import{s as gt,o as vt,n as Ie}from"../chunks/scheduler.36a0863c.js";import{S as yt,i as jt,g as o,s as c,u as C,A as Mt,h as p,f as t,c as i,j as k,v as x,y as $,k as b,o as g,a as s,w,d as _,t as Z,x as T}from"../chunks/index.d0f7a4c4.js";import{T as kt}from"../chunks/Tip.4c7d7eb1.js";import{I as ve}from"../chunks/IconCopyLink.bc5fad79.js";import{C as L}from"../chunks/CodeBlock.69433f87.js";import{F as Ct,M as $t}from"../chunks/Markdown.1d1f160a.js";function xt(E){let r,f='Recuerda, la arquitectura se refiere al esqueleto del modelo y los checkpoints son los pesos para una arquitectura dada. Por ejemplo, <a href="https://huggingface.co/bert-base-uncased" rel="nofollow">BERT</a> es una arquitectura, mientras que <code>bert-base-uncased</code> es un checkpoint. Modelo es un término general que puede significar una arquitectura o un checkpoint.';return{c(){r=o("p"),r.innerHTML=f},l(l){r=p(l,"P",{"data-svelte-h":!0}),$(r)!=="svelte-1wzhep4"&&(r.innerHTML=f)},m(l,u){s(l,r,u)},p:Ie,d(l){l&&t(r)}}}function wt(E){let r,f='Finalmente, las clases <code>AutoModelFor</code> te permiten cargar un modelo preentrenado para una tarea dada (revisa <a href="model_doc/auto">aquí</a> para conocer la lista completa de tareas disponibles). Por ejemplo, cargue un modelo para clasificación de secuencias con <code>AutoModelForSequenceClassification.from_pretrained()</code>:',l,u,m,d,J="Reutiliza fácilmente el mismo checkpoint para cargar una aquitectura para alguna tarea diferente:",j,v,q,y,W='Generalmente recomendamos utilizar las clases <code>AutoTokenizer</code> y <code>AutoModelFor</code> para cargar instancias pre-entrenadas de modelos. Ésto asegurará que cargues la arquitectura correcta en cada ocasión. En el siguiente <a href="preprocessing">tutorial</a>, aprende a usar tu tokenizador recién cargado, el extractor de características y el procesador para preprocesar un dataset para fine-tuning.',M;return u=new L({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>)`}}),v=new L({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24lMEElMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKCUyMmRpc3RpbGJlcnQtYmFzZS11bmNhc2VkJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),{c(){r=o("p"),r.innerHTML=f,l=c(),C(u.$$.fragment),m=c(),d=o("p"),d.textContent=J,j=c(),C(v.$$.fragment),q=c(),y=o("p"),y.innerHTML=W},l(a){r=p(a,"P",{"data-svelte-h":!0}),$(r)!=="svelte-3za6zf"&&(r.innerHTML=f),l=i(a),x(u.$$.fragment,a),m=i(a),d=p(a,"P",{"data-svelte-h":!0}),$(d)!=="svelte-17xqunq"&&(d.textContent=J),j=i(a),x(v.$$.fragment,a),q=i(a),y=p(a,"P",{"data-svelte-h":!0}),$(y)!=="svelte-6eqqlh"&&(y.innerHTML=W)},m(a,h){s(a,r,h),s(a,l,h),w(u,a,h),s(a,m,h),s(a,d,h),s(a,j,h),w(v,a,h),s(a,q,h),s(a,y,h),M=!0},p:Ie,i(a){M||(_(u.$$.fragment,a),_(v.$$.fragment,a),M=!0)},o(a){Z(u.$$.fragment,a),Z(v.$$.fragment,a),M=!1},d(a){a&&(t(r),t(l),t(m),t(d),t(j),t(q),t(y)),T(u,a),T(v,a)}}}function _t(E){let r,f;return r=new $t({props:{$$slots:{default:[wt]},$$scope:{ctx:E}}}),{c(){C(r.$$.fragment)},l(l){x(r.$$.fragment,l)},m(l,u){w(r,l,u),f=!0},p(l,u){const m={};u&2&&(m.$$scope={dirty:u,ctx:l}),r.$set(m)},i(l){f||(_(r.$$.fragment,l),f=!0)},o(l){Z(r.$$.fragment,l),f=!1},d(l){T(r,l)}}}function Zt(E){let r,f='Finalmente, la clase <code>TFAutoModelFor</code> te permite cargar tu modelo pre-entrenado para una tarea dada (revisa <a href="model_doc/auto">aquí</a> para conocer la lista completa de tareas disponibles). Por ejemplo, carga un modelo para clasificación de secuencias con <code>TFAutoModelForSequenceClassification.from_pretrained()</code>:',l,u,m,d,J="Reutiliza fácilmente el mismo checkpoint para cargar una aquitectura para alguna tarea diferente:",j,v,q,y,W='Generalmente recomendamos utilizar las clases <code>AutoTokenizer</code> y <code>TFAutoModelFor</code> para cargar instancias de modelos pre-entrenados. Ésto asegurará que cargues la arquitectura correcta cada vez. En el siguiente <a href="preprocessing">tutorial</a>, aprende a usar tu tokenizador recién cargado, el extractor de características y el procesador para preprocesar un dataset para fine-tuning.',M;return u=new L({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>)`}}),v=new L({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGQXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbiUwQSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMjJkaXN0aWxiZXJ0LWJhc2UtdW5jYXNlZCUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)`}}),{c(){r=o("p"),r.innerHTML=f,l=c(),C(u.$$.fragment),m=c(),d=o("p"),d.textContent=J,j=c(),C(v.$$.fragment),q=c(),y=o("p"),y.innerHTML=W},l(a){r=p(a,"P",{"data-svelte-h":!0}),$(r)!=="svelte-6xkpop"&&(r.innerHTML=f),l=i(a),x(u.$$.fragment,a),m=i(a),d=p(a,"P",{"data-svelte-h":!0}),$(d)!=="svelte-17xqunq"&&(d.textContent=J),j=i(a),x(v.$$.fragment,a),q=i(a),y=p(a,"P",{"data-svelte-h":!0}),$(y)!=="svelte-1ujd6p5"&&(y.innerHTML=W)},m(a,h){s(a,r,h),s(a,l,h),w(u,a,h),s(a,m,h),s(a,d,h),s(a,j,h),w(v,a,h),s(a,q,h),s(a,y,h),M=!0},p:Ie,i(a){M||(_(u.$$.fragment,a),_(v.$$.fragment,a),M=!0)},o(a){Z(u.$$.fragment,a),Z(v.$$.fragment,a),M=!1},d(a){a&&(t(r),t(l),t(m),t(d),t(j),t(q),t(y)),T(u,a),T(v,a)}}}function Tt(E){let r,f;return r=new $t({props:{$$slots:{default:[Zt]},$$scope:{ctx:E}}}),{c(){C(r.$$.fragment)},l(l){x(r.$$.fragment,l)},m(l,u){w(r,l,u),f=!0},p(l,u){const m={};u&2&&(m.$$scope={dirty:u,ctx:l}),r.$set(m)},i(l){f||(_(r.$$.fragment,l),f=!0)},o(l){Z(r.$$.fragment,l),f=!1},d(l){T(r,l)}}}function qt(E){let r,f,l,u,m,d,J,j,v,q,y="Carga instancias preentrenadas con un AutoClass",W,M,a="Con tantas arquitecturas diferentes de Transformer puede ser retador crear una para tu checkpoint. Como parte de la filosofía central de 🤗 Transformers para hacer que la biblioteca sea fácil, simple y flexible de usar; una <code>AutoClass</code> automáticamente infiere y carga la arquitectura correcta desde un checkpoint dado. El método <code>from_pretrained</code> te permite cargar rápidamente un modelo preentrenado para cualquier arquitectura, por lo que no tendrás que dedicar tiempo y recursos para entrenar uno desde cero. Producir este tipo de código con checkpoint implica que si funciona con uno, funcionará también con otro (siempre que haya sido entrenado para una tarea similar) incluso si la arquitectura es distinta.",h,V,ye,X,Qe="En este tutorial, aprenderás a:",je,Y,Be="<li>Cargar un tokenizador pre-entrenado.</li> <li>Cargar un extractor de características (feature extractor en inglés) pre-entrenado.</li> <li>Cargar un procesador pre-entrenado.</li> <li>Cargar un modelo pre-entrenado.</li>",Me,A,R,de,S,Le,ce,Ke="AutoTokenizer",ke,I,De="Casi cualquier tarea de Procesamiento de Lenguaje Natural comienza con un tokenizador. Un tokenizador convierte tu input a un formato que puede ser procesado por el modelo.",Ce,Q,Oe="Carga un tokenizador con <code>AutoTokenizer.from_pretrained()</code>:",xe,B,we,K,et="Luego tokeniza tu input como lo mostrado a continuación:",_e,D,Ze,U,H,he,O,Xe,ie,tt="AutoFeatureExtractor",Te,ee,at="Para tareas de audio y visión, un extractor de características procesa la señal de audio o imagen al formato de input correcto.",qe,te,st="Carga un extractor de características con <code>AutoFeatureExtractor.from_pretrained()</code>:",Ee,ae,Je,F,G,fe,se,Ye,ue,nt="AutoProcessor",We,ne,rt='Las tareas multimodales requieren un procesador que combine dos tipos de herramientas de preprocesamiento. Por ejemplo, el modelo <a href="model_doc/layoutlmv2">LayoutLMV2</a> requiere que un extractor de características maneje las imágenes y que un tokenizador maneje el texto; un procesador combina ambas.',Ae,re,lt="Carga un procesador con <code>AutoProcessor.from_pretrained()</code>:",Ue,le,Fe,z,N,be,oe,Se,me,ot="AutoModel",ze,P,Ve,$e,Re;return j=new ve({}),V=new kt({props:{$$slots:{default:[xt]},$$scope:{ctx:E}}}),S=new ve({}),B=new L({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMEElMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtdW5jYXNlZCUyMik=",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;bert-base-uncased&quot;</span>)`}}),D=new L({props:{code:"c2VxdWVuY2UlMjAlM0QlMjAlMjJJbiUyMGElMjBob2xlJTIwaW4lMjB0aGUlMjBncm91bmQlMjB0aGVyZSUyMGxpdmVkJTIwYSUyMGhvYmJpdC4lMjIlMEFwcmludCh0b2tlbml6ZXIoc2VxdWVuY2UpKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>sequence = <span class="hljs-string">&quot;In a hole in the ground there lived a hobbit.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(tokenizer(sequence))
{<span class="hljs-string">&#x27;input_ids&#x27;</span>: [<span class="hljs-number">101</span>, <span class="hljs-number">1999</span>, <span class="hljs-number">1037</span>, <span class="hljs-number">4920</span>, <span class="hljs-number">1999</span>, <span class="hljs-number">1996</span>, <span class="hljs-number">2598</span>, <span class="hljs-number">2045</span>, <span class="hljs-number">2973</span>, <span class="hljs-number">1037</span>, <span class="hljs-number">7570</span>, <span class="hljs-number">10322</span>, <span class="hljs-number">4183</span>, <span class="hljs-number">1012</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-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>, <span class="hljs-number">1</span>]}`}}),O=new ve({}),ae=new L({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9GZWF0dXJlRXh0cmFjdG9yJTBBJTBBZmVhdHVyZV9leHRyYWN0b3IlMjAlM0QlMjBBdXRvRmVhdHVyZUV4dHJhY3Rvci5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyZWhjYWxhYnJlcyUyRndhdjJ2ZWMyLWxnLXhsc3ItZW4tc3BlZWNoLWVtb3Rpb24tcmVjb2duaXRpb24lMjIlMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor

<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition&quot;</span>
<span class="hljs-meta">... </span>)`}}),se=new ve({}),le=new L({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Qcm9jZXNzb3IlMEElMEFwcm9jZXNzb3IlMjAlM0QlMjBBdXRvUHJvY2Vzc29yLmZyb21fcHJldHJhaW5lZCglMjJtaWNyb3NvZnQlMkZsYXlvdXRsbXYyLWJhc2UtdW5jYXNlZCUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;microsoft/layoutlmv2-base-uncased&quot;</span>)`}}),oe=new ve({}),P=new 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