2b:null 2c:["$","title",null,{"children":"Hello, topic modelling in JavaScript! | Kalle Westerling | Observable"}] 31:T799,You will notice that the `getTopics` function accepts a number of arguments: The first, and _required_ argument is an **array of sentences** (or a string, which will then be split up by line). The second argument is an object, which can contain any of the following parameters: ```js { topics: 4, stopwords: autoStopwords, returnType: "object", minLength: 2, stemmer: autoStemmer } ``` Let's go through each of those arguments: - _topics_: This is the number of topics that you want to generate from your sentences. It is automatically set to 4. - _stopwords_: This should be a list of stopwords that you want to exclude from the analysis. It is automatically a fairly comprehensive list, taken from Athar's codebase, and which can be found [further down in this file](https://observablehq.com/d/d2276f16b1a66be0#cell-18), if you are interested to look at them. - _returnType_: This should be set to `"html"` or `"object"` and will determine what is returned to you. In the example above, we have used `"object"` and, as you see in the cell above, the `topics` returned is an object that contains all the necessary data. If you set `returnType` to `"html"`, you will get a HTML representation of the topics returned instead. This is intended only for exploratory purposes. - _minLength_: Here, you can provide an integer which determines the minimum word length to take into consideration as a word. It is automatically set to 2, so any words longer than or equal to 2 will be analysed. - _stemmer_: This value can be set to `false` if you don't want the words from the sentences passed to be stemmed. It is automatically set to the auto-stemmer provided by this script, which is the [@stdlib/nlp-porter-stemmer](https://github.com/stdlib-js/nlp-porter-stemmer) library. You can pass your own stemmer as long as it is a function, which takes only one argument (the word) and returns a string value (the stemmed word).32:Tfee,getTopics = ( sentences, { topics = 4, stopwords = autoStopwords, returnType = "object", minLength = 2, stemmer = autoStemmer } = {} ) => { var documents = new Array(), f = {}, vocab = new Array(), docCount = 0; if (!Array.isArray(sentences)) sentences = sentences.split("\n"); sentences = sentences // remove links .map((sentence) => sentence.replace(/(?:https?|ftp):\/\/[\n\S]+/g, "")) // remove characters .map((sentence) => sentence.replace(/[^a-z\'A-Z ]+/g, " ")) .map((sentence) => sentence.trim()) .map((sentence) => sentence.toLowerCase()) .filter((d) => d); sentences.forEach((sentence) => { var words = sentence.split(/[\s,\"]+/); if (!words) return; var wordIndices = new Array(); words = words .filter((word) => word) .filter((word) => !stopwords[word]) .filter((word) => word.length >= minLength); if (stemmer) words = words.map((word) => stemmer(word)); words.forEach((word) => { if (f[word]) { f[word] = f[word] + 1; } else if (word) { f[word] = 1; vocab.push(word); } wordIndices.push(vocab.indexOf(word)); }); if (wordIndices && wordIndices.length > 0) { documents[docCount++] = wordIndices; } }); var V = vocab.length, M = documents.length, K = topics, alpha = 0.1, // per-document distributions over topics beta = 0.01; // per-topic distributions over words lda.configure(documents, V, 10000, 2000, 100, 10); lda.gibbs(K, alpha, beta); var theta = lda.getTheta(), phi = lda.getPhi(); var htmlOutput = "", data = { topics: {}, vocab, sentences, documents }; //topics var topicText = new Array(), topTerms = 20; phi.forEach((p, pix) => { topicText[pix] = ""; var tuples = new Array(); p.forEach((val, ix) => { tuples.push("" + val.toPrecision(2) + "_" + vocab[ix]); }); tuples.sort().reverse(); if (topTerms > vocab.length) topTerms = vocab.length; data.topics[pix] = []; Array(topTerms) .fill(0) .forEach((d, t) => { var topicTerm = tuples[t].split("_")[1]; var prob = parseInt(tuples[t].split("_")[0] * 100); if (prob < 0.0001) return; data.topics[pix].push([topicTerm, prob]); topicText[pix] += topicTerm + " "; }); topicText[pix] = topicText[pix].trim(); }); data.topicText = topicText; htmlOutput = '

'; theta.forEach((t, ix) => { htmlOutput += '

'; htmlOutput += '

'; t.forEach((k, tix) => { const width = parseInt("" + k * 100); htmlOutput += `

`; }); htmlOutput += `

${sentences[ix]}

`; }); data.theta = theta; if (returnType === "object") return data; const style = ` `; return html`${style}${htmlOutput}`; }33:T1325,lda = new (function () { var documents, z, nw, nd, nwsum, ndsum, thetasum, phisum, V, K, alpha, beta, THIN_INTERVAL = 20, BURN_IN = 100, ITERATIONS = 1000, SAMPLE_LAG, dispcol = 0, numstats = 0; this.configure = function ( docs, v, iterations, burnIn, thinInterval, sampleLag ) { this.ITERATIONS = iterations; this.BURN_IN = burnIn; this.THIN_INTERVAL = thinInterval; this.SAMPLE_LAG = sampleLag; this.documents = docs; this.V = v; this.dispcol = 0; this.numstats = 0; }; this.initialState = function (K) { var i, M = this.documents.length; this.nw = make2DArray(this.V, K); this.nd = make2DArray(M, K); this.nwsum = makeArray(K); this.ndsum = makeArray(M); this.z = new Array(); for (i = 0; i < M; i++) this.z[i] = new Array(); for (var m = 0; m < M; m++) { var N = this.documents[m].length; this.z[m] = new Array(); for (var n = 0; n < N; n++) { var topic = parseInt("" + Math.random() * K); this.z[m][n] = topic; this.nw[this.documents[m][n]][topic]++; this.nd[m][topic]++; this.nwsum[topic]++; } this.ndsum[m] = N; } }; this.gibbs = function (K, alpha, beta) { var i; this.K = K; this.alpha = alpha; this.beta = beta; if (this.SAMPLE_LAG > 0) { this.thetasum = make2DArray(this.documents.length, this.K); this.phisum = make2DArray(this.K, this.V); this.numstats = 0; } this.initialState(K); console.log( `Sampling ${this.ITERATIONS} iterations with burn-in of ${this.BURN_IN} (B/S=${this.THIN_INTERVAL}).` ); for (i = 0; i < this.ITERATIONS; i++) { for (var m = 0; m < this.z.length; m++) { for (var n = 0; n < this.z[m].length; n++) { var topic = this.sampleFullConditional(m, n); this.z[m][n] = topic; } } if (i < this.BURN_IN && i % this.THIN_INTERVAL == 0) { this.dispcol++; } if (i > this.BURN_IN && i % this.THIN_INTERVAL == 0) { this.dispcol++; } if (i > this.BURN_IN && this.SAMPLE_LAG > 0 && i % this.SAMPLE_LAG == 0) { this.updateParams(); if (i % this.THIN_INTERVAL != 0) this.dispcol++; } if (this.dispcol >= 100) { this.dispcol = 0; } } }; this.sampleFullConditional = function (m, n) { var topic = this.z[m][n]; this.nw[this.documents[m][n]][topic]--; this.nd[m][topic]--; this.nwsum[topic]--; this.ndsum[m]--; var p = makeArray(this.K); for (var k = 0; k < this.K; k++) { p[k] = (((this.nw[this.documents[m][n]][k] + this.beta) / (this.nwsum[k] + this.V * this.beta)) * (this.nd[m][k] + this.alpha)) / (this.ndsum[m] + this.K * this.alpha); } for (var k = 1; k < p.length; k++) { p[k] += p[k - 1]; } var u = Math.random() * p[this.K - 1]; for (topic = 0; topic < p.length; topic++) { if (u < p[topic]) break; } this.nw[this.documents[m][n]][topic]++; this.nd[m][topic]++; this.nwsum[topic]++; this.ndsum[m]++; return topic; }; this.updateParams = function () { for (var m = 0; m < this.documents.length; m++) { for (var k = 0; k < this.K; k++) { this.thetasum[m][k] += (this.nd[m][k] + this.alpha) / (this.ndsum[m] + this.K * this.alpha); } } for (var k = 0; k < this.K; k++) { for (var w = 0; w < this.V; w++) { this.phisum[k][w] += (this.nw[w][k] + this.beta) / (this.nwsum[k] + this.V * this.beta); } } this.numstats++; }; this.getTheta = function () { var theta = new Array(); for (var i = 0; i < this.documents.length; i++) theta[i] = new Array(); if (this.SAMPLE_LAG > 0) { for (var m = 0; m < this.documents.length; m++) { for (var k = 0; k < this.K; k++) { theta[m][k] = this.thetasum[m][k] / this.numstats; } } } else { for (var m = 0; m < this.documents.length; m++) { for (var k = 0; k < this.K; k++) { theta[m][k] = (this.nd[m][k] + this.alpha) / (this.ndsum[m] + this.K * this.alpha); } } } return theta; }; this.getPhi = function () { var phi = new Array(); for (var i = 0; i < this.K; i++) phi[i] = new Array(); if (this.SAMPLE_LAG > 0) { for (var k = 0; k < this.K; k++) { for (var w = 0; w < this.V; w++) { phi[k][w] = this.phisum[k][w] / this.numstats; } } } else { for (var k = 0; k < this.K; k++) { for (var w = 0; w < this.V; w++) { phi[k][w] = (this.nw[w][k] + this.beta) / (this.nwsum[k] + this.V * this.beta); } } } return phi; }; })()34:T1b03,autoStopwords = ({ a: 1, able: 1, about: 1, above: 1, according: 1, accordingly: 1, across: 1, actually: 1, after: 1, afterwards: 1, again: 1, against: 1, "ain't": 1, all: 1, allow: 1, allows: 1, almost: 1, alone: 1, along: 1, already: 1, also: 1, although: 1, always: 1, am: 1, among: 1, amongst: 1, an: 1, and: 1, another: 1, any: 1, anybody: 1, anyhow: 1, anyone: 1, anything: 1, anyway: 1, anyways: 1, anywhere: 1, apart: 1, appear: 1, appreciate: 1, appropriate: 1, are: 1, "aren't": 1, around: 1, as: 1, aside: 1, ask: 1, asking: 1, associated: 1, at: 1, available: 1, away: 1, awfully: 1, be: 1, became: 1, because: 1, become: 1, becomes: 1, becoming: 1, been: 1, before: 1, beforehand: 1, behind: 1, being: 1, believe: 1, below: 1, beside: 1, besides: 1, best: 1, better: 1, between: 1, beyond: 1, both: 1, brief: 1, but: 1, by: 1, "c'mon": 1, "c's": 1, came: 1, can: 1, "can't": 1, cannot: 1, cant: 1, cause: 1, causes: 1, certain: 1, certainly: 1, changes: 1, clearly: 1, co: 1, com: 1, come: 1, comes: 1, concerning: 1, consequently: 1, consider: 1, considering: 1, contain: 1, containing: 1, contains: 1, corresponding: 1, could: 1, "couldn't": 1, course: 1, currently: 1, definitely: 1, described: 1, despite: 1, did: 1, "didn't": 1, different: 1, do: 1, does: 1, "doesn't": 1, doing: 1, "don't": 1, done: 1, down: 1, downwards: 1, during: 1, each: 1, edu: 1, eg: 1, eight: 1, either: 1, else: 1, elsewhere: 1, enough: 1, entirely: 1, especially: 1, et: 1, etc: 1, even: 1, ever: 1, every: 1, everybody: 1, everyone: 1, everything: 1, everywhere: 1, ex: 1, exactly: 1, example: 1, except: 1, far: 1, few: 1, fifth: 1, first: 1, five: 1, followed: 1, following: 1, follows: 1, for: 1, former: 1, formerly: 1, forth: 1, four: 1, from: 1, further: 1, furthermore: 1, get: 1, gets: 1, getting: 1, given: 1, gives: 1, go: 1, goes: 1, going: 1, gone: 1, got: 1, gotten: 1, greetings: 1, had: 1, "hadn't": 1, happens: 1, hardly: 1, has: 1, "hasn't": 1, have: 1, "haven't": 1, having: 1, he: 1, "he's": 1, hello: 1, help: 1, hence: 1, her: 1, here: 1, "here's": 1, hereafter: 1, hereby: 1, herein: 1, hereupon: 1, hers: 1, herself: 1, hi: 1, him: 1, himself: 1, his: 1, hither: 1, hopefully: 1, how: 1, howbeit: 1, however: 1, "i'd": 1, "i'll": 1, "i'm": 1, "i've": 1, ie: 1, if: 1, ignored: 1, immediate: 1, in: 1, inasmuch: 1, inc: 1, indeed: 1, indicate: 1, indicated: 1, indicates: 1, inner: 1, insofar: 1, instead: 1, into: 1, inward: 1, is: 1, "isn't": 1, it: 1, "it'd": 1, "it'll": 1, "it's": 1, its: 1, itself: 1, just: 1, keep: 1, keeps: 1, kept: 1, know: 1, knows: 1, known: 1, last: 1, lately: 1, later: 1, latter: 1, latterly: 1, least: 1, less: 1, lest: 1, let: 1, "let's": 1, like: 1, liked: 1, likely: 1, little: 1, look: 1, looking: 1, looks: 1, ltd: 1, mainly: 1, many: 1, may: 1, maybe: 1, me: 1, mean: 1, meanwhile: 1, merely: 1, might: 1, more: 1, moreover: 1, most: 1, mostly: 1, much: 1, must: 1, my: 1, myself: 1, name: 1, namely: 1, nd: 1, near: 1, nearly: 1, necessary: 1, need: 1, needs: 1, neither: 1, never: 1, nevertheless: 1, new: 1, next: 1, nine: 1, no: 1, nobody: 1, non: 1, none: 1, noone: 1, nor: 1, normally: 1, not: 1, nothing: 1, novel: 1, now: 1, nowhere: 1, obviously: 1, of: 1, off: 1, often: 1, oh: 1, ok: 1, okay: 1, old: 1, on: 1, once: 1, one: 1, ones: 1, only: 1, onto: 1, or: 1, other: 1, others: 1, otherwise: 1, ought: 1, our: 1, ours: 1, ourselves: 1, out: 1, outside: 1, over: 1, overall: 1, own: 1, particular: 1, particularly: 1, per: 1, perhaps: 1, placed: 1, please: 1, plus: 1, possible: 1, presumably: 1, probably: 1, provides: 1, que: 1, quite: 1, qv: 1, rather: 1, rd: 1, re: 1, really: 1, reasonably: 1, regarding: 1, regardless: 1, regards: 1, relatively: 1, respectively: 1, right: 1, rt: 1, said: 1, same: 1, saw: 1, say: 1, saying: 1, says: 1, second: 1, secondly: 1, see: 1, seeing: 1, seem: 1, seemed: 1, seeming: 1, seems: 1, seen: 1, self: 1, selves: 1, sensible: 1, sent: 1, serious: 1, seriously: 1, seven: 1, several: 1, shall: 1, she: 1, should: 1, "shouldn't": 1, since: 1, six: 1, so: 1, some: 1, somebody: 1, somehow: 1, someone: 1, something: 1, sometime: 1, sometimes: 1, somewhat: 1, somewhere: 1, soon: 1, sorry: 1, specified: 1, specify: 1, specifying: 1, still: 1, sub: 1, such: 1, sup: 1, sure: 1, "t's": 1, take: 1, taken: 1, tell: 1, tends: 1, th: 1, than: 1, thank: 1, thanks: 1, thanx: 1, that: 1, "that's": 1, thats: 1, the: 1, their: 1, theirs: 1, them: 1, themselves: 1, then: 1, thence: 1, there: 1, "there's": 1, thereafter: 1, thereby: 1, therefore: 1, therein: 1, theres: 1, thereupon: 1, these: 1, they: 1, "they'd": 1, "they'll": 1, "they're": 1, "they've": 1, think: 1, third: 1, this: 1, thorough: 1, thoroughly: 1, those: 1, though: 1, three: 1, through: 1, throughout: 1, thru: 1, thus: 1, to: 1, together: 1, too: 1, took: 1, toward: 1, towards: 1, tried: 1, tries: 1, truly: 1, try: 1, trying: 1, twice: 1, two: 1, un: 1, under: 1, unfortunately: 1, unless: 1, unlikely: 1, until: 1, unto: 1, up: 1, upon: 1, us: 1, use: 1, used: 1, useful: 1, uses: 1, using: 1, usually: 1, value: 1, various: 1, very: 1, via: 1, viz: 1, vs: 1, want: 1, wants: 1, was: 1, "wasn't": 1, way: 1, we: 1, "we'd": 1, "we'll": 1, "we're": 1, "we've": 1, welcome: 1, well: 1, went: 1, were: 1, "weren't": 1, what: 1, "what's": 1, whatever: 1, when: 1, whence: 1, whenever: 1, where: 1, "where's": 1, whereafter: 1, whereas: 1, whereby: 1, wherein: 1, whereupon: 1, wherever: 1, whether: 1, which: 1, while: 1, whither: 1, who: 1, "who's": 1, whoever: 1, whole: 1, whom: 1, whose: 1, why: 1, will: 1, willing: 1, wish: 1, with: 1, within: 1, without: 1, "won't": 1, wonder: 1, would: 1, would: 1, "wouldn't": 1, yes: 1, yet: 1, you: 1, "you'd": 1, "you'll": 1, "you're": 1, "you've": 1, your: 1, yours: 1, yourself: 1, yourselves: 1, zero: 1 })35:T223d,testData = `Go until jurong 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Does that cause prob for u?"`36:[] 2f:{"id":"d2276f16b1a66be0","slug":"topic-modelling","title":"Hello, topic modelling in JavaScript!","thumbnail":"5f402fb6baa681d290d7fb75cec637a90a7d43d6c0804e224ec4063ef566a732","access_level":"public","create_time":"$D2023-07-11T17:25:58.739Z","update_time":"$D2023-07-11T17:59:35.622Z","view_time":null,"first_public_time":"$D2023-07-11T17:25:58.739Z","fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"32362545e9f3330d","login":"kallewesterling","name":"Kalle Westerling","avatar_url":"https://avatars-next.observableusercontent.com/f759a45dfbde6f2da561a90bcc8852bd0efd30d6a12ae3031f4d0bedc2464b65","tier":"starter_2024"},"version":143,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"a4098a3e48d12fd5","login":"kallewesterling","name":"Kalle Westerling","avatar_url":"https://avatars-next.observableusercontent.com/f759a45dfbde6f2da561a90bcc8852bd0efd30d6a12ae3031f4d0bedc2464b65","tier":null}],"members":[{"type":"individual","id":"a4098a3e48d12fd5","login":"kallewesterling","name":"Kalle Westerling","avatar_url":"https://avatars-next.observableusercontent.com/f759a45dfbde6f2da561a90bcc8852bd0efd30d6a12ae3031f4d0bedc2464b65","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Hello, topic modelling in JavaScript!","cells":[{"id":0,"value":"# Hello, topic modelling in JavaScript!\n\nThe script here is converted from Awais Athar's [lda.js package](https://github.com/awaisathar/lda.js) by Kalle Westerling.\n\nHis script aimed at creating [a HTML representation of the LDA result](http://awaisathar.github.io/lda.js/), explorable in some visualisations. The aim here is to extract the data that can then be further visualised using the Observable platform.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":35,"value":"## How to use","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":89,"value":"The first thing you'll want to do, if you want to use this code in your own notebook is to import the function `getTopics` from this notebook, like this:\n\n```js\nimport { getTopics } from 'kallewesterling/topic-modelling'\n```","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":63,"value":"Now we can get to the parts where we'll generate our topics. Start by defining your data. Here, we're going to use Athar's original data from the SMS spam corpus (see below)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":36,"value":"sentences = testData","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":67,"value":"Next, run the `getTopics` function:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":47,"value":"topics = getTopics(sentences, {\n returnType: \"object\",\n stemmer: false,\n topics: 5\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":71,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":101,"value":"Here's another example of generating topics with the same data as above, but with other parameters:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":105,"value":"topics2 = getTopics(sentences, {\n topics: 7, // generate 7 topics\n minLength: 10, // only include words longer than or equal to 10 characters\n stopwords: [], // skip stopwords\n returnType: \"object\" // return an object\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":118,"value":"Looking closer at the object's different properties, we can see that the vocabulary (`vocab`) only include 22 words:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":120,"value":"{\n const vocabulary = topics2.vocab;\n return html`

${vocabulary.join(\"

\")}`;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":130,"value":"Finally, let's have a look at the HTML output by running the same command as above but with a different `returnType`:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":134,"value":"topics3 = getTopics(sentences, {\n topics: 7, // generate 7 topics\n minLength: 10, // only include words longer than or equal to 10 characters\n stopwords: [], // skip stopwords\n returnType: \"html\" // return the **HTML table**\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":138,"value":"Now, you can see that it may not be recommendable to run the script with the `minLength` set as high as 10. **Try playing around with the values to see how things can work better.**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":48,"value":"-----","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":20,"value":"## Entrypoint: getTopics","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":21,"value":"$32","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":14,"value":"## LDA","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":15,"value":"$33","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":7,"value":"## Helper functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":9,"value":"makeArray = (x) => {\n var a = new Array();\n for (var i = 0; i < x; i++) {\n a[i] = 0;\n }\n return a;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":12,"value":"make2DArray = (x, y) => {\n var a = new Array();\n for (var i = 0; i < x; i++) {\n a[i] = new Array();\n for (var j = 0; j < y; j++) a[i][j] = 0;\n }\n return a;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":18,"value":"## Stopword constant","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":25,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":28,"value":"## Stemmer","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":30,"value":"autoStemmer = {\n const porterStemmer = await import(\n \"https://cdn.skypack.dev/@stdlib/nlp-porter-stemmer@0.0.7?min\"\n );\n return porterStemmer.default;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":39,"value":"## Test data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":43,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W36","stdlib":"1","titling":"auto"}} 30:"$2f:body"