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Fix ESLint react/no-unescaped-entities

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Marcus Blättermann 2022-11-21 12:45:00 +01:00
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4 changed files with 31 additions and 31 deletions
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2
website/src/templates/index.js
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@ -112,7 +112,7 @@ const AlertSpace = ({ nightly, legacy }) => {
)} )}
{!isOnline && ( {!isOnline && (
<Alert title="Looks like you're offline." icon="offline" variant="warning"> <Alert title="Looks like you're offline." icon="offline" variant="warning">
But don't worry, your visited pages should be saved for you. But don&apos;t worry, your visited pages should be saved for you.
</Alert> </Alert>
)} )}
</> </>

6
website/src/templates/universe.js
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@ -123,9 +123,9 @@ const UniverseContent = ({ content = [], categories, theme, pageContext, mdxComp
</Section> </Section>
)} )}
<section className="search-exclude"> <section className="search-exclude">
<H3>Found a mistake or something isn't working?</H3> <H3>Found a mistake or something isn&apos;t working?</H3>
<p> <p>
If you've come across a universe project that isn't working or is If you&apos;ve come across a universe project that isn&apos;t working or is
incompatible with the reported spaCy version, let us know by{' '} incompatible with the reported spaCy version, let us know by{' '}
<Link to="https://github.com/explosion/spaCy/discussions/new"> <Link to="https://github.com/explosion/spaCy/discussions/new">
opening a discussion thread opening a discussion thread
@ -234,7 +234,7 @@ const Project = ({ data, components }) => (
)} )}
{data.cran && ( {data.cran && (
<Aside title="Installation"> <Aside title="Installation">
<CodeBlock lang="r">install.packages("{data.cran}")</CodeBlock> <CodeBlock lang="r">install.packages(&quot;{data.cran}&quot;)</CodeBlock>
</Aside> </Aside>
)} )}

50
website/src/widgets/landing.js
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@ -75,15 +75,15 @@ const Landing = ({ data }) => {
<LandingCard title="Get things done" url="/usage/spacy-101" button="Get started"> <LandingCard title="Get things done" url="/usage/spacy-101" button="Get started">
spaCy is designed to help you do real work — to build real products, or gather 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. 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. It&apos;s easy to install, and its API is simple and productive.
</LandingCard> </LandingCard>
<LandingCard <LandingCard
title="Blazing fast" title="Blazing fast"
url="/usage/facts-figures" url="/usage/facts-figures"
button="Facts &amp; Figures" button="Facts &amp; Figures"
> >
spaCy excels at large-scale information extraction tasks. It's written from the spaCy excels at large-scale information extraction tasks. It&apos;s written from
ground up in carefully memory-managed Cython. If your application needs to the ground up in carefully memory-managed Cython. If your application needs to
process entire web dumps, spaCy is the library you want to be using. process entire web dumps, spaCy is the library you want to be using.
</LandingCard> </LandingCard>
@ -115,33 +115,33 @@ const Landing = ({ data }) => {
<img src={tailoredPipelinesImage} alt="spaCy Tailored Pipelines" /> <img src={tailoredPipelinesImage} alt="spaCy Tailored Pipelines" />
</Link> </Link>
<strong> <strong>
Get a custom spaCy pipeline, tailor-made for your NLP problem by spaCy's Get a custom spaCy pipeline, tailor-made for your NLP problem by
core developers. spaCy&apos;s core developers.
</strong> </strong>
<br /> <br />
<br /> <br />
<Ul> <Ul>
<Li emoji="🔥"> <Li emoji="🔥">
<strong>Streamlined.</strong> Nobody knows spaCy better than we do. Send <strong>Streamlined.</strong> Nobody knows spaCy better than we do. Send
us your pipeline requirements and we'll be ready to start producing your us your pipeline requirements and we&apos;ll be ready to start producing
solution in no time at all. your solution in no time at all.
</Li> </Li>
<Li emoji="🐿 "> <Li emoji="🐿 ">
<strong>Production ready.</strong> spaCy pipelines are robust and easy <strong>Production ready.</strong> spaCy pipelines are robust and easy
to deploy. You'll get a complete spaCy project folder which is ready to{' '} to deploy. You&apos;ll get a complete spaCy project folder which is
<InlineCode>spacy project run</InlineCode>. ready to <InlineCode>spacy project run</InlineCode>.
</Li> </Li>
<Li emoji="🔮"> <Li emoji="🔮">
<strong>Predictable.</strong> You'll know exactly what you're going to <strong>Predictable.</strong> You&apos;ll know exactly what you&apos;re
get and what it's going to cost. We quote fees up-front, let you try going to get and what it&apos;s going to cost. We quote fees up-front,
before you buy, and don't charge for over-runs at our end — all the risk let you try before you buy, and don&apos;t charge for over-runs at our
is on us. end — all the risk is on us.
</Li> </Li>
<Li emoji="🛠"> <Li emoji="🛠">
<strong>Maintainable.</strong> spaCy is an industry standard, and we'll <strong>Maintainable.</strong> spaCy is an industry standard, and
deliver your pipeline with full code, data, tests and documentation, so we&apos;ll deliver your pipeline with full code, data, tests and
your team can retrain, update and extend the solution as your documentation, so your team can retrain, update and extend the solution
requirements change. as your requirements change.
</Li> </Li>
</Ul> </Ul>
</LandingBanner> </LandingBanner>
@ -166,7 +166,7 @@ const Landing = ({ data }) => {
<br /> <br />
Prodigy is an <strong>annotation tool</strong> so efficient that data scientists Prodigy is an <strong>annotation tool</strong> so efficient that data scientists
can do the annotation themselves, enabling a new level of rapid iteration. can do the annotation themselves, enabling a new level of rapid iteration.
Whether you're working on entity recognition, intent detection or image Whether you&apos;re working on entity recognition, intent detection or image
classification, Prodigy can help you <strong>train and evaluate</strong> your classification, Prodigy can help you <strong>train and evaluate</strong> your
models faster. models faster.
</LandingBanner> </LandingBanner>
@ -214,7 +214,7 @@ const Landing = ({ data }) => {
<LandingCol> <LandingCol>
<H2>End-to-end workflows from prototype to production</H2> <H2>End-to-end workflows from prototype to production</H2>
<p> <p>
spaCy's new project system gives you a smooth path from prototype to spaCy&apos;s new project system gives you a smooth path from prototype to
production. It lets you keep track of all those{' '} production. It lets you keep track of all those{' '}
<strong>data transformation</strong>, preprocessing and{' '} <strong>data transformation</strong>, preprocessing and{' '}
<strong>training steps</strong>, so you can make sure your project is always <strong>training steps</strong>, so you can make sure your project is always
@ -237,11 +237,11 @@ const Landing = ({ data }) => {
small small
> >
spaCy v3.0 features all new <strong>transformer-based pipelines</strong> that spaCy v3.0 features all new <strong>transformer-based pipelines</strong> that
bring spaCy's accuracy right up to the current <strong>state-of-the-art</strong> bring spaCy&apos;s accuracy right up to the current{' '}
. You can use any pretrained transformer to train your own pipelines, and even <strong>state-of-the-art</strong>. You can use any pretrained transformer to
share one transformer between multiple components with{' '} train your own pipelines, and even share one transformer between multiple
<strong>multi-task learning</strong>. Training is now fully configurable and components with <strong>multi-task learning</strong>. Training is now fully
extensible, and you can define your own custom models using{' '} configurable and extensible, and you can define your own custom models using{' '}
<strong>PyTorch</strong>, <strong>TensorFlow</strong> and other frameworks. <strong>PyTorch</strong>, <strong>TensorFlow</strong> and other frameworks.
</LandingBanner> </LandingBanner>
<LandingBanner <LandingBanner
@ -271,7 +271,7 @@ const Landing = ({ data }) => {
<LandingCol> <LandingCol>
<H2>Benchmarks</H2> <H2>Benchmarks</H2>
<p> <p>
spaCy v3.0 introduces transformer-based pipelines that bring spaCy's spaCy v3.0 introduces transformer-based pipelines that bring spaCy&apos;s
accuracy right up to the current <strong>state-of-the-art</strong>. You can accuracy right up to the current <strong>state-of-the-art</strong>. You can
also use a CPU-optimized pipeline, which is less accurate but much cheaper also use a CPU-optimized pipeline, which is less accurate but much cheaper
to run. to run.

4
website/src/widgets/quickstart-models.js
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@ -93,7 +93,7 @@ const QuickstartInstall = ({ id, title, description, children }) => {
import spacy import spacy
</QS> </QS>
<QS load="spacy" prompt="python"> <QS load="spacy" prompt="python">
nlp = spacy.load("{pkg}") nlp = spacy.load(&quot;{pkg}&quot;)
</QS> </QS>
<QS load="module" prompt="python"> <QS load="module" prompt="python">
import {pkg} import {pkg}
@ -102,7 +102,7 @@ const QuickstartInstall = ({ id, title, description, children }) => {
nlp = {pkg}.load() nlp = {pkg}.load()
</QS> </QS>
<QS config="example" prompt="python"> <QS config="example" prompt="python">
doc = nlp("{exampleText}") doc = nlp(&quot;{exampleText}&quot;)
</QS> </QS>
<QS config="example" prompt="python"> <QS config="example" prompt="python">
print([ print([