import{S as p1,i as c1,s as f1,e as a,k as f,w as g,t as o,M as i1,c as r,d as s,m as i,a as p,x as b,h as n,b as y,G as e,g as h,y as M,q as v,o as E,B as T,v as d1,L as ve}from"../chunks/vendor-hf-doc-builder.js";import{T as Ol}from"../chunks/Tip-hf-doc-builder.js";import{Y as n1}from"../chunks/Youtube-hf-doc-builder.js";import{I as Ye}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as F}from"../chunks/CodeBlock-hf-doc-builder.js";import{D as m1}from"../chunks/DocNotebookDropdown-hf-doc-builder.js";import{F as Ml,M as me}from"../chunks/Markdown-hf-doc-builder.js";function h1(R){let l,m;return l=new F({props:{code:"cGlwJTIwaW5zdGFsbCUyMHRvcmNo",highlighted:"pip install torch"}}),{c(){g(l.$$.fragment)},l(c){b(l.$$.fragment,c)},m(c,u){M(l,c,u),m=!0},p:ve,i(c){m||(v(l.$$.fragment,c),m=!0)},o(c){E(l.$$.fragment,c),m=!1},d(c){T(l,c)}}}function u1(R){let l,m;return l=new me({props:{$$slots:{default:[h1]},$$scope:{ctx:R}}}),{c(){g(l.$$.fragment)},l(c){b(l.$$.fragment,c)},m(c,u){M(l,c,u),m=!0},p(c,u){const $={};u&2&&($.$$scope={dirty:u,ctx:c}),l.$set($)},i(c){m||(v(l.$$.fragment,c),m=!0)},o(c){E(l.$$.fragment,c),m=!1},d(c){T(l,c)}}}function $1(R){let l,m;return l=new F({props:{code:"cGlwJTIwaW5zdGFsbCUyMHRlbnNvcmZsb3c=",highlighted:"pip install tensorflow"}}),{c(){g(l.$$.fragment)},l(c){b(l.$$.fragment,c)},m(c,u){M(l,c,u),m=!0},p:ve,i(c){m||(v(l.$$.fragment,c),m=!0)},o(c){E(l.$$.fragment,c),m=!1},d(c){T(l,c)}}}function _1(R){let l,m;return l=new me({props:{$$slots:{default:[$1]},$$scope:{ctx:R}}}),{c(){g(l.$$.fragment)},l(c){b(l.$$.fragment,c)},m(c,u){M(l,c,u),m=!0},p(c,u){const $={};u&2&&($.$$scope={dirty:u,ctx:c}),l.$set($)},i(c){m||(v(l.$$.fragment,c),m=!0)},o(c){E(l.$$.fragment,c),m=!1},d(c){T(l,c)}}}function y1(R){let l,m,c,u,$;return{c(){l=a("p"),m=o("\uC0AC\uC6A9 \uAC00\uB2A5\uD55C \uC791\uC5C5\uC758 \uC804\uCCB4 \uBAA9\uB85D\uC740 "),c=a("a"),u=o("Pipelines API \uCC38\uC870"),$=o("\uB97C \uD655\uC778\uD558\uC138\uC694."),this.h()},l(w){l=r(w,"P",{});var J=p(l);m=n(J,"\uC0AC\uC6A9 \uAC00\uB2A5\uD55C \uC791\uC5C5\uC758 \uC804\uCCB4 \uBAA9\uB85D\uC740 "),c=r(J,"A",{href:!0});var _=p(c);u=n(_,"Pipelines API \uCC38\uC870"),_.forEach(s),$=n(J,"\uB97C \uD655\uC778\uD558\uC138\uC694."),J.forEach(s),this.h()},h(){y(c,"href","./main_classes/pipelines")},m(w,J){h(w,l,J),e(l,m),e(l,c),e(c,u),e(l,$)},d(w){w&&s(l)}}}function g1(R){let l,m,c,u,$,w,J,_,j,I,A,V,X;return V=new F({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uJTBBJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZChtb2RlbF9uYW1lKSUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKG1vZGVsX25hbWUp",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(){l=a("p"),m=a("code"),c=o("AutoModelForSequenceClassification"),u=o("\uACFC "),$=a("code"),w=o("AutoTokenizer"),J=o("\uB97C \uC0AC\uC6A9\uD558\uC5EC \uC0AC\uC804 \uD6C8\uB828\uB41C \uBAA8\uB378\uACFC \uAD00\uB828\uB41C \uD1A0\uD06C\uB098\uC774\uC800\uB97C \uB85C\uB4DC\uD558\uC138\uC694 (\uB2E4\uC74C \uC139\uC158\uC5D0\uC11C "),_=a("code"),j=o("AutoClass"),I=o("\uC5D0 \uB300\uD574 \uB354 \uC790\uC138\uD788 \uC54C\uC544\uBCF4\uACA0\uC2B5\uB2C8\uB2E4):"),A=f(),g(V.$$.fragment)},l(G){l=r(G,"P",{});var U=p(l);m=r(U,"CODE",{});var D=p(m);c=n(D,"AutoModelForSequenceClassification"),D.forEach(s),u=n(U,"\uACFC "),$=r(U,"CODE",{});var k=p($);w=n(k,"AutoTokenizer"),k.forEach(s),J=n(U,"\uB97C \uC0AC\uC6A9\uD558\uC5EC \uC0AC\uC804 \uD6C8\uB828\uB41C \uBAA8\uB378\uACFC \uAD00\uB828\uB41C \uD1A0\uD06C\uB098\uC774\uC800\uB97C \uB85C\uB4DC\uD558\uC138\uC694 (\uB2E4\uC74C \uC139\uC158\uC5D0\uC11C "),_=r(U,"CODE",{});var W=p(_);j=n(W,"AutoClass"),W.forEach(s),I=n(U,"\uC5D0 \uB300\uD574 \uB354 \uC790\uC138\uD788 \uC54C\uC544\uBCF4\uACA0\uC2B5\uB2C8\uB2E4):"),U.forEach(s),A=i(G),b(V.$$.fragment,G)},m(G,U){h(G,l,U),e(l,m),e(m,c),e(l,u),e(l,$),e($,w),e(l,J),e(l,_),e(_,j),e(l,I),h(G,A,U),M(V,G,U),X=!0},p:ve,i(G){X||(v(V.$$.fragment,G),X=!0)},o(G){E(V.$$.fragment,G),X=!1},d(G){G&&s(l),G&&s(A),T(V,G)}}}function b1(R){let l,m;return l=new me({props:{$$slots:{default:[g1]},$$scope:{ctx:R}}}),{c(){g(l.$$.fragment)},l(c){b(l.$$.fragment,c)},m(c,u){M(l,c,u),m=!0},p(c,u){const $={};u&2&&($.$$scope={dirty:u,ctx:c}),l.$set($)},i(c){m||(v(l.$$.fragment,c),m=!0)},o(c){E(l.$$.fragment,c),m=!1},d(c){T(l,c)}}}function M1(R){let l,m,c,u,$,w,J,_,j,I,A,V,X;return V=new F({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBURkF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24lMEElMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQobW9kZWxfbmFtZSklMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZChtb2RlbF9uYW1lKQ==",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(){l=a("p"),m=a("code"),c=o("TFAutoModelForSequenceClassification"),u=o("\uACFC "),$=a("code"),w=o("AutoTokenizer"),J=o("\uB97C \uC0AC\uC6A9\uD558\uC5EC \uC0AC\uC804 \uD6C8\uB828\uB41C \uBAA8\uB378\uACFC \uAD00\uB828\uB41C \uD1A0\uD06C\uB098\uC774\uC800\uB97C \uB85C\uB4DC\uD558\uC138\uC694 (\uB2E4\uC74C \uC139\uC158\uC5D0\uC11C "),_=a("code"),j=o("TFAutoClass"),I=o("\uC5D0 \uB300\uD574 \uB354 \uC790\uC138\uD788 \uC54C\uC544\uBCF4\uACA0\uC2B5\uB2C8\uB2E4):"),A=f(),g(V.$$.fragment)},l(G){l=r(G,"P",{});var U=p(l);m=r(U,"CODE",{});var D=p(m);c=n(D,"TFAutoModelForSequenceClassification"),D.forEach(s),u=n(U,"\uACFC "),$=r(U,"CODE",{});var k=p($);w=n(k,"AutoTokenizer"),k.forEach(s),J=n(U,"\uB97C \uC0AC\uC6A9\uD558\uC5EC \uC0AC\uC804 \uD6C8\uB828\uB41C \uBAA8\uB378\uACFC \uAD00\uB828\uB41C \uD1A0\uD06C\uB098\uC774\uC800\uB97C \uB85C\uB4DC\uD558\uC138\uC694 (\uB2E4\uC74C \uC139\uC158\uC5D0\uC11C "),_=r(U,"CODE",{});var W=p(_);j=n(W,"TFAutoClass"),W.forEach(s),I=n(U,"\uC5D0 \uB300\uD574 \uB354 \uC790\uC138\uD788 \uC54C\uC544\uBCF4\uACA0\uC2B5\uB2C8\uB2E4):"),U.forEach(s),A=i(G),b(V.$$.fragment,G)},m(G,U){h(G,l,U),e(l,m),e(m,c),e(l,u),e(l,$),e($,w),e(l,J),e(l,_),e(_,j),e(l,I),h(G,A,U),M(V,G,U),X=!0},p:ve,i(G){X||(v(V.$$.fragment,G),X=!0)},o(G){E(V.$$.fragment,G),X=!1},d(G){G&&s(l),G&&s(A),T(V,G)}}}function v1(R){let l,m;return l=new me({props:{$$slots:{default:[M1]},$$scope:{ctx:R}}}),{c(){g(l.$$.fragment)},l(c){b(l.$$.fragment,c)},m(c,u){M(l,c,u),m=!0},p(c,u){const $={};u&2&&($.$$scope={dirty:u,ctx:c}),l.$set($)},i(c){m||(v(l.$$.fragment,c),m=!0)},o(c){E(l.$$.fragment,c),m=!1},d(c){T(l,c)}}}function E1(R){let l,m;return l=new F({props:{code:"cHRfYmF0Y2glMjAlM0QlMjB0b2tlbml6ZXIoJTBBJTIwJTIwJTIwJTIwJTVCJTIyV2UlMjBhcmUlMjB2ZXJ5JTIwaGFwcHklMjB0byUyMHNob3clMjB5b3UlMjB0aGUlMjAlRjAlOUYlQTQlOTclMjBUcmFuc2Zvcm1lcnMlMjBsaWJyYXJ5LiUyMiUyQyUyMCUyMldlJTIwaG9wZSUyMHlvdSUyMGRvbid0JTIwaGF0ZSUyMGl0LiUyMiU1RCUyQyUwQSUyMCUyMCUyMCUyMHBhZGRpbmclM0RUcnVlJTJDJTBBJTIwJTIwJTIwJTIwdHJ1bmNhdGlvbiUzRFRydWUlMkMlMEElMjAlMjAlMjAlMjBtYXhfbGVuZ3RoJTNENTEyJTJDJTBBJTIwJTIwJTIwJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMiUyQyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>pt_batch = tokenizer(
<span class="hljs-meta">... </span>    [<span class="hljs-string">&quot;We are very happy to show you the \u{1F917} Transformers library.&quot;</span>, <span class="hljs-string">&quot;We hope you don&#x27;t hate it.&quot;</span>],
<span class="hljs-meta">... </span>    padding=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    truncation=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    max_length=<span class="hljs-number">512</span>,
<span class="hljs-meta">... </span>    return_tensors=<span class="hljs-string">&quot;pt&quot;</span>,
<span class="hljs-meta">... </span>)`}}),{c(){g(l.$$.fragment)},l(c){b(l.$$.fragment,c)},m(c,u){M(l,c,u),m=!0},p:ve,i(c){m||(v(l.$$.fragment,c),m=!0)},o(c){E(l.$$.fragment,c),m=!1},d(c){T(l,c)}}}function T1(R){let l,m;return l=new me({props:{$$slots:{default:[E1]},$$scope:{ctx:R}}}),{c(){g(l.$$.fragment)},l(c){b(l.$$.fragment,c)},m(c,u){M(l,c,u),m=!0},p(c,u){const $={};u&2&&($.$$scope={dirty:u,ctx:c}),l.$set($)},i(c){m||(v(l.$$.fragment,c),m=!0)},o(c){E(l.$$.fragment,c),m=!1},d(c){T(l,c)}}}function w1(R){let l,m;return l=new F({props:{code:"dGZfYmF0Y2glMjAlM0QlMjB0b2tlbml6ZXIoJTBBJTIwJTIwJTIwJTIwJTVCJTIyV2UlMjBhcmUlMjB2ZXJ5JTIwaGFwcHklMjB0byUyMHNob3clMjB5b3UlMjB0aGUlMjAlRjAlOUYlQTQlOTclMjBUcmFuc2Zvcm1lcnMlMjBsaWJyYXJ5LiUyMiUyQyUyMCUyMldlJTIwaG9wZSUyMHlvdSUyMGRvbid0JTIwaGF0ZSUyMGl0LiUyMiU1RCUyQyUwQSUyMCUyMCUyMCUyMHBhZGRpbmclM0RUcnVlJTJDJTBBJTIwJTIwJTIwJTIwdHJ1bmNhdGlvbiUzRFRydWUlMkMlMEElMjAlMjAlMjAlMjBtYXhfbGVuZ3RoJTNENTEyJTJDJTBBJTIwJTIwJTIwJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJ0ZiUyMiUyQyUwQSk=",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(l.$$.fragment)},l(c){b(l.$$.fragment,c)},m(c,u){M(l,c,u),m=!0},p:ve,i(c){m||(v(l.$$.fragment,c),m=!0)},o(c){E(l.$$.fragment,c),m=!1},d(c){T(l,c)}}}function j1(R){let l,m;return l=new me({props:{$$slots:{default:[w1]},$$scope:{ctx:R}}}),{c(){g(l.$$.fragment)},l(c){b(l.$$.fragment,c)},m(c,u){M(l,c,u),m=!0},p(c,u){const $={};u&2&&($.$$scope={dirty:u,ctx:c}),l.$set($)},i(c){m||(v(l.$$.fragment,c),m=!0)},o(c){E(l.$$.fragment,c),m=!1},d(c){T(l,c)}}}function k1(R){let l,m,c,u,$,w,J,_,j,I,A,V,X;return{c(){l=a("p"),m=a("a"),c=o("\uC804\uCC98\uB9AC"),u=o(" \uD29C\uD1A0\uB9AC\uC5BC\uC744 \uCC38\uC870\uD558\uC2DC\uBA74 \uD1A0\uD070\uD654\uC5D0 \uB300\uD55C \uC790\uC138\uD55C \uC124\uBA85\uACFC \uD568\uAED8 \uC774\uBBF8\uC9C0, \uC624\uB514\uC624\uC640 \uBA40\uD2F0\uBAA8\uB2EC \uC785\uB825\uC744 \uC804\uCC98\uB9AC\uD558\uAE30 \uC704\uD55C "),$=a("code"),w=o("AutoImageProcessor"),J=o("\uC640 "),_=a("code"),j=o("AutoFeatureExtractor"),I=o(", "),A=a("code"),V=o("AutoProcessor"),X=o("\uC758 \uC0AC\uC6A9\uBC29\uBC95\uB3C4 \uC54C \uC218 \uC788\uC2B5\uB2C8\uB2E4."),this.h()},l(G){l=r(G,"P",{});var U=p(l);m=r(U,"A",{href:!0});var D=p(m);c=n(D,"\uC804\uCC98\uB9AC"),D.forEach(s),u=n(U," \uD29C\uD1A0\uB9AC\uC5BC\uC744 \uCC38\uC870\uD558\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 "),$=r(U,"CODE",{});var k=p($);w=n(k,"AutoImageProcessor"),k.forEach(s),J=n(U,"\uC640 "),_=r(U,"CODE",{});var W=p(_);j=n(W,"AutoFeatureExtractor"),W.forEach(s),I=n(U,", "),A=r(U,"CODE",{});var S=p(A);V=n(S,"AutoProcessor"),S.forEach(s),X=n(U,"\uC758 \uC0AC\uC6A9\uBC29\uBC95\uB3C4 \uC54C \uC218 \uC788\uC2B5\uB2C8\uB2E4."),U.forEach(s),this.h()},h(){y(m,"href","./preprocessing")},m(G,U){h(G,l,U),e(l,m),e(m,c),e(l,u),e(l,$),e($,w),e(l,J),e(l,_),e(_,j),e(l,I),e(l,A),e(A,V),e(l,X)},d(G){G&&s(l)}}}function J1(R){let l,m,c,u,$,w,J;return{c(){l=a("p"),m=a("code"),c=o("AutoModel"),u=o(" \uD074\uB798\uC2A4\uC5D0\uC11C \uC9C0\uC6D0\uD558\uB294 \uACFC\uC5C5\uC5D0 \uB300\uD574\uC11C\uB294 "),$=a("a"),w=o("\uACFC\uC5C5 \uC694\uC57D"),J=o("\uC744 \uCC38\uC870\uD558\uC138\uC694."),this.h()},l(_){l=r(_,"P",{});var j=p(l);m=r(j,"CODE",{});var I=p(m);c=n(I,"AutoModel"),I.forEach(s),u=n(j," \uD074\uB798\uC2A4\uC5D0\uC11C \uC9C0\uC6D0\uD558\uB294 \uACFC\uC5C5\uC5D0 \uB300\uD574\uC11C\uB294 "),$=r(j,"A",{href:!0});var A=p($);w=n(A,"\uACFC\uC5C5 \uC694\uC57D"),A.forEach(s),J=n(j,"\uC744 \uCC38\uC870\uD558\uC138\uC694."),j.forEach(s),this.h()},h(){y($,"href","./task_summary")},m(_,j){h(_,l,j),e(l,m),e(m,c),e(l,u),e(l,$),e($,w),e(l,J)},d(_){_&&s(l)}}}function U1(R){let l,m,c,u,$,w,J,_,j,I,A,V,X,G,U,D,k,W,S,H,K,q,le,ye,P,x,ne,Q,ae,ee,pe,Ee,re,O,he,Z,N,te;return D=new F({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24lMEElMEFtb2RlbF9uYW1lJTIwJTNEJTIwJTIybmxwdG93biUyRmJlcnQtYmFzZS1tdWx0aWxpbmd1YWwtdW5jYXNlZC1zZW50aW1lbnQlMjIlMEFwdF9tb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKG1vZGVsX25hbWUp",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)`}}),W=new Ol({props:{$$slots:{default:[J1]},$$scope:{ctx:R}}}),x=new F({props:{code:"cHRfb3V0cHV0cyUyMCUzRCUyMHB0X21vZGVsKCoqcHRfYmF0Y2gp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>pt_outputs = pt_model(**pt_batch)'}}),N=new F({props:{code:"ZnJvbSUyMHRvcmNoJTIwaW1wb3J0JTIwbm4lMEElMEFwdF9wcmVkaWN0aW9ucyUyMCUzRCUyMG5uLmZ1bmN0aW9uYWwuc29mdG1heChwdF9vdXRwdXRzLmxvZ2l0cyUyQyUyMGRpbSUzRC0xKSUwQXByaW50KHB0X3ByZWRpY3Rpb25zKQ==",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)
tensor([[<span class="hljs-number">0.0021</span>, <span class="hljs-number">0.0018</span>, <span class="hljs-number">0.0115</span>, <span class="hljs-number">0.2121</span>, <span class="hljs-number">0.7725</span>],
        [<span class="hljs-number">0.2084</span>, <span class="hljs-number">0.1826</span>, <span class="hljs-number">0.1969</span>, <span class="hljs-number">0.1755</span>, <span class="hljs-number">0.2365</span>]], grad_fn=&lt;SoftmaxBackward0&gt;)`}}),{c(){l=a("p"),m=o("\u{1F917} Transformers\uB294 \uC0AC\uC804 \uD6C8\uB828\uB41C \uC778\uC2A4\uD134\uC2A4\uB97C \uAC04\uB2E8\uD558\uACE0 \uD1B5\uD569\uB41C \uBC29\uBC95\uC73C\uB85C \uB85C\uB4DC\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4. \uC989, "),c=a("code"),u=o("AutoTokenizer"),$=o("\uCC98\uB7FC "),w=a("code"),J=o("AutoModel"),_=o("\uC744 \uB85C\uB4DC\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4. \uC720\uC77C\uD55C \uCC28\uC774\uC810\uC740 \uACFC\uC5C5\uC5D0 \uC54C\uB9DE\uC740 "),j=a("code"),I=o("AutoModel"),A=o("\uC744 \uC120\uD0DD\uD574\uC57C \uD55C\uB2E4\uB294 \uC810\uC785\uB2C8\uB2E4. \uD14D\uC2A4\uD2B8 (\uB610\uB294 \uC2DC\uD000\uC2A4) \uBD84\uB958\uC758 \uACBD\uC6B0 "),V=a("code"),X=o("AutoModelForSequenceClassification"),G=o("\uC744 \uB85C\uB4DC\uD574\uC57C \uD569\uB2C8\uB2E4:"),U=f(),g(D.$$.fragment),k=f(),g(W.$$.fragment),S=f(),H=a("p"),K=o("\uC774\uC81C \uC804\uCC98\uB9AC\uB41C \uC785\uB825 \uBB36\uC74C\uC744 \uC9C1\uC811 \uBAA8\uB378\uC5D0 \uC804\uB2EC\uD574\uC57C \uD569\uB2C8\uB2E4. \uC544\uB798\uCC98\uB7FC "),q=a("code"),le=o("**"),ye=o("\uB97C \uC55E\uC5D0 \uBD99\uC5EC \uB515\uC154\uB108\uB9AC\uB97C \uD480\uC5B4\uC8FC\uBA74 \uB429\uB2C8\uB2E4:"),P=f(),g(x.$$.fragment),ne=f(),Q=a("p"),ae=o("\uBAA8\uB378\uC758 \uCD5C\uC885 \uD65C\uC131\uD654 \uD568\uC218 \uCD9C\uB825\uC740 "),ee=a("code"),pe=o("logits"),Ee=o(" \uC18D\uC131\uC5D0 \uB2F4\uACA8\uC788\uC2B5\uB2C8\uB2E4. 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<span class="hljs-meta">&gt;&gt;&gt; </span>model_name = <span class="hljs-string">&quot;nlptown/bert-base-multilingual-uncased-sentiment&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name)`}}),W=new Ol({props:{$$slots:{default:[C1]},$$scope:{ctx:R}}}),le=new F({props:{code:"dGZfb3V0cHV0cyUyMCUzRCUyMHRmX21vZGVsKHRmX2JhdGNoKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>tf_outputs = tf_model(tf_batch)'}}),O=new F({props:{code:"aW1wb3J0JTIwdGVuc29yZmxvdyUyMGFzJTIwdGYlMEElMEF0Zl9wcmVkaWN0aW9ucyUyMCUzRCUyMHRmLm5uLnNvZnRtYXgodGZfb3V0cHV0cy5sb2dpdHMlMkMlMjBheGlzJTNELTEpJTBBdGZfcHJlZGljdGlvbnM=",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>tf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-<span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tf_predictions`}}),{c(){l=a("p"),m=o("\u{1F917} Transformers\uB294 \uC0AC\uC804 \uD6C8\uB828\uB41C \uC778\uC2A4\uD134\uC2A4\uB97C \uAC04\uB2E8\uD558\uACE0 \uD1B5\uD569\uB41C \uBC29\uBC95\uC73C\uB85C \uB85C\uB4DC\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4. \uC989, "),c=a("code"),u=o("AutoTokenizer"),$=o("\uCC98\uB7FC "),w=a("code"),J=o("TFAutoModel"),_=o("\uC744 \uB85C\uB4DC\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4. \uC720\uC77C\uD55C \uCC28\uC774\uC810\uC740 \uACFC\uC5C5\uC5D0 \uC54C\uB9DE\uC740 "),j=a("code"),I=o("TFAutoModel"),A=o("\uC744 \uC120\uD0DD\uD574\uC57C \uD55C\uB2E4\uB294 \uC810\uC785\uB2C8\uB2E4. \uD14D\uC2A4\uD2B8 (\uB610\uB294 \uC2DC\uD000\uC2A4) \uBD84\uB958\uC758 \uACBD\uC6B0 "),V=a("code"),X=o("TFAutoModelForSequenceClassification"),G=o("\uC744 \uB85C\uB4DC\uD574\uC57C \uD569\uB2C8\uB2E4:"),U=f(),g(D.$$.fragment),k=f(),g(W.$$.fragment),S=f(),H=a("p"),K=o("\uC774\uC81C \uC804\uCC98\uB9AC\uB41C \uC785\uB825 \uBB36\uC74C\uC744 \uC9C1\uC811 \uBAA8\uB378\uC5D0 \uC804\uB2EC\uD574\uC57C \uD569\uB2C8\uB2E4. \uC544\uB798\uCC98\uB7FC \uADF8\uB300\uB85C \uD150\uC11C\uB97C \uC804\uB2EC\uD558\uBA74 \uB429\uB2C8\uB2E4:"),q=f(),g(le.$$.fragment),ye=f(),P=a("p"),x=o("\uBAA8\uB378\uC758 \uCD5C\uC885 \uD65C\uC131\uD654 \uD568\uC218 \uCD9C\uB825\uC740 "),ne=a("code"),Q=o("logits"),ae=o(" \uC18D\uC131\uC5D0 \uB2F4\uACA8\uC788\uC2B5\uB2C8\uB2E4. 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"),ee=r(fe,"CODE",{});var ge=p(ee);pe=n(ge,"logits"),ge.forEach(s),Ee=n(fe,"\uC5D0 softmax \uD568\uC218\uB97C \uC801\uC6A9\uD558\uC5EC \uD655\uB960\uC744 \uC5BB\uC744 \uC218 \uC788\uC2B5\uB2C8\uB2E4:"),fe.forEach(s),re=i(Z),b(O.$$.fragment,Z)},m(Z,N){h(Z,l,N),e(l,m),e(l,c),e(c,u),e(l,$),e(l,w),e(w,J),e(l,_),e(l,j),e(j,I),e(l,A),e(l,V),e(V,X),e(l,G),h(Z,U,N),M(D,Z,N),h(Z,k,N),M(W,Z,N),h(Z,S,N),h(Z,H,N),e(H,K),h(Z,q,N),M(le,Z,N),h(Z,ye,N),h(Z,P,N),e(P,x),e(P,ne),e(ne,Q),e(P,ae),e(P,ee),e(ee,pe),e(P,Ee),h(Z,re,N),M(O,Z,N),he=!0},p(Z,N){const te={};N&2&&(te.$$scope={dirty:N,ctx:Z}),W.$set(te)},i(Z){he||(v(D.$$.fragment,Z),v(W.$$.fragment,Z),v(le.$$.fragment,Z),v(O.$$.fragment,Z),he=!0)},o(Z){E(D.$$.fragment,Z),E(W.$$.fragment,Z),E(le.$$.fragment,Z),E(O.$$.fragment,Z),he=!1},d(Z){Z&&s(l),Z&&s(U),T(D,Z),Z&&s(k),T(W,Z),Z&&s(S),Z&&s(H),Z&&s(q),T(le,Z),Z&&s(ye),Z&&s(P),Z&&s(re),T(O,Z)}}}function G1(R){let l,m;return l=new me({props:{$$slots:{default:[R1]},$$scope:{ctx:R}}}),{c(){g(l.$$.fragment)},l(c){b(l.$$.fragment,c)},m(c,u){M(l,c,u),m=!0},p(c,u){const $={};u&2&&($.$$scope={dirty:u,ctx:c}),l.$set($)},i(c){m||(v(l.$$.fragment,c),m=!0)},o(c){E(l.$$.fragment,c),m=!1},d(c){T(l,c)}}}function W1(R){let l,m,c,u,$;return{c(){l=a("p"),m=o("\uBAA8\uB4E0 \u{1F917} Transformers \uBAA8\uB378(PyTorch \uB610\uB294 TensorFlow)\uC740 (softmax\uC640 \uAC19\uC740) \uCD5C\uC885 \uD65C\uC131\uD654 \uD568\uC218 "),c=a("em"),u=o("\uC774\uC804\uC5D0"),$=o(" \uD150\uC11C\uB97C \uCD9C\uB825\uD569\uB2C8\uB2E4. \uC65C\uB0D0\uD558\uBA74 \uCD5C\uC885 \uD65C\uC131\uD654 \uD568\uC218\uC758 \uCD9C\uB825\uC740 \uC885\uC885 \uC190\uC2E4 \uD568\uC218 \uCD9C\uB825\uACFC \uACB0\uD569\uB418\uAE30 \uB54C\uBB38\uC785\uB2C8\uB2E4. \uBAA8\uB378 \uCD9C\uB825\uC740 \uD2B9\uC218\uD55C \uB370\uC774\uD130 \uD074\uB798\uC2A4\uC774\uBBC0\uB85C IDE\uC5D0\uC11C \uC790\uB3D9 \uC644\uC131\uB429\uB2C8\uB2E4. \uBAA8\uB378 \uCD9C\uB825\uC740 \uD29C\uD50C\uC774\uB098 \uB515\uC154\uB108\uB9AC\uCC98\uB7FC \uB3D9\uC791\uD558\uBA70 (\uC815\uC218, \uC2AC\uB77C\uC774\uC2A4 \uB610\uB294 \uBB38\uC790\uC5F4\uB85C \uC778\uB371\uC2F1 \uAC00\uB2A5), None\uC778 \uC18D\uC131\uC740 \uBB34\uC2DC\uB429\uB2C8\uB2E4.")},l(w){l=r(w,"P",{});var J=p(l);m=n(J,"\uBAA8\uB4E0 \u{1F917} Transformers \uBAA8\uB378(PyTorch \uB610\uB294 TensorFlow)\uC740 (softmax\uC640 \uAC19\uC740) \uCD5C\uC885 \uD65C\uC131\uD654 \uD568\uC218 "),c=r(J,"EM",{});var _=p(c);u=n(_,"\uC774\uC804\uC5D0"),_.forEach(s),$=n(J," \uD150\uC11C\uB97C \uCD9C\uB825\uD569\uB2C8\uB2E4. \uC65C\uB0D0\uD558\uBA74 \uCD5C\uC885 \uD65C\uC131\uD654 \uD568\uC218\uC758 \uCD9C\uB825\uC740 \uC885\uC885 \uC190\uC2E4 \uD568\uC218 \uCD9C\uB825\uACFC \uACB0\uD569\uB418\uAE30 \uB54C\uBB38\uC785\uB2C8\uB2E4. \uBAA8\uB378 \uCD9C\uB825\uC740 \uD2B9\uC218\uD55C \uB370\uC774\uD130 \uD074\uB798\uC2A4\uC774\uBBC0\uB85C IDE\uC5D0\uC11C \uC790\uB3D9 \uC644\uC131\uB429\uB2C8\uB2E4. \uBAA8\uB378 \uCD9C\uB825\uC740 \uD29C\uD50C\uC774\uB098 \uB515\uC154\uB108\uB9AC\uCC98\uB7FC \uB3D9\uC791\uD558\uBA70 (\uC815\uC218, \uC2AC\uB77C\uC774\uC2A4 \uB610\uB294 \uBB38\uC790\uC5F4\uB85C \uC778\uB371\uC2F1 \uAC00\uB2A5), None\uC778 \uC18D\uC131\uC740 \uBB34\uC2DC\uB429\uB2C8\uB2E4."),J.forEach(s)},m(w,J){h(w,l,J),e(l,m),e(l,c),e(c,u),e(l,$)},d(w){w&&s(l)}}}function I1(R){let l,m,c,u,$,w,J,_,j,I,A,V,X,G,U,D;return J=new F({props:{code:"cHRfc2F2ZV9kaXJlY3RvcnklMjAlM0QlMjAlMjIuJTJGcHRfc2F2ZV9wcmV0cmFpbmVkJTIyJTBBdG9rZW5pemVyLnNhdmVfcHJldHJhaW5lZChwdF9zYXZlX2RpcmVjdG9yeSklMEFwdF9tb2RlbC5zYXZlX3ByZXRyYWluZWQocHRfc2F2ZV9kaXJlY3Rvcnkp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>pt_save_directory = <span class="hljs-string">&quot;./pt_save_pretrained&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.save_pretrained(pt_save_directory)
<span class="hljs-meta">&gt;&gt;&gt; </span>pt_model.save_pretrained(pt_save_directory)`}}),U=new F({props:{code:"cHRfbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMjIuJTJGcHRfc2F2ZV9wcmV0cmFpbmVkJTIyKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>pt_model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;./pt_save_pretrained&quot;</span>)'}}),{c(){l=a("p"),m=o("\uBBF8\uC138\uC870\uC815\uB41C \uBAA8\uB378\uC744 \uD1A0\uD06C\uB098\uC774\uC800\uC640 \uD568\uAED8 \uC800\uC7A5\uD558\uB824\uBA74 "),c=a("code"),u=o("PreTrainedModel.save_pretrained()"),$=o("\uB97C \uC0AC\uC6A9\uD558\uC138\uC694:"),w=f(),g(J.$$.fragment),_=f(),j=a("p"),I=o("\uBAA8\uB378\uC744 \uB2E4\uC2DC \uC0AC\uC6A9\uD558\uB824\uBA74 "),A=a("code"),V=o("PreTrainedModel.from_pretrained()"),X=o("\uB85C \uBAA8\uB378\uC744 \uB2E4\uC2DC \uB85C\uB4DC\uD558\uC138\uC694:"),G=f(),g(U.$$.fragment)},l(k){l=r(k,"P",{});var W=p(l);m=n(W,"\uBBF8\uC138\uC870\uC815\uB41C \uBAA8\uB378\uC744 \uD1A0\uD06C\uB098\uC774\uC800\uC640 \uD568\uAED8 \uC800\uC7A5\uD558\uB824\uBA74 "),c=r(W,"CODE",{});var S=p(c);u=n(S,"PreTrainedModel.save_pretrained()"),S.forEach(s),$=n(W,"\uB97C \uC0AC\uC6A9\uD558\uC138\uC694:"),W.forEach(s),w=i(k),b(J.$$.fragment,k),_=i(k),j=r(k,"P",{});var H=p(j);I=n(H,"\uBAA8\uB378\uC744 \uB2E4\uC2DC \uC0AC\uC6A9\uD558\uB824\uBA74 "),A=r(H,"CODE",{});var K=p(A);V=n(K,"PreTrainedModel.from_pretrained()"),K.forEach(s),X=n(H,"\uB85C \uBAA8\uB378\uC744 \uB2E4\uC2DC \uB85C\uB4DC\uD558\uC138\uC694:"),H.forEach(s),G=i(k),b(U.$$.fragment,k)},m(k,W){h(k,l,W),e(l,m),e(l,c),e(c,u),e(l,$),h(k,w,W),M(J,k,W),h(k,_,W),h(k,j,W),e(j,I),e(j,A),e(A,V),e(j,X),h(k,G,W),M(U,k,W),D=!0},p:ve,i(k){D||(v(J.$$.fragment,k),v(U.$$.fragment,k),D=!0)},o(k){E(J.$$.fragment,k),E(U.$$.fragment,k),D=!1},d(k){k&&s(l),k&&s(w),T(J,k),k&&s(_),k&&s(j),k&&s(G),T(U,k)}}}function A1(R){let l,m;return l=new me({props:{$$slots:{default:[I1]},$$scope:{ctx:R}}}),{c(){g(l.$$.fragment)},l(c){b(l.$$.fragment,c)},m(c,u){M(l,c,u),m=!0},p(c,u){const $={};u&2&&($.$$scope={dirty:u,ctx:c}),l.$set($)},i(c){m||(v(l.$$.fragment,c),m=!0)},o(c){E(l.$$.fragment,c),m=!1},d(c){T(l,c)}}}function V1(R){let l,m,c,u,$,w,J,_,j,I,A,V,X,G,U,D;return J=new F({props:{code:"dGZfc2F2ZV9kaXJlY3RvcnklMjAlM0QlMjAlMjIuJTJGdGZfc2F2ZV9wcmV0cmFpbmVkJTIyJTBBdG9rZW5pemVyLnNhdmVfcHJldHJhaW5lZCh0Zl9zYXZlX2RpcmVjdG9yeSklMEF0Zl9tb2RlbC5zYXZlX3ByZXRyYWluZWQodGZfc2F2ZV9kaXJlY3Rvcnkp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>tf_save_directory = <span class="hljs-string">&quot;./tf_save_pretrained&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.save_pretrained(tf_save_directory)
<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model.save_pretrained(tf_save_directory)`}}),U=new F({props:{code:"dGZfbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZ0Zl9zYXZlX3ByZXRyYWluZWQlMjIp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;./tf_save_pretrained&quot;</span>)'}}),{c(){l=a("p"),m=o("\uBBF8\uC138\uC870\uC815\uB41C \uBAA8\uB378\uC744 \uD1A0\uD06C\uB098\uC774\uC800\uC640 \uD568\uAED8 \uC800\uC7A5\uD558\uB824\uBA74 "),c=a("code"),u=o("TFPreTrainedModel.save_pretrained()"),$=o("\uB97C \uC0AC\uC6A9\uD558\uC138\uC694:"),w=f(),g(J.$$.fragment),_=f(),j=a("p"),I=o("\uBAA8\uB378\uC744 \uB2E4\uC2DC \uC0AC\uC6A9\uD558\uB824\uBA74 "),A=a("code"),V=o("TFPreTrainedModel.from_pretrained()"),X=o("\uB85C \uBAA8\uB378\uC744 \uB2E4\uC2DC \uB85C\uB4DC\uD558\uC138\uC694:"),G=f(),g(U.$$.fragment)},l(k){l=r(k,"P",{});var W=p(l);m=n(W,"\uBBF8\uC138\uC870\uC815\uB41C \uBAA8\uB378\uC744 \uD1A0\uD06C\uB098\uC774\uC800\uC640 \uD568\uAED8 \uC800\uC7A5\uD558\uB824\uBA74 "),c=r(W,"CODE",{});var S=p(c);u=n(S,"TFPreTrainedModel.save_pretrained()"),S.forEach(s),$=n(W,"\uB97C \uC0AC\uC6A9\uD558\uC138\uC694:"),W.forEach(s),w=i(k),b(J.$$.fragment,k),_=i(k),j=r(k,"P",{});var H=p(j);I=n(H,"\uBAA8\uB378\uC744 \uB2E4\uC2DC \uC0AC\uC6A9\uD558\uB824\uBA74 "),A=r(H,"CODE",{});var K=p(A);V=n(K,"TFPreTrainedModel.from_pretrained()"),K.forEach(s),X=n(H,"\uB85C \uBAA8\uB378\uC744 \uB2E4\uC2DC \uB85C\uB4DC\uD558\uC138\uC694:"),H.forEach(s),G=i(k),b(U.$$.fragment,k)},m(k,W){h(k,l,W),e(l,m),e(l,c),e(c,u),e(l,$),h(k,w,W),M(J,k,W),h(k,_,W),h(k,j,W),e(j,I),e(j,A),e(A,V),e(j,X),h(k,G,W),M(U,k,W),D=!0},p:ve,i(k){D||(v(J.$$.fragment,k),v(U.$$.fragment,k),D=!0)},o(k){E(J.$$.fragment,k),E(U.$$.fragment,k),D=!1},d(k){k&&s(l),k&&s(w),T(J,k),k&&s(_),k&&s(j),k&&s(G),T(U,k)}}}function F1(R){let l,m;return l=new me({props:{$$slots:{default:[V1]},$$scope:{ctx:R}}}),{c(){g(l.$$.fragment)},l(c){b(l.$$.fragment,c)},m(c,u){M(l,c,u),m=!0},p(c,u){const $={};u&2&&($.$$scope={dirty:u,ctx:c}),l.$set($)},i(c){m||(v(l.$$.fragment,c),m=!0)},o(c){E(l.$$.fragment,c),m=!1},d(c){T(l,c)}}}function N1(R){let l,m;return l=new F({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbCUwQSUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKHRmX3NhdmVfZGlyZWN0b3J5KSUwQXB0X21vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQodGZfc2F2ZV9kaXJlY3RvcnklMkMlMjBmcm9tX3RmJTNEVHJ1ZSk=",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(l.$$.fragment)},l(c){b(l.$$.fragment,c)},m(c,u){M(l,c,u),m=!0},p:ve,i(c){m||(v(l.$$.fragment,c),m=!0)},o(c){E(l.$$.fragment,c),m=!1},d(c){T(l,c)}}}function X1(R){let l,m;return l=new me({props:{$$slots:{default:[N1]},$$scope:{ctx:R}}}),{c(){g(l.$$.fragment)},l(c){b(l.$$.fragment,c)},m(c,u){M(l,c,u),m=!0},p(c,u){const $={};u&2&&($.$$scope={dirty:u,ctx:c}),l.$set($)},i(c){m||(v(l.$$.fragment,c),m=!0)},o(c){E(l.$$.fragment,c),m=!1},d(c){T(l,c)}}}function D1(R){let l,m;return l=new F({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGQXV0b01vZGVsJTBBJTBBdG9rZW5pemVyJTIwJTNEJTIwQXV0b1Rva2VuaXplci5mcm9tX3ByZXRyYWluZWQocHRfc2F2ZV9kaXJlY3RvcnkpJTBBdGZfbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKHB0X3NhdmVfZGlyZWN0b3J5JTJDJTIwZnJvbV9wdCUzRFRydWUp",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(l.$$.fragment)},l(c){b(l.$$.fragment,c)},m(c,u){M(l,c,u),m=!0},p:ve,i(c){m||(v(l.$$.fragment,c),m=!0)},o(c){E(l.$$.fragment,c),m=!1},d(c){T(l,c)}}}function z1(R){let l,m;return l=new me({props:{$$slots:{default:[D1]},$$scope:{ctx:R}}}),{c(){g(l.$$.fragment)},l(c){b(l.$$.fragment,c)},m(c,u){M(l,c,u),m=!0},p(c,u){const $={};u&2&&($.$$scope={dirty:u,ctx:c}),l.$set($)},i(c){m||(v(l.$$.fragment,c),m=!0)},o(c){E(l.$$.fragment,c),m=!1},d(c){T(l,c)}}}function B1(R){let l,m,c,u,$,w,J;return w=new F({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbCUwQSUwQW15X21vZGVsJTIwJTNEJTIwQXV0b01vZGVsLmZyb21fY29uZmlnKG15X2NvbmZpZyk=",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(){l=a("p"),m=a("code"),c=o("AutoModel.from_config()"),u=o("\uB97C \uC0AC\uC6A9\uD558\uC5EC \uBC14\uAFBC \uAD6C\uC131\uB300\uB85C \uBAA8\uB378\uC744 \uC0DD\uC131\uD558\uC138\uC694:"),$=f(),g(w.$$.fragment)},l(_){l=r(_,"P",{});var j=p(l);m=r(j,"CODE",{});var I=p(m);c=n(I,"AutoModel.from_config()"),I.forEach(s),u=n(j,"\uB97C \uC0AC\uC6A9\uD558\uC5EC \uBC14\uAFBC \uAD6C\uC131\uB300\uB85C \uBAA8\uB378\uC744 \uC0DD\uC131\uD558\uC138\uC694:"),j.forEach(s),$=i(_),b(w.$$.fragment,_)},m(_,j){h(_,l,j),e(l,m),e(m,c),e(l,u),h(_,$,j),M(w,_,j),J=!0},p:ve,i(_){J||(v(w.$$.fragment,_),J=!0)},o(_){E(w.$$.fragment,_),J=!1},d(_){_&&s(l),_&&s($),T(w,_)}}}function Y1(R){let l,m;return l=new me({props:{$$slots:{default:[B1]},$$scope:{ctx:R}}}),{c(){g(l.$$.fragment)},l(c){b(l.$$.fragment,c)},m(c,u){M(l,c,u),m=!0},p(c,u){const $={};u&2&&($.$$scope={dirty:u,ctx:c}),l.$set($)},i(c){m||(v(l.$$.fragment,c),m=!0)},o(c){E(l.$$.fragment,c),m=!1},d(c){T(l,c)}}}function H1(R){let l,m,c,u,$,w,J;return w=new F({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGQXV0b01vZGVsJTBBJTBBbXlfbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbC5mcm9tX2NvbmZpZyhteV9jb25maWcp",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(){l=a("p"),m=a("code"),c=o("TFAutoModel.from_config()"),u=o("\uB97C \uC0AC\uC6A9\uD558\uC5EC \uBC14\uAFBC \uAD6C\uC131\uB300\uB85C \uBAA8\uB378\uC744 \uC0DD\uC131\uD558\uC138\uC694:"),$=f(),g(w.$$.fragment)},l(_){l=r(_,"P",{});var j=p(l);m=r(j,"CODE",{});var I=p(m);c=n(I,"TFAutoModel.from_config()"),I.forEach(s),u=n(j,"\uB97C \uC0AC\uC6A9\uD558\uC5EC \uBC14\uAFBC \uAD6C\uC131\uB300\uB85C \uBAA8\uB378\uC744 \uC0DD\uC131\uD558\uC138\uC694:"),j.forEach(s),$=i(_),b(w.$$.fragment,_)},m(_,j){h(_,l,j),e(l,m),e(m,c),e(l,u),h(_,$,j),M(w,_,j),J=!0},p:ve,i(_){J||(v(w.$$.fragment,_),J=!0)},o(_){E(w.$$.fragment,_),J=!1},d(_){_&&s(l),_&&s($),T(w,_)}}}function S1(R){let l,m;return l=new me({props:{$$slots:{default:[H1]},$$scope:{ctx:R}}}),{c(){g(l.$$.fragment)},l(c){b(l.$$.fragment,c)},m(c,u){M(l,c,u),m=!0},p(c,u){const $={};u&2&&($.$$scope={dirty:u,ctx:c}),l.$set($)},i(c){m||(v(l.$$.fragment,c),m=!0)},o(c){E(l.$$.fragment,c),m=!1},d(c){T(l,c)}}}function P1(R){let l,m,c,u,$,w,J,_;return{c(){l=a("p"),m=o("\uBC88\uC5ED\uC774\uB098 \uC694\uC57D\uACFC \uAC19\uC774 \uC2DC\uD000\uC2A4-\uC2DC\uD000\uC2A4 \uBAA8\uB378\uC744 \uC0AC\uC6A9\uD558\uB294 \uACFC\uC5C5\uC5D0\uB294 "),c=a("code"),u=o("Seq2SeqTrainer"),$=o(" \uBC0F "),w=a("code"),J=o("Seq2SeqTrainingArguments"),_=o(" \uD074\uB798\uC2A4\uB97C \uC0AC\uC6A9\uD558\uC138\uC694.")},l(j){l=r(j,"P",{});var I=p(l);m=n(I,"\uBC88\uC5ED\uC774\uB098 \uC694\uC57D\uACFC \uAC19\uC774 \uC2DC\uD000\uC2A4-\uC2DC\uD000\uC2A4 \uBAA8\uB378\uC744 \uC0AC\uC6A9\uD558\uB294 \uACFC\uC5C5\uC5D0\uB294 "),c=r(I,"CODE",{});var A=p(c);u=n(A,"Seq2SeqTrainer"),A.forEach(s),$=n(I," \uBC0F "),w=r(I,"CODE",{});var V=p(w);J=n(V,"Seq2SeqTrainingArguments"),V.forEach(s),_=n(I," \uD074\uB798\uC2A4\uB97C \uC0AC\uC6A9\uD558\uC138\uC694."),I.forEach(s)},m(j,I){h(j,l,I),e(l,m),e(l,c),e(c,u),e(l,$),e(l,w),e(w,J),e(l,_)},d(j){j&&s(l)}}}function O1(R){let 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w=new Ye({}),A=new m1({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"}]}}),ae=new F({props:{code:"IXBpcCUyMGluc3RhbGwlMjB0cmFuc2Zvcm1lcnMlMjBkYXRhc2V0cw==",highlighted:"!pip install transformers datasets"}}),O=new Ml({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[_1],pytorch:[u1]},$$scope:{ctx:R}}}),C=new Ye({}),ue=new n1({props:{id:"tiZFewofSLM"}}),ht=new Ol({props:{$$slots:{default:[y1]},$$scope:{ctx:R}}}),fs=new F({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBY2xhc3NpZmllciUyMCUzRCUyMHBpcGVsaW5lKCUyMnNlbnRpbWVudC1hbmFseXNpcyUyMik=",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>)`}}),ds=new F({props:{code:"Y2xhc3NpZmllciglMjJXZSUyMGFyZSUyMHZlcnklMjBoYXBweSUyMHRvJTIwc2hvdyUyMHlvdSUyMHRoZSUyMCVGMCU5RiVBNCU5NyUyMFRyYW5zZm9ybWVycyUyMGxpYnJhcnkuJTIyKQ==",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>}]`}}),ms=new F({props:{code:"cmVzdWx0cyUyMCUzRCUyMGNsYXNzaWZpZXIoJTVCJTIyV2UlMjBhcmUlMjB2ZXJ5JTIwaGFwcHklMjB0byUyMHNob3clMjB5b3UlMjB0aGUlMjAlRjAlOUYlQTQlOTclMjBUcmFuc2Zvcm1lcnMlMjBsaWJyYXJ5LiUyMiUyQyUyMCUyMldlJTIwaG9wZSUyMHlvdSUyMGRvbid0JTIwaGF0ZSUyMGl0LiUyMiU1RCklMEFmb3IlMjByZXN1bHQlMjBpbiUyMHJlc3VsdHMlM0ElMEElMjAlMjAlMjAlMjBwcmludChmJTIybGFiZWwlM0ElMjAlN0JyZXN1bHQlNUInbGFiZWwnJTVEJTdEJTJDJTIwd2l0aCUyMHNjb3JlJTNBJTIwJTdCcm91bmQocmVzdWx0JTVCJ3Njb3JlJyU1RCUyQyUyMDQpJTdEJTIyKQ==",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>`}}),us=new F({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwcGlwZWxpbmUlMEElMEFzcGVlY2hfcmVjb2duaXplciUyMCUzRCUyMHBpcGVsaW5lKCUyMmF1dG9tYXRpYy1zcGVlY2gtcmVjb2duaXRpb24lMjIlMkMlMjBtb2RlbCUzRCUyMmZhY2Vib29rJTJGd2F2MnZlYzItYmFzZS05NjBoJTIyKQ==",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>)`}}),ys=new F({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTJDJTIwQXVkaW8lMEElMEFkYXRhc2V0JTIwJTNEJTIwbG9hZF9kYXRhc2V0KCUyMlBvbHlBSSUyRm1pbmRzMTQlMjIlMkMlMjBuYW1lJTNEJTIyZW4tVVMlMjIlMkMlMjBzcGxpdCUzRCUyMnRyYWluJTIyKQ==",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>)`}}),bs=new F({props:{code:"ZGF0YXNldCUyMCUzRCUyMGRhdGFzZXQuY2FzdF9jb2x1bW4oJTIyYXVkaW8lMjIlMkMlMjBBdWRpbyhzYW1wbGluZ19yYXRlJTNEc3BlZWNoX3JlY29nbml6ZXIuZmVhdHVyZV9leHRyYWN0b3Iuc2FtcGxpbmdfcmF0ZSkp",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))'}}),vs=new F({props:{code:"cmVzdWx0JTIwJTNEJTIwc3BlZWNoX3JlY29nbml6ZXIoZGF0YXNldCU1QiUzQTQlNUQlNUIlMjJhdWRpbyUyMiU1RCklMEFwcmludCglNUJkJTVCJTIydGV4dCUyMiU1RCUyMGZvciUyMGQlMjBpbiUyMHJlc3VsdCU1RCk=",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;FONDERING HOW I&#x27;D SET UP A JOIN TO HELL T 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 APSO 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 AN 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 FURN A JOINA COUT&#x27;</span>]`}}),Es=new Ye({}),js=new F({props:{code:"bW9kZWxfbmFtZSUyMCUzRCUyMCUyMm5scHRvd24lMkZiZXJ0LWJhc2UtbXVsdGlsaW5ndWFsLXVuY2FzZWQtc2VudGltZW50JTIy",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>model_name = <span class="hljs-string">&quot;nlptown/bert-base-multilingual-uncased-sentiment&quot;</span>'}}),bt=new Ml({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[v1],pytorch:[b1]},$$scope:{ctx:R}}}),ks=new F({props:{code:"Y2xhc3NpZmllciUyMCUzRCUyMHBpcGVsaW5lKCUyMnNlbnRpbWVudC1hbmFseXNpcyUyMiUyQyUyMG1vZGVsJTNEbW9kZWwlMkMlMjB0b2tlbml6ZXIlM0R0b2tlbml6ZXIpJTBBY2xhc3NpZmllciglMjJOb3VzJTIwc29tbWVzJTIwdHIlQzMlQThzJTIwaGV1cmV1eCUyMGRlJTIwdm91cyUyMHByJUMzJUE5c2VudGVyJTIwbGElMjBiaWJsaW90aCVDMyVBOHF1ZSUyMCVGMCU5RiVBNCU5NyUyMFRyYW5zZm9ybWVycy4lMjIp",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>}]`}}),Js=new Ye({}),Us=new n1({props:{id:"AhChOFRegn4"}}),Zs=new Ye({}),Rs=new F({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMEElMEFtb2RlbF9uYW1lJTIwJTNEJTIwJTIybmxwdG93biUyRmJlcnQtYmFzZS1tdWx0aWxpbmd1YWwtdW5jYXNlZC1zZW50aW1lbnQlMjIlMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZChtb2RlbF9uYW1lKQ==",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)`}}),Gs=new F({props:{code:"ZW5jb2RpbmclMjAlM0QlMjB0b2tlbml6ZXIoJTIyV2UlMjBhcmUlMjB2ZXJ5JTIwaGFwcHklMjB0byUyMHNob3clMjB5b3UlMjB0aGUlMjAlRjAlOUYlQTQlOTclMjBUcmFuc2Zvcm1lcnMlMjBsaWJyYXJ5LiUyMiklMEFwcmludChlbmNvZGluZyk=",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>]}`}}),jt=new Ml({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[j1],pytorch:[T1]},$$scope:{ctx:R}}}),kt=new Ol({props:{$$slots:{default:[k1]},$$scope:{ctx:R}}}),Ws=new Ye({}),Ut=new Ml({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[G1],pytorch:[Z1]},$$scope:{ctx:R}}}),Zt=new Ol({props:{$$slots:{default:[W1]},$$scope:{ctx:R}}}),Is=new Ye({}),Rt=new Ml({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[F1],pytorch:[A1]},$$scope:{ctx:R}}}),Gt=new Ml({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[z1],pytorch:[X1]},$$scope:{ctx:R}}}),As=new Ye({}),Vs=new F({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMEElMEFteV9jb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJkaXN0aWxiZXJ0LWJhc2UtdW5jYXNlZCUyMiUyQyUyMG5faGVhZHMlM0QxMik=",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>)`}}),It=new Ml({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[S1],pytorch:[Y1]},$$scope:{ctx:R}}}),Fs=new Ye({}),zs=new F({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24lMEElMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKCUyMmRpc3RpbGJlcnQtYmFzZS11bmNhc2VkJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),Ys=new F({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRyYWluaW5nQXJndW1lbnRzJTBBJTBBdHJhaW5pbmdfYXJncyUyMCUzRCUyMFRyYWluaW5nQXJndW1lbnRzKCUwQSUyMCUyMCUyMCUyMG91dHB1dF9kaXIlM0QlMjJwYXRoJTJGdG8lMkZzYXZlJTJGZm9sZGVyJTJGJTIyJTJDJTBBJTIwJTIwJTIwJTIwbGVhcm5pbmdfcmF0ZSUzRDJlLTUlMkMlMEElMjAlMjAlMjAlMjBwZXJfZGV2aWNlX3RyYWluX2JhdGNoX3NpemUlM0Q4JTJDJTBBJTIwJTIwJTIwJTIwcGVyX2RldmljZV9ldmFsX2JhdGNoX3NpemUlM0Q4JTJDJTBBJTIwJTIwJTIwJTIwbnVtX3RyYWluX2Vwb2NocyUzRDIlMkMlMEEp",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>)`}}),Ss=new F({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMEElMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjJkaXN0aWxiZXJ0LWJhc2UtdW5jYXNlZCUyMik=",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>)`}}),Os=new F({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBJTBBZGF0YXNldCUyMCUzRCUyMGxvYWRfZGF0YXNldCglMjJyb3R0ZW5fdG9tYXRvZXMlMjIpJTIwJTIwJTIzJTIwZG9jdGVzdCUzQSUyMCUyQklHTk9SRV9SRVNVTFQ=",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>`}}),Qs=new F({props:{code:"ZGVmJTIwdG9rZW5pemVfZGF0YXNldChkYXRhc2V0KSUzQSUwQSUyMCUyMCUyMCUyMHJldHVybiUyMHRva2VuaXplcihkYXRhc2V0JTVCJTIydGV4dCUyMiU1RCk=",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>])`}}),xs=new F({props:{code:"ZGF0YXNldCUyMCUzRCUyMGRhdGFzZXQubWFwKHRva2VuaXplX2RhdGFzZXQlMkMlMjBiYXRjaGVkJTNEVHJ1ZSk=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.<span class="hljs-built_in">map</span>(tokenize_dataset, batched=<span class="hljs-literal">True</span>)'}}),Ks=new F({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERhdGFDb2xsYXRvcldpdGhQYWRkaW5nJTBBJTBBZGF0YV9jb2xsYXRvciUyMCUzRCUyMERhdGFDb2xsYXRvcldpdGhQYWRkaW5nKHRva2VuaXplciUzRHRva2VuaXplcik=",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)`}}),el=new F({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRyYWluZXIlMEElMEF0cmFpbmVyJTIwJTNEJTIwVHJhaW5lciglMEElMjAlMjAlMjAlMjBtb2RlbCUzRG1vZGVsJTJDJTBBJTIwJTIwJTIwJTIwYXJncyUzRHRyYWluaW5nX2FyZ3MlMkMlMEElMjAlMjAlMjAlMjB0cmFpbl9kYXRhc2V0JTNEZGF0YXNldCU1QiUyMnRyYWluJTIyJTVEJTJDJTBBJTIwJTIwJTIwJTIwZXZhbF9kYXRhc2V0JTNEZGF0YXNldCU1QiUyMnRlc3QlMjIlNUQlMkMlMEElMjAlMjAlMjAlMjB0b2tlbml6ZXIlM0R0b2tlbml6ZXIlMkMlMEElMjAlMjAlMjAlMjBkYXRhX2NvbGxhdG9yJTNEZGF0YV9jb2xsYXRvciUyQyUwQSklMjAlMjAlMjMlMjBkb2N0ZXN0JTNBJTIwJTJCU0tJUA==",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>`}}),tl=new F({props:{code:"dHJhaW5lci50cmFpbigp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>trainer.train()'}}),zt=new Ol({props:{$$slots:{default:[P1]},$$scope:{ctx:R}}}),sl=new Ye({}),cl=new F({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGQXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbiUwQSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMjJkaXN0aWxiZXJ0LWJhc2UtdW5jYXNlZCUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),il=new F({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMEElMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjJkaXN0aWxiZXJ0LWJhc2UtdW5jYXNlZCUyMik=",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>)`}}),ml=new F({props:{code:"ZGVmJTIwdG9rZW5pemVfZGF0YXNldChkYXRhc2V0KSUzQSUwQSUyMCUyMCUyMCUyMHJldHVybiUyMHRva2VuaXplcihkYXRhc2V0JTVCJTIydGV4dCUyMiU1RCklMjAlMjAlMjMlMjBkb2N0ZXN0JTNBJTIwJTJCU0tJUA==",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>`}}),ul=new F({props:{code:"ZGF0YXNldCUyMCUzRCUyMGRhdGFzZXQubWFwKHRva2VuaXplX2RhdGFzZXQpJTIwJTIwJTIzJTIwZG9jdGVzdCUzQSUyMCUyQlNLSVAlMEF0Zl9kYXRhc2V0JTIwJTNEJTIwbW9kZWwucHJlcGFyZV90Zl9kYXRhc2V0KCUwQSUyMCUyMCUyMCUyMGRhdGFzZXQlNUIlMjJ0cmFpbiUyMiU1RCUyQyUyMGJhdGNoX3NpemUlM0QxNiUyQyUyMHNodWZmbGUlM0RUcnVlJTJDJTIwdG9rZW5pemVyJTNEdG9rZW5pemVyJTBBKSUyMCUyMCUyMyUyMGRvY3Rlc3QlM0ElMjAlMkJTS0lQ",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[<span class="hljs-string">&quot;train&quot;</span>], 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>`}}),_l=new F({props:{code:"ZnJvbSUyMHRlbnNvcmZsb3cua2VyYXMub3B0aW1pemVycyUyMGltcG9ydCUyMEFkYW0lMEElMEFtb2RlbC5jb21waWxlKG9wdGltaXplciUzREFkYW0oM2UtNSkpJTIwJTIwJTIzJTIwTm8lMjBsb3NzJTIwYXJndW1lbnQhJTBBbW9kZWwuZml0KHRmX2RhdGFzZXQpJTIwJTIwJTIzJTIwZG9jdGVzdCUzQSUyMCUyQlNLSVA=",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-comment"># No loss argument!</span>
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