Add spaCy IRL to landing [ci skip]

This commit is contained in:
Ines Montani 2019-03-30 20:32:03 +01:00
parent 68900066e0
commit 037ffdfd3f
4 changed files with 46 additions and 31 deletions

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@ -75,14 +75,28 @@ export const LandingBannerGrid = ({ children }) => (
</Grid> </Grid>
) )
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, { const contentClassNames = classNames(classes.bannerContent, {
[classes.bannerContentSmall]: small, [classes.bannerContentSmall]: small,
}) })
const textClassNames = classNames(classes.bannerText, { const textClassNames = classNames(classes.bannerText, {
[classes.bannerTextSmall]: small, [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 const Heading = small ? H2 : H1
return ( return (
<div className={classes.banner} style={style}> <div className={classes.banner} style={style}>
@ -113,7 +127,7 @@ export const LandingBanner = ({ title, label, to, button, small, background, col
export const LandingBannerButton = ({ to, small, children }) => ( export const LandingBannerButton = ({ to, small, children }) => (
<div className={classes.bannerButton}> <div className={classes.bannerButton}>
<Button to={to} variant="tertiary" large={!small}> <Button to={to} variant="tertiary" large={!small} className={classes.bannerButtonElement}>
{children} {children}
</Button> </Button>
</div> </div>

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@ -73,6 +73,7 @@
color: var(--color-back) color: var(--color-back)
padding: 5rem padding: 5rem
margin-bottom: var(--spacing-md) margin-bottom: var(--spacing-md)
background-size: cover
.banner-content .banner-content
margin-bottom: 0 margin-bottom: 0
@ -100,7 +101,7 @@
.banner-text-small p .banner-text-small p
font-size: 1.35rem font-size: 1.35rem
margin-bottom: 1rem margin-bottom: 1.5rem
@include breakpoint(min, md) @include breakpoint(min, md)
.banner-content .banner-content
@ -134,6 +135,9 @@
margin-bottom: var(--spacing-sm) margin-bottom: var(--spacing-sm)
text-align: right text-align: right
.banner-button-element
background: var(--color-theme)
.logos .logos
text-align: center text-align: center
padding-bottom: 1rem padding-bottom: 1rem

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@ -19,6 +19,7 @@ import { H2 } from '../components/typography'
import { Ul, Li } from '../components/list' import { Ul, Li } from '../components/list'
import Button from '../components/button' import Button from '../components/button'
import Link from '../components/link' import Link from '../components/link'
import irlBackground from '../images/spacy-irl.jpg'
import BenchmarksChoi from 'usage/_benchmarks-choi.md' import BenchmarksChoi from 'usage/_benchmarks-choi.md'
@ -151,19 +152,21 @@ const Landing = ({ data }) => {
<LandingBannerGrid> <LandingBannerGrid>
<LandingBanner <LandingBanner
title="BERT-style language model pretraining and more" title="spaCy IRL 2019: Two days of NLP"
label="New in v2.1" label="Join us in Berlin"
to="/usage/v2-1" to="https://irl.spacy.io/2019"
button="Read more" button="Get tickets"
background="#ffc194"
backgroundImage={irlBackground}
color="#1a1e23"
small small
> >
Learn more from small training corpora by initializing your models with{' '} We're pleased to invite the spaCy community and other folks working on Natural
<strong>knowledge from raw text</strong>. The new pretrain command teaches Language Processing to Berlin this summer for a small and intimate event{' '}
spaCy's CNN model to predict words based on their context, producing <strong>July 5-6, 2019</strong>. The event includes a hands-on training day for
representations of words in contexts. If you've seen Google's BERT system or teams using spaCy in production, followed by a one-track conference. We booked a
fast.ai's ULMFiT, spaCy's pretraining is similar but much more efficient. It's beautiful venue, hand-picked an awesome lineup of speakers and scheduled plenty
still experimental, but users are already reporting good results, so give it a of social time to get to know each other and exchange ideas.
try!
</LandingBanner> </LandingBanner>
<LandingBanner <LandingBanner
@ -191,23 +194,17 @@ const Landing = ({ data }) => {
<LandingLogos title="Featured on" logos={data.logosPublications} /> <LandingLogos title="Featured on" logos={data.logosPublications} />
<LandingBanner <LandingBanner
title="Convolutional neural network models" title="BERT-style language model pretraining"
label="New in v2.0" label="New in v2.1"
button="Download models" to="/usage/v2-1"
to="/models" button="Read more"
> >
spaCy v2.0 features new neural models for <strong>tagging</strong>,{' '} Learn more from small training corpora by initializing your models with{' '}
<strong>parsing</strong> and <strong>entity recognition</strong>. The models have <strong>knowledge from raw text</strong>. The new pretrain command teaches spaCy's
been designed and implemented from scratch specifically for spaCy, to give you an CNN model to predict words based on their context, producing representations of
unmatched balance of speed, size and accuracy. A novel bloom embedding strategy with words in contexts. If you've seen Google's BERT system or fast.ai's ULMFiT, spaCy's
subword features is used to support huge vocabularies in tiny tables. Convolutional pretraining is similar but much more efficient. It's still experimental, but users
layers with residual connections, layer normalization and maxout non-linearity are are already reporting good results, so give it a try!
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>,{' '}
<strong>20% more accurate</strong>, and
<strong>even cheaper to run</strong> than the previous generation.
</LandingBanner> </LandingBanner>
<LandingGrid cols={2}> <LandingGrid cols={2}>