Reggie hairy'.split())
-
+
y = sum(max(cos(w1, w2), 0) for w1 in tweet for w2 in accept)
n = sum(max(cos(w1, w2), 0) for w1 in tweet for w2 in reject)
-
+
if (y / (y + n)) >= 0.5 or True:
print(text)
-
-
+
+
def cos(v1, v2):
return dot(v1, v2) / (norm(v1) * norm(v2))
diff --git a/website/feed.xml.jade b/website/feed.xml.jade
deleted file mode 100644
index 0edbc2d5d..000000000
--- a/website/feed.xml.jade
+++ /dev/null
@@ -1,30 +0,0 @@
-include _includes/_mixins
-
-//- ----------------------------------------------------------------------------
-//- Blog Feed
-//- ----------------------------------------------------------------------------
-
-doctype xml
-rss(version='2.0', xmlns:atom='http://www.w3.org/2005/Atom')
- channel
- title= title
- link= url
- description= description
- atom:link(href='#{ url }/feed.xml', rel='self', type='application/rss+xml')
- for post, slug in public.blog._data
- if post.published != false
- item
- title=post.title
- description
- |
-
- if post.date
- pubDate=convertPubDate(post.date)
-
- link #{ url }/blog/#{ slug }
- guid(isPermaLink='true') #{ url }/blog/#{ slug }
diff --git a/website/index.jade b/website/index.jade
index 396604bee..35487dab3 100644
--- a/website/index.jade
+++ b/website/index.jade
@@ -1,186 +1,161 @@
+//- ----------------------------------
+//- 💫 LANDING PAGE
+//- ----------------------------------
+
include _includes/_mixins
-- var logos = [ ['chartbeat', 'https://chartbeat.com'], ['socrata', 'https://www.socrata.com'], ['keyreply', 'https://keyreply.com/'], [ 'kip', 'http://kipthis.com'], ['cytora', 'http://www.cytora.com'], ['signaln', 'http://signaln.com'] ]
-
-
-//- Landing Page
-//- ============================================================================
-
-header.header
- .header-body
- +h1.header-title.header-text
+header.o-header.u-pattern
+ h1.o-block-small.u-heading-0.u-text-shadow
| Industrial-strength#[br]
| Natural Language#[br]
| Processing
- +lead.header-text
- strong
- | Thousands of researchers are trying to make#[br]
- | computers understand text. They're succeeding.#[br]
- | We help you get their work out of papers and#[br]
- | into production.#[br]
+ .o-block-small.u-text-medium.u-text-strong.u-text-shadow
+ | Thousands of researchers are trying to make#[br]
+ | computers understand text. They're succeeding.#[br]
+ | spaCy is a Python NLP library that helps you get#[br]
+ | their work out of papers and into production.
- +button('secondary')(href='/docs/#install') Install spaCy
+ .o-inline-list
+ +button("/docs/#getting-started", true, "secondary")(target="_self") Install spaCy
-+divider('bar')
- | #[a(href='https://github.com/' + profiles.github + '/spaCy/releases' target='_blank'): #[strong Latest Release:] v#{spacy_version}]
- | #[a(href='https://github.com/' + profiles.github + '/spaCy' target='_blank'): +icon('github', 'secondary') #[strong #{spacy_stars}+ stars] on GitHub]
- | #[a(href='https://www.reddit.com/r/' + profiles.reddit target='_blank'): +icon('reddit', 'secondary') #[strong User Group] on Reddit]
+main.o-main
+ .o-inline-list.u-padding-small.u-text-center.u-text-label.u-color-subtle.u-border-bottom
+ +a("https://github.com/" + SOCIAL.github + "/spaCy/releases") #[strong Latest Release:] v#{SPACY_VERSION}
-main.main
+ +a("https://github.com/" + SOCIAL.github + "/spaCy") #[+icon("github")] #[strong #{SPACY_STARS}+ stars] on GitHub
- +grid('padding', 'space-between')
- +grid-col('half')
- +h2 Built for Production
-
- p.text-big.
- Most AI software is built for research. Over the last ten years,
- we've used a lot of that software, and built some of it ourselves,
- especially for natural language processing (NLP).
- But the faster the research has moved, the more impatient we've
- become. We want to see advanced NLP technologies get out into
- great products, as the basis of great businesses. We built spaCy
- to make that happen.
-
- +h2 Easy and Powerful
-
- p.text-big.
- For any NLP task, there are always lots of competing algorithms.
- We don't believe in implementing them all and letting you choose.
- Instead, we just implement one – the best one.
- When better algorithms are developed, we can update the library without
- breaking your code or bloating the API. This approach makes spaCy
- both #[strong easier] and #[strong more powerful] than a pluggable
- architecture. spaCy also features a #[strong unique whole-document design].
- Where other NLP libraries rely on sentence detection as
- a pre-process, spaCy reads the whole document at once, making it much more
- robust to informal and poorly formatted text.
-
- +h2 Permissive open-source license (MIT)
- p.text-big.
- We think spaCy is valuable software, so we made it free, to raise
- its value even higher.
- Making spaCy open-source puts us on the same side –
- we can tell you everything about how it works, and let
- you run it however you like. We think the software would be much
- less valuable as a service, which could disappear at any point.
-
- +grid-col('half', 'valign-bottom')
- +code-demo('lightning_tour.py').block.
- # pip install spacy && python -m spacy.en.download
- import spacy
-
- # Load English tokenizer, tagger, parser, NER and word vectors
- nlp = spacy.load('en')
- # Process a document, of any size
- text = open('war_and_peace.txt').read()
- doc = nlp(text)
-
- from spacy.attrs import *
- # All strings mapped to integers, for easy export to numpy
- np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
+ +a("https://www.reddit.com/r/" + SOCIAL.reddit) #[+icon("reddit")] #[strong User Group] on Reddit
- from reddit_corpus import RedditComments
- reddit = RedditComments('/path/to/reddit/corpus')
- # Parse a stream of documents, with multi-threading (no GIL!)
- # Processes over 100,000 tokens per second.
- for doc in nlp.pipe(reddit.texts, batch_size=10000, n_threads=4):
- # Multi-word expressions, such as names, dates etc
- # can be merged into single tokens
- for ent in doc.ents:
- ent.merge(ent.root.tag_, ent.text, ent.ent_type_)
- # Efficient, lossless serialization --- all annotations
- # saved, same size as uncompressed text
- byte_string = doc.to_bytes()
+ +grid.u-border-bottom
+ +grid-col("half").u-padding
+ +label Are you using spaCy?
+ +h(2)
+ +a("https://survey.spacy.io", true) Take the spaCy user survey
- +h2.text-center
- +label('strong') spaCy is trusted by
+ p.u-text-medium.
+ Two years after I started working on spaCy full time, I'm finally pushing forward with a #[strong 1.0 release]. It's also past time to take a bit of a census. I hope you'll take a few minutes to fill out this survey, to help me understand how you're using the library, and how it can be better.#[br]#[br]
- +grid('space-around', 'valign-center', 'padding')
- each logo in logos
- a(href=logo[1] target='_blank')
- img(src='assets/img/logos/' + logo[0] + '.png').logo--small
+ #[strong Thanks for your support!]#[br]
+ #[strong.u-heading-3.u-color-theme Matt]
+ #[br]#[br]
- +divider
+ #[+button("https://survey.spacy.io", true, "primary") Take the survey]
+
+ +grid-col("half").u-padding
+ +label The blog posts have moved
+ +h(2) Check out the new blog
+
+ p.u-text-medium We've updated the site to make it more focussed on the library itself. This will help us stay organised when we expand the tutorials section — by far the clearest message we've gotten from the survey so far. The blog posts have been moved to the new site for our consulting services, #[+a("https://explosion.ai", true) Explosion AI]. We've also updated our demos, and have open-sourced the services behind them. There are lots more releases to come. #[br]#[br]
+
+ +button("https://explosion.ai/blog", true, "primary") Go to the new blogs
+grid
- +grid-col('half', 'valign-center', 'align-center')
- .image-container
- img(src='assets/img/spacy_screen.png' style='display: block')
+ +grid-col("half").u-padding
+ +h(2) Built for Production
+ p.u-text-medium Most AI software is built for research. Over the last ten years, we've used a lot of that software, and built some of it ourselves, especially for natural language processing (NLP). But the faster the research has moved, the more impatient we've become. We want to see advanced NLP technologies get out into great products, as the basis of great businesses. We built spaCy to make that happen.
- +grid-col('half')
- +h2
- +label('strong') About spaCy
- .h2 What we do
+ +h(2) Easy and Powerful
+ p.u-text-medium For any NLP task, there are always lots of competing algorithms. We don't believe in implementing them all and letting you choose. Instead, we just implement one – the best one. When better algorithms are developed, we can update the library without breaking your code or bloating the API. This approach makes spaCy both #[strong easier] and #[strong more powerful] than a pluggable architecture. spaCy also features a #[strong unique whole-document design]. Where other NLP libraries rely on sentence detection as a pre-process, spaCy reads the whole document at once, making it much more robust to informal and poorly formatted text.
- p.text-big spaCy helps you write programs that do clever things with text. You give it a string of characters, it gives you an object that provides multiple useful views of its meaning and linguistic structure. Specifically, spaCy features a high performance tokenizer, part-of-speech tagger, named entity recognizer and syntactic dependency parser, with built-in support for word vectors. All of the functionality is united behind a clean high-level Python API, that makes it easy to use the different annotations together.
-
- p.text-big To make spaCy as fast and easy to install as we could, we built it #[strong from the ground up] from custom components, with #[strong custom implementations], and sometimes #[strong custom algorithms]. It's written in clean but efficient Cython code, which allows us to manage both low level details and the high-level Python API in a single codebase.
-
- +divider
+ +h(2) Permissive open-source license (MIT)
+ p.u-text-medium We think spaCy is valuable software, so we made it free, to raise its value even higher. Making spaCy open-source puts us on the same side – we can tell you everything about how it works, and let you run it however you like. We think the software would be much less valuable as a service, which could disappear at any point.
- +h2.text-center
- +label('strong') What our users say...
+ +grid-col("half").u-padding-medium
+ .x-terminal
+ .x-terminal__icons: span
+ .u-padding-small.u-text-label.u-text-strong.u-text-center lightning_tour.py
- +grid('padding')
- +grid-col('third', 'valign-center')
-
+ +code.x-terminal__code.
+ # pip install spacy && python -m spacy.en.download
+ import spacy
- +grid-col('third', 'valign-center')
-
+ # Load English tokenizer, tagger, parser, NER and word vectors
+ nlp = spacy.load('en')
+ # Process a document, of any size
+ text = open('war_and_peace.txt').read()
+ doc = nlp(text)
- +grid-col('third', 'valign-center')
-
+ from spacy.attrs import *
+ # All strings mapped to integers, for easy export to numpy
+ np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
- +divider
+ from reddit_corpus import RedditComments
+ reddit = RedditComments('/path/to/reddit/corpus')
+ # Parse a stream of documents, with multi-threading (no GIL!)
+ # Processes over 100,000 tokens per second.
+ for doc in nlp.pipe(reddit.texts, batch_size=10000, n_threads=4):
+ # Multi-word expressions, such as names, dates etc
+ # can be merged into single tokens
+ for ent in doc.ents:
+ ent.merge(ent.root.tag_, ent.text, ent.ent_type_)
+ # Efficient, lossless serialization --- all annotations
+ # saved, same size as uncompressed text
+ byte_string = doc.to_bytes()
- +grid('padding', 'valign-center')
- +grid-col('half')
- +h2
- .label-strong Benchmarks
- .h2 State-of-the-art speed and accuracy
+ .o-block.u-text-center
+ +label spaCy is trusted by
- p.text-big spaCy is committed to rigorous evaluation under standard methodology. Two peer-reviewed papers in 2015 confirm that it offers the #[strong fastest syntactic parser in the world] and that #[strong its accuracy is within 1% of the best] available. The few systems that are more accurate are 20× slower or more.
-
- p.text-big The first of the evaluations was published by #[strong Yahoo! Labs] and #[strong Emory University], as part of a survey of current parsing technologies #[a(href="http://aclweb.org/anthology/P/P15/P15-1038.pdf" target="_blank") (Choi et al., 2015)]. Their results and subsequent discussions helped us develop a novel psychologically-motivated technique to improve spaCy's accuracy, which we published in joint work with Macquarie University #[a(href="https://aclweb.org/anthology/D/D15/D15-1162.pdf" target="_blank") (Honnibal and Johnson, 2015)].
-
- +grid-col('half')
+ .o-block
+ each row in logos
+ .o-inline-list.u-padding-medium
+ each logo in row
+ +a(logo[1])
+ img(src="/assets/img/logos/" + logo[0] + ".png" width="150").u-padding-medium
+
+ .o-block.u-text-center.u-padding
+ +label What our users say...
+
+ +grid
+ +grid-col("third")
+
+
+ +grid-col("third")
+
+
+ +grid-col("third")
+
+
+ +grid
+ +grid-col("half").u-padding
+ +label Benchmarks
+ +h(2) State-of-the-art speed and accuracy
+
+ p.u-text-medium spaCy is committed to rigorous evaluation under standard methodology. Two peer-reviewed papers in 2015 confirm that it offers the #[strong fastest syntactic parser in the world] and that #[strong its accuracy is within 2% of the best] available.
+
+ sup #[+a("http://aclweb.org/anthology/P/P15/P15-1038.pdf") [1]], #[+a("https://aclweb.org/anthology/D/D15/D15-1162.pdf") [2]], #[+a("http://homes.cs.washington.edu/~lsz/papers/llz-naacl16.pdf") [3]]
+
+ +grid-col("half").u-padding
+table(["System", "Language", "Accuracy", "Speed (WPS)"])
- +row
- +cell #[+logo('tiny')]
- +cell #[strong Cython]
- +cell #[strong 91.8]
- +cell #[strong 13,963]
- +row
- +cell ClearNLP
- +cell Java
- +cell 91.7
- +cell 10,271
- +row
- +cell CoreNLP
- +cell Java
- +cell 89.6
- +cell 8,602
- +row
- +cell MATE
- +cell Java
- +cell 92.5
- +cell 550
- +row
- +cell Turbo
- +cell C++
- +cell 92.4
- +cell 349
+ +row
+ +cell
+ !=partial("_includes/_logo", { logo_size: "tiny" })
+ +cell #[strong Cython]
+ +cell #[strong 91.8]
+ +cell #[strong 13,963]
-
- +divider
+ +row
+ +cell ClearNLP
+ +cell Java
+ +cell 91.7
+ +cell 10,271
- +h2.text-center
- .label-strong Latest Blog Posts
- .h3 Read more about NLP
+ +row
+ +cell CoreNLP
+ +cell Java
+ +cell 89.6
+ +cell 8,602
- !=partial('_includes/_latest-posts', { max: 3 } )
+ +row
+ +cell MATE
+ +cell Java
+ +cell 92.5
+ +cell 550
- !=partial('_includes/_newsletter', { divider: 'top' } )
+ +row
+ +cell Turbo
+ +cell C++
+ +cell 92.4
+ +cell 349
- script(async src="https://platform.twitter.com/widgets.js" charset="utf-8")
+ include _includes/_newsletter
diff --git a/website/legal.jade b/website/legal.jade
deleted file mode 100644
index 792230536..000000000
--- a/website/legal.jade
+++ /dev/null
@@ -1,77 +0,0 @@
-include _includes/_mixins
-
-//- Legal / Imprint
-//- ============================================================================
-
-+h2 Imprint
-
-+label('strong') Editorial responsibility
-p spaCy GmbH
-
-+label('strong') Information according to § 5 TMG (Telekommunikationsgesetz)
-p.
- Buchholzer Str. 2#[br]
- 10437 Berlin#[br]
- Germany
-
-p.
- #[strong Authorized representative:]
- Henning Peters#[br]
-
-+label('strong') Contact
-p #[a(href='mailto:' + email)=email]
-
-p.
- #[strong Registergericht/district court:] Amtsgericht Berlin-Charlottenburg#[br]
- #[strong Handelsregister/Commercial Register:] HRB 168190 B#[br]
- #[strong Umsatzsteuer-Identifikationsnummer/ VAT–ID No.:] DE304746312#[br]
-
-p.
- #[strong Responsible for the content according to § 55 RStV (Rundfunkstaatsvertrag):]
- Henning Peters
-
-+h2 Credits
-
-p.
- #[strong Illustrations:]
- #[a(href='https://dribbble.com/kemal' target='_blank') Kemal Sanli]#[br]
- #[strong Photography:]
- #[a(href='http://johannawarda.com' target='_blank') Johanna Warda]
-
-+h2 Disclaimer
-
-+label('strong') Legal notice
-
-p.
- spaCy constantly checks and updates the information on its web pages. Despite
- the utmost care taken, we cannot rule out that some of the data may since have
- become outdated. Therefore, we cannot accept liability for the currency,
- accuracy and completeness of the information displayed. The same applies to
- all other web pages that can be reached via hyperlink. spaCy is not responsible
- for the content of websites which can be reached via such links. Furthermore,
- spaCy reserves the right to make changes or additions to the information
- displayed. Content and structure of the spaCy website is protected by
- copyright. The reproduction of information or data, in particular the use of
- texts, text excerpts or illustrative material, requires our prior approval.
- We wish you an informative stay on our pages and would welcome any suggestions,
- requests or criticism you may have.
-
-+label('strong') Web Analytics
-
-p.
- This website uses Google Analytics, a web analytics service provided by Google,
- Inc. Google Analytics uses “cookies”, which are text files placed on your
- computer, to help the website analyze how users use the site. The information
- generated by the cookie about your use of the website (including your IP
- address) will be transmitted to and stored by Google on servers in the United
- States . Google will use this information for the purpose of evaluating your
- use of the website, compiling reports on website activity for website operators
- and providing other services relating to website activity and internet usage.
- Google may also transfer this information to third parties where required to
- do so by law, or where such third parties process the information on Google’s
- behalf. Google will not associate your IP address with any other data held by
- Google. You may refuse the use of cookies by selecting the appropriate settings
- on your browser, however please note that if you do this you may not be able
- to use the full functionality of this website. By using this website, you
- consent to the processing of data about you by Google in the manner and for
- the purposes set out above.
diff --git a/website/robots.txt.jade b/website/robots.txt.jade
new file mode 100644
index 000000000..4ba095328
--- /dev/null
+++ b/website/robots.txt.jade
@@ -0,0 +1,7 @@
+//- ----------------------------------
+//- 💥 ROBOTS.TXT
+//- ----------------------------------
+
+if environment != "deploy"
+ | User-agent: *
+ | Disallow: /
diff --git a/website/styleguide.jade b/website/styleguide.jade
deleted file mode 100644
index 9a9bac87a..000000000
--- a/website/styleguide.jade
+++ /dev/null
@@ -1,391 +0,0 @@
-include _includes/_mixins
-
-- var colors_primary = { 'spaCy Blue' : '#09a3d5', 'spaCy Red': '#e4514f', 'spaCy Yellow' : '#f4c025', 'Black' : '#222222', 'Dark Grey': '#999999', 'Medium Grey': '#dddddd', 'Light Grey': '#f6f6f6', 'White': '#ffffff' }
-- var colors_secondary = { 'spaCy Blue' : '#09a3d5', 'spaCy Red': '#e4514f', 'Green': '#3ec930', 'Purple' : '#8130c9', 'Orange' : '#f47725', 'spaCy Yellow' : '#f4c025', 'Dark Grey': '#999999' }
-- var colors_logo = [colors_primary['Black'], colors_primary['spaCy Blue'], colors_primary['spaCy Red']]
-- var grid = { 'half': 2, 'third': 3, 'quarter': 4, 'fifth': 5 }
-
-
-//- Styleguide
-//- ============================================================================
-
-//- Introduction
-
-+section('introduction')
- +h2('introduction') Introduction
-
- p.text-big.
- This style guide is loosely based on the concept and principles of
- #[a(href='http://bradfrost.com/blog/post/atomic-web-design/' target='_blank') Atomic Design].
- The templates consist of small elements (atoms) which are combined and
- connected to form larger molecules and full components. CSS is written in
- #[a(href='http://sass-lang.com' target='_blank') Sass] and preprocessed
- using #[a(href='https://github.com/postcss/autoprefixer' target='_blank') Autoprefixer].
- It follows an adapted version of the standards presented in the
- #[a(href='https://pages.18f.gov/frontend/css-coding-styleguide/' target='_blank') 18F CSS Coding Styleguide]
- and #[a(href='https://en.bem.info' target='_blank') BEM]-oriented naming conventions.
-
- p.text-big.
- The site is compiled using #[a(href='#section-harp') Harp], a static web
- server with built-in preprocessing. Templates are written entirely in
- #[a(href='#section-jade') Jade], a clean, whitespace-sensitive templating
- language that compiles to HTML.
-
-
-+divider
-
-//- Design: Colors
-
-+section('colors')
- +h2('colors') Colors
- +button('secondary', 'small', 'source')(href='https://github.com/' + profiles.github + '/spacy/blob/master/website/assets/css/_variables.scss#L44' target='_blank') SCSS Color Swatches
-
- +grid('space-between', 'padding').block
- each hex, color in colors_primary
- +grid-col('quarter')
- .card
- .card-figure(style='background: ' + hex)
- +label #{color}
- div #{hex}
-
- +section('colors-syntax')
-
- +label('strong') Secondary Colors for Syntax Highlighting
-
- +grid
- each hex, color in colors_secondary
- .x-bubble(style='background: ' + hex data-tooltip=color + ': ' + hex)
-
-
-//- Design: Logo
-
-+section('logo')
- +h2('logo') Logo #[+button('secondary', 'small', 'source')(href='/assets/img/logo.svg' target='_blank') SVG] #[+button('secondary', 'small', 'source')(href='/assets/img/logo.png' target='_blank') PNG]
-
- p.text-big.
- The spaCy logo changes colour across the site and can be used in colour
- or in white on a coloured background.
-
- +grid('padding').has-aside
- each hex, color in colors_logo
- +grid-col('third')
- .card(style='color: ' + hex): +logo
-
- each hex, color in colors_logo
- +grid-col('third')
- .card(style='color: #fff; background: ' + hex + '; border-color: ' + hex): +logo
-
- +aside('Usage (Jade)')
- +code('jade').
- +logo(size)
-
- | Possible values for #[code size] are 'tiny', 'small', 'regular'
- | or 'large'.
-
-+divider
-
-//- Design: Typography
-
-+section('typography')
-
- +h2('typography', 'https://github.com/' + profiles.github + '/spacy/blob/master/website/assets/css/_base/_typography.sass') Typography
-
- +section('typography-headings').has-aside
-
- +h1 Heading Level One #[span.label Work Sans bold, 50px]
- +h2 Heading Level Two #[span.label Work Sans bold, 28px]
- +h3 Heading Level Three #[span.label Work Sans bold, 24px]
- +h4 Heading Level Four #[span.label Work Sans bold, 20px]
- +h5 Heading Level Five #[span.label Work Sans bold, 16px]
- +h6 Heading Level Six #[span.label Work Sans bold, 14px]
-
- +aside('Usage (Jade)')
- +code('jade').
- +h2(id, source) ...
-
- | If a unique #[code id] is specified, the headline becomes a permalink.
- | #[code source] takes a string containing a link that's used to add
- | a source button to the headline (both optional).
-
- +section('typography-paragraphs')
-
- +lead.
- This is a lead paragraph. It is bigger and can be used for introduction
- texts in blog posts. #[span.label Lato regular, 28px]
-
- p.text-big.
- This is a big text paragraph. The font size is slightly bigger than
- the regular base size which makes the content a lot easier to read.
- It's mainly used in blog posts. #[span.label Lato regular, 20px]
-
- p.
- This is a regular paragraph. It's used everywhere else across the whole
- website. It can contain #[strong bold text] and of course
- #[em italicized text] as well. #[span.label Lato regular, 16px]
-
-+divider
-
-
-//- Design: Grid
-
-+section('grid')
- +h2('grid', 'https://github.com/' + profiles.github + '/spacy/blob/master/website/assets/css/_base/_grid.sass') Grid
-
- +grid('padding').has-aside
- each count, col in grid
- - var i = 0
-
- while i < count
- +grid-col(col, 'text-center')
- .card=col
-
- - i++
-
- +aside('Usage (Jade)')
- +code('jade').
- +grid(...style)
- +grid-col(...style)
- ...
-
- | #[code style] arguments can be flexbox attributes like
- | 'space-around', alignment attributes like 'valign-bottom'
- | or the column type ('third', 'quarter' etc.) for grid columns.
-
-+divider
-
-//- Design: Elements
-
-+section('elements')
- +h2('elements') Elements
-
- +section('elements-buttons')
- +label('strong') Buttons
-
- +grid('margin-right').block.has-aside
- +button('primary')(href="#") Primary
- +button('secondary')(href="#") Secondary
- +button('tertiary')(href="#") Tertiary
-
- +aside('Usage (Jade)')
- +code('jade').
- +button(type, ...style)
- ...
-
- | Possible values for #[code type] are 'primary', 'secondary' and
- | 'tertiary'. #[code style] can be 'small'.
-
- +grid('margin-right')
- +button('primary', 'small')(href="#") Primary small
- +button('secondary', 'small')(href="#") Secondary small
- +button('tertiary', 'small')(href="#") Tertiary small
-
-
- +section('elements-tooltips')
- +label('strong') Tooltips
-
- +grid('space-between').has-aside
- p(data-tooltip='I am a tooltip!').visible This is text
- p(data-tooltip='Tooltip!'): strong Hover me
- p(data-tooltip='Type something here')
- +input('Input with tooltip')
-
- +aside('Usage (Jade)')
- +code('jade').
- a(data-tooltip='Tooltip text')
-
- | A tooltip can be attached to most elements, but should ideally
- | be used on only links and icons.
-
- +section('elements-icons')
- +label('strong') Icons
-
- +grid.has-aside
- each icon in [ 'Website', 'Feed', 'Permalink', 'Twitter', 'GitHub', 'Facebook', 'LinkedIn', 'HackerNews', 'Reddit', 'ProductHunt', 'Medium', 'Codepen']
- +icon(icon.toLowerCase(), 'large')(data-tooltip=icon)
-
- +aside('Usage (Jade)')
- +code('jade').
- +icon(type, ...style)
-
- | #[code type] takes the name of the icon, #[code style] can be
- | 'small', 'medium' or 'large'.
-
-
-+divider
-
-//- Design: Components
-
-+section('components')
- +h2('components', 'https://github.com/' + profiles.github + '/spacy/blob/master/website/assets/css/_components') Components
-
- +section('components-lists')
- +label('strong') Lists
-
- .has-aside
- +grid('space-between')
- +grid-col('half')
- +list
- +item I am a bulleted list
- +item I have nice bullets
- +item And I'm very
- +item flexible
-
- +grid-col('half')
- +list('numbers')
- +item I am an ordered list
- +item I have nice numbers
- +item And I'm very
- +item flexible
-
- +grid('space-between')
- +grid-col('half')
- +list('letters')
- +item I am an ordered list
- +item I have uppercase letters
- +item And I'm very
- +item flexible
-
- +grid-col('half')
- +list('roman')
- +item I am an ordered list
- +item I have roman numerals
- +item And I'm very
- +item flexible
-
- +aside('Usage (Jade)')
- +code('jade').
- +list(type, start)
- +item ...
- +item ...
-
- | Possible values for #[code type] are 'numbers' (default),
- | 'letters' and 'roman'. #[code start] is an integer and defines
- | the starting point of the list (defaults to 0).
-
- +section('components-blockquote')
-
- +label('strong') Blockquote
-
- .has-aside
- +quote('This is a source link', '#').
- This is a blockquote. It stands out from the rest of the content
- with the quotation marks being a bold visual statement. It can be
- used all over the site but its main purpose is displaying quotes
- in the blog posts.
-
- +aside('Usage (Jade)')
- +code('jade').
- +quote(source, link)
- ...
-
- | #[code source] (author / source of the quote) and #[code link]
- | (full link to source) both take strings (optional)
-
- +section('components-codeblock')
-
- +label('strong') Code block
-
- .has-aside
- +code('python', 'This is a label').
- # This is an example of a regular code block. It supports syntax highlighting.
- nlp = English()
-
- +aside('Usage (Jade)')
- +code('jade').
- +code(language, label).
- ...
-
- | #[code language] defaults to 'python', see
- | #[a(href='http://prismjs.com/download.html') Prism.js] for more
- | options. #[code label] takes a string (optional).
- | #[strong Important:] The #[code .] preserves whitespace and
- | prevents code from being interpreted.
-
- +section('components-table')
-
- +label('strong') Table
-
- .has-aside
- +table([ 'Column 1', 'Column 2', 'Column 3'], 'table--data')
- +row
- +cell This is a table
- +cell It displays data
- +cell #[code and even code!]
-
- +row
- +cell Tables are uncool but in this case
- +cell they're actually really useful.
- +cell('highlight') I am highlighted!
-
- +aside('Usage (Jade)')
- +code('jade').
- +table(head)
- +row
- +cell ...
- +cell ...
-
- | #[code head] takes an array of strings containing the column
- | heading names (optional, otherwise table is displayed without headings)
-
-
- +section('components-infobox')
-
- +label('strong') Info Box
-
- .has-aside
- +infobox('This is a label', 'has-aside').
- This is an infobox. It can be used to add notes,updates or warnings
- to blog articles or docs sections. It comes with an optional label
- or headline that's displayed at the top.
-
- +aside('Usage (Jade)')
- +code('jade').
- +infobox(label)
- ...
-
- #[code label] takes a string (optional).
-
-
- +section('components-aside')
-
- +label('strong') Aside
-
- .has-aside
- p.
- #[strong See example on the right.] Asides can be used in blog
- posts or the documentation to display additional information without
- spoiling the flow of reading (typically footnotes). They can contain
- a headline, links, images and even code blocks.
-
- p.
- Paragraphs (#[code p]) and table cells (#[code td] or #[code +cell])
- support nested asides by default. All other elements can be wrapped
- in the class #[code .has-aside] together with their respective aside.
-
- +aside('Usage (Jade)')
- +code('jade').
- +aside(headline)
- ...
-
- | #[code headline] takes a string (optional).
-
-+divider
-
-//- Code: Source
-
-+section('source')
- +h2('source') Source
-
- p.text-big.
- The source of this website is published on
- #[a(href='https://github.com/' + profiles.github + '/spaCy/tree/master/website' target='_blank') GitHub]
- under the MIT license. To compile the site, you need to install and run
- #[a(href='http://harpjs.com/' target='_blank') Harp]:
-
- +code('bash').
- sudo npm install --global harp
- git clone https://github.com/#{profiles.github}/spaCy
- cd spaCy/website
- harp server
-
-+divider
diff --git a/website/team.jade b/website/team.jade
deleted file mode 100644
index 1bdc556b1..000000000
--- a/website/team.jade
+++ /dev/null
@@ -1,19 +0,0 @@
-include _includes/_mixins
-
-//- Team
-//- ============================================================================
-
-+grid('align-center', 'padding')
- each member in team_members
- +grid-col('fifth', 'valign-center')
- .image-container
- img(src='/assets/img/profile_' + member + '.png')
-
- !=authors[member].name
- +label=(authors[member].title || 'job title')
-
- if authors[member].links
- .block
- each link, index in authors[member].links
- a(href=link[0] target='_blank')
- +icon(index)(data-tooltip=(link[1] || index))
