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Update global config and landing page

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ines 2017-10-03 14:28:18 +02:00
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49
website/_data.json
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@ -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"
}
}
}

80
website/_harp.json
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@ -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"
}
}
}
}

199
website/index.jade
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@ -8,61 +8,48 @@ include _includes/_mixins
| Natural Language#[br]
| Processing
h2.c-landing__title.o-block.u-heading-1
| in Python
h2.c-landing__title.o-block.u-heading-3
span.u-text-label.u-text-label--light in Python
+landing-badge(gh("spaCy") + "/releases/tag/v2.0.0-alpha", "v2alpha", "Try spaCy v2.0.0 alpha!")
+grid.o-content.c-landing__blocks
+grid-col("third").c-landing__card.o-card.o-grid.o-grid--space
+h(3) Fastest in the world
p
| spaCy excels at large-scale information extraction tasks.
| It's written from the ground up in carefully memory-managed
| Cython. Independent research has confirmed that spaCy is
| the fastest in the world. If your application needs to
| process entire web dumps, spaCy is the library you want to
| be using.
+grid.o-content
+grid-col("third").o-card
+h(2) Fastest in the world
p
| spaCy excels at large-scale information extraction tasks.
| It's written from the ground up in carefully memory-managed
| Cython. Independent research has confirmed that spaCy is
| the fastest in the world. If your application needs to
| process entire web dumps, spaCy is the library you want to
| be using.
+button("/usage/facts-figures", true, "primary")
| Facts & figures
+button("/docs/api", true, "primary")
| Facts & figures
+grid-col("third").c-landing__card.o-card.o-grid.o-grid--space
+h(3) Get things done
p
| spaCy is designed to help you do real work — to build real
| products, or gather real insights. The library respects
| your time, and tries to avoid wasting it. It's easy to
| install, and its API is simple and productive. We like to
| think of spaCy as the Ruby on Rails of Natural Language
| Processing.
+grid-col("third").o-card
+h(2) Get things done
p
| spaCy is designed to help you do real work — to build real
| products, or gather real insights. The library respects
| your time, and tries to avoid wasting it. It's easy to
| install, and its API is simple and productive. I like to
| think of spaCy as the Ruby on Rails of Natural Language
| Processing.
+button("/usage", true, "primary")
| Get started
+button("/docs/usage", true, "primary")
| Get started
+grid-col("third").c-landing__card.o-card.o-grid.o-grid--space
+h(3) Deep learning
p
| spaCy is the best way to prepare text for deep learning.
| It interoperates seamlessly with TensorFlow, PyTorch,
| scikit-learn, Gensim and the
| rest of Python's awesome AI ecosystem. spaCy helps you
| connect the statistical models trained by these libraries
| to the rest of your application.
+grid-col("third").o-card
+h(2) Deep learning
p
| spaCy is the best way to prepare text for deep learning.
| It interoperates seamlessly with
| #[+a("https://www.tensorflow.org") TensorFlow],
| #[+a("https://keras.io") Keras],
| #[+a("http://scikit-learn.org") Scikit-Learn],
| #[+a("https://radimrehurek.com/gensim") Gensim] and the
| rest of Python's awesome AI ecosystem. spaCy helps you
| connect the statistical models trained by these libraries
| to the rest of your application.
+button("/docs/usage/deep-learning", true, "primary")
| Read more
.o-inline-list.o-block.u-border-bottom.u-text-small.u-text-center.u-padding-small
+a(gh("spaCy") + "/releases")
strong.u-text-label.u-color-subtle #[+icon("code", 18)] Latest release:
| v#{SPACY_VERSION}
if LATEST_NEWS
+a(LATEST_NEWS.url) #[+tag.o-icon New!] #{LATEST_NEWS.title}
+button("/usage/deep-learning", true, "primary")
| Read more
.o-content
+grid
@ -92,67 +79,77 @@ include _includes/_mixins
+h(2) Features
+list
+item Non-destructive #[strong tokenization]
+item Syntax-driven sentence segmentation
+item Support for #[strong #{LANG_COUNT}+ languages]
+item #[strong #{MODEL_COUNT} statistical models] for #{MODEL_LANG_COUNT} languages
+item Pre-trained #[strong word vectors]
+item Easy #[strong deep learning] integration
+item Part-of-speech tagging
+item #[strong Named entity] recognition
+item Labelled dependency parsing
+item Syntax-driven sentence segmentation
+item Built in #[strong visualizers] for syntax and NER
+item Convenient string-to-hash mapping
+item Export to numpy data arrays
+item GIL-free #[strong multi-threading]
+item Efficient binary serialization
+item Easy #[strong deep learning] integration
+item Statistical models for #[strong English] and #[strong German]
+item Easy #[strong model packaging] and deployment
+item State-of-the-art speed
+item Robust, rigorously evaluated accuracy
+landing-banner("Convolutional neural network models", "New in v2.0")
p
| spaCy v2.0 features new neural models for #[strong tagging],
| #[strong parsing] and #[strong entity recognition]. The models have
| been designed and implemented from scratch specifically for spaCy, to
| give you an unmatched balance of speed, size and accuracy. A novel
| bloom embedding strategy with subword features is used to support
| huge vocabularies in tiny tables. Convolutional layers with residual
| connections, layer normalization and maxout non-linearity are used,
| giving much better efficiency than the standard BiLSTM solution.
| Finally, the parser and NER use an imitation learning objective to
| deliver accuracy in-line with the latest research systems,
| even when evaluated from raw text. With these innovations, spaCy
| v2.0's models are #[strong 10× smaller],
| #[strong 20% more accurate], and #[strong just as fast] as the
| previous generation.
.o-block-small.u-text-right
+button("/models", true, "secondary-light") Download models
+landing-logos("spaCy is trusted by", logos)
+button(gh("spacy") + "/stargazers", false, "secondary", "small")
| and many more
+landing-logos("Featured on", features).o-block-small
+landing-banner("Prodigy: Radically efficient machine teaching", "From the makers of spaCy")
p
| Prodigy is an #[strong annotation tool] so efficient that data scientists can
| do the annotation themselves, enabling a new level of rapid
| iteration. Whether you're working on entity recognition, intent
| detection or image classification, Prodigy can help you
| #[strong train and evaluate] your models faster. Stream in your own examples or
| real-world data from live APIs, update your model in real-time and
| chain models together to build more complex systems.
.o-block-small.u-text-right
+button("https://prodi.gy", true, "secondary-light") Try it out
.o-content
+grid
+grid-col("half")
+h(2) Benchmarks
p
| In 2015, independent researchers from Emory University and
| Yahoo! Labs showed that spaCy offered the
| #[strong fastest syntactic parser in the world] and that its
| accuracy was #[strong within 1% of the best] available
| (#[+a("https://aclweb.org/anthology/P/P15/P15-1038.pdf") Choi et al., 2015]).
| spaCy v2.0, released in 2017, is more accurate than any of
| the systems Choi et al. evaluated.
.o-inline-list
+button("/docs/usage/lightning-tour", true, "secondary")
| See examples
+button("/usage/facts-figures#benchmarks", true, "secondary") See details
.o-block.u-text-center.u-padding
h3.u-text-label.u-color-subtle.o-block spaCy is trusted by
each row in logos
+grid("center").o-inline-list
each details, name in row
+a(details[0])
img(src="/assets/img/logos/#{name}.png" alt=name width=(details[1] || 150)).u-padding-small
.u-pattern.u-padding
+grid.o-card.o-content
+grid-col("quarter")
img(src="/assets/img/profile_matt.png" width="280")
+grid-col("three-quarters")
+h(2) What's spaCy all about?
p
| By 2014, I'd been publishing NLP research for about 10
| years. During that time, I saw a huge gap open between the
| technology that Google-sized companies could take to market,
| and what was available to everyone else. This was especially
| clear when companies started trying to use my research. Like
| most researchers, my work was free to read, but expensive to
| apply. You could run my code, but its requirements were
| narrow. My code's mission in life was to print results
| tables for my papers — it was good at this job, and bad at
| all others.
p
| spaCy's #[+a("/docs/api/philosophy") mission] is to make
| cutting-edge NLP practical and commonly available. That's
| why I left academia in 2014, to build a production-quality
| open-source NLP library. It's why
| #[+a("https://twitter.com/_inesmontani") Ines] joined the
| project in 2015, to build visualisations, demos and
| annotation tools that make NLP technologies less abstract
| and easier to use. Together, we've founded
| #[+a(COMPANY_URL, true) Explosion AI], to develop data packs
| you can drop into spaCy to extend its capabilities. If
| you're processing Hindi insurance claims, you need a model
| for that. We can build it for you.
.o-block
+a("https://twitter.com/honnibal")
+svg("graphics", "matt-signature", 60, 45).u-color-theme
+grid-col("half")
include usage/_facts-figures/_benchmarks-choi-2015