import{s as _q,o as Mq,n as x}from"../chunks/scheduler.bdbef820.js";import{S as Tq,i as yq,g as i,s as r,r as p,A as Fq,h as l,f as d,c as a,j as C,u,x as f,k as w,y as t,a as T,v as h,d as b,t as _,w as M}from"../chunks/index.33f81d56.js";import{T as Nk}from"../chunks/Tip.34194030.js";import{D as k}from"../chunks/Docstring.64554317.js";import{C as $}from"../chunks/CodeBlock.362b34a4.js";import{E as L}from"../chunks/ExampleCodeBlock.4f2252c6.js";import{H as A,E as vq}from"../chunks/EditOnGithub.a9246e21.js";function Cq(v){let n,F='만약 <code>NewModelConfig</code>가 <a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>의 서브클래스라면, 해당 <code>model_type</code> 속성이 등록할 때 사용하는 키(여기서는 <code>&quot;new-model&quot;</code>)와 동일하게 설정되어 있는지 확인하세요.',c,s,m='마찬가지로, <code>NewModel</code>이 <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>의 서브클래스라면, 해당 <code>config_class</code> 속성이 등록할 때 사용하는 클래스(여기서는 <code>NewModelConfig</code>)와 동일하게 설정되어 있는지 확인하세요.';return{c(){n=i("p"),n.innerHTML=F,c=r(),s=i("p"),s.innerHTML=m},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-etcc7x"&&(n.innerHTML=F),c=a(e),s=l(e,"P",{"data-svelte-h":!0}),f(s)!=="svelte-gawjav"&&(s.innerHTML=m)},m(e,y){T(e,n,y),T(e,c,y),T(e,s,y)},p:x,d(e){e&&(d(n),d(c),d(s))}}}function wq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co (user-uploaded) and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;dbmdz/bert-base-german-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If configuration file is in a directory (e.g., was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*).</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./test/bert_saved_model/&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Load a specific configuration file.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./test/bert_saved_model/my_configuration.json&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Change some config attributes when loading a pretrained config.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-uncased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>, foo=<span class="hljs-literal">False</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>config, unused_kwargs = AutoConfig.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;google-bert/bert-base-uncased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>, foo=<span class="hljs-literal">False</span>, return_unused_kwargs=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>unused_kwargs
{<span class="hljs-string">&#x27;foo&#x27;</span>: <span class="hljs-literal">False</span>}`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function kq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",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><span class="hljs-comment"># Download vocabulary from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download vocabulary from huggingface.co (user-uploaded) and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;dbmdz/bert-base-german-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If vocabulary files are in a directory (e.g. tokenizer was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># tokenizer = AutoTokenizer.from_pretrained(&quot;./test/bert_saved_model/&quot;)</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download vocabulary from huggingface.co and define model-specific arguments</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;FacebookAI/roberta-base&quot;</span>, add_prefix_space=<span class="hljs-literal">True</span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function xq(v){let n,F="Passing <code>token=True</code> is required when you want to use a private model.";return{c(){n=i("p"),n.innerHTML=F},l(c){n=l(c,"P",{"data-svelte-h":!0}),f(n)!=="svelte-15auxyb"&&(n.innerHTML=F)},m(c,s){T(c,n,s)},p:x,d(c){c&&d(n)}}}function $q(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",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><span class="hljs-comment"># Download feature extractor from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-base-960h&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If feature extractor files are in a directory (e.g. feature extractor was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># feature_extractor = AutoFeatureExtractor.from_pretrained(&quot;./test/saved_model/&quot;)</span>`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Lq(v){let n,F="Passing <code>token=True</code> is required when you want to use a private model.";return{c(){n=i("p"),n.innerHTML=F},l(c){n=l(c,"P",{"data-svelte-h":!0}),f(n)!=="svelte-15auxyb"&&(n.innerHTML=F)},m(c,s){T(c,n,s)},p:x,d(c){c&&d(n)}}}function Aq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download image processor from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;google/vit-base-patch16-224-in21k&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If image processor files are in a directory (e.g. image processor was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># image_processor = AutoImageProcessor.from_pretrained(&quot;./test/saved_model/&quot;)</span>`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function jq(v){let n,F="Passing <code>token=True</code> is required when you want to use a private model.";return{c(){n=i("p"),n.innerHTML=F},l(c){n=l(c,"P",{"data-svelte-h":!0}),f(n)!=="svelte-15auxyb"&&(n.innerHTML=F)},m(c,s){T(c,n,s)},p:x,d(c){c&&d(n)}}}function Zq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",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><span class="hljs-comment"># Download processor from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-base-960h&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If processor files are in a directory (e.g. processor was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># processor = AutoProcessor.from_pretrained(&quot;./test/saved_model/&quot;)</span>`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Bq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWwlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbC5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Rq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Gq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbCUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWwuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModel.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Jq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModel.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModel.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModel.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Wq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModel.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Eq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQSUwQSUyMyUyMFVwZGF0ZSUyMGNvbmZpZ3VyYXRpb24lMjBkdXJpbmclMjBsb2FkaW5nJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiUyQyUyMG91dHB1dF9hdHRlbnRpb25zJTNEVHJ1ZSklMEFtb2RlbC5jb25maWcub3V0cHV0X2F0dGVudGlvbnMlMEElMEElMjMlMjBMb2FkaW5nJTIwZnJvbSUyMGElMjBQeVRvcmNoJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwVGVuc29yRmxvdyUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGcHRfbW9kZWwlMkZiZXJ0X3B0X21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbC5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnB0X21vZGVsJTJGYmVydF9weXRvcmNoX21vZGVsLmJpbiUyMiUyQyUyMGZyb21fcHQlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModel.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModel.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModel.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Vq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JQcmVUcmFpbmluZyUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yUHJlVHJhaW5pbmcuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForPreTraining.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Iq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForPreTraining.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Pq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclByZVRyYWluaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclByZVRyYWluaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForPreTraining.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Sq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclByZVRyYWluaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yUHJlVHJhaW5pbmcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQSUwQSUyMyUyMFVwZGF0ZSUyMGNvbmZpZ3VyYXRpb24lMjBkdXJpbmclMjBsb2FkaW5nJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclByZVRyYWluaW5nLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiUyQyUyMG91dHB1dF9hdHRlbnRpb25zJTNEVHJ1ZSklMEFtb2RlbC5jb25maWcub3V0cHV0X2F0dGVudGlvbnMlMEElMEElMjMlMjBMb2FkaW5nJTIwZnJvbSUyMGElMjBQeVRvcmNoJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwVGVuc29yRmxvdyUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGcHRfbW9kZWwlMkZiZXJ0X3B0X21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JQcmVUcmFpbmluZy5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnB0X21vZGVsJTJGYmVydF9weXRvcmNoX21vZGVsLmJpbiUyMiUyQyUyMGZyb21fcHQlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForPreTraining.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Uq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yUHJlVHJhaW5pbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JQcmVUcmFpbmluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForPreTraining.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Nq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForPreTraining.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function qq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JDYXVzYWxMTSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yQ2F1c2FsTE0uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Xq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Dq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckNhdXNhbExNJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvckNhdXNhbExNLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForCausalLM.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Hq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckNhdXNhbExNJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yQ2F1c2FsTE0uZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQSUwQSUyMyUyMFVwZGF0ZSUyMGNvbmZpZ3VyYXRpb24lMjBkdXJpbmclMjBsb2FkaW5nJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvckNhdXNhbExNLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiUyQyUyMG91dHB1dF9hdHRlbnRpb25zJTNEVHJ1ZSklMEFtb2RlbC5jb25maWcub3V0cHV0X2F0dGVudGlvbnMlMEElMEElMjMlMjBMb2FkaW5nJTIwZnJvbSUyMGElMjBQeVRvcmNoJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwVGVuc29yRmxvdyUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGcHRfbW9kZWwlMkZiZXJ0X3B0X21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JDYXVzYWxMTS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnB0X21vZGVsJTJGYmVydF9weXRvcmNoX21vZGVsLmJpbiUyMiUyQyUyMGZyb21fcHQlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForCausalLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Qq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yQ2F1c2FsTE0lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JDYXVzYWxMTS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForCausalLM.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Yq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForCausalLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function zq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JNYXNrZWRMTSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yTWFza2VkTE0uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedLM.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Oq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Kq(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck1hc2tlZExNJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvck1hc2tlZExNLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedLM.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function e3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function o3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yTWFza2VkTE0lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JNYXNrZWRMTS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMaskedLM.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function t3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMaskedLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function n3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTZXEyU2VxTE0lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtdDUlMkZ0NS1iYXNlJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU2VxMlNlcUxNLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-t5/t5-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function r3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;google-t5/t5-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;google-t5/t5-base&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/t5_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/t5_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function a3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNlcTJTZXFMTSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS10NSUyRnQ1LWJhc2UlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNlcTJTZXFMTS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-t5/t5-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function s3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNlcTJTZXFMTSUwQSUwQSUyMyUyMERvd25sb2FkJTIwbW9kZWwlMjBhbmQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNlcTJTZXFMTS5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLXQ1JTJGdDUtYmFzZSUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JTZXEyU2VxTE0uZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS10NSUyRnQ1LWJhc2UlMjIlMkMlMjBvdXRwdXRfYXR0ZW50aW9ucyUzRFRydWUpJTBBbW9kZWwuY29uZmlnLm91dHB1dF9hdHRlbnRpb25zJTBBJTBBJTIzJTIwTG9hZGluZyUyMGZyb20lMjBhJTIwUHlUb3JjaCUyMGNoZWNrcG9pbnQlMjBmaWxlJTIwaW5zdGVhZCUyMG9mJTIwYSUyMFRlbnNvckZsb3clMjBtb2RlbCUyMChzbG93ZXIpJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyLiUyRnB0X21vZGVsJTJGdDVfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNlcTJTZXFMTS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnB0X21vZGVsJTJGdDVfcHl0b3JjaF9tb2RlbC5iaW4lMjIlMkMlMjBmcm9tX3B0JTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;google-t5/t5-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;google-t5/t5-base&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/t5_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/t5_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function i3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yU2VxMlNlcUxNJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLXQ1JTJGdDUtYmFzZSUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JTZXEyU2VxTE0uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-t5/t5-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSeq2SeqLM.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function l3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;google-t5/t5-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;google-t5/t5-base&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/t5_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/t5_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function d3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function c3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function m3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function f3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function g3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSequenceClassification.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function p3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function u3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JNdWx0aXBsZUNob2ljZSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yTXVsdGlwbGVDaG9pY2UuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMultipleChoice.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function h3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JNdWx0aXBsZUNob2ljZSUwQSUwQSUyMyUyMERvd25sb2FkJTIwbW9kZWwlMjBhbmQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JNdWx0aXBsZUNob2ljZS5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvck11bHRpcGxlQ2hvaWNlLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiUyQyUyMG91dHB1dF9hdHRlbnRpb25zJTNEVHJ1ZSklMEFtb2RlbC5jb25maWcub3V0cHV0X2F0dGVudGlvbnMlMEElMEElMjMlMjBMb2FkaW5nJTIwZnJvbSUyMGElMjBURiUyMGNoZWNrcG9pbnQlMjBmaWxlJTIwaW5zdGVhZCUyMG9mJTIwYSUyMFB5VG9yY2glMjBtb2RlbCUyMChzbG93ZXIpJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyLiUyRnRmX21vZGVsJTJGYmVydF90Zl9tb2RlbF9jb25maWcuanNvbiUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvck11bHRpcGxlQ2hvaWNlLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX2NoZWNrcG9pbnQuY2twdC5pbmRleCUyMiUyQyUyMGZyb21fdGYlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMultipleChoice.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function b3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck11bHRpcGxlQ2hvaWNlJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvck11bHRpcGxlQ2hvaWNlLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMultipleChoice.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function _3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMultipleChoice.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function M3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yTXVsdGlwbGVDaG9pY2UlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JNdWx0aXBsZUNob2ljZS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMultipleChoice.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function T3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMultipleChoice.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function y3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JOZXh0U2VudGVuY2VQcmVkaWN0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JOZXh0U2VudGVuY2VQcmVkaWN0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForNextSentencePrediction.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function F3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForNextSentencePrediction.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function v3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck5leHRTZW50ZW5jZVByZWRpY3Rpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForNextSentencePrediction.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function C3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForNextSentencePrediction.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function w3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbEZvck5leHRTZW50ZW5jZVByZWRpY3Rpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForNextSentencePrediction.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function k3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForNextSentencePrediction.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function x3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function $3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQSUwQSUyMyUyMFVwZGF0ZSUyMGNvbmZpZ3VyYXRpb24lMjBkdXJpbmclMjBsb2FkaW5nJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiUyQyUyMG91dHB1dF9hdHRlbnRpb25zJTNEVHJ1ZSklMEFtb2RlbC5jb25maWcub3V0cHV0X2F0dGVudGlvbnMlMEElMEElMjMlMjBMb2FkaW5nJTIwZnJvbSUyMGElMjBURiUyMGNoZWNrcG9pbnQlMjBmaWxlJTIwaW5zdGVhZCUyMG9mJTIwYSUyMFB5VG9yY2glMjBtb2RlbCUyMChzbG93ZXIpJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyLiUyRnRmX21vZGVsJTJGYmVydF90Zl9tb2RlbF9jb25maWcuanNvbiUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMi4lMkZ0Zl9tb2RlbCUyRmJlcnRfdGZfY2hlY2twb2ludC5ja3B0LmluZGV4JTIyJTJDJTIwZnJvbV90ZiUzRFRydWUlMkMlMjBjb25maWclM0Rjb25maWclMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function L3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function A3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiUyQyUyMG91dHB1dF9hdHRlbnRpb25zJTNEVHJ1ZSklMEFtb2RlbC5jb25maWcub3V0cHV0X2F0dGVudGlvbnMlMEElMEElMjMlMjBMb2FkaW5nJTIwZnJvbSUyMGElMjBQeVRvcmNoJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwVGVuc29yRmxvdyUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGcHRfbW9kZWwlMkZiZXJ0X3B0X21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGcHRfbW9kZWwlMkZiZXJ0X3B5dG9yY2hfbW9kZWwuYmluJTIyJTJDJTIwZnJvbV9wdCUzRFRydWUlMkMlMjBjb25maWclM0Rjb25maWclMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function j3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForTokenClassification.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Z3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForTokenClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function B3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JRdWVzdGlvbkFuc3dlcmluZyUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yUXVlc3Rpb25BbnN3ZXJpbmcuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForQuestionAnswering.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function R3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function G3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclF1ZXN0aW9uQW5zd2VyaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForQuestionAnswering.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function J3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function W3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yUXVlc3Rpb25BbnN3ZXJpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JRdWVzdGlvbkFuc3dlcmluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForQuestionAnswering.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function E3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function V3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JEZXB0aEVzdGltYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckRlcHRoRXN0aW1hdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForDepthEstimation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDepthEstimation.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function I3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForDepthEstimation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDepthEstimation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDepthEstimation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDepthEstimation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function P3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JJbWFnZUNsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JJbWFnZUNsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function S3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function U3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckltYWdlQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9ySW1hZ2VDbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForImageClassification.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function N3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function q3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9ySW1hZ2VDbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbEZvckltYWdlQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForImageClassification.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function X3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function D3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JWaWRlb0NsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JWaWRlb0NsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVideoClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVideoClassification.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function H3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVideoClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVideoClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVideoClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVideoClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Q3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JNYXNrZWRJbWFnZU1vZGVsaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JNYXNrZWRJbWFnZU1vZGVsaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMaskedImageModeling

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedImageModeling.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function Y3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMaskedImageModeling

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedImageModeling.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedImageModeling.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedImageModeling.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function z3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck1hc2tlZEltYWdlTW9kZWxpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yTWFza2VkSW1hZ2VNb2RlbGluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMaskedImageModeling

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedImageModeling.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function O3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMaskedImageModeling

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedImageModeling.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedImageModeling.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedImageModeling.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function K3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JPYmplY3REZXRlY3Rpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvck9iamVjdERldGVjdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForObjectDetection

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function eX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForObjectDetection

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function oX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JJbWFnZVNlZ21lbnRhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9ySW1hZ2VTZWdtZW50YXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForImageSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageSegmentation.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function tX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForImageSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function nX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTZW1hbnRpY1NlZ21lbnRhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU2VtYW50aWNTZWdtZW50YXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSemanticSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSemanticSegmentation.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function rX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSemanticSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSemanticSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function aX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNlbWFudGljU2VnbWVudGF0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNlbWFudGljU2VnbWVudGF0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSemanticSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSemanticSegmentation.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function sX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSemanticSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSemanticSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function iX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JJbnN0YW5jZVNlZ21lbnRhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9ySW5zdGFuY2VTZWdtZW50YXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForInstanceSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForInstanceSegmentation.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function lX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForInstanceSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForInstanceSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForInstanceSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForInstanceSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function dX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JVbml2ZXJzYWxTZWdtZW50YXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclVuaXZlcnNhbFNlZ21lbnRhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForUniversalSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForUniversalSegmentation.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function cX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForUniversalSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForUniversalSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForUniversalSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForUniversalSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function mX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JaZXJvU2hvdEltYWdlQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclplcm9TaG90SW1hZ2VDbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForZeroShotImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotImageClassification.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function fX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JaZXJvU2hvdEltYWdlQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yWmVyb1Nob3RJbWFnZUNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yWmVyb1Nob3RJbWFnZUNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiUyQyUyMG91dHB1dF9hdHRlbnRpb25zJTNEVHJ1ZSklMEFtb2RlbC5jb25maWcub3V0cHV0X2F0dGVudGlvbnMlMEElMEElMjMlMjBMb2FkaW5nJTIwZnJvbSUyMGElMjBURiUyMGNoZWNrcG9pbnQlMjBmaWxlJTIwaW5zdGVhZCUyMG9mJTIwYSUyMFB5VG9yY2glMjBtb2RlbCUyMChzbG93ZXIpJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyLiUyRnRmX21vZGVsJTJGYmVydF90Zl9tb2RlbF9jb25maWcuanNvbiUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclplcm9TaG90SW1hZ2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnRmX21vZGVsJTJGYmVydF90Zl9jaGVja3BvaW50LmNrcHQuaW5kZXglMjIlMkMlMjBmcm9tX3RmJTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForZeroShotImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function gX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclplcm9TaG90SW1hZ2VDbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JaZXJvU2hvdEltYWdlQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForZeroShotImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForZeroShotImageClassification.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function pX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForZeroShotImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForZeroShotImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForZeroShotImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForZeroShotImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function uX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JaZXJvU2hvdE9iamVjdERldGVjdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yWmVyb1Nob3RPYmplY3REZXRlY3Rpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForZeroShotObjectDetection

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotObjectDetection.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function hX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForZeroShotObjectDetection

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotObjectDetection.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotObjectDetection.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotObjectDetection.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function bX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JBdWRpb0NsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JBdWRpb0NsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioClassification.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function _X(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function MX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckF1ZGlvQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yQXVkaW9DbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForAudioClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForAudioClassification.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function TX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForAudioClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForAudioClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function yX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JBdWRpb0ZyYW1lQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckF1ZGlvRnJhbWVDbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioFrameClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioFrameClassification.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function FX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioFrameClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioFrameClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioFrameClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioFrameClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function vX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JDVEMlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckNUQy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForCTC

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function CX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForCTC

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function wX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTcGVlY2hTZXEyU2VxJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JTcGVlY2hTZXEyU2VxLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSpeechSeq2Seq.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function kX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSpeechSeq2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function xX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNwZWVjaFNlcTJTZXElMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yU3BlZWNoU2VxMlNlcS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSpeechSeq2Seq.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function $X(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSpeechSeq2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function LX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yU3BlZWNoU2VxMlNlcSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbEZvclNwZWVjaFNlcTJTZXEuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSpeechSeq2Seq.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function AX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function jX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JBdWRpb1hWZWN0b3IlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckF1ZGlvWFZlY3Rvci5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioXVector

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioXVector.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function ZX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioXVector

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioXVector.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioXVector.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioXVector.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function BX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JUYWJsZVF1ZXN0aW9uQW5zd2VyaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlJTJGdGFwYXMtYmFzZS1maW5ldHVuZWQtd3RxJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVGFibGVRdWVzdGlvbkFuc3dlcmluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForTableQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTableQuestionAnswering.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function RX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForTableQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTableQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTableQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/tapas_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTableQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/tapas_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function GX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclRhYmxlUXVlc3Rpb25BbnN3ZXJpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUlMkZ0YXBhcy1iYXNlLWZpbmV0dW5lZC13dHElMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclRhYmxlUXVlc3Rpb25BbnN3ZXJpbmcuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForTableQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTableQuestionAnswering.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function JX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForTableQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTableQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTableQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/tapas_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTableQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/tapas_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function WX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JEb2N1bWVudFF1ZXN0aW9uQW5zd2VyaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyaW1waXJhJTJGbGF5b3V0bG0tZG9jdW1lbnQtcWElMjIlMkMlMjByZXZpc2lvbiUzRCUyMjUyZTAxYjMlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JEb2N1bWVudFF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForDocumentQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDocumentQuestionAnswering.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function EX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForDocumentQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDocumentQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDocumentQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/layoutlm_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDocumentQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/layoutlm_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function VX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckRvY3VtZW50UXVlc3Rpb25BbnN3ZXJpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJpbXBpcmElMkZsYXlvdXRsbS1kb2N1bWVudC1xYSUyMiUyQyUyMHJldmlzaW9uJTNEJTIyNTJlMDFiMyUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yRG9jdW1lbnRRdWVzdGlvbkFuc3dlcmluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForDocumentQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForDocumentQuestionAnswering.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function IX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForDocumentQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForDocumentQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForDocumentQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/layoutlm_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForDocumentQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/layoutlm_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function PX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JWaXN1YWxRdWVzdGlvbkFuc3dlcmluZyUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmRhbmRlbGluJTJGdmlsdC1iMzItZmluZXR1bmVkLXZxYSUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclZpc3VhbFF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVisualQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-finetuned-vqa&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVisualQuestionAnswering.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function SX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JWaXN1YWxRdWVzdGlvbkFuc3dlcmluZyUwQSUwQSUyMyUyMERvd25sb2FkJTIwbW9kZWwlMjBhbmQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JWaXN1YWxRdWVzdGlvbkFuc3dlcmluZy5mcm9tX3ByZXRyYWluZWQoJTIyZGFuZGVsaW4lMkZ2aWx0LWIzMi1maW5ldHVuZWQtdnFhJTIyKSUwQSUwQSUyMyUyMFVwZGF0ZSUyMGNvbmZpZ3VyYXRpb24lMjBkdXJpbmclMjBsb2FkaW5nJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JWaXN1YWxRdWVzdGlvbkFuc3dlcmluZy5mcm9tX3ByZXRyYWluZWQoJTIyZGFuZGVsaW4lMkZ2aWx0LWIzMi1maW5ldHVuZWQtdnFhJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFRGJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwUHlUb3JjaCUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGdGZfbW9kZWwlMkZ2aWx0X3RmX21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVmlzdWFsUXVlc3Rpb25BbnN3ZXJpbmcuZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMi4lMkZ0Zl9tb2RlbCUyRnZpbHRfdGZfY2hlY2twb2ludC5ja3B0LmluZGV4JTIyJTJDJTIwZnJvbV90ZiUzRFRydWUlMkMlMjBjb25maWclM0Rjb25maWclMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVisualQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVisualQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-finetuned-vqa&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVisualQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-finetuned-vqa&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/vilt_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVisualQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/vilt_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function UX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JWaXNpb24yU2VxJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JWaXNpb24yU2VxLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVision2Seq.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function NX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVision2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function qX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclZpc2lvbjJTZXElMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yVmlzaW9uMlNlcS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForVision2Seq.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function XX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForVision2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function DX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yVmlzaW9uMlNlcSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbEZvclZpc2lvbjJTZXEuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForVision2Seq.from_config(config)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function HX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForVision2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){n=i("p"),n.textContent=F,c=r(),p(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),f(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),u(s.$$.fragment,e)},m(e,y){T(e,n,y),T(e,c,y),h(s,e,y),m=!0},p:x,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){_(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),M(s,e)}}}function QX(v){let n,F,c,s,m,e,y,QE="많은 경우, 사용하려는 아키텍처는 <code>from_pretrained()</code> 메소드에서 제공하는 사전 훈련된 모델의 이름이나 경로로부터 유추할 수 있습니다. AutoClasses는 이 작업을 위해 존재하며, 사전 학습된 모델 가중치/구성/단어사전에 대한 이름/경로를 제공하면 자동으로 관련 모델을 가져오도록 도와줍니다.",ZC,vd,YE='<a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoConfig">AutoConfig</a>, <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel">AutoModel</a>, <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a> 중 하나를 인스턴스화하면 해당 아키텍처의 클래스를 직접 생성합니다. 예를 들어,',BC,Cd,RC,wd,zE='위 코드는 <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertModel">BertModel</a>의 인스턴스인 모델을 생성합니다.',GC,kd,OE="각 작업에 대해 하나의 <code>AutoModel</code> 클래스가 있으며, 각각의 백엔드(PyTorch, TensorFlow 또는 Flax)에 해당하는 클래스가 존재합니다.",JC,xd,WC,$d,KE="각 자동 클래스는 사용자의 커스텀 클래스로 확장될 수 있는 메소드를 가지고 있습니다. 예를 들어, <code>NewModel</code>이라는 커스텀 모델 클래스를 정의했다면, <code>NewModelConfig</code>를 준비한 후 다음과 같이 자동 클래스에 추가할 수 있습니다:",EC,Ld,VC,Ad,eV="이후에는 일반적으로 자동 클래스를 사용하는 것처럼 사용할 수 있습니다!",IC,vs,PC,jd,SC,ce,Zd,qk,_u,oV=`This is a generic configuration class that will be instantiated as one of the configuration classes of the library
when created with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoConfig.from_pretrained">from_pretrained()</a> class method.`,Xk,Mu,tV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Dk,Po,Bd,Hk,Tu,nV="Instantiate one of the configuration classes of the library from a pretrained model configuration.",Qk,yu,rV=`The configuration class to instantiate is selected based on the <code>model_type</code> property of the config object that
is loaded, or when it’s missing, by falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,Yk,Fu,aV='<li><strong>albert</strong> — <code>AlbertConfig</code> (ALBERT model)</li> <li><strong>align</strong> — <code>AlignConfig</code> (ALIGN model)</li> <li><strong>altclip</strong> — <a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPConfig">AltCLIPConfig</a> (AltCLIP model)</li> <li><strong>aria</strong> — <code>AriaConfig</code> (Aria model)</li> <li><strong>aria_text</strong> — <code>AriaTextConfig</code> (AriaText model)</li> <li><strong>audio-spectrogram-transformer</strong> — <code>ASTConfig</code> (Audio Spectrogram Transformer model)</li> <li><strong>autoformer</strong> — <a href="/docs/transformers/main/ko/model_doc/autoformer#transformers.AutoformerConfig">AutoformerConfig</a> (Autoformer model)</li> <li><strong>bamba</strong> — <code>BambaConfig</code> (Bamba model)</li> <li><strong>bark</strong> — <code>BarkConfig</code> (Bark model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartConfig">BartConfig</a> (BART model)</li> <li><strong>beit</strong> — <code>BeitConfig</code> (BEiT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> (BERT model)</li> <li><strong>bert-generation</strong> — <code>BertGenerationConfig</code> (Bert Generation model)</li> <li><strong>big_bird</strong> — <code>BigBirdConfig</code> (BigBird model)</li> <li><strong>bigbird_pegasus</strong> — <code>BigBirdPegasusConfig</code> (BigBird-Pegasus model)</li> <li><strong>biogpt</strong> — <a href="/docs/transformers/main/ko/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> (BioGpt model)</li> <li><strong>bit</strong> — <code>BitConfig</code> (BiT model)</li> <li><strong>blenderbot</strong> — <code>BlenderbotConfig</code> (Blenderbot model)</li> <li><strong>blenderbot-small</strong> — <code>BlenderbotSmallConfig</code> (BlenderbotSmall model)</li> <li><strong>blip</strong> — <a href="/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig">BlipConfig</a> (BLIP model)</li> <li><strong>blip-2</strong> — <a href="/docs/transformers/main/ko/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> (BLIP-2 model)</li> <li><strong>bloom</strong> — <code>BloomConfig</code> (BLOOM model)</li> <li><strong>bridgetower</strong> — <code>BridgeTowerConfig</code> (BridgeTower model)</li> <li><strong>bros</strong> — <code>BrosConfig</code> (BROS model)</li> <li><strong>camembert</strong> — <code>CamembertConfig</code> (CamemBERT model)</li> <li><strong>canine</strong> — <code>CanineConfig</code> (CANINE model)</li> <li><strong>chameleon</strong> — <a href="/docs/transformers/main/ko/model_doc/chameleon#transformers.ChameleonConfig">ChameleonConfig</a> (Chameleon model)</li> <li><strong>chinese_clip</strong> — <code>ChineseCLIPConfig</code> (Chinese-CLIP model)</li> <li><strong>chinese_clip_vision_model</strong> — <code>ChineseCLIPVisionConfig</code> (ChineseCLIPVisionModel model)</li> <li><strong>clap</strong> — <code>ClapConfig</code> (CLAP model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> (CLIP model)</li> <li><strong>clip_text_model</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTextConfig">CLIPTextConfig</a> (CLIPTextModel model)</li> <li><strong>clip_vision_model</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPVisionConfig">CLIPVisionConfig</a> (CLIPVisionModel model)</li> <li><strong>clipseg</strong> — <code>CLIPSegConfig</code> (CLIPSeg model)</li> <li><strong>clvp</strong> — <code>ClvpConfig</code> (CLVP model)</li> <li><strong>code_llama</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> (CodeLlama model)</li> <li><strong>codegen</strong> — <code>CodeGenConfig</code> (CodeGen model)</li> <li><strong>cohere</strong> — <a href="/docs/transformers/main/ko/model_doc/cohere#transformers.CohereConfig">CohereConfig</a> (Cohere model)</li> <li><strong>cohere2</strong> — <code>Cohere2Config</code> (Cohere2 model)</li> <li><strong>colpali</strong> — <code>ColPaliConfig</code> (ColPali model)</li> <li><strong>conditional_detr</strong> — <code>ConditionalDetrConfig</code> (Conditional DETR model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> (ConvBERT model)</li> <li><strong>convnext</strong> — <code>ConvNextConfig</code> (ConvNeXT model)</li> <li><strong>convnextv2</strong> — <code>ConvNextV2Config</code> (ConvNeXTV2 model)</li> <li><strong>cpmant</strong> — <code>CpmAntConfig</code> (CPM-Ant model)</li> <li><strong>ctrl</strong> — <code>CTRLConfig</code> (CTRL model)</li> <li><strong>cvt</strong> — <code>CvtConfig</code> (CvT model)</li> <li><strong>dac</strong> — <code>DacConfig</code> (DAC model)</li> <li><strong>data2vec-audio</strong> — <code>Data2VecAudioConfig</code> (Data2VecAudio model)</li> <li><strong>data2vec-text</strong> — <code>Data2VecTextConfig</code> (Data2VecText model)</li> <li><strong>data2vec-vision</strong> — <code>Data2VecVisionConfig</code> (Data2VecVision model)</li> <li><strong>dbrx</strong> — <a href="/docs/transformers/main/ko/model_doc/dbrx#transformers.DbrxConfig">DbrxConfig</a> (DBRX model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> (DeBERTa-v2 model)</li> <li><strong>decision_transformer</strong> — <code>DecisionTransformerConfig</code> (Decision Transformer model)</li> <li><strong>deformable_detr</strong> — <code>DeformableDetrConfig</code> (Deformable DETR model)</li> <li><strong>deit</strong> — <code>DeiTConfig</code> (DeiT model)</li> <li><strong>depth_anything</strong> — <code>DepthAnythingConfig</code> (Depth Anything model)</li> <li><strong>deta</strong> — <code>DetaConfig</code> (DETA model)</li> <li><strong>detr</strong> — <code>DetrConfig</code> (DETR model)</li> <li><strong>diffllama</strong> — <code>DiffLlamaConfig</code> (DiffLlama model)</li> <li><strong>dinat</strong> — <code>DinatConfig</code> (DiNAT model)</li> <li><strong>dinov2</strong> — <code>Dinov2Config</code> (DINOv2 model)</li> <li><strong>dinov2_with_registers</strong> — <code>Dinov2WithRegistersConfig</code> (DINOv2 with Registers model)</li> <li><strong>distilbert</strong> — <code>DistilBertConfig</code> (DistilBERT model)</li> <li><strong>donut-swin</strong> — <code>DonutSwinConfig</code> (DonutSwin model)</li> <li><strong>dpr</strong> — <code>DPRConfig</code> (DPR model)</li> <li><strong>dpt</strong> — <code>DPTConfig</code> (DPT model)</li> <li><strong>efficientformer</strong> — <code>EfficientFormerConfig</code> (EfficientFormer model)</li> <li><strong>efficientnet</strong> — <code>EfficientNetConfig</code> (EfficientNet model)</li> <li><strong>electra</strong> — <code>ElectraConfig</code> (ELECTRA model)</li> <li><strong>emu3</strong> — <code>Emu3Config</code> (Emu3 model)</li> <li><strong>encodec</strong> — <code>EncodecConfig</code> (EnCodec model)</li> <li><strong>encoder-decoder</strong> — <a href="/docs/transformers/main/ko/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> (Encoder decoder model)</li> <li><strong>ernie</strong> — <code>ErnieConfig</code> (ERNIE model)</li> <li><strong>ernie_m</strong> — <code>ErnieMConfig</code> (ErnieM model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/main/ko/model_doc/esm#transformers.EsmConfig">EsmConfig</a> (ESM model)</li> <li><strong>falcon</strong> — <code>FalconConfig</code> (Falcon model)</li> <li><strong>falcon_mamba</strong> — <code>FalconMambaConfig</code> (FalconMamba model)</li> <li><strong>fastspeech2_conformer</strong> — <code>FastSpeech2ConformerConfig</code> (FastSpeech2Conformer model)</li> <li><strong>flaubert</strong> — <code>FlaubertConfig</code> (FlauBERT model)</li> <li><strong>flava</strong> — <code>FlavaConfig</code> (FLAVA model)</li> <li><strong>fnet</strong> — <code>FNetConfig</code> (FNet model)</li> <li><strong>focalnet</strong> — <code>FocalNetConfig</code> (FocalNet model)</li> <li><strong>fsmt</strong> — <code>FSMTConfig</code> (FairSeq Machine-Translation model)</li> <li><strong>funnel</strong> — <code>FunnelConfig</code> (Funnel Transformer model)</li> <li><strong>fuyu</strong> — <code>FuyuConfig</code> (Fuyu model)</li> <li><strong>gemma</strong> — <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaConfig">GemmaConfig</a> (Gemma model)</li> <li><strong>gemma2</strong> — <a href="/docs/transformers/main/ko/model_doc/gemma2#transformers.Gemma2Config">Gemma2Config</a> (Gemma2 model)</li> <li><strong>git</strong> — <code>GitConfig</code> (GIT model)</li> <li><strong>glm</strong> — <code>GlmConfig</code> (GLM model)</li> <li><strong>glpn</strong> — <code>GLPNConfig</code> (GLPN model)</li> <li><strong>gpt-sw3</strong> — <code>GPT2Config</code> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <code>GPT2Config</code> (OpenAI GPT-2 model)</li> <li><strong>gpt_bigcode</strong> — <code>GPTBigCodeConfig</code> (GPTBigCode model)</li> <li><strong>gpt_neo</strong> — <code>GPTNeoConfig</code> (GPT Neo model)</li> <li><strong>gpt_neox</strong> — <code>GPTNeoXConfig</code> (GPT NeoX model)</li> <li><strong>gpt_neox_japanese</strong> — <a href="/docs/transformers/main/ko/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> (GPT NeoX Japanese model)</li> <li><strong>gptj</strong> — <code>GPTJConfig</code> (GPT-J model)</li> <li><strong>gptsan-japanese</strong> — <code>GPTSanJapaneseConfig</code> (GPTSAN-japanese model)</li> <li><strong>granite</strong> — <code>GraniteConfig</code> (Granite model)</li> <li><strong>granitemoe</strong> — <code>GraniteMoeConfig</code> (GraniteMoeMoe model)</li> <li><strong>granitevision</strong> — <code>LlavaNextConfig</code> (LLaVA-NeXT model)</li> <li><strong>graphormer</strong> — <a href="/docs/transformers/main/ko/model_doc/graphormer#transformers.GraphormerConfig">GraphormerConfig</a> (Graphormer model)</li> <li><strong>grounding-dino</strong> — <code>GroundingDinoConfig</code> (Grounding DINO model)</li> <li><strong>groupvit</strong> — <code>GroupViTConfig</code> (GroupViT model)</li> <li><strong>helium</strong> — <code>HeliumConfig</code> (Helium model)</li> <li><strong>hiera</strong> — <code>HieraConfig</code> (Hiera model)</li> <li><strong>hubert</strong> — <code>HubertConfig</code> (Hubert model)</li> <li><strong>ibert</strong> — <code>IBertConfig</code> (I-BERT model)</li> <li><strong>idefics</strong> — <code>IdeficsConfig</code> (IDEFICS model)</li> <li><strong>idefics2</strong> — <code>Idefics2Config</code> (Idefics2 model)</li> <li><strong>idefics3</strong> — <code>Idefics3Config</code> (Idefics3 model)</li> <li><strong>idefics3_vision</strong> — <code>Idefics3VisionConfig</code> (Idefics3VisionTransformer model)</li> <li><strong>ijepa</strong> — <code>IJepaConfig</code> (I-JEPA model)</li> <li><strong>imagegpt</strong> — <code>ImageGPTConfig</code> (ImageGPT model)</li> <li><strong>informer</strong> — <a href="/docs/transformers/main/ko/model_doc/informer#transformers.InformerConfig">InformerConfig</a> (Informer model)</li> <li><strong>instructblip</strong> — <code>InstructBlipConfig</code> (InstructBLIP model)</li> <li><strong>instructblipvideo</strong> — <code>InstructBlipVideoConfig</code> (InstructBlipVideo model)</li> <li><strong>jamba</strong> — <code>JambaConfig</code> (Jamba model)</li> <li><strong>jetmoe</strong> — <code>JetMoeConfig</code> (JetMoe model)</li> <li><strong>jukebox</strong> — <code>JukeboxConfig</code> (Jukebox model)</li> <li><strong>kosmos-2</strong> — <code>Kosmos2Config</code> (KOSMOS-2 model)</li> <li><strong>layoutlm</strong> — <code>LayoutLMConfig</code> (LayoutLM model)</li> <li><strong>layoutlmv2</strong> — <code>LayoutLMv2Config</code> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <code>LayoutLMv3Config</code> (LayoutLMv3 model)</li> <li><strong>led</strong> — <code>LEDConfig</code> (LED model)</li> <li><strong>levit</strong> — <code>LevitConfig</code> (LeViT model)</li> <li><strong>lilt</strong> — <code>LiltConfig</code> (LiLT model)</li> <li><strong>llama</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> (LLaMA model)</li> <li><strong>llava</strong> — <code>LlavaConfig</code> (LLaVa model)</li> <li><strong>llava_next</strong> — <code>LlavaNextConfig</code> (LLaVA-NeXT model)</li> <li><strong>llava_next_video</strong> — <code>LlavaNextVideoConfig</code> (LLaVa-NeXT-Video model)</li> <li><strong>llava_onevision</strong> — <code>LlavaOnevisionConfig</code> (LLaVA-Onevision model)</li> <li><strong>longformer</strong> — <code>LongformerConfig</code> (Longformer model)</li> <li><strong>longt5</strong> — <code>LongT5Config</code> (LongT5 model)</li> <li><strong>luke</strong> — <code>LukeConfig</code> (LUKE model)</li> <li><strong>lxmert</strong> — <code>LxmertConfig</code> (LXMERT model)</li> <li><strong>m2m_100</strong> — <code>M2M100Config</code> (M2M100 model)</li> <li><strong>mamba</strong> — <a href="/docs/transformers/main/ko/model_doc/mamba#transformers.MambaConfig">MambaConfig</a> (Mamba model)</li> <li><strong>mamba2</strong> — <a href="/docs/transformers/main/ko/model_doc/mamba2#transformers.Mamba2Config">Mamba2Config</a> (mamba2 model)</li> <li><strong>marian</strong> — <a href="/docs/transformers/main/ko/model_doc/marian#transformers.MarianConfig">MarianConfig</a> (Marian model)</li> <li><strong>markuplm</strong> — <code>MarkupLMConfig</code> (MarkupLM model)</li> <li><strong>mask2former</strong> — <code>Mask2FormerConfig</code> (Mask2Former model)</li> <li><strong>maskformer</strong> — <code>MaskFormerConfig</code> (MaskFormer model)</li> <li><strong>maskformer-swin</strong> — <code>MaskFormerSwinConfig</code> (MaskFormerSwin model)</li> <li><strong>mbart</strong> — <code>MBartConfig</code> (mBART model)</li> <li><strong>mctct</strong> — <code>MCTCTConfig</code> (M-CTC-T model)</li> <li><strong>mega</strong> — <code>MegaConfig</code> (MEGA model)</li> <li><strong>megatron-bert</strong> — <code>MegatronBertConfig</code> (Megatron-BERT model)</li> <li><strong>mgp-str</strong> — <code>MgpstrConfig</code> (MGP-STR model)</li> <li><strong>mimi</strong> — <code>MimiConfig</code> (Mimi model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/main/ko/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> (Mistral model)</li> <li><strong>mixtral</strong> — <code>MixtralConfig</code> (Mixtral model)</li> <li><strong>mllama</strong> — <code>MllamaConfig</code> (Mllama model)</li> <li><strong>mobilebert</strong> — <code>MobileBertConfig</code> (MobileBERT model)</li> <li><strong>mobilenet_v1</strong> — <code>MobileNetV1Config</code> (MobileNetV1 model)</li> <li><strong>mobilenet_v2</strong> — <code>MobileNetV2Config</code> (MobileNetV2 model)</li> <li><strong>mobilevit</strong> — <code>MobileViTConfig</code> (MobileViT model)</li> <li><strong>mobilevitv2</strong> — <code>MobileViTV2Config</code> (MobileViTV2 model)</li> <li><strong>modernbert</strong> — <code>ModernBertConfig</code> (ModernBERT model)</li> <li><strong>moonshine</strong> — <code>MoonshineConfig</code> (Moonshine model)</li> <li><strong>moshi</strong> — <code>MoshiConfig</code> (Moshi model)</li> <li><strong>mpnet</strong> — <code>MPNetConfig</code> (MPNet model)</li> <li><strong>mpt</strong> — <code>MptConfig</code> (MPT model)</li> <li><strong>mra</strong> — <code>MraConfig</code> (MRA model)</li> <li><strong>mt5</strong> — <code>MT5Config</code> (MT5 model)</li> <li><strong>musicgen</strong> — <code>MusicgenConfig</code> (MusicGen model)</li> <li><strong>musicgen_melody</strong> — <code>MusicgenMelodyConfig</code> (MusicGen Melody model)</li> <li><strong>mvp</strong> — <code>MvpConfig</code> (MVP model)</li> <li><strong>nat</strong> — <code>NatConfig</code> (NAT model)</li> <li><strong>nemotron</strong> — <code>NemotronConfig</code> (Nemotron model)</li> <li><strong>nezha</strong> — <code>NezhaConfig</code> (Nezha model)</li> <li><strong>nllb-moe</strong> — <code>NllbMoeConfig</code> (NLLB-MOE model)</li> <li><strong>nougat</strong> — <code>VisionEncoderDecoderConfig</code> (Nougat model)</li> <li><strong>nystromformer</strong> — <code>NystromformerConfig</code> (Nyströmformer model)</li> <li><strong>olmo</strong> — <code>OlmoConfig</code> (OLMo model)</li> <li><strong>olmo2</strong> — <code>Olmo2Config</code> (OLMo2 model)</li> <li><strong>olmoe</strong> — <code>OlmoeConfig</code> (OLMoE model)</li> <li><strong>omdet-turbo</strong> — <code>OmDetTurboConfig</code> (OmDet-Turbo model)</li> <li><strong>oneformer</strong> — <code>OneFormerConfig</code> (OneFormer model)</li> <li><strong>open-llama</strong> — <code>OpenLlamaConfig</code> (OpenLlama model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> (OpenAI GPT model)</li> <li><strong>opt</strong> — <code>OPTConfig</code> (OPT model)</li> <li><strong>owlv2</strong> — <code>Owlv2Config</code> (OWLv2 model)</li> <li><strong>owlvit</strong> — <code>OwlViTConfig</code> (OWL-ViT model)</li> <li><strong>paligemma</strong> — <a href="/docs/transformers/main/ko/model_doc/paligemma#transformers.PaliGemmaConfig">PaliGemmaConfig</a> (PaliGemma model)</li> <li><strong>patchtsmixer</strong> — <a href="/docs/transformers/main/ko/model_doc/patchtsmixer#transformers.PatchTSMixerConfig">PatchTSMixerConfig</a> (PatchTSMixer model)</li> <li><strong>patchtst</strong> — <a href="/docs/transformers/main/ko/model_doc/patchtst#transformers.PatchTSTConfig">PatchTSTConfig</a> (PatchTST model)</li> <li><strong>pegasus</strong> — <code>PegasusConfig</code> (Pegasus model)</li> <li><strong>pegasus_x</strong> — <code>PegasusXConfig</code> (PEGASUS-X model)</li> <li><strong>perceiver</strong> — <code>PerceiverConfig</code> (Perceiver model)</li> <li><strong>persimmon</strong> — <code>PersimmonConfig</code> (Persimmon model)</li> <li><strong>phi</strong> — <code>PhiConfig</code> (Phi model)</li> <li><strong>phi3</strong> — <code>Phi3Config</code> (Phi3 model)</li> <li><strong>phimoe</strong> — <code>PhimoeConfig</code> (Phimoe model)</li> <li><strong>pix2struct</strong> — <code>Pix2StructConfig</code> (Pix2Struct model)</li> <li><strong>pixtral</strong> — <code>PixtralVisionConfig</code> (Pixtral model)</li> <li><strong>plbart</strong> — <code>PLBartConfig</code> (PLBart model)</li> <li><strong>poolformer</strong> — <code>PoolFormerConfig</code> (PoolFormer model)</li> <li><strong>pop2piano</strong> — <code>Pop2PianoConfig</code> (Pop2Piano model)</li> <li><strong>prophetnet</strong> — <code>ProphetNetConfig</code> (ProphetNet model)</li> <li><strong>pvt</strong> — <code>PvtConfig</code> (PVT model)</li> <li><strong>pvt_v2</strong> — <code>PvtV2Config</code> (PVTv2 model)</li> <li><strong>qdqbert</strong> — <code>QDQBertConfig</code> (QDQBert model)</li> <li><strong>qwen2</strong> — <code>Qwen2Config</code> (Qwen2 model)</li> <li><strong>qwen2_5_vl</strong> — <code>Qwen2_5_VLConfig</code> (Qwen2_5_VL model)</li> <li><strong>qwen2_audio</strong> — <code>Qwen2AudioConfig</code> (Qwen2Audio model)</li> <li><strong>qwen2_audio_encoder</strong> — <code>Qwen2AudioEncoderConfig</code> (Qwen2AudioEncoder model)</li> <li><strong>qwen2_moe</strong> — <code>Qwen2MoeConfig</code> (Qwen2MoE model)</li> <li><strong>qwen2_vl</strong> — <code>Qwen2VLConfig</code> (Qwen2VL model)</li> <li><strong>rag</strong> — <a href="/docs/transformers/main/ko/model_doc/rag#transformers.RagConfig">RagConfig</a> (RAG model)</li> <li><strong>realm</strong> — <code>RealmConfig</code> (REALM model)</li> <li><strong>recurrent_gemma</strong> — <code>RecurrentGemmaConfig</code> (RecurrentGemma model)</li> <li><strong>reformer</strong> — <code>ReformerConfig</code> (Reformer model)</li> <li><strong>regnet</strong> — <code>RegNetConfig</code> (RegNet model)</li> <li><strong>rembert</strong> — <code>RemBertConfig</code> (RemBERT model)</li> <li><strong>resnet</strong> — <code>ResNetConfig</code> (ResNet model)</li> <li><strong>retribert</strong> — <code>RetriBertConfig</code> (RetriBERT model)</li> <li><strong>roberta</strong> — <code>RobertaConfig</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>RobertaPreLayerNormConfig</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <code>RoCBertConfig</code> (RoCBert model)</li> <li><strong>roformer</strong> — <code>RoFormerConfig</code> (RoFormer model)</li> <li><strong>rt_detr</strong> — <code>RTDetrConfig</code> (RT-DETR model)</li> <li><strong>rt_detr_resnet</strong> — <code>RTDetrResNetConfig</code> (RT-DETR-ResNet model)</li> <li><strong>rwkv</strong> — <code>RwkvConfig</code> (RWKV model)</li> <li><strong>sam</strong> — <code>SamConfig</code> (SAM model)</li> <li><strong>seamless_m4t</strong> — <code>SeamlessM4TConfig</code> (SeamlessM4T model)</li> <li><strong>seamless_m4t_v2</strong> — <code>SeamlessM4Tv2Config</code> (SeamlessM4Tv2 model)</li> <li><strong>segformer</strong> — <code>SegformerConfig</code> (SegFormer model)</li> <li><strong>seggpt</strong> — <code>SegGptConfig</code> (SegGPT model)</li> <li><strong>sew</strong> — <code>SEWConfig</code> (SEW model)</li> <li><strong>sew-d</strong> — <code>SEWDConfig</code> (SEW-D model)</li> <li><strong>siglip</strong> — <code>SiglipConfig</code> (SigLIP model)</li> <li><strong>siglip_vision_model</strong> — <code>SiglipVisionConfig</code> (SiglipVisionModel model)</li> <li><strong>speech-encoder-decoder</strong> — <code>SpeechEncoderDecoderConfig</code> (Speech Encoder decoder model)</li> <li><strong>speech_to_text</strong> — <code>Speech2TextConfig</code> (Speech2Text model)</li> <li><strong>speech_to_text_2</strong> — <code>Speech2Text2Config</code> (Speech2Text2 model)</li> <li><strong>speecht5</strong> — <code>SpeechT5Config</code> (SpeechT5 model)</li> <li><strong>splinter</strong> — <code>SplinterConfig</code> (Splinter model)</li> <li><strong>squeezebert</strong> — <code>SqueezeBertConfig</code> (SqueezeBERT model)</li> <li><strong>stablelm</strong> — <code>StableLmConfig</code> (StableLm model)</li> <li><strong>starcoder2</strong> — <code>Starcoder2Config</code> (Starcoder2 model)</li> <li><strong>superglue</strong> — <code>SuperGlueConfig</code> (SuperGlue model)</li> <li><strong>superpoint</strong> — <code>SuperPointConfig</code> (SuperPoint model)</li> <li><strong>swiftformer</strong> — <code>SwiftFormerConfig</code> (SwiftFormer model)</li> <li><strong>swin</strong> — <a href="/docs/transformers/main/ko/model_doc/swin#transformers.SwinConfig">SwinConfig</a> (Swin Transformer model)</li> <li><strong>swin2sr</strong> — <a href="/docs/transformers/main/ko/model_doc/swin2sr#transformers.Swin2SRConfig">Swin2SRConfig</a> (Swin2SR model)</li> <li><strong>swinv2</strong> — <a href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> (Swin Transformer V2 model)</li> <li><strong>switch_transformers</strong> — <code>SwitchTransformersConfig</code> (SwitchTransformers model)</li> <li><strong>t5</strong> — <code>T5Config</code> (T5 model)</li> <li><strong>table-transformer</strong> — <code>TableTransformerConfig</code> (Table Transformer model)</li> <li><strong>tapas</strong> — <code>TapasConfig</code> (TAPAS model)</li> <li><strong>textnet</strong> — <code>TextNetConfig</code> (TextNet model)</li> <li><strong>time_series_transformer</strong> — <a href="/docs/transformers/main/ko/model_doc/time_series_transformer#transformers.TimeSeriesTransformerConfig">TimeSeriesTransformerConfig</a> (Time Series Transformer model)</li> <li><strong>timesformer</strong> — <a href="/docs/transformers/main/ko/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a> (TimeSformer model)</li> <li><strong>timm_backbone</strong> — <code>TimmBackboneConfig</code> (TimmBackbone model)</li> <li><strong>timm_wrapper</strong> — <code>TimmWrapperConfig</code> (TimmWrapperModel model)</li> <li><strong>trajectory_transformer</strong> — <a href="/docs/transformers/main/ko/model_doc/trajectory_transformer#transformers.TrajectoryTransformerConfig">TrajectoryTransformerConfig</a> (Trajectory Transformer model)</li> <li><strong>transfo-xl</strong> — <code>TransfoXLConfig</code> (Transformer-XL model)</li> <li><strong>trocr</strong> — <code>TrOCRConfig</code> (TrOCR model)</li> <li><strong>tvlt</strong> — <code>TvltConfig</code> (TVLT model)</li> <li><strong>tvp</strong> — <code>TvpConfig</code> (TVP model)</li> <li><strong>udop</strong> — <code>UdopConfig</code> (UDOP model)</li> <li><strong>umt5</strong> — <code>UMT5Config</code> (UMT5 model)</li> <li><strong>unispeech</strong> — <code>UniSpeechConfig</code> (UniSpeech model)</li> <li><strong>unispeech-sat</strong> — <code>UniSpeechSatConfig</code> (UniSpeechSat model)</li> <li><strong>univnet</strong> — <code>UnivNetConfig</code> (UnivNet model)</li> <li><strong>upernet</strong> — <code>UperNetConfig</code> (UPerNet model)</li> <li><strong>van</strong> — <code>VanConfig</code> (VAN model)</li> <li><strong>video_llava</strong> — <code>VideoLlavaConfig</code> (VideoLlava model)</li> <li><strong>videomae</strong> — <code>VideoMAEConfig</code> (VideoMAE model)</li> <li><strong>vilt</strong> — <code>ViltConfig</code> (ViLT model)</li> <li><strong>vipllava</strong> — <code>VipLlavaConfig</code> (VipLlava model)</li> <li><strong>vision-encoder-decoder</strong> — <code>VisionEncoderDecoderConfig</code> (Vision Encoder decoder model)</li> <li><strong>vision-text-dual-encoder</strong> — <code>VisionTextDualEncoderConfig</code> (VisionTextDualEncoder model)</li> <li><strong>visual_bert</strong> — <code>VisualBertConfig</code> (VisualBERT model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTConfig">ViTConfig</a> (ViT model)</li> <li><strong>vit_hybrid</strong> — <code>ViTHybridConfig</code> (ViT Hybrid model)</li> <li><strong>vit_mae</strong> — <code>ViTMAEConfig</code> (ViTMAE model)</li> <li><strong>vit_msn</strong> — <code>ViTMSNConfig</code> (ViTMSN model)</li> <li><strong>vitdet</strong> — <code>VitDetConfig</code> (VitDet model)</li> <li><strong>vitmatte</strong> — <code>VitMatteConfig</code> (ViTMatte model)</li> <li><strong>vitpose</strong> — <code>VitPoseConfig</code> (ViTPose model)</li> <li><strong>vitpose_backbone</strong> — <code>VitPoseBackboneConfig</code> (ViTPoseBackbone model)</li> <li><strong>vits</strong> — <code>VitsConfig</code> (VITS model)</li> <li><strong>vivit</strong> — <a href="/docs/transformers/main/ko/model_doc/vivit#transformers.VivitConfig">VivitConfig</a> (ViViT model)</li> <li><strong>wav2vec2</strong> — <code>Wav2Vec2Config</code> (Wav2Vec2 model)</li> <li><strong>wav2vec2-bert</strong> — <code>Wav2Vec2BertConfig</code> (Wav2Vec2-BERT model)</li> <li><strong>wav2vec2-conformer</strong> — <code>Wav2Vec2ConformerConfig</code> (Wav2Vec2-Conformer model)</li> <li><strong>wavlm</strong> — <code>WavLMConfig</code> (WavLM model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> (Whisper model)</li> <li><strong>xclip</strong> — <code>XCLIPConfig</code> (X-CLIP model)</li> <li><strong>xglm</strong> — <code>XGLMConfig</code> (XGLM model)</li> <li><strong>xlm</strong> — <code>XLMConfig</code> (XLM model)</li> <li><strong>xlm-prophetnet</strong> — <code>XLMProphetNetConfig</code> (XLM-ProphetNet model)</li> <li><strong>xlm-roberta</strong> — <code>XLMRobertaConfig</code> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <code>XLMRobertaXLConfig</code> (XLM-RoBERTa-XL model)</li> <li><strong>xlnet</strong> — <code>XLNetConfig</code> (XLNet model)</li> <li><strong>xmod</strong> — <code>XmodConfig</code> (X-MOD model)</li> <li><strong>yolos</strong> — <code>YolosConfig</code> (YOLOS model)</li> <li><strong>yoso</strong> — <code>YosoConfig</code> (YOSO model)</li> <li><strong>zamba</strong> — <code>ZambaConfig</code> (Zamba model)</li> <li><strong>zamba2</strong> — <code>Zamba2Config</code> (Zamba2 model)</li> <li><strong>zoedepth</strong> — <code>ZoeDepthConfig</code> (ZoeDepth model)</li>',zk,Cs,Ok,ws,Rd,Kk,vu,sV="Register a new configuration for this class.",UC,Gd,NC,me,Jd,e1,Cu,iV=`This is a generic tokenizer class that will be instantiated as one of the tokenizer classes of the library when
created with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoTokenizer.from_pretrained">AutoTokenizer.from_pretrained()</a> class method.`,o1,wu,lV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",t1,So,Wd,n1,ku,dV="Instantiate one of the tokenizer classes of the library from a pretrained model vocabulary.",r1,xu,cV=`The tokenizer class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,a1,$u,mV='<li><strong>albert</strong> — <code>AlbertTokenizer</code> or <code>AlbertTokenizerFast</code> (ALBERT model)</li> <li><strong>align</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (ALIGN model)</li> <li><strong>aria</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Aria model)</li> <li><strong>bark</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (Bark model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartTokenizer">BartTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartTokenizerFast">BartTokenizerFast</a> (BART model)</li> <li><strong>barthez</strong> — <a href="/docs/transformers/main/ko/model_doc/barthez#transformers.BarthezTokenizer">BarthezTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/barthez#transformers.BarthezTokenizerFast">BarthezTokenizerFast</a> (BARThez model)</li> <li><strong>bartpho</strong> — <a href="/docs/transformers/main/ko/model_doc/bartpho#transformers.BartphoTokenizer">BartphoTokenizer</a> (BARTpho model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (BERT model)</li> <li><strong>bert-generation</strong> — <code>BertGenerationTokenizer</code> (Bert Generation model)</li> <li><strong>bert-japanese</strong> — <a href="/docs/transformers/main/ko/model_doc/bert-japanese#transformers.BertJapaneseTokenizer">BertJapaneseTokenizer</a> (BertJapanese model)</li> <li><strong>bertweet</strong> — <a href="/docs/transformers/main/ko/model_doc/bertweet#transformers.BertweetTokenizer">BertweetTokenizer</a> (BERTweet model)</li> <li><strong>big_bird</strong> — <code>BigBirdTokenizer</code> or <code>BigBirdTokenizerFast</code> (BigBird model)</li> <li><strong>bigbird_pegasus</strong> — <code>PegasusTokenizer</code> or <code>PegasusTokenizerFast</code> (BigBird-Pegasus model)</li> <li><strong>biogpt</strong> — <a href="/docs/transformers/main/ko/model_doc/biogpt#transformers.BioGptTokenizer">BioGptTokenizer</a> (BioGpt model)</li> <li><strong>blenderbot</strong> — <code>BlenderbotTokenizer</code> or <code>BlenderbotTokenizerFast</code> (Blenderbot model)</li> <li><strong>blenderbot-small</strong> — <code>BlenderbotSmallTokenizer</code> (BlenderbotSmall model)</li> <li><strong>blip</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (BLIP model)</li> <li><strong>blip-2</strong> — <code>GPT2Tokenizer</code> or <code>GPT2TokenizerFast</code> (BLIP-2 model)</li> <li><strong>bloom</strong> — <code>BloomTokenizerFast</code> (BLOOM model)</li> <li><strong>bridgetower</strong> — <code>RobertaTokenizer</code> or <code>RobertaTokenizerFast</code> (BridgeTower model)</li> <li><strong>bros</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (BROS model)</li> <li><strong>byt5</strong> — <code>ByT5Tokenizer</code> (ByT5 model)</li> <li><strong>camembert</strong> — <code>CamembertTokenizer</code> or <code>CamembertTokenizerFast</code> (CamemBERT model)</li> <li><strong>canine</strong> — <code>CanineTokenizer</code> (CANINE model)</li> <li><strong>chameleon</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Chameleon model)</li> <li><strong>chinese_clip</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (Chinese-CLIP model)</li> <li><strong>clap</strong> — <code>RobertaTokenizer</code> or <code>RobertaTokenizerFast</code> (CLAP model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTokenizerFast">CLIPTokenizerFast</a> (CLIP model)</li> <li><strong>clipseg</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTokenizerFast">CLIPTokenizerFast</a> (CLIPSeg model)</li> <li><strong>clvp</strong> — <code>ClvpTokenizer</code> (CLVP model)</li> <li><strong>code_llama</strong> — <code>CodeLlamaTokenizer</code> or <code>CodeLlamaTokenizerFast</code> (CodeLlama model)</li> <li><strong>codegen</strong> — <code>CodeGenTokenizer</code> or <code>CodeGenTokenizerFast</code> (CodeGen model)</li> <li><strong>cohere</strong> — <a href="/docs/transformers/main/ko/model_doc/cohere#transformers.CohereTokenizerFast">CohereTokenizerFast</a> (Cohere model)</li> <li><strong>cohere2</strong> — <a href="/docs/transformers/main/ko/model_doc/cohere#transformers.CohereTokenizerFast">CohereTokenizerFast</a> (Cohere2 model)</li> <li><strong>colpali</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (ColPali model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertTokenizer">ConvBertTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertTokenizerFast">ConvBertTokenizerFast</a> (ConvBERT model)</li> <li><strong>cpm</strong> — <code>CpmTokenizer</code> or <code>CpmTokenizerFast</code> (CPM model)</li> <li><strong>cpmant</strong> — <code>CpmAntTokenizer</code> (CPM-Ant model)</li> <li><strong>ctrl</strong> — <code>CTRLTokenizer</code> (CTRL model)</li> <li><strong>data2vec-audio</strong> — <code>Wav2Vec2CTCTokenizer</code> (Data2VecAudio model)</li> <li><strong>data2vec-text</strong> — <code>RobertaTokenizer</code> or <code>RobertaTokenizerFast</code> (Data2VecText model)</li> <li><strong>dbrx</strong> — <code>GPT2Tokenizer</code> or <code>GPT2TokenizerFast</code> (DBRX model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaTokenizer">DebertaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaTokenizerFast">DebertaTokenizerFast</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2Tokenizer">DebertaV2Tokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2TokenizerFast">DebertaV2TokenizerFast</a> (DeBERTa-v2 model)</li> <li><strong>diffllama</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (DiffLlama model)</li> <li><strong>distilbert</strong> — <code>DistilBertTokenizer</code> or <code>DistilBertTokenizerFast</code> (DistilBERT model)</li> <li><strong>dpr</strong> — <code>DPRQuestionEncoderTokenizer</code> or <code>DPRQuestionEncoderTokenizerFast</code> (DPR model)</li> <li><strong>electra</strong> — <code>ElectraTokenizer</code> or <code>ElectraTokenizerFast</code> (ELECTRA model)</li> <li><strong>emu3</strong> — <code>GPT2Tokenizer</code> or <code>GPT2TokenizerFast</code> (Emu3 model)</li> <li><strong>ernie</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (ERNIE model)</li> <li><strong>ernie_m</strong> — <code>ErnieMTokenizer</code> (ErnieM model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/main/ko/model_doc/esm#transformers.EsmTokenizer">EsmTokenizer</a> (ESM model)</li> <li><strong>falcon</strong> — <code>PreTrainedTokenizerFast</code> (Falcon model)</li> <li><strong>falcon_mamba</strong> — <code>GPTNeoXTokenizerFast</code> (FalconMamba model)</li> <li><strong>fastspeech2_conformer</strong> —  (FastSpeech2Conformer model)</li> <li><strong>flaubert</strong> — <code>FlaubertTokenizer</code> (FlauBERT model)</li> <li><strong>fnet</strong> — <code>FNetTokenizer</code> or <code>FNetTokenizerFast</code> (FNet model)</li> <li><strong>fsmt</strong> — <code>FSMTTokenizer</code> (FairSeq Machine-Translation model)</li> <li><strong>funnel</strong> — <code>FunnelTokenizer</code> or <code>FunnelTokenizerFast</code> (Funnel Transformer model)</li> <li><strong>gemma</strong> — <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaTokenizer">GemmaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaTokenizerFast">GemmaTokenizerFast</a> (Gemma model)</li> <li><strong>gemma2</strong> — <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaTokenizer">GemmaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaTokenizerFast">GemmaTokenizerFast</a> (Gemma2 model)</li> <li><strong>git</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (GIT model)</li> <li><strong>glm</strong> — <code>PreTrainedTokenizerFast</code> (GLM model)</li> <li><strong>gpt-sw3</strong> — <code>GPTSw3Tokenizer</code> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <code>GPT2Tokenizer</code> or <code>GPT2TokenizerFast</code> (OpenAI GPT-2 model)</li> <li><strong>gpt_bigcode</strong> — <code>GPT2Tokenizer</code> or <code>GPT2TokenizerFast</code> (GPTBigCode model)</li> <li><strong>gpt_neo</strong> — <code>GPT2Tokenizer</code> or <code>GPT2TokenizerFast</code> (GPT Neo model)</li> <li><strong>gpt_neox</strong> — <code>GPTNeoXTokenizerFast</code> (GPT NeoX model)</li> <li><strong>gpt_neox_japanese</strong> — <a href="/docs/transformers/main/ko/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseTokenizer">GPTNeoXJapaneseTokenizer</a> (GPT NeoX Japanese model)</li> <li><strong>gptj</strong> — <code>GPT2Tokenizer</code> or <code>GPT2TokenizerFast</code> (GPT-J model)</li> <li><strong>gptsan-japanese</strong> — <code>GPTSanJapaneseTokenizer</code> (GPTSAN-japanese model)</li> <li><strong>grounding-dino</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (Grounding DINO model)</li> <li><strong>groupvit</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTokenizerFast">CLIPTokenizerFast</a> (GroupViT model)</li> <li><strong>helium</strong> — <code>PreTrainedTokenizerFast</code> (Helium model)</li> <li><strong>herbert</strong> — <code>HerbertTokenizer</code> or <code>HerbertTokenizerFast</code> (HerBERT model)</li> <li><strong>hubert</strong> — <code>Wav2Vec2CTCTokenizer</code> (Hubert model)</li> <li><strong>ibert</strong> — <code>RobertaTokenizer</code> or <code>RobertaTokenizerFast</code> (I-BERT model)</li> <li><strong>idefics</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (IDEFICS model)</li> <li><strong>idefics2</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Idefics2 model)</li> <li><strong>idefics3</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Idefics3 model)</li> <li><strong>instructblip</strong> — <code>GPT2Tokenizer</code> or <code>GPT2TokenizerFast</code> (InstructBLIP model)</li> <li><strong>instructblipvideo</strong> — <code>GPT2Tokenizer</code> or <code>GPT2TokenizerFast</code> (InstructBlipVideo model)</li> <li><strong>jamba</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Jamba model)</li> <li><strong>jetmoe</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (JetMoe model)</li> <li><strong>jukebox</strong> — <code>JukeboxTokenizer</code> (Jukebox model)</li> <li><strong>kosmos-2</strong> — <code>XLMRobertaTokenizer</code> or <code>XLMRobertaTokenizerFast</code> (KOSMOS-2 model)</li> <li><strong>layoutlm</strong> — <code>LayoutLMTokenizer</code> or <code>LayoutLMTokenizerFast</code> (LayoutLM model)</li> <li><strong>layoutlmv2</strong> — <code>LayoutLMv2Tokenizer</code> or <code>LayoutLMv2TokenizerFast</code> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <code>LayoutLMv3Tokenizer</code> or <code>LayoutLMv3TokenizerFast</code> (LayoutLMv3 model)</li> <li><strong>layoutxlm</strong> — <code>LayoutXLMTokenizer</code> or <code>LayoutXLMTokenizerFast</code> (LayoutXLM model)</li> <li><strong>led</strong> — <code>LEDTokenizer</code> or <code>LEDTokenizerFast</code> (LED model)</li> <li><strong>lilt</strong> — <code>LayoutLMv3Tokenizer</code> or <code>LayoutLMv3TokenizerFast</code> (LiLT model)</li> <li><strong>llama</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (LLaMA model)</li> <li><strong>llava</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (LLaVa model)</li> <li><strong>llava_next</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (LLaVA-NeXT model)</li> <li><strong>llava_next_video</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (LLaVa-NeXT-Video model)</li> <li><strong>llava_onevision</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (LLaVA-Onevision model)</li> <li><strong>longformer</strong> — <code>LongformerTokenizer</code> or <code>LongformerTokenizerFast</code> (Longformer model)</li> <li><strong>longt5</strong> — <code>T5Tokenizer</code> or <code>T5TokenizerFast</code> (LongT5 model)</li> <li><strong>luke</strong> — <code>LukeTokenizer</code> (LUKE model)</li> <li><strong>lxmert</strong> — <code>LxmertTokenizer</code> or <code>LxmertTokenizerFast</code> (LXMERT model)</li> <li><strong>m2m_100</strong> — <code>M2M100Tokenizer</code> (M2M100 model)</li> <li><strong>mamba</strong> — <code>GPTNeoXTokenizerFast</code> (Mamba model)</li> <li><strong>mamba2</strong> — <code>GPTNeoXTokenizerFast</code> (mamba2 model)</li> <li><strong>marian</strong> — <a href="/docs/transformers/main/ko/model_doc/marian#transformers.MarianTokenizer">MarianTokenizer</a> (Marian model)</li> <li><strong>mbart</strong> — <code>MBartTokenizer</code> or <code>MBartTokenizerFast</code> (mBART model)</li> <li><strong>mbart50</strong> — <code>MBart50Tokenizer</code> or <code>MBart50TokenizerFast</code> (mBART-50 model)</li> <li><strong>mega</strong> — <code>RobertaTokenizer</code> or <code>RobertaTokenizerFast</code> (MEGA model)</li> <li><strong>megatron-bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (Megatron-BERT model)</li> <li><strong>mgp-str</strong> — <code>MgpstrTokenizer</code> (MGP-STR model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Mistral model)</li> <li><strong>mixtral</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Mixtral model)</li> <li><strong>mllama</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Mllama model)</li> <li><strong>mluke</strong> — <code>MLukeTokenizer</code> (mLUKE model)</li> <li><strong>mobilebert</strong> — <code>MobileBertTokenizer</code> or <code>MobileBertTokenizerFast</code> (MobileBERT model)</li> <li><strong>modernbert</strong> — <code>PreTrainedTokenizerFast</code> (ModernBERT model)</li> <li><strong>moonshine</strong> — <code>PreTrainedTokenizerFast</code> (Moonshine model)</li> <li><strong>moshi</strong> — <code>PreTrainedTokenizerFast</code> (Moshi model)</li> <li><strong>mpnet</strong> — <code>MPNetTokenizer</code> or <code>MPNetTokenizerFast</code> (MPNet model)</li> <li><strong>mpt</strong> — <code>GPTNeoXTokenizerFast</code> (MPT model)</li> <li><strong>mra</strong> — <code>RobertaTokenizer</code> or <code>RobertaTokenizerFast</code> (MRA model)</li> <li><strong>mt5</strong> — <code>MT5Tokenizer</code> or <code>MT5TokenizerFast</code> (MT5 model)</li> <li><strong>musicgen</strong> — <code>T5Tokenizer</code> or <code>T5TokenizerFast</code> (MusicGen model)</li> <li><strong>musicgen_melody</strong> — <code>T5Tokenizer</code> or <code>T5TokenizerFast</code> (MusicGen Melody model)</li> <li><strong>mvp</strong> — <code>MvpTokenizer</code> or <code>MvpTokenizerFast</code> (MVP model)</li> <li><strong>myt5</strong> — <code>MyT5Tokenizer</code> (myt5 model)</li> <li><strong>nemotron</strong> — <code>PreTrainedTokenizerFast</code> (Nemotron model)</li> <li><strong>nezha</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (Nezha model)</li> <li><strong>nllb</strong> — <code>NllbTokenizer</code> or <code>NllbTokenizerFast</code> (NLLB model)</li> <li><strong>nllb-moe</strong> — <code>NllbTokenizer</code> or <code>NllbTokenizerFast</code> (NLLB-MOE model)</li> <li><strong>nystromformer</strong> — <code>AlbertTokenizer</code> or <code>AlbertTokenizerFast</code> (Nyströmformer model)</li> <li><strong>olmo</strong> — <code>GPTNeoXTokenizerFast</code> (OLMo model)</li> <li><strong>olmo2</strong> — <code>GPTNeoXTokenizerFast</code> (OLMo2 model)</li> <li><strong>olmoe</strong> — <code>GPTNeoXTokenizerFast</code> (OLMoE model)</li> <li><strong>omdet-turbo</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTokenizerFast">CLIPTokenizerFast</a> (OmDet-Turbo model)</li> <li><strong>oneformer</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTokenizerFast">CLIPTokenizerFast</a> (OneFormer model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.OpenAIGPTTokenizer">OpenAIGPTTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.OpenAIGPTTokenizerFast">OpenAIGPTTokenizerFast</a> (OpenAI GPT model)</li> <li><strong>opt</strong> — <code>GPT2Tokenizer</code> or <code>GPT2TokenizerFast</code> (OPT model)</li> <li><strong>owlv2</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTokenizerFast">CLIPTokenizerFast</a> (OWLv2 model)</li> <li><strong>owlvit</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTokenizerFast">CLIPTokenizerFast</a> (OWL-ViT model)</li> <li><strong>paligemma</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (PaliGemma model)</li> <li><strong>pegasus</strong> — <code>PegasusTokenizer</code> or <code>PegasusTokenizerFast</code> (Pegasus model)</li> <li><strong>pegasus_x</strong> — <code>PegasusTokenizer</code> or <code>PegasusTokenizerFast</code> (PEGASUS-X model)</li> <li><strong>perceiver</strong> — <code>PerceiverTokenizer</code> (Perceiver model)</li> <li><strong>persimmon</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Persimmon model)</li> <li><strong>phi</strong> — <code>CodeGenTokenizer</code> or <code>CodeGenTokenizerFast</code> (Phi model)</li> <li><strong>phi3</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Phi3 model)</li> <li><strong>phimoe</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Phimoe model)</li> <li><strong>phobert</strong> — <code>PhobertTokenizer</code> (PhoBERT model)</li> <li><strong>pix2struct</strong> — <code>T5Tokenizer</code> or <code>T5TokenizerFast</code> (Pix2Struct model)</li> <li><strong>pixtral</strong> — <code>PreTrainedTokenizerFast</code> (Pixtral model)</li> <li><strong>plbart</strong> — <code>PLBartTokenizer</code> (PLBart model)</li> <li><strong>prophetnet</strong> — <code>ProphetNetTokenizer</code> (ProphetNet model)</li> <li><strong>qdqbert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (QDQBert model)</li> <li><strong>qwen2</strong> — <code>Qwen2Tokenizer</code> or <code>Qwen2TokenizerFast</code> (Qwen2 model)</li> <li><strong>qwen2_5_vl</strong> — <code>Qwen2Tokenizer</code> or <code>Qwen2TokenizerFast</code> (Qwen2_5_VL model)</li> <li><strong>qwen2_audio</strong> — <code>Qwen2Tokenizer</code> or <code>Qwen2TokenizerFast</code> (Qwen2Audio model)</li> <li><strong>qwen2_moe</strong> — <code>Qwen2Tokenizer</code> or <code>Qwen2TokenizerFast</code> (Qwen2MoE model)</li> <li><strong>qwen2_vl</strong> — <code>Qwen2Tokenizer</code> or <code>Qwen2TokenizerFast</code> (Qwen2VL model)</li> <li><strong>rag</strong> — <a href="/docs/transformers/main/ko/model_doc/rag#transformers.RagTokenizer">RagTokenizer</a> (RAG model)</li> <li><strong>realm</strong> — <code>RealmTokenizer</code> or <code>RealmTokenizerFast</code> (REALM model)</li> <li><strong>recurrent_gemma</strong> — <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaTokenizer">GemmaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaTokenizerFast">GemmaTokenizerFast</a> (RecurrentGemma model)</li> <li><strong>reformer</strong> — <code>ReformerTokenizer</code> or <code>ReformerTokenizerFast</code> (Reformer model)</li> <li><strong>rembert</strong> — <code>RemBertTokenizer</code> or <code>RemBertTokenizerFast</code> (RemBERT model)</li> <li><strong>retribert</strong> — <code>RetriBertTokenizer</code> or <code>RetriBertTokenizerFast</code> (RetriBERT model)</li> <li><strong>roberta</strong> — <code>RobertaTokenizer</code> or <code>RobertaTokenizerFast</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>RobertaTokenizer</code> or <code>RobertaTokenizerFast</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <code>RoCBertTokenizer</code> (RoCBert model)</li> <li><strong>roformer</strong> — <code>RoFormerTokenizer</code> or <code>RoFormerTokenizerFast</code> (RoFormer model)</li> <li><strong>rwkv</strong> — <code>GPTNeoXTokenizerFast</code> (RWKV model)</li> <li><strong>seamless_m4t</strong> — <code>SeamlessM4TTokenizer</code> or <code>SeamlessM4TTokenizerFast</code> (SeamlessM4T model)</li> <li><strong>seamless_m4t_v2</strong> — <code>SeamlessM4TTokenizer</code> or <code>SeamlessM4TTokenizerFast</code> (SeamlessM4Tv2 model)</li> <li><strong>siglip</strong> — <code>SiglipTokenizer</code> (SigLIP model)</li> <li><strong>speech_to_text</strong> — <code>Speech2TextTokenizer</code> (Speech2Text model)</li> <li><strong>speech_to_text_2</strong> — <code>Speech2Text2Tokenizer</code> (Speech2Text2 model)</li> <li><strong>speecht5</strong> — <code>SpeechT5Tokenizer</code> (SpeechT5 model)</li> <li><strong>splinter</strong> — <code>SplinterTokenizer</code> or <code>SplinterTokenizerFast</code> (Splinter model)</li> <li><strong>squeezebert</strong> — <code>SqueezeBertTokenizer</code> or <code>SqueezeBertTokenizerFast</code> (SqueezeBERT model)</li> <li><strong>stablelm</strong> — <code>GPTNeoXTokenizerFast</code> (StableLm model)</li> <li><strong>starcoder2</strong> — <code>GPT2Tokenizer</code> or <code>GPT2TokenizerFast</code> (Starcoder2 model)</li> <li><strong>switch_transformers</strong> — <code>T5Tokenizer</code> or <code>T5TokenizerFast</code> (SwitchTransformers model)</li> <li><strong>t5</strong> — <code>T5Tokenizer</code> or <code>T5TokenizerFast</code> (T5 model)</li> <li><strong>tapas</strong> — <code>TapasTokenizer</code> (TAPAS model)</li> <li><strong>tapex</strong> — <code>TapexTokenizer</code> (TAPEX model)</li> <li><strong>transfo-xl</strong> — <code>TransfoXLTokenizer</code> (Transformer-XL model)</li> <li><strong>tvp</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (TVP model)</li> <li><strong>udop</strong> — <code>UdopTokenizer</code> or <code>UdopTokenizerFast</code> (UDOP model)</li> <li><strong>umt5</strong> — <code>T5Tokenizer</code> or <code>T5TokenizerFast</code> (UMT5 model)</li> <li><strong>video_llava</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (VideoLlava model)</li> <li><strong>vilt</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (ViLT model)</li> <li><strong>vipllava</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (VipLlava model)</li> <li><strong>visual_bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (VisualBERT model)</li> <li><strong>vits</strong> — <code>VitsTokenizer</code> (VITS model)</li> <li><strong>wav2vec2</strong> — <code>Wav2Vec2CTCTokenizer</code> (Wav2Vec2 model)</li> <li><strong>wav2vec2-bert</strong> — <code>Wav2Vec2CTCTokenizer</code> (Wav2Vec2-BERT model)</li> <li><strong>wav2vec2-conformer</strong> — <code>Wav2Vec2CTCTokenizer</code> (Wav2Vec2-Conformer model)</li> <li><strong>wav2vec2_phoneme</strong> — <code>Wav2Vec2PhonemeCTCTokenizer</code> (Wav2Vec2Phoneme model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperTokenizer">WhisperTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperTokenizerFast">WhisperTokenizerFast</a> (Whisper model)</li> <li><strong>xclip</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTokenizerFast">CLIPTokenizerFast</a> (X-CLIP model)</li> <li><strong>xglm</strong> — <code>XGLMTokenizer</code> or <code>XGLMTokenizerFast</code> (XGLM model)</li> <li><strong>xlm</strong> — <code>XLMTokenizer</code> (XLM model)</li> <li><strong>xlm-prophetnet</strong> — <code>XLMProphetNetTokenizer</code> (XLM-ProphetNet model)</li> <li><strong>xlm-roberta</strong> — <code>XLMRobertaTokenizer</code> or <code>XLMRobertaTokenizerFast</code> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <code>XLMRobertaTokenizer</code> or <code>XLMRobertaTokenizerFast</code> (XLM-RoBERTa-XL model)</li> <li><strong>xlnet</strong> — <code>XLNetTokenizer</code> or <code>XLNetTokenizerFast</code> (XLNet model)</li> <li><strong>xmod</strong> — <code>XLMRobertaTokenizer</code> or <code>XLMRobertaTokenizerFast</code> (X-MOD model)</li> <li><strong>yoso</strong> — <code>AlbertTokenizer</code> or <code>AlbertTokenizerFast</code> (YOSO model)</li> <li><strong>zamba</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Zamba model)</li> <li><strong>zamba2</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Zamba2 model)</li>',s1,ks,i1,xs,Ed,l1,Lu,fV="Register a new tokenizer in this mapping.",qC,Vd,XC,fe,Id,d1,Au,gV=`This is a generic feature extractor class that will be instantiated as one of the feature extractor classes of the
library when created with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoFeatureExtractor.from_pretrained">AutoFeatureExtractor.from_pretrained()</a> class method.`,c1,ju,pV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",m1,j,Pd,f1,Zu,uV="Instantiate one of the feature extractor classes of the library from a pretrained model vocabulary.",g1,Bu,hV=`The feature extractor class to instantiate is selected based on the <code>model_type</code> property of the config object
(either passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s
missing, by falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,p1,Ru,bV='<li><strong>audio-spectrogram-transformer</strong> — <code>ASTFeatureExtractor</code> (Audio Spectrogram Transformer model)</li> <li><strong>beit</strong> — <code>BeitFeatureExtractor</code> (BEiT model)</li> <li><strong>chinese_clip</strong> — <code>ChineseCLIPFeatureExtractor</code> (Chinese-CLIP model)</li> <li><strong>clap</strong> — <code>ClapFeatureExtractor</code> (CLAP model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPFeatureExtractor">CLIPFeatureExtractor</a> (CLIP model)</li> <li><strong>clipseg</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTFeatureExtractor">ViTFeatureExtractor</a> (CLIPSeg model)</li> <li><strong>clvp</strong> — <code>ClvpFeatureExtractor</code> (CLVP model)</li> <li><strong>conditional_detr</strong> — <code>ConditionalDetrFeatureExtractor</code> (Conditional DETR model)</li> <li><strong>convnext</strong> — <code>ConvNextFeatureExtractor</code> (ConvNeXT model)</li> <li><strong>cvt</strong> — <code>ConvNextFeatureExtractor</code> (CvT model)</li> <li><strong>dac</strong> — <code>DacFeatureExtractor</code> (DAC model)</li> <li><strong>data2vec-audio</strong> — <code>Wav2Vec2FeatureExtractor</code> (Data2VecAudio model)</li> <li><strong>data2vec-vision</strong> — <code>BeitFeatureExtractor</code> (Data2VecVision model)</li> <li><strong>deformable_detr</strong> — <code>DeformableDetrFeatureExtractor</code> (Deformable DETR model)</li> <li><strong>deit</strong> — <code>DeiTFeatureExtractor</code> (DeiT model)</li> <li><strong>detr</strong> — <code>DetrFeatureExtractor</code> (DETR model)</li> <li><strong>dinat</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTFeatureExtractor">ViTFeatureExtractor</a> (DiNAT model)</li> <li><strong>donut-swin</strong> — <code>DonutFeatureExtractor</code> (DonutSwin model)</li> <li><strong>dpt</strong> — <code>DPTFeatureExtractor</code> (DPT model)</li> <li><strong>encodec</strong> — <code>EncodecFeatureExtractor</code> (EnCodec model)</li> <li><strong>flava</strong> — <code>FlavaFeatureExtractor</code> (FLAVA model)</li> <li><strong>glpn</strong> — <code>GLPNFeatureExtractor</code> (GLPN model)</li> <li><strong>groupvit</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPFeatureExtractor">CLIPFeatureExtractor</a> (GroupViT model)</li> <li><strong>hubert</strong> — <code>Wav2Vec2FeatureExtractor</code> (Hubert model)</li> <li><strong>imagegpt</strong> — <code>ImageGPTFeatureExtractor</code> (ImageGPT model)</li> <li><strong>layoutlmv2</strong> — <code>LayoutLMv2FeatureExtractor</code> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <code>LayoutLMv3FeatureExtractor</code> (LayoutLMv3 model)</li> <li><strong>levit</strong> — <code>LevitFeatureExtractor</code> (LeViT model)</li> <li><strong>maskformer</strong> — <code>MaskFormerFeatureExtractor</code> (MaskFormer model)</li> <li><strong>mctct</strong> — <code>MCTCTFeatureExtractor</code> (M-CTC-T model)</li> <li><strong>mimi</strong> — <code>EncodecFeatureExtractor</code> (Mimi model)</li> <li><strong>mobilenet_v1</strong> — <code>MobileNetV1FeatureExtractor</code> (MobileNetV1 model)</li> <li><strong>mobilenet_v2</strong> — <code>MobileNetV2FeatureExtractor</code> (MobileNetV2 model)</li> <li><strong>mobilevit</strong> — <code>MobileViTFeatureExtractor</code> (MobileViT model)</li> <li><strong>moonshine</strong> — <code>Wav2Vec2FeatureExtractor</code> (Moonshine model)</li> <li><strong>moshi</strong> — <code>EncodecFeatureExtractor</code> (Moshi model)</li> <li><strong>nat</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTFeatureExtractor">ViTFeatureExtractor</a> (NAT model)</li> <li><strong>owlvit</strong> — <code>OwlViTFeatureExtractor</code> (OWL-ViT model)</li> <li><strong>perceiver</strong> — <code>PerceiverFeatureExtractor</code> (Perceiver model)</li> <li><strong>poolformer</strong> — <code>PoolFormerFeatureExtractor</code> (PoolFormer model)</li> <li><strong>pop2piano</strong> — <code>Pop2PianoFeatureExtractor</code> (Pop2Piano model)</li> <li><strong>regnet</strong> — <code>ConvNextFeatureExtractor</code> (RegNet model)</li> <li><strong>resnet</strong> — <code>ConvNextFeatureExtractor</code> (ResNet model)</li> <li><strong>seamless_m4t</strong> — <code>SeamlessM4TFeatureExtractor</code> (SeamlessM4T model)</li> <li><strong>seamless_m4t_v2</strong> — <code>SeamlessM4TFeatureExtractor</code> (SeamlessM4Tv2 model)</li> <li><strong>segformer</strong> — <code>SegformerFeatureExtractor</code> (SegFormer model)</li> <li><strong>sew</strong> — <code>Wav2Vec2FeatureExtractor</code> (SEW model)</li> <li><strong>sew-d</strong> — <code>Wav2Vec2FeatureExtractor</code> (SEW-D model)</li> <li><strong>speech_to_text</strong> — <code>Speech2TextFeatureExtractor</code> (Speech2Text model)</li> <li><strong>speecht5</strong> — <code>SpeechT5FeatureExtractor</code> (SpeechT5 model)</li> <li><strong>swiftformer</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTFeatureExtractor">ViTFeatureExtractor</a> (SwiftFormer model)</li> <li><strong>swin</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTFeatureExtractor">ViTFeatureExtractor</a> (Swin Transformer model)</li> <li><strong>swinv2</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTFeatureExtractor">ViTFeatureExtractor</a> (Swin Transformer V2 model)</li> <li><strong>table-transformer</strong> — <code>DetrFeatureExtractor</code> (Table Transformer model)</li> <li><strong>timesformer</strong> — <code>VideoMAEFeatureExtractor</code> (TimeSformer model)</li> <li><strong>tvlt</strong> — <code>TvltFeatureExtractor</code> (TVLT model)</li> <li><strong>unispeech</strong> — <code>Wav2Vec2FeatureExtractor</code> (UniSpeech model)</li> <li><strong>unispeech-sat</strong> — <code>Wav2Vec2FeatureExtractor</code> (UniSpeechSat model)</li> <li><strong>univnet</strong> — <code>UnivNetFeatureExtractor</code> (UnivNet model)</li> <li><strong>van</strong> — <code>ConvNextFeatureExtractor</code> (VAN model)</li> <li><strong>videomae</strong> — <code>VideoMAEFeatureExtractor</code> (VideoMAE model)</li> <li><strong>vilt</strong> — <code>ViltFeatureExtractor</code> (ViLT model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTFeatureExtractor">ViTFeatureExtractor</a> (ViT model)</li> <li><strong>vit_mae</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTFeatureExtractor">ViTFeatureExtractor</a> (ViTMAE model)</li> <li><strong>vit_msn</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTFeatureExtractor">ViTFeatureExtractor</a> (ViTMSN model)</li> <li><strong>wav2vec2</strong> — <code>Wav2Vec2FeatureExtractor</code> (Wav2Vec2 model)</li> <li><strong>wav2vec2-bert</strong> — <code>Wav2Vec2FeatureExtractor</code> (Wav2Vec2-BERT model)</li> <li><strong>wav2vec2-conformer</strong> — <code>Wav2Vec2FeatureExtractor</code> (Wav2Vec2-Conformer model)</li> <li><strong>wavlm</strong> — <code>Wav2Vec2FeatureExtractor</code> (WavLM model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperFeatureExtractor">WhisperFeatureExtractor</a> (Whisper model)</li> <li><strong>xclip</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPFeatureExtractor">CLIPFeatureExtractor</a> (X-CLIP model)</li> <li><strong>yolos</strong> — <code>YolosFeatureExtractor</code> (YOLOS model)</li>',u1,$s,h1,Ls,b1,As,Sd,_1,Gu,_V="Register a new feature extractor for this class.",DC,Ud,HC,ge,Nd,M1,Ju,MV=`This is a generic image processor class that will be instantiated as one of the image processor classes of the
library when created with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoImageProcessor.from_pretrained">AutoImageProcessor.from_pretrained()</a> class method.`,T1,Wu,TV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",y1,Z,qd,F1,Eu,yV="Instantiate one of the image processor classes of the library from a pretrained model vocabulary.",v1,Vu,FV=`The image processor class to instantiate is selected based on the <code>model_type</code> property of the config object
(either passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s
missing, by falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,C1,Iu,vV='<li><strong>align</strong> — <code>EfficientNetImageProcessor</code> (ALIGN model)</li> <li><strong>aria</strong> — <code>A</code> or <code>r</code> (Aria model)</li> <li><strong>beit</strong> — <code>BeitImageProcessor</code> (BEiT model)</li> <li><strong>bit</strong> — <code>BitImageProcessor</code> (BiT model)</li> <li><strong>blip</strong> — <a href="/docs/transformers/main/ko/model_doc/blip#transformers.BlipImageProcessor">BlipImageProcessor</a> (BLIP model)</li> <li><strong>blip-2</strong> — <a href="/docs/transformers/main/ko/model_doc/blip#transformers.BlipImageProcessor">BlipImageProcessor</a> (BLIP-2 model)</li> <li><strong>bridgetower</strong> — <code>BridgeTowerImageProcessor</code> (BridgeTower model)</li> <li><strong>chameleon</strong> — <a href="/docs/transformers/main/ko/model_doc/chameleon#transformers.ChameleonImageProcessor">ChameleonImageProcessor</a> (Chameleon model)</li> <li><strong>chinese_clip</strong> — <code>ChineseCLIPImageProcessor</code> (Chinese-CLIP model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a> (CLIP model)</li> <li><strong>clipseg</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (CLIPSeg model)</li> <li><strong>conditional_detr</strong> — <code>ConditionalDetrImageProcessor</code> (Conditional DETR model)</li> <li><strong>convnext</strong> — <code>ConvNextImageProcessor</code> (ConvNeXT model)</li> <li><strong>convnextv2</strong> — <code>ConvNextImageProcessor</code> (ConvNeXTV2 model)</li> <li><strong>cvt</strong> — <code>ConvNextImageProcessor</code> (CvT model)</li> <li><strong>data2vec-vision</strong> — <code>BeitImageProcessor</code> (Data2VecVision model)</li> <li><strong>deformable_detr</strong> — <code>DeformableDetrImageProcessor</code> or <code>DeformableDetrImageProcessorFast</code> (Deformable DETR model)</li> <li><strong>deit</strong> — <code>DeiTImageProcessor</code> (DeiT model)</li> <li><strong>depth_anything</strong> — <code>DPTImageProcessor</code> (Depth Anything model)</li> <li><strong>deta</strong> — <code>DetaImageProcessor</code> (DETA model)</li> <li><strong>detr</strong> — <code>DetrImageProcessor</code> or <code>DetrImageProcessorFast</code> (DETR model)</li> <li><strong>dinat</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (DiNAT model)</li> <li><strong>dinov2</strong> — <code>BitImageProcessor</code> (DINOv2 model)</li> <li><strong>donut-swin</strong> — <code>DonutImageProcessor</code> (DonutSwin model)</li> <li><strong>dpt</strong> — <code>DPTImageProcessor</code> (DPT model)</li> <li><strong>efficientformer</strong> — <code>EfficientFormerImageProcessor</code> (EfficientFormer model)</li> <li><strong>efficientnet</strong> — <code>EfficientNetImageProcessor</code> (EfficientNet model)</li> <li><strong>flava</strong> — <code>FlavaImageProcessor</code> (FLAVA model)</li> <li><strong>focalnet</strong> — <code>BitImageProcessor</code> (FocalNet model)</li> <li><strong>fuyu</strong> — <code>FuyuImageProcessor</code> (Fuyu model)</li> <li><strong>git</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a> (GIT model)</li> <li><strong>glpn</strong> — <code>GLPNImageProcessor</code> (GLPN model)</li> <li><strong>grounding-dino</strong> — <code>GroundingDinoImageProcessor</code> (Grounding DINO model)</li> <li><strong>groupvit</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a> (GroupViT model)</li> <li><strong>hiera</strong> — <code>BitImageProcessor</code> (Hiera model)</li> <li><strong>idefics</strong> — <code>IdeficsImageProcessor</code> (IDEFICS model)</li> <li><strong>idefics2</strong> — <code>Idefics2ImageProcessor</code> (Idefics2 model)</li> <li><strong>idefics3</strong> — <code>Idefics3ImageProcessor</code> (Idefics3 model)</li> <li><strong>ijepa</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (I-JEPA model)</li> <li><strong>imagegpt</strong> — <code>ImageGPTImageProcessor</code> (ImageGPT model)</li> <li><strong>instructblip</strong> — <a href="/docs/transformers/main/ko/model_doc/blip#transformers.BlipImageProcessor">BlipImageProcessor</a> (InstructBLIP model)</li> <li><strong>instructblipvideo</strong> — <code>InstructBlipVideoImageProcessor</code> (InstructBlipVideo model)</li> <li><strong>kosmos-2</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a> (KOSMOS-2 model)</li> <li><strong>layoutlmv2</strong> — <code>LayoutLMv2ImageProcessor</code> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <code>LayoutLMv3ImageProcessor</code> (LayoutLMv3 model)</li> <li><strong>levit</strong> — <code>LevitImageProcessor</code> (LeViT model)</li> <li><strong>llava</strong> — <code>LlavaImageProcessor</code> (LLaVa model)</li> <li><strong>llava_next</strong> — <code>LlavaNextImageProcessor</code> (LLaVA-NeXT model)</li> <li><strong>llava_next_video</strong> — <code>LlavaNextVideoImageProcessor</code> (LLaVa-NeXT-Video model)</li> <li><strong>llava_onevision</strong> — <code>LlavaOnevisionImageProcessor</code> (LLaVA-Onevision model)</li> <li><strong>mask2former</strong> — <code>Mask2FormerImageProcessor</code> (Mask2Former model)</li> <li><strong>maskformer</strong> — <code>MaskFormerImageProcessor</code> (MaskFormer model)</li> <li><strong>mgp-str</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (MGP-STR model)</li> <li><strong>mllama</strong> — <code>MllamaImageProcessor</code> (Mllama model)</li> <li><strong>mobilenet_v1</strong> — <code>MobileNetV1ImageProcessor</code> (MobileNetV1 model)</li> <li><strong>mobilenet_v2</strong> — <code>MobileNetV2ImageProcessor</code> (MobileNetV2 model)</li> <li><strong>mobilevit</strong> — <code>MobileViTImageProcessor</code> (MobileViT model)</li> <li><strong>mobilevitv2</strong> — <code>MobileViTImageProcessor</code> (MobileViTV2 model)</li> <li><strong>nat</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (NAT model)</li> <li><strong>nougat</strong> — <code>NougatImageProcessor</code> (Nougat model)</li> <li><strong>oneformer</strong> — <code>OneFormerImageProcessor</code> (OneFormer model)</li> <li><strong>owlv2</strong> — <code>Owlv2ImageProcessor</code> (OWLv2 model)</li> <li><strong>owlvit</strong> — <code>OwlViTImageProcessor</code> (OWL-ViT model)</li> <li><strong>paligemma</strong> — <code>SiglipImageProcessor</code> (PaliGemma model)</li> <li><strong>perceiver</strong> — <code>PerceiverImageProcessor</code> (Perceiver model)</li> <li><strong>pix2struct</strong> — <code>Pix2StructImageProcessor</code> (Pix2Struct model)</li> <li><strong>pixtral</strong> — <code>PixtralImageProcessor</code> or <code>PixtralImageProcessorFast</code> (Pixtral model)</li> <li><strong>poolformer</strong> — <code>PoolFormerImageProcessor</code> (PoolFormer model)</li> <li><strong>pvt</strong> — <code>PvtImageProcessor</code> (PVT model)</li> <li><strong>pvt_v2</strong> — <code>PvtImageProcessor</code> (PVTv2 model)</li> <li><strong>qwen2_vl</strong> — <code>Qwen2VLImageProcessor</code> or <code>Qwen2VLImageProcessorFast</code> (Qwen2VL model)</li> <li><strong>regnet</strong> — <code>ConvNextImageProcessor</code> (RegNet model)</li> <li><strong>resnet</strong> — <code>ConvNextImageProcessor</code> (ResNet model)</li> <li><strong>rt_detr</strong> — <code>RTDetrImageProcessor</code> or <code>RTDetrImageProcessorFast</code> (RT-DETR model)</li> <li><strong>sam</strong> — <code>SamImageProcessor</code> (SAM model)</li> <li><strong>segformer</strong> — <code>SegformerImageProcessor</code> (SegFormer model)</li> <li><strong>seggpt</strong> — <code>SegGptImageProcessor</code> (SegGPT model)</li> <li><strong>siglip</strong> — <code>SiglipImageProcessor</code> (SigLIP model)</li> <li><strong>superglue</strong> — <code>S</code> or <code>u</code> (SuperGlue model)</li> <li><strong>swiftformer</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (SwiftFormer model)</li> <li><strong>swin</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (Swin Transformer model)</li> <li><strong>swin2sr</strong> — <a href="/docs/transformers/main/ko/model_doc/swin2sr#transformers.Swin2SRImageProcessor">Swin2SRImageProcessor</a> (Swin2SR model)</li> <li><strong>swinv2</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (Swin Transformer V2 model)</li> <li><strong>table-transformer</strong> — <code>DetrImageProcessor</code> (Table Transformer model)</li> <li><strong>timesformer</strong> — <code>VideoMAEImageProcessor</code> (TimeSformer model)</li> <li><strong>timm_wrapper</strong> — <code>TimmWrapperImageProcessor</code> (TimmWrapperModel model)</li> <li><strong>tvlt</strong> — <code>TvltImageProcessor</code> (TVLT model)</li> <li><strong>tvp</strong> — <code>TvpImageProcessor</code> (TVP model)</li> <li><strong>udop</strong> — <code>LayoutLMv3ImageProcessor</code> (UDOP model)</li> <li><strong>upernet</strong> — <code>SegformerImageProcessor</code> (UPerNet model)</li> <li><strong>van</strong> — <code>ConvNextImageProcessor</code> (VAN model)</li> <li><strong>videomae</strong> — <code>VideoMAEImageProcessor</code> (VideoMAE model)</li> <li><strong>vilt</strong> — <code>ViltImageProcessor</code> (ViLT model)</li> <li><strong>vipllava</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a> (VipLlava model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (ViT model)</li> <li><strong>vit_hybrid</strong> — <code>ViTHybridImageProcessor</code> (ViT Hybrid model)</li> <li><strong>vit_mae</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (ViTMAE model)</li> <li><strong>vit_msn</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (ViTMSN model)</li> <li><strong>vitmatte</strong> — <code>VitMatteImageProcessor</code> (ViTMatte model)</li> <li><strong>xclip</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a> (X-CLIP model)</li> <li><strong>yolos</strong> — <code>YolosImageProcessor</code> (YOLOS model)</li> <li><strong>zoedepth</strong> — <code>ZoeDepthImageProcessor</code> (ZoeDepth model)</li>',w1,js,k1,Zs,x1,Bs,Xd,$1,Pu,CV="Register a new image processor for this class.",QC,Dd,YC,pe,Hd,L1,Su,wV=`This is a generic processor class that will be instantiated as one of the processor classes of the library when
created with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoProcessor.from_pretrained">AutoProcessor.from_pretrained()</a> class method.`,A1,Uu,kV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",j1,B,Qd,Z1,Nu,xV="Instantiate one of the processor classes of the library from a pretrained model vocabulary.",B1,qu,$V=`The processor class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible):`,R1,Xu,LV='<li><strong>align</strong> — <code>AlignProcessor</code> (ALIGN model)</li> <li><strong>altclip</strong> — <a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPProcessor">AltCLIPProcessor</a> (AltCLIP model)</li> <li><strong>aria</strong> — <code>AriaProcessor</code> (Aria model)</li> <li><strong>bark</strong> — <code>BarkProcessor</code> (Bark model)</li> <li><strong>blip</strong> — <a href="/docs/transformers/main/ko/model_doc/blip#transformers.BlipProcessor">BlipProcessor</a> (BLIP model)</li> <li><strong>blip-2</strong> — <a href="/docs/transformers/main/ko/model_doc/blip-2#transformers.Blip2Processor">Blip2Processor</a> (BLIP-2 model)</li> <li><strong>bridgetower</strong> — <code>BridgeTowerProcessor</code> (BridgeTower model)</li> <li><strong>chameleon</strong> — <a href="/docs/transformers/main/ko/model_doc/chameleon#transformers.ChameleonProcessor">ChameleonProcessor</a> (Chameleon model)</li> <li><strong>chinese_clip</strong> — <code>ChineseCLIPProcessor</code> (Chinese-CLIP model)</li> <li><strong>clap</strong> — <code>ClapProcessor</code> (CLAP model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPProcessor">CLIPProcessor</a> (CLIP model)</li> <li><strong>clipseg</strong> — <code>CLIPSegProcessor</code> (CLIPSeg model)</li> <li><strong>clvp</strong> — <code>ClvpProcessor</code> (CLVP model)</li> <li><strong>colpali</strong> — <code>ColPaliProcessor</code> (ColPali model)</li> <li><strong>emu3</strong> — <code>Emu3Processor</code> (Emu3 model)</li> <li><strong>flava</strong> — <code>FlavaProcessor</code> (FLAVA model)</li> <li><strong>fuyu</strong> — <code>FuyuProcessor</code> (Fuyu model)</li> <li><strong>git</strong> — <code>GitProcessor</code> (GIT model)</li> <li><strong>grounding-dino</strong> — <code>GroundingDinoProcessor</code> (Grounding DINO model)</li> <li><strong>groupvit</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPProcessor">CLIPProcessor</a> (GroupViT model)</li> <li><strong>hubert</strong> — <code>Wav2Vec2Processor</code> (Hubert model)</li> <li><strong>idefics</strong> — <code>IdeficsProcessor</code> (IDEFICS model)</li> <li><strong>idefics2</strong> — <code>Idefics2Processor</code> (Idefics2 model)</li> <li><strong>idefics3</strong> — <code>Idefics3Processor</code> (Idefics3 model)</li> <li><strong>instructblip</strong> — <code>InstructBlipProcessor</code> (InstructBLIP model)</li> <li><strong>instructblipvideo</strong> — <code>InstructBlipVideoProcessor</code> (InstructBlipVideo model)</li> <li><strong>kosmos-2</strong> — <code>Kosmos2Processor</code> (KOSMOS-2 model)</li> <li><strong>layoutlmv2</strong> — <code>LayoutLMv2Processor</code> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <code>LayoutLMv3Processor</code> (LayoutLMv3 model)</li> <li><strong>llava</strong> — <code>LlavaProcessor</code> (LLaVa model)</li> <li><strong>llava_next</strong> — <code>LlavaNextProcessor</code> (LLaVA-NeXT model)</li> <li><strong>llava_next_video</strong> — <code>LlavaNextVideoProcessor</code> (LLaVa-NeXT-Video model)</li> <li><strong>llava_onevision</strong> — <code>LlavaOnevisionProcessor</code> (LLaVA-Onevision model)</li> <li><strong>markuplm</strong> — <code>MarkupLMProcessor</code> (MarkupLM model)</li> <li><strong>mctct</strong> — <code>MCTCTProcessor</code> (M-CTC-T model)</li> <li><strong>mgp-str</strong> — <code>MgpstrProcessor</code> (MGP-STR model)</li> <li><strong>mllama</strong> — <code>MllamaProcessor</code> (Mllama model)</li> <li><strong>moonshine</strong> — <code>Wav2Vec2Processor</code> (Moonshine model)</li> <li><strong>oneformer</strong> — <code>OneFormerProcessor</code> (OneFormer model)</li> <li><strong>owlv2</strong> — <code>Owlv2Processor</code> (OWLv2 model)</li> <li><strong>owlvit</strong> — <code>OwlViTProcessor</code> (OWL-ViT model)</li> <li><strong>paligemma</strong> — <a href="/docs/transformers/main/ko/model_doc/paligemma#transformers.PaliGemmaProcessor">PaliGemmaProcessor</a> (PaliGemma model)</li> <li><strong>pix2struct</strong> — <code>Pix2StructProcessor</code> (Pix2Struct model)</li> <li><strong>pixtral</strong> — <code>PixtralProcessor</code> (Pixtral model)</li> <li><strong>pop2piano</strong> — <code>Pop2PianoProcessor</code> (Pop2Piano model)</li> <li><strong>qwen2_5_vl</strong> — <code>Qwen2_5_VLProcessor</code> (Qwen2_5_VL model)</li> <li><strong>qwen2_audio</strong> — <code>Qwen2AudioProcessor</code> (Qwen2Audio model)</li> <li><strong>qwen2_vl</strong> — <code>Qwen2VLProcessor</code> (Qwen2VL model)</li> <li><strong>sam</strong> — <code>SamProcessor</code> (SAM model)</li> <li><strong>seamless_m4t</strong> — <code>SeamlessM4TProcessor</code> (SeamlessM4T model)</li> <li><strong>sew</strong> — <code>Wav2Vec2Processor</code> (SEW model)</li> <li><strong>sew-d</strong> — <code>Wav2Vec2Processor</code> (SEW-D model)</li> <li><strong>siglip</strong> — <code>SiglipProcessor</code> (SigLIP model)</li> <li><strong>speech_to_text</strong> — <code>Speech2TextProcessor</code> (Speech2Text model)</li> <li><strong>speech_to_text_2</strong> — <code>Speech2Text2Processor</code> (Speech2Text2 model)</li> <li><strong>speecht5</strong> — <code>SpeechT5Processor</code> (SpeechT5 model)</li> <li><strong>trocr</strong> — <code>TrOCRProcessor</code> (TrOCR model)</li> <li><strong>tvlt</strong> — <code>TvltProcessor</code> (TVLT model)</li> <li><strong>tvp</strong> — <code>TvpProcessor</code> (TVP model)</li> <li><strong>udop</strong> — <code>UdopProcessor</code> (UDOP model)</li> <li><strong>unispeech</strong> — <code>Wav2Vec2Processor</code> (UniSpeech model)</li> <li><strong>unispeech-sat</strong> — <code>Wav2Vec2Processor</code> (UniSpeechSat model)</li> <li><strong>video_llava</strong> — <code>VideoLlavaProcessor</code> (VideoLlava model)</li> <li><strong>vilt</strong> — <code>ViltProcessor</code> (ViLT model)</li> <li><strong>vipllava</strong> — <code>LlavaProcessor</code> (VipLlava model)</li> <li><strong>vision-text-dual-encoder</strong> — <code>VisionTextDualEncoderProcessor</code> (VisionTextDualEncoder model)</li> <li><strong>wav2vec2</strong> — <code>Wav2Vec2Processor</code> (Wav2Vec2 model)</li> <li><strong>wav2vec2-bert</strong> — <code>Wav2Vec2Processor</code> (Wav2Vec2-BERT model)</li> <li><strong>wav2vec2-conformer</strong> — <code>Wav2Vec2Processor</code> (Wav2Vec2-Conformer model)</li> <li><strong>wavlm</strong> — <code>Wav2Vec2Processor</code> (WavLM model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperProcessor">WhisperProcessor</a> (Whisper model)</li> <li><strong>xclip</strong> — <code>XCLIPProcessor</code> (X-CLIP model)</li>',G1,Rs,J1,Gs,W1,Js,Yd,E1,Du,AV="Register a new processor for this class.",zC,zd,OC,Od,jV="다음 자동 클래스들은 특정 헤드 없이 기본 모델 클래스를 인스턴스화하는 데 사용할 수 있습니다.",KC,Kd,e2,ue,ec,V1,Hu,ZV=`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,I1,Qu,BV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",P1,Jn,oc,S1,Yu,RV="Instantiates one of the base model classes of the library from a configuration.",U1,zu,GV=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,N1,Ws,q1,R,tc,X1,Ou,JV="Instantiate one of the base model classes of the library from a pretrained model.",D1,Ku,WV=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,H1,eh,EV='<li><strong>albert</strong> — <code>AlbertModel</code> (ALBERT model)</li> <li><strong>align</strong> — <code>AlignModel</code> (ALIGN model)</li> <li><strong>altclip</strong> — <a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li> <li><strong>aria</strong> — <code>AriaForConditionalGeneration</code> (Aria model)</li> <li><strong>aria_text</strong> — <code>AriaTextModel</code> (AriaText model)</li> <li><strong>audio-spectrogram-transformer</strong> — <code>ASTModel</code> (Audio Spectrogram Transformer model)</li> <li><strong>autoformer</strong> — <a href="/docs/transformers/main/ko/model_doc/autoformer#transformers.AutoformerModel">AutoformerModel</a> (Autoformer model)</li> <li><strong>bamba</strong> — <code>BambaModel</code> (Bamba model)</li> <li><strong>bark</strong> — <code>BarkModel</code> (Bark model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartModel">BartModel</a> (BART model)</li> <li><strong>beit</strong> — <code>BeitModel</code> (BEiT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertModel">BertModel</a> (BERT model)</li> <li><strong>bert-generation</strong> — <code>BertGenerationEncoder</code> (Bert Generation model)</li> <li><strong>big_bird</strong> — <code>BigBirdModel</code> (BigBird model)</li> <li><strong>bigbird_pegasus</strong> — <code>BigBirdPegasusModel</code> (BigBird-Pegasus model)</li> <li><strong>biogpt</strong> — <a href="/docs/transformers/main/ko/model_doc/biogpt#transformers.BioGptModel">BioGptModel</a> (BioGpt model)</li> <li><strong>bit</strong> — <code>BitModel</code> (BiT model)</li> <li><strong>blenderbot</strong> — <code>BlenderbotModel</code> (Blenderbot model)</li> <li><strong>blenderbot-small</strong> — <code>BlenderbotSmallModel</code> (BlenderbotSmall model)</li> <li><strong>blip</strong> — <a href="/docs/transformers/main/ko/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li> <li><strong>blip-2</strong> — <a href="/docs/transformers/main/ko/model_doc/blip-2#transformers.Blip2Model">Blip2Model</a> (BLIP-2 model)</li> <li><strong>bloom</strong> — <code>BloomModel</code> (BLOOM model)</li> <li><strong>bridgetower</strong> — <code>BridgeTowerModel</code> (BridgeTower model)</li> <li><strong>bros</strong> — <code>BrosModel</code> (BROS model)</li> <li><strong>camembert</strong> — <code>CamembertModel</code> (CamemBERT model)</li> <li><strong>canine</strong> — <code>CanineModel</code> (CANINE model)</li> <li><strong>chameleon</strong> — <a href="/docs/transformers/main/ko/model_doc/chameleon#transformers.ChameleonModel">ChameleonModel</a> (Chameleon model)</li> <li><strong>chinese_clip</strong> — <code>ChineseCLIPModel</code> (Chinese-CLIP model)</li> <li><strong>chinese_clip_vision_model</strong> — <code>ChineseCLIPVisionModel</code> (ChineseCLIPVisionModel model)</li> <li><strong>clap</strong> — <code>ClapModel</code> (CLAP model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li> <li><strong>clip_text_model</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTextModel">CLIPTextModel</a> (CLIPTextModel model)</li> <li><strong>clip_vision_model</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPVisionModel">CLIPVisionModel</a> (CLIPVisionModel model)</li> <li><strong>clipseg</strong> — <code>CLIPSegModel</code> (CLIPSeg model)</li> <li><strong>clvp</strong> — <code>ClvpModelForConditionalGeneration</code> (CLVP model)</li> <li><strong>code_llama</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaModel">LlamaModel</a> (CodeLlama model)</li> <li><strong>codegen</strong> — <code>CodeGenModel</code> (CodeGen model)</li> <li><strong>cohere</strong> — <a href="/docs/transformers/main/ko/model_doc/cohere#transformers.CohereModel">CohereModel</a> (Cohere model)</li> <li><strong>cohere2</strong> — <code>Cohere2Model</code> (Cohere2 model)</li> <li><strong>conditional_detr</strong> — <code>ConditionalDetrModel</code> (Conditional DETR model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertModel">ConvBertModel</a> (ConvBERT model)</li> <li><strong>convnext</strong> — <code>ConvNextModel</code> (ConvNeXT model)</li> <li><strong>convnextv2</strong> — <code>ConvNextV2Model</code> (ConvNeXTV2 model)</li> <li><strong>cpmant</strong> — <code>CpmAntModel</code> (CPM-Ant model)</li> <li><strong>ctrl</strong> — <code>CTRLModel</code> (CTRL model)</li> <li><strong>cvt</strong> — <code>CvtModel</code> (CvT model)</li> <li><strong>dac</strong> — <code>DacModel</code> (DAC model)</li> <li><strong>data2vec-audio</strong> — <code>Data2VecAudioModel</code> (Data2VecAudio model)</li> <li><strong>data2vec-text</strong> — <code>Data2VecTextModel</code> (Data2VecText model)</li> <li><strong>data2vec-vision</strong> — <code>Data2VecVisionModel</code> (Data2VecVision model)</li> <li><strong>dbrx</strong> — <a href="/docs/transformers/main/ko/model_doc/dbrx#transformers.DbrxModel">DbrxModel</a> (DBRX model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaModel">DebertaModel</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2Model">DebertaV2Model</a> (DeBERTa-v2 model)</li> <li><strong>decision_transformer</strong> — <code>DecisionTransformerModel</code> (Decision Transformer model)</li> <li><strong>deformable_detr</strong> — <code>DeformableDetrModel</code> (Deformable DETR model)</li> <li><strong>deit</strong> — <code>DeiTModel</code> (DeiT model)</li> <li><strong>deta</strong> — <code>DetaModel</code> (DETA model)</li> <li><strong>detr</strong> — <code>DetrModel</code> (DETR model)</li> <li><strong>diffllama</strong> — <code>DiffLlamaModel</code> (DiffLlama model)</li> <li><strong>dinat</strong> — <code>DinatModel</code> (DiNAT model)</li> <li><strong>dinov2</strong> — <code>Dinov2Model</code> (DINOv2 model)</li> <li><strong>dinov2_with_registers</strong> — <code>Dinov2WithRegistersModel</code> (DINOv2 with Registers model)</li> <li><strong>distilbert</strong> — <code>DistilBertModel</code> (DistilBERT model)</li> <li><strong>donut-swin</strong> — <code>DonutSwinModel</code> (DonutSwin model)</li> <li><strong>dpr</strong> — <code>DPRQuestionEncoder</code> (DPR model)</li> <li><strong>dpt</strong> — <code>DPTModel</code> (DPT model)</li> <li><strong>efficientformer</strong> — <code>EfficientFormerModel</code> (EfficientFormer model)</li> <li><strong>efficientnet</strong> — <code>EfficientNetModel</code> (EfficientNet model)</li> <li><strong>electra</strong> — <code>ElectraModel</code> (ELECTRA model)</li> <li><strong>encodec</strong> — <code>EncodecModel</code> (EnCodec model)</li> <li><strong>ernie</strong> — <code>ErnieModel</code> (ERNIE model)</li> <li><strong>ernie_m</strong> — <code>ErnieMModel</code> (ErnieM model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/main/ko/model_doc/esm#transformers.EsmModel">EsmModel</a> (ESM model)</li> <li><strong>falcon</strong> — <code>FalconModel</code> (Falcon model)</li> <li><strong>falcon_mamba</strong> — <code>FalconMambaModel</code> (FalconMamba model)</li> <li><strong>fastspeech2_conformer</strong> — <code>FastSpeech2ConformerModel</code> (FastSpeech2Conformer model)</li> <li><strong>flaubert</strong> — <code>FlaubertModel</code> (FlauBERT model)</li> <li><strong>flava</strong> — <code>FlavaModel</code> (FLAVA model)</li> <li><strong>fnet</strong> — <code>FNetModel</code> (FNet model)</li> <li><strong>focalnet</strong> — <code>FocalNetModel</code> (FocalNet model)</li> <li><strong>fsmt</strong> — <code>FSMTModel</code> (FairSeq Machine-Translation model)</li> <li><strong>funnel</strong> — <code>FunnelModel</code> or <code>FunnelBaseModel</code> (Funnel Transformer model)</li> <li><strong>gemma</strong> — <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaModel">GemmaModel</a> (Gemma model)</li> <li><strong>gemma2</strong> — <a href="/docs/transformers/main/ko/model_doc/gemma2#transformers.Gemma2Model">Gemma2Model</a> (Gemma2 model)</li> <li><strong>git</strong> — <code>GitModel</code> (GIT model)</li> <li><strong>glm</strong> — <code>GlmModel</code> (GLM model)</li> <li><strong>glpn</strong> — <code>GLPNModel</code> (GLPN model)</li> <li><strong>gpt-sw3</strong> — <code>GPT2Model</code> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <code>GPT2Model</code> (OpenAI GPT-2 model)</li> <li><strong>gpt_bigcode</strong> — <code>GPTBigCodeModel</code> (GPTBigCode model)</li> <li><strong>gpt_neo</strong> — <code>GPTNeoModel</code> (GPT Neo model)</li> <li><strong>gpt_neox</strong> — <code>GPTNeoXModel</code> (GPT NeoX model)</li> <li><strong>gpt_neox_japanese</strong> — <a href="/docs/transformers/main/ko/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseModel">GPTNeoXJapaneseModel</a> (GPT NeoX Japanese model)</li> <li><strong>gptj</strong> — <code>GPTJModel</code> (GPT-J model)</li> <li><strong>gptsan-japanese</strong> — <code>GPTSanJapaneseForConditionalGeneration</code> (GPTSAN-japanese model)</li> <li><strong>granite</strong> — <code>GraniteModel</code> (Granite model)</li> <li><strong>granitemoe</strong> — <code>GraniteMoeModel</code> (GraniteMoeMoe model)</li> <li><strong>graphormer</strong> — <a href="/docs/transformers/main/ko/model_doc/graphormer#transformers.GraphormerModel">GraphormerModel</a> (Graphormer model)</li> <li><strong>grounding-dino</strong> — <code>GroundingDinoModel</code> (Grounding DINO model)</li> <li><strong>groupvit</strong> — <code>GroupViTModel</code> (GroupViT model)</li> <li><strong>helium</strong> — <code>HeliumModel</code> (Helium model)</li> <li><strong>hiera</strong> — <code>HieraModel</code> (Hiera model)</li> <li><strong>hubert</strong> — <code>HubertModel</code> (Hubert model)</li> <li><strong>ibert</strong> — <code>IBertModel</code> (I-BERT model)</li> <li><strong>idefics</strong> — <code>IdeficsModel</code> (IDEFICS model)</li> <li><strong>idefics2</strong> — <code>Idefics2Model</code> (Idefics2 model)</li> <li><strong>idefics3</strong> — <code>Idefics3Model</code> (Idefics3 model)</li> <li><strong>idefics3_vision</strong> — <code>Idefics3VisionTransformer</code> (Idefics3VisionTransformer model)</li> <li><strong>ijepa</strong> — <code>IJepaModel</code> (I-JEPA model)</li> <li><strong>imagegpt</strong> — <code>ImageGPTModel</code> (ImageGPT model)</li> <li><strong>informer</strong> — <a href="/docs/transformers/main/ko/model_doc/informer#transformers.InformerModel">InformerModel</a> (Informer model)</li> <li><strong>jamba</strong> — <code>JambaModel</code> (Jamba model)</li> <li><strong>jetmoe</strong> — <code>JetMoeModel</code> (JetMoe model)</li> <li><strong>jukebox</strong> — <code>JukeboxModel</code> (Jukebox model)</li> <li><strong>kosmos-2</strong> — <code>Kosmos2Model</code> (KOSMOS-2 model)</li> <li><strong>layoutlm</strong> — <code>LayoutLMModel</code> (LayoutLM model)</li> <li><strong>layoutlmv2</strong> — <code>LayoutLMv2Model</code> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <code>LayoutLMv3Model</code> (LayoutLMv3 model)</li> <li><strong>led</strong> — <code>LEDModel</code> (LED model)</li> <li><strong>levit</strong> — <code>LevitModel</code> (LeViT model)</li> <li><strong>lilt</strong> — <code>LiltModel</code> (LiLT model)</li> <li><strong>llama</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaModel">LlamaModel</a> (LLaMA model)</li> <li><strong>longformer</strong> — <code>LongformerModel</code> (Longformer model)</li> <li><strong>longt5</strong> — <code>LongT5Model</code> (LongT5 model)</li> <li><strong>luke</strong> — <code>LukeModel</code> (LUKE model)</li> <li><strong>lxmert</strong> — <code>LxmertModel</code> (LXMERT model)</li> <li><strong>m2m_100</strong> — <code>M2M100Model</code> (M2M100 model)</li> <li><strong>mamba</strong> — <a href="/docs/transformers/main/ko/model_doc/mamba#transformers.MambaModel">MambaModel</a> (Mamba model)</li> <li><strong>mamba2</strong> — <a href="/docs/transformers/main/ko/model_doc/mamba2#transformers.Mamba2Model">Mamba2Model</a> (mamba2 model)</li> <li><strong>marian</strong> — <a href="/docs/transformers/main/ko/model_doc/marian#transformers.MarianModel">MarianModel</a> (Marian model)</li> <li><strong>markuplm</strong> — <code>MarkupLMModel</code> (MarkupLM model)</li> <li><strong>mask2former</strong> — <code>Mask2FormerModel</code> (Mask2Former model)</li> <li><strong>maskformer</strong> — <code>MaskFormerModel</code> (MaskFormer model)</li> <li><strong>maskformer-swin</strong> — <code>MaskFormerSwinModel</code> (MaskFormerSwin model)</li> <li><strong>mbart</strong> — <code>MBartModel</code> (mBART model)</li> <li><strong>mctct</strong> — <code>MCTCTModel</code> (M-CTC-T model)</li> <li><strong>mega</strong> — <code>MegaModel</code> (MEGA model)</li> <li><strong>megatron-bert</strong> — <code>MegatronBertModel</code> (Megatron-BERT model)</li> <li><strong>mgp-str</strong> — <code>MgpstrForSceneTextRecognition</code> (MGP-STR model)</li> <li><strong>mimi</strong> — <code>MimiModel</code> (Mimi model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/main/ko/model_doc/mistral#transformers.MistralModel">MistralModel</a> (Mistral model)</li> <li><strong>mixtral</strong> — <code>MixtralModel</code> (Mixtral model)</li> <li><strong>mobilebert</strong> — <code>MobileBertModel</code> (MobileBERT model)</li> <li><strong>mobilenet_v1</strong> — <code>MobileNetV1Model</code> (MobileNetV1 model)</li> <li><strong>mobilenet_v2</strong> — <code>MobileNetV2Model</code> (MobileNetV2 model)</li> <li><strong>mobilevit</strong> — <code>MobileViTModel</code> (MobileViT model)</li> <li><strong>mobilevitv2</strong> — <code>MobileViTV2Model</code> (MobileViTV2 model)</li> <li><strong>modernbert</strong> — <code>ModernBertModel</code> (ModernBERT model)</li> <li><strong>moonshine</strong> — <code>MoonshineModel</code> (Moonshine model)</li> <li><strong>moshi</strong> — <code>MoshiModel</code> (Moshi model)</li> <li><strong>mpnet</strong> — <code>MPNetModel</code> (MPNet model)</li> <li><strong>mpt</strong> — <code>MptModel</code> (MPT model)</li> <li><strong>mra</strong> — <code>MraModel</code> (MRA model)</li> <li><strong>mt5</strong> — <code>MT5Model</code> (MT5 model)</li> <li><strong>musicgen</strong> — <code>MusicgenModel</code> (MusicGen model)</li> <li><strong>musicgen_melody</strong> — <code>MusicgenMelodyModel</code> (MusicGen Melody model)</li> <li><strong>mvp</strong> — <code>MvpModel</code> (MVP model)</li> <li><strong>nat</strong> — <code>NatModel</code> (NAT model)</li> <li><strong>nemotron</strong> — <code>NemotronModel</code> (Nemotron model)</li> <li><strong>nezha</strong> — <code>NezhaModel</code> (Nezha model)</li> <li><strong>nllb-moe</strong> — <code>NllbMoeModel</code> (NLLB-MOE model)</li> <li><strong>nystromformer</strong> — <code>NystromformerModel</code> (Nyströmformer model)</li> <li><strong>olmo</strong> — <code>OlmoModel</code> (OLMo model)</li> <li><strong>olmo2</strong> — <code>Olmo2Model</code> (OLMo2 model)</li> <li><strong>olmoe</strong> — <code>OlmoeModel</code> (OLMoE model)</li> <li><strong>omdet-turbo</strong> — <code>OmDetTurboForObjectDetection</code> (OmDet-Turbo model)</li> <li><strong>oneformer</strong> — <code>OneFormerModel</code> (OneFormer model)</li> <li><strong>open-llama</strong> — <code>OpenLlamaModel</code> (OpenLlama model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.OpenAIGPTModel">OpenAIGPTModel</a> (OpenAI GPT model)</li> <li><strong>opt</strong> — <code>OPTModel</code> (OPT model)</li> <li><strong>owlv2</strong> — <code>Owlv2Model</code> (OWLv2 model)</li> <li><strong>owlvit</strong> — <code>OwlViTModel</code> (OWL-ViT model)</li> <li><strong>patchtsmixer</strong> — <a href="/docs/transformers/main/ko/model_doc/patchtsmixer#transformers.PatchTSMixerModel">PatchTSMixerModel</a> (PatchTSMixer model)</li> <li><strong>patchtst</strong> — <a href="/docs/transformers/main/ko/model_doc/patchtst#transformers.PatchTSTModel">PatchTSTModel</a> (PatchTST model)</li> <li><strong>pegasus</strong> — <code>PegasusModel</code> (Pegasus model)</li> <li><strong>pegasus_x</strong> — <code>PegasusXModel</code> (PEGASUS-X model)</li> <li><strong>perceiver</strong> — <code>PerceiverModel</code> (Perceiver model)</li> <li><strong>persimmon</strong> — <code>PersimmonModel</code> (Persimmon model)</li> <li><strong>phi</strong> — <code>PhiModel</code> (Phi model)</li> <li><strong>phi3</strong> — <code>Phi3Model</code> (Phi3 model)</li> <li><strong>phimoe</strong> — <code>PhimoeModel</code> (Phimoe model)</li> <li><strong>pixtral</strong> — <code>PixtralVisionModel</code> (Pixtral model)</li> <li><strong>plbart</strong> — <code>PLBartModel</code> (PLBart model)</li> <li><strong>poolformer</strong> — <code>PoolFormerModel</code> (PoolFormer model)</li> <li><strong>prophetnet</strong> — <code>ProphetNetModel</code> (ProphetNet model)</li> <li><strong>pvt</strong> — <code>PvtModel</code> (PVT model)</li> <li><strong>pvt_v2</strong> — <code>PvtV2Model</code> (PVTv2 model)</li> <li><strong>qdqbert</strong> — <code>QDQBertModel</code> (QDQBert model)</li> <li><strong>qwen2</strong> — <code>Qwen2Model</code> (Qwen2 model)</li> <li><strong>qwen2_5_vl</strong> — <code>Qwen2_5_VLModel</code> (Qwen2_5_VL model)</li> <li><strong>qwen2_audio_encoder</strong> — <code>Qwen2AudioEncoder</code> (Qwen2AudioEncoder model)</li> <li><strong>qwen2_moe</strong> — <code>Qwen2MoeModel</code> (Qwen2MoE model)</li> <li><strong>qwen2_vl</strong> — <code>Qwen2VLModel</code> (Qwen2VL model)</li> <li><strong>recurrent_gemma</strong> — <code>RecurrentGemmaModel</code> (RecurrentGemma model)</li> <li><strong>reformer</strong> — <code>ReformerModel</code> (Reformer model)</li> <li><strong>regnet</strong> — <code>RegNetModel</code> (RegNet model)</li> <li><strong>rembert</strong> — <code>RemBertModel</code> (RemBERT model)</li> <li><strong>resnet</strong> — <code>ResNetModel</code> (ResNet model)</li> <li><strong>retribert</strong> — <code>RetriBertModel</code> (RetriBERT model)</li> <li><strong>roberta</strong> — <code>RobertaModel</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>RobertaPreLayerNormModel</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <code>RoCBertModel</code> (RoCBert model)</li> <li><strong>roformer</strong> — <code>RoFormerModel</code> (RoFormer model)</li> <li><strong>rt_detr</strong> — <code>RTDetrModel</code> (RT-DETR model)</li> <li><strong>rwkv</strong> — <code>RwkvModel</code> (RWKV model)</li> <li><strong>sam</strong> — <code>SamModel</code> (SAM model)</li> <li><strong>seamless_m4t</strong> — <code>SeamlessM4TModel</code> (SeamlessM4T model)</li> <li><strong>seamless_m4t_v2</strong> — <code>SeamlessM4Tv2Model</code> (SeamlessM4Tv2 model)</li> <li><strong>segformer</strong> — <code>SegformerModel</code> (SegFormer model)</li> <li><strong>seggpt</strong> — <code>SegGptModel</code> (SegGPT model)</li> <li><strong>sew</strong> — <code>SEWModel</code> (SEW model)</li> <li><strong>sew-d</strong> — <code>SEWDModel</code> (SEW-D model)</li> <li><strong>siglip</strong> — <code>SiglipModel</code> (SigLIP model)</li> <li><strong>siglip_vision_model</strong> — <code>SiglipVisionModel</code> (SiglipVisionModel model)</li> <li><strong>speech_to_text</strong> — <code>Speech2TextModel</code> (Speech2Text model)</li> <li><strong>speecht5</strong> — <code>SpeechT5Model</code> (SpeechT5 model)</li> <li><strong>splinter</strong> — <code>SplinterModel</code> (Splinter model)</li> <li><strong>squeezebert</strong> — <code>SqueezeBertModel</code> (SqueezeBERT model)</li> <li><strong>stablelm</strong> — <code>StableLmModel</code> (StableLm model)</li> <li><strong>starcoder2</strong> — <code>Starcoder2Model</code> (Starcoder2 model)</li> <li><strong>superglue</strong> — <code>SuperGlueForKeypointMatching</code> (SuperGlue model)</li> <li><strong>swiftformer</strong> — <code>SwiftFormerModel</code> (SwiftFormer model)</li> <li><strong>swin</strong> — <a href="/docs/transformers/main/ko/model_doc/swin#transformers.SwinModel">SwinModel</a> (Swin Transformer model)</li> <li><strong>swin2sr</strong> — <a href="/docs/transformers/main/ko/model_doc/swin2sr#transformers.Swin2SRModel">Swin2SRModel</a> (Swin2SR model)</li> <li><strong>swinv2</strong> — <a href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2Model">Swinv2Model</a> (Swin Transformer V2 model)</li> <li><strong>switch_transformers</strong> — <code>SwitchTransformersModel</code> (SwitchTransformers model)</li> <li><strong>t5</strong> — <code>T5Model</code> (T5 model)</li> <li><strong>table-transformer</strong> — <code>TableTransformerModel</code> (Table Transformer model)</li> <li><strong>tapas</strong> — <code>TapasModel</code> (TAPAS model)</li> <li><strong>textnet</strong> — <code>TextNetModel</code> (TextNet model)</li> <li><strong>time_series_transformer</strong> — <a href="/docs/transformers/main/ko/model_doc/time_series_transformer#transformers.TimeSeriesTransformerModel">TimeSeriesTransformerModel</a> (Time Series Transformer model)</li> <li><strong>timesformer</strong> — <a href="/docs/transformers/main/ko/model_doc/timesformer#transformers.TimesformerModel">TimesformerModel</a> (TimeSformer model)</li> <li><strong>timm_backbone</strong> — <code>TimmBackbone</code> (TimmBackbone model)</li> <li><strong>timm_wrapper</strong> — <code>TimmWrapperModel</code> (TimmWrapperModel model)</li> <li><strong>trajectory_transformer</strong> — <a href="/docs/transformers/main/ko/model_doc/trajectory_transformer#transformers.TrajectoryTransformerModel">TrajectoryTransformerModel</a> (Trajectory Transformer model)</li> <li><strong>transfo-xl</strong> — <code>TransfoXLModel</code> (Transformer-XL model)</li> <li><strong>tvlt</strong> — <code>TvltModel</code> (TVLT model)</li> <li><strong>tvp</strong> — <code>TvpModel</code> (TVP model)</li> <li><strong>udop</strong> — <code>UdopModel</code> (UDOP model)</li> <li><strong>umt5</strong> — <code>UMT5Model</code> (UMT5 model)</li> <li><strong>unispeech</strong> — <code>UniSpeechModel</code> (UniSpeech model)</li> <li><strong>unispeech-sat</strong> — <code>UniSpeechSatModel</code> (UniSpeechSat model)</li> <li><strong>univnet</strong> — <code>UnivNetModel</code> (UnivNet model)</li> <li><strong>van</strong> — <code>VanModel</code> (VAN model)</li> <li><strong>videomae</strong> — <code>VideoMAEModel</code> (VideoMAE model)</li> <li><strong>vilt</strong> — <code>ViltModel</code> (ViLT model)</li> <li><strong>vision-text-dual-encoder</strong> — <code>VisionTextDualEncoderModel</code> (VisionTextDualEncoder model)</li> <li><strong>visual_bert</strong> — <code>VisualBertModel</code> (VisualBERT model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTModel">ViTModel</a> (ViT model)</li> <li><strong>vit_hybrid</strong> — <code>ViTHybridModel</code> (ViT Hybrid model)</li> <li><strong>vit_mae</strong> — <code>ViTMAEModel</code> (ViTMAE model)</li> <li><strong>vit_msn</strong> — <code>ViTMSNModel</code> (ViTMSN model)</li> <li><strong>vitdet</strong> — <code>VitDetModel</code> (VitDet model)</li> <li><strong>vits</strong> — <code>VitsModel</code> (VITS model)</li> <li><strong>vivit</strong> — <a href="/docs/transformers/main/ko/model_doc/vivit#transformers.VivitModel">VivitModel</a> (ViViT model)</li> <li><strong>wav2vec2</strong> — <code>Wav2Vec2Model</code> (Wav2Vec2 model)</li> <li><strong>wav2vec2-bert</strong> — <code>Wav2Vec2BertModel</code> (Wav2Vec2-BERT model)</li> <li><strong>wav2vec2-conformer</strong> — <code>Wav2Vec2ConformerModel</code> (Wav2Vec2-Conformer model)</li> <li><strong>wavlm</strong> — <code>WavLMModel</code> (WavLM model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperModel">WhisperModel</a> (Whisper model)</li> <li><strong>xclip</strong> — <code>XCLIPModel</code> (X-CLIP model)</li> <li><strong>xglm</strong> — <code>XGLMModel</code> (XGLM model)</li> <li><strong>xlm</strong> — <code>XLMModel</code> (XLM model)</li> <li><strong>xlm-prophetnet</strong> — <code>XLMProphetNetModel</code> (XLM-ProphetNet model)</li> <li><strong>xlm-roberta</strong> — <code>XLMRobertaModel</code> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <code>XLMRobertaXLModel</code> (XLM-RoBERTa-XL model)</li> <li><strong>xlnet</strong> — <code>XLNetModel</code> (XLNet model)</li> <li><strong>xmod</strong> — <code>XmodModel</code> (X-MOD model)</li> <li><strong>yolos</strong> — <code>YolosModel</code> (YOLOS model)</li> <li><strong>yoso</strong> — <code>YosoModel</code> (YOSO model)</li> <li><strong>zamba</strong> — <code>ZambaModel</code> (Zamba model)</li> <li><strong>zamba2</strong> — <code>Zamba2Model</code> (Zamba2 model)</li>',Q1,oh,VV=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,Y1,Es,o2,nc,t2,he,rc,z1,th,IV=`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,O1,nh,PV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",K1,Wn,ac,ex,rh,SV="Instantiates one of the base model classes of the library from a configuration.",ox,ah,UV=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,tx,Vs,nx,Uo,sc,rx,sh,NV="Instantiate one of the base model classes of the library from a pretrained model.",ax,ih,qV=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,sx,lh,XV='<li><strong>albert</strong> — <code>TFAlbertModel</code> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.TFBartModel">TFBartModel</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.TFBertModel">TFBertModel</a> (BERT model)</li> <li><strong>blenderbot</strong> — <code>TFBlenderbotModel</code> (Blenderbot model)</li> <li><strong>blenderbot-small</strong> — <code>TFBlenderbotSmallModel</code> (BlenderbotSmall model)</li> <li><strong>blip</strong> — <a href="/docs/transformers/main/ko/model_doc/blip#transformers.TFBlipModel">TFBlipModel</a> (BLIP model)</li> <li><strong>camembert</strong> — <code>TFCamembertModel</code> (CamemBERT model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.TFConvBertModel">TFConvBertModel</a> (ConvBERT model)</li> <li><strong>convnext</strong> — <code>TFConvNextModel</code> (ConvNeXT model)</li> <li><strong>convnextv2</strong> — <code>TFConvNextV2Model</code> (ConvNeXTV2 model)</li> <li><strong>ctrl</strong> — <code>TFCTRLModel</code> (CTRL model)</li> <li><strong>cvt</strong> — <code>TFCvtModel</code> (CvT model)</li> <li><strong>data2vec-vision</strong> — <code>TFData2VecVisionModel</code> (Data2VecVision model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.TFDebertaModel">TFDebertaModel</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.TFDebertaV2Model">TFDebertaV2Model</a> (DeBERTa-v2 model)</li> <li><strong>deit</strong> — <code>TFDeiTModel</code> (DeiT model)</li> <li><strong>distilbert</strong> — <code>TFDistilBertModel</code> (DistilBERT model)</li> <li><strong>dpr</strong> — <code>TFDPRQuestionEncoder</code> (DPR model)</li> <li><strong>efficientformer</strong> — <code>TFEfficientFormerModel</code> (EfficientFormer model)</li> <li><strong>electra</strong> — <code>TFElectraModel</code> (ELECTRA model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/main/ko/model_doc/esm#transformers.TFEsmModel">TFEsmModel</a> (ESM model)</li> <li><strong>flaubert</strong> — <code>TFFlaubertModel</code> (FlauBERT model)</li> <li><strong>funnel</strong> — <code>TFFunnelModel</code> or <code>TFFunnelBaseModel</code> (Funnel Transformer model)</li> <li><strong>gpt-sw3</strong> — <code>TFGPT2Model</code> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <code>TFGPT2Model</code> (OpenAI GPT-2 model)</li> <li><strong>gptj</strong> — <code>TFGPTJModel</code> (GPT-J model)</li> <li><strong>groupvit</strong> — <code>TFGroupViTModel</code> (GroupViT model)</li> <li><strong>hubert</strong> — <code>TFHubertModel</code> (Hubert model)</li> <li><strong>idefics</strong> — <code>TFIdeficsModel</code> (IDEFICS model)</li> <li><strong>layoutlm</strong> — <code>TFLayoutLMModel</code> (LayoutLM model)</li> <li><strong>layoutlmv3</strong> — <code>TFLayoutLMv3Model</code> (LayoutLMv3 model)</li> <li><strong>led</strong> — <code>TFLEDModel</code> (LED model)</li> <li><strong>longformer</strong> — <code>TFLongformerModel</code> (Longformer model)</li> <li><strong>lxmert</strong> — <code>TFLxmertModel</code> (LXMERT model)</li> <li><strong>marian</strong> — <a href="/docs/transformers/main/ko/model_doc/marian#transformers.TFMarianModel">TFMarianModel</a> (Marian model)</li> <li><strong>mbart</strong> — <code>TFMBartModel</code> (mBART model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/main/ko/model_doc/mistral#transformers.TFMistralModel">TFMistralModel</a> (Mistral model)</li> <li><strong>mobilebert</strong> — <code>TFMobileBertModel</code> (MobileBERT model)</li> <li><strong>mobilevit</strong> — <code>TFMobileViTModel</code> (MobileViT model)</li> <li><strong>mpnet</strong> — <code>TFMPNetModel</code> (MPNet model)</li> <li><strong>mt5</strong> — <code>TFMT5Model</code> (MT5 model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.TFOpenAIGPTModel">TFOpenAIGPTModel</a> (OpenAI GPT model)</li> <li><strong>opt</strong> — <code>TFOPTModel</code> (OPT model)</li> <li><strong>pegasus</strong> — <code>TFPegasusModel</code> (Pegasus model)</li> <li><strong>regnet</strong> — <code>TFRegNetModel</code> (RegNet model)</li> <li><strong>rembert</strong> — <code>TFRemBertModel</code> (RemBERT model)</li> <li><strong>resnet</strong> — <code>TFResNetModel</code> (ResNet model)</li> <li><strong>roberta</strong> — <code>TFRobertaModel</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>TFRobertaPreLayerNormModel</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <code>TFRoFormerModel</code> (RoFormer model)</li> <li><strong>sam</strong> — <code>TFSamModel</code> (SAM model)</li> <li><strong>segformer</strong> — <code>TFSegformerModel</code> (SegFormer model)</li> <li><strong>speech_to_text</strong> — <code>TFSpeech2TextModel</code> (Speech2Text model)</li> <li><strong>swiftformer</strong> — <code>TFSwiftFormerModel</code> (SwiftFormer model)</li> <li><strong>swin</strong> — <a href="/docs/transformers/main/ko/model_doc/swin#transformers.TFSwinModel">TFSwinModel</a> (Swin Transformer model)</li> <li><strong>t5</strong> — <code>TFT5Model</code> (T5 model)</li> <li><strong>tapas</strong> — <code>TFTapasModel</code> (TAPAS model)</li> <li><strong>transfo-xl</strong> — <code>TFTransfoXLModel</code> (Transformer-XL model)</li> <li><strong>vision-text-dual-encoder</strong> — <code>TFVisionTextDualEncoderModel</code> (VisionTextDualEncoder model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.TFViTModel">TFViTModel</a> (ViT model)</li> <li><strong>vit_mae</strong> — <code>TFViTMAEModel</code> (ViTMAE model)</li> <li><strong>wav2vec2</strong> — <code>TFWav2Vec2Model</code> (Wav2Vec2 model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.TFWhisperModel">TFWhisperModel</a> (Whisper model)</li> <li><strong>xglm</strong> — <code>TFXGLMModel</code> (XGLM model)</li> <li><strong>xlm</strong> — <code>TFXLMModel</code> (XLM model)</li> <li><strong>xlm-roberta</strong> — <code>TFXLMRobertaModel</code> (XLM-RoBERTa model)</li> <li><strong>xlnet</strong> — <code>TFXLNetModel</code> (XLNet model)</li>',ix,Is,n2,ic,r2,be,lc,lx,dh,DV=`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,dx,ch,HV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",cx,En,dc,mx,mh,QV="Instantiates one of the base model classes of the library from a configuration.",fx,fh,YV=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,gx,Ps,px,No,cc,ux,gh,zV="Instantiate one of the base model classes of the library from a pretrained model.",hx,ph,OV=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,bx,uh,KV='<li><strong>albert</strong> — <code>FlaxAlbertModel</code> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.FlaxBartModel">FlaxBartModel</a> (BART model)</li> <li><strong>beit</strong> — <code>FlaxBeitModel</code> (BEiT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.FlaxBertModel">FlaxBertModel</a> (BERT model)</li> <li><strong>big_bird</strong> — <code>FlaxBigBirdModel</code> (BigBird model)</li> <li><strong>blenderbot</strong> — <code>FlaxBlenderbotModel</code> (Blenderbot model)</li> <li><strong>blenderbot-small</strong> — <code>FlaxBlenderbotSmallModel</code> (BlenderbotSmall model)</li> <li><strong>bloom</strong> — <code>FlaxBloomModel</code> (BLOOM model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.FlaxCLIPModel">FlaxCLIPModel</a> (CLIP model)</li> <li><strong>dinov2</strong> — <code>FlaxDinov2Model</code> (DINOv2 model)</li> <li><strong>distilbert</strong> — <code>FlaxDistilBertModel</code> (DistilBERT model)</li> <li><strong>electra</strong> — <code>FlaxElectraModel</code> (ELECTRA model)</li> <li><strong>gemma</strong> — <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.FlaxGemmaModel">FlaxGemmaModel</a> (Gemma model)</li> <li><strong>gpt-sw3</strong> — <code>FlaxGPT2Model</code> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <code>FlaxGPT2Model</code> (OpenAI GPT-2 model)</li> <li><strong>gpt_neo</strong> — <code>FlaxGPTNeoModel</code> (GPT Neo model)</li> <li><strong>gptj</strong> — <code>FlaxGPTJModel</code> (GPT-J model)</li> <li><strong>llama</strong> — <code>FlaxLlamaModel</code> (LLaMA model)</li> <li><strong>longt5</strong> — <code>FlaxLongT5Model</code> (LongT5 model)</li> <li><strong>marian</strong> — <a href="/docs/transformers/main/ko/model_doc/marian#transformers.FlaxMarianModel">FlaxMarianModel</a> (Marian model)</li> <li><strong>mbart</strong> — <code>FlaxMBartModel</code> (mBART model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/main/ko/model_doc/mistral#transformers.FlaxMistralModel">FlaxMistralModel</a> (Mistral model)</li> <li><strong>mt5</strong> — <code>FlaxMT5Model</code> (MT5 model)</li> <li><strong>opt</strong> — <code>FlaxOPTModel</code> (OPT model)</li> <li><strong>pegasus</strong> — <code>FlaxPegasusModel</code> (Pegasus model)</li> <li><strong>regnet</strong> — <code>FlaxRegNetModel</code> (RegNet model)</li> <li><strong>resnet</strong> — <code>FlaxResNetModel</code> (ResNet model)</li> <li><strong>roberta</strong> — <code>FlaxRobertaModel</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>FlaxRobertaPreLayerNormModel</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <code>FlaxRoFormerModel</code> (RoFormer model)</li> <li><strong>t5</strong> — <code>FlaxT5Model</code> (T5 model)</li> <li><strong>vision-text-dual-encoder</strong> — <code>FlaxVisionTextDualEncoderModel</code> (VisionTextDualEncoder model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.FlaxViTModel">FlaxViTModel</a> (ViT model)</li> <li><strong>wav2vec2</strong> — <code>FlaxWav2Vec2Model</code> (Wav2Vec2 model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.FlaxWhisperModel">FlaxWhisperModel</a> (Whisper model)</li> <li><strong>xglm</strong> — <code>FlaxXGLMModel</code> (XGLM model)</li> <li><strong>xlm-roberta</strong> — <code>FlaxXLMRobertaModel</code> (XLM-RoBERTa model)</li>',_x,Ss,a2,mc,s2,fc,eI="다음 자동 클래스들은 사전 훈련 헤드가 포함된 모델을 인스턴스화하는 데 사용할 수 있습니다.",i2,gc,l2,_e,pc,Mx,hh,oI=`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,Tx,bh,tI="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",yx,Vn,uc,Fx,_h,nI="Instantiates one of the model classes of the library (with a pretraining head) from a configuration.",vx,Mh,rI=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,Cx,Us,wx,G,hc,kx,Th,aI="Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model.",xx,yh,sI=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,$x,Fh,iI='<li><strong>albert</strong> — <code>AlbertForPreTraining</code> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertForPreTraining">BertForPreTraining</a> (BERT model)</li> <li><strong>big_bird</strong> — <code>BigBirdForPreTraining</code> (BigBird model)</li> <li><strong>bloom</strong> — <code>BloomForCausalLM</code> (BLOOM model)</li> <li><strong>camembert</strong> — <code>CamembertForMaskedLM</code> (CamemBERT model)</li> <li><strong>colpali</strong> — <code>ColPaliForRetrieval</code> (ColPali model)</li> <li><strong>ctrl</strong> — <code>CTRLLMHeadModel</code> (CTRL model)</li> <li><strong>data2vec-text</strong> — <code>Data2VecTextForMaskedLM</code> (Data2VecText model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <code>DistilBertForMaskedLM</code> (DistilBERT model)</li> <li><strong>electra</strong> — <code>ElectraForPreTraining</code> (ELECTRA model)</li> <li><strong>ernie</strong> — <code>ErnieForPreTraining</code> (ERNIE model)</li> <li><strong>falcon_mamba</strong> — <code>FalconMambaForCausalLM</code> (FalconMamba model)</li> <li><strong>flaubert</strong> — <code>FlaubertWithLMHeadModel</code> (FlauBERT model)</li> <li><strong>flava</strong> — <code>FlavaForPreTraining</code> (FLAVA model)</li> <li><strong>fnet</strong> — <code>FNetForPreTraining</code> (FNet model)</li> <li><strong>fsmt</strong> — <code>FSMTForConditionalGeneration</code> (FairSeq Machine-Translation model)</li> <li><strong>funnel</strong> — <code>FunnelForPreTraining</code> (Funnel Transformer model)</li> <li><strong>gpt-sw3</strong> — <code>GPT2LMHeadModel</code> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <code>GPT2LMHeadModel</code> (OpenAI GPT-2 model)</li> <li><strong>gpt_bigcode</strong> — <code>GPTBigCodeForCausalLM</code> (GPTBigCode model)</li> <li><strong>gptsan-japanese</strong> — <code>GPTSanJapaneseForConditionalGeneration</code> (GPTSAN-japanese model)</li> <li><strong>hiera</strong> — <code>HieraForPreTraining</code> (Hiera model)</li> <li><strong>ibert</strong> — <code>IBertForMaskedLM</code> (I-BERT model)</li> <li><strong>idefics</strong> — <code>IdeficsForVisionText2Text</code> (IDEFICS model)</li> <li><strong>idefics2</strong> — <code>Idefics2ForConditionalGeneration</code> (Idefics2 model)</li> <li><strong>idefics3</strong> — <code>Idefics3ForConditionalGeneration</code> (Idefics3 model)</li> <li><strong>layoutlm</strong> — <code>LayoutLMForMaskedLM</code> (LayoutLM model)</li> <li><strong>llava</strong> — <code>LlavaForConditionalGeneration</code> (LLaVa model)</li> <li><strong>llava_next</strong> — <code>LlavaNextForConditionalGeneration</code> (LLaVA-NeXT model)</li> <li><strong>llava_next_video</strong> — <code>LlavaNextVideoForConditionalGeneration</code> (LLaVa-NeXT-Video model)</li> <li><strong>llava_onevision</strong> — <code>LlavaOnevisionForConditionalGeneration</code> (LLaVA-Onevision model)</li> <li><strong>longformer</strong> — <code>LongformerForMaskedLM</code> (Longformer model)</li> <li><strong>luke</strong> — <code>LukeForMaskedLM</code> (LUKE model)</li> <li><strong>lxmert</strong> — <code>LxmertForPreTraining</code> (LXMERT model)</li> <li><strong>mamba</strong> — <a href="/docs/transformers/main/ko/model_doc/mamba#transformers.MambaForCausalLM">MambaForCausalLM</a> (Mamba model)</li> <li><strong>mamba2</strong> — <a href="/docs/transformers/main/ko/model_doc/mamba2#transformers.Mamba2ForCausalLM">Mamba2ForCausalLM</a> (mamba2 model)</li> <li><strong>mega</strong> — <code>MegaForMaskedLM</code> (MEGA model)</li> <li><strong>megatron-bert</strong> — <code>MegatronBertForPreTraining</code> (Megatron-BERT model)</li> <li><strong>mllama</strong> — <code>MllamaForConditionalGeneration</code> (Mllama model)</li> <li><strong>mobilebert</strong> — <code>MobileBertForPreTraining</code> (MobileBERT model)</li> <li><strong>mpnet</strong> — <code>MPNetForMaskedLM</code> (MPNet model)</li> <li><strong>mpt</strong> — <code>MptForCausalLM</code> (MPT model)</li> <li><strong>mra</strong> — <code>MraForMaskedLM</code> (MRA model)</li> <li><strong>mvp</strong> — <code>MvpForConditionalGeneration</code> (MVP model)</li> <li><strong>nezha</strong> — <code>NezhaForPreTraining</code> (Nezha model)</li> <li><strong>nllb-moe</strong> — <code>NllbMoeForConditionalGeneration</code> (NLLB-MOE model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li> <li><strong>paligemma</strong> — <a href="/docs/transformers/main/ko/model_doc/paligemma#transformers.PaliGemmaForConditionalGeneration">PaliGemmaForConditionalGeneration</a> (PaliGemma model)</li> <li><strong>qwen2_audio</strong> — <code>Qwen2AudioForConditionalGeneration</code> (Qwen2Audio model)</li> <li><strong>retribert</strong> — <code>RetriBertModel</code> (RetriBERT model)</li> <li><strong>roberta</strong> — <code>RobertaForMaskedLM</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>RobertaPreLayerNormForMaskedLM</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <code>RoCBertForPreTraining</code> (RoCBert model)</li> <li><strong>rwkv</strong> — <code>RwkvForCausalLM</code> (RWKV model)</li> <li><strong>splinter</strong> — <code>SplinterForPreTraining</code> (Splinter model)</li> <li><strong>squeezebert</strong> — <code>SqueezeBertForMaskedLM</code> (SqueezeBERT model)</li> <li><strong>switch_transformers</strong> — <code>SwitchTransformersForConditionalGeneration</code> (SwitchTransformers model)</li> <li><strong>t5</strong> — <code>T5ForConditionalGeneration</code> (T5 model)</li> <li><strong>tapas</strong> — <code>TapasForMaskedLM</code> (TAPAS model)</li> <li><strong>transfo-xl</strong> — <code>TransfoXLLMHeadModel</code> (Transformer-XL model)</li> <li><strong>tvlt</strong> — <code>TvltForPreTraining</code> (TVLT model)</li> <li><strong>unispeech</strong> — <code>UniSpeechForPreTraining</code> (UniSpeech model)</li> <li><strong>unispeech-sat</strong> — <code>UniSpeechSatForPreTraining</code> (UniSpeechSat model)</li> <li><strong>video_llava</strong> — <code>VideoLlavaForConditionalGeneration</code> (VideoLlava model)</li> <li><strong>videomae</strong> — <code>VideoMAEForPreTraining</code> (VideoMAE model)</li> <li><strong>vipllava</strong> — <code>VipLlavaForConditionalGeneration</code> (VipLlava model)</li> <li><strong>visual_bert</strong> — <code>VisualBertForPreTraining</code> (VisualBERT model)</li> <li><strong>vit_mae</strong> — <code>ViTMAEForPreTraining</code> (ViTMAE model)</li> <li><strong>wav2vec2</strong> — <code>Wav2Vec2ForPreTraining</code> (Wav2Vec2 model)</li> <li><strong>wav2vec2-conformer</strong> — <code>Wav2Vec2ConformerForPreTraining</code> (Wav2Vec2-Conformer model)</li> <li><strong>xlm</strong> — <code>XLMWithLMHeadModel</code> (XLM model)</li> <li><strong>xlm-roberta</strong> — <code>XLMRobertaForMaskedLM</code> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <code>XLMRobertaXLForMaskedLM</code> (XLM-RoBERTa-XL model)</li> <li><strong>xlnet</strong> — <code>XLNetLMHeadModel</code> (XLNet model)</li> <li><strong>xmod</strong> — <code>XmodForMaskedLM</code> (X-MOD model)</li>',Lx,vh,lI=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,Ax,Ns,d2,bc,c2,Me,_c,jx,Ch,dI=`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,Zx,wh,cI="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Bx,In,Mc,Rx,kh,mI="Instantiates one of the model classes of the library (with a pretraining head) from a configuration.",Gx,xh,fI=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,Jx,qs,Wx,qo,Tc,Ex,$h,gI="Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model.",Vx,Lh,pI=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,Ix,Ah,uI='<li><strong>albert</strong> — <code>TFAlbertForPreTraining</code> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.TFBertForPreTraining">TFBertForPreTraining</a> (BERT model)</li> <li><strong>camembert</strong> — <code>TFCamembertForMaskedLM</code> (CamemBERT model)</li> <li><strong>ctrl</strong> — <code>TFCTRLLMHeadModel</code> (CTRL model)</li> <li><strong>distilbert</strong> — <code>TFDistilBertForMaskedLM</code> (DistilBERT model)</li> <li><strong>electra</strong> — <code>TFElectraForPreTraining</code> (ELECTRA model)</li> <li><strong>flaubert</strong> — <code>TFFlaubertWithLMHeadModel</code> (FlauBERT model)</li> <li><strong>funnel</strong> — <code>TFFunnelForPreTraining</code> (Funnel Transformer model)</li> <li><strong>gpt-sw3</strong> — <code>TFGPT2LMHeadModel</code> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <code>TFGPT2LMHeadModel</code> (OpenAI GPT-2 model)</li> <li><strong>idefics</strong> — <code>TFIdeficsForVisionText2Text</code> (IDEFICS model)</li> <li><strong>layoutlm</strong> — <code>TFLayoutLMForMaskedLM</code> (LayoutLM model)</li> <li><strong>lxmert</strong> — <code>TFLxmertForPreTraining</code> (LXMERT model)</li> <li><strong>mobilebert</strong> — <code>TFMobileBertForPreTraining</code> (MobileBERT model)</li> <li><strong>mpnet</strong> — <code>TFMPNetForMaskedLM</code> (MPNet model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li> <li><strong>roberta</strong> — <code>TFRobertaForMaskedLM</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>TFRobertaPreLayerNormForMaskedLM</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>t5</strong> — <code>TFT5ForConditionalGeneration</code> (T5 model)</li> <li><strong>tapas</strong> — <code>TFTapasForMaskedLM</code> (TAPAS model)</li> <li><strong>transfo-xl</strong> — <code>TFTransfoXLLMHeadModel</code> (Transformer-XL model)</li> <li><strong>vit_mae</strong> — <code>TFViTMAEForPreTraining</code> (ViTMAE model)</li> <li><strong>xlm</strong> — <code>TFXLMWithLMHeadModel</code> (XLM model)</li> <li><strong>xlm-roberta</strong> — <code>TFXLMRobertaForMaskedLM</code> (XLM-RoBERTa model)</li> <li><strong>xlnet</strong> — <code>TFXLNetLMHeadModel</code> (XLNet model)</li>',Px,Xs,m2,yc,f2,Te,Fc,Sx,jh,hI=`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,Ux,Zh,bI="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Nx,Pn,vc,qx,Bh,_I="Instantiates one of the model classes of the library (with a pretraining head) from a configuration.",Xx,Rh,MI=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,Dx,Ds,Hx,Xo,Cc,Qx,Gh,TI="Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model.",Yx,Jh,yI=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,zx,Wh,FI='<li><strong>albert</strong> — <code>FlaxAlbertForPreTraining</code> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.FlaxBertForPreTraining">FlaxBertForPreTraining</a> (BERT model)</li> <li><strong>big_bird</strong> — <code>FlaxBigBirdForPreTraining</code> (BigBird model)</li> <li><strong>electra</strong> — <code>FlaxElectraForPreTraining</code> (ELECTRA model)</li> <li><strong>longt5</strong> — <code>FlaxLongT5ForConditionalGeneration</code> (LongT5 model)</li> <li><strong>mbart</strong> — <code>FlaxMBartForConditionalGeneration</code> (mBART model)</li> <li><strong>mt5</strong> — <code>FlaxMT5ForConditionalGeneration</code> (MT5 model)</li> <li><strong>roberta</strong> — <code>FlaxRobertaForMaskedLM</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>FlaxRobertaPreLayerNormForMaskedLM</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <code>FlaxRoFormerForMaskedLM</code> (RoFormer model)</li> <li><strong>t5</strong> — <code>FlaxT5ForConditionalGeneration</code> (T5 model)</li> <li><strong>wav2vec2</strong> — <code>FlaxWav2Vec2ForPreTraining</code> (Wav2Vec2 model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li> <li><strong>xlm-roberta</strong> — <code>FlaxXLMRobertaForMaskedLM</code> (XLM-RoBERTa model)</li>',Ox,Hs,g2,wc,p2,kc,vI="다음 자동 클래스들은 아래의 자연어 처리 작업에 사용할 수 있습니다.",u2,xc,h2,ye,$c,Kx,Eh,CI=`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,e$,Vh,wI="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",o$,Sn,Lc,t$,Ih,kI="Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration.",n$,Ph,xI=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,r$,Qs,a$,J,Ac,s$,Sh,$I="Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model.",i$,Uh,LI=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,l$,Nh,AI='<li><strong>aria_text</strong> — <code>AriaTextForCausalLM</code> (AriaText model)</li> <li><strong>bamba</strong> — <code>BambaForCausalLM</code> (Bamba model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartForCausalLM">BartForCausalLM</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertLMHeadModel">BertLMHeadModel</a> (BERT model)</li> <li><strong>bert-generation</strong> — <code>BertGenerationDecoder</code> (Bert Generation model)</li> <li><strong>big_bird</strong> — <code>BigBirdForCausalLM</code> (BigBird model)</li> <li><strong>bigbird_pegasus</strong> — <code>BigBirdPegasusForCausalLM</code> (BigBird-Pegasus model)</li> <li><strong>biogpt</strong> — <a href="/docs/transformers/main/ko/model_doc/biogpt#transformers.BioGptForCausalLM">BioGptForCausalLM</a> (BioGpt model)</li> <li><strong>blenderbot</strong> — <code>BlenderbotForCausalLM</code> (Blenderbot model)</li> <li><strong>blenderbot-small</strong> — <code>BlenderbotSmallForCausalLM</code> (BlenderbotSmall model)</li> <li><strong>bloom</strong> — <code>BloomForCausalLM</code> (BLOOM model)</li> <li><strong>camembert</strong> — <code>CamembertForCausalLM</code> (CamemBERT model)</li> <li><strong>code_llama</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaForCausalLM">LlamaForCausalLM</a> (CodeLlama model)</li> <li><strong>codegen</strong> — <code>CodeGenForCausalLM</code> (CodeGen model)</li> <li><strong>cohere</strong> — <a href="/docs/transformers/main/ko/model_doc/cohere#transformers.CohereForCausalLM">CohereForCausalLM</a> (Cohere model)</li> <li><strong>cohere2</strong> — <code>Cohere2ForCausalLM</code> (Cohere2 model)</li> <li><strong>cpmant</strong> — <code>CpmAntForCausalLM</code> (CPM-Ant model)</li> <li><strong>ctrl</strong> — <code>CTRLLMHeadModel</code> (CTRL model)</li> <li><strong>data2vec-text</strong> — <code>Data2VecTextForCausalLM</code> (Data2VecText model)</li> <li><strong>dbrx</strong> — <a href="/docs/transformers/main/ko/model_doc/dbrx#transformers.DbrxForCausalLM">DbrxForCausalLM</a> (DBRX model)</li> <li><strong>diffllama</strong> — <code>DiffLlamaForCausalLM</code> (DiffLlama model)</li> <li><strong>electra</strong> — <code>ElectraForCausalLM</code> (ELECTRA model)</li> <li><strong>emu3</strong> — <code>Emu3ForCausalLM</code> (Emu3 model)</li> <li><strong>ernie</strong> — <code>ErnieForCausalLM</code> (ERNIE model)</li> <li><strong>falcon</strong> — <code>FalconForCausalLM</code> (Falcon model)</li> <li><strong>falcon_mamba</strong> — <code>FalconMambaForCausalLM</code> (FalconMamba model)</li> <li><strong>fuyu</strong> — <code>FuyuForCausalLM</code> (Fuyu model)</li> <li><strong>gemma</strong> — <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaForCausalLM">GemmaForCausalLM</a> (Gemma model)</li> <li><strong>gemma2</strong> — <a href="/docs/transformers/main/ko/model_doc/gemma2#transformers.Gemma2ForCausalLM">Gemma2ForCausalLM</a> (Gemma2 model)</li> <li><strong>git</strong> — <code>GitForCausalLM</code> (GIT model)</li> <li><strong>glm</strong> — <code>GlmForCausalLM</code> (GLM model)</li> <li><strong>gpt-sw3</strong> — <code>GPT2LMHeadModel</code> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <code>GPT2LMHeadModel</code> (OpenAI GPT-2 model)</li> <li><strong>gpt_bigcode</strong> — <code>GPTBigCodeForCausalLM</code> (GPTBigCode model)</li> <li><strong>gpt_neo</strong> — <code>GPTNeoForCausalLM</code> (GPT Neo model)</li> <li><strong>gpt_neox</strong> — <code>GPTNeoXForCausalLM</code> (GPT NeoX model)</li> <li><strong>gpt_neox_japanese</strong> — <a href="/docs/transformers/main/ko/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseForCausalLM">GPTNeoXJapaneseForCausalLM</a> (GPT NeoX Japanese model)</li> <li><strong>gptj</strong> — <code>GPTJForCausalLM</code> (GPT-J model)</li> <li><strong>granite</strong> — <code>GraniteForCausalLM</code> (Granite model)</li> <li><strong>granitemoe</strong> — <code>GraniteMoeForCausalLM</code> (GraniteMoeMoe model)</li> <li><strong>helium</strong> — <code>HeliumForCausalLM</code> (Helium model)</li> <li><strong>jamba</strong> — <code>JambaForCausalLM</code> (Jamba model)</li> <li><strong>jetmoe</strong> — <code>JetMoeForCausalLM</code> (JetMoe model)</li> <li><strong>llama</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaForCausalLM">LlamaForCausalLM</a> (LLaMA model)</li> <li><strong>mamba</strong> — <a href="/docs/transformers/main/ko/model_doc/mamba#transformers.MambaForCausalLM">MambaForCausalLM</a> (Mamba model)</li> <li><strong>mamba2</strong> — <a href="/docs/transformers/main/ko/model_doc/mamba2#transformers.Mamba2ForCausalLM">Mamba2ForCausalLM</a> (mamba2 model)</li> <li><strong>marian</strong> — <a href="/docs/transformers/main/ko/model_doc/marian#transformers.MarianForCausalLM">MarianForCausalLM</a> (Marian model)</li> <li><strong>mbart</strong> — <code>MBartForCausalLM</code> (mBART model)</li> <li><strong>mega</strong> — <code>MegaForCausalLM</code> (MEGA model)</li> <li><strong>megatron-bert</strong> — <code>MegatronBertForCausalLM</code> (Megatron-BERT model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/main/ko/model_doc/mistral#transformers.MistralForCausalLM">MistralForCausalLM</a> (Mistral model)</li> <li><strong>mixtral</strong> — <code>MixtralForCausalLM</code> (Mixtral model)</li> <li><strong>mllama</strong> — <code>MllamaForCausalLM</code> (Mllama model)</li> <li><strong>moshi</strong> — <code>MoshiForCausalLM</code> (Moshi model)</li> <li><strong>mpt</strong> — <code>MptForCausalLM</code> (MPT model)</li> <li><strong>musicgen</strong> — <code>MusicgenForCausalLM</code> (MusicGen model)</li> <li><strong>musicgen_melody</strong> — <code>MusicgenMelodyForCausalLM</code> (MusicGen Melody model)</li> <li><strong>mvp</strong> — <code>MvpForCausalLM</code> (MVP model)</li> <li><strong>nemotron</strong> — <code>NemotronForCausalLM</code> (Nemotron model)</li> <li><strong>olmo</strong> — <code>OlmoForCausalLM</code> (OLMo model)</li> <li><strong>olmo2</strong> — <code>Olmo2ForCausalLM</code> (OLMo2 model)</li> <li><strong>olmoe</strong> — <code>OlmoeForCausalLM</code> (OLMoE model)</li> <li><strong>open-llama</strong> — <code>OpenLlamaForCausalLM</code> (OpenLlama model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li> <li><strong>opt</strong> — <code>OPTForCausalLM</code> (OPT model)</li> <li><strong>pegasus</strong> — <code>PegasusForCausalLM</code> (Pegasus model)</li> <li><strong>persimmon</strong> — <code>PersimmonForCausalLM</code> (Persimmon model)</li> <li><strong>phi</strong> — <code>PhiForCausalLM</code> (Phi model)</li> <li><strong>phi3</strong> — <code>Phi3ForCausalLM</code> (Phi3 model)</li> <li><strong>phimoe</strong> — <code>PhimoeForCausalLM</code> (Phimoe model)</li> <li><strong>plbart</strong> — <code>PLBartForCausalLM</code> (PLBart model)</li> <li><strong>prophetnet</strong> — <code>ProphetNetForCausalLM</code> (ProphetNet model)</li> <li><strong>qdqbert</strong> — <code>QDQBertLMHeadModel</code> (QDQBert model)</li> <li><strong>qwen2</strong> — <code>Qwen2ForCausalLM</code> (Qwen2 model)</li> <li><strong>qwen2_moe</strong> — <code>Qwen2MoeForCausalLM</code> (Qwen2MoE model)</li> <li><strong>recurrent_gemma</strong> — <code>RecurrentGemmaForCausalLM</code> (RecurrentGemma model)</li> <li><strong>reformer</strong> — <code>ReformerModelWithLMHead</code> (Reformer model)</li> <li><strong>rembert</strong> — <code>RemBertForCausalLM</code> (RemBERT model)</li> <li><strong>roberta</strong> — <code>RobertaForCausalLM</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>RobertaPreLayerNormForCausalLM</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <code>RoCBertForCausalLM</code> (RoCBert model)</li> <li><strong>roformer</strong> — <code>RoFormerForCausalLM</code> (RoFormer model)</li> <li><strong>rwkv</strong> — <code>RwkvForCausalLM</code> (RWKV model)</li> <li><strong>speech_to_text_2</strong> — <code>Speech2Text2ForCausalLM</code> (Speech2Text2 model)</li> <li><strong>stablelm</strong> — <code>StableLmForCausalLM</code> (StableLm model)</li> <li><strong>starcoder2</strong> — <code>Starcoder2ForCausalLM</code> (Starcoder2 model)</li> <li><strong>transfo-xl</strong> — <code>TransfoXLLMHeadModel</code> (Transformer-XL model)</li> <li><strong>trocr</strong> — <code>TrOCRForCausalLM</code> (TrOCR model)</li> <li><strong>whisper</strong> — <code>WhisperForCausalLM</code> (Whisper model)</li> <li><strong>xglm</strong> — <code>XGLMForCausalLM</code> (XGLM model)</li> <li><strong>xlm</strong> — <code>XLMWithLMHeadModel</code> (XLM model)</li> <li><strong>xlm-prophetnet</strong> — <code>XLMProphetNetForCausalLM</code> (XLM-ProphetNet model)</li> <li><strong>xlm-roberta</strong> — <code>XLMRobertaForCausalLM</code> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <code>XLMRobertaXLForCausalLM</code> (XLM-RoBERTa-XL model)</li> <li><strong>xlnet</strong> — <code>XLNetLMHeadModel</code> (XLNet model)</li> <li><strong>xmod</strong> — <code>XmodForCausalLM</code> (X-MOD model)</li> <li><strong>zamba</strong> — <code>ZambaForCausalLM</code> (Zamba model)</li> <li><strong>zamba2</strong> — <code>Zamba2ForCausalLM</code> (Zamba2 model)</li>',d$,qh,jI=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,c$,Ys,b2,jc,_2,Fe,Zc,m$,Xh,ZI=`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,f$,Dh,BI="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",g$,Un,Bc,p$,Hh,RI="Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration.",u$,Qh,GI=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,h$,zs,b$,Do,Rc,_$,Yh,JI="Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model.",M$,zh,WI=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,T$,Oh,EI='<li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.TFBertLMHeadModel">TFBertLMHeadModel</a> (BERT model)</li> <li><strong>camembert</strong> — <code>TFCamembertForCausalLM</code> (CamemBERT model)</li> <li><strong>ctrl</strong> — <code>TFCTRLLMHeadModel</code> (CTRL model)</li> <li><strong>gpt-sw3</strong> — <code>TFGPT2LMHeadModel</code> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <code>TFGPT2LMHeadModel</code> (OpenAI GPT-2 model)</li> <li><strong>gptj</strong> — <code>TFGPTJForCausalLM</code> (GPT-J model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/main/ko/model_doc/mistral#transformers.TFMistralForCausalLM">TFMistralForCausalLM</a> (Mistral model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li> <li><strong>opt</strong> — <code>TFOPTForCausalLM</code> (OPT model)</li> <li><strong>rembert</strong> — <code>TFRemBertForCausalLM</code> (RemBERT model)</li> <li><strong>roberta</strong> — <code>TFRobertaForCausalLM</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>TFRobertaPreLayerNormForCausalLM</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <code>TFRoFormerForCausalLM</code> (RoFormer model)</li> <li><strong>transfo-xl</strong> — <code>TFTransfoXLLMHeadModel</code> (Transformer-XL model)</li> <li><strong>xglm</strong> — <code>TFXGLMForCausalLM</code> (XGLM model)</li> <li><strong>xlm</strong> — <code>TFXLMWithLMHeadModel</code> (XLM model)</li> <li><strong>xlm-roberta</strong> — <code>TFXLMRobertaForCausalLM</code> (XLM-RoBERTa model)</li> <li><strong>xlnet</strong> — <code>TFXLNetLMHeadModel</code> (XLNet model)</li>',y$,Os,M2,Gc,T2,ve,Jc,F$,Kh,VI=`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,v$,eb,II="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",C$,Nn,Wc,w$,ob,PI="Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration.",k$,tb,SI=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,x$,Ks,$$,Ho,Ec,L$,nb,UI="Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model.",A$,rb,NI=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,j$,ab,qI='<li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.FlaxBartForCausalLM">FlaxBartForCausalLM</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.FlaxBertForCausalLM">FlaxBertForCausalLM</a> (BERT model)</li> <li><strong>big_bird</strong> — <code>FlaxBigBirdForCausalLM</code> (BigBird model)</li> <li><strong>bloom</strong> — <code>FlaxBloomForCausalLM</code> (BLOOM model)</li> <li><strong>electra</strong> — <code>FlaxElectraForCausalLM</code> (ELECTRA model)</li> <li><strong>gemma</strong> — <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.FlaxGemmaForCausalLM">FlaxGemmaForCausalLM</a> (Gemma model)</li> <li><strong>gpt-sw3</strong> — <code>FlaxGPT2LMHeadModel</code> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <code>FlaxGPT2LMHeadModel</code> (OpenAI GPT-2 model)</li> <li><strong>gpt_neo</strong> — <code>FlaxGPTNeoForCausalLM</code> (GPT Neo model)</li> <li><strong>gptj</strong> — <code>FlaxGPTJForCausalLM</code> (GPT-J model)</li> <li><strong>llama</strong> — <code>FlaxLlamaForCausalLM</code> (LLaMA model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/main/ko/model_doc/mistral#transformers.FlaxMistralForCausalLM">FlaxMistralForCausalLM</a> (Mistral model)</li> <li><strong>opt</strong> — <code>FlaxOPTForCausalLM</code> (OPT model)</li> <li><strong>roberta</strong> — <code>FlaxRobertaForCausalLM</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>FlaxRobertaPreLayerNormForCausalLM</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>xglm</strong> — <code>FlaxXGLMForCausalLM</code> (XGLM model)</li> <li><strong>xlm-roberta</strong> — <code>FlaxXLMRobertaForCausalLM</code> (XLM-RoBERTa model)</li>',Z$,ei,y2,Vc,F2,Ce,Ic,B$,sb,XI=`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,R$,ib,DI="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",G$,qn,Pc,J$,lb,HI="Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration.",W$,db,QI=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,E$,oi,V$,W,Sc,I$,cb,YI="Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model.",P$,mb,zI=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,S$,fb,OI='<li><strong>albert</strong> — <code>AlbertForMaskedLM</code> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertForMaskedLM">BertForMaskedLM</a> (BERT model)</li> <li><strong>big_bird</strong> — <code>BigBirdForMaskedLM</code> (BigBird model)</li> <li><strong>camembert</strong> — <code>CamembertForMaskedLM</code> (CamemBERT model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertForMaskedLM">ConvBertForMaskedLM</a> (ConvBERT model)</li> <li><strong>data2vec-text</strong> — <code>Data2VecTextForMaskedLM</code> (Data2VecText model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <code>DistilBertForMaskedLM</code> (DistilBERT model)</li> <li><strong>electra</strong> — <code>ElectraForMaskedLM</code> (ELECTRA model)</li> <li><strong>ernie</strong> — <code>ErnieForMaskedLM</code> (ERNIE model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/main/ko/model_doc/esm#transformers.EsmForMaskedLM">EsmForMaskedLM</a> (ESM model)</li> <li><strong>flaubert</strong> — <code>FlaubertWithLMHeadModel</code> (FlauBERT model)</li> <li><strong>fnet</strong> — <code>FNetForMaskedLM</code> (FNet model)</li> <li><strong>funnel</strong> — <code>FunnelForMaskedLM</code> (Funnel Transformer model)</li> <li><strong>ibert</strong> — <code>IBertForMaskedLM</code> (I-BERT model)</li> <li><strong>layoutlm</strong> — <code>LayoutLMForMaskedLM</code> (LayoutLM model)</li> <li><strong>longformer</strong> — <code>LongformerForMaskedLM</code> (Longformer model)</li> <li><strong>luke</strong> — <code>LukeForMaskedLM</code> (LUKE model)</li> <li><strong>mbart</strong> — <code>MBartForConditionalGeneration</code> (mBART model)</li> <li><strong>mega</strong> — <code>MegaForMaskedLM</code> (MEGA model)</li> <li><strong>megatron-bert</strong> — <code>MegatronBertForMaskedLM</code> (Megatron-BERT model)</li> <li><strong>mobilebert</strong> — <code>MobileBertForMaskedLM</code> (MobileBERT model)</li> <li><strong>modernbert</strong> — <code>ModernBertForMaskedLM</code> (ModernBERT model)</li> <li><strong>mpnet</strong> — <code>MPNetForMaskedLM</code> (MPNet model)</li> <li><strong>mra</strong> — <code>MraForMaskedLM</code> (MRA model)</li> <li><strong>mvp</strong> — <code>MvpForConditionalGeneration</code> (MVP model)</li> <li><strong>nezha</strong> — <code>NezhaForMaskedLM</code> (Nezha model)</li> <li><strong>nystromformer</strong> — <code>NystromformerForMaskedLM</code> (Nyströmformer model)</li> <li><strong>perceiver</strong> — <code>PerceiverForMaskedLM</code> (Perceiver model)</li> <li><strong>qdqbert</strong> — <code>QDQBertForMaskedLM</code> (QDQBert model)</li> <li><strong>reformer</strong> — <code>ReformerForMaskedLM</code> (Reformer model)</li> <li><strong>rembert</strong> — <code>RemBertForMaskedLM</code> (RemBERT model)</li> <li><strong>roberta</strong> — <code>RobertaForMaskedLM</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>RobertaPreLayerNormForMaskedLM</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <code>RoCBertForMaskedLM</code> (RoCBert model)</li> <li><strong>roformer</strong> — <code>RoFormerForMaskedLM</code> (RoFormer model)</li> <li><strong>squeezebert</strong> — <code>SqueezeBertForMaskedLM</code> (SqueezeBERT model)</li> <li><strong>tapas</strong> — <code>TapasForMaskedLM</code> (TAPAS model)</li> <li><strong>wav2vec2</strong> — <code>Wav2Vec2ForMaskedLM</code> (Wav2Vec2 model)</li> <li><strong>xlm</strong> — <code>XLMWithLMHeadModel</code> (XLM model)</li> <li><strong>xlm-roberta</strong> — <code>XLMRobertaForMaskedLM</code> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <code>XLMRobertaXLForMaskedLM</code> (XLM-RoBERTa-XL model)</li> <li><strong>xmod</strong> — <code>XmodForMaskedLM</code> (X-MOD model)</li> <li><strong>yoso</strong> — <code>YosoForMaskedLM</code> (YOSO model)</li>',U$,gb,KI=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,N$,ti,v2,Uc,C2,we,Nc,q$,pb,eP=`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,X$,ub,oP="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",D$,Xn,qc,H$,hb,tP="Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration.",Q$,bb,nP=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,Y$,ni,z$,Qo,Xc,O$,_b,rP="Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model.",K$,Mb,aP=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,eL,Tb,sP='<li><strong>albert</strong> — <code>TFAlbertForMaskedLM</code> (ALBERT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.TFBertForMaskedLM">TFBertForMaskedLM</a> (BERT model)</li> <li><strong>camembert</strong> — <code>TFCamembertForMaskedLM</code> (CamemBERT model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.TFConvBertForMaskedLM">TFConvBertForMaskedLM</a> (ConvBERT model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.TFDebertaForMaskedLM">TFDebertaForMaskedLM</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.TFDebertaV2ForMaskedLM">TFDebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <code>TFDistilBertForMaskedLM</code> (DistilBERT model)</li> <li><strong>electra</strong> — <code>TFElectraForMaskedLM</code> (ELECTRA model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/main/ko/model_doc/esm#transformers.TFEsmForMaskedLM">TFEsmForMaskedLM</a> (ESM model)</li> <li><strong>flaubert</strong> — <code>TFFlaubertWithLMHeadModel</code> (FlauBERT model)</li> <li><strong>funnel</strong> — <code>TFFunnelForMaskedLM</code> (Funnel Transformer model)</li> <li><strong>layoutlm</strong> — <code>TFLayoutLMForMaskedLM</code> (LayoutLM model)</li> <li><strong>longformer</strong> — <code>TFLongformerForMaskedLM</code> (Longformer model)</li> <li><strong>mobilebert</strong> — <code>TFMobileBertForMaskedLM</code> (MobileBERT model)</li> <li><strong>mpnet</strong> — <code>TFMPNetForMaskedLM</code> (MPNet model)</li> <li><strong>rembert</strong> — <code>TFRemBertForMaskedLM</code> (RemBERT model)</li> <li><strong>roberta</strong> — <code>TFRobertaForMaskedLM</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>TFRobertaPreLayerNormForMaskedLM</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <code>TFRoFormerForMaskedLM</code> (RoFormer model)</li> <li><strong>tapas</strong> — <code>TFTapasForMaskedLM</code> (TAPAS model)</li> <li><strong>xlm</strong> — <code>TFXLMWithLMHeadModel</code> (XLM model)</li> <li><strong>xlm-roberta</strong> — <code>TFXLMRobertaForMaskedLM</code> (XLM-RoBERTa model)</li>',oL,ri,w2,Dc,k2,ke,Hc,tL,yb,iP=`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,nL,Fb,lP="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",rL,Dn,Qc,aL,vb,dP="Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration.",sL,Cb,cP=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,iL,ai,lL,Yo,Yc,dL,wb,mP="Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model.",cL,kb,fP=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,mL,xb,gP='<li><strong>albert</strong> — <code>FlaxAlbertForMaskedLM</code> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.FlaxBertForMaskedLM">FlaxBertForMaskedLM</a> (BERT model)</li> <li><strong>big_bird</strong> — <code>FlaxBigBirdForMaskedLM</code> (BigBird model)</li> <li><strong>distilbert</strong> — <code>FlaxDistilBertForMaskedLM</code> (DistilBERT model)</li> <li><strong>electra</strong> — <code>FlaxElectraForMaskedLM</code> (ELECTRA model)</li> <li><strong>mbart</strong> — <code>FlaxMBartForConditionalGeneration</code> (mBART model)</li> <li><strong>roberta</strong> — <code>FlaxRobertaForMaskedLM</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>FlaxRobertaPreLayerNormForMaskedLM</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <code>FlaxRoFormerForMaskedLM</code> (RoFormer model)</li> <li><strong>xlm-roberta</strong> — <code>FlaxXLMRobertaForMaskedLM</code> (XLM-RoBERTa model)</li>',fL,si,x2,zc,$2,Oc,Kc,L2,em,A2,om,tm,j2,nm,Z2,xe,rm,gL,$b,pP=`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,pL,Lb,uP="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",uL,Hn,am,hL,Ab,hP="Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration.",bL,jb,bP=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,_L,ii,ML,E,sm,TL,Zb,_P="Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model.",yL,Bb,MP=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,FL,Rb,TP='<li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li> <li><strong>bigbird_pegasus</strong> — <code>BigBirdPegasusForConditionalGeneration</code> (BigBird-Pegasus model)</li> <li><strong>blenderbot</strong> — <code>BlenderbotForConditionalGeneration</code> (Blenderbot model)</li> <li><strong>blenderbot-small</strong> — <code>BlenderbotSmallForConditionalGeneration</code> (BlenderbotSmall model)</li> <li><strong>encoder-decoder</strong> — <a href="/docs/transformers/main/ko/model_doc/encoder-decoder#transformers.EncoderDecoderModel">EncoderDecoderModel</a> (Encoder decoder model)</li> <li><strong>fsmt</strong> — <code>FSMTForConditionalGeneration</code> (FairSeq Machine-Translation model)</li> <li><strong>gptsan-japanese</strong> — <code>GPTSanJapaneseForConditionalGeneration</code> (GPTSAN-japanese model)</li> <li><strong>led</strong> — <code>LEDForConditionalGeneration</code> (LED model)</li> <li><strong>longt5</strong> — <code>LongT5ForConditionalGeneration</code> (LongT5 model)</li> <li><strong>m2m_100</strong> — <code>M2M100ForConditionalGeneration</code> (M2M100 model)</li> <li><strong>marian</strong> — <a href="/docs/transformers/main/ko/model_doc/marian#transformers.MarianMTModel">MarianMTModel</a> (Marian model)</li> <li><strong>mbart</strong> — <code>MBartForConditionalGeneration</code> (mBART model)</li> <li><strong>mt5</strong> — <code>MT5ForConditionalGeneration</code> (MT5 model)</li> <li><strong>mvp</strong> — <code>MvpForConditionalGeneration</code> (MVP model)</li> <li><strong>nllb-moe</strong> — <code>NllbMoeForConditionalGeneration</code> (NLLB-MOE model)</li> <li><strong>pegasus</strong> — <code>PegasusForConditionalGeneration</code> (Pegasus model)</li> <li><strong>pegasus_x</strong> — <code>PegasusXForConditionalGeneration</code> (PEGASUS-X model)</li> <li><strong>plbart</strong> — <code>PLBartForConditionalGeneration</code> (PLBart model)</li> <li><strong>prophetnet</strong> — <code>ProphetNetForConditionalGeneration</code> (ProphetNet model)</li> <li><strong>qwen2_audio</strong> — <code>Qwen2AudioForConditionalGeneration</code> (Qwen2Audio model)</li> <li><strong>seamless_m4t</strong> — <code>SeamlessM4TForTextToText</code> (SeamlessM4T model)</li> <li><strong>seamless_m4t_v2</strong> — <code>SeamlessM4Tv2ForTextToText</code> (SeamlessM4Tv2 model)</li> <li><strong>switch_transformers</strong> — <code>SwitchTransformersForConditionalGeneration</code> (SwitchTransformers model)</li> <li><strong>t5</strong> — <code>T5ForConditionalGeneration</code> (T5 model)</li> <li><strong>umt5</strong> — <code>UMT5ForConditionalGeneration</code> (UMT5 model)</li> <li><strong>xlm-prophetnet</strong> — <code>XLMProphetNetForConditionalGeneration</code> (XLM-ProphetNet model)</li>',vL,Gb,yP=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,CL,li,B2,im,R2,$e,lm,wL,Jb,FP=`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,kL,Wb,vP="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",xL,Qn,dm,$L,Eb,CP="Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration.",LL,Vb,wP=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,AL,di,jL,zo,cm,ZL,Ib,kP="Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model.",BL,Pb,xP=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,RL,Sb,$P='<li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li> <li><strong>blenderbot</strong> — <code>TFBlenderbotForConditionalGeneration</code> (Blenderbot model)</li> <li><strong>blenderbot-small</strong> — <code>TFBlenderbotSmallForConditionalGeneration</code> (BlenderbotSmall model)</li> <li><strong>encoder-decoder</strong> — <a href="/docs/transformers/main/ko/model_doc/encoder-decoder#transformers.TFEncoderDecoderModel">TFEncoderDecoderModel</a> (Encoder decoder model)</li> <li><strong>led</strong> — <code>TFLEDForConditionalGeneration</code> (LED model)</li> <li><strong>marian</strong> — <a href="/docs/transformers/main/ko/model_doc/marian#transformers.TFMarianMTModel">TFMarianMTModel</a> (Marian model)</li> <li><strong>mbart</strong> — <code>TFMBartForConditionalGeneration</code> (mBART model)</li> <li><strong>mt5</strong> — <code>TFMT5ForConditionalGeneration</code> (MT5 model)</li> <li><strong>pegasus</strong> — <code>TFPegasusForConditionalGeneration</code> (Pegasus model)</li> <li><strong>t5</strong> — <code>TFT5ForConditionalGeneration</code> (T5 model)</li>',GL,ci,G2,mm,J2,Le,fm,JL,Ub,LP=`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,WL,Nb,AP="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",EL,Yn,gm,VL,qb,jP="Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration.",IL,Xb,ZP=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,PL,mi,SL,Oo,pm,UL,Db,BP="Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model.",NL,Hb,RP=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,qL,Qb,GP='<li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li> <li><strong>blenderbot</strong> — <code>FlaxBlenderbotForConditionalGeneration</code> (Blenderbot model)</li> <li><strong>blenderbot-small</strong> — <code>FlaxBlenderbotSmallForConditionalGeneration</code> (BlenderbotSmall model)</li> <li><strong>encoder-decoder</strong> — <a href="/docs/transformers/main/ko/model_doc/encoder-decoder#transformers.FlaxEncoderDecoderModel">FlaxEncoderDecoderModel</a> (Encoder decoder model)</li> <li><strong>longt5</strong> — <code>FlaxLongT5ForConditionalGeneration</code> (LongT5 model)</li> <li><strong>marian</strong> — <a href="/docs/transformers/main/ko/model_doc/marian#transformers.FlaxMarianMTModel">FlaxMarianMTModel</a> (Marian model)</li> <li><strong>mbart</strong> — <code>FlaxMBartForConditionalGeneration</code> (mBART model)</li> <li><strong>mt5</strong> — <code>FlaxMT5ForConditionalGeneration</code> (MT5 model)</li> <li><strong>pegasus</strong> — <code>FlaxPegasusForConditionalGeneration</code> (Pegasus model)</li> <li><strong>t5</strong> — <code>FlaxT5ForConditionalGeneration</code> (T5 model)</li>',XL,fi,W2,um,E2,Ae,hm,DL,Yb,JP=`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,HL,zb,WP="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",QL,zn,bm,YL,Ob,EP="Instantiates one of the model classes of the library (with a sequence classification head) from a configuration.",zL,Kb,VP=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,OL,gi,KL,V,_m,eA,e_,IP="Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model.",oA,o_,PP=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,tA,t_,SP='<li><strong>albert</strong> — <code>AlbertForSequenceClassification</code> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartForSequenceClassification">BartForSequenceClassification</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertForSequenceClassification">BertForSequenceClassification</a> (BERT model)</li> <li><strong>big_bird</strong> — <code>BigBirdForSequenceClassification</code> (BigBird model)</li> <li><strong>bigbird_pegasus</strong> — <code>BigBirdPegasusForSequenceClassification</code> (BigBird-Pegasus model)</li> <li><strong>biogpt</strong> — <a href="/docs/transformers/main/ko/model_doc/biogpt#transformers.BioGptForSequenceClassification">BioGptForSequenceClassification</a> (BioGpt model)</li> <li><strong>bloom</strong> — <code>BloomForSequenceClassification</code> (BLOOM model)</li> <li><strong>camembert</strong> — <code>CamembertForSequenceClassification</code> (CamemBERT model)</li> <li><strong>canine</strong> — <code>CanineForSequenceClassification</code> (CANINE model)</li> <li><strong>code_llama</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaForSequenceClassification">LlamaForSequenceClassification</a> (CodeLlama model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertForSequenceClassification">ConvBertForSequenceClassification</a> (ConvBERT model)</li> <li><strong>ctrl</strong> — <code>CTRLForSequenceClassification</code> (CTRL model)</li> <li><strong>data2vec-text</strong> — <code>Data2VecTextForSequenceClassification</code> (Data2VecText model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaForSequenceClassification">DebertaForSequenceClassification</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2ForSequenceClassification">DebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li> <li><strong>diffllama</strong> — <code>DiffLlamaForSequenceClassification</code> (DiffLlama model)</li> <li><strong>distilbert</strong> — <code>DistilBertForSequenceClassification</code> (DistilBERT model)</li> <li><strong>electra</strong> — <code>ElectraForSequenceClassification</code> (ELECTRA model)</li> <li><strong>ernie</strong> — <code>ErnieForSequenceClassification</code> (ERNIE model)</li> <li><strong>ernie_m</strong> — <code>ErnieMForSequenceClassification</code> (ErnieM model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/main/ko/model_doc/esm#transformers.EsmForSequenceClassification">EsmForSequenceClassification</a> (ESM model)</li> <li><strong>falcon</strong> — <code>FalconForSequenceClassification</code> (Falcon model)</li> <li><strong>flaubert</strong> — <code>FlaubertForSequenceClassification</code> (FlauBERT model)</li> <li><strong>fnet</strong> — <code>FNetForSequenceClassification</code> (FNet model)</li> <li><strong>funnel</strong> — <code>FunnelForSequenceClassification</code> (Funnel Transformer model)</li> <li><strong>gemma</strong> — <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaForSequenceClassification">GemmaForSequenceClassification</a> (Gemma model)</li> <li><strong>gemma2</strong> — <a href="/docs/transformers/main/ko/model_doc/gemma2#transformers.Gemma2ForSequenceClassification">Gemma2ForSequenceClassification</a> (Gemma2 model)</li> <li><strong>glm</strong> — <code>GlmForSequenceClassification</code> (GLM model)</li> <li><strong>gpt-sw3</strong> — <code>GPT2ForSequenceClassification</code> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <code>GPT2ForSequenceClassification</code> (OpenAI GPT-2 model)</li> <li><strong>gpt_bigcode</strong> — <code>GPTBigCodeForSequenceClassification</code> (GPTBigCode model)</li> <li><strong>gpt_neo</strong> — <code>GPTNeoForSequenceClassification</code> (GPT Neo model)</li> <li><strong>gpt_neox</strong> — <code>GPTNeoXForSequenceClassification</code> (GPT NeoX model)</li> <li><strong>gptj</strong> — <code>GPTJForSequenceClassification</code> (GPT-J model)</li> <li><strong>helium</strong> — <code>HeliumForSequenceClassification</code> (Helium model)</li> <li><strong>ibert</strong> — <code>IBertForSequenceClassification</code> (I-BERT model)</li> <li><strong>jamba</strong> — <code>JambaForSequenceClassification</code> (Jamba model)</li> <li><strong>jetmoe</strong> — <code>JetMoeForSequenceClassification</code> (JetMoe model)</li> <li><strong>layoutlm</strong> — <code>LayoutLMForSequenceClassification</code> (LayoutLM model)</li> <li><strong>layoutlmv2</strong> — <code>LayoutLMv2ForSequenceClassification</code> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <code>LayoutLMv3ForSequenceClassification</code> (LayoutLMv3 model)</li> <li><strong>led</strong> — <code>LEDForSequenceClassification</code> (LED model)</li> <li><strong>lilt</strong> — <code>LiltForSequenceClassification</code> (LiLT model)</li> <li><strong>llama</strong> — <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaForSequenceClassification">LlamaForSequenceClassification</a> (LLaMA model)</li> <li><strong>longformer</strong> — <code>LongformerForSequenceClassification</code> (Longformer model)</li> <li><strong>luke</strong> — <code>LukeForSequenceClassification</code> (LUKE model)</li> <li><strong>markuplm</strong> — <code>MarkupLMForSequenceClassification</code> (MarkupLM model)</li> <li><strong>mbart</strong> — <code>MBartForSequenceClassification</code> (mBART model)</li> <li><strong>mega</strong> — <code>MegaForSequenceClassification</code> (MEGA model)</li> <li><strong>megatron-bert</strong> — <code>MegatronBertForSequenceClassification</code> (Megatron-BERT model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/main/ko/model_doc/mistral#transformers.MistralForSequenceClassification">MistralForSequenceClassification</a> (Mistral model)</li> <li><strong>mixtral</strong> — <code>MixtralForSequenceClassification</code> (Mixtral model)</li> <li><strong>mobilebert</strong> — <code>MobileBertForSequenceClassification</code> (MobileBERT model)</li> <li><strong>modernbert</strong> — <code>ModernBertForSequenceClassification</code> (ModernBERT model)</li> <li><strong>mpnet</strong> — <code>MPNetForSequenceClassification</code> (MPNet model)</li> <li><strong>mpt</strong> — <code>MptForSequenceClassification</code> (MPT model)</li> <li><strong>mra</strong> — <code>MraForSequenceClassification</code> (MRA model)</li> <li><strong>mt5</strong> — <code>MT5ForSequenceClassification</code> (MT5 model)</li> <li><strong>mvp</strong> — <code>MvpForSequenceClassification</code> (MVP model)</li> <li><strong>nemotron</strong> — <code>NemotronForSequenceClassification</code> (Nemotron model)</li> <li><strong>nezha</strong> — <code>NezhaForSequenceClassification</code> (Nezha model)</li> <li><strong>nystromformer</strong> — <code>NystromformerForSequenceClassification</code> (Nyströmformer model)</li> <li><strong>open-llama</strong> — <code>OpenLlamaForSequenceClassification</code> (OpenLlama model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.OpenAIGPTForSequenceClassification">OpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li> <li><strong>opt</strong> — <code>OPTForSequenceClassification</code> (OPT model)</li> <li><strong>perceiver</strong> — <code>PerceiverForSequenceClassification</code> (Perceiver model)</li> <li><strong>persimmon</strong> — <code>PersimmonForSequenceClassification</code> (Persimmon model)</li> <li><strong>phi</strong> — <code>PhiForSequenceClassification</code> (Phi model)</li> <li><strong>phi3</strong> — <code>Phi3ForSequenceClassification</code> (Phi3 model)</li> <li><strong>phimoe</strong> — <code>PhimoeForSequenceClassification</code> (Phimoe model)</li> <li><strong>plbart</strong> — <code>PLBartForSequenceClassification</code> (PLBart model)</li> <li><strong>qdqbert</strong> — <code>QDQBertForSequenceClassification</code> (QDQBert model)</li> <li><strong>qwen2</strong> — <code>Qwen2ForSequenceClassification</code> (Qwen2 model)</li> <li><strong>qwen2_moe</strong> — <code>Qwen2MoeForSequenceClassification</code> (Qwen2MoE model)</li> <li><strong>reformer</strong> — <code>ReformerForSequenceClassification</code> (Reformer model)</li> <li><strong>rembert</strong> — <code>RemBertForSequenceClassification</code> (RemBERT model)</li> <li><strong>roberta</strong> — <code>RobertaForSequenceClassification</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>RobertaPreLayerNormForSequenceClassification</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <code>RoCBertForSequenceClassification</code> (RoCBert model)</li> <li><strong>roformer</strong> — <code>RoFormerForSequenceClassification</code> (RoFormer model)</li> <li><strong>squeezebert</strong> — <code>SqueezeBertForSequenceClassification</code> (SqueezeBERT model)</li> <li><strong>stablelm</strong> — <code>StableLmForSequenceClassification</code> (StableLm model)</li> <li><strong>starcoder2</strong> — <code>Starcoder2ForSequenceClassification</code> (Starcoder2 model)</li> <li><strong>t5</strong> — <code>T5ForSequenceClassification</code> (T5 model)</li> <li><strong>tapas</strong> — <code>TapasForSequenceClassification</code> (TAPAS model)</li> <li><strong>transfo-xl</strong> — <code>TransfoXLForSequenceClassification</code> (Transformer-XL model)</li> <li><strong>umt5</strong> — <code>UMT5ForSequenceClassification</code> (UMT5 model)</li> <li><strong>xlm</strong> — <code>XLMForSequenceClassification</code> (XLM model)</li> <li><strong>xlm-roberta</strong> — <code>XLMRobertaForSequenceClassification</code> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <code>XLMRobertaXLForSequenceClassification</code> (XLM-RoBERTa-XL model)</li> <li><strong>xlnet</strong> — <code>XLNetForSequenceClassification</code> (XLNet model)</li> <li><strong>xmod</strong> — <code>XmodForSequenceClassification</code> (X-MOD model)</li> <li><strong>yoso</strong> — <code>YosoForSequenceClassification</code> (YOSO model)</li> <li><strong>zamba</strong> — <code>ZambaForSequenceClassification</code> (Zamba model)</li> <li><strong>zamba2</strong> — <code>Zamba2ForSequenceClassification</code> (Zamba2 model)</li>',nA,n_,UP=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,rA,pi,V2,Mm,I2,je,Tm,aA,r_,NP=`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,sA,a_,qP="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",iA,On,ym,lA,s_,XP="Instantiates one of the model classes of the library (with a sequence classification head) from a configuration.",dA,i_,DP=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,cA,ui,mA,Ko,Fm,fA,l_,HP="Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model.",gA,d_,QP=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,pA,c_,YP='<li><strong>albert</strong> — <code>TFAlbertForSequenceClassification</code> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.TFBartForSequenceClassification">TFBartForSequenceClassification</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.TFBertForSequenceClassification">TFBertForSequenceClassification</a> (BERT model)</li> <li><strong>camembert</strong> — <code>TFCamembertForSequenceClassification</code> (CamemBERT model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.TFConvBertForSequenceClassification">TFConvBertForSequenceClassification</a> (ConvBERT model)</li> <li><strong>ctrl</strong> — <code>TFCTRLForSequenceClassification</code> (CTRL model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.TFDebertaForSequenceClassification">TFDebertaForSequenceClassification</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.TFDebertaV2ForSequenceClassification">TFDebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <code>TFDistilBertForSequenceClassification</code> (DistilBERT model)</li> <li><strong>electra</strong> — <code>TFElectraForSequenceClassification</code> (ELECTRA model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/main/ko/model_doc/esm#transformers.TFEsmForSequenceClassification">TFEsmForSequenceClassification</a> (ESM model)</li> <li><strong>flaubert</strong> — <code>TFFlaubertForSequenceClassification</code> (FlauBERT model)</li> <li><strong>funnel</strong> — <code>TFFunnelForSequenceClassification</code> (Funnel Transformer model)</li> <li><strong>gpt-sw3</strong> — <code>TFGPT2ForSequenceClassification</code> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <code>TFGPT2ForSequenceClassification</code> (OpenAI GPT-2 model)</li> <li><strong>gptj</strong> — <code>TFGPTJForSequenceClassification</code> (GPT-J model)</li> <li><strong>layoutlm</strong> — <code>TFLayoutLMForSequenceClassification</code> (LayoutLM model)</li> <li><strong>layoutlmv3</strong> — <code>TFLayoutLMv3ForSequenceClassification</code> (LayoutLMv3 model)</li> <li><strong>longformer</strong> — <code>TFLongformerForSequenceClassification</code> (Longformer model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/main/ko/model_doc/mistral#transformers.TFMistralForSequenceClassification">TFMistralForSequenceClassification</a> (Mistral model)</li> <li><strong>mobilebert</strong> — <code>TFMobileBertForSequenceClassification</code> (MobileBERT model)</li> <li><strong>mpnet</strong> — <code>TFMPNetForSequenceClassification</code> (MPNet model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.TFOpenAIGPTForSequenceClassification">TFOpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li> <li><strong>rembert</strong> — <code>TFRemBertForSequenceClassification</code> (RemBERT model)</li> <li><strong>roberta</strong> — <code>TFRobertaForSequenceClassification</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>TFRobertaPreLayerNormForSequenceClassification</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <code>TFRoFormerForSequenceClassification</code> (RoFormer model)</li> <li><strong>tapas</strong> — <code>TFTapasForSequenceClassification</code> (TAPAS model)</li> <li><strong>transfo-xl</strong> — <code>TFTransfoXLForSequenceClassification</code> (Transformer-XL model)</li> <li><strong>xlm</strong> — <code>TFXLMForSequenceClassification</code> (XLM model)</li> <li><strong>xlm-roberta</strong> — <code>TFXLMRobertaForSequenceClassification</code> (XLM-RoBERTa model)</li> <li><strong>xlnet</strong> — <code>TFXLNetForSequenceClassification</code> (XLNet model)</li>',uA,hi,P2,vm,S2,Ze,Cm,hA,m_,zP=`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,bA,f_,OP="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",_A,Kn,wm,MA,g_,KP="Instantiates one of the model classes of the library (with a sequence classification head) from a configuration.",TA,p_,eS=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,yA,bi,FA,et,km,vA,u_,oS="Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model.",CA,h_,tS=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,wA,b_,nS='<li><strong>albert</strong> — <code>FlaxAlbertForSequenceClassification</code> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.FlaxBartForSequenceClassification">FlaxBartForSequenceClassification</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.FlaxBertForSequenceClassification">FlaxBertForSequenceClassification</a> (BERT model)</li> <li><strong>big_bird</strong> — <code>FlaxBigBirdForSequenceClassification</code> (BigBird model)</li> <li><strong>distilbert</strong> — <code>FlaxDistilBertForSequenceClassification</code> (DistilBERT model)</li> <li><strong>electra</strong> — <code>FlaxElectraForSequenceClassification</code> (ELECTRA model)</li> <li><strong>mbart</strong> — <code>FlaxMBartForSequenceClassification</code> (mBART model)</li> <li><strong>roberta</strong> — <code>FlaxRobertaForSequenceClassification</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>FlaxRobertaPreLayerNormForSequenceClassification</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <code>FlaxRoFormerForSequenceClassification</code> (RoFormer model)</li> <li><strong>xlm-roberta</strong> — <code>FlaxXLMRobertaForSequenceClassification</code> (XLM-RoBERTa model)</li>',kA,_i,U2,xm,N2,Be,$m,xA,__,rS=`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,$A,M_,aS="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",LA,er,Lm,AA,T_,sS="Instantiates one of the model classes of the library (with a multiple choice head) from a configuration.",jA,y_,iS=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,ZA,Mi,BA,I,Am,RA,F_,lS="Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model.",GA,v_,dS=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,JA,C_,cS='<li><strong>albert</strong> — <code>AlbertForMultipleChoice</code> (ALBERT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertForMultipleChoice">BertForMultipleChoice</a> (BERT model)</li> <li><strong>big_bird</strong> — <code>BigBirdForMultipleChoice</code> (BigBird model)</li> <li><strong>camembert</strong> — <code>CamembertForMultipleChoice</code> (CamemBERT model)</li> <li><strong>canine</strong> — <code>CanineForMultipleChoice</code> (CANINE model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertForMultipleChoice">ConvBertForMultipleChoice</a> (ConvBERT model)</li> <li><strong>data2vec-text</strong> — <code>Data2VecTextForMultipleChoice</code> (Data2VecText model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2ForMultipleChoice">DebertaV2ForMultipleChoice</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <code>DistilBertForMultipleChoice</code> (DistilBERT model)</li> <li><strong>electra</strong> — <code>ElectraForMultipleChoice</code> (ELECTRA model)</li> <li><strong>ernie</strong> — <code>ErnieForMultipleChoice</code> (ERNIE model)</li> <li><strong>ernie_m</strong> — <code>ErnieMForMultipleChoice</code> (ErnieM model)</li> <li><strong>flaubert</strong> — <code>FlaubertForMultipleChoice</code> (FlauBERT model)</li> <li><strong>fnet</strong> — <code>FNetForMultipleChoice</code> (FNet model)</li> <li><strong>funnel</strong> — <code>FunnelForMultipleChoice</code> (Funnel Transformer model)</li> <li><strong>ibert</strong> — <code>IBertForMultipleChoice</code> (I-BERT model)</li> <li><strong>longformer</strong> — <code>LongformerForMultipleChoice</code> (Longformer model)</li> <li><strong>luke</strong> — <code>LukeForMultipleChoice</code> (LUKE model)</li> <li><strong>mega</strong> — <code>MegaForMultipleChoice</code> (MEGA model)</li> <li><strong>megatron-bert</strong> — <code>MegatronBertForMultipleChoice</code> (Megatron-BERT model)</li> <li><strong>mobilebert</strong> — <code>MobileBertForMultipleChoice</code> (MobileBERT model)</li> <li><strong>mpnet</strong> — <code>MPNetForMultipleChoice</code> (MPNet model)</li> <li><strong>mra</strong> — <code>MraForMultipleChoice</code> (MRA model)</li> <li><strong>nezha</strong> — <code>NezhaForMultipleChoice</code> (Nezha model)</li> <li><strong>nystromformer</strong> — <code>NystromformerForMultipleChoice</code> (Nyströmformer model)</li> <li><strong>qdqbert</strong> — <code>QDQBertForMultipleChoice</code> (QDQBert model)</li> <li><strong>rembert</strong> — <code>RemBertForMultipleChoice</code> (RemBERT model)</li> <li><strong>roberta</strong> — <code>RobertaForMultipleChoice</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>RobertaPreLayerNormForMultipleChoice</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <code>RoCBertForMultipleChoice</code> (RoCBert model)</li> <li><strong>roformer</strong> — <code>RoFormerForMultipleChoice</code> (RoFormer model)</li> <li><strong>squeezebert</strong> — <code>SqueezeBertForMultipleChoice</code> (SqueezeBERT model)</li> <li><strong>xlm</strong> — <code>XLMForMultipleChoice</code> (XLM model)</li> <li><strong>xlm-roberta</strong> — <code>XLMRobertaForMultipleChoice</code> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <code>XLMRobertaXLForMultipleChoice</code> (XLM-RoBERTa-XL model)</li> <li><strong>xlnet</strong> — <code>XLNetForMultipleChoice</code> (XLNet model)</li> <li><strong>xmod</strong> — <code>XmodForMultipleChoice</code> (X-MOD model)</li> <li><strong>yoso</strong> — <code>YosoForMultipleChoice</code> (YOSO model)</li>',WA,w_,mS=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,EA,Ti,q2,jm,X2,Re,Zm,VA,k_,fS=`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,IA,x_,gS="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",PA,or,Bm,SA,$_,pS="Instantiates one of the model classes of the library (with a multiple choice head) from a configuration.",UA,L_,uS=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,NA,yi,qA,ot,Rm,XA,A_,hS="Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model.",DA,j_,bS=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,HA,Z_,_S='<li><strong>albert</strong> — <code>TFAlbertForMultipleChoice</code> (ALBERT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.TFBertForMultipleChoice">TFBertForMultipleChoice</a> (BERT model)</li> <li><strong>camembert</strong> — <code>TFCamembertForMultipleChoice</code> (CamemBERT model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.TFConvBertForMultipleChoice">TFConvBertForMultipleChoice</a> (ConvBERT model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.TFDebertaV2ForMultipleChoice">TFDebertaV2ForMultipleChoice</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <code>TFDistilBertForMultipleChoice</code> (DistilBERT model)</li> <li><strong>electra</strong> — <code>TFElectraForMultipleChoice</code> (ELECTRA model)</li> <li><strong>flaubert</strong> — <code>TFFlaubertForMultipleChoice</code> (FlauBERT model)</li> <li><strong>funnel</strong> — <code>TFFunnelForMultipleChoice</code> (Funnel Transformer model)</li> <li><strong>longformer</strong> — <code>TFLongformerForMultipleChoice</code> (Longformer model)</li> <li><strong>mobilebert</strong> — <code>TFMobileBertForMultipleChoice</code> (MobileBERT model)</li> <li><strong>mpnet</strong> — <code>TFMPNetForMultipleChoice</code> (MPNet model)</li> <li><strong>rembert</strong> — <code>TFRemBertForMultipleChoice</code> (RemBERT model)</li> <li><strong>roberta</strong> — <code>TFRobertaForMultipleChoice</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>TFRobertaPreLayerNormForMultipleChoice</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <code>TFRoFormerForMultipleChoice</code> (RoFormer model)</li> <li><strong>xlm</strong> — <code>TFXLMForMultipleChoice</code> (XLM model)</li> <li><strong>xlm-roberta</strong> — <code>TFXLMRobertaForMultipleChoice</code> (XLM-RoBERTa model)</li> <li><strong>xlnet</strong> — <code>TFXLNetForMultipleChoice</code> (XLNet model)</li>',QA,Fi,D2,Gm,H2,Ge,Jm,YA,B_,MS=`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,zA,R_,TS="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",OA,tr,Wm,KA,G_,yS="Instantiates one of the model classes of the library (with a multiple choice head) from a configuration.",ej,J_,FS=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,oj,vi,tj,tt,Em,nj,W_,vS="Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model.",rj,E_,CS=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,aj,V_,wS='<li><strong>albert</strong> — <code>FlaxAlbertForMultipleChoice</code> (ALBERT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.FlaxBertForMultipleChoice">FlaxBertForMultipleChoice</a> (BERT model)</li> <li><strong>big_bird</strong> — <code>FlaxBigBirdForMultipleChoice</code> (BigBird model)</li> <li><strong>distilbert</strong> — <code>FlaxDistilBertForMultipleChoice</code> (DistilBERT model)</li> <li><strong>electra</strong> — <code>FlaxElectraForMultipleChoice</code> (ELECTRA model)</li> <li><strong>roberta</strong> — <code>FlaxRobertaForMultipleChoice</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>FlaxRobertaPreLayerNormForMultipleChoice</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <code>FlaxRoFormerForMultipleChoice</code> (RoFormer model)</li> <li><strong>xlm-roberta</strong> — <code>FlaxXLMRobertaForMultipleChoice</code> (XLM-RoBERTa model)</li>',sj,Ci,Q2,Vm,Y2,Je,Im,ij,I_,kS=`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,lj,P_,xS="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",dj,nr,Pm,cj,S_,$S="Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration.",mj,U_,LS=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,fj,wi,gj,P,Sm,pj,N_,AS="Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model.",uj,q_,jS=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,hj,X_,ZS='<li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertForNextSentencePrediction">BertForNextSentencePrediction</a> (BERT model)</li> <li><strong>ernie</strong> — <code>ErnieForNextSentencePrediction</code> (ERNIE model)</li> <li><strong>fnet</strong> — <code>FNetForNextSentencePrediction</code> (FNet model)</li> <li><strong>megatron-bert</strong> — <code>MegatronBertForNextSentencePrediction</code> (Megatron-BERT model)</li> <li><strong>mobilebert</strong> — <code>MobileBertForNextSentencePrediction</code> (MobileBERT model)</li> <li><strong>nezha</strong> — <code>NezhaForNextSentencePrediction</code> (Nezha model)</li> <li><strong>qdqbert</strong> — <code>QDQBertForNextSentencePrediction</code> (QDQBert model)</li>',bj,D_,BS=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,_j,ki,z2,Um,O2,We,Nm,Mj,H_,RS=`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,Tj,Q_,GS="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",yj,rr,qm,Fj,Y_,JS="Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration.",vj,z_,WS=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,Cj,xi,wj,nt,Xm,kj,O_,ES="Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model.",xj,K_,VS=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,$j,eM,IS='<li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.TFBertForNextSentencePrediction">TFBertForNextSentencePrediction</a> (BERT model)</li> <li><strong>mobilebert</strong> — <code>TFMobileBertForNextSentencePrediction</code> (MobileBERT model)</li>',Lj,$i,K2,Dm,ew,Ee,Hm,Aj,oM,PS=`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,jj,tM,SS="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Zj,ar,Qm,Bj,nM,US="Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration.",Rj,rM,NS=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,Gj,Li,Jj,rt,Ym,Wj,aM,qS="Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model.",Ej,sM,XS=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,Vj,iM,DS='<li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.FlaxBertForNextSentencePrediction">FlaxBertForNextSentencePrediction</a> (BERT model)</li>',Ij,Ai,ow,zm,tw,Ve,Om,Pj,lM,HS=`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,Sj,dM,QS="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Uj,sr,Km,Nj,cM,YS="Instantiates one of the model classes of the library (with a token classification head) from a configuration.",qj,mM,zS=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,Xj,ji,Dj,S,ef,Hj,fM,OS="Instantiate one of the model classes of the library (with a token classification head) from a pretrained model.",Qj,gM,KS=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,Yj,pM,e9='<li><strong>albert</strong> — <code>AlbertForTokenClassification</code> (ALBERT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertForTokenClassification">BertForTokenClassification</a> (BERT model)</li> <li><strong>big_bird</strong> — <code>BigBirdForTokenClassification</code> (BigBird model)</li> <li><strong>biogpt</strong> — <a href="/docs/transformers/main/ko/model_doc/biogpt#transformers.BioGptForTokenClassification">BioGptForTokenClassification</a> (BioGpt model)</li> <li><strong>bloom</strong> — <code>BloomForTokenClassification</code> (BLOOM model)</li> <li><strong>bros</strong> — <code>BrosForTokenClassification</code> (BROS model)</li> <li><strong>camembert</strong> — <code>CamembertForTokenClassification</code> (CamemBERT model)</li> <li><strong>canine</strong> — <code>CanineForTokenClassification</code> (CANINE model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertForTokenClassification">ConvBertForTokenClassification</a> (ConvBERT model)</li> <li><strong>data2vec-text</strong> — <code>Data2VecTextForTokenClassification</code> (Data2VecText model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaForTokenClassification">DebertaForTokenClassification</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2ForTokenClassification">DebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li> <li><strong>diffllama</strong> — <code>DiffLlamaForTokenClassification</code> (DiffLlama model)</li> <li><strong>distilbert</strong> — <code>DistilBertForTokenClassification</code> (DistilBERT model)</li> <li><strong>electra</strong> — <code>ElectraForTokenClassification</code> (ELECTRA model)</li> <li><strong>ernie</strong> — <code>ErnieForTokenClassification</code> (ERNIE model)</li> <li><strong>ernie_m</strong> — <code>ErnieMForTokenClassification</code> (ErnieM model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/main/ko/model_doc/esm#transformers.EsmForTokenClassification">EsmForTokenClassification</a> (ESM model)</li> <li><strong>falcon</strong> — <code>FalconForTokenClassification</code> (Falcon model)</li> <li><strong>flaubert</strong> — <code>FlaubertForTokenClassification</code> (FlauBERT model)</li> <li><strong>fnet</strong> — <code>FNetForTokenClassification</code> (FNet model)</li> <li><strong>funnel</strong> — <code>FunnelForTokenClassification</code> (Funnel Transformer model)</li> <li><strong>gemma</strong> — <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaForTokenClassification">GemmaForTokenClassification</a> (Gemma model)</li> <li><strong>gemma2</strong> — <a href="/docs/transformers/main/ko/model_doc/gemma2#transformers.Gemma2ForTokenClassification">Gemma2ForTokenClassification</a> (Gemma2 model)</li> <li><strong>glm</strong> — <code>GlmForTokenClassification</code> (GLM model)</li> <li><strong>gpt-sw3</strong> — <code>GPT2ForTokenClassification</code> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <code>GPT2ForTokenClassification</code> (OpenAI GPT-2 model)</li> <li><strong>gpt_bigcode</strong> — <code>GPTBigCodeForTokenClassification</code> (GPTBigCode model)</li> <li><strong>gpt_neo</strong> — <code>GPTNeoForTokenClassification</code> (GPT Neo model)</li> <li><strong>gpt_neox</strong> — <code>GPTNeoXForTokenClassification</code> (GPT NeoX model)</li> <li><strong>helium</strong> — <code>HeliumForTokenClassification</code> (Helium model)</li> <li><strong>ibert</strong> — <code>IBertForTokenClassification</code> (I-BERT model)</li> <li><strong>layoutlm</strong> — <code>LayoutLMForTokenClassification</code> (LayoutLM model)</li> <li><strong>layoutlmv2</strong> — <code>LayoutLMv2ForTokenClassification</code> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <code>LayoutLMv3ForTokenClassification</code> (LayoutLMv3 model)</li> <li><strong>lilt</strong> — <code>LiltForTokenClassification</code> (LiLT model)</li> <li><strong>llama</strong> — <code>LlamaForTokenClassification</code> (LLaMA model)</li> <li><strong>longformer</strong> — <code>LongformerForTokenClassification</code> (Longformer model)</li> <li><strong>luke</strong> — <code>LukeForTokenClassification</code> (LUKE model)</li> <li><strong>markuplm</strong> — <code>MarkupLMForTokenClassification</code> (MarkupLM model)</li> <li><strong>mega</strong> — <code>MegaForTokenClassification</code> (MEGA model)</li> <li><strong>megatron-bert</strong> — <code>MegatronBertForTokenClassification</code> (Megatron-BERT model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/main/ko/model_doc/mistral#transformers.MistralForTokenClassification">MistralForTokenClassification</a> (Mistral model)</li> <li><strong>mixtral</strong> — <code>MixtralForTokenClassification</code> (Mixtral model)</li> <li><strong>mobilebert</strong> — <code>MobileBertForTokenClassification</code> (MobileBERT model)</li> <li><strong>modernbert</strong> — <code>ModernBertForTokenClassification</code> (ModernBERT model)</li> <li><strong>mpnet</strong> — <code>MPNetForTokenClassification</code> (MPNet model)</li> <li><strong>mpt</strong> — <code>MptForTokenClassification</code> (MPT model)</li> <li><strong>mra</strong> — <code>MraForTokenClassification</code> (MRA model)</li> <li><strong>mt5</strong> — <code>MT5ForTokenClassification</code> (MT5 model)</li> <li><strong>nemotron</strong> — <code>NemotronForTokenClassification</code> (Nemotron model)</li> <li><strong>nezha</strong> — <code>NezhaForTokenClassification</code> (Nezha model)</li> <li><strong>nystromformer</strong> — <code>NystromformerForTokenClassification</code> (Nyströmformer model)</li> <li><strong>persimmon</strong> — <code>PersimmonForTokenClassification</code> (Persimmon model)</li> <li><strong>phi</strong> — <code>PhiForTokenClassification</code> (Phi model)</li> <li><strong>phi3</strong> — <code>Phi3ForTokenClassification</code> (Phi3 model)</li> <li><strong>qdqbert</strong> — <code>QDQBertForTokenClassification</code> (QDQBert model)</li> <li><strong>qwen2</strong> — <code>Qwen2ForTokenClassification</code> (Qwen2 model)</li> <li><strong>qwen2_moe</strong> — <code>Qwen2MoeForTokenClassification</code> (Qwen2MoE model)</li> <li><strong>rembert</strong> — <code>RemBertForTokenClassification</code> (RemBERT model)</li> <li><strong>roberta</strong> — <code>RobertaForTokenClassification</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>RobertaPreLayerNormForTokenClassification</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <code>RoCBertForTokenClassification</code> (RoCBert model)</li> <li><strong>roformer</strong> — <code>RoFormerForTokenClassification</code> (RoFormer model)</li> <li><strong>squeezebert</strong> — <code>SqueezeBertForTokenClassification</code> (SqueezeBERT model)</li> <li><strong>stablelm</strong> — <code>StableLmForTokenClassification</code> (StableLm model)</li> <li><strong>starcoder2</strong> — <code>Starcoder2ForTokenClassification</code> (Starcoder2 model)</li> <li><strong>t5</strong> — <code>T5ForTokenClassification</code> (T5 model)</li> <li><strong>umt5</strong> — <code>UMT5ForTokenClassification</code> (UMT5 model)</li> <li><strong>xlm</strong> — <code>XLMForTokenClassification</code> (XLM model)</li> <li><strong>xlm-roberta</strong> — <code>XLMRobertaForTokenClassification</code> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <code>XLMRobertaXLForTokenClassification</code> (XLM-RoBERTa-XL model)</li> <li><strong>xlnet</strong> — <code>XLNetForTokenClassification</code> (XLNet model)</li> <li><strong>xmod</strong> — <code>XmodForTokenClassification</code> (X-MOD model)</li> <li><strong>yoso</strong> — <code>YosoForTokenClassification</code> (YOSO model)</li>',zj,uM,o9=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,Oj,Zi,nw,of,rw,Ie,tf,Kj,hM,t9=`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,eZ,bM,n9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",oZ,ir,nf,tZ,_M,r9="Instantiates one of the model classes of the library (with a token classification head) from a configuration.",nZ,MM,a9=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,rZ,Bi,aZ,at,rf,sZ,TM,s9="Instantiate one of the model classes of the library (with a token classification head) from a pretrained model.",iZ,yM,i9=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,lZ,FM,l9='<li><strong>albert</strong> — <code>TFAlbertForTokenClassification</code> (ALBERT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.TFBertForTokenClassification">TFBertForTokenClassification</a> (BERT model)</li> <li><strong>camembert</strong> — <code>TFCamembertForTokenClassification</code> (CamemBERT model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.TFConvBertForTokenClassification">TFConvBertForTokenClassification</a> (ConvBERT model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.TFDebertaForTokenClassification">TFDebertaForTokenClassification</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.TFDebertaV2ForTokenClassification">TFDebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <code>TFDistilBertForTokenClassification</code> (DistilBERT model)</li> <li><strong>electra</strong> — <code>TFElectraForTokenClassification</code> (ELECTRA model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/main/ko/model_doc/esm#transformers.TFEsmForTokenClassification">TFEsmForTokenClassification</a> (ESM model)</li> <li><strong>flaubert</strong> — <code>TFFlaubertForTokenClassification</code> (FlauBERT model)</li> <li><strong>funnel</strong> — <code>TFFunnelForTokenClassification</code> (Funnel Transformer model)</li> <li><strong>layoutlm</strong> — <code>TFLayoutLMForTokenClassification</code> (LayoutLM model)</li> <li><strong>layoutlmv3</strong> — <code>TFLayoutLMv3ForTokenClassification</code> (LayoutLMv3 model)</li> <li><strong>longformer</strong> — <code>TFLongformerForTokenClassification</code> (Longformer model)</li> <li><strong>mobilebert</strong> — <code>TFMobileBertForTokenClassification</code> (MobileBERT model)</li> <li><strong>mpnet</strong> — <code>TFMPNetForTokenClassification</code> (MPNet model)</li> <li><strong>rembert</strong> — <code>TFRemBertForTokenClassification</code> (RemBERT model)</li> <li><strong>roberta</strong> — <code>TFRobertaForTokenClassification</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>TFRobertaPreLayerNormForTokenClassification</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <code>TFRoFormerForTokenClassification</code> (RoFormer model)</li> <li><strong>xlm</strong> — <code>TFXLMForTokenClassification</code> (XLM model)</li> <li><strong>xlm-roberta</strong> — <code>TFXLMRobertaForTokenClassification</code> (XLM-RoBERTa model)</li> <li><strong>xlnet</strong> — <code>TFXLNetForTokenClassification</code> (XLNet model)</li>',dZ,Ri,aw,af,sw,Pe,sf,cZ,vM,d9=`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,mZ,CM,c9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",fZ,lr,lf,gZ,wM,m9="Instantiates one of the model classes of the library (with a token classification head) from a configuration.",pZ,kM,f9=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,uZ,Gi,hZ,st,df,bZ,xM,g9="Instantiate one of the model classes of the library (with a token classification head) from a pretrained model.",_Z,$M,p9=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,MZ,LM,u9='<li><strong>albert</strong> — <code>FlaxAlbertForTokenClassification</code> (ALBERT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.FlaxBertForTokenClassification">FlaxBertForTokenClassification</a> (BERT model)</li> <li><strong>big_bird</strong> — <code>FlaxBigBirdForTokenClassification</code> (BigBird model)</li> <li><strong>distilbert</strong> — <code>FlaxDistilBertForTokenClassification</code> (DistilBERT model)</li> <li><strong>electra</strong> — <code>FlaxElectraForTokenClassification</code> (ELECTRA model)</li> <li><strong>roberta</strong> — <code>FlaxRobertaForTokenClassification</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>FlaxRobertaPreLayerNormForTokenClassification</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <code>FlaxRoFormerForTokenClassification</code> (RoFormer model)</li> <li><strong>xlm-roberta</strong> — <code>FlaxXLMRobertaForTokenClassification</code> (XLM-RoBERTa model)</li>',TZ,Ji,iw,cf,lw,Se,mf,yZ,AM,h9=`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,FZ,jM,b9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",vZ,dr,ff,CZ,ZM,_9="Instantiates one of the model classes of the library (with a question answering head) from a configuration.",wZ,BM,M9=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,kZ,Wi,xZ,U,gf,$Z,RM,T9="Instantiate one of the model classes of the library (with a question answering head) from a pretrained model.",LZ,GM,y9=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,AZ,JM,F9='<li><strong>albert</strong> — <code>AlbertForQuestionAnswering</code> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartForQuestionAnswering">BartForQuestionAnswering</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertForQuestionAnswering">BertForQuestionAnswering</a> (BERT model)</li> <li><strong>big_bird</strong> — <code>BigBirdForQuestionAnswering</code> (BigBird model)</li> <li><strong>bigbird_pegasus</strong> — <code>BigBirdPegasusForQuestionAnswering</code> (BigBird-Pegasus model)</li> <li><strong>bloom</strong> — <code>BloomForQuestionAnswering</code> (BLOOM model)</li> <li><strong>camembert</strong> — <code>CamembertForQuestionAnswering</code> (CamemBERT model)</li> <li><strong>canine</strong> — <code>CanineForQuestionAnswering</code> (CANINE model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertForQuestionAnswering">ConvBertForQuestionAnswering</a> (ConvBERT model)</li> <li><strong>data2vec-text</strong> — <code>Data2VecTextForQuestionAnswering</code> (Data2VecText model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaForQuestionAnswering">DebertaForQuestionAnswering</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2ForQuestionAnswering">DebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li> <li><strong>diffllama</strong> — <code>DiffLlamaForQuestionAnswering</code> (DiffLlama model)</li> <li><strong>distilbert</strong> — <code>DistilBertForQuestionAnswering</code> (DistilBERT model)</li> <li><strong>electra</strong> — <code>ElectraForQuestionAnswering</code> (ELECTRA model)</li> <li><strong>ernie</strong> — <code>ErnieForQuestionAnswering</code> (ERNIE model)</li> <li><strong>ernie_m</strong> — <code>ErnieMForQuestionAnswering</code> (ErnieM model)</li> <li><strong>falcon</strong> — <code>FalconForQuestionAnswering</code> (Falcon model)</li> <li><strong>flaubert</strong> — <code>FlaubertForQuestionAnsweringSimple</code> (FlauBERT model)</li> <li><strong>fnet</strong> — <code>FNetForQuestionAnswering</code> (FNet model)</li> <li><strong>funnel</strong> — <code>FunnelForQuestionAnswering</code> (Funnel Transformer model)</li> <li><strong>gpt2</strong> — <code>GPT2ForQuestionAnswering</code> (OpenAI GPT-2 model)</li> <li><strong>gpt_neo</strong> — <code>GPTNeoForQuestionAnswering</code> (GPT Neo model)</li> <li><strong>gpt_neox</strong> — <code>GPTNeoXForQuestionAnswering</code> (GPT NeoX model)</li> <li><strong>gptj</strong> — <code>GPTJForQuestionAnswering</code> (GPT-J model)</li> <li><strong>ibert</strong> — <code>IBertForQuestionAnswering</code> (I-BERT model)</li> <li><strong>layoutlmv2</strong> — <code>LayoutLMv2ForQuestionAnswering</code> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <code>LayoutLMv3ForQuestionAnswering</code> (LayoutLMv3 model)</li> <li><strong>led</strong> — <code>LEDForQuestionAnswering</code> (LED model)</li> <li><strong>lilt</strong> — <code>LiltForQuestionAnswering</code> (LiLT model)</li> <li><strong>llama</strong> — <code>LlamaForQuestionAnswering</code> (LLaMA model)</li> <li><strong>longformer</strong> — <code>LongformerForQuestionAnswering</code> (Longformer model)</li> <li><strong>luke</strong> — <code>LukeForQuestionAnswering</code> (LUKE model)</li> <li><strong>lxmert</strong> — <code>LxmertForQuestionAnswering</code> (LXMERT model)</li> <li><strong>markuplm</strong> — <code>MarkupLMForQuestionAnswering</code> (MarkupLM model)</li> <li><strong>mbart</strong> — <code>MBartForQuestionAnswering</code> (mBART model)</li> <li><strong>mega</strong> — <code>MegaForQuestionAnswering</code> (MEGA model)</li> <li><strong>megatron-bert</strong> — <code>MegatronBertForQuestionAnswering</code> (Megatron-BERT model)</li> <li><strong>mistral</strong> — <code>MistralForQuestionAnswering</code> (Mistral model)</li> <li><strong>mixtral</strong> — <code>MixtralForQuestionAnswering</code> (Mixtral model)</li> <li><strong>mobilebert</strong> — <code>MobileBertForQuestionAnswering</code> (MobileBERT model)</li> <li><strong>mpnet</strong> — <code>MPNetForQuestionAnswering</code> (MPNet model)</li> <li><strong>mpt</strong> — <code>MptForQuestionAnswering</code> (MPT model)</li> <li><strong>mra</strong> — <code>MraForQuestionAnswering</code> (MRA model)</li> <li><strong>mt5</strong> — <code>MT5ForQuestionAnswering</code> (MT5 model)</li> <li><strong>mvp</strong> — <code>MvpForQuestionAnswering</code> (MVP model)</li> <li><strong>nemotron</strong> — <code>NemotronForQuestionAnswering</code> (Nemotron model)</li> <li><strong>nezha</strong> — <code>NezhaForQuestionAnswering</code> (Nezha model)</li> <li><strong>nystromformer</strong> — <code>NystromformerForQuestionAnswering</code> (Nyströmformer model)</li> <li><strong>opt</strong> — <code>OPTForQuestionAnswering</code> (OPT model)</li> <li><strong>qdqbert</strong> — <code>QDQBertForQuestionAnswering</code> (QDQBert model)</li> <li><strong>qwen2</strong> — <code>Qwen2ForQuestionAnswering</code> (Qwen2 model)</li> <li><strong>qwen2_moe</strong> — <code>Qwen2MoeForQuestionAnswering</code> (Qwen2MoE model)</li> <li><strong>reformer</strong> — <code>ReformerForQuestionAnswering</code> (Reformer model)</li> <li><strong>rembert</strong> — <code>RemBertForQuestionAnswering</code> (RemBERT model)</li> <li><strong>roberta</strong> — <code>RobertaForQuestionAnswering</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>RobertaPreLayerNormForQuestionAnswering</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <code>RoCBertForQuestionAnswering</code> (RoCBert model)</li> <li><strong>roformer</strong> — <code>RoFormerForQuestionAnswering</code> (RoFormer model)</li> <li><strong>splinter</strong> — <code>SplinterForQuestionAnswering</code> (Splinter model)</li> <li><strong>squeezebert</strong> — <code>SqueezeBertForQuestionAnswering</code> (SqueezeBERT model)</li> <li><strong>t5</strong> — <code>T5ForQuestionAnswering</code> (T5 model)</li> <li><strong>umt5</strong> — <code>UMT5ForQuestionAnswering</code> (UMT5 model)</li> <li><strong>xlm</strong> — <code>XLMForQuestionAnsweringSimple</code> (XLM model)</li> <li><strong>xlm-roberta</strong> — <code>XLMRobertaForQuestionAnswering</code> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <code>XLMRobertaXLForQuestionAnswering</code> (XLM-RoBERTa-XL model)</li> <li><strong>xlnet</strong> — <code>XLNetForQuestionAnsweringSimple</code> (XLNet model)</li> <li><strong>xmod</strong> — <code>XmodForQuestionAnswering</code> (X-MOD model)</li> <li><strong>yoso</strong> — <code>YosoForQuestionAnswering</code> (YOSO model)</li>',jZ,WM,v9=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,ZZ,Ei,dw,pf,cw,Ue,uf,BZ,EM,C9=`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,RZ,VM,w9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",GZ,cr,hf,JZ,IM,k9="Instantiates one of the model classes of the library (with a question answering head) from a configuration.",WZ,PM,x9=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,EZ,Vi,VZ,it,bf,IZ,SM,$9="Instantiate one of the model classes of the library (with a question answering head) from a pretrained model.",PZ,UM,L9=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,SZ,NM,A9='<li><strong>albert</strong> — <code>TFAlbertForQuestionAnswering</code> (ALBERT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.TFBertForQuestionAnswering">TFBertForQuestionAnswering</a> (BERT model)</li> <li><strong>camembert</strong> — <code>TFCamembertForQuestionAnswering</code> (CamemBERT model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.TFConvBertForQuestionAnswering">TFConvBertForQuestionAnswering</a> (ConvBERT model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.TFDebertaForQuestionAnswering">TFDebertaForQuestionAnswering</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.TFDebertaV2ForQuestionAnswering">TFDebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <code>TFDistilBertForQuestionAnswering</code> (DistilBERT model)</li> <li><strong>electra</strong> — <code>TFElectraForQuestionAnswering</code> (ELECTRA model)</li> <li><strong>flaubert</strong> — <code>TFFlaubertForQuestionAnsweringSimple</code> (FlauBERT model)</li> <li><strong>funnel</strong> — <code>TFFunnelForQuestionAnswering</code> (Funnel Transformer model)</li> <li><strong>gptj</strong> — <code>TFGPTJForQuestionAnswering</code> (GPT-J model)</li> <li><strong>layoutlmv3</strong> — <code>TFLayoutLMv3ForQuestionAnswering</code> (LayoutLMv3 model)</li> <li><strong>longformer</strong> — <code>TFLongformerForQuestionAnswering</code> (Longformer model)</li> <li><strong>mobilebert</strong> — <code>TFMobileBertForQuestionAnswering</code> (MobileBERT model)</li> <li><strong>mpnet</strong> — <code>TFMPNetForQuestionAnswering</code> (MPNet model)</li> <li><strong>rembert</strong> — <code>TFRemBertForQuestionAnswering</code> (RemBERT model)</li> <li><strong>roberta</strong> — <code>TFRobertaForQuestionAnswering</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>TFRobertaPreLayerNormForQuestionAnswering</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <code>TFRoFormerForQuestionAnswering</code> (RoFormer model)</li> <li><strong>xlm</strong> — <code>TFXLMForQuestionAnsweringSimple</code> (XLM model)</li> <li><strong>xlm-roberta</strong> — <code>TFXLMRobertaForQuestionAnswering</code> (XLM-RoBERTa model)</li> <li><strong>xlnet</strong> — <code>TFXLNetForQuestionAnsweringSimple</code> (XLNet model)</li>',UZ,Ii,mw,_f,fw,Ne,Mf,NZ,qM,j9=`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,qZ,XM,Z9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",XZ,mr,Tf,DZ,DM,B9="Instantiates one of the model classes of the library (with a question answering head) from a configuration.",HZ,HM,R9=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,QZ,Pi,YZ,lt,yf,zZ,QM,G9="Instantiate one of the model classes of the library (with a question answering head) from a pretrained model.",OZ,YM,J9=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,KZ,zM,W9='<li><strong>albert</strong> — <code>FlaxAlbertForQuestionAnswering</code> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/main/ko/model_doc/bart#transformers.FlaxBartForQuestionAnswering">FlaxBartForQuestionAnswering</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/main/ko/model_doc/bert#transformers.FlaxBertForQuestionAnswering">FlaxBertForQuestionAnswering</a> (BERT model)</li> <li><strong>big_bird</strong> — <code>FlaxBigBirdForQuestionAnswering</code> (BigBird model)</li> <li><strong>distilbert</strong> — <code>FlaxDistilBertForQuestionAnswering</code> (DistilBERT model)</li> <li><strong>electra</strong> — <code>FlaxElectraForQuestionAnswering</code> (ELECTRA model)</li> <li><strong>mbart</strong> — <code>FlaxMBartForQuestionAnswering</code> (mBART model)</li> <li><strong>roberta</strong> — <code>FlaxRobertaForQuestionAnswering</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>FlaxRobertaPreLayerNormForQuestionAnswering</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <code>FlaxRoFormerForQuestionAnswering</code> (RoFormer model)</li> <li><strong>xlm-roberta</strong> — <code>FlaxXLMRobertaForQuestionAnswering</code> (XLM-RoBERTa model)</li>',eB,Si,gw,Ff,pw,vf,Cf,uw,wf,hw,kf,xf,bw,$f,_w,Lf,E9="다음 자동 클래스들은 아래의 컴퓨터 비전 작업에 사용할 수 있습니다.",Mw,Af,Tw,qe,jf,oB,OM,V9=`This is a generic model class that will be instantiated as one of the model classes of the library (with a depth estimation head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,tB,KM,I9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",nB,fr,Zf,rB,eT,P9="Instantiates one of the model classes of the library (with a depth estimation head) from a configuration.",aB,oT,S9=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,sB,Ui,iB,N,Bf,lB,tT,U9="Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model.",dB,nT,N9=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,cB,rT,q9="<li><strong>depth_anything</strong> — <code>DepthAnythingForDepthEstimation</code> (Depth Anything model)</li> <li><strong>dpt</strong> — <code>DPTForDepthEstimation</code> (DPT model)</li> <li><strong>glpn</strong> — <code>GLPNForDepthEstimation</code> (GLPN model)</li> <li><strong>zoedepth</strong> — <code>ZoeDepthForDepthEstimation</code> (ZoeDepth model)</li>",mB,aT,X9=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,fB,Ni,yw,Rf,Fw,Xe,Gf,gB,sT,D9=`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,pB,iT,H9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",uB,gr,Jf,hB,lT,Q9="Instantiates one of the model classes of the library (with a image classification head) from a configuration.",bB,dT,Y9=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,_B,qi,MB,q,Wf,TB,cT,z9="Instantiate one of the model classes of the library (with a image classification head) from a pretrained model.",yB,mT,O9=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,FB,fT,K9='<li><strong>beit</strong> — <code>BeitForImageClassification</code> (BEiT model)</li> <li><strong>bit</strong> — <code>BitForImageClassification</code> (BiT model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPForImageClassification">CLIPForImageClassification</a> (CLIP model)</li> <li><strong>convnext</strong> — <code>ConvNextForImageClassification</code> (ConvNeXT model)</li> <li><strong>convnextv2</strong> — <code>ConvNextV2ForImageClassification</code> (ConvNeXTV2 model)</li> <li><strong>cvt</strong> — <code>CvtForImageClassification</code> (CvT model)</li> <li><strong>data2vec-vision</strong> — <code>Data2VecVisionForImageClassification</code> (Data2VecVision model)</li> <li><strong>deit</strong> — <code>DeiTForImageClassification</code> or <code>DeiTForImageClassificationWithTeacher</code> (DeiT model)</li> <li><strong>dinat</strong> — <code>DinatForImageClassification</code> (DiNAT model)</li> <li><strong>dinov2</strong> — <code>Dinov2ForImageClassification</code> (DINOv2 model)</li> <li><strong>dinov2_with_registers</strong> — <code>Dinov2WithRegistersForImageClassification</code> (DINOv2 with Registers model)</li> <li><strong>efficientformer</strong> — <code>EfficientFormerForImageClassification</code> or <code>EfficientFormerForImageClassificationWithTeacher</code> (EfficientFormer model)</li> <li><strong>efficientnet</strong> — <code>EfficientNetForImageClassification</code> (EfficientNet model)</li> <li><strong>focalnet</strong> — <code>FocalNetForImageClassification</code> (FocalNet model)</li> <li><strong>hiera</strong> — <code>HieraForImageClassification</code> (Hiera model)</li> <li><strong>ijepa</strong> — <code>IJepaForImageClassification</code> (I-JEPA model)</li> <li><strong>imagegpt</strong> — <code>ImageGPTForImageClassification</code> (ImageGPT model)</li> <li><strong>levit</strong> — <code>LevitForImageClassification</code> or <code>LevitForImageClassificationWithTeacher</code> (LeViT model)</li> <li><strong>mobilenet_v1</strong> — <code>MobileNetV1ForImageClassification</code> (MobileNetV1 model)</li> <li><strong>mobilenet_v2</strong> — <code>MobileNetV2ForImageClassification</code> (MobileNetV2 model)</li> <li><strong>mobilevit</strong> — <code>MobileViTForImageClassification</code> (MobileViT model)</li> <li><strong>mobilevitv2</strong> — <code>MobileViTV2ForImageClassification</code> (MobileViTV2 model)</li> <li><strong>nat</strong> — <code>NatForImageClassification</code> (NAT model)</li> <li><strong>perceiver</strong> — <code>PerceiverForImageClassificationLearned</code> or <code>PerceiverForImageClassificationFourier</code> or <code>PerceiverForImageClassificationConvProcessing</code> (Perceiver model)</li> <li><strong>poolformer</strong> — <code>PoolFormerForImageClassification</code> (PoolFormer model)</li> <li><strong>pvt</strong> — <code>PvtForImageClassification</code> (PVT model)</li> <li><strong>pvt_v2</strong> — <code>PvtV2ForImageClassification</code> (PVTv2 model)</li> <li><strong>regnet</strong> — <code>RegNetForImageClassification</code> (RegNet model)</li> <li><strong>resnet</strong> — <code>ResNetForImageClassification</code> (ResNet model)</li> <li><strong>segformer</strong> — <code>SegformerForImageClassification</code> (SegFormer model)</li> <li><strong>siglip</strong> — <code>SiglipForImageClassification</code> (SigLIP model)</li> <li><strong>swiftformer</strong> — <code>SwiftFormerForImageClassification</code> (SwiftFormer model)</li> <li><strong>swin</strong> — <a href="/docs/transformers/main/ko/model_doc/swin#transformers.SwinForImageClassification">SwinForImageClassification</a> (Swin Transformer model)</li> <li><strong>swinv2</strong> — <a href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2ForImageClassification">Swinv2ForImageClassification</a> (Swin Transformer V2 model)</li> <li><strong>textnet</strong> — <code>TextNetForImageClassification</code> (TextNet model)</li> <li><strong>timm_wrapper</strong> — <code>TimmWrapperForImageClassification</code> (TimmWrapperModel model)</li> <li><strong>van</strong> — <code>VanForImageClassification</code> (VAN model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTForImageClassification">ViTForImageClassification</a> (ViT model)</li> <li><strong>vit_hybrid</strong> — <code>ViTHybridForImageClassification</code> (ViT Hybrid model)</li> <li><strong>vit_msn</strong> — <code>ViTMSNForImageClassification</code> (ViTMSN model)</li>',vB,gT,eU=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,CB,Xi,vw,Ef,Cw,De,Vf,wB,pT,oU=`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,kB,uT,tU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",xB,pr,If,$B,hT,nU="Instantiates one of the model classes of the library (with a image classification head) from a configuration.",LB,bT,rU=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,AB,Di,jB,dt,Pf,ZB,_T,aU="Instantiate one of the model classes of the library (with a image classification head) from a pretrained model.",BB,MT,sU=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,RB,TT,iU='<li><strong>convnext</strong> — <code>TFConvNextForImageClassification</code> (ConvNeXT model)</li> <li><strong>convnextv2</strong> — <code>TFConvNextV2ForImageClassification</code> (ConvNeXTV2 model)</li> <li><strong>cvt</strong> — <code>TFCvtForImageClassification</code> (CvT model)</li> <li><strong>data2vec-vision</strong> — <code>TFData2VecVisionForImageClassification</code> (Data2VecVision model)</li> <li><strong>deit</strong> — <code>TFDeiTForImageClassification</code> or <code>TFDeiTForImageClassificationWithTeacher</code> (DeiT model)</li> <li><strong>efficientformer</strong> — <code>TFEfficientFormerForImageClassification</code> or <code>TFEfficientFormerForImageClassificationWithTeacher</code> (EfficientFormer model)</li> <li><strong>mobilevit</strong> — <code>TFMobileViTForImageClassification</code> (MobileViT model)</li> <li><strong>regnet</strong> — <code>TFRegNetForImageClassification</code> (RegNet model)</li> <li><strong>resnet</strong> — <code>TFResNetForImageClassification</code> (ResNet model)</li> <li><strong>segformer</strong> — <code>TFSegformerForImageClassification</code> (SegFormer model)</li> <li><strong>swiftformer</strong> — <code>TFSwiftFormerForImageClassification</code> (SwiftFormer model)</li> <li><strong>swin</strong> — <a href="/docs/transformers/main/ko/model_doc/swin#transformers.TFSwinForImageClassification">TFSwinForImageClassification</a> (Swin Transformer model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.TFViTForImageClassification">TFViTForImageClassification</a> (ViT model)</li>',GB,Hi,ww,Sf,kw,He,Uf,JB,yT,lU=`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,WB,FT,dU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",EB,ur,Nf,VB,vT,cU="Instantiates one of the model classes of the library (with a image classification head) from a configuration.",IB,CT,mU=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,PB,Qi,SB,ct,qf,UB,wT,fU="Instantiate one of the model classes of the library (with a image classification head) from a pretrained model.",NB,kT,gU=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,qB,xT,pU='<li><strong>beit</strong> — <code>FlaxBeitForImageClassification</code> (BEiT model)</li> <li><strong>dinov2</strong> — <code>FlaxDinov2ForImageClassification</code> (DINOv2 model)</li> <li><strong>regnet</strong> — <code>FlaxRegNetForImageClassification</code> (RegNet model)</li> <li><strong>resnet</strong> — <code>FlaxResNetForImageClassification</code> (ResNet model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.FlaxViTForImageClassification">FlaxViTForImageClassification</a> (ViT model)</li>',XB,Yi,xw,Xf,$w,Qe,Df,DB,$T,uU=`This is a generic model class that will be instantiated as one of the model classes of the library (with a video classification head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,HB,LT,hU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",QB,hr,Hf,YB,AT,bU="Instantiates one of the model classes of the library (with a video classification head) from a configuration.",zB,jT,_U=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,OB,zi,KB,X,Qf,eR,ZT,MU="Instantiate one of the model classes of the library (with a video classification head) from a pretrained model.",oR,BT,TU=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,tR,RT,yU='<li><strong>timesformer</strong> — <a href="/docs/transformers/main/ko/model_doc/timesformer#transformers.TimesformerForVideoClassification">TimesformerForVideoClassification</a> (TimeSformer model)</li> <li><strong>videomae</strong> — <code>VideoMAEForVideoClassification</code> (VideoMAE model)</li> <li><strong>vivit</strong> — <a href="/docs/transformers/main/ko/model_doc/vivit#transformers.VivitForVideoClassification">VivitForVideoClassification</a> (ViViT model)</li>',nR,GT,FU=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,rR,Oi,Lw,Yf,Aw,zf,Of,jw,Kf,Zw,Ye,eg,aR,JT,vU=`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,sR,WT,CU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",iR,br,og,lR,ET,wU="Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration.",dR,VT,kU=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,cR,Ki,mR,D,tg,fR,IT,xU="Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model.",gR,PT,$U=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,pR,ST,LU='<li><strong>deit</strong> — <code>DeiTForMaskedImageModeling</code> (DeiT model)</li> <li><strong>focalnet</strong> — <code>FocalNetForMaskedImageModeling</code> (FocalNet model)</li> <li><strong>swin</strong> — <a href="/docs/transformers/main/ko/model_doc/swin#transformers.SwinForMaskedImageModeling">SwinForMaskedImageModeling</a> (Swin Transformer model)</li> <li><strong>swinv2</strong> — <a href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2ForMaskedImageModeling">Swinv2ForMaskedImageModeling</a> (Swin Transformer V2 model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTForMaskedImageModeling">ViTForMaskedImageModeling</a> (ViT model)</li>',uR,UT,AU=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,hR,el,Bw,ng,Rw,ze,rg,bR,NT,jU=`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,_R,qT,ZU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",MR,_r,ag,TR,XT,BU="Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration.",yR,DT,RU=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,FR,ol,vR,mt,sg,CR,HT,GU="Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model.",wR,QT,JU=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,kR,YT,WU='<li><strong>deit</strong> — <code>TFDeiTForMaskedImageModeling</code> (DeiT model)</li> <li><strong>swin</strong> — <a href="/docs/transformers/main/ko/model_doc/swin#transformers.TFSwinForMaskedImageModeling">TFSwinForMaskedImageModeling</a> (Swin Transformer model)</li>',xR,tl,Gw,ig,Jw,Oe,lg,$R,zT,EU=`This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,LR,OT,VU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",AR,Mr,dg,jR,KT,IU="Instantiates one of the model classes of the library (with a object detection head) from a configuration.",ZR,ey,PU=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,BR,nl,RR,H,cg,GR,oy,SU="Instantiate one of the model classes of the library (with a object detection head) from a pretrained model.",JR,ty,UU=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,WR,ny,NU="<li><strong>conditional_detr</strong> — <code>ConditionalDetrForObjectDetection</code> (Conditional DETR model)</li> <li><strong>deformable_detr</strong> — <code>DeformableDetrForObjectDetection</code> (Deformable DETR model)</li> <li><strong>deta</strong> — <code>DetaForObjectDetection</code> (DETA model)</li> <li><strong>detr</strong> — <code>DetrForObjectDetection</code> (DETR model)</li> <li><strong>rt_detr</strong> — <code>RTDetrForObjectDetection</code> (RT-DETR model)</li> <li><strong>table-transformer</strong> — <code>TableTransformerForObjectDetection</code> (Table Transformer model)</li> <li><strong>yolos</strong> — <code>YolosForObjectDetection</code> (YOLOS model)</li>",ER,ry,qU=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,VR,rl,Ww,mg,Ew,Ke,fg,IR,ay,XU=`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,PR,sy,DU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",SR,Tr,gg,UR,iy,HU="Instantiates one of the model classes of the library (with a image segmentation head) from a configuration.",NR,ly,QU=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,qR,al,XR,Q,pg,DR,dy,YU="Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model.",HR,cy,zU=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,QR,my,OU="<li><strong>detr</strong> — <code>DetrForSegmentation</code> (DETR model)</li>",YR,fy,KU=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,zR,sl,Vw,ug,Iw,hg,bg,Pw,_g,Sw,eo,Mg,OR,gy,e5=`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,KR,py,o5="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",eG,yr,Tg,oG,uy,t5="Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration.",tG,hy,n5=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,nG,il,rG,Y,yg,aG,by,r5="Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model.",sG,_y,a5=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,iG,My,s5="<li><strong>beit</strong> — <code>BeitForSemanticSegmentation</code> (BEiT model)</li> <li><strong>data2vec-vision</strong> — <code>Data2VecVisionForSemanticSegmentation</code> (Data2VecVision model)</li> <li><strong>dpt</strong> — <code>DPTForSemanticSegmentation</code> (DPT model)</li> <li><strong>mobilenet_v2</strong> — <code>MobileNetV2ForSemanticSegmentation</code> (MobileNetV2 model)</li> <li><strong>mobilevit</strong> — <code>MobileViTForSemanticSegmentation</code> (MobileViT model)</li> <li><strong>mobilevitv2</strong> — <code>MobileViTV2ForSemanticSegmentation</code> (MobileViTV2 model)</li> <li><strong>segformer</strong> — <code>SegformerForSemanticSegmentation</code> (SegFormer model)</li> <li><strong>upernet</strong> — <code>UperNetForSemanticSegmentation</code> (UPerNet model)</li>",lG,Ty,i5=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,dG,ll,Uw,Fg,Nw,oo,vg,cG,yy,l5=`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,mG,Fy,d5="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",fG,Fr,Cg,gG,vy,c5="Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration.",pG,Cy,m5=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,uG,dl,hG,ft,wg,bG,wy,f5="Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model.",_G,ky,g5=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,MG,xy,p5="<li><strong>data2vec-vision</strong> — <code>TFData2VecVisionForSemanticSegmentation</code> (Data2VecVision model)</li> <li><strong>mobilevit</strong> — <code>TFMobileViTForSemanticSegmentation</code> (MobileViT model)</li> <li><strong>segformer</strong> — <code>TFSegformerForSemanticSegmentation</code> (SegFormer model)</li>",TG,cl,qw,kg,Xw,to,xg,yG,$y,u5=`This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,FG,Ly,h5="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",vG,vr,$g,CG,Ay,b5="Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration.",wG,jy,_5=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,kG,ml,xG,z,Lg,$G,Zy,M5="Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model.",LG,By,T5=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,AG,Ry,y5="<li><strong>maskformer</strong> — <code>MaskFormerForInstanceSegmentation</code> (MaskFormer model)</li>",jG,Gy,F5=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,ZG,fl,Dw,Ag,Hw,no,jg,BG,Jy,v5=`This is a generic model class that will be instantiated as one of the model classes of the library (with a universal image segmentation head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,RG,Wy,C5="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",GG,Cr,Zg,JG,Ey,w5="Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration.",WG,Vy,k5=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,EG,gl,VG,O,Bg,IG,Iy,x5="Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model.",PG,Py,$5=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,SG,Sy,L5="<li><strong>detr</strong> — <code>DetrForSegmentation</code> (DETR model)</li> <li><strong>mask2former</strong> — <code>Mask2FormerForUniversalSegmentation</code> (Mask2Former model)</li> <li><strong>maskformer</strong> — <code>MaskFormerForInstanceSegmentation</code> (MaskFormer model)</li> <li><strong>oneformer</strong> — <code>OneFormerForUniversalSegmentation</code> (OneFormer model)</li>",UG,Uy,A5=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,NG,pl,Qw,Rg,Yw,ro,Gg,qG,Ny,j5=`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,XG,qy,Z5="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",DG,wr,Jg,HG,Xy,B5="Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration.",QG,Dy,R5=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,YG,ul,zG,K,Wg,OG,Hy,G5="Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model.",KG,Qy,J5=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,eJ,Yy,W5='<li><strong>align</strong> — <code>AlignModel</code> (ALIGN model)</li> <li><strong>altclip</strong> — <a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li> <li><strong>blip</strong> — <a href="/docs/transformers/main/ko/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li> <li><strong>blip-2</strong> — <a href="/docs/transformers/main/ko/model_doc/blip-2#transformers.Blip2ForImageTextRetrieval">Blip2ForImageTextRetrieval</a> (BLIP-2 model)</li> <li><strong>chinese_clip</strong> — <code>ChineseCLIPModel</code> (Chinese-CLIP model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li> <li><strong>clipseg</strong> — <code>CLIPSegModel</code> (CLIPSeg model)</li> <li><strong>siglip</strong> — <code>SiglipModel</code> (SigLIP model)</li>',oJ,zy,E5=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,tJ,hl,zw,Eg,Ow,ao,Vg,nJ,Oy,V5=`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,rJ,Ky,I5="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",aJ,kr,Ig,sJ,eF,P5="Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration.",iJ,oF,S5=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,lJ,bl,dJ,gt,Pg,cJ,tF,U5="Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model.",mJ,nF,N5=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,fJ,rF,q5='<li><strong>blip</strong> — <a href="/docs/transformers/main/ko/model_doc/blip#transformers.TFBlipModel">TFBlipModel</a> (BLIP model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/main/ko/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>',gJ,_l,Kw,Sg,ek,so,Ug,pJ,aF,X5=`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot object detection head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,uJ,sF,D5="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",hJ,xr,Ng,bJ,iF,H5="Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration.",_J,lF,Q5=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,MJ,Ml,TJ,ee,qg,yJ,dF,Y5="Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model.",FJ,cF,z5=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,vJ,mF,O5="<li><strong>grounding-dino</strong> — <code>GroundingDinoForObjectDetection</code> (Grounding DINO model)</li> <li><strong>omdet-turbo</strong> — <code>OmDetTurboForObjectDetection</code> (OmDet-Turbo model)</li> <li><strong>owlv2</strong> — <code>Owlv2ForObjectDetection</code> (OWLv2 model)</li> <li><strong>owlvit</strong> — <code>OwlViTForObjectDetection</code> (OWL-ViT model)</li>",CJ,fF,K5=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,wJ,Tl,ok,Xg,tk,Dg,eN="다음 자동 클래스들은 아래의 오디오 작업에 사용할 수 있습니다.",nk,Hg,rk,io,Qg,kJ,gF,oN=`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,xJ,pF,tN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",$J,$r,Yg,LJ,uF,nN="Instantiates one of the model classes of the library (with a audio classification head) from a configuration.",AJ,hF,rN=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,jJ,yl,ZJ,oe,zg,BJ,bF,aN="Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model.",RJ,_F,sN=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,GJ,MF,iN='<li><strong>audio-spectrogram-transformer</strong> — <code>ASTForAudioClassification</code> (Audio Spectrogram Transformer model)</li> <li><strong>data2vec-audio</strong> — <code>Data2VecAudioForSequenceClassification</code> (Data2VecAudio model)</li> <li><strong>hubert</strong> — <code>HubertForSequenceClassification</code> (Hubert model)</li> <li><strong>sew</strong> — <code>SEWForSequenceClassification</code> (SEW model)</li> <li><strong>sew-d</strong> — <code>SEWDForSequenceClassification</code> (SEW-D model)</li> <li><strong>unispeech</strong> — <code>UniSpeechForSequenceClassification</code> (UniSpeech model)</li> <li><strong>unispeech-sat</strong> — <code>UniSpeechSatForSequenceClassification</code> (UniSpeechSat model)</li> <li><strong>wav2vec2</strong> — <code>Wav2Vec2ForSequenceClassification</code> (Wav2Vec2 model)</li> <li><strong>wav2vec2-bert</strong> — <code>Wav2Vec2BertForSequenceClassification</code> (Wav2Vec2-BERT model)</li> <li><strong>wav2vec2-conformer</strong> — <code>Wav2Vec2ConformerForSequenceClassification</code> (Wav2Vec2-Conformer model)</li> <li><strong>wavlm</strong> — <code>WavLMForSequenceClassification</code> (WavLM model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperForAudioClassification">WhisperForAudioClassification</a> (Whisper model)</li>',JJ,TF,lN=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,WJ,Fl,ak,Og,sk,lo,Kg,EJ,yF,dN=`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,VJ,FF,cN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",IJ,Lr,ep,PJ,vF,mN="Instantiates one of the model classes of the library (with a audio classification head) from a configuration.",SJ,CF,fN=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,UJ,vl,NJ,pt,op,qJ,wF,gN="Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model.",XJ,kF,pN=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,DJ,xF,uN="<li><strong>wav2vec2</strong> — <code>TFWav2Vec2ForSequenceClassification</code> (Wav2Vec2 model)</li>",HJ,Cl,ik,tp,lk,co,np,QJ,$F,hN=`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio frame (token) classification head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,YJ,LF,bN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",zJ,Ar,rp,OJ,AF,_N="Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration.",KJ,jF,MN=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,eW,wl,oW,te,ap,tW,ZF,TN="Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model.",nW,BF,yN=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,rW,RF,FN="<li><strong>data2vec-audio</strong> — <code>Data2VecAudioForAudioFrameClassification</code> (Data2VecAudio model)</li> <li><strong>unispeech-sat</strong> — <code>UniSpeechSatForAudioFrameClassification</code> (UniSpeechSat model)</li> <li><strong>wav2vec2</strong> — <code>Wav2Vec2ForAudioFrameClassification</code> (Wav2Vec2 model)</li> <li><strong>wav2vec2-bert</strong> — <code>Wav2Vec2BertForAudioFrameClassification</code> (Wav2Vec2-BERT model)</li> <li><strong>wav2vec2-conformer</strong> — <code>Wav2Vec2ConformerForAudioFrameClassification</code> (Wav2Vec2-Conformer model)</li> <li><strong>wavlm</strong> — <code>WavLMForAudioFrameClassification</code> (WavLM model)</li>",aW,GF,vN=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,sW,kl,dk,sp,ck,mo,ip,iW,JF,CN=`This is a generic model class that will be instantiated as one of the model classes of the library (with a connectionist temporal classification head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,lW,WF,wN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",dW,jr,lp,cW,EF,kN="Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration.",mW,VF,xN=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,fW,xl,gW,ne,dp,pW,IF,$N="Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model.",uW,PF,LN=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,hW,SF,AN="<li><strong>data2vec-audio</strong> — <code>Data2VecAudioForCTC</code> (Data2VecAudio model)</li> <li><strong>hubert</strong> — <code>HubertForCTC</code> (Hubert model)</li> <li><strong>mctct</strong> — <code>MCTCTForCTC</code> (M-CTC-T model)</li> <li><strong>sew</strong> — <code>SEWForCTC</code> (SEW model)</li> <li><strong>sew-d</strong> — <code>SEWDForCTC</code> (SEW-D model)</li> <li><strong>unispeech</strong> — <code>UniSpeechForCTC</code> (UniSpeech model)</li> <li><strong>unispeech-sat</strong> — <code>UniSpeechSatForCTC</code> (UniSpeechSat model)</li> <li><strong>wav2vec2</strong> — <code>Wav2Vec2ForCTC</code> (Wav2Vec2 model)</li> <li><strong>wav2vec2-bert</strong> — <code>Wav2Vec2BertForCTC</code> (Wav2Vec2-BERT model)</li> <li><strong>wav2vec2-conformer</strong> — <code>Wav2Vec2ConformerForCTC</code> (Wav2Vec2-Conformer model)</li> <li><strong>wavlm</strong> — <code>WavLMForCTC</code> (WavLM model)</li>",bW,UF,jN=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,_W,$l,mk,cp,fk,fo,mp,MW,NF,ZN=`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,TW,qF,BN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",yW,Zr,fp,FW,XF,RN="Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration.",vW,DF,GN=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,CW,Ll,wW,re,gp,kW,HF,JN="Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model.",xW,QF,WN=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,$W,YF,EN='<li><strong>moonshine</strong> — <code>MoonshineForConditionalGeneration</code> (Moonshine model)</li> <li><strong>pop2piano</strong> — <code>Pop2PianoForConditionalGeneration</code> (Pop2Piano model)</li> <li><strong>seamless_m4t</strong> — <code>SeamlessM4TForSpeechToText</code> (SeamlessM4T model)</li> <li><strong>seamless_m4t_v2</strong> — <code>SeamlessM4Tv2ForSpeechToText</code> (SeamlessM4Tv2 model)</li> <li><strong>speech-encoder-decoder</strong> — <code>SpeechEncoderDecoderModel</code> (Speech Encoder decoder model)</li> <li><strong>speech_to_text</strong> — <code>Speech2TextForConditionalGeneration</code> (Speech2Text model)</li> <li><strong>speecht5</strong> — <code>SpeechT5ForSpeechToText</code> (SpeechT5 model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperForConditionalGeneration">WhisperForConditionalGeneration</a> (Whisper model)</li>',LW,zF,VN=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,AW,Al,gk,pp,pk,go,up,jW,OF,IN=`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,ZW,KF,PN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",BW,Br,hp,RW,ev,SN="Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration.",GW,ov,UN=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,JW,jl,WW,ut,bp,EW,tv,NN="Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model.",VW,nv,qN=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,IW,rv,XN='<li><strong>speech_to_text</strong> — <code>TFSpeech2TextForConditionalGeneration</code> (Speech2Text model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.TFWhisperForConditionalGeneration">TFWhisperForConditionalGeneration</a> (Whisper model)</li>',PW,Zl,uk,_p,hk,po,Mp,SW,av,DN=`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,UW,sv,HN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",NW,Rr,Tp,qW,iv,QN="Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration.",XW,lv,YN=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,DW,Bl,HW,ht,yp,QW,dv,zN="Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model.",YW,cv,ON=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,zW,mv,KN='<li><strong>speech-encoder-decoder</strong> — <code>FlaxSpeechEncoderDecoderModel</code> (Speech Encoder decoder model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li>',OW,Rl,bk,Fp,_k,uo,vp,KW,fv,e4=`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio retrieval via x-vector head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,e0,gv,o4="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",o0,Gr,Cp,t0,pv,t4="Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration.",n0,uv,n4=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,r0,Gl,a0,ae,wp,s0,hv,r4="Instantiate one of the model classes of the library (with a audio retrieval via x-vector head) from a pretrained model.",i0,bv,a4=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,l0,_v,s4="<li><strong>data2vec-audio</strong> — <code>Data2VecAudioForXVector</code> (Data2VecAudio model)</li> <li><strong>unispeech-sat</strong> — <code>UniSpeechSatForXVector</code> (UniSpeechSat model)</li> <li><strong>wav2vec2</strong> — <code>Wav2Vec2ForXVector</code> (Wav2Vec2 model)</li> <li><strong>wav2vec2-bert</strong> — <code>Wav2Vec2BertForXVector</code> (Wav2Vec2-BERT model)</li> <li><strong>wav2vec2-conformer</strong> — <code>Wav2Vec2ConformerForXVector</code> (Wav2Vec2-Conformer model)</li> <li><strong>wavlm</strong> — <code>WavLMForXVector</code> (WavLM model)</li>",d0,Mv,i4=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,c0,Jl,Mk,kp,Tk,xp,$p,yk,Lp,Fk,Ap,jp,vk,Zp,Ck,Bp,l4="다음 자동 클래스들은 아래의 멀티모달 작업에 사용할 수 있습니다.",wk,Rp,kk,ho,Gp,m0,Tv,d4=`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,f0,yv,c4="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",g0,Jr,Jp,p0,Fv,m4="Instantiates one of the model classes of the library (with a table question answering head) from a configuration.",u0,vv,f4=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,h0,Wl,b0,se,Wp,_0,Cv,g4="Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model.",M0,wv,p4=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,T0,kv,u4="<li><strong>tapas</strong> — <code>TapasForQuestionAnswering</code> (TAPAS model)</li>",y0,xv,h4=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,F0,El,xk,Ep,$k,bo,Vp,v0,$v,b4=`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,C0,Lv,_4="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",w0,Wr,Ip,k0,Av,M4="Instantiates one of the model classes of the library (with a table question answering head) from a configuration.",x0,jv,T4=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,$0,Vl,L0,bt,Pp,A0,Zv,y4="Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model.",j0,Bv,F4=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,Z0,Rv,v4="<li><strong>tapas</strong> — <code>TFTapasForQuestionAnswering</code> (TAPAS model)</li>",B0,Il,Lk,Sp,Ak,_o,Up,R0,Gv,C4=`This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,G0,Jv,w4="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",J0,Er,Np,W0,Wv,k4="Instantiates one of the model classes of the library (with a document question answering head) from a configuration.",E0,Ev,x4=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,V0,Pl,I0,ie,qp,P0,Vv,$4="Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model.",S0,Iv,L4=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,U0,Pv,A4="<li><strong>layoutlm</strong> — <code>LayoutLMForQuestionAnswering</code> (LayoutLM model)</li> <li><strong>layoutlmv2</strong> — <code>LayoutLMv2ForQuestionAnswering</code> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <code>LayoutLMv3ForQuestionAnswering</code> (LayoutLMv3 model)</li>",N0,Sv,j4=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,q0,Sl,jk,Xp,Zk,Mo,Dp,X0,Uv,Z4=`This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,D0,Nv,B4="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",H0,Vr,Hp,Q0,qv,R4="Instantiates one of the model classes of the library (with a document question answering head) from a configuration.",Y0,Xv,G4=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,z0,Ul,O0,_t,Qp,K0,Dv,J4="Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model.",eE,Hv,W4=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,oE,Qv,E4="<li><strong>layoutlm</strong> — <code>TFLayoutLMForQuestionAnswering</code> (LayoutLM model)</li> <li><strong>layoutlmv3</strong> — <code>TFLayoutLMv3ForQuestionAnswering</code> (LayoutLMv3 model)</li>",tE,Nl,Bk,Yp,Rk,To,zp,nE,Yv,V4=`This is a generic model class that will be instantiated as one of the model classes of the library (with a visual question answering head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,rE,zv,I4="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",aE,Ir,Op,sE,Ov,P4="Instantiates one of the model classes of the library (with a visual question answering head) from a configuration.",iE,Kv,S4=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,lE,ql,dE,le,Kp,cE,eC,U4="Instantiate one of the model classes of the library (with a visual question answering head) from a pretrained model.",mE,oC,N4=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,fE,tC,q4='<li><strong>blip</strong> — <a href="/docs/transformers/main/ko/model_doc/blip#transformers.BlipForQuestionAnswering">BlipForQuestionAnswering</a> (BLIP model)</li> <li><strong>blip-2</strong> — <a href="/docs/transformers/main/ko/model_doc/blip-2#transformers.Blip2ForConditionalGeneration">Blip2ForConditionalGeneration</a> (BLIP-2 model)</li> <li><strong>vilt</strong> — <code>ViltForQuestionAnswering</code> (ViLT model)</li>',gE,nC,X4=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,pE,Xl,Gk,eu,Jk,yo,ou,uE,rC,D4=`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,hE,aC,H4="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",bE,Pr,tu,_E,sC,Q4="Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration.",ME,iC,Y4=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,TE,Dl,yE,de,nu,FE,lC,z4="Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model.",vE,dC,O4=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,CE,cC,K4='<li><strong>blip</strong> — <a href="/docs/transformers/main/ko/model_doc/blip#transformers.BlipForConditionalGeneration">BlipForConditionalGeneration</a> (BLIP model)</li> <li><strong>blip-2</strong> — <a href="/docs/transformers/main/ko/model_doc/blip-2#transformers.Blip2ForConditionalGeneration">Blip2ForConditionalGeneration</a> (BLIP-2 model)</li> <li><strong>chameleon</strong> — <a href="/docs/transformers/main/ko/model_doc/chameleon#transformers.ChameleonForConditionalGeneration">ChameleonForConditionalGeneration</a> (Chameleon model)</li> <li><strong>git</strong> — <code>GitForCausalLM</code> (GIT model)</li> <li><strong>idefics2</strong> — <code>Idefics2ForConditionalGeneration</code> (Idefics2 model)</li> <li><strong>idefics3</strong> — <code>Idefics3ForConditionalGeneration</code> (Idefics3 model)</li> <li><strong>instructblip</strong> — <code>InstructBlipForConditionalGeneration</code> (InstructBLIP model)</li> <li><strong>instructblipvideo</strong> — <code>InstructBlipVideoForConditionalGeneration</code> (InstructBlipVideo model)</li> <li><strong>kosmos-2</strong> — <code>Kosmos2ForConditionalGeneration</code> (KOSMOS-2 model)</li> <li><strong>llava</strong> — <code>LlavaForConditionalGeneration</code> (LLaVa model)</li> <li><strong>llava_next</strong> — <code>LlavaNextForConditionalGeneration</code> (LLaVA-NeXT model)</li> <li><strong>llava_next_video</strong> — <code>LlavaNextVideoForConditionalGeneration</code> (LLaVa-NeXT-Video model)</li> <li><strong>llava_onevision</strong> — <code>LlavaOnevisionForConditionalGeneration</code> (LLaVA-Onevision model)</li> <li><strong>mllama</strong> — <code>MllamaForConditionalGeneration</code> (Mllama model)</li> <li><strong>paligemma</strong> — <a href="/docs/transformers/main/ko/model_doc/paligemma#transformers.PaliGemmaForConditionalGeneration">PaliGemmaForConditionalGeneration</a> (PaliGemma model)</li> <li><strong>pix2struct</strong> — <code>Pix2StructForConditionalGeneration</code> (Pix2Struct model)</li> <li><strong>qwen2_5_vl</strong> — <code>Qwen2_5_VLForConditionalGeneration</code> (Qwen2_5_VL model)</li> <li><strong>qwen2_vl</strong> — <code>Qwen2VLForConditionalGeneration</code> (Qwen2VL model)</li> <li><strong>video_llava</strong> — <code>VideoLlavaForConditionalGeneration</code> (VideoLlava model)</li> <li><strong>vipllava</strong> — <code>VipLlavaForConditionalGeneration</code> (VipLlava model)</li> <li><strong>vision-encoder-decoder</strong> — <code>VisionEncoderDecoderModel</code> (Vision Encoder decoder model)</li>',wE,mC,eq=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,kE,Hl,Wk,ru,Ek,Fo,au,xE,fC,oq=`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,$E,gC,tq="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",LE,Sr,su,AE,pC,nq="Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration.",jE,uC,rq=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,ZE,Ql,BE,Mt,iu,RE,hC,aq="Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model.",GE,bC,sq=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,JE,_C,iq='<li><strong>blip</strong> — <a href="/docs/transformers/main/ko/model_doc/blip#transformers.TFBlipForConditionalGeneration">TFBlipForConditionalGeneration</a> (BLIP model)</li> <li><strong>vision-encoder-decoder</strong> — <code>TFVisionEncoderDecoderModel</code> (Vision Encoder decoder model)</li>',WE,Yl,Vk,lu,Ik,vo,du,EE,MC,lq=`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,VE,TC,dq="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",IE,Ur,cu,PE,yC,cq="Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration.",SE,FC,mq=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,UE,zl,NE,Tt,mu,qE,vC,fq="Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model.",XE,CC,gq=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,DE,wC,pq="<li><strong>vision-encoder-decoder</strong> — <code>FlaxVisionEncoderDecoderModel</code> (Vision Encoder decoder model)</li>",HE,Ol,Pk,fu,Sk,kC,Uk;return m=new A({props:{title:"Auto 클래스",local:"auto-classes",headingTag:"h1"}}),Cd=new $({props:{code:"bW9kZWwlMjAlM0QlMjBBdXRvTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKQ==",highlighted:'model = AutoModel.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)',wrap:!1}}),xd=new A({props:{title:"자동 클래스 확장",local:"extending-the-auto-classes",headingTag:"h2"}}),Ld=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWwlMEElMEFBdXRvQ29uZmlnLnJlZ2lzdGVyKCUyMm5ldy1tb2RlbCUyMiUyQyUyME5ld01vZGVsQ29uZmlnKSUwQUF1dG9Nb2RlbC5yZWdpc3RlcihOZXdNb2RlbENvbmZpZyUyQyUyME5ld01vZGVsKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModel

AutoConfig.register(<span class="hljs-string">&quot;new-model&quot;</span>, NewModelConfig)
AutoModel.register(NewModelConfig, NewModel)`,wrap:!1}}),vs=new Nk({props:{warning:!0,$$slots:{default:[Cq]},$$scope:{ctx:v}}}),jd=new A({props:{title:"AutoConfig",local:"transformers.AutoConfig ][ transformers.AutoConfig",headingTag:"h2"}}),Zd=new k({props:{name:"class transformers.AutoConfig",anchor:"transformers.AutoConfig",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/configuration_auto.py#L941"}}),Bd=new k({props:{name:"from_pretrained",anchor:"transformers.AutoConfig.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoConfig.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model configuration hosted inside a model repo on
huggingface.co.</li>
<li>A path to a <em>directory</em> containing a configuration file saved using the
<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.save_pretrained">save_pretrained()</a> method, or the <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> method,
e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a saved configuration JSON <em>file</em>, e.g.,
<code>./my_model_directory/configuration.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoConfig.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoConfig.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download the model weights and configuration files and override the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoConfig.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoConfig.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoConfig.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoConfig.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final configuration object.</p>
<p>If <code>True</code>, then this functions returns a <code>Tuple(config, unused_kwargs)</code> where <em>unused_kwargs</em> is a
dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
part of <code>kwargs</code> which has not been used to update <code>config</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoConfig.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoConfig.from_pretrained.kwargs(additional",description:`<strong>kwargs(additional</strong> keyword arguments, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are configuration attributes will be used to override the loaded
values. Behavior concerning key/value pairs whose keys are <em>not</em> configuration attributes is controlled
by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs(additional"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/configuration_auto.py#L964"}}),Cs=new L({props:{anchor:"transformers.AutoConfig.from_pretrained.example",$$slots:{default:[wq]},$$scope:{ctx:v}}}),Rd=new k({props:{name:"register",anchor:"transformers.AutoConfig.register",parameters:[{name:"model_type",val:""},{name:"config",val:""},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoConfig.register.model_type",description:"<strong>model_type</strong> (<code>str</code>) &#x2014; The model type like &#x201C;bert&#x201D; or &#x201C;gpt&#x201D;.",name:"model_type"},{anchor:"transformers.AutoConfig.register.config",description:'<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014; The config to register.',name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/configuration_auto.py#L1108"}}),Gd=new A({props:{title:"AutoTokenizer",local:"transformers.AutoTokenizer ][ transformers.AutoTokenizer",headingTag:"h2"}}),Jd=new k({props:{name:"class transformers.AutoTokenizer",anchor:"transformers.AutoTokenizer",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/tokenization_auto.py#L740"}}),Wd=new k({props:{name:"from_pretrained",anchor:"transformers.AutoTokenizer.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"*inputs",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoTokenizer.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a predefined tokenizer hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing vocabulary files required by the tokenizer, for instance saved
using the <a href="/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.save_pretrained">save_pretrained()</a> method, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a single saved vocabulary file if and only if the tokenizer only requires a
single vocabulary file (like Bert or XLNet), e.g.: <code>./my_model_directory/vocab.txt</code>. (Not
applicable to all derived classes)</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoTokenizer.from_pretrained.inputs",description:`<strong>inputs</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the Tokenizer <code>__init__()</code> method.`,name:"inputs"},{anchor:"transformers.AutoTokenizer.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
The configuration object used to determine the tokenizer class to instantiate.`,name:"config"},{anchor:"transformers.AutoTokenizer.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoTokenizer.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download the model weights and configuration files and override the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoTokenizer.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoTokenizer.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoTokenizer.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoTokenizer.from_pretrained.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>) &#x2014;
In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for
facebook/rag-token-base), specify it here.`,name:"subfolder"},{anchor:"transformers.AutoTokenizer.from_pretrained.use_fast",description:`<strong>use_fast</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Use a <a href="https://huggingface.co/docs/tokenizers/index" rel="nofollow">fast Rust-based tokenizer</a> if it is supported for
a given model. If a fast tokenizer is not available for a given model, a normal Python-based tokenizer
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Tokenizer type to be loaded.`,name:"tokenizer_type"},{anchor:"transformers.AutoTokenizer.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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Will be passed to the Tokenizer <code>__init__()</code> method. Can be used to set special tokens like
<code>bos_token</code>, <code>eos_token</code>, <code>unk_token</code>, <code>sep_token</code>, <code>pad_token</code>, <code>cls_token</code>, <code>mask_token</code>,
<code>additional_special_tokens</code>. See parameters in the <code>__init__()</code> for more details.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/tokenization_auto.py#L754"}}),ks=new L({props:{anchor:"transformers.AutoTokenizer.from_pretrained.example",$$slots:{default:[kq]},$$scope:{ctx:v}}}),Ed=new k({props:{name:"register",anchor:"transformers.AutoTokenizer.register",parameters:[{name:"config_class",val:""},{name:"slow_tokenizer_class",val:" = None"},{name:"fast_tokenizer_class",val:" = None"},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoTokenizer.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
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The slow tokenizer to register.`,name:"slow_tokenizer_class"},{anchor:"transformers.AutoTokenizer.register.fast_tokenizer_class",description:`<strong>fast_tokenizer_class</strong> (<code>PretrainedTokenizerFast</code>, <em>optional</em>) &#x2014;
The fast tokenizer to register.`,name:"fast_tokenizer_class"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/tokenization_auto.py#L978"}}),Vd=new A({props:{title:"AutoFeatureExtractor",local:"transformers.AutoFeatureExtractor ][ transformers.AutoFeatureExtractor",headingTag:"h2"}}),Id=new k({props:{name:"class transformers.AutoFeatureExtractor",anchor:"transformers.AutoFeatureExtractor",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/feature_extraction_auto.py#L244"}}),Pd=new k({props:{name:"from_pretrained",anchor:"transformers.AutoFeatureExtractor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoFeatureExtractor.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
huggingface.co.</li>
<li>a path to a <em>directory</em> containing a feature extractor file saved using the
<a href="/docs/transformers/main/ko/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained">save_pretrained()</a> method, e.g.,
<code>./my_model_directory/</code>.</li>
<li>a path or url to a saved feature extractor JSON <em>file</em>, e.g.,
<code>./my_model_directory/preprocessor_config.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the feature extractor files and override the cached versions
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Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final feature extractor object. If <code>True</code>, then this
functions returns a <code>Tuple(feature_extractor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
<code>kwargs</code> which has not been used to update <code>feature_extractor</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are feature extractor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are <em>not</em> feature extractor attributes is
controlled by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/feature_extraction_auto.py#L258"}}),$s=new Nk({props:{$$slots:{default:[xq]},$$scope:{ctx:v}}}),Ls=new L({props:{anchor:"transformers.AutoFeatureExtractor.from_pretrained.example",$$slots:{default:[$q]},$$scope:{ctx:v}}}),Sd=new k({props:{name:"register",anchor:"transformers.AutoFeatureExtractor.register",parameters:[{name:"config_class",val:""},{name:"feature_extractor_class",val:""},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoFeatureExtractor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoFeatureExtractor.register.feature_extractor_class",description:"<strong>feature_extractor_class</strong> (<code>FeatureExtractorMixin</code>) &#x2014; The feature extractor to register.",name:"feature_extractor_class"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/feature_extraction_auto.py#L394"}}),Ud=new A({props:{title:"AutoImageProcessor",local:"transformers.AutoImageProcessor ][ transformers.AutoImageProcessor",headingTag:"h2"}}),Nd=new k({props:{name:"class transformers.AutoImageProcessor",anchor:"transformers.AutoImageProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/image_processing_auto.py#L314"}}),qd=new k({props:{name:"from_pretrained",anchor:"transformers.AutoImageProcessor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"*inputs",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoImageProcessor.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained image_processor hosted inside a model repo on
huggingface.co.</li>
<li>a path to a <em>directory</em> containing a image processor file saved using the
<a href="/docs/transformers/main/ko/internal/image_processing_utils#transformers.ImageProcessingMixin.save_pretrained">save_pretrained()</a> method, e.g.,
<code>./my_model_directory/</code>.</li>
<li>a path or url to a saved image processor JSON <em>file</em>, e.g.,
<code>./my_model_directory/preprocessor_config.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoImageProcessor.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model image processor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoImageProcessor.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the image processor files and override the cached versions if
they exist.`,name:"force_download"},{anchor:"transformers.AutoImageProcessor.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
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A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoImageProcessor.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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Use a fast torchvision-base image processor if it is supported for a given model.
If a fast image processor is not available for a given model, a normal numpy-based image processor
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If <code>False</code>, then this function returns just the final image processor object. If <code>True</code>, then this
functions returns a <code>Tuple(image_processor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
consisting of the key/value pairs whose keys are not image processor attributes: i.e., the part of
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Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoImageProcessor.from_pretrained.image_processor_filename",description:`<strong>image_processor_filename</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;config.json&quot;</code>) &#x2014;
The name of the file in the model directory to use for the image processor config.`,name:"image_processor_filename"},{anchor:"transformers.AutoImageProcessor.from_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are image processor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are <em>not</em> image processor attributes is
controlled by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/image_processing_auto.py#L328"}}),js=new Nk({props:{$$slots:{default:[Lq]},$$scope:{ctx:v}}}),Zs=new L({props:{anchor:"transformers.AutoImageProcessor.from_pretrained.example",$$slots:{default:[Aq]},$$scope:{ctx:v}}}),Xd=new k({props:{name:"register",anchor:"transformers.AutoImageProcessor.register",parameters:[{name:"config_class",val:""},{name:"image_processor_class",val:" = None"},{name:"slow_image_processor_class",val:" = None"},{name:"fast_image_processor_class",val:" = None"},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoImageProcessor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
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This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
huggingface.co.</li>
<li>a path to a <em>directory</em> containing a processor files saved using the <code>save_pretrained()</code> method,
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</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoProcessor.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
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Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoProcessor.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoProcessor.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
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If <code>False</code>, then this function returns just the final feature extractor object. If <code>True</code>, then this
functions returns a <code>Tuple(feature_extractor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
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The values in kwargs of any keys which are feature extractor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are <em>not</em> feature extractor attributes is
controlled by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/processing_auto.py#L164"}}),Rs=new Nk({props:{$$slots:{default:[jq]},$$scope:{ctx:v}}}),Gs=new L({props:{anchor:"transformers.AutoProcessor.from_pretrained.example",$$slots:{default:[Zq]},$$scope:{ctx:v}}}),Yd=new k({props:{name:"register",anchor:"transformers.AutoProcessor.register",parameters:[{name:"config_class",val:""},{name:"processor_class",val:""},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoProcessor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoProcessor.register.processor_class",description:"<strong>processor_class</strong> (<code>FeatureExtractorMixin</code>) &#x2014; The processor to register.",name:"processor_class"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/processing_auto.py#L367"}}),zd=new A({props:{title:"일반적인 모델 클래스",local:"generic-model-classes",headingTag:"h2"}}),Kd=new A({props:{title:"AutoModel",local:"transformers.AutoModel ][ transformers.AutoModel",headingTag:"h3"}}),ec=new k({props:{name:"class transformers.AutoModel",anchor:"transformers.AutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1608"}}),oc=new k({props:{name:"from_config",anchor:"transformers.AutoModel.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModel.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>ASTConfig</code> configuration class: <code>ASTModel</code> (Audio Spectrogram Transformer model)</li>
<li><code>AlbertConfig</code> configuration class: <code>AlbertModel</code> (ALBERT model)</li>
<li><code>AlignConfig</code> configuration class: <code>AlignModel</code> (ALIGN model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPConfig">AltCLIPConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li>
<li><code>AriaConfig</code> configuration class: <code>AriaForConditionalGeneration</code> (Aria model)</li>
<li><code>AriaTextConfig</code> configuration class: <code>AriaTextModel</code> (AriaText model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/autoformer#transformers.AutoformerConfig">AutoformerConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/autoformer#transformers.AutoformerModel">AutoformerModel</a> (Autoformer model)</li>
<li><code>BambaConfig</code> configuration class: <code>BambaModel</code> (Bamba model)</li>
<li><code>BarkConfig</code> configuration class: <code>BarkModel</code> (Bark model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartModel">BartModel</a> (BART model)</li>
<li><code>BeitConfig</code> configuration class: <code>BeitModel</code> (BEiT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertModel">BertModel</a> (BERT model)</li>
<li><code>BertGenerationConfig</code> configuration class: <code>BertGenerationEncoder</code> (Bert Generation model)</li>
<li><code>BigBirdConfig</code> configuration class: <code>BigBirdModel</code> (BigBird model)</li>
<li><code>BigBirdPegasusConfig</code> configuration class: <code>BigBirdPegasusModel</code> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/biogpt#transformers.BioGptModel">BioGptModel</a> (BioGpt model)</li>
<li><code>BitConfig</code> configuration class: <code>BitModel</code> (BiT model)</li>
<li><code>BlenderbotConfig</code> configuration class: <code>BlenderbotModel</code> (Blenderbot model)</li>
<li><code>BlenderbotSmallConfig</code> configuration class: <code>BlenderbotSmallModel</code> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/blip-2#transformers.Blip2Model">Blip2Model</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li>
<li><code>BloomConfig</code> configuration class: <code>BloomModel</code> (BLOOM model)</li>
<li><code>BridgeTowerConfig</code> configuration class: <code>BridgeTowerModel</code> (BridgeTower model)</li>
<li><code>BrosConfig</code> configuration class: <code>BrosModel</code> (BROS model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><code>CLIPSegConfig</code> configuration class: <code>CLIPSegModel</code> (CLIPSeg model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTextConfig">CLIPTextConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTextModel">CLIPTextModel</a> (CLIPTextModel model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPVisionConfig">CLIPVisionConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPVisionModel">CLIPVisionModel</a> (CLIPVisionModel model)</li>
<li><code>CTRLConfig</code> configuration class: <code>CTRLModel</code> (CTRL model)</li>
<li><code>CamembertConfig</code> configuration class: <code>CamembertModel</code> (CamemBERT model)</li>
<li><code>CanineConfig</code> configuration class: <code>CanineModel</code> (CANINE model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/chameleon#transformers.ChameleonConfig">ChameleonConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/chameleon#transformers.ChameleonModel">ChameleonModel</a> (Chameleon model)</li>
<li><code>ChineseCLIPConfig</code> configuration class: <code>ChineseCLIPModel</code> (Chinese-CLIP model)</li>
<li><code>ChineseCLIPVisionConfig</code> configuration class: <code>ChineseCLIPVisionModel</code> (ChineseCLIPVisionModel model)</li>
<li><code>ClapConfig</code> configuration class: <code>ClapModel</code> (CLAP model)</li>
<li><code>ClvpConfig</code> configuration class: <code>ClvpModelForConditionalGeneration</code> (CLVP model)</li>
<li><code>CodeGenConfig</code> configuration class: <code>CodeGenModel</code> (CodeGen model)</li>
<li><code>Cohere2Config</code> configuration class: <code>Cohere2Model</code> (Cohere2 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/cohere#transformers.CohereConfig">CohereConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/cohere#transformers.CohereModel">CohereModel</a> (Cohere model)</li>
<li><code>ConditionalDetrConfig</code> configuration class: <code>ConditionalDetrModel</code> (Conditional DETR model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertModel">ConvBertModel</a> (ConvBERT model)</li>
<li><code>ConvNextConfig</code> configuration class: <code>ConvNextModel</code> (ConvNeXT model)</li>
<li><code>ConvNextV2Config</code> configuration class: <code>ConvNextV2Model</code> (ConvNeXTV2 model)</li>
<li><code>CpmAntConfig</code> configuration class: <code>CpmAntModel</code> (CPM-Ant model)</li>
<li><code>CvtConfig</code> configuration class: <code>CvtModel</code> (CvT model)</li>
<li><code>DPRConfig</code> configuration class: <code>DPRQuestionEncoder</code> (DPR model)</li>
<li><code>DPTConfig</code> configuration class: <code>DPTModel</code> (DPT model)</li>
<li><code>DacConfig</code> configuration class: <code>DacModel</code> (DAC model)</li>
<li><code>Data2VecAudioConfig</code> configuration class: <code>Data2VecAudioModel</code> (Data2VecAudio model)</li>
<li><code>Data2VecTextConfig</code> configuration class: <code>Data2VecTextModel</code> (Data2VecText model)</li>
<li><code>Data2VecVisionConfig</code> configuration class: <code>Data2VecVisionModel</code> (Data2VecVision model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/dbrx#transformers.DbrxConfig">DbrxConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/dbrx#transformers.DbrxModel">DbrxModel</a> (DBRX model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaModel">DebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2Model">DebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><code>DecisionTransformerConfig</code> configuration class: <code>DecisionTransformerModel</code> (Decision Transformer model)</li>
<li><code>DeformableDetrConfig</code> configuration class: <code>DeformableDetrModel</code> (Deformable DETR model)</li>
<li><code>DeiTConfig</code> configuration class: <code>DeiTModel</code> (DeiT model)</li>
<li><code>DetaConfig</code> configuration class: <code>DetaModel</code> (DETA model)</li>
<li><code>DetrConfig</code> configuration class: <code>DetrModel</code> (DETR model)</li>
<li><code>DiffLlamaConfig</code> configuration class: <code>DiffLlamaModel</code> (DiffLlama model)</li>
<li><code>DinatConfig</code> configuration class: <code>DinatModel</code> (DiNAT model)</li>
<li><code>Dinov2Config</code> configuration class: <code>Dinov2Model</code> (DINOv2 model)</li>
<li><code>Dinov2WithRegistersConfig</code> configuration class: <code>Dinov2WithRegistersModel</code> (DINOv2 with Registers model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>DistilBertModel</code> (DistilBERT model)</li>
<li><code>DonutSwinConfig</code> configuration class: <code>DonutSwinModel</code> (DonutSwin model)</li>
<li><code>EfficientFormerConfig</code> configuration class: <code>EfficientFormerModel</code> (EfficientFormer model)</li>
<li><code>EfficientNetConfig</code> configuration class: <code>EfficientNetModel</code> (EfficientNet model)</li>
<li><code>ElectraConfig</code> configuration class: <code>ElectraModel</code> (ELECTRA model)</li>
<li><code>EncodecConfig</code> configuration class: <code>EncodecModel</code> (EnCodec model)</li>
<li><code>ErnieConfig</code> configuration class: <code>ErnieModel</code> (ERNIE model)</li>
<li><code>ErnieMConfig</code> configuration class: <code>ErnieMModel</code> (ErnieM model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/esm#transformers.EsmModel">EsmModel</a> (ESM model)</li>
<li><code>FNetConfig</code> configuration class: <code>FNetModel</code> (FNet model)</li>
<li><code>FSMTConfig</code> configuration class: <code>FSMTModel</code> (FairSeq Machine-Translation model)</li>
<li><code>FalconConfig</code> configuration class: <code>FalconModel</code> (Falcon model)</li>
<li><code>FalconMambaConfig</code> configuration class: <code>FalconMambaModel</code> (FalconMamba model)</li>
<li><code>FastSpeech2ConformerConfig</code> configuration class: <code>FastSpeech2ConformerModel</code> (FastSpeech2Conformer model)</li>
<li><code>FlaubertConfig</code> configuration class: <code>FlaubertModel</code> (FlauBERT model)</li>
<li><code>FlavaConfig</code> configuration class: <code>FlavaModel</code> (FLAVA model)</li>
<li><code>FocalNetConfig</code> configuration class: <code>FocalNetModel</code> (FocalNet model)</li>
<li><code>FunnelConfig</code> configuration class: <code>FunnelModel</code> or <code>FunnelBaseModel</code> (Funnel Transformer model)</li>
<li><code>GLPNConfig</code> configuration class: <code>GLPNModel</code> (GLPN model)</li>
<li><code>GPT2Config</code> configuration class: <code>GPT2Model</code> (OpenAI GPT-2 model)</li>
<li><code>GPTBigCodeConfig</code> configuration class: <code>GPTBigCodeModel</code> (GPTBigCode model)</li>
<li><code>GPTJConfig</code> configuration class: <code>GPTJModel</code> (GPT-J model)</li>
<li><code>GPTNeoConfig</code> configuration class: <code>GPTNeoModel</code> (GPT Neo model)</li>
<li><code>GPTNeoXConfig</code> configuration class: <code>GPTNeoXModel</code> (GPT NeoX model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseModel">GPTNeoXJapaneseModel</a> (GPT NeoX Japanese model)</li>
<li><code>GPTSanJapaneseConfig</code> configuration class: <code>GPTSanJapaneseForConditionalGeneration</code> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/gemma2#transformers.Gemma2Config">Gemma2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/gemma2#transformers.Gemma2Model">Gemma2Model</a> (Gemma2 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaConfig">GemmaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaModel">GemmaModel</a> (Gemma model)</li>
<li><code>GitConfig</code> configuration class: <code>GitModel</code> (GIT model)</li>
<li><code>GlmConfig</code> configuration class: <code>GlmModel</code> (GLM model)</li>
<li><code>GraniteConfig</code> configuration class: <code>GraniteModel</code> (Granite model)</li>
<li><code>GraniteMoeConfig</code> configuration class: <code>GraniteMoeModel</code> (GraniteMoeMoe model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/graphormer#transformers.GraphormerConfig">GraphormerConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/graphormer#transformers.GraphormerModel">GraphormerModel</a> (Graphormer model)</li>
<li><code>GroundingDinoConfig</code> configuration class: <code>GroundingDinoModel</code> (Grounding DINO model)</li>
<li><code>GroupViTConfig</code> configuration class: <code>GroupViTModel</code> (GroupViT model)</li>
<li><code>HeliumConfig</code> configuration class: <code>HeliumModel</code> (Helium model)</li>
<li><code>HieraConfig</code> configuration class: <code>HieraModel</code> (Hiera model)</li>
<li><code>HubertConfig</code> configuration class: <code>HubertModel</code> (Hubert model)</li>
<li><code>IBertConfig</code> configuration class: <code>IBertModel</code> (I-BERT model)</li>
<li><code>IJepaConfig</code> configuration class: <code>IJepaModel</code> (I-JEPA model)</li>
<li><code>Idefics2Config</code> configuration class: <code>Idefics2Model</code> (Idefics2 model)</li>
<li><code>Idefics3Config</code> configuration class: <code>Idefics3Model</code> (Idefics3 model)</li>
<li><code>Idefics3VisionConfig</code> configuration class: <code>Idefics3VisionTransformer</code> (Idefics3VisionTransformer model)</li>
<li><code>IdeficsConfig</code> configuration class: <code>IdeficsModel</code> (IDEFICS model)</li>
<li><code>ImageGPTConfig</code> configuration class: <code>ImageGPTModel</code> (ImageGPT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/informer#transformers.InformerConfig">InformerConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/informer#transformers.InformerModel">InformerModel</a> (Informer model)</li>
<li><code>JambaConfig</code> configuration class: <code>JambaModel</code> (Jamba model)</li>
<li><code>JetMoeConfig</code> configuration class: <code>JetMoeModel</code> (JetMoe model)</li>
<li><code>JukeboxConfig</code> configuration class: <code>JukeboxModel</code> (Jukebox model)</li>
<li><code>Kosmos2Config</code> configuration class: <code>Kosmos2Model</code> (KOSMOS-2 model)</li>
<li><code>LEDConfig</code> configuration class: <code>LEDModel</code> (LED model)</li>
<li><code>LayoutLMConfig</code> configuration class: <code>LayoutLMModel</code> (LayoutLM model)</li>
<li><code>LayoutLMv2Config</code> configuration class: <code>LayoutLMv2Model</code> (LayoutLMv2 model)</li>
<li><code>LayoutLMv3Config</code> configuration class: <code>LayoutLMv3Model</code> (LayoutLMv3 model)</li>
<li><code>LevitConfig</code> configuration class: <code>LevitModel</code> (LeViT model)</li>
<li><code>LiltConfig</code> configuration class: <code>LiltModel</code> (LiLT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaModel">LlamaModel</a> (LLaMA model)</li>
<li><code>LongT5Config</code> configuration class: <code>LongT5Model</code> (LongT5 model)</li>
<li><code>LongformerConfig</code> configuration class: <code>LongformerModel</code> (Longformer model)</li>
<li><code>LukeConfig</code> configuration class: <code>LukeModel</code> (LUKE model)</li>
<li><code>LxmertConfig</code> configuration class: <code>LxmertModel</code> (LXMERT model)</li>
<li><code>M2M100Config</code> configuration class: <code>M2M100Model</code> (M2M100 model)</li>
<li><code>MBartConfig</code> configuration class: <code>MBartModel</code> (mBART model)</li>
<li><code>MCTCTConfig</code> configuration class: <code>MCTCTModel</code> (M-CTC-T model)</li>
<li><code>MPNetConfig</code> configuration class: <code>MPNetModel</code> (MPNet model)</li>
<li><code>MT5Config</code> configuration class: <code>MT5Model</code> (MT5 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/mamba2#transformers.Mamba2Config">Mamba2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/mamba2#transformers.Mamba2Model">Mamba2Model</a> (mamba2 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/mamba#transformers.MambaConfig">MambaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/mamba#transformers.MambaModel">MambaModel</a> (Mamba model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/marian#transformers.MarianModel">MarianModel</a> (Marian model)</li>
<li><code>MarkupLMConfig</code> configuration class: <code>MarkupLMModel</code> (MarkupLM model)</li>
<li><code>Mask2FormerConfig</code> configuration class: <code>Mask2FormerModel</code> (Mask2Former model)</li>
<li><code>MaskFormerConfig</code> configuration class: <code>MaskFormerModel</code> (MaskFormer model)</li>
<li><code>MaskFormerSwinConfig</code> configuration class: <code>MaskFormerSwinModel</code> (MaskFormerSwin model)</li>
<li><code>MegaConfig</code> configuration class: <code>MegaModel</code> (MEGA model)</li>
<li><code>MegatronBertConfig</code> configuration class: <code>MegatronBertModel</code> (Megatron-BERT model)</li>
<li><code>MgpstrConfig</code> configuration class: <code>MgpstrForSceneTextRecognition</code> (MGP-STR model)</li>
<li><code>MimiConfig</code> configuration class: <code>MimiModel</code> (Mimi model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/mistral#transformers.MistralModel">MistralModel</a> (Mistral model)</li>
<li><code>MixtralConfig</code> configuration class: <code>MixtralModel</code> (Mixtral model)</li>
<li><code>MobileBertConfig</code> configuration class: <code>MobileBertModel</code> (MobileBERT model)</li>
<li><code>MobileNetV1Config</code> configuration class: <code>MobileNetV1Model</code> (MobileNetV1 model)</li>
<li><code>MobileNetV2Config</code> configuration class: <code>MobileNetV2Model</code> (MobileNetV2 model)</li>
<li><code>MobileViTConfig</code> configuration class: <code>MobileViTModel</code> (MobileViT model)</li>
<li><code>MobileViTV2Config</code> configuration class: <code>MobileViTV2Model</code> (MobileViTV2 model)</li>
<li><code>ModernBertConfig</code> configuration class: <code>ModernBertModel</code> (ModernBERT model)</li>
<li><code>MoonshineConfig</code> configuration class: <code>MoonshineModel</code> (Moonshine model)</li>
<li><code>MoshiConfig</code> configuration class: <code>MoshiModel</code> (Moshi model)</li>
<li><code>MptConfig</code> configuration class: <code>MptModel</code> (MPT model)</li>
<li><code>MraConfig</code> configuration class: <code>MraModel</code> (MRA model)</li>
<li><code>MusicgenConfig</code> configuration class: <code>MusicgenModel</code> (MusicGen model)</li>
<li><code>MusicgenMelodyConfig</code> configuration class: <code>MusicgenMelodyModel</code> (MusicGen Melody model)</li>
<li><code>MvpConfig</code> configuration class: <code>MvpModel</code> (MVP model)</li>
<li><code>NatConfig</code> configuration class: <code>NatModel</code> (NAT model)</li>
<li><code>NemotronConfig</code> configuration class: <code>NemotronModel</code> (Nemotron model)</li>
<li><code>NezhaConfig</code> configuration class: <code>NezhaModel</code> (Nezha model)</li>
<li><code>NllbMoeConfig</code> configuration class: <code>NllbMoeModel</code> (NLLB-MOE model)</li>
<li><code>NystromformerConfig</code> configuration class: <code>NystromformerModel</code> (Nystr&#xF6;mformer model)</li>
<li><code>OPTConfig</code> configuration class: <code>OPTModel</code> (OPT model)</li>
<li><code>Olmo2Config</code> configuration class: <code>Olmo2Model</code> (OLMo2 model)</li>
<li><code>OlmoConfig</code> configuration class: <code>OlmoModel</code> (OLMo model)</li>
<li><code>OlmoeConfig</code> configuration class: <code>OlmoeModel</code> (OLMoE model)</li>
<li><code>OmDetTurboConfig</code> configuration class: <code>OmDetTurboForObjectDetection</code> (OmDet-Turbo model)</li>
<li><code>OneFormerConfig</code> configuration class: <code>OneFormerModel</code> (OneFormer model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.OpenAIGPTModel">OpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><code>OpenLlamaConfig</code> configuration class: <code>OpenLlamaModel</code> (OpenLlama model)</li>
<li><code>OwlViTConfig</code> configuration class: <code>OwlViTModel</code> (OWL-ViT model)</li>
<li><code>Owlv2Config</code> configuration class: <code>Owlv2Model</code> (OWLv2 model)</li>
<li><code>PLBartConfig</code> configuration class: <code>PLBartModel</code> (PLBart model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/patchtsmixer#transformers.PatchTSMixerConfig">PatchTSMixerConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/patchtsmixer#transformers.PatchTSMixerModel">PatchTSMixerModel</a> (PatchTSMixer model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/patchtst#transformers.PatchTSTConfig">PatchTSTConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/patchtst#transformers.PatchTSTModel">PatchTSTModel</a> (PatchTST model)</li>
<li><code>PegasusConfig</code> configuration class: <code>PegasusModel</code> (Pegasus model)</li>
<li><code>PegasusXConfig</code> configuration class: <code>PegasusXModel</code> (PEGASUS-X model)</li>
<li><code>PerceiverConfig</code> configuration class: <code>PerceiverModel</code> (Perceiver model)</li>
<li><code>PersimmonConfig</code> configuration class: <code>PersimmonModel</code> (Persimmon model)</li>
<li><code>Phi3Config</code> configuration class: <code>Phi3Model</code> (Phi3 model)</li>
<li><code>PhiConfig</code> configuration class: <code>PhiModel</code> (Phi model)</li>
<li><code>PhimoeConfig</code> configuration class: <code>PhimoeModel</code> (Phimoe model)</li>
<li><code>PixtralVisionConfig</code> configuration class: <code>PixtralVisionModel</code> (Pixtral model)</li>
<li><code>PoolFormerConfig</code> configuration class: <code>PoolFormerModel</code> (PoolFormer model)</li>
<li><code>ProphetNetConfig</code> configuration class: <code>ProphetNetModel</code> (ProphetNet model)</li>
<li><code>PvtConfig</code> configuration class: <code>PvtModel</code> (PVT model)</li>
<li><code>PvtV2Config</code> configuration class: <code>PvtV2Model</code> (PVTv2 model)</li>
<li><code>QDQBertConfig</code> configuration class: <code>QDQBertModel</code> (QDQBert model)</li>
<li><code>Qwen2AudioEncoderConfig</code> configuration class: <code>Qwen2AudioEncoder</code> (Qwen2AudioEncoder model)</li>
<li><code>Qwen2Config</code> configuration class: <code>Qwen2Model</code> (Qwen2 model)</li>
<li><code>Qwen2MoeConfig</code> configuration class: <code>Qwen2MoeModel</code> (Qwen2MoE model)</li>
<li><code>Qwen2VLConfig</code> configuration class: <code>Qwen2VLModel</code> (Qwen2VL model)</li>
<li><code>Qwen2_5_VLConfig</code> configuration class: <code>Qwen2_5_VLModel</code> (Qwen2_5_VL model)</li>
<li><code>RTDetrConfig</code> configuration class: <code>RTDetrModel</code> (RT-DETR model)</li>
<li><code>RecurrentGemmaConfig</code> configuration class: <code>RecurrentGemmaModel</code> (RecurrentGemma model)</li>
<li><code>ReformerConfig</code> configuration class: <code>ReformerModel</code> (Reformer model)</li>
<li><code>RegNetConfig</code> configuration class: <code>RegNetModel</code> (RegNet model)</li>
<li><code>RemBertConfig</code> configuration class: <code>RemBertModel</code> (RemBERT model)</li>
<li><code>ResNetConfig</code> configuration class: <code>ResNetModel</code> (ResNet model)</li>
<li><code>RetriBertConfig</code> configuration class: <code>RetriBertModel</code> (RetriBERT model)</li>
<li><code>RoCBertConfig</code> configuration class: <code>RoCBertModel</code> (RoCBert model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>RoFormerModel</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>RobertaModel</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>RobertaPreLayerNormModel</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>RwkvConfig</code> configuration class: <code>RwkvModel</code> (RWKV model)</li>
<li><code>SEWConfig</code> configuration class: <code>SEWModel</code> (SEW model)</li>
<li><code>SEWDConfig</code> configuration class: <code>SEWDModel</code> (SEW-D model)</li>
<li><code>SamConfig</code> configuration class: <code>SamModel</code> (SAM model)</li>
<li><code>SeamlessM4TConfig</code> configuration class: <code>SeamlessM4TModel</code> (SeamlessM4T model)</li>
<li><code>SeamlessM4Tv2Config</code> configuration class: <code>SeamlessM4Tv2Model</code> (SeamlessM4Tv2 model)</li>
<li><code>SegGptConfig</code> configuration class: <code>SegGptModel</code> (SegGPT model)</li>
<li><code>SegformerConfig</code> configuration class: <code>SegformerModel</code> (SegFormer model)</li>
<li><code>SiglipConfig</code> configuration class: <code>SiglipModel</code> (SigLIP model)</li>
<li><code>SiglipVisionConfig</code> configuration class: <code>SiglipVisionModel</code> (SiglipVisionModel model)</li>
<li><code>Speech2TextConfig</code> configuration class: <code>Speech2TextModel</code> (Speech2Text model)</li>
<li><code>SpeechT5Config</code> configuration class: <code>SpeechT5Model</code> (SpeechT5 model)</li>
<li><code>SplinterConfig</code> configuration class: <code>SplinterModel</code> (Splinter model)</li>
<li><code>SqueezeBertConfig</code> configuration class: <code>SqueezeBertModel</code> (SqueezeBERT model)</li>
<li><code>StableLmConfig</code> configuration class: <code>StableLmModel</code> (StableLm model)</li>
<li><code>Starcoder2Config</code> configuration class: <code>Starcoder2Model</code> (Starcoder2 model)</li>
<li><code>SuperGlueConfig</code> configuration class: <code>SuperGlueForKeypointMatching</code> (SuperGlue model)</li>
<li><code>SwiftFormerConfig</code> configuration class: <code>SwiftFormerModel</code> (SwiftFormer model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/swin2sr#transformers.Swin2SRConfig">Swin2SRConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/swin2sr#transformers.Swin2SRModel">Swin2SRModel</a> (Swin2SR model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/swin#transformers.SwinModel">SwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2Model">Swinv2Model</a> (Swin Transformer V2 model)</li>
<li><code>SwitchTransformersConfig</code> configuration class: <code>SwitchTransformersModel</code> (SwitchTransformers model)</li>
<li><code>T5Config</code> configuration class: <code>T5Model</code> (T5 model)</li>
<li><code>TableTransformerConfig</code> configuration class: <code>TableTransformerModel</code> (Table Transformer model)</li>
<li><code>TapasConfig</code> configuration class: <code>TapasModel</code> (TAPAS model)</li>
<li><code>TextNetConfig</code> configuration class: <code>TextNetModel</code> (TextNet model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/time_series_transformer#transformers.TimeSeriesTransformerConfig">TimeSeriesTransformerConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/time_series_transformer#transformers.TimeSeriesTransformerModel">TimeSeriesTransformerModel</a> (Time Series Transformer model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/timesformer#transformers.TimesformerModel">TimesformerModel</a> (TimeSformer model)</li>
<li><code>TimmBackboneConfig</code> configuration class: <code>TimmBackbone</code> (TimmBackbone model)</li>
<li><code>TimmWrapperConfig</code> configuration class: <code>TimmWrapperModel</code> (TimmWrapperModel model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/trajectory_transformer#transformers.TrajectoryTransformerConfig">TrajectoryTransformerConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/trajectory_transformer#transformers.TrajectoryTransformerModel">TrajectoryTransformerModel</a> (Trajectory Transformer model)</li>
<li><code>TransfoXLConfig</code> configuration class: <code>TransfoXLModel</code> (Transformer-XL model)</li>
<li><code>TvltConfig</code> configuration class: <code>TvltModel</code> (TVLT model)</li>
<li><code>TvpConfig</code> configuration class: <code>TvpModel</code> (TVP model)</li>
<li><code>UMT5Config</code> configuration class: <code>UMT5Model</code> (UMT5 model)</li>
<li><code>UdopConfig</code> configuration class: <code>UdopModel</code> (UDOP model)</li>
<li><code>UniSpeechConfig</code> configuration class: <code>UniSpeechModel</code> (UniSpeech model)</li>
<li><code>UniSpeechSatConfig</code> configuration class: <code>UniSpeechSatModel</code> (UniSpeechSat model)</li>
<li><code>UnivNetConfig</code> configuration class: <code>UnivNetModel</code> (UnivNet model)</li>
<li><code>VanConfig</code> configuration class: <code>VanModel</code> (VAN model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTModel">ViTModel</a> (ViT model)</li>
<li><code>ViTHybridConfig</code> configuration class: <code>ViTHybridModel</code> (ViT Hybrid model)</li>
<li><code>ViTMAEConfig</code> configuration class: <code>ViTMAEModel</code> (ViTMAE model)</li>
<li><code>ViTMSNConfig</code> configuration class: <code>ViTMSNModel</code> (ViTMSN model)</li>
<li><code>VideoMAEConfig</code> configuration class: <code>VideoMAEModel</code> (VideoMAE model)</li>
<li><code>ViltConfig</code> configuration class: <code>ViltModel</code> (ViLT model)</li>
<li><code>VisionTextDualEncoderConfig</code> configuration class: <code>VisionTextDualEncoderModel</code> (VisionTextDualEncoder model)</li>
<li><code>VisualBertConfig</code> configuration class: <code>VisualBertModel</code> (VisualBERT model)</li>
<li><code>VitDetConfig</code> configuration class: <code>VitDetModel</code> (VitDet model)</li>
<li><code>VitsConfig</code> configuration class: <code>VitsModel</code> (VITS model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/vivit#transformers.VivitConfig">VivitConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/vivit#transformers.VivitModel">VivitModel</a> (ViViT model)</li>
<li><code>Wav2Vec2BertConfig</code> configuration class: <code>Wav2Vec2BertModel</code> (Wav2Vec2-BERT model)</li>
<li><code>Wav2Vec2Config</code> configuration class: <code>Wav2Vec2Model</code> (Wav2Vec2 model)</li>
<li><code>Wav2Vec2ConformerConfig</code> configuration class: <code>Wav2Vec2ConformerModel</code> (Wav2Vec2-Conformer model)</li>
<li><code>WavLMConfig</code> configuration class: <code>WavLMModel</code> (WavLM model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperModel">WhisperModel</a> (Whisper model)</li>
<li><code>XCLIPConfig</code> configuration class: <code>XCLIPModel</code> (X-CLIP model)</li>
<li><code>XGLMConfig</code> configuration class: <code>XGLMModel</code> (XGLM model)</li>
<li><code>XLMConfig</code> configuration class: <code>XLMModel</code> (XLM model)</li>
<li><code>XLMProphetNetConfig</code> configuration class: <code>XLMProphetNetModel</code> (XLM-ProphetNet model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>XLMRobertaModel</code> (XLM-RoBERTa model)</li>
<li><code>XLMRobertaXLConfig</code> configuration class: <code>XLMRobertaXLModel</code> (XLM-RoBERTa-XL model)</li>
<li><code>XLNetConfig</code> configuration class: <code>XLNetModel</code> (XLNet model)</li>
<li><code>XmodConfig</code> configuration class: <code>XmodModel</code> (X-MOD model)</li>
<li><code>YolosConfig</code> configuration class: <code>YolosModel</code> (YOLOS model)</li>
<li><code>YosoConfig</code> configuration class: <code>YosoModel</code> (YOSO model)</li>
<li><code>Zamba2Config</code> configuration class: <code>Zamba2Model</code> (Zamba2 model)</li>
<li><code>ZambaConfig</code> configuration class: <code>ZambaModel</code> (Zamba model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModel.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Ws=new L({props:{anchor:"transformers.AutoModel.from_config.example",$$slots:{default:[Bq]},$$scope:{ctx:v}}}),tc=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModel.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModel.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModel.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModel.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModel.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModel.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModel.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModel.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModel.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModel.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModel.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModel.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModel.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModel.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModel.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Es=new L({props:{anchor:"transformers.AutoModel.from_pretrained.example",$$slots:{default:[Rq]},$$scope:{ctx:v}}}),nc=new A({props:{title:"TFAutoModel",local:"transformers.TFAutoModel ][ transformers.TFAutoModel",headingTag:"h3"}}),rc=new k({props:{name:"class transformers.TFAutoModel",anchor:"transformers.TFAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L537"}}),ac=new k({props:{name:"from_config",anchor:"transformers.TFAutoModel.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModel.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>TFAlbertModel</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bart#transformers.TFBartModel">TFBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.TFBertModel">TFBertModel</a> (BERT model)</li>
<li><code>BlenderbotConfig</code> configuration class: <code>TFBlenderbotModel</code> (Blenderbot model)</li>
<li><code>BlenderbotSmallConfig</code> configuration class: <code>TFBlenderbotSmallModel</code> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/blip#transformers.TFBlipModel">TFBlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>
<li><code>CTRLConfig</code> configuration class: <code>TFCTRLModel</code> (CTRL model)</li>
<li><code>CamembertConfig</code> configuration class: <code>TFCamembertModel</code> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.TFConvBertModel">TFConvBertModel</a> (ConvBERT model)</li>
<li><code>ConvNextConfig</code> configuration class: <code>TFConvNextModel</code> (ConvNeXT model)</li>
<li><code>ConvNextV2Config</code> configuration class: <code>TFConvNextV2Model</code> (ConvNeXTV2 model)</li>
<li><code>CvtConfig</code> configuration class: <code>TFCvtModel</code> (CvT model)</li>
<li><code>DPRConfig</code> configuration class: <code>TFDPRQuestionEncoder</code> (DPR model)</li>
<li><code>Data2VecVisionConfig</code> configuration class: <code>TFData2VecVisionModel</code> (Data2VecVision model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.TFDebertaModel">TFDebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.TFDebertaV2Model">TFDebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><code>DeiTConfig</code> configuration class: <code>TFDeiTModel</code> (DeiT model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>TFDistilBertModel</code> (DistilBERT model)</li>
<li><code>EfficientFormerConfig</code> configuration class: <code>TFEfficientFormerModel</code> (EfficientFormer model)</li>
<li><code>ElectraConfig</code> configuration class: <code>TFElectraModel</code> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/esm#transformers.TFEsmModel">TFEsmModel</a> (ESM model)</li>
<li><code>FlaubertConfig</code> configuration class: <code>TFFlaubertModel</code> (FlauBERT model)</li>
<li><code>FunnelConfig</code> configuration class: <code>TFFunnelModel</code> or <code>TFFunnelBaseModel</code> (Funnel Transformer model)</li>
<li><code>GPT2Config</code> configuration class: <code>TFGPT2Model</code> (OpenAI GPT-2 model)</li>
<li><code>GPTJConfig</code> configuration class: <code>TFGPTJModel</code> (GPT-J model)</li>
<li><code>GroupViTConfig</code> configuration class: <code>TFGroupViTModel</code> (GroupViT model)</li>
<li><code>HubertConfig</code> configuration class: <code>TFHubertModel</code> (Hubert model)</li>
<li><code>IdeficsConfig</code> configuration class: <code>TFIdeficsModel</code> (IDEFICS model)</li>
<li><code>LEDConfig</code> configuration class: <code>TFLEDModel</code> (LED model)</li>
<li><code>LayoutLMConfig</code> configuration class: <code>TFLayoutLMModel</code> (LayoutLM model)</li>
<li><code>LayoutLMv3Config</code> configuration class: <code>TFLayoutLMv3Model</code> (LayoutLMv3 model)</li>
<li><code>LongformerConfig</code> configuration class: <code>TFLongformerModel</code> (Longformer model)</li>
<li><code>LxmertConfig</code> configuration class: <code>TFLxmertModel</code> (LXMERT model)</li>
<li><code>MBartConfig</code> configuration class: <code>TFMBartModel</code> (mBART model)</li>
<li><code>MPNetConfig</code> configuration class: <code>TFMPNetModel</code> (MPNet model)</li>
<li><code>MT5Config</code> configuration class: <code>TFMT5Model</code> (MT5 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/marian#transformers.TFMarianModel">TFMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/mistral#transformers.TFMistralModel">TFMistralModel</a> (Mistral model)</li>
<li><code>MobileBertConfig</code> configuration class: <code>TFMobileBertModel</code> (MobileBERT model)</li>
<li><code>MobileViTConfig</code> configuration class: <code>TFMobileViTModel</code> (MobileViT model)</li>
<li><code>OPTConfig</code> configuration class: <code>TFOPTModel</code> (OPT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.TFOpenAIGPTModel">TFOpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><code>PegasusConfig</code> configuration class: <code>TFPegasusModel</code> (Pegasus model)</li>
<li><code>RegNetConfig</code> configuration class: <code>TFRegNetModel</code> (RegNet model)</li>
<li><code>RemBertConfig</code> configuration class: <code>TFRemBertModel</code> (RemBERT model)</li>
<li><code>ResNetConfig</code> configuration class: <code>TFResNetModel</code> (ResNet model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>TFRoFormerModel</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>TFRobertaModel</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>TFRobertaPreLayerNormModel</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>SamConfig</code> configuration class: <code>TFSamModel</code> (SAM model)</li>
<li><code>SegformerConfig</code> configuration class: <code>TFSegformerModel</code> (SegFormer model)</li>
<li><code>Speech2TextConfig</code> configuration class: <code>TFSpeech2TextModel</code> (Speech2Text model)</li>
<li><code>SwiftFormerConfig</code> configuration class: <code>TFSwiftFormerModel</code> (SwiftFormer model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/swin#transformers.TFSwinModel">TFSwinModel</a> (Swin Transformer model)</li>
<li><code>T5Config</code> configuration class: <code>TFT5Model</code> (T5 model)</li>
<li><code>TapasConfig</code> configuration class: <code>TFTapasModel</code> (TAPAS model)</li>
<li><code>TransfoXLConfig</code> configuration class: <code>TFTransfoXLModel</code> (Transformer-XL model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/vit#transformers.TFViTModel">TFViTModel</a> (ViT model)</li>
<li><code>ViTMAEConfig</code> configuration class: <code>TFViTMAEModel</code> (ViTMAE model)</li>
<li><code>VisionTextDualEncoderConfig</code> configuration class: <code>TFVisionTextDualEncoderModel</code> (VisionTextDualEncoder model)</li>
<li><code>Wav2Vec2Config</code> configuration class: <code>TFWav2Vec2Model</code> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.TFWhisperModel">TFWhisperModel</a> (Whisper model)</li>
<li><code>XGLMConfig</code> configuration class: <code>TFXGLMModel</code> (XGLM model)</li>
<li><code>XLMConfig</code> configuration class: <code>TFXLMModel</code> (XLM model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>TFXLMRobertaModel</code> (XLM-RoBERTa model)</li>
<li><code>XLNetConfig</code> configuration class: <code>TFXLNetModel</code> (XLNet model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModel.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Vs=new L({props:{anchor:"transformers.TFAutoModel.from_config.example",$$slots:{default:[Gq]},$$scope:{ctx:v}}}),sc=new k({props:{name:"from_pretrained",anchor:"transformers.TFAutoModel.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModel.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModel.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModel.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModel.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModel.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModel.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.TFAutoModel.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModel.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModel.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModel.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModel.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModel.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModel.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Is=new L({props:{anchor:"transformers.TFAutoModel.from_pretrained.example",$$slots:{default:[Jq]},$$scope:{ctx:v}}}),ic=new A({props:{title:"FlaxAutoModel",local:"transformers.FlaxAutoModel ][ transformers.FlaxAutoModel",headingTag:"h3"}}),lc=new k({props:{name:"class transformers.FlaxAutoModel",anchor:"transformers.FlaxAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L281"}}),dc=new k({props:{name:"from_config",anchor:"transformers.FlaxAutoModel.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModel.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>FlaxAlbertModel</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bart#transformers.FlaxBartModel">FlaxBartModel</a> (BART model)</li>
<li><code>BeitConfig</code> configuration class: <code>FlaxBeitModel</code> (BEiT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.FlaxBertModel">FlaxBertModel</a> (BERT model)</li>
<li><code>BigBirdConfig</code> configuration class: <code>FlaxBigBirdModel</code> (BigBird model)</li>
<li><code>BlenderbotConfig</code> configuration class: <code>FlaxBlenderbotModel</code> (Blenderbot model)</li>
<li><code>BlenderbotSmallConfig</code> configuration class: <code>FlaxBlenderbotSmallModel</code> (BlenderbotSmall model)</li>
<li><code>BloomConfig</code> configuration class: <code>FlaxBloomModel</code> (BLOOM model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/clip#transformers.FlaxCLIPModel">FlaxCLIPModel</a> (CLIP model)</li>
<li><code>Dinov2Config</code> configuration class: <code>FlaxDinov2Model</code> (DINOv2 model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>FlaxDistilBertModel</code> (DistilBERT model)</li>
<li><code>ElectraConfig</code> configuration class: <code>FlaxElectraModel</code> (ELECTRA model)</li>
<li><code>GPT2Config</code> configuration class: <code>FlaxGPT2Model</code> (OpenAI GPT-2 model)</li>
<li><code>GPTJConfig</code> configuration class: <code>FlaxGPTJModel</code> (GPT-J model)</li>
<li><code>GPTNeoConfig</code> configuration class: <code>FlaxGPTNeoModel</code> (GPT Neo model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaConfig">GemmaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.FlaxGemmaModel">FlaxGemmaModel</a> (Gemma model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> configuration class: <code>FlaxLlamaModel</code> (LLaMA model)</li>
<li><code>LongT5Config</code> configuration class: <code>FlaxLongT5Model</code> (LongT5 model)</li>
<li><code>MBartConfig</code> configuration class: <code>FlaxMBartModel</code> (mBART model)</li>
<li><code>MT5Config</code> configuration class: <code>FlaxMT5Model</code> (MT5 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/marian#transformers.FlaxMarianModel">FlaxMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/mistral#transformers.FlaxMistralModel">FlaxMistralModel</a> (Mistral model)</li>
<li><code>OPTConfig</code> configuration class: <code>FlaxOPTModel</code> (OPT model)</li>
<li><code>PegasusConfig</code> configuration class: <code>FlaxPegasusModel</code> (Pegasus model)</li>
<li><code>RegNetConfig</code> configuration class: <code>FlaxRegNetModel</code> (RegNet model)</li>
<li><code>ResNetConfig</code> configuration class: <code>FlaxResNetModel</code> (ResNet model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>FlaxRoFormerModel</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>FlaxRobertaModel</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>FlaxRobertaPreLayerNormModel</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>T5Config</code> configuration class: <code>FlaxT5Model</code> (T5 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/vit#transformers.FlaxViTModel">FlaxViTModel</a> (ViT model)</li>
<li><code>VisionTextDualEncoderConfig</code> configuration class: <code>FlaxVisionTextDualEncoderModel</code> (VisionTextDualEncoder model)</li>
<li><code>Wav2Vec2Config</code> configuration class: <code>FlaxWav2Vec2Model</code> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.FlaxWhisperModel">FlaxWhisperModel</a> (Whisper model)</li>
<li><code>XGLMConfig</code> configuration class: <code>FlaxXGLMModel</code> (XGLM model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>FlaxXLMRobertaModel</code> (XLM-RoBERTa model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModel.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Ps=new L({props:{anchor:"transformers.FlaxAutoModel.from_config.example",$$slots:{default:[Wq]},$$scope:{ctx:v}}}),cc=new k({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModel.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModel.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModel.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModel.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModel.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModel.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModel.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModel.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModel.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModel.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModel.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModel.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModel.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModel.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Ss=new L({props:{anchor:"transformers.FlaxAutoModel.from_pretrained.example",$$slots:{default:[Eq]},$$scope:{ctx:v}}}),mc=new A({props:{title:"일반적인 사전 학습 클래스",local:"generic-pretraining-classes",headingTag:"h2"}}),gc=new A({props:{title:"AutoModelForPreTraining",local:"transformers.AutoModelForPreTraining ][ transformers.AutoModelForPreTraining",headingTag:"h3"}}),pc=new k({props:{name:"class transformers.AutoModelForPreTraining",anchor:"transformers.AutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1615"}}),uc=new k({props:{name:"from_config",anchor:"transformers.AutoModelForPreTraining.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForPreTraining.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>AlbertForPreTraining</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertForPreTraining">BertForPreTraining</a> (BERT model)</li>
<li><code>BigBirdConfig</code> configuration class: <code>BigBirdForPreTraining</code> (BigBird model)</li>
<li><code>BloomConfig</code> configuration class: <code>BloomForCausalLM</code> (BLOOM model)</li>
<li><code>CTRLConfig</code> configuration class: <code>CTRLLMHeadModel</code> (CTRL model)</li>
<li><code>CamembertConfig</code> configuration class: <code>CamembertForMaskedLM</code> (CamemBERT model)</li>
<li><code>ColPaliConfig</code> configuration class: <code>ColPaliForRetrieval</code> (ColPali model)</li>
<li><code>Data2VecTextConfig</code> configuration class: <code>Data2VecTextForMaskedLM</code> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>DistilBertForMaskedLM</code> (DistilBERT model)</li>
<li><code>ElectraConfig</code> configuration class: <code>ElectraForPreTraining</code> (ELECTRA model)</li>
<li><code>ErnieConfig</code> configuration class: <code>ErnieForPreTraining</code> (ERNIE model)</li>
<li><code>FNetConfig</code> configuration class: <code>FNetForPreTraining</code> (FNet model)</li>
<li><code>FSMTConfig</code> configuration class: <code>FSMTForConditionalGeneration</code> (FairSeq Machine-Translation model)</li>
<li><code>FalconMambaConfig</code> configuration class: <code>FalconMambaForCausalLM</code> (FalconMamba model)</li>
<li><code>FlaubertConfig</code> configuration class: <code>FlaubertWithLMHeadModel</code> (FlauBERT model)</li>
<li><code>FlavaConfig</code> configuration class: <code>FlavaForPreTraining</code> (FLAVA model)</li>
<li><code>FunnelConfig</code> configuration class: <code>FunnelForPreTraining</code> (Funnel Transformer model)</li>
<li><code>GPT2Config</code> configuration class: <code>GPT2LMHeadModel</code> (OpenAI GPT-2 model)</li>
<li><code>GPTBigCodeConfig</code> configuration class: <code>GPTBigCodeForCausalLM</code> (GPTBigCode model)</li>
<li><code>GPTSanJapaneseConfig</code> configuration class: <code>GPTSanJapaneseForConditionalGeneration</code> (GPTSAN-japanese model)</li>
<li><code>HieraConfig</code> configuration class: <code>HieraForPreTraining</code> (Hiera model)</li>
<li><code>IBertConfig</code> configuration class: <code>IBertForMaskedLM</code> (I-BERT model)</li>
<li><code>Idefics2Config</code> configuration class: <code>Idefics2ForConditionalGeneration</code> (Idefics2 model)</li>
<li><code>Idefics3Config</code> configuration class: <code>Idefics3ForConditionalGeneration</code> (Idefics3 model)</li>
<li><code>IdeficsConfig</code> configuration class: <code>IdeficsForVisionText2Text</code> (IDEFICS model)</li>
<li><code>LayoutLMConfig</code> configuration class: <code>LayoutLMForMaskedLM</code> (LayoutLM model)</li>
<li><code>LlavaConfig</code> configuration class: <code>LlavaForConditionalGeneration</code> (LLaVa model)</li>
<li><code>LlavaNextConfig</code> configuration class: <code>LlavaNextForConditionalGeneration</code> (LLaVA-NeXT model)</li>
<li><code>LlavaNextVideoConfig</code> configuration class: <code>LlavaNextVideoForConditionalGeneration</code> (LLaVa-NeXT-Video model)</li>
<li><code>LlavaOnevisionConfig</code> configuration class: <code>LlavaOnevisionForConditionalGeneration</code> (LLaVA-Onevision model)</li>
<li><code>LongformerConfig</code> configuration class: <code>LongformerForMaskedLM</code> (Longformer model)</li>
<li><code>LukeConfig</code> configuration class: <code>LukeForMaskedLM</code> (LUKE model)</li>
<li><code>LxmertConfig</code> configuration class: <code>LxmertForPreTraining</code> (LXMERT model)</li>
<li><code>MPNetConfig</code> configuration class: <code>MPNetForMaskedLM</code> (MPNet model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/mamba2#transformers.Mamba2Config">Mamba2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/mamba2#transformers.Mamba2ForCausalLM">Mamba2ForCausalLM</a> (mamba2 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/mamba#transformers.MambaConfig">MambaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/mamba#transformers.MambaForCausalLM">MambaForCausalLM</a> (Mamba model)</li>
<li><code>MegaConfig</code> configuration class: <code>MegaForMaskedLM</code> (MEGA model)</li>
<li><code>MegatronBertConfig</code> configuration class: <code>MegatronBertForPreTraining</code> (Megatron-BERT model)</li>
<li><code>MllamaConfig</code> configuration class: <code>MllamaForConditionalGeneration</code> (Mllama model)</li>
<li><code>MobileBertConfig</code> configuration class: <code>MobileBertForPreTraining</code> (MobileBERT model)</li>
<li><code>MptConfig</code> configuration class: <code>MptForCausalLM</code> (MPT model)</li>
<li><code>MraConfig</code> configuration class: <code>MraForMaskedLM</code> (MRA model)</li>
<li><code>MvpConfig</code> configuration class: <code>MvpForConditionalGeneration</code> (MVP model)</li>
<li><code>NezhaConfig</code> configuration class: <code>NezhaForPreTraining</code> (Nezha model)</li>
<li><code>NllbMoeConfig</code> configuration class: <code>NllbMoeForConditionalGeneration</code> (NLLB-MOE model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/paligemma#transformers.PaliGemmaConfig">PaliGemmaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/paligemma#transformers.PaliGemmaForConditionalGeneration">PaliGemmaForConditionalGeneration</a> (PaliGemma model)</li>
<li><code>Qwen2AudioConfig</code> configuration class: <code>Qwen2AudioForConditionalGeneration</code> (Qwen2Audio model)</li>
<li><code>RetriBertConfig</code> configuration class: <code>RetriBertModel</code> (RetriBERT model)</li>
<li><code>RoCBertConfig</code> configuration class: <code>RoCBertForPreTraining</code> (RoCBert model)</li>
<li><code>RobertaConfig</code> configuration class: <code>RobertaForMaskedLM</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>RobertaPreLayerNormForMaskedLM</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>RwkvConfig</code> configuration class: <code>RwkvForCausalLM</code> (RWKV model)</li>
<li><code>SplinterConfig</code> configuration class: <code>SplinterForPreTraining</code> (Splinter model)</li>
<li><code>SqueezeBertConfig</code> configuration class: <code>SqueezeBertForMaskedLM</code> (SqueezeBERT model)</li>
<li><code>SwitchTransformersConfig</code> configuration class: <code>SwitchTransformersForConditionalGeneration</code> (SwitchTransformers model)</li>
<li><code>T5Config</code> configuration class: <code>T5ForConditionalGeneration</code> (T5 model)</li>
<li><code>TapasConfig</code> configuration class: <code>TapasForMaskedLM</code> (TAPAS model)</li>
<li><code>TransfoXLConfig</code> configuration class: <code>TransfoXLLMHeadModel</code> (Transformer-XL model)</li>
<li><code>TvltConfig</code> configuration class: <code>TvltForPreTraining</code> (TVLT model)</li>
<li><code>UniSpeechConfig</code> configuration class: <code>UniSpeechForPreTraining</code> (UniSpeech model)</li>
<li><code>UniSpeechSatConfig</code> configuration class: <code>UniSpeechSatForPreTraining</code> (UniSpeechSat model)</li>
<li><code>ViTMAEConfig</code> configuration class: <code>ViTMAEForPreTraining</code> (ViTMAE model)</li>
<li><code>VideoLlavaConfig</code> configuration class: <code>VideoLlavaForConditionalGeneration</code> (VideoLlava model)</li>
<li><code>VideoMAEConfig</code> configuration class: <code>VideoMAEForPreTraining</code> (VideoMAE model)</li>
<li><code>VipLlavaConfig</code> configuration class: <code>VipLlavaForConditionalGeneration</code> (VipLlava model)</li>
<li><code>VisualBertConfig</code> configuration class: <code>VisualBertForPreTraining</code> (VisualBERT model)</li>
<li><code>Wav2Vec2Config</code> configuration class: <code>Wav2Vec2ForPreTraining</code> (Wav2Vec2 model)</li>
<li><code>Wav2Vec2ConformerConfig</code> configuration class: <code>Wav2Vec2ConformerForPreTraining</code> (Wav2Vec2-Conformer model)</li>
<li><code>XLMConfig</code> configuration class: <code>XLMWithLMHeadModel</code> (XLM model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>XLMRobertaForMaskedLM</code> (XLM-RoBERTa model)</li>
<li><code>XLMRobertaXLConfig</code> configuration class: <code>XLMRobertaXLForMaskedLM</code> (XLM-RoBERTa-XL model)</li>
<li><code>XLNetConfig</code> configuration class: <code>XLNetLMHeadModel</code> (XLNet model)</li>
<li><code>XmodConfig</code> configuration class: <code>XmodForMaskedLM</code> (X-MOD model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForPreTraining.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Us=new L({props:{anchor:"transformers.AutoModelForPreTraining.from_config.example",$$slots:{default:[Vq]},$$scope:{ctx:v}}}),hc=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForPreTraining.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForPreTraining.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Ns=new L({props:{anchor:"transformers.AutoModelForPreTraining.from_pretrained.example",$$slots:{default:[Iq]},$$scope:{ctx:v}}}),bc=new A({props:{title:"TFAutoModelForPreTraining",local:"transformers.TFAutoModelForPreTraining ][ transformers.TFAutoModelForPreTraining",headingTag:"h3"}}),_c=new k({props:{name:"class transformers.TFAutoModelForPreTraining",anchor:"transformers.TFAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L553"}}),Mc=new k({props:{name:"from_config",anchor:"transformers.TFAutoModelForPreTraining.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForPreTraining.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>TFAlbertForPreTraining</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.TFBertForPreTraining">TFBertForPreTraining</a> (BERT model)</li>
<li><code>CTRLConfig</code> configuration class: <code>TFCTRLLMHeadModel</code> (CTRL model)</li>
<li><code>CamembertConfig</code> configuration class: <code>TFCamembertForMaskedLM</code> (CamemBERT model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>TFDistilBertForMaskedLM</code> (DistilBERT model)</li>
<li><code>ElectraConfig</code> configuration class: <code>TFElectraForPreTraining</code> (ELECTRA model)</li>
<li><code>FlaubertConfig</code> configuration class: <code>TFFlaubertWithLMHeadModel</code> (FlauBERT model)</li>
<li><code>FunnelConfig</code> configuration class: <code>TFFunnelForPreTraining</code> (Funnel Transformer model)</li>
<li><code>GPT2Config</code> configuration class: <code>TFGPT2LMHeadModel</code> (OpenAI GPT-2 model)</li>
<li><code>IdeficsConfig</code> configuration class: <code>TFIdeficsForVisionText2Text</code> (IDEFICS model)</li>
<li><code>LayoutLMConfig</code> configuration class: <code>TFLayoutLMForMaskedLM</code> (LayoutLM model)</li>
<li><code>LxmertConfig</code> configuration class: <code>TFLxmertForPreTraining</code> (LXMERT model)</li>
<li><code>MPNetConfig</code> configuration class: <code>TFMPNetForMaskedLM</code> (MPNet model)</li>
<li><code>MobileBertConfig</code> configuration class: <code>TFMobileBertForPreTraining</code> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><code>RobertaConfig</code> configuration class: <code>TFRobertaForMaskedLM</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>TFRobertaPreLayerNormForMaskedLM</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>T5Config</code> configuration class: <code>TFT5ForConditionalGeneration</code> (T5 model)</li>
<li><code>TapasConfig</code> configuration class: <code>TFTapasForMaskedLM</code> (TAPAS model)</li>
<li><code>TransfoXLConfig</code> configuration class: <code>TFTransfoXLLMHeadModel</code> (Transformer-XL model)</li>
<li><code>ViTMAEConfig</code> configuration class: <code>TFViTMAEForPreTraining</code> (ViTMAE model)</li>
<li><code>XLMConfig</code> configuration class: <code>TFXLMWithLMHeadModel</code> (XLM model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>TFXLMRobertaForMaskedLM</code> (XLM-RoBERTa model)</li>
<li><code>XLNetConfig</code> configuration class: <code>TFXLNetLMHeadModel</code> (XLNet model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForPreTraining.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),qs=new L({props:{anchor:"transformers.TFAutoModelForPreTraining.from_config.example",$$slots:{default:[Pq]},$$scope:{ctx:v}}}),Tc=new k({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForPreTraining.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Xs=new L({props:{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[Sq]},$$scope:{ctx:v}}}),yc=new A({props:{title:"FlaxAutoModelForPreTraining",local:"transformers.FlaxAutoModelForPreTraining ][ transformers.FlaxAutoModelForPreTraining",headingTag:"h3"}}),Fc=new k({props:{name:"class transformers.FlaxAutoModelForPreTraining",anchor:"transformers.FlaxAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L288"}}),vc=new k({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForPreTraining.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>FlaxAlbertForPreTraining</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.FlaxBertForPreTraining">FlaxBertForPreTraining</a> (BERT model)</li>
<li><code>BigBirdConfig</code> configuration class: <code>FlaxBigBirdForPreTraining</code> (BigBird model)</li>
<li><code>ElectraConfig</code> configuration class: <code>FlaxElectraForPreTraining</code> (ELECTRA model)</li>
<li><code>LongT5Config</code> configuration class: <code>FlaxLongT5ForConditionalGeneration</code> (LongT5 model)</li>
<li><code>MBartConfig</code> configuration class: <code>FlaxMBartForConditionalGeneration</code> (mBART model)</li>
<li><code>MT5Config</code> configuration class: <code>FlaxMT5ForConditionalGeneration</code> (MT5 model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>FlaxRoFormerForMaskedLM</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>FlaxRobertaForMaskedLM</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>FlaxRobertaPreLayerNormForMaskedLM</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>T5Config</code> configuration class: <code>FlaxT5ForConditionalGeneration</code> (T5 model)</li>
<li><code>Wav2Vec2Config</code> configuration class: <code>FlaxWav2Vec2ForPreTraining</code> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>FlaxXLMRobertaForMaskedLM</code> (XLM-RoBERTa model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Ds=new L({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.example",$$slots:{default:[Uq]},$$scope:{ctx:v}}}),Cc=new k({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Hs=new L({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[Nq]},$$scope:{ctx:v}}}),wc=new A({props:{title:"자연어 처리",local:"natural-language-processing",headingTag:"h2"}}),xc=new A({props:{title:"AutoModelForCausalLM",local:"transformers.AutoModelForCausalLM ][ transformers.AutoModelForCausalLM",headingTag:"h3"}}),$c=new k({props:{name:"class transformers.AutoModelForCausalLM",anchor:"transformers.AutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1630"}}),Lc=new k({props:{name:"from_config",anchor:"transformers.AutoModelForCausalLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCausalLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AriaTextConfig</code> configuration class: <code>AriaTextForCausalLM</code> (AriaText model)</li>
<li><code>BambaConfig</code> configuration class: <code>BambaForCausalLM</code> (Bamba model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartForCausalLM">BartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertLMHeadModel">BertLMHeadModel</a> (BERT model)</li>
<li><code>BertGenerationConfig</code> configuration class: <code>BertGenerationDecoder</code> (Bert Generation model)</li>
<li><code>BigBirdConfig</code> configuration class: <code>BigBirdForCausalLM</code> (BigBird model)</li>
<li><code>BigBirdPegasusConfig</code> configuration class: <code>BigBirdPegasusForCausalLM</code> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/biogpt#transformers.BioGptForCausalLM">BioGptForCausalLM</a> (BioGpt model)</li>
<li><code>BlenderbotConfig</code> configuration class: <code>BlenderbotForCausalLM</code> (Blenderbot model)</li>
<li><code>BlenderbotSmallConfig</code> configuration class: <code>BlenderbotSmallForCausalLM</code> (BlenderbotSmall model)</li>
<li><code>BloomConfig</code> configuration class: <code>BloomForCausalLM</code> (BLOOM model)</li>
<li><code>CTRLConfig</code> configuration class: <code>CTRLLMHeadModel</code> (CTRL model)</li>
<li><code>CamembertConfig</code> configuration class: <code>CamembertForCausalLM</code> (CamemBERT model)</li>
<li><code>CodeGenConfig</code> configuration class: <code>CodeGenForCausalLM</code> (CodeGen model)</li>
<li><code>Cohere2Config</code> configuration class: <code>Cohere2ForCausalLM</code> (Cohere2 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/cohere#transformers.CohereConfig">CohereConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/cohere#transformers.CohereForCausalLM">CohereForCausalLM</a> (Cohere model)</li>
<li><code>CpmAntConfig</code> configuration class: <code>CpmAntForCausalLM</code> (CPM-Ant model)</li>
<li><code>Data2VecTextConfig</code> configuration class: <code>Data2VecTextForCausalLM</code> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/dbrx#transformers.DbrxConfig">DbrxConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/dbrx#transformers.DbrxForCausalLM">DbrxForCausalLM</a> (DBRX model)</li>
<li><code>DiffLlamaConfig</code> configuration class: <code>DiffLlamaForCausalLM</code> (DiffLlama model)</li>
<li><code>ElectraConfig</code> configuration class: <code>ElectraForCausalLM</code> (ELECTRA model)</li>
<li><code>Emu3Config</code> configuration class: <code>Emu3ForCausalLM</code> (Emu3 model)</li>
<li><code>ErnieConfig</code> configuration class: <code>ErnieForCausalLM</code> (ERNIE model)</li>
<li><code>FalconConfig</code> configuration class: <code>FalconForCausalLM</code> (Falcon model)</li>
<li><code>FalconMambaConfig</code> configuration class: <code>FalconMambaForCausalLM</code> (FalconMamba model)</li>
<li><code>FuyuConfig</code> configuration class: <code>FuyuForCausalLM</code> (Fuyu model)</li>
<li><code>GPT2Config</code> configuration class: <code>GPT2LMHeadModel</code> (OpenAI GPT-2 model)</li>
<li><code>GPTBigCodeConfig</code> configuration class: <code>GPTBigCodeForCausalLM</code> (GPTBigCode model)</li>
<li><code>GPTJConfig</code> configuration class: <code>GPTJForCausalLM</code> (GPT-J model)</li>
<li><code>GPTNeoConfig</code> configuration class: <code>GPTNeoForCausalLM</code> (GPT Neo model)</li>
<li><code>GPTNeoXConfig</code> configuration class: <code>GPTNeoXForCausalLM</code> (GPT NeoX model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseForCausalLM">GPTNeoXJapaneseForCausalLM</a> (GPT NeoX Japanese model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/gemma2#transformers.Gemma2Config">Gemma2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/gemma2#transformers.Gemma2ForCausalLM">Gemma2ForCausalLM</a> (Gemma2 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaConfig">GemmaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaForCausalLM">GemmaForCausalLM</a> (Gemma model)</li>
<li><code>GitConfig</code> configuration class: <code>GitForCausalLM</code> (GIT model)</li>
<li><code>GlmConfig</code> configuration class: <code>GlmForCausalLM</code> (GLM model)</li>
<li><code>GraniteConfig</code> configuration class: <code>GraniteForCausalLM</code> (Granite model)</li>
<li><code>GraniteMoeConfig</code> configuration class: <code>GraniteMoeForCausalLM</code> (GraniteMoeMoe model)</li>
<li><code>HeliumConfig</code> configuration class: <code>HeliumForCausalLM</code> (Helium model)</li>
<li><code>JambaConfig</code> configuration class: <code>JambaForCausalLM</code> (Jamba model)</li>
<li><code>JetMoeConfig</code> configuration class: <code>JetMoeForCausalLM</code> (JetMoe model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaForCausalLM">LlamaForCausalLM</a> (LLaMA model)</li>
<li><code>MBartConfig</code> configuration class: <code>MBartForCausalLM</code> (mBART model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/mamba2#transformers.Mamba2Config">Mamba2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/mamba2#transformers.Mamba2ForCausalLM">Mamba2ForCausalLM</a> (mamba2 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/mamba#transformers.MambaConfig">MambaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/mamba#transformers.MambaForCausalLM">MambaForCausalLM</a> (Mamba model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/marian#transformers.MarianForCausalLM">MarianForCausalLM</a> (Marian model)</li>
<li><code>MegaConfig</code> configuration class: <code>MegaForCausalLM</code> (MEGA model)</li>
<li><code>MegatronBertConfig</code> configuration class: <code>MegatronBertForCausalLM</code> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/mistral#transformers.MistralForCausalLM">MistralForCausalLM</a> (Mistral model)</li>
<li><code>MixtralConfig</code> configuration class: <code>MixtralForCausalLM</code> (Mixtral model)</li>
<li><code>MllamaConfig</code> configuration class: <code>MllamaForCausalLM</code> (Mllama model)</li>
<li><code>MoshiConfig</code> configuration class: <code>MoshiForCausalLM</code> (Moshi model)</li>
<li><code>MptConfig</code> configuration class: <code>MptForCausalLM</code> (MPT model)</li>
<li><code>MusicgenConfig</code> configuration class: <code>MusicgenForCausalLM</code> (MusicGen model)</li>
<li><code>MusicgenMelodyConfig</code> configuration class: <code>MusicgenMelodyForCausalLM</code> (MusicGen Melody model)</li>
<li><code>MvpConfig</code> configuration class: <code>MvpForCausalLM</code> (MVP model)</li>
<li><code>NemotronConfig</code> configuration class: <code>NemotronForCausalLM</code> (Nemotron model)</li>
<li><code>OPTConfig</code> configuration class: <code>OPTForCausalLM</code> (OPT model)</li>
<li><code>Olmo2Config</code> configuration class: <code>Olmo2ForCausalLM</code> (OLMo2 model)</li>
<li><code>OlmoConfig</code> configuration class: <code>OlmoForCausalLM</code> (OLMo model)</li>
<li><code>OlmoeConfig</code> configuration class: <code>OlmoeForCausalLM</code> (OLMoE model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><code>OpenLlamaConfig</code> configuration class: <code>OpenLlamaForCausalLM</code> (OpenLlama model)</li>
<li><code>PLBartConfig</code> configuration class: <code>PLBartForCausalLM</code> (PLBart model)</li>
<li><code>PegasusConfig</code> configuration class: <code>PegasusForCausalLM</code> (Pegasus model)</li>
<li><code>PersimmonConfig</code> configuration class: <code>PersimmonForCausalLM</code> (Persimmon model)</li>
<li><code>Phi3Config</code> configuration class: <code>Phi3ForCausalLM</code> (Phi3 model)</li>
<li><code>PhiConfig</code> configuration class: <code>PhiForCausalLM</code> (Phi model)</li>
<li><code>PhimoeConfig</code> configuration class: <code>PhimoeForCausalLM</code> (Phimoe model)</li>
<li><code>ProphetNetConfig</code> configuration class: <code>ProphetNetForCausalLM</code> (ProphetNet model)</li>
<li><code>QDQBertConfig</code> configuration class: <code>QDQBertLMHeadModel</code> (QDQBert model)</li>
<li><code>Qwen2Config</code> configuration class: <code>Qwen2ForCausalLM</code> (Qwen2 model)</li>
<li><code>Qwen2MoeConfig</code> configuration class: <code>Qwen2MoeForCausalLM</code> (Qwen2MoE model)</li>
<li><code>RecurrentGemmaConfig</code> configuration class: <code>RecurrentGemmaForCausalLM</code> (RecurrentGemma model)</li>
<li><code>ReformerConfig</code> configuration class: <code>ReformerModelWithLMHead</code> (Reformer model)</li>
<li><code>RemBertConfig</code> configuration class: <code>RemBertForCausalLM</code> (RemBERT model)</li>
<li><code>RoCBertConfig</code> configuration class: <code>RoCBertForCausalLM</code> (RoCBert model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>RoFormerForCausalLM</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>RobertaForCausalLM</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>RobertaPreLayerNormForCausalLM</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>RwkvConfig</code> configuration class: <code>RwkvForCausalLM</code> (RWKV model)</li>
<li><code>Speech2Text2Config</code> configuration class: <code>Speech2Text2ForCausalLM</code> (Speech2Text2 model)</li>
<li><code>StableLmConfig</code> configuration class: <code>StableLmForCausalLM</code> (StableLm model)</li>
<li><code>Starcoder2Config</code> configuration class: <code>Starcoder2ForCausalLM</code> (Starcoder2 model)</li>
<li><code>TrOCRConfig</code> configuration class: <code>TrOCRForCausalLM</code> (TrOCR model)</li>
<li><code>TransfoXLConfig</code> configuration class: <code>TransfoXLLMHeadModel</code> (Transformer-XL model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <code>WhisperForCausalLM</code> (Whisper model)</li>
<li><code>XGLMConfig</code> configuration class: <code>XGLMForCausalLM</code> (XGLM model)</li>
<li><code>XLMConfig</code> configuration class: <code>XLMWithLMHeadModel</code> (XLM model)</li>
<li><code>XLMProphetNetConfig</code> configuration class: <code>XLMProphetNetForCausalLM</code> (XLM-ProphetNet model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>XLMRobertaForCausalLM</code> (XLM-RoBERTa model)</li>
<li><code>XLMRobertaXLConfig</code> configuration class: <code>XLMRobertaXLForCausalLM</code> (XLM-RoBERTa-XL model)</li>
<li><code>XLNetConfig</code> configuration class: <code>XLNetLMHeadModel</code> (XLNet model)</li>
<li><code>XmodConfig</code> configuration class: <code>XmodForCausalLM</code> (X-MOD model)</li>
<li><code>Zamba2Config</code> configuration class: <code>Zamba2ForCausalLM</code> (Zamba2 model)</li>
<li><code>ZambaConfig</code> configuration class: <code>ZambaForCausalLM</code> (Zamba model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForCausalLM.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Qs=new L({props:{anchor:"transformers.AutoModelForCausalLM.from_config.example",$$slots:{default:[qq]},$$scope:{ctx:v}}}),Ac=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForCausalLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCausalLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Ys=new L({props:{anchor:"transformers.AutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Xq]},$$scope:{ctx:v}}}),jc=new A({props:{title:"TFAutoModelForCausalLM",local:"transformers.TFAutoModelForCausalLM ][ transformers.TFAutoModelForCausalLM",headingTag:"h3"}}),Zc=new k({props:{name:"class transformers.TFAutoModelForCausalLM",anchor:"transformers.TFAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L568"}}),Bc=new k({props:{name:"from_config",anchor:"transformers.TFAutoModelForCausalLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForCausalLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.TFBertLMHeadModel">TFBertLMHeadModel</a> (BERT model)</li>
<li><code>CTRLConfig</code> configuration class: <code>TFCTRLLMHeadModel</code> (CTRL model)</li>
<li><code>CamembertConfig</code> configuration class: <code>TFCamembertForCausalLM</code> (CamemBERT model)</li>
<li><code>GPT2Config</code> configuration class: <code>TFGPT2LMHeadModel</code> (OpenAI GPT-2 model)</li>
<li><code>GPTJConfig</code> configuration class: <code>TFGPTJForCausalLM</code> (GPT-J model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/mistral#transformers.TFMistralForCausalLM">TFMistralForCausalLM</a> (Mistral model)</li>
<li><code>OPTConfig</code> configuration class: <code>TFOPTForCausalLM</code> (OPT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><code>RemBertConfig</code> configuration class: <code>TFRemBertForCausalLM</code> (RemBERT model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>TFRoFormerForCausalLM</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>TFRobertaForCausalLM</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>TFRobertaPreLayerNormForCausalLM</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>TransfoXLConfig</code> configuration class: <code>TFTransfoXLLMHeadModel</code> (Transformer-XL model)</li>
<li><code>XGLMConfig</code> configuration class: <code>TFXGLMForCausalLM</code> (XGLM model)</li>
<li><code>XLMConfig</code> configuration class: <code>TFXLMWithLMHeadModel</code> (XLM model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>TFXLMRobertaForCausalLM</code> (XLM-RoBERTa model)</li>
<li><code>XLNetConfig</code> configuration class: <code>TFXLNetLMHeadModel</code> (XLNet model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForCausalLM.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),zs=new L({props:{anchor:"transformers.TFAutoModelForCausalLM.from_config.example",$$slots:{default:[Dq]},$$scope:{ctx:v}}}),Rc=new k({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForCausalLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Os=new L({props:{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Hq]},$$scope:{ctx:v}}}),Gc=new A({props:{title:"FlaxAutoModelForCausalLM",local:"transformers.FlaxAutoModelForCausalLM ][ transformers.FlaxAutoModelForCausalLM",headingTag:"h3"}}),Jc=new k({props:{name:"class transformers.FlaxAutoModelForCausalLM",anchor:"transformers.FlaxAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L295"}}),Wc=new k({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForCausalLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bart#transformers.FlaxBartForCausalLM">FlaxBartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.FlaxBertForCausalLM">FlaxBertForCausalLM</a> (BERT model)</li>
<li><code>BigBirdConfig</code> configuration class: <code>FlaxBigBirdForCausalLM</code> (BigBird model)</li>
<li><code>BloomConfig</code> configuration class: <code>FlaxBloomForCausalLM</code> (BLOOM model)</li>
<li><code>ElectraConfig</code> configuration class: <code>FlaxElectraForCausalLM</code> (ELECTRA model)</li>
<li><code>GPT2Config</code> configuration class: <code>FlaxGPT2LMHeadModel</code> (OpenAI GPT-2 model)</li>
<li><code>GPTJConfig</code> configuration class: <code>FlaxGPTJForCausalLM</code> (GPT-J model)</li>
<li><code>GPTNeoConfig</code> configuration class: <code>FlaxGPTNeoForCausalLM</code> (GPT Neo model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaConfig">GemmaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.FlaxGemmaForCausalLM">FlaxGemmaForCausalLM</a> (Gemma model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> configuration class: <code>FlaxLlamaForCausalLM</code> (LLaMA model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/mistral#transformers.FlaxMistralForCausalLM">FlaxMistralForCausalLM</a> (Mistral model)</li>
<li><code>OPTConfig</code> configuration class: <code>FlaxOPTForCausalLM</code> (OPT model)</li>
<li><code>RobertaConfig</code> configuration class: <code>FlaxRobertaForCausalLM</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>FlaxRobertaPreLayerNormForCausalLM</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>XGLMConfig</code> configuration class: <code>FlaxXGLMForCausalLM</code> (XGLM model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>FlaxXLMRobertaForCausalLM</code> (XLM-RoBERTa model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Ks=new L({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.example",$$slots:{default:[Qq]},$$scope:{ctx:v}}}),Ec=new k({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),ei=new L({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Yq]},$$scope:{ctx:v}}}),Vc=new A({props:{title:"AutoModelForMaskedLM",local:"transformers.AutoModelForMaskedLM ][ transformers.AutoModelForMaskedLM",headingTag:"h3"}}),Ic=new k({props:{name:"class transformers.AutoModelForMaskedLM",anchor:"transformers.AutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1637"}}),Pc=new k({props:{name:"from_config",anchor:"transformers.AutoModelForMaskedLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>AlbertForMaskedLM</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertForMaskedLM">BertForMaskedLM</a> (BERT model)</li>
<li><code>BigBirdConfig</code> configuration class: <code>BigBirdForMaskedLM</code> (BigBird model)</li>
<li><code>CamembertConfig</code> configuration class: <code>CamembertForMaskedLM</code> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertForMaskedLM">ConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><code>Data2VecTextConfig</code> configuration class: <code>Data2VecTextForMaskedLM</code> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>DistilBertForMaskedLM</code> (DistilBERT model)</li>
<li><code>ElectraConfig</code> configuration class: <code>ElectraForMaskedLM</code> (ELECTRA model)</li>
<li><code>ErnieConfig</code> configuration class: <code>ErnieForMaskedLM</code> (ERNIE model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/esm#transformers.EsmForMaskedLM">EsmForMaskedLM</a> (ESM model)</li>
<li><code>FNetConfig</code> configuration class: <code>FNetForMaskedLM</code> (FNet model)</li>
<li><code>FlaubertConfig</code> configuration class: <code>FlaubertWithLMHeadModel</code> (FlauBERT model)</li>
<li><code>FunnelConfig</code> configuration class: <code>FunnelForMaskedLM</code> (Funnel Transformer model)</li>
<li><code>IBertConfig</code> configuration class: <code>IBertForMaskedLM</code> (I-BERT model)</li>
<li><code>LayoutLMConfig</code> configuration class: <code>LayoutLMForMaskedLM</code> (LayoutLM model)</li>
<li><code>LongformerConfig</code> configuration class: <code>LongformerForMaskedLM</code> (Longformer model)</li>
<li><code>LukeConfig</code> configuration class: <code>LukeForMaskedLM</code> (LUKE model)</li>
<li><code>MBartConfig</code> configuration class: <code>MBartForConditionalGeneration</code> (mBART model)</li>
<li><code>MPNetConfig</code> configuration class: <code>MPNetForMaskedLM</code> (MPNet model)</li>
<li><code>MegaConfig</code> configuration class: <code>MegaForMaskedLM</code> (MEGA model)</li>
<li><code>MegatronBertConfig</code> configuration class: <code>MegatronBertForMaskedLM</code> (Megatron-BERT model)</li>
<li><code>MobileBertConfig</code> configuration class: <code>MobileBertForMaskedLM</code> (MobileBERT model)</li>
<li><code>ModernBertConfig</code> configuration class: <code>ModernBertForMaskedLM</code> (ModernBERT model)</li>
<li><code>MraConfig</code> configuration class: <code>MraForMaskedLM</code> (MRA model)</li>
<li><code>MvpConfig</code> configuration class: <code>MvpForConditionalGeneration</code> (MVP model)</li>
<li><code>NezhaConfig</code> configuration class: <code>NezhaForMaskedLM</code> (Nezha model)</li>
<li><code>NystromformerConfig</code> configuration class: <code>NystromformerForMaskedLM</code> (Nystr&#xF6;mformer model)</li>
<li><code>PerceiverConfig</code> configuration class: <code>PerceiverForMaskedLM</code> (Perceiver model)</li>
<li><code>QDQBertConfig</code> configuration class: <code>QDQBertForMaskedLM</code> (QDQBert model)</li>
<li><code>ReformerConfig</code> configuration class: <code>ReformerForMaskedLM</code> (Reformer model)</li>
<li><code>RemBertConfig</code> configuration class: <code>RemBertForMaskedLM</code> (RemBERT model)</li>
<li><code>RoCBertConfig</code> configuration class: <code>RoCBertForMaskedLM</code> (RoCBert model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>RoFormerForMaskedLM</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>RobertaForMaskedLM</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>RobertaPreLayerNormForMaskedLM</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>SqueezeBertConfig</code> configuration class: <code>SqueezeBertForMaskedLM</code> (SqueezeBERT model)</li>
<li><code>TapasConfig</code> configuration class: <code>TapasForMaskedLM</code> (TAPAS model)</li>
<li><code>Wav2Vec2Config</code> configuration class: <code>Wav2Vec2ForMaskedLM</code> (Wav2Vec2 model)</li>
<li><code>XLMConfig</code> configuration class: <code>XLMWithLMHeadModel</code> (XLM model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>XLMRobertaForMaskedLM</code> (XLM-RoBERTa model)</li>
<li><code>XLMRobertaXLConfig</code> configuration class: <code>XLMRobertaXLForMaskedLM</code> (XLM-RoBERTa-XL model)</li>
<li><code>XmodConfig</code> configuration class: <code>XmodForMaskedLM</code> (X-MOD model)</li>
<li><code>YosoConfig</code> configuration class: <code>YosoForMaskedLM</code> (YOSO model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMaskedLM.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),oi=new L({props:{anchor:"transformers.AutoModelForMaskedLM.from_config.example",$$slots:{default:[zq]},$$scope:{ctx:v}}}),Sc=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForMaskedLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),ti=new L({props:{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[Oq]},$$scope:{ctx:v}}}),Uc=new A({props:{title:"TFAutoModelForMaskedLM",local:"transformers.TFAutoModelForMaskedLM ][ transformers.TFAutoModelForMaskedLM",headingTag:"h3"}}),Nc=new k({props:{name:"class transformers.TFAutoModelForMaskedLM",anchor:"transformers.TFAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L618"}}),qc=new k({props:{name:"from_config",anchor:"transformers.TFAutoModelForMaskedLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMaskedLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>TFAlbertForMaskedLM</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.TFBertForMaskedLM">TFBertForMaskedLM</a> (BERT model)</li>
<li><code>CamembertConfig</code> configuration class: <code>TFCamembertForMaskedLM</code> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.TFConvBertForMaskedLM">TFConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.TFDebertaForMaskedLM">TFDebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.TFDebertaV2ForMaskedLM">TFDebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>TFDistilBertForMaskedLM</code> (DistilBERT model)</li>
<li><code>ElectraConfig</code> configuration class: <code>TFElectraForMaskedLM</code> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/esm#transformers.TFEsmForMaskedLM">TFEsmForMaskedLM</a> (ESM model)</li>
<li><code>FlaubertConfig</code> configuration class: <code>TFFlaubertWithLMHeadModel</code> (FlauBERT model)</li>
<li><code>FunnelConfig</code> configuration class: <code>TFFunnelForMaskedLM</code> (Funnel Transformer model)</li>
<li><code>LayoutLMConfig</code> configuration class: <code>TFLayoutLMForMaskedLM</code> (LayoutLM model)</li>
<li><code>LongformerConfig</code> configuration class: <code>TFLongformerForMaskedLM</code> (Longformer model)</li>
<li><code>MPNetConfig</code> configuration class: <code>TFMPNetForMaskedLM</code> (MPNet model)</li>
<li><code>MobileBertConfig</code> configuration class: <code>TFMobileBertForMaskedLM</code> (MobileBERT model)</li>
<li><code>RemBertConfig</code> configuration class: <code>TFRemBertForMaskedLM</code> (RemBERT model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>TFRoFormerForMaskedLM</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>TFRobertaForMaskedLM</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>TFRobertaPreLayerNormForMaskedLM</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>TapasConfig</code> configuration class: <code>TFTapasForMaskedLM</code> (TAPAS model)</li>
<li><code>XLMConfig</code> configuration class: <code>TFXLMWithLMHeadModel</code> (XLM model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>TFXLMRobertaForMaskedLM</code> (XLM-RoBERTa model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMaskedLM.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),ni=new L({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_config.example",$$slots:{default:[Kq]},$$scope:{ctx:v}}}),Xc=new k({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),ri=new L({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[e3]},$$scope:{ctx:v}}}),Dc=new A({props:{title:"FlaxAutoModelForMaskedLM",local:"transformers.FlaxAutoModelForMaskedLM ][ transformers.FlaxAutoModelForMaskedLM",headingTag:"h3"}}),Hc=new k({props:{name:"class transformers.FlaxAutoModelForMaskedLM",anchor:"transformers.FlaxAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L302"}}),Qc=new k({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForMaskedLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>FlaxAlbertForMaskedLM</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.FlaxBertForMaskedLM">FlaxBertForMaskedLM</a> (BERT model)</li>
<li><code>BigBirdConfig</code> configuration class: <code>FlaxBigBirdForMaskedLM</code> (BigBird model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>FlaxDistilBertForMaskedLM</code> (DistilBERT model)</li>
<li><code>ElectraConfig</code> configuration class: <code>FlaxElectraForMaskedLM</code> (ELECTRA model)</li>
<li><code>MBartConfig</code> configuration class: <code>FlaxMBartForConditionalGeneration</code> (mBART model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>FlaxRoFormerForMaskedLM</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>FlaxRobertaForMaskedLM</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>FlaxRobertaPreLayerNormForMaskedLM</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>FlaxXLMRobertaForMaskedLM</code> (XLM-RoBERTa model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),ai=new L({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.example",$$slots:{default:[o3]},$$scope:{ctx:v}}}),Yc=new k({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),si=new L({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[t3]},$$scope:{ctx:v}}}),zc=new A({props:{title:"AutoModelForMaskGeneration",local:"transformers.AutoModelForMaskGeneration ][ transformers.AutoModelForMaskGeneration",headingTag:"h3"}}),Kc=new k({props:{name:"class transformers.AutoModelForMaskGeneration",anchor:"transformers.AutoModelForMaskGeneration",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1592"}}),em=new A({props:{title:"TFAutoModelForMaskGeneration",local:"transformers.TFAutoModelForMaskGeneration ][ transformers.TFAutoModelForMaskGeneration",headingTag:"h3"}}),tm=new k({props:{name:"class transformers.TFAutoModelForMaskGeneration",anchor:"transformers.TFAutoModelForMaskGeneration",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L529"}}),nm=new A({props:{title:"AutoModelForSeq2SeqLM",local:"transformers.AutoModelForSeq2SeqLM ][ transformers.AutoModelForSeq2SeqLM",headingTag:"h3"}}),rm=new k({props:{name:"class transformers.AutoModelForSeq2SeqLM",anchor:"transformers.AutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1644"}}),am=new k({props:{name:"from_config",anchor:"transformers.AutoModelForSeq2SeqLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><code>BigBirdPegasusConfig</code> configuration class: <code>BigBirdPegasusForConditionalGeneration</code> (BigBird-Pegasus model)</li>
<li><code>BlenderbotConfig</code> configuration class: <code>BlenderbotForConditionalGeneration</code> (Blenderbot model)</li>
<li><code>BlenderbotSmallConfig</code> configuration class: <code>BlenderbotSmallForConditionalGeneration</code> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/encoder-decoder#transformers.EncoderDecoderModel">EncoderDecoderModel</a> (Encoder decoder model)</li>
<li><code>FSMTConfig</code> configuration class: <code>FSMTForConditionalGeneration</code> (FairSeq Machine-Translation model)</li>
<li><code>GPTSanJapaneseConfig</code> configuration class: <code>GPTSanJapaneseForConditionalGeneration</code> (GPTSAN-japanese model)</li>
<li><code>LEDConfig</code> configuration class: <code>LEDForConditionalGeneration</code> (LED model)</li>
<li><code>LongT5Config</code> configuration class: <code>LongT5ForConditionalGeneration</code> (LongT5 model)</li>
<li><code>M2M100Config</code> configuration class: <code>M2M100ForConditionalGeneration</code> (M2M100 model)</li>
<li><code>MBartConfig</code> configuration class: <code>MBartForConditionalGeneration</code> (mBART model)</li>
<li><code>MT5Config</code> configuration class: <code>MT5ForConditionalGeneration</code> (MT5 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/marian#transformers.MarianMTModel">MarianMTModel</a> (Marian model)</li>
<li><code>MvpConfig</code> configuration class: <code>MvpForConditionalGeneration</code> (MVP model)</li>
<li><code>NllbMoeConfig</code> configuration class: <code>NllbMoeForConditionalGeneration</code> (NLLB-MOE model)</li>
<li><code>PLBartConfig</code> configuration class: <code>PLBartForConditionalGeneration</code> (PLBart model)</li>
<li><code>PegasusConfig</code> configuration class: <code>PegasusForConditionalGeneration</code> (Pegasus model)</li>
<li><code>PegasusXConfig</code> configuration class: <code>PegasusXForConditionalGeneration</code> (PEGASUS-X model)</li>
<li><code>ProphetNetConfig</code> configuration class: <code>ProphetNetForConditionalGeneration</code> (ProphetNet model)</li>
<li><code>Qwen2AudioConfig</code> configuration class: <code>Qwen2AudioForConditionalGeneration</code> (Qwen2Audio model)</li>
<li><code>SeamlessM4TConfig</code> configuration class: <code>SeamlessM4TForTextToText</code> (SeamlessM4T model)</li>
<li><code>SeamlessM4Tv2Config</code> configuration class: <code>SeamlessM4Tv2ForTextToText</code> (SeamlessM4Tv2 model)</li>
<li><code>SwitchTransformersConfig</code> configuration class: <code>SwitchTransformersForConditionalGeneration</code> (SwitchTransformers model)</li>
<li><code>T5Config</code> configuration class: <code>T5ForConditionalGeneration</code> (T5 model)</li>
<li><code>UMT5Config</code> configuration class: <code>UMT5ForConditionalGeneration</code> (UMT5 model)</li>
<li><code>XLMProphetNetConfig</code> configuration class: <code>XLMProphetNetForConditionalGeneration</code> (XLM-ProphetNet model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),ii=new L({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[n3]},$$scope:{ctx:v}}}),sm=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),li=new L({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[r3]},$$scope:{ctx:v}}}),im=new A({props:{title:"TFAutoModelForSeq2SeqLM",local:"transformers.TFAutoModelForSeq2SeqLM ][ transformers.TFAutoModelForSeq2SeqLM",headingTag:"h3"}}),lm=new k({props:{name:"class transformers.TFAutoModelForSeq2SeqLM",anchor:"transformers.TFAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L625"}}),dm=new k({props:{name:"from_config",anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><code>BlenderbotConfig</code> configuration class: <code>TFBlenderbotForConditionalGeneration</code> (Blenderbot model)</li>
<li><code>BlenderbotSmallConfig</code> configuration class: <code>TFBlenderbotSmallForConditionalGeneration</code> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/encoder-decoder#transformers.TFEncoderDecoderModel">TFEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><code>LEDConfig</code> configuration class: <code>TFLEDForConditionalGeneration</code> (LED model)</li>
<li><code>MBartConfig</code> configuration class: <code>TFMBartForConditionalGeneration</code> (mBART model)</li>
<li><code>MT5Config</code> configuration class: <code>TFMT5ForConditionalGeneration</code> (MT5 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/marian#transformers.TFMarianMTModel">TFMarianMTModel</a> (Marian model)</li>
<li><code>PegasusConfig</code> configuration class: <code>TFPegasusForConditionalGeneration</code> (Pegasus model)</li>
<li><code>T5Config</code> configuration class: <code>TFT5ForConditionalGeneration</code> (T5 model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),di=new L({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[a3]},$$scope:{ctx:v}}}),cm=new k({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),ci=new L({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[s3]},$$scope:{ctx:v}}}),mm=new A({props:{title:"FlaxAutoModelForSeq2SeqLM",local:"transformers.FlaxAutoModelForSeq2SeqLM ][ transformers.FlaxAutoModelForSeq2SeqLM",headingTag:"h3"}}),fm=new k({props:{name:"class transformers.FlaxAutoModelForSeq2SeqLM",anchor:"transformers.FlaxAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L309"}}),gm=new k({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><code>BlenderbotConfig</code> configuration class: <code>FlaxBlenderbotForConditionalGeneration</code> (Blenderbot model)</li>
<li><code>BlenderbotSmallConfig</code> configuration class: <code>FlaxBlenderbotSmallForConditionalGeneration</code> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/encoder-decoder#transformers.FlaxEncoderDecoderModel">FlaxEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><code>LongT5Config</code> configuration class: <code>FlaxLongT5ForConditionalGeneration</code> (LongT5 model)</li>
<li><code>MBartConfig</code> configuration class: <code>FlaxMBartForConditionalGeneration</code> (mBART model)</li>
<li><code>MT5Config</code> configuration class: <code>FlaxMT5ForConditionalGeneration</code> (MT5 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/marian#transformers.FlaxMarianMTModel">FlaxMarianMTModel</a> (Marian model)</li>
<li><code>PegasusConfig</code> configuration class: <code>FlaxPegasusForConditionalGeneration</code> (Pegasus model)</li>
<li><code>T5Config</code> configuration class: <code>FlaxT5ForConditionalGeneration</code> (T5 model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),mi=new L({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[i3]},$$scope:{ctx:v}}}),pm=new k({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),fi=new L({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[l3]},$$scope:{ctx:v}}}),um=new A({props:{title:"AutoModelForSequenceClassification",local:"transformers.AutoModelForSequenceClassification ][ transformers.AutoModelForSequenceClassification",headingTag:"h3"}}),hm=new k({props:{name:"class transformers.AutoModelForSequenceClassification",anchor:"transformers.AutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1655"}}),bm=new k({props:{name:"from_config",anchor:"transformers.AutoModelForSequenceClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSequenceClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>AlbertForSequenceClassification</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartForSequenceClassification">BartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertForSequenceClassification">BertForSequenceClassification</a> (BERT model)</li>
<li><code>BigBirdConfig</code> configuration class: <code>BigBirdForSequenceClassification</code> (BigBird model)</li>
<li><code>BigBirdPegasusConfig</code> configuration class: <code>BigBirdPegasusForSequenceClassification</code> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/biogpt#transformers.BioGptForSequenceClassification">BioGptForSequenceClassification</a> (BioGpt model)</li>
<li><code>BloomConfig</code> configuration class: <code>BloomForSequenceClassification</code> (BLOOM model)</li>
<li><code>CTRLConfig</code> configuration class: <code>CTRLForSequenceClassification</code> (CTRL model)</li>
<li><code>CamembertConfig</code> configuration class: <code>CamembertForSequenceClassification</code> (CamemBERT model)</li>
<li><code>CanineConfig</code> configuration class: <code>CanineForSequenceClassification</code> (CANINE model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertForSequenceClassification">ConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><code>Data2VecTextConfig</code> configuration class: <code>Data2VecTextForSequenceClassification</code> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaForSequenceClassification">DebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2ForSequenceClassification">DebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><code>DiffLlamaConfig</code> configuration class: <code>DiffLlamaForSequenceClassification</code> (DiffLlama model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>DistilBertForSequenceClassification</code> (DistilBERT model)</li>
<li><code>ElectraConfig</code> configuration class: <code>ElectraForSequenceClassification</code> (ELECTRA model)</li>
<li><code>ErnieConfig</code> configuration class: <code>ErnieForSequenceClassification</code> (ERNIE model)</li>
<li><code>ErnieMConfig</code> configuration class: <code>ErnieMForSequenceClassification</code> (ErnieM model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/esm#transformers.EsmForSequenceClassification">EsmForSequenceClassification</a> (ESM model)</li>
<li><code>FNetConfig</code> configuration class: <code>FNetForSequenceClassification</code> (FNet model)</li>
<li><code>FalconConfig</code> configuration class: <code>FalconForSequenceClassification</code> (Falcon model)</li>
<li><code>FlaubertConfig</code> configuration class: <code>FlaubertForSequenceClassification</code> (FlauBERT model)</li>
<li><code>FunnelConfig</code> configuration class: <code>FunnelForSequenceClassification</code> (Funnel Transformer model)</li>
<li><code>GPT2Config</code> configuration class: <code>GPT2ForSequenceClassification</code> (OpenAI GPT-2 model)</li>
<li><code>GPTBigCodeConfig</code> configuration class: <code>GPTBigCodeForSequenceClassification</code> (GPTBigCode model)</li>
<li><code>GPTJConfig</code> configuration class: <code>GPTJForSequenceClassification</code> (GPT-J model)</li>
<li><code>GPTNeoConfig</code> configuration class: <code>GPTNeoForSequenceClassification</code> (GPT Neo model)</li>
<li><code>GPTNeoXConfig</code> configuration class: <code>GPTNeoXForSequenceClassification</code> (GPT NeoX model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/gemma2#transformers.Gemma2Config">Gemma2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/gemma2#transformers.Gemma2ForSequenceClassification">Gemma2ForSequenceClassification</a> (Gemma2 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaConfig">GemmaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaForSequenceClassification">GemmaForSequenceClassification</a> (Gemma model)</li>
<li><code>GlmConfig</code> configuration class: <code>GlmForSequenceClassification</code> (GLM model)</li>
<li><code>HeliumConfig</code> configuration class: <code>HeliumForSequenceClassification</code> (Helium model)</li>
<li><code>IBertConfig</code> configuration class: <code>IBertForSequenceClassification</code> (I-BERT model)</li>
<li><code>JambaConfig</code> configuration class: <code>JambaForSequenceClassification</code> (Jamba model)</li>
<li><code>JetMoeConfig</code> configuration class: <code>JetMoeForSequenceClassification</code> (JetMoe model)</li>
<li><code>LEDConfig</code> configuration class: <code>LEDForSequenceClassification</code> (LED model)</li>
<li><code>LayoutLMConfig</code> configuration class: <code>LayoutLMForSequenceClassification</code> (LayoutLM model)</li>
<li><code>LayoutLMv2Config</code> configuration class: <code>LayoutLMv2ForSequenceClassification</code> (LayoutLMv2 model)</li>
<li><code>LayoutLMv3Config</code> configuration class: <code>LayoutLMv3ForSequenceClassification</code> (LayoutLMv3 model)</li>
<li><code>LiltConfig</code> configuration class: <code>LiltForSequenceClassification</code> (LiLT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaForSequenceClassification">LlamaForSequenceClassification</a> (LLaMA model)</li>
<li><code>LongformerConfig</code> configuration class: <code>LongformerForSequenceClassification</code> (Longformer model)</li>
<li><code>LukeConfig</code> configuration class: <code>LukeForSequenceClassification</code> (LUKE model)</li>
<li><code>MBartConfig</code> configuration class: <code>MBartForSequenceClassification</code> (mBART model)</li>
<li><code>MPNetConfig</code> configuration class: <code>MPNetForSequenceClassification</code> (MPNet model)</li>
<li><code>MT5Config</code> configuration class: <code>MT5ForSequenceClassification</code> (MT5 model)</li>
<li><code>MarkupLMConfig</code> configuration class: <code>MarkupLMForSequenceClassification</code> (MarkupLM model)</li>
<li><code>MegaConfig</code> configuration class: <code>MegaForSequenceClassification</code> (MEGA model)</li>
<li><code>MegatronBertConfig</code> configuration class: <code>MegatronBertForSequenceClassification</code> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/mistral#transformers.MistralForSequenceClassification">MistralForSequenceClassification</a> (Mistral model)</li>
<li><code>MixtralConfig</code> configuration class: <code>MixtralForSequenceClassification</code> (Mixtral model)</li>
<li><code>MobileBertConfig</code> configuration class: <code>MobileBertForSequenceClassification</code> (MobileBERT model)</li>
<li><code>ModernBertConfig</code> configuration class: <code>ModernBertForSequenceClassification</code> (ModernBERT model)</li>
<li><code>MptConfig</code> configuration class: <code>MptForSequenceClassification</code> (MPT model)</li>
<li><code>MraConfig</code> configuration class: <code>MraForSequenceClassification</code> (MRA model)</li>
<li><code>MvpConfig</code> configuration class: <code>MvpForSequenceClassification</code> (MVP model)</li>
<li><code>NemotronConfig</code> configuration class: <code>NemotronForSequenceClassification</code> (Nemotron model)</li>
<li><code>NezhaConfig</code> configuration class: <code>NezhaForSequenceClassification</code> (Nezha model)</li>
<li><code>NystromformerConfig</code> configuration class: <code>NystromformerForSequenceClassification</code> (Nystr&#xF6;mformer model)</li>
<li><code>OPTConfig</code> configuration class: <code>OPTForSequenceClassification</code> (OPT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.OpenAIGPTForSequenceClassification">OpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><code>OpenLlamaConfig</code> configuration class: <code>OpenLlamaForSequenceClassification</code> (OpenLlama model)</li>
<li><code>PLBartConfig</code> configuration class: <code>PLBartForSequenceClassification</code> (PLBart model)</li>
<li><code>PerceiverConfig</code> configuration class: <code>PerceiverForSequenceClassification</code> (Perceiver model)</li>
<li><code>PersimmonConfig</code> configuration class: <code>PersimmonForSequenceClassification</code> (Persimmon model)</li>
<li><code>Phi3Config</code> configuration class: <code>Phi3ForSequenceClassification</code> (Phi3 model)</li>
<li><code>PhiConfig</code> configuration class: <code>PhiForSequenceClassification</code> (Phi model)</li>
<li><code>PhimoeConfig</code> configuration class: <code>PhimoeForSequenceClassification</code> (Phimoe model)</li>
<li><code>QDQBertConfig</code> configuration class: <code>QDQBertForSequenceClassification</code> (QDQBert model)</li>
<li><code>Qwen2Config</code> configuration class: <code>Qwen2ForSequenceClassification</code> (Qwen2 model)</li>
<li><code>Qwen2MoeConfig</code> configuration class: <code>Qwen2MoeForSequenceClassification</code> (Qwen2MoE model)</li>
<li><code>ReformerConfig</code> configuration class: <code>ReformerForSequenceClassification</code> (Reformer model)</li>
<li><code>RemBertConfig</code> configuration class: <code>RemBertForSequenceClassification</code> (RemBERT model)</li>
<li><code>RoCBertConfig</code> configuration class: <code>RoCBertForSequenceClassification</code> (RoCBert model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>RoFormerForSequenceClassification</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>RobertaForSequenceClassification</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>RobertaPreLayerNormForSequenceClassification</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>SqueezeBertConfig</code> configuration class: <code>SqueezeBertForSequenceClassification</code> (SqueezeBERT model)</li>
<li><code>StableLmConfig</code> configuration class: <code>StableLmForSequenceClassification</code> (StableLm model)</li>
<li><code>Starcoder2Config</code> configuration class: <code>Starcoder2ForSequenceClassification</code> (Starcoder2 model)</li>
<li><code>T5Config</code> configuration class: <code>T5ForSequenceClassification</code> (T5 model)</li>
<li><code>TapasConfig</code> configuration class: <code>TapasForSequenceClassification</code> (TAPAS model)</li>
<li><code>TransfoXLConfig</code> configuration class: <code>TransfoXLForSequenceClassification</code> (Transformer-XL model)</li>
<li><code>UMT5Config</code> configuration class: <code>UMT5ForSequenceClassification</code> (UMT5 model)</li>
<li><code>XLMConfig</code> configuration class: <code>XLMForSequenceClassification</code> (XLM model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>XLMRobertaForSequenceClassification</code> (XLM-RoBERTa model)</li>
<li><code>XLMRobertaXLConfig</code> configuration class: <code>XLMRobertaXLForSequenceClassification</code> (XLM-RoBERTa-XL model)</li>
<li><code>XLNetConfig</code> configuration class: <code>XLNetForSequenceClassification</code> (XLNet model)</li>
<li><code>XmodConfig</code> configuration class: <code>XmodForSequenceClassification</code> (X-MOD model)</li>
<li><code>YosoConfig</code> configuration class: <code>YosoForSequenceClassification</code> (YOSO model)</li>
<li><code>Zamba2Config</code> configuration class: <code>Zamba2ForSequenceClassification</code> (Zamba2 model)</li>
<li><code>ZambaConfig</code> configuration class: <code>ZambaForSequenceClassification</code> (Zamba model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSequenceClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),gi=new L({props:{anchor:"transformers.AutoModelForSequenceClassification.from_config.example",$$slots:{default:[d3]},$$scope:{ctx:v}}}),_m=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSequenceClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),pi=new L({props:{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[c3]},$$scope:{ctx:v}}}),Mm=new A({props:{title:"TFAutoModelForSequenceClassification",local:"transformers.TFAutoModelForSequenceClassification ][ transformers.TFAutoModelForSequenceClassification",headingTag:"h3"}}),Tm=new k({props:{name:"class transformers.TFAutoModelForSequenceClassification",anchor:"transformers.TFAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L636"}}),ym=new k({props:{name:"from_config",anchor:"transformers.TFAutoModelForSequenceClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>TFAlbertForSequenceClassification</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bart#transformers.TFBartForSequenceClassification">TFBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.TFBertForSequenceClassification">TFBertForSequenceClassification</a> (BERT model)</li>
<li><code>CTRLConfig</code> configuration class: <code>TFCTRLForSequenceClassification</code> (CTRL model)</li>
<li><code>CamembertConfig</code> configuration class: <code>TFCamembertForSequenceClassification</code> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.TFConvBertForSequenceClassification">TFConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.TFDebertaForSequenceClassification">TFDebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.TFDebertaV2ForSequenceClassification">TFDebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>TFDistilBertForSequenceClassification</code> (DistilBERT model)</li>
<li><code>ElectraConfig</code> configuration class: <code>TFElectraForSequenceClassification</code> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/esm#transformers.TFEsmForSequenceClassification">TFEsmForSequenceClassification</a> (ESM model)</li>
<li><code>FlaubertConfig</code> configuration class: <code>TFFlaubertForSequenceClassification</code> (FlauBERT model)</li>
<li><code>FunnelConfig</code> configuration class: <code>TFFunnelForSequenceClassification</code> (Funnel Transformer model)</li>
<li><code>GPT2Config</code> configuration class: <code>TFGPT2ForSequenceClassification</code> (OpenAI GPT-2 model)</li>
<li><code>GPTJConfig</code> configuration class: <code>TFGPTJForSequenceClassification</code> (GPT-J model)</li>
<li><code>LayoutLMConfig</code> configuration class: <code>TFLayoutLMForSequenceClassification</code> (LayoutLM model)</li>
<li><code>LayoutLMv3Config</code> configuration class: <code>TFLayoutLMv3ForSequenceClassification</code> (LayoutLMv3 model)</li>
<li><code>LongformerConfig</code> configuration class: <code>TFLongformerForSequenceClassification</code> (Longformer model)</li>
<li><code>MPNetConfig</code> configuration class: <code>TFMPNetForSequenceClassification</code> (MPNet model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/mistral#transformers.TFMistralForSequenceClassification">TFMistralForSequenceClassification</a> (Mistral model)</li>
<li><code>MobileBertConfig</code> configuration class: <code>TFMobileBertForSequenceClassification</code> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/openai-gpt#transformers.TFOpenAIGPTForSequenceClassification">TFOpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><code>RemBertConfig</code> configuration class: <code>TFRemBertForSequenceClassification</code> (RemBERT model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>TFRoFormerForSequenceClassification</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>TFRobertaForSequenceClassification</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>TFRobertaPreLayerNormForSequenceClassification</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>TapasConfig</code> configuration class: <code>TFTapasForSequenceClassification</code> (TAPAS model)</li>
<li><code>TransfoXLConfig</code> configuration class: <code>TFTransfoXLForSequenceClassification</code> (Transformer-XL model)</li>
<li><code>XLMConfig</code> configuration class: <code>TFXLMForSequenceClassification</code> (XLM model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>TFXLMRobertaForSequenceClassification</code> (XLM-RoBERTa model)</li>
<li><code>XLNetConfig</code> configuration class: <code>TFXLNetForSequenceClassification</code> (XLNet model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),ui=new L({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.example",$$slots:{default:[m3]},$$scope:{ctx:v}}}),Fm=new k({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),hi=new L({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[f3]},$$scope:{ctx:v}}}),vm=new A({props:{title:"FlaxAutoModelForSequenceClassification",local:"transformers.FlaxAutoModelForSequenceClassification ][ transformers.FlaxAutoModelForSequenceClassification",headingTag:"h3"}}),Cm=new k({props:{name:"class transformers.FlaxAutoModelForSequenceClassification",anchor:"transformers.FlaxAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L320"}}),wm=new k({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>FlaxAlbertForSequenceClassification</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bart#transformers.FlaxBartForSequenceClassification">FlaxBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.FlaxBertForSequenceClassification">FlaxBertForSequenceClassification</a> (BERT model)</li>
<li><code>BigBirdConfig</code> configuration class: <code>FlaxBigBirdForSequenceClassification</code> (BigBird model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>FlaxDistilBertForSequenceClassification</code> (DistilBERT model)</li>
<li><code>ElectraConfig</code> configuration class: <code>FlaxElectraForSequenceClassification</code> (ELECTRA model)</li>
<li><code>MBartConfig</code> configuration class: <code>FlaxMBartForSequenceClassification</code> (mBART model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>FlaxRoFormerForSequenceClassification</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>FlaxRobertaForSequenceClassification</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>FlaxRobertaPreLayerNormForSequenceClassification</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>FlaxXLMRobertaForSequenceClassification</code> (XLM-RoBERTa model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),bi=new L({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.example",$$slots:{default:[g3]},$$scope:{ctx:v}}}),km=new k({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),_i=new L({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[p3]},$$scope:{ctx:v}}}),xm=new A({props:{title:"AutoModelForMultipleChoice",local:"transformers.AutoModelForMultipleChoice ][ transformers.AutoModelForMultipleChoice",headingTag:"h3"}}),$m=new k({props:{name:"class transformers.AutoModelForMultipleChoice",anchor:"transformers.AutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1711"}}),Lm=new k({props:{name:"from_config",anchor:"transformers.AutoModelForMultipleChoice.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMultipleChoice.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>AlbertForMultipleChoice</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertForMultipleChoice">BertForMultipleChoice</a> (BERT model)</li>
<li><code>BigBirdConfig</code> configuration class: <code>BigBirdForMultipleChoice</code> (BigBird model)</li>
<li><code>CamembertConfig</code> configuration class: <code>CamembertForMultipleChoice</code> (CamemBERT model)</li>
<li><code>CanineConfig</code> configuration class: <code>CanineForMultipleChoice</code> (CANINE model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertForMultipleChoice">ConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><code>Data2VecTextConfig</code> configuration class: <code>Data2VecTextForMultipleChoice</code> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2ForMultipleChoice">DebertaV2ForMultipleChoice</a> (DeBERTa-v2 model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>DistilBertForMultipleChoice</code> (DistilBERT model)</li>
<li><code>ElectraConfig</code> configuration class: <code>ElectraForMultipleChoice</code> (ELECTRA model)</li>
<li><code>ErnieConfig</code> configuration class: <code>ErnieForMultipleChoice</code> (ERNIE model)</li>
<li><code>ErnieMConfig</code> configuration class: <code>ErnieMForMultipleChoice</code> (ErnieM model)</li>
<li><code>FNetConfig</code> configuration class: <code>FNetForMultipleChoice</code> (FNet model)</li>
<li><code>FlaubertConfig</code> configuration class: <code>FlaubertForMultipleChoice</code> (FlauBERT model)</li>
<li><code>FunnelConfig</code> configuration class: <code>FunnelForMultipleChoice</code> (Funnel Transformer model)</li>
<li><code>IBertConfig</code> configuration class: <code>IBertForMultipleChoice</code> (I-BERT model)</li>
<li><code>LongformerConfig</code> configuration class: <code>LongformerForMultipleChoice</code> (Longformer model)</li>
<li><code>LukeConfig</code> configuration class: <code>LukeForMultipleChoice</code> (LUKE model)</li>
<li><code>MPNetConfig</code> configuration class: <code>MPNetForMultipleChoice</code> (MPNet model)</li>
<li><code>MegaConfig</code> configuration class: <code>MegaForMultipleChoice</code> (MEGA model)</li>
<li><code>MegatronBertConfig</code> configuration class: <code>MegatronBertForMultipleChoice</code> (Megatron-BERT model)</li>
<li><code>MobileBertConfig</code> configuration class: <code>MobileBertForMultipleChoice</code> (MobileBERT model)</li>
<li><code>MraConfig</code> configuration class: <code>MraForMultipleChoice</code> (MRA model)</li>
<li><code>NezhaConfig</code> configuration class: <code>NezhaForMultipleChoice</code> (Nezha model)</li>
<li><code>NystromformerConfig</code> configuration class: <code>NystromformerForMultipleChoice</code> (Nystr&#xF6;mformer model)</li>
<li><code>QDQBertConfig</code> configuration class: <code>QDQBertForMultipleChoice</code> (QDQBert model)</li>
<li><code>RemBertConfig</code> configuration class: <code>RemBertForMultipleChoice</code> (RemBERT model)</li>
<li><code>RoCBertConfig</code> configuration class: <code>RoCBertForMultipleChoice</code> (RoCBert model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>RoFormerForMultipleChoice</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>RobertaForMultipleChoice</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>RobertaPreLayerNormForMultipleChoice</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>SqueezeBertConfig</code> configuration class: <code>SqueezeBertForMultipleChoice</code> (SqueezeBERT model)</li>
<li><code>XLMConfig</code> configuration class: <code>XLMForMultipleChoice</code> (XLM model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>XLMRobertaForMultipleChoice</code> (XLM-RoBERTa model)</li>
<li><code>XLMRobertaXLConfig</code> configuration class: <code>XLMRobertaXLForMultipleChoice</code> (XLM-RoBERTa-XL model)</li>
<li><code>XLNetConfig</code> configuration class: <code>XLNetForMultipleChoice</code> (XLNet model)</li>
<li><code>XmodConfig</code> configuration class: <code>XmodForMultipleChoice</code> (X-MOD model)</li>
<li><code>YosoConfig</code> configuration class: <code>YosoForMultipleChoice</code> (YOSO model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMultipleChoice.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Mi=new L({props:{anchor:"transformers.AutoModelForMultipleChoice.from_config.example",$$slots:{default:[u3]},$$scope:{ctx:v}}}),Am=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForMultipleChoice.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Ti=new L({props:{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[h3]},$$scope:{ctx:v}}}),jm=new A({props:{title:"TFAutoModelForMultipleChoice",local:"transformers.TFAutoModelForMultipleChoice ][ transformers.TFAutoModelForMultipleChoice",headingTag:"h3"}}),Zm=new k({props:{name:"class transformers.TFAutoModelForMultipleChoice",anchor:"transformers.TFAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L683"}}),Bm=new k({props:{name:"from_config",anchor:"transformers.TFAutoModelForMultipleChoice.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>TFAlbertForMultipleChoice</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.TFBertForMultipleChoice">TFBertForMultipleChoice</a> (BERT model)</li>
<li><code>CamembertConfig</code> configuration class: <code>TFCamembertForMultipleChoice</code> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.TFConvBertForMultipleChoice">TFConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.TFDebertaV2ForMultipleChoice">TFDebertaV2ForMultipleChoice</a> (DeBERTa-v2 model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>TFDistilBertForMultipleChoice</code> (DistilBERT model)</li>
<li><code>ElectraConfig</code> configuration class: <code>TFElectraForMultipleChoice</code> (ELECTRA model)</li>
<li><code>FlaubertConfig</code> configuration class: <code>TFFlaubertForMultipleChoice</code> (FlauBERT model)</li>
<li><code>FunnelConfig</code> configuration class: <code>TFFunnelForMultipleChoice</code> (Funnel Transformer model)</li>
<li><code>LongformerConfig</code> configuration class: <code>TFLongformerForMultipleChoice</code> (Longformer model)</li>
<li><code>MPNetConfig</code> configuration class: <code>TFMPNetForMultipleChoice</code> (MPNet model)</li>
<li><code>MobileBertConfig</code> configuration class: <code>TFMobileBertForMultipleChoice</code> (MobileBERT model)</li>
<li><code>RemBertConfig</code> configuration class: <code>TFRemBertForMultipleChoice</code> (RemBERT model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>TFRoFormerForMultipleChoice</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>TFRobertaForMultipleChoice</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>TFRobertaPreLayerNormForMultipleChoice</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>XLMConfig</code> configuration class: <code>TFXLMForMultipleChoice</code> (XLM model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>TFXLMRobertaForMultipleChoice</code> (XLM-RoBERTa model)</li>
<li><code>XLNetConfig</code> configuration class: <code>TFXLNetForMultipleChoice</code> (XLNet model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),yi=new L({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.example",$$slots:{default:[b3]},$$scope:{ctx:v}}}),Rm=new k({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Fi=new L({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[_3]},$$scope:{ctx:v}}}),Gm=new A({props:{title:"FlaxAutoModelForMultipleChoice",local:"transformers.FlaxAutoModelForMultipleChoice ][ transformers.FlaxAutoModelForMultipleChoice",headingTag:"h3"}}),Jm=new k({props:{name:"class transformers.FlaxAutoModelForMultipleChoice",anchor:"transformers.FlaxAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L345"}}),Wm=new k({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>FlaxAlbertForMultipleChoice</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.FlaxBertForMultipleChoice">FlaxBertForMultipleChoice</a> (BERT model)</li>
<li><code>BigBirdConfig</code> configuration class: <code>FlaxBigBirdForMultipleChoice</code> (BigBird model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>FlaxDistilBertForMultipleChoice</code> (DistilBERT model)</li>
<li><code>ElectraConfig</code> configuration class: <code>FlaxElectraForMultipleChoice</code> (ELECTRA model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>FlaxRoFormerForMultipleChoice</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>FlaxRobertaForMultipleChoice</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>FlaxRobertaPreLayerNormForMultipleChoice</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>FlaxXLMRobertaForMultipleChoice</code> (XLM-RoBERTa model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),vi=new L({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.example",$$slots:{default:[M3]},$$scope:{ctx:v}}}),Em=new k({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Ci=new L({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[T3]},$$scope:{ctx:v}}}),Vm=new A({props:{title:"AutoModelForNextSentencePrediction",local:"transformers.AutoModelForNextSentencePrediction ][ transformers.AutoModelForNextSentencePrediction",headingTag:"h3"}}),Im=new k({props:{name:"class transformers.AutoModelForNextSentencePrediction",anchor:"transformers.AutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1718"}}),Pm=new k({props:{name:"from_config",anchor:"transformers.AutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertForNextSentencePrediction">BertForNextSentencePrediction</a> (BERT model)</li>
<li><code>ErnieConfig</code> configuration class: <code>ErnieForNextSentencePrediction</code> (ERNIE model)</li>
<li><code>FNetConfig</code> configuration class: <code>FNetForNextSentencePrediction</code> (FNet model)</li>
<li><code>MegatronBertConfig</code> configuration class: <code>MegatronBertForNextSentencePrediction</code> (Megatron-BERT model)</li>
<li><code>MobileBertConfig</code> configuration class: <code>MobileBertForNextSentencePrediction</code> (MobileBERT model)</li>
<li><code>NezhaConfig</code> configuration class: <code>NezhaForNextSentencePrediction</code> (Nezha model)</li>
<li><code>QDQBertConfig</code> configuration class: <code>QDQBertForNextSentencePrediction</code> (QDQBert model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),wi=new L({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[y3]},$$scope:{ctx:v}}}),Sm=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),ki=new L({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[F3]},$$scope:{ctx:v}}}),Um=new A({props:{title:"TFAutoModelForNextSentencePrediction",local:"transformers.TFAutoModelForNextSentencePrediction ][ transformers.TFAutoModelForNextSentencePrediction",headingTag:"h3"}}),Nm=new k({props:{name:"class transformers.TFAutoModelForNextSentencePrediction",anchor:"transformers.TFAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L690"}}),qm=new k({props:{name:"from_config",anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.TFBertForNextSentencePrediction">TFBertForNextSentencePrediction</a> (BERT model)</li>
<li><code>MobileBertConfig</code> configuration class: <code>TFMobileBertForNextSentencePrediction</code> (MobileBERT model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),xi=new L({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[v3]},$$scope:{ctx:v}}}),Xm=new k({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),$i=new L({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[C3]},$$scope:{ctx:v}}}),Dm=new A({props:{title:"FlaxAutoModelForNextSentencePrediction",local:"transformers.FlaxAutoModelForNextSentencePrediction ][ transformers.FlaxAutoModelForNextSentencePrediction",headingTag:"h3"}}),Hm=new k({props:{name:"class transformers.FlaxAutoModelForNextSentencePrediction",anchor:"transformers.FlaxAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L352"}}),Qm=new k({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.FlaxBertForNextSentencePrediction">FlaxBertForNextSentencePrediction</a> (BERT model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Li=new L({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[w3]},$$scope:{ctx:v}}}),Ym=new k({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Ai=new L({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[k3]},$$scope:{ctx:v}}}),zm=new A({props:{title:"AutoModelForTokenClassification",local:"transformers.AutoModelForTokenClassification ][ transformers.AutoModelForTokenClassification",headingTag:"h3"}}),Om=new k({props:{name:"class transformers.AutoModelForTokenClassification",anchor:"transformers.AutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1704"}}),Km=new k({props:{name:"from_config",anchor:"transformers.AutoModelForTokenClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTokenClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>AlbertForTokenClassification</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertForTokenClassification">BertForTokenClassification</a> (BERT model)</li>
<li><code>BigBirdConfig</code> configuration class: <code>BigBirdForTokenClassification</code> (BigBird model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/biogpt#transformers.BioGptForTokenClassification">BioGptForTokenClassification</a> (BioGpt model)</li>
<li><code>BloomConfig</code> configuration class: <code>BloomForTokenClassification</code> (BLOOM model)</li>
<li><code>BrosConfig</code> configuration class: <code>BrosForTokenClassification</code> (BROS model)</li>
<li><code>CamembertConfig</code> configuration class: <code>CamembertForTokenClassification</code> (CamemBERT model)</li>
<li><code>CanineConfig</code> configuration class: <code>CanineForTokenClassification</code> (CANINE model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertForTokenClassification">ConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><code>Data2VecTextConfig</code> configuration class: <code>Data2VecTextForTokenClassification</code> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaForTokenClassification">DebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2ForTokenClassification">DebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><code>DiffLlamaConfig</code> configuration class: <code>DiffLlamaForTokenClassification</code> (DiffLlama model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>DistilBertForTokenClassification</code> (DistilBERT model)</li>
<li><code>ElectraConfig</code> configuration class: <code>ElectraForTokenClassification</code> (ELECTRA model)</li>
<li><code>ErnieConfig</code> configuration class: <code>ErnieForTokenClassification</code> (ERNIE model)</li>
<li><code>ErnieMConfig</code> configuration class: <code>ErnieMForTokenClassification</code> (ErnieM model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/esm#transformers.EsmForTokenClassification">EsmForTokenClassification</a> (ESM model)</li>
<li><code>FNetConfig</code> configuration class: <code>FNetForTokenClassification</code> (FNet model)</li>
<li><code>FalconConfig</code> configuration class: <code>FalconForTokenClassification</code> (Falcon model)</li>
<li><code>FlaubertConfig</code> configuration class: <code>FlaubertForTokenClassification</code> (FlauBERT model)</li>
<li><code>FunnelConfig</code> configuration class: <code>FunnelForTokenClassification</code> (Funnel Transformer model)</li>
<li><code>GPT2Config</code> configuration class: <code>GPT2ForTokenClassification</code> (OpenAI GPT-2 model)</li>
<li><code>GPTBigCodeConfig</code> configuration class: <code>GPTBigCodeForTokenClassification</code> (GPTBigCode model)</li>
<li><code>GPTNeoConfig</code> configuration class: <code>GPTNeoForTokenClassification</code> (GPT Neo model)</li>
<li><code>GPTNeoXConfig</code> configuration class: <code>GPTNeoXForTokenClassification</code> (GPT NeoX model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/gemma2#transformers.Gemma2Config">Gemma2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/gemma2#transformers.Gemma2ForTokenClassification">Gemma2ForTokenClassification</a> (Gemma2 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaConfig">GemmaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.GemmaForTokenClassification">GemmaForTokenClassification</a> (Gemma model)</li>
<li><code>GlmConfig</code> configuration class: <code>GlmForTokenClassification</code> (GLM model)</li>
<li><code>HeliumConfig</code> configuration class: <code>HeliumForTokenClassification</code> (Helium model)</li>
<li><code>IBertConfig</code> configuration class: <code>IBertForTokenClassification</code> (I-BERT model)</li>
<li><code>LayoutLMConfig</code> configuration class: <code>LayoutLMForTokenClassification</code> (LayoutLM model)</li>
<li><code>LayoutLMv2Config</code> configuration class: <code>LayoutLMv2ForTokenClassification</code> (LayoutLMv2 model)</li>
<li><code>LayoutLMv3Config</code> configuration class: <code>LayoutLMv3ForTokenClassification</code> (LayoutLMv3 model)</li>
<li><code>LiltConfig</code> configuration class: <code>LiltForTokenClassification</code> (LiLT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> configuration class: <code>LlamaForTokenClassification</code> (LLaMA model)</li>
<li><code>LongformerConfig</code> configuration class: <code>LongformerForTokenClassification</code> (Longformer model)</li>
<li><code>LukeConfig</code> configuration class: <code>LukeForTokenClassification</code> (LUKE model)</li>
<li><code>MPNetConfig</code> configuration class: <code>MPNetForTokenClassification</code> (MPNet model)</li>
<li><code>MT5Config</code> configuration class: <code>MT5ForTokenClassification</code> (MT5 model)</li>
<li><code>MarkupLMConfig</code> configuration class: <code>MarkupLMForTokenClassification</code> (MarkupLM model)</li>
<li><code>MegaConfig</code> configuration class: <code>MegaForTokenClassification</code> (MEGA model)</li>
<li><code>MegatronBertConfig</code> configuration class: <code>MegatronBertForTokenClassification</code> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/mistral#transformers.MistralForTokenClassification">MistralForTokenClassification</a> (Mistral model)</li>
<li><code>MixtralConfig</code> configuration class: <code>MixtralForTokenClassification</code> (Mixtral model)</li>
<li><code>MobileBertConfig</code> configuration class: <code>MobileBertForTokenClassification</code> (MobileBERT model)</li>
<li><code>ModernBertConfig</code> configuration class: <code>ModernBertForTokenClassification</code> (ModernBERT model)</li>
<li><code>MptConfig</code> configuration class: <code>MptForTokenClassification</code> (MPT model)</li>
<li><code>MraConfig</code> configuration class: <code>MraForTokenClassification</code> (MRA model)</li>
<li><code>NemotronConfig</code> configuration class: <code>NemotronForTokenClassification</code> (Nemotron model)</li>
<li><code>NezhaConfig</code> configuration class: <code>NezhaForTokenClassification</code> (Nezha model)</li>
<li><code>NystromformerConfig</code> configuration class: <code>NystromformerForTokenClassification</code> (Nystr&#xF6;mformer model)</li>
<li><code>PersimmonConfig</code> configuration class: <code>PersimmonForTokenClassification</code> (Persimmon model)</li>
<li><code>Phi3Config</code> configuration class: <code>Phi3ForTokenClassification</code> (Phi3 model)</li>
<li><code>PhiConfig</code> configuration class: <code>PhiForTokenClassification</code> (Phi model)</li>
<li><code>QDQBertConfig</code> configuration class: <code>QDQBertForTokenClassification</code> (QDQBert model)</li>
<li><code>Qwen2Config</code> configuration class: <code>Qwen2ForTokenClassification</code> (Qwen2 model)</li>
<li><code>Qwen2MoeConfig</code> configuration class: <code>Qwen2MoeForTokenClassification</code> (Qwen2MoE model)</li>
<li><code>RemBertConfig</code> configuration class: <code>RemBertForTokenClassification</code> (RemBERT model)</li>
<li><code>RoCBertConfig</code> configuration class: <code>RoCBertForTokenClassification</code> (RoCBert model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>RoFormerForTokenClassification</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>RobertaForTokenClassification</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>RobertaPreLayerNormForTokenClassification</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>SqueezeBertConfig</code> configuration class: <code>SqueezeBertForTokenClassification</code> (SqueezeBERT model)</li>
<li><code>StableLmConfig</code> configuration class: <code>StableLmForTokenClassification</code> (StableLm model)</li>
<li><code>Starcoder2Config</code> configuration class: <code>Starcoder2ForTokenClassification</code> (Starcoder2 model)</li>
<li><code>T5Config</code> configuration class: <code>T5ForTokenClassification</code> (T5 model)</li>
<li><code>UMT5Config</code> configuration class: <code>UMT5ForTokenClassification</code> (UMT5 model)</li>
<li><code>XLMConfig</code> configuration class: <code>XLMForTokenClassification</code> (XLM model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>XLMRobertaForTokenClassification</code> (XLM-RoBERTa model)</li>
<li><code>XLMRobertaXLConfig</code> configuration class: <code>XLMRobertaXLForTokenClassification</code> (XLM-RoBERTa-XL model)</li>
<li><code>XLNetConfig</code> configuration class: <code>XLNetForTokenClassification</code> (XLNet model)</li>
<li><code>XmodConfig</code> configuration class: <code>XmodForTokenClassification</code> (X-MOD model)</li>
<li><code>YosoConfig</code> configuration class: <code>YosoForTokenClassification</code> (YOSO model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForTokenClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),ji=new L({props:{anchor:"transformers.AutoModelForTokenClassification.from_config.example",$$slots:{default:[x3]},$$scope:{ctx:v}}}),ef=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForTokenClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Zi=new L({props:{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[$3]},$$scope:{ctx:v}}}),of=new A({props:{title:"TFAutoModelForTokenClassification",local:"transformers.TFAutoModelForTokenClassification ][ transformers.TFAutoModelForTokenClassification",headingTag:"h3"}}),tf=new k({props:{name:"class transformers.TFAutoModelForTokenClassification",anchor:"transformers.TFAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L674"}}),nf=new k({props:{name:"from_config",anchor:"transformers.TFAutoModelForTokenClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTokenClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>TFAlbertForTokenClassification</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.TFBertForTokenClassification">TFBertForTokenClassification</a> (BERT model)</li>
<li><code>CamembertConfig</code> configuration class: <code>TFCamembertForTokenClassification</code> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.TFConvBertForTokenClassification">TFConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.TFDebertaForTokenClassification">TFDebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.TFDebertaV2ForTokenClassification">TFDebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>TFDistilBertForTokenClassification</code> (DistilBERT model)</li>
<li><code>ElectraConfig</code> configuration class: <code>TFElectraForTokenClassification</code> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/esm#transformers.TFEsmForTokenClassification">TFEsmForTokenClassification</a> (ESM model)</li>
<li><code>FlaubertConfig</code> configuration class: <code>TFFlaubertForTokenClassification</code> (FlauBERT model)</li>
<li><code>FunnelConfig</code> configuration class: <code>TFFunnelForTokenClassification</code> (Funnel Transformer model)</li>
<li><code>LayoutLMConfig</code> configuration class: <code>TFLayoutLMForTokenClassification</code> (LayoutLM model)</li>
<li><code>LayoutLMv3Config</code> configuration class: <code>TFLayoutLMv3ForTokenClassification</code> (LayoutLMv3 model)</li>
<li><code>LongformerConfig</code> configuration class: <code>TFLongformerForTokenClassification</code> (Longformer model)</li>
<li><code>MPNetConfig</code> configuration class: <code>TFMPNetForTokenClassification</code> (MPNet model)</li>
<li><code>MobileBertConfig</code> configuration class: <code>TFMobileBertForTokenClassification</code> (MobileBERT model)</li>
<li><code>RemBertConfig</code> configuration class: <code>TFRemBertForTokenClassification</code> (RemBERT model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>TFRoFormerForTokenClassification</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>TFRobertaForTokenClassification</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>TFRobertaPreLayerNormForTokenClassification</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>XLMConfig</code> configuration class: <code>TFXLMForTokenClassification</code> (XLM model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>TFXLMRobertaForTokenClassification</code> (XLM-RoBERTa model)</li>
<li><code>XLNetConfig</code> configuration class: <code>TFXLNetForTokenClassification</code> (XLNet model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForTokenClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Bi=new L({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_config.example",$$slots:{default:[L3]},$$scope:{ctx:v}}}),rf=new k({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Ri=new L({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[A3]},$$scope:{ctx:v}}}),af=new A({props:{title:"FlaxAutoModelForTokenClassification",local:"transformers.FlaxAutoModelForTokenClassification ][ transformers.FlaxAutoModelForTokenClassification",headingTag:"h3"}}),sf=new k({props:{name:"class transformers.FlaxAutoModelForTokenClassification",anchor:"transformers.FlaxAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L336"}}),lf=new k({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForTokenClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>FlaxAlbertForTokenClassification</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.FlaxBertForTokenClassification">FlaxBertForTokenClassification</a> (BERT model)</li>
<li><code>BigBirdConfig</code> configuration class: <code>FlaxBigBirdForTokenClassification</code> (BigBird model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>FlaxDistilBertForTokenClassification</code> (DistilBERT model)</li>
<li><code>ElectraConfig</code> configuration class: <code>FlaxElectraForTokenClassification</code> (ELECTRA model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>FlaxRoFormerForTokenClassification</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>FlaxRobertaForTokenClassification</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>FlaxRobertaPreLayerNormForTokenClassification</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>FlaxXLMRobertaForTokenClassification</code> (XLM-RoBERTa model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Gi=new L({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.example",$$slots:{default:[j3]},$$scope:{ctx:v}}}),df=new k({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Ji=new L({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[Z3]},$$scope:{ctx:v}}}),cf=new A({props:{title:"AutoModelForQuestionAnswering",local:"transformers.AutoModelForQuestionAnswering ][ transformers.AutoModelForQuestionAnswering",headingTag:"h3"}}),mf=new k({props:{name:"class transformers.AutoModelForQuestionAnswering",anchor:"transformers.AutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1664"}}),ff=new k({props:{name:"from_config",anchor:"transformers.AutoModelForQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>AlbertForQuestionAnswering</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartForQuestionAnswering">BartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertForQuestionAnswering">BertForQuestionAnswering</a> (BERT model)</li>
<li><code>BigBirdConfig</code> configuration class: <code>BigBirdForQuestionAnswering</code> (BigBird model)</li>
<li><code>BigBirdPegasusConfig</code> configuration class: <code>BigBirdPegasusForQuestionAnswering</code> (BigBird-Pegasus model)</li>
<li><code>BloomConfig</code> configuration class: <code>BloomForQuestionAnswering</code> (BLOOM model)</li>
<li><code>CamembertConfig</code> configuration class: <code>CamembertForQuestionAnswering</code> (CamemBERT model)</li>
<li><code>CanineConfig</code> configuration class: <code>CanineForQuestionAnswering</code> (CANINE model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertForQuestionAnswering">ConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><code>Data2VecTextConfig</code> configuration class: <code>Data2VecTextForQuestionAnswering</code> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaForQuestionAnswering">DebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2ForQuestionAnswering">DebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><code>DiffLlamaConfig</code> configuration class: <code>DiffLlamaForQuestionAnswering</code> (DiffLlama model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>DistilBertForQuestionAnswering</code> (DistilBERT model)</li>
<li><code>ElectraConfig</code> configuration class: <code>ElectraForQuestionAnswering</code> (ELECTRA model)</li>
<li><code>ErnieConfig</code> configuration class: <code>ErnieForQuestionAnswering</code> (ERNIE model)</li>
<li><code>ErnieMConfig</code> configuration class: <code>ErnieMForQuestionAnswering</code> (ErnieM model)</li>
<li><code>FNetConfig</code> configuration class: <code>FNetForQuestionAnswering</code> (FNet model)</li>
<li><code>FalconConfig</code> configuration class: <code>FalconForQuestionAnswering</code> (Falcon model)</li>
<li><code>FlaubertConfig</code> configuration class: <code>FlaubertForQuestionAnsweringSimple</code> (FlauBERT model)</li>
<li><code>FunnelConfig</code> configuration class: <code>FunnelForQuestionAnswering</code> (Funnel Transformer model)</li>
<li><code>GPT2Config</code> configuration class: <code>GPT2ForQuestionAnswering</code> (OpenAI GPT-2 model)</li>
<li><code>GPTJConfig</code> configuration class: <code>GPTJForQuestionAnswering</code> (GPT-J model)</li>
<li><code>GPTNeoConfig</code> configuration class: <code>GPTNeoForQuestionAnswering</code> (GPT Neo model)</li>
<li><code>GPTNeoXConfig</code> configuration class: <code>GPTNeoXForQuestionAnswering</code> (GPT NeoX model)</li>
<li><code>IBertConfig</code> configuration class: <code>IBertForQuestionAnswering</code> (I-BERT model)</li>
<li><code>LEDConfig</code> configuration class: <code>LEDForQuestionAnswering</code> (LED model)</li>
<li><code>LayoutLMv2Config</code> configuration class: <code>LayoutLMv2ForQuestionAnswering</code> (LayoutLMv2 model)</li>
<li><code>LayoutLMv3Config</code> configuration class: <code>LayoutLMv3ForQuestionAnswering</code> (LayoutLMv3 model)</li>
<li><code>LiltConfig</code> configuration class: <code>LiltForQuestionAnswering</code> (LiLT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> configuration class: <code>LlamaForQuestionAnswering</code> (LLaMA model)</li>
<li><code>LongformerConfig</code> configuration class: <code>LongformerForQuestionAnswering</code> (Longformer model)</li>
<li><code>LukeConfig</code> configuration class: <code>LukeForQuestionAnswering</code> (LUKE model)</li>
<li><code>LxmertConfig</code> configuration class: <code>LxmertForQuestionAnswering</code> (LXMERT model)</li>
<li><code>MBartConfig</code> configuration class: <code>MBartForQuestionAnswering</code> (mBART model)</li>
<li><code>MPNetConfig</code> configuration class: <code>MPNetForQuestionAnswering</code> (MPNet model)</li>
<li><code>MT5Config</code> configuration class: <code>MT5ForQuestionAnswering</code> (MT5 model)</li>
<li><code>MarkupLMConfig</code> configuration class: <code>MarkupLMForQuestionAnswering</code> (MarkupLM model)</li>
<li><code>MegaConfig</code> configuration class: <code>MegaForQuestionAnswering</code> (MEGA model)</li>
<li><code>MegatronBertConfig</code> configuration class: <code>MegatronBertForQuestionAnswering</code> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> configuration class: <code>MistralForQuestionAnswering</code> (Mistral model)</li>
<li><code>MixtralConfig</code> configuration class: <code>MixtralForQuestionAnswering</code> (Mixtral model)</li>
<li><code>MobileBertConfig</code> configuration class: <code>MobileBertForQuestionAnswering</code> (MobileBERT model)</li>
<li><code>MptConfig</code> configuration class: <code>MptForQuestionAnswering</code> (MPT model)</li>
<li><code>MraConfig</code> configuration class: <code>MraForQuestionAnswering</code> (MRA model)</li>
<li><code>MvpConfig</code> configuration class: <code>MvpForQuestionAnswering</code> (MVP model)</li>
<li><code>NemotronConfig</code> configuration class: <code>NemotronForQuestionAnswering</code> (Nemotron model)</li>
<li><code>NezhaConfig</code> configuration class: <code>NezhaForQuestionAnswering</code> (Nezha model)</li>
<li><code>NystromformerConfig</code> configuration class: <code>NystromformerForQuestionAnswering</code> (Nystr&#xF6;mformer model)</li>
<li><code>OPTConfig</code> configuration class: <code>OPTForQuestionAnswering</code> (OPT model)</li>
<li><code>QDQBertConfig</code> configuration class: <code>QDQBertForQuestionAnswering</code> (QDQBert model)</li>
<li><code>Qwen2Config</code> configuration class: <code>Qwen2ForQuestionAnswering</code> (Qwen2 model)</li>
<li><code>Qwen2MoeConfig</code> configuration class: <code>Qwen2MoeForQuestionAnswering</code> (Qwen2MoE model)</li>
<li><code>ReformerConfig</code> configuration class: <code>ReformerForQuestionAnswering</code> (Reformer model)</li>
<li><code>RemBertConfig</code> configuration class: <code>RemBertForQuestionAnswering</code> (RemBERT model)</li>
<li><code>RoCBertConfig</code> configuration class: <code>RoCBertForQuestionAnswering</code> (RoCBert model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>RoFormerForQuestionAnswering</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>RobertaForQuestionAnswering</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>RobertaPreLayerNormForQuestionAnswering</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>SplinterConfig</code> configuration class: <code>SplinterForQuestionAnswering</code> (Splinter model)</li>
<li><code>SqueezeBertConfig</code> configuration class: <code>SqueezeBertForQuestionAnswering</code> (SqueezeBERT model)</li>
<li><code>T5Config</code> configuration class: <code>T5ForQuestionAnswering</code> (T5 model)</li>
<li><code>UMT5Config</code> configuration class: <code>UMT5ForQuestionAnswering</code> (UMT5 model)</li>
<li><code>XLMConfig</code> configuration class: <code>XLMForQuestionAnsweringSimple</code> (XLM model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>XLMRobertaForQuestionAnswering</code> (XLM-RoBERTa model)</li>
<li><code>XLMRobertaXLConfig</code> configuration class: <code>XLMRobertaXLForQuestionAnswering</code> (XLM-RoBERTa-XL model)</li>
<li><code>XLNetConfig</code> configuration class: <code>XLNetForQuestionAnsweringSimple</code> (XLNet model)</li>
<li><code>XmodConfig</code> configuration class: <code>XmodForQuestionAnswering</code> (X-MOD model)</li>
<li><code>YosoConfig</code> configuration class: <code>YosoForQuestionAnswering</code> (YOSO model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForQuestionAnswering.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Wi=new L({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_config.example",$$slots:{default:[B3]},$$scope:{ctx:v}}}),gf=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Ei=new L({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[R3]},$$scope:{ctx:v}}}),pf=new A({props:{title:"TFAutoModelForQuestionAnswering",local:"transformers.TFAutoModelForQuestionAnswering ][ transformers.TFAutoModelForQuestionAnswering",headingTag:"h3"}}),uf=new k({props:{name:"class transformers.TFAutoModelForQuestionAnswering",anchor:"transformers.TFAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L645"}}),hf=new k({props:{name:"from_config",anchor:"transformers.TFAutoModelForQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>TFAlbertForQuestionAnswering</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.TFBertForQuestionAnswering">TFBertForQuestionAnswering</a> (BERT model)</li>
<li><code>CamembertConfig</code> configuration class: <code>TFCamembertForQuestionAnswering</code> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/convbert#transformers.TFConvBertForQuestionAnswering">TFConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta#transformers.TFDebertaForQuestionAnswering">TFDebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/deberta-v2#transformers.TFDebertaV2ForQuestionAnswering">TFDebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>TFDistilBertForQuestionAnswering</code> (DistilBERT model)</li>
<li><code>ElectraConfig</code> configuration class: <code>TFElectraForQuestionAnswering</code> (ELECTRA model)</li>
<li><code>FlaubertConfig</code> configuration class: <code>TFFlaubertForQuestionAnsweringSimple</code> (FlauBERT model)</li>
<li><code>FunnelConfig</code> configuration class: <code>TFFunnelForQuestionAnswering</code> (Funnel Transformer model)</li>
<li><code>GPTJConfig</code> configuration class: <code>TFGPTJForQuestionAnswering</code> (GPT-J model)</li>
<li><code>LayoutLMv3Config</code> configuration class: <code>TFLayoutLMv3ForQuestionAnswering</code> (LayoutLMv3 model)</li>
<li><code>LongformerConfig</code> configuration class: <code>TFLongformerForQuestionAnswering</code> (Longformer model)</li>
<li><code>MPNetConfig</code> configuration class: <code>TFMPNetForQuestionAnswering</code> (MPNet model)</li>
<li><code>MobileBertConfig</code> configuration class: <code>TFMobileBertForQuestionAnswering</code> (MobileBERT model)</li>
<li><code>RemBertConfig</code> configuration class: <code>TFRemBertForQuestionAnswering</code> (RemBERT model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>TFRoFormerForQuestionAnswering</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>TFRobertaForQuestionAnswering</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>TFRobertaPreLayerNormForQuestionAnswering</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>XLMConfig</code> configuration class: <code>TFXLMForQuestionAnsweringSimple</code> (XLM model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>TFXLMRobertaForQuestionAnswering</code> (XLM-RoBERTa model)</li>
<li><code>XLNetConfig</code> configuration class: <code>TFXLNetForQuestionAnsweringSimple</code> (XLNet model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Vi=new L({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[G3]},$$scope:{ctx:v}}}),bf=new k({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Ii=new L({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[J3]},$$scope:{ctx:v}}}),_f=new A({props:{title:"FlaxAutoModelForQuestionAnswering",local:"transformers.FlaxAutoModelForQuestionAnswering ][ transformers.FlaxAutoModelForQuestionAnswering",headingTag:"h3"}}),Mf=new k({props:{name:"class transformers.FlaxAutoModelForQuestionAnswering",anchor:"transformers.FlaxAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L329"}}),Tf=new k({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlbertConfig</code> configuration class: <code>FlaxAlbertForQuestionAnswering</code> (ALBERT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bart#transformers.FlaxBartForQuestionAnswering">FlaxBartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/bert#transformers.FlaxBertForQuestionAnswering">FlaxBertForQuestionAnswering</a> (BERT model)</li>
<li><code>BigBirdConfig</code> configuration class: <code>FlaxBigBirdForQuestionAnswering</code> (BigBird model)</li>
<li><code>DistilBertConfig</code> configuration class: <code>FlaxDistilBertForQuestionAnswering</code> (DistilBERT model)</li>
<li><code>ElectraConfig</code> configuration class: <code>FlaxElectraForQuestionAnswering</code> (ELECTRA model)</li>
<li><code>MBartConfig</code> configuration class: <code>FlaxMBartForQuestionAnswering</code> (mBART model)</li>
<li><code>RoFormerConfig</code> configuration class: <code>FlaxRoFormerForQuestionAnswering</code> (RoFormer model)</li>
<li><code>RobertaConfig</code> configuration class: <code>FlaxRobertaForQuestionAnswering</code> (RoBERTa model)</li>
<li><code>RobertaPreLayerNormConfig</code> configuration class: <code>FlaxRobertaPreLayerNormForQuestionAnswering</code> (RoBERTa-PreLayerNorm model)</li>
<li><code>XLMRobertaConfig</code> configuration class: <code>FlaxXLMRobertaForQuestionAnswering</code> (XLM-RoBERTa model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Pi=new L({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[W3]},$$scope:{ctx:v}}}),yf=new k({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Si=new L({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[E3]},$$scope:{ctx:v}}}),Ff=new A({props:{title:"AutoModelForTextEncoding",local:"transformers.AutoModelForTextEncoding ][ transformers.AutoModelForTextEncoding",headingTag:"h3"}}),Cf=new k({props:{name:"class transformers.AutoModelForTextEncoding",anchor:"transformers.AutoModelForTextEncoding",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1600"}}),wf=new A({props:{title:"TFAutoModelForTextEncoding",local:"transformers.TFAutoModelForTextEncoding ][ transformers.TFAutoModelForTextEncoding",headingTag:"h3"}}),xf=new k({props:{name:"class transformers.TFAutoModelForTextEncoding",anchor:"transformers.TFAutoModelForTextEncoding",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L533"}}),$f=new A({props:{title:"컴퓨터 비전",local:"computer-vision",headingTag:"h2"}}),Af=new A({props:{title:"AutoModelForDepthEstimation",local:"transformers.AutoModelForDepthEstimation ][ transformers.AutoModelForDepthEstimation",headingTag:"h3"}}),jf=new k({props:{name:"class transformers.AutoModelForDepthEstimation",anchor:"transformers.AutoModelForDepthEstimation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1793"}}),Zf=new k({props:{name:"from_config",anchor:"transformers.AutoModelForDepthEstimation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDepthEstimation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>DPTConfig</code> configuration class: <code>DPTForDepthEstimation</code> (DPT model)</li>
<li><code>DepthAnythingConfig</code> configuration class: <code>DepthAnythingForDepthEstimation</code> (Depth Anything model)</li>
<li><code>GLPNConfig</code> configuration class: <code>GLPNForDepthEstimation</code> (GLPN model)</li>
<li><code>ZoeDepthConfig</code> configuration class: <code>ZoeDepthForDepthEstimation</code> (ZoeDepth model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForDepthEstimation.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Ui=new L({props:{anchor:"transformers.AutoModelForDepthEstimation.from_config.example",$$slots:{default:[V3]},$$scope:{ctx:v}}}),Bf=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForDepthEstimation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Ni=new L({props:{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.example",$$slots:{default:[I3]},$$scope:{ctx:v}}}),Rf=new A({props:{title:"AutoModelForImageClassification",local:"transformers.AutoModelForImageClassification ][ transformers.AutoModelForImageClassification",headingTag:"h3"}}),Gf=new k({props:{name:"class transformers.AutoModelForImageClassification",anchor:"transformers.AutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1727"}}),Jf=new k({props:{name:"from_config",anchor:"transformers.AutoModelForImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>BeitConfig</code> configuration class: <code>BeitForImageClassification</code> (BEiT model)</li>
<li><code>BitConfig</code> configuration class: <code>BitForImageClassification</code> (BiT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPForImageClassification">CLIPForImageClassification</a> (CLIP model)</li>
<li><code>ConvNextConfig</code> configuration class: <code>ConvNextForImageClassification</code> (ConvNeXT model)</li>
<li><code>ConvNextV2Config</code> configuration class: <code>ConvNextV2ForImageClassification</code> (ConvNeXTV2 model)</li>
<li><code>CvtConfig</code> configuration class: <code>CvtForImageClassification</code> (CvT model)</li>
<li><code>Data2VecVisionConfig</code> configuration class: <code>Data2VecVisionForImageClassification</code> (Data2VecVision model)</li>
<li><code>DeiTConfig</code> configuration class: <code>DeiTForImageClassification</code> or <code>DeiTForImageClassificationWithTeacher</code> (DeiT model)</li>
<li><code>DinatConfig</code> configuration class: <code>DinatForImageClassification</code> (DiNAT model)</li>
<li><code>Dinov2Config</code> configuration class: <code>Dinov2ForImageClassification</code> (DINOv2 model)</li>
<li><code>Dinov2WithRegistersConfig</code> configuration class: <code>Dinov2WithRegistersForImageClassification</code> (DINOv2 with Registers model)</li>
<li><code>EfficientFormerConfig</code> configuration class: <code>EfficientFormerForImageClassification</code> or <code>EfficientFormerForImageClassificationWithTeacher</code> (EfficientFormer model)</li>
<li><code>EfficientNetConfig</code> configuration class: <code>EfficientNetForImageClassification</code> (EfficientNet model)</li>
<li><code>FocalNetConfig</code> configuration class: <code>FocalNetForImageClassification</code> (FocalNet model)</li>
<li><code>HieraConfig</code> configuration class: <code>HieraForImageClassification</code> (Hiera model)</li>
<li><code>IJepaConfig</code> configuration class: <code>IJepaForImageClassification</code> (I-JEPA model)</li>
<li><code>ImageGPTConfig</code> configuration class: <code>ImageGPTForImageClassification</code> (ImageGPT model)</li>
<li><code>LevitConfig</code> configuration class: <code>LevitForImageClassification</code> or <code>LevitForImageClassificationWithTeacher</code> (LeViT model)</li>
<li><code>MobileNetV1Config</code> configuration class: <code>MobileNetV1ForImageClassification</code> (MobileNetV1 model)</li>
<li><code>MobileNetV2Config</code> configuration class: <code>MobileNetV2ForImageClassification</code> (MobileNetV2 model)</li>
<li><code>MobileViTConfig</code> configuration class: <code>MobileViTForImageClassification</code> (MobileViT model)</li>
<li><code>MobileViTV2Config</code> configuration class: <code>MobileViTV2ForImageClassification</code> (MobileViTV2 model)</li>
<li><code>NatConfig</code> configuration class: <code>NatForImageClassification</code> (NAT model)</li>
<li><code>PerceiverConfig</code> configuration class: <code>PerceiverForImageClassificationLearned</code> or <code>PerceiverForImageClassificationFourier</code> or <code>PerceiverForImageClassificationConvProcessing</code> (Perceiver model)</li>
<li><code>PoolFormerConfig</code> configuration class: <code>PoolFormerForImageClassification</code> (PoolFormer model)</li>
<li><code>PvtConfig</code> configuration class: <code>PvtForImageClassification</code> (PVT model)</li>
<li><code>PvtV2Config</code> configuration class: <code>PvtV2ForImageClassification</code> (PVTv2 model)</li>
<li><code>RegNetConfig</code> configuration class: <code>RegNetForImageClassification</code> (RegNet model)</li>
<li><code>ResNetConfig</code> configuration class: <code>ResNetForImageClassification</code> (ResNet model)</li>
<li><code>SegformerConfig</code> configuration class: <code>SegformerForImageClassification</code> (SegFormer model)</li>
<li><code>SiglipConfig</code> configuration class: <code>SiglipForImageClassification</code> (SigLIP model)</li>
<li><code>SwiftFormerConfig</code> configuration class: <code>SwiftFormerForImageClassification</code> (SwiftFormer model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/swin#transformers.SwinForImageClassification">SwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2ForImageClassification">Swinv2ForImageClassification</a> (Swin Transformer V2 model)</li>
<li><code>TextNetConfig</code> configuration class: <code>TextNetForImageClassification</code> (TextNet model)</li>
<li><code>TimmWrapperConfig</code> configuration class: <code>TimmWrapperForImageClassification</code> (TimmWrapperModel model)</li>
<li><code>VanConfig</code> configuration class: <code>VanForImageClassification</code> (VAN model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTForImageClassification">ViTForImageClassification</a> (ViT model)</li>
<li><code>ViTHybridConfig</code> configuration class: <code>ViTHybridForImageClassification</code> (ViT Hybrid model)</li>
<li><code>ViTMSNConfig</code> configuration class: <code>ViTMSNForImageClassification</code> (ViTMSN model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForImageClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),qi=new L({props:{anchor:"transformers.AutoModelForImageClassification.from_config.example",$$slots:{default:[P3]},$$scope:{ctx:v}}}),Wf=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Xi=new L({props:{anchor:"transformers.AutoModelForImageClassification.from_pretrained.example",$$slots:{default:[S3]},$$scope:{ctx:v}}}),Ef=new A({props:{title:"TFAutoModelForImageClassification",local:"transformers.TFAutoModelForImageClassification ][ transformers.TFAutoModelForImageClassification",headingTag:"h3"}}),Vf=new k({props:{name:"class transformers.TFAutoModelForImageClassification",anchor:"transformers.TFAutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L584"}}),If=new k({props:{name:"from_config",anchor:"transformers.TFAutoModelForImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>ConvNextConfig</code> configuration class: <code>TFConvNextForImageClassification</code> (ConvNeXT model)</li>
<li><code>ConvNextV2Config</code> configuration class: <code>TFConvNextV2ForImageClassification</code> (ConvNeXTV2 model)</li>
<li><code>CvtConfig</code> configuration class: <code>TFCvtForImageClassification</code> (CvT model)</li>
<li><code>Data2VecVisionConfig</code> configuration class: <code>TFData2VecVisionForImageClassification</code> (Data2VecVision model)</li>
<li><code>DeiTConfig</code> configuration class: <code>TFDeiTForImageClassification</code> or <code>TFDeiTForImageClassificationWithTeacher</code> (DeiT model)</li>
<li><code>EfficientFormerConfig</code> configuration class: <code>TFEfficientFormerForImageClassification</code> or <code>TFEfficientFormerForImageClassificationWithTeacher</code> (EfficientFormer model)</li>
<li><code>MobileViTConfig</code> configuration class: <code>TFMobileViTForImageClassification</code> (MobileViT model)</li>
<li><code>RegNetConfig</code> configuration class: <code>TFRegNetForImageClassification</code> (RegNet model)</li>
<li><code>ResNetConfig</code> configuration class: <code>TFResNetForImageClassification</code> (ResNet model)</li>
<li><code>SegformerConfig</code> configuration class: <code>TFSegformerForImageClassification</code> (SegFormer model)</li>
<li><code>SwiftFormerConfig</code> configuration class: <code>TFSwiftFormerForImageClassification</code> (SwiftFormer model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/swin#transformers.TFSwinForImageClassification">TFSwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/vit#transformers.TFViTForImageClassification">TFViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForImageClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Di=new L({props:{anchor:"transformers.TFAutoModelForImageClassification.from_config.example",$$slots:{default:[U3]},$$scope:{ctx:v}}}),Pf=new k({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Hi=new L({props:{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[N3]},$$scope:{ctx:v}}}),Sf=new A({props:{title:"FlaxAutoModelForImageClassification",local:"transformers.FlaxAutoModelForImageClassification ][ transformers.FlaxAutoModelForImageClassification",headingTag:"h3"}}),Uf=new k({props:{name:"class transformers.FlaxAutoModelForImageClassification",anchor:"transformers.FlaxAutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L361"}}),Nf=new k({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>BeitConfig</code> configuration class: <code>FlaxBeitForImageClassification</code> (BEiT model)</li>
<li><code>Dinov2Config</code> configuration class: <code>FlaxDinov2ForImageClassification</code> (DINOv2 model)</li>
<li><code>RegNetConfig</code> configuration class: <code>FlaxRegNetForImageClassification</code> (RegNet model)</li>
<li><code>ResNetConfig</code> configuration class: <code>FlaxResNetForImageClassification</code> (ResNet model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/vit#transformers.FlaxViTForImageClassification">FlaxViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Qi=new L({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_config.example",$$slots:{default:[q3]},$$scope:{ctx:v}}}),qf=new k({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Yi=new L({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[X3]},$$scope:{ctx:v}}}),Xf=new A({props:{title:"AutoModelForVideoClassification",local:"transformers.AutoModelForVideoClassification ][ transformers.AutoModelForVideoClassification",headingTag:"h3"}}),Df=new k({props:{name:"class transformers.AutoModelForVideoClassification",anchor:"transformers.AutoModelForVideoClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1800"}}),Hf=new k({props:{name:"from_config",anchor:"transformers.AutoModelForVideoClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVideoClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/ko/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/timesformer#transformers.TimesformerForVideoClassification">TimesformerForVideoClassification</a> (TimeSformer model)</li>
<li><code>VideoMAEConfig</code> configuration class: <code>VideoMAEForVideoClassification</code> (VideoMAE model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/vivit#transformers.VivitConfig">VivitConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/vivit#transformers.VivitForVideoClassification">VivitForVideoClassification</a> (ViViT model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVideoClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),zi=new L({props:{anchor:"transformers.AutoModelForVideoClassification.from_config.example",$$slots:{default:[D3]},$$scope:{ctx:v}}}),Qf=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForVideoClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Oi=new L({props:{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.example",$$slots:{default:[H3]},$$scope:{ctx:v}}}),Yf=new A({props:{title:"AutoModelForKeypointDetection",local:"transformers.AutoModelForKeypointDetection ][ transformers.AutoModelForKeypointDetection",headingTag:"h3"}}),Of=new k({props:{name:"class transformers.AutoModelForKeypointDetection",anchor:"transformers.AutoModelForKeypointDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1596"}}),Kf=new A({props:{title:"AutoModelForMaskedImageModeling",local:"transformers.AutoModelForMaskedImageModeling ][ transformers.AutoModelForMaskedImageModeling",headingTag:"h3"}}),eg=new k({props:{name:"class transformers.AutoModelForMaskedImageModeling",anchor:"transformers.AutoModelForMaskedImageModeling",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1872"}}),og=new k({props:{name:"from_config",anchor:"transformers.AutoModelForMaskedImageModeling.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>DeiTConfig</code> configuration class: <code>DeiTForMaskedImageModeling</code> (DeiT model)</li>
<li><code>FocalNetConfig</code> configuration class: <code>FocalNetForMaskedImageModeling</code> (FocalNet model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/swin#transformers.SwinForMaskedImageModeling">SwinForMaskedImageModeling</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2ForMaskedImageModeling">Swinv2ForMaskedImageModeling</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTForMaskedImageModeling">ViTForMaskedImageModeling</a> (ViT model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Ki=new L({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[Q3]},$$scope:{ctx:v}}}),tg=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),el=new L({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.example",$$slots:{default:[Y3]},$$scope:{ctx:v}}}),ng=new A({props:{title:"TFAutoModelForMaskedImageModeling",local:"transformers.TFAutoModelForMaskedImageModeling ][ transformers.TFAutoModelForMaskedImageModeling",headingTag:"h3"}}),rg=new k({props:{name:"class transformers.TFAutoModelForMaskedImageModeling",anchor:"transformers.TFAutoModelForMaskedImageModeling",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L575"}}),ag=new k({props:{name:"from_config",anchor:"transformers.TFAutoModelForMaskedImageModeling.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>DeiTConfig</code> configuration class: <code>TFDeiTForMaskedImageModeling</code> (DeiT model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/swin#transformers.TFSwinForMaskedImageModeling">TFSwinForMaskedImageModeling</a> (Swin Transformer model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
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Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
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Load the model weights from a PyTorch checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
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already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),tl=new L({props:{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.example",$$slots:{default:[O3]},$$scope:{ctx:v}}}),ig=new A({props:{title:"AutoModelForObjectDetection",local:"transformers.AutoModelForObjectDetection ][ transformers.AutoModelForObjectDetection",headingTag:"h3"}}),lg=new k({props:{name:"class transformers.AutoModelForObjectDetection",anchor:"transformers.AutoModelForObjectDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1777"}}),dg=new k({props:{name:"from_config",anchor:"transformers.AutoModelForObjectDetection.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForObjectDetection.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>ConditionalDetrConfig</code> configuration class: <code>ConditionalDetrForObjectDetection</code> (Conditional DETR model)</li>
<li><code>DeformableDetrConfig</code> configuration class: <code>DeformableDetrForObjectDetection</code> (Deformable DETR model)</li>
<li><code>DetaConfig</code> configuration class: <code>DetaForObjectDetection</code> (DETA model)</li>
<li><code>DetrConfig</code> configuration class: <code>DetrForObjectDetection</code> (DETR model)</li>
<li><code>RTDetrConfig</code> configuration class: <code>RTDetrForObjectDetection</code> (RT-DETR model)</li>
<li><code>TableTransformerConfig</code> configuration class: <code>TableTransformerForObjectDetection</code> (Table Transformer model)</li>
<li><code>YolosConfig</code> configuration class: <code>YolosForObjectDetection</code> (YOLOS model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForObjectDetection.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
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Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
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Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),rl=new L({props:{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.example",$$slots:{default:[eX]},$$scope:{ctx:v}}}),mg=new A({props:{title:"AutoModelForImageSegmentation",local:"transformers.AutoModelForImageSegmentation ][ transformers.AutoModelForImageSegmentation",headingTag:"h3"}}),fg=new k({props:{name:"class transformers.AutoModelForImageSegmentation",anchor:"transformers.AutoModelForImageSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1743"}}),gg=new k({props:{name:"from_config",anchor:"transformers.AutoModelForImageSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>DetrConfig</code> configuration class: <code>DetrForSegmentation</code> (DETR model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForImageSegmentation.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),al=new L({props:{anchor:"transformers.AutoModelForImageSegmentation.from_config.example",$$slots:{default:[oX]},$$scope:{ctx:v}}}),pg=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForImageSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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</ul>`,name:"config"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
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<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),sl=new L({props:{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.example",$$slots:{default:[tX]},$$scope:{ctx:v}}}),ug=new A({props:{title:"AutoModelForImageToImage",local:"transformers.AutoModelForImageToImage ][ transformers.AutoModelForImageToImage",headingTag:"h3"}}),bg=new k({props:{name:"class transformers.AutoModelForImageToImage",anchor:"transformers.AutoModelForImageToImage",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1604"}}),_g=new A({props:{title:"AutoModelForSemanticSegmentation",local:"transformers.AutoModelForSemanticSegmentation ][ transformers.AutoModelForSemanticSegmentation",headingTag:"h3"}}),Mg=new k({props:{name:"class transformers.AutoModelForSemanticSegmentation",anchor:"transformers.AutoModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1750"}}),Tg=new k({props:{name:"from_config",anchor:"transformers.AutoModelForSemanticSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>BeitConfig</code> configuration class: <code>BeitForSemanticSegmentation</code> (BEiT model)</li>
<li><code>DPTConfig</code> configuration class: <code>DPTForSemanticSegmentation</code> (DPT model)</li>
<li><code>Data2VecVisionConfig</code> configuration class: <code>Data2VecVisionForSemanticSegmentation</code> (Data2VecVision model)</li>
<li><code>MobileNetV2Config</code> configuration class: <code>MobileNetV2ForSemanticSegmentation</code> (MobileNetV2 model)</li>
<li><code>MobileViTConfig</code> configuration class: <code>MobileViTForSemanticSegmentation</code> (MobileViT model)</li>
<li><code>MobileViTV2Config</code> configuration class: <code>MobileViTV2ForSemanticSegmentation</code> (MobileViTV2 model)</li>
<li><code>SegformerConfig</code> configuration class: <code>SegformerForSemanticSegmentation</code> (SegFormer model)</li>
<li><code>UperNetConfig</code> configuration class: <code>UperNetForSemanticSegmentation</code> (UPerNet model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),il=new L({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[nX]},$$scope:{ctx:v}}}),yg=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),ll=new L({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[rX]},$$scope:{ctx:v}}}),Fg=new A({props:{title:"TFAutoModelForSemanticSegmentation",local:"transformers.TFAutoModelForSemanticSegmentation ][ transformers.TFAutoModelForSemanticSegmentation",headingTag:"h3"}}),vg=new k({props:{name:"class transformers.TFAutoModelForSemanticSegmentation",anchor:"transformers.TFAutoModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L602"}}),Cg=new k({props:{name:"from_config",anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>Data2VecVisionConfig</code> configuration class: <code>TFData2VecVisionForSemanticSegmentation</code> (Data2VecVision model)</li>
<li><code>MobileViTConfig</code> configuration class: <code>TFMobileViTForSemanticSegmentation</code> (MobileViT model)</li>
<li><code>SegformerConfig</code> configuration class: <code>TFSegformerForSemanticSegmentation</code> (SegFormer model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),dl=new L({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[aX]},$$scope:{ctx:v}}}),wg=new k({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>MaskFormerConfig</code> configuration class: <code>MaskFormerForInstanceSegmentation</code> (MaskFormer model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),ml=new L({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.example",$$slots:{default:[iX]},$$scope:{ctx:v}}}),Lg=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
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PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
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<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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</ul>`,name:"config"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Deprecated and ignored. All downloads are now resumed by default when possible.
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),fl=new L({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.example",$$slots:{default:[lX]},$$scope:{ctx:v}}}),Ag=new A({props:{title:"AutoModelForUniversalSegmentation",local:"transformers.AutoModelForUniversalSegmentation ][ transformers.AutoModelForUniversalSegmentation",headingTag:"h3"}}),jg=new k({props:{name:"class transformers.AutoModelForUniversalSegmentation",anchor:"transformers.AutoModelForUniversalSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1759"}}),Zg=new k({props:{name:"from_config",anchor:"transformers.AutoModelForUniversalSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForUniversalSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>DetrConfig</code> configuration class: <code>DetrForSegmentation</code> (DETR model)</li>
<li><code>Mask2FormerConfig</code> configuration class: <code>Mask2FormerForUniversalSegmentation</code> (Mask2Former model)</li>
<li><code>MaskFormerConfig</code> configuration class: <code>MaskFormerForInstanceSegmentation</code> (MaskFormer model)</li>
<li><code>OneFormerConfig</code> configuration class: <code>OneFormerForUniversalSegmentation</code> (OneFormer model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
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Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
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<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),pl=new L({props:{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.example",$$slots:{default:[cX]},$$scope:{ctx:v}}}),Rg=new A({props:{title:"AutoModelForZeroShotImageClassification",local:"transformers.AutoModelForZeroShotImageClassification ][ transformers.AutoModelForZeroShotImageClassification",headingTag:"h3"}}),Gg=new k({props:{name:"class transformers.AutoModelForZeroShotImageClassification",anchor:"transformers.AutoModelForZeroShotImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1734"}}),Jg=new k({props:{name:"from_config",anchor:"transformers.AutoModelForZeroShotImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>AlignConfig</code> configuration class: <code>AlignModel</code> (ALIGN model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPConfig">AltCLIPConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/blip-2#transformers.Blip2ForImageTextRetrieval">Blip2ForImageTextRetrieval</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><code>CLIPSegConfig</code> configuration class: <code>CLIPSegModel</code> (CLIPSeg model)</li>
<li><code>ChineseCLIPConfig</code> configuration class: <code>ChineseCLIPModel</code> (Chinese-CLIP model)</li>
<li><code>SiglipConfig</code> configuration class: <code>SiglipModel</code> (SigLIP model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),ul=new L({props:{anchor:"transformers.AutoModelForZeroShotImageClassification.from_config.example",$$slots:{default:[mX]},$$scope:{ctx:v}}}),Wg=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
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PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
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<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
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<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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<p>This option can be used if you want to create a model from a pretrained configuration but load your own
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
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Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Deprecated and ignored. All downloads are now resumed by default when possible.
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
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<ul>
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already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/blip#transformers.TFBlipModel">TFBlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>
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Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
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<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
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automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>GroundingDinoConfig</code> configuration class: <code>GroundingDinoForObjectDetection</code> (Grounding DINO model)</li>
<li><code>OmDetTurboConfig</code> configuration class: <code>OmDetTurboForObjectDetection</code> (OmDet-Turbo model)</li>
<li><code>OwlViTConfig</code> configuration class: <code>OwlViTForObjectDetection</code> (OWL-ViT model)</li>
<li><code>Owlv2Config</code> configuration class: <code>Owlv2ForObjectDetection</code> (OWLv2 model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
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Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Tl=new L({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.example",$$slots:{default:[hX]},$$scope:{ctx:v}}}),Xg=new A({props:{title:"오디오",local:"audio",headingTag:"h2"}}),Hg=new A({props:{title:"AutoModelForAudioClassification",local:"transformers.AutoModelForAudioClassification ][ transformers.AutoModelForAudioClassification",headingTag:"h3"}}),Qg=new k({props:{name:"class transformers.AutoModelForAudioClassification",anchor:"transformers.AutoModelForAudioClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1821"}}),Yg=new k({props:{name:"from_config",anchor:"transformers.AutoModelForAudioClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>ASTConfig</code> configuration class: <code>ASTForAudioClassification</code> (Audio Spectrogram Transformer model)</li>
<li><code>Data2VecAudioConfig</code> configuration class: <code>Data2VecAudioForSequenceClassification</code> (Data2VecAudio model)</li>
<li><code>HubertConfig</code> configuration class: <code>HubertForSequenceClassification</code> (Hubert model)</li>
<li><code>SEWConfig</code> configuration class: <code>SEWForSequenceClassification</code> (SEW model)</li>
<li><code>SEWDConfig</code> configuration class: <code>SEWDForSequenceClassification</code> (SEW-D model)</li>
<li><code>UniSpeechConfig</code> configuration class: <code>UniSpeechForSequenceClassification</code> (UniSpeech model)</li>
<li><code>UniSpeechSatConfig</code> configuration class: <code>UniSpeechSatForSequenceClassification</code> (UniSpeechSat model)</li>
<li><code>Wav2Vec2BertConfig</code> configuration class: <code>Wav2Vec2BertForSequenceClassification</code> (Wav2Vec2-BERT model)</li>
<li><code>Wav2Vec2Config</code> configuration class: <code>Wav2Vec2ForSequenceClassification</code> (Wav2Vec2 model)</li>
<li><code>Wav2Vec2ConformerConfig</code> configuration class: <code>Wav2Vec2ConformerForSequenceClassification</code> (Wav2Vec2-Conformer model)</li>
<li><code>WavLMConfig</code> configuration class: <code>WavLMForSequenceClassification</code> (WavLM model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperForAudioClassification">WhisperForAudioClassification</a> (Whisper model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),yl=new L({props:{anchor:"transformers.AutoModelForAudioClassification.from_config.example",$$slots:{default:[bX]},$$scope:{ctx:v}}}),zg=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForAudioClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Fl=new L({props:{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[_X]},$$scope:{ctx:v}}}),Og=new A({props:{title:"TFAutoModelForAudioClassification",local:"transformers.TFAutoModelForAudioClassification ][ transformers.TFAutoModelForAudioClassification",headingTag:"h3"}}),Kg=new k({props:{name:"class transformers.TFAutoModelForAudioClassification",anchor:"transformers.TFAutoModelForAudioClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L544"}}),ep=new k({props:{name:"from_config",anchor:"transformers.TFAutoModelForAudioClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForAudioClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>Wav2Vec2Config</code> configuration class: <code>TFWav2Vec2ForSequenceClassification</code> (Wav2Vec2 model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForAudioClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),vl=new L({props:{anchor:"transformers.TFAutoModelForAudioClassification.from_config.example",$$slots:{default:[MX]},$$scope:{ctx:v}}}),op=new k({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Cl=new L({props:{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[TX]},$$scope:{ctx:v}}}),tp=new A({props:{title:"AutoModelForAudioFrameClassification",local:"transformers.AutoModelForAudioFrameClassification ][ transformers.AutoModelForAudioFrameClassification",headingTag:"h3"}}),np=new k({props:{name:"class transformers.AutoModelForAudioFrameClassification",anchor:"transformers.AutoModelForAudioFrameClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1844"}}),rp=new k({props:{name:"from_config",anchor:"transformers.AutoModelForAudioFrameClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>Data2VecAudioConfig</code> configuration class: <code>Data2VecAudioForAudioFrameClassification</code> (Data2VecAudio model)</li>
<li><code>UniSpeechSatConfig</code> configuration class: <code>UniSpeechSatForAudioFrameClassification</code> (UniSpeechSat model)</li>
<li><code>Wav2Vec2BertConfig</code> configuration class: <code>Wav2Vec2BertForAudioFrameClassification</code> (Wav2Vec2-BERT model)</li>
<li><code>Wav2Vec2Config</code> configuration class: <code>Wav2Vec2ForAudioFrameClassification</code> (Wav2Vec2 model)</li>
<li><code>Wav2Vec2ConformerConfig</code> configuration class: <code>Wav2Vec2ConformerForAudioFrameClassification</code> (Wav2Vec2-Conformer model)</li>
<li><code>WavLMConfig</code> configuration class: <code>WavLMForAudioFrameClassification</code> (WavLM model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),wl=new L({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.example",$$slots:{default:[yX]},$$scope:{ctx:v}}}),ap=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),kl=new L({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.example",$$slots:{default:[FX]},$$scope:{ctx:v}}}),sp=new A({props:{title:"AutoModelForCTC",local:"transformers.AutoModelForCTC ][ transformers.AutoModelForCTC",headingTag:"h3"}}),ip=new k({props:{name:"class transformers.AutoModelForCTC",anchor:"transformers.AutoModelForCTC",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1828"}}),lp=new k({props:{name:"from_config",anchor:"transformers.AutoModelForCTC.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCTC.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>Data2VecAudioConfig</code> configuration class: <code>Data2VecAudioForCTC</code> (Data2VecAudio model)</li>
<li><code>HubertConfig</code> configuration class: <code>HubertForCTC</code> (Hubert model)</li>
<li><code>MCTCTConfig</code> configuration class: <code>MCTCTForCTC</code> (M-CTC-T model)</li>
<li><code>SEWConfig</code> configuration class: <code>SEWForCTC</code> (SEW model)</li>
<li><code>SEWDConfig</code> configuration class: <code>SEWDForCTC</code> (SEW-D model)</li>
<li><code>UniSpeechConfig</code> configuration class: <code>UniSpeechForCTC</code> (UniSpeech model)</li>
<li><code>UniSpeechSatConfig</code> configuration class: <code>UniSpeechSatForCTC</code> (UniSpeechSat model)</li>
<li><code>Wav2Vec2BertConfig</code> configuration class: <code>Wav2Vec2BertForCTC</code> (Wav2Vec2-BERT model)</li>
<li><code>Wav2Vec2Config</code> configuration class: <code>Wav2Vec2ForCTC</code> (Wav2Vec2 model)</li>
<li><code>Wav2Vec2ConformerConfig</code> configuration class: <code>Wav2Vec2ConformerForCTC</code> (Wav2Vec2-Conformer model)</li>
<li><code>WavLMConfig</code> configuration class: <code>WavLMForCTC</code> (WavLM model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForCTC.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),xl=new L({props:{anchor:"transformers.AutoModelForCTC.from_config.example",$$slots:{default:[vX]},$$scope:{ctx:v}}}),dp=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForCTC.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCTC.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForCTC.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForCTC.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForCTC.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForCTC.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForCTC.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForCTC.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForCTC.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForCTC.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForCTC.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForCTC.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForCTC.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForCTC.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForCTC.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForCTC.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),$l=new L({props:{anchor:"transformers.AutoModelForCTC.from_pretrained.example",$$slots:{default:[CX]},$$scope:{ctx:v}}}),cp=new A({props:{title:"AutoModelForSpeechSeq2Seq",local:"transformers.AutoModelForSpeechSeq2Seq ][ transformers.AutoModelForSpeechSeq2Seq",headingTag:"h3"}}),mp=new k({props:{name:"class transformers.AutoModelForSpeechSeq2Seq",anchor:"transformers.AutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1835"}}),fp=new k({props:{name:"from_config",anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>MoonshineConfig</code> configuration class: <code>MoonshineForConditionalGeneration</code> (Moonshine model)</li>
<li><code>Pop2PianoConfig</code> configuration class: <code>Pop2PianoForConditionalGeneration</code> (Pop2Piano model)</li>
<li><code>SeamlessM4TConfig</code> configuration class: <code>SeamlessM4TForSpeechToText</code> (SeamlessM4T model)</li>
<li><code>SeamlessM4Tv2Config</code> configuration class: <code>SeamlessM4Tv2ForSpeechToText</code> (SeamlessM4Tv2 model)</li>
<li><code>Speech2TextConfig</code> configuration class: <code>Speech2TextForConditionalGeneration</code> (Speech2Text model)</li>
<li><code>SpeechEncoderDecoderConfig</code> configuration class: <code>SpeechEncoderDecoderModel</code> (Speech Encoder decoder model)</li>
<li><code>SpeechT5Config</code> configuration class: <code>SpeechT5ForSpeechToText</code> (SpeechT5 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperForConditionalGeneration">WhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Ll=new L({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[wX]},$$scope:{ctx:v}}}),gp=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Al=new L({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[kX]},$$scope:{ctx:v}}}),pp=new A({props:{title:"TFAutoModelForSpeechSeq2Seq",local:"transformers.TFAutoModelForSpeechSeq2Seq ][ transformers.TFAutoModelForSpeechSeq2Seq",headingTag:"h3"}}),up=new k({props:{name:"class transformers.TFAutoModelForSpeechSeq2Seq",anchor:"transformers.TFAutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L699"}}),hp=new k({props:{name:"from_config",anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>Speech2TextConfig</code> configuration class: <code>TFSpeech2TextForConditionalGeneration</code> (Speech2Text model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.TFWhisperForConditionalGeneration">TFWhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),jl=new L({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[xX]},$$scope:{ctx:v}}}),bp=new k({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Zl=new L({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[$X]},$$scope:{ctx:v}}}),_p=new A({props:{title:"FlaxAutoModelForSpeechSeq2Seq",local:"transformers.FlaxAutoModelForSpeechSeq2Seq ][ transformers.FlaxAutoModelForSpeechSeq2Seq",headingTag:"h3"}}),Mp=new k({props:{name:"class transformers.FlaxAutoModelForSpeechSeq2Seq",anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L377"}}),Tp=new k({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>SpeechEncoderDecoderConfig</code> configuration class: <code>FlaxSpeechEncoderDecoderModel</code> (Speech Encoder decoder model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Bl=new L({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[LX]},$$scope:{ctx:v}}}),yp=new k({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Rl=new L({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[AX]},$$scope:{ctx:v}}}),Fp=new A({props:{title:"AutoModelForAudioXVector",local:"transformers.AutoModelForAudioXVector ][ transformers.AutoModelForAudioXVector",headingTag:"h3"}}),vp=new k({props:{name:"class transformers.AutoModelForAudioXVector",anchor:"transformers.AutoModelForAudioXVector",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1853"}}),Cp=new k({props:{name:"from_config",anchor:"transformers.AutoModelForAudioXVector.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioXVector.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>Data2VecAudioConfig</code> configuration class: <code>Data2VecAudioForXVector</code> (Data2VecAudio model)</li>
<li><code>UniSpeechSatConfig</code> configuration class: <code>UniSpeechSatForXVector</code> (UniSpeechSat model)</li>
<li><code>Wav2Vec2BertConfig</code> configuration class: <code>Wav2Vec2BertForXVector</code> (Wav2Vec2-BERT model)</li>
<li><code>Wav2Vec2Config</code> configuration class: <code>Wav2Vec2ForXVector</code> (Wav2Vec2 model)</li>
<li><code>Wav2Vec2ConformerConfig</code> configuration class: <code>Wav2Vec2ConformerForXVector</code> (Wav2Vec2-Conformer model)</li>
<li><code>WavLMConfig</code> configuration class: <code>WavLMForXVector</code> (WavLM model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioXVector.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Gl=new L({props:{anchor:"transformers.AutoModelForAudioXVector.from_config.example",$$slots:{default:[jX]},$$scope:{ctx:v}}}),wp=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForAudioXVector.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
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<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
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Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Jl=new L({props:{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.example",$$slots:{default:[ZX]},$$scope:{ctx:v}}}),kp=new A({props:{title:"AutoModelForTextToSpectrogram",local:"transformers.AutoModelForTextToSpectrogram ][ transformers.AutoModelForTextToSpectrogram",headingTag:"h3"}}),$p=new k({props:{name:"class transformers.AutoModelForTextToSpectrogram",anchor:"transformers.AutoModelForTextToSpectrogram",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1857"}}),Lp=new A({props:{title:"AutoModelForTextToWaveform",local:"transformers.AutoModelForTextToWaveform ][ transformers.AutoModelForTextToWaveform",headingTag:"h3"}}),jp=new k({props:{name:"class transformers.AutoModelForTextToWaveform",anchor:"transformers.AutoModelForTextToWaveform",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1861"}}),Zp=new A({props:{title:"멀티모달",local:"multimodal",headingTag:"h2"}}),Rp=new A({props:{title:"AutoModelForTableQuestionAnswering",local:"transformers.AutoModelForTableQuestionAnswering ][ transformers.AutoModelForTableQuestionAnswering",headingTag:"h3"}}),Gp=new k({props:{name:"class transformers.AutoModelForTableQuestionAnswering",anchor:"transformers.AutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1671"}}),Jp=new k({props:{name:"from_config",anchor:"transformers.AutoModelForTableQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>TapasConfig</code> configuration class: <code>TapasForQuestionAnswering</code> (TAPAS model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Wl=new L({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[BX]},$$scope:{ctx:v}}}),Wp=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
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<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
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Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
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Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
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automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),El=new L({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[RX]},$$scope:{ctx:v}}}),Ep=new A({props:{title:"TFAutoModelForTableQuestionAnswering",local:"transformers.TFAutoModelForTableQuestionAnswering ][ transformers.TFAutoModelForTableQuestionAnswering",headingTag:"h3"}}),Vp=new k({props:{name:"class transformers.TFAutoModelForTableQuestionAnswering",anchor:"transformers.TFAutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L663"}}),Ip=new k({props:{name:"from_config",anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>TapasConfig</code> configuration class: <code>TFTapasForQuestionAnswering</code> (TAPAS model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Vl=new L({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[GX]},$$scope:{ctx:v}}}),Pp=new k({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Il=new L({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[JX]},$$scope:{ctx:v}}}),Sp=new A({props:{title:"AutoModelForDocumentQuestionAnswering",local:"transformers.AutoModelForDocumentQuestionAnswering ][ transformers.AutoModelForDocumentQuestionAnswering",headingTag:"h3"}}),Up=new k({props:{name:"class transformers.AutoModelForDocumentQuestionAnswering",anchor:"transformers.AutoModelForDocumentQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1693"}}),Np=new k({props:{name:"from_config",anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>LayoutLMConfig</code> configuration class: <code>LayoutLMForQuestionAnswering</code> (LayoutLM model)</li>
<li><code>LayoutLMv2Config</code> configuration class: <code>LayoutLMv2ForQuestionAnswering</code> (LayoutLMv2 model)</li>
<li><code>LayoutLMv3Config</code> configuration class: <code>LayoutLMv3ForQuestionAnswering</code> (LayoutLMv3 model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Pl=new L({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[WX]},$$scope:{ctx:v}}}),qp=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Sl=new L({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[EX]},$$scope:{ctx:v}}}),Xp=new A({props:{title:"TFAutoModelForDocumentQuestionAnswering",local:"transformers.TFAutoModelForDocumentQuestionAnswering ][ transformers.TFAutoModelForDocumentQuestionAnswering",headingTag:"h3"}}),Dp=new k({props:{name:"class transformers.TFAutoModelForDocumentQuestionAnswering",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L652"}}),Hp=new k({props:{name:"from_config",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>LayoutLMConfig</code> configuration class: <code>TFLayoutLMForQuestionAnswering</code> (LayoutLM model)</li>
<li><code>LayoutLMv3Config</code> configuration class: <code>TFLayoutLMv3ForQuestionAnswering</code> (LayoutLMv3 model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
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Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Nl=new L({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[IX]},$$scope:{ctx:v}}}),Yp=new A({props:{title:"AutoModelForVisualQuestionAnswering",local:"transformers.AutoModelForVisualQuestionAnswering ][ transformers.AutoModelForVisualQuestionAnswering",headingTag:"h3"}}),zp=new k({props:{name:"class transformers.AutoModelForVisualQuestionAnswering",anchor:"transformers.AutoModelForVisualQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1682"}}),Op=new k({props:{name:"from_config",anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/ko/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/blip-2#transformers.Blip2ForConditionalGeneration">Blip2ForConditionalGeneration</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/blip#transformers.BlipForQuestionAnswering">BlipForQuestionAnswering</a> (BLIP model)</li>
<li><code>ViltConfig</code> configuration class: <code>ViltForQuestionAnswering</code> (ViLT model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),ql=new L({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.example",$$slots:{default:[PX]},$$scope:{ctx:v}}}),Kp=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L447"}}),Xl=new L({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.example",$$slots:{default:[SX]},$$scope:{ctx:v}}}),eu=new A({props:{title:"AutoModelForVision2Seq",local:"transformers.AutoModelForVision2Seq ][ transformers.AutoModelForVision2Seq",headingTag:"h3"}}),ou=new k({props:{name:"class transformers.AutoModelForVision2Seq",anchor:"transformers.AutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1807"}}),tu=new k({props:{name:"from_config",anchor:"transformers.AutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/ko/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/blip-2#transformers.Blip2ForConditionalGeneration">Blip2ForConditionalGeneration</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/blip#transformers.BlipForConditionalGeneration">BlipForConditionalGeneration</a> (BLIP model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/chameleon#transformers.ChameleonConfig">ChameleonConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/chameleon#transformers.ChameleonForConditionalGeneration">ChameleonForConditionalGeneration</a> (Chameleon model)</li>
<li><code>GitConfig</code> configuration class: <code>GitForCausalLM</code> (GIT model)</li>
<li><code>Idefics2Config</code> configuration class: <code>Idefics2ForConditionalGeneration</code> (Idefics2 model)</li>
<li><code>Idefics3Config</code> configuration class: <code>Idefics3ForConditionalGeneration</code> (Idefics3 model)</li>
<li><code>InstructBlipConfig</code> configuration class: <code>InstructBlipForConditionalGeneration</code> (InstructBLIP model)</li>
<li><code>InstructBlipVideoConfig</code> configuration class: <code>InstructBlipVideoForConditionalGeneration</code> (InstructBlipVideo model)</li>
<li><code>Kosmos2Config</code> configuration class: <code>Kosmos2ForConditionalGeneration</code> (KOSMOS-2 model)</li>
<li><code>LlavaConfig</code> configuration class: <code>LlavaForConditionalGeneration</code> (LLaVa model)</li>
<li><code>LlavaNextConfig</code> configuration class: <code>LlavaNextForConditionalGeneration</code> (LLaVA-NeXT model)</li>
<li><code>LlavaNextVideoConfig</code> configuration class: <code>LlavaNextVideoForConditionalGeneration</code> (LLaVa-NeXT-Video model)</li>
<li><code>LlavaOnevisionConfig</code> configuration class: <code>LlavaOnevisionForConditionalGeneration</code> (LLaVA-Onevision model)</li>
<li><code>MllamaConfig</code> configuration class: <code>MllamaForConditionalGeneration</code> (Mllama model)</li>
<li><a href="/docs/transformers/main/ko/model_doc/paligemma#transformers.PaliGemmaConfig">PaliGemmaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/paligemma#transformers.PaliGemmaForConditionalGeneration">PaliGemmaForConditionalGeneration</a> (PaliGemma model)</li>
<li><code>Pix2StructConfig</code> configuration class: <code>Pix2StructForConditionalGeneration</code> (Pix2Struct model)</li>
<li><code>Qwen2VLConfig</code> configuration class: <code>Qwen2VLForConditionalGeneration</code> (Qwen2VL model)</li>
<li><code>Qwen2_5_VLConfig</code> configuration class: <code>Qwen2_5_VLForConditionalGeneration</code> (Qwen2_5_VL model)</li>
<li><code>VideoLlavaConfig</code> configuration class: <code>VideoLlavaForConditionalGeneration</code> (VideoLlava model)</li>
<li><code>VipLlavaConfig</code> configuration class: <code>VipLlavaForConditionalGeneration</code> (VipLlava model)</li>
<li><code>VisionEncoderDecoderConfig</code> configuration class: <code>VisionEncoderDecoderModel</code> (Vision Encoder decoder model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVision2Seq.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L418"}}),Dl=new L({props:{anchor:"transformers.AutoModelForVision2Seq.from_config.example",$$slots:{default:[UX]},$$scope:{ctx:v}}}),nu=new k({props:{name:"from_pretrained",anchor:"transformers.AutoModelForVision2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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The model class to instantiate is selected based on the configuration class:</p>
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<li>A path to a <em>directory</em> containing model weights saved using
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<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
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