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import"../chunks/DsnmJJEf.js";import{i as z,h as w,C as v,H as V,a as l,E as C,s as N}from"../chunks/DVlE9Y_t.js";import{p as Q,o as F,s as a,f as i,a as c,b as B,c as r,g as E,h as p,n as G,m as Y}from"../chunks/D2kUtL-w.js";import{i as J}from"../chunks/BmnfoIg9.js";import{C as j}from"../chunks/CDBILFp-.js";import{F as I}from"../chunks/rwKEN8mv.js";const _='{"title":"Utilizarea modelelor preantrenate","local":"using-pretrained-models","sections":[],"depth":1}';var A=r('<meta name="hf:doc:metadata"/>'),X=r('<!> <p>Însă, recomandăm utilizarea <a href="https://huggingface.co/transformers/model_doc/auto?highlight=auto#auto-classes" rel="nofollow">claselor <code>Auto*</code></a>, deoarece acestea sunt proiectate să fie architecture-agnostic. În timp ce codul precedent limita utilizatorii la checkpoints loadable în CamemBERT architecture, utilizarea claselor <code>Auto*</code> face schimbarea checkpointurilor simplă:</p> <!>',1),S=r('<!> <p>Însă, recomandăm utilizarea <a href="https://huggingface.co/transformers/model_doc/auto?highlight=auto#auto-classes" rel="nofollow">claselor <code>TFAuto*</code></a>, deoarece acestea sunt proiectate să fie architecture-agnostic. În timp ce codul precedent limita utilizatorii la checkpoints loadable în CamemBERT architecture, utilizarea claselor <code>TFAuto*</code> face schimbarea checkpointurilor simplă:</p> <!>',1),R=r('<p></p> <!> <!> <!> <!> <p>Hubul Model oferă o modalitate simplă de a selecta modelul adecvat, astfel încât utilizarea sa în orice bibliotecă downstream poate fi efectuată în câteva linii de cod. Să vedem cum se utilizează efectiv unul dintre aceste modele și cum putem contribui înapoi la comunitate.</p> <p>Să presupunem că noi căutăm un French-based model ce poate face mask filling.</p> <div class="flex justify-center"><img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter4/camembert.gif" alt="Selecting the Camembert model." width="80%"/></div> <p>Alegem checkpointul „camembert-base” pentru al încerca. Identificatorul <code>camembert-base</code> este tot de ce avem nevoie pentru a începe. În capitolele precedente, am văzut cum putem inițializa modelul folosind funcția <code>pipeline()</code>:</p> <!> <!> <p>În mod evident, încărcarea unui model într-un pipeline este extrem de simplu. Singurul lucru la care trebuie să atrageți atenția este că checkpointul ales este adecvat pentru sarcina pe care urmează să o execute. De exemplu, în momentul în care încărăm checkpoint-ul „camembert-base” în pipelineul <code>fill-mask</code>, este perfect în regulă. Dar dacă îl încărcam în pipelineul <code>text-classification</code>, rezultatele nu vor avea nici o logică, pentru că headul „camembert-base” nu este adecvat pentru această sarcină! Recomandăm utilizarea task selectorului în interfața Hugging Face în scopul selectării checkpointurilor adecvate:</p> <div class="flex justify-center"><img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter4/tasks.png" alt="Selectorul de sarcini pe interfața web." width="80%"/></div> <p>Puteți inițializa checkpointul în mod direct folosind arhitectura modelului:</p> <!> <blockquote class="tip"><p>În momentul în care folosiți un model preantrenat, asigurați-vă să verificați cum a fost antrenat și pe ce date se bazează. De asemenea, trebuie să cunoasceți limitele și prejudecățile sale. Toată această informație va fi indicată pe cartea modelului.</p></blockquote> <!> <p></p>',1);function P(T,U){Q(U,!1);let n=Y("pt");F(()=>{const e=new URLSearchParams(window.location.search);E(n,e.get("fw")||"pt")}),z();var m=R();w("za6r8j",e=>{var s=A();N(s,"content",_),c(e,s)});var u=a(i(m),2);I(u,{get fw(){return p(n)}});var d=a(u,2);v(d,{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"});var b=a(d,2);V(b,{title:"Utilizarea modelelor preantrenate",local:"using-pretrained-models",headingTag:"h1"});var h=a(b,2);{var f=e=>{j(e,{chapter:4,classNames:"absolute z-10 right-0 top-0",notebooks:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter4/section2_pt.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter4/section2_pt.ipynb"}]})},k=e=>{j(e,{chapter:4,classNames:"absolute z-10 right-0 top-0",notebooks:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter4/section2_tf.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter4/section2_tf.ipynb"}]})};J(h,e=>{p(n)==="pt"?e(f):e(k,-1)})}var y=a(h,10);l(y,{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>)`,lang:"py",wrap:!1});var M=a(y,2);l(M,{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>}
]`,lang:"python",wrap:!1});var g=a(M,8);{var x=e=>{var s=X(),t=i(s);l(t,{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>)`,lang:"py",wrap:!1});var o=a(t,4);l(o,{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>)`,lang:"py",wrap:!1}),c(e,s)},Z=e=>{var s=S(),t=i(s);l(t,{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>)`,lang:"py",wrap:!1});var o=a(t,4);l(o,{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>)`,lang:"py",wrap:!1}),c(e,s)};J(g,e=>{p(n)==="pt"?e(x):e(Z,-1)})}var W=a(g,4);C(W,{source:"https://github.com/huggingface/course/blob/main/chapters/ro/chapter4/2.mdx"}),G(2),c(T,m),B()}export{P as component};

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	import"../chunks/DsnmJJEf.js";import{i as z,h as w,C as v,H as V,a as l,E as C,s as N}from"../chunks/DVlE9Y_t.js";import{p as Q,o as F,s as a,f as i,a as c,b as B,c as r,g as E,h as p,n as G,m as Y}from"../chunks/D2kUtL-w.js";import{i as J}from"../chunks/BmnfoIg9.js";import{C as j}from"../chunks/CDBILFp-.js";import{F as I}from"../chunks/rwKEN8mv.js";const _='{"title":"Utilizarea modelelor preantrenate","local":"using-pretrained-models","sections":[],"depth":1}';var A=r('<meta name="hf:doc:metadata"/>'),X=r('<!> <p>Însă, recomandăm utilizarea <a href="https://huggingface.co/transformers/model_doc/auto?highlight=auto#auto-classes" rel="nofollow">claselor <code>Auto</code></a>, deoarece acestea sunt proiectate să fie architecture-agnostic. În timp ce codul precedent limita utilizatorii la checkpoints loadable în CamemBERT architecture, utilizarea claselor <code>Auto</code> face schimbarea checkpointurilor simplă:</p> <!>',1),S=r('<!> <p>Însă, recomandăm utilizarea <a href="https://huggingface.co/transformers/model_doc/auto?highlight=auto#auto-classes" rel="nofollow">claselor <code>TFAuto</code></a>, deoarece acestea sunt proiectate să fie architecture-agnostic. În timp ce codul precedent limita utilizatorii la checkpoints loadable în CamemBERT architecture, utilizarea claselor <code>TFAuto</code> face schimbarea checkpointurilor simplă:</p> <!>',1),R=r('<p></p> <!> <!> <!> <!> <p>Hubul Model oferă o modalitate simplă de a selecta modelul adecvat, astfel încât utilizarea sa în orice bibliotecă downstream poate fi efectuată în câteva linii de cod. Să vedem cum se utilizează efectiv unul dintre aceste modele și cum putem contribui înapoi la comunitate.</p> <p>Să presupunem că noi căutăm un French-based model ce poate face mask filling.</p> <div class="flex justify-center"><img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter4/camembert.gif" alt="Selecting the Camembert model." width="80%"/></div> <p>Alegem checkpointul „camembert-base” pentru al încerca. Identificatorul <code>camembert-base</code> este tot de ce avem nevoie pentru a începe. În capitolele precedente, am văzut cum putem inițializa modelul folosind funcția <code>pipeline()</code>:</p> <!> <!> <p>În mod evident, încărcarea unui model într-un pipeline este extrem de simplu. Singurul lucru la care trebuie să atrageți atenția este că checkpointul ales este adecvat pentru sarcina pe care urmează să o execute. De exemplu, în momentul în care încărăm checkpoint-ul „camembert-base” în pipelineul <code>fill-mask</code>, este perfect în regulă. Dar dacă îl încărcam în pipelineul <code>text-classification</code>, rezultatele nu vor avea nici o logică, pentru că headul „camembert-base” nu este adecvat pentru această sarcină! Recomandăm utilizarea task selectorului în interfața Hugging Face în scopul selectării checkpointurilor adecvate:</p> <div class="flex justify-center"><img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter4/tasks.png" alt="Selectorul de sarcini pe interfața web." width="80%"/></div> <p>Puteți inițializa checkpointul în mod direct folosind arhitectura modelului:</p> <!> <blockquote class="tip"><p>În momentul în care folosiți un model preantrenat, asigurați-vă să verificați cum a fost antrenat și pe ce date se bazează. De asemenea, trebuie să cunoasceți limitele și prejudecățile sale. Toată această informație va fi indicată pe cartea modelului.</p></blockquote> <!> <p></p>',1);function P(T,U){Q(U,!1);let n=Y("pt");F(()=>{const e=new URLSearchParams(window.location.search);E(n,e.get("fw")\|\|"pt")}),z();var m=R();w("za6r8j",e=>{var s=A();N(s,"content",_),c(e,s)});var u=a(i(m),2);I(u,{get fw(){return p(n)}});var d=a(u,2);v(d,{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"});var b=a(d,2);V(b,{title:"Utilizarea modelelor preantrenate",local:"using-pretrained-models",headingTag:"h1"});var h=a(b,2);{var f=e=>{j(e,{chapter:4,classNames:"absolute z-10 right-0 top-0",notebooks:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter4/section2_pt.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter4/section2_pt.ipynb"}]})},k=e=>{j(e,{chapter:4,classNames:"absolute z-10 right-0 top-0",notebooks:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter4/section2_tf.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter4/section2_tf.ipynb"}]})};J(h,e=>{p(n)==="pt"?e(f):e(k,-1)})}var y=a(h,10);l(y,{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">"fill-mask"</span>, model=<span class="hljs-string">"camembert-base"</span>)
	results = camembert_fill_mask(<span class="hljs-string">"Le camembert est <mask> :)"</span>)`,lang:"py",wrap:!1});var M=a(y,2);l(M,{code:"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",highlighted:`[
	{<span class="hljs-string">'sequence'</span>: <span class="hljs-string">'Le camembert est délicieux :)'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.49091005325317383</span>, <span class="hljs-string">'token'</span>: <span class="hljs-number">7200</span>, <span class="hljs-string">'token_str'</span>: <span class="hljs-string">'délicieux'</span>},
	{<span class="hljs-string">'sequence'</span>: <span class="hljs-string">'Le camembert est excellent :)'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.1055697426199913</span>, <span class="hljs-string">'token'</span>: <span class="hljs-number">2183</span>, <span class="hljs-string">'token_str'</span>: <span class="hljs-string">'excellent'</span>},
	{<span class="hljs-string">'sequence'</span>: <span class="hljs-string">'Le camembert est succulent :)'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.03453313186764717</span>, <span class="hljs-string">'token'</span>: <span class="hljs-number">26202</span>, <span class="hljs-string">'token_str'</span>: <span class="hljs-string">'succulent'</span>},
	{<span class="hljs-string">'sequence'</span>: <span class="hljs-string">'Le camembert est meilleur :)'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.0330314114689827</span>, <span class="hljs-string">'token'</span>: <span class="hljs-number">528</span>, <span class="hljs-string">'token_str'</span>: <span class="hljs-string">'meilleur'</span>},
	{<span class="hljs-string">'sequence'</span>: <span class="hljs-string">'Le camembert est parfait :)'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.03007650189101696</span>, <span class="hljs-string">'token'</span>: <span class="hljs-number">1654</span>, <span class="hljs-string">'token_str'</span>: <span class="hljs-string">'parfait'</span>}
	]`,lang:"python",wrap:!1});var g=a(M,8);{var x=e=>{var s=X(),t=i(s);l(t,{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">"camembert-base"</span>)
	model = CamembertForMaskedLM.from_pretrained(<span class="hljs-string">"camembert-base"</span>)`,lang:"py",wrap:!1});var o=a(t,4);l(o,{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">"camembert-base"</span>)
	model = AutoModelForMaskedLM.from_pretrained(<span class="hljs-string">"camembert-base"</span>)`,lang:"py",wrap:!1}),c(e,s)},Z=e=>{var s=S(),t=i(s);l(t,{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">"camembert-base"</span>)
	model = TFCamembertForMaskedLM.from_pretrained(<span class="hljs-string">"camembert-base"</span>)`,lang:"py",wrap:!1});var o=a(t,4);l(o,{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">"camembert-base"</span>)
	model = TFAutoModelForMaskedLM.from_pretrained(<span class="hljs-string">"camembert-base"</span>)`,lang:"py",wrap:!1}),c(e,s)};J(g,e=>{p(n)==="pt"?e(x):e(Z,-1)})}var W=a(g,4);C(W,{source:"https://github.com/huggingface/course/blob/main/chapters/ro/chapter4/2.mdx"}),G(2),c(T,m),B()}export{P as component};