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https://github.com/explosion/spaCy.git
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Fix ESLint react/no-unescaped-entities
This commit is contained in:
Marcus Blättermann
parent
cf460065bf
commit
dbfdc55989
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@ -112,7 +112,7 @@ const AlertSpace = ({ nightly, legacy }) => {
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)}
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{!isOnline && (
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<Alert title="Looks like you're offline." icon="offline" variant="warning">
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But don't worry, your visited pages should be saved for you.
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But don't worry, your visited pages should be saved for you.
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</Alert>
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)}
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</>
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@ -123,9 +123,9 @@ const UniverseContent = ({ content = [], categories, theme, pageContext, mdxComp
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</Section>
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)}
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<section className="search-exclude">
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<H3>Found a mistake or something isn't working?</H3>
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<H3>Found a mistake or something isn't working?</H3>
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<p>
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If you've come across a universe project that isn't working or is
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If you've come across a universe project that isn't working or is
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incompatible with the reported spaCy version, let us know by{' '}
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<Link to="https://github.com/explosion/spaCy/discussions/new">
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opening a discussion thread
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@ -228,7 +228,7 @@ const Project = ({ data, components }) => (
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)}
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{data.cran && (
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<Aside title="Installation">
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<CodeBlock lang="r">install.packages("{data.cran}")</CodeBlock>
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<CodeBlock lang="r">install.packages("{data.cran}")</CodeBlock>
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</Aside>
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)}
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@ -75,15 +75,15 @@ const Landing = ({ data }) => {
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<LandingCard title="Get things done" url="/usage/spacy-101" button="Get started">
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spaCy is designed to help you do real work — to build real products, or gather
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real insights. The library respects your time, and tries to avoid wasting it.
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It's easy to install, and its API is simple and productive.
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It's easy to install, and its API is simple and productive.
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</LandingCard>
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<LandingCard
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title="Blazing fast"
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url="/usage/facts-figures"
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button="Facts & Figures"
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>
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spaCy excels at large-scale information extraction tasks. It's written from the
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ground up in carefully memory-managed Cython. If your application needs to
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spaCy excels at large-scale information extraction tasks. It's written from
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the ground up in carefully memory-managed Cython. If your application needs to
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process entire web dumps, spaCy is the library you want to be using.
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</LandingCard>
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@ -115,33 +115,33 @@ const Landing = ({ data }) => {
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<img src={tailoredPipelinesImage} alt="spaCy Tailored Pipelines" />
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</Link>
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<strong>
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Get a custom spaCy pipeline, tailor-made for your NLP problem by spaCy's
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core developers.
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Get a custom spaCy pipeline, tailor-made for your NLP problem by
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spaCy's core developers.
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</strong>
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<br />
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<br />
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<Ul>
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<Li emoji="🔥">
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<strong>Streamlined.</strong> Nobody knows spaCy better than we do. Send
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us your pipeline requirements and we'll be ready to start producing your
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solution in no time at all.
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us your pipeline requirements and we'll be ready to start producing
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your solution in no time at all.
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</Li>
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<Li emoji="🐿 ">
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<strong>Production ready.</strong> spaCy pipelines are robust and easy
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to deploy. You'll get a complete spaCy project folder which is ready to{' '}
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<InlineCode>spacy project run</InlineCode>.
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to deploy. You'll get a complete spaCy project folder which is
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ready to <InlineCode>spacy project run</InlineCode>.
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</Li>
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<Li emoji="🔮">
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<strong>Predictable.</strong> You'll know exactly what you're going to
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get and what it's going to cost. We quote fees up-front, let you try
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before you buy, and don't charge for over-runs at our end — all the risk
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is on us.
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<strong>Predictable.</strong> You'll know exactly what you're
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going to get and what it's going to cost. We quote fees up-front,
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let you try before you buy, and don't charge for over-runs at our
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end — all the risk is on us.
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</Li>
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<Li emoji="🛠">
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<strong>Maintainable.</strong> spaCy is an industry standard, and we'll
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deliver your pipeline with full code, data, tests and documentation, so
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your team can retrain, update and extend the solution as your
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requirements change.
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<strong>Maintainable.</strong> spaCy is an industry standard, and
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we'll deliver your pipeline with full code, data, tests and
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documentation, so your team can retrain, update and extend the solution
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as your requirements change.
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</Li>
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</Ul>
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</LandingBanner>
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@ -166,7 +166,7 @@ const Landing = ({ data }) => {
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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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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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@ -214,7 +214,7 @@ const Landing = ({ data }) => {
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<LandingCol>
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<H2>End-to-end workflows from prototype to production</H2>
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<p>
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spaCy's new project system gives you a smooth path from prototype to
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spaCy's new project system gives you a smooth path from prototype to
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production. It lets you keep track of all those{' '}
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<strong>data transformation</strong>, preprocessing and{' '}
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<strong>training steps</strong>, so you can make sure your project is always
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@ -237,11 +237,11 @@ const Landing = ({ data }) => {
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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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bring spaCy's accuracy right up to the current{' '}
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<strong>state-of-the-art</strong>. You can use any pretrained transformer to
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train your own pipelines, and even share one transformer between multiple
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components with <strong>multi-task learning</strong>. Training is now fully
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configurable and extensible, and you can define your own custom models using{' '}
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<strong>PyTorch</strong>, <strong>TensorFlow</strong> and other frameworks.
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</LandingBanner>
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<LandingBanner
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@ -271,7 +271,7 @@ const Landing = ({ data }) => {
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<LandingCol>
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<H2>Benchmarks</H2>
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<p>
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spaCy v3.0 introduces transformer-based pipelines that bring spaCy's
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spaCy v3.0 introduces transformer-based pipelines that bring spaCy's
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accuracy right up to the current <strong>state-of-the-art</strong>. You can
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also use a CPU-optimized pipeline, which is less accurate but much cheaper
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to run.
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@ -93,7 +93,7 @@ const QuickstartInstall = ({ id, title, description, children }) => {
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import spacy
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</QS>
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<QS load="spacy" prompt="python">
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nlp = spacy.load("{pkg}")
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nlp = spacy.load("{pkg}")
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</QS>
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<QS load="module" prompt="python">
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import {pkg}
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@ -102,7 +102,7 @@ const QuickstartInstall = ({ id, title, description, children }) => {
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nlp = {pkg}.load()
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</QS>
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<QS config="example" prompt="python">
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doc = nlp("{exampleText}")
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doc = nlp("{exampleText}")
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</QS>
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<QS config="example" prompt="python">
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print([
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