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hf-doc-build/doc-dev / course /pr_1120 /pt /_app /immutable /nodes /32.1908aa0a.js

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	import{s as ut,n as mt,o as Mt}from"../chunks/scheduler.505acc25.js";import{S as Jt,i as yt,e as o,s as t,c,h as ht,a as i,d as e,b as n,f as dt,g as r,j as p,k as y,l as Tt,m as l,n as d,t as u,o as m,p as M}from"../chunks/index.1238bded.js";import{C as Ut,H as ca,E as jt}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.076cb552.js";import{Y as bt}from"../chunks/Youtube.2fb63721.js";import{C as J}from"../chunks/CodeBlock.b4575fa8.js";import{C as gt}from"../chunks/CourseFloatingBanner.2e302d0f.js";function wt(ol){let h,ua,ra,ma,X,Ma,k,Ja,N,ya,z,il='Às vezes, o conjunto de dados de que você precisa para criar um aplicativo de PLN não existe, portanto, você mesmo precisará criá-lo. Nesta seção, mostraremos como criar um corpus de <a href="https://github.com/features/issues/" rel="nofollow">issues do GitHub</a>, que são comumente usados para rastrear bugs ou recursos nos repositórios do GitHub. Este corpus pode ser usado para vários fins, incluindo:',ha,V,pl="<li>Explorar quanto tempo leva para fechar as issues abertos ou pull requests</li> <li>Treinar um <em>classificador multilabel</em> que pode marcar issues com metadados com base na descrição da issue (por exemplo, “bug”, “melhoria” ou “pergunta”)</li> <li>Criando um mecanismo de pesquisa semântica para descobrir quais issues correspondem à consulta de um usuário</li>",Ta,_,cl="Aqui nos concentraremos na criação do corpus e, na próxima seção, abordaremos o aplicativo de pesquisa semântica. Para manter a meta, usaremos as issues do GitHub associados a um projeto de código aberto popular: 🤗 Datasets! Vamos dar uma olhada em como obter os dados e explorar as informações contidas nessas edições.",Ua,B,ja,H,rl='Você pode encontrar todos as issues em 🤗 Datasets navegando até a <a href="https://github.com/huggingface/datasets/issues" rel="nofollow">guia de issues</a> do repositório. Conforme mostrado na captura de tela a seguir, no momento da redação, havia 331 issues abertos e 668 fechados.',ba,T,dl='<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%"/>',ga,W,ul="Se você clicar em uma dessas issues, verá que ele contém um título, uma descrição e um conjunto de rótulos que caracterizam a issue. Um exemplo é mostrado na captura de tela abaixo.",wa,U,ml='<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%"/>',fa,F,Ml='Para baixar todos as issues do repositório, usaremos a <a href="https://docs.github.com/en/rest" rel="nofollow">GitHub REST API</a> para pesquisar o [<code>Issues</code> endpoint](<a href="https://docs.github." rel="nofollow">https://docs.github.</a> com/en/rest/reference/issues#list-repository-issues). Esse endpoint retorna uma lista de objetos JSON, com cada objeto contendo um grande número de campos que incluem o título e a descrição, bem como metadados sobre o status da issue e assim por diante.',xa,Q,Jl="Uma maneira conveniente de baixar as issues é por meio da biblioteca <code>requests</code>, que é a maneira padrão de fazer solicitações HTTP em Python. Você pode instalar a biblioteca executando:",Za,Y,Ga,$,yl="Uma vez que a biblioteca esteja instalada, você pode fazer solicitações GET para o endpoint <code>Issues</code> invocando a função <code>requests.get()</code>. Por exemplo, você pode executar o seguinte comando para recuperar a primeira issue na primeira página:",Ra,E,Ia,S,hl="O objeto <code>response</code> contém muitas informações úteis sobre a solicitação, incluindo o código de status HTTP:",va,A,Ca,q,Xa,L,Tl='onde um status <code>200</code> significa que a solicitação foi bem-sucedida (você pode encontrar uma lista de possíveis códigos de status HTTP <a href="https://en.wikipedia.org/wiki/List_of_HTTP_status_codes" rel="nofollow">aqui</a>). O que realmente nos interessa, porém, é o <em>payload</em>, que pode ser acessado em vários formatos como bytes, strings ou JSON. Como sabemos que nossas issues estão no formato JSON, vamos inspecionar o payload da seguinte forma:',ka,D,Na,P,za,O,Ul="Uau, é muita informação! Podemos ver campos úteis como <code>title</code>, <code>body</code> e <code>number</code> que descrevem a issue, bem como informações sobre o usuário do GitHub que abriu a issue.",Va,j,jl="<p>✏️ <strong>Experimente!</strong> Clique em alguns dos URLs na carga JSON acima para ter uma ideia de que tipo de informação cada issue do GitHub está vinculado.</p>",_a,K,bl='Conforme descrito na [documentação] do GitHub (<a href="https://docs.github.com/en/rest/overview/resources-in-the-rest-api#rate-limiting" rel="nofollow">https://docs.github.com/en/rest/overview/resources-in-the-rest-api#rate-limiting</a>), as solicitações não autenticadas são limitadas a 60 solicitações por hora. Embora você possa aumentar o parâmetro de consulta <code>per_page</code> para reduzir o número de solicitações feitas, você ainda atingirá o limite de taxa em qualquer repositório que tenha mais do que alguns milhares de issues. Então, em vez disso, você deve seguir as [instruções] do GitHub (<a href="https://docs.github.com/en/github/authenticating-to-github/creating-a-personal-access-token" rel="nofollow">https://docs.github.com/en/github/authenticating-to-github/creating-a-personal-access-token</a>) sobre como criar um <em>token de acesso pessoal</em> para que você pode aumentar o limite de taxa para 5.000 solicitações por hora. Depois de ter seu token, você pode incluí-lo como parte do cabeçalho da solicitação:',Ba,ss,Ha,b,gl='<p>⚠️ Não compartilhe um notebook com seu <code>GITHUB_TOKEN</code> colado nele. Recomendamos que você exclua a última célula depois de executá-la para evitar o vazamento dessas informações acidentalmente. Melhor ainda, armazene o token em um arquivo <em>.env</em> e use a <a href="https://github.com/theskumar/python-dotenv" rel="nofollow"><code>python-dotenv</code> library</a> para carregá-lo automaticamente para você como uma variável de ambiente.</p>',Wa,as,wl="Agora que temos nosso token de acesso, vamos criar uma função que possa baixar todas as issues de um repositório do GitHub:",Fa,es,Qa,ls,fl="Agora, quando chamamos <code>fetch_issues()</code>, ele fará o download de todas as issues em lotes para evitar exceder o limite do GitHub no número de solicitações por hora; o resultado será armazenado em um arquivo <em>repository_name-issues.jsonl</em>, onde cada linha é um objeto JSON que representa uma issue. Vamos usar esta função para pegar todas as issues de 🤗 Datasets:",Ya,ts,$a,ns,xl='Depois que as issues forem baixadas, podemos carregá-las localmente usando nossas novas habilidades da <a href="/course/chapter5/2">seção 2</a>:',Ea,os,Sa,is,Aa,ps,Zl='Ótimo, criamos nosso primeiro conjunto de dados do zero! Mas por que existem vários milhares de issues quando a <a href="https://github.com/huggingface/datasets/issues" rel="nofollow">guia Issue</a> do repositório 🤗 Datasets mostra apenas cerca de 1.000 issues no total 🤔? Conforme descrito na [documentação] do GitHub (<a href="https://docs.github.com/en/rest/reference/issues#list-issues-assigned-to-the-authenticated-user" rel="nofollow">https://docs.github.com/en/rest/reference/issues#list-issues-assigned-to-the-authenticated-user</a>), isso ocorre porque baixamos todos os pull request também:',qa,cs,Gl="<p>A API REST v3 do GitHub considera cada pull request como uma issue, mas nem toda issue é um pull request. Por esse motivo, os endpoints de “issues” podem retornar issues e solicitações de pull na resposta. Você pode identificar solicitações de pull pela chave <code>pull_request</code>. Esteja ciente de que o <code>id</code> de uma solicitação pull retornada de endpoints “issues” será um ID de issue.</p>",La,rs,Rl="Como o conteúdo das issues e dos pull request são bem diferentes, vamos fazer um pequeno pré-processamento para nos permitir distinguir entre eles.",Da,ds,Pa,us,Il='O trecho acima da documentação do GitHub nos diz que a coluna <code>pull_request</code> pode ser usada para diferenciar entre issues e solicitações de pull request. Vamos olhar para uma amostra aleatória para ver qual é a diferença. Como fizemos na <a href="/course/chapter5/3">seção 3</a>, vamos encadear <code>Dataset.shuffle()</code> e <code>Dataset.select()</code> para criar uma amostra aleatória e então compactar o <code>html_url</code> e <code>pull_request</code> para que possamos comparar os vários URLs:',Oa,ms,Ka,Ms,se,Js,vl="Aqui podemos ver que cada pull request está associado a vários URLs, enquanto as issues comuns têm uma entrada <code>None</code>. Podemos usar essa distinção para criar uma nova coluna <code>is_pull_request</code> que verifica se o campo <code>pull_request</code> é <code>None</code> ou não:",ae,ys,ee,g,Cl="<p>✏️ <strong>Experimente!</strong> Calcule o tempo médio que leva para fechar as issues em 🤗 Datasets. Você pode achar a função <code>Dataset.filter()</code> útil para filtrar os pull requests e as issues abertas, e você pode usar a função <code>Dataset.set_format()</code> para converter o conjunto de dados em um <code>DataFrame</code> para que você possa manipular facilmente os timestamps <code>created_at</code> e <code>closed_at</code>. Para pontos de bônus, calcule o tempo médio que leva para fechar os pull requests.</p>",le,hs,Xl="Embora possamos continuar a limpar o conjunto de dados descartando ou renomeando algumas colunas, geralmente é uma boa prática manter o conjunto de dados o mais “bruto” possível neste estágio para que possa ser facilmente usado em vários aplicativos.",te,Ts,kl="Antes de enviarmos nosso conjunto de dados para o Hugging Face Hub, vamos lidar com uma coisa que está faltando: os comentários associados a cada issue e pull request. Vamos adicioná-los a seguir - você adivinhou - a API REST do GitHub!",ne,Us,oe,js,Nl="Conforme mostrado na captura de tela a seguir, os comentários associados a uma issue ou a pull request fornecem uma rica fonte de informações, especialmente se estivermos interessados em criar um mecanismo de pesquisa para responder às consultas dos usuários sobre a biblioteca.",ie,w,zl='<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%"/>',pe,bs,Vl='A API REST do GitHub fornece um <a href="https://docs.github.com/en/rest/reference/issues#list-issue-comments" rel="nofollow">endpoint <code>Comments</code></a> que retorna todos os comentários associados a uma issue. Vamos testar o endpoint para ver o que ele retorna:',ce,gs,re,ws,de,fs,_l="Podemos ver que o comentário está armazenado no campo <code>body</code>, então vamos escrever uma função simples que retorna todos os comentários associados a uma issue selecionando o conteúdo do <code>body</code> para cada elemento em <code>response.json()</code>:",ue,xs,me,Zs,Me,Gs,Bl="Isso parece certo, então vamos usar <code>Dataset.map()</code> para adicionar uma nova coluna <code>comments</code> para cada issue em nosso conjunto de dados:",Je,Rs,ye,Is,Hl="A etapa final é salvar o conjunto de dados aumentado junto com nossos dados brutos para que possamos enviá-los para o Hub:",he,vs,Te,Cs,Ue,Xs,je,ks,Wl='Agora que temos nosso conjunto de dados aumentado, é hora de enviá-lo para o Hub para que possamos compartilhá-lo com a comunidade! Para fazer o upload do conjunto de dados, usaremos a <a href="https://github.com/huggingface/huggingface_hub" rel="nofollow">🤗 Hub library</a>, que nos permite interagir com o Hugging Face Hub por meio de uma API Python. 🤗 Hub vem pré-instalado com 🤗 Transformers, para que possamos usá-lo diretamente. Por exemplo, podemos usar a função <code>list_datasets()</code> para obter informações sobre todos os conjuntos de dados públicos atualmente hospedados no Hub:',be,Ns,ge,zs,we,Vs,Fl="Podemos ver que atualmente existem cerca de 1.500 conjuntos de dados no Hub, e a função <code>list_datasets()</code> também fornece alguns metadados básicos sobre cada repositório de conjuntos de dados.",fe,_s,Ql="Para nossos propósitos, a primeira coisa que precisamos fazer é criar um novo repositório de conjunto de dados no Hub. Para fazer isso, precisamos de um token de autenticação, que pode ser obtido primeiro entrando no Hugging Face Hub com a função <code>notebook_login()</code>:",xe,Bs,Ze,Hs,Yl="Isso criará um widget onde você poderá inserir seu nome de usuário e senha, e um token de API será salvo em <em>~/.huggingface/token</em>. Se você estiver executando o código em um terminal, poderá fazer login via CLI:",Ge,Ws,Re,Fs,$l="Feito isso, podemos criar um novo repositório de conjunto de dados com a função <code>create_repo()</code>:",Ie,Qs,ve,Ys,Ce,$s,El="Neste exemplo, criamos um repositório de conjunto de dados vazio chamado <code>github-issues</code> sob o nome de usuário <code>lewtun</code> (o nome de usuário deve ser seu nome de usuário do Hub quando você estiver executando este código!).",Xe,f,Sl="<p>✏️ <strong>Experimente!</strong> Use seu nome de usuário e senha do Hugging Face Hub para obter um token e criar um repositório vazio chamado <code>github-issues</code>. Lembre-se de <strong>nunca salvar suas credenciais</strong> no Colab ou em qualquer outro repositório, pois essas informações podem ser exploradas por agentes mal-intencionados.</p>",ke,Es,Al="Em seguida, vamos clonar o repositório do Hub para nossa máquina local e copiar nosso arquivo de conjunto de dados para ele. O 🤗 Hub fornece uma classe <code>Repository</code> útil que envolve muitos dos comandos comuns do Git, portanto, para clonar o repositório remoto, basta fornecer o URL e o caminho local para o qual desejamos clonar:",Ne,Ss,ze,As,ql="Por padrão, várias extensões de arquivo (como <em>.bin</em>, <em>.gz</em> e <em>.zip</em>) são rastreadas com o Git LFS para que arquivos grandes possam ser versionados no mesmo fluxo de trabalho do Git. Você pode encontrar uma lista de extensões de arquivos rastreados dentro do arquivo <em>.gitattributes</em> do repositório. Para incluir o formato JSON Lines na lista, podemos executar o seguinte comando:",Ve,qs,_e,Ls,Ll="Então podemos usar <code>Repository.push_to_hub()</code> para enviar o conjunto de dados para o Hub:",Be,Ds,He,Ps,Dl="Se navegarmos para a URL contida em <code>repo_url</code>, veremos agora que nosso arquivo de conjunto de dados foi carregado.",We,x,Pl='<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%"/>',Fe,Os,Ol="A partir daqui, qualquer um pode baixar o conjunto de dados simplesmente fornecendo <code>load_dataset()</code> com o ID do repositório como o argumento <code>path</code>:",Qe,Ks,Ye,sa,$e,aa,Kl="Legal, nós enviamos nosso conjunto de dados para o Hub e está disponível para outros usarem! Há apenas uma coisa importante a fazer: adicionar um <em>cartão de conjunto de dados</em> que explica como o corpus foi criado e fornece outras informações úteis para a comunidade.",Ee,Z,st='<p>💡 Você também pode enviar um conjunto de dados para o Hugging Face Hub diretamente do terminal usando <code>huggingface-cli</code> e um pouco de magia Git. Consulte o <a href="https://huggingface.co/docs/datasets/share#share-a-dataset-using-the-cli" rel="nofollow">guia do 🤗 Datasets</a> para obter detalhes sobre como fazer isso.</p>',Se,ea,Ae,la,at="Conjuntos de dados bem documentados são mais propensos a serem úteis para outras pessoas (incluindo você mesmo no futuro!), pois fornecem o contexto para permitir que os usuários decidam se o conjunto de dados é relevante para sua tarefa e avaliem possíveis vieses ou riscos associados ao uso o conjunto de dados.",qe,ta,et="No Hugging Face Hub, essas informações são armazenadas no arquivo <em>README.md</em> de cada repositório de conjunto de dados. Há duas etapas principais que você deve seguir antes de criar este arquivo:",Le,na,lt='<li>Use a aplicação <a href="https://huggingface.co/datasets/tagging/" rel="nofollow"><code>datasets-tagging</code></a> para criar tags de metadados no formato YAML. Essas tags são usadas para uma variedade de recursos de pesquisa no Hugging Face Hub e garantem que seu conjunto de dados possa ser facilmente encontrado pelos membros da comunidade. Como criamos um conjunto de dados personalizado aqui, você precisará clonar o repositório <code>datasets-tagging</code> e executar o aplicativo localmente. Veja como é a interface:</li>',De,G,tt='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter5/datasets-tagger.png" alt="The 'datasets-tagging' interface." width="80%"/>',Pe,R,nt='<li>Leia o <a href="https://github.com/huggingface/datasets/blob/master/templates/README_guide.md" rel="nofollow">guia do 🤗 datasets</a> sobre como criar cartões informativos de conjuntos de dados e use-os como modelo.</li>',Oe,oa,ot="Você pode criar o arquivo <em>README.md</em> diretamente no Hub e encontrar um cartão de conjunto de dados de modelo no repositório de conjunto de dados <code>lewtun/github-issues</code>. Uma captura de tela do cartão de conjunto de dados preenchido é mostrada abaixo.",Ke,I,it='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter5/dataset-card.png" alt="A dataset card." width="80%"/>',sl,v,pt='<p>✏️ <strong>Experimente!</strong> Use o aplicativo <code>dataset-tagging</code> e <a href="https://github.com/huggingface/datasets/blob/master/templates/README_guide.md" rel="nofollow">guia do 🤗 datasets</a> para concluir o <em>Arquivo README.md</em> para o conjunto de dados de issues do GitHub.</p>',al,ia,ct="É isso! Vimos nesta seção que criar um bom conjunto de dados pode ser bastante complicado, mas felizmente carregá-lo e compartilhá-lo com a comunidade não é. Na próxima seção, usaremos nosso novo conjunto de dados para criar um mecanismo de pesquisa semântica com o 🤗 datasets que podem corresponder perguntas as issues e comentários mais relevantes.",el,C,rt="<p>✏️ <strong>Experimente!</strong> Siga as etapas que seguimos nesta seção para criar um conjunto de dados de issues do GitHub para sua biblioteca de código aberto favorita (escolha algo diferente do 🤗 datasets, é claro!). Para pontos de bônus, ajuste um classificador multilabel para prever as tags presentes no campo <code>labels</code>.</p>",ll,pa,tl,da,nl;return X=new Ut({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),k=new ca({props:{title:"Criando seu próprio dataset",local:"criando-seu-próprio-dataset",headingTag:"h1"}}),N=new gt({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/pt/chapter5/section5.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/pt/chapter5/section5.ipynb"}]}}),B=new ca({props:{title:"Obtendo os dados",local:"obtendo-os-dados",headingTag:"h2"}}),Y=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

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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:"R0lUSFVCX1RPS0VOJTIwJTNEJTIweHh4JTIwJTIwJTIzJTIwQ29weSUyMHlvdXIlMjBHaXRIdWIlMjB0b2tlbiUyMGhlcmUlMEFoZWFkZXJzJTIwJTNEJTIwJTdCJTIyQXV0aG9yaXphdGlvbiUyMiUzQSUyMGYlMjJ0b2tlbiUyMCU3QkdJVEhVQl9UT0tFTiU3RCUyMiU3RA==",highlighted:`GITHUB_TOKEN = xxx <span class="hljs-comment"># Copy your GitHub token here</span>
	headers = {<span class="hljs-string">"Authorization"</span>: <span class="hljs-string">f"token <span class="hljs-subst">{GITHUB_TOKEN}</span>"</span>}`,wrap:!1}}),es=new J({props:{code:"aW1wb3J0JTIwdGltZSUwQWltcG9ydCUyMG1hdGglMEFmcm9tJTIwcGF0aGxpYiUyMGltcG9ydCUyMFBhdGglMEFpbXBvcnQlMjBwYW5kYXMlMjBhcyUyMHBkJTBBZnJvbSUyMHRxZG0ubm90ZWJvb2slMjBpbXBvcnQlMjB0cWRtJTBBJTBBJTBBZGVmJTIwZmV0Y2hfaXNzdWVzKCUwQSUyMCUyMCUyMCUyMG93bmVyJTNEJTIyaHVnZ2luZ2ZhY2UlMjIlMkMlMEElMjAlMjAlMjAlMjByZXBvJTNEJTIyZGF0YXNldHMlMjIlMkMlMEElMjAlMjAlMjAlMjBudW1faXNzdWVzJTNEMTBfMDAwJTJDJTBBJTIwJTIwJTIwJTIwcmF0ZV9saW1pdCUzRDVfMDAwJTJDJTBBJTIwJTIwJTIwJTIwaXNzdWVzX3BhdGglM0RQYXRoKCUyMi4lMjIpJTJDJTBBKSUzQSUwQSUyMCUyMCUyMCUyMGlmJTIwbm90JTIwaXNzdWVzX3BhdGguaXNfZGlyKCklM0ElMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBpc3N1ZXNfcGF0aC5ta2RpcihleGlzdF9vayUzRFRydWUpJTBBJTBBJTIwJTIwJTIwJTIwYmF0Y2glMjAlM0QlMjAlNUIlNUQlMEElMjAlMjAlMjAlMjBhbGxfaXNzdWVzJTIwJTNEJTIwJTVCJTVEJTBBJTIwJTIwJTIwJTIwcGVyX3BhZ2UlMjAlM0QlMjAxMDAlMjAlMjAlMjMlMjBOdW1iZXIlMjBvZiUyMGlzc3VlcyUyMHRvJTIwcmV0dXJuJTIwcGVyJTIwcGFnZSUwQSUyMCUyMCUyMCUyMG51bV9wYWdlcyUyMCUzRCUyMG1hdGguY2VpbChudW1faXNzdWVzJTIwJTJGJTIwcGVyX3BhZ2UpJTBBJTIwJTIwJTIwJTIwYmFzZV91cmwlMjAlM0QlMjAlMjJodHRwcyUzQSUyRiUyRmFwaS5naXRodWIuY29tJTJGcmVwb3MlMjIlMEElMEElMjAlMjAlMjAlMjBmb3IlMjBwYWdlJTIwaW4lMjB0cWRtKHJhbmdlKG51bV9wYWdlcykpJTNBJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIzJTIwUXVlcnklMjB3aXRoJTIwc3RhdGUlM0RhbGwlMjB0byUyMGdldCUyMGJvdGglMjBvcGVuJTIwYW5kJTIwY2xvc2VkJTIwaXNzdWVzJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwcXVlcnklMjAlM0QlMjBmJTIyaXNzdWVzJTNGcGFnZSUzRCU3QnBhZ2UlN0QlMjZwZXJfcGFnZSUzRCU3QnBlcl9wYWdlJTdEJTI2c3RhdGUlM0RhbGwlMjIlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBpc3N1ZXMlMjAlM0QlMjByZXF1ZXN0cy5nZXQoZiUyMiU3QmJhc2VfdXJsJTdEJTJGJTdCb3duZXIlN0QlMkYlN0JyZXBvJTdEJTJGJTdCcXVlcnklN0QlMjIlMkMlMjBoZWFkZXJzJTNEaGVhZGVycyklMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBiYXRjaC5leHRlbmQoaXNzdWVzLmpzb24oKSklMEElMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBpZiUyMGxlbihiYXRjaCklMjAlM0UlMjByYXRlX2xpbWl0JTIwYW5kJTIwbGVuKGFsbF9pc3N1ZXMpJTIwJTNDJTIwbnVtX2lzc3VlcyUzQSUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMGFsbF9pc3N1ZXMuZXh0ZW5kKGJhdGNoKSUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMGJhdGNoJTIwJTNEJTIwJTVCJTVEJTIwJTIwJTIzJTIwRmx1c2glMjBiYXRjaCUyMGZvciUyMG5leHQlMjB0aW1lJTIwcGVyaW9kJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwcHJpbnQoZiUyMlJlYWNoZWQlMjBHaXRIdWIlMjByYXRlJTIwbGltaXQuJTIwU2xlZXBpbmclMjBmb3IlMjBvbmUlMjBob3VyJTIwLi4uJTIyKSUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMHRpbWUuc2xlZXAoNjAlMjAqJTIwNjAlMjAlMkIlMjAxKSUwQSUwQSUyMCUyMCUyMCUyMGFsbF9pc3N1ZXMuZXh0ZW5kKGJhdGNoKSUwQSUyMCUyMCUyMCUyMGRmJTIwJTNEJTIwcGQuRGF0YUZyYW1lLmZyb21fcmVjb3JkcyhhbGxfaXNzdWVzKSUwQSUyMCUyMCUyMCUyMGRmLnRvX2pzb24oZiUyMiU3Qmlzc3Vlc19wYXRoJTdEJTJGJTdCcmVwbyU3RC1pc3N1ZXMuanNvbmwlMjIlMkMlMjBvcmllbnQlM0QlMjJyZWNvcmRzJTIyJTJDJTIwbGluZXMlM0RUcnVlKSUwQSUyMCUyMCUyMCUyMHByaW50KCUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMGYlMjJEb3dubG9hZGVkJTIwYWxsJTIwdGhlJTIwaXNzdWVzJTIwZm9yJTIwJTdCcmVwbyU3RCElMjBEYXRhc2V0JTIwc3RvcmVkJTIwYXQlMjAlN0Jpc3N1ZXNfcGF0aCU3RCUyRiU3QnJlcG8lN0QtaXNzdWVzLmpzb25sJTIyJTBBJTIwJTIwJTIwJTIwKQ==",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"># Number of issues to return per page</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"># Query with state=all to get both open and closed issues</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"># Flush batch for next time period</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:"JTIzJTIwRGVwZW5kaW5nJTIwb24lMjB5b3VyJTIwaW50ZXJuZXQlMjBjb25uZWN0aW9uJTJDJTIwdGhpcyUyMGNhbiUyMHRha2UlMjBzZXZlcmFsJTIwbWludXRlcyUyMHRvJTIwcnVuLi4uJTBBZmV0Y2hfaXNzdWVzKCk=",highlighted:`<span class="hljs-comment"># Depending on your internet connection, this can take several minutes to run...</span>
	fetch_issues()`,wrap:!1}}),os=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}}),is=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 ca({props:{title:"Limpando os dados",local:"limpando-os-dados",headingTag:"h2"}}),ms=new J({props:{code:"c2FtcGxlJTIwJTNEJTIwaXNzdWVzX2RhdGFzZXQuc2h1ZmZsZShzZWVkJTNENjY2KS5zZWxlY3QocmFuZ2UoMykpJTBBJTBBJTIzJTIwUHJpbnQlMjBvdXQlMjB0aGUlMjBVUkwlMjBhbmQlMjBwdWxsJTIwcmVxdWVzdCUyMGVudHJpZXMlMEFmb3IlMjB1cmwlMkMlMjBwciUyMGluJTIwemlwKHNhbXBsZSU1QiUyMmh0bWxfdXJsJTIyJTVEJTJDJTIwc2FtcGxlJTVCJTIycHVsbF9yZXF1ZXN0JTIyJTVEKSUzQSUwQSUyMCUyMCUyMCUyMHByaW50KGYlMjIlM0UlM0UlMjBVUkwlM0ElMjAlN0J1cmwlN0QlMjIpJTBBJTIwJTIwJTIwJTIwcHJpbnQoZiUyMiUzRSUzRSUyMFB1bGwlMjByZXF1ZXN0JTNBJTIwJTdCcHIlN0QlNUNuJTIyKQ==",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"># Print out the URL and pull request entries</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}}),Ms=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}}),ys=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}}),Us=new ca({props:{title:"Aumentando o conjunto de dados",local:"aumentando-o-conjunto-de-dados",headingTag:"h2"}}),gs=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}}),ws=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}}),xs=new J({props:{code:"ZGVmJTIwZ2V0X2NvbW1lbnRzKGlzc3VlX251bWJlciklM0ElMEElMjAlMjAlMjAlMjB1cmwlMjAlM0QlMjBmJTIyaHR0cHMlM0ElMkYlMkZhcGkuZ2l0aHViLmNvbSUyRnJlcG9zJTJGaHVnZ2luZ2ZhY2UlMkZkYXRhc2V0cyUyRmlzc3VlcyUyRiU3Qmlzc3VlX251bWJlciU3RCUyRmNvbW1lbnRzJTIyJTBBJTIwJTIwJTIwJTIwcmVzcG9uc2UlMjAlM0QlMjByZXF1ZXN0cy5nZXQodXJsJTJDJTIwaGVhZGVycyUzRGhlYWRlcnMpJTBBJTIwJTIwJTIwJTIwcmV0dXJuJTIwJTVCciU1QiUyMmJvZHklMjIlNUQlMjBmb3IlMjByJTIwaW4lMjByZXNwb25zZS5qc29uKCklNUQlMEElMEElMEElMjMlMjBUZXN0JTIwb3VyJTIwZnVuY3Rpb24lMjB3b3JrcyUyMGFzJTIwZXhwZWN0ZWQlMEFnZXRfY29tbWVudHMoMjc5Mik=",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"># Test our function works as expected</span>
	get_comments(<span class="hljs-number">2792</span>)`,wrap:!1}}),Zs=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}}),Rs=new J({props:{code:"JTIzJTIwRGVwZW5kaW5nJTIwb24lMjB5b3VyJTIwaW50ZXJuZXQlMjBjb25uZWN0aW9uJTJDJTIwdGhpcyUyMGNhbiUyMHRha2UlMjBhJTIwZmV3JTIwbWludXRlcy4uLiUwQWlzc3Vlc193aXRoX2NvbW1lbnRzX2RhdGFzZXQlMjAlM0QlMjBpc3N1ZXNfZGF0YXNldC5tYXAoJTBBJTIwJTIwJTIwJTIwbGFtYmRhJTIweCUzQSUyMCU3QiUyMmNvbW1lbnRzJTIyJTNBJTIwZ2V0X2NvbW1lbnRzKHglNUIlMjJudW1iZXIlMjIlNUQpJTdEJTBBKQ==",highlighted:`<span class="hljs-comment"># Depending on your internet connection, this can take a few minutes...</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}}),vs=new J({props:{code:"aXNzdWVzX3dpdGhfY29tbWVudHNfZGF0YXNldC50b19qc29uKCUyMmlzc3Vlcy1kYXRhc2V0cy13aXRoLWNvbW1lbnRzLmpzb25sJTIyKQ==",highlighted:'issues_with_comments_dataset.to_json(<span class="hljs-string">"issues-datasets-with-comments.jsonl"</span>)',wrap:!1}}),Cs=new ca({props:{title:"Carregando o conjunto de dados para o Hugging Face Hub",local:"carregando-o-conjunto-de-dados-para-o-hugging-face-hub",headingTag:"h2"}}),Xs=new bt({props:{id:"HaN6qCr_Afc"}}),Ns=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}}),zs=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}}),Bs=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}}),Ws=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}}),Ys=new J({props:{code:"J2h0dHBzJTNBJTJGJTJGaHVnZ2luZ2ZhY2UuY28lMkZkYXRhc2V0cyUyRmxld3R1biUyRmdpdGh1Yi1pc3N1ZXMn",highlighted:'<span class="hljs-string">'https://huggingface.co/datasets/lewtun/github-issues'</span>',wrap:!1}}),Ss=new J({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMFJlcG9zaXRvcnklMEElMEFyZXBvJTIwJTNEJTIwUmVwb3NpdG9yeShsb2NhbF9kaXIlM0QlMjJnaXRodWItaXNzdWVzJTIyJTJDJTIwY2xvbmVfZnJvbSUzRHJlcG9fdXJsKSUwQSFjcCUyMGRhdGFzZXRzLWlzc3Vlcy13aXRoLWNvbW1lbnRzLmpzb25sJTIwZ2l0aHViLWlzc3VlcyUyRg==",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 datasets-issues-<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}}),Ds=new J({props:{code:"cmVwby5wdXNoX3RvX2h1Yigp",highlighted:"repo.push_to_hub()",wrap:!1}}),Ks=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}}),sa=new J({props:{code:"RGF0YXNldCglN0IlMEElMjAlMjAlMjAlMjBmZWF0dXJlcyUzQSUyMCU1Qid1cmwnJTJDJTIwJ3JlcG9zaXRvcnlfdXJsJyUyQyUyMCdsYWJlbHNfdXJsJyUyQyUyMCdjb21tZW50c191cmwnJTJDJTIwJ2V2ZW50c191cmwnJTJDJTIwJ2h0bWxfdXJsJyUyQyUyMCdpZCclMkMlMjAnbm9kZV9pZCclMkMlMjAnbnVtYmVyJyUyQyUyMCd0aXRsZSclMkMlMjAndXNlciclMkMlMjAnbGFiZWxzJyUyQyUyMCdzdGF0ZSclMkMlMjAnbG9ja2VkJyUyQyUyMCdhc3NpZ25lZSclMkMlMjAnYXNzaWduZWVzJyUyQyUyMCdtaWxlc3RvbmUnJTJDJTIwJ2NvbW1lbnRzJyUyQyUyMCdjcmVhdGVkX2F0JyUyQyUyMCd1cGRhdGVkX2F0JyUyQyUyMCdjbG9zZWRfYXQnJTJDJTIwJ2F1dGhvcl9hc3NvY2lhdGlvbiclMkMlMjAnYWN0aXZlX2xvY2tfcmVhc29uJyUyQyUyMCdwdWxsX3JlcXVlc3QnJTJDJTIwJ2JvZHknJTJDJTIwJ3BlcmZvcm1lZF92aWFfZ2l0aHViX2FwcCclMkMlMjAnaXNfcHVsbF9yZXF1ZXN0JyU1RCUyQyUwQSUyMCUyMCUyMCUyMG51bV9yb3dzJTNBJTIwMjg1NSUwQSU3RCk=",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}}),ea=new ca({props:{title:"Criando um cartão do datasets",local:"criando-um-cartão-do-datasets",headingTag:"h2"}}),pa=new 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