import{s as Mq,o as _q,n as k}from"../chunks/scheduler.bdbef820.js";import{S as yq,i as Tq,g as i,s as r,r as u,A as Fq,h as l,f as d,c as a,j as w,u as p,x as g,k as C,y as t,a as y,v as h,d as b,t as M,w as _}from"../chunks/index.33f81d56.js";import{T as N1}from"../chunks/Tip.34194030.js";import{D as x}from"../chunks/Docstring.64554317.js";import{C as $}from"../chunks/CodeBlock.362b34a4.js";import{E as j}from"../chunks/ExampleCodeBlock.4f2252c6.js";import{H as Z,E as vq}from"../chunks/EditOnGithub.a9246e21.js";function wq(v){let n,F="만약 <code>NewModelConfig</code>가 <code>PretrainedConfig</code>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 <code>PreTrainedModel</code>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}),g(n)!=="svelte-1ha31hq"&&(n.innerHTML=F),c=a(e),s=l(e,"P",{"data-svelte-h":!0}),g(s)!=="svelte-1b84ayq"&&(s.innerHTML=m)},m(e,T){y(e,n,T),y(e,c,T),y(e,s,T)},p:k,d(e){e&&(d(n),d(c),d(s))}}}function Cq(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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> 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function kq(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}),g(n)!=="svelte-15auxyb"&&(n.innerHTML=F)},m(c,s){y(c,n,s)},p:k,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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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}),g(n)!=="svelte-15auxyb"&&(n.innerHTML=F)},m(c,s){y(c,n,s)},p:k,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> 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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}),g(n)!=="svelte-15auxyb"&&(n.innerHTML=F)},m(c,s){y(c,n,s)},p:k,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> 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function Jq(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function Gq(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function Eq(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function Uq(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function Sq(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function Pq(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function Hq(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function oX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function nX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function aX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function iX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function dX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function mX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function gX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function fX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function uX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function pX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function bX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function MX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function _X(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function yX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function TX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function vX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function wX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function CX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function xX(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwbW9kZWwlMjBhbmQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JOZXh0U2VudGVuY2VQcmVkaWN0aW9uLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiUyQyUyMG91dHB1dF9hdHRlbnRpb25zJTNEVHJ1ZSklMEFtb2RlbC5jb25maWcub3V0cHV0X2F0dGVudGlvbnMlMEElMEElMjMlMjBMb2FkaW5nJTIwZnJvbSUyMGElMjBQeVRvcmNoJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwVGVuc29yRmxvdyUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGcHRfbW9kZWwlMkZiZXJ0X3B0X21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbEZvck5leHRTZW50ZW5jZVByZWRpY3Rpb24uZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHl0b3JjaF9tb2RlbC5iaW4lMjIlMkMlMjBmcm9tX3B0JTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function kX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function $X(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function jX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function LX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function BX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function JX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function WX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function GX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function VX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function UX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function PX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function qX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function DX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function QX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function YX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function zX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function OX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function KX(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function o3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function n3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function a3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function i3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function d3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function m3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function g3(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function f3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function u3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function p3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function b3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function M3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function _3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function T3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function v3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function w3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function C3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function k3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function $3(v){let n,F="Examples:",c,s,m;return s=new $({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNwZWVjaFNlcTJTZXElMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JTcGVlY2hTZXEyU2VxLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JTcGVlY2hTZXEyU2VxLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiUyQyUyMG91dHB1dF9hdHRlbnRpb25zJTNEVHJ1ZSklMEFtb2RlbC5jb25maWcub3V0cHV0X2F0dGVudGlvbnMlMEElMEElMjMlMjBMb2FkaW5nJTIwZnJvbSUyMGElMjBQeVRvcmNoJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwVGVuc29yRmxvdyUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGcHRfbW9kZWwlMkZiZXJ0X3B0X21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JTcGVlY2hTZXEyU2VxLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGcHRfbW9kZWwlMkZiZXJ0X3B5dG9yY2hfbW9kZWwuYmluJTIyJTJDJTIwZnJvbV9wdCUzRFRydWUlMkMlMjBjb25maWclM0Rjb25maWclMEEp",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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function j3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function L3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function A3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function B3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function J3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function W3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function G3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function V3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function U3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function S3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function P3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function q3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function D3(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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(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, 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(),u(s.$$.fragment)},l(e){n=l(e,"P",{"data-svelte-h":!0}),g(n)!=="svelte-kvfsh7"&&(n.textContent=F),c=a(e),p(s.$$.fragment,e)},m(e,T){y(e,n,T),y(e,c,T),h(s,e,T),m=!0},p:k,i(e){m||(b(s.$$.fragment,e),m=!0)},o(e){M(s.$$.fragment,e),m=!1},d(e){e&&(d(n),d(c)),_(s,e)}}}function Q3(v){let n,F,c,s,m,e,T,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.",A2,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,',B2,wd,R2,Cd,zE="위 코드는 <code>BertModel</code>의 인스턴스인 모델을 생성합니다.",J2,xd,OE="각 작업에 대해 하나의 <code>AutoModel</code> 클래스가 있으며, 각각의 백엔드(PyTorch, TensorFlow 또는 Flax)에 해당하는 클래스가 존재합니다.",W2,kd,G2,$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:",E2,jd,V2,Zd,eV="이후에는 일반적으로 자동 클래스를 사용하는 것처럼 사용할 수 있습니다!",I2,vs,U2,Ld,S2,ce,Ad,q1,Mp,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.`,X1,_p,tV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",D1,Uo,Bd,H1,yp,nV="Instantiate one of the configuration classes of the library from a pretrained model configuration.",Q1,Tp,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>:`,Y1,Fp,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> — <code>AltCLIPConfig</code> (AltCLIP model)</li> <li><strong>audio-spectrogram-transformer</strong> — <code>ASTConfig</code> (Audio Spectrogram Transformer model)</li> <li><strong>autoformer</strong> — <code>AutoformerConfig</code> (Autoformer model)</li> <li><strong>bark</strong> — <code>BarkConfig</code> (Bark model)</li> <li><strong>bart</strong> — <code>BartConfig</code> (BART model)</li> <li><strong>beit</strong> — <code>BeitConfig</code> (BEiT model)</li> <li><strong>bert</strong> — <code>BertConfig</code> (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> — <code>BioGptConfig</code> (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> — <code>BlipConfig</code> (BLIP model)</li> <li><strong>blip-2</strong> — <code>Blip2Config</code> (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> — <code>CLIPConfig</code> (CLIP model)</li> <li><strong>clip_text_model</strong> — <code>CLIPTextConfig</code> (CLIPTextModel model)</li> <li><strong>clip_vision_model</strong> — <code>CLIPVisionConfig</code> (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/llama2#transformers.LlamaConfig">LlamaConfig</a> (CodeLlama model)</li> <li><strong>codegen</strong> — <code>CodeGenConfig</code> (CodeGen model)</li> <li><strong>cohere</strong> — <code>CohereConfig</code> (Cohere model)</li> <li><strong>conditional_detr</strong> — <code>ConditionalDetrConfig</code> (Conditional DETR model)</li> <li><strong>convbert</strong> — <code>ConvBertConfig</code> (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> — <code>DbrxConfig</code> (DBRX model)</li> <li><strong>deberta</strong> — <code>DebertaConfig</code> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <code>DebertaV2Config</code> (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>dinat</strong> — <code>DinatConfig</code> (DiNAT model)</li> <li><strong>dinov2</strong> — <code>Dinov2Config</code> (DINOv2 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>encodec</strong> — <code>EncodecConfig</code> (EnCodec model)</li> <li><strong>encoder-decoder</strong> — <code>EncoderDecoderConfig</code> (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> — <code>Gemma2Config</code> (Gemma2 model)</li> <li><strong>git</strong> — <code>GitConfig</code> (GIT 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> — <code>GPTNeoXJapaneseConfig</code> (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>graphormer</strong> — <code>GraphormerConfig</code> (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>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>imagegpt</strong> — <code>ImageGPTConfig</code> (ImageGPT model)</li> <li><strong>informer</strong> — <code>InformerConfig</code> (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/llama2#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> — <code>MambaConfig</code> (Mamba model)</li> <li><strong>mamba2</strong> — <code>Mamba2Config</code> (mamba2 model)</li> <li><strong>marian</strong> — <code>MarianConfig</code> (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> — <code>MistralConfig</code> (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>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>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> — <code>OpenAIGPTConfig</code> (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> — <code>PaliGemmaConfig</code> (PaliGemma model)</li> <li><strong>patchtsmixer</strong> — <code>PatchTSMixerConfig</code> (PatchTSMixer model)</li> <li><strong>patchtst</strong> — <code>PatchTSTConfig</code> (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_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> — <code>RagConfig</code> (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>superpoint</strong> — <code>SuperPointConfig</code> (SuperPoint model)</li> <li><strong>swiftformer</strong> — <code>SwiftFormerConfig</code> (SwiftFormer model)</li> <li><strong>swin</strong> — <code>SwinConfig</code> (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>time_series_transformer</strong> — <code>TimeSeriesTransformerConfig</code> (Time Series Transformer model)</li> <li><strong>timesformer</strong> — <code>TimesformerConfig</code> (TimeSformer model)</li> <li><strong>timm_backbone</strong> — <code>TimmBackboneConfig</code> (TimmBackbone model)</li> <li><strong>trajectory_transformer</strong> — <code>TrajectoryTransformerConfig</code> (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>vits</strong> — <code>VitsConfig</code> (VITS model)</li> <li><strong>vivit</strong> — <code>VivitConfig</code> (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>zoedepth</strong> — <code>ZoeDepthConfig</code> (ZoeDepth model)</li>',z1,ws,O1,Cs,Rd,K1,vp,sV="Register a new configuration for this class.",P2,Jd,N2,me,Wd,ex,wp,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.`,ox,Cp,lV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",tx,So,Gd,nx,xp,dV="Instantiate one of the tokenizer classes of the library from a pretrained model vocabulary.",rx,kp,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>:`,ax,$p,mV='<li><strong>albert</strong> — <code>AlbertTokenizer</code> or <code>AlbertTokenizerFast</code> (ALBERT model)</li> <li><strong>align</strong> — <code>BertTokenizer</code> or <code>BertTokenizerFast</code> (ALIGN model)</li> <li><strong>bark</strong> — <code>BertTokenizer</code> or <code>BertTokenizerFast</code> (Bark model)</li> <li><strong>bart</strong> — <code>BartTokenizer</code> or <code>BartTokenizerFast</code> (BART model)</li> <li><strong>barthez</strong> — <code>BarthezTokenizer</code> or <code>BarthezTokenizerFast</code> (BARThez model)</li> <li><strong>bartpho</strong> — <code>BartphoTokenizer</code> (BARTpho model)</li> <li><strong>bert</strong> — <code>BertTokenizer</code> or <code>BertTokenizerFast</code> (BERT model)</li> <li><strong>bert-generation</strong> — <code>BertGenerationTokenizer</code> (Bert Generation model)</li> <li><strong>bert-japanese</strong> — <code>BertJapaneseTokenizer</code> (BertJapanese model)</li> <li><strong>bertweet</strong> — <code>BertweetTokenizer</code> (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> — <code>BioGptTokenizer</code> (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> — <code>BertTokenizer</code> or <code>BertTokenizerFast</code> (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> — <code>BertTokenizer</code> or <code>BertTokenizerFast</code> (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/llama2#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Chameleon model)</li> <li><strong>chinese_clip</strong> — <code>BertTokenizer</code> or <code>BertTokenizerFast</code> (Chinese-CLIP model)</li> <li><strong>clap</strong> — <code>RobertaTokenizer</code> or <code>RobertaTokenizerFast</code> (CLAP model)</li> <li><strong>clip</strong> — <code>CLIPTokenizer</code> or <code>CLIPTokenizerFast</code> (CLIP model)</li> <li><strong>clipseg</strong> — <code>CLIPTokenizer</code> or <code>CLIPTokenizerFast</code> (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> — <code>CohereTokenizerFast</code> (Cohere model)</li> <li><strong>convbert</strong> — <code>ConvBertTokenizer</code> or <code>ConvBertTokenizerFast</code> (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> — <code>DebertaTokenizer</code> or <code>DebertaTokenizerFast</code> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <code>DebertaV2Tokenizer</code> or <code>DebertaV2TokenizerFast</code> (DeBERTa-v2 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>ernie</strong> — <code>BertTokenizer</code> or <code>BertTokenizerFast</code> (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> — <code>BertTokenizer</code> or <code>BertTokenizerFast</code> (GIT 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> — <code>GPTNeoXJapaneseTokenizer</code> (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> — <code>BertTokenizer</code> or <code>BertTokenizerFast</code> (Grounding DINO model)</li> <li><strong>groupvit</strong> — <code>CLIPTokenizer</code> or <code>CLIPTokenizerFast</code> (GroupViT 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/llama2#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (IDEFICS model)</li> <li><strong>idefics2</strong> — <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Idefics2 model)</li> <li><strong>idefics3</strong> — <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama2#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/llama2#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Jamba model)</li> <li><strong>jetmoe</strong> — <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama2#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/llama2#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (LLaMA model)</li> <li><strong>llava</strong> — <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (LLaVa model)</li> <li><strong>llava_next</strong> — <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (LLaVA-NeXT model)</li> <li><strong>llava_next_video</strong> — <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (LLaVa-NeXT-Video 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> — <code>MarianTokenizer</code> (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> — <code>BertTokenizer</code> or <code>BertTokenizerFast</code> (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/llama2#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Mistral model)</li> <li><strong>mixtral</strong> — <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Mixtral model)</li> <li><strong>mllama</strong> — <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama2#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>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>nezha</strong> — <code>BertTokenizer</code> or <code>BertTokenizerFast</code> (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>olmoe</strong> — <code>GPTNeoXTokenizerFast</code> (OLMoE model)</li> <li><strong>omdet-turbo</strong> — <code>CLIPTokenizer</code> or <code>CLIPTokenizerFast</code> (OmDet-Turbo model)</li> <li><strong>oneformer</strong> — <code>CLIPTokenizer</code> or <code>CLIPTokenizerFast</code> (OneFormer model)</li> <li><strong>openai-gpt</strong> — <code>OpenAIGPTTokenizer</code> or <code>OpenAIGPTTokenizerFast</code> (OpenAI GPT model)</li> <li><strong>opt</strong> — <code>GPT2Tokenizer</code> or <code>GPT2TokenizerFast</code> (OPT model)</li> <li><strong>owlv2</strong> — <code>CLIPTokenizer</code> or <code>CLIPTokenizerFast</code> (OWLv2 model)</li> <li><strong>owlvit</strong> — <code>CLIPTokenizer</code> or <code>CLIPTokenizerFast</code> (OWL-ViT model)</li> <li><strong>paligemma</strong> — <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama2#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/llama2#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama2#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/llama2#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Phi3 model)</li> <li><strong>phimoe</strong> — <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama2#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> — <code>BertTokenizer</code> or <code>BertTokenizerFast</code> (QDQBert model)</li> <li><strong>qwen2</strong> — <code>Qwen2Tokenizer</code> or <code>Qwen2TokenizerFast</code> (Qwen2 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>rag</strong> — <code>RagTokenizer</code> (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> — <code>BertTokenizer</code> or <code>BertTokenizerFast</code> (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/llama2#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (VideoLlava model)</li> <li><strong>vilt</strong> — <code>BertTokenizer</code> or <code>BertTokenizerFast</code> (ViLT model)</li> <li><strong>vipllava</strong> — <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (VipLlava model)</li> <li><strong>visual_bert</strong> — <code>BertTokenizer</code> or <code>BertTokenizerFast</code> (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> — <code>CLIPTokenizer</code> or <code>CLIPTokenizerFast</code> (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/llama2#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Zamba model)</li>',sx,xs,ix,ks,Ed,lx,jp,gV="Register a new tokenizer in this mapping.",q2,Vd,X2,ge,Id,dx,Zp,fV=`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.`,cx,Lp,uV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",mx,L,Ud,gx,Ap,pV="Instantiate one of the feature extractor classes of the library from a pretrained model vocabulary.",fx,Bp,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>:`,ux,Rp,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> — <code>CLIPFeatureExtractor</code> (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> — <code>CLIPFeatureExtractor</code> (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>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> — <code>CLIPFeatureExtractor</code> (X-CLIP model)</li> <li><strong>yolos</strong> — <code>YolosFeatureExtractor</code> (YOLOS model)</li>',px,$s,hx,js,bx,Zs,Sd,Mx,Jp,MV="Register a new feature extractor for this class.",D2,Pd,H2,fe,Nd,_x,Wp,_V=`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.`,yx,Gp,yV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Tx,A,qd,Fx,Ep,TV="Instantiate one of the image processor classes of the library from a pretrained model vocabulary.",vx,Vp,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>:`,wx,Ip,vV='<li><strong>align</strong> — <code>EfficientNetImageProcessor</code> (ALIGN 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> — <code>BlipImageProcessor</code> (BLIP model)</li> <li><strong>blip-2</strong> — <code>BlipImageProcessor</code> (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> — <code>CLIPImageProcessor</code> (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> (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> (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> — <code>CLIPImageProcessor</code> (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> — <code>CLIPImageProcessor</code> (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>imagegpt</strong> — <code>ImageGPTImageProcessor</code> (ImageGPT model)</li> <li><strong>instructblip</strong> — <code>BlipImageProcessor</code> (InstructBLIP model)</li> <li><strong>instructblipvideo</strong> — <code>InstructBlipVideoImageProcessor</code> (InstructBlipVideo model)</li> <li><strong>kosmos-2</strong> — <code>CLIPImageProcessor</code> (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>CLIPImageProcessor</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>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> (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> (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>R</code> or <code>T</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>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>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> — <code>CLIPImageProcessor</code> (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> — <code>CLIPImageProcessor</code> (X-CLIP model)</li> <li><strong>yolos</strong> — <code>YolosImageProcessor</code> (YOLOS model)</li> <li><strong>zoedepth</strong> — <code>ZoeDepthImageProcessor</code> (ZoeDepth model)</li>',Cx,Ls,xx,As,kx,Bs,Xd,$x,Up,wV="Register a new image processor for this class.",Q2,Dd,Y2,ue,Hd,jx,Sp,CV=`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.`,Zx,Pp,xV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Lx,B,Qd,Ax,Np,kV="Instantiate one of the processor classes of the library from a pretrained model vocabulary.",Bx,qp,$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):`,Rx,Xp,jV='<li><strong>align</strong> — <code>AlignProcessor</code> (ALIGN model)</li> <li><strong>altclip</strong> — <code>AltCLIPProcessor</code> (AltCLIP model)</li> <li><strong>bark</strong> — <code>BarkProcessor</code> (Bark model)</li> <li><strong>blip</strong> — <code>BlipProcessor</code> (BLIP model)</li> <li><strong>blip-2</strong> — <code>Blip2Processor</code> (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> — <code>CLIPProcessor</code> (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>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> — <code>CLIPProcessor</code> (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>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> — <code>PaliGemmaProcessor</code> (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_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>',Jx,Rs,Wx,Js,Gx,Ws,Yd,Ex,Dp,ZV="Register a new processor for this class.",z2,zd,O2,Od,LV="다음 자동 클래스들은 특정 헤드 없이 기본 모델 클래스를 인스턴스화하는 데 사용할 수 있습니다.",K2,Kd,ew,pe,ec,Vx,Hp,AV=`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.`,Ix,Qp,BV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Ux,Wn,oc,Sx,Yp,RV="Instantiates one of the base model classes of the library from a configuration.",Px,zp,JV=`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.`,Nx,Gs,qx,R,tc,Xx,Op,WV="Instantiate one of the base model classes of the library from a pretrained model.",Dx,Kp,GV=`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>:`,Hx,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> — <code>AltCLIPModel</code> (AltCLIP model)</li> <li><strong>audio-spectrogram-transformer</strong> — <code>ASTModel</code> (Audio Spectrogram Transformer model)</li> <li><strong>autoformer</strong> — <code>AutoformerModel</code> (Autoformer model)</li> <li><strong>bark</strong> — <code>BarkModel</code> (Bark model)</li> <li><strong>bart</strong> — <code>BartModel</code> (BART model)</li> <li><strong>beit</strong> — <code>BeitModel</code> (BEiT model)</li> <li><strong>bert</strong> — <code>BertModel</code> (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> — <code>BioGptModel</code> (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> — <code>BlipModel</code> (BLIP model)</li> <li><strong>blip-2</strong> — <code>Blip2Model</code> (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> — <code>CLIPModel</code> (CLIP model)</li> <li><strong>clip_text_model</strong> — <code>CLIPTextModel</code> (CLIPTextModel model)</li> <li><strong>clip_vision_model</strong> — <code>CLIPVisionModel</code> (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/llama2#transformers.LlamaModel">LlamaModel</a> (CodeLlama model)</li> <li><strong>codegen</strong> — <code>CodeGenModel</code> (CodeGen model)</li> <li><strong>cohere</strong> — <code>CohereModel</code> (Cohere model)</li> <li><strong>conditional_detr</strong> — <code>ConditionalDetrModel</code> (Conditional DETR model)</li> <li><strong>convbert</strong> — <code>ConvBertModel</code> (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> — <code>DbrxModel</code> (DBRX model)</li> <li><strong>deberta</strong> — <code>DebertaModel</code> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <code>DebertaV2Model</code> (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>dinat</strong> — <code>DinatModel</code> (DiNAT model)</li> <li><strong>dinov2</strong> — <code>Dinov2Model</code> (DINOv2 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> — <code>Gemma2Model</code> (Gemma2 model)</li> <li><strong>git</strong> — <code>GitModel</code> (GIT 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> — <code>GPTNeoXJapaneseModel</code> (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> — <code>GraphormerModel</code> (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>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>imagegpt</strong> — <code>ImageGPTModel</code> (ImageGPT model)</li> <li><strong>informer</strong> — <code>InformerModel</code> (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/llama2#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> — <code>MambaModel</code> (Mamba model)</li> <li><strong>mamba2</strong> — <code>Mamba2Model</code> (mamba2 model)</li> <li><strong>marian</strong> — <code>MarianModel</code> (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> — <code>MistralModel</code> (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>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>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> — <code>OpenAIGPTModel</code> (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> — <code>PatchTSMixerModel</code> (PatchTSMixer model)</li> <li><strong>patchtst</strong> — <code>PatchTSTModel</code> (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_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>swiftformer</strong> — <code>SwiftFormerModel</code> (SwiftFormer model)</li> <li><strong>swin</strong> — <code>SwinModel</code> (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>time_series_transformer</strong> — <code>TimeSeriesTransformerModel</code> (Time Series Transformer model)</li> <li><strong>timesformer</strong> — <code>TimesformerModel</code> (TimeSformer model)</li> <li><strong>timm_backbone</strong> — <code>TimmBackbone</code> (TimmBackbone model)</li> <li><strong>trajectory_transformer</strong> — <code>TrajectoryTransformerModel</code> (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> — <code>VivitModel</code> (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>',Qx,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>`,Yx,Es,ow,nc,tw,he,rc,zx,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.`,Ox,nh,UV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Kx,Gn,ac,ek,rh,SV="Instantiates one of the base model classes of the library from a configuration.",ok,ah,PV=`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.`,tk,Vs,nk,Po,sc,rk,sh,NV="Instantiate one of the base model classes of the library from a pretrained model.",ak,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>:`,sk,lh,XV='<li><strong>albert</strong> — <code>TFAlbertModel</code> (ALBERT model)</li> <li><strong>bart</strong> — <code>TFBartModel</code> (BART model)</li> <li><strong>bert</strong> — <code>TFBertModel</code> (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> — <code>TFBlipModel</code> (BLIP model)</li> <li><strong>camembert</strong> — <code>TFCamembertModel</code> (CamemBERT model)</li> <li><strong>clip</strong> — <code>TFCLIPModel</code> (CLIP model)</li> <li><strong>convbert</strong> — <code>TFConvBertModel</code> (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> — <code>TFDebertaModel</code> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <code>TFDebertaV2Model</code> (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> — <code>TFMarianModel</code> (Marian model)</li> <li><strong>mbart</strong> — <code>TFMBartModel</code> (mBART model)</li> <li><strong>mistral</strong> — <code>TFMistralModel</code> (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> — <code>TFOpenAIGPTModel</code> (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> — <code>TFSwinModel</code> (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>',ik,Is,nw,ic,rw,be,lc,lk,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.`,dk,ch,HV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",ck,En,dc,mk,mh,QV="Instantiates one of the base model classes of the library from a configuration.",gk,gh,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.`,fk,Us,uk,No,cc,pk,fh,zV="Instantiate one of the base model classes of the library from a pretrained model.",hk,uh,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>:`,bk,ph,KV='<li><strong>albert</strong> — <code>FlaxAlbertModel</code> (ALBERT model)</li> <li><strong>bart</strong> — <code>FlaxBartModel</code> (BART model)</li> <li><strong>beit</strong> — <code>FlaxBeitModel</code> (BEiT model)</li> <li><strong>bert</strong> — <code>FlaxBertModel</code> (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> — <code>FlaxCLIPModel</code> (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> — <code>FlaxMarianModel</code> (Marian model)</li> <li><strong>mbart</strong> — <code>FlaxMBartModel</code> (mBART model)</li> <li><strong>mistral</strong> — <code>FlaxMistralModel</code> (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>',Mk,Ss,aw,mc,sw,gc,eI="다음 자동 클래스들은 사전 훈련 헤드가 포함된 모델을 인스턴스화하는 데 사용할 수 있습니다.",iw,fc,lw,Me,uc,_k,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.`,yk,bh,tI="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Tk,Vn,pc,Fk,Mh,nI="Instantiates one of the model classes of the library (with a pretraining head) from a configuration.",vk,_h,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.`,wk,Ps,Ck,J,hc,xk,yh,aI="Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model.",kk,Th,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>:`,$k,Fh,iI="<li><strong>albert</strong> — <code>AlbertForPreTraining</code> (ALBERT model)</li> <li><strong>bart</strong> — <code>BartForConditionalGeneration</code> (BART model)</li> <li><strong>bert</strong> — <code>BertForPreTraining</code> (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>ctrl</strong> — <code>CTRLLMHeadModel</code> (CTRL model)</li> <li><strong>data2vec-text</strong> — <code>Data2VecTextForMaskedLM</code> (Data2VecText model)</li> <li><strong>deberta</strong> — <code>DebertaForMaskedLM</code> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <code>DebertaV2ForMaskedLM</code> (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> — <code>MambaForCausalLM</code> (Mamba model)</li> <li><strong>mamba2</strong> — <code>Mamba2ForCausalLM</code> (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> — <code>OpenAIGPTLMHeadModel</code> (OpenAI GPT model)</li> <li><strong>paligemma</strong> — <code>PaliGemmaForConditionalGeneration</code> (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>",jk,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>`,Zk,Ns,dw,bc,cw,_e,Mc,Lk,wh,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.`,Ak,Ch,cI="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Bk,In,_c,Rk,xh,mI="Instantiates one of the model classes of the library (with a pretraining head) from a configuration.",Jk,kh,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.`,Wk,qs,Gk,qo,yc,Ek,$h,fI="Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model.",Vk,jh,uI=`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>:`,Ik,Zh,pI="<li><strong>albert</strong> — <code>TFAlbertForPreTraining</code> (ALBERT model)</li> <li><strong>bart</strong> — <code>TFBartForConditionalGeneration</code> (BART model)</li> <li><strong>bert</strong> — <code>TFBertForPreTraining</code> (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> — <code>TFOpenAIGPTLMHeadModel</code> (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>",Uk,Xs,mw,Tc,gw,ye,Fc,Sk,Lh,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.`,Pk,Ah,bI="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Nk,Un,vc,qk,Bh,MI="Instantiates one of the model classes of the library (with a pretraining head) from a configuration.",Xk,Rh,_I=`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.`,Dk,Ds,Hk,Xo,wc,Qk,Jh,yI="Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model.",Yk,Wh,TI=`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>:`,zk,Gh,FI='<li><strong>albert</strong> — <code>FlaxAlbertForPreTraining</code> (ALBERT model)</li> <li><strong>bart</strong> — <code>FlaxBartForConditionalGeneration</code> (BART model)</li> <li><strong>bert</strong> — <code>FlaxBertForPreTraining</code> (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>',Ok,Hs,fw,Cc,uw,xc,vI="다음 자동 클래스들은 아래의 자연어 처리 작업에 사용할 수 있습니다.",pw,kc,hw,Te,$c,Kk,Eh,wI=`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$,Vh,CI="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",o$,Sn,jc,t$,Ih,xI="Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration.",n$,Uh,kI=`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$,Qs,a$,W,Zc,s$,Sh,$I="Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model.",i$,Ph,jI=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,l$,Nh,ZI='<li><strong>bart</strong> — <code>BartForCausalLM</code> (BART model)</li> <li><strong>bert</strong> — <code>BertLMHeadModel</code> (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> — <code>BioGptForCausalLM</code> (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/llama2#transformers.LlamaForCausalLM">LlamaForCausalLM</a> (CodeLlama model)</li> <li><strong>codegen</strong> — <code>CodeGenForCausalLM</code> (CodeGen model)</li> <li><strong>cohere</strong> — <code>CohereForCausalLM</code> (Cohere 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> — <code>DbrxForCausalLM</code> (DBRX model)</li> <li><strong>electra</strong> — <code>ElectraForCausalLM</code> (ELECTRA 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> — <code>Gemma2ForCausalLM</code> (Gemma2 model)</li> <li><strong>git</strong> — <code>GitForCausalLM</code> (GIT 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> — <code>GPTNeoXJapaneseForCausalLM</code> (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>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/llama2#transformers.LlamaForCausalLM">LlamaForCausalLM</a> (LLaMA model)</li> <li><strong>mamba</strong> — <code>MambaForCausalLM</code> (Mamba model)</li> <li><strong>mamba2</strong> — <code>Mamba2ForCausalLM</code> (mamba2 model)</li> <li><strong>marian</strong> — <code>MarianForCausalLM</code> (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> — <code>MistralForCausalLM</code> (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>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>olmoe</strong> — <code>OlmoeForCausalLM</code> (OLMoE model)</li> <li><strong>open-llama</strong> — <code>OpenLlamaForCausalLM</code> (OpenLlama model)</li> <li><strong>openai-gpt</strong> — <code>OpenAIGPTLMHeadModel</code> (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>',d$,qh,LI=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,c$,Ys,bw,Lc,Mw,Fe,Ac,m$,Xh,AI=`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,g$,Dh,BI="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",f$,Pn,Bc,u$,Hh,RI="Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration.",p$,Qh,JI=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,h$,zs,b$,Do,Rc,M$,Yh,WI="Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model.",_$,zh,GI=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,y$,Oh,EI="<li><strong>bert</strong> — <code>TFBertLMHeadModel</code> (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> — <code>TFMistralForCausalLM</code> (Mistral model)</li> <li><strong>openai-gpt</strong> — <code>TFOpenAIGPTLMHeadModel</code> (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>",T$,Os,_w,Jc,yw,ve,Wc,F$,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$,eb,II="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",w$,Nn,Gc,C$,ob,UI="Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration.",x$,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.`,k$,Ks,$$,Ho,Ec,j$,nb,PI="Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model.",Z$,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>:`,L$,ab,qI='<li><strong>bart</strong> — <code>FlaxBartForCausalLM</code> (BART model)</li> <li><strong>bert</strong> — <code>FlaxBertForCausalLM</code> (BERT model)</li> <li><strong>big_bird</strong> — <code>FlaxBigBirdForCausalLM</code> (BigBird model)</li> <li><strong>bloom</strong> — <code>FlaxBloomForCausalLM</code> (BLOOM model)</li> <li><strong>electra</strong> — <code>FlaxElectraForCausalLM</code> (ELECTRA model)</li> <li><strong>gemma</strong> — <a href="/docs/transformers/main/ko/model_doc/gemma#transformers.FlaxGemmaForCausalLM">FlaxGemmaForCausalLM</a> (Gemma model)</li> <li><strong>gpt-sw3</strong> — <code>FlaxGPT2LMHeadModel</code> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <code>FlaxGPT2LMHeadModel</code> (OpenAI GPT-2 model)</li> <li><strong>gpt_neo</strong> — <code>FlaxGPTNeoForCausalLM</code> (GPT Neo model)</li> <li><strong>gptj</strong> — <code>FlaxGPTJForCausalLM</code> (GPT-J model)</li> <li><strong>llama</strong> — <code>FlaxLlamaForCausalLM</code> (LLaMA model)</li> <li><strong>mistral</strong> — <code>FlaxMistralForCausalLM</code> (Mistral model)</li> <li><strong>opt</strong> — <code>FlaxOPTForCausalLM</code> (OPT model)</li> <li><strong>roberta</strong> — <code>FlaxRobertaForCausalLM</code> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <code>FlaxRobertaPreLayerNormForCausalLM</code> (RoBERTa-PreLayerNorm model)</li> <li><strong>xglm</strong> — <code>FlaxXGLMForCausalLM</code> (XGLM model)</li> <li><strong>xlm-roberta</strong> — <code>FlaxXLMRobertaForCausalLM</code> (XLM-RoBERTa model)</li>',A$,ei,Tw,Vc,Fw,we,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).",J$,qn,Uc,W$,lb,HI="Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration.",G$,db,QI=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,E$,oi,V$,G,Sc,I$,cb,YI="Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model.",U$,mb,zI=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,S$,gb,OI='<li><strong>albert</strong> — <code>AlbertForMaskedLM</code> (ALBERT model)</li> <li><strong>bart</strong> — <code>BartForConditionalGeneration</code> (BART model)</li> <li><strong>bert</strong> — <code>BertForMaskedLM</code> (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> — <code>ConvBertForMaskedLM</code> (ConvBERT model)</li> <li><strong>data2vec-text</strong> — <code>Data2VecTextForMaskedLM</code> (Data2VecText model)</li> <li><strong>deberta</strong> — <code>DebertaForMaskedLM</code> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <code>DebertaV2ForMaskedLM</code> (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>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>',P$,fb,KI=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,N$,ti,vw,Pc,ww,Ce,Nc,q$,ub,e9=`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,X$,pb,o9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",D$,Xn,qc,H$,hb,t9="Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration.",Q$,bb,n9=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,Y$,ni,z$,Qo,Xc,O$,Mb,r9="Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model.",K$,_b,a9=`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,yb,s9='<li><strong>albert</strong> — <code>TFAlbertForMaskedLM</code> (ALBERT model)</li> <li><strong>bert</strong> — <code>TFBertForMaskedLM</code> (BERT model)</li> <li><strong>camembert</strong> — <code>TFCamembertForMaskedLM</code> (CamemBERT model)</li> <li><strong>convbert</strong> — <code>TFConvBertForMaskedLM</code> (ConvBERT model)</li> <li><strong>deberta</strong> — <code>TFDebertaForMaskedLM</code> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <code>TFDebertaV2ForMaskedLM</code> (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>',oj,ri,Cw,Dc,xw,xe,Hc,tj,Tb,i9=`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.`,nj,Fb,l9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",rj,Dn,Qc,aj,vb,d9="Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration.",sj,wb,c9=`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.`,ij,ai,lj,Yo,Yc,dj,Cb,m9="Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model.",cj,xb,g9=`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>:`,mj,kb,f9="<li><strong>albert</strong> — <code>FlaxAlbertForMaskedLM</code> (ALBERT model)</li> <li><strong>bart</strong> — <code>FlaxBartForConditionalGeneration</code> (BART model)</li> <li><strong>bert</strong> — <code>FlaxBertForMaskedLM</code> (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>",gj,si,kw,zc,$w,Oc,Kc,jw,em,Zw,om,tm,Lw,nm,Aw,ke,rm,fj,$b,u9=`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.`,uj,jb,p9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",pj,Hn,am,hj,Zb,h9="Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration.",bj,Lb,b9=`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,ii,_j,E,sm,yj,Ab,M9="Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model.",Tj,Bb,_9=`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,Rb,y9="<li><strong>bart</strong> — <code>BartForConditionalGeneration</code> (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> — <code>EncoderDecoderModel</code> (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> — <code>MarianMTModel</code> (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>",vj,Jb,T9=`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,li,Bw,im,Rw,$e,lm,Cj,Wb,F9=`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.`,xj,Gb,v9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",kj,Qn,dm,$j,Eb,w9="Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration.",jj,Vb,C9=`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.`,Zj,di,Lj,zo,cm,Aj,Ib,x9="Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model.",Bj,Ub,k9=`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>:`,Rj,Sb,$9="<li><strong>bart</strong> — <code>TFBartForConditionalGeneration</code> (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> — <code>TFEncoderDecoderModel</code> (Encoder decoder model)</li> <li><strong>led</strong> — <code>TFLEDForConditionalGeneration</code> (LED model)</li> <li><strong>marian</strong> — <code>TFMarianMTModel</code> (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>",Jj,ci,Jw,mm,Ww,je,gm,Wj,Pb,j9=`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.`,Gj,Nb,Z9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Ej,Yn,fm,Vj,qb,L9="Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration.",Ij,Xb,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.`,Uj,mi,Sj,Oo,um,Pj,Db,B9="Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model.",Nj,Hb,R9=`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>:`,qj,Qb,J9="<li><strong>bart</strong> — <code>FlaxBartForConditionalGeneration</code> (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> — <code>FlaxEncoderDecoderModel</code> (Encoder decoder model)</li> <li><strong>longt5</strong> — <code>FlaxLongT5ForConditionalGeneration</code> (LongT5 model)</li> <li><strong>marian</strong> — <code>FlaxMarianMTModel</code> (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>",Xj,gi,Gw,pm,Ew,Ze,hm,Dj,Yb,W9=`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.`,Hj,zb,G9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Qj,zn,bm,Yj,Ob,E9="Instantiates one of the model classes of the library (with a sequence classification head) from a configuration.",zj,Kb,V9=`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,fi,Kj,V,Mm,eZ,eM,I9="Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model.",oZ,oM,U9=`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>:`,tZ,tM,S9='<li><strong>albert</strong> — <code>AlbertForSequenceClassification</code> (ALBERT model)</li> <li><strong>bart</strong> — <code>BartForSequenceClassification</code> (BART model)</li> <li><strong>bert</strong> — <code>BertForSequenceClassification</code> (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> — <code>BioGptForSequenceClassification</code> (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/llama2#transformers.LlamaForSequenceClassification">LlamaForSequenceClassification</a> (CodeLlama model)</li> <li><strong>convbert</strong> — <code>ConvBertForSequenceClassification</code> (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> — <code>DebertaForSequenceClassification</code> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <code>DebertaV2ForSequenceClassification</code> (DeBERTa-v2 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> — <code>Gemma2ForSequenceClassification</code> (Gemma2 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>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/llama2#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> — <code>MistralForSequenceClassification</code> (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>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> — <code>OpenAIGPTForSequenceClassification</code> (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>',nZ,nM,P9=`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>`,rZ,ui,Vw,_m,Iw,Le,ym,aZ,rM,N9=`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.`,sZ,aM,q9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",iZ,On,Tm,lZ,sM,X9="Instantiates one of the model classes of the library (with a sequence classification head) from a configuration.",dZ,iM,D9=`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.`,cZ,pi,mZ,Ko,Fm,gZ,lM,H9="Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model.",fZ,dM,Q9=`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>:`,uZ,cM,Y9='<li><strong>albert</strong> — <code>TFAlbertForSequenceClassification</code> (ALBERT model)</li> <li><strong>bart</strong> — <code>TFBartForSequenceClassification</code> (BART model)</li> <li><strong>bert</strong> — <code>TFBertForSequenceClassification</code> (BERT model)</li> <li><strong>camembert</strong> — <code>TFCamembertForSequenceClassification</code> (CamemBERT model)</li> <li><strong>convbert</strong> — <code>TFConvBertForSequenceClassification</code> (ConvBERT model)</li> <li><strong>ctrl</strong> — <code>TFCTRLForSequenceClassification</code> (CTRL model)</li> <li><strong>deberta</strong> — <code>TFDebertaForSequenceClassification</code> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <code>TFDebertaV2ForSequenceClassification</code> (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> — <code>TFMistralForSequenceClassification</code> (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> — <code>TFOpenAIGPTForSequenceClassification</code> (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>',pZ,hi,Uw,vm,Sw,Ae,wm,hZ,mM,z9=`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.`,bZ,gM,O9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",MZ,Kn,Cm,_Z,fM,K9="Instantiates one of the model classes of the library (with a sequence classification head) from a configuration.",yZ,uM,eU=`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.`,TZ,bi,FZ,et,xm,vZ,pM,oU="Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model.",wZ,hM,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>:`,CZ,bM,nU="<li><strong>albert</strong> — <code>FlaxAlbertForSequenceClassification</code> (ALBERT model)</li> <li><strong>bart</strong> — <code>FlaxBartForSequenceClassification</code> (BART model)</li> <li><strong>bert</strong> — <code>FlaxBertForSequenceClassification</code> (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>",xZ,Mi,Pw,km,Nw,Be,$m,kZ,MM,rU=`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.`,$Z,_M,aU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",jZ,er,jm,ZZ,yM,sU="Instantiates one of the model classes of the library (with a multiple choice head) from a configuration.",LZ,TM,iU=`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.`,AZ,_i,BZ,I,Zm,RZ,FM,lU="Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model.",JZ,vM,dU=`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>:`,WZ,wM,cU="<li><strong>albert</strong> — <code>AlbertForMultipleChoice</code> (ALBERT model)</li> <li><strong>bert</strong> — <code>BertForMultipleChoice</code> (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> — <code>ConvBertForMultipleChoice</code> (ConvBERT model)</li> <li><strong>data2vec-text</strong> — <code>Data2VecTextForMultipleChoice</code> (Data2VecText model)</li> <li><strong>deberta-v2</strong> — <code>DebertaV2ForMultipleChoice</code> (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>",GZ,CM,mU=`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>`,EZ,yi,qw,Lm,Xw,Re,Am,VZ,xM,gU=`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.`,IZ,kM,fU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",UZ,or,Bm,SZ,$M,uU="Instantiates one of the model classes of the library (with a multiple choice head) from a configuration.",PZ,jM,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.`,NZ,Ti,qZ,ot,Rm,XZ,ZM,hU="Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model.",DZ,LM,bU=`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>:`,HZ,AM,MU="<li><strong>albert</strong> — <code>TFAlbertForMultipleChoice</code> (ALBERT model)</li> <li><strong>bert</strong> — <code>TFBertForMultipleChoice</code> (BERT model)</li> <li><strong>camembert</strong> — <code>TFCamembertForMultipleChoice</code> (CamemBERT model)</li> <li><strong>convbert</strong> — <code>TFConvBertForMultipleChoice</code> (ConvBERT model)</li> <li><strong>deberta-v2</strong> — <code>TFDebertaV2ForMultipleChoice</code> (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>",QZ,Fi,Dw,Jm,Hw,Je,Wm,YZ,BM,_U=`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.`,zZ,RM,yU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",OZ,tr,Gm,KZ,JM,TU="Instantiates one of the model classes of the library (with a multiple choice head) from a configuration.",eL,WM,FU=`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,vi,tL,tt,Em,nL,GM,vU="Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model.",rL,EM,wU=`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>:`,aL,VM,CU="<li><strong>albert</strong> — <code>FlaxAlbertForMultipleChoice</code> (ALBERT model)</li> <li><strong>bert</strong> — <code>FlaxBertForMultipleChoice</code> (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>",sL,wi,Qw,Vm,Yw,We,Im,iL,IM,xU=`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.`,lL,UM,kU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",dL,nr,Um,cL,SM,$U="Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration.",mL,PM,jU=`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.`,gL,Ci,fL,U,Sm,uL,NM,ZU="Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model.",pL,qM,LU=`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>:`,hL,XM,AU="<li><strong>bert</strong> — <code>BertForNextSentencePrediction</code> (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>",bL,DM,BU=`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>`,ML,xi,zw,Pm,Ow,Ge,Nm,_L,HM,RU=`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.`,yL,QM,JU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",TL,rr,qm,FL,YM,WU="Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration.",vL,zM,GU=`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.`,wL,ki,CL,nt,Xm,xL,OM,EU="Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model.",kL,KM,VU=`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,e_,IU="<li><strong>bert</strong> — <code>TFBertForNextSentencePrediction</code> (BERT model)</li> <li><strong>mobilebert</strong> — <code>TFMobileBertForNextSentencePrediction</code> (MobileBERT model)</li>",jL,$i,Kw,Dm,eC,Ee,Hm,ZL,o_,UU=`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.`,LL,t_,SU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",AL,ar,Qm,BL,n_,PU="Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration.",RL,r_,NU=`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.`,JL,ji,WL,rt,Ym,GL,a_,qU="Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model.",EL,s_,XU=`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>:`,VL,i_,DU="<li><strong>bert</strong> — <code>FlaxBertForNextSentencePrediction</code> (BERT model)</li>",IL,Zi,oC,zm,tC,Ve,Om,UL,l_,HU=`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.`,SL,d_,QU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",PL,sr,Km,NL,c_,YU="Instantiates one of the model classes of the library (with a token classification head) from a configuration.",qL,m_,zU=`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.`,XL,Li,DL,S,eg,HL,g_,OU="Instantiate one of the model classes of the library (with a token classification head) from a pretrained model.",QL,f_,KU=`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>:`,YL,u_,eS='<li><strong>albert</strong> — <code>AlbertForTokenClassification</code> (ALBERT model)</li> <li><strong>bert</strong> — <code>BertForTokenClassification</code> (BERT model)</li> <li><strong>big_bird</strong> — <code>BigBirdForTokenClassification</code> (BigBird model)</li> <li><strong>biogpt</strong> — <code>BioGptForTokenClassification</code> (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> — <code>ConvBertForTokenClassification</code> (ConvBERT model)</li> <li><strong>data2vec-text</strong> — <code>Data2VecTextForTokenClassification</code> (Data2VecText model)</li> <li><strong>deberta</strong> — <code>DebertaForTokenClassification</code> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <code>DebertaV2ForTokenClassification</code> (DeBERTa-v2 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> — <code>Gemma2ForTokenClassification</code> (Gemma2 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>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> — <code>MistralForTokenClassification</code> (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>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>',zL,p_,oS=`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>`,OL,Ai,nC,og,rC,Ie,tg,KL,h_,tS=`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.`,eA,b_,nS="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",oA,ir,ng,tA,M_,rS="Instantiates one of the model classes of the library (with a token classification head) from a configuration.",nA,__,aS=`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.`,rA,Bi,aA,at,rg,sA,y_,sS="Instantiate one of the model classes of the library (with a token classification head) from a pretrained model.",iA,T_,iS=`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>:`,lA,F_,lS='<li><strong>albert</strong> — <code>TFAlbertForTokenClassification</code> (ALBERT model)</li> <li><strong>bert</strong> — <code>TFBertForTokenClassification</code> (BERT model)</li> <li><strong>camembert</strong> — <code>TFCamembertForTokenClassification</code> (CamemBERT model)</li> <li><strong>convbert</strong> — <code>TFConvBertForTokenClassification</code> (ConvBERT model)</li> <li><strong>deberta</strong> — <code>TFDebertaForTokenClassification</code> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <code>TFDebertaV2ForTokenClassification</code> (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>',dA,Ri,aC,ag,sC,Ue,sg,cA,v_,dS=`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.`,mA,w_,cS="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",gA,lr,ig,fA,C_,mS="Instantiates one of the model classes of the library (with a token classification head) from a configuration.",uA,x_,gS=`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.`,pA,Ji,hA,st,lg,bA,k_,fS="Instantiate one of the model classes of the library (with a token classification head) from a pretrained model.",MA,$_,uS=`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>:`,_A,j_,pS="<li><strong>albert</strong> — <code>FlaxAlbertForTokenClassification</code> (ALBERT model)</li> <li><strong>bert</strong> — <code>FlaxBertForTokenClassification</code> (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>",yA,Wi,iC,dg,lC,Se,cg,TA,Z_,hS=`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.`,FA,L_,bS="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",vA,dr,mg,wA,A_,MS="Instantiates one of the model classes of the library (with a question answering head) from a configuration.",CA,B_,_S=`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.`,xA,Gi,kA,P,gg,$A,R_,yS="Instantiate one of the model classes of the library (with a question answering head) from a pretrained model.",jA,J_,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>:`,ZA,W_,FS="<li><strong>albert</strong> — <code>AlbertForQuestionAnswering</code> (ALBERT model)</li> <li><strong>bart</strong> — <code>BartForQuestionAnswering</code> (BART model)</li> <li><strong>bert</strong> — <code>BertForQuestionAnswering</code> (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> — <code>ConvBertForQuestionAnswering</code> (ConvBERT model)</li> <li><strong>data2vec-text</strong> — <code>Data2VecTextForQuestionAnswering</code> (Data2VecText model)</li> <li><strong>deberta</strong> — <code>DebertaForQuestionAnswering</code> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <code>DebertaV2ForQuestionAnswering</code> (DeBERTa-v2 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>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>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>",LA,G_,vS=`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>`,AA,Ei,dC,fg,cC,Pe,ug,BA,E_,wS=`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.`,RA,V_,CS="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",JA,cr,pg,WA,I_,xS="Instantiates one of the model classes of the library (with a question answering head) from a configuration.",GA,U_,kS=`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.`,EA,Vi,VA,it,hg,IA,S_,$S="Instantiate one of the model classes of the library (with a question answering head) from a pretrained model.",UA,P_,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>:`,SA,N_,ZS="<li><strong>albert</strong> — <code>TFAlbertForQuestionAnswering</code> (ALBERT model)</li> <li><strong>bert</strong> — <code>TFBertForQuestionAnswering</code> (BERT model)</li> <li><strong>camembert</strong> — <code>TFCamembertForQuestionAnswering</code> (CamemBERT model)</li> <li><strong>convbert</strong> — <code>TFConvBertForQuestionAnswering</code> (ConvBERT model)</li> <li><strong>deberta</strong> — <code>TFDebertaForQuestionAnswering</code> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <code>TFDebertaV2ForQuestionAnswering</code> (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>",PA,Ii,mC,bg,gC,Ne,Mg,NA,q_,LS=`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.`,qA,X_,AS="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",XA,mr,_g,DA,D_,BS="Instantiates one of the model classes of the library (with a question answering head) from a configuration.",HA,H_,RS=`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.`,QA,Ui,YA,lt,yg,zA,Q_,JS="Instantiate one of the model classes of the library (with a question answering head) from a pretrained model.",OA,Y_,WS=`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>:`,KA,z_,GS="<li><strong>albert</strong> — <code>FlaxAlbertForQuestionAnswering</code> (ALBERT model)</li> <li><strong>bart</strong> — <code>FlaxBartForQuestionAnswering</code> (BART model)</li> <li><strong>bert</strong> — <code>FlaxBertForQuestionAnswering</code> (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,fC,Tg,uC,Fg,vg,pC,wg,hC,Cg,xg,bC,kg,MC,$g,ES="다음 자동 클래스들은 아래의 컴퓨터 비전 작업에 사용할 수 있습니다.",_C,jg,yC,qe,Zg,oB,O_,VS=`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,K_,IS="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",nB,gr,Lg,rB,ey,US="Instantiates one of the model classes of the library (with a depth estimation head) from a configuration.",aB,oy,SS=`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,Pi,iB,N,Ag,lB,ty,PS="Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model.",dB,ny,NS=`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,ry,qS="<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,ay,XS=`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>`,gB,Ni,TC,Bg,FC,Xe,Rg,fB,sy,DS=`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.`,uB,iy,HS="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",pB,fr,Jg,hB,ly,QS="Instantiates one of the model classes of the library (with a image classification head) from a configuration.",bB,dy,YS=`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.`,MB,qi,_B,q,Wg,yB,cy,zS="Instantiate one of the model classes of the library (with a image classification head) from a pretrained model.",TB,my,OS=`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,gy,KS='<li><strong>beit</strong> — <code>BeitForImageClassification</code> (BEiT model)</li> <li><strong>bit</strong> — <code>BitForImageClassification</code> (BiT model)</li> <li><strong>clip</strong> — <code>CLIPForImageClassification</code> (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>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>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> — <code>SwinForImageClassification</code> (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>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,fy,eP=`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>`,wB,Xi,vC,Gg,wC,De,Eg,CB,uy,oP=`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.`,xB,py,tP="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",kB,ur,Vg,$B,hy,nP="Instantiates one of the model classes of the library (with a image classification head) from a configuration.",jB,by,rP=`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.`,ZB,Di,LB,dt,Ig,AB,My,aP="Instantiate one of the model classes of the library (with a image classification head) from a pretrained model.",BB,_y,sP=`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,yy,iP='<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> — <code>TFSwinForImageClassification</code> (Swin Transformer model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/main/ko/model_doc/vit#transformers.TFViTForImageClassification">TFViTForImageClassification</a> (ViT model)</li>',JB,Hi,CC,Ug,xC,He,Sg,WB,Ty,lP=`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.`,GB,Fy,dP="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",EB,pr,Pg,VB,vy,cP="Instantiates one of the model classes of the library (with a image classification head) from a configuration.",IB,wy,mP=`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.`,UB,Qi,SB,ct,Ng,PB,Cy,gP="Instantiate one of the model classes of the library (with a image classification head) from a pretrained model.",NB,xy,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>:`,qB,ky,uP='<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,kC,qg,$C,Qe,Xg,DB,$y,pP=`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,jy,hP="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",QB,hr,Dg,YB,Zy,bP="Instantiates one of the model classes of the library (with a video classification head) from a configuration.",zB,Ly,MP=`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,Hg,eR,Ay,_P="Instantiate one of the model classes of the library (with a video classification head) from a pretrained model.",oR,By,yP=`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,Ry,TP="<li><strong>timesformer</strong> — <code>TimesformerForVideoClassification</code> (TimeSformer model)</li> <li><strong>videomae</strong> — <code>VideoMAEForVideoClassification</code> (VideoMAE model)</li> <li><strong>vivit</strong> — <code>VivitForVideoClassification</code> (ViViT model)</li>",nR,Jy,FP=`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,jC,Qg,ZC,Yg,zg,LC,Og,AC,Ye,Kg,aR,Wy,vP=`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,Gy,wP="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",iR,br,ef,lR,Ey,CP="Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration.",dR,Vy,xP=`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,of,gR,Iy,kP="Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model.",fR,Uy,$P=`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>:`,uR,Sy,jP='<li><strong>deit</strong> — <code>DeiTForMaskedImageModeling</code> (DeiT model)</li> <li><strong>focalnet</strong> — <code>FocalNetForMaskedImageModeling</code> (FocalNet model)</li> <li><strong>swin</strong> — <code>SwinForMaskedImageModeling</code> (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>',pR,Py,ZP=`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,BC,tf,RC,ze,nf,bR,Ny,LP=`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.`,MR,qy,AP="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",_R,Mr,rf,yR,Xy,BP="Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration.",TR,Dy,RP=`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,af,wR,Hy,JP="Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model.",CR,Qy,WP=`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>:`,xR,Yy,GP="<li><strong>deit</strong> — <code>TFDeiTForMaskedImageModeling</code> (DeiT model)</li> <li><strong>swin</strong> — <code>TFSwinForMaskedImageModeling</code> (Swin Transformer model)</li>",kR,tl,JC,sf,WC,Oe,lf,$R,zy,EP=`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.`,jR,Oy,VP="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",ZR,_r,df,LR,Ky,IP="Instantiates one of the model classes of the library (with a object detection head) from a configuration.",AR,eT,UP=`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,cf,JR,oT,SP="Instantiate one of the model classes of the library (with a object detection head) from a pretrained model.",WR,tT,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>:`,GR,nT,NP="<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,rT,qP=`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,GC,mf,EC,Ke,gf,IR,aT,XP=`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.`,UR,sT,DP="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",SR,yr,ff,PR,iT,HP="Instantiates one of the model classes of the library (with a image segmentation head) from a configuration.",NR,lT,QP=`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,uf,DR,dT,YP="Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model.",HR,cT,zP=`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,mT,OP="<li><strong>detr</strong> — <code>DetrForSegmentation</code> (DETR model)</li>",YR,gT,KP=`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,VC,pf,IC,hf,bf,UC,Mf,SC,eo,_f,OR,fT,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,uT,o5="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",eJ,Tr,yf,oJ,pT,t5="Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration.",tJ,hT,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.`,nJ,il,rJ,Y,Tf,aJ,bT,r5="Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model.",sJ,MT,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>:`,iJ,_T,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>",lJ,yT,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>`,dJ,ll,PC,Ff,NC,oo,vf,cJ,TT,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.`,mJ,FT,d5="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",gJ,Fr,wf,fJ,vT,c5="Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration.",uJ,wT,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.`,pJ,dl,hJ,gt,Cf,bJ,CT,g5="Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model.",MJ,xT,f5=`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,kT,u5="<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>",yJ,cl,qC,xf,XC,to,kf,TJ,$T,p5=`This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created
with the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,FJ,jT,h5="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",vJ,vr,$f,wJ,ZT,b5="Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration.",CJ,LT,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.`,xJ,ml,kJ,z,jf,$J,AT,_5="Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model.",jJ,BT,y5=`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>:`,ZJ,RT,T5="<li><strong>maskformer</strong> — <code>MaskFormerForInstanceSegmentation</code> (MaskFormer model)</li>",LJ,JT,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>`,AJ,gl,DC,Zf,HC,no,Lf,BJ,WT,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.`,RJ,GT,w5="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",JJ,wr,Af,WJ,ET,C5="Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration.",GJ,VT,x5=`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.`,EJ,fl,VJ,O,Bf,IJ,IT,k5="Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model.",UJ,UT,$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>:`,SJ,ST,j5="<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>",PJ,PT,Z5=`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>`,NJ,ul,QC,Rf,YC,ro,Jf,qJ,NT,L5=`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.`,XJ,qT,A5="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",DJ,Cr,Wf,HJ,XT,B5="Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration.",QJ,DT,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.`,YJ,pl,zJ,K,Gf,OJ,HT,J5="Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model.",KJ,QT,W5=`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>:`,eW,YT,G5="<li><strong>align</strong> — <code>AlignModel</code> (ALIGN model)</li> <li><strong>altclip</strong> — <code>AltCLIPModel</code> (AltCLIP model)</li> <li><strong>blip</strong> — <code>BlipModel</code> (BLIP model)</li> <li><strong>blip-2</strong> — <code>Blip2ForImageTextRetrieval</code> (BLIP-2 model)</li> <li><strong>chinese_clip</strong> — <code>ChineseCLIPModel</code> (Chinese-CLIP model)</li> <li><strong>clip</strong> — <code>CLIPModel</code> (CLIP model)</li> <li><strong>clipseg</strong> — <code>CLIPSegModel</code> (CLIPSeg model)</li> <li><strong>siglip</strong> — <code>SiglipModel</code> (SigLIP model)</li>",oW,zT,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>`,tW,hl,zC,Ef,OC,ao,Vf,nW,OT,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.`,rW,KT,I5="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",aW,xr,If,sW,eF,U5="Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration.",iW,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.`,lW,bl,dW,ft,Uf,cW,tF,P5="Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model.",mW,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>:`,gW,rF,q5="<li><strong>blip</strong> — <code>TFBlipModel</code> (BLIP model)</li> <li><strong>clip</strong> — <code>TFCLIPModel</code> (CLIP model)</li>",fW,Ml,KC,Sf,e1,so,Pf,uW,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.`,pW,sF,D5="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",hW,kr,Nf,bW,iF,H5="Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration.",MW,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.`,_W,_l,yW,ee,qf,TW,dF,Y5="Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model.",FW,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>:`,vW,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>",wW,gF,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>`,CW,yl,o1,Xf,t1,Df,eN="다음 자동 클래스들은 아래의 오디오 작업에 사용할 수 있습니다.",n1,Hf,r1,io,Qf,xW,fF,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.`,kW,uF,tN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",$W,$r,Yf,jW,pF,nN="Instantiates one of the model classes of the library (with a audio classification head) from a configuration.",ZW,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.`,LW,Tl,AW,oe,zf,BW,bF,aN="Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model.",RW,MF,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>:`,JW,_F,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>',WW,yF,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>`,GW,Fl,a1,Of,s1,lo,Kf,EW,TF,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.`,VW,FF,cN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",IW,jr,eu,UW,vF,mN="Instantiates one of the model classes of the library (with a audio classification head) from a configuration.",SW,wF,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.`,PW,vl,NW,ut,ou,qW,CF,fN="Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model.",XW,xF,uN=`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>:`,DW,kF,pN="<li><strong>wav2vec2</strong> — <code>TFWav2Vec2ForSequenceClassification</code> (Wav2Vec2 model)</li>",HW,wl,i1,tu,l1,co,nu,QW,$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.`,YW,jF,bN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",zW,Zr,ru,OW,ZF,MN="Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration.",KW,LF,_N=`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,Cl,oG,te,au,tG,AF,yN="Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model.",nG,BF,TN=`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>:`,rG,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>",aG,JF,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>`,sG,xl,d1,su,c1,mo,iu,iG,WF,wN=`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.`,lG,GF,CN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",dG,Lr,lu,cG,EF,xN="Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration.",mG,VF,kN=`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.`,gG,kl,fG,ne,du,uG,IF,$N="Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model.",pG,UF,jN=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,hG,SF,ZN="<li><strong>data2vec-audio</strong> — <code>Data2VecAudioForCTC</code> (Data2VecAudio model)</li> <li><strong>hubert</strong> — <code>HubertForCTC</code> (Hubert model)</li> <li><strong>mctct</strong> — <code>MCTCTForCTC</code> (M-CTC-T model)</li> <li><strong>sew</strong> — <code>SEWForCTC</code> (SEW model)</li> <li><strong>sew-d</strong> — <code>SEWDForCTC</code> (SEW-D model)</li> <li><strong>unispeech</strong> — <code>UniSpeechForCTC</code> (UniSpeech model)</li> <li><strong>unispeech-sat</strong> — <code>UniSpeechSatForCTC</code> (UniSpeechSat model)</li> <li><strong>wav2vec2</strong> — <code>Wav2Vec2ForCTC</code> (Wav2Vec2 model)</li> <li><strong>wav2vec2-bert</strong> — <code>Wav2Vec2BertForCTC</code> (Wav2Vec2-BERT model)</li> <li><strong>wav2vec2-conformer</strong> — <code>Wav2Vec2ConformerForCTC</code> (Wav2Vec2-Conformer model)</li> <li><strong>wavlm</strong> — <code>WavLMForCTC</code> (WavLM model)</li>",bG,PF,LN=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,MG,$l,m1,cu,g1,go,mu,_G,NF,AN=`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.`,yG,qF,BN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",TG,Ar,gu,FG,XF,RN="Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration.",vG,DF,JN=`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.`,wG,jl,CG,re,fu,xG,HF,WN="Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model.",kG,QF,GN=`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>:`,$G,YF,EN='<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>',jG,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>`,ZG,Zl,f1,uu,u1,fo,pu,LG,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.`,AG,KF,UN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",BG,Br,hu,RG,ev,SN="Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration.",JG,ov,PN=`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.`,WG,Ll,GG,pt,bu,EG,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.",VG,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>:`,IG,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>',UG,Al,p1,Mu,h1,uo,_u,SG,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.`,PG,sv,HN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",NG,Rr,yu,qG,iv,QN="Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration.",XG,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.`,DG,Bl,HG,ht,Tu,QG,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.",YG,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>:`,zG,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>',OG,Rl,b1,Fu,M1,po,vu,KG,gv,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,fv,o4="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",o0,Jr,wu,t0,uv,t4="Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration.",n0,pv,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,Jl,a0,ae,Cu,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,Mv,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,_v,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,Wl,_1,xu,y1,ku,$u,T1,ju,F1,Zu,Lu,v1,Au,w1,Bu,l4="다음 자동 클래스들은 아래의 멀티모달 작업에 사용할 수 있습니다.",C1,Ru,x1,ho,Ju,m0,yv,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.`,g0,Tv,c4="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",f0,Wr,Wu,u0,Fv,m4="Instantiates one of the model classes of the library (with a table question answering head) from a configuration.",p0,vv,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.`,h0,Gl,b0,se,Gu,M0,wv,f4="Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model.",_0,Cv,u4=`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>:`,y0,xv,p4="<li><strong>tapas</strong> — <code>TapasForQuestionAnswering</code> (TAPAS model)</li>",T0,kv,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,k1,Eu,$1,bo,Vu,v0,$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.`,w0,jv,M4="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",C0,Gr,Iu,x0,Zv,_4="Instantiates one of the model classes of the library (with a table question answering head) from a configuration.",k0,Lv,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.`,$0,Vl,j0,bt,Uu,Z0,Av,T4="Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model.",L0,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>:`,A0,Rv,v4="<li><strong>tapas</strong> — <code>TFTapasForQuestionAnswering</code> (TAPAS model)</li>",B0,Il,j1,Su,Z1,Mo,Pu,R0,Jv,w4=`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.`,J0,Wv,C4="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",W0,Er,Nu,G0,Gv,x4="Instantiates one of the model classes of the library (with a document question answering head) from a configuration.",E0,Ev,k4=`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,Ul,I0,ie,qu,U0,Vv,$4="Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model.",S0,Iv,j4=`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>:`,P0,Uv,Z4="<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,L4=`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,L1,Xu,A1,_o,Du,X0,Pv,A4=`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,Hu,Q0,qv,R4="Instantiates one of the model classes of the library (with a document question answering head) from a configuration.",Y0,Xv,J4=`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,Pl,O0,Mt,Qu,K0,Dv,W4="Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model.",eE,Hv,G4=`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,B1,Yu,R1,yo,zu,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,Ou,sE,Ov,U4="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,Ku,cE,e2,P4="Instantiate one of the model classes of the library (with a visual question answering head) from a pretrained model.",mE,o2,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>:`,gE,t2,q4="<li><strong>blip</strong> — <code>BlipForQuestionAnswering</code> (BLIP model)</li> <li><strong>blip-2</strong> — <code>Blip2ForConditionalGeneration</code> (BLIP-2 model)</li> <li><strong>vilt</strong> — <code>ViltForQuestionAnswering</code> (ViLT model)</li>",fE,n2,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>`,uE,Xl,J1,ep,W1,To,op,pE,r2,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,a2,H4="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",bE,Ur,tp,ME,s2,Q4="Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration.",_E,i2,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.`,yE,Dl,TE,de,np,FE,l2,z4="Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model.",vE,d2,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>:`,wE,c2,K4='<li><strong>blip</strong> — <code>BlipForConditionalGeneration</code> (BLIP model)</li> <li><strong>blip-2</strong> — <code>Blip2ForConditionalGeneration</code> (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> — <code>PaliGemmaForConditionalGeneration</code> (PaliGemma model)</li> <li><strong>pix2struct</strong> — <code>Pix2StructForConditionalGeneration</code> (Pix2Struct 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>',CE,m2,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>`,xE,Hl,G1,rp,E1,Fo,ap,kE,g2,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,f2,tq="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",jE,Sr,sp,ZE,u2,nq="Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration.",LE,p2,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.`,AE,Ql,BE,_t,ip,RE,h2,aq="Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model.",JE,b2,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>:`,WE,M2,iq="<li><strong>blip</strong> — <code>TFBlipForConditionalGeneration</code> (BLIP model)</li> <li><strong>vision-encoder-decoder</strong> — <code>TFVisionEncoderDecoderModel</code> (Vision Encoder decoder model)</li>",GE,Yl,V1,lp,I1,vo,dp,EE,_2,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,y2,dq="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",IE,Pr,cp,UE,T2,cq="Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration.",SE,F2,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.`,PE,zl,NE,yt,mp,qE,v2,gq="Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model.",XE,w2,fq=`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,C2,uq="<li><strong>vision-encoder-decoder</strong> — <code>FlaxVisionEncoderDecoderModel</code> (Vision Encoder decoder model)</li>",HE,Ol,U1,gp,S1,x2,P1;return m=new Z({props:{title:"Auto 클래스",local:"auto-classes",headingTag:"h1"}}),wd=new $({props:{code:"bW9kZWwlMjAlM0QlMjBBdXRvTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKQ==",highlighted:'model = AutoModel.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)',wrap:!1}}),kd=new Z({props:{title:"자동 클래스 확장",local:"extending-the-auto-classes",headingTag:"h2"}}),jd=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 N1({props:{warning:!0,$$slots:{default:[wq]},$$scope:{ctx:v}}}),Ld=new Z({props:{title:"AutoConfig",local:"transformers.AutoConfig ][ transformers.AutoConfig",headingTag:"h2"}}),Ad=new x({props:{name:"class transformers.AutoConfig",anchor:"transformers.AutoConfig",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/configuration_auto.py#L889"}}),Bd=new x({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
<code>save_pretrained()</code> method, or the <code>save_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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
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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#L912"}}),ws=new j({props:{anchor:"transformers.AutoConfig.from_pretrained.example",$$slots:{default:[Cq]},$$scope:{ctx:v}}}),Rd=new x({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> (<code>PretrainedConfig</code>) &#x2014; The config to register.",name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/configuration_auto.py#L1051"}}),Jd=new Z({props:{title:"AutoTokenizer",local:"transformers.AutoTokenizer ][ transformers.AutoTokenizer",headingTag:"h2"}}),Wd=new x({props:{name:"class transformers.AutoTokenizer",anchor:"transformers.AutoTokenizer",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/tokenization_auto.py#L713"}}),Gd=new x({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 <code>save_pretrained()</code> 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;
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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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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
is returned instead.`,name:"use_fast"},{anchor:"transformers.AutoTokenizer.from_pretrained.tokenizer_type",description:`<strong>tokenizer_type</strong> (<code>str</code>, <em>optional</em>) &#x2014;
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
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.AutoTokenizer.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
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#L727"}}),xs=new j({props:{anchor:"transformers.AutoTokenizer.from_pretrained.example",$$slots:{default:[xq]},$$scope:{ctx:v}}}),Ed=new x({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> (<code>PretrainedConfig</code>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoTokenizer.register.slow_tokenizer_class",description:`<strong>slow_tokenizer_class</strong> (<code>PretrainedTokenizer</code>, <em>optional</em>) &#x2014;
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#L951"}}),Vd=new Z({props:{title:"AutoFeatureExtractor",local:"transformers.AutoFeatureExtractor ][ transformers.AutoFeatureExtractor",headingTag:"h2"}}),Id=new x({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#L242"}}),Ud=new x({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
<code>save_pretrained()</code> 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
if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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
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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.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
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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#L256"}}),$s=new N1({props:{$$slots:{default:[kq]},$$scope:{ctx:v}}}),js=new j({props:{anchor:"transformers.AutoFeatureExtractor.from_pretrained.example",$$slots:{default:[$q]},$$scope:{ctx:v}}}),Sd=new x({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> (<code>PretrainedConfig</code>) &#x2014;
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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
<code>save_pretrained()</code> 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.,
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</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
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Whether or not to force to (re-)download the image processor files and override the cached versions if
they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoImageProcessor.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.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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identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoImageProcessor.from_pretrained.use_fast",description:`<strong>use_fast</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
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is returned instead.`,name:"use_fast"},{anchor:"transformers.AutoImageProcessor.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 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
<code>kwargs</code> which has not been used to update <code>image_processor</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoImageProcessor.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.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#L321"}}),Ls=new N1({props:{$$slots:{default:[jq]},$$scope:{ctx:v}}}),As=new j({props:{anchor:"transformers.AutoImageProcessor.from_pretrained.example",$$slots:{default:[Zq]},$$scope:{ctx:v}}}),Xd=new x({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> (<code>PretrainedConfig</code>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoImageProcessor.register.image_processor_class",description:"<strong>image_processor_class</strong> (<code>ImageProcessingMixin</code>) &#x2014; The image processor to register.",name:"image_processor_class"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/image_processing_auto.py#L499"}}),Dd=new Z({props:{title:"AutoProcessor",local:"transformers.AutoProcessor ][ transformers.AutoProcessor",headingTag:"h2"}}),Hd=new x({props:{name:"class transformers.AutoProcessor",anchor:"transformers.AutoProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/processing_auto.py#L145"}}),Qd=new x({props:{name:"from_pretrained",anchor:"transformers.AutoProcessor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoProcessor.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 processor files saved using the <code>save_pretrained()</code> method,
e.g., <code>./my_model_directory/</code>.</li>
</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
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoProcessor.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
if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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
when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"transformers.AutoProcessor.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.AutoProcessor.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.AutoProcessor.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.AutoProcessor.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/processing_auto.py#L159"}}),Rs=new N1({props:{$$slots:{default:[Lq]},$$scope:{ctx:v}}}),Js=new j({props:{anchor:"transformers.AutoProcessor.from_pretrained.example",$$slots:{default:[Aq]},$$scope:{ctx:v}}}),Yd=new x({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> (<code>PretrainedConfig</code>) &#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#L362"}}),zd=new Z({props:{title:"일반적인 모델 클래스",local:"generic-model-classes",headingTag:"h2"}}),Kd=new Z({props:{title:"AutoModel",local:"transformers.AutoModel ][ transformers.AutoModel",headingTag:"h3"}}),ec=new x({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#L1539"}}),oc=new x({props:{name:"from_config",anchor:"transformers.AutoModel.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModel.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>AltCLIPConfig</code> configuration class: <code>AltCLIPModel</code> (AltCLIP model)</li>
<li><code>AutoformerConfig</code> configuration class: <code>AutoformerModel</code> (Autoformer model)</li>
<li><code>BarkConfig</code> configuration class: <code>BarkModel</code> (Bark model)</li>
<li><code>BartConfig</code> configuration class: <code>BartModel</code> (BART model)</li>
<li><code>BeitConfig</code> configuration class: <code>BeitModel</code> (BEiT model)</li>
<li><code>BertConfig</code> configuration class: <code>BertModel</code> (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><code>BioGptConfig</code> configuration class: <code>BioGptModel</code> (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><code>Blip2Config</code> configuration class: <code>Blip2Model</code> (BLIP-2 model)</li>
<li><code>BlipConfig</code> configuration class: <code>BlipModel</code> (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><code>CLIPConfig</code> configuration class: <code>CLIPModel</code> (CLIP model)</li>
<li><code>CLIPSegConfig</code> configuration class: <code>CLIPSegModel</code> (CLIPSeg model)</li>
<li><code>CLIPTextConfig</code> configuration class: <code>CLIPTextModel</code> (CLIPTextModel model)</li>
<li><code>CLIPVisionConfig</code> configuration class: <code>CLIPVisionModel</code> (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>CohereConfig</code> configuration class: <code>CohereModel</code> (Cohere model)</li>
<li><code>ConditionalDetrConfig</code> configuration class: <code>ConditionalDetrModel</code> (Conditional DETR model)</li>
<li><code>ConvBertConfig</code> configuration class: <code>ConvBertModel</code> (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><code>DbrxConfig</code> configuration class: <code>DbrxModel</code> (DBRX model)</li>
<li><code>DebertaConfig</code> configuration class: <code>DebertaModel</code> (DeBERTa model)</li>
<li><code>DebertaV2Config</code> configuration class: <code>DebertaV2Model</code> (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>DinatConfig</code> configuration class: <code>DinatModel</code> (DiNAT model)</li>
<li><code>Dinov2Config</code> configuration class: <code>Dinov2Model</code> (DINOv2 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><code>GPTNeoXJapaneseConfig</code> configuration class: <code>GPTNeoXJapaneseModel</code> (GPT NeoX Japanese model)</li>
<li><code>GPTSanJapaneseConfig</code> configuration class: <code>GPTSanJapaneseForConditionalGeneration</code> (GPTSAN-japanese model)</li>
<li><code>Gemma2Config</code> configuration class: <code>Gemma2Model</code> (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>GraniteConfig</code> configuration class: <code>GraniteModel</code> (Granite model)</li>
<li><code>GraniteMoeConfig</code> configuration class: <code>GraniteMoeModel</code> (GraniteMoeMoe model)</li>
<li><code>GraphormerConfig</code> configuration class: <code>GraphormerModel</code> (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>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>Idefics2Config</code> configuration class: <code>Idefics2Model</code> (Idefics2 model)</li>
<li><code>Idefics3Config</code> configuration class: <code>Idefics3Model</code> (Idefics3 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><code>InformerConfig</code> configuration class: <code>InformerModel</code> (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/llama2#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/llama2#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><code>Mamba2Config</code> configuration class: <code>Mamba2Model</code> (mamba2 model)</li>
<li><code>MambaConfig</code> configuration class: <code>MambaModel</code> (Mamba model)</li>
<li><code>MarianConfig</code> configuration class: <code>MarianModel</code> (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><code>MistralConfig</code> configuration class: <code>MistralModel</code> (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>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>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><code>OpenAIGPTConfig</code> configuration class: <code>OpenAIGPTModel</code> (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><code>PatchTSMixerConfig</code> configuration class: <code>PatchTSMixerModel</code> (PatchTSMixer model)</li>
<li><code>PatchTSTConfig</code> configuration class: <code>PatchTSTModel</code> (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>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>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><code>SwinConfig</code> configuration class: <code>SwinModel</code> (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>TimeSeriesTransformerConfig</code> configuration class: <code>TimeSeriesTransformerModel</code> (Time Series Transformer model)</li>
<li><code>TimesformerConfig</code> configuration class: <code>TimesformerModel</code> (TimeSformer model)</li>
<li><code>TimmBackboneConfig</code> configuration class: <code>TimmBackbone</code> (TimmBackbone model)</li>
<li><code>TrajectoryTransformerConfig</code> configuration class: <code>TrajectoryTransformerModel</code> (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><code>VivitConfig</code> configuration class: <code>VivitModel</code> (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>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"}}),Gs=new j({props:{anchor:"transformers.AutoModel.from_config.example",$$slots:{default:[Bq]},$$scope:{ctx:v}}}),tc=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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 <code>save_pretrained()</code> and
<code>from_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.AutoModel.from_pretrained.example",$$slots:{default:[Rq]},$$scope:{ctx:v}}}),nc=new Z({props:{title:"TFAutoModel",local:"transformers.TFAutoModel ][ transformers.TFAutoModel",headingTag:"h3"}}),rc=new x({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 x({props:{name:"from_config",anchor:"transformers.TFAutoModel.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModel.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>BartConfig</code> configuration class: <code>TFBartModel</code> (BART model)</li>
<li><code>BertConfig</code> configuration class: <code>TFBertModel</code> (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><code>BlipConfig</code> configuration class: <code>TFBlipModel</code> (BLIP model)</li>
<li><code>CLIPConfig</code> configuration class: <code>TFCLIPModel</code> (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><code>ConvBertConfig</code> configuration class: <code>TFConvBertModel</code> (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><code>DebertaConfig</code> configuration class: <code>TFDebertaModel</code> (DeBERTa model)</li>
<li><code>DebertaV2Config</code> configuration class: <code>TFDebertaV2Model</code> (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><code>MarianConfig</code> configuration class: <code>TFMarianModel</code> (Marian model)</li>
<li><code>MistralConfig</code> configuration class: <code>TFMistralModel</code> (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><code>OpenAIGPTConfig</code> configuration class: <code>TFOpenAIGPTModel</code> (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><code>SwinConfig</code> configuration class: <code>TFSwinModel</code> (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 j({props:{anchor:"transformers.TFAutoModel.from_config.example",$$slots:{default:[Jq]},$$scope:{ctx:v}}}),sc=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.TFAutoModel.from_pretrained.example",$$slots:{default:[Wq]},$$scope:{ctx:v}}}),ic=new Z({props:{title:"FlaxAutoModel",local:"transformers.FlaxAutoModel ][ transformers.FlaxAutoModel",headingTag:"h3"}}),lc=new x({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 x({props:{name:"from_config",anchor:"transformers.FlaxAutoModel.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModel.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>BartConfig</code> configuration class: <code>FlaxBartModel</code> (BART model)</li>
<li><code>BeitConfig</code> configuration class: <code>FlaxBeitModel</code> (BEiT model)</li>
<li><code>BertConfig</code> configuration class: <code>FlaxBertModel</code> (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><code>CLIPConfig</code> configuration class: <code>FlaxCLIPModel</code> (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/llama2#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><code>MarianConfig</code> configuration class: <code>FlaxMarianModel</code> (Marian model)</li>
<li><code>MistralConfig</code> configuration class: <code>FlaxMistralModel</code> (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"}}),Us=new j({props:{anchor:"transformers.FlaxAutoModel.from_config.example",$$slots:{default:[Gq]},$$scope:{ctx:v}}}),cc=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.FlaxAutoModel.from_pretrained.example",$$slots:{default:[Eq]},$$scope:{ctx:v}}}),mc=new Z({props:{title:"일반적인 사전 학습 클래스",local:"generic-pretraining-classes",headingTag:"h2"}}),fc=new Z({props:{title:"AutoModelForPreTraining",local:"transformers.AutoModelForPreTraining ][ transformers.AutoModelForPreTraining",headingTag:"h3"}}),uc=new x({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#L1546"}}),pc=new x({props:{name:"from_config",anchor:"transformers.AutoModelForPreTraining.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForPreTraining.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>BartConfig</code> configuration class: <code>BartForConditionalGeneration</code> (BART model)</li>
<li><code>BertConfig</code> configuration class: <code>BertForPreTraining</code> (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>Data2VecTextConfig</code> configuration class: <code>Data2VecTextForMaskedLM</code> (Data2VecText model)</li>
<li><code>DebertaConfig</code> configuration class: <code>DebertaForMaskedLM</code> (DeBERTa model)</li>
<li><code>DebertaV2Config</code> configuration class: <code>DebertaV2ForMaskedLM</code> (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><code>Mamba2Config</code> configuration class: <code>Mamba2ForCausalLM</code> (mamba2 model)</li>
<li><code>MambaConfig</code> configuration class: <code>MambaForCausalLM</code> (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><code>OpenAIGPTConfig</code> configuration class: <code>OpenAIGPTLMHeadModel</code> (OpenAI GPT model)</li>
<li><code>PaliGemmaConfig</code> configuration class: <code>PaliGemmaForConditionalGeneration</code> (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"}}),Ps=new j({props:{anchor:"transformers.AutoModelForPreTraining.from_config.example",$$slots:{default:[Vq]},$$scope:{ctx:v}}}),hc=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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 <code>save_pretrained()</code> and
<code>from_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.AutoModelForPreTraining.from_pretrained.example",$$slots:{default:[Iq]},$$scope:{ctx:v}}}),bc=new Z({props:{title:"TFAutoModelForPreTraining",local:"transformers.TFAutoModelForPreTraining ][ transformers.TFAutoModelForPreTraining",headingTag:"h3"}}),Mc=new x({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"}}),_c=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForPreTraining.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForPreTraining.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>BartConfig</code> configuration class: <code>TFBartForConditionalGeneration</code> (BART model)</li>
<li><code>BertConfig</code> configuration class: <code>TFBertForPreTraining</code> (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><code>OpenAIGPTConfig</code> configuration class: <code>TFOpenAIGPTLMHeadModel</code> (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 j({props:{anchor:"transformers.TFAutoModelForPreTraining.from_config.example",$$slots:{default:[Uq]},$$scope:{ctx:v}}}),yc=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[Sq]},$$scope:{ctx:v}}}),Tc=new Z({props:{title:"FlaxAutoModelForPreTraining",local:"transformers.FlaxAutoModelForPreTraining ][ transformers.FlaxAutoModelForPreTraining",headingTag:"h3"}}),Fc=new x({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 x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForPreTraining.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>BartConfig</code> configuration class: <code>FlaxBartForConditionalGeneration</code> (BART model)</li>
<li><code>BertConfig</code> configuration class: <code>FlaxBertForPreTraining</code> (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 j({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.example",$$slots:{default:[Pq]},$$scope:{ctx:v}}}),wc=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[Nq]},$$scope:{ctx:v}}}),Cc=new Z({props:{title:"자연어 처리",local:"natural-language-processing",headingTag:"h2"}}),kc=new Z({props:{title:"AutoModelForCausalLM",local:"transformers.AutoModelForCausalLM ][ transformers.AutoModelForCausalLM",headingTag:"h3"}}),$c=new x({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#L1561"}}),jc=new x({props:{name:"from_config",anchor:"transformers.AutoModelForCausalLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCausalLM.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>BartConfig</code> configuration class: <code>BartForCausalLM</code> (BART model)</li>
<li><code>BertConfig</code> configuration class: <code>BertLMHeadModel</code> (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><code>BioGptConfig</code> configuration class: <code>BioGptForCausalLM</code> (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>CohereConfig</code> configuration class: <code>CohereForCausalLM</code> (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><code>DbrxConfig</code> configuration class: <code>DbrxForCausalLM</code> (DBRX model)</li>
<li><code>ElectraConfig</code> configuration class: <code>ElectraForCausalLM</code> (ELECTRA 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><code>GPTNeoXJapaneseConfig</code> configuration class: <code>GPTNeoXJapaneseForCausalLM</code> (GPT NeoX Japanese model)</li>
<li><code>Gemma2Config</code> configuration class: <code>Gemma2ForCausalLM</code> (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>GraniteConfig</code> configuration class: <code>GraniteForCausalLM</code> (Granite model)</li>
<li><code>GraniteMoeConfig</code> configuration class: <code>GraniteMoeForCausalLM</code> (GraniteMoeMoe 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/llama2#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/llama2#transformers.LlamaForCausalLM">LlamaForCausalLM</a> (LLaMA model)</li>
<li><code>MBartConfig</code> configuration class: <code>MBartForCausalLM</code> (mBART model)</li>
<li><code>Mamba2Config</code> configuration class: <code>Mamba2ForCausalLM</code> (mamba2 model)</li>
<li><code>MambaConfig</code> configuration class: <code>MambaForCausalLM</code> (Mamba model)</li>
<li><code>MarianConfig</code> configuration class: <code>MarianForCausalLM</code> (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><code>MistralConfig</code> configuration class: <code>MistralForCausalLM</code> (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>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>OlmoConfig</code> configuration class: <code>OlmoForCausalLM</code> (OLMo model)</li>
<li><code>OlmoeConfig</code> configuration class: <code>OlmoeForCausalLM</code> (OLMoE model)</li>
<li><code>OpenAIGPTConfig</code> configuration class: <code>OpenAIGPTLMHeadModel</code> (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>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 j({props:{anchor:"transformers.AutoModelForCausalLM.from_config.example",$$slots:{default:[qq]},$$scope:{ctx:v}}}),Zc=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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 <code>save_pretrained()</code> and
<code>from_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.AutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Xq]},$$scope:{ctx:v}}}),Lc=new Z({props:{title:"TFAutoModelForCausalLM",local:"transformers.TFAutoModelForCausalLM ][ transformers.TFAutoModelForCausalLM",headingTag:"h3"}}),Ac=new x({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 x({props:{name:"from_config",anchor:"transformers.TFAutoModelForCausalLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForCausalLM.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>BertConfig</code> configuration class: <code>TFBertLMHeadModel</code> (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><code>MistralConfig</code> configuration class: <code>TFMistralForCausalLM</code> (Mistral model)</li>
<li><code>OPTConfig</code> configuration class: <code>TFOPTForCausalLM</code> (OPT model)</li>
<li><code>OpenAIGPTConfig</code> configuration class: <code>TFOpenAIGPTLMHeadModel</code> (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 j({props:{anchor:"transformers.TFAutoModelForCausalLM.from_config.example",$$slots:{default:[Dq]},$$scope:{ctx:v}}}),Rc=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Hq]},$$scope:{ctx:v}}}),Jc=new Z({props:{title:"FlaxAutoModelForCausalLM",local:"transformers.FlaxAutoModelForCausalLM ][ transformers.FlaxAutoModelForCausalLM",headingTag:"h3"}}),Wc=new x({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"}}),Gc=new x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForCausalLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>BartConfig</code> configuration class: <code>FlaxBartForCausalLM</code> (BART model)</li>
<li><code>BertConfig</code> configuration class: <code>FlaxBertForCausalLM</code> (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/llama2#transformers.LlamaConfig">LlamaConfig</a> configuration class: <code>FlaxLlamaForCausalLM</code> (LLaMA model)</li>
<li><code>MistralConfig</code> configuration class: <code>FlaxMistralForCausalLM</code> (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 j({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.example",$$slots:{default:[Qq]},$$scope:{ctx:v}}}),Ec=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Yq]},$$scope:{ctx:v}}}),Vc=new Z({props:{title:"AutoModelForMaskedLM",local:"transformers.AutoModelForMaskedLM ][ transformers.AutoModelForMaskedLM",headingTag:"h3"}}),Ic=new x({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#L1568"}}),Uc=new x({props:{name:"from_config",anchor:"transformers.AutoModelForMaskedLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedLM.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>BartConfig</code> configuration class: <code>BartForConditionalGeneration</code> (BART model)</li>
<li><code>BertConfig</code> configuration class: <code>BertForMaskedLM</code> (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><code>ConvBertConfig</code> configuration class: <code>ConvBertForMaskedLM</code> (ConvBERT model)</li>
<li><code>Data2VecTextConfig</code> configuration class: <code>Data2VecTextForMaskedLM</code> (Data2VecText model)</li>
<li><code>DebertaConfig</code> configuration class: <code>DebertaForMaskedLM</code> (DeBERTa model)</li>
<li><code>DebertaV2Config</code> configuration class: <code>DebertaV2ForMaskedLM</code> (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>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 j({props:{anchor:"transformers.AutoModelForMaskedLM.from_config.example",$$slots:{default:[zq]},$$scope:{ctx:v}}}),Sc=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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 <code>save_pretrained()</code> and
<code>from_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[Oq]},$$scope:{ctx:v}}}),Pc=new Z({props:{title:"TFAutoModelForMaskedLM",local:"transformers.TFAutoModelForMaskedLM ][ transformers.TFAutoModelForMaskedLM",headingTag:"h3"}}),Nc=new x({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 x({props:{name:"from_config",anchor:"transformers.TFAutoModelForMaskedLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMaskedLM.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>BertConfig</code> configuration class: <code>TFBertForMaskedLM</code> (BERT model)</li>
<li><code>CamembertConfig</code> configuration class: <code>TFCamembertForMaskedLM</code> (CamemBERT model)</li>
<li><code>ConvBertConfig</code> configuration class: <code>TFConvBertForMaskedLM</code> (ConvBERT model)</li>
<li><code>DebertaConfig</code> configuration class: <code>TFDebertaForMaskedLM</code> (DeBERTa model)</li>
<li><code>DebertaV2Config</code> configuration class: <code>TFDebertaV2ForMaskedLM</code> (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 j({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_config.example",$$slots:{default:[Kq]},$$scope:{ctx:v}}}),Xc=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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;
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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.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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[eX]},$$scope:{ctx:v}}}),Dc=new Z({props:{title:"FlaxAutoModelForMaskedLM",local:"transformers.FlaxAutoModelForMaskedLM ][ transformers.FlaxAutoModelForMaskedLM",headingTag:"h3"}}),Hc=new x({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 x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForMaskedLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>BartConfig</code> configuration class: <code>FlaxBartForConditionalGeneration</code> (BART model)</li>
<li><code>BertConfig</code> configuration class: <code>FlaxBertForMaskedLM</code> (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 j({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.example",$$slots:{default:[oX]},$$scope:{ctx:v}}}),Yc=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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;
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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.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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[tX]},$$scope:{ctx:v}}}),zc=new Z({props:{title:"AutoModelForMaskGeneration",local:"transformers.AutoModelForMaskGeneration ][ transformers.AutoModelForMaskGeneration",headingTag:"h3"}}),Kc=new x({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#L1523"}}),em=new Z({props:{title:"TFAutoModelForMaskGeneration",local:"transformers.TFAutoModelForMaskGeneration ][ transformers.TFAutoModelForMaskGeneration",headingTag:"h3"}}),tm=new x({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 Z({props:{title:"AutoModelForSeq2SeqLM",local:"transformers.AutoModelForSeq2SeqLM ][ transformers.AutoModelForSeq2SeqLM",headingTag:"h3"}}),rm=new x({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#L1575"}}),am=new x({props:{name:"from_config",anchor:"transformers.AutoModelForSeq2SeqLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>BartConfig</code> configuration class: <code>BartForConditionalGeneration</code> (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><code>EncoderDecoderConfig</code> configuration class: <code>EncoderDecoderModel</code> (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><code>MarianConfig</code> configuration class: <code>MarianMTModel</code> (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 j({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[nX]},$$scope:{ctx:v}}}),sm=new x({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
<code>save_pretrained()</code>, 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
model).</li>
<li>The model was saved using <code>save_pretrained()</code> 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 <code>save_pretrained()</code> and
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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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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
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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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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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 (<code>from_pretrained()</code>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
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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>BartConfig</code> configuration class: <code>TFBartForConditionalGeneration</code> (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><code>EncoderDecoderConfig</code> configuration class: <code>TFEncoderDecoderModel</code> (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><code>MarianConfig</code> configuration class: <code>TFMarianMTModel</code> (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;
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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
<code>save_pretrained()</code>, 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
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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 <code>save_pretrained()</code> 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
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.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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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.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 (<code>from_pretrained()</code>). 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>BartConfig</code> configuration class: <code>FlaxBartForConditionalGeneration</code> (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><code>EncoderDecoderConfig</code> configuration class: <code>FlaxEncoderDecoderModel</code> (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><code>MarianConfig</code> configuration class: <code>FlaxMarianMTModel</code> (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 j({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[iX]},$$scope:{ctx:v}}}),um=new x({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
<code>save_pretrained()</code>, 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;
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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 <code>save_pretrained()</code> 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
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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"}}),gi=new j({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[lX]},$$scope:{ctx:v}}}),pm=new Z({props:{title:"AutoModelForSequenceClassification",local:"transformers.AutoModelForSequenceClassification ][ transformers.AutoModelForSequenceClassification",headingTag:"h3"}}),hm=new x({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#L1586"}}),bm=new x({props:{name:"from_config",anchor:"transformers.AutoModelForSequenceClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSequenceClassification.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>BartConfig</code> configuration class: <code>BartForSequenceClassification</code> (BART model)</li>
<li><code>BertConfig</code> configuration class: <code>BertForSequenceClassification</code> (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><code>BioGptConfig</code> configuration class: <code>BioGptForSequenceClassification</code> (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><code>ConvBertConfig</code> configuration class: <code>ConvBertForSequenceClassification</code> (ConvBERT model)</li>
<li><code>Data2VecTextConfig</code> configuration class: <code>Data2VecTextForSequenceClassification</code> (Data2VecText model)</li>
<li><code>DebertaConfig</code> configuration class: <code>DebertaForSequenceClassification</code> (DeBERTa model)</li>
<li><code>DebertaV2Config</code> configuration class: <code>DebertaV2ForSequenceClassification</code> (DeBERTa-v2 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><code>Gemma2Config</code> configuration class: <code>Gemma2ForSequenceClassification</code> (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>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/llama2#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/main/ko/model_doc/llama2#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><code>MistralConfig</code> configuration class: <code>MistralForSequenceClassification</code> (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>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><code>OpenAIGPTConfig</code> configuration class: <code>OpenAIGPTForSequenceClassification</code> (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>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"}}),fi=new j({props:{anchor:"transformers.AutoModelForSequenceClassification.from_config.example",$$slots:{default:[dX]},$$scope:{ctx:v}}}),Mm=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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 <code>save_pretrained()</code> and
<code>from_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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"}}),ui=new j({props:{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[cX]},$$scope:{ctx:v}}}),_m=new Z({props:{title:"TFAutoModelForSequenceClassification",local:"transformers.TFAutoModelForSequenceClassification ][ transformers.TFAutoModelForSequenceClassification",headingTag:"h3"}}),ym=new x({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"}}),Tm=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForSequenceClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>BartConfig</code> configuration class: <code>TFBartForSequenceClassification</code> (BART model)</li>
<li><code>BertConfig</code> configuration class: <code>TFBertForSequenceClassification</code> (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><code>ConvBertConfig</code> configuration class: <code>TFConvBertForSequenceClassification</code> (ConvBERT model)</li>
<li><code>DebertaConfig</code> configuration class: <code>TFDebertaForSequenceClassification</code> (DeBERTa model)</li>
<li><code>DebertaV2Config</code> configuration class: <code>TFDebertaV2ForSequenceClassification</code> (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><code>MistralConfig</code> configuration class: <code>TFMistralForSequenceClassification</code> (Mistral model)</li>
<li><code>MobileBertConfig</code> configuration class: <code>TFMobileBertForSequenceClassification</code> (MobileBERT model)</li>
<li><code>OpenAIGPTConfig</code> configuration class: <code>TFOpenAIGPTForSequenceClassification</code> (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"}}),pi=new j({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.example",$$slots:{default:[mX]},$$scope:{ctx:v}}}),Fm=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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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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.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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[gX]},$$scope:{ctx:v}}}),vm=new Z({props:{title:"FlaxAutoModelForSequenceClassification",local:"transformers.FlaxAutoModelForSequenceClassification ][ transformers.FlaxAutoModelForSequenceClassification",headingTag:"h3"}}),wm=new x({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"}}),Cm=new x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>BartConfig</code> configuration class: <code>FlaxBartForSequenceClassification</code> (BART model)</li>
<li><code>BertConfig</code> configuration class: <code>FlaxBertForSequenceClassification</code> (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 j({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.example",$$slots:{default:[fX]},$$scope:{ctx:v}}}),xm=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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
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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.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 (<code>from_pretrained()</code>). 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"}}),Mi=new j({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[uX]},$$scope:{ctx:v}}}),km=new Z({props:{title:"AutoModelForMultipleChoice",local:"transformers.AutoModelForMultipleChoice ][ transformers.AutoModelForMultipleChoice",headingTag:"h3"}}),$m=new x({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#L1642"}}),jm=new x({props:{name:"from_config",anchor:"transformers.AutoModelForMultipleChoice.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMultipleChoice.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>BertConfig</code> configuration class: <code>BertForMultipleChoice</code> (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><code>ConvBertConfig</code> configuration class: <code>ConvBertForMultipleChoice</code> (ConvBERT model)</li>
<li><code>Data2VecTextConfig</code> configuration class: <code>Data2VecTextForMultipleChoice</code> (Data2VecText model)</li>
<li><code>DebertaV2Config</code> configuration class: <code>DebertaV2ForMultipleChoice</code> (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"}}),_i=new j({props:{anchor:"transformers.AutoModelForMultipleChoice.from_config.example",$$slots:{default:[pX]},$$scope:{ctx:v}}}),Zm=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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 <code>save_pretrained()</code> and
<code>from_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[hX]},$$scope:{ctx:v}}}),Lm=new Z({props:{title:"TFAutoModelForMultipleChoice",local:"transformers.TFAutoModelForMultipleChoice ][ transformers.TFAutoModelForMultipleChoice",headingTag:"h3"}}),Am=new x({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 x({props:{name:"from_config",anchor:"transformers.TFAutoModelForMultipleChoice.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>BertConfig</code> configuration class: <code>TFBertForMultipleChoice</code> (BERT model)</li>
<li><code>CamembertConfig</code> configuration class: <code>TFCamembertForMultipleChoice</code> (CamemBERT model)</li>
<li><code>ConvBertConfig</code> configuration class: <code>TFConvBertForMultipleChoice</code> (ConvBERT model)</li>
<li><code>DebertaV2Config</code> configuration class: <code>TFDebertaV2ForMultipleChoice</code> (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"}}),Ti=new j({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.example",$$slots:{default:[bX]},$$scope:{ctx:v}}}),Rm=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[MX]},$$scope:{ctx:v}}}),Jm=new Z({props:{title:"FlaxAutoModelForMultipleChoice",local:"transformers.FlaxAutoModelForMultipleChoice ][ transformers.FlaxAutoModelForMultipleChoice",headingTag:"h3"}}),Wm=new x({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"}}),Gm=new x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>BertConfig</code> configuration class: <code>FlaxBertForMultipleChoice</code> (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 j({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.example",$$slots:{default:[_X]},$$scope:{ctx:v}}}),Em=new x({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
<code>save_pretrained()</code>, 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>
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<li>The model was saved using <code>save_pretrained()</code> and is reloaded by supplying the
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
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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.
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already been done)</li>
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initialization function (<code>from_pretrained()</code>). Each key of <code>kwargs</code> that
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<li><code>BertConfig</code> configuration class: <code>BertForNextSentencePrediction</code> (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>
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<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>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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<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 <code>save_pretrained()</code> 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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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 <code>save_pretrained()</code> and
<code>from_pretrained()</code> 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
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Load the model weights from a TensorFlow 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
cached versions if they exist.
resume_download &#x2014;
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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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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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 (<code>from_pretrained()</code>). 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>BertConfig</code> configuration class: <code>TFBertForNextSentencePrediction</code> (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;
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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
<code>save_pretrained()</code>, 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;
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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 <code>save_pretrained()</code> 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
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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;
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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.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
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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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[wX]},$$scope:{ctx:v}}}),Dm=new Z({props:{title:"FlaxAutoModelForNextSentencePrediction",local:"transformers.FlaxAutoModelForNextSentencePrediction ][ transformers.FlaxAutoModelForNextSentencePrediction",headingTag:"h3"}}),Hm=new x({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 x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>BertConfig</code> configuration class: <code>FlaxBertForNextSentencePrediction</code> (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"}}),ji=new j({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[CX]},$$scope:{ctx:v}}}),Ym=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[xX]},$$scope:{ctx:v}}}),zm=new Z({props:{title:"AutoModelForTokenClassification",local:"transformers.AutoModelForTokenClassification ][ transformers.AutoModelForTokenClassification",headingTag:"h3"}}),Om=new x({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#L1635"}}),Km=new x({props:{name:"from_config",anchor:"transformers.AutoModelForTokenClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTokenClassification.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>BertConfig</code> configuration class: <code>BertForTokenClassification</code> (BERT model)</li>
<li><code>BigBirdConfig</code> configuration class: <code>BigBirdForTokenClassification</code> (BigBird model)</li>
<li><code>BioGptConfig</code> configuration class: <code>BioGptForTokenClassification</code> (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><code>ConvBertConfig</code> configuration class: <code>ConvBertForTokenClassification</code> (ConvBERT model)</li>
<li><code>Data2VecTextConfig</code> configuration class: <code>Data2VecTextForTokenClassification</code> (Data2VecText model)</li>
<li><code>DebertaConfig</code> configuration class: <code>DebertaForTokenClassification</code> (DeBERTa model)</li>
<li><code>DebertaV2Config</code> configuration class: <code>DebertaV2ForTokenClassification</code> (DeBERTa-v2 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><code>Gemma2Config</code> configuration class: <code>Gemma2ForTokenClassification</code> (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>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/llama2#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><code>MistralConfig</code> configuration class: <code>MistralForTokenClassification</code> (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>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"}}),Li=new j({props:{anchor:"transformers.AutoModelForTokenClassification.from_config.example",$$slots:{default:[kX]},$$scope:{ctx:v}}}),eg=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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 <code>save_pretrained()</code> and
<code>from_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[$X]},$$scope:{ctx:v}}}),og=new Z({props:{title:"TFAutoModelForTokenClassification",local:"transformers.TFAutoModelForTokenClassification ][ transformers.TFAutoModelForTokenClassification",headingTag:"h3"}}),tg=new x({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"}}),ng=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForTokenClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTokenClassification.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>BertConfig</code> configuration class: <code>TFBertForTokenClassification</code> (BERT model)</li>
<li><code>CamembertConfig</code> configuration class: <code>TFCamembertForTokenClassification</code> (CamemBERT model)</li>
<li><code>ConvBertConfig</code> configuration class: <code>TFConvBertForTokenClassification</code> (ConvBERT model)</li>
<li><code>DebertaConfig</code> configuration class: <code>TFDebertaForTokenClassification</code> (DeBERTa model)</li>
<li><code>DebertaV2Config</code> configuration class: <code>TFDebertaV2ForTokenClassification</code> (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 j({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_config.example",$$slots:{default:[jX]},$$scope:{ctx:v}}}),rg=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[ZX]},$$scope:{ctx:v}}}),ag=new Z({props:{title:"FlaxAutoModelForTokenClassification",local:"transformers.FlaxAutoModelForTokenClassification ][ transformers.FlaxAutoModelForTokenClassification",headingTag:"h3"}}),sg=new x({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"}}),ig=new x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForTokenClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>BertConfig</code> configuration class: <code>FlaxBertForTokenClassification</code> (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"}}),Ji=new j({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.example",$$slots:{default:[LX]},$$scope:{ctx:v}}}),lg=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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;
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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.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 (<code>from_pretrained()</code>). 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"}}),Wi=new j({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[AX]},$$scope:{ctx:v}}}),dg=new Z({props:{title:"AutoModelForQuestionAnswering",local:"transformers.AutoModelForQuestionAnswering ][ transformers.AutoModelForQuestionAnswering",headingTag:"h3"}}),cg=new x({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#L1595"}}),mg=new x({props:{name:"from_config",anchor:"transformers.AutoModelForQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForQuestionAnswering.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>BartConfig</code> configuration class: <code>BartForQuestionAnswering</code> (BART model)</li>
<li><code>BertConfig</code> configuration class: <code>BertForQuestionAnswering</code> (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><code>ConvBertConfig</code> configuration class: <code>ConvBertForQuestionAnswering</code> (ConvBERT model)</li>
<li><code>Data2VecTextConfig</code> configuration class: <code>Data2VecTextForQuestionAnswering</code> (Data2VecText model)</li>
<li><code>DebertaConfig</code> configuration class: <code>DebertaForQuestionAnswering</code> (DeBERTa model)</li>
<li><code>DebertaV2Config</code> configuration class: <code>DebertaV2ForQuestionAnswering</code> (DeBERTa-v2 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/llama2#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><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>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"}}),Gi=new j({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_config.example",$$slots:{default:[BX]},$$scope:{ctx:v}}}),gg=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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 <code>save_pretrained()</code> and
<code>from_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[RX]},$$scope:{ctx:v}}}),fg=new Z({props:{title:"TFAutoModelForQuestionAnswering",local:"transformers.TFAutoModelForQuestionAnswering ][ transformers.TFAutoModelForQuestionAnswering",headingTag:"h3"}}),ug=new x({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"}}),pg=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>BertConfig</code> configuration class: <code>TFBertForQuestionAnswering</code> (BERT model)</li>
<li><code>CamembertConfig</code> configuration class: <code>TFCamembertForQuestionAnswering</code> (CamemBERT model)</li>
<li><code>ConvBertConfig</code> configuration class: <code>TFConvBertForQuestionAnswering</code> (ConvBERT model)</li>
<li><code>DebertaConfig</code> configuration class: <code>TFDebertaForQuestionAnswering</code> (DeBERTa model)</li>
<li><code>DebertaV2Config</code> configuration class: <code>TFDebertaV2ForQuestionAnswering</code> (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 j({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[JX]},$$scope:{ctx:v}}}),hg=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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
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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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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>
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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 (<code>from_pretrained()</code>). 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>AlbertConfig</code> configuration class: <code>FlaxAlbertForQuestionAnswering</code> (ALBERT model)</li>
<li><code>BartConfig</code> configuration class: <code>FlaxBartForQuestionAnswering</code> (BART model)</li>
<li><code>BertConfig</code> configuration class: <code>FlaxBertForQuestionAnswering</code> (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>
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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 j({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[GX]},$$scope:{ctx:v}}}),yg=new x({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
<code>save_pretrained()</code>, 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>
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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 <code>save_pretrained()</code> 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
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.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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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.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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[EX]},$$scope:{ctx:v}}}),Tg=new Z({props:{title:"AutoModelForTextEncoding",local:"transformers.AutoModelForTextEncoding ][ transformers.AutoModelForTextEncoding",headingTag:"h3"}}),vg=new x({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#L1531"}}),wg=new Z({props:{title:"TFAutoModelForTextEncoding",local:"transformers.TFAutoModelForTextEncoding ][ transformers.TFAutoModelForTextEncoding",headingTag:"h3"}}),xg=new x({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"}}),kg=new Z({props:{title:"컴퓨터 비전",local:"computer-vision",headingTag:"h2"}}),jg=new Z({props:{title:"AutoModelForDepthEstimation",local:"transformers.AutoModelForDepthEstimation ][ transformers.AutoModelForDepthEstimation",headingTag:"h3"}}),Zg=new x({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#L1724"}}),Lg=new x({props:{name:"from_config",anchor:"transformers.AutoModelForDepthEstimation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDepthEstimation.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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"}}),Pi=new j({props:{anchor:"transformers.AutoModelForDepthEstimation.from_config.example",$$slots:{default:[VX]},$$scope:{ctx:v}}}),Ag=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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 <code>save_pretrained()</code> and
<code>from_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.example",$$slots:{default:[IX]},$$scope:{ctx:v}}}),Bg=new Z({props:{title:"AutoModelForImageClassification",local:"transformers.AutoModelForImageClassification ][ transformers.AutoModelForImageClassification",headingTag:"h3"}}),Rg=new x({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#L1658"}}),Jg=new x({props:{name:"from_config",anchor:"transformers.AutoModelForImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageClassification.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>CLIPConfig</code> configuration class: <code>CLIPForImageClassification</code> (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>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>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><code>SwinConfig</code> configuration class: <code>SwinForImageClassification</code> (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>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 j({props:{anchor:"transformers.AutoModelForImageClassification.from_config.example",$$slots:{default:[UX]},$$scope:{ctx:v}}}),Wg=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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 <code>save_pretrained()</code> and
<code>from_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.AutoModelForImageClassification.from_pretrained.example",$$slots:{default:[SX]},$$scope:{ctx:v}}}),Gg=new Z({props:{title:"TFAutoModelForImageClassification",local:"transformers.TFAutoModelForImageClassification ][ transformers.TFAutoModelForImageClassification",headingTag:"h3"}}),Eg=new x({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"}}),Vg=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForImageClassification.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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><code>SwinConfig</code> configuration class: <code>TFSwinForImageClassification</code> (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 j({props:{anchor:"transformers.TFAutoModelForImageClassification.from_config.example",$$slots:{default:[PX]},$$scope:{ctx:v}}}),Ig=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[NX]},$$scope:{ctx:v}}}),Ug=new Z({props:{title:"FlaxAutoModelForImageClassification",local:"transformers.FlaxAutoModelForImageClassification ][ transformers.FlaxAutoModelForImageClassification",headingTag:"h3"}}),Sg=new x({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"}}),Pg=new x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForImageClassification.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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;
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<li><code>VideoMAEConfig</code> configuration class: <code>VideoMAEForVideoClassification</code> (VideoMAE model)</li>
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<li>The model was saved using <code>save_pretrained()</code> and is reloaded by supplying the
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
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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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resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
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<ul>
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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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<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><code>SwinConfig</code> configuration class: <code>SwinForMaskedImageModeling</code> (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>
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<li>A path to a <em>directory</em> containing model weights saved using
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<li>The model was saved using <code>save_pretrained()</code> 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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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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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
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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 (<code>from_pretrained()</code>). 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>DeiTConfig</code> configuration class: <code>TFDeiTForMaskedImageModeling</code> (DeiT model)</li>
<li><code>SwinConfig</code> configuration class: <code>TFSwinForMaskedImageModeling</code> (Swin Transformer 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
<code>save_pretrained()</code>, 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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<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 <code>save_pretrained()</code> 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.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
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.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
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.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.TFAutoModelForMaskedImageModeling.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.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
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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.example",$$slots:{default:[OX]},$$scope:{ctx:v}}}),sf=new Z({props:{title:"AutoModelForObjectDetection",local:"transformers.AutoModelForObjectDetection ][ transformers.AutoModelForObjectDetection",headingTag:"h3"}}),lf=new x({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#L1708"}}),df=new x({props:{name:"from_config",anchor:"transformers.AutoModelForObjectDetection.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForObjectDetection.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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;
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"}}),nl=new j({props:{anchor:"transformers.AutoModelForObjectDetection.from_config.example",$$slots:{default:[KX]},$$scope:{ctx:v}}}),cf=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForObjectDetection.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForObjectDetection.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
<code>save_pretrained()</code>, 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;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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 <code>save_pretrained()</code> and
<code>from_pretrained()</code> 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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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForObjectDetection.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.AutoModelForObjectDetection.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.AutoModelForObjectDetection.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.AutoModelForObjectDetection.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.AutoModelForObjectDetection.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.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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.example",$$slots:{default:[e3]},$$scope:{ctx:v}}}),mf=new Z({props:{title:"AutoModelForImageSegmentation",local:"transformers.AutoModelForImageSegmentation ][ transformers.AutoModelForImageSegmentation",headingTag:"h3"}}),gf=new x({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#L1674"}}),ff=new x({props:{name:"from_config",anchor:"transformers.AutoModelForImageSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageSegmentation.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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;
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<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>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
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<li>The model was saved using <code>save_pretrained()</code> and is reloaded by supplying the
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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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resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
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<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>
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<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>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
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<li>The model was saved using <code>save_pretrained()</code> 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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Path to a directory in which a downloaded pretrained model configuration should be cached if the
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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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resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
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Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
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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 (<code>from_pretrained()</code>). 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>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>
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<li>A path to a <em>directory</em> containing model weights saved using
<code>save_pretrained()</code>, 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>
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<li>The model was saved using <code>save_pretrained()</code> 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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Path to a directory in which a downloaded pretrained model configuration should be cached if the
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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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resume_download &#x2014;
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<li><code>Mask2FormerConfig</code> configuration class: <code>Mask2FormerForUniversalSegmentation</code> (Mask2Former model)</li>
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<ul>
<li><code>AlignConfig</code> configuration class: <code>AlignModel</code> (ALIGN model)</li>
<li><code>AltCLIPConfig</code> configuration class: <code>AltCLIPModel</code> (AltCLIP model)</li>
<li><code>Blip2Config</code> configuration class: <code>Blip2ForImageTextRetrieval</code> (BLIP-2 model)</li>
<li><code>BlipConfig</code> configuration class: <code>BlipModel</code> (BLIP model)</li>
<li><code>CLIPConfig</code> configuration class: <code>CLIPModel</code> (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>
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<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>
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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.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
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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.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 (<code>from_pretrained()</code>). 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"}}),yl=new j({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.example",$$slots:{default:[h3]},$$scope:{ctx:v}}}),Xf=new Z({props:{title:"오디오",local:"audio",headingTag:"h2"}}),Hf=new Z({props:{title:"AutoModelForAudioClassification",local:"transformers.AutoModelForAudioClassification ][ transformers.AutoModelForAudioClassification",headingTag:"h3"}}),Qf=new x({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#L1752"}}),Yf=new x({props:{name:"from_config",anchor:"transformers.AutoModelForAudioClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioClassification.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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"}}),Tl=new j({props:{anchor:"transformers.AutoModelForAudioClassification.from_config.example",$$slots:{default:[b3]},$$scope:{ctx:v}}}),zf=new x({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
<code>save_pretrained()</code>, 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;
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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 <code>save_pretrained()</code> 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 <code>save_pretrained()</code> and
<code>from_pretrained()</code> 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
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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;
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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.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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[M3]},$$scope:{ctx:v}}}),Of=new Z({props:{title:"TFAutoModelForAudioClassification",local:"transformers.TFAutoModelForAudioClassification ][ transformers.TFAutoModelForAudioClassification",headingTag:"h3"}}),Kf=new x({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"}}),eu=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForAudioClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForAudioClassification.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#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 j({props:{anchor:"transformers.TFAutoModelForAudioClassification.from_config.example",$$slots:{default:[_3]},$$scope:{ctx:v}}}),ou=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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;
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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.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 (<code>from_pretrained()</code>). 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>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"}}),Cl=new j({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.example",$$slots:{default:[T3]},$$scope:{ctx:v}}}),au=new x({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
<code>save_pretrained()</code>, 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;
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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 <code>save_pretrained()</code> 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 <code>save_pretrained()</code> and
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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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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#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.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;
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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.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
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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 (<code>from_pretrained()</code>). 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>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"}}),kl=new j({props:{anchor:"transformers.AutoModelForCTC.from_config.example",$$slots:{default:[v3]},$$scope:{ctx:v}}}),du=new x({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
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<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>
</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;
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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 <code>save_pretrained()</code> 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 <code>save_pretrained()</code> and
<code>from_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.AutoModelForCTC.from_pretrained.example",$$slots:{default:[w3]},$$scope:{ctx:v}}}),cu=new Z({props:{title:"AutoModelForSpeechSeq2Seq",local:"transformers.AutoModelForSpeechSeq2Seq ][ transformers.AutoModelForSpeechSeq2Seq",headingTag:"h3"}}),mu=new x({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#L1766"}}),gu=new x({props:{name:"from_config",anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<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"}}),jl=new j({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[C3]},$$scope:{ctx:v}}}),fu=new x({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
<code>save_pretrained()</code>, 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;
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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 <code>save_pretrained()</code> 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 <code>save_pretrained()</code> and
<code>from_pretrained()</code> 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
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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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resume_download &#x2014;
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
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.,
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automatically loaded:</p>
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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 (<code>from_pretrained()</code>). 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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<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>
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Can be either:</p>
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<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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model).</li>
<li>The model was saved using <code>save_pretrained()</code> 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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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
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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;
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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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Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
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<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 (<code>from_pretrained()</code>). 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>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 j({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[j3]},$$scope:{ctx:v}}}),Tu=new x({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
<code>save_pretrained()</code>, 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> (<code>PretrainedConfig</code>, <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 <code>save_pretrained()</code> 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
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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
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#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.FlaxAutoModelForSpeechSeq2Seq.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.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
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.FlaxAutoModelForSpeechSeq2Seq.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.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 (<code>from_pretrained()</code>). 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>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;
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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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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.,
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initialization function (<code>from_pretrained()</code>). Each key of <code>kwargs</code> that
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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>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;
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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>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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<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 <code>save_pretrained()</code> 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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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 <code>save_pretrained()</code> and
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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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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#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.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;
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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.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 (<code>from_pretrained()</code>). 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>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
<code>save_pretrained()</code>, 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
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Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>, <em>optional</em>) &#x2014;
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 <code>save_pretrained()</code> 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
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.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.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.,
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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 (<code>from_pretrained()</code>). 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>Blip2Config</code> configuration class: <code>Blip2ForConditionalGeneration</code> (BLIP-2 model)</li>
<li><code>BlipConfig</code> configuration class: <code>BlipForQuestionAnswering</code> (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;
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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
<code>save_pretrained()</code>, 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;
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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 <code>save_pretrained()</code> 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 <code>save_pretrained()</code> and
<code>from_pretrained()</code> 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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_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 (<code>from_pretrained()</code>). 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 j({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.example",$$slots:{default:[S3]},$$scope:{ctx:v}}}),ep=new Z({props:{title:"AutoModelForVision2Seq",local:"transformers.AutoModelForVision2Seq ][ transformers.AutoModelForVision2Seq",headingTag:"h3"}}),op=new x({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#L1738"}}),tp=new x({props:{name:"from_config",anchor:"transformers.AutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>Blip2Config</code> configuration class: <code>Blip2ForConditionalGeneration</code> (BLIP-2 model)</li>
<li><code>BlipConfig</code> configuration class: <code>BlipForConditionalGeneration</code> (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><code>PaliGemmaConfig</code> configuration class: <code>PaliGemmaForConditionalGeneration</code> (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>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 j({props:{anchor:"transformers.AutoModelForVision2Seq.from_config.example",$$slots:{default:[P3]},$$scope:{ctx:v}}}),np=new x({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
<code>save_pretrained()</code>, 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;
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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 <code>save_pretrained()</code> 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 <code>save_pretrained()</code> and
<code>from_pretrained()</code> 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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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForVision2Seq.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.AutoModelForVision2Seq.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.AutoModelForVision2Seq.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.AutoModelForVision2Seq.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.AutoModelForVision2Seq.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.AutoModelForVision2Seq.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 (<code>from_pretrained()</code>). 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"}}),Hl=new j({props:{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[N3]},$$scope:{ctx:v}}}),rp=new Z({props:{title:"TFAutoModelForVision2Seq",local:"transformers.TFAutoModelForVision2Seq ][ transformers.TFAutoModelForVision2Seq",headingTag:"h3"}}),ap=new x({props:{name:"class transformers.TFAutoModelForVision2Seq",anchor:"transformers.TFAutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L611"}}),sp=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<code>PretrainedConfig</code>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><code>BlipConfig</code> configuration class: <code>TFBlipForConditionalGeneration</code> (BLIP model)</li>
<li><code>VisionEncoderDecoderConfig</code> configuration class: <code>TFVisionEncoderDecoderModel</code> (Vision Encoder decoder model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForVision2Seq.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
<code>save_pretrained()</code>, 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.TFAutoModelForVision2Seq.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 <code>save_pretrained()</code> 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.TFAutoModelForVision2Seq.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
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
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