import{s as Te,o as ke}from"../chunks/scheduler.37c15a92.js";import{S as je,i as Ue,g as y,s as m,r as j,A as xe,h as g,f as a,c as p,j as Me,u as U,x as w,k as se,y as $e,a as i,v as x,t as f,b as ye,d as h,w as $,m as we,n as _e,p as ge}from"../chunks/index.2bf4358c.js";import{T as Ze}from"../chunks/Tip.363c041f.js";import{C as q}from"../chunks/CodeBlock.4f5fc1ad.js";import{C as Je}from"../chunks/CourseFloatingBanner.15ba07e6.js";import{F as ve}from"../chunks/FrameworkSwitchCourse.8d4d4ab6.js";import{H as Ce}from"../chunks/Heading.8ada512a.js";function We(M){let t,r;return t=new Je({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.$$.fragment)},l(l){U(t.$$.fragment,l)},m(l,d){x(t,l,d),r=!0},i(l){r||(h(t.$$.fragment,l),r=!0)},o(l){f(t.$$.fragment,l),r=!1},d(l){$(t,l)}}}function Ve(M){let t,r;return t=new Je({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.$$.fragment)},l(l){U(t.$$.fragment,l)},m(l,d){x(t,l,d),r=!0},i(l){r||(h(t.$$.fragment,l),r=!0)},o(l){f(t.$$.fragment,l),r=!1},d(l){$(t,l)}}}function Ne(M){let t,r,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,c,b;return t=new q({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}}),c=new q({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.$$.fragment),r=m(),l=y("p"),l.innerHTML=d,u=m(),j(c.$$.fragment)},l(s){U(t.$$.fragment,s),r=p(s),l=g(s,"P",{"data-svelte-h":!0}),w(l)!=="svelte-83gnjn"&&(l.innerHTML=d),u=p(s),U(c.$$.fragment,s)},m(s,o){x(t,s,o),i(s,r,o),i(s,l,o),i(s,u,o),x(c,s,o),b=!0},i(s){b||(h(t.$$.fragment,s),h(c.$$.fragment,s),b=!0)},o(s){f(t.$$.fragment,s),f(c.$$.fragment,s),b=!1},d(s){s&&(a(r),a(l),a(u)),$(t,s),$(c,s)}}}function ze(M){let t,r,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,c,b;return t=new q({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}}),c=new q({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.$$.fragment),r=m(),l=y("p"),l.innerHTML=d,u=m(),j(c.$$.fragment)},l(s){U(t.$$.fragment,s),r=p(s),l=g(s,"P",{"data-svelte-h":!0}),w(l)!=="svelte-8qvpr"&&(l.innerHTML=d),u=p(s),U(c.$$.fragment,s)},m(s,o){x(t,s,o),i(s,r,o),i(s,l,o),i(s,u,o),x(c,s,o),b=!0},i(s){b||(h(t.$$.fragment,s),h(c.$$.fragment,s),b=!0)},o(s){f(t.$$.fragment,s),f(c.$$.fragment,s),b=!1},d(s){s&&(a(r),a(l),a(u)),$(t,s),$(c,s)}}}function Ee(M){let t;return{c(){t=we("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(r){t=_e(r,"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(r,l){i(r,t,l)},d(r){r&&a(t)}}}function Fe(M){let t,r,l,d,u,c,b,s,o,J,I,C,ne="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.",A,W,ae="Supposons que nous recherchions un modèle basé sur le français, capable de remplir des masques.",X,_,re='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter4/camembert.gif" alt="Selecting the Camembert model." width="80%"/>',L,V,ie="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> :",R,N,S,z,H,E,oe="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 :",D,Z,ce='<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%"/>',P,F,me="Vous pouvez également instancier le <em>checkpoint</em> en utilisant directement l’architecture du modèle :",K,T,k,Y,v,O,G,ee;u=new ve({props:{fw:M[0]}}),b=new Ce({props:{title:"Utilisation de modèles pré-entraînés",local:"utilisation-de-modèles-pré-entraînés",headingTag:"h1"}});const pe=[Ve,We],Q=[];function ue(e,n){return e[0]==="pt"?0:1}o=ue(M),J=Q[o]=pe[o](M),N=new q({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 q({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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