import{s as Ka,o as tr,n as xl}from"../chunks/scheduler.889ebb13.js";import{S as er,i as lr,g as r,s,u as d,A as ar,h as n,f as e,c as o,j as v,v as y,y as h,k as u,o as c,a,w as J,d as T,t as _,x as U}from"../chunks/index.8980e686.js";import{I as b}from"../chunks/IconCopyLink.357761cd.js";import{C as j}from"../chunks/CodeBlock.25fc3430.js";import{F as Oa,M as Zl}from"../chunks/Markdown.31057263.js";function rr(C){let i,w=`예제 스크립트는 🤗 <a href="https://huggingface.co/docs/datasets/" rel="nofollow">Datasets</a> 라이브러리에서 데이터 세트를 다운로드하고 전처리합니다.
그런 다음 스크립트는 요약 기능을 지원하는 아키텍처에서 <a href="https://huggingface.co/docs/transformers/main_classes/trainer" rel="nofollow">Trainer</a>를 사용하여 데이터 세트를 미세 조정합니다.
다음 예는 <a href="https://huggingface.co/datasets/cnn_dailymail" rel="nofollow">CNN/DailyMail</a> 데이터 세트에서 <a href="https://huggingface.co/t5-small" rel="nofollow">T5-small</a>을 미세 조정합니다.
T5 모델은 훈련 방식에 따라 추가 <code>source_prefix</code> 인수가 필요하며, 이 프롬프트는 요약 작업임을 T5에 알려줍니다.`,p,M,f;return M=new j({props:{code:"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",highlighted:`python examples/pytorch/summarization/run_summarization.py \\
    --model_name_or_path t5-small \\
    --do_train \\
    --do_eval \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --source_prefix <span class="hljs-string">&quot;summarize: &quot;</span> \\
    --output_dir /tmp/tst-summarization \\
    --per_device_train_batch_size=4 \\
    --per_device_eval_batch_size=4 \\
    --overwrite_output_dir \\
    --predict_with_generate`}}),{c(){i=r("p"),i.innerHTML=w,p=s(),d(M.$$.fragment)},l(m){i=n(m,"P",{"data-svelte-h":!0}),h(i)!=="svelte-h1sv3m"&&(i.innerHTML=w),p=o(m),y(M.$$.fragment,m)},m(m,g){a(m,i,g),a(m,p,g),J(M,m,g),f=!0},p:xl,i(m){f||(T(M.$$.fragment,m),f=!0)},o(m){_(M.$$.fragment,m),f=!1},d(m){m&&(e(i),e(p)),U(M,m)}}}function nr(C){let i,w;return i=new Zl({props:{$$slots:{default:[rr]},$$scope:{ctx:C}}}),{c(){d(i.$$.fragment)},l(p){y(i.$$.fragment,p)},m(p,M){J(i,p,M),w=!0},p(p,M){const f={};M&2&&(f.$$scope={dirty:M,ctx:p}),i.$set(f)},i(p){w||(T(i.$$.fragment,p),w=!0)},o(p){_(i.$$.fragment,p),w=!1},d(p){U(i,p)}}}function ir(C){let i,w=`예제 스크립트는 🤗 <a href="https://huggingface.co/docs/datasets/" rel="nofollow">Datasets</a> 라이브러리에서 데이터 세트를 다운로드하고 전처리합니다.
그런 다음 스크립트는 요약 기능을 지원하는 아키텍처에서 Keras를 사용하여 데이터 세트를 미세 조정합니다.
다음 예는 <a href="https://huggingface.co/datasets/cnn_dailymail" rel="nofollow">CNN/DailyMail</a> 데이터 세트에서 <a href="https://huggingface.co/t5-small" rel="nofollow">T5-small</a>을 미세 조정합니다.
T5 모델은 훈련 방식에 따라 추가 <code>source_prefix</code> 인수가 필요하며, 이 프롬프트는 요약 작업임을 T5에 알려줍니다.`,p,M,f;return M=new j({props:{code:"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",highlighted:`python examples/tensorflow/summarization/run_summarization.py  \\
    --model_name_or_path t5-small \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --output_dir /tmp/tst-summarization  \\
    --per_device_train_batch_size 8 \\
    --per_device_eval_batch_size 16 \\
    --num_train_epochs 3 \\
    --do_train \\
    --do_eval`}}),{c(){i=r("p"),i.innerHTML=w,p=s(),d(M.$$.fragment)},l(m){i=n(m,"P",{"data-svelte-h":!0}),h(i)!=="svelte-1dpi2fx"&&(i.innerHTML=w),p=o(m),y(M.$$.fragment,m)},m(m,g){a(m,i,g),a(m,p,g),J(M,m,g),f=!0},p:xl,i(m){f||(T(M.$$.fragment,m),f=!0)},o(m){_(M.$$.fragment,m),f=!1},d(m){m&&(e(i),e(p)),U(M,m)}}}function sr(C){let i,w;return i=new Zl({props:{$$slots:{default:[ir]},$$scope:{ctx:C}}}),{c(){d(i.$$.fragment)},l(p){y(i.$$.fragment,p)},m(p,M){J(i,p,M),w=!0},p(p,M){const f={};M&2&&(f.$$scope={dirty:M,ctx:p}),i.$set(f)},i(p){w||(T(i.$$.fragment,p),w=!0)},o(p){_(i.$$.fragment,p),w=!1},d(p){U(i,p)}}}function or(C){let i,w=`Tensor Processing Units (TPUs)는 성능을 가속화하기 위해 특별히 설계되었습니다.
PyTorch는 <a href="https://www.tensorflow.org/xla" rel="nofollow">XLA</a> 딥러닝 컴파일러와 함께 TPU를 지원합니다(자세한 내용은 <a href="https://github.com/pytorch/xla/blob/master/README.md" rel="nofollow">여기</a> 참조).
TPU를 사용하려면 <code>xla_spawn.py</code> 스크립트를 실행하고 <code>num_cores</code> 인수를 사용하여 사용하려는 TPU 코어 수를 설정합니다.`,p,M,f;return M=new j({props:{code:"cHl0aG9uJTIweGxhX3NwYXduLnB5JTIwLS1udW1fY29yZXMlMjA4JTIwJTVDJTBBJTIwJTIwJTIwJTIwc3VtbWFyaXphdGlvbiUyRnJ1bl9zdW1tYXJpemF0aW9uLnB5JTIwJTVDJTBBJTIwJTIwJTIwJTIwLS1tb2RlbF9uYW1lX29yX3BhdGglMjB0NS1zbWFsbCUyMCU1QyUwQSUyMCUyMCUyMCUyMC0tZG9fdHJhaW4lMjAlNUMlMEElMjAlMjAlMjAlMjAtLWRvX2V2YWwlMjAlNUMlMEElMjAlMjAlMjAlMjAtLWRhdGFzZXRfbmFtZSUyMGNubl9kYWlseW1haWwlMjAlNUMlMEElMjAlMjAlMjAlMjAtLWRhdGFzZXRfY29uZmlnJTIwJTIyMy4wLjAlMjIlMjAlNUMlMEElMjAlMjAlMjAlMjAtLXNvdXJjZV9wcmVmaXglMjAlMjJzdW1tYXJpemUlM0ElMjAlMjIlMjAlNUMlMEElMjAlMjAlMjAlMjAtLW91dHB1dF9kaXIlMjAlMkZ0bXAlMkZ0c3Qtc3VtbWFyaXphdGlvbiUyMCU1QyUwQSUyMCUyMCUyMCUyMC0tcGVyX2RldmljZV90cmFpbl9iYXRjaF9zaXplJTNENCUyMCU1QyUwQSUyMCUyMCUyMCUyMC0tcGVyX2RldmljZV9ldmFsX2JhdGNoX3NpemUlM0Q0JTIwJTVDJTBBJTIwJTIwJTIwJTIwLS1vdmVyd3JpdGVfb3V0cHV0X2RpciUyMCU1QyUwQSUyMCUyMCUyMCUyMC0tcHJlZGljdF93aXRoX2dlbmVyYXRl",highlighted:`python xla_spawn.py --num_cores 8 \\
    summarization/run_summarization.py \\
    --model_name_or_path t5-small \\
    --do_train \\
    --do_eval \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --source_prefix <span class="hljs-string">&quot;summarize: &quot;</span> \\
    --output_dir /tmp/tst-summarization \\
    --per_device_train_batch_size=4 \\
    --per_device_eval_batch_size=4 \\
    --overwrite_output_dir \\
    --predict_with_generate`}}),{c(){i=r("p"),i.innerHTML=w,p=s(),d(M.$$.fragment)},l(m){i=n(m,"P",{"data-svelte-h":!0}),h(i)!=="svelte-1uupy1q"&&(i.innerHTML=w),p=o(m),y(M.$$.fragment,m)},m(m,g){a(m,i,g),a(m,p,g),J(M,m,g),f=!0},p:xl,i(m){f||(T(M.$$.fragment,m),f=!0)},o(m){_(M.$$.fragment,m),f=!1},d(m){m&&(e(i),e(p)),U(M,m)}}}function pr(C){let i,w;return i=new Zl({props:{$$slots:{default:[or]},$$scope:{ctx:C}}}),{c(){d(i.$$.fragment)},l(p){y(i.$$.fragment,p)},m(p,M){J(i,p,M),w=!0},p(p,M){const f={};M&2&&(f.$$scope={dirty:M,ctx:p}),i.$set(f)},i(p){w||(T(i.$$.fragment,p),w=!0)},o(p){_(i.$$.fragment,p),w=!1},d(p){U(i,p)}}}function mr(C){let i,w=`Tensor Processing Units (TPUs)는 성능을 가속화하기 위해 특별히 설계되었습니다.
TensorFlow 스크립트는 TPU를 훈련에 사용하기 위해 <a href="https://www.tensorflow.org/guide/distributed_training#tpustrategy" rel="nofollow"><code>TPUStrategy</code></a>를 활용합니다.
TPU를 사용하려면 TPU 리소스의 이름을 <code>tpu</code> 인수에 전달합니다.`,p,M,f;return M=new j({props:{code:"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",highlighted:`python run_summarization.py  \\
    --tpu name_of_tpu_resource \\
    --model_name_or_path t5-small \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --output_dir /tmp/tst-summarization  \\
    --per_device_train_batch_size 8 \\
    --per_device_eval_batch_size 16 \\
    --num_train_epochs 3 \\
    --do_train \\
    --do_eval`}}),{c(){i=r("p"),i.innerHTML=w,p=s(),d(M.$$.fragment)},l(m){i=n(m,"P",{"data-svelte-h":!0}),h(i)!=="svelte-14rbp0b"&&(i.innerHTML=w),p=o(m),y(M.$$.fragment,m)},m(m,g){a(m,i,g),a(m,p,g),J(M,m,g),f=!0},p:xl,i(m){f||(T(M.$$.fragment,m),f=!0)},o(m){_(M.$$.fragment,m),f=!1},d(m){m&&(e(i),e(p)),U(M,m)}}}function Mr(C){let i,w;return i=new Zl({props:{$$slots:{default:[mr]},$$scope:{ctx:C}}}),{c(){d(i.$$.fragment)},l(p){y(i.$$.fragment,p)},m(p,M){J(i,p,M),w=!0},p(p,M){const f={};M&2&&(f.$$scope={dirty:M,ctx:p}),i.$set(f)},i(p){w||(T(i.$$.fragment,p),w=!0)},o(p){_(i.$$.fragment,p),w=!1},d(p){U(i,p)}}}function hr(C){let i,w,p,M,f,m,g,P,Nl,Dt,kl="스크립트로 실행하기",Je,q,Hl='🤗 Transformers 노트북과 함께 <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch" rel="nofollow">PyTorch</a>, <a href="https://github.com/huggingface/transformers/tree/main/examples/tensorflow" rel="nofollow">TensorFlow</a>, 또는 <a href="https://github.com/huggingface/transformers/tree/main/examples/flax" rel="nofollow">JAX/Flax</a>를 사용해 특정 태스크에 대한 모델을 훈련하는 방법을 보여주는 예제 스크립트도 있습니다.',Te,D,Ql=`또한 <a href="https://github.com/huggingface/transformers/tree/main/examples/research_projects" rel="nofollow">연구 프로젝트</a> 및 <a href="https://github.com/huggingface/transformers/tree/main/examples/legacy" rel="nofollow">레거시 예제</a>에서 대부분 커뮤니티에서 제공한 스크립트를 찾을 수 있습니다.
이러한 스크립트는 적극적으로 유지 관리되지 않으며 최신 버전의 라이브러리와 호환되지 않을 가능성이 높은 특정 버전의 🤗 Transformers를 필요로 합니다.`,_e,O,Pl=`예제 스크립트가 모든 문제에서 바로 작동하는 것은 아니며, 해결하려는 문제에 맞게 스크립트를 변경해야 할 수도 있습니다.
이를 위해 대부분의 스크립트에는 데이터 전처리 방법이 나와있어 필요에 따라 수정할 수 있습니다.`,Ue,K,ql=`예제 스크립트에 구현하고 싶은 기능이 있으면 pull request를 제출하기 전에 <a href="https://discuss.huggingface.co/" rel="nofollow">포럼</a> 또는 <a href="https://github.com/huggingface/transformers/issues" rel="nofollow">이슈</a>에서 논의해 주세요.
버그 수정은 환영하지만 가독성을 희생하면서까지 더 많은 기능을 추가하는 pull request는 병합(merge)하지 않을 가능성이 높습니다.`,ve,tt,Dl=`이 가이드에서는 <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization" rel="nofollow">PyTorch</a> 및 <a href="https://github.com/huggingface/transformers/tree/main/examples/tensorflow/summarization" rel="nofollow">TensorFlow</a>에서 요약 훈련하는
스크립트 예제를 실행하는 방법을 설명합니다.
특별한 설명이 없는 한 모든 예제는 두 프레임워크 모두에서 작동할 것으로 예상됩니다.`,ge,$,R,oe,et,Ll,Ot,Ol="설정하기",je,lt,Kl="최신 버전의 예제 스크립트를 성공적으로 실행하려면 새 가상 환경에서 <strong>소스로부터 🤗 Transformers를 설치</strong>해야 합니다:",Ce,at,be,rt,ta="이전 버전의 예제 스크립트를 보려면 아래 토글을 클릭하세요:",$e,nt,ea='<summary>이전 버전의 🤗 Transformers 예제</summary> <ul><li><a href="https://github.com/huggingface/transformers/tree/v4.5.1/examples">v4.5.1</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v4.4.2/examples">v4.4.2</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v4.3.3/examples">v4.3.3</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v4.2.2/examples">v4.2.2</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v4.1.1/examples">v4.1.1</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v4.0.1/examples">v4.0.1</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v3.5.1/examples">v3.5.1</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v3.4.0/examples">v3.4.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v3.3.1/examples">v3.3.1</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v3.2.0/examples">v3.2.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v3.1.0/examples">v3.1.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v3.0.2/examples">v3.0.2</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.11.0/examples">v2.11.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.10.0/examples">v2.10.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.9.1/examples">v2.9.1</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.8.0/examples">v2.8.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.7.0/examples">v2.7.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.6.0/examples">v2.6.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.5.1/examples">v2.5.1</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.4.0/examples">v2.4.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.3.0/examples">v2.3.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.2.0/examples">v2.2.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.1.0/examples">v2.1.1</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v2.0.0/examples">v2.0.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v1.2.0/examples">v1.2.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v1.1.0/examples">v1.1.0</a></li> <li><a href="https://github.com/huggingface/transformers/tree/v1.0.0/examples">v1.0.0</a></li></ul>',Xe,it,la="그리고 다음과 같이 복제(clone)해온 🤗 Transformers 버전을 특정 버전(예: v3.5.1)으로 전환하세요:",Ae,st,Ie,ot,aa="올바른 라이브러리 버전을 설정한 후 원하는 예제 폴더로 이동하여 예제별로 라이브러리에 대한 요구 사항(requirements)을 설치합니다:",We,pt,xe,X,V,pe,mt,Rl,Kt,ra="스크립트 실행하기",Ze,G,Ne,A,B,me,Mt,Vl,te,na="혼합 정밀도(mixed precision)로 분산 훈련하기",Le,ht,ia=`<a href="https://huggingface.co/docs/transformers/main_classes/trainer" rel="nofollow">Trainer</a> 클래스는 분산 훈련과 혼합 정밀도(mixed precision)를 지원하므로 스크립트에서도 사용할 수 있습니다.
이 두 가지 기능을 모두 활성화하려면 다음 두 가지를 설정해야 합니다:`,Re,ct,sa="<li><code>fp16</code> 인수를 추가해 혼합 정밀도(mixed precision)를 활성화합니다.</li> <li><code>nproc_per_node</code> 인수를 추가해 사용할 GPU 개수를 설정합니다.</li>",Ve,ut,Ge,ft,oa=`TensorFlow 스크립트는 분산 훈련을 위해 <a href="https://www.tensorflow.org/guide/distributed_training#mirroredstrategy" rel="nofollow"><code>MirroredStrategy</code></a>를 활용하며, 훈련 스크립트에 인수를 추가할 필요가 없습니다.
다중 GPU 환경이라면, TensorFlow 스크립트는 기본적으로 여러 개의 GPU를 사용합니다.`,Be,I,F,Me,wt,Gl,ee,pa="TPU 위에서 스크립트 실행하기",Fe,Y,Ye,W,z,he,dt,Bl,le,ma="🤗 Accelerate로 스크립트 실행하기",ze,yt,Ma=`🤗 <a href="https://huggingface.co/docs/accelerate" rel="nofollow">Accelerate</a>는 PyTorch 훈련 과정에 대한 완전한 가시성을 유지하면서 여러 유형의 설정(CPU 전용, 다중 GPU, TPU)에서 모델을 훈련할 수 있는 통합 방법을 제공하는 PyTorch 전용 라이브러리입니다.
🤗 Accelerate가 설치되어 있는지 확인하세요:`,Ee,E,ae,ha="참고: Accelerate는 빠르게 개발 중이므로 스크립트를 실행하려면 accelerate를 설치해야 합니다.",Fl,Jt,Se,Tt,ca=`<code>run_summarization.py</code> 스크립트 대신 <code>run_summarization_no_trainer.py</code> 스크립트를 사용해야 합니다.
🤗 Accelerate 클래스가 지원되는 스크립트는 폴더에 <code>task_no_trainer.py</code> 파일이 있습니다.
다음 명령을 실행하여 구성 파일을 생성하고 저장합니다:`,ke,_t,He,Ut,ua="설정을 테스트하여 올바르게 구성되었는지 확인합니다:",Qe,vt,Pe,gt,fa="이제 훈련을 시작할 준비가 되었습니다:",qe,jt,De,x,S,ce,Ct,Yl,re,wa="사용자 정의 데이터 세트 사용하기",Oe,bt,da=`요약 스크립트는 사용자 지정 데이터 세트가 CSV 또는 JSON 파일인 경우 지원합니다.
사용자 지정 데이터 세트를 사용하는 경우에는 몇 가지 추가 인수를 지정해야 합니다:`,Ke,$t,ya="<li><code>train_file</code>과 <code>validation_file</code>은 훈련 및 검증 파일의 경로를 지정합니다.</li> <li><code>text_column</code>은 요약할 입력 텍스트입니다.</li> <li><code>summary_column</code>은 출력할 대상 텍스트입니다.</li>",tl,Xt,Ja="사용자 지정 데이터 세트를 사용하는 요약 스크립트는 다음과 같습니다:",el,At,ll,Z,k,ue,It,zl,ne,Ta="스크립트 테스트하기",al,Wt,_a="전체 데이터 세트를 대상으로 훈련을 완료하는데 꽤 오랜 시간이 걸리기 때문에, 작은 데이터 세트에서 모든 것이 예상대로 실행되는지 확인하는 것이 좋습니다.",rl,xt,Ua="다음 인수를 사용하여 데이터 세트를 최대 샘플 수로 잘라냅니다:",nl,Zt,va="<li><code>max_train_samples</code></li> <li><code>max_eval_samples</code></li> <li><code>max_predict_samples</code></li>",il,Nt,sl,Lt,ga=`모든 예제 스크립트가 <code>max_predict_samples</code> 인수를 지원하지는 않습니다.
스크립트가 이 인수를 지원하는지 확실하지 않은 경우 <code>-h</code> 인수를 추가하여 확인하세요:`,ol,Rt,pl,N,H,fe,Vt,El,ie,ja="체크포인트(checkpoint)에서 훈련 이어서 하기",ml,Gt,Ca=`또 다른 유용한 옵션은 이전 체크포인트에서 훈련을 재개하는 것입니다.
이렇게 하면 훈련이 중단되더라도 처음부터 다시 시작하지 않고 중단한 부분부터 다시 시작할 수 있습니다.
체크포인트에서 훈련을 재개하는 방법에는 두 가지가 있습니다.`,Ml,Bt,ba=`첫 번째는 <code>output_dir previous_output_dir</code> 인수를 사용하여 <code>output_dir</code>에 저장된 최신 체크포인트부터 훈련을 재개하는 방법입니다.
이 경우 <code>overwrite_output_dir</code>을 제거해야 합니다:`,hl,Ft,cl,Yt,$a="두 번째는 <code>resume_from_checkpoint path_to_specific_checkpoint</code> 인수를 사용하여 특정 체크포인트 폴더에서 훈련을 재개하는 방법입니다.",ul,zt,fl,L,Q,we,Et,Sl,se,Xa="모델 공유하기",wl,St,Aa=`모든 스크립트는 최종 모델을 <a href="https://huggingface.co/models" rel="nofollow">Model Hub</a>에 업로드할 수 있습니다.
시작하기 전에 Hugging Face에 로그인했는지 확인하세요:`,dl,kt,yl,Ht,Ia=`그런 다음 스크립트에 <code>push_to_hub</code> 인수를 추가합니다.
이 인수는 Hugging Face 사용자 이름과 <code>output_dir</code>에 지정된 폴더 이름으로 저장소를 생성합니다.`,Jl,Qt,Wa=`저장소에 특정 이름을 지정하려면 <code>push_to_hub_model_id</code> 인수를 사용하여 추가합니다.
저장소는 네임스페이스 아래에 자동으로 나열됩니다.
다음 예는 특정 저장소 이름으로 모델을 업로드하는 방법입니다:`,Tl,Pt,_l,de,Ul;return P=new b({}),et=new b({}),at=new j({props:{code:"Z2l0JTIwY2xvbmUlMjBodHRwcyUzQSUyRiUyRmdpdGh1Yi5jb20lMkZodWdnaW5nZmFjZSUyRnRyYW5zZm9ybWVycyUwQWNkJTIwdHJhbnNmb3JtZXJzJTBBcGlwJTIwaW5zdGFsbCUyMC4=",highlighted:`git <span class="hljs-built_in">clone</span> https://github.com/huggingface/transformers
<span class="hljs-built_in">cd</span> transformers
pip install .`}}),st=new j({props:{code:"Z2l0JTIwY2hlY2tvdXQlMjB0YWdzJTJGdjMuNS4x",highlighted:"git checkout tags/v3.5.1"}}),pt=new j({props:{code:"cGlwJTIwaW5zdGFsbCUyMC1yJTIwcmVxdWlyZW1lbnRzLnR4dA==",highlighted:"pip install -r requirements.txt"}}),mt=new b({}),G=new Oa({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[sr],pytorch:[nr]},$$scope:{ctx:C}}}),Mt=new b({}),ut=new j({props:{code:"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",highlighted:`python -m torch.distributed.launch \\
    --nproc_per_node 8 pytorch/summarization/run_summarization.py \\
    --fp16 \\
    --model_name_or_path t5-small \\
    --do_train \\
    --do_eval \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --source_prefix <span class="hljs-string">&quot;summarize: &quot;</span> \\
    --output_dir /tmp/tst-summarization \\
    --per_device_train_batch_size=4 \\
    --per_device_eval_batch_size=4 \\
    --overwrite_output_dir \\
    --predict_with_generate`}}),wt=new b({}),Y=new Oa({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Mr],pytorch:[pr]},$$scope:{ctx:C}}}),dt=new b({}),Jt=new j({props:{code:"cGlwJTIwaW5zdGFsbCUyMGdpdCUyQmh0dHBzJTNBJTJGJTJGZ2l0aHViLmNvbSUyRmh1Z2dpbmdmYWNlJTJGYWNjZWxlcmF0ZQ==",highlighted:"pip install git+https://github.com/huggingface/accelerate"}}),_t=new j({props:{code:"YWNjZWxlcmF0ZSUyMGNvbmZpZw==",highlighted:"accelerate config"}}),vt=new j({props:{code:"YWNjZWxlcmF0ZSUyMHRlc3Q=",highlighted:'accelerate <span class="hljs-built_in">test</span>'}}),jt=new j({props:{code:"YWNjZWxlcmF0ZSUyMGxhdW5jaCUyMHJ1bl9zdW1tYXJpemF0aW9uX25vX3RyYWluZXIucHklMjAlNUMlMEElMjAlMjAlMjAlMjAtLW1vZGVsX25hbWVfb3JfcGF0aCUyMHQ1LXNtYWxsJTIwJTVDJTBBJTIwJTIwJTIwJTIwLS1kYXRhc2V0X25hbWUlMjBjbm5fZGFpbHltYWlsJTIwJTVDJTBBJTIwJTIwJTIwJTIwLS1kYXRhc2V0X2NvbmZpZyUyMCUyMjMuMC4wJTIyJTIwJTVDJTBBJTIwJTIwJTIwJTIwLS1zb3VyY2VfcHJlZml4JTIwJTIyc3VtbWFyaXplJTNBJTIwJTIyJTIwJTVDJTBBJTIwJTIwJTIwJTIwLS1vdXRwdXRfZGlyJTIwfiUyRnRtcCUyRnRzdC1zdW1tYXJpemF0aW9u",highlighted:`accelerate launch run_summarization_no_trainer.py \\
    --model_name_or_path t5-small \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --source_prefix <span class="hljs-string">&quot;summarize: &quot;</span> \\
    --output_dir ~/tmp/tst-summarization`}}),Ct=new b({}),At=new j({props:{code:"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",highlighted:`python examples/pytorch/summarization/run_summarization.py \\
    --model_name_or_path t5-small \\
    --do_train \\
    --do_eval \\
    --train_file path_to_csv_or_jsonlines_file \\
    --validation_file path_to_csv_or_jsonlines_file \\
    --text_column text_column_name \\
    --summary_column summary_column_name \\
    --source_prefix <span class="hljs-string">&quot;summarize: &quot;</span> \\
    --output_dir /tmp/tst-summarization \\
    --overwrite_output_dir \\
    --per_device_train_batch_size=4 \\
    --per_device_eval_batch_size=4 \\
    --predict_with_generate`}}),It=new b({}),Nt=new j({props:{code:"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",highlighted:`python examples/pytorch/summarization/run_summarization.py \\
    --model_name_or_path t5-small \\
    --max_train_samples 50 \\
    --max_eval_samples 50 \\
    --max_predict_samples 50 \\
    --do_train \\
    --do_eval \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --source_prefix <span class="hljs-string">&quot;summarize: &quot;</span> \\
    --output_dir /tmp/tst-summarization \\
    --per_device_train_batch_size=4 \\
    --per_device_eval_batch_size=4 \\
    --overwrite_output_dir \\
    --predict_with_generate`}}),Rt=new j({props:{code:"ZXhhbXBsZXMlMkZweXRvcmNoJTJGc3VtbWFyaXphdGlvbiUyRnJ1bl9zdW1tYXJpemF0aW9uLnB5JTIwLWg=",highlighted:"examples/pytorch/summarization/run_summarization.py -h"}}),Vt=new b({}),Ft=new j({props:{code:"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",highlighted:`python examples/pytorch/summarization/run_summarization.py
    --model_name_or_path t5-small \\
    --do_train \\
    --do_eval \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --source_prefix <span class="hljs-string">&quot;summarize: &quot;</span> \\
    --output_dir /tmp/tst-summarization \\
    --per_device_train_batch_size=4 \\
    --per_device_eval_batch_size=4 \\
    --output_dir previous_output_dir \\
    --predict_with_generate`}}),zt=new j({props:{code:"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",highlighted:`python examples/pytorch/summarization/run_summarization.py
    --model_name_or_path t5-small \\
    --do_train \\
    --do_eval \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --source_prefix <span class="hljs-string">&quot;summarize: &quot;</span> \\
    --output_dir /tmp/tst-summarization \\
    --per_device_train_batch_size=4 \\
    --per_device_eval_batch_size=4 \\
    --overwrite_output_dir \\
    --resume_from_checkpoint path_to_specific_checkpoint \\
    --predict_with_generate`}}),Et=new b({}),kt=new j({props:{code:"aHVnZ2luZ2ZhY2UtY2xpJTIwbG9naW4=",highlighted:"huggingface-cli login"}}),Pt=new j({props:{code:"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",highlighted:`python examples/pytorch/summarization/run_summarization.py
    --model_name_or_path t5-small \\
    --do_train \\
    --do_eval \\
    --dataset_name cnn_dailymail \\
    --dataset_config <span class="hljs-string">&quot;3.0.0&quot;</span> \\
    --source_prefix <span class="hljs-string">&quot;summarize: &quot;</span> \\
    --push_to_hub \\
    --push_to_hub_model_id finetuned-t5-cnn_dailymail \\
    --output_dir /tmp/tst-summarization \\
    --per_device_train_batch_size=4 \\
    --per_device_eval_batch_size=4 \\
    --overwrite_output_dir \\
    --predict_with_generate`}}),{c(){i=r("meta"),w=s(),p=r("p"),M=s(),f=r("h1"),m=r("a"),g=r("span"),d(P.$$.fragment),Nl=s(),Dt=r("span"),Dt.textContent=kl,Je=s(),q=r("p"),q.innerHTML=Hl,Te=s(),D=r("p"),D.innerHTML=Ql,_e=s(),O=r("p"),O.textContent=Pl,Ue=s(),K=r("p"),K.innerHTML=ql,ve=s(),tt=r("p"),tt.innerHTML=Dl,ge=s(),$=r("h2"),R=r("a"),oe=r("span"),d(et.$$.fragment),Ll=s(),Ot=r("span"),Ot.textContent=Ol,je=s(),lt=r("p"),lt.innerHTML=Kl,Ce=s(),d(at.$$.fragment),be=s(),rt=r("p"),rt.textContent=ta,$e=s(),nt=r("details"),nt.innerHTML=ea,Xe=s(),it=r("p"),it.textContent=la,Ae=s(),d(st.$$.fragment),Ie=s(),ot=r("p"),ot.textContent=aa,We=s(),d(pt.$$.fragment),xe=s(),X=r("h2"),V=r("a"),pe=r("span"),d(mt.$$.fragment),Rl=s(),Kt=r("span"),Kt.textContent=ra,Ze=s(),d(G.$$.fragment),Ne=s(),A=r("h2"),B=r("a"),me=r("span"),d(Mt.$$.fragment),Vl=s(),te=r("span"),te.textContent=na,Le=s(),ht=r("p"),ht.innerHTML=ia,Re=s(),ct=r("ul"),ct.innerHTML=sa,Ve=s(),d(ut.$$.fragment),Ge=s(),ft=r("p"),ft.innerHTML=oa,Be=s(),I=r("h2"),F=r("a"),Me=r("span"),d(wt.$$.fragment),Gl=s(),ee=r("span"),ee.textContent=pa,Fe=s(),d(Y.$$.fragment),Ye=s(),W=r("h2"),z=r("a"),he=r("span"),d(dt.$$.fragment),Bl=s(),le=r("span"),le.textContent=ma,ze=s(),yt=r("p"),yt.innerHTML=Ma,Ee=s(),E=r("blockquote"),ae=r("p"),ae.textContent=ha,Fl=s(),d(Jt.$$.fragment),Se=s(),Tt=r("p"),Tt.innerHTML=ca,ke=s(),d(_t.$$.fragment),He=s(),Ut=r("p"),Ut.textContent=ua,Qe=s(),d(vt.$$.fragment),Pe=s(),gt=r("p"),gt.textContent=fa,qe=s(),d(jt.$$.fragment),De=s(),x=r("h2"),S=r("a"),ce=r("span"),d(Ct.$$.fragment),Yl=s(),re=r("span"),re.textContent=wa,Oe=s(),bt=r("p"),bt.textContent=da,Ke=s(),$t=r("ul"),$t.innerHTML=ya,tl=s(),Xt=r("p"),Xt.textContent=Ja,el=s(),d(At.$$.fragment),ll=s(),Z=r("h2"),k=r("a"),ue=r("span"),d(It.$$.fragment),zl=s(),ne=r("span"),ne.textContent=Ta,al=s(),Wt=r("p"),Wt.textContent=_a,rl=s(),xt=r("p"),xt.textContent=Ua,nl=s(),Zt=r("ul"),Zt.innerHTML=va,il=s(),d(Nt.$$.fragment),sl=s(),Lt=r("p"),Lt.innerHTML=ga,ol=s(),d(Rt.$$.fragment),pl=s(),N=r("h2"),H=r("a"),fe=r("span"),d(Vt.$$.fragment),El=s(),ie=r("span"),ie.textContent=ja,ml=s(),Gt=r("p"),Gt.textContent=Ca,Ml=s(),Bt=r("p"),Bt.innerHTML=ba,hl=s(),d(Ft.$$.fragment),cl=s(),Yt=r("p"),Yt.innerHTML=$a,ul=s(),d(zt.$$.fragment),fl=s(),L=r("h2"),Q=r("a"),we=r("span"),d(Et.$$.fragment),Sl=s(),se=r("span"),se.textContent=Xa,wl=s(),St=r("p"),St.innerHTML=Aa,dl=s(),d(kt.$$.fragment),yl=s(),Ht=r("p"),Ht.innerHTML=Ia,Jl=s(),Qt=r("p"),Qt.innerHTML=Wa,Tl=s(),d(Pt.$$.fragment),_l=s(),de=r("p"),this.h()},l(t){const 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