transformers/pr_37155/ko/_app/immutable/nodes/167.699cb7fe.js

import{s as Yt,o as Dt,n as St}from"../chunks/scheduler.bdbef820.js";import{S as qt,i as Kt,g as r,s as n,r as o,A as Ot,h as p,f as s,c as a,j as Pt,u as i,x as d,k as ht,y as es,a as l,v as m,d as f,t as u,w as c}from"../chunks/index.33f81d56.js";import{T as Xt}from"../chunks/Tip.34194030.js";import{Y as zt}from"../chunks/Youtube.0e329b00.js";import{C as M}from"../chunks/CodeBlock.3bad7fc9.js";import{H as T,E as ts}from"../chunks/index.474b463a.js";function ss(he){let g,h='메모리 절약 기술에 대한 자세한 내용은 성능 <a href="performance">가이드</a>를 참조하세요.';return{c(){g=r("p"),g.innerHTML=h},l($){g=p($,"P",{"data-svelte-h":!0}),d(g)!=="svelte-74wrmh"&&(g.innerHTML=h)},m($,j){l($,g,j)},p:St,d($){$&&s(g)}}}function ls(he){let g,h="일반적으로 토크나이저는 특정 토크나이저의 기본 값을 기준으로 사용자에 대한 ‘attention_mask’를 만듭니다.";return{c(){g=r("p"),g.textContent=h},l($){g=p($,"P",{"data-svelte-h":!0}),d(g)!=="svelte-1afkfxz"&&(g.textContent=h)},m($,j){l($,g,j)},p:St,d($){$&&s(g)}}}function ns(he){let g,h,$,j,U,be,C,jt="때때로 오류가 발생할 수 있지만, 저희가 도와드리겠습니다! 이 가이드는 현재까지 확인된 가장 일반적인 문제 몇 가지와 그것들을 해결하는 방법에 대해 다룹니다. 그러나 이 가이드는 모든 🤗 Transformers 문제를 포괄적으로 다루고 있지 않습니다. 문제 해결에 더 많은 도움을 받으려면 다음을 시도해보세요:",ye,v,we,_,bt='<li><a href="https://discuss.huggingface.co/" rel="nofollow">포럼</a>에서 도움을 요청하세요. <a href="https://discuss.huggingface.co/c/beginners/5" rel="nofollow">Beginners</a> 또는 <a href="https://discuss.huggingface.co/c/transformers/9" rel="nofollow">🤗 Transformers</a>와 같은 특정 카테고리에 질문을 게시할 수 있습니다. 재현 가능한 코드와 함께 잘 서술된 포럼 게시물을 작성하여 여러분의 문제가 해결될 가능성을 극대화하세요!</li>',Te,J,Ue,b,yt='<li><p>라이브러리와 관련된 버그이면 🤗 Transformers 저장소에서 <a href="https://github.com/huggingface/transformers/issues/new/choose" rel="nofollow">이슈</a>를 생성하세요. 버그에 대해 설명하는 정보를 가능한 많이 포함하려고 노력하여, 무엇이 잘못 되었는지와 어떻게 수정할 수 있는지 더 잘 파악할 수 있도록 도와주세요.</p></li> <li><p>이전 버전의 🤗 Transformers을 사용하는 경우 중요한 변경 사항이 버전 사이에 도입되었기 때문에 <a href="migration">마이그레이션</a> 가이드를 확인하세요.</p></li>',Ce,k,wt='문제 해결 및 도움 매뉴얼에 대한 자세한 내용은 Hugging Face 강좌의 <a href="https://huggingface.co/course/chapter8/1?fw=pt" rel="nofollow">8장</a>을 참조하세요.',ve,V,_e,Z,Tt="클라우드 및 내부망(intranet) 설정의 일부 GPU 인스턴스는 외부 연결에 대한 방화벽으로 차단되어 연결 오류가 발생할 수 있습니다. 스크립트가 모델 가중치나 데이터를 다운로드하려고 할 때, 다운로드가 중단되고 다음 메시지와 함께 시간 초과됩니다:",Je,G,ke,W,Ut='이 경우에는 연결 오류를 피하기 위해 🤗 Transformers를 <a href="installation#offline-mode">오프라인 모드</a>로 실행해야 합니다.',Ve,x,Ze,I,Ct="수백만 개의 매개변수로 대규모 모델을 훈련하는 것은 적절한 하드웨어 없이 어려울 수 있습니다. GPU 메모리가 부족한 경우 발생할 수 있는 일반적인 오류는 다음과 같습니다:",Ge,Q,We,B,vt="다음은 메모리 사용을 줄이기 위해 시도해 볼 수 있는 몇 가지 잠재적인 해결책입니다:",xe,H,_t='<li><a href="/docs/transformers/pr_37155/ko/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>의 <a href="main_classes/trainer#transformers.TrainingArguments.per_device_train_batch_size"><code>per_device_train_batch_size</code></a> 값을 줄이세요.</li> <li><a href="/docs/transformers/pr_37155/ko/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>의 <a href="main_classes/trainer#transformers.TrainingArguments.gradient_accumulation_steps"><code>gradient_accumulation_steps</code></a>은 전체 배치 크기를 효과적으로 늘리세요.</li>',Ie,y,Qe,E,Be,A,Jt='TensorFlow의 <a href="https://www.tensorflow.org/tutorials/keras/save_and_load#save_the_entire_model" rel="nofollow">model.save</a> 메소드는 아키텍처, 가중치, 훈련 구성 등 전체 모델을 단일 파일에 저장합니다. 그러나 모델 파일을 다시 가져올 때 🤗 Transformers는 모델 파일에 있는 모든 TensorFlow 관련 객체를 가져오지 않을 수 있기 때문에 오류가 발생할 수 있습니다. TensorFlow 모델 저장 및 가져오기 문제를 피하려면 다음을 권장합니다:',He,F,kt='<li>모델 가중치를 <code>h5</code> 파일 확장자로 <a href="https://www.tensorflow.org/tutorials/keras/save_and_load#save_the_entire_model" rel="nofollow"><code>model.save_weights</code></a>로 저장한 다음 <a href="/docs/transformers/pr_37155/ko/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a>로 모델을 다시 가져옵니다:</li>',Ee,R,Ae,L,Vt='<li>모델을 <code>~TFPretrainedModel.save_pretrained</code>로 저장하고 <a href="/docs/transformers/pr_37155/ko/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a>로 다시 가져옵니다:</li>',Fe,N,Re,P,Le,X,Zt="특히 최신 모델인 경우 만날 수 있는 다른 일반적인 오류는 <code>ImportError</code>입니다:",Ne,z,Pe,S,Gt="이러한 오류 유형의 경우 최신 모델에 액세스할 수 있도록 최신 버전의 🤗 Transformers가 설치되어 있는지 확인하세요:",Xe,Y,ze,D,Se,q,Wt="때때로 장치 코드 오류에 대한 일반적인 CUDA 오류가 발생할 수 있습니다.",Ye,K,De,O,xt="더 자세한 오류 메시지를 얻으려면 우선 코드를 CPU에서 실행합니다. 다음 환경 변수를 코드의 시작 부분에 추가하여 CPU로 전환하세요:",qe,ee,Ke,te,It="또 다른 옵션은 GPU에서 더 나은 역추적(traceback)을 얻는 것입니다. 다음 환경 변수를 코드의 시작 부분에 추가하여 역추적이 오류가 발생한 소스를 가리키도록 하세요:",Oe,se,et,le,tt,ne,Qt="경우에 따라 <code>input_ids</code>에 패딩 토큰이 포함된 경우 <code>hidden_state</code> 출력이 올바르지 않을 수 있습니다. 데모를 위해 모델과 토크나이저를 가져오세요. 모델의 <code>pad_token_id</code>에 액세스하여 해당 값을 확인할 수 있습니다. 일부 모델의 경우 <code>pad_token_id</code>가 <code>None</code>일 수 있지만 언제든지 수동으로 설정할 수 있습니다.",st,ae,lt,re,Bt="다음 예제는 패딩 토큰을 마스킹하지 않은 출력을 보여줍니다:",nt,pe,at,oe,Ht="다음은 두 번째 시퀀스의 실제 출력입니다:",rt,ie,pt,me,Et="대부분의 경우 모델에 <code>attention_mask</code>를 제공하여 패딩 토큰을 무시해야 이러한 조용한 오류를 방지할 수 있습니다. 이제 두 번째 시퀀스의 출력이 실제 출력과 일치합니다:",ot,w,it,fe,mt,ue,At="🤗 Transformers는 패딩 토큰이 제공된 경우 패딩 토큰을 마스킹하기 위한 <code>attention_mask</code>를 자동으로 생성하지 않습니다. 그 이유는 다음과 같습니다:",ft,ce,Ft="<li>일부 모델에는 패딩 토큰이 없습니다.</li> <li>일부 사용 사례의 경우 사용자가 모델이 패딩 토큰을 관리하기를 원합니다.</li>",ut,de,ct,ge,Rt=`일반적으로, 사전 학습된 모델의 인스턴스를 가져오기 위해 <a href="/docs/transformers/pr_37155/ko/model_doc/auto#transformers.AutoModel">AutoModel</a> 클래스를 사용하는 것이 좋습니다.
이 클래스는 구성에 따라 주어진 체크포인트에서 올바른 아키텍처를 자동으로 추론하고 가져올 수 있습니다.
모델을 체크포인트에서 가져올 때 이 <code>ValueError</code>가 발생하면, 이는 Auto 클래스가 주어진 체크포인트의 구성에서
가져오려는 모델 유형과 매핑을 찾을 수 없다는 것을 의미합니다. 가장 흔하게 발생하는 경우는
체크포인트가 주어진 태스크를 지원하지 않을 때입니다.
예를 들어, 다음 예제에서 질의응답에 대한 GPT2가 없기 때문에 오류가 발생합니다:`,dt,$e,gt,Me,$t,je,Mt;return U=new T({props:{title:"문제 해결",local:"troubleshoot",headingTag:"h1"}}),v=new zt({props:{id:"S2EEG3JIt2A"}}),J=new zt({props:{id:"_PAli-V4wj0"}}),V=new T({props:{title:"방화벽 환경",local:"firewalled-environments",headingTag:"h2"}}),G=new M({props:{code:"VmFsdWVFcnJvciUzQSUyMENvbm5lY3Rpb24lMjBlcnJvciUyQyUyMGFuZCUyMHdlJTIwY2Fubm90JTIwZmluZCUyMHRoZSUyMHJlcXVlc3RlZCUyMGZpbGVzJTIwaW4lMjB0aGUlMjBjYWNoZWQlMjBwYXRoLiUwQVBsZWFzZSUyMHRyeSUyMGFnYWluJTIwb3IlMjBtYWtlJTIwc3VyZSUyMHlvdXIlMjBJbnRlcm5ldCUyMGNvbm5lY3Rpb24lMjBpcyUyMG9uLg==",highlighted:`ValueError: Connection error, <span class="hljs-built_in">and</span> we cannot <span class="hljs-keyword">find</span> the requested <span class="hljs-keyword">files</span> in the cached path.
Please <span class="hljs-keyword">try</span> again <span class="hljs-built_in">or</span> <span class="hljs-keyword">make</span> sure your Internet connection <span class="hljs-keyword">is</span> <span class="hljs-keyword">on</span>.`,wrap:!1}}),x=new T({props:{title:"CUDA 메모리 부족(CUDA out of memory)",local:"cuda-out-of-memory",headingTag:"h2"}}),Q=new M({props:{code:"Q1VEQSUyMG91dCUyMG9mJTIwbWVtb3J5LiUyMFRyaWVkJTIwdG8lMjBhbGxvY2F0ZSUyMDI1Ni4wMCUyME1pQiUyMChHUFUlMjAwJTNCJTIwMTEuMTclMjBHaUIlMjB0b3RhbCUyMGNhcGFjaXR5JTNCJTIwOS43MCUyMEdpQiUyMGFscmVhZHklMjBhbGxvY2F0ZWQlM0IlMjAxNzkuODElMjBNaUIlMjBmcmVlJTNCJTIwOS44NSUyMEdpQiUyMHJlc2VydmVkJTIwaW4lMjB0b3RhbCUyMGJ5JTIwUHlUb3JjaCk=",highlighted:'<span class="hljs-attribute">CUDA</span> out of memory. Tried to allocate <span class="hljs-number">256</span>.<span class="hljs-number">00</span> MiB (GPU <span class="hljs-number">0</span>; <span class="hljs-number">11</span>.<span class="hljs-number">17</span> GiB total capacity; <span class="hljs-number">9</span>.<span class="hljs-number">70</span> GiB already allocated; <span class="hljs-number">179</span>.<span class="hljs-number">81</span> MiB free; <span class="hljs-number">9</span>.<span class="hljs-number">85</span> GiB reserved in total by PyTorch)',wrap:!1}}),y=new Xt({props:{$$slots:{default:[ss]},$$scope:{ctx:he}}}),E=new T({props:{title:"저장된 TensorFlow 모델을 가져올 수 없습니다(Unable to load a saved TensorFlow model)",local:"unable-to-load-a-saved-uensorFlow-model",headingTag:"h2"}}),R=new M({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGUHJlVHJhaW5lZE1vZGVsJTBBZnJvbSUyMHRlbnNvcmZsb3clMjBpbXBvcnQlMjBrZXJhcyUwQSUwQW1vZGVsLnNhdmVfd2VpZ2h0cyglMjJzb21lX2ZvbGRlciUyRnRmX21vZGVsLmg1JTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZQcmVUcmFpbmVkTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMnNvbWVfZm9sZGVyJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFPreTrainedModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> tensorflow <span class="hljs-keyword">import</span> keras

<span class="hljs-meta">&gt;&gt;&gt; </span>model.save_weights(<span class="hljs-string">&quot;some_folder/tf_model.h5&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFPreTrainedModel.from_pretrained(<span class="hljs-string">&quot;some_folder&quot;</span>)`,wrap:!1}}),N=new M({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGUHJlVHJhaW5lZE1vZGVsJTBBJTBBbW9kZWwuc2F2ZV9wcmV0cmFpbmVkKCUyMnBhdGhfdG8lMkZtb2RlbCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGUHJlVHJhaW5lZE1vZGVsLmZyb21fcHJldHJhaW5lZCglMjJwYXRoX3RvJTJGbW9kZWwlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFPreTrainedModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model.save_pretrained(<span class="hljs-string">&quot;path_to/model&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFPreTrainedModel.from_pretrained(<span class="hljs-string">&quot;path_to/model&quot;</span>)`,wrap:!1}}),P=new T({props:{title:"ImportError",local:"importerror",headingTag:"h2"}}),z=new M({props:{code:"SW1wb3J0RXJyb3IlM0ElMjBjYW5ub3QlMjBpbXBvcnQlMjBuYW1lJTIwJ0ltYWdlR1BUSW1hZ2VQcm9jZXNzb3InJTIwZnJvbSUyMCd0cmFuc2Zvcm1lcnMnJTIwKHVua25vd24lMjBsb2NhdGlvbik=",highlighted:'ImportError: cannot <span class="hljs-keyword">import</span> <span class="hljs-type">name</span> <span class="hljs-string">&#x27;ImageGPTImageProcessor&#x27;</span> <span class="hljs-keyword">from</span> <span class="hljs-string">&#x27;transformers&#x27;</span> (<span class="hljs-type">unknown</span> <span class="hljs-keyword">location</span>)',wrap:!1}}),Y=new M({props:{code:"cGlwJTIwaW5zdGFsbCUyMHRyYW5zZm9ybWVycyUyMC0tdXBncmFkZQ==",highlighted:"pip install transformers --upgrade",wrap:!1}}),D=new T({props:{title:"CUDA error: device-side assert triggered",local:"cuda-error-deviceside-assert-triggered",headingTag:"h2"}}),K=new M({props:{code:"UnVudGltZUVycm9yJTNBJTIwQ1VEQSUyMGVycm9yJTNBJTIwZGV2aWNlLXNpZGUlMjBhc3NlcnQlMjB0cmlnZ2VyZWQ=",highlighted:'RuntimeError: CUDA <span class="hljs-literal">error</span>: device-<span class="hljs-literal">side</span> <span class="hljs-keyword">assert</span> triggered',wrap:!1}}),ee=new M({props:{code:"aW1wb3J0JTIwb3MlMEElMEFvcy5lbnZpcm9uJTVCJTIyQ1VEQV9WSVNJQkxFX0RFVklDRVMlMjIlNUQlMjAlM0QlMjAlMjIlMjI=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> os

<span class="hljs-meta">&gt;&gt;&gt; </span>os.environ[<span class="hljs-string">&quot;CUDA_VISIBLE_DEVICES&quot;</span>] = <span class="hljs-string">&quot;&quot;</span>`,wrap:!1}}),se=new M({props:{code:"aW1wb3J0JTIwb3MlMEElMEFvcy5lbnZpcm9uJTVCJTIyQ1VEQV9MQVVOQ0hfQkxPQ0tJTkclMjIlNUQlMjAlM0QlMjAlMjIxJTIy",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> os

<span class="hljs-meta">&gt;&gt;&gt; </span>os.environ[<span class="hljs-string">&quot;CUDA_LAUNCH_BLOCKING&quot;</span>] = <span class="hljs-string">&quot;1&quot;</span>`,wrap:!1}}),le=new T({props:{title:"패딩 토큰이 마스킹되지 않은 경우 잘못된 출력(Incorrect output when padding tokens aren’t masked)",local:"incorrect-output-when-padding-tokens-arent-masked",headingTag:"h2"}}),ae=new M({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24lMEFpbXBvcnQlMjB0b3JjaCUwQSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtdW5jYXNlZCUyMiklMEFtb2RlbC5jb25maWcucGFkX3Rva2VuX2lk",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><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.pad_token_id
<span class="hljs-number">0</span>`,wrap:!1}}),pe=new M({props:{code:"aW5wdXRfaWRzJTIwJTNEJTIwdG9yY2gudGVuc29yKCU1QiU1Qjc1OTIlMkMlMjAyMDU3JTJDJTIwMjA5NyUyQyUyMDIzOTMlMkMlMjA5NjExJTJDJTIwMjExNSU1RCUyQyUyMCU1Qjc1OTIlMkMlMjAwJTJDJTIwMCUyQyUyMDAlMkMlMjAwJTJDJTIwMCU1RCU1RCklMEFvdXRwdXQlMjAlM0QlMjBtb2RlbChpbnB1dF9pZHMpJTBBcHJpbnQob3V0cHV0LmxvZ2l0cyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = torch.tensor([[<span class="hljs-number">7592</span>, <span class="hljs-number">2057</span>, <span class="hljs-number">2097</span>, <span class="hljs-number">2393</span>, <span class="hljs-number">9611</span>, <span class="hljs-number">2115</span>], [<span class="hljs-number">7592</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]])
<span class="hljs-meta">&gt;&gt;&gt; </span>output = model(input_ids)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(output.logits)
tensor([[ <span class="hljs-number">0.0082</span>, -<span class="hljs-number">0.2307</span>],
        [ <span class="hljs-number">0.1317</span>, -<span class="hljs-number">0.1683</span>]], grad_fn=&lt;AddmmBackward0&gt;)`,wrap:!1}}),ie=new M({props:{code:"aW5wdXRfaWRzJTIwJTNEJTIwdG9yY2gudGVuc29yKCU1QiU1Qjc1OTIlNUQlNUQpJTBBb3V0cHV0JTIwJTNEJTIwbW9kZWwoaW5wdXRfaWRzKSUwQXByaW50KG91dHB1dC5sb2dpdHMp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = torch.tensor([[<span class="hljs-number">7592</span>]])
<span class="hljs-meta">&gt;&gt;&gt; </span>output = model(input_ids)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(output.logits)
tensor([[-<span class="hljs-number">0.1008</span>, -<span class="hljs-number">0.4061</span>]], grad_fn=&lt;AddmmBackward0&gt;)`,wrap:!1}}),w=new Xt({props:{$$slots:{default:[ls]},$$scope:{ctx:he}}}),fe=new M({props:{code:"YXR0ZW50aW9uX21hc2slMjAlM0QlMjB0b3JjaC50ZW5zb3IoJTVCJTVCMSUyQyUyMDElMkMlMjAxJTJDJTIwMSUyQyUyMDElMkMlMjAxJTVEJTJDJTIwJTVCMSUyQyUyMDAlMkMlMjAwJTJDJTIwMCUyQyUyMDAlMkMlMjAwJTVEJTVEKSUwQW91dHB1dCUyMCUzRCUyMG1vZGVsKGlucHV0X2lkcyUyQyUyMGF0dGVudGlvbl9tYXNrJTNEYXR0ZW50aW9uX21hc2spJTBBcHJpbnQob3V0cHV0LmxvZ2l0cyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>attention_mask = torch.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">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]])
<span class="hljs-meta">&gt;&gt;&gt; </span>output = model(input_ids, attention_mask=attention_mask)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(output.logits)
tensor([[ <span class="hljs-number">0.0082</span>, -<span class="hljs-number">0.2307</span>],
        [-<span class="hljs-number">0.1008</span>, -<span class="hljs-number">0.4061</span>]], grad_fn=&lt;AddmmBackward0&gt;)`,wrap:!1}}),de=new T({props:{title:"ValueError: 이 유형의 AutoModel에 대해 인식할 수 없는 XYZ 구성 클래스(ValueError: Unrecognized configuration class XYZ for this kind of AutoModel)",local:"valueerror-unrecognized-configuration-class-xyz-for-this-kind-of-automodel",headingTag:"h2"}}),$e=new M({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Qcm9jZXNzb3IlMkMlMjBBdXRvTW9kZWxGb3JRdWVzdGlvbkFuc3dlcmluZyUwQSUwQXByb2Nlc3NvciUyMCUzRCUyMEF1dG9Qcm9jZXNzb3IuZnJvbV9wcmV0cmFpbmVkKCUyMm9wZW5haS1jb21tdW5pdHklMkZncHQyLW1lZGl1bSUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fcHJldHJhaW5lZCglMjJvcGVuYWktY29tbXVuaXR5JTJGZ3B0Mi1tZWRpdW0lMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, AutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;openai-community/gpt2-medium&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;openai-community/gpt2-medium&quot;</span>)
ValueError: Unrecognized configuration <span class="hljs-keyword">class</span> &lt;<span class="hljs-keyword">class</span> <span class="hljs-string">&#x27;transformers.models.gpt2.configuration_gpt2.GPT2Config&#x27;</span>&gt; <span class="hljs-keyword">for</span> this kind of AutoModel: AutoModelForQuestionAnswering.
Model <span class="hljs-built_in">type</span> should be one of AlbertConfig, BartConfig, BertConfig, BigBirdConfig, BigBirdPegasusConfig, BloomConfig, ...`,wrap:!1}}),Me=new 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