mirror of https://github.com/explosion/spaCy.git
307 lines
14 KiB
TypeScript
307 lines
14 KiB
TypeScript
import React from 'react'
|
||
import PropTypes from 'prop-types'
|
||
|
||
import {
|
||
LandingHeader,
|
||
LandingTitle,
|
||
LandingSubtitle,
|
||
LandingGrid,
|
||
LandingCard,
|
||
LandingCol,
|
||
LandingDemo,
|
||
LandingBannerGrid,
|
||
LandingBanner,
|
||
} from '../src/components/landing'
|
||
import { H2 } from '../src/components/typography'
|
||
import { InlineCode } from '../src/components/inlineCode'
|
||
import { Ul, Li } from '../src/components/list'
|
||
import Button from '../src/components/button'
|
||
import Link from '../src/components/link'
|
||
|
||
import QuickstartTraining from '../src/widgets/quickstart-training'
|
||
import Project from '../src/widgets/project'
|
||
import Features from '../src/widgets/features'
|
||
import Layout from '../src/templates'
|
||
import courseImage from '../public/images/course.jpg'
|
||
import prodigyImage from '../public/images/prodigy_overview.jpg'
|
||
import projectsImage from '../public/images/projects.png'
|
||
import tailoredPipelinesImage from '../public/images/spacy-tailored-pipelines_wide.png'
|
||
import { nightly, legacy } from '../meta/dynamicMeta.mjs'
|
||
|
||
import Benchmarks from '../docs/usage/_benchmarks-models.mdx'
|
||
import { ImageFill } from '../src/components/embed'
|
||
|
||
function getCodeExample(nightly) {
|
||
return `# pip install -U ${nightly ? 'spacy-nightly --pre' : 'spacy'}
|
||
# python -m spacy download en_core_web_sm
|
||
import spacy
|
||
|
||
# Load English tokenizer, tagger, parser and NER
|
||
nlp = spacy.load("en_core_web_sm")
|
||
|
||
# Process whole documents
|
||
text = ("When Sebastian Thrun started working on self-driving cars at "
|
||
"Google in 2007, few people outside of the company took him "
|
||
"seriously. “I can tell you very senior CEOs of major American "
|
||
"car companies would shake my hand and turn away because I wasn’t "
|
||
"worth talking to,” said Thrun, in an interview with Recode earlier "
|
||
"this week.")
|
||
doc = nlp(text)
|
||
|
||
# Analyze syntax
|
||
print("Noun phrases:", [chunk.text for chunk in doc.noun_chunks])
|
||
print("Verbs:", [token.lemma_ for token in doc if token.pos_ == "VERB"])
|
||
|
||
# Find named entities, phrases and concepts
|
||
for entity in doc.ents:
|
||
print(entity.text, entity.label_)
|
||
`
|
||
}
|
||
|
||
const Landing = () => {
|
||
const codeExample = getCodeExample(nightly)
|
||
return (
|
||
<Layout>
|
||
<LandingHeader nightly={nightly} legacy={legacy}>
|
||
<LandingTitle>
|
||
Industrial-Strength
|
||
<br />
|
||
Natural Language
|
||
<br />
|
||
Processing
|
||
</LandingTitle>
|
||
<LandingSubtitle>in Python</LandingSubtitle>
|
||
</LandingHeader>
|
||
<LandingGrid blocks>
|
||
<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
|
||
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.
|
||
</LandingCard>
|
||
<LandingCard
|
||
title="Blazing fast"
|
||
url="/usage/facts-figures"
|
||
button="Facts & Figures"
|
||
>
|
||
spaCy excels at large-scale information extraction tasks. It's written from
|
||
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.
|
||
</LandingCard>
|
||
|
||
<LandingCard title="Awesome ecosystem" url="/usage/projects" button="Read more">
|
||
Since its release in 2015, spaCy has become an industry standard with a huge
|
||
ecosystem. Choose from a variety of plugins, integrate with your machine
|
||
learning stack and build custom components and workflows.
|
||
</LandingCard>
|
||
</LandingGrid>
|
||
|
||
<LandingGrid>
|
||
<LandingDemo title="Edit the code & try spaCy">{codeExample}</LandingDemo>
|
||
|
||
<LandingCol>
|
||
<H2>Features</H2>
|
||
<Features />
|
||
</LandingCol>
|
||
</LandingGrid>
|
||
|
||
<LandingBannerGrid>
|
||
<LandingBanner
|
||
to="https://explosion.ai/custom-solutions"
|
||
button="Learn more"
|
||
background="#E4F4F9"
|
||
color="#1e1935"
|
||
small
|
||
>
|
||
<p>
|
||
<Link to="https://explosion.ai/custom-solutions" hidden>
|
||
<ImageFill
|
||
image={tailoredPipelinesImage}
|
||
alt="spaCy Tailored Pipelines"
|
||
/>
|
||
</Link>
|
||
</p>
|
||
<p>
|
||
<strong>
|
||
Get a custom spaCy pipeline, tailor-made for your NLP problem by
|
||
spaCy's core developers.
|
||
</strong>
|
||
</p>
|
||
<Ul>
|
||
<Li emoji="🔥">
|
||
<strong>Streamlined.</strong> Nobody knows spaCy better than we do. Send
|
||
us your pipeline requirements and we'll be ready to start producing
|
||
your solution in no time at all.
|
||
</Li>
|
||
<Li emoji="🐿 ">
|
||
<strong>Production ready.</strong> spaCy pipelines are robust and easy
|
||
to deploy. You'll get a complete spaCy project folder which is
|
||
ready to <InlineCode>spacy project run</InlineCode>.
|
||
</Li>
|
||
<Li emoji="🔮">
|
||
<strong>Predictable.</strong> You'll know exactly what you're
|
||
going to get and what it's going to cost. We quote fees up-front,
|
||
let you try before you buy, and don't charge for over-runs at our
|
||
end — all the risk is on us.
|
||
</Li>
|
||
<Li emoji="🛠">
|
||
<strong>Maintainable.</strong> spaCy is an industry standard, and
|
||
we'll deliver your pipeline with full code, data, tests and
|
||
documentation, so your team can retrain, update and extend the solution
|
||
as your requirements change.
|
||
</Li>
|
||
</Ul>
|
||
</LandingBanner>
|
||
|
||
<LandingBanner
|
||
title="Prodigy: Radically efficient machine teaching"
|
||
label="From the makers of spaCy"
|
||
to="https://prodi.gy"
|
||
button="Try it out"
|
||
background="#f6f6f6"
|
||
color="#000"
|
||
small
|
||
>
|
||
<p>
|
||
<Link to="https://prodi.gy" noLinkLayout>
|
||
<ImageFill
|
||
image={prodigyImage}
|
||
alt="Prodigy: Radically efficient machine teaching"
|
||
/>
|
||
</Link>
|
||
</p>
|
||
<p>
|
||
Prodigy is an <strong>annotation tool</strong> so efficient that data
|
||
scientists can do the annotation themselves, enabling a new level of rapid
|
||
iteration. Whether you're working on entity recognition, intent
|
||
detection or image classification, Prodigy can help you{' '}
|
||
<strong>train and evaluate</strong> your models faster.
|
||
</p>
|
||
</LandingBanner>
|
||
</LandingBannerGrid>
|
||
|
||
<LandingGrid cols={2} style={{ gridTemplateColumns: '1fr calc(80ch + 14rem)' }}>
|
||
<LandingCol>
|
||
<H2>Reproducible training for custom pipelines</H2>
|
||
<p>
|
||
spaCy v3.0 introduces a comprehensive and extensible system for{' '}
|
||
<strong>configuring your training runs</strong>. Your configuration file
|
||
will describe every detail of your training run, with no hidden defaults,
|
||
making it easy to <strong>rerun your experiments</strong> and track changes.
|
||
You can use the quickstart widget or the{' '}
|
||
<Link to="/api/cli#init-config">
|
||
<InlineCode>init config</InlineCode>
|
||
</Link>{' '}
|
||
command to get started, or clone a project template for an end-to-end
|
||
workflow.
|
||
</p>
|
||
<p>
|
||
<Button to="/usage/training">Get started</Button>
|
||
</p>
|
||
</LandingCol>
|
||
<LandingCol>
|
||
<QuickstartTraining />
|
||
</LandingCol>
|
||
</LandingGrid>
|
||
|
||
<LandingGrid cols={2}>
|
||
<LandingCol>
|
||
<Link to="/usage/projects" hidden>
|
||
<ImageFill
|
||
image={projectsImage}
|
||
alt="Illustration of project workflow and commands"
|
||
/>
|
||
</Link>
|
||
<br />
|
||
<br />
|
||
<br />
|
||
<Project id="pipelines/tagger_parser_ud" title="Get started">
|
||
The easiest way to get started is to clone a project template and run it
|
||
– for example, this template for training a{' '}
|
||
<strong>part-of-speech tagger</strong> and{' '}
|
||
<strong>dependency parser</strong> on a Universal Dependencies treebank.
|
||
</Project>
|
||
</LandingCol>
|
||
<LandingCol>
|
||
<H2>End-to-end workflows from prototype to production</H2>
|
||
<p>
|
||
spaCy's new project system gives you a smooth path from prototype to
|
||
production. It lets you keep track of all those{' '}
|
||
<strong>data transformation</strong>, preprocessing and{' '}
|
||
<strong>training steps</strong>, so you can make sure your project is always
|
||
ready to hand over for automation. It features source asset download,
|
||
command execution, checksum verification, and caching with a variety of
|
||
backends and integrations.
|
||
</p>
|
||
<p>
|
||
<Button to="/usage/projects">Try it out</Button>
|
||
</p>
|
||
</LandingCol>
|
||
</LandingGrid>
|
||
|
||
<LandingBannerGrid>
|
||
<LandingBanner
|
||
label="New in v3.0"
|
||
title="Transformer-based pipelines, new training system, project templates & more"
|
||
to="/usage/v3"
|
||
button="See what's new"
|
||
small
|
||
>
|
||
<p>
|
||
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>. You can use any pretrained transformer to
|
||
train your own pipelines, and even share one transformer between multiple
|
||
components with <strong>multi-task learning</strong>. Training is now fully
|
||
configurable and extensible, and you can define your own custom models using{' '}
|
||
<strong>PyTorch</strong>, <strong>TensorFlow</strong> and other frameworks.
|
||
</p>
|
||
</LandingBanner>
|
||
<LandingBanner
|
||
to="https://course.spacy.io"
|
||
button="Start the course"
|
||
background="#f6f6f6"
|
||
color="#252a33"
|
||
small
|
||
>
|
||
<p>
|
||
<Link to="https://course.spacy.io" hidden>
|
||
<ImageFill
|
||
image={courseImage}
|
||
alt="Advanced NLP with spaCy: A free online course"
|
||
/>
|
||
</Link>
|
||
</p>
|
||
<p>
|
||
In this <strong>free and interactive online course</strong> you’ll learn how
|
||
to use spaCy to build advanced natural language understanding systems, using
|
||
both rule-based and machine learning approaches. It includes{' '}
|
||
<strong>55 exercises</strong> featuring videos, slide decks, multiple-choice
|
||
questions and interactive coding practice in the browser.
|
||
</p>
|
||
</LandingBanner>
|
||
</LandingBannerGrid>
|
||
|
||
<LandingGrid cols={2} style={{ gridTemplateColumns: '1fr 60%' }}>
|
||
<LandingCol>
|
||
<H2>Benchmarks</H2>
|
||
<p>
|
||
spaCy v3.0 introduces transformer-based pipelines that bring spaCy's
|
||
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
|
||
to run.
|
||
</p>
|
||
<p>
|
||
<Button to="/usage/facts-figures#benchmarks">More results</Button>
|
||
</p>
|
||
</LandingCol>
|
||
|
||
<LandingCol>
|
||
<Benchmarks />
|
||
</LandingCol>
|
||
</LandingGrid>
|
||
</Layout>
|
||
)
|
||
}
|
||
|
||
export default Landing
|