mirror of
https://github.com/explosion/spaCy.git
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e597110d31
<!--- Provide a general summary of your changes in the title. --> ## Description The new website is implemented using [Gatsby](https://www.gatsbyjs.org) with [Remark](https://github.com/remarkjs/remark) and [MDX](https://mdxjs.com/). This allows authoring content in **straightforward Markdown** without the usual limitations. Standard elements can be overwritten with powerful [React](http://reactjs.org/) components and wherever Markdown syntax isn't enough, JSX components can be used. Hopefully, this update will also make it much easier to contribute to the docs. Once this PR is merged, I'll implement auto-deployment via [Netlify](https://netlify.com) on a specific branch (to avoid building the website on every PR). There's a bunch of other cool stuff that the new setup will allow us to do – including writing front-end tests, service workers, offline support, implementing a search and so on. This PR also includes various new docs pages and content. Resolves #3270. Resolves #3222. Resolves #2947. Resolves #2837. ### Types of change enhancement ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
282 lines
12 KiB
JavaScript
282 lines
12 KiB
JavaScript
import React from 'react'
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import PropTypes from 'prop-types'
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import { StaticQuery, graphql } from 'gatsby'
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import { LandingHeader, LandingTitle, LandingSubtitle, LandingGrid } from '../components/landing'
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import { LandingCard, LandingButton, LandingDemo } from '../components/landing'
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import { LandingBannerGrid, LandingBanner, LandingLogos } from '../components/landing'
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import { H2 } from '../components/typography'
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import { Ul, Li } from '../components/list'
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import Button from '../components/button'
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import Link from '../components/link'
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import BenchmarksChoi from 'usage/_benchmarks-choi.md'
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const CODE_EXAMPLE = `# pip install 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, NER and word vectors
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nlp = spacy.load("en_core_web_sm")
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# Process whole documents
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text = (u"When Sebastian Thrun started working on self-driving cars at "
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u"Google in 2007, few people outside of the company took him "
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u"seriously. “I can tell you very senior CEOs of major American "
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u"car companies would shake my hand and turn away because I wasn’t "
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u"worth talking to,” said Thrun, now the co-founder and CEO of "
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u"online higher education startup Udacity, in an interview with "
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u"Recode earlier this week.")
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doc = nlp(text)
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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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# Determine semantic similarities
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doc1 = nlp(u"my fries were super gross")
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doc2 = nlp(u"such disgusting fries")
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similarity = doc1.similarity(doc2)
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print(doc1.text, doc2.text, similarity)
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`
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/**
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* Compute the overall total counts of models and languages
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*/
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function getCounts(langs = []) {
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return {
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langs: langs.length,
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modelLangs: langs.filter(({ models }) => models && !!models.length).length,
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models: langs.map(({ models }) => (models ? models.length : 0)).reduce((a, b) => a + b, 0),
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}
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}
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const Landing = ({ data }) => {
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const counts = getCounts(data.languages)
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return (
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<>
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<LandingHeader>
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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="Fastest in the world">
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<p>
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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. Independent research has
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confirmed that spaCy is the fastest in the world. If your application needs
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to process entire web dumps, spaCy is the library you want to be using.
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</p>
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<LandingButton to="/usage/facts-figures">Facts & Figures</LandingButton>
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</LandingCard>
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<LandingCard title="Get things done">
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<p>
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spaCy is designed to help you do real work — to build real products, or
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gather real insights. The library respects your time, and tries to avoid
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wasting it. It's easy to install, and its API is simple and productive. We
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like to think of spaCy as the Ruby on Rails of Natural Language Processing.
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</p>
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<LandingButton to="/usage">Get started</LandingButton>
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</LandingCard>
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<LandingCard title="Deep learning">
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<p>
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spaCy is the best way to prepare text for deep learning. It interoperates
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seamlessly with TensorFlow, PyTorch, scikit-learn, Gensim and the rest of
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Python's awesome AI ecosystem. With spaCy, you can easily construct
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linguistically sophisticated statistical models for a variety of NLP
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problems.
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</p>
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<LandingButton to="/usage/training">Read more</LandingButton>
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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">{CODE_EXAMPLE}</LandingDemo>
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<div>
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<H2>Features</H2>
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<Ul>
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<Li>
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Non-destructive <strong>tokenization</strong>
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</Li>
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<Li>
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<strong>Named entity</strong> recognition
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</Li>
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<Li>
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Support for <strong>{counts.langs}+ languages</strong>
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</Li>
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<Li>
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<strong>{counts.models} statistical models</strong> for{' '}
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{counts.modelLangs} languages
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</Li>
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<Li>
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Pre-trained <strong>word vectors</strong>
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</Li>
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<Li>
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Easy <strong>deep learning</strong> integration
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</Li>
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<Li>Part-of-speech tagging</Li>
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<Li>Labelled dependency parsing</Li>
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<Li>Syntax-driven sentence segmentation</Li>
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<Li>
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Built in <strong>visualizers</strong> for syntax and NER
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</Li>
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<Li>Convenient string-to-hash mapping</Li>
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<Li>Export to numpy data arrays</Li>
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<Li>Efficient binary serialization</Li>
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<Li>
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Easy <strong>model packaging</strong> and deployment
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</Li>
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<Li>State-of-the-art speed</Li>
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<Li>Robust, rigorously evaluated accuracy</Li>
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</Ul>
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</div>
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</LandingGrid>
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<LandingBannerGrid>
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<LandingBanner
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title="BERT-style language model pretraining and more"
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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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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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<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="#252a33"
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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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</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="Convolutional neural network models"
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label="New in v2.0"
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button="Download models"
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to="/models"
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>
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spaCy v2.0 features new neural models for <strong>tagging</strong>,{' '}
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<strong>parsing</strong> and <strong>entity recognition</strong>. The models have
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been designed and implemented from scratch specifically for spaCy, to give you an
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unmatched balance of speed, size and accuracy. A novel bloom embedding strategy with
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subword features is used to support huge vocabularies in tiny tables. Convolutional
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layers with residual connections, layer normalization and maxout non-linearity are
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used, giving much better efficiency than the standard BiLSTM solution. Finally, the
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parser and NER use an imitation learning objective to deliver accuracy in-line with
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the latest research systems, even when evaluated from raw text. With these
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innovations, spaCy v2.0's models are <strong>10× smaller</strong>,{' '}
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<strong>20% more accurate</strong>, and
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<strong>even cheaper to run</strong> than the previous generation.
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</LandingBanner>
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<LandingGrid cols={2}>
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<div>
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<H2>Benchmarks</H2>
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<p>
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In 2015, independent researchers from Emory University and Yahoo! Labs
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showed that spaCy offered the
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<strong>fastest syntactic parser in the world</strong> and that its accuracy
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was <strong>within 1% of the best</strong> available (
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<Link to="https://aclweb.org/anthology/P/P15/P15-1038.pdf">
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Choi et al., 2015
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</Link>
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). spaCy v2.0, released in 2017, is more accurate than any of the systems
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Choi et al. evaluated.
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</p>
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<p>
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<Button to="/usage/facts-figures#benchmarks" large>
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See details
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</Button>
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</p>
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</div>
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<div>
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<BenchmarksChoi />
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</div>
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</LandingGrid>
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</>
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)
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}
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Landing.propTypes = {
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data: PropTypes.shape({
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repo: PropTypes.string,
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languages: PropTypes.arrayOf(
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PropTypes.shape({
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models: PropTypes.arrayOf(PropTypes.string),
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})
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),
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logosUsers: PropTypes.arrayOf(
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PropTypes.shape({
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id: PropTypes.string.isRequired,
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url: PropTypes.string.isRequired,
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})
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),
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logosPublications: PropTypes.arrayOf(
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PropTypes.shape({
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id: PropTypes.string.isRequired,
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url: PropTypes.string.isRequired,
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})
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),
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}),
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}
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export default () => (
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<StaticQuery query={landingQuery} render={({ site }) => <Landing data={site.siteMetadata} />} />
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)
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const landingQuery = graphql`
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query LandingQuery {
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site {
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siteMetadata {
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repo
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languages {
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models
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}
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logosUsers {
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id
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url
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}
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logosPublications {
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id
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url
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}
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}
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}
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}
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`
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