import{s as je,o as Ue}from"../chunks/scheduler.37c15a92.js";import{S as xe,i as $e,g as y,s as o,r as J,A as we,h as g,f as a,c as m,j as ge,u as T,x as w,k as le,y as _e,a as r,v as k,t as f,b as Je,d as h,w as j,m as Ze,n as ve,p as Te}from"../chunks/index.2bf4358c.js";import{T as Ce}from"../chunks/Tip.363c041f.js";import{C as G}from"../chunks/CodeBlock.4e987730.js";import{C as ke}from"../chunks/CourseFloatingBanner.6add7356.js";import{F as We}from"../chunks/FrameworkSwitchCourse.8d4d4ab6.js";import{H as Ve,E as Ne}from"../chunks/getInferenceSnippets.80a69898.js";function ze(M){let t,i;return t=new ke({props:{chapter:4,classNames:"absolute z-10 right-0 top-0",notebooks:[{label:"English",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter4/section2_tf.ipynb"},{label:"Français",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/fr/chapter4/section2_tf.ipynb"},{label:"English",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter4/section2_tf.ipynb"},{label:"Français",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/fr/chapter4/section2_tf.ipynb"}]}}),{c(){J(t.$$.fragment)},l(l){T(t.$$.fragment,l)},m(l,d){k(t,l,d),i=!0},i(l){i||(h(t.$$.fragment,l),i=!0)},o(l){f(t.$$.fragment,l),i=!1},d(l){j(t,l)}}}function Ee(M){let t,i;return t=new ke({props:{chapter:4,classNames:"absolute z-10 right-0 top-0",notebooks:[{label:"English",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter4/section2_pt.ipynb"},{label:"Français",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/fr/chapter4/section2_pt.ipynb"},{label:"English",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter4/section2_pt.ipynb"},{label:"Français",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/fr/chapter4/section2_pt.ipynb"}]}}),{c(){J(t.$$.fragment)},l(l){T(t.$$.fragment,l)},m(l,d){k(t,l,d),i=!0},i(l){i||(h(t.$$.fragment,l),i=!0)},o(l){f(t.$$.fragment,l),i=!1},d(l){j(t,l)}}}function Fe(M){let t,i,l,d='Cependant, nous recommandons d’utiliser les classes <a href="https://huggingface.co/transformers/model_doc/auto.html?highlight=auto#auto-classes" rel="nofollow"><code>TFAuto*</code></a> à la place, car elles sont par conception indépendantes de l’architecture. Alors que l’exemple de code précédent limite les utilisateurs aux <em>checkpoints</em> chargeables dans l’architecture CamemBERT, l’utilisation des classes <code>TFAuto*</code> facilite le changement de <em>checkpoint</em> :',u,p,b;return t=new G({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMENhbWVtYmVydFRva2VuaXplciUyQyUyMFRGQ2FtZW1iZXJ0Rm9yTWFza2VkTE0lMEElMEF0b2tlbml6ZXIlMjAlM0QlMjBDYW1lbWJlcnRUb2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmNhbWVtYmVydC1iYXNlJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZDYW1lbWJlcnRGb3JNYXNrZWRMTS5mcm9tX3ByZXRyYWluZWQoJTIyY2FtZW1iZXJ0LWJhc2UlMjIp",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> CamembertTokenizer, TFCamembertForMaskedLM

tokenizer = CamembertTokenizer.from_pretrained(<span class="hljs-string">&quot;camembert-base&quot;</span>)
model = TFCamembertForMaskedLM.from_pretrained(<span class="hljs-string">&quot;camembert-base&quot;</span>)`,wrap:!1}}),p=new G({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBURkF1dG9Nb2RlbEZvck1hc2tlZExNJTBBJTBBdG9rZW5pemVyJTIwJTNEJTIwQXV0b1Rva2VuaXplci5mcm9tX3ByZXRyYWluZWQoJTIyY2FtZW1iZXJ0LWJhc2UlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvck1hc2tlZExNLmZyb21fcHJldHJhaW5lZCglMjJjYW1lbWJlcnQtYmFzZSUyMik=",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFAutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;camembert-base&quot;</span>)
model = TFAutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;camembert-base&quot;</span>)`,wrap:!1}}),{c(){J(t.$$.fragment),i=o(),l=y("p"),l.innerHTML=d,u=o(),J(p.$$.fragment)},l(s){T(t.$$.fragment,s),i=m(s),l=g(s,"P",{"data-svelte-h":!0}),w(l)!=="svelte-83gnjn"&&(l.innerHTML=d),u=m(s),T(p.$$.fragment,s)},m(s,c){k(t,s,c),r(s,i,c),r(s,l,c),r(s,u,c),k(p,s,c),b=!0},i(s){b||(h(t.$$.fragment,s),h(p.$$.fragment,s),b=!0)},o(s){f(t.$$.fragment,s),f(p.$$.fragment,s),b=!1},d(s){s&&(a(i),a(l),a(u)),j(t,s),j(p,s)}}}function Qe(M){let t,i,l,d='Cependant, nous recommandons d’utiliser les classes <a href="https://huggingface.co/transformers/model_doc/auto.html?highlight=auto#auto-classes" rel="nofollow"><code>Auto*</code></a> à la place, car elles sont par conception indépendantes de l’architecture. Alors que l’exemple de code précédent limite les utilisateurs aux <em>checkpoints</em> chargeables dans l’architecture CamemBERT, l’utilisation des classes <code>Auto*</code> facilite le changement de <em>checkpoint</em> :',u,p,b;return t=new G({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMENhbWVtYmVydFRva2VuaXplciUyQyUyMENhbWVtYmVydEZvck1hc2tlZExNJTBBJTBBdG9rZW5pemVyJTIwJTNEJTIwQ2FtZW1iZXJ0VG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjJjYW1lbWJlcnQtYmFzZSUyMiklMEFtb2RlbCUyMCUzRCUyMENhbWVtYmVydEZvck1hc2tlZExNLmZyb21fcHJldHJhaW5lZCglMjJjYW1lbWJlcnQtYmFzZSUyMik=",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> CamembertTokenizer, CamembertForMaskedLM

tokenizer = CamembertTokenizer.from_pretrained(<span class="hljs-string">&quot;camembert-base&quot;</span>)
model = CamembertForMaskedLM.from_pretrained(<span class="hljs-string">&quot;camembert-base&quot;</span>)`,wrap:!1}}),p=new G({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBBdXRvTW9kZWxGb3JNYXNrZWRMTSUwQSUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmNhbWVtYmVydC1iYXNlJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yTWFza2VkTE0uZnJvbV9wcmV0cmFpbmVkKCUyMmNhbWVtYmVydC1iYXNlJTIyKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;camembert-base&quot;</span>)
model = AutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;camembert-base&quot;</span>)`,wrap:!1}}),{c(){J(t.$$.fragment),i=o(),l=y("p"),l.innerHTML=d,u=o(),J(p.$$.fragment)},l(s){T(t.$$.fragment,s),i=m(s),l=g(s,"P",{"data-svelte-h":!0}),w(l)!=="svelte-8qvpr"&&(l.innerHTML=d),u=m(s),T(p.$$.fragment,s)},m(s,c){k(t,s,c),r(s,i,c),r(s,l,c),r(s,u,c),k(p,s,c),b=!0},i(s){b||(h(t.$$.fragment,s),h(p.$$.fragment,s),b=!0)},o(s){f(t.$$.fragment,s),f(p.$$.fragment,s),b=!1},d(s){s&&(a(i),a(l),a(u)),j(t,s),j(p,s)}}}function Be(M){let t;return{c(){t=Ze("Lorsque vous utilisez un modèle pré-entraîné, assurez-vous de vérifier comment il a été entraîné, sur quels jeux de données, ses limites et ses biais. Toutes ces informations doivent être indiquées dans sa carte.")},l(i){t=ve(i,"Lorsque vous utilisez un modèle pré-entraîné, assurez-vous de vérifier comment il a été entraîné, sur quels jeux de données, ses limites et ses biais. Toutes ces informations doivent être indiquées dans sa carte.")},m(i,l){r(i,t,l)},d(i){i&&a(t)}}}function Ie(M){let t,i,l,d,u,p,b,s,c,U,Y,C,re="Le <em>Hub</em> rend simple la sélection d’un modèle et permet alors que celui-ci puisse être utilisé dans toute bibliothèque en aval en seulement quelques lignes de code. Voyons comment utiliser concrètement l’un de ces modèles et comment contribuer au développement de la communauté.",X,W,ie="Supposons que nous recherchions un modèle basé sur le français, capable de remplir des masques.",L,_,ce='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter4/camembert.gif" alt="Selecting the Camembert model." width="80%"/>',R,V,oe="Nous choisissons le <em>checkpoint</em> <code>camembert-base</code> pour essayer. L’identifiant <code>camembert-base</code> est tout ce dont nous avons besoin pour commencer à utiliser le modèle ! Comme vous l’avez vu dans les chapitres précédents, nous pouvons l’instancier en utilisant la fonction <code>pipeline()</code> :",S,N,H,z,D,E,me="Comme vous pouvez le constater, le chargement d’un modèle dans un pipeline est extrêmement simple. La seule chose à laquelle vous devez faire attention est que le <em>checkpoint</em> choisi soit adapté à la tâche pour laquelle il va être utilisé. Par exemple, ici nous chargeons le <em>checkpoint</em> <code>camembert-base</code> dans le pipeline <code>fill-mask</code>, ce qui est tout à fait correct. Mais si nous chargerions ce <em>checkpoint</em> dans le pipeline <code>text-classification</code>, les résultats n’auraient aucun sens car la tête de <code>camembert-base</code> n’est pas adaptée à cette tâche ! Nous recommandons d’utiliser le sélecteur de tâche dans l’interface du <em>Hub</em> afin de sélectionner les <em>checkpoints</em> appropriés :",P,Z,pe='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter4/tasks.png" alt="The task selector on the web interface." width="80%"/>',K,F,ue="Vous pouvez également instancier le <em>checkpoint</em> en utilisant directement l’architecture du modèle :",O,x,$,q,v,ee,Q,se,A,te;u=new We({props:{fw:M[0]}}),b=new Ve({props:{title:"Utilisation de modèles pré-entraînés",local:"utilisation-de-modèles-pré-entraînés",headingTag:"h1"}});const be=[Ee,ze],B=[];function de(e,n){return e[0]==="pt"?0:1}c=de(M),U=B[c]=be[c](M),N=new G({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBY2FtZW1iZXJ0X2ZpbGxfbWFzayUyMCUzRCUyMHBpcGVsaW5lKCUyMmZpbGwtbWFzayUyMiUyQyUyMG1vZGVsJTNEJTIyY2FtZW1iZXJ0LWJhc2UlMjIpJTBBcmVzdWx0cyUyMCUzRCUyMGNhbWVtYmVydF9maWxsX21hc2soJTIyTGUlMjBjYW1lbWJlcnQlMjBlc3QlMjAlM0NtYXNrJTNFJTIwJTNBKSUyMik=",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

camembert_fill_mask = pipeline(<span class="hljs-string">&quot;fill-mask&quot;</span>, model=<span class="hljs-string">&quot;camembert-base&quot;</span>)
results = camembert_fill_mask(<span class="hljs-string">&quot;Le camembert est &lt;mask&gt; :)&quot;</span>)`,wrap:!1}}),z=new G({props:{code:"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",highlighted:`[
  {<span class="hljs-string">&#x27;sequence&#x27;</span>: <span class="hljs-string">&#x27;Le camembert est délicieux :)&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.49091005325317383</span>, <span class="hljs-string">&#x27;token&#x27;</span>: <span class="hljs-number">7200</span>, <span class="hljs-string">&#x27;token_str&#x27;</span>: <span class="hljs-string">&#x27;délicieux&#x27;</span>}, 
  {<span class="hljs-string">&#x27;sequence&#x27;</span>: <span class="hljs-string">&#x27;Le camembert est excellent :)&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.1055697426199913</span>, <span class="hljs-string">&#x27;token&#x27;</span>: <span class="hljs-number">2183</span>, <span class="hljs-string">&#x27;token_str&#x27;</span>: <span class="hljs-string">&#x27;excellent&#x27;</span>}, 
  {<span class="hljs-string">&#x27;sequence&#x27;</span>: <span class="hljs-string">&#x27;Le camembert est succulent :)&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.03453313186764717</span>, <span class="hljs-string">&#x27;token&#x27;</span>: <span class="hljs-number">26202</span>, <span class="hljs-string">&#x27;token_str&#x27;</span>: <span class="hljs-string">&#x27;succulent&#x27;</span>}, 
  {<span class="hljs-string">&#x27;sequence&#x27;</span>: <span class="hljs-string">&#x27;Le camembert est meilleur :)&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.0330314114689827</span>, <span class="hljs-string">&#x27;token&#x27;</span>: <span class="hljs-number">528</span>, <span class="hljs-string">&#x27;token_str&#x27;</span>: <span class="hljs-string">&#x27;meilleur&#x27;</span>}, 
  {<span class="hljs-string">&#x27;sequence&#x27;</span>: <span class="hljs-string">&#x27;Le camembert est parfait :)&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.03007650189101696</span>, <span class="hljs-string">&#x27;token&#x27;</span>: <span class="hljs-number">1654</span>, <span class="hljs-string">&#x27;token_str&#x27;</span>: <span class="hljs-string">&#x27;parfait&#x27;</span>}
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