import{S as Ia,i as Na,s as Sa,e as o,k as d,w as T,t,M as Fa,c as p,d as l,m as M,a as c,x as U,h as a,b,G as s,g as m,y as g,q as E,o as k,B as v,v as Aa,L as et}from"../../chunks/vendor-hf-doc-builder.js";import{T as Kl}from"../../chunks/Tip-hf-doc-builder.js";import{Y as Ca}from"../../chunks/Youtube-hf-doc-builder.js";import{I as Qs}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as q}from"../../chunks/CodeBlock-hf-doc-builder.js";import{D as za}from"../../chunks/DocNotebookDropdown-hf-doc-builder.js";import{F as xt,M as xs}from"../../chunks/Markdown-hf-doc-builder.js";function Ya(z){let n,i,r,y,_,B,R,G,w,X,V,Y,W,A,I,O,F,H,Q,se,S,N,P,h,J,pe,D,ce,K,x,Me,ye,fe,ge,Ge,ie,je,ee,we,Ve,Je,le,he,te,$e,ae,We,re,Ce,ue,ne,$,C,oe,Fe,de,L,Ie,me,Ee,be,Be,_e,Re,Ue,ke,Te,ve,Ne,Ze;return{c(){n=t(`\uC774 \uD0DC\uC2A4\uD06C \uAC00\uC774\uB4DC\uB294 \uC544\uB798 \uBAA8\uB378 \uC544\uD0A4\uD14D\uCC98\uC5D0\uB3C4 \uC751\uC6A9\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4.

`),i=o("p"),r=o("a"),y=t("BART"),_=t(", "),B=o("a"),R=t("BigBird-Pegasus"),G=t(", "),w=o("a"),X=t("Blenderbot"),V=t(", "),Y=o("a"),W=t("BlenderbotSmall"),A=t(", "),I=o("a"),O=t("Encoder decoder"),F=t(", "),H=o("a"),Q=t("FairSeq Machine-Translation"),se=t(", "),S=o("a"),N=t("GPTSAN-japanese"),P=t(", "),h=o("a"),J=t("LED"),pe=t(", "),D=o("a"),ce=t("LongT5"),K=t(", "),x=o("a"),Me=t("M2M100"),ye=t(", "),fe=o("a"),ge=t("Marian"),Ge=t(", "),ie=o("a"),je=t("mBART"),ee=t(", "),we=o("a"),Ve=t("MT5"),Je=t(", "),le=o("a"),he=t("MVP"),te=t(", "),$e=o("a"),ae=t("NLLB"),We=t(", "),re=o("a"),Ce=t("NLLB-MOE"),ue=t(", "),ne=o("a"),$=t("Pegasus"),C=t(", "),oe=o("a"),Fe=t("PEGASUS-X"),de=t(", "),L=o("a"),Ie=t("PLBart"),me=t(", "),Ee=o("a"),be=t("ProphetNet"),Be=t(", "),_e=o("a"),Re=t("SwitchTransformers"),Ue=t(", "),ke=o("a"),Te=t("T5"),ve=t(", "),Ne=o("a"),Ze=t("XLM-ProphetNet"),this.h()},l(Xe){n=a(Xe,`\uC774 \uD0DC\uC2A4\uD06C \uAC00\uC774\uB4DC\uB294 \uC544\uB798 \uBAA8\uB378 \uC544\uD0A4\uD14D\uCC98\uC5D0\uB3C4 \uC751\uC6A9\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4.

`),i=p(Xe,"P",{});var j=c(i);r=p(j,"A",{href:!0});var f=c(r);y=a(f,"BART"),f.forEach(l),_=a(j,", "),B=p(j,"A",{href:!0});var Z=c(B);R=a(Z,"BigBird-Pegasus"),Z.forEach(l),G=a(j,", "),w=p(j,"A",{href:!0});var Qe=c(w);X=a(Qe,"Blenderbot"),Qe.forEach(l),V=a(j,", "),Y=p(j,"A",{href:!0});var xe=c(Y);W=a(xe,"BlenderbotSmall"),xe.forEach(l),A=a(j,", "),I=p(j,"A",{href:!0});var ts=c(I);O=a(ts,"Encoder decoder"),ts.forEach(l),F=a(j,", "),H=p(j,"A",{href:!0});var Ae=c(H);Q=a(Ae,"FairSeq Machine-Translation"),Ae.forEach(l),se=a(j,", "),S=p(j,"A",{href:!0});var Se=c(S);N=a(Se,"GPTSAN-japanese"),Se.forEach(l),P=a(j,", "),h=p(j,"A",{href:!0});var es=c(h);J=a(es,"LED"),es.forEach(l),pe=a(j,", "),D=p(j,"A",{href:!0});var ze=c(D);ce=a(ze,"LongT5"),ze.forEach(l),K=a(j,", "),x=p(j,"A",{href:!0});var ss=c(x);Me=a(ss,"M2M100"),ss.forEach(l),ye=a(j,", "),fe=p(j,"A",{href:!0});var ls=c(fe);ge=a(ls,"Marian"),ls.forEach(l),Ge=a(j,", "),ie=p(j,"A",{href:!0});var qe=c(ie);je=a(qe,"mBART"),qe.forEach(l),ee=a(j,", "),we=p(j,"A",{href:!0});var as=c(we);Ve=a(as,"MT5"),as.forEach(l),Je=a(j,", "),le=p(j,"A",{href:!0});var Ye=c(le);he=a(Ye,"MVP"),Ye.forEach(l),te=a(j,", "),$e=p(j,"A",{href:!0});var Pe=c($e);ae=a(Pe,"NLLB"),Pe.forEach(l),We=a(j,", "),re=p(j,"A",{href:!0});var De=c(re);Ce=a(De,"NLLB-MOE"),De.forEach(l),ue=a(j,", "),ne=p(j,"A",{href:!0});var fs=c(ne);$=a(fs,"Pegasus"),fs.forEach(l),C=a(j,", "),oe=p(j,"A",{href:!0});var Le=c(oe);Fe=a(Le,"PEGASUS-X"),Le.forEach(l),de=a(j,", "),L=p(j,"A",{href:!0});var He=c(L);Ie=a(He,"PLBart"),He.forEach(l),me=a(j,", "),Ee=p(j,"A",{href:!0});var rs=c(Ee);be=a(rs,"ProphetNet"),rs.forEach(l),Be=a(j,", "),_e=p(j,"A",{href:!0});var Oe=c(_e);Re=a(Oe,"SwitchTransformers"),Oe.forEach(l),Ue=a(j,", "),ke=p(j,"A",{href:!0});var Is=c(ke);Te=a(Is,"T5"),Is.forEach(l),ve=a(j,", "),Ne=p(j,"A",{href:!0});var Us=c(Ne);Ze=a(Us,"XLM-ProphetNet"),Us.forEach(l),j.forEach(l),this.h()},h(){b(r,"href","../model_doc/bart"),b(B,"href","../model_doc/bigbird_pegasus"),b(w,"href","../model_doc/blenderbot"),b(Y,"href","../model_doc/blenderbot-small"),b(I,"href","../model_doc/encoder-decoder"),b(H,"href","../model_doc/fsmt"),b(S,"href","../model_doc/gptsan-japanese"),b(h,"href","../model_doc/led"),b(D,"href","../model_doc/longt5"),b(x,"href","../model_doc/m2m_100"),b(fe,"href","../model_doc/marian"),b(ie,"href","../model_doc/mbart"),b(we,"href","../model_doc/mt5"),b(le,"href","../model_doc/mvp"),b($e,"href","../model_doc/nllb"),b(re,"href","../model_doc/nllb-moe"),b(ne,"href","../model_doc/pegasus"),b(oe,"href","../model_doc/pegasus_x"),b(L,"href","../model_doc/plbart"),b(Ee,"href","../model_doc/prophetnet"),b(_e,"href","../model_doc/switch_transformers"),b(ke,"href","../model_doc/t5"),b(Ne,"href","../model_doc/xlm-prophetnet")},m(Xe,j){m(Xe,n,j),m(Xe,i,j),s(i,r),s(r,y),s(i,_),s(i,B),s(B,R),s(i,G),s(i,w),s(w,X),s(i,V),s(i,Y),s(Y,W),s(i,A),s(i,I),s(I,O),s(i,F),s(i,H),s(H,Q),s(i,se),s(i,S),s(S,N),s(i,P),s(i,h),s(h,J),s(i,pe),s(i,D),s(D,ce),s(i,K),s(i,x),s(x,Me),s(i,ye),s(i,fe),s(fe,ge),s(i,Ge),s(i,ie),s(ie,je),s(i,ee),s(i,we),s(we,Ve),s(i,Je),s(i,le),s(le,he),s(i,te),s(i,$e),s($e,ae),s(i,We),s(i,re),s(re,Ce),s(i,ue),s(i,ne),s(ne,$),s(i,C),s(i,oe),s(oe,Fe),s(i,de),s(i,L),s(L,Ie),s(i,me),s(i,Ee),s(Ee,be),s(i,Be),s(i,_e),s(_e,Re),s(i,Ue),s(i,ke),s(ke,Te),s(i,ve),s(i,Ne),s(Ne,Ze)},d(Xe){Xe&&l(n),Xe&&l(i)}}}function Ha(z){let n,i;return n=new q({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERhdGFDb2xsYXRvckZvclNlcTJTZXElMEElMEFkYXRhX2NvbGxhdG9yJTIwJTNEJTIwRGF0YUNvbGxhdG9yRm9yU2VxMlNlcSh0b2tlbml6ZXIlM0R0b2tlbml6ZXIlMkMlMjBtb2RlbCUzRGNoZWNrcG9pbnQp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span>data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint)`}}),{c(){T(n.$$.fragment)},l(r){U(n.$$.fragment,r)},m(r,y){g(n,r,y),i=!0},p:et,i(r){i||(E(n.$$.fragment,r),i=!0)},o(r){k(n.$$.fragment,r),i=!1},d(r){v(n,r)}}}function Qa(z){let n,i;return n=new xs({props:{$$slots:{default:[Ha]},$$scope:{ctx:z}}}),{c(){T(n.$$.fragment)},l(r){U(n.$$.fragment,r)},m(r,y){g(n,r,y),i=!0},p(r,y){const _={};y&2&&(_.$$scope={dirty:y,ctx:r}),n.$set(_)},i(r){i||(E(n.$$.fragment,r),i=!0)},o(r){k(n.$$.fragment,r),i=!1},d(r){v(n,r)}}}function xa(z){let n,i;return n=new q({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERhdGFDb2xsYXRvckZvclNlcTJTZXElMEElMEFkYXRhX2NvbGxhdG9yJTIwJTNEJTIwRGF0YUNvbGxhdG9yRm9yU2VxMlNlcSh0b2tlbml6ZXIlM0R0b2tlbml6ZXIlMkMlMjBtb2RlbCUzRGNoZWNrcG9pbnQlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnRmJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span>data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)`}}),{c(){T(n.$$.fragment)},l(r){U(n.$$.fragment,r)},m(r,y){g(n,r,y),i=!0},p:et,i(r){i||(E(n.$$.fragment,r),i=!0)},o(r){k(n.$$.fragment,r),i=!1},d(r){v(n,r)}}}function qa(z){let n,i;return n=new xs({props:{$$slots:{default:[xa]},$$scope:{ctx:z}}}),{c(){T(n.$$.fragment)},l(r){U(n.$$.fragment,r)},m(r,y){g(n,r,y),i=!0},p(r,y){const _={};y&2&&(_.$$scope={dirty:y,ctx:r}),n.$set(_)},i(r){i||(E(n.$$.fragment,r),i=!0)},o(r){k(n.$$.fragment,r),i=!1},d(r){v(n,r)}}}function Pa(z){let n,i,r,y,_,B,R;return{c(){n=o("p"),i=o("code"),r=t("Trainer"),y=t("\uB85C \uBAA8\uB378\uC744 \uD30C\uC778\uD29C\uB2DD\uD558\uB294 \uBC29\uBC95\uC5D0 \uC775\uC219\uD558\uC9C0 \uC54A\uB2E4\uBA74 "),_=o("a"),B=t("\uC5EC\uAE30"),R=t("\uC5D0\uC11C \uAE30\uBCF8 \uD29C\uD1A0\uB9AC\uC5BC\uC744 \uC0B4\uD3B4\uBCF4\uC2DC\uAE30 \uBC14\uB78D\uB2C8\uB2E4!"),this.h()},l(G){n=p(G,"P",{});var w=c(n);i=p(w,"CODE",{});var X=c(i);r=a(X,"Trainer"),X.forEach(l),y=a(w,"\uB85C \uBAA8\uB378\uC744 \uD30C\uC778\uD29C\uB2DD\uD558\uB294 \uBC29\uBC95\uC5D0 \uC775\uC219\uD558\uC9C0 \uC54A\uB2E4\uBA74 "),_=p(w,"A",{href:!0});var V=c(_);B=a(V,"\uC5EC\uAE30"),V.forEach(l),R=a(w,"\uC5D0\uC11C \uAE30\uBCF8 \uD29C\uD1A0\uB9AC\uC5BC\uC744 \uC0B4\uD3B4\uBCF4\uC2DC\uAE30 \uBC14\uB78D\uB2C8\uB2E4!"),w.forEach(l),this.h()},h(){b(_,"href","../training#train-with-pytorch-trainer")},m(G,w){m(G,n,w),s(n,i),s(i,r),s(n,y),s(n,_),s(_,B),s(n,R)},d(G){G&&l(n)}}}function Da(z){let n,i,r,y,_,B,R,G,w,X,V,Y,W,A,I,O,F,H,Q,se,S,N,P,h,J,pe,D,ce,K,x,Me,ye,fe,ge,Ge,ie,je,ee,we,Ve,Je,le,he,te,$e,ae,We,re,Ce,ue,ne;return n=new Kl({props:{$$slots:{default:[Pa]},$$scope:{ctx:z}}}),w=new q({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvclNlcTJTZXFMTSUyQyUyMFNlcTJTZXFUcmFpbmluZ0FyZ3VtZW50cyUyQyUyMFNlcTJTZXFUcmFpbmVyJTBBJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JTZXEyU2VxTE0uZnJvbV9wcmV0cmFpbmVkKGNoZWNrcG9pbnQp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)`}}),le=new q({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>training_args = Seq2SeqTrainingArguments(
<span class="hljs-meta">... </span>    output_dir=<span class="hljs-string">&quot;my_awesome_opus_books_model&quot;</span>,
<span class="hljs-meta">... </span>    evaluation_strategy=<span class="hljs-string">&quot;epoch&quot;</span>,
<span class="hljs-meta">... </span>    learning_rate=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span>    per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span>    per_device_eval_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span>    weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span>    save_total_limit=<span class="hljs-number">3</span>,
<span class="hljs-meta">... </span>    num_train_epochs=<span class="hljs-number">2</span>,
<span class="hljs-meta">... </span>    predict_with_generate=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    fp16=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    push_to_hub=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>trainer = Seq2SeqTrainer(
<span class="hljs-meta">... </span>    model=model,
<span class="hljs-meta">... </span>    args=training_args,
<span class="hljs-meta">... </span>    train_dataset=tokenized_books[<span class="hljs-string">&quot;train&quot;</span>],
<span class="hljs-meta">... </span>    eval_dataset=tokenized_books[<span class="hljs-string">&quot;test&quot;</span>],
<span class="hljs-meta">... </span>    tokenizer=tokenizer,
<span class="hljs-meta">... </span>    data_collator=data_collator,
<span class="hljs-meta">... </span>    compute_metrics=compute_metrics,
<span class="hljs-meta">... </span>)

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<span class="hljs-meta">&gt;&gt;&gt; </span>optimizer = AdamWeightDecay(learning_rate=<span class="hljs-number">2e-5</span>, weight_decay_rate=<span class="hljs-number">0.01</span>)`}}),V=new q({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGQXV0b01vZGVsRm9yU2VxMlNlcUxNJTBBJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNlcTJTZXFMTS5mcm9tX3ByZXRyYWluZWQoY2hlY2twb2ludCk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(checkpoint)`}}),S=new q({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>tf_train_set = model.prepare_tf_dataset(
<span class="hljs-meta">... </span>    tokenized_books[<span class="hljs-string">&quot;train&quot;</span>],
<span class="hljs-meta">... </span>    shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span>    collate_fn=data_collator,
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>tf_test_set = model.prepare_tf_dataset(
<span class="hljs-meta">... </span>    tokenized_books[<span class="hljs-string">&quot;test&quot;</span>],
<span class="hljs-meta">... </span>    shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>    batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span>    collate_fn=data_collator,
<span class="hljs-meta">... </span>)`}}),x=new q({props:{code:"aW1wb3J0JTIwdGVuc29yZmxvdyUyMGFzJTIwdGYlMEElMEFtb2RlbC5jb21waWxlKG9wdGltaXplciUzRG9wdGltaXplcik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>model.<span class="hljs-built_in">compile</span>(optimizer=optimizer)`}}),ae=new q({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycy5rZXJhc19jYWxsYmFja3MlMjBpbXBvcnQlMjBLZXJhc01ldHJpY0NhbGxiYWNrJTBBJTBBbWV0cmljX2NhbGxiYWNrJTIwJTNEJTIwS2VyYXNNZXRyaWNDYWxsYmFjayhtZXRyaWNfZm4lM0Rjb21wdXRlX21ldHJpY3MlMkMlMjBldmFsX2RhdGFzZXQlM0R0Zl92YWxpZGF0aW9uX3NldCk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers.keras_callbacks <span class="hljs-keyword">import</span> KerasMetricCallback

<span class="hljs-meta">&gt;&gt;&gt; </span>metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)`}}),oe=new q({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycy5rZXJhc19jYWxsYmFja3MlMjBpbXBvcnQlMjBQdXNoVG9IdWJDYWxsYmFjayUwQSUwQXB1c2hfdG9faHViX2NhbGxiYWNrJTIwJTNEJTIwUHVzaFRvSHViQ2FsbGJhY2soJTBBJTIwJTIwJTIwJTIwb3V0cHV0X2RpciUzRCUyMm15X2F3ZXNvbWVfb3B1c19ib29rc19tb2RlbCUyMiUyQyUwQSUyMCUyMCUyMCUyMHRva2VuaXplciUzRHRva2VuaXplciUyQyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers.keras_callbacks <span class="hljs-keyword">import</span> PushToHubCallback

<span class="hljs-meta">&gt;&gt;&gt; </span>push_to_hub_callback = PushToHubCallback(
<span class="hljs-meta">... </span>    output_dir=<span class="hljs-string">&quot;my_awesome_opus_books_model&quot;</span>,
<span class="hljs-meta">... </span>    tokenizer=tokenizer,
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<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;my_awesome_opus_books_model&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).input_ids`}}),F=new q({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvclNlcTJTZXFMTSUwQSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU2VxMlNlcUxNLmZyb21fcHJldHJhaW5lZCglMjJteV9hd2Vzb21lX29wdXNfYm9va3NfbW9kZWwlMjIpJTBBb3V0cHV0cyUyMCUzRCUyMG1vZGVsLmdlbmVyYXRlKGlucHV0cyUyQyUyMG1heF9uZXdfdG9rZW5zJTNENDAlMkMlMjBkb19zYW1wbGUlM0RUcnVlJTJDJTIwdG9wX2slM0QzMCUyQyUyMHRvcF9wJTNEMC45NSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;my_awesome_opus_books_model&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model.generate(inputs, max_new_tokens=<span class="hljs-number">40</span>, do_sample=<span class="hljs-literal">True</span>, top_k=<span class="hljs-number">30</span>, top_p=<span class="hljs-number">0.95</span>)`}}),N=new q({props:{code:"dG9rZW5pemVyLmRlY29kZShvdXRwdXRzJTVCMCU1RCUyQyUyMHNraXBfc3BlY2lhbF90b2tlbnMlM0RUcnVlKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.decode(outputs[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>)
<span class="hljs-string">&#x27;Les lign\xE9es partagent des ressources avec des bact\xE9ries enfixant l&#x27;</span>azote.<span class="hljs-string">&#x27;</span>`}}),{c(){n=o("p"),i=t("\uD14D\uC2A4\uD2B8\uB97C \uD1A0\uD070\uD654\uD558\uACE0 "),r=o("code"),y=t("input_ids"),_=t("\uB97C PyTorch \uD150\uC11C\uB85C \uBC18\uD658\uD558\uC138\uC694:"),B=d(),T(R.$$.fragment),G=d(),w=o("p"),X=o("code"),V=t("generate()"),Y=t(" \uBA54\uC11C\uB4DC\uB85C \uBC88\uC5ED\uC744 \uC0DD\uC131\uD558\uC138\uC694. \uB2E4\uC591\uD55C \uD14D\uC2A4\uD2B8 \uC0DD\uC131 \uC804\uB7B5 \uBC0F \uC0DD\uC131\uC744 \uC81C\uC5B4\uD558\uAE30 \uC704\uD55C \uB9E4\uAC1C\uBCC0\uC218\uC5D0 \uB300\uD55C \uC790\uC138\uD55C \uB0B4\uC6A9\uC740 "),W=o("a"),A=t("Text Generation"),I=t(" API\uB97C \uC0B4\uD3B4\uBCF4\uC2DC\uAE30 \uBC14\uB78D\uB2C8\uB2E4."),O=d(),T(F.$$.fragment),H=d(),Q=o("p"),se=t("\uC0DD\uC131\uB41C \uD1A0\uD070 ID\uB4E4\uC744 \uB2E4\uC2DC \uD14D\uC2A4\uD2B8\uB85C \uB514\uCF54\uB529\uD558\uC138\uC694:"),S=d(),T(N.$$.fragment),this.h()},l(h){n=p(h,"P",{});var J=c(n);i=a(J,"\uD14D\uC2A4\uD2B8\uB97C \uD1A0\uD070\uD654\uD558\uACE0 "),r=p(J,"CODE",{});var pe=c(r);y=a(pe,"input_ids"),pe.forEach(l),_=a(J,"\uB97C PyTorch \uD150\uC11C\uB85C \uBC18\uD658\uD558\uC138\uC694:"),J.forEach(l),B=M(h),U(R.$$.fragment,h),G=M(h),w=p(h,"P",{});var D=c(w);X=p(D,"CODE",{});var ce=c(X);V=a(ce,"generate()"),ce.forEach(l),Y=a(D," \uBA54\uC11C\uB4DC\uB85C \uBC88\uC5ED\uC744 \uC0DD\uC131\uD558\uC138\uC694. \uB2E4\uC591\uD55C \uD14D\uC2A4\uD2B8 \uC0DD\uC131 \uC804\uB7B5 \uBC0F \uC0DD\uC131\uC744 \uC81C\uC5B4\uD558\uAE30 \uC704\uD55C \uB9E4\uAC1C\uBCC0\uC218\uC5D0 \uB300\uD55C \uC790\uC138\uD55C \uB0B4\uC6A9\uC740 "),W=p(D,"A",{href:!0});var K=c(W);A=a(K,"Text Generation"),K.forEach(l),I=a(D," API\uB97C \uC0B4\uD3B4\uBCF4\uC2DC\uAE30 \uBC14\uB78D\uB2C8\uB2E4."),D.forEach(l),O=M(h),U(F.$$.fragment,h),H=M(h),Q=p(h,"P",{});var x=c(Q);se=a(x,"\uC0DD\uC131\uB41C \uD1A0\uD070 ID\uB4E4\uC744 \uB2E4\uC2DC \uD14D\uC2A4\uD2B8\uB85C \uB514\uCF54\uB529\uD558\uC138\uC694:"),x.forEach(l),S=M(h),U(N.$$.fragment,h),this.h()},h(){b(W,"href","../main_classes/text_generation")},m(h,J){m(h,n,J),s(n,i),s(n,r),s(r,y),s(n,_),m(h,B,J),g(R,h,J),m(h,G,J),m(h,w,J),s(w,X),s(X,V),s(w,Y),s(w,W),s(W,A),s(w,I),m(h,O,J),g(F,h,J),m(h,H,J),m(h,Q,J),s(Q,se),m(h,S,J),g(N,h,J),P=!0},p:et,i(h){P||(E(R.$$.fragment,h),E(F.$$.fragment,h),E(N.$$.fragment,h),P=!0)},o(h){k(R.$$.fragment,h),k(F.$$.fragment,h),k(N.$$.fragment,h),P=!1},d(h){h&&l(n),h&&l(B),v(R,h),h&&l(G),h&&l(w),h&&l(O),v(F,h),h&&l(H),h&&l(Q),h&&l(S),v(N,h)}}}function tr(z){let n,i;return n=new xs({props:{$$slots:{default:[lr]},$$scope:{ctx:z}}}),{c(){T(n.$$.fragment)},l(r){U(n.$$.fragment,r)},m(r,y){g(n,r,y),i=!0},p(r,y){const _={};y&2&&(_.$$scope={dirty:y,ctx:r}),n.$set(_)},i(r){i||(E(n.$$.fragment,r),i=!0)},o(r){k(n.$$.fragment,r),i=!1},d(r){v(n,r)}}}function ar(z){let n,i,r,y,_,B,R,G,w,X,V,Y,W,A,I,O,F,H,Q,se,S,N,P;return R=new q({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMEElMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjJteV9hd2Vzb21lX29wdXNfYm9va3NfbW9kZWwlMjIpJTBBaW5wdXRzJTIwJTNEJTIwdG9rZW5pemVyKHRleHQlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnRmJTIyKS5pbnB1dF9pZHM=",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;my_awesome_opus_books_model&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(text, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>).input_ids`}}),F=new q({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGQXV0b01vZGVsRm9yU2VxMlNlcUxNJTBBJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNlcTJTZXFMTS5mcm9tX3ByZXRyYWluZWQoJTIybXlfYXdlc29tZV9vcHVzX2Jvb2tzX21vZGVsJTIyKSUwQW91dHB1dHMlMjAlM0QlMjBtb2RlbC5nZW5lcmF0ZShpbnB1dHMlMkMlMjBtYXhfbmV3X3Rva2VucyUzRDQwJTJDJTIwZG9fc2FtcGxlJTNEVHJ1ZSUyQyUyMHRvcF9rJTNEMzAlMkMlMjB0b3BfcCUzRDAuOTUp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;my_awesome_opus_books_model&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model.generate(inputs, max_new_tokens=<span class="hljs-number">40</span>, do_sample=<span class="hljs-literal">True</span>, top_k=<span class="hljs-number">30</span>, top_p=<span class="hljs-number">0.95</span>)`}}),N=new q({props:{code:"dG9rZW5pemVyLmRlY29kZShvdXRwdXRzJTVCMCU1RCUyQyUyMHNraXBfc3BlY2lhbF90b2tlbnMlM0RUcnVlKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.decode(outputs[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>)
<span class="hljs-string">&#x27;Les lugumes partagent les ressources avec des bact\xE9ries fixatrices d&#x27;</span>azote.<span class="hljs-string">&#x27;</span>`}}),{c(){n=o("p"),i=t("\uD14D\uC2A4\uD2B8\uB97C \uD1A0\uD070\uD654\uD558\uACE0 "),r=o("code"),y=t("input_ids"),_=t("\uB97C TensorFlow \uD150\uC11C\uB85C \uBC18\uD658\uD558\uC138\uC694:"),B=d(),T(R.$$.fragment),G=d(),w=o("p"),X=o("code"),V=t("generate()"),Y=t(" \uBA54\uC11C\uB4DC\uB85C \uBC88\uC5ED\uC744 \uC0DD\uC131\uD558\uC138\uC694. \uB2E4\uC591\uD55C \uD14D\uC2A4\uD2B8 \uC0DD\uC131 \uC804\uB7B5 \uBC0F \uC0DD\uC131\uC744 \uC81C\uC5B4\uD558\uAE30 \uC704\uD55C \uB9E4\uAC1C\uBCC0\uC218\uC5D0 \uB300\uD55C \uC790\uC138\uD55C \uB0B4\uC6A9\uC740 "),W=o("a"),A=t("Text Generation"),I=t(" API\uB97C \uC0B4\uD3B4\uBCF4\uC2DC\uAE30 \uBC14\uB78D\uB2C8\uB2E4."),O=d(),T(F.$$.fragment),H=d(),Q=o("p"),se=t("\uC0DD\uC131\uB41C \uD1A0\uD070 ID\uB4E4\uC744 \uB2E4\uC2DC \uD14D\uC2A4\uD2B8\uB85C \uB514\uCF54\uB529\uD558\uC138\uC694:"),S=d(),T(N.$$.fragment),this.h()},l(h){n=p(h,"P",{});var J=c(n);i=a(J,"\uD14D\uC2A4\uD2B8\uB97C \uD1A0\uD070\uD654\uD558\uACE0 "),r=p(J,"CODE",{});var pe=c(r);y=a(pe,"input_ids"),pe.forEach(l),_=a(J,"\uB97C TensorFlow \uD150\uC11C\uB85C \uBC18\uD658\uD558\uC138\uC694:"),J.forEach(l),B=M(h),U(R.$$.fragment,h),G=M(h),w=p(h,"P",{});var D=c(w);X=p(D,"CODE",{});var ce=c(X);V=a(ce,"generate()"),ce.forEach(l),Y=a(D," \uBA54\uC11C\uB4DC\uB85C \uBC88\uC5ED\uC744 \uC0DD\uC131\uD558\uC138\uC694. \uB2E4\uC591\uD55C \uD14D\uC2A4\uD2B8 \uC0DD\uC131 \uC804\uB7B5 \uBC0F \uC0DD\uC131\uC744 \uC81C\uC5B4\uD558\uAE30 \uC704\uD55C \uB9E4\uAC1C\uBCC0\uC218\uC5D0 \uB300\uD55C \uC790\uC138\uD55C \uB0B4\uC6A9\uC740 "),W=p(D,"A",{href:!0});var K=c(W);A=a(K,"Text Generation"),K.forEach(l),I=a(D," API\uB97C \uC0B4\uD3B4\uBCF4\uC2DC\uAE30 \uBC14\uB78D\uB2C8\uB2E4."),D.forEach(l),O=M(h),U(F.$$.fragment,h),H=M(h),Q=p(h,"P",{});var x=c(Q);se=a(x,"\uC0DD\uC131\uB41C \uD1A0\uD070 ID\uB4E4\uC744 \uB2E4\uC2DC \uD14D\uC2A4\uD2B8\uB85C \uB514\uCF54\uB529\uD558\uC138\uC694:"),x.forEach(l),S=M(h),U(N.$$.fragment,h),this.h()},h(){b(W,"href","../main_classes/text_generation")},m(h,J){m(h,n,J),s(n,i),s(n,r),s(r,y),s(n,_),m(h,B,J),g(R,h,J),m(h,G,J),m(h,w,J),s(w,X),s(X,V),s(w,Y),s(w,W),s(W,A),s(w,I),m(h,O,J),g(F,h,J),m(h,H,J),m(h,Q,J),s(Q,se),m(h,S,J),g(N,h,J),P=!0},p:et,i(h){P||(E(R.$$.fragment,h),E(F.$$.fragment,h),E(N.$$.fragment,h),P=!0)},o(h){k(R.$$.fragment,h),k(F.$$.fragment,h),k(N.$$.fragment,h),P=!1},d(h){h&&l(n),h&&l(B),v(R,h),h&&l(G),h&&l(w),h&&l(O),v(F,h),h&&l(H),h&&l(Q),h&&l(S),v(N,h)}}}function rr(z){let n,i;return n=new xs({props:{$$slots:{default:[ar]},$$scope:{ctx:z}}}),{c(){T(n.$$.fragment)},l(r){U(n.$$.fragment,r)},m(r,y){g(n,r,y),i=!0},p(r,y){const _={};y&2&&(_.$$scope={dirty:y,ctx:r}),n.$set(_)},i(r){i||(E(n.$$.fragment,r),i=!0)},o(r){k(n.$$.fragment,r),i=!1},d(r){v(n,r)}}}function nr(z){let n,i,r,y,_,B,R,G,w,X,V,Y,W,A,I,O,F,H,Q,se,S,N,P,h,J,pe,D,ce,K,x,Me,ye,fe,ge,Ge,ie,je,ee,we,Ve,Je,le,he,te,$e,ae,We,re,Ce,ue,ne,$,C,oe,Fe,de,L,Ie,me,Ee,be,Be,_e,Re,Ue,ke,Te,ve,Ne,Ze,Xe,j,f,Z,Qe,xe,ts,Ae,Se,es,ze,ss,ls,qe,as,Ye,Pe,De,fs,Le,He,rs,Oe,Is,Us,ns,qs,st,lt,Ps,tt,at,Ns,Ds,rt,nt,yl,gs,$l,Ke,ot,Ls,pt,ct,Os,ft,mt,Ks,it,ht,bl,Es,_l,os,ut,el,dt,Mt,sl,yt,$t,jl,us,wl,ms,ds,ll,ks,bt,tl,_t,Jl,ps,jt,vs,wt,Jt,Zs,Tt,Ut,Tl,Bs,Ul,Ms,gt,al,Et,kt,gl,Rs,El,ys,vt,rl,Zt,Bt,kl,is,$s,nl,Xs,Rt,ol,Xt,vl,bs,Zl,_s,Bl,hs,js,pl,Gs,Gt,cl,Vt,Rl,Ss,Wt,Xl,Fs,Ct,Gl,Vs,Vl,cs,It,fl,Nt,St,ml,Ft,At,Wl,Ws,Cl,ws,zt,il,Yt,Ht,Il,Js,Nl;return B=new Qs({}),V=new za({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/translation.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/translation.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/translation.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/translation.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/translation.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/translation.ipynb"}]}}),W=new Ca({props:{id:"1JvfrvZgi6c"}}),Me=new Kl({props:{$$slots:{default:[Ya]},$$scope:{ctx:z}}}),ie=new q({props:{code:"cGlwJTIwaW5zdGFsbCUyMHRyYW5zZm9ybWVycyUyMGRhdGFzZXRzJTIwZXZhbHVhdGUlMjBzYWNyZWJsZXU=",highlighted:"pip install transformers datasets evaluate sacrebleu"}}),Je=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMG5vdGVib29rX2xvZ2luJTBBJTBBbm90ZWJvb2tfbG9naW4oKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login

<span class="hljs-meta">&gt;&gt;&gt; </span>notebook_login()`}}),ae=new Qs({}),L=new q({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBJTBBYm9va3MlMjAlM0QlMjBsb2FkX2RhdGFzZXQoJTIyb3B1c19ib29rcyUyMiUyQyUyMCUyMmVuLWZyJTIyKQ==",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>books = load_dataset(<span class="hljs-string">&quot;opus_books&quot;</span>, <span class="hljs-string">&quot;en-fr&quot;</span>)`}}),Ue=new q({props:{code:"Ym9va3MlMjAlM0QlMjBib29rcyU1QiUyMnRyYWluJTIyJTVELnRyYWluX3Rlc3Rfc3BsaXQodGVzdF9zaXplJTNEMC4yKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>books = books[<span class="hljs-string">&quot;train&quot;</span>].train_test_split(test_size=<span class="hljs-number">0.2</span>)'}}),Ze=new q({props:{code:"Ym9va3MlNUIlMjJ0cmFpbiUyMiU1RCU1QjAlNUQ=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>books[<span class="hljs-string">&quot;train&quot;</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">&#x27;id&#x27;</span>: <span class="hljs-string">&#x27;90560&#x27;</span>,
 <span class="hljs-string">&#x27;translation&#x27;</span>: {<span class="hljs-string">&#x27;en&#x27;</span>: <span class="hljs-string">&#x27;But this lofty plateau measured only a few fathoms, and soon we reentered Our Element.&#x27;</span>,
  <span class="hljs-string">&#x27;fr&#x27;</span>: <span class="hljs-string">&#x27;Mais ce plateau \xE9lev\xE9 ne mesurait que quelques toises, et bient\xF4t nous f\xFBmes rentr\xE9s dans notre \xE9l\xE9ment.&#x27;</span>}}`}}),ze=new Qs({}),Ye=new Ca({props:{id:"XAR8jnZZuUs"}}),He=new q({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMEElMEFjaGVja3BvaW50JTIwJTNEJTIwJTIydDUtc21hbGwlMjIlMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZChjaGVja3BvaW50KQ==",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>checkpoint = <span class="hljs-string">&quot;t5-small&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(checkpoint)`}}),gs=new q({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>source_lang = <span class="hljs-string">&quot;en&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>target_lang = <span class="hljs-string">&quot;fr&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>prefix = <span class="hljs-string">&quot;translate English to French: &quot;</span>


<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>    inputs = [prefix + example[source_lang] <span class="hljs-keyword">for</span> example <span class="hljs-keyword">in</span> examples[<span class="hljs-string">&quot;translation&quot;</span>]]
<span class="hljs-meta">... </span>    targets = [example[target_lang] <span class="hljs-keyword">for</span> example <span class="hljs-keyword">in</span> examples[<span class="hljs-string">&quot;translation&quot;</span>]]
<span class="hljs-meta">... </span>    model_inputs = tokenizer(inputs, text_target=targets, max_length=<span class="hljs-number">128</span>, truncation=<span class="hljs-literal">True</span>)
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> model_inputs`}}),Es=new q({props:{code:"dG9rZW5pemVkX2Jvb2tzJTIwJTNEJTIwYm9va3MubWFwKHByZXByb2Nlc3NfZnVuY3Rpb24lMkMlMjBiYXRjaGVkJTNEVHJ1ZSk=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>tokenized_books = books.<span class="hljs-built_in">map</span>(preprocess_function, batched=<span class="hljs-literal">True</span>)'}}),us=new xt({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[qa],pytorch:[Qa]},$$scope:{ctx:z}}}),ks=new Qs({}),Bs=new q({props:{code:"aW1wb3J0JTIwZXZhbHVhdGUlMEElMEFtZXRyaWMlMjAlM0QlMjBldmFsdWF0ZS5sb2FkKCUyMnNhY3JlYmxldSUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> evaluate

<span class="hljs-meta">&gt;&gt;&gt; </span>metric = evaluate.load(<span class="hljs-string">&quot;sacrebleu&quot;</span>)`}}),Rs=new q({props:{code:"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",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">def</span> <span class="hljs-title function_">postprocess_text</span>(<span class="hljs-params">preds, labels</span>):
<span class="hljs-meta">... </span>    preds = [pred.strip() <span class="hljs-keyword">for</span> pred <span class="hljs-keyword">in</span> preds]
<span class="hljs-meta">... </span>    labels = [[label.strip()] <span class="hljs-keyword">for</span> label <span class="hljs-keyword">in</span> labels]

<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> preds, labels


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">compute_metrics</span>(<span class="hljs-params">eval_preds</span>):
<span class="hljs-meta">... </span>    preds, labels = eval_preds
<span class="hljs-meta">... </span>    <span class="hljs-keyword">if</span> <span class="hljs-built_in">isinstance</span>(preds, <span class="hljs-built_in">tuple</span>):
<span class="hljs-meta">... </span>        preds = preds[<span class="hljs-number">0</span>]
<span class="hljs-meta">... </span>    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=<span class="hljs-literal">True</span>)

<span class="hljs-meta">... </span>    labels = np.where(labels != -<span class="hljs-number">100</span>, labels, tokenizer.pad_token_id)
<span class="hljs-meta">... </span>    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=<span class="hljs-literal">True</span>)

<span class="hljs-meta">... </span>    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)

<span class="hljs-meta">... </span>    result = metric.compute(predictions=decoded_preds, references=decoded_labels)
<span class="hljs-meta">... </span>    result = {<span class="hljs-string">&quot;bleu&quot;</span>: result[<span class="hljs-string">&quot;score&quot;</span>]}

<span class="hljs-meta">... </span>    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) <span class="hljs-keyword">for</span> pred <span class="hljs-keyword">in</span> preds]
<span class="hljs-meta">... </span>    result[<span class="hljs-string">&quot;gen_len&quot;</span>] = np.mean(prediction_lens)
<span class="hljs-meta">... </span>    result = {k: <span class="hljs-built_in">round</span>(v, <span class="hljs-number">4</span>) <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> result.items()}
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> result`}}),Xs=new Qs({}),bs=new xt({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[er],pytorch:[La]},$$scope:{ctx:z}}}),_s=new Kl({props:{$$slots:{default:[sr]},$$scope:{ctx:z}}}),Gs=new Qs({}),Vs=new q({props:{code:"dGV4dCUyMCUzRCUyMCUyMnRyYW5zbGF0ZSUyMEVuZ2xpc2glMjB0byUyMEZyZW5jaCUzQSUyMExlZ3VtZXMlMjBzaGFyZSUyMHJlc291cmNlcyUyMHdpdGglMjBuaXRyb2dlbi1maXhpbmclMjBiYWN0ZXJpYS4lMjI=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;translate English to French: Legumes share resources with nitrogen-fixing bacteria.&quot;</span>'}}),Ws=new q({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBdHJhbnNsYXRvciUyMCUzRCUyMHBpcGVsaW5lKCUyMnRyYW5zbGF0aW9uJTIyJTJDJTIwbW9kZWwlM0QlMjJteV9hd2Vzb21lX29wdXNfYm9va3NfbW9kZWwlMjIpJTBBdHJhbnNsYXRvcih0ZXh0KQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>translator = pipeline(<span class="hljs-string">&quot;translation&quot;</span>, model=<span class="hljs-string">&quot;my_awesome_opus_books_model&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>translator(text)
[{<span class="hljs-string">&#x27;translation_text&#x27;</span>: <span class="hljs-string">&#x27;Legumes partagent des ressources avec des bact\xE9ries azotantes.&#x27;</span>}]`}}),Js=new xt({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[rr],pytorch:[tr]},$$scope:{ctx:z}}}),{c(){n=o("meta"),i=d(),r=o("h1"),y=o("a"),_=o("span"),T(B.$$.fragment),R=d(),G=o("span"),w=t("\uBC88\uC5ED"),X=d(),T(V.$$.fragment),Y=d(),T(W.$$.fragment),A=d(),I=o("p"),O=t("\uBC88\uC5ED\uC740 \uD55C \uC5B8\uC5B4\uB85C \uB41C \uC2DC\uD000\uC2A4\uB97C \uB2E4\uB978 \uC5B8\uC5B4\uB85C \uBCC0\uD658\uD569\uB2C8\uB2E4. \uBC88\uC5ED\uC774\uB098 \uC694\uC57D\uC740 \uC785\uB825\uC744 \uBC1B\uC544 \uC77C\uB828\uC758 \uCD9C\uB825\uC744 \uBC18\uD658\uD558\uB294 \uAC15\uB825\uD55C \uD504\uB808\uC784\uC6CC\uD06C\uC778 \uC2DC\uD000\uC2A4-\uD22C-\uC2DC\uD000\uC2A4 \uBB38\uC81C\uB85C \uAD6C\uC131\uD560 \uC218 \uC788\uB294 \uB300\uD45C\uC801\uC778 \uD0DC\uC2A4\uD06C\uC785\uB2C8\uB2E4. \uBC88\uC5ED 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