import{s as At,o as Ut,n as lt}from"../chunks/scheduler.889ebb13.js";import{S as Et,i as Ft,g as o,s as i,u as j,A as Nt,h as p,f as t,c,j as y,v as M,y as v,k as $,o as b,a as s,w,d as _,t as x,x as C}from"../chunks/index.8980e686.js";import{T as Wt}from"../chunks/Tip.7191df11.js";import{I as ge}from"../chunks/IconCopyLink.357761cd.js";import{C as L}from"../chunks/CodeBlock.25fc3430.js";import{F as Gt,M as Jt}from"../chunks/Markdown.31057263.js";function Vt(E){let a,d='아키텍처는 모델의 골격을 의미하며 체크포인트는 주어진 아키텍처에 대한 가중치입니다. 예를 들어, <a href="https://huggingface.co/bert-base-uncased" rel="nofollow">BERT</a>는 아키텍처이고, <code>bert-base-uncased</code>는 체크포인트입니다. 모델은 아키텍처 또는 체크포인트를 의미할 수 있는 일반적인 용어입니다.';return{c(){a=o("p"),a.innerHTML=d},l(n){a=p(n,"P",{"data-svelte-h":!0}),v(a)!=="svelte-16p0tdi"&&(a.innerHTML=d)},m(n,u){s(n,a,u)},p:lt,d(n){n&&t(a)}}}function Ht(E){let a,d=`PyTorch모델의 경우 <code>from_pretrained()</code> 메서드는 내부적으로 피클을 사용하여 안전하지 않은 것으로 알려진 <code>torch.load()</code>를 사용합니다.
일반적으로 신뢰할 수 없는 소스에서 가져왔거나 변조되었을 수 있는 모델은 로드하지 마세요. 허깅 페이스 허브에서 호스팅되는 공개 모델의 경우 이러한 보안 위험이 부분적으로 완화되며, 각 커밋 시 멀웨어를 <a href="https://huggingface.co/docs/hub/security-malware" rel="nofollow">검사합니다</a>. GPG를 사용해 서명된 <a href="https://huggingface.co/docs/hub/security-gpg#signing-commits-with-gpg" rel="nofollow">커밋 검증</a>과 같은 모범사례는 <a href="https://huggingface.co/docs/hub/security" rel="nofollow">문서</a>를 참조하세요.`,n,u,f="텐서플로우와 Flax 체크포인트는 영향을 받지 않으며, <code>from_pretrained</code>메서드에 <code>from_tf</code> 와 <code>from_flax</code> 키워드 가변 인자를 사용하여 이 문제를 우회할 수 있습니다.";return{c(){a=o("p"),a.innerHTML=d,n=i(),u=o("p"),u.innerHTML=f},l(h){a=p(h,"P",{"data-svelte-h":!0}),v(a)!=="svelte-1u3kbez"&&(a.innerHTML=d),n=c(h),u=p(h,"P",{"data-svelte-h":!0}),v(u)!=="svelte-7z7rov"&&(u.innerHTML=f)},m(h,U){s(h,a,U),s(h,n,U),s(h,u,U)},p:lt,d(h){h&&(t(a),t(n),t(u))}}}function Pt(E){let a,d='마지막으로 AutoModelFor클래스를 사용하면 주어진 작업에 대해 미리 학습된 모델을 로드할 수 있습니다 (사용 가능한 작업의 전체 목록은 <a href="model_doc/auto">여기</a>를 참조하세요). 예를 들어, <code>AutoModelForSequenceClassification.from_pretrained()</code>를 사용하여 시퀀스 분류용 모델을 로드할 수 있습니다:',n,u,f,h,U="동일한 체크포인트를 쉽게 재사용하여 다른 작업에 아키텍처를 로드할 수 있습니다:",J,k,A,Z,F,g,m='일반적으로 AutoTokenizer 클래스와 AutoModelFor 클래스를 사용하여 미리 학습된 모델 인스턴스를 로드하는 것이 좋습니다. 이렇게 하면 매번 올바른 아키텍처를 로드할 수 있습니다. 다음 <a href="preprocessing">튜토리얼</a>에서는 새롭게 로드한 토크나이저, 이미지 프로세서, 특징 추출기를 사용하여 미세 튜닝용 데이터 세트를 전처리하는 방법에 대해 알아봅니다.',T;return u=new L({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24lMEElMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKCUyMmRpc3RpbGJlcnQtYmFzZS11bmNhc2VkJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),k=new L({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24lMEElMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKCUyMmRpc3RpbGJlcnQtYmFzZS11bmNhc2VkJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),Z=new Wt({props:{warning:!0,$$slots:{default:[Ht]},$$scope:{ctx:E}}}),{c(){a=o("p"),a.innerHTML=d,n=i(),j(u.$$.fragment),f=i(),h=o("p"),h.textContent=U,J=i(),j(k.$$.fragment),A=i(),j(Z.$$.fragment),F=i(),g=o("p"),g.innerHTML=m},l(r){a=p(r,"P",{"data-svelte-h":!0}),v(a)!=="svelte-18uszv6"&&(a.innerHTML=d),n=c(r),M(u.$$.fragment,r),f=c(r),h=p(r,"P",{"data-svelte-h":!0}),v(h)!=="svelte-7ro1yz"&&(h.textContent=U),J=c(r),M(k.$$.fragment,r),A=c(r),M(Z.$$.fragment,r),F=c(r),g=p(r,"P",{"data-svelte-h":!0}),v(g)!=="svelte-4umuej"&&(g.innerHTML=m)},m(r,W){s(r,a,W),s(r,n,W),w(u,r,W),s(r,f,W),s(r,h,W),s(r,J,W),w(k,r,W),s(r,A,W),w(Z,r,W),s(r,F,W),s(r,g,W),T=!0},p(r,W){const N={};W&2&&(N.$$scope={dirty:W,ctx:r}),Z.$set(N)},i(r){T||(_(u.$$.fragment,r),_(k.$$.fragment,r),_(Z.$$.fragment,r),T=!0)},o(r){x(u.$$.fragment,r),x(k.$$.fragment,r),x(Z.$$.fragment,r),T=!1},d(r){r&&(t(a),t(n),t(f),t(h),t(J),t(A),t(F),t(g)),C(u,r),C(k,r),C(Z,r)}}}function Rt(E){let a,d;return a=new Jt({props:{$$slots:{default:[Pt]},$$scope:{ctx:E}}}),{c(){j(a.$$.fragment)},l(n){M(a.$$.fragment,n)},m(n,u){w(a,n,u),d=!0},p(n,u){const f={};u&2&&(f.$$scope={dirty:u,ctx:n}),a.$set(f)},i(n){d||(_(a.$$.fragment,n),d=!0)},o(n){x(a.$$.fragment,n),d=!1},d(n){C(a,n)}}}function Lt(E){let a,d='마지막으로 <code>TFAutoModelFor</code> 클래스를 사용하면 주어진 작업에 대해 사전 훈련된 모델을 로드할 수 있습니다. (사용 가능한 작업의 전체 목록은 <a href="model_doc/auto">여기</a>를 참조하세요. 예를 들어, <code>TFAutoModelForSequenceClassification.from_pretrained()</code>로 시퀀스 분류를 위한 모델을 로드합니다:',n,u,f,h,U="쉽게 동일한 체크포인트를 재사용하여 다른 작업에 아키텍처를 로드할 수 있습니다:",J,k,A,Z,F='일반적으로, <code>AutoTokenizer</code>클래스와 <code>TFAutoModelFor</code> 클래스를 사용하여 미리 학습된 모델 인스턴스를 로드하는 것이 좋습니다. 이렇게 하면 매번 올바른 아키텍처를 로드할 수 있습니다. 다음 <a href="preprocessing">튜토리얼</a>에서는 새롭게 로드한 토크나이저, 이미지 프로세서, 특징 추출기를 사용하여 미세 튜닝용 데이터 세트를 전처리하는 방법에 대해 알아봅니다.',g;return u=new L({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGQXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbiUwQSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMjJkaXN0aWxiZXJ0LWJhc2UtdW5jYXNlZCUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),k=new L({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGQXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbiUwQSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMjJkaXN0aWxiZXJ0LWJhc2UtdW5jYXNlZCUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),{c(){a=o("p"),a.innerHTML=d,n=i(),j(u.$$.fragment),f=i(),h=o("p"),h.textContent=U,J=i(),j(k.$$.fragment),A=i(),Z=o("p"),Z.innerHTML=F},l(m){a=p(m,"P",{"data-svelte-h":!0}),v(a)!=="svelte-xsgejq"&&(a.innerHTML=d),n=c(m),M(u.$$.fragment,m),f=c(m),h=p(m,"P",{"data-svelte-h":!0}),v(h)!=="svelte-1ilzt8b"&&(h.textContent=U),J=c(m),M(k.$$.fragment,m),A=c(m),Z=p(m,"P",{"data-svelte-h":!0}),v(Z)!=="svelte-1w046dp"&&(Z.innerHTML=F)},m(m,T){s(m,a,T),s(m,n,T),w(u,m,T),s(m,f,T),s(m,h,T),s(m,J,T),w(k,m,T),s(m,A,T),s(m,Z,T),g=!0},p:lt,i(m){g||(_(u.$$.fragment,m),_(k.$$.fragment,m),g=!0)},o(m){x(u.$$.fragment,m),x(k.$$.fragment,m),g=!1},d(m){m&&(t(a),t(n),t(f),t(h),t(J),t(A),t(Z)),C(u,m),C(k,m)}}}function Xt(E){let a,d;return a=new Jt({props:{$$slots:{default:[Lt]},$$scope:{ctx:E}}}),{c(){j(a.$$.fragment)},l(n){M(a.$$.fragment,n)},m(n,u){w(a,n,u),d=!0},p(n,u){const f={};u&2&&(f.$$scope={dirty:u,ctx:n}),a.$set(f)},i(n){d||(_(a.$$.fragment,n),d=!0)},o(n){x(a.$$.fragment,n),d=!1},d(n){C(a,n)}}}function zt(E){let a,d,n,u,f,h,U,J,k,A,Z="AutoClass로 사전 학습된 인스턴스 로드",F,g,m=`트랜스포머 아키텍처가 매우 다양하기 때문에 체크포인트에 맞는 아키텍처를 생성하는 것이 어려울 수 있습니다. 라이브러리를 쉽고 간단하며 유연하게 사용하기 위한 Transformer 핵심 철학의 일환으로, <code>AutoClass</code>는 주어진 체크포인트에서 올바른 아키텍처를 자동으로 추론하여 로드합니다. <code>from_pretrained()</code> 메서드를 사용하면 모든 아키텍처에 대해 사전 학습된 모델을 빠르게 로드할 수 있으므로 모델을 처음부터 학습하는 데 시간과 리소스를 투입할 필요가 없습니다.
체크포인트에 구애받지 않는 코드를 생성한다는 것은 코드가 한 체크포인트에서 작동하면 아키텍처가 다르더라도 다른 체크포인트(유사한 작업에 대해 학습된 경우)에서도 작동한다는 것을 의미합니다.`,T,r,W,N,nt="이 튜토리얼에서는 다음을 학습합니다:",ke,Q,rt="<li>사전 학습된 토크나이저 로드하기.</li> <li>사전 학습된 이미지 프로세서 로드하기.</li> <li>사전 학습된 특징 추출기 로드하기.</li> <li>사전 훈련된 프로세서 로드하기.</li> <li>사전 학습된 모델 로드하기.</li>",Ze,G,X,ye,B,Oe,fe,ot="AutoTokenizer",Te,K,pt=`거의 모든 NLP 작업은 토크나이저로 시작됩니다. 토크나이저는 사용자의 입력을 모델에서 처리할 수 있는 형식으로 변환합니다.
<code>AutoTokenizer.from_pretrained()</code>로 토크나이저를 로드합니다:`,We,D,Je,O,it="그리고 아래와 같이 입력을 토큰화합니다:",Ae,ee,Ue,V,z,je,te,et,de,ct="AutoImageProcessor",Ee,se,ut="비전 작업의 경우 이미지 프로세서가 이미지를 올바른 입력 형식으로 처리합니다.",Fe,ae,Ne,H,Y,Me,le,tt,$e,mt="AutoFeatureExtractor",Ge,ne,ht="오디오 작업의 경우 특징 추출기가 오디오 신호를 올바른 입력 형식으로 처리합니다.",Ve,re,ft="<code>AutoFeatureExtractor.from_pretrained()</code>로 특징 추출기를 로드합니다:",He,oe,Pe,P,S,we,pe,st,be,dt="AutoProcessor",Re,ie,$t="멀티모달 작업에는 두 가지 유형의 전처리 도구를 결합한 프로세서가 필요합니다. 예를 들어 LayoutLMV2 모델에는 이미지를 처리하는 이미지 프로세서와 텍스트를 처리하는 토크나이저가 필요하며, 프로세서는 이 두 가지를 결합합니다.",Le,ce,bt="<code>AutoProcessor.from_pretrained()</code>로 프로세서를 로드합니다:",Xe,ue,ze,R,q,_e,me,at,ve,vt="AutoModel",Ye,I,Se,xe,qe;return J=new ge({}),r=new Wt({props:{$$slots:{default:[Vt]},$$scope:{ctx:E}}}),B=new ge({}),D=new L({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMEElMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtdW5jYXNlZCUyMik=",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-uncased&quot;</span>)`}}),ee=new L({props:{code:"c2VxdWVuY2UlMjAlM0QlMjAlMjJJbiUyMGElMjBob2xlJTIwaW4lMjB0aGUlMjBncm91bmQlMjB0aGVyZSUyMGxpdmVkJTIwYSUyMGhvYmJpdC4lMjIlMEFwcmludCh0b2tlbml6ZXIoc2VxdWVuY2UpKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>sequence = <span class="hljs-string">&quot;In a hole in the ground there lived a hobbit.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(tokenizer(sequence))
{<span class="hljs-string">&#x27;input_ids&#x27;</span>: [<span class="hljs-number">101</span>, <span class="hljs-number">1999</span>, <span class="hljs-number">1037</span>, <span class="hljs-number">4920</span>, <span class="hljs-number">1999</span>, <span class="hljs-number">1996</span>, <span class="hljs-number">2598</span>, <span class="hljs-number">2045</span>, <span class="hljs-number">2973</span>, <span class="hljs-number">1037</span>, <span class="hljs-number">7570</span>, <span class="hljs-number">10322</span>, <span class="hljs-number">4183</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-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>]}`}}),te=new ge({}),ae=new L({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

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