import{s as Cl,o as kl,n as Q}from"../chunks/scheduler.889ebb13.js";import{S as Jl,i as Wl,g as i,s as p,u as d,A as Vl,h as c,f as e,c as o,j as J,v as g,y as f,k as Z,o as q,a as l,w as $,d as y,t as v,x as j}from"../chunks/index.8980e686.js";import{T as be}from"../chunks/Tip.7191df11.js";import{I as rt}from"../chunks/IconCopyLink.357761cd.js";import{C as W}from"../chunks/CodeBlock.25fc3430.js";import{F as ql,M as ks}from"../chunks/Markdown.31057263.js";function xl(k){let n,h='configuration 파일을 딕셔너리로 저장하거나 사용자 정의 configuration 속성과 기본 configuration 속성의 차이점만 저장할 수도 있습니다! 자세한 내용은 <a href="main_classes/configuration">configuration</a> 문서를 참조하세요.';return{c(){n=i("p"),n.innerHTML=h},l(a){n=c(a,"P",{"data-svelte-h":!0}),f(n)!=="svelte-p4ht4o"&&(n.innerHTML=h)},m(a,m){l(a,n,m)},p:Q,d(a){a&&e(n)}}}function Rl(k){let n,h="사용자 지정 configuration 속성을 모델에 가져옵니다:",a,m,_,b,U="이제 사전 학습된 가중치 대신 임의의 값을 가진 모델이 생성됩니다. 이 모델을 훈련하기 전까지는 유용하게 사용할 수 없습니다. 훈련은 비용과 시간이 많이 소요되는 프로세스입니다. 일반적으로 훈련에 필요한 리소스의 일부만 사용하면서 더 나은 결과를 더 빨리 얻으려면 사전 훈련된 모델을 사용하는 것이 좋습니다.",C,T,V="사전 학습된 모델을 <code>from_pretrained()</code>로 생성합니다:",u,M,z,x,E="🤗 Transformers에서 제공한 모델의 사전 학습된 가중치를 사용하는 경우 기본 모델 configuration을 자동으로 불러옵니다. 그러나 원하는 경우 기본 모델 configuration 속성의 일부 또는 전부를 사용자 지정으로 바꿀 수 있습니다:",G,R,H;return m=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERpc3RpbEJlcnRNb2RlbCUwQSUwQW15X2NvbmZpZyUyMCUzRCUyMERpc3RpbEJlcnRDb25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZ5b3VyX21vZGVsX3NhdmVfcGF0aCUyRmNvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwRGlzdGlsQmVydE1vZGVsKG15X2NvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertModel

<span class="hljs-meta">&gt;&gt;&gt; </span>my_config = DistilBertConfig.from_pretrained(<span class="hljs-string">&quot;./your_model_save_path/config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertModel(my_config)`}}),M=new W({props:{code:"bW9kZWwlMjAlM0QlMjBEaXN0aWxCZXJ0TW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMmRpc3RpbGJlcnQtYmFzZS11bmNhc2VkJTIyKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertModel.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)'}}),R=new W({props:{code:"bW9kZWwlMjAlM0QlMjBEaXN0aWxCZXJ0TW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMmRpc3RpbGJlcnQtYmFzZS11bmNhc2VkJTIyJTJDJTIwY29uZmlnJTNEbXlfY29uZmlnKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertModel.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>, config=my_config)'}}),{c(){n=i("p"),n.textContent=h,a=p(),d(m.$$.fragment),_=p(),b=i("p"),b.textContent=U,C=p(),T=i("p"),T.innerHTML=V,u=p(),d(M.$$.fragment),z=p(),x=i("p"),x.textContent=E,G=p(),d(R.$$.fragment)},l(r){n=c(r,"P",{"data-svelte-h":!0}),f(n)!=="svelte-14tvdkn"&&(n.textContent=h),a=o(r),g(m.$$.fragment,r),_=o(r),b=c(r,"P",{"data-svelte-h":!0}),f(b)!=="svelte-1x90d88"&&(b.textContent=U),C=o(r),T=c(r,"P",{"data-svelte-h":!0}),f(T)!=="svelte-157o62u"&&(T.innerHTML=V),u=o(r),g(M.$$.fragment,r),z=o(r),x=c(r,"P",{"data-svelte-h":!0}),f(x)!=="svelte-yuz877"&&(x.textContent=E),G=o(r),g(R.$$.fragment,r)},m(r,w){l(r,n,w),l(r,a,w),$(m,r,w),l(r,_,w),l(r,b,w),l(r,C,w),l(r,T,w),l(r,u,w),$(M,r,w),l(r,z,w),l(r,x,w),l(r,G,w),$(R,r,w),H=!0},p:Q,i(r){H||(y(m.$$.fragment,r),y(M.$$.fragment,r),y(R.$$.fragment,r),H=!0)},o(r){v(m.$$.fragment,r),v(M.$$.fragment,r),v(R.$$.fragment,r),H=!1},d(r){r&&(e(n),e(a),e(_),e(b),e(C),e(T),e(u),e(z),e(x),e(G)),j(m,r),j(M,r),j(R,r)}}}function Ul(k){let n,h;return n=new ks({props:{$$slots:{default:[Rl]},$$scope:{ctx:k}}}),{c(){d(n.$$.fragment)},l(a){g(n.$$.fragment,a)},m(a,m){$(n,a,m),h=!0},p(a,m){const _={};m&2&&(_.$$scope={dirty:m,ctx:a}),n.$set(_)},i(a){h||(y(n.$$.fragment,a),h=!0)},o(a){v(n.$$.fragment,a),h=!1},d(a){j(n,a)}}}function zl(k){let n,h="사용자 지정 configuration 속성을 모델에 불러옵니다:",a,m,_,b,U="이제 사전 학습된 가중치 대신 임의의 값을 가진 모델이 생성됩니다. 이 모델을 훈련하기 전까지는 유용하게 사용할 수 없습니다. 훈련은 비용과 시간이 많이 소요되는 프로세스입니다. 일반적으로 훈련에 필요한 리소스의 일부만 사용하면서 더 나은 결과를 더 빨리 얻으려면 사전 훈련된 모델을 사용하는 것이 좋습니다.",C,T,V="사전 학습된 모델을 <code>from_pretrained()</code>로 생성합니다:",u,M,z,x,E="🤗 Transformers에서 제공한 모델의 사전 학습된 가중치를 사용하는 경우 기본 모델 configuration을 자동으로 불러옵니다. 그러나 원하는 경우 기본 모델 configuration 속성의 일부 또는 전부를 사용자 지정으로 바꿀 수 있습니다:",G,R,H;return m=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGRGlzdGlsQmVydE1vZGVsJTBBJTBBbXlfY29uZmlnJTIwJTNEJTIwRGlzdGlsQmVydENvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyLiUyRnlvdXJfbW9kZWxfc2F2ZV9wYXRoJTJGbXlfY29uZmlnLmpzb24lMjIpJTBBdGZfbW9kZWwlMjAlM0QlMjBURkRpc3RpbEJlcnRNb2RlbChteV9jb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFDistilBertModel

<span class="hljs-meta">&gt;&gt;&gt; </span>my_config = DistilBertConfig.from_pretrained(<span class="hljs-string">&quot;./your_model_save_path/my_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model = TFDistilBertModel(my_config)`}}),M=new W({props:{code:"dGZfbW9kZWwlMjAlM0QlMjBURkRpc3RpbEJlcnRNb2RlbC5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model = TFDistilBertModel.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)'}}),R=new W({props:{code:"dGZfbW9kZWwlMjAlM0QlMjBURkRpc3RpbEJlcnRNb2RlbC5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIlMkMlMjBjb25maWclM0RteV9jb25maWcp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model = TFDistilBertModel.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>, config=my_config)'}}),{c(){n=i("p"),n.textContent=h,a=p(),d(m.$$.fragment),_=p(),b=i("p"),b.textContent=U,C=p(),T=i("p"),T.innerHTML=V,u=p(),d(M.$$.fragment),z=p(),x=i("p"),x.textContent=E,G=p(),d(R.$$.fragment)},l(r){n=c(r,"P",{"data-svelte-h":!0}),f(n)!=="svelte-1jf8wr"&&(n.textContent=h),a=o(r),g(m.$$.fragment,r),_=o(r),b=c(r,"P",{"data-svelte-h":!0}),f(b)!=="svelte-1x90d88"&&(b.textContent=U),C=o(r),T=c(r,"P",{"data-svelte-h":!0}),f(T)!=="svelte-157o62u"&&(T.innerHTML=V),u=o(r),g(M.$$.fragment,r),z=o(r),x=c(r,"P",{"data-svelte-h":!0}),f(x)!=="svelte-yuz877"&&(x.textContent=E),G=o(r),g(R.$$.fragment,r)},m(r,w){l(r,n,w),l(r,a,w),$(m,r,w),l(r,_,w),l(r,b,w),l(r,C,w),l(r,T,w),l(r,u,w),$(M,r,w),l(r,z,w),l(r,x,w),l(r,G,w),$(R,r,w),H=!0},p:Q,i(r){H||(y(m.$$.fragment,r),y(M.$$.fragment,r),y(R.$$.fragment,r),H=!0)},o(r){v(m.$$.fragment,r),v(M.$$.fragment,r),v(R.$$.fragment,r),H=!1},d(r){r&&(e(n),e(a),e(_),e(b),e(C),e(T),e(u),e(z),e(x),e(G)),j(m,r),j(M,r),j(R,r)}}}function Gl(k){let n,h;return n=new ks({props:{$$slots:{default:[zl]},$$scope:{ctx:k}}}),{c(){d(n.$$.fragment)},l(a){g(n.$$.fragment,a)},m(a,m){$(n,a,m),h=!0},p(a,m){const _={};m&2&&(_.$$scope={dirty:m,ctx:a}),n.$set(_)},i(a){h||(y(n.$$.fragment,a),h=!0)},o(a){v(n.$$.fragment,a),h=!1},d(a){j(n,a)}}}function Hl(k){let n,h="예를 들어, <code>DistilBertForSequenceClassification</code>은 시퀀스 분류 헤드가 있는 기본 DistilBERT 모델입니다. 시퀀스 분류 헤드는 풀링된 출력 위에 있는 선형 레이어입니다.",a,m,_,b,U="다른 모델 헤드로 전환하여 이 체크포인트를 다른 작업에 쉽게 재사용할 수 있습니다. 질의응답 작업의 경우, <code>DistilBertForQuestionAnswering</code> 모델 헤드를 사용할 수 있습니다. 질의응답 헤드는 숨겨진 상태 출력 위에 선형 레이어가 있다는 점을 제외하면 시퀀스 분류 헤드와 유사합니다.",C,T,V;return m=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERpc3RpbEJlcnRGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uJTBBJTBBbW9kZWwlMjAlM0QlMjBEaXN0aWxCZXJ0Rm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),T=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERpc3RpbEJlcnRGb3JRdWVzdGlvbkFuc3dlcmluZyUwQSUwQW1vZGVsJTIwJTNEJTIwRGlzdGlsQmVydEZvclF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fcHJldHJhaW5lZCglMjJkaXN0aWxiZXJ0LWJhc2UtdW5jYXNlZCUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),{c(){n=i("p"),n.innerHTML=h,a=p(),d(m.$$.fragment),_=p(),b=i("p"),b.innerHTML=U,C=p(),d(T.$$.fragment)},l(u){n=c(u,"P",{"data-svelte-h":!0}),f(n)!=="svelte-1d1i7tu"&&(n.innerHTML=h),a=o(u),g(m.$$.fragment,u),_=o(u),b=c(u,"P",{"data-svelte-h":!0}),f(b)!=="svelte-127ugzm"&&(b.innerHTML=U),C=o(u),g(T.$$.fragment,u)},m(u,M){l(u,n,M),l(u,a,M),$(m,u,M),l(u,_,M),l(u,b,M),l(u,C,M),$(T,u,M),V=!0},p:Q,i(u){V||(y(m.$$.fragment,u),y(T.$$.fragment,u),V=!0)},o(u){v(m.$$.fragment,u),v(T.$$.fragment,u),V=!1},d(u){u&&(e(n),e(a),e(_),e(b),e(C)),j(m,u),j(T,u)}}}function El(k){let n,h;return n=new ks({props:{$$slots:{default:[Hl]},$$scope:{ctx:k}}}),{c(){d(n.$$.fragment)},l(a){g(n.$$.fragment,a)},m(a,m){$(n,a,m),h=!0},p(a,m){const _={};m&2&&(_.$$scope={dirty:m,ctx:a}),n.$set(_)},i(a){h||(y(n.$$.fragment,a),h=!0)},o(a){v(n.$$.fragment,a),h=!1},d(a){j(n,a)}}}function Fl(k){let n,h="예를 들어, <code>TFDistilBertForSequenceClassification</code>은 시퀀스 분류 헤드가 있는 기본 DistilBERT 모델입니다. 시퀀스 분류 헤드는 풀링된 출력 위에 있는 선형 레이어입니다.",a,m,_,b,U="다른 모델 헤드로 전환하여 이 체크포인트를 다른 작업에 쉽게 재사용할 수 있습니다. 질의응답 작업의 경우, <code>TFDistilBertForQuestionAnswering</code> 모델 헤드를 사용할 수 있습니다. 질의응답 헤드는 숨겨진 상태 출력 위에 선형 레이어가 있다는 점을 제외하면 시퀀스 분류 헤드와 유사합니다.",C,T,V;return m=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGRGlzdGlsQmVydEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24lMEElMEF0Zl9tb2RlbCUyMCUzRCUyMFRGRGlzdGlsQmVydEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKCUyMmRpc3RpbGJlcnQtYmFzZS11bmNhc2VkJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFDistilBertForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model = TFDistilBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),T=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGRGlzdGlsQmVydEZvclF1ZXN0aW9uQW5zd2VyaW5nJTBBJTBBdGZfbW9kZWwlMjAlM0QlMjBURkRpc3RpbEJlcnRGb3JRdWVzdGlvbkFuc3dlcmluZy5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFDistilBertForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model = TFDistilBertForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),{c(){n=i("p"),n.innerHTML=h,a=p(),d(m.$$.fragment),_=p(),b=i("p"),b.innerHTML=U,C=p(),d(T.$$.fragment)},l(u){n=c(u,"P",{"data-svelte-h":!0}),f(n)!=="svelte-io5rj8"&&(n.innerHTML=h),a=o(u),g(m.$$.fragment,u),_=o(u),b=c(u,"P",{"data-svelte-h":!0}),f(b)!=="svelte-87qals"&&(b.innerHTML=U),C=o(u),g(T.$$.fragment,u)},m(u,M){l(u,n,M),l(u,a,M),$(m,u,M),l(u,_,M),l(u,b,M),l(u,C,M),$(T,u,M),V=!0},p:Q,i(u){V||(y(m.$$.fragment,u),y(T.$$.fragment,u),V=!0)},o(u){v(m.$$.fragment,u),v(T.$$.fragment,u),V=!1},d(u){u&&(e(n),e(a),e(_),e(b),e(C)),j(m,u),j(T,u)}}}function Xl(k){let n,h;return n=new ks({props:{$$slots:{default:[Fl]},$$scope:{ctx:k}}}),{c(){d(n.$$.fragment)},l(a){g(n.$$.fragment,a)},m(a,m){$(n,a,m),h=!0},p(a,m){const _={};m&2&&(_.$$scope={dirty:m,ctx:a}),n.$set(_)},i(a){h||(y(n.$$.fragment,a),h=!0)},o(a){v(n.$$.fragment,a),h=!1},d(a){j(n,a)}}}function Bl(k){let n,h='모든 모델이 빠른 토크나이저를 지원하는 것은 아닙니다. 이 <a href="index#supported-frameworks">표</a>에서 모델의 빠른 토크나이저 지원 여부를 확인하세요.';return{c(){n=i("p"),n.innerHTML=h},l(a){n=c(a,"P",{"data-svelte-h":!0}),f(n)!=="svelte-w2xdht"&&(n.innerHTML=h)},m(a,m){l(a,n,m)},p:Q,d(a){a&&e(n)}}}function Yl(k){let n,h="<code>AutoTokenizer</code>는 기본적으로 빠른 토크나이저를 가져오려고 합니다. 이 동작을 비활성화하려면 <code>from_pretrained</code>에서 <code>use_fast=False</code>를 설정하면 됩니다.";return{c(){n=i("p"),n.innerHTML=h},l(a){n=c(a,"P",{"data-svelte-h":!0}),f(n)!=="svelte-7sn66f"&&(n.innerHTML=h)},m(a,m){l(a,n,m)},p:Q,d(a){a&&e(n)}}}function Ll(k){let n,h="사용자 지정을 원하지 않는 경우 <code>from_pretrained</code> 메소드를 사용하여 모델의 기본 이미지 프로세서 매개변수를 불러오면 됩니다.";return{c(){n=i("p"),n.innerHTML=h},l(a){n=c(a,"P",{"data-svelte-h":!0}),f(n)!=="svelte-f8vs2z"&&(n.innerHTML=h)},m(a,m){l(a,n,m)},p:Q,d(a){a&&e(n)}}}function Nl(k){let n,h="사용자 지정이 필요하지 않은 경우 <code>from_pretrained</code> 메소드를 사용하여 모델의 기본 특성 추출기 ㅁ개변수를 불러 오면 됩니다.";return{c(){n=i("p"),n.innerHTML=h},l(a){n=c(a,"P",{"data-svelte-h":!0}),f(n)!=="svelte-vnhffe"&&(n.innerHTML=h)},m(a,m){l(a,n,m)},p:Q,d(a){a&&e(n)}}}function Ql(k){let n,h,a,m,_,b,U,C,T,V,u="맞춤형 아키텍처 만들기",M,z,x='<a href="model_doc/auto"><code>AutoClass</code></a>는 모델 아키텍처를 자동으로 추론하고 미리 학습된 configuration과 가중치를 다운로드합니다. 일반적으로 체크포인트에 구애받지 않는 코드를 생성하려면 <code>AutoClass</code>를 사용하는 것이 좋습니다. 하지만 특정 모델 파라미터를 보다 세밀하게 제어하고자 하는 사용자는 몇 가지 기본 클래스만으로 커스텀 🤗 Transformers 모델을 생성할 수 있습니다. 이는 🤗 Transformers 모델을 연구, 교육 또는 실험하는 데 관심이 있는 모든 사용자에게 특히 유용할 수 있습니다. 이 가이드에서는 ‘AutoClass’를 사용하지 않고 커스텀 모델을 만드는 방법에 대해 알아보겠습니다:',E,G,R="<li>모델 configuration을 가져오고 사용자 지정합니다.</li> <li>모델 아키텍처를 생성합니다.</li> <li>텍스트에 사용할 느리거나 빠른 토큰화기를 만듭니다.</li> <li>비전 작업을 위한 이미지 프로세서를 생성합니다.</li> <li>오디오 작업을 위한 특성 추출기를 생성합니다.</li> <li>멀티모달 작업용 프로세서를 생성합니다.</li>",H,r,w,ce,pt,Js,le,Gs="Configuration",Me,ot,Hs='<a href="main_classes/configuration">configuration</a>은 모델의 특정 속성을 나타냅니다. 각 모델 구성에는 서로 다른 속성이 있습니다. 예를 들어, 모든 NLP 모델에는 <code>hidden_size</code>, <code>num_attention_heads</code>, <code>num_hidden_layers</code> 및 <code>vocab_size</code> 속성이 공통으로 있습니다. 이러한 속성은 모델을 구성할 attention heads 또는 hidden layers의 수를 지정합니다.',we,it,Es='<a href="model_doc/distilbert">DistilBERT</a> 속성을 검사하기 위해 <code>DistilBertConfig</code>에 접근하여 자세히 살펴봅니다:',Te,ct,Ze,mt,Fs="<code>DistilBertConfig</code>는 기본 <code>DistilBertModel</code>을 빌드하는 데 사용되는 모든 기본 속성을 표시합니다. 모든 속성은 커스터마이징이 가능하므로 실험을 위한 공간을 만들 수 있습니다. 예를 들어 기본 모델을 다음과 같이 커스터마이즈할 수 있습니다:",qe,ut,Xs="<li><code>activation</code> 파라미터로 다른 활성화 함수를 사용해 보세요.</li> <li><code>attention_dropout</code> 파라미터를 사용하여 어텐션 확률에 더 높은 드롭아웃 비율을 사용하세요.</li>",Ce,ft,ke,ht,Bs="사전 학습된 모델 속성은 <code>from_pretrained()</code> 함수에서 수정할 수 있습니다:",Je,dt,We,gt,Ys="모델 구성이 만족스러우면 <code>save_pretrained()</code>로 저장할 수 있습니다. 설정 파일은 지정된 작업 경로에 JSON 파일로 저장됩니다:",Ve,$t,xe,yt,Ls="configuration 파일을 재사용하려면 <code>from_pretrained()</code>를 사용하여 가져오세요:",Re,vt,Ue,P,ze,F,I,me,jt,Ws,ne,Ns="모델",Ge,_t,Qs='다음 단계는 <a href="main_classes/models">모델(model)</a>을 만드는 것입니다. 느슨하게 아키텍처라고도 불리는 모델은 각 계층이 수행하는 동작과 발생하는 작업을 정의합니다. configuration의 <code>num_hidden_layers</code>와 같은 속성은 아키텍처를 정의하는 데 사용됩니다. 모든 모델은 기본 클래스 <code>PreTrainedModel</code>과 입력 임베딩 크기 조정 및 셀프 어텐션 헤드 가지 치기와 같은 몇 가지 일반적인 메소드를 공유합니다. 또한 모든 모델은 <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html" rel="nofollow"><code>torch.nn.Module</code></a>, <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow"><code>tf.keras.Model</code></a> 또는 <a href="https://flax.readthedocs.io/en/latest/flax.linen.html#module" rel="nofollow"><code>flax.linen.Module</code></a>의 서브클래스이기도 합니다. 즉, 모델은 각 프레임워크의 사용법과 호환됩니다.',He,S,Ee,X,D,ue,bt,Vs,ae,Ps="모델 헤드",Fe,Mt,Is="이 시점에서 <em>은닉 상태(hidden state)</em>를 출력하는 기본 DistilBERT 모델을 갖게 됩니다. 은닉 상태는 최종 출력을 생성하기 위해 모델 헤드에 입력으로 전달됩니다. 🤗 Transformers는 모델이 해당 작업을 지원하는 한 각 작업마다 다른 모델 헤드를 제공합니다(즉, 번역과 같은 시퀀스 간 작업에는 DistilBERT를 사용할 수 없음).",Xe,A,Be,B,K,fe,wt,xs,re,Ss="토크나이저",Ye,Tt,Ds='텍스트 데이터에 모델을 사용하기 전에 마지막으로 필요한 기본 클래스는 원시 텍스트를 텐서로 변환하는 <a href="main_classes/tokenizer">토크나이저</a>입니다. 🤗 Transformers에 사용할 수 있는 토크나이저는 두 가지 유형이 있습니다:',Le,Zt,As=`<li><code>PreTrainedTokenizer</code>: 파이썬으로 구현된 토크나이저입니다.</li> <li><code>PreTrainedTokenizerFast</code>: Rust 기반 <a href="https://huggingface.co/docs/tokenizers/python/latest/" rel="nofollow">🤗 Tokenizer</a> 라이브러리로 만들어진 토크나이저입니다. 이 토크나이저는 Rust로 구현되어 배치 토큰화에서 특히 빠릅니다. 빠른 토크나이저는 토큰을 원래 단어나 문자에 매핑하는 <em>오프셋 매핑</em>과 같은 추가 메소드도 제공합니다.
두 토크나이저 모두 인코딩 및 디코딩, 새 토큰 추가, 특수 토큰 관리와 같은 일반적인 방법을 지원합니다.</li>`,Ne,O,Qe,qt,Ks="토크나이저를 직접 학습한 경우, <em>어휘(vocabulary)</em> 파일에서 토크나이저를 만들 수 있습니다:",Pe,Ct,Ie,kt,Os="사용자 지정 토크나이저의 어휘는 사전 학습된 모델의 토크나이저에서 생성된 어휘와 다를 수 있다는 점을 기억하는 것이 중요합니다. 사전 학습된 모델을 사용하는 경우 사전 학습된 모델의 어휘를 사용해야 하며, 그렇지 않으면 입력이 의미를 갖지 못합니다. <code>DistilBertTokenizer</code> 클래스를 사용하여 사전 학습된 모델의 어휘로 토크나이저를 생성합니다:",Se,Jt,De,Wt,tl="<code>DistilBertTokenizerFast</code> 클래스로 빠른 토크나이저를 생성합니다:",Ae,Vt,Ke,tt,Oe,Y,et,he,xt,Rs,pe,el="이미지 프로세서",ts,Rt,sl="이미지 프로세서(image processor)는 비전 입력을 처리합니다. 기본 <code>ImageProcessingMixin</code> 클래스에서 상속합니다.",es,Ut,ll='사용하려면 사용 중인 모델과 연결된 이미지 프로세서를 생성합니다. 예를 들어, 이미지 분류에 <a href="model_doc/vit">ViT</a>를 사용하는 경우 기본 <code>ViTImageProcessor</code>를 생성합니다:',ss,zt,ls,st,ns,Gt,nl="사용자 지정 이미지 프로세서를 생성하려면 <code>ViTImageProcessor</code> 파라미터를 수정합니다:",as,Ht,rs,L,lt,de,Et,Us,oe,al="특성 추출기",ps,Ft,rl="특성 추출기(feature extractor)는 오디오 입력을 처리합니다. 기본 <code>FeatureExtractionMixin</code> 클래스에서 상속되며, 오디오 입력을 처리하기 위해 <code>SequenceFeatureExtractor</code> 클래스에서 상속할 수도 있습니다.",os,Xt,pl='사용하려면 사용 중인 모델과 연결된 특성 추출기를 생성합니다. 예를 들어, 오디오 분류에 <a href="model_doc/wav2vec2">Wav2Vec2</a>를 사용하는 경우 기본 <code>Wav2Vec2FeatureExtractor</code>를 생성합니다:',is,Bt,cs,nt,ms,Yt,ol="사용자 지정 특성 추출기를 만들려면 <code>Wav2Vec2FeatureExtractor</code> 매개변수를 수정합니다:",us,Lt,fs,N,at,ge,Nt,zs,ie,il="프로세서",hs,Qt,cl="멀티모달 작업을 지원하는 모델의 경우, 🤗 Transformers는 특성 추출기 및 토크나이저와 같은 처리 클래스를 단일 객체로 편리하게 래핑하는 프로세서 클래스를 제공합니다. 예를 들어, 자동 음성 인식 작업(Automatic Speech Recognition task (ASR))에 <code>Wav2Vec2Processor</code>를 사용한다고 가정해 보겠습니다. 자동 음성 인식 작업은 오디오를 텍스트로 변환하므로 특성 추출기와 토크나이저가 필요합니다.",ds,Pt,ml="오디오 입력을 처리할 특성 추출기를 만듭니다:",gs,It,$s,St,ul="텍스트 입력을 처리할 토크나이저를 만듭니다:",ys,Dt,vs,At,fl="<code>Wav2Vec2Processor</code>에서 특성 추출기와 토크나이저를 결합합니다:",js,Kt,_s,Ot,hl="configuration과 모델이라는 두 가지 기본 클래스와 추가 전처리 클래스(토크나이저, 이미지 프로세서, 특성 추출기 또는 프로세서)를 사용하면 🤗 Transformers에서 지원하는 모든 모델을 만들 수 있습니다. 이러한 각 기본 클래스는 구성이 가능하므로 원하는 특정 속성을 사용할 수 있습니다. 학습을 위해 모델을 쉽게 설정하거나 기존의 사전 학습된 모델을 수정하여 미세 조정할 수 있습니다.",bs,$e,Ms;return C=new rt({}),pt=new rt({}),ct=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERpc3RpbEJlcnRDb25maWclMEElMEFjb25maWclMjAlM0QlMjBEaXN0aWxCZXJ0Q29uZmlnKCklMEFwcmludChjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertConfig

<span class="hljs-meta">&gt;&gt;&gt; </span>config = DistilBertConfig()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(config)
DistilBertConfig {
  <span class="hljs-string">&quot;activation&quot;</span>: <span class="hljs-string">&quot;gelu&quot;</span>,
  <span class="hljs-string">&quot;attention_dropout&quot;</span>: <span class="hljs-number">0.1</span>,
  <span class="hljs-string">&quot;dim&quot;</span>: <span class="hljs-number">768</span>,
  <span class="hljs-string">&quot;dropout&quot;</span>: <span class="hljs-number">0.1</span>,
  <span class="hljs-string">&quot;hidden_dim&quot;</span>: <span class="hljs-number">3072</span>,
  <span class="hljs-string">&quot;initializer_range&quot;</span>: <span class="hljs-number">0.02</span>,
  <span class="hljs-string">&quot;max_position_embeddings&quot;</span>: <span class="hljs-number">512</span>,
  <span class="hljs-string">&quot;model_type&quot;</span>: <span class="hljs-string">&quot;distilbert&quot;</span>,
  <span class="hljs-string">&quot;n_heads&quot;</span>: <span class="hljs-number">12</span>,
  <span class="hljs-string">&quot;n_layers&quot;</span>: <span class="hljs-number">6</span>,
  <span class="hljs-string">&quot;pad_token_id&quot;</span>: <span class="hljs-number">0</span>,
  <span class="hljs-string">&quot;qa_dropout&quot;</span>: <span class="hljs-number">0.1</span>,
  <span class="hljs-string">&quot;seq_classif_dropout&quot;</span>: <span class="hljs-number">0.2</span>,
  <span class="hljs-string">&quot;sinusoidal_pos_embds&quot;</span>: false,
  <span class="hljs-string">&quot;transformers_version&quot;</span>: <span class="hljs-string">&quot;4.16.2&quot;</span>,
  <span class="hljs-string">&quot;vocab_size&quot;</span>: <span class="hljs-number">30522</span>
}`}}),ft=new W({props:{code:"bXlfY29uZmlnJTIwJTNEJTIwRGlzdGlsQmVydENvbmZpZyhhY3RpdmF0aW9uJTNEJTIycmVsdSUyMiUyQyUyMGF0dGVudGlvbl9kcm9wb3V0JTNEMC40KSUwQXByaW50KG15X2NvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>my_config = DistilBertConfig(activation=<span class="hljs-string">&quot;relu&quot;</span>, attention_dropout=<span class="hljs-number">0.4</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(my_config)
DistilBertConfig {
  <span class="hljs-string">&quot;activation&quot;</span>: <span class="hljs-string">&quot;relu&quot;</span>,
  <span class="hljs-string">&quot;attention_dropout&quot;</span>: <span class="hljs-number">0.4</span>,
  <span class="hljs-string">&quot;dim&quot;</span>: <span class="hljs-number">768</span>,
  <span class="hljs-string">&quot;dropout&quot;</span>: <span class="hljs-number">0.1</span>,
  <span class="hljs-string">&quot;hidden_dim&quot;</span>: <span class="hljs-number">3072</span>,
  <span class="hljs-string">&quot;initializer_range&quot;</span>: <span class="hljs-number">0.02</span>,
  <span class="hljs-string">&quot;max_position_embeddings&quot;</span>: <span class="hljs-number">512</span>,
  <span class="hljs-string">&quot;model_type&quot;</span>: <span class="hljs-string">&quot;distilbert&quot;</span>,
  <span class="hljs-string">&quot;n_heads&quot;</span>: <span class="hljs-number">12</span>,
  <span class="hljs-string">&quot;n_layers&quot;</span>: <span class="hljs-number">6</span>,
  <span class="hljs-string">&quot;pad_token_id&quot;</span>: <span class="hljs-number">0</span>,
  <span class="hljs-string">&quot;qa_dropout&quot;</span>: <span class="hljs-number">0.1</span>,
  <span class="hljs-string">&quot;seq_classif_dropout&quot;</span>: <span class="hljs-number">0.2</span>,
  <span class="hljs-string">&quot;sinusoidal_pos_embds&quot;</span>: false,
  <span class="hljs-string">&quot;transformers_version&quot;</span>: <span class="hljs-string">&quot;4.16.2&quot;</span>,
  <span class="hljs-string">&quot;vocab_size&quot;</span>: <span class="hljs-number">30522</span>
}`}}),dt=new W({props:{code:"bXlfY29uZmlnJTIwJTNEJTIwRGlzdGlsQmVydENvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIlMkMlMjBhY3RpdmF0aW9uJTNEJTIycmVsdSUyMiUyQyUyMGF0dGVudGlvbl9kcm9wb3V0JTNEMC40KQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>my_config = DistilBertConfig.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>, activation=<span class="hljs-string">&quot;relu&quot;</span>, attention_dropout=<span class="hljs-number">0.4</span>)'}}),$t=new W({props:{code:"bXlfY29uZmlnLnNhdmVfcHJldHJhaW5lZChzYXZlX2RpcmVjdG9yeSUzRCUyMi4lMkZ5b3VyX21vZGVsX3NhdmVfcGF0aCUyMik=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>my_config.save_pretrained(save_directory=<span class="hljs-string">&quot;./your_model_save_path&quot;</span>)'}}),vt=new W({props:{code:"bXlfY29uZmlnJTIwJTNEJTIwRGlzdGlsQmVydENvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyLiUyRnlvdXJfbW9kZWxfc2F2ZV9wYXRoJTJGY29uZmlnLmpzb24lMjIp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>my_config = DistilBertConfig.from_pretrained(<span class="hljs-string">&quot;./your_model_save_path/config.json&quot;</span>)'}}),P=new be({props:{$$slots:{default:[xl]},$$scope:{ctx:k}}}),jt=new rt({}),S=new ql({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Gl],pytorch:[Ul]},$$scope:{ctx:k}}}),bt=new rt({}),A=new ql({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Xl],pytorch:[El]},$$scope:{ctx:k}}}),wt=new rt({}),O=new be({props:{warning:!0,$$slots:{default:[Bl]},$$scope:{ctx:k}}}),Ct=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERpc3RpbEJlcnRUb2tlbml6ZXIlMEElMEFteV90b2tlbml6ZXIlMjAlM0QlMjBEaXN0aWxCZXJ0VG9rZW5pemVyKHZvY2FiX2ZpbGUlM0QlMjJteV92b2NhYl9maWxlLnR4dCUyMiUyQyUyMGRvX2xvd2VyX2Nhc2UlM0RGYWxzZSUyQyUyMHBhZGRpbmdfc2lkZSUzRCUyMmxlZnQlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>my_tokenizer = DistilBertTokenizer(vocab_file=<span class="hljs-string">&quot;my_vocab_file.txt&quot;</span>, do_lower_case=<span class="hljs-literal">False</span>, padding_side=<span class="hljs-string">&quot;left&quot;</span>)`}}),Jt=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERpc3RpbEJlcnRUb2tlbml6ZXIlMEElMEFzbG93X3Rva2VuaXplciUyMCUzRCUyMERpc3RpbEJlcnRUb2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmRpc3RpbGJlcnQtYmFzZS11bmNhc2VkJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>slow_tokenizer = DistilBertTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),Vt=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERpc3RpbEJlcnRUb2tlbml6ZXJGYXN0JTBBJTBBZmFzdF90b2tlbml6ZXIlMjAlM0QlMjBEaXN0aWxCZXJ0VG9rZW5pemVyRmFzdC5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertTokenizerFast

<span class="hljs-meta">&gt;&gt;&gt; </span>fast_tokenizer = DistilBertTokenizerFast.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),tt=new be({props:{$$slots:{default:[Yl]},$$scope:{ctx:k}}}),xt=new rt({}),zt=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFZpVEltYWdlUHJvY2Vzc29yJTBBJTBBdml0X2V4dHJhY3RvciUyMCUzRCUyMFZpVEltYWdlUHJvY2Vzc29yKCklMEFwcmludCh2aXRfZXh0cmFjdG9yKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ViTImageProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span>vit_extractor = ViTImageProcessor()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(vit_extractor)
ViTImageProcessor {
  <span class="hljs-string">&quot;do_normalize&quot;</span>: true,
  <span class="hljs-string">&quot;do_resize&quot;</span>: true,
  <span class="hljs-string">&quot;feature_extractor_type&quot;</span>: <span class="hljs-string">&quot;ViTImageProcessor&quot;</span>,
  <span class="hljs-string">&quot;image_mean&quot;</span>: [
    <span class="hljs-number">0.5</span>,
    <span class="hljs-number">0.5</span>,
    <span class="hljs-number">0.5</span>
  ],
  <span class="hljs-string">&quot;image_std&quot;</span>: [
    <span class="hljs-number">0.5</span>,
    <span class="hljs-number">0.5</span>,
    <span class="hljs-number">0.5</span>
  ],
  <span class="hljs-string">&quot;resample&quot;</span>: <span class="hljs-number">2</span>,
  <span class="hljs-string">&quot;size&quot;</span>: <span class="hljs-number">224</span>
}`}}),st=new be({props:{$$slots:{default:[Ll]},$$scope:{ctx:k}}}),Ht=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFZpVEltYWdlUHJvY2Vzc29yJTBBJTBBbXlfdml0X2V4dHJhY3RvciUyMCUzRCUyMFZpVEltYWdlUHJvY2Vzc29yKHJlc2FtcGxlJTNEJTIyUElMLkltYWdlLkJPWCUyMiUyQyUyMGRvX25vcm1hbGl6ZSUzREZhbHNlJTJDJTIwaW1hZ2VfbWVhbiUzRCU1QjAuMyUyQyUyMDAuMyUyQyUyMDAuMyU1RCklMEFwcmludChteV92aXRfZXh0cmFjdG9yKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ViTImageProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span>my_vit_extractor = ViTImageProcessor(resample=<span class="hljs-string">&quot;PIL.Image.BOX&quot;</span>, do_normalize=<span class="hljs-literal">False</span>, image_mean=[<span class="hljs-number">0.3</span>, <span class="hljs-number">0.3</span>, <span class="hljs-number">0.3</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(my_vit_extractor)
ViTImageProcessor {
  <span class="hljs-string">&quot;do_normalize&quot;</span>: false,
  <span class="hljs-string">&quot;do_resize&quot;</span>: true,
  <span class="hljs-string">&quot;feature_extractor_type&quot;</span>: <span class="hljs-string">&quot;ViTImageProcessor&quot;</span>,
  <span class="hljs-string">&quot;image_mean&quot;</span>: [
    <span class="hljs-number">0.3</span>,
    <span class="hljs-number">0.3</span>,
    <span class="hljs-number">0.3</span>
  ],
  <span class="hljs-string">&quot;image_std&quot;</span>: [
    <span class="hljs-number">0.5</span>,
    <span class="hljs-number">0.5</span>,
    <span class="hljs-number">0.5</span>
  ],
  <span class="hljs-string">&quot;resample&quot;</span>: <span class="hljs-string">&quot;PIL.Image.BOX&quot;</span>,
  <span class="hljs-string">&quot;size&quot;</span>: <span class="hljs-number">224</span>
}`}}),Et=new rt({}),Bt=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFdhdjJWZWMyRmVhdHVyZUV4dHJhY3RvciUwQSUwQXcydjJfZXh0cmFjdG9yJTIwJTNEJTIwV2F2MlZlYzJGZWF0dXJlRXh0cmFjdG9yKCklMEFwcmludCh3MnYyX2V4dHJhY3Rvcik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Wav2Vec2FeatureExtractor

<span class="hljs-meta">&gt;&gt;&gt; </span>w2v2_extractor = Wav2Vec2FeatureExtractor()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(w2v2_extractor)
Wav2Vec2FeatureExtractor {
  <span class="hljs-string">&quot;do_normalize&quot;</span>: true,
  <span class="hljs-string">&quot;feature_extractor_type&quot;</span>: <span class="hljs-string">&quot;Wav2Vec2FeatureExtractor&quot;</span>,
  <span class="hljs-string">&quot;feature_size&quot;</span>: <span class="hljs-number">1</span>,
  <span class="hljs-string">&quot;padding_side&quot;</span>: <span class="hljs-string">&quot;right&quot;</span>,
  <span class="hljs-string">&quot;padding_value&quot;</span>: <span class="hljs-number">0.0</span>,
  <span class="hljs-string">&quot;return_attention_mask&quot;</span>: false,
  <span class="hljs-string">&quot;sampling_rate&quot;</span>: <span class="hljs-number">16000</span>
}`}}),nt=new be({props:{$$slots:{default:[Nl]},$$scope:{ctx:k}}}),Lt=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFdhdjJWZWMyRmVhdHVyZUV4dHJhY3RvciUwQSUwQXcydjJfZXh0cmFjdG9yJTIwJTNEJTIwV2F2MlZlYzJGZWF0dXJlRXh0cmFjdG9yKHNhbXBsaW5nX3JhdGUlM0Q4MDAwJTJDJTIwZG9fbm9ybWFsaXplJTNERmFsc2UpJTBBcHJpbnQodzJ2Ml9leHRyYWN0b3Ip",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Wav2Vec2FeatureExtractor

<span class="hljs-meta">&gt;&gt;&gt; </span>w2v2_extractor = Wav2Vec2FeatureExtractor(sampling_rate=<span class="hljs-number">8000</span>, do_normalize=<span class="hljs-literal">False</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(w2v2_extractor)
Wav2Vec2FeatureExtractor {
  <span class="hljs-string">&quot;do_normalize&quot;</span>: false,
  <span class="hljs-string">&quot;feature_extractor_type&quot;</span>: <span class="hljs-string">&quot;Wav2Vec2FeatureExtractor&quot;</span>,
  <span class="hljs-string">&quot;feature_size&quot;</span>: <span class="hljs-number">1</span>,
  <span class="hljs-string">&quot;padding_side&quot;</span>: <span class="hljs-string">&quot;right&quot;</span>,
  <span class="hljs-string">&quot;padding_value&quot;</span>: <span class="hljs-number">0.0</span>,
  <span class="hljs-string">&quot;return_attention_mask&quot;</span>: false,
  <span class="hljs-string">&quot;sampling_rate&quot;</span>: <span class="hljs-number">8000</span>
}`}}),Nt=new rt({}),It=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFdhdjJWZWMyRmVhdHVyZUV4dHJhY3RvciUwQSUwQWZlYXR1cmVfZXh0cmFjdG9yJTIwJTNEJTIwV2F2MlZlYzJGZWF0dXJlRXh0cmFjdG9yKHBhZGRpbmdfdmFsdWUlM0QxLjAlMkMlMjBkb19ub3JtYWxpemUlM0RUcnVlKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Wav2Vec2FeatureExtractor

<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = Wav2Vec2FeatureExtractor(padding_value=<span class="hljs-number">1.0</span>, do_normalize=<span class="hljs-literal">True</span>)`}}),Dt=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFdhdjJWZWMyQ1RDVG9rZW5pemVyJTBBJTBBdG9rZW5pemVyJTIwJTNEJTIwV2F2MlZlYzJDVENUb2tlbml6ZXIodm9jYWJfZmlsZSUzRCUyMm15X3ZvY2FiX2ZpbGUudHh0JTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Wav2Vec2CTCTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = Wav2Vec2CTCTokenizer(vocab_file=<span class="hljs-string">&quot;my_vocab_file.txt&quot;</span>)`}}),Kt=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFdhdjJWZWMyUHJvY2Vzc29yJTBBJTBBcHJvY2Vzc29yJTIwJTNEJTIwV2F2MlZlYzJQcm9jZXNzb3IoZmVhdHVyZV9leHRyYWN0b3IlM0RmZWF0dXJlX2V4dHJhY3RvciUyQyUyMHRva2VuaXplciUzRHRva2VuaXplcik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Wav2Vec2Processor

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)`}}),{c(){n=i("meta"),h=p(),a=i("p"),m=p(),_=i("h1"),b=i("a"),U=i("span"),d(C.$$.fragment),T=p(),V=i("span"),V.textContent=u,M=p(),z=i("p"),z.innerHTML=x,E=p(),G=i("ul"),G.innerHTML=R,H=p(),r=i("h2"),w=i("a"),ce=i("span"),d(pt.$$.fragment),Js=p(),le=i("span"),le.textContent=Gs,Me=p(),ot=i("p"),ot.innerHTML=Hs,we=p(),it=i("p"),it.innerHTML=Es,Te=p(),d(ct.$$.fragment),Ze=p(),mt=i("p"),mt.innerHTML=Fs,qe=p(),ut=i("ul"),ut.innerHTML=Xs,Ce=p(),d(ft.$$.fragment),ke=p(),ht=i("p"),ht.innerHTML=Bs,Je=p(),d(dt.$$.fragment),We=p(),gt=i("p"),gt.innerHTML=Ys,Ve=p(),d($t.$$.fragment),xe=p(),yt=i("p"),yt.innerHTML=Ls,Re=p(),d(vt.$$.fragment),Ue=p(),d(P.$$.fragment),ze=p(),F=i("h2"),I=i("a"),me=i("span"),d(jt.$$.fragment),Ws=p(),ne=i("span"),ne.textContent=Ns,Ge=p(),_t=i("p"),_t.innerHTML=Qs,He=p(),d(S.$$.fragment),Ee=p(),X=i("h3"),D=i("a"),ue=i("span"),d(bt.$$.fragment),Vs=p(),ae=i("span"),ae.textContent=Ps,Fe=p(),Mt=i("p"),Mt.innerHTML=Is,Xe=p(),d(A.$$.fragment),Be=p(),B=i("h2"),K=i("a"),fe=i("span"),d(wt.$$.fragment),xs=p(),re=i("span"),re.textContent=Ss,Ye=p(),Tt=i("p"),Tt.innerHTML=Ds,Le=p(),Zt=i("ul"),Zt.innerHTML=As,Ne=p(),d(O.$$.fragment),Qe=p(),qt=i("p"),qt.innerHTML=Ks,Pe=p(),d(Ct.$$.fragment),Ie=p(),kt=i("p"),kt.innerHTML=Os,Se=p(),d(Jt.$$.fragment),De=p(),Wt=i("p"),Wt.innerHTML=tl,Ae=p(),d(Vt.$$.fragment),Ke=p(),d(tt.$$.fragment),Oe=p(),Y=i("h2"),et=i("a"),he=i("span"),d(xt.$$.fragment),Rs=p(),pe=i("span"),pe.textContent=el,ts=p(),Rt=i("p"),Rt.innerHTML=sl,es=p(),Ut=i("p"),Ut.innerHTML=ll,ss=p(),d(zt.$$.fragment),ls=p(),d(st.$$.fragment),ns=p(),Gt=i("p"),Gt.innerHTML=nl,as=p(),d(Ht.$$.fragment),rs=p(),L=i("h2"),lt=i("a"),de=i("span"),d(Et.$$.fragment),Us=p(),oe=i("span"),oe.textContent=al,ps=p(),Ft=i("p"),Ft.innerHTML=rl,os=p(),Xt=i("p"),Xt.innerHTML=pl,is=p(),d(Bt.$$.fragment),cs=p(),d(nt.$$.fragment),ms=p(),Yt=i("p"),Yt.innerHTML=ol,us=p(),d(Lt.$$.fragment),fs=p(),N=i("h2"),at=i("a"),ge=i("span"),d(Nt.$$.fragment),zs=p(),ie=i("span"),ie.textContent=il,hs=p(),Qt=i("p"),Qt.innerHTML=cl,ds=p(),Pt=i("p"),Pt.textContent=ml,gs=p(),d(It.$$.fragment),$s=p(),St=i("p"),St.textContent=ul,ys=p(),d(Dt.$$.fragment),vs=p(),At=i("p"),At.innerHTML=fl,js=p(),d(Kt.$$.fragment),_s=p(),Ot=i("p"),Ot.textContent=hl,bs=p(),$e=i("p"),this.h()},l(t){const 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