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hf-doc-build/doc-dev / course /pr_1139 /it /_app /immutable /nodes /37.a7caeba2.js

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	import{s as Jt,n as yt,o as ht}from"../chunks/scheduler.505acc25.js";import{S as Tt,i as Ut,e as i,s as t,c as r,h as jt,a as p,d as l,b as n,f as mt,g as o,j as c,k as y,l as gt,m as a,n as u,t as d,o as M,p as m}from"../chunks/index.1238bded.js";import{C as bt,H as oe,E as wt}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.34bb763b.js";import{Y as ft}from"../chunks/Youtube.2fb63721.js";import{C as J}from"../chunks/CodeBlock.919579ab.js";import{C as xt}from"../chunks/CourseFloatingBanner.0b6e065b.js";function Zt(ca){let h,Me,ue,me,X,Je,v,ye,N,he,k,ra='A volte il dataset che ti serve per la tua applicazione NLP non esiste, per cui dovrai crearlo da te. In questa sezione ti mostreremo come creare un corpus di <a href="https://github.com/features/issues" rel="nofollow">issue da GitHub</a>, usate solitamente per tenere traccia dei bug e delle feature nelle repository su GitHub. Questo corpus può essere usato in diversi modi, ad esempio:',Te,_,oa="<li>Esplorare il tempo impiegato per chiudere un issue, o per effettuare dei pull</li> <li>Addestrare un <em>classificatore multiclasse</em> che assegna a ogni issue dei metadati sulla base della descrizione dell’issue (ad esempio, “bug”, “enhancement”, “question”)</li> <li>Creare un motore di ricerca semantico per trovare quale issue corrisponde a una richiesta dell’utente</li>",Ue,V,ua="Ci focalizzeremo sulla creazione del corpus, e nella prossima sezione affronteremo la creazione di un motore di ricerca semantico. Useremo gli issue GitHub associate a un progetto open source molto popolare: 🤗 Datasets! Diamo un’occhiata a come recuperare i dati e come esplorare le informazioni contenute negli issue.",je,B,ge,H,da='Puoi trovare tutte gli issue in 🤗 Datasets navigando nella <a href="https://github.com/huggingface/datasets/issues" rel="nofollow">sezione Issues della repository</a>. Come si vede dallo screenshot, al momento della scrittura c’erano 331 issue aperti e 668 issue chiusi.',be,T,Ma='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter5/datasets-issues.png" alt="The GitHub issues associated with 🤗 Datasets." width="80%"/>',we,W,ma="Se clicchi su una di questi issue vedrai che contiene un titolo, una descrizione, e un set di etichette che caratterizzano l’issue. Un esempio è mostrato nello screenshot successivo.",fe,U,Ja='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter5/datasets-issues-single.png" alt="A typical GitHub issue in the 🤗 Datasets repository." width="80%"/>',xe,Y,ya='Per scaricare gli issue della repository, useremo la <a href="https://docs.github.com/en/rest" rel="nofollow">REST API di GitHub</a> per interrogare l’<a href="https://docs.github.com/en/rest/reference/issues#list-repository-issues" rel="nofollow">endpoint <code>Issues</code></a>. Questo endpoint restituisce una lista di oggetti JSON, e ogni oggetto contiene un gran numero di campi, tra cui il titolo e la descrizione, così come dei metadati circo lo status dell’issue e altro ancora.',Ze,F,ha="Una maniera conveniente di scaricare gli issue è attraverso la libreria <code>requests</code>, che rappresenta il metodo standard di fare richieste HTTP su Python. Puoi installa la libreria attraverso il codice:",Ge,Q,Re,$,Ta="Una volta che la libreria è stata installata, puoi effettuare una richiesta GET all’endpoint <code>Issues</code> utilizzando la funzione <code>requests.get()</code>. Ad esempio, puoi eseguire il comando mostrato di seguito per recuperare il primo issue nella prima pagina:",Ie,E,Ce,S,Ua="L’oggetto <code>response</code> contiene un sacco di informazioni utili sulla richiesta, compreso il codice di stato HTTP:",ze,A,Xe,L,ve,q,ja='Lo status <code>200</code> indica che la richiesta ha avuto buon fine (puoi trovare una lista di codici di stato HTTTP <a href="https://it.wikipedia.org/wiki/Codici_di_stato_HTTP" rel="nofollow">qui</a>). Ma ciò che ci interessa davvero è il <em>payload</em>, a cui è possibile accedere utilizzando diversi formati come byte, stringh, o JSON. Visto che sappiamo che i nostri issue sono in formato JSON, diamo un’occhiata al payload come segue:',Ne,D,ke,P,_e,O,ga="Wow, quante informazioni! Possiamo vedere alcuni campi utili come <code>title</code>, <code>body</code> e <code>number</code> che descrivono l’issue, così come informazioni sull’utente che l’ha aperto.",Ve,j,ba="<p>✏️ <strong>Prova tu!</strong> Clicca su alcuni degli URL nel payload JSON per farti un’idea del tipo di informazione a cui è collegato ogni issue GitHub.</p>",Be,K,wa='Come descritto nella <a href="https://docs.github.com/en/rest/overview/resources-in-the-rest-api#rate-limiting" rel="nofollow">documentazione di GitHub</a>, le richieste senza autenticazione sono limitate a 60 ogni ora. Benché possiamo aumentare il parametro della query <code>per_page</code> per ridurre il numero di richieste, raggiungerai comunque il limite su qualunque repository che ha qualche migliaio di issue. Quindi, dovresti seguire le <a href="https://docs.github.com/en/github/authenticating-to-github/creating-a-personal-access-token" rel="nofollow">istruzioni</a> su come creare un <em>token di accesso personale</em> così che puoi aumentare il limite a 5.000 richieste ogni ora. Una volta che hai ottenuto il tuo token, puoi includerlo come parte dell’header della richiesta:',He,ss,We,g,fa='<p>⚠️ Fai attenzione a non condividere un notebook con il tuo <code>GITHUB_TOKEN</code> al suo interno. Ti consigliamo di cancellare l’ultima cella una volta che l’hai eseguita per evitare di far trapelare quest’informazione accidentalmente. Meglio ancora, salva il tuo token in un file <em>.env</em> e usa la <a href="https://github.com/theskumar/python-dotenv" rel="nofollow">libreria <code>python-dotenv</code></a> per caricarlo automaticamente come una variabile d’ambiente.</p>',Ye,es,xa="Ora che abbiamo il nostro token di accesso, creiamo una funzione che scarichi tutti gli issue da una repository GitHub:",Fe,ls,Qe,as,Za="Ora quando eseguiremo <code>fetch_issues()</code>, scaricherà tutti gli issue in batch per evitare di superare il limite di GitHub del numero di richieste per ora; il risultato sarà conservato in un file <em>repository_name-issues.jsonl</em>, in cui ogni linea è un oggetto JSON che rappresenta un issue. Usiamo questa funzione per recuperare tutti gli issue da 🤗 Datasets:",$e,ts,Ee,ns,Ga='Una volta che gli issue sono stati scaricati, possiamo caricarli in locale usando le nuove abilità imparate nella <a href="/course/chapter5/2">sezione 2</a>:',Se,is,Ae,ps,Le,cs,Ra='Benissimo, abbiamo creato il nostro primo dataset da zero! Ma perché ci sono migliaia di issue quando la <a href="https://github.com/huggingface/datasets/issues" rel="nofollow">sezione Issues</a> della repository 🤗 Datasets mostra circa 1,000 issue in totale 🤔? Come indicato nella <a href="https://docs.github.com/en/rest/reference/issues#list-issues-assigned-to-the-authenticated-user" rel="nofollow">documentazione di GitHub</a>, è perché abbiamo scaricato anche le richieste di pull:',qe,rs,Ia="<p>GitHub’s REST API v3 considers every pull request an issue, but not every issue is a pull request. For this reason, “Issues” endpoints may return both issues and pull requests in the response. You can identify pull requests by the <code>pull_request</code> key. Be aware that the <code>id</code> of a pull request returned from “Issues” endpoints will be an issue id.</p>",De,os,Ca="(<em>La REST API v3 di GitHub considera ogni richiesta di pull un issue, ma non ogni issue è una richiesta di pull. Per questa ragione, gli endpoint “Issues” potrebbe tornare sia gli issue che le richieste di pull. È possibile identificare le richieste di pull utilizzando la chiave <code>pull_request</code>. Tieni presente che l’<code>id</code> di una richiesta di pull resituita dagli endpoint <code>Issues</code> sarà un id di un issue.</em>)",Pe,us,za="Poichè i contenuti degli issue e delle richieste di pull sono molto diversi, facciamo un po’ di preprocessing per permetterci di distinguere tra i due.",Oe,ds,Ke,Ms,Xa='Il frammento precedente della documentazione di GitHub ci dice che la colonna <code>pull_request</code> può essere utilizzata per distinguere gli issue e le richieste di pull. Diamo uno sguardo a un esempio casuale per vedere qual è la differenza. Come abbiamo fatto nella <a href="/course/chapter5/3">sezione 3</a>, concateneremo <code>Dataset.shuffle()</code> e <code>Dataset.select()</code> per creare un campione random, e poi zipperemo le colonne <code>html_url</code> e <code>pull_request</code> così da poter paragonare i diversi URL:',sl,ms,el,Js,ll,ys,va="Possiamo vedere che ogni richiesta di pull è associata a diversi URL, mentre i comuni issue hanno un’entrata <code>None</code>. Possiamo usare questa distinzione per crare una nuova colonna <code>is_pull_request</code> che controlla se il campo <code>pull_request</code> sia <code>None</code> o meno:",al,hs,tl,b,Na="<p>✏️ <strong>Prova tu!</strong> Calcola il tempo medio che ci vuole a chiudere un issue su 🤗 Datasets. Potrebbe essere utile usare la funzione <code>Dataset.filter()</code> per eliminare le richieste di pull e gli issue aperti, e puoi usare la funzione <code>Dataset.set_format()</code> per convertire il dataset in un <code>DataFrame</code> così che puoi facilmente manipolare i timestamp <code>created_at</code> e <code>closed_at</code>. Per dei punti bonus, calcola il tempo medio che ci vuole a chiudere le richieste di pull.</p>",nl,Ts,ka="Benché potremmo procedere e pulire ulteriormente il dataset eliminando o rinominando alcune colonne, è solitamente buona prassi lasciare il dataset quando più intatto è possibile in questo stadio, così che può essere utilizzato facilmente in più applicazioni.",il,Us,_a="Prima di caricare il nostro dataset sull’Hub Hugging Face, dobbiamo occuparci di una cosa che manca: i commenti associati a ogni issue e richiesta di pull. Hai indovinato, li aggiungeremo utilizzando la REST API di GitHub!",pl,js,cl,gs,Va="Come mostrato negli screenshot di seguito, i commenti associati a un issue o una richiesta di pull offrono una fonte molto ricca di informazioni, soprattutto se siamo interessati a costruire un motore di ricerca per rispondere alle richieste degli utenti sulla libreria.",rl,w,Ba='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter5/datasets-issues-comment.png" alt="Comments associated with an issue about 🤗 Datasets." width="80%"/>',ol,bs,Ha='La REST API di GitHub offre un <a href="https://docs.github.com/en/rest/reference/issues#list-issue-comments" rel="nofollow">endpoint <code>Comments</code></a> che restituisce tutti i commenti associati con un numero di issue. Testiamo quest’endpoint per vedere cosa restituisce:',ul,ws,dl,fs,Ml,xs,Wa="Possiamo vedere che il commento è archiviato nel campo <code>body</code>, quindi possiamo scvrivere una semplice funzione che restituisce tutti i commenti associati con un issue estraendo i contenuti di <code>body</code> per ogni elemento in <code>response.json()</code>:",ml,Zs,Jl,Gs,yl,Rs,Ya="Sembra andar bene, quindi possiamo usare <code>Dataset.map()</code> per aggiungere una nuova colonna <code>comments</code> a ogni usse nel nostro dataset:",hl,Is,Tl,Cs,Fa="Come passaggio finale, salviamo il dataset esteso assieme ai nostri dati non processati, così da poter caricare entrambi sull’Hub:",Ul,zs,jl,Xs,gl,vs,bl,Ns,Qa='Ora che abbiamo il nostro dataset esteso, è arrivato il momento di caricarlo sull’Hub, così da poterlo condividere con la community! Per caricare il dataset useremo la <a href="https://github.com/huggingface/huggingface_hub" rel="nofollow">libreria 🤗 Hub</a>, che ci permette di interagire con l’Hub di Hugging Face attraverso un’API di Python. 🤗 Hub è preinstallato con 🤗 Transformers, così possiamo usarlo da subito. Ad esempio, possiamo usare la funzione <code>list_datastes()</code> per avere informazioni su tutti i dataset pubblici attualmente presenti sull’Hub:',wl,ks,fl,_s,xl,Vs,$a="Possiamo vedere che al momento ci sono circa 1.500 dataset sull’Hub, e la funzione <code>list_datasets()</code> inoltre permette di avere alcuni metadati su ciascuna repository.",Zl,Bs,Ea="Per ciò che ci riguarda, la prima cosa che dobbiamo fare è crare una nuova repository nell’Hub. Per far ciò abbiamo bisogno di un token di autentificazione, che pouò essere ottenuto effettuando l’accesso nell’Hub Hugging Face con la funzione <code>notebook_login()</code>:",Gl,Hs,Rl,Ws,Sa="Questo creerà un widget in cui puoi inserire il tuo username e la tua password, e un token API verrà salvato in <em>~/.huggingface/token</em>. Se stai eseguendo il codice in un terminale, puoi effettuare l’accesso attraverso il comando:",Il,Ys,Cl,Fs,Aa="Una volta fatto questo, possiamo crare una nuova repository con la funzione <code>create_repo()</code>:",zl,Qs,Xl,$s,vl,Es,La="In quest’esempio, abbiamo creato una repository vuota chiamata <code>github-issues</code> con l’username <code>lewtun</code> (l’username dovrebbe essere quello del tuo account Hub quando esegui questo codice!).",Nl,f,qa="<p>✏️ <strong>Prova tu!</strong> Usa le tue credenziali dell’Hub Hugging Face per ottenere un token e creare una repository vuota chiamata <code>github-issues</code>. Ricordati di <strong>non salvere mai le tue credenziali</strong> su Colab o qualunque altra repository, perché potrebbero essere recuperate da malintenzionati.</p>",kl,Ss,Da="Ora, cloniamo la repository dall’Hub alla nostra macchina e copiamo al suo interno i file del nostro dataset. 🤗 Hub contiene una classe <code>Repository</code> che ha al suo interno molti dei comandi più comuni di Git, per cui per clonare la repository in remoto dobbiamo semplicemente fornire l’URL e il percorso locale in cui desideriamo clonare:",_l,As,Vl,Ls,Pa="Di default, diverse estensioni file (ad esempio <em>.bin</em>, <em>.gz</em> e <em>.zip</em>) sono registrate da Git LFS, così che i file di grandi dimensioni possono essere gestiti all’interno dello stesso workflow. Puoi trovare una lista delle estensioni di file monitorati nel file <em>.gitattributes</em> della repository. Per includere il formato JSON Lines a questa lista, possiamo utilizzare il comando:",Bl,qs,Hl,Ds,Oa="Ora possiamo usare <code>Repository.push_to_hub()</code> per caricare il dataset sull’Hub:",Wl,Ps,Yl,Os,Ka="Se navighiamo fino all’URL contenuto in <code>repo_url</code>, vedremo che il file del nostro dataset è stato caricato.",Fl,x,st='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter5/hub-repo.png" alt="Our dataset repository on the Hugging Face Hub." width="80%"/>',Ql,Ks,et="Da qui, chiunque può scaricare il dataset semplicemente inserendo l’ID della repository come argomento <code>path</code> di <code>load_dataset()</code>:",$l,se,El,ee,Sl,le,lt="Bene, abbiamo caricato il nostro dataset sull’Hub, e può essere utilizzato da tutti! C’è un’altra cosa importante che dobbiamo fare: aggiungere una <em>dataset card</em> che spiega come è stato creato il corpus, e offre altre informazioni utili per la community.",Al,Z,at='<p>💡 Puoi caricare un dataset nell’Hub di Hugging Face anche direttamente dal terminale usando <code>huggingface-cli</code> e un po’ di magia Git. La <a href="https://huggingface.co/docs/datasets/share#share-a-dataset-using-the-cli" rel="nofollow">guida a 🤗 Datasets</a> spiega come farlo.</p>',Ll,ae,ql,te,tt="I dataset ben-documentati sono più utili agli altri utenti (compreso il futuro te!), poiché spiegano il contesto per permettere agli utenti di decidere se un dataset può essere utile, e valutare gli eventuali bias o rischi associati nell’utilizzo del dataset.",Dl,ne,nt="Sull’Hug di Hugging Face, queste informazioni si trovano nel file <em>README.md</em> della repository. Ci sono due passaggi principali che dovresti seguire prima di creare questo file:",Pl,ie,it='<li>Usa l’<a href="https://huggingface.co/datasets/tagging/" rel="nofollow">applicatione <code>datasets-tagging</code></a> per creare tag di metadati in formato YAML. Questi tag sono usato per una serie di funzioni di ricerca sull’Hub di Hugging Face, e assicurano che il tuo dataset possa essere facilmente trovato dai membri della community. Poichè abbiamo creato un nostro dataset, dovrai clonare la repository <code>datasets-tagging</code>, ed eseguire l’applicazione in locale. Ecco com’è l’interfaccia:</li>',Ol,G,pt='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter5/datasets-tagger.png" alt="The 'datasets-tagging' interface." width="80%"/>',Kl,R,ct='<li>Leggi la <a href="https://github.com/huggingface/datasets/blob/master/templates/README_guide.md" rel="nofollow">guida 🤗 Datasets</a> sulla creazione di dataset card informative, e usala come template.</li>',sa,pe,rt="Puoi creare il file <em>README.md</em> direttamente sull’Hub, e puoi trovare un modello per una dataset card nella repository <code>lewtun/github-issues</code>. Di seguito è mostrato uno screenshot di una dataset card già compilata.",ea,I,ot='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter5/dataset-card.png" alt="A dataset card." width="80%"/>',la,C,ut='<p>✏️ <strong>Prova tu!</strong> Usa l’applicazione <code>dataset-tagging</code> e la <a href="https://github.com/huggingface/datasets/blob/master/templates/README_guide.md" rel="nofollow">guida 🤗 Datasets</a> per completare il file <em>README.md</em> per il tuo dataset di issue di GitHub.</p>',aa,ce,dt="È tutto! Abbiamo visto in questa sezione che creare un buon dataset può essere un’impresa, ma per fortuna caricarlo e condividerlo con la community è molto più semplice. Nella prossima sezione useremo il nostro nuovo dataset per creare un motore di ricerca semantico con 🤗 Datasets, che abbina alle domande gli issue e i commenti più rilevanti.",ta,z,Mt="<p>✏️ <strong>Prova tu!</strong> Segui i passi che abbiamo eseguito in questa sezione per creare un dataset di issue GitHub per la tua libreria open source preferita (ovviamente scegli qualcosa di diverso da 🤗 Datasets!). Per punti bonus, esegui il fine-tuning di un classificatore multiclasse per predirre i tag presenti nel campo <code>labels</code>.</p>",na,re,ia,de,pa;return X=new bt({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),v=new oe({props:{title:"Creare il proprio dataset",local:"creare-il-proprio-dataset",headingTag:"h1"}}),N=new xt({props:{chapter:5,classNames:"absolute z-10 right-0 top-0",notebooks:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/it/chapter5/section5.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/it/chapter5/section5.ipynb"}]}}),B=new oe({props:{title:"Recuperare i dati",local:"recuperare-i-dati",headingTag:"h2"}}),Q=new J({props:{code:"IXBpcCUyMGluc3RhbGwlMjByZXF1ZXN0cw==",highlighted:"!pip install requests",wrap:!1}}),E=new J({props:{code:"aW1wb3J0JTIwcmVxdWVzdHMlMEElMEF1cmwlMjAlM0QlMjAlMjJodHRwcyUzQSUyRiUyRmFwaS5naXRodWIuY29tJTJGcmVwb3MlMkZodWdnaW5nZmFjZSUyRmRhdGFzZXRzJTJGaXNzdWVzJTNGcGFnZSUzRDElMjZwZXJfcGFnZSUzRDElMjIlMEFyZXNwb25zZSUyMCUzRCUyMHJlcXVlc3RzLmdldCh1cmwp",highlighted:`<span class="hljs-keyword">import</span> requests

	url = <span class="hljs-string">"https://api.github.com/repos/huggingface/datasets/issues?page=1&per_page=1"</span>
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	<span class="hljs-string">'body'</span>: <span class="hljs-string">'[GooAQ](https://github.com/allenai/gooaq) dataset was recently updated after splits were added for the same. This PR contains new updated GooAQ with train/val/test splits and updated README as well.'</span>,
	<span class="hljs-string">'performed_via_github_app'</span>: <span class="hljs-literal">None</span>}]`,wrap:!1}}),ss=new J({props:{code:"R0lUSFVCX1RPS0VOJTIwJTNEJTIweHh4JTIwJTIwJTIzJTIwaW5zZXJpc2NpJTIwcXVpJTIwaWwlMjB0dW8lMjB0b2tlbiUyMEdpdEh1YiUwQWhlYWRlcnMlMjAlM0QlMjAlN0IlMjJBdXRob3JpemF0aW9uJTIyJTNBJTIwZiUyMnRva2VuJTIwJTdCR0lUSFVCX1RPS0VOJTdEJTIyJTdE",highlighted:`GITHUB_TOKEN = xxx <span class="hljs-comment"># inserisci qui il tuo token GitHub</span>
	headers = {<span class="hljs-string">"Authorization"</span>: <span class="hljs-string">f"token <span class="hljs-subst">{GITHUB_TOKEN}</span>"</span>}`,wrap:!1}}),ls=new J({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> time
	<span class="hljs-keyword">import</span> math
	<span class="hljs-keyword">from</span> pathlib <span class="hljs-keyword">import</span> Path
	<span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd
	<span class="hljs-keyword">from</span> tqdm.notebook <span class="hljs-keyword">import</span> tqdm


	<span class="hljs-keyword">def</span> <span class="hljs-title function_">fetch_issues</span>(<span class="hljs-params">
	owner=<span class="hljs-string">"huggingface"</span>,
	repo=<span class="hljs-string">"datasets"</span>,
	num_issues=<span class="hljs-number">10_000</span>,
	rate_limit=<span class="hljs-number">5_000</span>,
	issues_path=Path(<span class="hljs-params"><span class="hljs-string">"."</span></span>),
	</span>):
	<span class="hljs-keyword">if</span> <span class="hljs-keyword">not</span> issues_path.is_dir():
	issues_path.mkdir(exist_ok=<span class="hljs-literal">True</span>)

	batch = []
	all_issues = []
	per_page = <span class="hljs-number">100</span> <span class="hljs-comment"># Numero di issue da restituire per pagina</span>
	num_pages = math.ceil(num_issues / per_page)
	base_url = <span class="hljs-string">"https://api.github.com/repos"</span>

	<span class="hljs-keyword">for</span> page <span class="hljs-keyword">in</span> tqdm(<span class="hljs-built_in">range</span>(num_pages)):
	<span class="hljs-comment"># La query ha state=all per ottenere sia gli issue aperti che quelli chiusi</span>
	query = <span class="hljs-string">f"issues?page=<span class="hljs-subst">{page}</span>&per_page=<span class="hljs-subst">{per_page}</span>&state=all"</span>
	issues = requests.get(<span class="hljs-string">f"<span class="hljs-subst">{base_url}</span>/<span class="hljs-subst">{owner}</span>/<span class="hljs-subst">{repo}</span>/<span class="hljs-subst">{query}</span>"</span>, headers=headers)
	batch.extend(issues.json())

	<span class="hljs-keyword">if</span> <span class="hljs-built_in">len</span>(batch) > rate_limit <span class="hljs-keyword">and</span> <span class="hljs-built_in">len</span>(all_issues) < num_issues:
	all_issues.extend(batch)
	batch = [] <span class="hljs-comment"># puliamo la batch per il termine successivo</span>
	<span class="hljs-built_in">print</span>(<span class="hljs-string">f"Reached GitHub rate limit. Sleeping for one hour ..."</span>)
	time.sleep(<span class="hljs-number">60</span> * <span class="hljs-number">60</span> + <span class="hljs-number">1</span>)

	all_issues.extend(batch)
	df = pd.DataFrame.from_records(all_issues)
	df.to_json(<span class="hljs-string">f"<span class="hljs-subst">{issues_path}</span>/<span class="hljs-subst">{repo}</span>-issues.jsonl"</span>, orient=<span class="hljs-string">"records"</span>, lines=<span class="hljs-literal">True</span>)
	<span class="hljs-built_in">print</span>(
	<span class="hljs-string">f"Downloaded all the issues for <span class="hljs-subst">{repo}</span>! Dataset stored at <span class="hljs-subst">{issues_path}</span>/<span class="hljs-subst">{repo}</span>-issues.jsonl"</span>
	)`,wrap:!1}}),ts=new J({props:{code:"JTIzJTIwQSUyMHNlY29uZGElMjBkZWxsYSUyMHR1YSUyMGNvbm5lc3Npb25lJTIwaW50ZXJuZXQlMkMlMjBjaSUyMHBvdHJlYmJlJTIwdm9sZXJlJTIwcXVhbGNoZSUyMHNlY29uZG8uLi4lMEFmZXRjaF9pc3N1ZXMoKQ==",highlighted:`<span class="hljs-comment"># A seconda della tua connessione internet, ci potrebbe volere qualche secondo...</span>
	fetch_issues()`,wrap:!1}}),is=new J({props:{code:"aXNzdWVzX2RhdGFzZXQlMjAlM0QlMjBsb2FkX2RhdGFzZXQoJTIyanNvbiUyMiUyQyUyMGRhdGFfZmlsZXMlM0QlMjJkYXRhc2V0cy1pc3N1ZXMuanNvbmwlMjIlMkMlMjBzcGxpdCUzRCUyMnRyYWluJTIyKSUwQWlzc3Vlc19kYXRhc2V0",highlighted:`issues_dataset = load_dataset(<span class="hljs-string">"json"</span>, data_files=<span class="hljs-string">"datasets-issues.jsonl"</span>, split=<span class="hljs-string">"train"</span>)
	issues_dataset`,wrap:!1}}),ps=new J({props:{code:"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",highlighted:`Dataset({
	features: [<span class="hljs-string">'url'</span>, <span class="hljs-string">'repository_url'</span>, <span class="hljs-string">'labels_url'</span>, <span class="hljs-string">'comments_url'</span>, <span class="hljs-string">'events_url'</span>, <span class="hljs-string">'html_url'</span>, <span class="hljs-string">'id'</span>, <span class="hljs-string">'node_id'</span>, <span class="hljs-string">'number'</span>, <span class="hljs-string">'title'</span>, <span class="hljs-string">'user'</span>, <span class="hljs-string">'labels'</span>, <span class="hljs-string">'state'</span>, <span class="hljs-string">'locked'</span>, <span class="hljs-string">'assignee'</span>, <span class="hljs-string">'assignees'</span>, <span class="hljs-string">'milestone'</span>, <span class="hljs-string">'comments'</span>, <span class="hljs-string">'created_at'</span>, <span class="hljs-string">'updated_at'</span>, <span class="hljs-string">'closed_at'</span>, <span class="hljs-string">'author_association'</span>, <span class="hljs-string">'active_lock_reason'</span>, <span class="hljs-string">'pull_request'</span>, <span class="hljs-string">'body'</span>, <span class="hljs-string">'timeline_url'</span>, <span class="hljs-string">'performed_via_github_app'</span>],
	num_rows: <span class="hljs-number">3019</span>
	})`,wrap:!1}}),ds=new oe({props:{title:"Pulire i dati",local:"pulire-i-dati",headingTag:"h2"}}),ms=new J({props:{code:"c2FtcGxlJTIwJTNEJTIwaXNzdWVzX2RhdGFzZXQuc2h1ZmZsZShzZWVkJTNENjY2KS5zZWxlY3QocmFuZ2UoMykpJTBBJTBBJTIzJTIwU3RhbXBpYW1vJTIwbGUlMjBlbnRyYXRlJTIwJTYwVVJMJTYwJTIwZSUyMCU2MHB1bGxfcmVxdWVzdCU2MCUwQWZvciUyMHVybCUyQyUyMHByJTIwaW4lMjB6aXAoc2FtcGxlJTVCJTIyaHRtbF91cmwlMjIlNUQlMkMlMjBzYW1wbGUlNUIlMjJwdWxsX3JlcXVlc3QlMjIlNUQpJTNBJTBBJTIwJTIwJTIwJTIwcHJpbnQoZiUyMiUzRSUzRSUyMFVSTCUzQSUyMCU3QnVybCU3RCUyMiklMEElMjAlMjAlMjAlMjBwcmludChmJTIyJTNFJTNFJTIwUHVsbCUyMHJlcXVlc3QlM0ElMjAlN0JwciU3RCU1Q24lMjIp",highlighted:`sample = issues_dataset.shuffle(seed=<span class="hljs-number">666</span>).select(<span class="hljs-built_in">range</span>(<span class="hljs-number">3</span>))

	<span class="hljs-comment"># Stampiamo le entrate \`URL\` e \`pull_request\`</span>
	<span class="hljs-keyword">for</span> url, pr <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(sample[<span class="hljs-string">"html_url"</span>], sample[<span class="hljs-string">"pull_request"</span>]):
	<span class="hljs-built_in">print</span>(<span class="hljs-string">f">> URL: <span class="hljs-subst">{url}</span>"</span>)
	<span class="hljs-built_in">print</span>(<span class="hljs-string">f">> Pull request: <span class="hljs-subst">{pr}</span>\\n"</span>)`,wrap:!1}}),Js=new J({props:{code:"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",highlighted:`>> URL: https://github.com/huggingface/datasets/pull/<span class="hljs-number">850</span>
	>> Pull request: {<span class="hljs-string">'url'</span>: <span class="hljs-string">'https://api.github.com/repos/huggingface/datasets/pulls/850'</span>, <span class="hljs-string">'html_url'</span>: <span class="hljs-string">'https://github.com/huggingface/datasets/pull/850'</span>, <span class="hljs-string">'diff_url'</span>: <span class="hljs-string">'https://github.com/huggingface/datasets/pull/850.diff'</span>, <span class="hljs-string">'patch_url'</span>: <span class="hljs-string">'https://github.com/huggingface/datasets/pull/850.patch'</span>}

	>> URL: https://github.com/huggingface/datasets/issues/<span class="hljs-number">2773</span>
	>> Pull request: <span class="hljs-literal">None</span>

	>> URL: https://github.com/huggingface/datasets/pull/<span class="hljs-number">783</span>
	>> Pull request: {<span class="hljs-string">'url'</span>: <span class="hljs-string">'https://api.github.com/repos/huggingface/datasets/pulls/783'</span>, <span class="hljs-string">'html_url'</span>: <span class="hljs-string">'https://github.com/huggingface/datasets/pull/783'</span>, <span class="hljs-string">'diff_url'</span>: <span class="hljs-string">'https://github.com/huggingface/datasets/pull/783.diff'</span>, <span class="hljs-string">'patch_url'</span>: <span class="hljs-string">'https://github.com/huggingface/datasets/pull/783.patch'</span>}`,wrap:!1}}),hs=new J({props:{code:"aXNzdWVzX2RhdGFzZXQlMjAlM0QlMjBpc3N1ZXNfZGF0YXNldC5tYXAoJTBBJTIwJTIwJTIwJTIwbGFtYmRhJTIweCUzQSUyMCU3QiUyMmlzX3B1bGxfcmVxdWVzdCUyMiUzQSUyMEZhbHNlJTIwaWYlMjB4JTVCJTIycHVsbF9yZXF1ZXN0JTIyJTVEJTIwaXMlMjBOb25lJTIwZWxzZSUyMFRydWUlN0QlMEEp",highlighted:`issues_dataset = issues_dataset.<span class="hljs-built_in">map</span>(
	<span class="hljs-keyword">lambda</span> x: {<span class="hljs-string">"is_pull_request"</span>: <span class="hljs-literal">False</span> <span class="hljs-keyword">if</span> x[<span class="hljs-string">"pull_request"</span>] <span class="hljs-keyword">is</span> <span class="hljs-literal">None</span> <span class="hljs-keyword">else</span> <span class="hljs-literal">True</span>}
	)`,wrap:!1}}),js=new oe({props:{title:"Estendere il dataset",local:"estendere-il-dataset",headingTag:"h2"}}),ws=new J({props:{code:"aXNzdWVfbnVtYmVyJTIwJTNEJTIwMjc5MiUwQXVybCUyMCUzRCUyMGYlMjJodHRwcyUzQSUyRiUyRmFwaS5naXRodWIuY29tJTJGcmVwb3MlMkZodWdnaW5nZmFjZSUyRmRhdGFzZXRzJTJGaXNzdWVzJTJGJTdCaXNzdWVfbnVtYmVyJTdEJTJGY29tbWVudHMlMjIlMEFyZXNwb25zZSUyMCUzRCUyMHJlcXVlc3RzLmdldCh1cmwlMkMlMjBoZWFkZXJzJTNEaGVhZGVycyklMEFyZXNwb25zZS5qc29uKCk=",highlighted:`issue_number = <span class="hljs-number">2792</span>
	url = <span class="hljs-string">f"https://api.github.com/repos/huggingface/datasets/issues/<span class="hljs-subst">{issue_number}</span>/comments"</span>
	response = requests.get(url, headers=headers)
	response.json()`,wrap:!1}}),fs=new J({props:{code:"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",highlighted:`[{<span class="hljs-string">'url'</span>: <span class="hljs-string">'https://api.github.com/repos/huggingface/datasets/issues/comments/897594128'</span>,
	<span class="hljs-string">'html_url'</span>: <span class="hljs-string">'https://github.com/huggingface/datasets/pull/2792#issuecomment-897594128'</span>,
	<span class="hljs-string">'issue_url'</span>: <span class="hljs-string">'https://api.github.com/repos/huggingface/datasets/issues/2792'</span>,
	<span class="hljs-string">'id'</span>: <span class="hljs-number">897594128</span>,
	<span class="hljs-string">'node_id'</span>: <span class="hljs-string">'IC_kwDODunzps41gDMQ'</span>,
	<span class="hljs-string">'user'</span>: {<span class="hljs-string">'login'</span>: <span class="hljs-string">'bhavitvyamalik'</span>,
	<span class="hljs-string">'id'</span>: <span class="hljs-number">19718818</span>,
	<span class="hljs-string">'node_id'</span>: <span class="hljs-string">'MDQ6VXNlcjE5NzE4ODE4'</span>,
	<span class="hljs-string">'avatar_url'</span>: <span class="hljs-string">'https://avatars.githubusercontent.com/u/19718818?v=4'</span>,
	<span class="hljs-string">'gravatar_id'</span>: <span class="hljs-string">''</span>,
	<span class="hljs-string">'url'</span>: <span class="hljs-string">'https://api.github.com/users/bhavitvyamalik'</span>,
	<span class="hljs-string">'html_url'</span>: <span class="hljs-string">'https://github.com/bhavitvyamalik'</span>,
	<span class="hljs-string">'followers_url'</span>: <span class="hljs-string">'https://api.github.com/users/bhavitvyamalik/followers'</span>,
	<span class="hljs-string">'following_url'</span>: <span class="hljs-string">'https://api.github.com/users/bhavitvyamalik/following{/other_user}'</span>,
	<span class="hljs-string">'gists_url'</span>: <span class="hljs-string">'https://api.github.com/users/bhavitvyamalik/gists{/gist_id}'</span>,
	<span class="hljs-string">'starred_url'</span>: <span class="hljs-string">'https://api.github.com/users/bhavitvyamalik/starred{/owner}{/repo}'</span>,
	<span class="hljs-string">'subscriptions_url'</span>: <span class="hljs-string">'https://api.github.com/users/bhavitvyamalik/subscriptions'</span>,
	<span class="hljs-string">'organizations_url'</span>: <span class="hljs-string">'https://api.github.com/users/bhavitvyamalik/orgs'</span>,
	<span class="hljs-string">'repos_url'</span>: <span class="hljs-string">'https://api.github.com/users/bhavitvyamalik/repos'</span>,
	<span class="hljs-string">'events_url'</span>: <span class="hljs-string">'https://api.github.com/users/bhavitvyamalik/events{/privacy}'</span>,
	<span class="hljs-string">'received_events_url'</span>: <span class="hljs-string">'https://api.github.com/users/bhavitvyamalik/received_events'</span>,
	<span class="hljs-string">'type'</span>: <span class="hljs-string">'User'</span>,
	<span class="hljs-string">'site_admin'</span>: <span class="hljs-literal">False</span>},
	<span class="hljs-string">'created_at'</span>: <span class="hljs-string">'2021-08-12T12:21:52Z'</span>,
	<span class="hljs-string">'updated_at'</span>: <span class="hljs-string">'2021-08-12T12:31:17Z'</span>,
	<span class="hljs-string">'author_association'</span>: <span class="hljs-string">'CONTRIBUTOR'</span>,
	<span class="hljs-string">'body'</span>: <span class="hljs-string">"@albertvillanova my tests are failing here:\\r\\n\`\`\`\\r\\ndataset_name = 'gooaq'\\r\\n\\r\\n def test_load_dataset(self, dataset_name):\\r\\n configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)[:1]\\r\\n> self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True, use_local_dummy_data=True)\\r\\n\\r\\ntests/test_dataset_common.py:234: \\r\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\r\\ntests/test_dataset_common.py:187: in check_load_dataset\\r\\n self.parent.assertTrue(len(dataset[split]) > 0)\\r\\nE AssertionError: False is not true\\r\\n\`\`\`\\r\\nWhen I try loading dataset on local machine it works fine. Any suggestions on how can I avoid this error?"</span>,
	<span class="hljs-string">'performed_via_github_app'</span>: <span class="hljs-literal">None</span>}]`,wrap:!1}}),Zs=new J({props:{code:"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",highlighted:`<span class="hljs-keyword">def</span> <span class="hljs-title function_">get_comments</span>(<span class="hljs-params">issue_number</span>):
	url = <span class="hljs-string">f"https://api.github.com/repos/huggingface/datasets/issues/<span class="hljs-subst">{issue_number}</span>/comments"</span>
	response = requests.get(url, headers=headers)
	<span class="hljs-keyword">return</span> [r[<span class="hljs-string">"body"</span>] <span class="hljs-keyword">for</span> r <span class="hljs-keyword">in</span> response.json()]


	<span class="hljs-comment"># Testiamo la nostra funzione</span>
	get_comments(<span class="hljs-number">2792</span>)`,wrap:!1}}),Gs=new J({props:{code:"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",highlighted:'[<span class="hljs-string">"@albertvillanova my tests are failing here:\\r\\n```\\r\\ndataset_name = 'gooaq'\\r\\n\\r\\n def test_load_dataset(self, dataset_name):\\r\\n configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)[:1]\\r\\n> self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True, use_local_dummy_data=True)\\r\\n\\r\\ntests/test_dataset_common.py:234: \\r\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\r\\ntests/test_dataset_common.py:187: in check_load_dataset\\r\\n self.parent.assertTrue(len(dataset[split]) > 0)\\r\\nE AssertionError: False is not true\\r\\n```\\r\\nWhen I try loading dataset on local machine it works fine. Any suggestions on how can I avoid this error?"</span>]',wrap:!1}}),Is=new J({props:{code:"JTIzJTIwQSUyMHNlY29uZGElMjBkZWxsYSUyMHR1YSUyMGNvbm5lc3Npb25lJTJDJTIwcG90cmViYmUlMjB2b2xlcmNpJTIwcXVhbGNoZSUyMHNlY29uZG8uLi4lMEFpc3N1ZXNfd2l0aF9jb21tZW50c19kYXRhc2V0JTIwJTNEJTIwaXNzdWVzX2RhdGFzZXQubWFwKCUwQSUyMCUyMCUyMCUyMGxhbWJkYSUyMHglM0ElMjAlN0IlMjJjb21tZW50cyUyMiUzQSUyMGdldF9jb21tZW50cyh4JTVCJTIybnVtYmVyJTIyJTVEKSU3RCUwQSk=",highlighted:`<span class="hljs-comment"># A seconda della tua connessione, potrebbe volerci qualche secondo...</span>
	issues_with_comments_dataset = issues_dataset.<span class="hljs-built_in">map</span>(
	<span class="hljs-keyword">lambda</span> x: {<span class="hljs-string">"comments"</span>: get_comments(x[<span class="hljs-string">"number"</span>])}
	)`,wrap:!1}}),zs=new J({props:{code:"aXNzdWVzX3dpdGhfY29tbWVudHNfZGF0YXNldC50b19qc29uKCUyMmlzc3Vlcy1kYXRhc2V0cy13aXRoLWNvbW1lbnRzLmpzb25sJTIyKQ==",highlighted:'issues_with_comments_dataset.to_json(<span class="hljs-string">"issues-datasets-with-comments.jsonl"</span>)',wrap:!1}}),Xs=new oe({props:{title:"Caricare il dataset sull’Hub Hugging Face",local:"caricare-il-dataset-sullhub-hugging-face",headingTag:"h2"}}),vs=new ft({props:{id:"HaN6qCr_Afc"}}),ks=new J({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMGxpc3RfZGF0YXNldHMlMEElMEFhbGxfZGF0YXNldHMlMjAlM0QlMjBsaXN0X2RhdGFzZXRzKCklMEFwcmludChmJTIyTnVtYmVyJTIwb2YlMjBkYXRhc2V0cyUyMG9uJTIwSHViJTNBJTIwJTdCbGVuKGFsbF9kYXRhc2V0cyklN0QlMjIpJTBBcHJpbnQoYWxsX2RhdGFzZXRzJTVCMCU1RCk=",highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> list_datasets

	all_datasets = list_datasets()
	<span class="hljs-built_in">print</span>(<span class="hljs-string">f"Number of datasets on Hub: <span class="hljs-subst">{<span class="hljs-built_in">len</span>(all_datasets)}</span>"</span>)
	<span class="hljs-built_in">print</span>(all_datasets[<span class="hljs-number">0</span>])`,wrap:!1}}),_s=new J({props:{code:"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",highlighted:`Number of datasets on Hub: <span class="hljs-number">1487</span>
	Dataset Name: acronym_identification, Tags: [<span class="hljs-string">'annotations_creators:expert-generated'</span>, <span class="hljs-string">'language_creators:found'</span>, <span class="hljs-string">'languages:en'</span>, <span class="hljs-string">'licenses:mit'</span>, <span class="hljs-string">'multilinguality:monolingual'</span>, <span class="hljs-string">'size_categories:10K<n<100K'</span>, <span class="hljs-string">'source_datasets:original'</span>, <span class="hljs-string">'task_categories:structure-prediction'</span>, <span class="hljs-string">'task_ids:structure-prediction-other-acronym-identification'</span>]`,wrap:!1}}),Hs=new J({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMG5vdGVib29rX2xvZ2luJTBBJTBBbm90ZWJvb2tfbG9naW4oKQ==",highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login

	notebook_login()`,wrap:!1}}),Ys=new J({props:{code:"aHVnZ2luZ2ZhY2UtY2xpJTIwbG9naW4=",highlighted:"huggingface-cli login",wrap:!1}}),Qs=new J({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMGNyZWF0ZV9yZXBvJTBBJTBBcmVwb191cmwlMjAlM0QlMjBjcmVhdGVfcmVwbyhuYW1lJTNEJTIyZ2l0aHViLWlzc3VlcyUyMiUyQyUyMHJlcG9fdHlwZSUzRCUyMmRhdGFzZXQlMjIpJTBBcmVwb191cmw=",highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> create_repo

	repo_url = create_repo(name=<span class="hljs-string">"github-issues"</span>, repo_type=<span class="hljs-string">"dataset"</span>)
	repo_url`,wrap:!1}}),$s=new J({props:{code:"J2h0dHBzJTNBJTJGJTJGaHVnZ2luZ2ZhY2UuY28lMkZkYXRhc2V0cyUyRmxld3R1biUyRmdpdGh1Yi1pc3N1ZXMn",highlighted:'<span class="hljs-string">'https://huggingface.co/datasets/lewtun/github-issues'</span>',wrap:!1}}),As=new J({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMFJlcG9zaXRvcnklMEElMEFyZXBvJTIwJTNEJTIwUmVwb3NpdG9yeShsb2NhbF9kaXIlM0QlMjJnaXRodWItaXNzdWVzJTIyJTJDJTIwY2xvbmVfZnJvbSUzRHJlcG9fdXJsKSUwQSFjcCUyMGlzc3Vlcy1kYXRhc2V0cy13aXRoLWNvbW1lbnRzLmpzb25sJTIwZ2l0aHViLWlzc3VlcyUyRg==",highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> Repository

	repo = Repository(local_dir=<span class="hljs-string">"github-issues"</span>, clone_from=repo_url)
	!cp issues-datasets-<span class="hljs-keyword">with</span>-comments.jsonl github-issues/`,wrap:!1}}),qs=new J({props:{code:"cmVwby5sZnNfdHJhY2soJTIyKi5qc29ubCUyMik=",highlighted:'repo.lfs_track(<span class="hljs-string">"*.jsonl"</span>)',wrap:!1}}),Ps=new J({props:{code:"cmVwby5wdXNoX3RvX2h1Yigp",highlighted:"repo.push_to_hub()",wrap:!1}}),se=new J({props:{code:"cmVtb3RlX2RhdGFzZXQlMjAlM0QlMjBsb2FkX2RhdGFzZXQoJTIybGV3dHVuJTJGZ2l0aHViLWlzc3VlcyUyMiUyQyUyMHNwbGl0JTNEJTIydHJhaW4lMjIpJTBBcmVtb3RlX2RhdGFzZXQ=",highlighted:`remote_dataset = load_dataset(<span class="hljs-string">"lewtun/github-issues"</span>, split=<span class="hljs-string">"train"</span>)
	remote_dataset`,wrap:!1}}),ee=new J({props:{code:"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",highlighted:`Dataset({
	features: [<span class="hljs-string">'url'</span>, <span class="hljs-string">'repository_url'</span>, <span class="hljs-string">'labels_url'</span>, <span class="hljs-string">'comments_url'</span>, <span class="hljs-string">'events_url'</span>, <span class="hljs-string">'html_url'</span>, <span class="hljs-string">'id'</span>, <span class="hljs-string">'node_id'</span>, <span class="hljs-string">'number'</span>, <span class="hljs-string">'title'</span>, <span class="hljs-string">'user'</span>, <span class="hljs-string">'labels'</span>, <span class="hljs-string">'state'</span>, <span class="hljs-string">'locked'</span>, <span class="hljs-string">'assignee'</span>, <span class="hljs-string">'assignees'</span>, <span class="hljs-string">'milestone'</span>, <span class="hljs-string">'comments'</span>, <span class="hljs-string">'created_at'</span>, <span class="hljs-string">'updated_at'</span>, <span class="hljs-string">'closed_at'</span>, <span class="hljs-string">'author_association'</span>, <span class="hljs-string">'active_lock_reason'</span>, <span class="hljs-string">'pull_request'</span>, <span class="hljs-string">'body'</span>, <span class="hljs-string">'performed_via_github_app'</span>, <span class="hljs-string">'is_pull_request'</span>],
	num_rows: <span class="hljs-number">2855</span>
	})`,wrap:!1}}),ae=new oe({props:{title:"Creare una dataset card",local:"creare-una-dataset-card",headingTag:"h2"}}),re=new 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