mirror of https://github.com/explosion/spaCy.git
Update landing [ci skip]
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Ines Montani
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@ -37,8 +37,8 @@ export const LandingSubtitle = ({ children }) => (
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)
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export const LandingGrid = ({ cols = 3, blocks = false, children }) => (
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<Content className={classNames(classes.grid, { [classes.blocks]: blocks })}>
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<Grid cols={cols} narrow={blocks}>
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<Content className={classNames({ [classes.blocks]: blocks })}>
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<Grid cols={cols} narrow={blocks} className={classes.grid}>
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{children}
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</Grid>
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</Content>
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@ -76,7 +76,7 @@
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.banner
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background: var(--color-theme)
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color: var(--color-back)
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padding: 5rem
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padding: 1rem 5rem
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margin-bottom: var(--spacing-md)
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background-size: cover
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@ -128,14 +128,17 @@
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padding-right: 2rem
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@include breakpoint(max, md)
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.banner
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padding: 1rem 3rem
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.banner-content
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display: block
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.banner-text
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padding-top: 0
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.col
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grid-column: 1 / span 2
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.grid
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grid-template-columns: 1fr !important
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.banner-button
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margin-bottom: var(--spacing-sm)
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@ -20,6 +20,7 @@ import Button from '../components/button'
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import Link from '../components/link'
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import courseImage from '../../docs/images/course.jpg'
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import prodigyImage from '../../docs/images/prodigy_overview.jpg'
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import BenchmarksChoi from 'usage/_benchmarks-choi.md'
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@ -147,6 +148,59 @@ const Landing = ({ data }) => {
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</LandingCol>
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</LandingGrid>
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<LandingBannerGrid>
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<LandingBanner
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title="spaCy v3.0 nightly: Transformer-based pipelines, new training system, project templates & more"
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label="Try the pre-release"
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to="https://nightly.spacy.io"
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button="See what's new"
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background="#8758fe"
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color="#ffffff"
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small
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>
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spaCy v3.0 features all new <strong>transformer-based pipelines</strong> that
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bring spaCy's accuracy right up to the current <strong>state-of-the-art</strong>
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. You can use any pretrained transformer to train your own pipelines, and even
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share one transformer between multiple components with{' '}
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<strong>multi-task learning</strong>. Training is now fully configurable and
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extensible, and you can define your own custom models using{' '}
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<strong>PyTorch</strong>, <strong>TensorFlow</strong> and other frameworks. The
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new spaCy projects system lets you describe whole{' '}
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<strong>end-to-end workflows</strong> in a single file, giving you an easy path
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from prototype to production, and making it easy to clone and adapt
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best-practice projects for your own use cases.
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</LandingBanner>
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<LandingBanner
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title="Prodigy: Radically efficient machine teaching"
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label="From the makers of spaCy"
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to="https://prodi.gy"
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button="Try it out"
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background="#f6f6f6"
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color="#000"
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small
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>
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<Link to="https://prodi.gy" hidden>
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<img
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src={prodigyImage}
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alt="Prodigy: Radically efficient machine teaching"
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/>
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</Link>
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<br />
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<br />
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Prodigy is an <strong>annotation tool</strong> so efficient that data scientists
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can do the annotation themselves, enabling a new level of rapid iteration.
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Whether you're working on entity recognition, intent detection or image
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classification, Prodigy can help you <strong>train and evaluate</strong> your
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models faster.
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</LandingBanner>
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</LandingBannerGrid>
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<LandingLogos title="spaCy is trusted by" logos={data.logosUsers}>
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<Button to={`https://github.com/${data.repo}/stargazers`}>and many more</Button>
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</LandingLogos>
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<LandingLogos title="Featured on" logos={data.logosPublications} />
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<LandingBannerGrid>
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<LandingBanner
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to="https://course.spacy.io"
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@ -169,45 +223,23 @@ const Landing = ({ data }) => {
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<strong>55 exercises</strong> featuring videos, slide decks, multiple-choice
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questions and interactive coding practice in the browser.
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</LandingBanner>
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<LandingBanner
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title="Prodigy: Radically efficient machine teaching"
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label="From the makers of spaCy"
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to="https://prodi.gy"
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button="Try it out"
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background="#eee"
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color="#000"
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title="BERT-style language model pretraining"
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label="New in v2.1"
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to="/usage/v2-1"
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button="Read more"
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small
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>
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Prodigy is an <strong>annotation tool</strong> so efficient that data scientists
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can do the annotation themselves, enabling a new level of rapid iteration.
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Whether you're working on entity recognition, intent detection or image
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classification, Prodigy can help you <strong>train and evaluate</strong> your
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models faster. Stream in your own examples or real-world data from live APIs,
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update your model in real-time and chain models together to build more complex
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systems.
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Learn more from small training corpora by initializing your models with{' '}
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<strong>knowledge from raw text</strong>. The new pretrain command teaches
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spaCy's CNN model to predict words based on their context, producing
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representations of words in contexts. If you've seen Google's BERT system or
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fast.ai's ULMFiT, spaCy's pretraining is similar – but much more efficient. It's
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still experimental, but users are already reporting good results, so give it a
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try!
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</LandingBanner>
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</LandingBannerGrid>
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<LandingLogos title="spaCy is trusted by" logos={data.logosUsers}>
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<Button to={`https://github.com/${data.repo}/stargazers`}>and many more</Button>
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</LandingLogos>
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<LandingLogos title="Featured on" logos={data.logosPublications} />
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<LandingBanner
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title="BERT-style language model pretraining"
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label="New in v2.1"
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to="/usage/v2-1"
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button="Read more"
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>
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Learn more from small training corpora by initializing your models with{' '}
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<strong>knowledge from raw text</strong>. The new pretrain command teaches spaCy's
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CNN model to predict words based on their context, producing representations of
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words in contexts. If you've seen Google's BERT system or fast.ai's ULMFiT, spaCy's
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pretraining is similar – but much more efficient. It's still experimental, but users
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are already reporting good results, so give it a try!
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</LandingBanner>
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<LandingGrid cols={2}>
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<LandingCol>
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<H2>Benchmarks</H2>
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