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training_args = TrainingArguments(
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)`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TrainingArguments

training_args = TrainingArguments(
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checkpoint = "camembert-base"

model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)
tokenizer = AutoTokenizer.from_pretrained(checkpoint)`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForMaskedLM, AutoTokenizer

checkpoint = <span class="hljs-string">&quot;camembert-base&quot;</span>

model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)
tokenizer = AutoTokenizer.from_pretrained(checkpoint)`}}),{c(){j(m.$$.fragment)},l(p){T(m.$$.fragment,p)},m(p,h){P(m,p,h),c=!0},i(p){c||($(m.$$.fragment,p),c=!0)},o(p){b(m.$$.fragment,p),c=!1},d(p){G(m,p)}}}function Z_(A){let m,c;return m=new R({props:{code:`from transformers import AutoModelForMaskedLM, AutoTokenizer

checkpoint = "camembert-base"

model = AutoModelForMaskedLM.from_pretrained(checkpoint)
tokenizer = AutoTokenizer.from_pretrained(checkpoint)`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForMaskedLM, AutoTokenizer

checkpoint = <span class="hljs-string">&quot;camembert-base&quot;</span>

model = AutoModelForMaskedLM.from_pretrained(checkpoint)
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model.save_pretrained("<path_to_dummy_folder>")
tokenizer.save_pretrained("<path_to_dummy_folder>")`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForMaskedLM, AutoTokenizer

checkpoint = <span class="hljs-string">&quot;camembert-base&quot;</span>

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model.save_pretrained(<span class="hljs-string">&quot;&lt;path_to_dummy_folder&gt;&quot;</span>)
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model.save_pretrained("<path_to_dummy_folder>")
tokenizer.save_pretrained("<path_to_dummy_folder>")`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForMaskedLM, AutoTokenizer

checkpoint = <span class="hljs-string">&quot;camembert-base&quot;</span>

model = AutoModelForMaskedLM.from_pretrained(checkpoint)
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hc={local:"",sections:[{local:"pushtohub-api",title:"\u4F7F\u7528 push_to_hub API"},{local:"huggingfacehub-python",title:"\u4F7F\u7528 huggingface_hub python\u5E93"},{local:"",title:"\u4F7F\u7528\u7F51\u7EDC\u754C\u9762"},{local:"",sections:[{local:"the-uploadfile-approach",title:"The `upload_file` approach"},{local:"the-repository-class",title:"The `Repository` class"},{local:"the-gitbased-approach",title:"The git-based approach"}],title:"\u4E0A\u4F20\u6A21\u578B\u6587\u4EF6"}],title:"\u5171\u4EAB\u9884\u8BAD\u7EC3\u6A21\u578B"};function dc(A,m,c){let p="pt";return x_(()=>{const h=new URLSearchParams(window.location.search);c(0,p=h.get("fw")||"pt")}),[p]}class Tc extends H_{constructor(m){super();L_(this,m,dc,gc,C_,{})}}export{Tc as default,hc as metadata};
