import{S as Ms,i as Os,s as Us,e as o,k as f,w as g,t as e,M as Bs,c as r,d as s,m as c,a as i,x as _,h as n,b as d,G as t,g as p,y as v,L as zs,q as j,o as w,B as y,v as Vs}from"../chunks/vendor-hf-doc-builder.js";import{I as ot}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as sa}from"../chunks/CodeBlock-hf-doc-builder.js";function Gs(is){let x,Aa,k,P,ha,O,rt,ua,it,Pa,q,pt,U,dt,ft,qa,$,ct,B,ht,ut,z,mt,gt,Ia,E,I,ma,V,_t,ga,vt,Sa,S,jt,ea,wt,yt,La,L,$t,na,bt,xt,Ca,G,Ta,C,kt,la,Et,Dt,Ha,J,Na,D,T,_a,K,At,va,Pt,Fa,h,qt,ja,It,St,wa,Lt,Ct,oa,Tt,Ht,Ra,Z,Ma,u,Nt,ya,Ft,Rt,$a,Mt,Ot,ra,Ut,Bt,Oa,Q,Ua,b,zt,ba,Vt,Gt,ia,Jt,Kt,Ba,W,za,A,H,xa,X,Zt,ka,Qt,Va,m,Wt,Y,Xt,Yt,Ea,as,ts,Da,ss,es,Ga,N,ns,pa,ls,os,Ja,aa,Ka,da,rs,Za,ta,Qa;return O=new ot({}),V=new ot({}),G=new sa({props:{code:`from datasets import load_dataset_builder
ds_builder = load_dataset_builder("rotten_tomatoes")

ds_builder.info.description

ds_builder.info.features`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset_builder
<span class="hljs-meta">&gt;&gt;&gt; </span>ds_builder = load_dataset_builder(<span class="hljs-string">&quot;rotten_tomatoes&quot;</span>)

<span class="hljs-comment"># Inspect dataset description</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>ds_builder.info.description
Movie Review Dataset. This <span class="hljs-keyword">is</span> a dataset of containing <span class="hljs-number">5</span>,<span class="hljs-number">331</span> positive <span class="hljs-keyword">and</span> <span class="hljs-number">5</span>,<span class="hljs-number">331</span> negative processed sentences <span class="hljs-keyword">from</span> Rotten Tomatoes movie reviews. This data was first used <span class="hljs-keyword">in</span> Bo Pang <span class="hljs-keyword">and</span> Lillian Lee, \`\`Seeing stars: Exploiting <span class="hljs-keyword">class</span> <span class="hljs-title class_">relationships</span> <span class="hljs-keyword">for</span> sentiment categorization <span class="hljs-keyword">with</span> respect to rating scales.<span class="hljs-string">&#x27;&#x27;</span>, Proceedings of the ACL, <span class="hljs-number">2005.</span>

<span class="hljs-comment"># Inspect dataset features</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>ds_builder.info.features
{<span class="hljs-string">&#x27;label&#x27;</span>: ClassLabel(num_classes=<span class="hljs-number">2</span>, names=[<span class="hljs-string">&#x27;neg&#x27;</span>, <span class="hljs-string">&#x27;pos&#x27;</span>], <span class="hljs-built_in">id</span>=<span class="hljs-literal">None</span>),
 <span class="hljs-string">&#x27;text&#x27;</span>: Value(dtype=<span class="hljs-string">&#x27;string&#x27;</span>, <span class="hljs-built_in">id</span>=<span class="hljs-literal">None</span>)}`}}),J=new sa({props:{code:`from datasets import load_dataset

dataset = load_dataset("rotten_tomatoes", split="train")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;rotten_tomatoes&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)`}}),K=new ot({}),Z=new sa({props:{code:`from datasets import get_dataset_split_names

get_dataset_split_names("rotten_tomatoes")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> get_dataset_split_names

<span class="hljs-meta">&gt;&gt;&gt; </span>get_dataset_split_names(<span class="hljs-string">&quot;rotten_tomatoes&quot;</span>)
[<span class="hljs-string">&#x27;train&#x27;</span>, <span class="hljs-string">&#x27;validation&#x27;</span>, <span class="hljs-string">&#x27;test&#x27;</span>]`}}),Q=new sa({props:{code:`from datasets import load_dataset

dataset = load_dataset("rotten_tomatoes", split="train")
dataset`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;rotten_tomatoes&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset
Dataset({
    features: [<span class="hljs-string">&#x27;text&#x27;</span>, <span class="hljs-string">&#x27;label&#x27;</span>],
    num_rows: <span class="hljs-number">8530</span>
})`}}),W=new sa({props:{code:`from datasets import load_dataset

dataset = load_dataset("rotten_tomatoes")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;rotten_tomatoes&quot;</span>)
DatasetDict({
    train: Dataset({
        features: [<span class="hljs-string">&#x27;text&#x27;</span>, <span class="hljs-string">&#x27;label&#x27;</span>],
        num_rows: <span class="hljs-number">8530</span>
    })
    validation: Dataset({
        features: [<span class="hljs-string">&#x27;text&#x27;</span>, <span class="hljs-string">&#x27;label&#x27;</span>],
        num_rows: <span class="hljs-number">1066</span>
    })
    test: Dataset({
        features: [<span class="hljs-string">&#x27;text&#x27;</span>, <span class="hljs-string">&#x27;label&#x27;</span>],
        num_rows: <span class="hljs-number">1066</span>
    })
})`}}),X=new ot({}),aa=new sa({props:{code:`from datasets import get_dataset_config_names

configs = get_dataset_config_names("PolyAI/minds14")
print(configs)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> get_dataset_config_names

<span class="hljs-meta">&gt;&gt;&gt; </span>configs = get_dataset_config_names(<span class="hljs-string">&quot;PolyAI/minds14&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(configs)
[<span class="hljs-string">&#x27;cs-CZ&#x27;</span>, <span class="hljs-string">&#x27;de-DE&#x27;</span>, <span class="hljs-string">&#x27;en-AU&#x27;</span>, <span class="hljs-string">&#x27;en-GB&#x27;</span>, <span class="hljs-string">&#x27;en-US&#x27;</span>, <span class="hljs-string">&#x27;es-ES&#x27;</span>, <span class="hljs-string">&#x27;fr-FR&#x27;</span>, <span class="hljs-string">&#x27;it-IT&#x27;</span>, <span class="hljs-string">&#x27;ko-KR&#x27;</span>, <span class="hljs-string">&#x27;nl-NL&#x27;</span>, <span class="hljs-string">&#x27;pl-PL&#x27;</span>, <span class="hljs-string">&#x27;pt-PT&#x27;</span>, <span class="hljs-string">&#x27;ru-RU&#x27;</span>, <span class="hljs-string">&#x27;zh-CN&#x27;</span>, <span class="hljs-string">&#x27;all&#x27;</span>]`}}),ta=new sa({props:{code:`from datasets import load_dataset

mindsFR = load_dataset("PolyAI/minds14", "fr-FR", split="train")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>mindsFR = load_dataset(<span class="hljs-string">&quot;PolyAI/minds14&quot;</span>, <span class="hljs-string">&quot;fr-FR&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)`}}),{c(){x=o("meta"),Aa=f(),k=o("h1"),P=o("a"),ha=o("span"),g(O.$$.fragment),rt=f(),ua=o("span"),it=e("Load a dataset from the Hub"),Pa=f(),q=o("p"),pt=e("Finding high-quality datasets that are reproducible and accessible can be difficult. One of \u{1F917} Datasets main goals is to provide a simple way to load a dataset of any format or type. 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