import{S as oe,i as he,s as me,e as p,k as c,w as v,t as l,M as ce,c as r,d as a,m as i,a as o,x as $,h as t,b as m,G as e,g as h,y as w,L as ie,q as k,o as y,B as x,v as ue}from"../chunks/vendor-hf-doc-builder.js";import{I as Ia}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as ts}from"../chunks/CodeBlock-hf-doc-builder.js";function be(Da){let j,js,d,E,ps,I,Js,rs,Rs,ds,K,Ks,gs,q,D,Qs,Q,Vs,Ws,Xs,os,Zs,_s,A,sa,M,aa,ea,vs,g,P,hs,C,na,ms,la,$s,T,ta,V,pa,ra,ws,z,oa,B,ha,ma,ks,F,ys,u,ca,cs,ia,ua,is,ba,fa,W,ja,da,xs,O,Es,b,ga,X,_a,va,us,$a,wa,qs,H,As,_,N,bs,U,ka,fs,ya,Ps,f,xa,Z,Ea,qa,Y,Aa,Pa,Ts,G,zs,ss,Ta,Ns,J,Ls,L,za,as,Na,La,Ss,R,Is,es,Sa,Ds;return I=new Ia({}),C=new Ia({}),F=new ts({props:{code:`from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)`}}),O=new ts({props:{code:`dataset = dataset.map(lambda examples: tokenizer(examples["text"]), batched=True)
dataset[0]`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.<span class="hljs-built_in">map</span>(<span class="hljs-keyword">lambda</span> examples: tokenizer(examples[<span class="hljs-string">&quot;text&quot;</span>]), batched=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset[<span class="hljs-number">0</span>]
{<span class="hljs-string">&#x27;text&#x27;</span>: <span class="hljs-string">&#x27;the rock is destined to be the 21st century\\&#x27;s new &quot; conan &quot; and that he\\&#x27;s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .&#x27;</span>, 
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mnli = load_dataset("glue", "mnli", split="train")
mnli_aligned = mnli.align_labels_with_mapping(label2id, "label")`,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>mnli = load_dataset(<span class="hljs-string">&quot;glue&quot;</span>, <span class="hljs-string">&quot;mnli&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>mnli_aligned = mnli.align_labels_with_mapping(label2id, <span class="hljs-string">&quot;label&quot;</span>)`}}),{c(){j=p("meta"),js=c(),d=p("h1"),E=p("a"),ps=p("span"),v(I.$$.fragment),Js=c(),rs=p("span"),Rs=l("Process text data"),ds=c(),K=p("p"),Ks=l("This guide shows specific methods for processing text datasets. 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