mirror of
https://github.com/explosion/spaCy.git
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122 lines
4.1 KiB
JavaScript
122 lines
4.1 KiB
JavaScript
import React, { useState } from 'react'
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import { StaticQuery, graphql } from 'gatsby'
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import highlightCode from 'gatsby-remark-prismjs/highlight-code.js'
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import { Quickstart } from '../components/quickstart'
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import generator, { DATA as GENERATOR_DATA } from './quickstart-training-generator'
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import { htmlToReact } from '../components/util'
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const DEFAULT_LANG = 'en'
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const DEFAULT_HARDWARE = 'cpu'
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const DEFAULT_OPT = 'efficiency'
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const COMPONENTS = ['tagger', 'parser', 'ner', 'textcat']
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const COMMENT = `# This is an auto-generated partial config. To use it with 'spacy train'
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# you can run spacy init fill-config to auto-fill all default settings:
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# python -m spacy init fill-config ./base_config.cfg ./config.cfg`
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const DATA = [
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{
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id: 'lang',
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title: 'Language',
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defaultValue: DEFAULT_LANG,
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},
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{
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id: 'components',
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title: 'Components',
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help: 'Pipeline components to train. Requires training data for those annotations.',
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options: COMPONENTS.map(id => ({ id, title: id })),
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multiple: true,
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},
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{
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id: 'hardware',
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title: 'Hardware',
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options: [
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{ id: 'cpu', title: 'CPU', checked: DEFAULT_HARDWARE === 'cpu' },
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{ id: 'gpu', title: 'GPU (transformer)', checked: DEFAULT_HARDWARE === 'gpu' },
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],
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},
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{
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id: 'optimize',
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title: 'Optimize for',
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help:
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'Optimize for efficiency (faster inference, smaller model, lower memory consumption) or higher accuracy (potentially larger & slower model). Will impact the choice of architecture, pretrained weights and hyperparameters.',
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options: [
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{ id: 'efficiency', title: 'efficiency', checked: DEFAULT_OPT === 'efficiency' },
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{ id: 'accuracy', title: 'accuracy', checked: DEFAULT_OPT === 'accuracy' },
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],
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},
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]
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export default function QuickstartTraining({ id, title, download = 'base_config.cfg' }) {
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const [lang, setLang] = useState(DEFAULT_LANG)
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const [components, setComponents] = useState([])
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const [[hardware], setHardware] = useState([DEFAULT_HARDWARE])
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const [[optimize], setOptimize] = useState([DEFAULT_OPT])
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const setters = {
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lang: setLang,
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components: setComponents,
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hardware: setHardware,
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optimize: setOptimize,
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}
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const reco = GENERATOR_DATA[lang] || GENERATOR_DATA.__default__
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const content = generator({
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lang,
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components,
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optimize,
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hardware,
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transformer_data: reco.transformer,
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word_vectors: reco.word_vectors,
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has_letters: reco.has_letters,
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})
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const rawStr = content.trim().replace(/\n\n\n+/g, '\n\n')
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const rawContent = `${COMMENT}\n${rawStr}`
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const displayContent = highlightCode('ini', rawContent)
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.split('\n')
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.map(line => (line.startsWith('#') ? `<span class="token comment">${line}</span>` : line))
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.join('\n')
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return (
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<StaticQuery
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query={query}
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render={({ site }) => {
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const langs = site.siteMetadata.languages
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DATA[0].dropdown = langs
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.map(({ name, code }) => ({
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id: code,
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title: name,
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}))
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.sort((a, b) => a.title.localeCompare(b.title))
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return (
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<Quickstart
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id="quickstart-widget"
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Container="div"
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download={download}
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rawContent={rawContent}
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data={DATA}
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title={title}
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id={id}
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setters={setters}
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hidePrompts
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small
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codeLang="ini"
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>
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{htmlToReact(displayContent)}
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</Quickstart>
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)
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}}
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/>
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)
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}
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const query = graphql`
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query QuickstartTrainingQuery {
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site {
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siteMetadata {
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languages {
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code
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name
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}
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}
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}
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}
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`
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