From 037ffdfd3fa275b6b4303ee0617f3d26475a900f Mon Sep 17 00:00:00 2001 From: Ines Montani Date: Sat, 30 Mar 2019 20:32:03 +0100 Subject: [PATCH] Add spaCy IRL to landing [ci skip] --- website/src/components/landing.js | 20 ++++++++-- website/src/images/spacy-irl.jpg | Bin 0 -> 77173 bytes website/src/styles/landing.module.sass | 6 ++- website/src/widgets/landing.js | 51 ++++++++++++------------- 4 files changed, 46 insertions(+), 31 deletions(-) create mode 100644 website/src/images/spacy-irl.jpg diff --git a/website/src/components/landing.js b/website/src/components/landing.js index e84534820..16c342e3f 100644 --- a/website/src/components/landing.js +++ b/website/src/components/landing.js @@ -75,14 +75,28 @@ export const LandingBannerGrid = ({ children }) => ( ) -export const LandingBanner = ({ title, label, to, button, small, background, color, children }) => { +export const LandingBanner = ({ + title, + label, + to, + button, + small, + background, + backgroundImage, + color, + children, +}) => { const contentClassNames = classNames(classes.bannerContent, { [classes.bannerContentSmall]: small, }) const textClassNames = classNames(classes.bannerText, { [classes.bannerTextSmall]: small, }) - const style = { '--color-theme': background, '--color-back': color } + const style = { + '--color-theme': background, + '--color-back': color, + backgroundImage: backgroundImage ? `url(${backgroundImage})` : null, + } const Heading = small ? H2 : H1 return (
@@ -113,7 +127,7 @@ export const LandingBanner = ({ title, label, to, button, small, background, col export const LandingBannerButton = ({ to, small, children }) => (
-
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The new pretrain command teaches - spaCy's CNN model to predict words based on their context, producing - representations of words in contexts. If you've seen Google's BERT system or - fast.ai's ULMFiT, spaCy's pretraining is similar – but much more efficient. It's - still experimental, but users are already reporting good results, so give it a - try! + We're pleased to invite the spaCy community and other folks working on Natural + Language Processing to Berlin this summer for a small and intimate event{' '} + July 5-6, 2019. The event includes a hands-on training day for + teams using spaCy in production, followed by a one-track conference. We booked a + beautiful venue, hand-picked an awesome lineup of speakers and scheduled plenty + of social time to get to know each other and exchange ideas. { - spaCy v2.0 features new neural models for tagging,{' '} - parsing and 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 10× smaller,{' '} - 20% more accurate, and - even cheaper to run than the previous generation. + Learn more from small training corpora by initializing your models with{' '} + knowledge from raw text. The new pretrain command teaches spaCy's + CNN model to predict words based on their context, producing representations of + words in contexts. If you've seen Google's BERT system or fast.ai's ULMFiT, spaCy's + pretraining is similar – but much more efficient. It's still experimental, but users + are already reporting good results, so give it a try!