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056b73468c
* Extract `CodeBlock` component into own file * Extract `InlineCode` component into own file * Extract `TypeAnnotation` component into own file * Convert named `export` to `default export` * Remove unused `export` * Simplify `TypeAnnotation` to remove dependency for Prism * Load `Code` component dynamically * Extract `MarkdownToReact` component into own file * WIP Code Dynamic * Load `MarkdownToReact` component dynamically * Extract `htmlToReact` to own file * Load `htmlToReact` component dynamically * Dynamically load `Juniper`
307 lines
14 KiB
TypeScript
307 lines
14 KiB
TypeScript
import React from 'react'
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import PropTypes from 'prop-types'
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import {
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LandingHeader,
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LandingTitle,
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LandingSubtitle,
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LandingGrid,
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LandingCard,
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LandingCol,
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LandingDemo,
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LandingBannerGrid,
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LandingBanner,
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} from '../src/components/landing'
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import { H2 } from '../src/components/typography'
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import { InlineCode } from '../src/components/inlineCode'
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import { Ul, Li } from '../src/components/list'
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import Button from '../src/components/button'
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import Link from '../src/components/link'
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import QuickstartTraining from '../src/widgets/quickstart-training'
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import Project from '../src/widgets/project'
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import Features from '../src/widgets/features'
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import Layout from '../src/templates'
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import courseImage from '../public/images/course.jpg'
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import prodigyImage from '../public/images/prodigy_overview.jpg'
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import projectsImage from '../public/images/projects.png'
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import tailoredPipelinesImage from '../public/images/spacy-tailored-pipelines_wide.png'
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import { nightly, legacy } from '../meta/dynamicMeta.mjs'
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import Benchmarks from '../docs/usage/_benchmarks-models.mdx'
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import { ImageFill } from '../src/components/embed'
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function getCodeExample(nightly) {
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return `# pip install -U ${nightly ? 'spacy-nightly --pre' : 'spacy'}
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# python -m spacy download en_core_web_sm
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import spacy
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# Load English tokenizer, tagger, parser and NER
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nlp = spacy.load("en_core_web_sm")
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# Process whole documents
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text = ("When Sebastian Thrun started working on self-driving cars at "
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"Google in 2007, few people outside of the company took him "
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"seriously. “I can tell you very senior CEOs of major American "
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"car companies would shake my hand and turn away because I wasn’t "
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"worth talking to,” said Thrun, in an interview with Recode earlier "
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"this week.")
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doc = nlp(text)
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# Analyze syntax
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print("Noun phrases:", [chunk.text for chunk in doc.noun_chunks])
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print("Verbs:", [token.lemma_ for token in doc if token.pos_ == "VERB"])
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# Find named entities, phrases and concepts
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for entity in doc.ents:
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print(entity.text, entity.label_)
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`
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}
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const Landing = () => {
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const codeExample = getCodeExample(nightly)
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return (
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<Layout>
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<LandingHeader nightly={nightly} legacy={legacy}>
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<LandingTitle>
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Industrial-Strength
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<br />
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Natural Language
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<br />
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Processing
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</LandingTitle>
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<LandingSubtitle>in Python</LandingSubtitle>
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</LandingHeader>
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<LandingGrid blocks>
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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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</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
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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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<LandingCard title="Awesome ecosystem" url="/usage/projects" button="Read more">
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Since its release in 2015, spaCy has become an industry standard with a huge
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ecosystem. Choose from a variety of plugins, integrate with your machine
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learning stack and build custom components and workflows.
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</LandingCard>
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</LandingGrid>
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<LandingGrid>
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<LandingDemo title="Edit the code & try spaCy">{codeExample}</LandingDemo>
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<LandingCol>
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<H2>Features</H2>
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<Features />
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</LandingCol>
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</LandingGrid>
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<LandingBannerGrid>
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<LandingBanner
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to="https://explosion.ai/custom-solutions"
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button="Learn more"
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background="#E4F4F9"
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color="#1e1935"
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small
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>
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<p>
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<Link to="https://explosion.ai/custom-solutions" hidden>
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<ImageFill
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image={tailoredPipelinesImage}
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alt="spaCy Tailored Pipelines"
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/>
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</Link>
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</p>
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<p>
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<strong>
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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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</p>
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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
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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
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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
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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
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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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<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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<p>
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<Link to="https://prodi.gy" noLinkLayout>
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<ImageFill
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image={prodigyImage}
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alt="Prodigy: Radically efficient machine teaching"
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/>
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</Link>
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</p>
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<p>
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Prodigy is an <strong>annotation tool</strong> so efficient that data
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scientists can do the annotation themselves, enabling a new level of rapid
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iteration. Whether you're working on entity recognition, intent
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detection or image classification, Prodigy can help you{' '}
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<strong>train and evaluate</strong> your models faster.
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</p>
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</LandingBanner>
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</LandingBannerGrid>
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<LandingGrid cols={2} style={{ gridTemplateColumns: '1fr calc(80ch + 14rem)' }}>
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<LandingCol>
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<H2>Reproducible training for custom pipelines</H2>
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<p>
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spaCy v3.0 introduces a comprehensive and extensible system for{' '}
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<strong>configuring your training runs</strong>. Your configuration file
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will describe every detail of your training run, with no hidden defaults,
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making it easy to <strong>rerun your experiments</strong> and track changes.
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You can use the quickstart widget or the{' '}
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<Link to="/api/cli#init-config">
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<InlineCode>init config</InlineCode>
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</Link>{' '}
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command to get started, or clone a project template for an end-to-end
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workflow.
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</p>
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<p>
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<Button to="/usage/training">Get started</Button>
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</p>
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</LandingCol>
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<LandingCol>
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<QuickstartTraining />
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</LandingCol>
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</LandingGrid>
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<LandingGrid cols={2}>
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<LandingCol>
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<Link to="/usage/projects" hidden>
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<ImageFill
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image={projectsImage}
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alt="Illustration of project workflow and commands"
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/>
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</Link>
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<br />
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<br />
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<br />
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<Project id="pipelines/tagger_parser_ud" title="Get started">
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The easiest way to get started is to clone a project template and run it
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– for example, this template for training a{' '}
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<strong>part-of-speech tagger</strong> and{' '}
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<strong>dependency parser</strong> on a Universal Dependencies treebank.
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</Project>
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</LandingCol>
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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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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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ready to hand over for automation. It features source asset download,
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command execution, checksum verification, and caching with a variety of
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backends and integrations.
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</p>
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<p>
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<Button to="/usage/projects">Try it out</Button>
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</p>
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</LandingCol>
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</LandingGrid>
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<LandingBannerGrid>
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<LandingBanner
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label="New in v3.0"
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title="Transformer-based pipelines, new training system, project templates & more"
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to="/usage/v3"
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button="See what's new"
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small
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>
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<p>
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spaCy v3.0 features all new <strong>transformer-based pipelines</strong>{' '}
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that 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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</p>
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</LandingBanner>
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<LandingBanner
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to="https://course.spacy.io"
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button="Start the course"
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background="#f6f6f6"
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color="#252a33"
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small
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>
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<p>
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<Link to="https://course.spacy.io" hidden>
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<ImageFill
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image={courseImage}
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alt="Advanced NLP with spaCy: A free online course"
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/>
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</Link>
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</p>
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<p>
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In this <strong>free and interactive online course</strong> you’ll learn how
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to use spaCy to build advanced natural language understanding systems, using
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both rule-based and machine learning approaches. It includes{' '}
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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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</p>
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</LandingBanner>
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</LandingBannerGrid>
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<LandingGrid cols={2} style={{ gridTemplateColumns: '1fr 60%' }}>
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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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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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</p>
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<p>
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<Button to="/usage/facts-figures#benchmarks">More results</Button>
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</p>
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</LandingCol>
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<LandingCol>
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<Benchmarks />
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</LandingCol>
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</LandingGrid>
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</Layout>
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)
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}
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export default Landing
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