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(factory) {\n typeof define === 'function' && define.amd ? define(factory) :\n factory();\n}((function () { 'use strict';\n\n // Copyright 2018 The Distill Template Authors\n //\n // Licensed under the Apache License, Version 2.0 (the \"License\");\n // you may not use this file except in compliance with the License.\n // You may obtain a copy of the License at\n //\n // http://www.apache.org/licenses/LICENSE-2.0\n //\n // Unless required by applicable law or agreed to in writing, software\n // distributed under the License is distributed on an \"AS IS\" BASIS,\n // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n // See the License for the specific language governing permissions and\n // limitations under the License.\n\n const days = ['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'];\n const months = ['Jan.', 'Feb.', 'March', 'April', 'May', 'June', 'July', 'Aug.', 'Sept.', 'Oct.', 'Nov.', 'Dec.'];\n const zeroPad = n => n < 10 ? '0' + n : n;\n\n const RFC = function(date) {\n const day = days[date.getDay()].substring(0, 3);\n const paddedDate = zeroPad(date.getDate());\n const month = months[date.getMonth()].substring(0,3);\n const year = date.getFullYear().toString();\n const hours = date.getUTCHours().toString();\n const minutes = date.getUTCMinutes().toString();\n const seconds = date.getUTCSeconds().toString();\n return `${day}, ${paddedDate} ${month} ${year} ${hours}:${minutes}:${seconds} Z`;\n };\n\n const objectFromMap = function(map) {\n const object = Array.from(map).reduce((object, [key, value]) => (\n Object.assign(object, { [key]: value }) // Be careful! Maps can have non-String keys; object literals can't.\n ), {});\n return object;\n };\n\n const mapFromObject = function(object) {\n const map = new Map();\n for (var property in object) {\n if (object.hasOwnProperty(property)) {\n map.set(property, object[property]);\n }\n }\n return map;\n };\n\n class Author {\n\n // constructor(name='', personalURL='', affiliation='', affiliationURL='') {\n // this.name = name; // 'Chris Olah'\n // this.personalURL = personalURL; // 'https://colah.github.io'\n // this.affiliation = affiliation; // 'Google Brain'\n // this.affiliationURL = affiliationURL; // 'https://g.co/brain'\n // }\n\n constructor(object) {\n this.name = object.author; // 'Chris Olah'\n this.personalURL = object.authorURL; // 'https://colah.github.io'\n this.affiliation = object.affiliation; // 'Google Brain'\n this.affiliationURL = object.affiliationURL; // 'https://g.co/brain'\n this.affiliations = object.affiliations || []; // new-style affiliations\n }\n\n // 'Chris'\n get firstName() {\n const names = this.name.split(' ');\n return names.slice(0, names.length - 1).join(' ');\n }\n\n // 'Olah'\n get lastName() {\n const names = this.name.split(' ');\n return names[names.length -1];\n }\n }\n\n function mergeFromYMLFrontmatter(target, source) {\n target.title = source.title;\n if (source.published) {\n if (source.published instanceof Date) {\n target.publishedDate = source.published;\n } else if (source.published.constructor === String) {\n target.publishedDate = new Date(source.published);\n }\n }\n if (source.publishedDate) {\n if (source.publishedDate instanceof Date) {\n target.publishedDate = source.publishedDate;\n } else if (source.publishedDate.constructor === String) {\n target.publishedDate = new Date(source.publishedDate);\n } else {\n console.error('Don\\'t know what to do with published date: ' + source.publishedDate);\n }\n }\n target.description = source.description;\n target.authors = source.authors.map( (authorObject) => new Author(authorObject));\n target.katex = source.katex;\n target.password = source.password;\n if (source.doi) {\n target.doi = source.doi;\n }\n }\n\n class FrontMatter {\n constructor() {\n this.title = 'unnamed article'; // 'Attention and Augmented Recurrent Neural Networks'\n this.description = ''; // 'A visual overview of neural attention...'\n this.authors = []; // Array of Author(s)\n\n this.bibliography = new Map();\n this.bibliographyParsed = false;\n // {\n // 'gregor2015draw': {\n // 'title': 'DRAW: A recurrent neural network for image generation',\n // 'author': 'Gregor, Karol and Danihelka, Ivo and Graves, Alex and Rezende, Danilo Jimenez and Wierstra, Daan',\n // 'journal': 'arXiv preprint arXiv:1502.04623',\n // 'year': '2015',\n // 'url': 'https://arxiv.org/pdf/1502.04623.pdf',\n // 'type': 'article'\n // },\n // }\n\n // Citation keys should be listed in the order that they are appear in the document.\n // Each key refers to a key in the bibliography dictionary.\n this.citations = []; // [ 'gregor2015draw', 'mercier2011humans' ]\n this.citationsCollected = false;\n\n //\n // Assigned from posts.csv\n //\n\n // publishedDate: 2016-09-08T07:00:00.000Z,\n // tags: [ 'rnn' ],\n // distillPath: '2016/augmented-rnns',\n // githubPath: 'distillpub/post--augmented-rnns',\n // doiSuffix: 1,\n\n //\n // Assigned from journal\n //\n this.journal = {};\n // journal: {\n // 'title': 'Distill',\n // 'full_title': 'Distill',\n // 'abbrev_title': 'Distill',\n // 'url': 'http://distill.pub',\n // 'doi': '10.23915/distill',\n // 'publisherName': 'Distill Working Group',\n // 'publisherEmail': 'admin@distill.pub',\n // 'issn': '2476-0757',\n // 'editors': [...],\n // 'committee': [...]\n // }\n // volume: 1,\n // issue: 9,\n\n this.katex = {};\n\n //\n // Assigned from publishing process\n //\n\n // githubCompareUpdatesUrl: 'https://github.com/distillpub/post--augmented-rnns/compare/1596e094d8943d2dc0ea445d92071129c6419c59...3bd9209e0c24d020f87cf6152dcecc6017cbc193',\n // updatedDate: 2017-03-21T07:13:16.000Z,\n // doi: '10.23915/distill.00001',\n this.doi = undefined;\n this.publishedDate = undefined;\n }\n\n // Example:\n // title: Demo Title Attention and Augmented Recurrent Neural Networks\n // published: Jan 10, 2017\n // authors:\n // - Chris Olah:\n // - Shan Carter: http://shancarter.com\n // affiliations:\n // - Google Brain:\n // - Google Brain: http://g.co/brain\n\n //\n // Computed Properties\n //\n\n // 'http://distill.pub/2016/augmented-rnns',\n set url(value) {\n this._url = value;\n }\n get url() {\n if (this._url) {\n return this._url;\n } else if (this.distillPath && this.journal.url) {\n return this.journal.url + '/' + this.distillPath;\n } else if (this.journal.url) {\n return this.journal.url;\n }\n }\n\n // 'https://github.com/distillpub/post--augmented-rnns',\n get githubUrl() {\n if (this.githubPath) {\n return 'https://github.com/' + this.githubPath;\n } else {\n return undefined;\n }\n }\n\n // TODO resolve differences in naming of URL/Url/url.\n // 'http://distill.pub/2016/augmented-rnns/thumbnail.jpg',\n set previewURL(value) {\n this._previewURL = value;\n }\n get previewURL() {\n return this._previewURL ? this._previewURL : this.url + '/thumbnail.jpg';\n }\n\n // 'Thu, 08 Sep 2016 00:00:00 -0700',\n get publishedDateRFC() {\n return RFC(this.publishedDate);\n }\n\n // 'Thu, 08 Sep 2016 00:00:00 -0700',\n get updatedDateRFC() {\n return RFC(this.updatedDate);\n }\n\n // 2016,\n get publishedYear() {\n return this.publishedDate.getFullYear();\n }\n\n // 'Sept',\n get publishedMonth() {\n return months[this.publishedDate.getMonth()];\n }\n\n // 8,\n get publishedDay() {\n return this.publishedDate.getDate();\n }\n\n // '09',\n get publishedMonthPadded() {\n return zeroPad(this.publishedDate.getMonth() + 1);\n }\n\n // '08',\n get publishedDayPadded() {\n return zeroPad(this.publishedDate.getDate());\n }\n\n get publishedISODateOnly() {\n return this.publishedDate.toISOString().split('T')[0];\n }\n\n get volume() {\n const volume = this.publishedYear - 2015;\n if (volume < 1) {\n throw new Error('Invalid publish date detected during computing volume');\n }\n return volume;\n }\n\n get issue() {\n return this.publishedDate.getMonth() + 1;\n }\n\n // 'Olah & Carter',\n get concatenatedAuthors() {\n if (this.authors.length > 2) {\n return this.authors[0].lastName + ', et al.';\n } else if (this.authors.length === 2) {\n return this.authors[0].lastName + ' & ' + this.authors[1].lastName;\n } else if (this.authors.length === 1) {\n return this.authors[0].lastName;\n }\n }\n\n // 'Olah, Chris and Carter, Shan',\n get bibtexAuthors() {\n return this.authors.map(author => {\n return author.lastName + ', ' + author.firstName;\n }).join(' and ');\n }\n\n // 'olah2016attention'\n get slug() {\n let slug = '';\n if (this.authors.length) {\n slug += this.authors[0].lastName.toLowerCase();\n slug += this.publishedYear;\n slug += this.title.split(' ')[0].toLowerCase();\n }\n return slug || 'Untitled';\n }\n\n get bibliographyEntries() {\n return new Map(this.citations.map( citationKey => {\n const entry = this.bibliography.get(citationKey);\n return [citationKey, entry];\n }));\n }\n\n set bibliography(bibliography) {\n if (bibliography instanceof Map) {\n this._bibliography = bibliography;\n } else if (typeof bibliography === 'object') {\n this._bibliography = mapFromObject(bibliography);\n }\n }\n\n get bibliography() {\n return this._bibliography;\n }\n\n static fromObject(source) {\n const frontMatter = new FrontMatter();\n Object.assign(frontMatter, source);\n return frontMatter;\n }\n\n assignToObject(target) {\n Object.assign(target, this);\n target.bibliography = objectFromMap(this.bibliographyEntries);\n target.url = this.url;\n target.doi = this.doi;\n target.githubUrl = this.githubUrl;\n target.previewURL = this.previewURL;\n if (this.publishedDate) {\n target.volume = this.volume;\n target.issue = this.issue;\n target.publishedDateRFC = this.publishedDateRFC;\n target.publishedYear = this.publishedYear;\n target.publishedMonth = this.publishedMonth;\n target.publishedDay = this.publishedDay;\n target.publishedMonthPadded = this.publishedMonthPadded;\n target.publishedDayPadded = this.publishedDayPadded;\n }\n if (this.updatedDate) {\n target.updatedDateRFC = this.updatedDateRFC;\n }\n target.concatenatedAuthors = this.concatenatedAuthors;\n target.bibtexAuthors = this.bibtexAuthors;\n target.slug = this.slug;\n }\n\n }\n\n // Copyright 2018 The Distill Template Authors\n //\n // Licensed under the Apache License, Version 2.0 (the \"License\");\n // you may not use this file except in compliance with the License.\n // You may obtain a copy of the License at\n //\n // http://www.apache.org/licenses/LICENSE-2.0\n //\n // Unless required by applicable law or agreed to in writing, software\n // distributed under the License is distributed on an \"AS IS\" BASIS,\n // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n // See the License for the specific language governing permissions and\n // limitations under the License.\n\n const Mutating = (superclass) => {\n return class extends superclass {\n\n constructor() {\n super();\n\n // set up mutation observer\n const options = {childList: true, characterData: true, subtree: true};\n const observer = new MutationObserver( () => {\n observer.disconnect();\n this.renderIfPossible();\n observer.observe(this, options);\n });\n\n // ...and listen for changes\n observer.observe(this, options);\n }\n\n connectedCallback() {\n super.connectedCallback();\n\n this.renderIfPossible();\n }\n\n // potential TODO: check if this is enough for all our usecases\n // maybe provide a custom function to tell if we have enough information to render\n renderIfPossible() {\n if (this.textContent && this.root) {\n this.renderContent();\n }\n }\n\n renderContent() {\n console.error(`Your class ${this.constructor.name} must provide a custom renderContent() method!` );\n }\n\n }; // end class\n }; // end mixin function\n\n // Copyright 2018 The Distill Template Authors\n //\n // Licensed under the Apache License, Version 2.0 (the \"License\");\n // you may not use this file except in compliance with the License.\n // You may obtain a copy of the License at\n //\n // http://www.apache.org/licenses/LICENSE-2.0\n //\n // Unless required by applicable law or agreed to in writing, software\n // distributed under the License is distributed on an \"AS IS\" BASIS,\n // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n // See the License for the specific language governing permissions and\n // limitations under the License.\n\n /*global ShadyCSS*/\n\n const Template = (name, templateString, useShadow = true) => {\n\n return (superclass) => {\n\n const template = document.createElement('template');\n template.innerHTML = templateString;\n\n if (useShadow && 'ShadyCSS' in window) {\n ShadyCSS.prepareTemplate(template, name);\n }\n\n return class extends superclass {\n\n static get is() { return name; }\n\n constructor() {\n super();\n\n this.clone = document.importNode(template.content, true);\n if (useShadow) {\n this.attachShadow({mode: 'open'});\n this.shadowRoot.appendChild(this.clone);\n }\n }\n\n connectedCallback() {\n if (this.hasAttribute('distill-prerendered')) {\n return;\n }\n if (useShadow) {\n if ('ShadyCSS' in window) {\n ShadyCSS.styleElement(this);\n }\n } else {\n this.insertBefore(this.clone, this.firstChild);\n }\n }\n\n get root() {\n if (useShadow) {\n return this.shadowRoot;\n } else {\n return this;\n }\n }\n\n /* TODO: Are we using these? Should we even? */\n $(query) {\n return this.root.querySelector(query);\n }\n\n $$(query) {\n return this.root.querySelectorAll(query);\n }\n };\n };\n };\n\n var math = \"/*\\n * Copyright 2018 The Distill Template Authors\\n *\\n * Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n * you may not use this file except in compliance with the License.\\n * You may obtain a copy of the License at\\n *\\n * http://www.apache.org/licenses/LICENSE-2.0\\n *\\n * Unless required by applicable law or agreed to in writing, software\\n * distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n * See the License for the specific language governing permissions and\\n * limitations under the License.\\n */\\n\\nspan.katex-display {\\n text-align: left;\\n padding: 8px 0 8px 0;\\n margin: 0.5em 0 0.5em 1em;\\n}\\n\\nspan.katex {\\n -webkit-font-smoothing: antialiased;\\n color: rgba(0, 0, 0, 0.8);\\n font-size: 1.18em;\\n}\\n\";\n\n // Copyright 2018 The Distill Template Authors\n //\n // Licensed under the Apache License, Version 2.0 (the \"License\");\n // you may not use this file except in compliance with the License.\n // You may obtain a copy of the License at\n //\n // http://www.apache.org/licenses/LICENSE-2.0\n //\n // Unless required by applicable law or agreed to in writing, software\n // distributed under the License is distributed on an \"AS IS\" BASIS,\n // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n // See the License for the specific language governing permissions and\n // limitations under the License.\n\n // This is a straight concatenation of code from KaTeX's contrib folder,\n // but we aren't using some of their helpers that don't work well outside a browser environment.\n\n /*global katex */\n\n const findEndOfMath = function(delimiter, text, startIndex) {\n // Adapted from\n // https://github.com/Khan/perseus/blob/master/src/perseus-markdown.jsx\n let index = startIndex;\n let braceLevel = 0;\n\n const delimLength = delimiter.length;\n\n while (index < text.length) {\n const character = text[index];\n\n if (\n braceLevel <= 0 &&\n text.slice(index, index + delimLength) === delimiter\n ) {\n return index;\n } else if (character === \"\\\\\") {\n index++;\n } else if (character === \"{\") {\n braceLevel++;\n } else if (character === \"}\") {\n braceLevel--;\n }\n\n index++;\n }\n\n return -1;\n };\n\n const splitAtDelimiters = function(startData, leftDelim, rightDelim, display) {\n const finalData = [];\n\n for (let i = 0; i < startData.length; i++) {\n if (startData[i].type === \"text\") {\n const text = startData[i].data;\n\n let lookingForLeft = true;\n let currIndex = 0;\n let nextIndex;\n\n nextIndex = text.indexOf(leftDelim);\n if (nextIndex !== -1) {\n currIndex = nextIndex;\n finalData.push({\n type: \"text\",\n data: text.slice(0, currIndex)\n });\n lookingForLeft = false;\n }\n\n while (true) {\n // eslint-disable-line no-constant-condition\n if (lookingForLeft) {\n nextIndex = text.indexOf(leftDelim, currIndex);\n if (nextIndex === -1) {\n break;\n }\n\n finalData.push({\n type: \"text\",\n data: text.slice(currIndex, nextIndex)\n });\n\n currIndex = nextIndex;\n } else {\n nextIndex = findEndOfMath(\n rightDelim,\n text,\n currIndex + leftDelim.length\n );\n if (nextIndex === -1) {\n break;\n }\n\n finalData.push({\n type: \"math\",\n data: text.slice(currIndex + leftDelim.length, nextIndex),\n rawData: text.slice(currIndex, nextIndex + rightDelim.length),\n display: display\n });\n\n currIndex = nextIndex + rightDelim.length;\n }\n\n lookingForLeft = !lookingForLeft;\n }\n\n finalData.push({\n type: \"text\",\n data: text.slice(currIndex)\n });\n } else {\n finalData.push(startData[i]);\n }\n }\n\n return finalData;\n };\n\n const splitWithDelimiters = function(text, delimiters) {\n let data = [{ type: \"text\", data: text }];\n for (let i = 0; i < delimiters.length; i++) {\n const delimiter = delimiters[i];\n data = splitAtDelimiters(\n data,\n delimiter.left,\n delimiter.right,\n delimiter.display || false\n );\n }\n return data;\n };\n\n /* Note: optionsCopy is mutated by this method. If it is ever exposed in the\n * API, we should copy it before mutating.\n */\n const renderMathInText = function(text, optionsCopy) {\n const data = splitWithDelimiters(text, optionsCopy.delimiters);\n const fragment = document.createDocumentFragment();\n\n for (let i = 0; i < data.length; i++) {\n if (data[i].type === \"text\") {\n fragment.appendChild(document.createTextNode(data[i].data));\n } else {\n const tag = document.createElement(\"d-math\");\n const math = data[i].data;\n // Override any display mode defined in the settings with that\n // defined by the text itself\n optionsCopy.displayMode = data[i].display;\n try {\n tag.textContent = math;\n if (optionsCopy.displayMode) {\n tag.setAttribute(\"block\", \"\");\n }\n } catch (e) {\n if (!(e instanceof katex.ParseError)) {\n throw e;\n }\n optionsCopy.errorCallback(\n \"KaTeX auto-render: Failed to parse `\" + data[i].data + \"` with \",\n e\n );\n fragment.appendChild(document.createTextNode(data[i].rawData));\n continue;\n }\n fragment.appendChild(tag);\n }\n }\n\n return fragment;\n };\n\n const renderElem = function(elem, optionsCopy) {\n for (let i = 0; i < elem.childNodes.length; i++) {\n const childNode = elem.childNodes[i];\n if (childNode.nodeType === 3) {\n // Text node\n const text = childNode.textContent;\n if (optionsCopy.mightHaveMath(text)) {\n const frag = renderMathInText(text, optionsCopy);\n i += frag.childNodes.length - 1;\n elem.replaceChild(frag, childNode);\n }\n } else if (childNode.nodeType === 1) {\n // Element node\n const shouldRender =\n optionsCopy.ignoredTags.indexOf(childNode.nodeName.toLowerCase()) ===\n -1;\n\n if (shouldRender) {\n renderElem(childNode, optionsCopy);\n }\n }\n // Otherwise, it's something else, and ignore it.\n }\n };\n\n const defaultAutoRenderOptions = {\n delimiters: [\n { left: \"$$\", right: \"$$\", display: true },\n { left: \"\\\\[\", right: \"\\\\]\", display: true },\n { left: \"\\\\(\", right: \"\\\\)\", display: false }\n // LaTeX uses this, but it ruins the display of normal `$` in text:\n // {left: '$', right: '$', display: false},\n ],\n\n ignoredTags: [\n \"script\",\n \"noscript\",\n \"style\",\n \"textarea\",\n \"pre\",\n \"code\",\n \"svg\"\n ],\n\n errorCallback: function(msg, err) {\n console.error(msg, err);\n }\n };\n\n const renderMathInElement = function(elem, options) {\n if (!elem) {\n throw new Error(\"No element provided to render\");\n }\n\n const optionsCopy = Object.assign({}, defaultAutoRenderOptions, options);\n const delimiterStrings = optionsCopy.delimiters.flatMap(d => [\n d.left,\n d.right\n ]);\n const mightHaveMath = text =>\n delimiterStrings.some(d => text.indexOf(d) !== -1);\n optionsCopy.mightHaveMath = mightHaveMath;\n renderElem(elem, optionsCopy);\n };\n\n // Copyright 2018 The Distill Template Authors\n\n const katexJSURL = 'https://distill.pub/third-party/katex/katex.min.js';\n const katexCSSTag = '[${label || \"link\"}] `;\n } /* else if (\"doi\" in ent){\n return ` [DOI] `;\n }*/ else {\n return \"\";\n }\n }\n function doi_string(ent, new_line) {\n if (\"doi\" in ent) {\n return `${new_line ? \"DOI: ${ent.doi} `;\n } else {\n return \"\";\n }\n }\n\n function title_string(ent) {\n return '' + ent.title + \" \";\n }\n\n function bibliography_cite(ent, fancy) {\n if (ent) {\n var cite = title_string(ent);\n cite += link_string(ent) + \"\" + ent.title + \" . \";\n cite += venue_string(ent);\n cite += doi_string(ent);\n cite += link_string(ent);\n return cite*/\n } else {\n return \"?\";\n }\n }\n\n function hover_cite(ent) {\n if (ent) {\n var cite = \"\";\n cite += \"\" + ent.title + \" \";\n cite += link_string(ent);\n cite += \" or tag. We found the following text: ' + text);\n const wrapper = document.createElement('span');\n wrapper.innerHTML = addedNode.nodeValue;\n addedNode.parentNode.insertBefore(wrapper, addedNode);\n addedNode.parentNode.removeChild(addedNode);\n }\n } break;\n }\n }\n }\n }).observe(this, {childList: true});\n }\n\n }\n\n var commonjsGlobal = typeof globalThis !== 'undefined' ? globalThis : typeof window !== 'undefined' ? window : typeof global !== 'undefined' ? global : typeof self !== 'undefined' ? self : {};\n\n function createCommonjsModule(fn, module) {\n \treturn module = { exports: {} }, fn(module, module.exports), module.exports;\n }\n\n var bibtexParse = createCommonjsModule(function (module, exports) {\n /* start bibtexParse 0.0.22 */\n\n //Original work by Henrik Muehe (c) 2010\n //\n //CommonJS port by Mikola Lysenko 2013\n //\n //Port to Browser lib by ORCID / RCPETERS\n //\n //Issues:\n //no comment handling within strings\n //no string concatenation\n //no variable values yet\n //Grammar implemented here:\n //bibtex -> (string | preamble | comment | entry)*;\n //string -> '@STRING' '{' key_equals_value '}';\n //preamble -> '@PREAMBLE' '{' value '}';\n //comment -> '@COMMENT' '{' value '}';\n //entry -> '@' key '{' key ',' key_value_list '}';\n //key_value_list -> key_equals_value (',' key_equals_value)*;\n //key_equals_value -> key '=' value;\n //value -> value_quotes | value_braces | key;\n //value_quotes -> '\"' .*? '\"'; // not quite\n //value_braces -> '{' .*? '\"'; // not quite\n (function(exports) {\n\n function BibtexParser() {\n\n this.months = [\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"];\n this.notKey = [',','{','}',' ','='];\n this.pos = 0;\n this.input = \"\";\n this.entries = new Array();\n\n this.currentEntry = \"\";\n\n this.setInput = function(t) {\n this.input = t;\n };\n\n this.getEntries = function() {\n return this.entries;\n };\n\n this.isWhitespace = function(s) {\n return (s == ' ' || s == '\\r' || s == '\\t' || s == '\\n');\n };\n\n this.match = function(s, canCommentOut) {\n if (canCommentOut == undefined || canCommentOut == null)\n canCommentOut = true;\n this.skipWhitespace(canCommentOut);\n if (this.input.substring(this.pos, this.pos + s.length) == s) {\n this.pos += s.length;\n } else {\n throw \"Token mismatch, expected \" + s + \", found \"\n + this.input.substring(this.pos);\n } this.skipWhitespace(canCommentOut);\n };\n\n this.tryMatch = function(s, canCommentOut) {\n if (canCommentOut == undefined || canCommentOut == null)\n canCommentOut = true;\n this.skipWhitespace(canCommentOut);\n if (this.input.substring(this.pos, this.pos + s.length) == s) {\n return true;\n } else {\n return false;\n } };\n\n /* when search for a match all text can be ignored, not just white space */\n this.matchAt = function() {\n while (this.input.length > this.pos && this.input[this.pos] != '@') {\n this.pos++;\n }\n if (this.input[this.pos] == '@') {\n return true;\n } return false;\n };\n\n this.skipWhitespace = function(canCommentOut) {\n while (this.isWhitespace(this.input[this.pos])) {\n this.pos++;\n } if (this.input[this.pos] == \"%\" && canCommentOut == true) {\n while (this.input[this.pos] != \"\\n\") {\n this.pos++;\n } this.skipWhitespace(canCommentOut);\n } };\n\n this.value_braces = function() {\n var bracecount = 0;\n this.match(\"{\", false);\n var start = this.pos;\n var escaped = false;\n while (true) {\n if (!escaped) {\n if (this.input[this.pos] == '}') {\n if (bracecount > 0) {\n bracecount--;\n } else {\n var end = this.pos;\n this.match(\"}\", false);\n return this.input.substring(start, end);\n } } else if (this.input[this.pos] == '{') {\n bracecount++;\n } else if (this.pos >= this.input.length - 1) {\n throw \"Unterminated value\";\n } } if (this.input[this.pos] == '\\\\' && escaped == false)\n escaped = true;\n else\n escaped = false;\n this.pos++;\n } };\n\n this.value_comment = function() {\n var str = '';\n var brcktCnt = 0;\n while (!(this.tryMatch(\"}\", false) && brcktCnt == 0)) {\n str = str + this.input[this.pos];\n if (this.input[this.pos] == '{')\n brcktCnt++;\n if (this.input[this.pos] == '}')\n brcktCnt--;\n if (this.pos >= this.input.length - 1) {\n throw \"Unterminated value:\" + this.input.substring(start);\n } this.pos++;\n } return str;\n };\n\n this.value_quotes = function() {\n this.match('\"', false);\n var start = this.pos;\n var escaped = false;\n while (true) {\n if (!escaped) {\n if (this.input[this.pos] == '\"') {\n var end = this.pos;\n this.match('\"', false);\n return this.input.substring(start, end);\n } else if (this.pos >= this.input.length - 1) {\n throw \"Unterminated value:\" + this.input.substring(start);\n } }\n if (this.input[this.pos] == '\\\\' && escaped == false)\n escaped = true;\n else\n escaped = false;\n this.pos++;\n } };\n\n this.single_value = function() {\n var start = this.pos;\n if (this.tryMatch(\"{\")) {\n return this.value_braces();\n } else if (this.tryMatch('\"')) {\n return this.value_quotes();\n } else {\n var k = this.key();\n if (k.match(\"^[0-9]+$\"))\n return k;\n else if (this.months.indexOf(k.toLowerCase()) >= 0)\n return k.toLowerCase();\n else\n throw \"Value expected:\" + this.input.substring(start) + ' for key: ' + k;\n\n } };\n\n this.value = function() {\n var values = [];\n values.push(this.single_value());\n while (this.tryMatch(\"#\")) {\n this.match(\"#\");\n values.push(this.single_value());\n } return values.join(\"\");\n };\n\n this.key = function() {\n var start = this.pos;\n while (true) {\n if (this.pos >= this.input.length) {\n throw \"Runaway key\";\n } // а-яА-Я is Cyrillic\n //console.log(this.input[this.pos]);\n if (this.notKey.indexOf(this.input[this.pos]) >= 0) {\n return this.input.substring(start, this.pos);\n } else {\n this.pos++;\n\n } } };\n\n this.key_equals_value = function() {\n var key = this.key();\n if (this.tryMatch(\"=\")) {\n this.match(\"=\");\n var val = this.value();\n return [ key, val ];\n } else {\n throw \"... = value expected, equals sign missing:\"\n + this.input.substring(this.pos);\n } };\n\n this.key_value_list = function() {\n var kv = this.key_equals_value();\n this.currentEntry['entryTags'] = {};\n this.currentEntry['entryTags'][kv[0]] = kv[1];\n while (this.tryMatch(\",\")) {\n this.match(\",\");\n // fixes problems with commas at the end of a list\n if (this.tryMatch(\"}\")) {\n break;\n }\n kv = this.key_equals_value();\n this.currentEntry['entryTags'][kv[0]] = kv[1];\n } };\n\n this.entry_body = function(d) {\n this.currentEntry = {};\n this.currentEntry['citationKey'] = this.key();\n this.currentEntry['entryType'] = d.substring(1);\n this.match(\",\");\n this.key_value_list();\n this.entries.push(this.currentEntry);\n };\n\n this.directive = function() {\n this.match(\"@\");\n return \"@\" + this.key();\n };\n\n this.preamble = function() {\n this.currentEntry = {};\n this.currentEntry['entryType'] = 'PREAMBLE';\n this.currentEntry['entry'] = this.value_comment();\n this.entries.push(this.currentEntry);\n };\n\n this.comment = function() {\n this.currentEntry = {};\n this.currentEntry['entryType'] = 'COMMENT';\n this.currentEntry['entry'] = this.value_comment();\n this.entries.push(this.currentEntry);\n };\n\n this.entry = function(d) {\n this.entry_body(d);\n };\n\n this.bibtex = function() {\n while (this.matchAt()) {\n var d = this.directive();\n this.match(\"{\");\n if (d == \"@STRING\") {\n this.string();\n } else if (d == \"@PREAMBLE\") {\n this.preamble();\n } else if (d == \"@COMMENT\") {\n this.comment();\n } else {\n this.entry(d);\n }\n this.match(\"}\");\n } };\n }\n exports.toJSON = function(bibtex) {\n var b = new BibtexParser();\n b.setInput(bibtex);\n b.bibtex();\n return b.entries;\n };\n\n /* added during hackathon don't hate on me */\n exports.toBibtex = function(json) {\n var out = '';\n for ( var i in json) {\n out += \"@\" + json[i].entryType;\n out += '{';\n if (json[i].citationKey)\n out += json[i].citationKey + ', ';\n if (json[i].entry)\n out += json[i].entry ;\n if (json[i].entryTags) {\n var tags = '';\n for (var jdx in json[i].entryTags) {\n if (tags.length != 0)\n tags += ', ';\n tags += jdx + '= {' + json[i].entryTags[jdx] + '}';\n }\n out += tags;\n }\n out += '}\\n\\n';\n }\n return out;\n\n };\n\n })( exports);\n\n /* end bibtexParse */\n });\n\n // Copyright 2018 The Distill Template Authors\n\n function normalizeTag(string) {\n return string\n .replace(/[\\t\\n ]+/g, ' ')\n .replace(/{\\\\[\"^`.'acu~Hvs]( )?([a-zA-Z])}/g, (full, x, char) => char)\n .replace(/{\\\\([a-zA-Z])}/g, (full, char) => char);\n }\n\n function parseBibtex(bibtex) {\n const bibliography = new Map();\n const parsedEntries = bibtexParse.toJSON(bibtex);\n for (const entry of parsedEntries) {\n // normalize tags; note entryTags is an object, not Map\n for (const [key, value] of Object.entries(entry.entryTags)) {\n entry.entryTags[key.toLowerCase()] = normalizeTag(value);\n }\n entry.entryTags.type = entry.entryType;\n // add to bibliography\n bibliography.set(entry.citationKey, entry.entryTags);\n }\n return bibliography;\n }\n\n function serializeFrontmatterToBibtex(frontMatter) {\n return `@article{${frontMatter.slug},\n author = {${frontMatter.bibtexAuthors}},\n title = {${frontMatter.title}},\n journal = {${frontMatter.journal.title}},\n year = {${frontMatter.publishedYear}},\n note = {${frontMatter.url}},\n doi = {${frontMatter.doi}}\n}`;\n }\n\n // Copyright 2018 The Distill Template Authors\n\n class Bibliography extends HTMLElement {\n\n static get is() { return 'd-bibliography'; }\n\n constructor() {\n super();\n\n // set up mutation observer\n const options = {childList: true, characterData: true, subtree: true};\n const observer = new MutationObserver( (entries) => {\n for (const entry of entries) {\n if (entry.target.nodeName === 'SCRIPT' || entry.type === 'characterData') {\n this.parseIfPossible();\n }\n }\n });\n observer.observe(this, options);\n }\n\n connectedCallback() {\n requestAnimationFrame(() => {\n this.parseIfPossible();\n });\n }\n\n parseIfPossible() {\n const scriptTag = this.querySelector('script');\n if (!scriptTag) return;\n if (scriptTag.type == 'text/bibtex') {\n const newBibtex = scriptTag.textContent;\n if (this.bibtex !== newBibtex) {\n this.bibtex = newBibtex;\n const bibliography = parseBibtex(this.bibtex);\n this.notify(bibliography);\n }\n } else if (scriptTag.type == 'text/json') {\n const bibliography = new Map(JSON.parse(scriptTag.textContent));\n this.notify(bibliography);\n } else {\n console.warn('Unsupported bibliography script tag type: ' + scriptTag.type);\n }\n }\n\n notify(bibliography) {\n const options = { detail: bibliography, bubbles: true };\n const event = new CustomEvent('onBibliographyChanged', options);\n this.dispatchEvent(event);\n }\n\n /* observe 'src' attribute */\n\n static get observedAttributes() {\n return ['src'];\n }\n\n receivedBibtex(event) {\n const bibliography = parseBibtex(event.target.response);\n this.notify(bibliography);\n }\n\n attributeChangedCallback(name, oldValue, newValue) {\n var oReq = new XMLHttpRequest();\n oReq.onload = (e) => this.receivedBibtex(e);\n oReq.onerror = () => console.warn(`Could not load Bibtex! (tried ${newValue})`);\n oReq.responseType = 'text';\n oReq.open('GET', newValue, true);\n oReq.send();\n }\n\n\n }\n\n // Copyright 2018 The Distill Template Authors\n //\n // Licensed under the Apache License, Version 2.0 (the \"License\");\n // you may not use this file except in compliance with the License.\n // You may obtain a copy of the License at\n //\n // http://www.apache.org/licenses/LICENSE-2.0\n //\n // Unless required by applicable law or agreed to in writing, software\n // distributed under the License is distributed on an \"AS IS\" BASIS,\n // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n // See the License for the specific language governing permissions and\n // limitations under the License.\n\n // import style from '../styles/d-byline.css';\n\n function bylineTemplate(frontMatter) {\n return `\n
\n
\n
\n
\n
Published \n
May 31, 2024
\n
\n
\n
\n`\n );\n\n class Cite extends T$3(HTMLElement) {\n /* Lifecycle */\n constructor() {\n super();\n this._numbers = [];\n this._entries = [];\n }\n\n connectedCallback() {\n this.outerSpan = this.root.querySelector(\"#citation-\");\n this.innerSpan = this.root.querySelector(\".citation-number\");\n this.hoverBox = this.root.querySelector(\"d-hover-box\");\n window.customElements.whenDefined(\"d-hover-box\").then(() => {\n this.hoverBox.listen(this);\n });\n // in case this component got connected after values were set\n if (this.numbers) {\n this.displayNumbers(this.numbers);\n }\n if (this.entries) {\n this.displayEntries(this.entries);\n }\n }\n\n //TODO This causes an infinite loop on firefox with polyfills.\n // This is only needed for interactive editing so no priority.\n // disconnectedCallback() {\n // const options = { detail: [this, this.keys], bubbles: true };\n // const event = new CustomEvent('onCiteKeyRemoved', options);\n // document.dispatchEvent(event);\n // }\n\n /* observe 'key' attribute */\n\n static get observedAttributes() {\n return [\"key\", \"bibtex-key\"];\n }\n\n attributeChangedCallback(name, oldValue, newValue) {\n const eventName = oldValue ? \"onCiteKeyChanged\" : \"onCiteKeyCreated\";\n const keys = newValue.split(\",\").map(k => k.trim());\n const options = { detail: [this, keys], bubbles: true };\n const event = new CustomEvent(eventName, options);\n document.dispatchEvent(event);\n }\n\n set key(value) {\n this.setAttribute(\"key\", value);\n }\n\n get key() {\n return this.getAttribute(\"key\") || this.getAttribute(\"bibtex-key\");\n }\n\n get keys() {\n const result = this.key.split(\",\");\n console.log(result);\n return result;\n }\n\n /* Setters & Rendering */\n\n set numbers(numbers) {\n this._numbers = numbers;\n this.displayNumbers(numbers);\n }\n\n get numbers() {\n return this._numbers;\n }\n\n displayNumbers(numbers) {\n if (!this.innerSpan) return;\n const numberStrings = numbers.map(index => {\n return index == -1 ? \"?\" : index + 1 + \"\";\n });\n const textContent = \"[\" + numberStrings.join(\", \") + \"]\";\n this.innerSpan.textContent = textContent;\n }\n\n set entries(entries) {\n this._entries = entries;\n this.displayEntries(entries);\n }\n\n get entries() {\n return this._entries;\n }\n\n displayEntries(entries) {\n if (!this.hoverBox) return;\n this.hoverBox.innerHTML = `\n ${entries\n .map(hover_cite)\n .map(html => `${html} `)\n .join(\"\\n\")}\n `;\n }\n }\n\n // Copyright 2018 The Distill Template Authors\n\n const styles$1 = `\nd-citation-list {\n contain: style;\n}\n\nd-citation-list .references {\n grid-column: text;\n}\n\nd-citation-list .references .title {\n font-weight: 500;\n}\n`;\n\n function renderCitationList(element, entries, dom=document) {\n if (entries.size > 0) {\n element.style.display = '';\n let list = element.querySelector('.references');\n if (list) {\n list.innerHTML = '';\n } else {\n const stylesTag = dom.createElement('style');\n stylesTag.innerHTML = styles$1;\n element.appendChild(stylesTag);\n\n const heading = dom.createElement('h3');\n heading.id = 'references';\n heading.textContent = 'References';\n element.appendChild(heading);\n\n list = dom.createElement('ol');\n list.id = 'references-list';\n list.className = 'references';\n element.appendChild(list);\n }\n\n for (const [key, entry] of entries) {\n const listItem = dom.createElement('li');\n listItem.id = key;\n listItem.innerHTML = bibliography_cite(entry);\n list.appendChild(listItem);\n }\n } else {\n element.style.display = 'none';\n }\n }\n\n class CitationList extends HTMLElement {\n\n static get is() { return 'd-citation-list'; }\n\n connectedCallback() {\n if (!this.hasAttribute('distill-prerendered')) {\n this.style.display = 'none';\n }\n }\n\n set citations(citations) {\n renderCitationList(this, citations);\n }\n\n }\n\n var prism = createCommonjsModule(function (module) {\n /* **********************************************\n Begin prism-core.js\n ********************************************** */\n\n var _self = (typeof window !== 'undefined')\n \t? window // if in browser\n \t: (\n \t\t(typeof WorkerGlobalScope !== 'undefined' && self instanceof WorkerGlobalScope)\n \t\t? self // if in worker\n \t\t: {} // if in node js\n \t);\n\n /**\n * Prism: Lightweight, robust, elegant syntax highlighting\n * MIT license http://www.opensource.org/licenses/mit-license.php/\n * @author Lea Verou http://lea.verou.me\n */\n\n var Prism = (function (_self){\n\n // Private helper vars\n var lang = /\\blang(?:uage)?-([\\w-]+)\\b/i;\n var uniqueId = 0;\n\n\n var _ = {\n \tmanual: _self.Prism && _self.Prism.manual,\n \tdisableWorkerMessageHandler: _self.Prism && _self.Prism.disableWorkerMessageHandler,\n \tutil: {\n \t\tencode: function encode(tokens) {\n \t\t\tif (tokens instanceof Token) {\n \t\t\t\treturn new Token(tokens.type, encode(tokens.content), tokens.alias);\n \t\t\t} else if (Array.isArray(tokens)) {\n \t\t\t\treturn tokens.map(encode);\n \t\t\t} else {\n \t\t\t\treturn tokens.replace(/&/g, '&').replace(/' + env.content + '';\n };\n\n /**\n * @param {string} text\n * @param {LinkedList} tokenList\n * @param {any} grammar\n * @param {LinkedListNode} startNode\n * @param {number} startPos\n * @param {boolean} [oneshot=false]\n * @param {string} [target]\n */\n function matchGrammar(text, tokenList, grammar, startNode, startPos, oneshot, target) {\n \tfor (var token in grammar) {\n \t\tif (!grammar.hasOwnProperty(token) || !grammar[token]) {\n \t\t\tcontinue;\n \t\t}\n\n \t\tvar patterns = grammar[token];\n \t\tpatterns = Array.isArray(patterns) ? patterns : [patterns];\n\n \t\tfor (var j = 0; j < patterns.length; ++j) {\n \t\t\tif (target && target == token + ',' + j) {\n \t\t\t\treturn;\n \t\t\t}\n\n \t\t\tvar pattern = patterns[j],\n \t\t\t\tinside = pattern.inside,\n \t\t\t\tlookbehind = !!pattern.lookbehind,\n \t\t\t\tgreedy = !!pattern.greedy,\n \t\t\t\tlookbehindLength = 0,\n \t\t\t\talias = pattern.alias;\n\n \t\t\tif (greedy && !pattern.pattern.global) {\n \t\t\t\t// Without the global flag, lastIndex won't work\n \t\t\t\tvar flags = pattern.pattern.toString().match(/[imsuy]*$/)[0];\n \t\t\t\tpattern.pattern = RegExp(pattern.pattern.source, flags + 'g');\n \t\t\t}\n\n \t\t\tpattern = pattern.pattern || pattern;\n\n \t\t\tfor ( // iterate the token list and keep track of the current token/string position\n \t\t\t\tvar currentNode = startNode.next, pos = startPos;\n \t\t\t\tcurrentNode !== tokenList.tail;\n \t\t\t\tpos += currentNode.value.length, currentNode = currentNode.next\n \t\t\t) {\n\n \t\t\t\tvar str = currentNode.value;\n\n \t\t\t\tif (tokenList.length > text.length) {\n \t\t\t\t\t// Something went terribly wrong, ABORT, ABORT!\n \t\t\t\t\treturn;\n \t\t\t\t}\n\n \t\t\t\tif (str instanceof Token) {\n \t\t\t\t\tcontinue;\n \t\t\t\t}\n\n \t\t\t\tvar removeCount = 1; // this is the to parameter of removeBetween\n\n \t\t\t\tif (greedy && currentNode != tokenList.tail.prev) {\n \t\t\t\t\tpattern.lastIndex = pos;\n \t\t\t\t\tvar match = pattern.exec(text);\n \t\t\t\t\tif (!match) {\n \t\t\t\t\t\tbreak;\n \t\t\t\t\t}\n\n \t\t\t\t\tvar from = match.index + (lookbehind && match[1] ? match[1].length : 0);\n \t\t\t\t\tvar to = match.index + match[0].length;\n \t\t\t\t\tvar p = pos;\n\n \t\t\t\t\t// find the node that contains the match\n \t\t\t\t\tp += currentNode.value.length;\n \t\t\t\t\twhile (from >= p) {\n \t\t\t\t\t\tcurrentNode = currentNode.next;\n \t\t\t\t\t\tp += currentNode.value.length;\n \t\t\t\t\t}\n \t\t\t\t\t// adjust pos (and p)\n \t\t\t\t\tp -= currentNode.value.length;\n \t\t\t\t\tpos = p;\n\n \t\t\t\t\t// the current node is a Token, then the match starts inside another Token, which is invalid\n \t\t\t\t\tif (currentNode.value instanceof Token) {\n \t\t\t\t\t\tcontinue;\n \t\t\t\t\t}\n\n \t\t\t\t\t// find the last node which is affected by this match\n \t\t\t\t\tfor (\n \t\t\t\t\t\tvar k = currentNode;\n \t\t\t\t\t\tk !== tokenList.tail && (p < to || (typeof k.value === 'string' && !k.prev.value.greedy));\n \t\t\t\t\t\tk = k.next\n \t\t\t\t\t) {\n \t\t\t\t\t\tremoveCount++;\n \t\t\t\t\t\tp += k.value.length;\n \t\t\t\t\t}\n \t\t\t\t\tremoveCount--;\n\n \t\t\t\t\t// replace with the new match\n \t\t\t\t\tstr = text.slice(pos, p);\n \t\t\t\t\tmatch.index -= pos;\n \t\t\t\t} else {\n \t\t\t\t\tpattern.lastIndex = 0;\n\n \t\t\t\t\tvar match = pattern.exec(str);\n \t\t\t\t}\n\n \t\t\t\tif (!match) {\n \t\t\t\t\tif (oneshot) {\n \t\t\t\t\t\tbreak;\n \t\t\t\t\t}\n\n \t\t\t\t\tcontinue;\n \t\t\t\t}\n\n \t\t\t\tif (lookbehind) {\n \t\t\t\t\tlookbehindLength = match[1] ? match[1].length : 0;\n \t\t\t\t}\n\n \t\t\t\tvar from = match.index + lookbehindLength,\n \t\t\t\t\tmatch = match[0].slice(lookbehindLength),\n \t\t\t\t\tto = from + match.length,\n \t\t\t\t\tbefore = str.slice(0, from),\n \t\t\t\t\tafter = str.slice(to);\n\n \t\t\t\tvar removeFrom = currentNode.prev;\n\n \t\t\t\tif (before) {\n \t\t\t\t\tremoveFrom = addAfter(tokenList, removeFrom, before);\n \t\t\t\t\tpos += before.length;\n \t\t\t\t}\n\n \t\t\t\tremoveRange(tokenList, removeFrom, removeCount);\n\n \t\t\t\tvar wrapped = new Token(token, inside ? _.tokenize(match, inside) : match, alias, match, greedy);\n \t\t\t\tcurrentNode = addAfter(tokenList, removeFrom, wrapped);\n\n \t\t\t\tif (after) {\n \t\t\t\t\taddAfter(tokenList, currentNode, after);\n \t\t\t\t}\n\n\n \t\t\t\tif (removeCount > 1)\n \t\t\t\t\tmatchGrammar(text, tokenList, grammar, currentNode.prev, pos, true, token + ',' + j);\n\n \t\t\t\tif (oneshot)\n \t\t\t\t\tbreak;\n \t\t\t}\n \t\t}\n \t}\n }\n\n /**\n * @typedef LinkedListNode\n * @property {T} value\n * @property {LinkedListNode | null} prev The previous node.\n * @property {LinkedListNode | null} next The next node.\n * @template T\n */\n\n /**\n * @template T\n */\n function LinkedList() {\n \t/** @type {LinkedListNode} */\n \tvar head = { value: null, prev: null, next: null };\n \t/** @type {LinkedListNode} */\n \tvar tail = { value: null, prev: head, next: null };\n \thead.next = tail;\n\n \t/** @type {LinkedListNode} */\n \tthis.head = head;\n \t/** @type {LinkedListNode} */\n \tthis.tail = tail;\n \tthis.length = 0;\n }\n\n /**\n * Adds a new node with the given value to the list.\n * @param {LinkedList} list\n * @param {LinkedListNode} node\n * @param {T} value\n * @returns {LinkedListNode} The added node.\n * @template T\n */\n function addAfter(list, node, value) {\n \t// assumes that node != list.tail && values.length >= 0\n \tvar next = node.next;\n\n \tvar newNode = { value: value, prev: node, next: next };\n \tnode.next = newNode;\n \tnext.prev = newNode;\n \tlist.length++;\n\n \treturn newNode;\n }\n /**\n * Removes `count` nodes after the given node. The given node will not be removed.\n * @param {LinkedList} list\n * @param {LinkedListNode} node\n * @param {number} count\n * @template T\n */\n function removeRange(list, node, count) {\n \tvar next = node.next;\n \tfor (var i = 0; i < count && next !== list.tail; i++) {\n \t\tnext = next.next;\n \t}\n \tnode.next = next;\n \tnext.prev = node;\n \tlist.length -= i;\n }\n /**\n * @param {LinkedList} list\n * @returns {T[]}\n * @template T\n */\n function toArray(list) {\n \tvar array = [];\n \tvar node = list.head.next;\n \twhile (node !== list.tail) {\n \t\tarray.push(node.value);\n \t\tnode = node.next;\n \t}\n \treturn array;\n }\n\n\n if (!_self.document) {\n \tif (!_self.addEventListener) {\n \t\t// in Node.js\n \t\treturn _;\n \t}\n\n \tif (!_.disableWorkerMessageHandler) {\n \t\t// In worker\n \t\t_self.addEventListener('message', function (evt) {\n \t\t\tvar message = JSON.parse(evt.data),\n \t\t\t\tlang = message.language,\n \t\t\t\tcode = message.code,\n \t\t\t\timmediateClose = message.immediateClose;\n\n \t\t\t_self.postMessage(_.highlight(code, _.languages[lang], lang));\n \t\t\tif (immediateClose) {\n \t\t\t\t_self.close();\n \t\t\t}\n \t\t}, false);\n \t}\n\n \treturn _;\n }\n\n //Get current script and highlight\n var script = _.util.currentScript();\n\n if (script) {\n \t_.filename = script.src;\n\n \tif (script.hasAttribute('data-manual')) {\n \t\t_.manual = true;\n \t}\n }\n\n function highlightAutomaticallyCallback() {\n \tif (!_.manual) {\n \t\t_.highlightAll();\n \t}\n }\n\n if (!_.manual) {\n \t// If the document state is \"loading\", then we'll use DOMContentLoaded.\n \t// If the document state is \"interactive\" and the prism.js script is deferred, then we'll also use the\n \t// DOMContentLoaded event because there might be some plugins or languages which have also been deferred and they\n \t// might take longer one animation frame to execute which can create a race condition where only some plugins have\n \t// been loaded when Prism.highlightAll() is executed, depending on how fast resources are loaded.\n \t// See https://github.com/PrismJS/prism/issues/2102\n \tvar readyState = document.readyState;\n \tif (readyState === 'loading' || readyState === 'interactive' && script && script.defer) {\n \t\tdocument.addEventListener('DOMContentLoaded', highlightAutomaticallyCallback);\n \t} else {\n \t\tif (window.requestAnimationFrame) {\n \t\t\twindow.requestAnimationFrame(highlightAutomaticallyCallback);\n \t\t} else {\n \t\t\twindow.setTimeout(highlightAutomaticallyCallback, 16);\n \t\t}\n \t}\n }\n\n return _;\n\n })(_self);\n\n if ( module.exports) {\n \tmodule.exports = Prism;\n }\n\n // hack for components to work correctly in node.js\n if (typeof commonjsGlobal !== 'undefined') {\n \tcommonjsGlobal.Prism = Prism;\n }\n\n\n /* **********************************************\n Begin prism-markup.js\n ********************************************** */\n\n Prism.languages.markup = {\n \t'comment': //,\n \t'prolog': /<\\?[\\s\\S]+?\\?>/,\n \t'doctype': {\n \t\tpattern: /\"'[\\]]|\"[^\"]*\"|'[^']*')+(?:\\[(?:(?!)*\\]\\s*)?>/i,\n \t\tgreedy: true\n \t},\n \t'cdata': //i,\n \t'tag': {\n \t\tpattern: /<\\/?(?!\\d)[^\\s>\\/=$<%]+(?:\\s(?:\\s*[^\\s>\\/=]+(?:\\s*=\\s*(?:\"[^\"]*\"|'[^']*'|[^\\s'\">=]+(?=[\\s>]))|(?=[\\s/>])))+)?\\s*\\/?>/i,\n \t\tgreedy: true,\n \t\tinside: {\n \t\t\t'tag': {\n \t\t\t\tpattern: /^<\\/?[^\\s>\\/]+/i,\n \t\t\t\tinside: {\n \t\t\t\t\t'punctuation': /^<\\/?/,\n \t\t\t\t\t'namespace': /^[^\\s>\\/:]+:/\n \t\t\t\t}\n \t\t\t},\n \t\t\t'attr-value': {\n \t\t\t\tpattern: /=\\s*(?:\"[^\"]*\"|'[^']*'|[^\\s'\">=]+)/i,\n \t\t\t\tinside: {\n \t\t\t\t\t'punctuation': [\n \t\t\t\t\t\t/^=/,\n \t\t\t\t\t\t{\n \t\t\t\t\t\t\tpattern: /^(\\s*)[\"']|[\"']$/,\n \t\t\t\t\t\t\tlookbehind: true\n \t\t\t\t\t\t}\n \t\t\t\t\t]\n \t\t\t\t}\n \t\t\t},\n \t\t\t'punctuation': /\\/?>/,\n \t\t\t'attr-name': {\n \t\t\t\tpattern: /[^\\s>\\/]+/,\n \t\t\t\tinside: {\n \t\t\t\t\t'namespace': /^[^\\s>\\/:]+:/\n \t\t\t\t}\n \t\t\t}\n\n \t\t}\n \t},\n \t'entity': /&#?[\\da-z]{1,8};/i\n };\n\n Prism.languages.markup['tag'].inside['attr-value'].inside['entity'] =\n \tPrism.languages.markup['entity'];\n\n // Plugin to make entity title show the real entity, idea by Roman Komarov\n Prism.hooks.add('wrap', function(env) {\n\n \tif (env.type === 'entity') {\n \t\tenv.attributes['title'] = env.content.replace(/&/, '&');\n \t}\n });\n\n Object.defineProperty(Prism.languages.markup.tag, 'addInlined', {\n \t/**\n \t * Adds an inlined language to markup.\n \t *\n \t * An example of an inlined language is CSS with `\n\n block to let us know how to highlight your code; e.g.:\\n zeros = np.zeros(shape) .');\n return;\n }\n const language = prism.languages[this.languageName];\n if (language == undefined) {\n console.warn(`Distill does not yet support highlighting your code block in \"${this.languageName}'.`);\n return;\n }\n\n let content = this.textContent;\n const codeTag = this.shadowRoot.querySelector('#code-container');\n\n if (this.hasAttribute('block')) {\n // normalize the tab indents\n content = content.replace(/\\n/, '');\n const tabs = content.match(/\\s*/);\n content = content.replace(new RegExp('\\n' + tabs, 'g'), '\\n');\n content = content.trim();\n // wrap code block in pre tag if needed\n if (codeTag.parentNode instanceof ShadowRoot) {\n const preTag = document.createElement('pre');\n this.shadowRoot.removeChild(codeTag);\n preTag.appendChild(codeTag);\n this.shadowRoot.appendChild(preTag);\n }\n\n }\n\n codeTag.className = `language-${this.languageName}`;\n codeTag.innerHTML = prism.highlight(content, language);\n }\n\n }\n\n // Copyright 2018 The Distill Template Authors\n\n const T$5 = Template('d-footnote', `\n\n\n\n \n \n\n\n \n\n`);\n\n class Footnote extends T$5(HTMLElement) {\n\n constructor() {\n super();\n\n const options = {childList: true, characterData: true, subtree: true};\n const observer = new MutationObserver(this.notify);\n observer.observe(this, options);\n }\n\n notify() {\n const options = { detail: this, bubbles: true };\n const event = new CustomEvent('onFootnoteChanged', options);\n document.dispatchEvent(event);\n }\n\n connectedCallback() {\n // listen and notify about changes to slotted content\n // const slot = this.shadowRoot.querySelector('#slot');\n // console.warn(slot.textContent);\n // slot.addEventListener('slotchange', this.notify);\n this.hoverBox = this.root.querySelector('d-hover-box');\n window.customElements.whenDefined('d-hover-box').then(() => {\n this.hoverBox.listen(this);\n });\n // create numeric ID\n Footnote.currentFootnoteId += 1;\n const IdString = Footnote.currentFootnoteId.toString();\n this.root.host.id = 'd-footnote-' + IdString;\n\n // set up hidden hover box\n const id = 'dt-fn-hover-box-' + IdString;\n this.hoverBox.id = id;\n\n // set up visible footnote marker\n const span = this.root.querySelector('#fn-');\n span.setAttribute('id', 'fn-' + IdString);\n span.setAttribute('data-hover-ref', id);\n span.textContent = IdString;\n }\n\n }\n\n Footnote.currentFootnoteId = 0;\n\n // Copyright 2018 The Distill Template Authors\n\n const T$6 = Template('d-footnote-list', `\n\n\nFootnotes \nTable of contents \n `;\n\n for (const el of headings) {\n // should element be included in TOC?\n const isInTitle = el.parentElement.tagName == 'D-TITLE';\n const isException = el.getAttribute('no-toc');\n if (isInTitle || isException) continue;\n // create TOC entry\n const title = el.textContent;\n const link = '#' + el.getAttribute('id');\n\n let newLine = '' + '' + title + ' ' + ' ';\n if (el.tagName == 'H3') {\n newLine = '';\n } else {\n newLine += ' ';\n element.innerHTML = ToC;\n }\n\n // Copyright 2018 The Distill Template Authors\n //\n // Licensed under the Apache License, Version 2.0 (the \"License\");\n // you may not use this file except in compliance with the License.\n // You may obtain a copy of the License at\n //\n // http://www.apache.org/licenses/LICENSE-2.0\n //\n // Unless required by applicable law or agreed to in writing, software\n // distributed under the License is distributed on an \"AS IS\" BASIS,\n // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n // See the License for the specific language governing permissions and\n // limitations under the License.\n\n // Figure\n //\n // d-figure provides a state-machine of visibility events:\n //\n // scroll out of view\n // +----------------+\n // *do work here* | |\n // +----------------+ +-+---------+ +-v---------+\n // | ready +----> onscreen | | offscreen |\n // +----------------+ +---------^-+ +---------+-+\n // | |\n // +----------------+\n // scroll into view\n //\n\n class Figure extends HTMLElement {\n\n static get is() { return 'd-figure'; 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+
+
+
+
+
+ FineWeb: decanting the web for the finest text data at scale
+
+
+
+
+ 🍷 FineWeb: decanting the web for the finest text data at scale
+
+
+
+
+
+
+
+
+
+
+
+ The performance of a large language model (LLM) depends heavily on the quality and size of its pretraining dataset.
+ However, the pretraining datasets for state-of-the-art open LLMs like Llama 3
+ Reading time: 45 min. For the best reading experience, we recommend not using a mobile phone.
+
+ Recently, we released 🍷 FineWeb 15-trillion tokens, 44TB disk space ) dataset for LLM pretraining. FineWeb is derived from 96 CommonCrawl snapshots and produces better-performing LLMs than other open pretraining datasets . To bring more clarity in machine learning and advance the open understanding of how to train good quality large language models, we decided to carefully document and ablate all of the design choices used in FineWeb, including in-depth investigations of deduplication and filtering strategies. The present long form report is a deep dive in how to create a large and high-quality web-scale dataset for LLM pretraining. The dataset it-self, 🍷 FineWeb, is available here .
+
+ We are extremely thankful to the whole distill.pub team (Christopher Olah, Shan Carter, Ludwig Schubert in particular) for creating the template on which we based this blog post. Thanks also for inspiring us with exquisitely crafted articles and blog posts.
+
+
In this report we also introduce 📚 FineWeb-Edu 📚 FineWeb-Edu is available in two sizes/filtering-level: 1.3 trillion (very high educational content) and 5.4 trillion (high educational content) tokens (all tokens are measured with GPT2 tokenizer here .
+ Both datasets are released under the permissive ODC-By 1.0 license
+
+ TLDR: This blog covers a discussion on processing and evaluating data quality at scale, the 🍷 FineWeb
+ recipe (listing and explaining all of our design choices), and the process followed to create its 📚 FineWeb-Edu subset.
+
+ What's web data
+ Finding the data
+ A common question often asked regarding web datasets used
+ to train LLMs is “where do they even get all that data?”. There are generally two options:
+
+ you either crawl it yourself, like companies such as OpenAI or Anthropic (among others) do (see here and here )
+
+
+
+ you use a public repository of crawled webpages, like the one maintained by
+ the non-profit CommonCrawl
+
+ To build 🍷 FineWeb, following what has been done in the past by a number of LLM training teams,
+ we used CommonCrawl (CC) as a starting point.
+ The Common Crawl non–profit organization has been crawling the web since 2007 and
+ releases a new crawl containing 200 to 400 TiB of textual content obtained via automatic web crawling usually
+ every 1 or 2 months.
+ As an example, the latest CC crawl (April 2024) contains 2.7
+ billion web pages, totaling 386 TiB of uncompressed HTML text contentNote that the size changes from crawl to crawl. Note also that we use "dump" or "crawl" interchangeability in this report. .
+ Ninety-six crawls have been released since 2013 and 3 crawls from 2008 to 2012, which are in a different (older) format.
+ We have not processed these 3 older crawls.
+
+ Processing at scale
+ Given the sheer size of the data involved, one of the main
+ challenges we had to overcome was having a modular, scalable codebase that would allow us to quickly iterate
+ on our processing decisions and easily try out new ideas, while appropriately parallelizing our workloads
+ and providing clear insights into the data.
+ For this purpose, we developed datatrovelibrary . You will find the exact scripts we used in the
+ datatrove repository
+
+ What is good data?
+ This is probably the main question to keep in mind when
+ creating a dataset. In most contexts and, in particular, in the context of large language model pretraining Note that this report is focused on the special field of web-scale datasets ("web-scale" typically meaning >100 billion tokens obtained from the web) used to pretrain a Large Language Model (by pretraining we mean the very first step in the training of a model, starting from random weights). We don't pretend to cover any other field of dataset creation nor that the lessons or hypothesis we develop in this document can extend to any field besides this specific field. , "high quality" is not a very well defined term
+ It is still common to train a model on a given corpus considered "clean"
+ (typically wikipediaEven though as we mentioned above the notion of "clean" is so ill-defined that it should probably not been seen as equivalent to wikipedia-type of text ) and use it to check the perplexity on the dataset
+ that we were trying to curate"Small" in comparison to standard sizes of today's LLMs, i.e. small in comparison to 7-70 billion parameters. In this work "small" means about 1-2 billion parameters on a representative subset of our dataset and evaluate them on
+ a set of evaluation tasks. Small models are used because training costs and time are a function of model size. In this second approach, it is important to
+ choose a diverse and representative set of dataset-evaluation tasks and try not to overfit to any one individual benchmark as it would risk hurting the generality of the obtained LLM after pretraining.
+ Yet another way to compare different datasets would be to
+ train a model on each dataset and have humans rate and compare the generations of the models (like on the LMSYS Chatbot Arena )
+ In this work, we went with the approach of training small
+ models and evaluating them on a set of "early-signal" benchmark tasks. We believe this is a reasonable proxy for the quality
+ of the data used to train these models, when keeping in mind the above-mentioned caveat around overfitting on the evaluation benchmarks.
+ Ablations and evaluation setup
+ To compare the impact of a given processing
+ step, we trained two models on two versions of the dataset, one version processed with the extra step (the one we wish to evaluate) and another version with this step
+ ablated (cut/removed). Apart from the data, these two models would be otherwise identical: the same number of parameters, architecture hyper-parameters, and trained
+ on an equal number of randomly sampled tokens from each version of the data, for a single epoch — the only difference being thus the
+ training data. We then evaluated each model on the same set of tasks and compared average
+ scores.
+ Our ablation models were trained using nanotron
+ We'll make the configuration to reproduce these ablation models available soon in Nanotron.
+ We evaluated the models using lightevallighteval:
+
+ small variance between runs trained on different samplings of the same
+ dataset: we want our runs on a subset of the data to be representative of the whole dataset, and the
+ resulting scores to be, in the limit of what is possible, less sensitive to exact data point choices than to our filter ablations.
+
+
+
+ performance increasing monotonically (or close) over a training run:
+ ideally, as the number of seen tokens increases, the performance on a high-signal benchmark should not decrease
+ (which would be indicative of unreliable results at a small scale)
+
+
+
+ performance above random baseline for this task by at least a few standard deviations: given our small ablation models and trainings we usually don't reach extremely high scores on any benchmark, but we want to make sure that the scores we get are above random noise.
+
+
+ After consideration, we selected the following list of benchmarks:
+
+ CommonSense QA
+ HellaSwag
+ OpenBook QA
+ PIQA
+ SIQA
+ WinoGrande
+ ARC
+ MMLU
+
+ To
+ ensure our checkpoint evaluation stayed within a limited timeframe, we capped the longer benchmarks at 1000 samples (wall-clock evaluation taking less than 5
+ min on a single node of 8 GPUs - done in parallel to the training).
+ You can find the full list of tasks and prompts we used here .
+ The 🍷 FineWeb recipe
+ In the next subsections we will explain each of the steps
+ taken to produce the FineWeb dataset.
+
+
+ You can find a fully reproducible datatrove config here .
+ Starting point: text extraction
+ CommonCrawl data is available in two main formats: WARC
+ and WET. WARC (Web ARChive format) files contain the raw data from the crawl, including the
+ full page HTML and request metadata. WET (WARC Encapsulated Text) files provide a text only
+ version of those websites.
+ A large number of datasets take the WET files as their
+ starting point. In our experience the default text extraction used by Common Crawl to create these WET files is suboptimal for the goals of LLM pretrainingIn particular we suspect that it keeps too much boilerplate content and navigation menus. and there are a variety of open-source libraries that
+ provide better text extraction. We extracted
+ the text content from the WARC files using the trafilatura library
+ You can find a benchmark comparing several text extraction libraries here .
+ To validate this decision, we processed the 2019-18 dump
+ directly using the WET files and with text extracted from WARC files using trafilaturaWe used trafilatura default options with favour_precision=True. . We applied the same
+ processing to each one (our base filtering+minhash, detailed below) and trained two models. While the
+ resulting dataset is about 25% larger for the WET data (around 254 billion tokens), it proves to be of much worse
+ quality than the one that used trafilatura to extract text from WARC files (which is around 200 billion tokens). Visual inspection of some samples confirms that many of
+ these additional tokens on the WET files are unnecessary page boilerplate.
+ It is important to note, however, that text extraction is one of the most costly steps of our
+ processing, so we believe that using the readily available WET data could be a reasonable trade-off for
+ lower budget teams.
+
+
+
+
First steps of filtering
+ Filtering is an important part of the curation process. It consists in
+ removing part of the data (which can consists in removing words, lines, or even full documents) that lower the performances of the model and is thus
+ deemed to be “lower quality” in our eval-driven process of dataset crafting.
+ As a basis for our filtering we used part of the setup
+ from RefinedWeb
+
+ Applied URL filtering using a blocklist to remove adult content
+
+
+
+
+ Applied quality and repetition filters from MassiveText
+
+ After applying this filtering to each of the text
+ extracted dumps (there are currently 96 dumps) we obtained roughly 36 trillion tokens of dataAs everywhere in this report: this is the number of tokens when tokenized with the gpt2 tokenizer .
+ Deduplicating the data
+ Deduplication is one of the most important steps when creating large web datasets for LLM pretraining. Methods to deduplicate datasets attempt to identify and remove redundant/repeated data from the dataset.
+ Why deduplicate?
+ The web has many aggregators, mirrors, templated pages or
+ just otherwise repeated content spread over different domains and webpages. Sometimes, these duplicated pages
+ can even be introduced by the crawler itself, when different links point to the same page.
+ Removing these duplicates (deduplicating) has been correlated with improvements in model performance
+ There are different ways to identify and even define
+ duplicated data. Common approaches rely on hashing techniques to speed up the process, or on building
+ efficient data structures to index the data (like suffix arrays). Methods can also be “fuzzy”, by using some
+ similarity metric to mark documents as duplicates, or “exact” by checking for exact matches between two
+ documents (or lines, paragraphs, or whatever other granularity level being used)Note that here, even when we discuss "fuzzy" deduplication, we are only employing methods that operate on character/word matches, aka surface-level text. A more complex concept of deduplication is concerned with "semantic" deduplication: comparing/removing texts which are relative to the same concepts and use for instance synonyms or periphrase. We don't discuss these topics here but note that they can be important in the field of large-scale synthetic data generation for instance (see our Cosmopedia release on this topic) .
+
+ Our deduplication parameters
+ Following RefinedWebOur units are "words", computed in the MinHash processing function with a language-specific word tokenizer . and compute minhashes using
+ 112 hash functions in total, split into 14 buckets of 8 hashes each — targeting documents that are at least
+ 75% similar. Documents with the same 8 minhashes in any bucket are considered a duplicate of each other.
+ This would mean that for two documents with a similarity ($$s$$)
+ of 0.7, 0.75, 0.8 and 0.85, the probability that they would be identified as duplicates would be 56%, 77%,
+ 92% and 98.8% respectively ($$1-(1-s^8)^{14}$$). See the plot below for a match probability
+ comparison between our setup with 112 hashes and the one from RefinedWeb, with 9000 hashes, divided into 450
+ buckets of 20 hashes (that requires a substantially larger amount of compute resources, as each individual hash must be computed, stored and then compared with hashes from other documents):
+
+
+
+
While the high number of hash functions in RefinedWeb
+ allows for a steeper, more well defined cut off (documents with real similarity near the threshold are more likely to be correctly identified), we believe the compute and storage savings are a reasonable
+ trade off.
+ It should also be noted that intra-document deduplication is already handled by our repetition filter, which removes documents with many repeated lines and paragraphs.
+
+ More deduplication is always better, right?
+ We started the project with the assumption that more deduplication is always better , so our initial approach was to take the entire dataset (all
+ 90+ dumps) and deduplicate them together as one big dataset using MinHash.
+ We did this in an iterative manner: starting with the most
+ recent dump (which at the time was 2023-50) and proceeding chronologically until we reached the oldest crawl. We deduplicated each dump
+ not only within itself, but removing any document matching any other documents in the previously processed
+ dumps.
+ For instance, for the second most recent dump (2023-40 at
+ the time), we deduplicated it against the most recent one in addition to within itself. As a result, the older the dumps, the higher the number of dumps it was deduplicated against and the more we removed data from it (indeed, in the oldest dumps we removed more than 90% of the data in the deduplication step).
+ Deduplicating the dataset in this manner resulted in 4
+ trillion tokens of data, but, quite surprisingly to us, when training on a randomly sampled 350 billion
+ tokens subset, our ablation models showed no improvement over a model trained on the non deduplicated data, scoring far below its predecessor RefinedWeb on our aggregate of tasks (see graph below).
+
+
+
+
This was challenging our assumption that more deduplication was always better so we decided to take a closer look at one of the oldest dumps, dump 2013-48:
+
+ pre deduplication, this dump had ~490 billion tokens
+
+
+ after our iterative MinHash, ~31 billion tokens remained (94% of data had been
+ removed)
+
+
+ As an experiment, we tried training two models on 28 billion tokens
+ sampled from the following data from 2013-48:
+
+ the fully deduplicated remaining ~31 billion tokens (originally kept
+ data )
+
+
+
+ 171 billion tokens obtained by individually deduplicating (without
+ considering the other dumps) the ~460 billion tokens that had been removed from this dump in the
+ iterative dedup process (originally removed data )While there may be documents in originally kept
+ data similar to documents in originally removed data , we estimate the overlap to be small (around 4 billion tokens)
+
+
+
+
+
+
These results show that, for this older dump taken in isolation, the data that was kept (10% of the original data) was actually worse than the 90% of data we
+ removedNote that these ablation models are trained only on data from this dump so it's considered independently of all the other dumps. . This is also confirmed by visual inspection: originally kept
+ data contains far more ads, lists of keywords and generally badly formatted text than originally removed data .
+ Taking a step back: individual dump dedup
+ We decided to experimence with alternative approaches: we deduplicated
+ each dump with MinHash individually (independently of the other dumps). This resulted in 20 trillion
+ tokens of data.
+ When training on a random sample from this dataset we see
+ that it now matches RefinedWeb’s performance (see curves below):
+
+
+
+
We hypothesize that the main improvement gained from
+ deduplication is the removal of very large clusters that are present in every single dump (you will find
+ some examples of these clusters in the RefinedWeb paper, each containing hundreds of thousands of
+ documents) and that further deduplication for clusters with a low number of duplicates (less than ~100 i.e. the number
+ of dumps) actually harms performance: data that does not find a duplicate match in any other dump might
+ actually be worse quality/more out of distribution (as evidenced by the results on the 2013-48 data).
+ While you might see some performance improvement when
+ deduplicating a few dumps together, at the scale of the entire dataset (all the dumps), the effect from this upsampling of lower quality data side
+ effect seems to be more impactful.
+ One possibility to consider is that as filtering quality
+ improves, this effect may not be as prevalent, since the filtering might be able to remove some of this
+ lower quality data. We also experimented with applying different, and often “lighter”, deduplication
+ approaches on top of the individually deduplicated dumps. You can read about them further below.
+
+ A note on measuring the effect of deduplication
+ Given the nature of deduplication, its effect is not
+ always very visible in a smaller slice of the dataset (such as 28B tokens, the size we used for our
+ filtering ablations). Furthermore, one must consider the fact that there are specific effects at play when
+ deduplicating across all CommonCrawl dumps, as some URLs/pages are recrawled from one dump to the next.
+ To visualize the effect of scaling the number of training
+ tokens on measuring deduplication impact, we considered the following (very extreme and unrealistic
+ regarding the degree of duplication observed) theoretical scenario:
+
+ there are 100 CommonCrawl dumps (roughly accurate)
+
+
+ each dump has been perfectly individually deduplicated (every single
+ document in a is unique in this dump)
+
+
+
+ each dump is a perfect copy of each other (maximum possible duplication
+ across dumps, effectively the worst case scenario)
+
+
+
+ each dump has 200 billion tokens (for a total of 20 trillion, the resulting
+ size of our individual dedup above)
+
+
+
+ each dump is made up of documents of 1k tokens (200M documents per dump)
+
+
+ We then simulated uniformly sampling documents from this
+ entire dataset of 20 trillion tokens, to obtain subsets of 1B, 10B, 100B, 350B and 1T tokens. In the image
+ below you can see how often each document would be repeated.
+
+
+
+
For 1B almost all documents would be unique
+ (#duplicates=1), despite the fact that in the entire dataset each document is repeated 100 times (once per
+ dump). We start seeing some changes at the 100B scale (0.5% of the total dataset), with a large number of
+ documents being repeated twice, and a few even 4-8 times. At the larger scale of 1T (5% of the total
+ dataset), the majority of the documents are repeated up to 8 times, with some being repeated up to 16
+ times.
+ We ran our performance evaluations for the deduplicated
+ data at the 350B scale, which would, under this theoretical scenario, be made up of a significant portion of
+ documents duplicated up to 8 times. This simulation illustrates the inherent difficulties associated with
+ measuring deduplication impact on the training of LLMs, once the biggest duplicate clusters have been
+ removed.
+
+ Other (failed) global approaches
+ To build on top of our newly found method (independently deduplicating each dump). We attempted to further improve the performance further deduplicating the
+ independently minhash deduped 20 trillion tokens of data (globally, over all dumps). We explored the following methods:
+
+ URL deduplication, where we only kept one document per normalized
+ (lowercased) URL (71.5% of tokens removed, 5.6T left) — FineWeb URL dedup
+
+
+ Line deduplication:
+
+ remove all but 1 (randomly chosen) occurrence of each duplicated line (77.8% of
+ tokens dropped, 4.4T left) — FineWeb line dedup
+
+
+ same as above, but only removing duplicate lines with at least 10
+ words and dropping documents with fewer than 3 sentences after deduplication (85% of tokens
+ dropped, 2.9T left) — FineWeb line dedup w/ min words
+
+
+ remove all but 1 occurrence of each span of 3 duplicated lines
+ with each number treated as 0 when finding duplicates, (80.9% of tokens removed, 3.7T left) — FineWeb 3-line
+ dedup
+
+
+
+ The performance of the models trained on each of these was
+ consistently worse (even if to different degrees) than that of the original independently deduplicated
+ data:
+
+
+
+
Filtering the data even more for quality
+ By this point we had reached the same performance of the previous work we attempted to reproduce and extend:
+ RefinedWeb, using our base filtering and independent MinHash. Still, on our aggregate of tasks, another heavily filtered dataset, the C4 dataset
+ We therefore set out to find new filtering steps that
+ would, at first, allow us to match the performance of C4 and, at a second stage, surpass it. A natural starting point
+ was to look into the processing of C4 itself.
+ C4: A dataset that has stood the test of time
+ The C4
+ dataset was first released in 2019. It was obtained from the 2019-18 CommonCrawl dump by
+ removing non english data, applying some heuristic filters on both the line and document level,
+ deduplicating on the line level, and removing documents containing words from a word blocklist.
+ Despite its age and limited size for current standards (around 175B gpt2 tokens), this dataset is, to this day, a common sub-set of typical LLM training, being used in models such as the relatively recent Llama1
+
+
+
+
+ applying “All filters” (drop lines not ending on punctuation marks,
+ mentioning javascript and cookie notices + drop documents outside length thresholds, containing “lorem
+ ipsum” or a curly bracket, {) allows us to match C4’s HellaSwag performance ("All filter" versus "C4" curves).
+
+
+
+ The curly bracket filter, and the word lengths filter only give a small
+ boost, removing 2.8% and 4.3% of tokens, respectively
+
+
+
+ The terminal punctuation filter, by itself, gives the biggest individual
+ boost, but removes around 30% of all tokens (!)
+
+
+
+ The lorem_ipsum, javascript and policy rules each remove <0.5% of
+ training tokens, so we did not train on them individually
+
+
+
+ "All filters except the (very destructive) terminal_punct" performs better than
+ terminal_punct by itself, while removing less in total (~7%)
+
+
+ We decided to apply all C4 filters mentioned above except
+ the terminal punctuation one. We validated these results with a longer run, which you will find in a plot in
+ the next section.
+ A statistical approach to develop heuristic filters
+ To develop new heuristic filters and select their thresholds we devised a systematic process:
+ we started by collecting a very large list of high level statistics of our datasets (over fifty different metrics) ranging from common document-level
+ metrics (e.g. number of lines, avg. line/word length, etc) to inter-document repetition metrics (inspired by MassiveText), on both a high quality and a lower quality web dataset;
+ we selected the metrics for which the Wasserstein distance between the two distributions (of the metric computed on each dataset) was larger;
+ we inspected the histograms of the two distributions and empirically chose a threshold that would make the lower quality dataset more closely resemble the higher quality one on this metric;
+ we validated the resulting filter (metric-threshold pair) by using it on a reference dataset and running small ablations.
+ Due to our (new) assumption that global MinHash greatly upsamples lower quality data in the oldest dumps, we computed metrics on both the independently
+ MinHashed and the (worse quality) global MinHashed versions of the 2013-48 and 2015-22 crawls (two older crawls). We then compared the
+ statistics at a macro level, by looking at the distribution of these metrics for each one.
+ Perhaps not too surprisingly given our findings for deduplication, we found significant
+ disparities in most of the metrics for the two deduplication methods. For instance, the line-char-duplicates
+ metric (nb. of characters in duplicated lines / nb. characters), roughly doubled from the independent dedup
+ (0.0053 for 2015-22 and 0.0058 for 2013-48), to the global dedup (0.011 for 2015-22 and 0.01 for 2013-48),
+ indicating that the latter had higher inter-document repetition.
+ Following the process listed above for these datasets yielded seventeen candidate
+ metric-threshold pairs. In the image below, you can see three of these histograms:
+
+
+
+
As an example, we inspected the histograms of "fraction of lines ending with punctuation" (see the image above) and observed an increased document density of global MinHash at around 0.12.
+ We then filtered with this threshold and found that the removed data had a higher amount of short lists or consisted of only document layout text ("Home", "Sign up", etc).
+
+
+ We then assessed the effectiveness of these seventeen newly created
+ filters, by conducting several of our 28 billion tokens ablation runs on the 2019-18 crawl . Out
+ of all those runs, we identified three filters (the ones based on the histograms above) that demonstrated
+ the most significant improvements on the aggregate score:
+
+ Remove documents where the fraction of lines ending with punctuation ≤ 0.12
+ (10.14% of tokens removed) — vs the 30% from the original C4 terminal punct filter
+
+
+
+ Remove documents where the fraction of characters in duplicated lines ≥ 0.1
+ (12.47% of tokens removed) — the original MassiveText threshold for this ratio is ≥ 0.2
+
+
+
+ Remove documents where the fraction of lines shorter than 30 characters ≥
+ 0.67 (3.73% of tokens removed)
+
+
+
+ When applying the three together, ~22% of tokens were removed.
+
+
+
+
+
These filters allowed us to further improve performance and to, notably, surpass the C4 dataset performance while providing a much larger dataset at the same time.
+
+ The final FineWeb dataset
+ The final 🍷 FineWeb dataset comprises 15T tokens and
+ includes the following previously mentioned steps, in order, each providing a performance boost on our group
+ of benchmark tasks:
+
+
+ independent MinHash deduplication per dump
+
+
+ a selection of C4 filters
+
+
+ our custom filters (mentioned in the previous section)
+
+
+
+
+
Comparisons with other web-scale datasets
+ We compared 🍷 FineWeb with the following datasets that are usually considered the highest quality web-scale datasets openly accessible (we also indicate for each the approximate number of tokens in the public version of the dataset):
+
+
+
+ Dolma v1.6 (3T tokens) (the
+ CommonCrawl part) There is a newer version of Dolma, v1.7, which is smaller
+
+
+
+
+
+ and our new 🍷 FineWeb (15T tokens) (this report)
+
+
+ You will find the 350B-tokens-trained ablation models openly accessible and gathered in this
+ collection . We have uploaded checkpoints at every 1000 training steps. You will also find our full evaluation
+ results here .
+
+
+
+
🍷 FineWeb is thus –up to our knowledge– the dataset leading to the current highest model performances while allowing to train on several trillion of openly accessible unique tokens.
+
+ 📚 FineWeb-Edu
+
+
+ 📚 FineWeb-Edu outperforms 🍷 FineWeb and all other open web datasets on our group of evaluation tasks.
+
+ 📚 FineWeb-Edu is an additional developement of FineWeb that we are excited to introduce in this tech report and openly release. FineWeb-Edu is based on a new approach that recently emerged for filtering LLM training datasets: using synthetic data to develop classifiers for identifying educational content. This technique was notably used in the trainings of Llama 3
+ The popular Phi3 models were trained on 3.3 and 4.8 trillion tokens, with the paper
+ Our training data consists of heavily filtered publicly available web data (according to the 'educational level') from various open internet sources, as well as synthetic LLM-generated data.
+ Similarly, Llama 3 blog post
+ We found that previous generations of Llama are good at identifying high-quality data, so we used Llama 2 to help build the text-quality classifiers that are powering Llama 3.
+ However, these classifiers and filtered datasets are not publicly available. To further enhance 🍷 FineWeb's quality, we developed an educational quality classifier using annotations generated by Llama-3-70B-Instruct to create 📚 FineWeb-Edu
+
+ Annotating for educational quality at scale
+ We used Llama-3-70B-Instruct to annotate 500k samples from 🍷 FineWeb, scoring each for their educational quality on a scale from 0 to 5.
+ We explored various prompt format to automatically extract an educational score using an LLM and found that the additive scale by Yuan et al.
+
+
+
Prompt used for Llama3 annotations of the educational score, also available here .
+
In terms of open-weight model to use for annotating the data, we experimented with several models including Mixtral-8x7B-Instruct and Mixtral-8x22B-Instruct , Llama-3-70B-Instruct as well as a jury gathering these three models
+
+ Training a classifier
+ To scale our annotation to the trillion tokens of FineWeb, we trained a classifier from the 450k annotation of our Llama3-70B model. The model we used was a Snowflake-arctic-embed embedding model with a classification head with a single regression output on top of it. We trained this model on 450,000 Llama 3 annotations for 20 epochs with a learning rate of 3e-4, freezing the embedding and encoder layers. We saved the checkpoint with the highest F1 score on our held-out validation set of 45k samples, treating Llama 3 annotations as ground-truth. After training, we rounded the scores to integers from 0 to 5.
+ We then converted the problem to a binary classification task by using a fixed threshold to determine if a file is educational. With a threshold of 3, the model achieved an F1 score of 82% on the validation set, indicating strong performance in distinguishing high-quality educational content.
+ The classifier is available at: HuggingFaceFW/fineweb-edu-classifier . The training and inference code is available on GitHub .
+
+ Filtering and results
+ We applied the classifier to the 15T tokens of 🍷 FineWeb, a process that required 6,000 H100 GPU hours. We investigated the impact of using different thresholds for the filtering and found that using a threshold of 3 gave the best overall results. Although using a threshold higher than 3 improves performance on knowledge and reasoning intensive benchmarks, it significantly degrades performance on HellaSwag and PIQA. The plot below shows the performance of each threshold compared to FineWeb on six different benchmarks; it uses a 1.82B model trained on 8B tokens.
+
+
+
+
+
Note: this ablation was conducted on 8B tokens from the 2024-10 dump for both the FineWeb and FineWeb-Edu subsets, which might not be representative of the entire dataset. The next ablation shows that the findings for threshold 3 hold on a longer run of 350B tokens from all FineWeb dumps, except for HellaSwag, where we noticed a slight performance degradation.
+ We built 📚 FineWeb-Edu by filtering out samples with scores lower than 3. This removed 92% of the dataset, leaving us with 1.3 trillion educational tokens. To evaluate the effectiveness of this filtering at a larger scale, we conducted an ablation using a 1.82B model trained on 350 billion tokens, similar to the FineWeb filtering ablation mentioned above:
+
+
+
+
+
Here are the key highlights of the ablation results above:
+
+ 📚 FineWeb-Edu surpasses 🍷 FineWeb and all other open web datasets, with remarkable improvements on educational benchmarks such as MMLU, ARC, and OpenBookQA.
+ It achieves the same performance with significantly less data, requiring 10x fewer tokens compared to C4 and Dolma to match MMLU results.
+ This demonstrates the effectiveness of using classifiers trained on LLM annotations for large-scale data filtering.
+
+ Given that a threshold of 2 also demonstrated strong performance while retaining more data, we are releasing an additional dataset filtered with this threshold, containing 5.4 trillion tokens under HuggingFaceFW/fineweb-edu-score-2 .
+ You can find the two datasets along with the classifier used for the filtering in this collection .
+
+
+
+ Bonus: CommonCrawl over time
+
+ Just like fine wine, not all crawls are created equal.
+
+ While ablating filtering steps, we noticed that certain crawls outperformed others by a significant margin. We decided to investigate this phenomenon.
+ Benchmark performance by crawl
+ For each crawl, we trained two 1.8B models on 27 billion tokens randomly sampled from that crawl's data (after the
+ base filtering and MinHash deduplication steps), where each run had a different random 27BT sampling of this data. We trained 192 such models, totaling over 60 thousand H100 GPU-hours. We subsequently took
+ the last 3 checkpoints for both runs and plotted the average of these 6 data points per crawl.
+ The plot below clearly shows that some dumps perform far
+ worse than others. Each year has a different color, and the number of crawls per year also varies.
+
+
+
+
+
We investigated possible causes for this behaviour such as changes in the most common URLs of each dump, as well as potential benchmark contamination, but could not find any conclusive explanation. We leave further investigation for future work.
+
+ Synthetic data
+ We wondered if the strong performance of the last few crawls could be, in part, attributed to the presence of a larger quantity of synthetic data (data
+ generated by LLMs). Such a change would not be surprising due to the recent increase in popularity of LLMs,
+ notably of ChatGPT.
+ Since, to the best of our knowledge, there is no foolproof method to detect synthetic data, we opted to use a proxy metric: we measured the frequency of the
+ following words in each crawl: "delve", "as a large language model", "it's important to note", "rich tapestry",
+ "intertwined", "certainly!", "dive into", all of which are commonly used by ChatGPT.
+ It is important to note that not all samples containing
+ one of these phrases were necessarily generated by ChatGPT (and also that many ChatGPT generated samples do
+ not contain any of these phrases), but assuming that the amount of synthetic data were to not change across
+ crawls, one would expect these frequencies to remain approximately constant over time.
+ The results are shown in the following plot:
+ While the frequency remained approximately constant until
+ 2023-14 (ChatGPT was released at the end of 2022), we find a steep increase of our proxy metric
+ in recent crawls. While this simple test is not enough to conclude that ChatGPT completions and other synthetic data is improving the quality of the most recent crawl, it at the very least does not seem to drastically harm it.
+ We expect to continue seeing increasing quantities of synthetic data on new CC crawls. However, while for relatively small trainings this data does not seem to harm performance (and might actually improve it), it is not clear that this holds for much larger trainings.
+
+ Conclusion and looking forward
+ Through our open science efforts we hope to open more and more the black box around training high performance large language models as well as give every model trainer the ability to create state-of-the-art LLMs. We're excited to continue iterating on FineWeb and increasingly better filtered subsets of web data, in a fully open and reproducible manner.
+ In particular in the short term, while English currently dominates the large language model landscape, we're looking forward to applying the learnings we make in this project to make high quality training data available in other languages as well and as accessible as possible.
+ In a nutshell: the future is bright and exciting for studying the science of creating datasets at scale and in the open 🤗.
+
+
+
+
+
+
+
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