import{s as lt,o as et,n as X}from"../chunks/scheduler.56730f09.js";import{S as pt,i as tt,g as t,s as e,u,A as rt,h as r,f as n,c as p,j as $,v as j,y as h,k as g,o as J,a as l,w as b,d as i,t as o,x as f}from"../chunks/index.ba464511.js";import{T as Pa}from"../chunks/Tip.70442610.js";import{Y as ct}from"../chunks/Youtube.7b20230c.js";import{I as N}from"../chunks/IconCopyLink.89bde88e.js";import{C as T}from"../chunks/CodeBlock.2302fd0d.js";import{D as mt}from"../chunks/DocNotebookDropdown.68fd8653.js";import{F as ht,M as nt}from"../chunks/Markdown.6303afae.js";function ut(w){let c,d="<code>AutoProcessor</code>는 <strong>언제나</strong> 작동하여 토크나이저, 이미지 프로세서, 특성 추출기 또는 프로세서 등 사용 중인 모델에 맞는 클래스를 자동으로 선택합니다.";return{c(){c=t("p"),c.innerHTML=d},l(m){c=r(m,"P",{"data-svelte-h":!0}),h(c)!=="svelte-8prefl"&&(c.innerHTML=d)},m(m,M){l(m,c,M)},p:X,d(m){m&&n(c)}}}function jt(w){let c,d="사전훈련된 모델을 사용할 계획이라면 모델과 함께 사전훈련된 토크나이저를 사용하는 것이 중요합니다. 이렇게 하면 텍스트가 사전훈련 말뭉치와 동일한 방식으로 분할되고 사전훈련 중에 동일한 해당 토큰-인덱스 쌍(일반적으로 <em>vocab</em>이라고 함)을 사용합니다.";return{c(){c=t("p"),c.innerHTML=d},l(m){c=r(m,"P",{"data-svelte-h":!0}),h(c)!=="svelte-1qm7hmc"&&(c.innerHTML=d)},m(m,M){l(m,c,M)},p:X,d(m){m&&n(c)}}}function bt(w){let c,d='다양한 패딩과 잘라내기 인수에 대해 더 알아보려면 <a href="./pad_truncation">패딩과 잘라내기</a> 개념 가이드를 확인해보세요.';return{c(){c=t("p"),c.innerHTML=d},l(m){c=r(m,"P",{"data-svelte-h":!0}),h(c)!=="svelte-5nyjl5"&&(c.innerHTML=d)},m(m,M){l(m,c,M)},p:X,d(m){m&&n(c)}}}function it(w){let c,d;return c=new T({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>batch_sentences = [
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;But what about second breakfast?&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Don&#x27;t think he knows about second breakfast, Pip.&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;What about elevensies?&quot;</span>,
<span class="hljs-meta">... </span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>encoded_input = tokenizer(batch_sentences, padding=<span class="hljs-literal">True</span>, truncation=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(encoded_input)
{<span class="hljs-string">&#x27;input_ids&#x27;</span>: tensor([[<span class="hljs-number">101</span>, <span class="hljs-number">1252</span>, <span class="hljs-number">1184</span>, <span class="hljs-number">1164</span>, <span class="hljs-number">1248</span>, <span class="hljs-number">6462</span>, <span class="hljs-number">136</span>, <span class="hljs-number">102</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
                      [<span class="hljs-number">101</span>, <span class="hljs-number">1790</span>, <span class="hljs-number">112</span>, <span class="hljs-number">189</span>, <span class="hljs-number">1341</span>, <span class="hljs-number">1119</span>, <span class="hljs-number">3520</span>, <span class="hljs-number">1164</span>, <span class="hljs-number">1248</span>, <span class="hljs-number">6462</span>, <span class="hljs-number">117</span>, <span class="hljs-number">21902</span>, <span class="hljs-number">1643</span>, <span class="hljs-number">119</span>, <span class="hljs-number">102</span>],
                      [<span class="hljs-number">101</span>, <span class="hljs-number">1327</span>, <span class="hljs-number">1164</span>, <span class="hljs-number">5450</span>, <span class="hljs-number">23434</span>, <span class="hljs-number">136</span>, <span class="hljs-number">102</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]]),
 <span class="hljs-string">&#x27;token_type_ids&#x27;</span>: tensor([[<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
                           [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
                           [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]]),
 <span class="hljs-string">&#x27;attention_mask&#x27;</span>: tensor([[<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
                           [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>],
                           [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]])}`,wrap:!1}}),{c(){u(c.$$.fragment)},l(m){j(c.$$.fragment,m)},m(m,M){b(c,m,M),d=!0},p:X,i(m){d||(i(c.$$.fragment,m),d=!0)},o(m){o(c.$$.fragment,m),d=!1},d(m){f(c,m)}}}function ot(w){let c,d;return c=new nt({props:{$$slots:{default:[it]},$$scope:{ctx:w}}}),{c(){u(c.$$.fragment)},l(m){j(c.$$.fragment,m)},m(m,M){b(c,m,M),d=!0},p(m,M){const v={};M&2&&(v.$$scope={dirty:M,ctx:m}),c.$set(v)},i(m){d||(i(c.$$.fragment,m),d=!0)},o(m){o(c.$$.fragment,m),d=!1},d(m){f(c,m)}}}function ft(w){let c,d;return c=new T({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>batch_sentences = [
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;But what about second breakfast?&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Don&#x27;t think he knows about second breakfast, Pip.&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;What about elevensies?&quot;</span>,
<span class="hljs-meta">... </span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>encoded_input = tokenizer(batch_sentences, padding=<span class="hljs-literal">True</span>, truncation=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(encoded_input)
{<span class="hljs-string">&#x27;input_ids&#x27;</span>: &lt;tf.Tensor: shape=(<span class="hljs-number">2</span>, <span class="hljs-number">9</span>), dtype=int32, numpy=
array([[<span class="hljs-number">101</span>, <span class="hljs-number">1252</span>, <span class="hljs-number">1184</span>, <span class="hljs-number">1164</span>, <span class="hljs-number">1248</span>, <span class="hljs-number">6462</span>, <span class="hljs-number">136</span>, <span class="hljs-number">102</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
       [<span class="hljs-number">101</span>, <span class="hljs-number">1790</span>, <span class="hljs-number">112</span>, <span class="hljs-number">189</span>, <span class="hljs-number">1341</span>, <span class="hljs-number">1119</span>, <span class="hljs-number">3520</span>, <span class="hljs-number">1164</span>, <span class="hljs-number">1248</span>, <span class="hljs-number">6462</span>, <span class="hljs-number">117</span>, <span class="hljs-number">21902</span>, <span class="hljs-number">1643</span>, <span class="hljs-number">119</span>, <span class="hljs-number">102</span>],
       [<span class="hljs-number">101</span>, <span class="hljs-number">1327</span>, <span class="hljs-number">1164</span>, <span class="hljs-number">5450</span>, <span class="hljs-number">23434</span>, <span class="hljs-number">136</span>, <span class="hljs-number">102</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]],
      dtype=int32)&gt;,
 <span class="hljs-string">&#x27;token_type_ids&#x27;</span>: &lt;tf.Tensor: shape=(<span class="hljs-number">2</span>, <span class="hljs-number">9</span>), dtype=int32, numpy=
array([[<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
       [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
       [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]], dtype=int32)&gt;,
 <span class="hljs-string">&#x27;attention_mask&#x27;</span>: &lt;tf.Tensor: shape=(<span class="hljs-number">2</span>, <span class="hljs-number">9</span>), dtype=int32, numpy=
array([[<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
       [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>],
       [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]], dtype=int32)&gt;}`,wrap:!1}}),{c(){u(c.$$.fragment)},l(m){j(c.$$.fragment,m)},m(m,M){b(c,m,M),d=!0},p:X,i(m){d||(i(c.$$.fragment,m),d=!0)},o(m){o(c.$$.fragment,m),d=!1},d(m){f(c,m)}}}function dt(w){let c,d;return c=new nt({props:{$$slots:{default:[ft]},$$scope:{ctx:w}}}),{c(){u(c.$$.fragment)},l(m){j(c.$$.fragment,m)},m(m,M){b(c,m,M),d=!0},p(m,M){const v={};M&2&&(v.$$scope={dirty:M,ctx:m}),c.$set(v)},i(m){d||(i(c.$$.fragment,m),d=!0)},o(m){o(c.$$.fragment,m),d=!1},d(m){f(c,m)}}}function gt(w){let c,d=`이미지 전처리는 이미지 증강 기법을 몇 가지 적용한 뒤에 할 수도 있습니다.
이미지 전처리 및 이미지 증강은 모두 이미지 데이터를 변형하지만, 서로 다른 목적을 가지고 있습니다:`,m,M,v=`<li>이미지 증강은 과적합(over-fitting)을 방지하고 모델의 견고함(resiliency)을 높이는 데 도움이 되는 방식으로 이미지를 수정합니다.
밝기와 색상 조정, 자르기, 회전, 크기 조정, 확대/축소 등 다양한 방법으로 데이터를 증강할 수 있습니다.
그러나 증강으로 이미지의 의미가 바뀌지 않도록 주의해야 합니다.</li> <li>이미지 전처리는 이미지가 모델이 예상하는 입력 형식과 일치하도록 보장합니다.
컴퓨터 비전 모델을 미세 조정할 때 이미지는 모델이 초기에 훈련될 때와 정확히 같은 방식으로 전처리되어야 합니다.</li>`,y,_,x="이미지 증강에는 원하는 라이브러리를 무엇이든 사용할 수 있습니다. 이미지 전처리에는 모델과 연결된 <code>ImageProcessor</code>를 사용합니다.";return{c(){c=t("p"),c.textContent=d,m=e(),M=t("ul"),M.innerHTML=v,y=e(),_=t("p"),_.innerHTML=x},l(I){c=r(I,"P",{"data-svelte-h":!0}),h(c)!=="svelte-fh9zuk"&&(c.textContent=d),m=p(I),M=r(I,"UL",{"data-svelte-h":!0}),h(M)!=="svelte-1gppyte"&&(M.innerHTML=v),y=p(I),_=r(I,"P",{"data-svelte-h":!0}),h(_)!=="svelte-wokouf"&&(_.innerHTML=x)},m(I,U){l(I,c,U),l(I,m,U),l(I,M,U),l(I,y,U),l(I,_,U)},p:X,d(I){I&&(n(c),n(m),n(M),n(y),n(_))}}}function Mt(w){let c,d="데이터 세트가 상당히 크기 때문에 🤗 Datasets의 <code>split</code> 매개변수를 사용하여 훈련 세트에서 작은 샘플만 가져오세요!";return{c(){c=t("p"),c.innerHTML=d},l(m){c=r(m,"P",{"data-svelte-h":!0}),h(c)!=="svelte-1ozj54"&&(c.innerHTML=d)},m(m,M){l(m,c,M)},p:X,d(m){m&&n(c)}}}function Jt(w){let c,d=`위의 예에서는 이미지 증강 중에 이미지 크기를 조정했기 때문에 <code>do_resize=False</code>로 설정하고, 해당 <code>image_processor</code>에서 <code>size</code> 속성을 활용했습니다.
이미지 증강 중에 이미지 크기를 조정하지 않은 경우 이 매개변수를 생략하세요.
기본적으로는 <code>ImageProcessor</code>가 크기 조정을 처리합니다.`,m,M,v="증강 변환 과정에서 이미지를 정규화하려면 <code>image_processor.image_mean</code> 및 <code>image_processor.image_std</code> 값을 사용하세요.";return{c(){c=t("p"),c.innerHTML=d,m=e(),M=t("p"),M.innerHTML=v},l(y){c=r(y,"P",{"data-svelte-h":!0}),h(c)!=="svelte-1yu321o"&&(c.innerHTML=d),m=p(y),M=r(y,"P",{"data-svelte-h":!0}),h(M)!=="svelte-mx1c62"&&(M.innerHTML=v)},m(y,_){l(y,c,_),l(y,m,_),l(y,M,_)},p:X,d(y){y&&(n(c),n(m),n(M))}}}function Tt(w){let c,d=`<code>ImageProcessor</code>는 객체 감지, 시맨틱 세그멘테이션(semantic segmentation), 인스턴스 세그멘테이션(instance segmentation), 파놉틱 세그멘테이션(panoptic segmentation)과 같은 작업에 대한 후처리 방법을 제공합니다.
이러한 방법은 모델의 원시 출력을 경계 상자나 세그멘테이션 맵과 같은 의미 있는 예측으로 변환해줍니다.`;return{c(){c=t("p"),c.innerHTML=d},l(m){c=r(m,"P",{"data-svelte-h":!0}),h(c)!=="svelte-1nya0re"&&(c.innerHTML=d)},m(m,M){l(m,c,M)},p:X,d(m){m&&n(c)}}}function $t(w){let c,d,m,M,v,y,_,x,I,U,Ge="전처리",Mn,ms,Jn,hs,Ve="모델을 훈련하려면 데이터 세트를 모델에 맞는 입력 형식으로 전처리해야 합니다. 텍스트, 이미지 또는 오디오인지 관계없이 데이터를 텐서 배치로 변환하고 조립할 필요가 있습니다. 🤗 Transformers는 모델에 대한 데이터를 준비하는 데 도움이 되는 일련의 전처리 클래스를 제공합니다. 이 튜토리얼에서는 다음 내용을 배울 수 있습니다:",Tn,us,Be='<li>텍스트는 <a href="./main_classes/tokenizer">Tokenizer</a>를 사용하여 토큰 시퀀스로 변환하고 토큰의 숫자 표현을 만든 후 텐서로 조립합니다.</li> <li>음성 및 오디오는 <a href="./main_classes/feature_extractor">Feature extractor</a>를 사용하여 오디오 파형에서 시퀀스 특성을 파악하여 텐서로 변환합니다.</li> <li>이미지 입력은 <a href="./main_classes/image">ImageProcessor</a>을 사용하여 이미지를 텐서로 변환합니다.</li> <li>멀티모달 입력은 <a href="./main_classes/processors">Processor</a>을 사용하여 토크나이저와 특성 추출기 또는 이미지 프로세서를 결합합니다.</li>',$n,R,wn,js,We="시작하기 전에 🤗 Datasets를 설치하여 실험에 사용할 데이터를 불러올 수 있습니다:",yn,bs,vn,C,Y,en,is,ve,Sa,He="자연어처리",_n,os,In,fs,Ne='텍스트 데이터를 전처리하기 위한 기본 도구는 <a href="main_classes/tokenizer">tokenizer</a>입니다. 토크나이저는 일련의 규칙에 따라 텍스트를 <em>토큰</em>으로 나눕니다. 토큰은 숫자로 변환되고 텐서는 모델 입력이 됩니다. 모델에 필요한 추가 입력은 토크나이저에 의해 추가됩니다.',Un,q,xn,ds,Xe="시작하려면 <code>AutoTokenizer.from_pretrained()</code> 메소드를 사용하여 사전훈련된 토크나이저를 불러오세요. 모델과 함께 사전훈련된 <em>vocab</em>을 다운로드합니다:",Cn,gs,kn,Ms,Re="그 다음으로 텍스트를 토크나이저에 넣어주세요:",Zn,Js,Gn,Ts,Ye="토크나이저는 세 가지 중요한 항목을 포함한 딕셔너리를 반환합니다:",Vn,$s,qe='<li><a href="glossary#input-ids">input_ids</a>는 문장의 각 토큰에 해당하는 인덱스입니다.</li> <li><a href="glossary#attention-mask">attention_mask</a>는 토큰을 처리해야 하는지 여부를 나타냅니다.</li> <li><a href="glossary#token-type-ids">token_type_ids</a>는 두 개 이상의 시퀀스가 있을 때 토큰이 속한 시퀀스를 식별합니다.</li>',Bn,ws,Ee="<code>input_ids</code>를 디코딩하여 입력을 반환합니다:",Wn,ys,Hn,vs,ze=`토크나이저가 두 개의 특수한 토큰(분류 토큰 <code>CLS</code>와 분할 토큰 <code>SEP</code>)을 문장에 추가했습니다.
모든 모델에 특수한 토큰이 필요한 것은 아니지만, 필요하다면 토크나이저가 자동으로 추가합니다.`,Nn,_s,Ae="전처리할 문장이 여러 개 있는 경우에는 리스트로 토크나이저에 전달합니다:",Xn,Is,Rn,k,E,pn,Us,_e,Da,Fe="패딩",Yn,xs,Le="모델 입력인 텐서는 모양이 균일해야 하지만, 문장의 길이가 항상 같지는 않기 때문에 문제가 될 수 있습니다. 패딩은 짧은 문장에 특수한 <em>패딩 토큰</em>을 추가하여 텐서를 직사각형 모양이 되도록 하는 전략입니다.",qn,Cs,Qe="<code>padding</code> 매개변수를 <code>True</code>로 설정하여 배치 내의 짧은 시퀀스를 가장 긴 시퀀스에 맞춰 패딩합니다.",En,ks,zn,Zs,Pe="길이가 짧은 첫 문장과 세 번째 문장이 이제 <code>0</code>으로 채워졌습니다.",An,Z,z,tn,Gs,Ie,Ka,Se="잘라내기",Fn,Vs,De="한편, 때로는 시퀀스가 모델에서 처리하기에 너무 길 수도 있습니다. 이 경우, 시퀀스를 더 짧게 줄일 필요가 있습니다.",Ln,Bs,Ke="모델에서 허용하는 최대 길이로 시퀀스를 자르려면 <code>truncation</code> 매개변수를 <code>True</code>로 설정하세요:",Qn,Ws,Pn,A,Sn,G,F,rn,Hs,Ue,Oa,Oe="텐서 만들기",Dn,Ns,sp="마지막으로, 토크나이저가 모델에 공급되는 실제 텐서를 반환하도록 합니다.",Kn,Xs,ap="<code>return_tensors</code> 매개변수를 PyTorch의 경우 <code>pt</code>, TensorFlow의 경우 <code>tf</code>로 설정하세요:",On,L,sl,V,Q,cn,Rs,xe,sn,np="오디오",al,Ys,lp='오디오 작업은 모델에 맞는 데이터 세트를 준비하기 위해 <a href="main_classes/feature_extractor">특성 추출기</a>가 필요합니다. 특성 추출기는 원시 오디오 데이터에서 특성를 추출하고 이를 텐서로 변환하는 것이 목적입니다.',nl,qs,ep='오디오 데이터 세트에 특성 추출기를 사용하는 방법을 보기 위해 <a href="https://huggingface.co/datasets/PolyAI/minds14" rel="nofollow">MInDS-14</a> 데이터 세트를 가져오세요. (데이터 세트를 가져오는 방법은 🤗 <a href="https://huggingface.co/docs/datasets/load_hub.html" rel="nofollow">데이터 세트 튜토리얼</a>에서 자세히 설명하고 있습니다.)',ll,Es,el,zs,pp="<code>audio</code> 열의 첫 번째 요소에 접근하여 입력을 살펴보세요. <code>audio</code> 열을 호출하면 오디오 파일을 자동으로 가져오고 리샘플링합니다.",pl,As,tl,Fs,tp="이렇게 하면 세 가지 항목이 반환됩니다:",rl,Ls,rp="<li><code>array</code>는 1D 배열로 가져와서 (필요한 경우) 리샘플링된 음성 신호입니다.</li> <li><code>path</code>는 오디오 파일의 위치를 가리킵니다.</li> <li><code>sampling_rate</code>는 음성 신호에서 초당 측정되는 데이터 포인트 수를 나타냅니다.</li>",cl,Qs,cp=`이 튜토리얼에서는 <a href="https://huggingface.co/facebook/wav2vec2-base" rel="nofollow">Wav2Vec2</a> 모델을 사용합니다. 모델 카드를 보면 Wav2Vec2가 16kHz 샘플링된 음성 오디오를 기반으로 사전훈련된 것을 알 수 있습니다.
모델을 사전훈련하는 데 사용된 데이터 세트의 샘플링 레이트와 오디오 데이터의 샘플링 레이트가 일치해야 합니다. 데이터의 샘플링 레이트가 다르면 데이터를 리샘플링해야 합니다.`,ml,Ps,mp="<li>🤗 Datasets의 <code>cast_column</code> 메소드를 사용하여 샘플링 레이트를 16kHz로 업샘플링하세요:</li>",hl,Ss,ul,P,hp="<li>오디오 파일을 리샘플링하기 위해 <code>audio</code> 열을 다시 호출합니다:</li>",jl,Ds,bl,Ks,up=`다음으로, 입력을 정규화하고 패딩할 특성 추출기를 가져오세요. 텍스트 데이터의 경우, 더 짧은 시퀀스에 대해 <code>0</code>이 추가됩니다. 오디오 데이터에도 같은 개념이 적용됩니다.
특성 추출기는 배열에 <code>0</code>(묵음으로 해석)을 추가합니다.`,il,Os,jp="<code>AutoFeatureExtractor.from_pretrained()</code>를 사용하여 특성 추출기를 가져오세요:",ol,sa,fl,aa,bp="오디오 <code>array</code>를 특성 추출기에 전달하세요. 또한, 발생할 수 있는 조용한 오류(silent errors)를 더 잘 디버깅할 수 있도록 특성 추출기에 <code>sampling_rate</code> 인수를 추가하는 것을 권장합니다.",dl,na,gl,la,ip="토크나이저와 마찬가지로 배치 내에서 가변적인 시퀀스를 처리하기 위해 패딩 또는 잘라내기를 적용할 수 있습니다. 이 두 개의 오디오 샘플의 시퀀스 길이를 확인해보세요:",Ml,ea,Jl,pa,op="오디오 샘플의 길이가 동일하도록 데이터 세트를 전처리하는 함수를 만드세요. 최대 샘플 길이를 지정하면 특성 추출기가 해당 길이에 맞춰 시퀀스를 패딩하거나 잘라냅니다:",Tl,ta,$l,ra,fp="<code>preprocess_function</code>을 데이터 세트의 처음 예시 몇 개에 적용해보세요:",wl,ca,yl,ma,dp="이제 샘플 길이가 모두 같고 지정된 최대 길이에 맞게 되었습니다. 드디어 전처리된 데이터 세트를 모델에 전달할 수 있습니다!",vl,ha,_l,B,S,mn,ua,Ce,an,gp="컴퓨터 비전",Il,ja,Mp=`컴퓨터 비전 작업의 경우, 모델에 대한 데이터 세트를 준비하기 위해 <a href="main_classes/image_processor">이미지 프로세서</a>가 필요합니다.
이미지 전처리는 이미지를 모델이 예상하는 입력으로 변환하는 여러 단계로 이루어집니다.
이러한 단계에는 크기 조정, 정규화, 색상 채널 보정, 이미지의 텐서 변환 등이 포함됩니다.`,Ul,D,xl,ba,Jp=`<a href="https://huggingface.co/datasets/food101" rel="nofollow">food101</a> 데이터 세트를 가져와서 컴퓨터 비전 데이터 세트에서 이미지 프로세서를 어떻게 사용하는지 알아보세요.
데이터 세트를 불러오는 방법은 🤗 <a href="https://huggingface.co/docs/datasets/load_hub.html" rel="nofollow">데이터 세트 튜토리얼</a>을 참고하세요.`,Cl,K,kl,ia,Zl,oa,Tp='다음으로, 🤗 Datasets의 <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=image#datasets.Image" rel="nofollow"><code>image</code></a>로 이미지를 확인해보세요:',Gl,fa,Vl,O,$p='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/vision-preprocess-tutorial.png"/>',Bl,da,wp="<code>AutoImageProcessor.from_pretrained()</code>로 이미지 프로세서를 가져오세요:",Wl,ga,Hl,Ma,yp=`먼저 이미지 증강 단계를 추가해 봅시다. 아무 라이브러리나 사용해도 괜찮지만, 이번 튜토리얼에서는 torchvision의 <a href="https://pytorch.org/vision/stable/transforms.html" rel="nofollow"><code>transforms</code></a> 모듈을 사용하겠습니다.
다른 데이터 증강 라이브러리를 사용해보고 싶다면, <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_albumentations.ipynb" rel="nofollow">Albumentations</a> 또는 <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_kornia.ipynb" rel="nofollow">Kornia notebooks</a>에서 어떻게 사용하는지 배울 수 있습니다.`,Nl,Ja,vp=`<li><a href="https://pytorch.org/vision/master/generated/torchvision.transforms.Compose.html" rel="nofollow"><code>Compose</code></a>로  <a href="https://pytorch.org/vision/main/generated/torchvision.transforms.RandomResizedCrop.html" rel="nofollow"><code>RandomResizedCrop</code></a>와 <a href="https://pytorch.org/vision/main/generated/torchvision.transforms.ColorJitter.html" rel="nofollow"><code>ColorJitter</code></a> 등 변환을 몇 가지 연결하세요.
참고로 크기 조정에 필요한 이미지의 크기 요구사항은 <code>image_processor</code>에서 가져올 수 있습니다.
일부 모델은 정확한 높이와 너비를 요구하지만, 제일 짧은 변의 길이(<code>shortest_edge</code>)만 정의된 모델도 있습니다.</li>`,Xl,Ta,Rl,ss,_p=`<li>모델은 입력으로 <a href="model_doc/visionencoderdecoder#transformers.VisionEncoderDecoderModel.forward.pixel_values"><code>pixel_values</code></a>를 받습니다.
<code>ImageProcessor</code>는 이미지 정규화 및 적절한 텐서 생성을 처리할 수 있습니다.
배치 이미지에 대한 이미지 증강 및 이미지 전처리를 결합하고 <code>pixel_values</code>를 생성하는 함수를 만듭니다:</li>`,Yl,$a,ql,as,El,ns,Ip='<li>🤗 Datasets의 <a href="https://huggingface.co/docs/datasets/process.html#format-transform" rel="nofollow"><code>set_transform</code></a>를 사용하여 실시간으로 변환을 적용합니다:</li>',zl,wa,Al,ls,Up=`<li>이제 이미지에 접근하면 이미지 프로세서가 <code>pixel_values</code>를 추가한 것을 알 수 있습니다.
드디어 처리된 데이터 세트를 모델에 전달할 수 있습니다!</li>`,Fl,ya,Ll,va,xp="다음은 변형이 적용된 후의 이미지입니다. 이미지가 무작위로 잘려나갔고 색상 속성이 다릅니다.",Ql,_a,Pl,es,Cp='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/preprocessed_image.png"/>',Sl,ps,Dl,W,ts,hn,Ia,ke,nn,kp="패딩",Kl,Ua,Zp=`예를 들어, <a href="./model_doc/detr">DETR</a>와 같은 경우에는 모델이 훈련할 때 크기 조정 증강을 적용합니다.
이로 인해 배치 내 이미지 크기가 달라질 수 있습니다.
<code>DetrImageProcessor</code>의 <code>DetrImageProcessor.pad()</code>를 사용하고 사용자 정의 <code>collate_fn</code>을 정의해서 배치 이미지를 처리할 수 있습니다.`,Ol,xa,se,H,rs,un,Ca,Ze,ln,Gp="멀티모달",ae,ka,Vp=`멀티모달 입력이 필요한 작업의 경우, 모델에 데이터 세트를 준비하기 위한 <a href="main_classes/processors">프로세서</a>가 필요합니다.
프로세서는 토크나이저와 특성 추출기와 같은 두 가지 처리 객체를 결합합니다.`,ne,Za,Bp=`<a href="https://huggingface.co/datasets/lj_speech" rel="nofollow">LJ Speech</a> 데이터 세트를 가져와서 자동 음성 인식(ASR)을 위한 프로세서를 사용하는 방법을 확인하세요.
(데이터 세트를 가져오는 방법에 대한 자세한 내용은 🤗 <a href="https://huggingface.co/docs/datasets/load_hub.html" rel="nofollow">데이터 세트 튜토리얼</a>에서 볼 수 있습니다.)`,le,Ga,ee,Va,Wp="자동 음성 인식(ASR)에서는 <code>audio</code>와 <code>text</code>에만 집중하면 되므로, 다른 열들은 제거할 수 있습니다:",pe,Ba,te,Wa,Hp="이제 <code>audio</code>와 <code>text</code>열을 살펴보세요:",re,Ha,ce,Na,Np='기존에 사전훈련된 모델에서 사용된 데이터 세트와 새로운 오디오 데이터 세트의 샘플링 레이트를 일치시키기 위해 오디오 데이터 세트의 샘플링 레이트를 <a href="preprocessing#audio">리샘플링</a>해야 합니다!',me,Xa,he,Ra,Xp="<code>AutoProcessor.from_pretrained()</code>로 프로세서를 가져오세요:",ue,Ya,je,qa,Rp=`<li><code>array</code>에 들어 있는 오디오 데이터를 <code>input_values</code>로 변환하고 <code>text</code>를 토큰화하여 <code>labels</code>로 변환하는 함수를 만듭니다.
모델의 입력은 다음과 같습니다:</li>`,be,Ea,ie,cs,Yp="<li>샘플을 <code>prepare_dataset</code> 함수에 적용하세요:</li>",oe,za,fe,Aa,qp=`이제 프로세서가 <code>input_values</code>와 <code>labels</code>를 추가하고, 샘플링 레이트도 올바르게 16kHz로 다운샘플링했습니다.
드디어 처리된 데이터 세트를 모델에 전달할 수 있습니다!`,de,jn,ge;return x=new N({}),ms=new mt({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/preprocessing.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/preprocessing.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/preprocessing.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/preprocessing.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/preprocessing.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/preprocessing.ipynb"}]}}),R=new Pa({props:{$$slots:{default:[ut]},$$scope:{ctx:w}}}),bs=new T({props:{code:"cGlwJTIwaW5zdGFsbCUyMGRhdGFzZXRz",highlighted:"pip install datasets",wrap:!1}}),is=new N({}),os=new ct({props:{id:"Yffk5aydLzg"}}),q=new Pa({props:{$$slots:{default:[jt]},$$scope:{ctx:w}}}),gs=new T({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMEElMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIp",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>)`,wrap:!1}}),Js=new T({props:{code:"ZW5jb2RlZF9pbnB1dCUyMCUzRCUyMHRva2VuaXplciglMjJEbyUyMG5vdCUyMG1lZGRsZSUyMGluJTIwdGhlJTIwYWZmYWlycyUyMG9mJTIwd2l6YXJkcyUyQyUyMGZvciUyMHRoZXklMjBhcmUlMjBzdWJ0bGUlMjBhbmQlMjBxdWljayUyMHRvJTIwYW5nZXIuJTIyKSUwQXByaW50KGVuY29kZWRfaW5wdXQp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>encoded_input = tokenizer(<span class="hljs-string">&quot;Do not meddle in the affairs of wizards, for they are subtle and quick to anger.&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(encoded_input)
{<span class="hljs-string">&#x27;input_ids&#x27;</span>: [<span class="hljs-number">101</span>, <span class="hljs-number">2079</span>, <span class="hljs-number">2025</span>, <span class="hljs-number">19960</span>, <span class="hljs-number">10362</span>, <span class="hljs-number">1999</span>, <span class="hljs-number">1996</span>, <span class="hljs-number">3821</span>, <span class="hljs-number">1997</span>, <span class="hljs-number">16657</span>, <span class="hljs-number">1010</span>, <span class="hljs-number">2005</span>, <span class="hljs-number">2027</span>, <span class="hljs-number">2024</span>, <span class="hljs-number">11259</span>, <span class="hljs-number">1998</span>, <span class="hljs-number">4248</span>, <span class="hljs-number">2000</span>, <span class="hljs-number">4963</span>, <span class="hljs-number">1012</span>, <span class="hljs-number">102</span>],
 <span class="hljs-string">&#x27;token_type_ids&#x27;</span>: [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
 <span class="hljs-string">&#x27;attention_mask&#x27;</span>: [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>]}`,wrap:!1}}),ys=new T({props:{code:"dG9rZW5pemVyLmRlY29kZShlbmNvZGVkX2lucHV0JTVCJTIyaW5wdXRfaWRzJTIyJTVEKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.decode(encoded_input[<span class="hljs-string">&quot;input_ids&quot;</span>])
<span class="hljs-string">&#x27;[CLS] Do not meddle in the affairs of wizards, for they are subtle and quick to anger. [SEP]&#x27;</span>`,wrap:!1}}),Is=new T({props:{code:"YmF0Y2hfc2VudGVuY2VzJTIwJTNEJTIwJTVCJTBBJTIwJTIwJTIwJTIwJTIyQnV0JTIwd2hhdCUyMGFib3V0JTIwc2Vjb25kJTIwYnJlYWtmYXN0JTNGJTIyJTJDJTBBJTIwJTIwJTIwJTIwJTIyRG9uJ3QlMjB0aGluayUyMGhlJTIwa25vd3MlMjBhYm91dCUyMHNlY29uZCUyMGJyZWFrZmFzdCUyQyUyMFBpcC4lMjIlMkMlMEElMjAlMjAlMjAlMjAlMjJXaGF0JTIwYWJvdXQlMjBlbGV2ZW5zaWVzJTNGJTIyJTJDJTBBJTVEJTBBZW5jb2RlZF9pbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIoYmF0Y2hfc2VudGVuY2VzKSUwQXByaW50KGVuY29kZWRfaW5wdXRzKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>batch_sentences = [
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;But what about second breakfast?&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Don&#x27;t think he knows about second breakfast, Pip.&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;What about elevensies?&quot;</span>,
<span class="hljs-meta">... </span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>encoded_inputs = tokenizer(batch_sentences)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(encoded_inputs)
{<span class="hljs-string">&#x27;input_ids&#x27;</span>: [[<span class="hljs-number">101</span>, <span class="hljs-number">1252</span>, <span class="hljs-number">1184</span>, <span class="hljs-number">1164</span>, <span class="hljs-number">1248</span>, <span class="hljs-number">6462</span>, <span class="hljs-number">136</span>, <span class="hljs-number">102</span>],
               [<span class="hljs-number">101</span>, <span class="hljs-number">1790</span>, <span class="hljs-number">112</span>, <span class="hljs-number">189</span>, <span class="hljs-number">1341</span>, <span class="hljs-number">1119</span>, <span class="hljs-number">3520</span>, <span class="hljs-number">1164</span>, <span class="hljs-number">1248</span>, <span class="hljs-number">6462</span>, <span class="hljs-number">117</span>, <span class="hljs-number">21902</span>, <span class="hljs-number">1643</span>, <span class="hljs-number">119</span>, <span class="hljs-number">102</span>],
               [<span class="hljs-number">101</span>, <span class="hljs-number">1327</span>, <span class="hljs-number">1164</span>, <span class="hljs-number">5450</span>, <span class="hljs-number">23434</span>, <span class="hljs-number">136</span>, <span class="hljs-number">102</span>]],
 <span class="hljs-string">&#x27;token_type_ids&#x27;</span>: [[<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
                    [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
                    [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]],
 <span class="hljs-string">&#x27;attention_mask&#x27;</span>: [[<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>],
                    [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>],
                    [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>]]}`,wrap:!1}}),Us=new N({}),ks=new T({props:{code:"YmF0Y2hfc2VudGVuY2VzJTIwJTNEJTIwJTVCJTBBJTIwJTIwJTIwJTIwJTIyQnV0JTIwd2hhdCUyMGFib3V0JTIwc2Vjb25kJTIwYnJlYWtmYXN0JTNGJTIyJTJDJTBBJTIwJTIwJTIwJTIwJTIyRG9uJ3QlMjB0aGluayUyMGhlJTIwa25vd3MlMjBhYm91dCUyMHNlY29uZCUyMGJyZWFrZmFzdCUyQyUyMFBpcC4lMjIlMkMlMEElMjAlMjAlMjAlMjAlMjJXaGF0JTIwYWJvdXQlMjBlbGV2ZW5zaWVzJTNGJTIyJTJDJTBBJTVEJTBBZW5jb2RlZF9pbnB1dCUyMCUzRCUyMHRva2VuaXplcihiYXRjaF9zZW50ZW5jZXMlMkMlMjBwYWRkaW5nJTNEVHJ1ZSklMEFwcmludChlbmNvZGVkX2lucHV0KQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>batch_sentences = [
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;But what about second breakfast?&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Don&#x27;t think he knows about second breakfast, Pip.&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;What about elevensies?&quot;</span>,
<span class="hljs-meta">... </span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>encoded_input = tokenizer(batch_sentences, padding=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(encoded_input)
{<span class="hljs-string">&#x27;input_ids&#x27;</span>: [[<span class="hljs-number">101</span>, <span class="hljs-number">1252</span>, <span class="hljs-number">1184</span>, <span class="hljs-number">1164</span>, <span class="hljs-number">1248</span>, <span class="hljs-number">6462</span>, <span class="hljs-number">136</span>, <span class="hljs-number">102</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
               [<span class="hljs-number">101</span>, <span class="hljs-number">1790</span>, <span class="hljs-number">112</span>, <span class="hljs-number">189</span>, <span class="hljs-number">1341</span>, <span class="hljs-number">1119</span>, <span class="hljs-number">3520</span>, <span class="hljs-number">1164</span>, <span class="hljs-number">1248</span>, <span class="hljs-number">6462</span>, <span class="hljs-number">117</span>, <span class="hljs-number">21902</span>, <span class="hljs-number">1643</span>, <span class="hljs-number">119</span>, <span class="hljs-number">102</span>],
               [<span class="hljs-number">101</span>, <span class="hljs-number">1327</span>, <span class="hljs-number">1164</span>, <span class="hljs-number">5450</span>, <span class="hljs-number">23434</span>, <span class="hljs-number">136</span>, <span class="hljs-number">102</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]],
 <span class="hljs-string">&#x27;token_type_ids&#x27;</span>: [[<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
                    [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
                    [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]],
 <span class="hljs-string">&#x27;attention_mask&#x27;</span>: [[<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
                    [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>],
                    [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]]}`,wrap:!1}}),Gs=new N({}),Ws=new T({props:{code:"YmF0Y2hfc2VudGVuY2VzJTIwJTNEJTIwJTVCJTBBJTIwJTIwJTIwJTIwJTIyQnV0JTIwd2hhdCUyMGFib3V0JTIwc2Vjb25kJTIwYnJlYWtmYXN0JTNGJTIyJTJDJTBBJTIwJTIwJTIwJTIwJTIyRG9uJ3QlMjB0aGluayUyMGhlJTIwa25vd3MlMjBhYm91dCUyMHNlY29uZCUyMGJyZWFrZmFzdCUyQyUyMFBpcC4lMjIlMkMlMEElMjAlMjAlMjAlMjAlMjJXaGF0JTIwYWJvdXQlMjBlbGV2ZW5zaWVzJTNGJTIyJTJDJTBBJTVEJTBBZW5jb2RlZF9pbnB1dCUyMCUzRCUyMHRva2VuaXplcihiYXRjaF9zZW50ZW5jZXMlMkMlMjBwYWRkaW5nJTNEVHJ1ZSUyQyUyMHRydW5jYXRpb24lM0RUcnVlKSUwQXByaW50KGVuY29kZWRfaW5wdXQp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>batch_sentences = [
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;But what about second breakfast?&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Don&#x27;t think he knows about second breakfast, Pip.&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;What about elevensies?&quot;</span>,
<span class="hljs-meta">... </span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>encoded_input = tokenizer(batch_sentences, padding=<span class="hljs-literal">True</span>, truncation=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(encoded_input)
{<span class="hljs-string">&#x27;input_ids&#x27;</span>: [[<span class="hljs-number">101</span>, <span class="hljs-number">1252</span>, <span class="hljs-number">1184</span>, <span class="hljs-number">1164</span>, <span class="hljs-number">1248</span>, <span class="hljs-number">6462</span>, <span class="hljs-number">136</span>, <span class="hljs-number">102</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
               [<span class="hljs-number">101</span>, <span class="hljs-number">1790</span>, <span class="hljs-number">112</span>, <span class="hljs-number">189</span>, <span class="hljs-number">1341</span>, <span class="hljs-number">1119</span>, <span class="hljs-number">3520</span>, <span class="hljs-number">1164</span>, <span class="hljs-number">1248</span>, <span class="hljs-number">6462</span>, <span class="hljs-number">117</span>, <span class="hljs-number">21902</span>, <span class="hljs-number">1643</span>, <span class="hljs-number">119</span>, <span class="hljs-number">102</span>],
               [<span class="hljs-number">101</span>, <span class="hljs-number">1327</span>, <span class="hljs-number">1164</span>, <span class="hljs-number">5450</span>, <span class="hljs-number">23434</span>, <span class="hljs-number">136</span>, <span class="hljs-number">102</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]],
 <span class="hljs-string">&#x27;token_type_ids&#x27;</span>: [[<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
                    [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
                    [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]],
 <span class="hljs-string">&#x27;attention_mask&#x27;</span>: [[<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
                    [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>],
                    [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]]}`,wrap:!1}}),A=new Pa({props:{$$slots:{default:[bt]},$$scope:{ctx:w}}}),Hs=new N({}),L=new ht({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[dt],pytorch:[ot]},$$scope:{ctx:w}}}),Rs=new N({}),Es=new T({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTJDJTIwQXVkaW8lMEElMEFkYXRhc2V0JTIwJTNEJTIwbG9hZF9kYXRhc2V0KCUyMlBvbHlBSSUyRm1pbmRzMTQlMjIlMkMlMjBuYW1lJTNEJTIyZW4tVVMlMjIlMkMlMjBzcGxpdCUzRCUyMnRyYWluJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset, Audio

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;PolyAI/minds14&quot;</span>, name=<span class="hljs-string">&quot;en-US&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)`,wrap:!1}}),As=new T({props:{code:"ZGF0YXNldCU1QjAlNUQlNUIlMjJhdWRpbyUyMiU1RA==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>]
{<span class="hljs-string">&#x27;array&#x27;</span>: array([ <span class="hljs-number">0.</span>        ,  <span class="hljs-number">0.00024414</span>, -<span class="hljs-number">0.00024414</span>, ..., -<span class="hljs-number">0.00024414</span>,
         <span class="hljs-number">0.</span>        ,  <span class="hljs-number">0.</span>        ], dtype=float32),
 <span class="hljs-string">&#x27;path&#x27;</span>: <span class="hljs-string">&#x27;/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav&#x27;</span>,
 <span class="hljs-string">&#x27;sampling_rate&#x27;</span>: <span class="hljs-number">8000</span>}`,wrap:!1}}),Ss=new T({props:{code:"ZGF0YXNldCUyMCUzRCUyMGRhdGFzZXQuY2FzdF9jb2x1bW4oJTIyYXVkaW8lMjIlMkMlMjBBdWRpbyhzYW1wbGluZ19yYXRlJTNEMTZfMDAwKSk=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.cast_column(<span class="hljs-string">&quot;audio&quot;</span>, Audio(sampling_rate=<span class="hljs-number">16_000</span>))',wrap:!1}}),Ds=new T({props:{code:"ZGF0YXNldCU1QjAlNUQlNUIlMjJhdWRpbyUyMiU1RA==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>]
{<span class="hljs-string">&#x27;array&#x27;</span>: array([ <span class="hljs-number">2.3443763e-05</span>,  <span class="hljs-number">2.1729663e-04</span>,  <span class="hljs-number">2.2145823e-04</span>, ...,
         <span class="hljs-number">3.8356509e-05</span>, -<span class="hljs-number">7.3497440e-06</span>, -<span class="hljs-number">2.1754686e-05</span>], dtype=float32),
 <span class="hljs-string">&#x27;path&#x27;</span>: <span class="hljs-string">&#x27;/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav&#x27;</span>,
 <span class="hljs-string">&#x27;sampling_rate&#x27;</span>: <span class="hljs-number">16000</span>}`,wrap:!1}}),sa=new T({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9GZWF0dXJlRXh0cmFjdG9yJTBBJTBBZmVhdHVyZV9leHRyYWN0b3IlMjAlM0QlMjBBdXRvRmVhdHVyZUV4dHJhY3Rvci5mcm9tX3ByZXRyYWluZWQoJTIyZmFjZWJvb2slMkZ3YXYydmVjMi1iYXNlJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor

<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-base&quot;</span>)`,wrap:!1}}),na=new T({props:{code:"YXVkaW9faW5wdXQlMjAlM0QlMjAlNUJkYXRhc2V0JTVCMCU1RCU1QiUyMmF1ZGlvJTIyJTVEJTVCJTIyYXJyYXklMjIlNUQlNUQlMEFmZWF0dXJlX2V4dHJhY3RvcihhdWRpb19pbnB1dCUyQyUyMHNhbXBsaW5nX3JhdGUlM0QxNjAwMCk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>audio_input = [dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>]]
<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor(audio_input, sampling_rate=<span class="hljs-number">16000</span>)
{<span class="hljs-string">&#x27;input_values&#x27;</span>: [array([ <span class="hljs-number">3.8106556e-04</span>,  <span class="hljs-number">2.7506407e-03</span>,  <span class="hljs-number">2.8015103e-03</span>, ...,
        <span class="hljs-number">5.6335266e-04</span>,  <span class="hljs-number">4.6588284e-06</span>, -<span class="hljs-number">1.7142107e-04</span>], dtype=float32)]}`,wrap:!1}}),ea=new T({props:{code:"ZGF0YXNldCU1QjAlNUQlNUIlMjJhdWRpbyUyMiU1RCU1QiUyMmFycmF5JTIyJTVELnNoYXBlJTBBJTBBZGF0YXNldCU1QjElNUQlNUIlMjJhdWRpbyUyMiU1RCU1QiUyMmFycmF5JTIyJTVELnNoYXBl",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>].shape
(<span class="hljs-number">173398</span>,)

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset[<span class="hljs-number">1</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>].shape
(<span class="hljs-number">106496</span>,)`,wrap:!1}}),ta=new T({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_function</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span>    audio_arrays = [x[<span class="hljs-string">&quot;array&quot;</span>] <span class="hljs-keyword">for</span> x <span class="hljs-keyword">in</span> examples[<span class="hljs-string">&quot;audio&quot;</span>]]
<span class="hljs-meta">... </span>    inputs = feature_extractor(
<span class="hljs-meta">... </span>        audio_arrays,
<span class="hljs-meta">... </span>        sampling_rate=<span class="hljs-number">16000</span>,
<span class="hljs-meta">... </span>        padding=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>        max_length=<span class="hljs-number">100000</span>,
<span class="hljs-meta">... </span>        truncation=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    )
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> inputs`,wrap:!1}}),ca=new T({props:{code:"cHJvY2Vzc2VkX2RhdGFzZXQlMjAlM0QlMjBwcmVwcm9jZXNzX2Z1bmN0aW9uKGRhdGFzZXQlNUIlM0E1JTVEKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>processed_dataset = preprocess_function(dataset[:<span class="hljs-number">5</span>])',wrap:!1}}),ha=new T({props:{code:"cHJvY2Vzc2VkX2RhdGFzZXQlNUIlMjJpbnB1dF92YWx1ZXMlMjIlNUQlNUIwJTVELnNoYXBlJTBBJTBBcHJvY2Vzc2VkX2RhdGFzZXQlNUIlMjJpbnB1dF92YWx1ZXMlMjIlNUQlNUIxJTVELnNoYXBl",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>processed_dataset[<span class="hljs-string">&quot;input_values&quot;</span>][<span class="hljs-number">0</span>].shape
(<span class="hljs-number">100000</span>,)

<span class="hljs-meta">&gt;&gt;&gt; </span>processed_dataset[<span class="hljs-string">&quot;input_values&quot;</span>][<span class="hljs-number">1</span>].shape
(<span class="hljs-number">100000</span>,)`,wrap:!1}}),ua=new N({}),D=new Pa({props:{$$slots:{default:[gt]},$$scope:{ctx:w}}}),K=new Pa({props:{$$slots:{default:[Mt]},$$scope:{ctx:w}}}),ia=new T({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBJTBBZGF0YXNldCUyMCUzRCUyMGxvYWRfZGF0YXNldCglMjJmb29kMTAxJTIyJTJDJTIwc3BsaXQlM0QlMjJ0cmFpbiU1QiUzQTEwMCU1RCUyMik=",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;food101&quot;</span>, split=<span class="hljs-string">&quot;train[:100]&quot;</span>)`,wrap:!1}}),fa=new T({props:{code:"ZGF0YXNldCU1QjAlNUQlNUIlMjJpbWFnZSUyMiU1RA==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;image&quot;</span>]',wrap:!1}}),ga=new T({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9JbWFnZVByb2Nlc3NvciUwQSUwQWltYWdlX3Byb2Nlc3NvciUyMCUzRCUyMEF1dG9JbWFnZVByb2Nlc3Nvci5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlJTJGdml0LWJhc2UtcGF0Y2gxNi0yMjQlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;google/vit-base-patch16-224&quot;</span>)`,wrap:!1}}),Ta=new T({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> torchvision.transforms <span class="hljs-keyword">import</span> RandomResizedCrop, ColorJitter, Compose

<span class="hljs-meta">&gt;&gt;&gt; </span>size = (
<span class="hljs-meta">... </span>    image_processor.size[<span class="hljs-string">&quot;shortest_edge&quot;</span>]
<span class="hljs-meta">... </span>    <span class="hljs-keyword">if</span> <span class="hljs-string">&quot;shortest_edge&quot;</span> <span class="hljs-keyword">in</span> image_processor.size
<span class="hljs-meta">... </span>    <span class="hljs-keyword">else</span> (image_processor.size[<span class="hljs-string">&quot;height&quot;</span>], image_processor.size[<span class="hljs-string">&quot;width&quot;</span>])
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>_transforms = Compose([RandomResizedCrop(size), ColorJitter(brightness=<span class="hljs-number">0.5</span>, hue=<span class="hljs-number">0.5</span>)])`,wrap:!1}}),$a=new T({props:{code:"ZGVmJTIwdHJhbnNmb3JtcyhleGFtcGxlcyklM0ElMEElMjAlMjAlMjAlMjBpbWFnZXMlMjAlM0QlMjAlNUJfdHJhbnNmb3JtcyhpbWcuY29udmVydCglMjJSR0IlMjIpKSUyMGZvciUyMGltZyUyMGluJTIwZXhhbXBsZXMlNUIlMjJpbWFnZSUyMiU1RCU1RCUwQSUyMCUyMCUyMCUyMGV4YW1wbGVzJTVCJTIycGl4ZWxfdmFsdWVzJTIyJTVEJTIwJTNEJTIwaW1hZ2VfcHJvY2Vzc29yKGltYWdlcyUyQyUyMGRvX3Jlc2l6ZSUzREZhbHNlJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMiklNUIlMjJwaXhlbF92YWx1ZXMlMjIlNUQlMEElMjAlMjAlMjAlMjByZXR1cm4lMjBleGFtcGxlcw==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">transforms</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span>    images = [_transforms(img.convert(<span class="hljs-string">&quot;RGB&quot;</span>)) <span class="hljs-keyword">for</span> img <span class="hljs-keyword">in</span> examples[<span class="hljs-string">&quot;image&quot;</span>]]
<span class="hljs-meta">... </span>    examples[<span class="hljs-string">&quot;pixel_values&quot;</span>] = image_processor(images, do_resize=<span class="hljs-literal">False</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)[<span class="hljs-string">&quot;pixel_values&quot;</span>]
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> examples`,wrap:!1}}),as=new Pa({props:{$$slots:{default:[Jt]},$$scope:{ctx:w}}}),wa=new T({props:{code:"ZGF0YXNldC5zZXRfdHJhbnNmb3JtKHRyYW5zZm9ybXMp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>dataset.set_transform(transforms)',wrap:!1}}),ya=new T({props:{code:"ZGF0YXNldCU1QjAlNUQua2V5cygp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>dataset[<span class="hljs-number">0</span>].keys()',wrap:!1}}),_a=new T({props:{code:"aW1wb3J0JTIwbnVtcHklMjBhcyUyMG5wJTBBaW1wb3J0JTIwbWF0cGxvdGxpYi5weXBsb3QlMjBhcyUyMHBsdCUwQSUwQWltZyUyMCUzRCUyMGRhdGFzZXQlNUIwJTVEJTVCJTIycGl4ZWxfdmFsdWVzJTIyJTVEJTBBcGx0Lmltc2hvdyhpbWcucGVybXV0ZSgxJTJDJTIwMiUyQyUyMDApKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt

<span class="hljs-meta">&gt;&gt;&gt; </span>img = dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;pixel_values&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>plt.imshow(img.permute(<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">0</span>))`,wrap:!1}}),ps=new Pa({props:{$$slots:{default:[Tt]},$$scope:{ctx:w}}}),Ia=new N({}),xa=new T({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">collate_fn</span>(<span class="hljs-params">batch</span>):
<span class="hljs-meta">... </span>    pixel_values = [item[<span class="hljs-string">&quot;pixel_values&quot;</span>] <span class="hljs-keyword">for</span> item <span class="hljs-keyword">in</span> batch]
<span class="hljs-meta">... </span>    encoding = image_processor.pad(pixel_values, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">... </span>    labels = [item[<span class="hljs-string">&quot;labels&quot;</span>] <span class="hljs-keyword">for</span> item <span class="hljs-keyword">in</span> batch]
<span class="hljs-meta">... </span>    batch = {}
<span class="hljs-meta">... </span>    batch[<span class="hljs-string">&quot;pixel_values&quot;</span>] = encoding[<span class="hljs-string">&quot;pixel_values&quot;</span>]
<span class="hljs-meta">... </span>    batch[<span class="hljs-string">&quot;pixel_mask&quot;</span>] = encoding[<span class="hljs-string">&quot;pixel_mask&quot;</span>]
<span class="hljs-meta">... </span>    batch[<span class="hljs-string">&quot;labels&quot;</span>] = labels
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> batch`,wrap:!1}}),Ca=new N({}),Ga=new T({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBJTBBbGpfc3BlZWNoJTIwJTNEJTIwbG9hZF9kYXRhc2V0KCUyMmxqX3NwZWVjaCUyMiUyQyUyMHNwbGl0JTNEJTIydHJhaW4lMjIp",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>lj_speech = load_dataset(<span class="hljs-string">&quot;lj_speech&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)`,wrap:!1}}),Ba=new T({props:{code:"bGpfc3BlZWNoJTIwJTNEJTIwbGpfc3BlZWNoLm1hcChyZW1vdmVfY29sdW1ucyUzRCU1QiUyMmZpbGUlMjIlMkMlMjAlMjJpZCUyMiUyQyUyMCUyMm5vcm1hbGl6ZWRfdGV4dCUyMiU1RCk=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>lj_speech = lj_speech.<span class="hljs-built_in">map</span>(remove_columns=[<span class="hljs-string">&quot;file&quot;</span>, <span class="hljs-string">&quot;id&quot;</span>, <span class="hljs-string">&quot;normalized_text&quot;</span>])',wrap:!1}}),Ha=new T({props:{code:"bGpfc3BlZWNoJTVCMCU1RCU1QiUyMmF1ZGlvJTIyJTVEJTBBJTBBbGpfc3BlZWNoJTVCMCU1RCU1QiUyMnRleHQlMjIlNUQ=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>lj_speech[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>]
{<span class="hljs-string">&#x27;array&#x27;</span>: array([-<span class="hljs-number">7.3242188e-04</span>, -<span class="hljs-number">7.6293945e-04</span>, -<span class="hljs-number">6.4086914e-04</span>, ...,
         <span class="hljs-number">7.3242188e-04</span>,  <span class="hljs-number">2.1362305e-04</span>,  <span class="hljs-number">6.1035156e-05</span>], dtype=float32),
 <span class="hljs-string">&#x27;path&#x27;</span>: <span class="hljs-string">&#x27;/root/.cache/huggingface/datasets/downloads/extracted/917ece08c95cf0c4115e45294e3cd0dee724a1165b7fc11798369308a465bd26/LJSpeech-1.1/wavs/LJ001-0001.wav&#x27;</span>,
 <span class="hljs-string">&#x27;sampling_rate&#x27;</span>: <span class="hljs-number">22050</span>}

<span class="hljs-meta">&gt;&gt;&gt; </span>lj_speech[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;text&quot;</span>]
<span class="hljs-string">&#x27;Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition&#x27;</span>`,wrap:!1}}),Xa=new T({props:{code:"bGpfc3BlZWNoJTIwJTNEJTIwbGpfc3BlZWNoLmNhc3RfY29sdW1uKCUyMmF1ZGlvJTIyJTJDJTIwQXVkaW8oc2FtcGxpbmdfcmF0ZSUzRDE2XzAwMCkp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>lj_speech = lj_speech.cast_column(<span class="hljs-string">&quot;audio&quot;</span>, Audio(sampling_rate=<span class="hljs-number">16_000</span>))',wrap:!1}}),Ya=new T({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Qcm9jZXNzb3IlMEElMEFwcm9jZXNzb3IlMjAlM0QlMjBBdXRvUHJvY2Vzc29yLmZyb21fcHJldHJhaW5lZCglMjJmYWNlYm9vayUyRndhdjJ2ZWMyLWJhc2UtOTYwaCUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-base-960h&quot;</span>)`,wrap:!1}}),Ea=new T({props:{code:"ZGVmJTIwcHJlcGFyZV9kYXRhc2V0KGV4YW1wbGUpJTNBJTBBJTIwJTIwJTIwJTIwYXVkaW8lMjAlM0QlMjBleGFtcGxlJTVCJTIyYXVkaW8lMjIlNUQlMEElMEElMjAlMjAlMjAlMjBleGFtcGxlLnVwZGF0ZShwcm9jZXNzb3IoYXVkaW8lM0RhdWRpbyU1QiUyMmFycmF5JTIyJTVEJTJDJTIwdGV4dCUzRGV4YW1wbGUlNUIlMjJ0ZXh0JTIyJTVEJTJDJTIwc2FtcGxpbmdfcmF0ZSUzRDE2MDAwKSklMEElMEElMjAlMjAlMjAlMjByZXR1cm4lMjBleGFtcGxl",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">prepare_dataset</span>(<span class="hljs-params">example</span>):
<span class="hljs-meta">... </span>    audio = example[<span class="hljs-string">&quot;audio&quot;</span>]

<span class="hljs-meta">... </span>    example.update(processor(audio=audio[<span class="hljs-string">&quot;array&quot;</span>], text=example[<span class="hljs-string">&quot;text&quot;</span>], sampling_rate=<span class="hljs-number">16000</span>))

<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> example`,wrap:!1}}),za=new T({props:{code:"cHJlcGFyZV9kYXRhc2V0KGxqX3NwZWVjaCU1QjAlNUQp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>prepare_dataset(lj_speech[<span class="hljs-number">0</span>])',wrap:!1}}),{c(){c=t("meta"),d=e(),m=t("p"),M=e(),v=t("h1"),y=t("a"),_=t("span"),u(x.$$.fragment),I=e(),U=t("span"),U.textContent=Ge,Mn=e(),u(ms.$$.fragment),Jn=e(),hs=t("p"),hs.textContent=Ve,Tn=e(),us=t("ul"),us.innerHTML=Be,$n=e(),u(R.$$.fragment),wn=e(),js=t("p"),js.textContent=We,yn=e(),u(bs.$$.fragment),vn=e(),C=t("h2"),Y=t("a"),en=t("span"),u(is.$$.fragment),ve=e(),Sa=t("span"),Sa.textContent=He,_n=e(),u(os.$$.fragment),In=e(),fs=t("p"),fs.innerHTML=Ne,Un=e(),u(q.$$.fragment),xn=e(),ds=t("p"),ds.innerHTML=Xe,Cn=e(),u(gs.$$.fragment),kn=e(),Ms=t("p"),Ms.textContent=Re,Zn=e(),u(Js.$$.fragment),Gn=e(),Ts=t("p"),Ts.textContent=Ye,Vn=e(),$s=t("ul"),$s.innerHTML=qe,Bn=e(),ws=t("p"),ws.innerHTML=Ee,Wn=e(),u(ys.$$.fragment),Hn=e(),vs=t("p"),vs.innerHTML=ze,Nn=e(),_s=t("p"),_s.textContent=Ae,Xn=e(),u(Is.$$.fragment),Rn=e(),k=t("h3"),E=t("a"),pn=t("span"),u(Us.$$.fragment),_e=e(),Da=t("span"),Da.textContent=Fe,Yn=e(),xs=t("p"),xs.innerHTML=Le,qn=e(),Cs=t("p"),Cs.innerHTML=Qe,En=e(),u(ks.$$.fragment),zn=e(),Zs=t("p"),Zs.innerHTML=Pe,An=e(),Z=t("h3"),z=t("a"),tn=t("span"),u(Gs.$$.fragment),Ie=e(),Ka=t("span"),Ka.textContent=Se,Fn=e(),Vs=t("p"),Vs.textContent=De,Ln=e(),Bs=t("p"),Bs.innerHTML=Ke,Qn=e(),u(Ws.$$.fragment),Pn=e(),u(A.$$.fragment),Sn=e(),G=t("h3"),F=t("a"),rn=t("span"),u(Hs.$$.fragment),Ue=e(),Oa=t("span"),Oa.textContent=Oe,Dn=e(),Ns=t("p"),Ns.textContent=sp,Kn=e(),Xs=t("p"),Xs.innerHTML=ap,On=e(),u(L.$$.fragment),sl=e(),V=t("h2"),Q=t("a"),cn=t("span"),u(Rs.$$.fragment),xe=e(),sn=t("span"),sn.textContent=np,al=e(),Ys=t("p"),Ys.innerHTML=lp,nl=e(),qs=t("p"),qs.innerHTML=ep,ll=e(),u(Es.$$.fragment),el=e(),zs=t("p"),zs.innerHTML=pp,pl=e(),u(As.$$.fragment),tl=e(),Fs=t("p"),Fs.textContent=tp,rl=e(),Ls=t("ul"),Ls.innerHTML=rp,cl=e(),Qs=t("p"),Qs.innerHTML=cp,ml=e(),Ps=t("ol"),Ps.innerHTML=mp,hl=e(),u(Ss.$$.fragment),ul=e(),P=t("ol"),P.innerHTML=hp,jl=e(),u(Ds.$$.fragment),bl=e(),Ks=t("p"),Ks.innerHTML=up,il=e(),Os=t("p"),Os.innerHTML=jp,ol=e(),u(sa.$$.fragment),fl=e(),aa=t("p"),aa.innerHTML=bp,dl=e(),u(na.$$.fragment),gl=e(),la=t("p"),la.textContent=ip,Ml=e(),u(ea.$$.fragment),Jl=e(),pa=t("p"),pa.textContent=op,Tl=e(),u(ta.$$.fragment),$l=e(),ra=t("p"),ra.innerHTML=fp,wl=e(),u(ca.$$.fragment),yl=e(),ma=t("p"),ma.textContent=dp,vl=e(),u(ha.$$.fragment),_l=e(),B=t("h2"),S=t("a"),mn=t("span"),u(ua.$$.fragment),Ce=e(),an=t("span"),an.textContent=gp,Il=e(),ja=t("p"),ja.innerHTML=Mp,Ul=e(),u(D.$$.fragment),xl=e(),ba=t("p"),ba.innerHTML=Jp,Cl=e(),u(K.$$.fragment),kl=e(),u(ia.$$.fragment),Zl=e(),oa=t("p"),oa.innerHTML=Tp,Gl=e(),u(fa.$$.fragment),Vl=e(),O=t("div"),O.innerHTML=$p,Bl=e(),da=t("p"),da.innerHTML=wp,Wl=e(),u(ga.$$.fragment),Hl=e(),Ma=t("p"),Ma.innerHTML=yp,Nl=e(),Ja=t("ol"),Ja.innerHTML=vp,Xl=e(),u(Ta.$$.fragment),Rl=e(),ss=t("ol"),ss.innerHTML=_p,Yl=e(),u($a.$$.fragment),ql=e(),u(as.$$.fragment),El=e(),ns=t("ol"),ns.innerHTML=Ip,zl=e(),u(wa.$$.fragment),Al=e(),ls=t("ol"),ls.innerHTML=Up,Fl=e(),u(ya.$$.fragment),Ll=e(),va=t("p"),va.textContent=xp,Ql=e(),u(_a.$$.fragment),Pl=e(),es=t("div"),es.innerHTML=Cp,Sl=e(),u(ps.$$.fragment),Dl=e(),W=t("h3"),ts=t("a"),hn=t("span"),u(Ia.$$.fragment),ke=e(),nn=t("span"),nn.textContent=kp,Kl=e(),Ua=t("p"),Ua.innerHTML=Zp,Ol=e(),u(xa.$$.fragment),se=e(),H=t("h2"),rs=t("a"),un=t("span"),u(Ca.$$.fragment),Ze=e(),ln=t("span"),ln.textContent=Gp,ae=e(),ka=t("p"),ka.innerHTML=Vp,ne=e(),Za=t("p"),Za.innerHTML=Bp,le=e(),u(Ga.$$.fragment),ee=e(),Va=t("p"),Va.innerHTML=Wp,pe=e(),u(Ba.$$.fragment),te=e(),Wa=t("p"),Wa.innerHTML=Hp,re=e(),u(Ha.$$.fragment),ce=e(),Na=t("p"),Na.innerHTML=Np,me=e(),u(Xa.$$.fragment),he=e(),Ra=t("p"),Ra.innerHTML=Xp,ue=e(),u(Ya.$$.fragment),je=e(),qa=t("ol"),qa.innerHTML=Rp,be=e(),u(Ea.$$.fragment),ie=e(),cs=t("ol"),cs.innerHTML=Yp,oe=e(),u(za.$$.fragment),fe=e(),Aa=t("p"),Aa.innerHTML=qp,de=e(),jn=t("p"),this.h()},l(s){const 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