diff --git a/website/_includes/_mixins-base.jade b/website/_includes/_mixins-base.jade index 752423d79..31bb641cd 100644 --- a/website/_includes/_mixins-base.jade +++ b/website/_includes/_mixins-base.jade @@ -45,10 +45,11 @@ mixin icon(name, width, height) icon - [string] "pro", "con" or "neutral" (default: "neutral") size - [integer] icon size (optional) -mixin procon(icon, size) - - colors = { pro: "green", con: "red", neutral: "subtle" } - +icon("circle", size || 16)(class="u-color-#{colors[icon] || 'subtle'}" aria-label=icon)&attributes(attributes) - +mixin procon(icon, label, show_label, size) + - var colors = { yes: "green", no: "red", neutral: "subtle" } + span.u-nowrap + +icon(icon, size || 20)(class="u-color-#{colors[icon] || 'subtle'}").o-icon--inline&attributes(attributes) + span.u-text-small(class=show_label ? null : "u-hidden")=(label || icon) //- Headlines Helper Mixin level - [integer] 1, 2, 3, 4, or 5 diff --git a/website/_includes/_svg.jade b/website/_includes/_svg.jade index f9d7a2b53..144f9dc1a 100644 --- a/website/_includes/_svg.jade +++ b/website/_includes/_svg.jade @@ -16,8 +16,14 @@ svg(style="position: absolute; visibility: hidden; width: 0; height: 0;" width=" symbol#svg_book(viewBox="0 0 20 20") path(d="M15.5 11h-11c-0.275 0-0.5 0.225-0.5 0.5v1c0 0.276 0.225 0.5 0.5 0.5h11c0.276 0 0.5-0.224 0.5-0.5v-1c0-0.275-0.224-0.5-0.5-0.5zM15.5 7h-11c-0.275 0-0.5 0.225-0.5 0.5v1c0 0.276 0.225 0.5 0.5 0.5h11c0.276 0 0.5-0.224 0.5-0.5v-1c0-0.275-0.224-0.5-0.5-0.5zM10.5 15h-6c-0.275 0-0.5 0.225-0.5 0.5v1c0 0.276 0.225 0.5 0.5 0.5h6c0.276 0 0.5-0.224 0.5-0.5v-1c0-0.275-0.224-0.5-0.5-0.5zM15.5 3h-11c-0.275 0-0.5 0.225-0.5 0.5v1c0 0.276 0.225 0.5 0.5 0.5h11c0.276 0 0.5-0.224 0.5-0.5v-1c0-0.275-0.224-0.5-0.5-0.5z") - symbol#svg_circle(viewBox="0 0 18 18") - ellipse(rx="9" ry="9" cx="9" cy="9") + symbol#svg_yes(viewBox="0 0 24 24") + path(d="M9.984 17.016l9-9-1.406-1.453-7.594 7.594-3.563-3.563-1.406 1.406zM12 2.016c5.531 0 9.984 4.453 9.984 9.984s-4.453 9.984-9.984 9.984-9.984-4.453-9.984-9.984 4.453-9.984 9.984-9.984z") + + symbol#svg_no(viewBox="0 0 24 24") + path(d="M17.016 15.609l-3.609-3.609 3.609-3.609-1.406-1.406-3.609 3.609-3.609-3.609-1.406 1.406 3.609 3.609-3.609 3.609 1.406 1.406 3.609-3.609 3.609 3.609zM12 2.016c5.531 0 9.984 4.453 9.984 9.984s-4.453 9.984-9.984 9.984-9.984-4.453-9.984-9.984 4.453-9.984 9.984-9.984z") + + symbol#svg_neutral(viewBox="0 0 24 24") + path(d="M12 2.016c5.531 0 9.984 4.453 9.984 9.984s-4.453 9.984-9.984 9.984-9.984-4.453-9.984-9.984 4.453-9.984 9.984-9.984z") symbol#svg_chat(viewBox="0 0 30 30") path(d="M28.74 25.2c-1.73-.3-3.77-1.46-4.74-3.6 3.64-2.2 6-5.68 6-9.6 0-6.63-6.72-12-15-12S0 5.37 0 12s6.72 12 15 12c1.1 0 2.2-.1 3.23-.3 2.86 2 6.25 2.62 10.4 2.15.26-.02.37-.15.37-.32 0-.16-.1-.3-.26-.32zM23 14c0 .55-.45 1-1 1H8c-.55 0-1-.45-1-1s.45-1 1-1h14c.55 0 1 .45 1 1zm0-4c0 .55-.45 1-1 1H8c-.55 0-1-.45-1-1s.45-1 1-1h14c.55 0 1 .45 1 1z") diff --git a/website/styleguide.jade b/website/styleguide.jade index 42e70ed73..638e1aed1 100644 --- a/website/styleguide.jade +++ b/website/styleguide.jade @@ -145,7 +145,7 @@ include _includes/_mixins | mixin, using their name and an optional size value in #[code px]. +infobox.u-text-center - each icon in ["code", "arrow-right", "book", "circle", "chat", "star", "help", "accept", "reject", "markdown", "course", "github", "jupyter"] + each icon in ["code", "arrow-right", "book", "chat", "star", "help_o", "help", "yes", "no", "neutral", "accept", "reject", "markdown", "course", "github", "jupyter"] .u-inline-block.u-padding-small.u-color-dark(data-tooltip=icon data-tooltip-style="code" aria-label=icon) +icon(icon, 20) diff --git a/website/usage/_facts-figures/_feature-comparison.jade b/website/usage/_facts-figures/_feature-comparison.jade index c8fa5ffbe..3f970f16c 100644 --- a/website/usage/_facts-figures/_feature-comparison.jade +++ b/website/usage/_facts-figures/_feature-comparison.jade @@ -14,45 +14,45 @@ p +row +cell Neural network models - each icon in ["pro", "pro", "con", "pro"] - +cell.u-text-center #[+procon(icon)] + each answer in ["yes", "yes", "no", "yes"] + +cell.u-text-center #[+procon(answer)] +row +cell Integrated word vectors - each icon in ["pro", "con", "con", "con"] - +cell.u-text-center #[+procon(icon)] + each answer in ["yes", "no", "no", "no"] + +cell.u-text-center #[+procon(answer)] +row +cell Multi-language support - each icon in ["pro", "pro", "pro", "pro"] - +cell.u-text-center #[+procon(icon)] + each answer in ["yes", "yes", "yes", "yes"] + +cell.u-text-center #[+procon(answer)] +row +cell Tokenization - each icon in ["pro", "pro", "pro", "pro"] - +cell.u-text-center #[+procon(icon)] + each answer in ["yes", "yes", "yes", "yes"] + +cell.u-text-center #[+procon(answer)] +row +cell Part-of-speech tagging - each icon in ["pro", "pro", "pro", "pro"] - +cell.u-text-center #[+procon(icon)] + each answer in ["yes", "yes", "yes", "yes"] + +cell.u-text-center #[+procon(answer)] +row +cell Sentence segmentation - each icon in ["pro", "pro", "pro", "pro"] - +cell.u-text-center #[+procon(icon)] + each answer in ["yes", "yes", "yes", "yes"] + +cell.u-text-center #[+procon(answer)] +row +cell Dependency parsing - each icon in ["pro", "pro", "con", "pro"] - +cell.u-text-center #[+procon(icon)] + each answer in ["yes", "yes", "no", "yes"] + +cell.u-text-center #[+procon(answer)] +row +cell Entity recognition - each icon in ["pro", "con", "pro", "pro"] - +cell.u-text-center #[+procon(icon)] + each answer in ["yes", "no", "yes", "yes"] + +cell.u-text-center #[+procon(answer)] +row +cell Coreference resolution - each icon in ["con", "con", "con", "pro"] - +cell.u-text-center #[+procon(icon)] + each answer in ["no", "no", "no", "yes"] + +cell.u-text-center #[+procon(answer)] diff --git a/website/usage/_spacy-101/_similarity.jade b/website/usage/_spacy-101/_similarity.jade index e8ce692f0..cb3611f92 100644 --- a/website/usage/_spacy-101/_similarity.jade +++ b/website/usage/_spacy-101/_similarity.jade @@ -24,17 +24,18 @@ p print(token1.similarity(token2)) +aside - | #[strong #[+procon("neutral", 16)] similarity:] identical#[br] - | #[strong #[+procon("pro", 16)] similarity:] similar (higher is more similar) #[br] - | #[strong #[+procon("con", 16)] similarity:] dissimilar (lower is less similar) + | #[strong #[+procon("neutral", "identical", false, 16)] similarity:] identical#[br] + | #[strong #[+procon("yes", "similar", false, 16)] similarity:] similar (higher is more similar) #[br] + | #[strong #[+procon("no", "dissimilar", false, 16)] similarity:] dissimilar (lower is less similar) +table(["", "dog", "cat", "banana"]) each cells, label in {"dog": [1, 0.8, 0.24], "cat": [0.8, 1, 0.28], "banana": [0.24, 0.28, 1]} +row +cell.u-text-label.u-color-theme=label for cell in cells - +cell.u-text-center #[code=cell.toFixed(2)] - | #[+procon(cell < 0.5 ? "con" : cell != 1 ? "pro" : "neutral")] + +cell.u-text-center + - var result = cell < 0.5 ? ["yes", "similar"] : cell != 1 ? ["no", "dissimilar"] : ["neutral", "identical"] + | #[code=cell.toFixed(2)] #[+procon(...result)] p | In this case, the model's predictions are pretty on point. A dog is very diff --git a/website/usage/_training/_basics.jade b/website/usage/_training/_basics.jade index 05e67c2c1..77df3c433 100644 --- a/website/usage/_training/_basics.jade +++ b/website/usage/_training/_basics.jade @@ -30,15 +30,15 @@ p +table(["Text", "Entity", "Start", "End", "Label", ""]) - var style = [0, 0, 1, 1, 1] +annotation-row(["Uber blew through $1 million a week", "Uber", 0, 4, "ORG"], style) - +cell #[+procon("pro")] + +cell #[+procon("yes", "right", true)] +annotation-row(["Android Pay expands to Canada", "Android", 0, 7, "PERSON"], style) - +cell #[+procon("con")] + +cell #[+procon("no", "wrong", true)] +annotation-row(["Android Pay expands to Canada", "Canada", 23, 30, "GPE"], style) - +cell #[+procon("pro")] + +cell #[+procon("yes", "right", true)] +annotation-row(["Spotify steps up Asia expansion", "Spotify", 0, 8, "ORG"], style) - +cell #[+procon("pro")] + +cell #[+procon("yes", "right", true)] +annotation-row(["Spotify steps up Asia expansion", "Asia", 17, 21, "NORP"], style) - +cell #[+procon("con")] + +cell #[+procon("no", "wrong", true)] p | Alternatively, the @@ -50,13 +50,13 @@ p +table(["Text", "Entity", "Start", "End", "Label", ""]) - var style = [0, 0, 1, 1, 1] +annotation-row(["let me google this for you", "google", 7, 13, "ORG"], style) - +cell #[+procon("con")] + +cell #[+procon("no", "wrong", true)] +annotation-row(["Google Maps launches location sharing", "Google", 0, 6, "ORG"], style) - +cell #[+procon("con")] + +cell #[+procon("no", "wrong", true)] +annotation-row(["Google rebrands its business apps", "Google", 0, 6, "ORG"], style) - +cell #[+procon("pro")] + +cell #[+procon("yes", "right", true)] +annotation-row(["look what i found on google! 😂", "google", 21, 27, "ORG"], style) - +cell #[+procon("con")] + +cell #[+procon("no", "wrong", true)] p | Based on the few examples above, you can already create six training diff --git a/website/usage/_vectors-similarity/_in-context.jade b/website/usage/_vectors-similarity/_in-context.jade index d8e864d9d..6d4fb8b3d 100644 --- a/website/usage/_vectors-similarity/_in-context.jade +++ b/website/usage/_vectors-similarity/_in-context.jade @@ -36,15 +36,15 @@ p +table(["Context", "labrador.similarity(dog)"]) +row +cell The #[strong labrador] barked. - +cell #[code 0.56] #[+procon("pro")] + +cell #[code 0.56] #[+procon("yes", "similar")] +row +cell The #[strong labrador] swam. - +cell #[code 0.48] #[+procon("con")] + +cell #[code 0.48] #[+procon("no", "dissimilar")] +row +cell the #[strong labrador] people live in canada. - +cell #[code 0.39] #[+procon("con")] + +cell #[code 0.39] #[+procon("no", "dissimilar")] p | The same also works for whole documents. Here, the variance of the @@ -81,8 +81,9 @@ p +row(counter ? null : "divider") +cell=label for cell in cells - +cell.u-text-center #[code=cell.toFixed(2)] - | #[+procon(cell < 0.7 ? "con" : cell != 1 ? "pro" : "neutral")] + +cell.u-text-center + - var result = cell < 0.7 ? ["no", "dissimilar"] : cell != 1 ? ["yes", "similar"] : ["neutral", "identical"] + | #[code=cell.toFixed(2)] #[+procon(...result)] - counter++ p @@ -117,6 +118,7 @@ p +row(counter ? null : "divider") +cell=label for cell in cells - +cell.u-text-center #[code=cell.toFixed(2)] - | #[+procon(cell < 0.7 ? "con" : cell != 1 ? "pro" : "neutral")] + +cell.u-text-center + - var result = cell < 0.7 ? ["no", "dissimilar"] : cell != 1 ? ["yes", "similar"] : ["neutral", "identical"] + | #[code=cell.toFixed(2)] #[+procon(...result)] - counter++ diff --git a/website/usage/spacy-101.jade b/website/usage/spacy-101.jade index 3b75202f7..8a2741e71 100644 --- a/website/usage/spacy-101.jade +++ b/website/usage/spacy-101.jade @@ -99,69 +99,69 @@ p +row +cell #[strong Tokenization] +cell Segmenting text into words, punctuations marks etc. - +cell #[+procon("con")] + +cell #[+procon("no", "no", true)] +row +cell #[strong Part-of-speech] (POS) #[strong Tagging] +cell Assigning word types to tokens, like verb or noun. - +cell #[+procon("pro")] + +cell #[+procon("yes", "yes", true)] +row +cell #[strong Dependency Parsing] +cell | Assigning syntactic dependency labels, describing the | relations between individual tokens, like subject or object. - +cell #[+procon("pro")] + +cell #[+procon("yes", "yes", true)] +row +cell #[strong Lemmatization] +cell | Assigning the base forms of words. For example, the lemma of | "was" is "be", and the lemma of "rats" is "rat". - +cell #[+procon("pro")] + +cell #[+procon("no", "no", true)] +row +cell #[strong Sentence Boundary Detection] (SBD) +cell Finding and segmenting individual sentences. - +cell #[+procon("pro")] + +cell #[+procon("yes", "yes", true)] +row +cell #[strong Named Entity Recongition] (NER) +cell | Labelling named "real-world" objects, like persons, companies | or locations. - +cell #[+procon("pro")] + +cell #[+procon("yes", "yes", true)] +row +cell #[strong Similarity] +cell | Comparing words, text spans and documents and how similar | they are to each other. - +cell #[+procon("pro")] + +cell #[+procon("yes", "yes", true)] +row +cell #[strong Text Classification] +cell | Assigning categories or labels to a whole document, or parts | of a document. - +cell #[+procon("pro")] + +cell #[+procon("yes", "yes", true)] +row +cell #[strong Rule-based Matching] +cell | Finding sequences of tokens based on their texts and | linguistic annotations, similar to regular expressions. - +cell #[+procon("con")] + +cell #[+procon("no", "no", true)] +row +cell #[strong Training] +cell Updating and improving a statistical model's predictions. - +cell #[+procon("neutral")] + +cell #[+procon("no", "no", true)] +row +cell #[strong Serialization] +cell Saving objects to files or byte strings. - +cell #[+procon("neutral")] + +cell #[+procon("no", "no", true)] +h(2, "annotations") Linguistic annotations