import{S as g_,i as v_,s as E_,e as r,k as f,w as g,t as n,M as k_,c as o,d as s,m as c,a as l,x as v,h as p,b as T,G as e,g as u,y as E,q as k,o as w,B as j,v as w_,L as Ee}from"../chunks/vendor-hf-doc-builder.js";import{T as Tl}from"../chunks/Tip-hf-doc-builder.js";import{Y as __}from"../chunks/Youtube-hf-doc-builder.js";import{I as Ie}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as I}from"../chunks/CodeBlock-hf-doc-builder.js";import{D as j_}from"../chunks/DocNotebookDropdown-hf-doc-builder.js";import{F as ha,M as ue}from"../chunks/Markdown-hf-doc-builder.js";function T_(D){let a,m;return a=new I({props:{code:"pip install torch",highlighted:"pip install torch"}}),{c(){g(a.$$.fragment)},l(i){v(a.$$.fragment,i)},m(i,h){E(a,i,h),m=!0},p:Ee,i(i){m||(k(a.$$.fragment,i),m=!0)},o(i){w(a.$$.fragment,i),m=!1},d(i){j(a,i)}}}function b_(D){let a,m;return a=new ue({props:{$$slots:{default:[T_]},$$scope:{ctx:D}}}),{c(){g(a.$$.fragment)},l(i){v(a.$$.fragment,i)},m(i,h){E(a,i,h),m=!0},p(i,h){const $={};h&2&&($.$$scope={dirty:h,ctx:i}),a.$set($)},i(i){m||(k(a.$$.fragment,i),m=!0)},o(i){w(a.$$.fragment,i),m=!1},d(i){j(a,i)}}}function A_(D){let a,m;return a=new I({props:{code:"pip install tensorflow",highlighted:"pip install tensorflow"}}),{c(){g(a.$$.fragment)},l(i){v(a.$$.fragment,i)},m(i,h){E(a,i,h),m=!0},p:Ee,i(i){m||(k(a.$$.fragment,i),m=!0)},o(i){w(a.$$.fragment,i),m=!1},d(i){j(a,i)}}}function y_(D){let a,m;return a=new ue({props:{$$slots:{default:[A_]},$$scope:{ctx:D}}}),{c(){g(a.$$.fragment)},l(i){v(a.$$.fragment,i)},m(i,h){E(a,i,h),m=!0},p(i,h){const $={};h&2&&($.$$scope={dirty:h,ctx:i}),a.$set($)},i(i){m||(k(a.$$.fragment,i),m=!0)},o(i){w(a.$$.fragment,i),m=!1},d(i){j(a,i)}}}function C_(D){let a,m,i,h,$,y,C,_,b,S,x,F,H;return F=new I({props:{code:`from transformers import AutoTokenizer, AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(model_name)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(model_name)`}}),{c(){a=r("p"),m=r("code"),i=n("AutoModelForSequenceClassification"),h=n("\uACFC "),$=r("code"),y=n("AutoTokenizer"),C=n("\uB85C \uC0AC\uC804\uD559\uC2B5\uB41C \uBAA8\uB378\uACFC \uD568\uAED8 \uC5F0\uAD00\uB41C \uD1A0\uD06C\uB098\uC774\uC800\uB97C \uBD88\uB7EC\uC635\uB2C8\uB2E4. ("),_=r("code"),b=n("AutoClass"),S=n("\uC5D0 \uB300\uD55C \uB0B4\uC6A9\uC740 \uB2E4\uC74C \uC139\uC158\uC5D0\uC11C \uC0B4\uD3B4\uBCF4\uACA0\uC2B5\uB2C8\uB2E4)"),x=f(),g(F.$$.fragment)},l(z){a=o(z,"P",{});var O=l(a);m=o(O,"CODE",{});var L=l(m);i=p(L,"AutoModelForSequenceClassification"),L.forEach(s),h=p(O,"\uACFC "),$=o(O,"CODE",{});var A=l($);y=p(A,"AutoTokenizer"),A.forEach(s),C=p(O,"\uB85C \uC0AC\uC804\uD559\uC2B5\uB41C \uBAA8\uB378\uACFC \uD568\uAED8 \uC5F0\uAD00\uB41C \uD1A0\uD06C\uB098\uC774\uC800\uB97C \uBD88\uB7EC\uC635\uB2C8\uB2E4. ("),_=o(O,"CODE",{});var M=l(_);b=p(M,"AutoClass"),M.forEach(s),S=p(O,"\uC5D0 \uB300\uD55C \uB0B4\uC6A9\uC740 \uB2E4\uC74C \uC139\uC158\uC5D0\uC11C \uC0B4\uD3B4\uBCF4\uACA0\uC2B5\uB2C8\uB2E4)"),O.forEach(s),x=c(z),v(F.$$.fragment,z)},m(z,O){u(z,a,O),e(a,m),e(m,i),e(a,h),e(a,$),e($,y),e(a,C),e(a,_),e(_,b),e(a,S),u(z,x,O),E(F,z,O),H=!0},p:Ee,i(z){H||(k(F.$$.fragment,z),H=!0)},o(z){w(F.$$.fragment,z),H=!1},d(z){z&&s(a),z&&s(x),j(F,z)}}}function P_(D){let a,m;return a=new ue({props:{$$slots:{default:[C_]},$$scope:{ctx:D}}}),{c(){g(a.$$.fragment)},l(i){v(a.$$.fragment,i)},m(i,h){E(a,i,h),m=!0},p(i,h){const $={};h&2&&($.$$scope={dirty:h,ctx:i}),a.$set($)},i(i){m||(k(a.$$.fragment,i),m=!0)},o(i){w(a.$$.fragment,i),m=!1},d(i){j(a,i)}}}function D_(D){let a,m,i,h,$,y,C,_,b,S,x,F,H;return F=new I({props:{code:`from transformers import AutoTokenizer, TFAutoModelForSequenceClassification

model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(model_name)`}}),{c(){a=r("p"),m=r("code"),i=n("TFAutoModelForSequenceClassification"),h=n("\uACFC "),$=r("code"),y=n("AutoTokenizer"),C=n("\uB85C \uC0AC\uC804\uD559\uC2B5\uB41C \uBAA8\uB378\uACFC \uD568\uAED8 \uC5F0\uAD00\uB41C \uD1A0\uD06C\uB098\uC774\uC800\uB97C \uBD88\uB7EC\uC635\uB2C8\uB2E4. ("),_=r("code"),b=n("TFAutoClass"),S=n("\uC5D0 \uB300\uD55C \uB0B4\uC6A9\uC740 \uB2E4\uC74C \uC139\uC158\uC5D0\uC11C \uC0B4\uD3B4\uBCF4\uACA0\uC2B5\uB2C8\uB2E4)"),x=f(),g(F.$$.fragment)},l(z){a=o(z,"P",{});var O=l(a);m=o(O,"CODE",{});var L=l(m);i=p(L,"TFAutoModelForSequenceClassification"),L.forEach(s),h=p(O,"\uACFC "),$=o(O,"CODE",{});var A=l($);y=p(A,"AutoTokenizer"),A.forEach(s),C=p(O,"\uB85C \uC0AC\uC804\uD559\uC2B5\uB41C \uBAA8\uB378\uACFC \uD568\uAED8 \uC5F0\uAD00\uB41C \uD1A0\uD06C\uB098\uC774\uC800\uB97C \uBD88\uB7EC\uC635\uB2C8\uB2E4. ("),_=o(O,"CODE",{});var M=l(_);b=p(M,"TFAutoClass"),M.forEach(s),S=p(O,"\uC5D0 \uB300\uD55C \uB0B4\uC6A9\uC740 \uB2E4\uC74C \uC139\uC158\uC5D0\uC11C \uC0B4\uD3B4\uBCF4\uACA0\uC2B5\uB2C8\uB2E4)"),O.forEach(s),x=c(z),v(F.$$.fragment,z)},m(z,O){u(z,a,O),e(a,m),e(m,i),e(a,h),e(a,$),e($,y),e(a,C),e(a,_),e(_,b),e(a,S),u(z,x,O),E(F,z,O),H=!0},p:Ee,i(z){H||(k(F.$$.fragment,z),H=!0)},o(z){w(F.$$.fragment,z),H=!1},d(z){z&&s(a),z&&s(x),j(F,z)}}}function q_(D){let a,m;return a=new ue({props:{$$slots:{default:[D_]},$$scope:{ctx:D}}}),{c(){g(a.$$.fragment)},l(i){v(a.$$.fragment,i)},m(i,h){E(a,i,h),m=!0},p(i,h){const $={};h&2&&($.$$scope={dirty:h,ctx:i}),a.$set($)},i(i){m||(k(a.$$.fragment,i),m=!0)},o(i){w(a.$$.fragment,i),m=!1},d(i){j(a,i)}}}function O_(D){let a,m;return a=new I({props:{code:`pt_batch = tokenizer(
    ["We are very happy to show you the \u{1F917} Transformers library.", "We hope you don't hate it."],
    padding=True,
    truncation=True,
    max_length=512,
    return_tensors="pt",
)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>pt_batch = tokenizer(
<span class="hljs-meta">... </span>    [<span class="hljs-string">&quot;We are very happy to show you the \u{1F917} Transformers library.&quot;</span>, <span class="hljs-string">&quot;We hope you don&#x27;t hate it.&quot;</span>],
<span class="hljs-meta">... </span>    padding=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    truncation=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    max_length=<span class="hljs-number">512</span>,
<span class="hljs-meta">... </span>    return_tensors=<span class="hljs-string">&quot;pt&quot;</span>,
<span class="hljs-meta">... </span>)`}}),{c(){g(a.$$.fragment)},l(i){v(a.$$.fragment,i)},m(i,h){E(a,i,h),m=!0},p:Ee,i(i){m||(k(a.$$.fragment,i),m=!0)},o(i){w(a.$$.fragment,i),m=!1},d(i){j(a,i)}}}function z_(D){let a,m;return a=new ue({props:{$$slots:{default:[O_]},$$scope:{ctx:D}}}),{c(){g(a.$$.fragment)},l(i){v(a.$$.fragment,i)},m(i,h){E(a,i,h),m=!0},p(i,h){const $={};h&2&&($.$$scope={dirty:h,ctx:i}),a.$set($)},i(i){m||(k(a.$$.fragment,i),m=!0)},o(i){w(a.$$.fragment,i),m=!1},d(i){j(a,i)}}}function M_(D){let a,m;return a=new I({props:{code:`tf_batch = tokenizer(
    ["We are very happy to show you the \u{1F917} Transformers library.", "We hope you don't hate it."],
    padding=True,
    truncation=True,
    max_length=512,
    return_tensors="tf",
)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>tf_batch = tokenizer(
<span class="hljs-meta">... </span>    [<span class="hljs-string">&quot;We are very happy to show you the \u{1F917} Transformers library.&quot;</span>, <span class="hljs-string">&quot;We hope you don&#x27;t hate it.&quot;</span>],
<span class="hljs-meta">... </span>    padding=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    truncation=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    max_length=<span class="hljs-number">512</span>,
<span class="hljs-meta">... </span>    return_tensors=<span class="hljs-string">&quot;tf&quot;</span>,
<span class="hljs-meta">... </span>)`}}),{c(){g(a.$$.fragment)},l(i){v(a.$$.fragment,i)},m(i,h){E(a,i,h),m=!0},p:Ee,i(i){m||(k(a.$$.fragment,i),m=!0)},o(i){w(a.$$.fragment,i),m=!1},d(i){j(a,i)}}}function S_(D){let a,m;return a=new ue({props:{$$slots:{default:[M_]},$$scope:{ctx:D}}}),{c(){g(a.$$.fragment)},l(i){v(a.$$.fragment,i)},m(i,h){E(a,i,h),m=!0},p(i,h){const $={};h&2&&($.$$scope={dirty:h,ctx:i}),a.$set($)},i(i){m||(k(a.$$.fragment,i),m=!0)},o(i){w(a.$$.fragment,i),m=!1},d(i){j(a,i)}}}function F_(D){let a,m,i,h,$,y,C,_,b,S;return{c(){a=r("p"),m=r("a"),i=n("\uC804\uCC98\uB9AC"),h=n(" \uD29C\uD1A0\uB9AC\uC5BC\uC744 \uBCF4\uC2DC\uBA74 \uD1A0\uD070\uD654\uC5D0 \uB300\uD55C \uC790\uC138\uD55C \uC124\uBA85\uACFC \uD568\uAED8 \uC774\uBBF8\uC9C0, \uC624\uB514\uC624\uC640 \uBA40\uD2F0\uBAA8\uB2EC \uC785\uB825\uC744 \uC804\uCC98\uB9AC\uD558\uAE30 \uC704\uD55C "),$=r("code"),y=n("AutoFeatureExtractor"),C=n("\uACFC "),_=r("code"),b=n("AutoProcessor"),S=n("\uC758 \uC0AC\uC6A9\uBC29\uBC95\uB3C4 \uC54C \uC218 \uC788\uC2B5\uB2C8\uB2E4."),this.h()},l(x){a=o(x,"P",{});var F=l(a);m=o(F,"A",{href:!0});var H=l(m);i=p(H,"\uC804\uCC98\uB9AC"),H.forEach(s),h=p(F," \uD29C\uD1A0\uB9AC\uC5BC\uC744 \uBCF4\uC2DC\uBA74 \uD1A0\uD070\uD654\uC5D0 \uB300\uD55C \uC790\uC138\uD55C \uC124\uBA85\uACFC \uD568\uAED8 \uC774\uBBF8\uC9C0, \uC624\uB514\uC624\uC640 \uBA40\uD2F0\uBAA8\uB2EC \uC785\uB825\uC744 \uC804\uCC98\uB9AC\uD558\uAE30 \uC704\uD55C "),$=o(F,"CODE",{});var z=l($);y=p(z,"AutoFeatureExtractor"),z.forEach(s),C=p(F,"\uACFC "),_=o(F,"CODE",{});var O=l(_);b=p(O,"AutoProcessor"),O.forEach(s),S=p(F,"\uC758 \uC0AC\uC6A9\uBC29\uBC95\uB3C4 \uC54C \uC218 \uC788\uC2B5\uB2C8\uB2E4."),F.forEach(s),this.h()},h(){T(m,"href","./preprocessing")},m(x,F){u(x,a,F),e(a,m),e(m,i),e(a,h),e(a,$),e($,y),e(a,C),e(a,_),e(_,b),e(a,S)},d(x){x&&s(a)}}}function x_(D){let a,m,i,h,$,y,C;return{c(){a=r("p"),m=r("code"),i=n("AutoModel"),h=n(" \uD074\uB798\uC2A4\uC5D0\uC11C \uC9C0\uC6D0\uD558\uB294 \uD0DC\uC2A4\uD06C\uB4E4\uC740 "),$=r("a"),y=n("\uD0DC\uC2A4\uD06C \uC815\uB9AC"),C=n(" \uBB38\uC11C\uB97C \uCC38\uACE0\uD574\uC8FC\uC138\uC694."),this.h()},l(_){a=o(_,"P",{});var b=l(a);m=o(b,"CODE",{});var S=l(m);i=p(S,"AutoModel"),S.forEach(s),h=p(b," \uD074\uB798\uC2A4\uC5D0\uC11C \uC9C0\uC6D0\uD558\uB294 \uD0DC\uC2A4\uD06C\uB4E4\uC740 "),$=o(b,"A",{href:!0});var x=l($);y=p(x,"\uD0DC\uC2A4\uD06C \uC815\uB9AC"),x.forEach(s),C=p(b," \uBB38\uC11C\uB97C \uCC38\uACE0\uD574\uC8FC\uC138\uC694."),b.forEach(s),this.h()},h(){T($,"href","./task_summary")},m(_,b){u(_,a,b),e(a,m),e(m,i),e(a,h),e(a,$),e($,y),e(a,C)},d(_){_&&s(a)}}}function I_(D){let a,m,i,h,$,y,C,_,b,S,x,F,H,z,O,L,A,M,Z,W,G,le,B,te,V,X,pe,J,ke,re,ne,_e,Q,se,ge,P,N,ie;return L=new I({props:{code:`from transformers import AutoModelForSequenceClassification

model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
pt_model = AutoModelForSequenceClassification.from_pretrained(model_name)`,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_name = <span class="hljs-string">&quot;nlptown/bert-base-multilingual-uncased-sentiment&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pt_model = AutoModelForSequenceClassification.from_pretrained(model_name)`}}),M=new Tl({props:{$$slots:{default:[x_]},$$scope:{ctx:D}}}),X=new I({props:{code:"pt_outputs = pt_model(**pt_batch)",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>pt_outputs = pt_model(**pt_batch)'}}),N=new I({props:{code:`from torch import nn

pt_predictions = nn.functional.softmax(pt_outputs.logits, dim=-1)
print(pt_predictions)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> torch <span class="hljs-keyword">import</span> nn

<span class="hljs-meta">&gt;&gt;&gt; </span>pt_predictions = nn.functional.softmax(pt_outputs.logits, dim=-<span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(pt_predictions)
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tokenizer.save_pretrained(pt_save_directory)
pt_model.save_pretrained(pt_save_directory)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>pt_save_directory = <span class="hljs-string">&quot;./pt_save_pretrained&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.save_pretrained(pt_save_directory)
<span class="hljs-meta">&gt;&gt;&gt; </span>pt_model.save_pretrained(pt_save_directory)`}}),O=new I({props:{code:'pt_model = AutoModelForSequenceClassification.from_pretrained("./pt_save_pretrained")',highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>pt_model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;./pt_save_pretrained&quot;</span>)'}}),{c(){a=r("p"),m=n("\uBAA8\uB378\uC744 \uD30C\uC778\uD29C\uB2DD\uD55C \uB4A4\uC5D0\uB294 "),i=r("code"),h=n("PreTrainedModel.save_pretrained()"),$=n("\uB85C \uBAA8\uB378\uC744 \uD1A0\uD06C\uB098\uC774\uC800\uC640 \uD568\uAED8 \uC800\uC7A5\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4."),y=f(),g(C.$$.fragment),_=f(),b=r("p"),S=n("\uBAA8\uB378\uC744 \uB2E4\uC2DC \uC0AC\uC6A9\uD560 \uB54C\uB294 "),x=r("code"),F=n("PreTrainedModel.from_pretrained()"),H=n("\uB85C \uB2E4\uC2DC \uBD88\uB7EC\uC624\uBA74 \uB429\uB2C8\uB2E4."),z=f(),g(O.$$.fragment)},l(A){a=o(A,"P",{});var M=l(a);m=p(M,"\uBAA8\uB378\uC744 \uD30C\uC778\uD29C\uB2DD\uD55C \uB4A4\uC5D0\uB294 "),i=o(M,"CODE",{});var Z=l(i);h=p(Z,"PreTrainedModel.save_pretrained()"),Z.forEach(s),$=p(M,"\uB85C \uBAA8\uB378\uC744 \uD1A0\uD06C\uB098\uC774\uC800\uC640 \uD568\uAED8 \uC800\uC7A5\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4."),M.forEach(s),y=c(A),v(C.$$.fragment,A),_=c(A),b=o(A,"P",{});var W=l(b);S=p(W,"\uBAA8\uB378\uC744 \uB2E4\uC2DC \uC0AC\uC6A9\uD560 \uB54C\uB294 "),x=o(W,"CODE",{});var G=l(x);F=p(G,"PreTrainedModel.from_pretrained()"),G.forEach(s),H=p(W,"\uB85C \uB2E4\uC2DC \uBD88\uB7EC\uC624\uBA74 \uB429\uB2C8\uB2E4."),W.forEach(s),z=c(A),v(O.$$.fragment,A)},m(A,M){u(A,a,M),e(a,m),e(a,i),e(i,h),e(a,$),u(A,y,M),E(C,A,M),u(A,_,M),u(A,b,M),e(b,S),e(b,x),e(x,F),e(b,H),u(A,z,M),E(O,A,M),L=!0},p:Ee,i(A){L||(k(C.$$.fragment,A),k(O.$$.fragment,A),L=!0)},o(A){w(C.$$.fragment,A),w(O.$$.fragment,A),L=!1},d(A){A&&s(a),A&&s(y),j(C,A),A&&s(_),A&&s(b),A&&s(z),j(O,A)}}}function K_(D){let a,m;return a=new ue({props:{$$slots:{default:[U_]},$$scope:{ctx:D}}}),{c(){g(a.$$.fragment)},l(i){v(a.$$.fragment,i)},m(i,h){E(a,i,h),m=!0},p(i,h){const $={};h&2&&($.$$scope={dirty:h,ctx:i}),a.$set($)},i(i){m||(k(a.$$.fragment,i),m=!0)},o(i){w(a.$$.fragment,i),m=!1},d(i){j(a,i)}}}function G_(D){let a,m,i,h,$,y,C,_,b,S,x,F,H,z,O,L;return C=new I({props:{code:`tf_save_directory = "./tf_save_pretrained"
tokenizer.save_pretrained(tf_save_directory)
tf_model.save_pretrained(tf_save_directory)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>tf_save_directory = <span class="hljs-string">&quot;./tf_save_pretrained&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.save_pretrained(tf_save_directory)
<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model.save_pretrained(tf_save_directory)`}}),O=new I({props:{code:'tf_model = TFAutoModelForSequenceClassification.from_pretrained("./tf_save_pretrained")',highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;./tf_save_pretrained&quot;</span>)'}}),{c(){a=r("p"),m=n("\uBAA8\uB378\uC744 \uD30C\uC778\uD29C\uB2DD\uD55C \uB4A4\uC5D0\uB294 "),i=r("code"),h=n("TFPreTrainedModel.save_pretrained()"),$=n("\uB85C \uBAA8\uB378\uC744 \uD1A0\uD06C\uB098\uC774\uC800\uC640 \uD568\uAED8 \uC800\uC7A5\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4."),y=f(),g(C.$$.fragment),_=f(),b=r("p"),S=n("\uBAA8\uB378\uC744 \uB2E4\uC2DC \uC0AC\uC6A9\uD560 \uB54C\uB294 "),x=r("code"),F=n("TFPreTrainedModel.from_pretrained()"),H=n("\uB85C \uB2E4\uC2DC \uBD88\uB7EC\uC624\uBA74 \uB429\uB2C8\uB2E4."),z=f(),g(O.$$.fragment)},l(A){a=o(A,"P",{});var M=l(a);m=p(M,"\uBAA8\uB378\uC744 \uD30C\uC778\uD29C\uB2DD\uD55C \uB4A4\uC5D0\uB294 "),i=o(M,"CODE",{});var Z=l(i);h=p(Z,"TFPreTrainedModel.save_pretrained()"),Z.forEach(s),$=p(M,"\uB85C \uBAA8\uB378\uC744 \uD1A0\uD06C\uB098\uC774\uC800\uC640 \uD568\uAED8 \uC800\uC7A5\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4."),M.forEach(s),y=c(A),v(C.$$.fragment,A),_=c(A),b=o(A,"P",{});var W=l(b);S=p(W,"\uBAA8\uB378\uC744 \uB2E4\uC2DC \uC0AC\uC6A9\uD560 \uB54C\uB294 "),x=o(W,"CODE",{});var G=l(x);F=p(G,"TFPreTrainedModel.from_pretrained()"),G.forEach(s),H=p(W,"\uB85C \uB2E4\uC2DC \uBD88\uB7EC\uC624\uBA74 \uB429\uB2C8\uB2E4."),W.forEach(s),z=c(A),v(O.$$.fragment,A)},m(A,M){u(A,a,M),e(a,m),e(a,i),e(i,h),e(a,$),u(A,y,M),E(C,A,M),u(A,_,M),u(A,b,M),e(b,S),e(b,x),e(x,F),e(b,H),u(A,z,M),E(O,A,M),L=!0},p:Ee,i(A){L||(k(C.$$.fragment,A),k(O.$$.fragment,A),L=!0)},o(A){w(C.$$.fragment,A),w(O.$$.fragment,A),L=!1},d(A){A&&s(a),A&&s(y),j(C,A),A&&s(_),A&&s(b),A&&s(z),j(O,A)}}}function B_(D){let a,m;return a=new ue({props:{$$slots:{default:[G_]},$$scope:{ctx:D}}}),{c(){g(a.$$.fragment)},l(i){v(a.$$.fragment,i)},m(i,h){E(a,i,h),m=!0},p(i,h){const $={};h&2&&($.$$scope={dirty:h,ctx:i}),a.$set($)},i(i){m||(k(a.$$.fragment,i),m=!0)},o(i){w(a.$$.fragment,i),m=!1},d(i){j(a,i)}}}function V_(D){let a,m;return a=new I({props:{code:`from transformers import AutoModel

tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
<span class="hljs-meta">&gt;&gt;&gt; </span>pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=<span class="hljs-literal">True</span>)`}}),{c(){g(a.$$.fragment)},l(i){v(a.$$.fragment,i)},m(i,h){E(a,i,h),m=!0},p:Ee,i(i){m||(k(a.$$.fragment,i),m=!0)},o(i){w(a.$$.fragment,i),m=!1},d(i){j(a,i)}}}function Y_(D){let a,m;return a=new ue({props:{$$slots:{default:[V_]},$$scope:{ctx:D}}}),{c(){g(a.$$.fragment)},l(i){v(a.$$.fragment,i)},m(i,h){E(a,i,h),m=!0},p(i,h){const $={};h&2&&($.$$scope={dirty:h,ctx:i}),a.$set($)},i(i){m||(k(a.$$.fragment,i),m=!0)},o(i){w(a.$$.fragment,i),m=!1},d(i){j(a,i)}}}function J_(D){let a,m;return a=new I({props:{code:`from transformers import TFAutoModel

tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=<span class="hljs-literal">True</span>)`}}),{c(){g(a.$$.fragment)},l(i){v(a.$$.fragment,i)},m(i,h){E(a,i,h),m=!0},p:Ee,i(i){m||(k(a.$$.fragment,i),m=!0)},o(i){w(a.$$.fragment,i),m=!1},d(i){j(a,i)}}}function Q_(D){let a,m;return a=new ue({props:{$$slots:{default:[J_]},$$scope:{ctx:D}}}),{c(){g(a.$$.fragment)},l(i){v(a.$$.fragment,i)},m(i,h){E(a,i,h),m=!0},p(i,h){const $={};h&2&&($.$$scope={dirty:h,ctx:i}),a.$set($)},i(i){m||(k(a.$$.fragment,i),m=!0)},o(i){w(a.$$.fragment,i),m=!1},d(i){j(a,i)}}}function Z_(D){let a,m,i,h,$,y,C;return y=new I({props:{code:`from transformers import AutoModel

my_model = AutoModel.from_config(my_config)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span>my_model = AutoModel.from_config(my_config)`}}),{c(){a=r("p"),m=r("code"),i=n("AutoModel.from_config()"),h=n("\uB97C \uC0AC\uC6A9\uD558\uC5EC \uCEE4\uC2A4\uD140 \uAD6C\uC131\uB300\uB85C \uBAA8\uB378\uC744 \uC0DD\uC131\uD569\uB2C8\uB2E4."),$=f(),g(y.$$.fragment)},l(_){a=o(_,"P",{});var b=l(a);m=o(b,"CODE",{});var S=l(m);i=p(S,"AutoModel.from_config()"),S.forEach(s),h=p(b,"\uB97C \uC0AC\uC6A9\uD558\uC5EC \uCEE4\uC2A4\uD140 \uAD6C\uC131\uB300\uB85C \uBAA8\uB378\uC744 \uC0DD\uC131\uD569\uB2C8\uB2E4."),b.forEach(s),$=c(_),v(y.$$.fragment,_)},m(_,b){u(_,a,b),e(a,m),e(m,i),e(a,h),u(_,$,b),E(y,_,b),C=!0},p:Ee,i(_){C||(k(y.$$.fragment,_),C=!0)},o(_){w(y.$$.fragment,_),C=!1},d(_){_&&s(a),_&&s($),j(y,_)}}}function X_(D){let a,m;return a=new ue({props:{$$slots:{default:[Z_]},$$scope:{ctx:D}}}),{c(){g(a.$$.fragment)},l(i){v(a.$$.fragment,i)},m(i,h){E(a,i,h),m=!0},p(i,h){const $={};h&2&&($.$$scope={dirty:h,ctx:i}),a.$set($)},i(i){m||(k(a.$$.fragment,i),m=!0)},o(i){w(a.$$.fragment,i),m=!1},d(i){j(a,i)}}}function eg(D){let a,m,i,h,$,y,C;return y=new I({props:{code:`from transformers import TFAutoModel

my_model = TFAutoModel.from_config(my_config)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span>my_model = TFAutoModel.from_config(my_config)`}}),{c(){a=r("p"),m=r("code"),i=n("TFAutoModel.from_config()"),h=n("\uB97C \uC0AC\uC6A9\uD558\uC5EC \uCEE4\uC2A4\uD140 \uAD6C\uC131\uB300\uB85C \uBAA8\uB378\uC744 \uC0DD\uC131\uD569\uB2C8\uB2E4."),$=f(),g(y.$$.fragment)},l(_){a=o(_,"P",{});var b=l(a);m=o(b,"CODE",{});var S=l(m);i=p(S,"TFAutoModel.from_config()"),S.forEach(s),h=p(b,"\uB97C \uC0AC\uC6A9\uD558\uC5EC \uCEE4\uC2A4\uD140 \uAD6C\uC131\uB300\uB85C \uBAA8\uB378\uC744 \uC0DD\uC131\uD569\uB2C8\uB2E4."),b.forEach(s),$=c(_),v(y.$$.fragment,_)},m(_,b){u(_,a,b),e(a,m),e(m,i),e(a,h),u(_,$,b),E(y,_,b),C=!0},p:Ee,i(_){C||(k(y.$$.fragment,_),C=!0)},o(_){w(y.$$.fragment,_),C=!1},d(_){_&&s(a),_&&s($),j(y,_)}}}function tg(D){let a,m;return a=new ue({props:{$$slots:{default:[eg]},$$scope:{ctx:D}}}),{c(){g(a.$$.fragment)},l(i){v(a.$$.fragment,i)},m(i,h){E(a,i,h),m=!0},p(i,h){const $={};h&2&&($.$$scope={dirty:h,ctx:i}),a.$set($)},i(i){m||(k(a.$$.fragment,i),m=!0)},o(i){w(a.$$.fragment,i),m=!1},d(i){j(a,i)}}}function sg(D){let a,m,i,h,$,y,C,_;return{c(){a=r("p"),m=n("sequence-to-sequence \uBAA8\uB378\uC744 \uC0AC\uC6A9\uD558\uB294 (\uBC88\uC5ED\uC774\uB098 \uC694\uC57D \uAC19\uC740) \uD0DC\uC2A4\uD06C\uC758 \uACBD\uC6B0 "),i=r("code"),h=n("Seq2SeqTrainer"),$=n("\uC640 "),y=r("code"),C=n("Seq2SeqTrainingArguments"),_=n(" \uD074\uB798\uC2A4\uB97C \uB300\uC2E0 \uC0AC\uC6A9\uD558\uC2DC\uAE30 \uBC14\uB78D\uB2C8\uB2E4.")},l(b){a=o(b,"P",{});var S=l(a);m=p(S,"sequence-to-sequence \uBAA8\uB378\uC744 \uC0AC\uC6A9\uD558\uB294 (\uBC88\uC5ED\uC774\uB098 \uC694\uC57D \uAC19\uC740) \uD0DC\uC2A4\uD06C\uC758 \uACBD\uC6B0 "),i=o(S,"CODE",{});var x=l(i);h=p(x,"Seq2SeqTrainer"),x.forEach(s),$=p(S,"\uC640 "),y=o(S,"CODE",{});var F=l(y);C=p(F,"Seq2SeqTrainingArguments"),F.forEach(s),_=p(S," \uD074\uB798\uC2A4\uB97C \uB300\uC2E0 \uC0AC\uC6A9\uD558\uC2DC\uAE30 \uBC14\uB78D\uB2C8\uB2E4."),S.forEach(s)},m(b,S){u(b,a,S),e(a,m),e(a,i),e(i,h),e(a,$),e(a,y),e(y,C),e(a,_)},d(b){b&&s(a)}}}function ag(D){let 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y=new Ie({}),x=new j_({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/quicktour.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/quicktour.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/quicktour.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/quicktour.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/quicktour.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/quicktour.ipynb"}]}}),L=new I({props:{code:"!pip install transformers datasets",highlighted:"!pip install transformers datasets"}}),G=new ha({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[y_],pytorch:[b_]},$$scope:{ctx:D}}}),X=new Ie({}),ne=new __({props:{id:"tiZFewofSLM"}}),ts=new I({props:{code:`from transformers import pipeline

classifier = pipeline("sentiment-analysis")`,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>classifier = pipeline(<span class="hljs-string">&quot;sentiment-analysis&quot;</span>)`}}),as=new I({props:{code:'classifier("We are very happy to show you the \u{1F917} Transformers library.")',highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>classifier(<span class="hljs-string">&quot;We are very happy to show you the \u{1F917} Transformers library.&quot;</span>)
[{<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;POSITIVE&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9998</span>}]`}}),rs=new I({props:{code:`results = classifier(["We are very happy to show you the \u{1F917} Transformers library.", "We hope you don't hate it."])
for result in results:
    print(f"label: {result['label']}, with score: {round(result['score'], 4)}")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>results = classifier([<span class="hljs-string">&quot;We are very happy to show you the \u{1F917} Transformers library.&quot;</span>, <span class="hljs-string">&quot;We hope you don&#x27;t hate it.&quot;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> result <span class="hljs-keyword">in</span> results:
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;label: <span class="hljs-subst">{result[<span class="hljs-string">&#x27;label&#x27;</span>]}</span>, with score: <span class="hljs-subst">{<span class="hljs-built_in">round</span>(result[<span class="hljs-string">&#x27;score&#x27;</span>], <span class="hljs-number">4</span>)}</span>&quot;</span>)
label: POSITIVE, <span class="hljs-keyword">with</span> score: <span class="hljs-number">0.9998</span>
label: NEGATIVE, <span class="hljs-keyword">with</span> score: <span class="hljs-number">0.5309</span>`}}),ls=new I({props:{code:`import torch
from transformers import pipeline

speech_recognizer = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<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>speech_recognizer = pipeline(<span class="hljs-string">&quot;automatic-speech-recognition&quot;</span>, model=<span class="hljs-string">&quot;facebook/wav2vec2-base-960h&quot;</span>)`}}),is=new I({props:{code:`from datasets import load_dataset, Audio

dataset = load_dataset("PolyAI/minds14", name="en-US", split="train")`,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>)`}}),cs=new I({props:{code:'dataset = dataset.cast_column("audio", Audio(sampling_rate=speech_recognizer.feature_extractor.sampling_rate))',highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.cast_column(<span class="hljs-string">&quot;audio&quot;</span>, Audio(sampling_rate=speech_recognizer.feature_extractor.sampling_rate))'}}),ds=new I({props:{code:`result = speech_recognizer(dataset[:4]["audio"])
print([d["text"] for d in result])`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>result = speech_recognizer(dataset[:<span class="hljs-number">4</span>][<span class="hljs-string">&quot;audio&quot;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>([d[<span class="hljs-string">&quot;text&quot;</span>] <span class="hljs-keyword">for</span> d <span class="hljs-keyword">in</span> result])
[<span class="hljs-string">&#x27;I WOULD LIKE TO SET UP A JOINT ACCOUNT WITH MY PARTNER HOW DO I PROCEED WITH DOING THAT&#x27;</span>, <span class="hljs-string">&quot;FODING HOW I&#x27;D SET UP A JOIN TO HET WITH MY WIFE AND WHERE THE AP MIGHT BE&quot;</span>, <span class="hljs-string">&quot;I I&#x27;D LIKE TOY SET UP A JOINT ACCOUNT WITH MY PARTNER I&#x27;M NOT SEEING THE OPTION TO DO IT ON THE AP SO I CALLED IN TO GET SOME HELP CAN I JUST DO IT OVER THE PHONE WITH YOU AND GIVE YOU THE INFORMATION OR SHOULD I DO IT IN THE AP AND I&#x27;M MISSING SOMETHING UQUETTE HAD PREFERRED TO JUST DO IT OVER THE PHONE OF POSSIBLE THINGS&quot;</span>, <span class="hljs-string">&#x27;HOW DO I THURN A JOIN A COUNT&#x27;</span>]`}}),ms=new Ie({}),$s=new I({props:{code:'model_name = "nlptown/bert-base-multilingual-uncased-sentiment"',highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>model_name = <span class="hljs-string">&quot;nlptown/bert-base-multilingual-uncased-sentiment&quot;</span>'}}),ht=new ha({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[q_],pytorch:[P_]},$$scope:{ctx:D}}}),_s=new I({props:{code:`classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
classifier("Nous sommes tr\xE8s heureux de vous pr\xE9senter la biblioth\xE8que \u{1F917} Transformers.")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>classifier = pipeline(<span class="hljs-string">&quot;sentiment-analysis&quot;</span>, model=model, tokenizer=tokenizer)
<span class="hljs-meta">&gt;&gt;&gt; </span>classifier(<span class="hljs-string">&quot;Nous sommes tr\xE8s heureux de vous pr\xE9senter la biblioth\xE8que \u{1F917} Transformers.&quot;</span>)
[{<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;5 stars&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.7273</span>}]`}}),gs=new Ie({}),vs=new __({props:{id:"AhChOFRegn4"}}),Es=new Ie({}),ws=new I({props:{code:`from transformers import AutoTokenizer

model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
tokenizer = AutoTokenizer.from_pretrained(model_name)`,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>model_name = <span class="hljs-string">&quot;nlptown/bert-base-multilingual-uncased-sentiment&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(model_name)`}}),js=new I({props:{code:`encoding = tokenizer("We are very happy to show you the \u{1F917} Transformers library.")
print(encoding)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>encoding = tokenizer(<span class="hljs-string">&quot;We are very happy to show you the \u{1F917} Transformers library.&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(encoding)
{<span class="hljs-string">&#x27;input_ids&#x27;</span>: [<span class="hljs-number">101</span>, <span class="hljs-number">11312</span>, <span class="hljs-number">10320</span>, <span class="hljs-number">12495</span>, <span class="hljs-number">19308</span>, <span class="hljs-number">10114</span>, <span class="hljs-number">11391</span>, <span class="hljs-number">10855</span>, <span class="hljs-number">10103</span>, <span class="hljs-number">100</span>, <span class="hljs-number">58263</span>, <span class="hljs-number">13299</span>, <span class="hljs-number">119</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-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>]}`}}),Et=new ha({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[S_],pytorch:[z_]},$$scope:{ctx:D}}}),kt=new Tl({props:{$$slots:{default:[F_]},$$scope:{ctx:D}}}),Ts=new Ie({}),jt=new ha({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[R_],pytorch:[N_]},$$scope:{ctx:D}}}),Tt=new Tl({props:{$$slots:{default:[W_]},$$scope:{ctx:D}}}),bs=new Ie({}),At=new ha({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[B_],pytorch:[K_]},$$scope:{ctx:D}}}),yt=new ha({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Q_],pytorch:[Y_]},$$scope:{ctx:D}}}),As=new Ie({}),ys=new I({props:{code:`from transformers import AutoConfig

my_config = AutoConfig.from_pretrained("distilbert-base-uncased", n_heads=12)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig

<span class="hljs-meta">&gt;&gt;&gt; </span>my_config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>, n_heads=<span class="hljs-number">12</span>)`}}),Pt=new ha({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[tg],pytorch:[X_]},$$scope:{ctx:D}}}),Cs=new Ie({}),Os=new I({props:{code:`from transformers import AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")`,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>)`}}),Ms=new I({props:{code:`from transformers import TrainingArguments

training_args = TrainingArguments(
    output_dir="path/to/save/folder/",
    learning_rate=2e-5,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    num_train_epochs=2,
)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TrainingArguments

<span class="hljs-meta">&gt;&gt;&gt; </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span>    output_dir=<span class="hljs-string">&quot;path/to/save/folder/&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">8</span>,
<span class="hljs-meta">... </span>    per_device_eval_batch_size=<span class="hljs-number">8</span>,
<span class="hljs-meta">... </span>    num_train_epochs=<span class="hljs-number">2</span>,
<span class="hljs-meta">... </span>)`}}),Fs=new I({props:{code:`from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")`,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;distilbert-base-uncased&quot;</span>)`}}),Is=new I({props:{code:`from datasets import load_dataset

dataset = load_dataset("rotten_tomatoes")  # doctest: +IGNORE_RESULT`,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;rotten_tomatoes&quot;</span>)  <span class="hljs-comment"># doctest: +IGNORE_RESULT</span>`}}),Hs=new I({props:{code:`def tokenize_dataset(dataset):
    return tokenizer(dataset["text"])`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">tokenize_dataset</span>(<span class="hljs-params">dataset</span>):
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> tokenizer(dataset[<span class="hljs-string">&quot;text&quot;</span>])`}}),Ws=new I({props:{code:"",highlighted:""}}),Gs=new I({props:{code:`from transformers import DataCollatorWithPadding

data_collator = DataCollatorWithPadding(tokenizer=tokenizer)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorWithPadding

<span class="hljs-meta">&gt;&gt;&gt; </span>data_collator = DataCollatorWithPadding(tokenizer=tokenizer)`}}),Bs=new I({props:{code:`from transformers import Trainer

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset["train"],
    eval_dataset=dataset["test"],
    tokenizer=tokenizer,
    data_collator=data_collator,
)  # doctest: +SKIP`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Trainer

<span class="hljs-meta">&gt;&gt;&gt; </span>trainer = Trainer(
<span class="hljs-meta">... </span>    model=model,
<span class="hljs-meta">... </span>    args=training_args,
<span class="hljs-meta">... </span>    train_dataset=dataset[<span class="hljs-string">&quot;train&quot;</span>],
<span class="hljs-meta">... </span>    eval_dataset=dataset[<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>)  <span class="hljs-comment"># doctest: +SKIP</span>`}}),Vs=new I({props:{code:"trainer.train()",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>trainer.train()'}}),Ft=new Tl({props:{$$slots:{default:[sg]},$$scope:{ctx:D}}}),Ys=new Ie({}),sa=new I({props:{code:`from transformers import TFAutoModelForSequenceClassification

model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")`,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>)`}}),ra=new I({props:{code:`from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")`,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;distilbert-base-uncased&quot;</span>)`}}),la=new I({props:{code:`def tokenize_dataset(dataset):
    return tokenizer(dataset["text"])  # doctest: +SKIP`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">tokenize_dataset</span>(<span class="hljs-params">dataset</span>):
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> tokenizer(dataset[<span class="hljs-string">&quot;text&quot;</span>])  <span class="hljs-comment"># doctest: +SKIP</span>`}}),pa=new I({props:{code:`dataset = dataset.map(tokenize_dataset)  # doctest: +SKIP
tf_dataset = model.prepare_tf_dataset(
    dataset, batch_size=16, shuffle=True, tokenizer=tokenizer
)  # doctest: +SKIP`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.<span class="hljs-built_in">map</span>(tokenize_dataset)  <span class="hljs-comment"># doctest: +SKIP</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tf_dataset = model.prepare_tf_dataset(
<span class="hljs-meta">... </span>    dataset, batch_size=<span class="hljs-number">16</span>, shuffle=<span class="hljs-literal">True</span>, tokenizer=tokenizer
<span class="hljs-meta">... </span>)  <span class="hljs-comment"># doctest: +SKIP</span>`}}),fa=new I({props:{code:`from tensorflow.keras.optimizers import Adam

model.compile(optimizer=Adam(3e-5))
model.fit(dataset)  # doctest: +SKIP`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> tensorflow.keras.optimizers <span class="hljs-keyword">import</span> Adam

<span class="hljs-meta">&gt;&gt;&gt; </span>model.<span class="hljs-built_in">compile</span>(optimizer=Adam(<span class="hljs-number">3e-5</span>))
<span class="hljs-meta">&gt;&gt;&gt; </span>model.fit(dataset)  <span class="hljs-comment"># doctest: +SKIP</span>`}}),ca=new Ie({}),{c(){a=r("meta"),m=f(),i=r("h1"),h=r("a"),$=r("span"),g(y.$$.fragment),C=f(),_=r("span"),b=n("\uB458\uB7EC\uBCF4\uAE30"),S=f(),g(x.$$.fragment),F=n("\n\u{1F917} Transformer\uB97C \uC2DC\uC791\uD574\uBD10\uC694! \uB458\uB7EC\uBCF4\uAE30\uB294 \uAC1C\uBC1C\uC790\uC640 \uC77C\uBC18 \uC0AC\uC6A9\uC790 \uBAA8\uB450\uB97C \uC704\uD574 \uC4F0\uC5EC\uC84C\uC2B5\uB2C8\uB2E4. `pipeline()`\uC73C\uB85C \uCD94\uB860\uD558\uB294 \uBC29\uBC95, [AutoClass](./model_doc/auto)\uB85C \uC0AC\uC804\uD559\uC2B5\uB41C \uBAA8\uB378\uACFC \uC804\uCC98\uB9AC\uAE30\uB97C \uC801\uC7AC\uD558\uB294 \uBC29\uBC95\uACFC PyTorch \uB610\uB294 TensorFlow\uB85C \uC2E0\uC18D\uD558\uAC8C \uBAA8\uB378\uC744 \uD6C8\uB828\uC2DC\uD0A4\uB294 \uBC29\uBC95\uC744 \uBCF4\uC5EC\uC90D\uB2C8\uB2E4. \uAE30\uBCF8\uC744 \uBC30\uC6B0\uACE0 \uC2F6\uB2E4\uBA74 \uD29C\uD1A0\uB9AC\uC5BC\uC774\uB098 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