import{S as els,i as ols,s as rls,e as a,k as l,w as b,t as o,M as tls,c as n,d as t,m as i,a as s,x as M,h as r,b as d,G as e,g as v,y as T,q as F,o as E,B as y,v as als,L as V}from"../../chunks/vendor-hf-doc-builder.js";import{T as Wjo}from"../../chunks/Tip-hf-doc-builder.js";import{D as G}from"../../chunks/Docstring-hf-doc-builder.js";import{C as P}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as te}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Z}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function nls(x){let h,C,u,f,p,c,g,ze,Vc,Xg,Vt,Wc,Jc,JZ,Dg,Qe,oo,Uc,zs,UZ,Hs,Ks,XZ,Xc,el,DZ,Dc,qg,Bn;return{c(){h=a("p"),C=o("If your "),u=a("code"),f=o("NewModelConfig"),p=o(" is a subclass of "),c=a("code"),g=o("~transformer.PretrainedConfig"),ze=o(`, make sure its
`),Vc=a("code"),Xg=o("model_type"),Vt=o(" attribute is set to the same key you use when registering the config (here "),Wc=a("code"),Jc=o('"new-model"'),JZ=o(")."),Dg=l(),Qe=a("p"),oo=o("Likewise, if your "),Uc=a("code"),zs=o("NewModel"),UZ=o(" is a subclass of "),Hs=a("a"),Ks=o("PreTrainedModel"),XZ=o(`, make sure its
`),Xc=a("code"),el=o("config_class"),DZ=o(` attribute is set to the same class you use when registering the model (here
`),Dc=a("code"),qg=o("NewModelConfig"),Bn=o(")."),this.h()},l(ro){h=n(ro,"P",{});var Ie=s(h);C=r(Ie,"If your "),u=n(Ie,"CODE",{});var VQ=s(u);f=r(VQ,"NewModelConfig"),VQ.forEach(t),p=r(Ie," is a subclass of "),c=n(Ie,"CODE",{});var qc=s(c);g=r(qc,"~transformer.PretrainedConfig"),qc.forEach(t),ze=r(Ie,`, make sure its
`),Vc=n(Ie,"CODE",{});var WQ=s(Vc);Xg=r(WQ,"model_type"),WQ.forEach(t),Vt=r(Ie," attribute is set to the same key you use when registering the config (here "),Wc=n(Ie,"CODE",{});var JQ=s(Wc);Jc=r(JQ,'"new-model"'),JQ.forEach(t),JZ=r(Ie,")."),Ie.forEach(t),Dg=i(ro),Qe=n(ro,"P",{});var jo=s(Qe);oo=r(jo,"Likewise, if your "),Uc=n(jo,"CODE",{});var In=s(Uc);zs=r(In,"NewModel"),In.forEach(t),UZ=r(jo," is a subclass of "),Hs=n(jo,"A",{href:!0});var UQ=s(Hs);Ks=r(UQ,"PreTrainedModel"),UQ.forEach(t),XZ=r(jo,`, make sure its
`),Xc=n(jo,"CODE",{});var Og=s(Xc);el=r(Og,"config_class"),Og.forEach(t),DZ=r(jo,` attribute is set to the same class you use when registering the model (here
`),Dc=n(jo,"CODE",{});var XQ=s(Dc);qg=r(XQ,"NewModelConfig"),XQ.forEach(t),Bn=r(jo,")."),jo.forEach(t),this.h()},h(){d(Hs,"href","/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel")},m(ro,Ie){v(ro,h,Ie),e(h,C),e(h,u),e(u,f),e(h,p),e(h,c),e(c,g),e(h,ze),e(h,Vc),e(Vc,Xg),e(h,Vt),e(h,Wc),e(Wc,Jc),e(h,JZ),v(ro,Dg,Ie),v(ro,Qe,Ie),e(Qe,oo),e(Qe,Uc),e(Uc,zs),e(Qe,UZ),e(Qe,Hs),e(Hs,Ks),e(Qe,XZ),e(Qe,Xc),e(Xc,el),e(Qe,DZ),e(Qe,Dc),e(Dc,qg),e(Qe,Bn)},d(ro){ro&&t(h),ro&&t(Dg),ro&&t(Qe)}}}function sls(x){let h,C,u,f,p;return f=new P({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;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;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;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>}`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function lls(x){let h,C,u,f,p;return f=new P({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;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;roberta-base&quot;</span>, add_prefix_space=<span class="hljs-literal">True</span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function ils(x){let h,C,u,f,p;return{c(){h=a("p"),C=o("Passing "),u=a("code"),f=o("token=True"),p=o(" is required when you want to use a private model.")},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Passing "),u=n(g,"CODE",{});var ze=s(u);f=r(ze,"token=True"),ze.forEach(t),p=r(g," is required when you want to use a private model."),g.forEach(t)},m(c,g){v(c,h,g),e(h,C),e(h,u),e(u,f),e(h,p)},d(c){c&&t(h)}}}function dls(x){let h,C,u,f,p;return f=new P({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>`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function cls(x){let h,C,u,f,p;return{c(){h=a("p"),C=o("Passing "),u=a("code"),f=o("token=True"),p=o(" is required when you want to use a private model.")},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Passing "),u=n(g,"CODE",{});var ze=s(u);f=r(ze,"token=True"),ze.forEach(t),p=r(g," is required when you want to use a private model."),g.forEach(t)},m(c,g){v(c,h,g),e(h,C),e(h,u),e(u,f),e(h,p)},d(c){c&&t(h)}}}function mls(x){let h,C,u,f,p;return f=new P({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>`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function fls(x){let h,C,u,f,p;return{c(){h=a("p"),C=o("Passing "),u=a("code"),f=o("token=True"),p=o(" is required when you want to use a private model.")},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Passing "),u=n(g,"CODE",{});var ze=s(u);f=r(ze,"token=True"),ze.forEach(t),p=r(g," is required when you want to use a private model."),g.forEach(t)},m(c,g){v(c,h,g),e(h,C),e(h,u),e(u,f),e(h,p)},d(c){c&&t(h)}}}function hls(x){let h,C,u,f,p;return f=new P({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>`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function gls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWwlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWwuZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function uls(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function pls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbCUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsLmZyb21fY29uZmlnKGNvbmZpZyk=",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModel.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function _ls(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function vls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbC5mcm9tX2NvbmZpZyhjb25maWcp",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModel.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function bls(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Mls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JQcmVUcmFpbmluZyUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclByZVRyYWluaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForPreTraining.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Tls(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Fls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclByZVRyYWluaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JQcmVUcmFpbmluZy5mcm9tX2NvbmZpZyhjb25maWcp",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForPreTraining.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Els(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function yls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yUHJlVHJhaW5pbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yUHJlVHJhaW5pbmcuZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForPreTraining.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Cls(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function wls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JDYXVzYWxMTSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckNhdXNhbExNLmZyb21fY29uZmlnKGNvbmZpZyk=",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Lls(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Als(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckNhdXNhbExNJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JDYXVzYWxMTS5mcm9tX2NvbmZpZyhjb25maWcp",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForCausalLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Rls(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function kls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yQ2F1c2FsTE0lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yQ2F1c2FsTE0uZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForCausalLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Sls(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function xls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JNYXNrZWRMTSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvck1hc2tlZExNLmZyb21fY29uZmlnKGNvbmZpZyk=",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Gls(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Bls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck1hc2tlZExNJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JNYXNrZWRMTS5mcm9tX2NvbmZpZyhjb25maWcp",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ils(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Nls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yTWFza2VkTE0lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yTWFza2VkTE0uZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMaskedLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function $ls(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function jls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTZXEyU2VxTE0lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJ0NS1iYXNlJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU2VxMlNlcUxNLmZyb21fY29uZmlnKGNvbmZpZyk=",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;t5-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Pls(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Zls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNlcTJTZXFMTSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMnQ1LWJhc2UlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNlcTJTZXFMTS5mcm9tX2NvbmZpZyhjb25maWcp",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;t5-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Vls(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Wls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yU2VxMlNlcUxNJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIydDUtYmFzZSUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JTZXEyU2VxTE0uZnJvbV9jb25maWcoY29uZmlnKQ==",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;t5-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSeq2SeqLM.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Jls(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Uls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Xls(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Dls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function qls(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ols(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSequenceClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Qls(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Yls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JNdWx0aXBsZUNob2ljZSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvck11bHRpcGxlQ2hvaWNlLmZyb21fY29uZmlnKGNvbmZpZyk=",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMultipleChoice.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function zls(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Hls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck11bHRpcGxlQ2hvaWNlJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JNdWx0aXBsZUNob2ljZS5mcm9tX2NvbmZpZyhjb25maWcp",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMultipleChoice.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Kls(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function eis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yTXVsdGlwbGVDaG9pY2UlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yTXVsdGlwbGVDaG9pY2UuZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMultipleChoice.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function ois(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function ris(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JOZXh0U2VudGVuY2VQcmVkaWN0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForNextSentencePrediction.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function tis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JOZXh0U2VudGVuY2VQcmVkaWN0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvck5leHRTZW50ZW5jZVByZWRpY3Rpb24uZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFRGJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwUHlUb3JjaCUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbi5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnRmX21vZGVsJTJGYmVydF90Zl9jaGVja3BvaW50LmNrcHQuaW5kZXglMjIlMkMlMjBmcm9tX3RmJTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function ais(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck5leHRTZW50ZW5jZVByZWRpY3Rpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvck5leHRTZW50ZW5jZVByZWRpY3Rpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForNextSentencePrediction.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function nis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck5leHRTZW50ZW5jZVByZWRpY3Rpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JOZXh0U2VudGVuY2VQcmVkaWN0aW9uLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvck5leHRTZW50ZW5jZVByZWRpY3Rpb24uZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHl0b3JjaF9tb2RlbC5iaW4lMjIlMkMlMjBmcm9tX3B0JTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function sis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JOZXh0U2VudGVuY2VQcmVkaWN0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForNextSentencePrediction.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function lis(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function iis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function dis(x){let h,C,u,f,p;return f=new P({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, 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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function cis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function mis(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function fis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForTokenClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function his(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function gis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JRdWVzdGlvbkFuc3dlcmluZyUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForQuestionAnswering.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function uis(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function pis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclF1ZXN0aW9uQW5zd2VyaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JRdWVzdGlvbkFuc3dlcmluZy5mcm9tX2NvbmZpZyhjb25maWcp",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForQuestionAnswering.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function _is(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function vis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yUXVlc3Rpb25BbnN3ZXJpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yUXVlc3Rpb25BbnN3ZXJpbmcuZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForQuestionAnswering.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function bis(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Mis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JEZXB0aEVzdGltYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JEZXB0aEVzdGltYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDepthEstimation.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Tis(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Fis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JJbWFnZUNsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9ySW1hZ2VDbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Eis(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function yis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckltYWdlQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvckltYWdlQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForImageClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Cis(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function wis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9ySW1hZ2VDbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JJbWFnZUNsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForImageClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Lis(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ais(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JWaWRlb0NsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVmlkZW9DbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVideoClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ris(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function kis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JNYXNrZWRJbWFnZU1vZGVsaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yTWFza2VkSW1hZ2VNb2RlbGluZy5mcm9tX2NvbmZpZyhjb25maWcp",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedImageModeling.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Sis(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function xis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck1hc2tlZEltYWdlTW9kZWxpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvck1hc2tlZEltYWdlTW9kZWxpbmcuZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedImageModeling.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Gis(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Bis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JPYmplY3REZXRlY3Rpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JPYmplY3REZXRlY3Rpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Iis(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Nis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JJbWFnZVNlZ21lbnRhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckltYWdlU2VnbWVudGF0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageSegmentation.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function $is(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function jis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTZW1hbnRpY1NlZ21lbnRhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclNlbWFudGljU2VnbWVudGF0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSemanticSegmentation.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Pis(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Zis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNlbWFudGljU2VnbWVudGF0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JTZW1hbnRpY1NlZ21lbnRhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSemanticSegmentation.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Vis(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Wis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JJbnN0YW5jZVNlZ21lbnRhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckluc3RhbmNlU2VnbWVudGF0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForInstanceSegmentation.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Jis(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Uis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JVbml2ZXJzYWxTZWdtZW50YXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JVbml2ZXJzYWxTZWdtZW50YXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForUniversalSegmentation.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Xis(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Dis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JaZXJvU2hvdEltYWdlQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JaZXJvU2hvdEltYWdlQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotImageClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function qis(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ois(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclplcm9TaG90SW1hZ2VDbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yWmVyb1Nob3RJbWFnZUNsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForZeroShotImageClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Qis(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Yis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JaZXJvU2hvdE9iamVjdERldGVjdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclplcm9TaG90T2JqZWN0RGV0ZWN0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotObjectDetection.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function zis(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function His(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JBdWRpb0NsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yQXVkaW9DbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Kis(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function eds(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckF1ZGlvQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvckF1ZGlvQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForAudioClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function ods(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function rds(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JBdWRpb0ZyYW1lQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JBdWRpb0ZyYW1lQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioFrameClassification.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function tds(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function ads(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JDVEMlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JDVEMuZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function nds(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function sds(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTcGVlY2hTZXEyU2VxJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU3BlZWNoU2VxMlNlcS5mcm9tX2NvbmZpZyhjb25maWcp",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSpeechSeq2Seq.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function lds(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function ids(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNwZWVjaFNlcTJTZXElMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNwZWVjaFNlcTJTZXEuZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSpeechSeq2Seq.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function dds(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNwZWVjaFNlcTJTZXElMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JTcGVlY2hTZXEyU2VxLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yU3BlZWNoU2VxMlNlcS5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNwZWVjaFNlcTJTZXEuZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHl0b3JjaF9tb2RlbC5iaW4lMjIlMkMlMjBmcm9tX3B0JTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function cds(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yU3BlZWNoU2VxMlNlcSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JTcGVlY2hTZXEyU2VxLmZyb21fY29uZmlnKGNvbmZpZyk=",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSpeechSeq2Seq.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function mds(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function fds(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JBdWRpb1hWZWN0b3IlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JBdWRpb1hWZWN0b3IuZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioXVector.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function hds(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JBdWRpb1hWZWN0b3IlMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yQXVkaW9YVmVjdG9yLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckF1ZGlvWFZlY3Rvci5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFRGJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwUHlUb3JjaCUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yQXVkaW9YVmVjdG9yLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX2NoZWNrcG9pbnQuY2twdC5pbmRleCUyMiUyQyUyMGZyb21fdGYlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function gds(x){let h,C,u,f,p;return f=new P({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)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function uds(x){let h,C,u,f,p;return f=new P({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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function pds(x){let h,C,u,f,p;return f=new P({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)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function _ds(x){let h,C,u,f,p;return f=new P({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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function vds(x){let h,C,u,f,p;return f=new P({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)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function bds(x){let h,C,u,f,p;return f=new P({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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Mds(x){let h,C,u,f,p;return f=new P({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)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Tds(x){let h,C,u,f,p;return f=new P({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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Fds(x){let h,C,u,f,p;return f=new P({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)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Eds(x){let h,C,u,f,p;return f=new P({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, 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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function yds(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JWaXNpb24yU2VxJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVmlzaW9uMlNlcS5mcm9tX2NvbmZpZyhjb25maWcp",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVision2Seq.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Cds(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function wds(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclZpc2lvbjJTZXElMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclZpc2lvbjJTZXEuZnJvbV9jb25maWcoY29uZmlnKQ==",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForVision2Seq.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Lds(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Ads(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yVmlzaW9uMlNlcSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JWaXNpb24yU2VxLmZyb21fY29uZmlnKGNvbmZpZyk=",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;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForVision2Seq.from_config(config)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function Rds(x){let h,C,u,f,p;return f=new P({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;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;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>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),b(f.$$.fragment)},l(c){h=n(c,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(c),M(f.$$.fragment,c)},m(c,g){v(c,h,g),e(h,C),v(c,u,g),T(f,c,g),p=!0},p:V,i(c){p||(F(f.$$.fragment,c),p=!0)},o(c){E(f.$$.fragment,c),p=!1},d(c){c&&t(h),c&&t(u),y(f,c)}}}function kds(x){let 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c=new te({}),Bn=new P({props:{code:"bW9kZWwlMjAlM0QlMjBBdXRvTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMik=",highlighted:'model = AutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)'}}),qZ=new te({}),OZ=new P({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)`}}),Yg=new Wjo({props:{warning:!0,$$slots:{default:[nls]},$$scope:{ctx:x}}}),QZ=new te({}),YZ=new G({props:{name:"class transformers.AutoConfig",anchor:"transformers.AutoConfig",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/configuration_auto.py#L885"}}),KZ=new G({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. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing a configuration file saved using the
<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.save_pretrained">save_pretrained()</a> method, or the <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> method,
e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a saved configuration JSON <em>file</em>, e.g.,
<code>./my_model_directory/configuration.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoConfig.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoConfig.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download the model weights and configuration files and override the
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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<li>A string, the <em>model id</em> of a predefined tokenizer hosted inside a model repo on huggingface.co.
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<li>A path to a <em>directory</em> containing vocabulary files required by the tokenizer, for instance saved
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<li>A path or url to a single saved vocabulary file if and only if the tokenizer only requires a
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<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
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<li>a path to a <em>directory</em> containing a feature extractor file saved using the
<a href="/docs/transformers/v4.32.0/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained">save_pretrained()</a> method, e.g.,
<code>./my_model_directory/</code>.</li>
<li>a path or url to a saved feature extractor JSON <em>file</em>, e.g.,
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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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If <code>False</code>, then this function returns just the final feature extractor object. If <code>True</code>, then this
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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. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
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<li>a path to a <em>directory</em> containing a image processor file saved using the
<a href="/docs/transformers/v4.32.0/en/internal/image_processing_utils#transformers.ImageProcessingMixin.save_pretrained">save_pretrained()</a> method, e.g.,
<code>./my_model_directory/</code>.</li>
<li>a path or url to a saved image processor JSON <em>file</em>, e.g.,
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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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If <code>False</code>, then this function returns just the final image processor object. If <code>True</code>, then this
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<ul>
<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
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<li>a path to a <em>directory</em> containing a processor files saved using the <code>save_pretrained()</code> method,
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if they exist.`,name:"force_download"},{anchor:"transformers.AutoProcessor.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.`,name:"resume_download"},{anchor:"transformers.AutoProcessor.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoProcessor.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
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/v4.32.0/src/transformers/models/auto/processing_auto.py#L131"}}),e3=new Wjo({props:{$$slots:{default:[fls]},$$scope:{ctx:x}}}),o3=new Z({props:{anchor:"transformers.AutoProcessor.from_pretrained.example",$$slots:{default:[hls]},$$scope:{ctx:x}}}),yV=new G({props:{name:"register",anchor:"transformers.AutoProcessor.register",parameters:[{name:"config_class",val:""},{name:"processor_class",val:""}],parametersDescription:[{anchor:"transformers.AutoProcessor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoProcessor.register.processor_class",description:"<strong>processor_class</strong> (<code>FeatureExtractorMixin</code>) &#x2014; The processor to register.",name:"processor_class"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/processing_auto.py#L320"}}),CV=new te({}),wV=new te({}),LV=new G({props:{name:"class transformers.AutoModel",anchor:"transformers.AutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1193"}}),RV=new G({props:{name:"from_config",anchor:"transformers.AutoModel.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModel.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/audio-spectrogram-transformer#transformers.ASTConfig">ASTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/audio-spectrogram-transformer#transformers.ASTModel">ASTModel</a> (Audio Spectrogram Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertModel">AlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/align#transformers.AlignConfig">AlignConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/align#transformers.AlignModel">AlignModel</a> (ALIGN model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/altclip#transformers.AltCLIPConfig">AltCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/autoformer#transformers.AutoformerConfig">AutoformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/autoformer#transformers.AutoformerModel">AutoformerModel</a> (Autoformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bark#transformers.BarkConfig">BarkConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bark#transformers.BarkModel">BarkModel</a> (Bark model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartModel">BartModel</a> (BART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/beit#transformers.BeitModel">BeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertModel">BertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert-generation#transformers.BertGenerationEncoder">BertGenerationEncoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdModel">BigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusModel">BigBirdPegasusModel</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/biogpt#transformers.BioGptModel">BioGptModel</a> (BioGpt model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bit#transformers.BitConfig">BitConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bit#transformers.BitModel">BitModel</a> (BiT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot#transformers.BlenderbotModel">BlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallModel">BlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blip-2#transformers.Blip2Model">Blip2Model</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bloom#transformers.BloomModel">BloomModel</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bridgetower#transformers.BridgeTowerConfig">BridgeTowerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bridgetower#transformers.BridgeTowerModel">BridgeTowerModel</a> (BridgeTower model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/clipseg#transformers.CLIPSegConfig">CLIPSegConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/clipseg#transformers.CLIPSegModel">CLIPSegModel</a> (CLIPSeg model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ctrl#transformers.CTRLModel">CTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertModel">CamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/canine#transformers.CanineModel">CanineModel</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/chinese_clip#transformers.ChineseCLIPConfig">ChineseCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/chinese_clip#transformers.ChineseCLIPModel">ChineseCLIPModel</a> (Chinese-CLIP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/clap#transformers.ClapConfig">ClapConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/clap#transformers.ClapModel">ClapModel</a> (CLAP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/codegen#transformers.CodeGenConfig">CodeGenConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/codegen#transformers.CodeGenModel">CodeGenModel</a> (CodeGen model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/conditional_detr#transformers.ConditionalDetrConfig">ConditionalDetrConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/conditional_detr#transformers.ConditionalDetrModel">ConditionalDetrModel</a> (Conditional DETR model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.ConvBertModel">ConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/convnext#transformers.ConvNextModel">ConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/convnextv2#transformers.ConvNextV2Config">ConvNextV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/convnextv2#transformers.ConvNextV2Model">ConvNextV2Model</a> (ConvNeXTV2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/cpmant#transformers.CpmAntConfig">CpmAntConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/cpmant#transformers.CpmAntModel">CpmAntModel</a> (CPM-Ant model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/cvt#transformers.CvtModel">CvtModel</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/dpt#transformers.DPTModel">DPTModel</a> (DPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecAudioModel">Data2VecAudioModel</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecTextModel">Data2VecTextModel</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecVisionModel">Data2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.DebertaModel">DebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.DebertaV2Model">DebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/decision_transformer#transformers.DecisionTransformerConfig">DecisionTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/decision_transformer#transformers.DecisionTransformerModel">DecisionTransformerModel</a> (Decision Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deformable_detr#transformers.DeformableDetrConfig">DeformableDetrConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deformable_detr#transformers.DeformableDetrModel">DeformableDetrModel</a> (Deformable DETR model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deit#transformers.DeiTModel">DeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deta#transformers.DetaConfig">DetaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deta#transformers.DetaModel">DetaModel</a> (DETA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/detr#transformers.DetrModel">DetrModel</a> (DETR model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/dinat#transformers.DinatConfig">DinatConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/dinat#transformers.DinatModel">DinatModel</a> (DiNAT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/dinov2#transformers.Dinov2Config">Dinov2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/dinov2#transformers.Dinov2Model">Dinov2Model</a> (DINOv2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/donut#transformers.DonutSwinConfig">DonutSwinConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/donut#transformers.DonutSwinModel">DonutSwinModel</a> (DonutSwin model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/efficientformer#transformers.EfficientFormerModel">EfficientFormerModel</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/efficientnet#transformers.EfficientNetConfig">EfficientNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/efficientnet#transformers.EfficientNetModel">EfficientNetModel</a> (EfficientNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraModel">ElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/encodec#transformers.EncodecConfig">EncodecConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/encodec#transformers.EncodecModel">EncodecModel</a> (EnCodec model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ernie#transformers.ErnieModel">ErnieModel</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ernie_m#transformers.ErnieMModel">ErnieMModel</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/esm#transformers.EsmModel">EsmModel</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/fnet#transformers.FNetModel">FNetModel</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/fsmt#transformers.FSMTModel">FSMTModel</a> (FairSeq Machine-Translation model)</li>
<li><code>FalconConfig</code> configuration class: <code>FalconModel</code> (Falcon model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertModel">FlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/flava#transformers.FlavaModel">FlavaModel</a> (FLAVA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/focalnet#transformers.FocalNetConfig">FocalNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/focalnet#transformers.FocalNetModel">FocalNetModel</a> (FocalNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelModel">FunnelModel</a> or <a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelBaseModel">FunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/glpn#transformers.GLPNModel">GLPNModel</a> (GLPN model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.GPT2Model">GPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeModel">GPTBigCodeModel</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.GPTJModel">GPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neo#transformers.GPTNeoModel">GPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neox#transformers.GPTNeoXModel">GPTNeoXModel</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseModel">GPTNeoXJapaneseModel</a> (GPT NeoX Japanese model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/git#transformers.GitConfig">GitConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/git#transformers.GitModel">GitModel</a> (GIT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/graphormer#transformers.GraphormerConfig">GraphormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/graphormer#transformers.GraphormerModel">GraphormerModel</a> (Graphormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/groupvit#transformers.GroupViTModel">GroupViTModel</a> (GroupViT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/hubert#transformers.HubertModel">HubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ibert#transformers.IBertModel">IBertModel</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/idefics#transformers.IdeficsConfig">IdeficsConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/idefics#transformers.IdeficsModel">IdeficsModel</a> (IDEFICS model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/imagegpt#transformers.ImageGPTModel">ImageGPTModel</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/informer#transformers.InformerConfig">InformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/informer#transformers.InformerModel">InformerModel</a> (Informer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/jukebox#transformers.JukeboxConfig">JukeboxConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/jukebox#transformers.JukeboxModel">JukeboxModel</a> (Jukebox model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/led#transformers.LEDModel">LEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.LayoutLMModel">LayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2Model">LayoutLMv2Model</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Model">LayoutLMv3Model</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/levit#transformers.LevitModel">LevitModel</a> (LeViT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/lilt#transformers.LiltModel">LiltModel</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/llama2#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/llama2#transformers.LlamaModel">LlamaModel</a> (LLaMA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/longt5#transformers.LongT5Model">LongT5Model</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerModel">LongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/luke#transformers.LukeModel">LukeModel</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/lxmert#transformers.LxmertModel">LxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/m2m_100#transformers.M2M100Model">M2M100Model</a> (M2M100 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartModel">MBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mctct#transformers.MCTCTModel">MCTCTModel</a> (M-CTC-T model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetModel">MPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mt5#transformers.MT5Model">MT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/marian#transformers.MarianModel">MarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/markuplm#transformers.MarkupLMModel">MarkupLMModel</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mask2former#transformers.Mask2FormerConfig">Mask2FormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mask2former#transformers.Mask2FormerModel">Mask2FormerModel</a> (Mask2Former model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/maskformer#transformers.MaskFormerModel">MaskFormerModel</a> (MaskFormer model)</li>
<li><code>MaskFormerSwinConfig</code> configuration class: <code>MaskFormerSwinModel</code> (MaskFormerSwin model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mega#transformers.MegaModel">MegaModel</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/megatron-bert#transformers.MegatronBertModel">MegatronBertModel</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mgp-str#transformers.MgpstrConfig">MgpstrConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mgp-str#transformers.MgpstrForSceneTextRecognition">MgpstrForSceneTextRecognition</a> (MGP-STR model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertModel">MobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config">MobileNetV1Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilenet_v1#transformers.MobileNetV1Model">MobileNetV1Model</a> (MobileNetV1 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2Model">MobileNetV2Model</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilevit#transformers.MobileViTModel">MobileViTModel</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilevitv2#transformers.MobileViTV2Config">MobileViTV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilevitv2#transformers.MobileViTV2Model">MobileViTV2Model</a> (MobileViTV2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpt#transformers.MptModel">MptModel</a> (MPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mra#transformers.MraModel">MraModel</a> (MRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mvp#transformers.MvpModel">MvpModel</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/nat#transformers.NatConfig">NatConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/nat#transformers.NatModel">NatModel</a> (NAT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/nezha#transformers.NezhaModel">NezhaModel</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/nllb-moe#transformers.NllbMoeConfig">NllbMoeConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/nllb-moe#transformers.NllbMoeModel">NllbMoeModel</a> (NLLB-MOE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/nystromformer#transformers.NystromformerModel">NystromformerModel</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/opt#transformers.OPTModel">OPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/oneformer#transformers.OneFormerConfig">OneFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/oneformer#transformers.OneFormerModel">OneFormerModel</a> (OneFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/openai-gpt#transformers.OpenAIGPTModel">OpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/open-llama#transformers.OpenLlamaModel">OpenLlamaModel</a> (OpenLlama model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/owlvit#transformers.OwlViTModel">OwlViTModel</a> (OWL-ViT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/plbart#transformers.PLBartModel">PLBartModel</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/pegasus#transformers.PegasusModel">PegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/pegasus_x#transformers.PegasusXConfig">PegasusXConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/pegasus_x#transformers.PegasusXModel">PegasusXModel</a> (PEGASUS-X model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/perceiver#transformers.PerceiverModel">PerceiverModel</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/poolformer#transformers.PoolFormerModel">PoolFormerModel</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/prophetnet#transformers.ProphetNetModel">ProphetNetModel</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/pvt#transformers.PvtConfig">PvtConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/pvt#transformers.PvtModel">PvtModel</a> (PVT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/qdqbert#transformers.QDQBertModel">QDQBertModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/reformer#transformers.ReformerModel">ReformerModel</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/regnet#transformers.RegNetModel">RegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertModel">RemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/resnet#transformers.ResNetModel">ResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roc_bert#transformers.RoCBertModel">RoCBertModel</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerModel">RoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaModel">RobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormModel">RobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/rwkv#transformers.RwkvConfig">RwkvConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/rwkv#transformers.RwkvModel">RwkvModel</a> (RWKV model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/sew#transformers.SEWModel">SEWModel</a> (SEW model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/sew-d#transformers.SEWDModel">SEWDModel</a> (SEW-D model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/sam#transformers.SamConfig">SamConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/sam#transformers.SamModel">SamModel</a> (SAM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/segformer#transformers.SegformerModel">SegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/speech_to_text#transformers.Speech2TextModel">Speech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/speecht5#transformers.SpeechT5Config">SpeechT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/speecht5#transformers.SpeechT5Model">SpeechT5Model</a> (SpeechT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/splinter#transformers.SplinterModel">SplinterModel</a> (Splinter model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/squeezebert#transformers.SqueezeBertModel">SqueezeBertModel</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/swiftformer#transformers.SwiftFormerConfig">SwiftFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/swiftformer#transformers.SwiftFormerModel">SwiftFormerModel</a> (SwiftFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/swin2sr#transformers.Swin2SRConfig">Swin2SRConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/swin2sr#transformers.Swin2SRModel">Swin2SRModel</a> (Swin2SR model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/swin#transformers.SwinModel">SwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/swinv2#transformers.Swinv2Model">Swinv2Model</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/switch_transformers#transformers.SwitchTransformersModel">SwitchTransformersModel</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.T5Model">T5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/table-transformer#transformers.TableTransformerConfig">TableTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/table-transformer#transformers.TableTransformerModel">TableTransformerModel</a> (Table Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TapasModel">TapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerConfig">TimeSeriesTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerModel">TimeSeriesTransformerModel</a> (Time Series Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/timesformer#transformers.TimesformerModel">TimesformerModel</a> (TimeSformer model)</li>
<li><code>TimmBackboneConfig</code> configuration class: <code>TimmBackbone</code> (TimmBackbone model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerConfig">TrajectoryTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerModel">TrajectoryTransformerModel</a> (Trajectory Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/transfo-xl#transformers.TransfoXLModel">TransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/tvlt#transformers.TvltConfig">TvltConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/tvlt#transformers.TvltModel">TvltModel</a> (TVLT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/umt5#transformers.UMT5Model">UMT5Model</a> (UMT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/unispeech#transformers.UniSpeechModel">UniSpeechModel</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel">UniSpeechSatModel</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/van#transformers.VanModel">VanModel</a> (VAN model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vit#transformers.ViTModel">ViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vit_hybrid#transformers.ViTHybridConfig">ViTHybridConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vit_hybrid#transformers.ViTHybridModel">ViTHybridModel</a> (ViT Hybrid model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vit_mae#transformers.ViTMAEModel">ViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vit_msn#transformers.ViTMSNModel">ViTMSNModel</a> (ViTMSN model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/videomae#transformers.VideoMAEModel">VideoMAEModel</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vilt#transformers.ViltModel">ViltModel</a> (ViLT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderModel">VisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/visual_bert#transformers.VisualBertModel">VisualBertModel</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vivit#transformers.VivitConfig">VivitConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vivit#transformers.VivitModel">VivitModel</a> (ViViT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Model">Wav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerModel">Wav2Vec2ConformerModel</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wavlm#transformers.WavLMModel">WavLMModel</a> (WavLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/whisper#transformers.WhisperModel">WhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xclip#transformers.XCLIPConfig">XCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xclip#transformers.XCLIPModel">XCLIPModel</a> (X-CLIP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xglm#transformers.XGLMModel">XGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMModel">XLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetModel">XLMProphetNetModel</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaModel">XLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLModel">XLMRobertaXLModel</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetModel">XLNetModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xmod#transformers.XmodModel">XmodModel</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/yolos#transformers.YolosModel">YolosModel</a> (YOLOS model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/yoso#transformers.YosoModel">YosoModel</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),n3=new Z({props:{anchor:"transformers.AutoModel.from_config.example",$$slots:{default:[gls]},$$scope:{ctx:x}}}),kV=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModel.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModel.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModel.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModel.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModel.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModel.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModel.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModel.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModel.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModel.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModel.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModel.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModel.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),BT=new Z({props:{anchor:"transformers.AutoModel.from_pretrained.example",$$slots:{default:[uls]},$$scope:{ctx:x}}}),SV=new te({}),xV=new G({props:{name:"class transformers.TFAutoModel",anchor:"transformers.TFAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_tf_auto.py#L527"}}),BV=new G({props:{name:"from_config",anchor:"transformers.TFAutoModel.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModel.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.TFAlbertModel">TFAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.TFBartModel">TFBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.TFBertModel">TFBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot#transformers.TFBlenderbotModel">TFBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallModel">TFBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blip#transformers.TFBlipModel">TFBlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ctrl#transformers.TFCTRLModel">TFCTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.TFCamembertModel">TFCamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.TFConvBertModel">TFConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/convnext#transformers.TFConvNextModel">TFConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/cvt#transformers.TFCvtModel">TFCvtModel</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/dpr#transformers.TFDPRQuestionEncoder">TFDPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.TFData2VecVisionModel">TFData2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.TFDebertaModel">TFDebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.TFDebertaV2Model">TFDebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deit#transformers.TFDeiTModel">TFDeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.TFDistilBertModel">TFDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/efficientformer#transformers.TFEfficientFormerModel">TFEfficientFormerModel</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.TFElectraModel">TFElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/esm#transformers.TFEsmModel">TFEsmModel</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.TFFlaubertModel">TFFlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.TFFunnelModel">TFFunnelModel</a> or <a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.TFFunnelBaseModel">TFFunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.TFGPT2Model">TFGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.TFGPTJModel">TFGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/groupvit#transformers.TFGroupViTModel">TFGroupViTModel</a> (GroupViT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/hubert#transformers.TFHubertModel">TFHubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/led#transformers.TFLEDModel">TFLEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.TFLayoutLMModel">TFLayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3Model">TFLayoutLMv3Model</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.TFLongformerModel">TFLongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/lxmert#transformers.TFLxmertModel">TFLxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.TFMBartModel">TFMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.TFMPNetModel">TFMPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mt5#transformers.TFMT5Model">TFMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/marian#transformers.TFMarianModel">TFMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.TFMobileBertModel">TFMobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilevit#transformers.TFMobileViTModel">TFMobileViTModel</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/opt#transformers.TFOPTModel">TFOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/openai-gpt#transformers.TFOpenAIGPTModel">TFOpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/pegasus#transformers.TFPegasusModel">TFPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/regnet#transformers.TFRegNetModel">TFRegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.TFRemBertModel">TFRemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/resnet#transformers.TFResNetModel">TFResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.TFRoFormerModel">TFRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.TFRobertaModel">TFRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormModel">TFRobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/sam#transformers.SamConfig">SamConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/sam#transformers.TFSamModel">TFSamModel</a> (SAM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/segformer#transformers.TFSegformerModel">TFSegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/speech_to_text#transformers.TFSpeech2TextModel">TFSpeech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/swin#transformers.TFSwinModel">TFSwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.TFT5Model">TFT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TFTapasModel">TFTapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/transfo-xl#transformers.TFTransfoXLModel">TFTransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vit#transformers.TFViTModel">TFViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vit_mae#transformers.TFViTMAEModel">TFViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vision-text-dual-encoder#transformers.TFVisionTextDualEncoderModel">TFVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.TFWav2Vec2Model">TFWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/whisper#transformers.TFWhisperModel">TFWhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xglm#transformers.TFXGLMModel">TFXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.TFXLMModel">TFXLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaModel">TFXLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.TFXLNetModel">TFXLNetModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),NT=new Z({props:{anchor:"transformers.TFAutoModel.from_config.example",$$slots:{default:[pls]},$$scope:{ctx:x}}}),IV=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModel.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModel.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModel.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModel.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModel.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModel.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModel.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModel.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModel.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModel.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModel.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModel.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),DF=new Z({props:{anchor:"transformers.TFAutoModel.from_pretrained.example",$$slots:{default:[_ls]},$$scope:{ctx:x}}}),NV=new te({}),$V=new G({props:{name:"class transformers.FlaxAutoModel",anchor:"transformers.FlaxAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_flax_auto.py#L274"}}),PV=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModel.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModel.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.FlaxAlbertModel">FlaxAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.FlaxBartModel">FlaxBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/beit#transformers.FlaxBeitModel">FlaxBeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.FlaxBertModel">FlaxBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.FlaxBigBirdModel">FlaxBigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot#transformers.FlaxBlenderbotModel">FlaxBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallModel">FlaxBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bloom#transformers.FlaxBloomModel">FlaxBloomModel</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/clip#transformers.FlaxCLIPModel">FlaxCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.FlaxDistilBertModel">FlaxDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.FlaxElectraModel">FlaxElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.FlaxGPT2Model">FlaxGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.FlaxGPTJModel">FlaxGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neo#transformers.FlaxGPTNeoModel">FlaxGPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/longt5#transformers.FlaxLongT5Model">FlaxLongT5Model</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.FlaxMBartModel">FlaxMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mt5#transformers.FlaxMT5Model">FlaxMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/marian#transformers.FlaxMarianModel">FlaxMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/opt#transformers.FlaxOPTModel">FlaxOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/pegasus#transformers.FlaxPegasusModel">FlaxPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/regnet#transformers.FlaxRegNetModel">FlaxRegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/resnet#transformers.FlaxResNetModel">FlaxResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.FlaxRoFormerModel">FlaxRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.FlaxRobertaModel">FlaxRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormModel">FlaxRobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.FlaxT5Model">FlaxT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vit#transformers.FlaxViTModel">FlaxViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vision-text-dual-encoder#transformers.FlaxVisionTextDualEncoderModel">FlaxVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2Model">FlaxWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/whisper#transformers.FlaxWhisperModel">FlaxWhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xglm#transformers.FlaxXGLMModel">FlaxXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaModel">FlaxXLMRobertaModel</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),OF=new Z({props:{anchor:"transformers.FlaxAutoModel.from_config.example",$$slots:{default:[vls]},$$scope:{ctx:x}}}),ZV=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModel.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModel.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModel.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModel.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModel.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModel.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModel.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModel.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModel.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModel.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModel.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModel.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),AE=new Z({props:{anchor:"transformers.FlaxAutoModel.from_pretrained.example",$$slots:{default:[bls]},$$scope:{ctx:x}}}),VV=new te({}),WV=new te({}),JV=new G({props:{name:"class transformers.AutoModelForPreTraining",anchor:"transformers.AutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1200"}}),XV=new G({props:{name:"from_config",anchor:"transformers.AutoModelForPreTraining.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForPreTraining.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertForPreTraining">AlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertForPreTraining">BertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdForPreTraining">BigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraForPreTraining">ElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ernie#transformers.ErnieForPreTraining">ErnieForPreTraining</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/fnet#transformers.FNetForPreTraining">FNetForPreTraining</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/flava#transformers.FlavaForPreTraining">FlavaForPreTraining</a> (FLAVA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelForPreTraining">FunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForCausalLM">GPTBigCodeForCausalLM</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/idefics#transformers.IdeficsConfig">IdeficsConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/idefics#transformers.IdeficsForVisionText2Text">IdeficsForVisionText2Text</a> (IDEFICS model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/lxmert#transformers.LxmertForPreTraining">LxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mega#transformers.MegaForMaskedLM">MegaForMaskedLM</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/megatron-bert#transformers.MegatronBertForPreTraining">MegatronBertForPreTraining</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertForPreTraining">MobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpt#transformers.MptForCausalLM">MptForCausalLM</a> (MPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mra#transformers.MraForMaskedLM">MraForMaskedLM</a> (MRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/nezha#transformers.NezhaForPreTraining">NezhaForPreTraining</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/nllb-moe#transformers.NllbMoeConfig">NllbMoeConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/nllb-moe#transformers.NllbMoeForConditionalGeneration">NllbMoeForConditionalGeneration</a> (NLLB-MOE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roc_bert#transformers.RoCBertForPreTraining">RoCBertForPreTraining</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMaskedLM">RobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/rwkv#transformers.RwkvConfig">RwkvConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/rwkv#transformers.RwkvForCausalLM">RwkvForCausalLM</a> (RWKV model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/splinter#transformers.SplinterForPreTraining">SplinterForPreTraining</a> (Splinter model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/switch_transformers#transformers.SwitchTransformersForConditionalGeneration">SwitchTransformersForConditionalGeneration</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/tvlt#transformers.TvltConfig">TvltConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/tvlt#transformers.TvltForPreTraining">TvltForPreTraining</a> (TVLT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/unispeech#transformers.UniSpeechForPreTraining">UniSpeechForPreTraining</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatForPreTraining">UniSpeechSatForPreTraining</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vit_mae#transformers.ViTMAEForPreTraining">ViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/videomae#transformers.VideoMAEForPreTraining">VideoMAEForPreTraining</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/visual_bert#transformers.VisualBertForPreTraining">VisualBertForPreTraining</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2ForPreTraining">Wav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForPreTraining">Wav2Vec2ConformerForPreTraining</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xmod#transformers.XmodForMaskedLM">XmodForMaskedLM</a> (X-MOD model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),SE=new Z({props:{anchor:"transformers.AutoModelForPreTraining.from_config.example",$$slots:{default:[Mls]},$$scope:{ctx:x}}}),DV=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),Vy=new Z({props:{anchor:"transformers.AutoModelForPreTraining.from_pretrained.example",$$slots:{default:[Tls]},$$scope:{ctx:x}}}),qV=new te({}),OV=new G({props:{name:"class transformers.TFAutoModelForPreTraining",anchor:"transformers.TFAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_tf_auto.py#L543"}}),YV=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForPreTraining.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForPreTraining.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.TFAlbertForPreTraining">TFAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.TFBertForPreTraining">TFBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.TFElectraForPreTraining">TFElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.TFFunnelForPreTraining">TFFunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/lxmert#transformers.TFLxmertForPreTraining">TFLxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.TFMobileBertForPreTraining">TFMobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMaskedLM">TFRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vit_mae#transformers.TFViTMAEForPreTraining">TFViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),Jy=new Z({props:{anchor:"transformers.TFAutoModelForPreTraining.from_config.example",$$slots:{default:[Fls]},$$scope:{ctx:x}}}),zV=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),u5=new Z({props:{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[Els]},$$scope:{ctx:x}}}),HV=new te({}),KV=new G({props:{name:"class transformers.FlaxAutoModelForPreTraining",anchor:"transformers.FlaxAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_flax_auto.py#L281"}}),oW=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForPreTraining.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.FlaxAlbertForPreTraining">FlaxAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.FlaxBertForPreTraining">FlaxBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.FlaxBigBirdForPreTraining">FlaxBigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.FlaxElectraForPreTraining">FlaxElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMaskedLM">FlaxRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2ForPreTraining">FlaxWav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),_5=new Z({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.example",$$slots:{default:[yls]},$$scope:{ctx:x}}}),rW=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),G5=new Z({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[Cls]},$$scope:{ctx:x}}}),tW=new te({}),aW=new te({}),nW=new G({props:{name:"class transformers.AutoModelForCausalLM",anchor:"transformers.AutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1215"}}),lW=new G({props:{name:"from_config",anchor:"transformers.AutoModelForCausalLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCausalLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartForCausalLM">BartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertLMHeadModel">BertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert-generation#transformers.BertGenerationDecoder">BertGenerationDecoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdForCausalLM">BigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForCausalLM">BigBirdPegasusForCausalLM</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/biogpt#transformers.BioGptForCausalLM">BioGptForCausalLM</a> (BioGpt model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot#transformers.BlenderbotForCausalLM">BlenderbotForCausalLM</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForCausalLM">BlenderbotSmallForCausalLM</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertForCausalLM">CamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/codegen#transformers.CodeGenConfig">CodeGenConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/codegen#transformers.CodeGenForCausalLM">CodeGenForCausalLM</a> (CodeGen model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/cpmant#transformers.CpmAntConfig">CpmAntConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/cpmant#transformers.CpmAntForCausalLM">CpmAntForCausalLM</a> (CPM-Ant model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecTextForCausalLM">Data2VecTextForCausalLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraForCausalLM">ElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ernie#transformers.ErnieForCausalLM">ErnieForCausalLM</a> (ERNIE model)</li>
<li><code>FalconConfig</code> configuration class: <code>FalconForCausalLM</code> (Falcon model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForCausalLM">GPTBigCodeForCausalLM</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.GPTJForCausalLM">GPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neo#transformers.GPTNeoForCausalLM">GPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neox#transformers.GPTNeoXForCausalLM">GPTNeoXForCausalLM</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseForCausalLM">GPTNeoXJapaneseForCausalLM</a> (GPT NeoX Japanese model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/git#transformers.GitConfig">GitConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/git#transformers.GitForCausalLM">GitForCausalLM</a> (GIT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/llama2#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/llama2#transformers.LlamaForCausalLM">LlamaForCausalLM</a> (LLaMA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartForCausalLM">MBartForCausalLM</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/marian#transformers.MarianForCausalLM">MarianForCausalLM</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mega#transformers.MegaForCausalLM">MegaForCausalLM</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/megatron-bert#transformers.MegatronBertForCausalLM">MegatronBertForCausalLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpt#transformers.MptForCausalLM">MptForCausalLM</a> (MPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/musicgen#transformers.MusicgenConfig">MusicgenConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/musicgen#transformers.MusicgenForCausalLM">MusicgenForCausalLM</a> (MusicGen model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mvp#transformers.MvpForCausalLM">MvpForCausalLM</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/opt#transformers.OPTForCausalLM">OPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/open-llama#transformers.OpenLlamaForCausalLM">OpenLlamaForCausalLM</a> (OpenLlama model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/plbart#transformers.PLBartForCausalLM">PLBartForCausalLM</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/pegasus#transformers.PegasusForCausalLM">PegasusForCausalLM</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/prophetnet#transformers.ProphetNetForCausalLM">ProphetNetForCausalLM</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/qdqbert#transformers.QDQBertLMHeadModel">QDQBertLMHeadModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/reformer#transformers.ReformerModelWithLMHead">ReformerModelWithLMHead</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertForCausalLM">RemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roc_bert#transformers.RoCBertForCausalLM">RoCBertForCausalLM</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerForCausalLM">RoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaForCausalLM">RobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForCausalLM">RobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/rwkv#transformers.RwkvConfig">RwkvConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/rwkv#transformers.RwkvForCausalLM">RwkvForCausalLM</a> (RWKV model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/speech_to_text_2#transformers.Speech2Text2Config">Speech2Text2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/speech_to_text_2#transformers.Speech2Text2ForCausalLM">Speech2Text2ForCausalLM</a> (Speech2Text2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/trocr#transformers.TrOCRConfig">TrOCRConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/trocr#transformers.TrOCRForCausalLM">TrOCRForCausalLM</a> (TrOCR model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xglm#transformers.XGLMForCausalLM">XGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForCausalLM">XLMProphetNetForCausalLM</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForCausalLM">XLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForCausalLM">XLMRobertaXLForCausalLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xmod#transformers.XmodForCausalLM">XmodForCausalLM</a> (X-MOD model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),N5=new Z({props:{anchor:"transformers.AutoModelForCausalLM.from_config.example",$$slots:{default:[wls]},$$scope:{ctx:x}}}),iW=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),V9=new Z({props:{anchor:"transformers.AutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Lls]},$$scope:{ctx:x}}}),dW=new te({}),cW=new G({props:{name:"class transformers.TFAutoModelForCausalLM",anchor:"transformers.TFAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_tf_auto.py#L558"}}),fW=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForCausalLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForCausalLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.TFBertLMHeadModel">TFBertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.TFCamembertForCausalLM">TFCamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.TFGPTJForCausalLM">TFGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/opt#transformers.TFOPTForCausalLM">TFOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.TFRemBertForCausalLM">TFRemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.TFRoFormerForCausalLM">TFRoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.TFRobertaForCausalLM">TFRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForCausalLM">TFRobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xglm#transformers.TFXGLMForCausalLM">TFXGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForCausalLM">TFXLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),J9=new Z({props:{anchor:"transformers.TFAutoModelForCausalLM.from_config.example",$$slots:{default:[Als]},$$scope:{ctx:x}}}),hW=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),lC=new Z({props:{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Rls]},$$scope:{ctx:x}}}),gW=new te({}),uW=new G({props:{name:"class transformers.FlaxAutoModelForCausalLM",anchor:"transformers.FlaxAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_flax_auto.py#L288"}}),_W=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForCausalLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.FlaxBartForCausalLM">FlaxBartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.FlaxBertForCausalLM">FlaxBertForCausalLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.FlaxBigBirdForCausalLM">FlaxBigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bloom#transformers.FlaxBloomForCausalLM">FlaxBloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.FlaxElectraForCausalLM">FlaxElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.FlaxGPT2LMHeadModel">FlaxGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.FlaxGPTJForCausalLM">FlaxGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neo#transformers.FlaxGPTNeoForCausalLM">FlaxGPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/opt#transformers.FlaxOPTForCausalLM">FlaxOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.FlaxRobertaForCausalLM">FlaxRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForCausalLM">FlaxRobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xglm#transformers.FlaxXGLMForCausalLM">FlaxXGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForCausalLM">FlaxXLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),dC=new Z({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.example",$$slots:{default:[kls]},$$scope:{ctx:x}}}),vW=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),yC=new Z({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Sls]},$$scope:{ctx:x}}}),bW=new te({}),MW=new G({props:{name:"class transformers.AutoModelForMaskedLM",anchor:"transformers.AutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1222"}}),FW=new G({props:{name:"from_config",anchor:"transformers.AutoModelForMaskedLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertForMaskedLM">AlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertForMaskedLM">BertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdForMaskedLM">BigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.ConvBertForMaskedLM">ConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraForMaskedLM">ElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ernie#transformers.ErnieForMaskedLM">ErnieForMaskedLM</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/esm#transformers.EsmForMaskedLM">EsmForMaskedLM</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/fnet#transformers.FNetForMaskedLM">FNetForMaskedLM</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelForMaskedLM">FunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mega#transformers.MegaForMaskedLM">MegaForMaskedLM</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/megatron-bert#transformers.MegatronBertForMaskedLM">MegatronBertForMaskedLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertForMaskedLM">MobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mra#transformers.MraForMaskedLM">MraForMaskedLM</a> (MRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/nezha#transformers.NezhaForMaskedLM">NezhaForMaskedLM</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/nystromformer#transformers.NystromformerForMaskedLM">NystromformerForMaskedLM</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/perceiver#transformers.PerceiverForMaskedLM">PerceiverForMaskedLM</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/qdqbert#transformers.QDQBertForMaskedLM">QDQBertForMaskedLM</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/reformer#transformers.ReformerForMaskedLM">ReformerForMaskedLM</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertForMaskedLM">RemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roc_bert#transformers.RoCBertForMaskedLM">RoCBertForMaskedLM</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerForMaskedLM">RoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMaskedLM">RobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <code>Wav2Vec2ForMaskedLM</code> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xmod#transformers.XmodForMaskedLM">XmodForMaskedLM</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/yoso#transformers.YosoForMaskedLM">YosoForMaskedLM</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),wC=new Z({props:{anchor:"transformers.AutoModelForMaskedLM.from_config.example",$$slots:{default:[xls]},$$scope:{ctx:x}}}),EW=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),bw=new Z({props:{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[Gls]},$$scope:{ctx:x}}}),yW=new te({}),CW=new G({props:{name:"class transformers.TFAutoModelForMaskedLM",anchor:"transformers.TFAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_tf_auto.py#L608"}}),LW=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForMaskedLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMaskedLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.TFAlbertForMaskedLM">TFAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.TFBertForMaskedLM">TFBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.TFConvBertForMaskedLM">TFConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.TFDebertaForMaskedLM">TFDebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.TFDebertaV2ForMaskedLM">TFDebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.TFElectraForMaskedLM">TFElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/esm#transformers.TFEsmForMaskedLM">TFEsmForMaskedLM</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.TFFunnelForMaskedLM">TFFunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.TFLongformerForMaskedLM">TFLongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.TFMobileBertForMaskedLM">TFMobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.TFRemBertForMaskedLM">TFRemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.TFRoFormerForMaskedLM">TFRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMaskedLM">TFRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),Tw=new Z({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_config.example",$$slots:{default:[Bls]},$$scope:{ctx:x}}}),AW=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),Uw=new Z({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[Ils]},$$scope:{ctx:x}}}),RW=new te({}),kW=new G({props:{name:"class transformers.FlaxAutoModelForMaskedLM",anchor:"transformers.FlaxAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_flax_auto.py#L295"}}),xW=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForMaskedLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.FlaxAlbertForMaskedLM">FlaxAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.FlaxBertForMaskedLM">FlaxBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.FlaxBigBirdForMaskedLM">FlaxBigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.FlaxDistilBertForMaskedLM">FlaxDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.FlaxElectraForMaskedLM">FlaxElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMaskedLM">FlaxRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),Dw=new Z({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.example",$$slots:{default:[Nls]},$$scope:{ctx:x}}}),GW=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),a6=new Z({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[$ls]},$$scope:{ctx:x}}}),BW=new te({}),NW=new G({props:{name:"class transformers.AutoModelForMaskGeneration",anchor:"transformers.AutoModelForMaskGeneration",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1185"}}),$W=new te({}),PW=new G({props:{name:"class transformers.TFAutoModelForMaskGeneration",anchor:"transformers.TFAutoModelForMaskGeneration",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_tf_auto.py#L519"}}),ZW=new te({}),VW=new G({props:{name:"class transformers.AutoModelForSeq2SeqLM",anchor:"transformers.AutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1229"}}),JW=new G({props:{name:"from_config",anchor:"transformers.AutoModelForSeq2SeqLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForConditionalGeneration">BigBirdPegasusForConditionalGeneration</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot#transformers.BlenderbotForConditionalGeneration">BlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForConditionalGeneration">BlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/encoder-decoder#transformers.EncoderDecoderModel">EncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/led#transformers.LEDForConditionalGeneration">LEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/longt5#transformers.LongT5ForConditionalGeneration">LongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/m2m_100#transformers.M2M100ForConditionalGeneration">M2M100ForConditionalGeneration</a> (M2M100 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mt5#transformers.MT5ForConditionalGeneration">MT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/marian#transformers.MarianMTModel">MarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/nllb-moe#transformers.NllbMoeConfig">NllbMoeConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/nllb-moe#transformers.NllbMoeForConditionalGeneration">NllbMoeForConditionalGeneration</a> (NLLB-MOE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/plbart#transformers.PLBartForConditionalGeneration">PLBartForConditionalGeneration</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/pegasus#transformers.PegasusForConditionalGeneration">PegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/pegasus_x#transformers.PegasusXConfig">PegasusXConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/pegasus_x#transformers.PegasusXForConditionalGeneration">PegasusXForConditionalGeneration</a> (PEGASUS-X model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/prophetnet#transformers.ProphetNetForConditionalGeneration">ProphetNetForConditionalGeneration</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/switch_transformers#transformers.SwitchTransformersForConditionalGeneration">SwitchTransformersForConditionalGeneration</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/umt5#transformers.UMT5ForConditionalGeneration">UMT5ForConditionalGeneration</a> (UMT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForConditionalGeneration">XLMProphetNetForConditionalGeneration</a> (XLM-ProphetNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),i6=new Z({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[jls]},$$scope:{ctx:x}}}),UW=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),G6=new Z({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[Pls]},$$scope:{ctx:x}}}),XW=new te({}),DW=new G({props:{name:"class transformers.TFAutoModelForSeq2SeqLM",anchor:"transformers.TFAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_tf_auto.py#L615"}}),OW=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot#transformers.TFBlenderbotForConditionalGeneration">TFBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallForConditionalGeneration">TFBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/encoder-decoder#transformers.TFEncoderDecoderModel">TFEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/led#transformers.TFLEDForConditionalGeneration">TFLEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.TFMBartForConditionalGeneration">TFMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mt5#transformers.TFMT5ForConditionalGeneration">TFMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/marian#transformers.TFMarianMTModel">TFMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/pegasus#transformers.TFPegasusForConditionalGeneration">TFPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),I6=new Z({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[Zls]},$$scope:{ctx:x}}}),QW=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),D6=new Z({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[Vls]},$$scope:{ctx:x}}}),YW=new te({}),zW=new G({props:{name:"class transformers.FlaxAutoModelForSeq2SeqLM",anchor:"transformers.FlaxAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_flax_auto.py#L302"}}),KW=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot#transformers.FlaxBlenderbotForConditionalGeneration">FlaxBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallForConditionalGeneration">FlaxBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/encoder-decoder#transformers.FlaxEncoderDecoderModel">FlaxEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/marian#transformers.FlaxMarianMTModel">FlaxMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/pegasus#transformers.FlaxPegasusForConditionalGeneration">FlaxPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),O6=new Z({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[Wls]},$$scope:{ctx:x}}}),eJ=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),n8=new Z({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[Jls]},$$scope:{ctx:x}}}),oJ=new te({}),rJ=new G({props:{name:"class transformers.AutoModelForSequenceClassification",anchor:"transformers.AutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1238"}}),aJ=new G({props:{name:"from_config",anchor:"transformers.AutoModelForSequenceClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSequenceClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertForSequenceClassification">AlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartForSequenceClassification">BartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertForSequenceClassification">BertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdForSequenceClassification">BigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForSequenceClassification">BigBirdPegasusForSequenceClassification</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/biogpt#transformers.BioGptForSequenceClassification">BioGptForSequenceClassification</a> (BioGpt model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bloom#transformers.BloomForSequenceClassification">BloomForSequenceClassification</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ctrl#transformers.CTRLForSequenceClassification">CTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertForSequenceClassification">CamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/canine#transformers.CanineForSequenceClassification">CanineForSequenceClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.ConvBertForSequenceClassification">ConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecTextForSequenceClassification">Data2VecTextForSequenceClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.DebertaForSequenceClassification">DebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.DebertaV2ForSequenceClassification">DebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraForSequenceClassification">ElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ernie#transformers.ErnieForSequenceClassification">ErnieForSequenceClassification</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ernie_m#transformers.ErnieMForSequenceClassification">ErnieMForSequenceClassification</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/esm#transformers.EsmForSequenceClassification">EsmForSequenceClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/fnet#transformers.FNetForSequenceClassification">FNetForSequenceClassification</a> (FNet model)</li>
<li><code>FalconConfig</code> configuration class: <code>FalconForSequenceClassification</code> (Falcon model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertForSequenceClassification">FlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelForSequenceClassification">FunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.GPT2ForSequenceClassification">GPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForSequenceClassification">GPTBigCodeForSequenceClassification</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.GPTJForSequenceClassification">GPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neo#transformers.GPTNeoForSequenceClassification">GPTNeoForSequenceClassification</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neox#transformers.GPTNeoXForSequenceClassification">GPTNeoXForSequenceClassification</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ibert#transformers.IBertForSequenceClassification">IBertForSequenceClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/led#transformers.LEDForSequenceClassification">LEDForSequenceClassification</a> (LED model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.LayoutLMForSequenceClassification">LayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForSequenceClassification">LayoutLMv2ForSequenceClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForSequenceClassification">LayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/lilt#transformers.LiltForSequenceClassification">LiltForSequenceClassification</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/llama2#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/llama2#transformers.LlamaForSequenceClassification">LlamaForSequenceClassification</a> (LLaMA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerForSequenceClassification">LongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/luke#transformers.LukeForSequenceClassification">LukeForSequenceClassification</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartForSequenceClassification">MBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetForSequenceClassification">MPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mt5#transformers.MT5ForSequenceClassification">MT5ForSequenceClassification</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/markuplm#transformers.MarkupLMForSequenceClassification">MarkupLMForSequenceClassification</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mega#transformers.MegaForSequenceClassification">MegaForSequenceClassification</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/megatron-bert#transformers.MegatronBertForSequenceClassification">MegatronBertForSequenceClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertForSequenceClassification">MobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpt#transformers.MptForSequenceClassification">MptForSequenceClassification</a> (MPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mra#transformers.MraForSequenceClassification">MraForSequenceClassification</a> (MRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mvp#transformers.MvpForSequenceClassification">MvpForSequenceClassification</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/nezha#transformers.NezhaForSequenceClassification">NezhaForSequenceClassification</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/nystromformer#transformers.NystromformerForSequenceClassification">NystromformerForSequenceClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/opt#transformers.OPTForSequenceClassification">OPTForSequenceClassification</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/openai-gpt#transformers.OpenAIGPTForSequenceClassification">OpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/open-llama#transformers.OpenLlamaForSequenceClassification">OpenLlamaForSequenceClassification</a> (OpenLlama model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/plbart#transformers.PLBartForSequenceClassification">PLBartForSequenceClassification</a> (PLBart model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/perceiver#transformers.PerceiverForSequenceClassification">PerceiverForSequenceClassification</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/qdqbert#transformers.QDQBertForSequenceClassification">QDQBertForSequenceClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/reformer#transformers.ReformerForSequenceClassification">ReformerForSequenceClassification</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertForSequenceClassification">RemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roc_bert#transformers.RoCBertForSequenceClassification">RoCBertForSequenceClassification</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerForSequenceClassification">RoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaForSequenceClassification">RobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForSequenceClassification">RobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/squeezebert#transformers.SqueezeBertForSequenceClassification">SqueezeBertForSequenceClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.T5ForSequenceClassification">T5ForSequenceClassification</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TapasForSequenceClassification">TapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/transfo-xl#transformers.TransfoXLForSequenceClassification">TransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/umt5#transformers.UMT5ForSequenceClassification">UMT5ForSequenceClassification</a> (UMT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMForSequenceClassification">XLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForSequenceClassification">XLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForSequenceClassification">XLMRobertaXLForSequenceClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetForSequenceClassification">XLNetForSequenceClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xmod#transformers.XmodForSequenceClassification">XmodForSequenceClassification</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/yoso#transformers.YosoForSequenceClassification">YosoForSequenceClassification</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),l8=new Z({props:{anchor:"transformers.AutoModelForSequenceClassification.from_config.example",$$slots:{default:[Uls]},$$scope:{ctx:x}}}),nJ=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),A7=new Z({props:{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[Xls]},$$scope:{ctx:x}}}),sJ=new te({}),lJ=new G({props:{name:"class transformers.TFAutoModelForSequenceClassification",anchor:"transformers.TFAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_tf_auto.py#L624"}}),dJ=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForSequenceClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.TFAlbertForSequenceClassification">TFAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.TFBartForSequenceClassification">TFBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.TFBertForSequenceClassification">TFBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ctrl#transformers.TFCTRLForSequenceClassification">TFCTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.TFCamembertForSequenceClassification">TFCamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.TFConvBertForSequenceClassification">TFConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.TFDebertaForSequenceClassification">TFDebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.TFDebertaV2ForSequenceClassification">TFDebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.TFDistilBertForSequenceClassification">TFDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.TFElectraForSequenceClassification">TFElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/esm#transformers.TFEsmForSequenceClassification">TFEsmForSequenceClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.TFFlaubertForSequenceClassification">TFFlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.TFFunnelForSequenceClassification">TFFunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.TFGPT2ForSequenceClassification">TFGPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.TFGPTJForSequenceClassification">TFGPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.TFLayoutLMForSequenceClassification">TFLayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForSequenceClassification">TFLayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.TFLongformerForSequenceClassification">TFLongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.TFMPNetForSequenceClassification">TFMPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.TFMobileBertForSequenceClassification">TFMobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/openai-gpt#transformers.TFOpenAIGPTForSequenceClassification">TFOpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.TFRemBertForSequenceClassification">TFRemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.TFRoFormerForSequenceClassification">TFRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.TFRobertaForSequenceClassification">TFRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForSequenceClassification">TFRobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TFTapasForSequenceClassification">TFTapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/transfo-xl#transformers.TFTransfoXLForSequenceClassification">TFTransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.TFXLMForSequenceClassification">TFXLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForSequenceClassification">TFXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.TFXLNetForSequenceClassification">TFXLNetForSequenceClassification</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),k7=new Z({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.example",$$slots:{default:[Dls]},$$scope:{ctx:x}}}),cJ=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSequenceClassification.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.TFAutoModelForSequenceClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),iL=new Z({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[qls]},$$scope:{ctx:x}}}),mJ=new te({}),fJ=new G({props:{name:"class transformers.FlaxAutoModelForSequenceClassification",anchor:"transformers.FlaxAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_flax_auto.py#L311"}}),gJ=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.FlaxAlbertForSequenceClassification">FlaxAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.FlaxBartForSequenceClassification">FlaxBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.FlaxBertForSequenceClassification">FlaxBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.FlaxBigBirdForSequenceClassification">FlaxBigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.FlaxDistilBertForSequenceClassification">FlaxDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.FlaxElectraForSequenceClassification">FlaxElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.FlaxMBartForSequenceClassification">FlaxMBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.FlaxRoFormerForSequenceClassification">FlaxRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.FlaxRobertaForSequenceClassification">FlaxRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForSequenceClassification">FlaxRobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForSequenceClassification">FlaxXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),cL=new Z({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.example",$$slots:{default:[Ols]},$$scope:{ctx:x}}}),uJ=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.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.FlaxAutoModelForSequenceClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),FL=new Z({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[Qls]},$$scope:{ctx:x}}}),pJ=new te({}),_J=new G({props:{name:"class transformers.AutoModelForMultipleChoice",anchor:"transformers.AutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1294"}}),bJ=new G({props:{name:"from_config",anchor:"transformers.AutoModelForMultipleChoice.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMultipleChoice.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertForMultipleChoice">AlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertForMultipleChoice">BertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdForMultipleChoice">BigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertForMultipleChoice">CamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/canine#transformers.CanineForMultipleChoice">CanineForMultipleChoice</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.ConvBertForMultipleChoice">ConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecTextForMultipleChoice">Data2VecTextForMultipleChoice</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.DebertaV2ForMultipleChoice">DebertaV2ForMultipleChoice</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertForMultipleChoice">DistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraForMultipleChoice">ElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ernie#transformers.ErnieForMultipleChoice">ErnieForMultipleChoice</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ernie_m#transformers.ErnieMForMultipleChoice">ErnieMForMultipleChoice</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/fnet#transformers.FNetForMultipleChoice">FNetForMultipleChoice</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertForMultipleChoice">FlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelForMultipleChoice">FunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ibert#transformers.IBertForMultipleChoice">IBertForMultipleChoice</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerForMultipleChoice">LongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/luke#transformers.LukeForMultipleChoice">LukeForMultipleChoice</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetForMultipleChoice">MPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mega#transformers.MegaForMultipleChoice">MegaForMultipleChoice</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/megatron-bert#transformers.MegatronBertForMultipleChoice">MegatronBertForMultipleChoice</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertForMultipleChoice">MobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mra#transformers.MraForMultipleChoice">MraForMultipleChoice</a> (MRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/nezha#transformers.NezhaForMultipleChoice">NezhaForMultipleChoice</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/nystromformer#transformers.NystromformerForMultipleChoice">NystromformerForMultipleChoice</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/qdqbert#transformers.QDQBertForMultipleChoice">QDQBertForMultipleChoice</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertForMultipleChoice">RemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roc_bert#transformers.RoCBertForMultipleChoice">RoCBertForMultipleChoice</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerForMultipleChoice">RoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaForMultipleChoice">RobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMultipleChoice">RobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/squeezebert#transformers.SqueezeBertForMultipleChoice">SqueezeBertForMultipleChoice</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMForMultipleChoice">XLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForMultipleChoice">XLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMultipleChoice">XLMRobertaXLForMultipleChoice</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetForMultipleChoice">XLNetForMultipleChoice</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xmod#transformers.XmodForMultipleChoice">XmodForMultipleChoice</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/yoso#transformers.YosoForMultipleChoice">YosoForMultipleChoice</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),yL=new Z({props:{anchor:"transformers.AutoModelForMultipleChoice.from_config.example",$$slots:{default:[Yls]},$$scope:{ctx:x}}}),MJ=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),cA=new Z({props:{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[zls]},$$scope:{ctx:x}}}),TJ=new te({}),FJ=new G({props:{name:"class transformers.TFAutoModelForMultipleChoice",anchor:"transformers.TFAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_tf_auto.py#L671"}}),yJ=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForMultipleChoice.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.TFAlbertForMultipleChoice">TFAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.TFBertForMultipleChoice">TFBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.TFCamembertForMultipleChoice">TFCamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.TFConvBertForMultipleChoice">TFConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.TFDistilBertForMultipleChoice">TFDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.TFElectraForMultipleChoice">TFElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.TFFlaubertForMultipleChoice">TFFlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.TFFunnelForMultipleChoice">TFFunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.TFLongformerForMultipleChoice">TFLongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.TFMPNetForMultipleChoice">TFMPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.TFMobileBertForMultipleChoice">TFMobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.TFRemBertForMultipleChoice">TFRemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.TFRoFormerForMultipleChoice">TFRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.TFRobertaForMultipleChoice">TFRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMultipleChoice">TFRobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.TFXLMForMultipleChoice">TFXLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMultipleChoice">TFXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.TFXLNetForMultipleChoice">TFXLNetForMultipleChoice</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),fA=new Z({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.example",$$slots:{default:[Hls]},$$scope:{ctx:x}}}),CJ=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),SA=new Z({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[Kls]},$$scope:{ctx:x}}}),wJ=new te({}),LJ=new G({props:{name:"class transformers.FlaxAutoModelForMultipleChoice",anchor:"transformers.FlaxAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_flax_auto.py#L336"}}),RJ=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.FlaxAlbertForMultipleChoice">FlaxAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.FlaxBertForMultipleChoice">FlaxBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.FlaxBigBirdForMultipleChoice">FlaxBigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.FlaxDistilBertForMultipleChoice">FlaxDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.FlaxElectraForMultipleChoice">FlaxElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.FlaxRoFormerForMultipleChoice">FlaxRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.FlaxRobertaForMultipleChoice">FlaxRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMultipleChoice">FlaxRobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMultipleChoice">FlaxXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),GA=new Z({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.example",$$slots:{default:[eis]},$$scope:{ctx:x}}}),kJ=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),JA=new Z({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[ois]},$$scope:{ctx:x}}}),SJ=new te({}),xJ=new G({props:{name:"class transformers.AutoModelForNextSentencePrediction",anchor:"transformers.AutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1301"}}),BJ=new G({props:{name:"from_config",anchor:"transformers.AutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertForNextSentencePrediction">BertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ernie#transformers.ErnieForNextSentencePrediction">ErnieForNextSentencePrediction</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/fnet#transformers.FNetForNextSentencePrediction">FNetForNextSentencePrediction</a> (FNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/megatron-bert#transformers.MegatronBertForNextSentencePrediction">MegatronBertForNextSentencePrediction</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertForNextSentencePrediction">MobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/nezha#transformers.NezhaForNextSentencePrediction">NezhaForNextSentencePrediction</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/qdqbert#transformers.QDQBertForNextSentencePrediction">QDQBertForNextSentencePrediction</a> (QDQBert model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),XA=new Z({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[ris]},$$scope:{ctx:x}}}),IJ=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),eR=new Z({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[tis]},$$scope:{ctx:x}}}),NJ=new te({}),$J=new G({props:{name:"class transformers.TFAutoModelForNextSentencePrediction",anchor:"transformers.TFAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_tf_auto.py#L678"}}),PJ=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.TFBertForNextSentencePrediction">TFBertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.TFMobileBertForNextSentencePrediction">TFMobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),rR=new Z({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[ais]},$$scope:{ctx:x}}}),ZJ=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),nR=new Z({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[nis]},$$scope:{ctx:x}}}),WJ=new te({}),JJ=new G({props:{name:"class transformers.FlaxAutoModelForNextSentencePrediction",anchor:"transformers.FlaxAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_flax_auto.py#L343"}}),XJ=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.FlaxBertForNextSentencePrediction">FlaxBertForNextSentencePrediction</a> (BERT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),lR=new Z({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[sis]},$$scope:{ctx:x}}}),DJ=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),dR=new Z({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[lis]},$$scope:{ctx:x}}}),qJ=new te({}),OJ=new G({props:{name:"class transformers.AutoModelForTokenClassification",anchor:"transformers.AutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1287"}}),YJ=new G({props:{name:"from_config",anchor:"transformers.AutoModelForTokenClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTokenClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertForTokenClassification">AlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertForTokenClassification">BertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdForTokenClassification">BigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/biogpt#transformers.BioGptForTokenClassification">BioGptForTokenClassification</a> (BioGpt model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bloom#transformers.BloomForTokenClassification">BloomForTokenClassification</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertForTokenClassification">CamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/canine#transformers.CanineForTokenClassification">CanineForTokenClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.ConvBertForTokenClassification">ConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecTextForTokenClassification">Data2VecTextForTokenClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.DebertaForTokenClassification">DebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.DebertaV2ForTokenClassification">DebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertForTokenClassification">DistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraForTokenClassification">ElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ernie#transformers.ErnieForTokenClassification">ErnieForTokenClassification</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ernie_m#transformers.ErnieMForTokenClassification">ErnieMForTokenClassification</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/esm#transformers.EsmForTokenClassification">EsmForTokenClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/fnet#transformers.FNetForTokenClassification">FNetForTokenClassification</a> (FNet model)</li>
<li><code>FalconConfig</code> configuration class: <code>FalconForTokenClassification</code> (Falcon model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertForTokenClassification">FlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelForTokenClassification">FunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.GPT2ForTokenClassification">GPT2ForTokenClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForTokenClassification">GPTBigCodeForTokenClassification</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neo#transformers.GPTNeoForTokenClassification">GPTNeoForTokenClassification</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neox#transformers.GPTNeoXForTokenClassification">GPTNeoXForTokenClassification</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ibert#transformers.IBertForTokenClassification">IBertForTokenClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.LayoutLMForTokenClassification">LayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForTokenClassification">LayoutLMv2ForTokenClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForTokenClassification">LayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/lilt#transformers.LiltForTokenClassification">LiltForTokenClassification</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerForTokenClassification">LongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/luke#transformers.LukeForTokenClassification">LukeForTokenClassification</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetForTokenClassification">MPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/markuplm#transformers.MarkupLMForTokenClassification">MarkupLMForTokenClassification</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mega#transformers.MegaForTokenClassification">MegaForTokenClassification</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/megatron-bert#transformers.MegatronBertForTokenClassification">MegatronBertForTokenClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertForTokenClassification">MobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpt#transformers.MptForTokenClassification">MptForTokenClassification</a> (MPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mra#transformers.MraForTokenClassification">MraForTokenClassification</a> (MRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/nezha#transformers.NezhaForTokenClassification">NezhaForTokenClassification</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/nystromformer#transformers.NystromformerForTokenClassification">NystromformerForTokenClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/qdqbert#transformers.QDQBertForTokenClassification">QDQBertForTokenClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertForTokenClassification">RemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roc_bert#transformers.RoCBertForTokenClassification">RoCBertForTokenClassification</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerForTokenClassification">RoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaForTokenClassification">RobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForTokenClassification">RobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/squeezebert#transformers.SqueezeBertForTokenClassification">SqueezeBertForTokenClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMForTokenClassification">XLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForTokenClassification">XLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForTokenClassification">XLMRobertaXLForTokenClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetForTokenClassification">XLNetForTokenClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xmod#transformers.XmodForTokenClassification">XmodForTokenClassification</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/yoso#transformers.YosoForTokenClassification">YosoForTokenClassification</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),mR=new Z({props:{anchor:"transformers.AutoModelForTokenClassification.from_config.example",$$slots:{default:[iis]},$$scope:{ctx:x}}}),zJ=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),hk=new Z({props:{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[dis]},$$scope:{ctx:x}}}),HJ=new te({}),KJ=new G({props:{name:"class transformers.TFAutoModelForTokenClassification",anchor:"transformers.TFAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_tf_auto.py#L662"}}),oU=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForTokenClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTokenClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.TFAlbertForTokenClassification">TFAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.TFBertForTokenClassification">TFBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.TFCamembertForTokenClassification">TFCamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.TFConvBertForTokenClassification">TFConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.TFDebertaForTokenClassification">TFDebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.TFDebertaV2ForTokenClassification">TFDebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.TFDistilBertForTokenClassification">TFDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.TFElectraForTokenClassification">TFElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/esm#transformers.TFEsmForTokenClassification">TFEsmForTokenClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.TFFlaubertForTokenClassification">TFFlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.TFFunnelForTokenClassification">TFFunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.TFLayoutLMForTokenClassification">TFLayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForTokenClassification">TFLayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.TFLongformerForTokenClassification">TFLongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.TFMPNetForTokenClassification">TFMPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.TFMobileBertForTokenClassification">TFMobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.TFRemBertForTokenClassification">TFRemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.TFRoFormerForTokenClassification">TFRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.TFRobertaForTokenClassification">TFRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForTokenClassification">TFRobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.TFXLMForTokenClassification">TFXLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForTokenClassification">TFXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.TFXLNetForTokenClassification">TFXLNetForTokenClassification</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),uk=new Z({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_config.example",$$slots:{default:[cis]},$$scope:{ctx:x}}}),rU=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),Pk=new Z({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[mis]},$$scope:{ctx:x}}}),tU=new te({}),aU=new G({props:{name:"class transformers.FlaxAutoModelForTokenClassification",anchor:"transformers.FlaxAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_flax_auto.py#L327"}}),sU=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForTokenClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.FlaxAlbertForTokenClassification">FlaxAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.FlaxBertForTokenClassification">FlaxBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.FlaxBigBirdForTokenClassification">FlaxBigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.FlaxDistilBertForTokenClassification">FlaxDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.FlaxElectraForTokenClassification">FlaxElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.FlaxRoFormerForTokenClassification">FlaxRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.FlaxRobertaForTokenClassification">FlaxRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForTokenClassification">FlaxRobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForTokenClassification">FlaxXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),Vk=new Z({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.example",$$slots:{default:[fis]},$$scope:{ctx:x}}}),lU=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),zk=new Z({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[his]},$$scope:{ctx:x}}}),iU=new te({}),dU=new G({props:{name:"class transformers.AutoModelForQuestionAnswering",anchor:"transformers.AutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1247"}}),mU=new G({props:{name:"from_config",anchor:"transformers.AutoModelForQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertForQuestionAnswering">AlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartForQuestionAnswering">BartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertForQuestionAnswering">BertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdForQuestionAnswering">BigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForQuestionAnswering">BigBirdPegasusForQuestionAnswering</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bloom#transformers.BloomForQuestionAnswering">BloomForQuestionAnswering</a> (BLOOM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertForQuestionAnswering">CamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/canine#transformers.CanineForQuestionAnswering">CanineForQuestionAnswering</a> (CANINE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.ConvBertForQuestionAnswering">ConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecTextForQuestionAnswering">Data2VecTextForQuestionAnswering</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.DebertaForQuestionAnswering">DebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.DebertaV2ForQuestionAnswering">DebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraForQuestionAnswering">ElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ernie#transformers.ErnieForQuestionAnswering">ErnieForQuestionAnswering</a> (ERNIE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ernie_m#transformers.ErnieMForQuestionAnswering">ErnieMForQuestionAnswering</a> (ErnieM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/fnet#transformers.FNetForQuestionAnswering">FNetForQuestionAnswering</a> (FNet model)</li>
<li><code>FalconConfig</code> configuration class: <code>FalconForQuestionAnswering</code> (Falcon model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertForQuestionAnsweringSimple">FlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelForQuestionAnswering">FunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt2#transformers.GPT2ForQuestionAnswering">GPT2ForQuestionAnswering</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.GPTJForQuestionAnswering">GPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neo#transformers.GPTNeoForQuestionAnswering">GPTNeoForQuestionAnswering</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gpt_neox#transformers.GPTNeoXForQuestionAnswering">GPTNeoXForQuestionAnswering</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/ibert#transformers.IBertForQuestionAnswering">IBertForQuestionAnswering</a> (I-BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/led#transformers.LEDForQuestionAnswering">LEDForQuestionAnswering</a> (LED model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/lilt#transformers.LiltForQuestionAnswering">LiltForQuestionAnswering</a> (LiLT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerForQuestionAnswering">LongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/luke#transformers.LukeForQuestionAnswering">LukeForQuestionAnswering</a> (LUKE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/lxmert#transformers.LxmertForQuestionAnswering">LxmertForQuestionAnswering</a> (LXMERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartForQuestionAnswering">MBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetForQuestionAnswering">MPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mt5#transformers.MT5ForQuestionAnswering">MT5ForQuestionAnswering</a> (MT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/markuplm#transformers.MarkupLMForQuestionAnswering">MarkupLMForQuestionAnswering</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mega#transformers.MegaForQuestionAnswering">MegaForQuestionAnswering</a> (MEGA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/megatron-bert#transformers.MegatronBertForQuestionAnswering">MegatronBertForQuestionAnswering</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertForQuestionAnswering">MobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpt#transformers.MptForQuestionAnswering">MptForQuestionAnswering</a> (MPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mra#transformers.MraForQuestionAnswering">MraForQuestionAnswering</a> (MRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mvp#transformers.MvpForQuestionAnswering">MvpForQuestionAnswering</a> (MVP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/nezha#transformers.NezhaForQuestionAnswering">NezhaForQuestionAnswering</a> (Nezha model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/nystromformer#transformers.NystromformerForQuestionAnswering">NystromformerForQuestionAnswering</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/opt#transformers.OPTForQuestionAnswering">OPTForQuestionAnswering</a> (OPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/qdqbert#transformers.QDQBertForQuestionAnswering">QDQBertForQuestionAnswering</a> (QDQBert model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/reformer#transformers.ReformerForQuestionAnswering">ReformerForQuestionAnswering</a> (Reformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertForQuestionAnswering">RemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roc_bert#transformers.RoCBertForQuestionAnswering">RoCBertForQuestionAnswering</a> (RoCBert model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerForQuestionAnswering">RoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaForQuestionAnswering">RobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForQuestionAnswering">RobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/splinter#transformers.SplinterForQuestionAnswering">SplinterForQuestionAnswering</a> (Splinter model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/squeezebert#transformers.SqueezeBertForQuestionAnswering">SqueezeBertForQuestionAnswering</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/t5#transformers.T5ForQuestionAnswering">T5ForQuestionAnswering</a> (T5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/umt5#transformers.UMT5ForQuestionAnswering">UMT5ForQuestionAnswering</a> (UMT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMForQuestionAnsweringSimple">XLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaForQuestionAnswering">XLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForQuestionAnswering">XLMRobertaXLForQuestionAnswering</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetForQuestionAnsweringSimple">XLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xmod#transformers.XmodForQuestionAnswering">XmodForQuestionAnswering</a> (X-MOD model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/yoso#transformers.YosoForQuestionAnswering">YosoForQuestionAnswering</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),Kk=new Z({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_config.example",$$slots:{default:[gis]},$$scope:{ctx:x}}}),fU=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),dx=new Z({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[uis]},$$scope:{ctx:x}}}),hU=new te({}),gU=new G({props:{name:"class transformers.TFAutoModelForQuestionAnswering",anchor:"transformers.TFAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_tf_auto.py#L633"}}),pU=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.TFAlbertForQuestionAnswering">TFAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.TFBertForQuestionAnswering">TFBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/camembert#transformers.TFCamembertForQuestionAnswering">TFCamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/convbert#transformers.TFConvBertForQuestionAnswering">TFConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta#transformers.TFDebertaForQuestionAnswering">TFDebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deberta-v2#transformers.TFDebertaV2ForQuestionAnswering">TFDebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.TFDistilBertForQuestionAnswering">TFDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.TFElectraForQuestionAnswering">TFElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/flaubert#transformers.TFFlaubertForQuestionAnsweringSimple">TFFlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/funnel#transformers.TFFunnelForQuestionAnswering">TFFunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/gptj#transformers.TFGPTJForQuestionAnswering">TFGPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForQuestionAnswering">TFLayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/longformer#transformers.TFLongformerForQuestionAnswering">TFLongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mpnet#transformers.TFMPNetForQuestionAnswering">TFMPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilebert#transformers.TFMobileBertForQuestionAnswering">TFMobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/rembert#transformers.TFRemBertForQuestionAnswering">TFRemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.TFRoFormerForQuestionAnswering">TFRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.TFRobertaForQuestionAnswering">TFRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForQuestionAnswering">TFRobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm#transformers.TFXLMForQuestionAnsweringSimple">TFXLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForQuestionAnswering">TFXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlnet#transformers.TFXLNetForQuestionAnsweringSimple">TFXLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),mx=new Z({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[pis]},$$scope:{ctx:x}}}),_U=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),Bx=new Z({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[_is]},$$scope:{ctx:x}}}),vU=new te({}),bU=new G({props:{name:"class transformers.FlaxAutoModelForQuestionAnswering",anchor:"transformers.FlaxAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_flax_auto.py#L320"}}),TU=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/albert#transformers.FlaxAlbertForQuestionAnswering">FlaxAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bart#transformers.FlaxBartForQuestionAnswering">FlaxBartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bert#transformers.FlaxBertForQuestionAnswering">FlaxBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/big_bird#transformers.FlaxBigBirdForQuestionAnswering">FlaxBigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/distilbert#transformers.FlaxDistilBertForQuestionAnswering">FlaxDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/electra#transformers.FlaxElectraForQuestionAnswering">FlaxElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mbart#transformers.FlaxMBartForQuestionAnswering">FlaxMBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roformer#transformers.FlaxRoFormerForQuestionAnswering">FlaxRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta#transformers.FlaxRobertaForQuestionAnswering">FlaxRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForQuestionAnswering">FlaxRobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForQuestionAnswering">FlaxXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),Nx=new Z({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[vis]},$$scope:{ctx:x}}}),FU=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),Ox=new Z({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[bis]},$$scope:{ctx:x}}}),EU=new te({}),CU=new G({props:{name:"class transformers.AutoModelForTextEncoding",anchor:"transformers.AutoModelForTextEncoding",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1189"}}),wU=new te({}),AU=new G({props:{name:"class transformers.TFAutoModelForTextEncoding",anchor:"transformers.TFAutoModelForTextEncoding",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_tf_auto.py#L523"}}),RU=new te({}),kU=new te({}),SU=new G({props:{name:"class transformers.AutoModelForDepthEstimation",anchor:"transformers.AutoModelForDepthEstimation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1376"}}),GU=new G({props:{name:"from_config",anchor:"transformers.AutoModelForDepthEstimation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDepthEstimation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/dpt#transformers.DPTForDepthEstimation">DPTForDepthEstimation</a> (DPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/glpn#transformers.GLPNForDepthEstimation">GLPNForDepthEstimation</a> (GLPN model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),Kx=new Z({props:{anchor:"transformers.AutoModelForDepthEstimation.from_config.example",$$slots:{default:[Mis]},$$scope:{ctx:x}}}),BU=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),tG=new Z({props:{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.example",$$slots:{default:[Tis]},$$scope:{ctx:x}}}),NU=new te({}),$U=new G({props:{name:"class transformers.AutoModelForImageClassification",anchor:"transformers.AutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1310"}}),PU=new G({props:{name:"from_config",anchor:"transformers.AutoModelForImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/beit#transformers.BeitForImageClassification">BeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/bit#transformers.BitConfig">BitConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/bit#transformers.BitForImageClassification">BitForImageClassification</a> (BiT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/convnext#transformers.ConvNextForImageClassification">ConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/convnextv2#transformers.ConvNextV2Config">ConvNextV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/convnextv2#transformers.ConvNextV2ForImageClassification">ConvNextV2ForImageClassification</a> (ConvNeXTV2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/cvt#transformers.CvtForImageClassification">CvtForImageClassification</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecVisionForImageClassification">Data2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deit#transformers.DeiTForImageClassification">DeiTForImageClassification</a> or <a href="/docs/transformers/v4.32.0/en/model_doc/deit#transformers.DeiTForImageClassificationWithTeacher">DeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/dinat#transformers.DinatConfig">DinatConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/dinat#transformers.DinatForImageClassification">DinatForImageClassification</a> (DiNAT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/dinov2#transformers.Dinov2Config">Dinov2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/dinov2#transformers.Dinov2ForImageClassification">Dinov2ForImageClassification</a> (DINOv2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassification">EfficientFormerForImageClassification</a> or <a href="/docs/transformers/v4.32.0/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassificationWithTeacher">EfficientFormerForImageClassificationWithTeacher</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/efficientnet#transformers.EfficientNetConfig">EfficientNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/efficientnet#transformers.EfficientNetForImageClassification">EfficientNetForImageClassification</a> (EfficientNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/focalnet#transformers.FocalNetConfig">FocalNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/focalnet#transformers.FocalNetForImageClassification">FocalNetForImageClassification</a> (FocalNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/imagegpt#transformers.ImageGPTForImageClassification">ImageGPTForImageClassification</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/levit#transformers.LevitForImageClassification">LevitForImageClassification</a> or <a href="/docs/transformers/v4.32.0/en/model_doc/levit#transformers.LevitForImageClassificationWithTeacher">LevitForImageClassificationWithTeacher</a> (LeViT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config">MobileNetV1Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilenet_v1#transformers.MobileNetV1ForImageClassification">MobileNetV1ForImageClassification</a> (MobileNetV1 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForImageClassification">MobileNetV2ForImageClassification</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilevit#transformers.MobileViTForImageClassification">MobileViTForImageClassification</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilevitv2#transformers.MobileViTV2Config">MobileViTV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilevitv2#transformers.MobileViTV2ForImageClassification">MobileViTV2ForImageClassification</a> (MobileViTV2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/nat#transformers.NatConfig">NatConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/nat#transformers.NatForImageClassification">NatForImageClassification</a> (NAT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/perceiver#transformers.PerceiverForImageClassificationLearned">PerceiverForImageClassificationLearned</a> or <a href="/docs/transformers/v4.32.0/en/model_doc/perceiver#transformers.PerceiverForImageClassificationFourier">PerceiverForImageClassificationFourier</a> or <a href="/docs/transformers/v4.32.0/en/model_doc/perceiver#transformers.PerceiverForImageClassificationConvProcessing">PerceiverForImageClassificationConvProcessing</a> (Perceiver model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/poolformer#transformers.PoolFormerForImageClassification">PoolFormerForImageClassification</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/pvt#transformers.PvtConfig">PvtConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/pvt#transformers.PvtForImageClassification">PvtForImageClassification</a> (PVT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/regnet#transformers.RegNetForImageClassification">RegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/resnet#transformers.ResNetForImageClassification">ResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/segformer#transformers.SegformerForImageClassification">SegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/swiftformer#transformers.SwiftFormerConfig">SwiftFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/swiftformer#transformers.SwiftFormerForImageClassification">SwiftFormerForImageClassification</a> (SwiftFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/swin#transformers.SwinForImageClassification">SwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/swinv2#transformers.Swinv2ForImageClassification">Swinv2ForImageClassification</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/van#transformers.VanForImageClassification">VanForImageClassification</a> (VAN model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vit#transformers.ViTForImageClassification">ViTForImageClassification</a> (ViT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vit_hybrid#transformers.ViTHybridConfig">ViTHybridConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vit_hybrid#transformers.ViTHybridForImageClassification">ViTHybridForImageClassification</a> (ViT Hybrid model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vit_msn#transformers.ViTMSNForImageClassification">ViTMSNForImageClassification</a> (ViTMSN model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),nG=new Z({props:{anchor:"transformers.AutoModelForImageClassification.from_config.example",$$slots:{default:[Fis]},$$scope:{ctx:x}}}),ZU=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),IG=new Z({props:{anchor:"transformers.AutoModelForImageClassification.from_pretrained.example",$$slots:{default:[Eis]},$$scope:{ctx:x}}}),VU=new te({}),WU=new G({props:{name:"class transformers.TFAutoModelForImageClassification",anchor:"transformers.TFAutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_tf_auto.py#L574"}}),UU=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/convnext#transformers.TFConvNextForImageClassification">TFConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/cvt#transformers.TFCvtForImageClassification">TFCvtForImageClassification</a> (CvT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.TFData2VecVisionForImageClassification">TFData2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deit#transformers.TFDeiTForImageClassification">TFDeiTForImageClassification</a> or <a href="/docs/transformers/v4.32.0/en/model_doc/deit#transformers.TFDeiTForImageClassificationWithTeacher">TFDeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/efficientformer#transformers.TFEfficientFormerForImageClassification">TFEfficientFormerForImageClassification</a> or <a href="/docs/transformers/v4.32.0/en/model_doc/efficientformer#transformers.TFEfficientFormerForImageClassificationWithTeacher">TFEfficientFormerForImageClassificationWithTeacher</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilevit#transformers.TFMobileViTForImageClassification">TFMobileViTForImageClassification</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/regnet#transformers.TFRegNetForImageClassification">TFRegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/resnet#transformers.TFResNetForImageClassification">TFResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/segformer#transformers.TFSegformerForImageClassification">TFSegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/swin#transformers.TFSwinForImageClassification">TFSwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vit#transformers.TFViTForImageClassification">TFViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),$G=new Z({props:{anchor:"transformers.TFAutoModelForImageClassification.from_config.example",$$slots:{default:[yis]},$$scope:{ctx:x}}}),XU=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),qG=new Z({props:{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[Cis]},$$scope:{ctx:x}}}),DU=new te({}),qU=new G({props:{name:"class transformers.FlaxAutoModelForImageClassification",anchor:"transformers.FlaxAutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_flax_auto.py#L352"}}),QU=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/beit#transformers.FlaxBeitForImageClassification">FlaxBeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/regnet#transformers.FlaxRegNetForImageClassification">FlaxRegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/resnet#transformers.FlaxResNetForImageClassification">FlaxResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vit#transformers.FlaxViTForImageClassification">FlaxViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),QG=new Z({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_config.example",$$slots:{default:[wis]},$$scope:{ctx:x}}}),YU=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
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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 delete incompletely received files. Will attempt to resume the download if such a
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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>
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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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corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/timesformer#transformers.TimesformerForVideoClassification">TimesformerForVideoClassification</a> (TimeSformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/videomae#transformers.VideoMAEForVideoClassification">VideoMAEForVideoClassification</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vivit#transformers.VivitConfig">VivitConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vivit#transformers.VivitForVideoClassification">VivitForVideoClassification</a> (ViViT 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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
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PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
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<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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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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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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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.AutoModelForVideoClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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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.AutoModelForVideoClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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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.AutoModelForVideoClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deit#transformers.DeiTForMaskedImageModeling">DeiTForMaskedImageModeling</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/focalnet#transformers.FocalNetConfig">FocalNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/focalnet#transformers.FocalNetForMaskedImageModeling">FocalNetForMaskedImageModeling</a> (FocalNet model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/swin#transformers.SwinForMaskedImageModeling">SwinForMaskedImageModeling</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/swinv2#transformers.Swinv2ForMaskedImageModeling">Swinv2ForMaskedImageModeling</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vit#transformers.ViTForMaskedImageModeling">ViTForMaskedImageModeling</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),dB=new Z({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[kis]},$$scope:{ctx:x}}}),sX=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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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.AutoModelForMaskedImageModeling.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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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.AutoModelForMaskedImageModeling.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),pB=new Z({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.example",$$slots:{default:[Sis]},$$scope:{ctx:x}}}),lX=new te({}),iX=new G({props:{name:"class transformers.TFAutoModelForMaskedImageModeling",anchor:"transformers.TFAutoModelForMaskedImageModeling",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_tf_auto.py#L565"}}),cX=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForMaskedImageModeling.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deit#transformers.TFDeiTForMaskedImageModeling">TFDeiTForMaskedImageModeling</a> (DeiT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/swin#transformers.TFSwinForMaskedImageModeling">TFSwinForMaskedImageModeling</a> (Swin Transformer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),vB=new Z({props:{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[xis]},$$scope:{ctx:x}}}),mX=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMaskedImageModeling.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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.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.TFAutoModelForMaskedImageModeling.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.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
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.TFAutoModelForMaskedImageModeling.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.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 (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),TB=new Z({props:{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.example",$$slots:{default:[Gis]},$$scope:{ctx:x}}}),hX=new te({}),gX=new G({props:{name:"class transformers.AutoModelForObjectDetection",anchor:"transformers.AutoModelForObjectDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1360"}}),pX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForObjectDetection.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForObjectDetection.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/conditional_detr#transformers.ConditionalDetrConfig">ConditionalDetrConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/conditional_detr#transformers.ConditionalDetrForObjectDetection">ConditionalDetrForObjectDetection</a> (Conditional DETR model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deformable_detr#transformers.DeformableDetrConfig">DeformableDetrConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deformable_detr#transformers.DeformableDetrForObjectDetection">DeformableDetrForObjectDetection</a> (Deformable DETR model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/deta#transformers.DetaConfig">DetaConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/deta#transformers.DetaForObjectDetection">DetaForObjectDetection</a> (DETA model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/detr#transformers.DetrForObjectDetection">DetrForObjectDetection</a> (DETR model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/table-transformer#transformers.TableTransformerConfig">TableTransformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/table-transformer#transformers.TableTransformerForObjectDetection">TableTransformerForObjectDetection</a> (Table Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/yolos#transformers.YolosForObjectDetection">YolosForObjectDetection</a> (YOLOS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),EB=new Z({props:{anchor:"transformers.AutoModelForObjectDetection.from_config.example",$$slots:{default:[Bis]},$$scope:{ctx:x}}}),_X=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_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;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForObjectDetection.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.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 (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),SB=new Z({props:{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.example",$$slots:{default:[Iis]},$$scope:{ctx:x}}}),vX=new te({}),bX=new G({props:{name:"class transformers.AutoModelForImageSegmentation",anchor:"transformers.AutoModelForImageSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1326"}}),TX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForImageSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),GB=new Z({props:{anchor:"transformers.AutoModelForImageSegmentation.from_config.example",$$slots:{default:[Nis]},$$scope:{ctx:x}}}),FX=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForImageSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
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<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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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
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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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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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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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.AutoModelForImageSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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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.AutoModelForImageSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/beit#transformers.BeitForSemanticSegmentation">BeitForSemanticSegmentation</a> (BEiT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/dpt#transformers.DPTForSemanticSegmentation">DPTForSemanticSegmentation</a> (DPT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecVisionForSemanticSegmentation">Data2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForSemanticSegmentation">MobileNetV2ForSemanticSegmentation</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilevit#transformers.MobileViTForSemanticSegmentation">MobileViTForSemanticSegmentation</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilevitv2#transformers.MobileViTV2Config">MobileViTV2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilevitv2#transformers.MobileViTV2ForSemanticSegmentation">MobileViTV2ForSemanticSegmentation</a> (MobileViTV2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/segformer#transformers.SegformerForSemanticSegmentation">SegformerForSemanticSegmentation</a> (SegFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/upernet#transformers.UperNetConfig">UperNetConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation">UperNetForSemanticSegmentation</a> (UPerNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),jB=new Z({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[jis]},$$scope:{ctx:x}}}),LX=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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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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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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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.AutoModelForSemanticSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),OB=new Z({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[Pis]},$$scope:{ctx:x}}}),AX=new te({}),RX=new G({props:{name:"class transformers.TFAutoModelForSemanticSegmentation",anchor:"transformers.TFAutoModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_tf_auto.py#L592"}}),SX=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.TFData2VecVisionForSemanticSegmentation">TFData2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mobilevit#transformers.TFMobileViTForSemanticSegmentation">TFMobileViTForSemanticSegmentation</a> (MobileViT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/segformer#transformers.TFSegformerForSemanticSegmentation">TFSegformerForSemanticSegmentation</a> (SegFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),YB=new Z({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[Zis]},$$scope:{ctx:x}}}),xX=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
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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.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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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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<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
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supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
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<li><a href="/docs/transformers/v4.32.0/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer 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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
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<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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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.AutoModelForInstanceSegmentation.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.AutoModelForInstanceSegmentation.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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<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
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corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mask2former#transformers.Mask2FormerConfig">Mask2FormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mask2former#transformers.Mask2FormerForUniversalSegmentation">Mask2FormerForUniversalSegmentation</a> (Mask2Former model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/oneformer#transformers.OneFormerConfig">OneFormerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/oneformer#transformers.OneFormerForUniversalSegmentation">OneFormerForUniversalSegmentation</a> (OneFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),lI=new Z({props:{anchor:"transformers.AutoModelForUniversalSegmentation.from_config.example",$$slots:{default:[Uis]},$$scope:{ctx:x}}}),WX=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForUniversalSegmentation.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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
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<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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</ul>`,name:"config"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
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<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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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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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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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.AutoModelForUniversalSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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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.AutoModelForUniversalSegmentation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),hI=new Z({props:{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.example",$$slots:{default:[Xis]},$$scope:{ctx:x}}}),JX=new te({}),UX=new G({props:{name:"class transformers.AutoModelForZeroShotImageClassification",anchor:"transformers.AutoModelForZeroShotImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1317"}}),DX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForZeroShotImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/align#transformers.AlignConfig">AlignConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/align#transformers.AlignModel">AlignModel</a> (ALIGN model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/altclip#transformers.AltCLIPConfig">AltCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/clipseg#transformers.CLIPSegConfig">CLIPSegConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/clipseg#transformers.CLIPSegModel">CLIPSegModel</a> (CLIPSeg model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/chinese_clip#transformers.ChineseCLIPConfig">ChineseCLIPConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/chinese_clip#transformers.ChineseCLIPModel">ChineseCLIPModel</a> (Chinese-CLIP model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),uI=new Z({props:{anchor:"transformers.AutoModelForZeroShotImageClassification.from_config.example",$$slots:{default:[Dis]},$$scope:{ctx:x}}}),qX=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
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<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForZeroShotImageClassification.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 delete incompletely received files. Will attempt to resume the download if such a
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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.AutoModelForZeroShotImageClassification.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
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allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForZeroShotImageClassification.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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<ul>
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already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blip#transformers.TFBlipModel">TFBlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>
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Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.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.TFAutoModelForZeroShotImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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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.TFAutoModelForZeroShotImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.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.TFAutoModelForZeroShotImageClassification.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.TFAutoModelForZeroShotImageClassification.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.TFAutoModelForZeroShotImageClassification.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.TFAutoModelForZeroShotImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/owlvit#transformers.OwlViTForObjectDetection">OwlViTForObjectDetection</a> (OWL-ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),kI=new Z({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config.example",$$slots:{default:[Yis]},$$scope:{ctx:x}}}),aD=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotObjectDetection.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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),GI=new Z({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.example",$$slots:{default:[zis]},$$scope:{ctx:x}}}),nD=new te({}),sD=new te({}),lD=new G({props:{name:"class transformers.AutoModelForAudioClassification",anchor:"transformers.AutoModelForAudioClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1397"}}),dD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForAudioClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/audio-spectrogram-transformer#transformers.ASTConfig">ASTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/audio-spectrogram-transformer#transformers.ASTForAudioClassification">ASTForAudioClassification</a> (Audio Spectrogram Transformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecAudioForSequenceClassification">Data2VecAudioForSequenceClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/hubert#transformers.HubertForSequenceClassification">HubertForSequenceClassification</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/sew#transformers.SEWForSequenceClassification">SEWForSequenceClassification</a> (SEW model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/sew-d#transformers.SEWDForSequenceClassification">SEWDForSequenceClassification</a> (SEW-D model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/unispeech#transformers.UniSpeechForSequenceClassification">UniSpeechForSequenceClassification</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatForSequenceClassification">UniSpeechSatForSequenceClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2ForSequenceClassification">Wav2Vec2ForSequenceClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForSequenceClassification">Wav2Vec2ConformerForSequenceClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wavlm#transformers.WavLMForSequenceClassification">WavLMForSequenceClassification</a> (WavLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/whisper#transformers.WhisperForAudioClassification">WhisperForAudioClassification</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),NI=new Z({props:{anchor:"transformers.AutoModelForAudioClassification.from_config.example",$$slots:{default:[His]},$$scope:{ctx:x}}}),cD=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),QI=new Z({props:{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[Kis]},$$scope:{ctx:x}}}),mD=new te({}),fD=new G({props:{name:"class transformers.TFAutoModelForAudioClassification",anchor:"transformers.TFAutoModelForAudioClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_tf_auto.py#L534"}}),gD=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForAudioClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForAudioClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.TFWav2Vec2ForSequenceClassification">TFWav2Vec2ForSequenceClassification</a> (Wav2Vec2 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),zI=new Z({props:{anchor:"transformers.TFAutoModelForAudioClassification.from_config.example",$$slots:{default:[eds]},$$scope:{ctx:x}}}),uD=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecAudioForAudioFrameClassification">Data2VecAudioForAudioFrameClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatForAudioFrameClassification">UniSpeechSatForAudioFrameClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2ForAudioFrameClassification">Wav2Vec2ForAudioFrameClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForAudioFrameClassification">Wav2Vec2ConformerForAudioFrameClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wavlm#transformers.WavLMForAudioFrameClassification">WavLMForAudioFrameClassification</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),oN=new Z({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.example",$$slots:{default:[rds]},$$scope:{ctx:x}}}),MD=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
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Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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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.`,name:"force_download"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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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
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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 (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),iN=new Z({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.example",$$slots:{default:[tds]},$$scope:{ctx:x}}}),TD=new te({}),FD=new G({props:{name:"class transformers.AutoModelForCTC",anchor:"transformers.AutoModelForCTC",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1404"}}),yD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForCTC.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCTC.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecAudioForCTC">Data2VecAudioForCTC</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/hubert#transformers.HubertForCTC">HubertForCTC</a> (Hubert model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/mctct#transformers.MCTCTForCTC">MCTCTForCTC</a> (M-CTC-T model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/sew#transformers.SEWForCTC">SEWForCTC</a> (SEW model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/sew-d#transformers.SEWDForCTC">SEWDForCTC</a> (SEW-D model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/unispeech#transformers.UniSpeechForCTC">UniSpeechForCTC</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatForCTC">UniSpeechSatForCTC</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC">Wav2Vec2ForCTC</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForCTC">Wav2Vec2ConformerForCTC</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wavlm#transformers.WavLMForCTC">WavLMForCTC</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),cN=new Z({props:{anchor:"transformers.AutoModelForCTC.from_config.example",$$slots:{default:[ads]},$$scope:{ctx:x}}}),CD=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForCTC.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForCTC.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForCTC.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForCTC.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForCTC.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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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.AutoModelForCTC.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForCTC.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForCTC.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForCTC.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForCTC.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForCTC.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),FN=new Z({props:{anchor:"transformers.AutoModelForCTC.from_pretrained.example",$$slots:{default:[nds]},$$scope:{ctx:x}}}),wD=new te({}),LD=new G({props:{name:"class transformers.AutoModelForSpeechSeq2Seq",anchor:"transformers.AutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1411"}}),RD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/speech_to_text#transformers.Speech2TextForConditionalGeneration">Speech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderConfig">SpeechEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderModel">SpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/speecht5#transformers.SpeechT5Config">SpeechT5Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/speecht5#transformers.SpeechT5ForSpeechToText">SpeechT5ForSpeechToText</a> (SpeechT5 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/whisper#transformers.WhisperForConditionalGeneration">WhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),yN=new Z({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[sds]},$$scope:{ctx:x}}}),kD=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.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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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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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.AutoModelForSpeechSeq2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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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.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),kN=new Z({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[lds]},$$scope:{ctx:x}}}),SD=new te({}),xD=new G({props:{name:"class transformers.TFAutoModelForSpeechSeq2Seq",anchor:"transformers.TFAutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_tf_auto.py#L687"}}),BD=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/speech_to_text#transformers.TFSpeech2TextForConditionalGeneration">TFSpeech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/whisper#transformers.TFWhisperForConditionalGeneration">TFWhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),xN=new Z({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[ids]},$$scope:{ctx:x}}}),ID=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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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.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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Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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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.TFAutoModelForSpeechSeq2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
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automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderConfig">SpeechEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/speech-encoder-decoder#transformers.FlaxSpeechEncoderDecoderModel">FlaxSpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),$N=new Z({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[cds]},$$scope:{ctx:x}}}),VD=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
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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 delete incompletely received files. Will attempt to resume the download if such a
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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.FlaxAutoModelForSpeechSeq2Seq.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.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.,
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automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/data2vec#transformers.Data2VecAudioForXVector">Data2VecAudioForXVector</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/unispeech-sat#transformers.UniSpeechSatForXVector">UniSpeechSatForXVector</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2#transformers.Wav2Vec2ForXVector">Wav2Vec2ForXVector</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForXVector">Wav2Vec2ConformerForXVector</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/wavlm#transformers.WavLMForXVector">WavLMForXVector</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),WN=new Z({props:{anchor:"transformers.AutoModelForAudioXVector.from_config.example",$$slots:{default:[fds]},$$scope:{ctx:x}}}),qD=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForAudioXVector.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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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.`,name:"force_download"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForAudioXVector.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.AutoModelForAudioXVector.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),QN=new Z({props:{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.example",$$slots:{default:[hds]},$$scope:{ctx:x}}}),OD=new te({}),YD=new G({props:{name:"class transformers.AutoModelForTextToSpectrogram",anchor:"transformers.AutoModelForTextToSpectrogram",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1433"}}),zD=new te({}),KD=new G({props:{name:"class transformers.AutoModelForTextToWaveform",anchor:"transformers.AutoModelForTextToWaveform",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1437"}}),eq=new te({}),oq=new te({}),rq=new G({props:{name:"class transformers.AutoModelForTableQuestionAnswering",anchor:"transformers.AutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1254"}}),aq=new G({props:{name:"from_config",anchor:"transformers.AutoModelForTableQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TapasForQuestionAnswering">TapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),e$=new Z({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[gds]},$$scope:{ctx:x}}}),nq=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
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
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<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForTableQuestionAnswering.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.AutoModelForTableQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForTableQuestionAnswering.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.AutoModelForTableQuestionAnswering.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.AutoModelForTableQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),t$=new Z({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[uds]},$$scope:{ctx:x}}}),sq=new te({}),lq=new G({props:{name:"class transformers.TFAutoModelForTableQuestionAnswering",anchor:"transformers.TFAutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_tf_auto.py#L651"}}),dq=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/tapas#transformers.TFTapasForQuestionAnswering">TFTapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),n$=new Z({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[pds]},$$scope:{ctx:x}}}),cq=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.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.TFAutoModelForTableQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),l$=new Z({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[_ds]},$$scope:{ctx:x}}}),mq=new te({}),fq=new G({props:{name:"class transformers.AutoModelForDocumentQuestionAnswering",anchor:"transformers.AutoModelForDocumentQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1276"}}),gq=new G({props:{name:"from_config",anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.LayoutLMForQuestionAnswering">LayoutLMForQuestionAnswering</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),d$=new Z({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[vds]},$$scope:{ctx:x}}}),uq=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),g$=new Z({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[bds]},$$scope:{ctx:x}}}),pq=new te({}),_q=new G({props:{name:"class transformers.TFAutoModelForDocumentQuestionAnswering",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_tf_auto.py#L640"}}),bq=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/layoutlm#transformers.TFLayoutLMForQuestionAnswering">TFLayoutLMForQuestionAnswering</a> (LayoutLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),p$=new Z({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[Mds]},$$scope:{ctx:x}}}),Mq=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
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<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
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Load the model weights from a PyTorch checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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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.TFAutoModelForDocumentQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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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 (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vilt#transformers.ViltForQuestionAnswering">ViltForQuestionAnswering</a> (ViLT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),M$=new Z({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.example",$$slots:{default:[Fds]},$$scope:{ctx:x}}}),Cq=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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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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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L438"}}),E$=new Z({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.example",$$slots:{default:[Eds]},$$scope:{ctx:x}}}),wq=new te({}),Lq=new G({props:{name:"class transformers.AutoModelForVision2Seq",anchor:"transformers.AutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/modeling_auto.py#L1390"}}),Rq=new G({props:{name:"from_config",anchor:"transformers.AutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration">Blip2ForConditionalGeneration</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/blip#transformers.BlipForConditionalGeneration">BlipForConditionalGeneration</a> (BLIP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/git#transformers.GitConfig">GitConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/git#transformers.GitForCausalLM">GitForCausalLM</a> (GIT model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/instructblip#transformers.InstructBlipConfig">InstructBlipConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/instructblip#transformers.InstructBlipForConditionalGeneration">InstructBlipForConditionalGeneration</a> (InstructBLIP model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/pix2struct#transformers.Pix2StructConfig">Pix2StructConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/pix2struct#transformers.Pix2StructForConditionalGeneration">Pix2StructForConditionalGeneration</a> (Pix2Struct model)</li>
<li><a href="/docs/transformers/v4.32.0/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/v4.32.0/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderModel">VisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/auto/auto_factory.py#L407"}}),C$=new Z({props:{anchor:"transformers.AutoModelForVision2Seq.from_config.example",$$slots:{default:[yds]},$$scope:{ctx:x}}}),kq=new G({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.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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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.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
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"),aee=a("a"),Msr=o("GPT2TokenizerFast"),Tsr=o(" (BLIP-2 model)"),Fsr=l(),D2=a("li"),lAe=a("strong"),Esr=o("bloom"),ysr=o(" \u2014 "),nee=a("a"),Csr=o("BloomTokenizerFast"),wsr=o(" (BLOOM model)"),Lsr=l(),hl=a("li"),iAe=a("strong"),Asr=o("bridgetower"),Rsr=o(" \u2014 "),see=a("a"),ksr=o("RobertaTokenizer"),Ssr=o(" or "),lee=a("a"),xsr=o("RobertaTokenizerFast"),Gsr=o(" (BridgeTower model)"),Bsr=l(),q2=a("li"),dAe=a("strong"),Isr=o("byt5"),Nsr=o(" \u2014 "),iee=a("a"),$sr=o("ByT5Tokenizer"),jsr=o(" (ByT5 model)"),Psr=l(),gl=a("li"),cAe=a("strong"),Zsr=o("camembert"),Vsr=o(" \u2014 "),dee=a("a"),Wsr=o("CamembertTokenizer"),Jsr=o(" or "),cee=a("a"),Usr=o("CamembertTokenizerFast"),Xsr=o(" (CamemBERT model)"),Dsr=l(),O2=a("li"),mAe=a("strong"),qsr=o("canine"),Osr=o(" \u2014 "),mee=a("a"),Qsr=o("CanineTokenizer"),Ysr=o(" (CANINE model)"),zsr=l(),ul=a("li"),fAe=a("strong"),Hsr=o("chinese_clip"),Ksr=o(" \u2014 "),fee=a("a"),elr=o("BertTokenizer"),olr=o(" or "),hee=a("a"),rlr=o("BertTokenizerFast"),tlr=o(" (Chinese-CLIP model)"),alr=l(),pl=a("li"),hAe=a("strong"),nlr=o("clap"),slr=o(" \u2014 "),gee=a("a"),llr=o("RobertaTokenizer"),ilr=o(" or "),uee=a("a"),dlr=o("RobertaTokenizerFast"),clr=o(" (CLAP model)"),mlr=l(),_l=a("li"),gAe=a("strong"),flr=o("clip"),hlr=o(" \u2014 "),pee=a("a"),glr=o("CLIPTokenizer"),ulr=o(" or "),_ee=a("a"),plr=o("CLIPTokenizerFast"),_lr=o(" (CLIP model)"),vlr=l(),vl=a("li"),uAe=a("strong"),blr=o("clipseg"),Mlr=o(" \u2014 "),vee=a("a"),Tlr=o("CLIPTokenizer"),Flr=o(" or "),bee=a("a"),Elr=o("CLIPTokenizerFast"),ylr=o(" (CLIPSeg model)"),Clr=l(),bl=a("li"),pAe=a("strong"),wlr=o("codegen"),Llr=o(" \u2014 "),Mee=a("a"),Alr=o("CodeGenTokenizer"),Rlr=o(" or "),Tee=a("a"),klr=o("CodeGenTokenizerFast"),Slr=o(" (CodeGen model)"),xlr=l(),Ml=a("li"),_Ae=a("strong"),Glr=o("convbert"),Blr=o(" \u2014 "),Fee=a("a"),Ilr=o("ConvBertTokenizer"),Nlr=o(" or "),Eee=a("a"),$lr=o("ConvBertTokenizerFast"),jlr=o(" (ConvBERT model)"),Plr=l(),Tl=a("li"),vAe=a("strong"),Zlr=o("cpm"),Vlr=o(" \u2014 "),yee=a("a"),Wlr=o("CpmTokenizer"),Jlr=o(" or "),Cee=a("a"),Ulr=o("CpmTokenizerFast"),Xlr=o(" (CPM model)"),Dlr=l(),Q2=a("li"),bAe=a("strong"),qlr=o("cpmant"),Olr=o(" \u2014 "),wee=a("a"),Qlr=o("CpmAntTokenizer"),Ylr=o(" (CPM-Ant model)"),zlr=l(),Y2=a("li"),MAe=a("strong"),Hlr=o("ctrl"),Klr=o(" \u2014 "),Lee=a("a"),eir=o("CTRLTokenizer"),oir=o(" (CTRL model)"),rir=l(),Fl=a("li"),TAe=a("strong"),tir=o("data2vec-text"),air=o(" \u2014 "),Aee=a("a"),nir=o("RobertaTokenizer"),sir=o(" or "),Ree=a("a"),lir=o("RobertaTokenizerFast"),iir=o(" (Data2VecText model)"),dir=l(),El=a("li"),FAe=a("strong"),cir=o("deberta"),mir=o(" \u2014 "),kee=a("a"),fir=o("DebertaTokenizer"),hir=o(" or "),See=a("a"),gir=o("DebertaTokenizerFast"),uir=o(" (DeBERTa model)"),pir=l(),yl=a("li"),EAe=a("strong"),_ir=o("deberta-v2"),vir=o(" \u2014 "),xee=a("a"),bir=o("DebertaV2Tokenizer"),Mir=o(" or "),Gee=a("a"),Tir=o("DebertaV2TokenizerFast"),Fir=o(" (DeBERTa-v2 model)"),Eir=l(),Cl=a("li"),yAe=a("strong"),yir=o("distilbert"),Cir=o(" \u2014 "),Bee=a("a"),wir=o("DistilBertTokenizer"),Lir=o(" or "),Iee=a("a"),Air=o("DistilBertTokenizerFast"),Rir=o(" (DistilBERT model)"),kir=l(),wl=a("li"),CAe=a("strong"),Sir=o("dpr"),xir=o(" \u2014 "),Nee=a("a"),Gir=o("DPRQuestionEncoderTokenizer"),Bir=o(" or "),$ee=a("a"),Iir=o("DPRQuestionEncoderTokenizerFast"),Nir=o(" (DPR model)"),$ir=l(),Ll=a("li"),wAe=a("strong"),jir=o("electra"),Pir=o(" \u2014 "),jee=a("a"),Zir=o("ElectraTokenizer"),Vir=o(" or "),Pee=a("a"),Wir=o("ElectraTokenizerFast"),Jir=o(" (ELECTRA model)"),Uir=l(),Al=a("li"),LAe=a("strong"),Xir=o("ernie"),Dir=o(" \u2014 "),Zee=a("a"),qir=o("BertTokenizer"),Oir=o(" or "),Vee=a("a"),Qir=o("BertTokenizerFast"),Yir=o(" (ERNIE model)"),zir=l(),z2=a("li"),AAe=a("strong"),Hir=o("ernie_m"),Kir=o(" \u2014 "),Wee=a("a"),edr=o("ErnieMTokenizer"),odr=o(" (ErnieM model)"),rdr=l(),H2=a("li"),RAe=a("strong"),tdr=o("esm"),adr=o(" \u2014 "),Jee=a("a"),ndr=o("EsmTokenizer"),sdr=o(" (ESM model)"),ldr=l(),K2=a("li"),kAe=a("strong"),idr=o("flaubert"),ddr=o(" \u2014 "),Uee=a("a"),cdr=o("FlaubertTokenizer"),mdr=o(" (FlauBERT model)"),fdr=l(),Rl=a("li"),SAe=a("strong"),hdr=o("fnet"),gdr=o(" \u2014 "),Xee=a("a"),udr=o("FNetTokenizer"),pdr=o(" or "),Dee=a("a"),_dr=o("FNetTokenizerFast"),vdr=o(" (FNet model)"),bdr=l(),ev=a("li"),xAe=a("strong"),Mdr=o("fsmt"),Tdr=o(" \u2014 "),qee=a("a"),Fdr=o("FSMTTokenizer"),Edr=o(" (FairSeq Machine-Translation model)"),ydr=l(),kl=a("li"),GAe=a("strong"),Cdr=o("funnel"),wdr=o(" \u2014 "),Oee=a("a"),Ldr=o("FunnelTokenizer"),Adr=o(" or "),Qee=a("a"),Rdr=o("FunnelTokenizerFast"),kdr=o(" (Funnel Transformer model)"),Sdr=l(),Sl=a("li"),BAe=a("strong"),xdr=o("git"),Gdr=o(" \u2014 "),Yee=a("a"),Bdr=o("BertTokenizer"),Idr=o(" or "),zee=a("a"),Ndr=o("BertTokenizerFast"),$dr=o(" (GIT model)"),jdr=l(),ov=a("li"),IAe=a("strong"),Pdr=o("gpt-sw3"),Zdr=o(" \u2014 "),Hee=a("a"),Vdr=o("GPTSw3Tokenizer"),Wdr=o(" (GPT-Sw3 model)"),Jdr=l(),xl=a("li"),NAe=a("strong"),Udr=o("gpt2"),Xdr=o(" \u2014 "),Kee=a("a"),Ddr=o("GPT2Tokenizer"),qdr=o(" or "),eoe=a("a"),Odr=o("GPT2TokenizerFast"),Qdr=o(" (OpenAI GPT-2 model)"),Ydr=l(),Gl=a("li"),$Ae=a("strong"),zdr=o("gpt_bigcode"),Hdr=o(" \u2014 "),ooe=a("a"),Kdr=o("GPT2Tokenizer"),ecr=o(" or "),roe=a("a"),ocr=o("GPT2TokenizerFast"),rcr=o(" (GPTBigCode model)"),tcr=l(),Bl=a("li"),jAe=a("strong"),acr=o("gpt_neo"),ncr=o(" \u2014 "),toe=a("a"),scr=o("GPT2Tokenizer"),lcr=o(" or "),aoe=a("a"),icr=o("GPT2TokenizerFast"),dcr=o(" (GPT Neo model)"),ccr=l(),rv=a("li"),PAe=a("strong"),mcr=o("gpt_neox"),fcr=o(" \u2014 "),noe=a("a"),hcr=o("GPTNeoXTokenizerFast"),gcr=o(" (GPT NeoX model)"),ucr=l(),tv=a("li"),ZAe=a("strong"),pcr=o("gpt_neox_japanese"),_cr=o(" \u2014 "),soe=a("a"),vcr=o("GPTNeoXJapaneseTokenizer"),bcr=o(" (GPT NeoX Japanese model)"),Mcr=l(),Il=a("li"),VAe=a("strong"),Tcr=o("gptj"),Fcr=o(" \u2014 "),loe=a("a"),Ecr=o("GPT2Tokenizer"),ycr=o(" or "),ioe=a("a"),Ccr=o("GPT2TokenizerFast"),wcr=o(" (GPT-J model)"),Lcr=l(),av=a("li"),WAe=a("strong"),Acr=o("gptsan-japanese"),Rcr=o(" \u2014 "),doe=a("a"),kcr=o("GPTSanJapaneseTokenizer"),Scr=o(" (GPTSAN-japanese model)"),xcr=l(),Nl=a("li"),JAe=a("strong"),Gcr=o("groupvit"),Bcr=o(" \u2014 "),coe=a("a"),Icr=o("CLIPTokenizer"),Ncr=o(" or "),moe=a("a"),$cr=o("CLIPTokenizerFast"),jcr=o(" (GroupViT model)"),Pcr=l(),$l=a("li"),UAe=a("strong"),Zcr=o("herbert"),Vcr=o(" \u2014 "),foe=a("a"),Wcr=o("HerbertTokenizer"),Jcr=o(" or "),hoe=a("a"),Ucr=o("HerbertTokenizerFast"),Xcr=o(" (HerBERT model)"),Dcr=l(),nv=a("li"),XAe=a("strong"),qcr=o("hubert"),Ocr=o(" \u2014 "),goe=a("a"),Qcr=o("Wav2Vec2CTCTokenizer"),Ycr=o(" (Hubert model)"),zcr=l(),jl=a("li"),DAe=a("strong"),Hcr=o("ibert"),Kcr=o(" \u2014 "),uoe=a("a"),emr=o("RobertaTokenizer"),omr=o(" or "),poe=a("a"),rmr=o("RobertaTokenizerFast"),tmr=o(" (I-BERT model)"),amr=l(),sv=a("li"),qAe=a("strong"),nmr=o("idefics"),smr=o(" \u2014 "),_oe=a("a"),lmr=o("LlamaTokenizerFast"),imr=o(" (IDEFICS model)"),dmr=l(),Pl=a("li"),OAe=a("strong"),cmr=o("instructblip"),mmr=o(" \u2014 "),voe=a("a"),fmr=o("GPT2Tokenizer"),hmr=o(" or "),boe=a("a"),gmr=o("GPT2TokenizerFast"),umr=o(" (InstructBLIP model)"),pmr=l(),lv=a("li"),QAe=a("strong"),_mr=o("jukebox"),vmr=o(" \u2014 "),Moe=a("a"),bmr=o("JukeboxTokenizer"),Mmr=o(" (Jukebox model)"),Tmr=l(),Zl=a("li"),YAe=a("strong"),Fmr=o("layoutlm"),Emr=o(" \u2014 "),Toe=a("a"),ymr=o("LayoutLMTokenizer"),Cmr=o(" or "),Foe=a("a"),wmr=o("LayoutLMTokenizerFast"),Lmr=o(" (LayoutLM model)"),Amr=l(),Vl=a("li"),zAe=a("strong"),Rmr=o("layoutlmv2"),kmr=o(" \u2014 "),Eoe=a("a"),Smr=o("LayoutLMv2Tokenizer"),xmr=o(" or "),yoe=a("a"),Gmr=o("LayoutLMv2TokenizerFast"),Bmr=o(" (LayoutLMv2 model)"),Imr=l(),Wl=a("li"),HAe=a("strong"),Nmr=o("layoutlmv3"),$mr=o(" \u2014 "),Coe=a("a"),jmr=o("LayoutLMv3Tokenizer"),Pmr=o(" or "),woe=a("a"),Zmr=o("LayoutLMv3TokenizerFast"),Vmr=o(" (LayoutLMv3 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"),Doe=a("a"),ihr=o("MBart50Tokenizer"),dhr=o(" or "),qoe=a("a"),chr=o("MBart50TokenizerFast"),mhr=o(" (mBART-50 model)"),fhr=l(),Hl=a("li"),mRe=a("strong"),hhr=o("mega"),ghr=o(" \u2014 "),Ooe=a("a"),uhr=o("RobertaTokenizer"),phr=o(" or "),Qoe=a("a"),_hr=o("RobertaTokenizerFast"),vhr=o(" (MEGA model)"),bhr=l(),Kl=a("li"),fRe=a("strong"),Mhr=o("megatron-bert"),Thr=o(" \u2014 "),Yoe=a("a"),Fhr=o("BertTokenizer"),Ehr=o(" or "),zoe=a("a"),yhr=o("BertTokenizerFast"),Chr=o(" (Megatron-BERT model)"),whr=l(),mv=a("li"),hRe=a("strong"),Lhr=o("mgp-str"),Ahr=o(" \u2014 "),Hoe=a("a"),Rhr=o("MgpstrTokenizer"),khr=o(" (MGP-STR model)"),Shr=l(),fv=a("li"),gRe=a("strong"),xhr=o("mluke"),Ghr=o(" \u2014 "),Koe=a("a"),Bhr=o("MLukeTokenizer"),Ihr=o(" (mLUKE model)"),Nhr=l(),ei=a("li"),uRe=a("strong"),$hr=o("mobilebert"),jhr=o(" \u2014 "),ere=a("a"),Phr=o("MobileBertTokenizer"),Zhr=o(" or "),ore=a("a"),Vhr=o("MobileBertTokenizerFast"),Whr=o(" (MobileBERT 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"),Mme=a("a"),CYr=o("TFMobileBertModel"),wYr=o(" (MobileBERT model)"),LYr=l(),vF=a("li"),Nje=a("strong"),AYr=o("mobilevit"),RYr=o(" \u2014 "),Tme=a("a"),kYr=o("TFMobileViTModel"),SYr=o(" (MobileViT model)"),xYr=l(),bF=a("li"),$je=a("strong"),GYr=o("mpnet"),BYr=o(" \u2014 "),Fme=a("a"),IYr=o("TFMPNetModel"),NYr=o(" (MPNet model)"),$Yr=l(),MF=a("li"),jje=a("strong"),jYr=o("mt5"),PYr=o(" \u2014 "),Eme=a("a"),ZYr=o("TFMT5Model"),VYr=o(" (MT5 model)"),WYr=l(),TF=a("li"),Pje=a("strong"),JYr=o("openai-gpt"),UYr=o(" \u2014 "),yme=a("a"),XYr=o("TFOpenAIGPTModel"),DYr=o(" (OpenAI GPT model)"),qYr=l(),FF=a("li"),Zje=a("strong"),OYr=o("opt"),QYr=o(" \u2014 "),Cme=a("a"),YYr=o("TFOPTModel"),zYr=o(" (OPT model)"),HYr=l(),EF=a("li"),Vje=a("strong"),KYr=o("pegasus"),ezr=o(" \u2014 "),wme=a("a"),ozr=o("TFPegasusModel"),rzr=o(" (Pegasus model)"),tzr=l(),yF=a("li"),Wje=a("strong"),azr=o("regnet"),nzr=o(" \u2014 "),Lme=a("a"),szr=o("TFRegNetModel"),lzr=o(" (RegNet model)"),izr=l(),CF=a("li"),Jje=a("strong"),dzr=o("rembert"),czr=o(" \u2014 "),Ame=a("a"),mzr=o("TFRemBertModel"),fzr=o(" (RemBERT model)"),hzr=l(),wF=a("li"),Uje=a("strong"),gzr=o("resnet"),uzr=o(" \u2014 "),Rme=a("a"),pzr=o("TFResNetModel"),_zr=o(" (ResNet model)"),vzr=l(),LF=a("li"),Xje=a("strong"),bzr=o("roberta"),Mzr=o(" \u2014 "),kme=a("a"),Tzr=o("TFRobertaModel"),Fzr=o(" (RoBERTa model)"),Ezr=l(),AF=a("li"),Dje=a("strong"),yzr=o("roberta-prelayernorm"),Czr=o(" \u2014 "),Sme=a("a"),wzr=o("TFRobertaPreLayerNormModel"),Lzr=o(" (RoBERTa-PreLayerNorm model)"),Azr=l(),RF=a("li"),qje=a("strong"),Rzr=o("roformer"),kzr=o(" \u2014 "),xme=a("a"),Szr=o("TFRoFormerModel"),xzr=o(" (RoFormer model)"),Gzr=l(),kF=a("li"),Oje=a("strong"),Bzr=o("sam"),Izr=o(" \u2014 "),Gme=a("a"),Nzr=o("TFSamModel"),$zr=o(" (SAM model)"),jzr=l(),SF=a("li"),Qje=a("strong"),Pzr=o("segformer"),Zzr=o(" \u2014 "),Bme=a("a"),Vzr=o("TFSegformerModel"),Wzr=o(" (SegFormer model)"),Jzr=l(),xF=a("li"),Yje=a("strong"),Uzr=o("speech_to_text"),Xzr=o(" \u2014 "),Ime=a("a"),Dzr=o("TFSpeech2TextModel"),qzr=o(" (Speech2Text model)"),Ozr=l(),GF=a("li"),zje=a("strong"),Qzr=o("swin"),Yzr=o(" \u2014 "),Nme=a("a"),zzr=o("TFSwinModel"),Hzr=o(" (Swin Transformer model)"),Kzr=l(),BF=a("li"),Hje=a("strong"),eHr=o("t5"),oHr=o(" \u2014 "),$me=a("a"),rHr=o("TFT5Model"),tHr=o(" (T5 model)"),aHr=l(),IF=a("li"),Kje=a("strong"),nHr=o("tapas"),sHr=o(" \u2014 "),jme=a("a"),lHr=o("TFTapasModel"),iHr=o(" (TAPAS model)"),dHr=l(),NF=a("li"),ePe=a("strong"),cHr=o("transfo-xl"),mHr=o(" \u2014 "),Pme=a("a"),fHr=o("TFTransfoXLModel"),hHr=o(" (Transformer-XL model)"),gHr=l(),$F=a("li"),oPe=a("strong"),uHr=o("vision-text-dual-encoder"),pHr=o(" \u2014 "),Zme=a("a"),_Hr=o("TFVisionTextDualEncoderModel"),vHr=o(" (VisionTextDualEncoder model)"),bHr=l(),jF=a("li"),rPe=a("strong"),MHr=o("vit"),THr=o(" \u2014 "),Vme=a("a"),FHr=o("TFViTModel"),EHr=o(" (ViT model)"),yHr=l(),PF=a("li"),tPe=a("strong"),CHr=o("vit_mae"),wHr=o(" \u2014 "),Wme=a("a"),LHr=o("TFViTMAEModel"),AHr=o(" (ViTMAE model)"),RHr=l(),ZF=a("li"),aPe=a("strong"),kHr=o("wav2vec2"),SHr=o(" \u2014 "),Jme=a("a"),xHr=o("TFWav2Vec2Model"),GHr=o(" (Wav2Vec2 model)"),BHr=l(),VF=a("li"),nPe=a("strong"),IHr=o("whisper"),NHr=o(" \u2014 "),Ume=a("a"),$Hr=o("TFWhisperModel"),jHr=o(" (Whisper model)"),PHr=l(),WF=a("li"),sPe=a("strong"),ZHr=o("xglm"),VHr=o(" \u2014 "),Xme=a("a"),WHr=o("TFXGLMModel"),JHr=o(" (XGLM model)"),UHr=l(),JF=a("li"),lPe=a("strong"),XHr=o("xlm"),DHr=o(" \u2014 "),Dme=a("a"),qHr=o("TFXLMModel"),OHr=o(" (XLM model)"),QHr=l(),UF=a("li"),iPe=a("strong"),YHr=o("xlm-roberta"),zHr=o(" \u2014 "),qme=a("a"),HHr=o("TFXLMRobertaModel"),KHr=o(" (XLM-RoBERTa model)"),eKr=l(),XF=a("li"),dPe=a("strong"),oKr=o("xlnet"),rKr=o(" \u2014 "),Ome=a("a"),tKr=o("TFXLNetModel"),aKr=o(" (XLNet model)"),nKr=l(),b(DF.$$.fragment),dGo=l(),dm=a("h3"),qF=a("a"),cPe=a("span"),b(NV.$$.fragment),sKr=l(),mPe=a("span"),lKr=o("FlaxAutoModel"),cGo=l(),Do=a("div"),b($V.$$.fragment),iKr=l(),cm=a("p"),dKr=o(`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),Qme=a("a"),cKr=o("from_pretrained()"),mKr=o(" class method or the "),Yme=a("a"),fKr=o("from_config()"),hKr=o(` class
method.`),gKr=l(),jV=a("p"),uKr=o("This class cannot be instantiated directly using "),fPe=a("code"),pKr=o("__init__()"),_Kr=o(" (throws an error)."),vKr=l(),Ut=a("div"),b(PV.$$.fragment),bKr=l(),hPe=a("p"),MKr=o("Instantiates one of the base model classes of the library from a configuration."),TKr=l(),mm=a("p"),FKr=o(`Note:
Loading a model from its configuration file does `),gPe=a("strong"),EKr=o("not"),yKr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),zme=a("a"),CKr=o("from_pretrained()"),wKr=o(" to load the model weights."),LKr=l(),b(OF.$$.fragment),AKr=l(),at=a("div"),b(ZV.$$.fragment),RKr=l(),uPe=a("p"),kKr=o("Instantiate one of the base model classes of the library from a pretrained model."),SKr=l(),Vn=a("p"),xKr=o("The model class to instantiate is selected based on the "),pPe=a("code"),GKr=o("model_type"),BKr=o(` property of the config object (either
passed as an argument or loaded from `),_Pe=a("code"),IKr=o("pretrained_model_name_or_path"),NKr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),vPe=a("code"),$Kr=o("pretrained_model_name_or_path"),jKr=o(":"),PKr=l(),se=a("ul"),QF=a("li"),bPe=a("strong"),ZKr=o("albert"),VKr=o(" \u2014 "),Hme=a("a"),WKr=o("FlaxAlbertModel"),JKr=o(" (ALBERT model)"),UKr=l(),YF=a("li"),MPe=a("strong"),XKr=o("bart"),DKr=o(" \u2014 "),Kme=a("a"),qKr=o("FlaxBartModel"),OKr=o(" (BART model)"),QKr=l(),zF=a("li"),TPe=a("strong"),YKr=o("beit"),zKr=o(" \u2014 "),efe=a("a"),HKr=o("FlaxBeitModel"),KKr=o(" (BEiT model)"),eet=l(),HF=a("li"),FPe=a("strong"),oet=o("bert"),ret=o(" \u2014 "),ofe=a("a"),tet=o("FlaxBertModel"),aet=o(" (BERT model)"),net=l(),KF=a("li"),EPe=a("strong"),set=o("big_bird"),iet=o(" \u2014 "),rfe=a("a"),det=o("FlaxBigBirdModel"),cet=o(" (BigBird model)"),met=l(),eE=a("li"),yPe=a("strong"),fet=o("blenderbot"),het=o(" \u2014 "),tfe=a("a"),get=o("FlaxBlenderbotModel"),uet=o(" (Blenderbot model)"),pet=l(),oE=a("li"),CPe=a("strong"),_et=o("blenderbot-small"),vet=o(" \u2014 "),afe=a("a"),bet=o("FlaxBlenderbotSmallModel"),Met=o(" (BlenderbotSmall model)"),Tet=l(),rE=a("li"),wPe=a("strong"),Fet=o("bloom"),Eet=o(" \u2014 "),nfe=a("a"),yet=o("FlaxBloomModel"),Cet=o(" (BLOOM model)"),wet=l(),tE=a("li"),LPe=a("strong"),Let=o("clip"),Aet=o(" \u2014 "),sfe=a("a"),Ret=o("FlaxCLIPModel"),ket=o(" (CLIP model)"),Set=l(),aE=a("li"),APe=a("strong"),xet=o("distilbert"),Get=o(" \u2014 "),lfe=a("a"),Bet=o("FlaxDistilBertModel"),Iet=o(" (DistilBERT model)"),Net=l(),nE=a("li"),RPe=a("strong"),$et=o("electra"),jet=o(" \u2014 "),ife=a("a"),Pet=o("FlaxElectraModel"),Zet=o(" (ELECTRA model)"),Vet=l(),sE=a("li"),kPe=a("strong"),Wet=o("gpt-sw3"),Jet=o(" \u2014 "),dfe=a("a"),Uet=o("FlaxGPT2Model"),Xet=o(" (GPT-Sw3 model)"),Det=l(),lE=a("li"),SPe=a("strong"),qet=o("gpt2"),Oet=o(" \u2014 "),cfe=a("a"),Qet=o("FlaxGPT2Model"),Yet=o(" (OpenAI GPT-2 model)"),zet=l(),iE=a("li"),xPe=a("strong"),Het=o("gpt_neo"),Ket=o(" \u2014 "),mfe=a("a"),eot=o("FlaxGPTNeoModel"),oot=o(" (GPT Neo model)"),rot=l(),dE=a("li"),GPe=a("strong"),tot=o("gptj"),aot=o(" \u2014 "),ffe=a("a"),not=o("FlaxGPTJModel"),sot=o(" (GPT-J model)"),lot=l(),cE=a("li"),BPe=a("strong"),iot=o("longt5"),dot=o(" \u2014 "),hfe=a("a"),cot=o("FlaxLongT5Model"),mot=o(" (LongT5 model)"),fot=l(),mE=a("li"),IPe=a("strong"),hot=o("marian"),got=o(" \u2014 "),gfe=a("a"),uot=o("FlaxMarianModel"),pot=o(" (Marian model)"),_ot=l(),fE=a("li"),NPe=a("strong"),vot=o("mbart"),bot=o(" \u2014 "),ufe=a("a"),Mot=o("FlaxMBartModel"),Tot=o(" (mBART model)"),Fot=l(),hE=a("li"),$Pe=a("strong"),Eot=o("mt5"),yot=o(" \u2014 "),pfe=a("a"),Cot=o("FlaxMT5Model"),wot=o(" (MT5 model)"),Lot=l(),gE=a("li"),jPe=a("strong"),Aot=o("opt"),Rot=o(" \u2014 "),_fe=a("a"),kot=o("FlaxOPTModel"),Sot=o(" (OPT model)"),xot=l(),uE=a("li"),PPe=a("strong"),Got=o("pegasus"),Bot=o(" \u2014 "),vfe=a("a"),Iot=o("FlaxPegasusModel"),Not=o(" (Pegasus model)"),$ot=l(),pE=a("li"),ZPe=a("strong"),jot=o("regnet"),Pot=o(" \u2014 "),bfe=a("a"),Zot=o("FlaxRegNetModel"),Vot=o(" (RegNet model)"),Wot=l(),_E=a("li"),VPe=a("strong"),Jot=o("resnet"),Uot=o(" \u2014 "),Mfe=a("a"),Xot=o("FlaxResNetModel"),Dot=o(" (ResNet model)"),qot=l(),vE=a("li"),WPe=a("strong"),Oot=o("roberta"),Qot=o(" \u2014 "),Tfe=a("a"),Yot=o("FlaxRobertaModel"),zot=o(" (RoBERTa model)"),Hot=l(),bE=a("li"),JPe=a("strong"),Kot=o("roberta-prelayernorm"),ert=o(" \u2014 "),Ffe=a("a"),ort=o("FlaxRobertaPreLayerNormModel"),rrt=o(" (RoBERTa-PreLayerNorm model)"),trt=l(),ME=a("li"),UPe=a("strong"),art=o("roformer"),nrt=o(" \u2014 "),Efe=a("a"),srt=o("FlaxRoFormerModel"),lrt=o(" (RoFormer model)"),irt=l(),TE=a("li"),XPe=a("strong"),drt=o("t5"),crt=o(" \u2014 "),yfe=a("a"),mrt=o("FlaxT5Model"),frt=o(" (T5 model)"),hrt=l(),FE=a("li"),DPe=a("strong"),grt=o("vision-text-dual-encoder"),urt=o(" \u2014 "),Cfe=a("a"),prt=o("FlaxVisionTextDualEncoderModel"),_rt=o(" (VisionTextDualEncoder model)"),vrt=l(),EE=a("li"),qPe=a("strong"),brt=o("vit"),Mrt=o(" \u2014 "),wfe=a("a"),Trt=o("FlaxViTModel"),Frt=o(" (ViT model)"),Ert=l(),yE=a("li"),OPe=a("strong"),yrt=o("wav2vec2"),Crt=o(" \u2014 "),Lfe=a("a"),wrt=o("FlaxWav2Vec2Model"),Lrt=o(" (Wav2Vec2 model)"),Art=l(),CE=a("li"),QPe=a("strong"),Rrt=o("whisper"),krt=o(" \u2014 "),Afe=a("a"),Srt=o("FlaxWhisperModel"),xrt=o(" (Whisper model)"),Grt=l(),wE=a("li"),YPe=a("strong"),Brt=o("xglm"),Irt=o(" \u2014 "),Rfe=a("a"),Nrt=o("FlaxXGLMModel"),$rt=o(" (XGLM model)"),jrt=l(),LE=a("li"),zPe=a("strong"),Prt=o("xlm-roberta"),Zrt=o(" \u2014 "),kfe=a("a"),Vrt=o("FlaxXLMRobertaModel"),Wrt=o(" (XLM-RoBERTa model)"),Jrt=l(),b(AE.$$.fragment),mGo=l(),fm=a("h2"),RE=a("a"),HPe=a("span"),b(VV.$$.fragment),Urt=l(),KPe=a("span"),Xrt=o("Generic pretraining classes"),fGo=l(),Sfe=a("p"),Drt=o("The following auto classes are available for instantiating a model with a pretraining head."),hGo=l(),hm=a("h3"),kE=a("a"),eZe=a("span"),b(WV.$$.fragment),qrt=l(),oZe=a("span"),Ort=o("AutoModelForPreTraining"),gGo=l(),qo=a("div"),b(JV.$$.fragment),Qrt=l(),gm=a("p"),Yrt=o(`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 `),xfe=a("a"),zrt=o("from_pretrained()"),Hrt=o(" class method or the "),Gfe=a("a"),Krt=o("from_config()"),ett=o(` class
method.`),ott=l(),UV=a("p"),rtt=o("This class cannot be instantiated directly using "),rZe=a("code"),ttt=o("__init__()"),att=o(" (throws an error)."),ntt=l(),Xt=a("div"),b(XV.$$.fragment),stt=l(),tZe=a("p"),ltt=o("Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),itt=l(),um=a("p"),dtt=o(`Note:
Loading a model from its configuration file does `),aZe=a("strong"),ctt=o("not"),mtt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Bfe=a("a"),ftt=o("from_pretrained()"),htt=o(" to load the model weights."),gtt=l(),b(SE.$$.fragment),utt=l(),lo=a("div"),b(DV.$$.fragment),ptt=l(),nZe=a("p"),_tt=o("Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),vtt=l(),Wn=a("p"),btt=o("The model class to instantiate is selected based on the "),sZe=a("code"),Mtt=o("model_type"),Ttt=o(` property of the config object (either
passed as an argument or loaded from `),lZe=a("code"),Ftt=o("pretrained_model_name_or_path"),Ett=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),iZe=a("code"),ytt=o("pretrained_model_name_or_path"),Ctt=o(":"),wtt=l(),U=a("ul"),xE=a("li"),dZe=a("strong"),Ltt=o("albert"),Att=o(" \u2014 "),Ife=a("a"),Rtt=o("AlbertForPreTraining"),ktt=o(" (ALBERT model)"),Stt=l(),GE=a("li"),cZe=a("strong"),xtt=o("bart"),Gtt=o(" \u2014 "),Nfe=a("a"),Btt=o("BartForConditionalGeneration"),Itt=o(" (BART model)"),Ntt=l(),BE=a("li"),mZe=a("strong"),$tt=o("bert"),jtt=o(" \u2014 "),$fe=a("a"),Ptt=o("BertForPreTraining"),Ztt=o(" (BERT model)"),Vtt=l(),IE=a("li"),fZe=a("strong"),Wtt=o("big_bird"),Jtt=o(" \u2014 "),jfe=a("a"),Utt=o("BigBirdForPreTraining"),Xtt=o(" (BigBird model)"),Dtt=l(),NE=a("li"),hZe=a("strong"),qtt=o("bloom"),Ott=o(" \u2014 "),Pfe=a("a"),Qtt=o("BloomForCausalLM"),Ytt=o(" (BLOOM model)"),ztt=l(),$E=a("li"),gZe=a("strong"),Htt=o("camembert"),Ktt=o(" \u2014 "),Zfe=a("a"),eat=o("CamembertForMaskedLM"),oat=o(" (CamemBERT model)"),rat=l(),jE=a("li"),uZe=a("strong"),tat=o("ctrl"),aat=o(" \u2014 "),Vfe=a("a"),nat=o("CTRLLMHeadModel"),sat=o(" (CTRL model)"),lat=l(),PE=a("li"),pZe=a("strong"),iat=o("data2vec-text"),dat=o(" \u2014 "),Wfe=a("a"),cat=o("Data2VecTextForMaskedLM"),mat=o(" (Data2VecText model)"),fat=l(),ZE=a("li"),_Ze=a("strong"),hat=o("deberta"),gat=o(" \u2014 "),Jfe=a("a"),uat=o("DebertaForMaskedLM"),pat=o(" (DeBERTa model)"),_at=l(),VE=a("li"),vZe=a("strong"),vat=o("deberta-v2"),bat=o(" \u2014 "),Ufe=a("a"),Mat=o("DebertaV2ForMaskedLM"),Tat=o(" (DeBERTa-v2 model)"),Fat=l(),WE=a("li"),bZe=a("strong"),Eat=o("distilbert"),yat=o(" \u2014 "),Xfe=a("a"),Cat=o("DistilBertForMaskedLM"),wat=o(" (DistilBERT model)"),Lat=l(),JE=a("li"),MZe=a("strong"),Aat=o("electra"),Rat=o(" \u2014 "),Dfe=a("a"),kat=o("ElectraForPreTraining"),Sat=o(" (ELECTRA model)"),xat=l(),UE=a("li"),TZe=a("strong"),Gat=o("ernie"),Bat=o(" \u2014 "),qfe=a("a"),Iat=o("ErnieForPreTraining"),Nat=o(" (ERNIE model)"),$at=l(),XE=a("li"),FZe=a("strong"),jat=o("flaubert"),Pat=o(" \u2014 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model\u2019s configuration. Use `),bge=a("a"),Bft=o("from_pretrained()"),Ift=o(" to load the model weights."),Nft=l(),b(_5.$$.fragment),$ft=l(),st=a("div"),b(rW.$$.fragment),jft=l(),aWe=a("p"),Pft=o("Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),Zft=l(),Un=a("p"),Vft=o("The model class to instantiate is selected based on the "),nWe=a("code"),Wft=o("model_type"),Jft=o(` property of the config object (either
passed as an argument or loaded from `),sWe=a("code"),Uft=o("pretrained_model_name_or_path"),Xft=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),lWe=a("code"),Dft=o("pretrained_model_name_or_path"),qft=o(":"),Oft=l(),we=a("ul"),v5=a("li"),iWe=a("strong"),Qft=o("albert"),Yft=o(" \u2014 "),Mge=a("a"),zft=o("FlaxAlbertForPreTraining"),Hft=o(" (ALBERT model)"),Kft=l(),b5=a("li"),dWe=a("strong"),eht=o("bart"),oht=o(" \u2014 "),Tge=a("a"),rht=o("FlaxBartForConditionalGeneration"),tht=o(" (BART model)"),aht=l(),M5=a("li"),cWe=a("strong"),nht=o("bert"),sht=o(" \u2014 "),Fge=a("a"),lht=o("FlaxBertForPreTraining"),iht=o(" (BERT model)"),dht=l(),T5=a("li"),mWe=a("strong"),cht=o("big_bird"),mht=o(" \u2014 "),Ege=a("a"),fht=o("FlaxBigBirdForPreTraining"),hht=o(" (BigBird model)"),ght=l(),F5=a("li"),fWe=a("strong"),uht=o("electra"),pht=o(" \u2014 "),yge=a("a"),_ht=o("FlaxElectraForPreTraining"),vht=o(" (ELECTRA model)"),bht=l(),E5=a("li"),hWe=a("strong"),Mht=o("longt5"),Tht=o(" \u2014 "),Cge=a("a"),Fht=o("FlaxLongT5ForConditionalGeneration"),Eht=o(" (LongT5 model)"),yht=l(),y5=a("li"),gWe=a("strong"),Cht=o("mbart"),wht=o(" \u2014 "),wge=a("a"),Lht=o("FlaxMBartForConditionalGeneration"),Aht=o(" (mBART model)"),Rht=l(),C5=a("li"),uWe=a("strong"),kht=o("mt5"),Sht=o(" \u2014 "),Lge=a("a"),xht=o("FlaxMT5ForConditionalGeneration"),Ght=o(" (MT5 model)"),Bht=l(),w5=a("li"),pWe=a("strong"),Iht=o("roberta"),Nht=o(" \u2014 "),Age=a("a"),$ht=o("FlaxRobertaForMaskedLM"),jht=o(" (RoBERTa model)"),Pht=l(),L5=a("li"),_We=a("strong"),Zht=o("roberta-prelayernorm"),Vht=o(" \u2014 "),Rge=a("a"),Wht=o("FlaxRobertaPreLayerNormForMaskedLM"),Jht=o(" (RoBERTa-PreLayerNorm model)"),Uht=l(),A5=a("li"),vWe=a("strong"),Xht=o("roformer"),Dht=o(" \u2014 "),kge=a("a"),qht=o("FlaxRoFormerForMaskedLM"),Oht=o(" (RoFormer model)"),Qht=l(),R5=a("li"),bWe=a("strong"),Yht=o("t5"),zht=o(" \u2014 "),Sge=a("a"),Hht=o("FlaxT5ForConditionalGeneration"),Kht=o(" (T5 model)"),egt=l(),k5=a("li"),MWe=a("strong"),ogt=o("wav2vec2"),rgt=o(" \u2014 "),xge=a("a"),tgt=o("FlaxWav2Vec2ForPreTraining"),agt=o(" (Wav2Vec2 model)"),ngt=l(),S5=a("li"),TWe=a("strong"),sgt=o("whisper"),lgt=o(" \u2014 "),Gge=a("a"),igt=o("FlaxWhisperForConditionalGeneration"),dgt=o(" (Whisper model)"),cgt=l(),x5=a("li"),FWe=a("strong"),mgt=o("xlm-roberta"),fgt=o(" \u2014 "),Bge=a("a"),hgt=o("FlaxXLMRobertaForMaskedLM"),ggt=o(" (XLM-RoBERTa model)"),ugt=l(),b(G5.$$.fragment),bGo=l(),Fm=a("h2"),B5=a("a"),EWe=a("span"),b(tW.$$.fragment),pgt=l(),yWe=a("span"),_gt=o("Natural Language Processing"),MGo=l(),Ige=a("p"),vgt=o("The following auto classes are available for the following natural language processing tasks."),TGo=l(),Em=a("h3"),I5=a("a"),CWe=a("span"),b(aW.$$.fragment),bgt=l(),wWe=a("span"),Mgt=o("AutoModelForCausalLM"),FGo=l(),Yo=a("div"),b(nW.$$.fragment),Tgt=l(),ym=a("p"),Fgt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),Nge=a("a"),Egt=o("from_pretrained()"),ygt=o(" class method or the "),$ge=a("a"),Cgt=o("from_config()"),wgt=o(` class
method.`),Lgt=l(),sW=a("p"),Agt=o("This class cannot be instantiated directly using "),LWe=a("code"),Rgt=o("__init__()"),kgt=o(" (throws an error)."),Sgt=l(),Ot=a("div"),b(lW.$$.fragment),xgt=l(),AWe=a("p"),Ggt=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),Bgt=l(),Cm=a("p"),Igt=o(`Note:
Loading a model from its configuration file does `),RWe=a("strong"),Ngt=o("not"),$gt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),jge=a("a"),jgt=o("from_pretrained()"),Pgt=o(" to load the model weights."),Zgt=l(),b(N5.$$.fragment),Vgt=l(),io=a("div"),b(iW.$$.fragment),Wgt=l(),kWe=a("p"),Jgt=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),Ugt=l(),Xn=a("p"),Xgt=o("The model class to instantiate is selected based on the "),SWe=a("code"),Dgt=o("model_type"),qgt=o(` property of the config object (either
passed as an argument or loaded from `),xWe=a("code"),Ogt=o("pretrained_model_name_or_path"),Qgt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),GWe=a("code"),Ygt=o("pretrained_model_name_or_path"),zgt=o(":"),Hgt=l(),Q=a("ul"),$5=a("li"),BWe=a("strong"),Kgt=o("bart"),eut=o(" \u2014 "),Pge=a("a"),out=o("BartForCausalLM"),rut=o(" (BART model)"),tut=l(),j5=a("li"),IWe=a("strong"),aut=o("bert"),nut=o(" \u2014 "),Zge=a("a"),sut=o("BertLMHeadModel"),lut=o(" (BERT model)"),iut=l(),P5=a("li"),NWe=a("strong"),dut=o("bert-generation"),cut=o(" \u2014 "),Vge=a("a"),mut=o("BertGenerationDecoder"),fut=o(" (Bert Generation model)"),hut=l(),Z5=a("li"),$We=a("strong"),gut=o("big_bird"),uut=o(" \u2014 "),Wge=a("a"),put=o("BigBirdForCausalLM"),_ut=o(" (BigBird model)"),vut=l(),V5=a("li"),jWe=a("strong"),but=o("bigbird_pegasus"),Mut=o(" \u2014 "),Jge=a("a"),Tut=o("BigBirdPegasusForCausalLM"),Fut=o(" (BigBird-Pegasus model)"),Eut=l(),W5=a("li"),PWe=a("strong"),yut=o("biogpt"),Cut=o(" \u2014 "),Uge=a("a"),wut=o("BioGptForCausalLM"),Lut=o(" (BioGpt model)"),Aut=l(),J5=a("li"),ZWe=a("strong"),Rut=o("blenderbot"),kut=o(" \u2014 "),Xge=a("a"),Sut=o("BlenderbotForCausalLM"),xut=o(" (Blenderbot model)"),Gut=l(),U5=a("li"),VWe=a("strong"),But=o("blenderbot-small"),Iut=o(" \u2014 "),Dge=a("a"),Nut=o("BlenderbotSmallForCausalLM"),$ut=o(" (BlenderbotSmall model)"),jut=l(),X5=a("li"),WWe=a("strong"),Put=o("bloom"),Zut=o(" \u2014 "),qge=a("a"),Vut=o("BloomForCausalLM"),Wut=o(" (BLOOM model)"),Jut=l(),D5=a("li"),JWe=a("strong"),Uut=o("camembert"),Xut=o(" \u2014 "),Oge=a("a"),Dut=o("CamembertForCausalLM"),qut=o(" (CamemBERT model)"),Out=l(),q5=a("li"),UWe=a("strong"),Qut=o("codegen"),Yut=o(" \u2014 "),Qge=a("a"),zut=o("CodeGenForCausalLM"),Hut=o(" (CodeGen model)"),Kut=l(),O5=a("li"),XWe=a("strong"),ept=o("cpmant"),opt=o(" \u2014 "),Yge=a("a"),rpt=o("CpmAntForCausalLM"),tpt=o(" (CPM-Ant model)"),apt=l(),Q5=a("li"),DWe=a("strong"),npt=o("ctrl"),spt=o(" \u2014 "),zge=a("a"),lpt=o("CTRLLMHeadModel"),ipt=o(" (CTRL model)"),dpt=l(),Y5=a("li"),qWe=a("strong"),cpt=o("data2vec-text"),mpt=o(" \u2014 "),Hge=a("a"),fpt=o("Data2VecTextForCausalLM"),hpt=o(" (Data2VecText model)"),gpt=l(),z5=a("li"),OWe=a("strong"),upt=o("electra"),ppt=o(" \u2014 "),Kge=a("a"),_pt=o("ElectraForCausalLM"),vpt=o(" (ELECTRA model)"),bpt=l(),H5=a("li"),QWe=a("strong"),Mpt=o("ernie"),Tpt=o(" \u2014 "),eue=a("a"),Fpt=o("ErnieForCausalLM"),Ept=o(" (ERNIE model)"),ypt=l(),K5=a("li"),YWe=a("strong"),Cpt=o("falcon"),wpt=o(" \u2014 "),zWe=a("code"),Lpt=o("FalconForCausalLM"),Apt=o(" (Falcon model)"),Rpt=l(),e9=a("li"),HWe=a("strong"),kpt=o("git"),Spt=o(" \u2014 "),oue=a("a"),xpt=o("GitForCausalLM"),Gpt=o(" (GIT model)"),Bpt=l(),o9=a("li"),KWe=a("strong"),Ipt=o("gpt-sw3"),Npt=o(" \u2014 "),rue=a("a"),$pt=o("GPT2LMHeadModel"),jpt=o(" (GPT-Sw3 model)"),Ppt=l(),r9=a("li"),eJe=a("strong"),Zpt=o("gpt2"),Vpt=o(" \u2014 "),tue=a("a"),Wpt=o("GPT2LMHeadModel"),Jpt=o(" (OpenAI GPT-2 model)"),Upt=l(),t9=a("li"),oJe=a("strong"),Xpt=o("gpt_bigcode"),Dpt=o(" \u2014 "),aue=a("a"),qpt=o("GPTBigCodeForCausalLM"),Opt=o(" (GPTBigCode model)"),Qpt=l(),a9=a("li"),rJe=a("strong"),Ypt=o("gpt_neo"),zpt=o(" \u2014 "),nue=a("a"),Hpt=o("GPTNeoForCausalLM"),Kpt=o(" (GPT Neo model)"),e_t=l(),n9=a("li"),tJe=a("strong"),o_t=o("gpt_neox"),r_t=o(" \u2014 "),sue=a("a"),t_t=o("GPTNeoXForCausalLM"),a_t=o(" (GPT NeoX model)"),n_t=l(),s9=a("li"),aJe=a("strong"),s_t=o("gpt_neox_japanese"),l_t=o(" \u2014 "),lue=a("a"),i_t=o("GPTNeoXJapaneseForCausalLM"),d_t=o(" (GPT NeoX Japanese model)"),c_t=l(),l9=a("li"),nJe=a("strong"),m_t=o("gptj"),f_t=o(" \u2014 "),iue=a("a"),h_t=o("GPTJForCausalLM"),g_t=o(" (GPT-J model)"),u_t=l(),i9=a("li"),sJe=a("strong"),p_t=o("llama"),__t=o(" \u2014 "),due=a("a"),v_t=o("LlamaForCausalLM"),b_t=o(" (LLaMA model)"),M_t=l(),d9=a("li"),lJe=a("strong"),T_t=o("marian"),F_t=o(" \u2014 "),cue=a("a"),E_t=o("MarianForCausalLM"),y_t=o(" (Marian model)"),C_t=l(),c9=a("li"),iJe=a("strong"),w_t=o("mbart"),L_t=o(" \u2014 "),mue=a("a"),A_t=o("MBartForCausalLM"),R_t=o(" (mBART model)"),k_t=l(),m9=a("li"),dJe=a("strong"),S_t=o("mega"),x_t=o(" \u2014 "),fue=a("a"),G_t=o("MegaForCausalLM"),B_t=o(" (MEGA model)"),I_t=l(),f9=a("li"),cJe=a("strong"),N_t=o("megatron-bert"),$_t=o(" \u2014 "),hue=a("a"),j_t=o("MegatronBertForCausalLM"),P_t=o(" (Megatron-BERT model)"),Z_t=l(),h9=a("li"),mJe=a("strong"),V_t=o("mpt"),W_t=o(" \u2014 "),gue=a("a"),J_t=o("MptForCausalLM"),U_t=o(" (MPT model)"),X_t=l(),g9=a("li"),fJe=a("strong"),D_t=o("musicgen"),q_t=o(" \u2014 "),uue=a("a"),O_t=o("MusicgenForCausalLM"),Q_t=o(" (MusicGen model)"),Y_t=l(),u9=a("li"),hJe=a("strong"),z_t=o("mvp"),H_t=o(" \u2014 "),pue=a("a"),K_t=o("MvpForCausalLM"),e2t=o(" (MVP model)"),o2t=l(),p9=a("li"),gJe=a("strong"),r2t=o("open-llama"),t2t=o(" \u2014 "),_ue=a("a"),a2t=o("OpenLlamaForCausalLM"),n2t=o(" (OpenLlama model)"),s2t=l(),_9=a("li"),uJe=a("strong"),l2t=o("openai-gpt"),i2t=o(" \u2014 "),vue=a("a"),d2t=o("OpenAIGPTLMHeadModel"),c2t=o(" (OpenAI GPT model)"),m2t=l(),v9=a("li"),pJe=a("strong"),f2t=o("opt"),h2t=o(" \u2014 "),bue=a("a"),g2t=o("OPTForCausalLM"),u2t=o(" (OPT model)"),p2t=l(),b9=a("li"),_Je=a("strong"),_2t=o("pegasus"),v2t=o(" \u2014 "),Mue=a("a"),b2t=o("PegasusForCausalLM"),M2t=o(" (Pegasus model)"),T2t=l(),M9=a("li"),vJe=a("strong"),F2t=o("plbart"),E2t=o(" \u2014 "),Tue=a("a"),y2t=o("PLBartForCausalLM"),C2t=o(" (PLBart model)"),w2t=l(),T9=a("li"),bJe=a("strong"),L2t=o("prophetnet"),A2t=o(" \u2014 "),Fue=a("a"),R2t=o("ProphetNetForCausalLM"),k2t=o(" (ProphetNet model)"),S2t=l(),F9=a("li"),MJe=a("strong"),x2t=o("qdqbert"),G2t=o(" \u2014 "),Eue=a("a"),B2t=o("QDQBertLMHeadModel"),I2t=o(" (QDQBert model)"),N2t=l(),E9=a("li"),TJe=a("strong"),$2t=o("reformer"),j2t=o(" \u2014 "),yue=a("a"),P2t=o("ReformerModelWithLMHead"),Z2t=o(" (Reformer model)"),V2t=l(),y9=a("li"),FJe=a("strong"),W2t=o("rembert"),J2t=o(" \u2014 "),Cue=a("a"),U2t=o("RemBertForCausalLM"),X2t=o(" (RemBERT model)"),D2t=l(),C9=a("li"),EJe=a("strong"),q2t=o("roberta"),O2t=o(" \u2014 "),wue=a("a"),Q2t=o("RobertaForCausalLM"),Y2t=o(" (RoBERTa model)"),z2t=l(),w9=a("li"),yJe=a("strong"),H2t=o("roberta-prelayernorm"),K2t=o(" \u2014 "),Lue=a("a"),evt=o("RobertaPreLayerNormForCausalLM"),ovt=o(" (RoBERTa-PreLayerNorm model)"),rvt=l(),L9=a("li"),CJe=a("strong"),tvt=o("roc_bert"),avt=o(" \u2014 "),Aue=a("a"),nvt=o("RoCBertForCausalLM"),svt=o(" (RoCBert model)"),lvt=l(),A9=a("li"),wJe=a("strong"),ivt=o("roformer"),dvt=o(" \u2014 "),Rue=a("a"),cvt=o("RoFormerForCausalLM"),mvt=o(" (RoFormer model)"),fvt=l(),R9=a("li"),LJe=a("strong"),hvt=o("rwkv"),gvt=o(" \u2014 "),kue=a("a"),uvt=o("RwkvForCausalLM"),pvt=o(" (RWKV model)"),_vt=l(),k9=a("li"),AJe=a("strong"),vvt=o("speech_to_text_2"),bvt=o(" \u2014 "),Sue=a("a"),Mvt=o("Speech2Text2ForCausalLM"),Tvt=o(" (Speech2Text2 model)"),Fvt=l(),S9=a("li"),RJe=a("strong"),Evt=o("transfo-xl"),yvt=o(" \u2014 "),xue=a("a"),Cvt=o("TransfoXLLMHeadModel"),wvt=o(" (Transformer-XL model)"),Lvt=l(),x9=a("li"),kJe=a("strong"),Avt=o("trocr"),Rvt=o(" \u2014 "),Gue=a("a"),kvt=o("TrOCRForCausalLM"),Svt=o(" (TrOCR model)"),xvt=l(),G9=a("li"),SJe=a("strong"),Gvt=o("xglm"),Bvt=o(" \u2014 "),Bue=a("a"),Ivt=o("XGLMForCausalLM"),Nvt=o(" (XGLM model)"),$vt=l(),B9=a("li"),xJe=a("strong"),jvt=o("xlm"),Pvt=o(" \u2014 "),Iue=a("a"),Zvt=o("XLMWithLMHeadModel"),Vvt=o(" (XLM model)"),Wvt=l(),I9=a("li"),GJe=a("strong"),Jvt=o("xlm-prophetnet"),Uvt=o(" \u2014 "),Nue=a("a"),Xvt=o("XLMProphetNetForCausalLM"),Dvt=o(" (XLM-ProphetNet model)"),qvt=l(),N9=a("li"),BJe=a("strong"),Ovt=o("xlm-roberta"),Qvt=o(" \u2014 "),$ue=a("a"),Yvt=o("XLMRobertaForCausalLM"),zvt=o(" (XLM-RoBERTa model)"),Hvt=l(),$9=a("li"),IJe=a("strong"),Kvt=o("xlm-roberta-xl"),e1t=o(" \u2014 "),jue=a("a"),o1t=o("XLMRobertaXLForCausalLM"),r1t=o(" (XLM-RoBERTa-XL model)"),t1t=l(),j9=a("li"),NJe=a("strong"),a1t=o("xlnet"),n1t=o(" \u2014 "),Pue=a("a"),s1t=o("XLNetLMHeadModel"),l1t=o(" (XLNet model)"),i1t=l(),P9=a("li"),$Je=a("strong"),d1t=o("xmod"),c1t=o(" \u2014 "),Zue=a("a"),m1t=o("XmodForCausalLM"),f1t=o(" (X-MOD model)"),h1t=l(),Z9=a("p"),g1t=o("The model is set in evaluation mode by default using "),jJe=a("code"),u1t=o("model.eval()"),p1t=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),PJe=a("code"),_1t=o("model.train()"),v1t=l(),b(V9.$$.fragment),EGo=l(),wm=a("h3"),W9=a("a"),ZJe=a("span"),b(dW.$$.fragment),b1t=l(),VJe=a("span"),M1t=o("TFAutoModelForCausalLM"),yGo=l(),zo=a("div"),b(cW.$$.fragment),T1t=l(),Lm=a("p"),F1t=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),Vue=a("a"),E1t=o("from_pretrained()"),y1t=o(" class method or the "),Wue=a("a"),C1t=o("from_config()"),w1t=o(` class
method.`),L1t=l(),mW=a("p"),A1t=o("This class cannot be instantiated directly using "),WJe=a("code"),R1t=o("__init__()"),k1t=o(" (throws an error)."),S1t=l(),Qt=a("div"),b(fW.$$.fragment),x1t=l(),JJe=a("p"),G1t=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),B1t=l(),Am=a("p"),I1t=o(`Note:
Loading a model from its configuration file does `),UJe=a("strong"),N1t=o("not"),$1t=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Jue=a("a"),j1t=o("from_pretrained()"),P1t=o(" to load the model weights."),Z1t=l(),b(J9.$$.fragment),V1t=l(),lt=a("div"),b(hW.$$.fragment),W1t=l(),XJe=a("p"),J1t=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),U1t=l(),Dn=a("p"),X1t=o("The model class to instantiate is selected based on the "),DJe=a("code"),D1t=o("model_type"),q1t=o(` property of the config object (either
passed as an argument or loaded from `),qJe=a("code"),O1t=o("pretrained_model_name_or_path"),Q1t=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),OJe=a("code"),Y1t=o("pretrained_model_name_or_path"),z1t=o(":"),H1t=l(),Ee=a("ul"),U9=a("li"),QJe=a("strong"),K1t=o("bert"),ebt=o(" \u2014 "),Uue=a("a"),obt=o("TFBertLMHeadModel"),rbt=o(" (BERT model)"),tbt=l(),X9=a("li"),YJe=a("strong"),abt=o("camembert"),nbt=o(" \u2014 "),Xue=a("a"),sbt=o("TFCamembertForCausalLM"),lbt=o(" (CamemBERT model)"),ibt=l(),D9=a("li"),zJe=a("strong"),dbt=o("ctrl"),cbt=o(" \u2014 "),Due=a("a"),mbt=o("TFCTRLLMHeadModel"),fbt=o(" (CTRL model)"),hbt=l(),q9=a("li"),HJe=a("strong"),gbt=o("gpt-sw3"),ubt=o(" \u2014 "),que=a("a"),pbt=o("TFGPT2LMHeadModel"),_bt=o(" (GPT-Sw3 model)"),vbt=l(),O9=a("li"),KJe=a("strong"),bbt=o("gpt2"),Mbt=o(" \u2014 "),Oue=a("a"),Tbt=o("TFGPT2LMHeadModel"),Fbt=o(" (OpenAI GPT-2 model)"),Ebt=l(),Q9=a("li"),eUe=a("strong"),ybt=o("gptj"),Cbt=o(" \u2014 "),Que=a("a"),wbt=o("TFGPTJForCausalLM"),Lbt=o(" (GPT-J model)"),Abt=l(),Y9=a("li"),oUe=a("strong"),Rbt=o("openai-gpt"),kbt=o(" \u2014 "),Yue=a("a"),Sbt=o("TFOpenAIGPTLMHeadModel"),xbt=o(" (OpenAI GPT model)"),Gbt=l(),z9=a("li"),rUe=a("strong"),Bbt=o("opt"),Ibt=o(" \u2014 "),zue=a("a"),Nbt=o("TFOPTForCausalLM"),$bt=o(" (OPT model)"),jbt=l(),H9=a("li"),tUe=a("strong"),Pbt=o("rembert"),Zbt=o(" \u2014 "),Hue=a("a"),Vbt=o("TFRemBertForCausalLM"),Wbt=o(" (RemBERT model)"),Jbt=l(),K9=a("li"),aUe=a("strong"),Ubt=o("roberta"),Xbt=o(" \u2014 "),Kue=a("a"),Dbt=o("TFRobertaForCausalLM"),qbt=o(" (RoBERTa model)"),Obt=l(),eC=a("li"),nUe=a("strong"),Qbt=o("roberta-prelayernorm"),Ybt=o(" \u2014 "),epe=a("a"),zbt=o("TFRobertaPreLayerNormForCausalLM"),Hbt=o(" (RoBERTa-PreLayerNorm model)"),Kbt=l(),oC=a("li"),sUe=a("strong"),e0t=o("roformer"),o0t=o(" \u2014 "),ope=a("a"),r0t=o("TFRoFormerForCausalLM"),t0t=o(" (RoFormer model)"),a0t=l(),rC=a("li"),lUe=a("strong"),n0t=o("transfo-xl"),s0t=o(" \u2014 "),rpe=a("a"),l0t=o("TFTransfoXLLMHeadModel"),i0t=o(" (Transformer-XL model)"),d0t=l(),tC=a("li"),iUe=a("strong"),c0t=o("xglm"),m0t=o(" \u2014 "),tpe=a("a"),f0t=o("TFXGLMForCausalLM"),h0t=o(" (XGLM model)"),g0t=l(),aC=a("li"),dUe=a("strong"),u0t=o("xlm"),p0t=o(" \u2014 "),ape=a("a"),_0t=o("TFXLMWithLMHeadModel"),v0t=o(" (XLM model)"),b0t=l(),nC=a("li"),cUe=a("strong"),M0t=o("xlm-roberta"),T0t=o(" \u2014 "),npe=a("a"),F0t=o("TFXLMRobertaForCausalLM"),E0t=o(" (XLM-RoBERTa model)"),y0t=l(),sC=a("li"),mUe=a("strong"),C0t=o("xlnet"),w0t=o(" \u2014 "),spe=a("a"),L0t=o("TFXLNetLMHeadModel"),A0t=o(" (XLNet model)"),R0t=l(),b(lC.$$.fragment),CGo=l(),Rm=a("h3"),iC=a("a"),fUe=a("span"),b(gW.$$.fragment),k0t=l(),hUe=a("span"),S0t=o("FlaxAutoModelForCausalLM"),wGo=l(),Ho=a("div"),b(uW.$$.fragment),x0t=l(),km=a("p"),G0t=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),lpe=a("a"),B0t=o("from_pretrained()"),I0t=o(" class method or the "),ipe=a("a"),N0t=o("from_config()"),$0t=o(` class
method.`),j0t=l(),pW=a("p"),P0t=o("This class cannot be instantiated directly using "),gUe=a("code"),Z0t=o("__init__()"),V0t=o(" (throws an error)."),W0t=l(),Yt=a("div"),b(_W.$$.fragment),J0t=l(),uUe=a("p"),U0t=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),X0t=l(),Sm=a("p"),D0t=o(`Note:
Loading a model from its configuration file does `),pUe=a("strong"),q0t=o("not"),O0t=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),dpe=a("a"),Q0t=o("from_pretrained()"),Y0t=o(" to load the model weights."),z0t=l(),b(dC.$$.fragment),H0t=l(),it=a("div"),b(vW.$$.fragment),K0t=l(),_Ue=a("p"),e3t=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),o3t=l(),qn=a("p"),r3t=o("The model class to instantiate is selected based on the "),vUe=a("code"),t3t=o("model_type"),a3t=o(` property of the config object (either
passed as an argument or loaded from `),bUe=a("code"),n3t=o("pretrained_model_name_or_path"),s3t=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),MUe=a("code"),l3t=o("pretrained_model_name_or_path"),i3t=o(":"),d3t=l(),Le=a("ul"),cC=a("li"),TUe=a("strong"),c3t=o("bart"),m3t=o(" \u2014 "),cpe=a("a"),f3t=o("FlaxBartForCausalLM"),h3t=o(" (BART model)"),g3t=l(),mC=a("li"),FUe=a("strong"),u3t=o("bert"),p3t=o(" \u2014 "),mpe=a("a"),_3t=o("FlaxBertForCausalLM"),v3t=o(" (BERT model)"),b3t=l(),fC=a("li"),EUe=a("strong"),M3t=o("big_bird"),T3t=o(" \u2014 "),fpe=a("a"),F3t=o("FlaxBigBirdForCausalLM"),E3t=o(" (BigBird model)"),y3t=l(),hC=a("li"),yUe=a("strong"),C3t=o("bloom"),w3t=o(" \u2014 "),hpe=a("a"),L3t=o("FlaxBloomForCausalLM"),A3t=o(" (BLOOM model)"),R3t=l(),gC=a("li"),CUe=a("strong"),k3t=o("electra"),S3t=o(" \u2014 "),gpe=a("a"),x3t=o("FlaxElectraForCausalLM"),G3t=o(" (ELECTRA model)"),B3t=l(),uC=a("li"),wUe=a("strong"),I3t=o("gpt-sw3"),N3t=o(" \u2014 "),upe=a("a"),$3t=o("FlaxGPT2LMHeadModel"),j3t=o(" (GPT-Sw3 model)"),P3t=l(),pC=a("li"),LUe=a("strong"),Z3t=o("gpt2"),V3t=o(" \u2014 "),ppe=a("a"),W3t=o("FlaxGPT2LMHeadModel"),J3t=o(" (OpenAI GPT-2 model)"),U3t=l(),_C=a("li"),AUe=a("strong"),X3t=o("gpt_neo"),D3t=o(" \u2014 "),_pe=a("a"),q3t=o("FlaxGPTNeoForCausalLM"),O3t=o(" (GPT Neo model)"),Q3t=l(),vC=a("li"),RUe=a("strong"),Y3t=o("gptj"),z3t=o(" \u2014 "),vpe=a("a"),H3t=o("FlaxGPTJForCausalLM"),K3t=o(" (GPT-J model)"),e4t=l(),bC=a("li"),kUe=a("strong"),o4t=o("opt"),r4t=o(" \u2014 "),bpe=a("a"),t4t=o("FlaxOPTForCausalLM"),a4t=o(" (OPT model)"),n4t=l(),MC=a("li"),SUe=a("strong"),s4t=o("roberta"),l4t=o(" \u2014 "),Mpe=a("a"),i4t=o("FlaxRobertaForCausalLM"),d4t=o(" (RoBERTa model)"),c4t=l(),TC=a("li"),xUe=a("strong"),m4t=o("roberta-prelayernorm"),f4t=o(" \u2014 "),Tpe=a("a"),h4t=o("FlaxRobertaPreLayerNormForCausalLM"),g4t=o(" (RoBERTa-PreLayerNorm model)"),u4t=l(),FC=a("li"),GUe=a("strong"),p4t=o("xglm"),_4t=o(" \u2014 "),Fpe=a("a"),v4t=o("FlaxXGLMForCausalLM"),b4t=o(" (XGLM model)"),M4t=l(),EC=a("li"),BUe=a("strong"),T4t=o("xlm-roberta"),F4t=o(" \u2014 "),Epe=a("a"),E4t=o("FlaxXLMRobertaForCausalLM"),y4t=o(" (XLM-RoBERTa model)"),C4t=l(),b(yC.$$.fragment),LGo=l(),xm=a("h3"),CC=a("a"),IUe=a("span"),b(bW.$$.fragment),w4t=l(),NUe=a("span"),L4t=o("AutoModelForMaskedLM"),AGo=l(),Ko=a("div"),b(MW.$$.fragment),A4t=l(),Gm=a("p"),R4t=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the `),ype=a("a"),k4t=o("from_pretrained()"),S4t=o(" class method or the "),Cpe=a("a"),x4t=o("from_config()"),G4t=o(` class
method.`),B4t=l(),TW=a("p"),I4t=o("This class cannot be instantiated directly using "),$Ue=a("code"),N4t=o("__init__()"),$4t=o(" (throws an error)."),j4t=l(),zt=a("div"),b(FW.$$.fragment),P4t=l(),jUe=a("p"),Z4t=o("Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),V4t=l(),Bm=a("p"),W4t=o(`Note:
Loading a model from its configuration file does `),PUe=a("strong"),J4t=o("not"),U4t=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),wpe=a("a"),X4t=o("from_pretrained()"),D4t=o(" to load the model weights."),q4t=l(),b(wC.$$.fragment),O4t=l(),co=a("div"),b(EW.$$.fragment),Q4t=l(),ZUe=a("p"),Y4t=o("Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),z4t=l(),On=a("p"),H4t=o("The model class to instantiate is selected based on the "),VUe=a("code"),K4t=o("model_type"),eMt=o(` property of the config object (either
passed as an argument or loaded from `),WUe=a("code"),oMt=o("pretrained_model_name_or_path"),rMt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),JUe=a("code"),tMt=o("pretrained_model_name_or_path"),aMt=o(":"),nMt=l(),K=a("ul"),LC=a("li"),UUe=a("strong"),sMt=o("albert"),lMt=o(" \u2014 "),Lpe=a("a"),iMt=o("AlbertForMaskedLM"),dMt=o(" (ALBERT model)"),cMt=l(),AC=a("li"),XUe=a("strong"),mMt=o("bart"),fMt=o(" \u2014 "),Ape=a("a"),hMt=o("BartForConditionalGeneration"),gMt=o(" (BART model)"),uMt=l(),RC=a("li"),DUe=a("strong"),pMt=o("bert"),_Mt=o(" \u2014 "),Rpe=a("a"),vMt=o("BertForMaskedLM"),bMt=o(" (BERT model)"),MMt=l(),kC=a("li"),qUe=a("strong"),TMt=o("big_bird"),FMt=o(" \u2014 "),kpe=a("a"),EMt=o("BigBirdForMaskedLM"),yMt=o(" (BigBird model)"),CMt=l(),SC=a("li"),OUe=a("strong"),wMt=o("camembert"),LMt=o(" \u2014 "),Spe=a("a"),AMt=o("CamembertForMaskedLM"),RMt=o(" (CamemBERT model)"),kMt=l(),xC=a("li"),QUe=a("strong"),SMt=o("convbert"),xMt=o(" \u2014 "),xpe=a("a"),GMt=o("ConvBertForMaskedLM"),BMt=o(" (ConvBERT model)"),IMt=l(),GC=a("li"),YUe=a("strong"),NMt=o("data2vec-text"),$Mt=o(" \u2014 "),Gpe=a("a"),jMt=o("Data2VecTextForMaskedLM"),PMt=o(" (Data2VecText model)"),ZMt=l(),BC=a("li"),zUe=a("strong"),VMt=o("deberta"),WMt=o(" \u2014 "),Bpe=a("a"),JMt=o("DebertaForMaskedLM"),UMt=o(" (DeBERTa model)"),XMt=l(),IC=a("li"),HUe=a("strong"),DMt=o("deberta-v2"),qMt=o(" \u2014 "),Ipe=a("a"),OMt=o("DebertaV2ForMaskedLM"),QMt=o(" (DeBERTa-v2 model)"),YMt=l(),NC=a("li"),KUe=a("strong"),zMt=o("distilbert"),HMt=o(" \u2014 "),Npe=a("a"),KMt=o("DistilBertForMaskedLM"),eTt=o(" (DistilBERT model)"),oTt=l(),$C=a("li"),eXe=a("strong"),rTt=o("electra"),tTt=o(" \u2014 "),$pe=a("a"),aTt=o("ElectraForMaskedLM"),nTt=o(" (ELECTRA model)"),sTt=l(),jC=a("li"),oXe=a("strong"),lTt=o("ernie"),iTt=o(" \u2014 "),jpe=a("a"),dTt=o("ErnieForMaskedLM"),cTt=o(" (ERNIE model)"),mTt=l(),PC=a("li"),rXe=a("strong"),fTt=o("esm"),hTt=o(" \u2014 "),Ppe=a("a"),gTt=o("EsmForMaskedLM"),uTt=o(" (ESM model)"),pTt=l(),ZC=a("li"),tXe=a("strong"),_Tt=o("flaubert"),vTt=o(" \u2014 "),Zpe=a("a"),bTt=o("FlaubertWithLMHeadModel"),MTt=o(" (FlauBERT model)"),TTt=l(),VC=a("li"),aXe=a("strong"),FTt=o("fnet"),ETt=o(" \u2014 "),Vpe=a("a"),yTt=o("FNetForMaskedLM"),CTt=o(" (FNet model)"),wTt=l(),WC=a("li"),nXe=a("strong"),LTt=o("funnel"),ATt=o(" \u2014 "),Wpe=a("a"),RTt=o("FunnelForMaskedLM"),kTt=o(" (Funnel Transformer model)"),STt=l(),JC=a("li"),sXe=a("strong"),xTt=o("ibert"),GTt=o(" \u2014 "),Jpe=a("a"),BTt=o("IBertForMaskedLM"),ITt=o(" (I-BERT model)"),NTt=l(),UC=a("li"),lXe=a("strong"),$Tt=o("layoutlm"),jTt=o(" \u2014 "),Upe=a("a"),PTt=o("LayoutLMForMaskedLM"),ZTt=o(" (LayoutLM model)"),VTt=l(),XC=a("li"),iXe=a("strong"),WTt=o("longformer"),JTt=o(" \u2014 "),Xpe=a("a"),UTt=o("LongformerForMaskedLM"),XTt=o(" (Longformer model)"),DTt=l(),DC=a("li"),dXe=a("strong"),qTt=o("luke"),OTt=o(" \u2014 "),Dpe=a("a"),QTt=o("LukeForMaskedLM"),YTt=o(" (LUKE model)"),zTt=l(),qC=a("li"),cXe=a("strong"),HTt=o("mbart"),KTt=o(" \u2014 "),qpe=a("a"),eFt=o("MBartForConditionalGeneration"),oFt=o(" (mBART model)"),rFt=l(),OC=a("li"),mXe=a("strong"),tFt=o("mega"),aFt=o(" \u2014 "),Ope=a("a"),nFt=o("MegaForMaskedLM"),sFt=o(" (MEGA model)"),lFt=l(),QC=a("li"),fXe=a("strong"),iFt=o("megatron-bert"),dFt=o(" \u2014 "),Qpe=a("a"),cFt=o("MegatronBertForMaskedLM"),mFt=o(" (Megatron-BERT model)"),fFt=l(),YC=a("li"),hXe=a("strong"),hFt=o("mobilebert"),gFt=o(" \u2014 "),Ype=a("a"),uFt=o("MobileBertForMaskedLM"),pFt=o(" (MobileBERT model)"),_Ft=l(),zC=a("li"),gXe=a("strong"),vFt=o("mpnet"),bFt=o(" \u2014 "),zpe=a("a"),MFt=o("MPNetForMaskedLM"),TFt=o(" (MPNet model)"),FFt=l(),HC=a("li"),uXe=a("strong"),EFt=o("mra"),yFt=o(" \u2014 "),Hpe=a("a"),CFt=o("MraForMaskedLM"),wFt=o(" (MRA model)"),LFt=l(),KC=a("li"),pXe=a("strong"),AFt=o("mvp"),RFt=o(" \u2014 "),Kpe=a("a"),kFt=o("MvpForConditionalGeneration"),SFt=o(" (MVP model)"),xFt=l(),ew=a("li"),_Xe=a("strong"),GFt=o("nezha"),BFt=o(" \u2014 "),e_e=a("a"),IFt=o("NezhaForMaskedLM"),NFt=o(" (Nezha model)"),$Ft=l(),ow=a("li"),vXe=a("strong"),jFt=o("nystromformer"),PFt=o(" \u2014 "),o_e=a("a"),ZFt=o("NystromformerForMaskedLM"),VFt=o(" (Nystr\xF6mformer model)"),WFt=l(),rw=a("li"),bXe=a("strong"),JFt=o("perceiver"),UFt=o(" \u2014 "),r_e=a("a"),XFt=o("PerceiverForMaskedLM"),DFt=o(" (Perceiver model)"),qFt=l(),tw=a("li"),MXe=a("strong"),OFt=o("qdqbert"),QFt=o(" \u2014 "),t_e=a("a"),YFt=o("QDQBertForMaskedLM"),zFt=o(" (QDQBert model)"),HFt=l(),aw=a("li"),TXe=a("strong"),KFt=o("reformer"),eEt=o(" \u2014 "),a_e=a("a"),oEt=o("ReformerForMaskedLM"),rEt=o(" (Reformer model)"),tEt=l(),nw=a("li"),FXe=a("strong"),aEt=o("rembert"),nEt=o(" \u2014 "),n_e=a("a"),sEt=o("RemBertForMaskedLM"),lEt=o(" (RemBERT model)"),iEt=l(),sw=a("li"),EXe=a("strong"),dEt=o("roberta"),cEt=o(" \u2014 "),s_e=a("a"),mEt=o("RobertaForMaskedLM"),fEt=o(" (RoBERTa model)"),hEt=l(),lw=a("li"),yXe=a("strong"),gEt=o("roberta-prelayernorm"),uEt=o(" \u2014 "),l_e=a("a"),pEt=o("RobertaPreLayerNormForMaskedLM"),_Et=o(" (RoBERTa-PreLayerNorm model)"),vEt=l(),iw=a("li"),CXe=a("strong"),bEt=o("roc_bert"),MEt=o(" \u2014 "),i_e=a("a"),TEt=o("RoCBertForMaskedLM"),FEt=o(" (RoCBert model)"),EEt=l(),dw=a("li"),wXe=a("strong"),yEt=o("roformer"),CEt=o(" \u2014 "),d_e=a("a"),wEt=o("RoFormerForMaskedLM"),LEt=o(" (RoFormer model)"),AEt=l(),cw=a("li"),LXe=a("strong"),REt=o("squeezebert"),kEt=o(" \u2014 "),c_e=a("a"),SEt=o("SqueezeBertForMaskedLM"),xEt=o(" (SqueezeBERT model)"),GEt=l(),mw=a("li"),AXe=a("strong"),BEt=o("tapas"),IEt=o(" \u2014 "),m_e=a("a"),NEt=o("TapasForMaskedLM"),$Et=o(" (TAPAS model)"),jEt=l(),fw=a("li"),RXe=a("strong"),PEt=o("wav2vec2"),ZEt=o(" \u2014 "),kXe=a("code"),VEt=o("Wav2Vec2ForMaskedLM"),WEt=o(" (Wav2Vec2 model)"),JEt=l(),hw=a("li"),SXe=a("strong"),UEt=o("xlm"),XEt=o(" \u2014 "),f_e=a("a"),DEt=o("XLMWithLMHeadModel"),qEt=o(" (XLM model)"),OEt=l(),gw=a("li"),xXe=a("strong"),QEt=o("xlm-roberta"),YEt=o(" \u2014 "),h_e=a("a"),zEt=o("XLMRobertaForMaskedLM"),HEt=o(" (XLM-RoBERTa 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method.`),Ayt=l(),wW=a("p"),Ryt=o("This class cannot be instantiated directly using "),ZXe=a("code"),kyt=o("__init__()"),Syt=o(" (throws an error)."),xyt=l(),Ht=a("div"),b(LW.$$.fragment),Gyt=l(),VXe=a("p"),Byt=o("Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),Iyt=l(),$m=a("p"),Nyt=o(`Note:
Loading a model from its configuration file does `),WXe=a("strong"),$yt=o("not"),jyt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),b_e=a("a"),Pyt=o("from_pretrained()"),Zyt=o(" to load the model weights."),Vyt=l(),b(Tw.$$.fragment),Wyt=l(),dt=a("div"),b(AW.$$.fragment),Jyt=l(),JXe=a("p"),Uyt=o("Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),Xyt=l(),Qn=a("p"),Dyt=o("The model class to instantiate is selected based on the "),UXe=a("code"),qyt=o("model_type"),Oyt=o(` property of the config object (either
passed as an argument or loaded from `),XXe=a("code"),Qyt=o("pretrained_model_name_or_path"),Yyt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),DXe=a("code"),zyt=o("pretrained_model_name_or_path"),Hyt=o(":"),Kyt=l(),pe=a("ul"),Fw=a("li"),qXe=a("strong"),e5t=o("albert"),o5t=o(" \u2014 "),M_e=a("a"),r5t=o("TFAlbertForMaskedLM"),t5t=o(" (ALBERT model)"),a5t=l(),Ew=a("li"),OXe=a("strong"),n5t=o("bert"),s5t=o(" \u2014 "),T_e=a("a"),l5t=o("TFBertForMaskedLM"),i5t=o(" (BERT model)"),d5t=l(),yw=a("li"),QXe=a("strong"),c5t=o("camembert"),m5t=o(" \u2014 "),F_e=a("a"),f5t=o("TFCamembertForMaskedLM"),h5t=o(" (CamemBERT model)"),g5t=l(),Cw=a("li"),YXe=a("strong"),u5t=o("convbert"),p5t=o(" \u2014 "),E_e=a("a"),_5t=o("TFConvBertForMaskedLM"),v5t=o(" (ConvBERT model)"),b5t=l(),ww=a("li"),zXe=a("strong"),M5t=o("deberta"),T5t=o(" \u2014 "),y_e=a("a"),F5t=o("TFDebertaForMaskedLM"),E5t=o(" (DeBERTa model)"),y5t=l(),Lw=a("li"),HXe=a("strong"),C5t=o("deberta-v2"),w5t=o(" \u2014 "),C_e=a("a"),L5t=o("TFDebertaV2ForMaskedLM"),A5t=o(" (DeBERTa-v2 model)"),R5t=l(),Aw=a("li"),KXe=a("strong"),k5t=o("distilbert"),S5t=o(" \u2014 "),w_e=a("a"),x5t=o("TFDistilBertForMaskedLM"),G5t=o(" (DistilBERT model)"),B5t=l(),Rw=a("li"),eDe=a("strong"),I5t=o("electra"),N5t=o(" \u2014 "),L_e=a("a"),$5t=o("TFElectraForMaskedLM"),j5t=o(" (ELECTRA model)"),P5t=l(),kw=a("li"),oDe=a("strong"),Z5t=o("esm"),V5t=o(" \u2014 "),A_e=a("a"),W5t=o("TFEsmForMaskedLM"),J5t=o(" (ESM model)"),U5t=l(),Sw=a("li"),rDe=a("strong"),X5t=o("flaubert"),D5t=o(" \u2014 "),R_e=a("a"),q5t=o("TFFlaubertWithLMHeadModel"),O5t=o(" (FlauBERT model)"),Q5t=l(),xw=a("li"),tDe=a("strong"),Y5t=o("funnel"),z5t=o(" \u2014 "),k_e=a("a"),H5t=o("TFFunnelForMaskedLM"),K5t=o(" (Funnel Transformer model)"),e9t=l(),Gw=a("li"),aDe=a("strong"),o9t=o("layoutlm"),r9t=o(" \u2014 "),S_e=a("a"),t9t=o("TFLayoutLMForMaskedLM"),a9t=o(" (LayoutLM model)"),n9t=l(),Bw=a("li"),nDe=a("strong"),s9t=o("longformer"),l9t=o(" \u2014 "),x_e=a("a"),i9t=o("TFLongformerForMaskedLM"),d9t=o(" (Longformer model)"),c9t=l(),Iw=a("li"),sDe=a("strong"),m9t=o("mobilebert"),f9t=o(" \u2014 "),G_e=a("a"),h9t=o("TFMobileBertForMaskedLM"),g9t=o(" (MobileBERT model)"),u9t=l(),Nw=a("li"),lDe=a("strong"),p9t=o("mpnet"),_9t=o(" \u2014 "),B_e=a("a"),v9t=o("TFMPNetForMaskedLM"),b9t=o(" (MPNet model)"),M9t=l(),$w=a("li"),iDe=a("strong"),T9t=o("rembert"),F9t=o(" \u2014 "),I_e=a("a"),E9t=o("TFRemBertForMaskedLM"),y9t=o(" (RemBERT model)"),C9t=l(),jw=a("li"),dDe=a("strong"),w9t=o("roberta"),L9t=o(" \u2014 "),N_e=a("a"),A9t=o("TFRobertaForMaskedLM"),R9t=o(" (RoBERTa model)"),k9t=l(),Pw=a("li"),cDe=a("strong"),S9t=o("roberta-prelayernorm"),x9t=o(" \u2014 "),$_e=a("a"),G9t=o("TFRobertaPreLayerNormForMaskedLM"),B9t=o(" (RoBERTa-PreLayerNorm model)"),I9t=l(),Zw=a("li"),mDe=a("strong"),N9t=o("roformer"),$9t=o(" \u2014 "),j_e=a("a"),j9t=o("TFRoFormerForMaskedLM"),P9t=o(" (RoFormer model)"),Z9t=l(),Vw=a("li"),fDe=a("strong"),V9t=o("tapas"),W9t=o(" \u2014 "),P_e=a("a"),J9t=o("TFTapasForMaskedLM"),U9t=o(" (TAPAS model)"),X9t=l(),Ww=a("li"),hDe=a("strong"),D9t=o("xlm"),q9t=o(" \u2014 "),Z_e=a("a"),O9t=o("TFXLMWithLMHeadModel"),Q9t=o(" (XLM model)"),Y9t=l(),Jw=a("li"),gDe=a("strong"),z9t=o("xlm-roberta"),H9t=o(" \u2014 "),V_e=a("a"),K9t=o("TFXLMRobertaForMaskedLM"),eCt=o(" (XLM-RoBERTa model)"),oCt=l(),b(Uw.$$.fragment),SGo=l(),jm=a("h3"),Xw=a("a"),uDe=a("span"),b(RW.$$.fragment),rCt=l(),pDe=a("span"),tCt=o("FlaxAutoModelForMaskedLM"),xGo=l(),or=a("div"),b(kW.$$.fragment),aCt=l(),Pm=a("p"),nCt=o(`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
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method.`),cCt=l(),SW=a("p"),mCt=o("This class cannot be instantiated directly using "),_De=a("code"),fCt=o("__init__()"),hCt=o(" (throws an error)."),gCt=l(),Kt=a("div"),b(xW.$$.fragment),uCt=l(),vDe=a("p"),pCt=o("Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),_Ct=l(),Zm=a("p"),vCt=o(`Note:
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model\u2019s configuration. Use `),U_e=a("a"),TCt=o("from_pretrained()"),FCt=o(" to load the model weights."),ECt=l(),b(Dw.$$.fragment),yCt=l(),ct=a("div"),b(GW.$$.fragment),CCt=l(),MDe=a("p"),wCt=o("Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),LCt=l(),Yn=a("p"),ACt=o("The model class to instantiate is selected based on the "),TDe=a("code"),RCt=o("model_type"),kCt=o(` property of the config object (either
passed as an argument or loaded from `),FDe=a("code"),SCt=o("pretrained_model_name_or_path"),xCt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),EDe=a("code"),GCt=o("pretrained_model_name_or_path"),BCt=o(":"),ICt=l(),ke=a("ul"),qw=a("li"),yDe=a("strong"),NCt=o("albert"),$Ct=o(" \u2014 "),X_e=a("a"),jCt=o("FlaxAlbertForMaskedLM"),PCt=o(" (ALBERT model)"),ZCt=l(),Ow=a("li"),CDe=a("strong"),VCt=o("bart"),WCt=o(" \u2014 "),D_e=a("a"),JCt=o("FlaxBartForConditionalGeneration"),UCt=o(" (BART model)"),XCt=l(),Qw=a("li"),wDe=a("strong"),DCt=o("bert"),qCt=o(" \u2014 "),q_e=a("a"),OCt=o("FlaxBertForMaskedLM"),QCt=o(" (BERT model)"),YCt=l(),Yw=a("li"),LDe=a("strong"),zCt=o("big_bird"),HCt=o(" \u2014 "),O_e=a("a"),KCt=o("FlaxBigBirdForMaskedLM"),ewt=o(" (BigBird model)"),owt=l(),zw=a("li"),ADe=a("strong"),rwt=o("distilbert"),twt=o(" \u2014 "),Q_e=a("a"),awt=o("FlaxDistilBertForMaskedLM"),nwt=o(" (DistilBERT model)"),swt=l(),Hw=a("li"),RDe=a("strong"),lwt=o("electra"),iwt=o(" \u2014 "),Y_e=a("a"),dwt=o("FlaxElectraForMaskedLM"),cwt=o(" (ELECTRA model)"),mwt=l(),Kw=a("li"),kDe=a("strong"),fwt=o("mbart"),hwt=o(" \u2014 "),z_e=a("a"),gwt=o("FlaxMBartForConditionalGeneration"),uwt=o(" (mBART model)"),pwt=l(),e6=a("li"),SDe=a("strong"),_wt=o("roberta"),vwt=o(" \u2014 "),H_e=a("a"),bwt=o("FlaxRobertaForMaskedLM"),Mwt=o(" (RoBERTa model)"),Twt=l(),o6=a("li"),xDe=a("strong"),Fwt=o("roberta-prelayernorm"),Ewt=o(" \u2014 "),K_e=a("a"),ywt=o("FlaxRobertaPreLayerNormForMaskedLM"),Cwt=o(" (RoBERTa-PreLayerNorm model)"),wwt=l(),r6=a("li"),GDe=a("strong"),Lwt=o("roformer"),Awt=o(" \u2014 "),e2e=a("a"),Rwt=o("FlaxRoFormerForMaskedLM"),kwt=o(" (RoFormer model)"),Swt=l(),t6=a("li"),BDe=a("strong"),xwt=o("xlm-roberta"),Gwt=o(" \u2014 "),o2e=a("a"),Bwt=o("FlaxXLMRobertaForMaskedLM"),Iwt=o(" (XLM-RoBERTa model)"),Nwt=l(),b(a6.$$.fragment),GGo=l(),Vm=a("h3"),n6=a("a"),IDe=a("span"),b(BW.$$.fragment),$wt=l(),NDe=a("span"),jwt=o("AutoModelForMaskGeneration"),BGo=l(),IW=a("div"),b(NW.$$.fragment),IGo=l(),Wm=a("h3"),s6=a("a"),$De=a("span"),b($W.$$.fragment),Pwt=l(),jDe=a("span"),Zwt=o("TFAutoModelForMaskGeneration"),NGo=l(),jW=a("div"),b(PW.$$.fragment),$Go=l(),Jm=a("h3"),l6=a("a"),PDe=a("span"),b(ZW.$$.fragment),Vwt=l(),ZDe=a("span"),Wwt=o("AutoModelForSeq2SeqLM"),jGo=l(),rr=a("div"),b(VW.$$.fragment),Jwt=l(),Um=a("p"),Uwt=o(`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 `),r2e=a("a"),Xwt=o("from_pretrained()"),Dwt=o(" class method or the "),t2e=a("a"),qwt=o("from_config()"),Owt=o(` class
method.`),Qwt=l(),WW=a("p"),Ywt=o("This class cannot be instantiated directly using "),VDe=a("code"),zwt=o("__init__()"),Hwt=o(" (throws an error)."),Kwt=l(),ea=a("div"),b(JW.$$.fragment),e6t=l(),WDe=a("p"),o6t=o("Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),r6t=l(),Xm=a("p"),t6t=o(`Note:
Loading a model from its configuration file does `),JDe=a("strong"),a6t=o("not"),n6t=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),a2e=a("a"),s6t=o("from_pretrained()"),l6t=o(" to load the model weights."),i6t=l(),b(i6.$$.fragment),d6t=l(),mo=a("div"),b(UW.$$.fragment),c6t=l(),UDe=a("p"),m6t=o("Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),f6t=l(),zn=a("p"),h6t=o("The model class to instantiate is selected based on the "),XDe=a("code"),g6t=o("model_type"),u6t=o(` property of the config object (either
passed as an argument or loaded from `),DDe=a("code"),p6t=o("pretrained_model_name_or_path"),_6t=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),qDe=a("code"),v6t=o("pretrained_model_name_or_path"),b6t=o(":"),M6t=l(),ge=a("ul"),d6=a("li"),ODe=a("strong"),T6t=o("bart"),F6t=o(" \u2014 "),n2e=a("a"),E6t=o("BartForConditionalGeneration"),y6t=o(" (BART model)"),C6t=l(),c6=a("li"),QDe=a("strong"),w6t=o("bigbird_pegasus"),L6t=o(" \u2014 "),s2e=a("a"),A6t=o("BigBirdPegasusForConditionalGeneration"),R6t=o(" (BigBird-Pegasus model)"),k6t=l(),m6=a("li"),YDe=a("strong"),S6t=o("blenderbot"),x6t=o(" \u2014 "),l2e=a("a"),G6t=o("BlenderbotForConditionalGeneration"),B6t=o(" (Blenderbot model)"),I6t=l(),f6=a("li"),zDe=a("strong"),N6t=o("blenderbot-small"),$6t=o(" \u2014 "),i2e=a("a"),j6t=o("BlenderbotSmallForConditionalGeneration"),P6t=o(" (BlenderbotSmall model)"),Z6t=l(),h6=a("li"),HDe=a("strong"),V6t=o("encoder-decoder"),W6t=o(" \u2014 "),d2e=a("a"),J6t=o("EncoderDecoderModel"),U6t=o(" (Encoder decoder model)"),X6t=l(),g6=a("li"),KDe=a("strong"),D6t=o("fsmt"),q6t=o(" \u2014 "),c2e=a("a"),O6t=o("FSMTForConditionalGeneration"),Q6t=o(" (FairSeq Machine-Translation model)"),Y6t=l(),u6=a("li"),eqe=a("strong"),z6t=o("gptsan-japanese"),H6t=o(" \u2014 "),m2e=a("a"),K6t=o("GPTSanJapaneseForConditionalGeneration"),e8t=o(" (GPTSAN-japanese model)"),o8t=l(),p6=a("li"),oqe=a("strong"),r8t=o("led"),t8t=o(" \u2014 "),f2e=a("a"),a8t=o("LEDForConditionalGeneration"),n8t=o(" (LED model)"),s8t=l(),_6=a("li"),rqe=a("strong"),l8t=o("longt5"),i8t=o(" \u2014 "),h2e=a("a"),d8t=o("LongT5ForConditionalGeneration"),c8t=o(" (LongT5 model)"),m8t=l(),v6=a("li"),tqe=a("strong"),f8t=o("m2m_100"),h8t=o(" \u2014 "),g2e=a("a"),g8t=o("M2M100ForConditionalGeneration"),u8t=o(" (M2M100 model)"),p8t=l(),b6=a("li"),aqe=a("strong"),_8t=o("marian"),v8t=o(" \u2014 "),u2e=a("a"),b8t=o("MarianMTModel"),M8t=o(" (Marian model)"),T8t=l(),M6=a("li"),nqe=a("strong"),F8t=o("mbart"),E8t=o(" \u2014 "),p2e=a("a"),y8t=o("MBartForConditionalGeneration"),C8t=o(" (mBART model)"),w8t=l(),T6=a("li"),sqe=a("strong"),L8t=o("mt5"),A8t=o(" \u2014 "),_2e=a("a"),R8t=o("MT5ForConditionalGeneration"),k8t=o(" (MT5 model)"),S8t=l(),F6=a("li"),lqe=a("strong"),x8t=o("mvp"),G8t=o(" \u2014 "),v2e=a("a"),B8t=o("MvpForConditionalGeneration"),I8t=o(" (MVP model)"),N8t=l(),E6=a("li"),iqe=a("strong"),$8t=o("nllb-moe"),j8t=o(" \u2014 "),b2e=a("a"),P8t=o("NllbMoeForConditionalGeneration"),Z8t=o(" (NLLB-MOE model)"),V8t=l(),y6=a("li"),dqe=a("strong"),W8t=o("pegasus"),J8t=o(" \u2014 "),M2e=a("a"),U8t=o("PegasusForConditionalGeneration"),X8t=o(" (Pegasus model)"),D8t=l(),C6=a("li"),cqe=a("strong"),q8t=o("pegasus_x"),O8t=o(" \u2014 "),T2e=a("a"),Q8t=o("PegasusXForConditionalGeneration"),Y8t=o(" (PEGASUS-X model)"),z8t=l(),w6=a("li"),mqe=a("strong"),H8t=o("plbart"),K8t=o(" \u2014 "),F2e=a("a"),e7t=o("PLBartForConditionalGeneration"),o7t=o(" (PLBart model)"),r7t=l(),L6=a("li"),fqe=a("strong"),t7t=o("prophetnet"),a7t=o(" \u2014 "),E2e=a("a"),n7t=o("ProphetNetForConditionalGeneration"),s7t=o(" (ProphetNet model)"),l7t=l(),A6=a("li"),hqe=a("strong"),i7t=o("switch_transformers"),d7t=o(" \u2014 "),y2e=a("a"),c7t=o("SwitchTransformersForConditionalGeneration"),m7t=o(" (SwitchTransformers model)"),f7t=l(),R6=a("li"),gqe=a("strong"),h7t=o("t5"),g7t=o(" \u2014 "),C2e=a("a"),u7t=o("T5ForConditionalGeneration"),p7t=o(" (T5 model)"),_7t=l(),k6=a("li"),uqe=a("strong"),v7t=o("umt5"),b7t=o(" \u2014 "),w2e=a("a"),M7t=o("UMT5ForConditionalGeneration"),T7t=o(" (UMT5 model)"),F7t=l(),S6=a("li"),pqe=a("strong"),E7t=o("xlm-prophetnet"),y7t=o(" \u2014 "),L2e=a("a"),C7t=o("XLMProphetNetForConditionalGeneration"),w7t=o(" (XLM-ProphetNet model)"),L7t=l(),x6=a("p"),A7t=o("The model is set in evaluation mode by default using "),_qe=a("code"),R7t=o("model.eval()"),k7t=o(` (so for instance, dropout modules are
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falling back to using pattern matching on `),Dqe=a("code"),ZAt=o("pretrained_model_name_or_path"),VAt=o(":"),WAt=l(),$e=a("ul"),Q6=a("li"),qqe=a("strong"),JAt=o("bart"),UAt=o(" \u2014 "),U2e=a("a"),XAt=o("FlaxBartForConditionalGeneration"),DAt=o(" (BART model)"),qAt=l(),Y6=a("li"),Oqe=a("strong"),OAt=o("blenderbot"),QAt=o(" \u2014 "),X2e=a("a"),YAt=o("FlaxBlenderbotForConditionalGeneration"),zAt=o(" (Blenderbot model)"),HAt=l(),z6=a("li"),Qqe=a("strong"),KAt=o("blenderbot-small"),eRt=o(" \u2014 "),D2e=a("a"),oRt=o("FlaxBlenderbotSmallForConditionalGeneration"),rRt=o(" (BlenderbotSmall model)"),tRt=l(),H6=a("li"),Yqe=a("strong"),aRt=o("encoder-decoder"),nRt=o(" \u2014 "),q2e=a("a"),sRt=o("FlaxEncoderDecoderModel"),lRt=o(" (Encoder decoder model)"),iRt=l(),K6=a("li"),zqe=a("strong"),dRt=o("longt5"),cRt=o(" \u2014 "),O2e=a("a"),mRt=o("FlaxLongT5ForConditionalGeneration"),fRt=o(" (LongT5 model)"),hRt=l(),e8=a("li"),Hqe=a("strong"),gRt=o("marian"),uRt=o(" \u2014 "),Q2e=a("a"),pRt=o("FlaxMarianMTModel"),_Rt=o(" (Marian model)"),vRt=l(),o8=a("li"),Kqe=a("strong"),bRt=o("mbart"),MRt=o(" \u2014 "),Y2e=a("a"),TRt=o("FlaxMBartForConditionalGeneration"),FRt=o(" (mBART model)"),ERt=l(),r8=a("li"),eOe=a("strong"),yRt=o("mt5"),CRt=o(" \u2014 "),z2e=a("a"),wRt=o("FlaxMT5ForConditionalGeneration"),LRt=o(" (MT5 model)"),ARt=l(),t8=a("li"),oOe=a("strong"),RRt=o("pegasus"),kRt=o(" \u2014 "),H2e=a("a"),SRt=o("FlaxPegasusForConditionalGeneration"),xRt=o(" (Pegasus model)"),GRt=l(),a8=a("li"),rOe=a("strong"),BRt=o("t5"),IRt=o(" \u2014 "),K2e=a("a"),NRt=o("FlaxT5ForConditionalGeneration"),$Rt=o(" (T5 model)"),jRt=l(),b(n8.$$.fragment),JGo=l(),Hm=a("h3"),s8=a("a"),tOe=a("span"),b(oJ.$$.fragment),PRt=l(),aOe=a("span"),ZRt=o("AutoModelForSequenceClassification"),UGo=l(),nr=a("div"),b(rJ.$$.fragment),VRt=l(),Km=a("p"),WRt=o(`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 `),eve=a("a"),JRt=o("from_pretrained()"),URt=o(" class method or the "),ove=a("a"),XRt=o("from_config()"),DRt=o(` class
method.`),qRt=l(),tJ=a("p"),ORt=o("This class cannot be instantiated directly using "),nOe=a("code"),QRt=o("__init__()"),YRt=o(" (throws an error)."),zRt=l(),ta=a("div"),b(aJ.$$.fragment),HRt=l(),sOe=a("p"),KRt=o("Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),ekt=l(),ef=a("p"),okt=o(`Note:
Loading a model from its configuration file does `),lOe=a("strong"),rkt=o("not"),tkt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),rve=a("a"),akt=o("from_pretrained()"),nkt=o(" to load the model weights."),skt=l(),b(l8.$$.fragment),lkt=l(),fo=a("div"),b(nJ.$$.fragment),ikt=l(),iOe=a("p"),dkt=o("Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),ckt=l(),es=a("p"),mkt=o("The model class to instantiate is selected based on the "),dOe=a("code"),fkt=o("model_type"),hkt=o(` property of the config object (either
passed as an argument or loaded from `),cOe=a("code"),gkt=o("pretrained_model_name_or_path"),ukt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mOe=a("code"),pkt=o("pretrained_model_name_or_path"),_kt=o(":"),vkt=l(),B=a("ul"),i8=a("li"),fOe=a("strong"),bkt=o("albert"),Mkt=o(" \u2014 "),tve=a("a"),Tkt=o("AlbertForSequenceClassification"),Fkt=o(" (ALBERT model)"),Ekt=l(),d8=a("li"),hOe=a("strong"),ykt=o("bart"),Ckt=o(" \u2014 "),ave=a("a"),wkt=o("BartForSequenceClassification"),Lkt=o(" (BART model)"),Akt=l(),c8=a("li"),gOe=a("strong"),Rkt=o("bert"),kkt=o(" \u2014 "),nve=a("a"),Skt=o("BertForSequenceClassification"),xkt=o(" (BERT model)"),Gkt=l(),m8=a("li"),uOe=a("strong"),Bkt=o("big_bird"),Ikt=o(" \u2014 "),sve=a("a"),Nkt=o("BigBirdForSequenceClassification"),$kt=o(" (BigBird model)"),jkt=l(),f8=a("li"),pOe=a("strong"),Pkt=o("bigbird_pegasus"),Zkt=o(" \u2014 "),lve=a("a"),Vkt=o("BigBirdPegasusForSequenceClassification"),Wkt=o(" (BigBird-Pegasus model)"),Jkt=l(),h8=a("li"),_Oe=a("strong"),Ukt=o("biogpt"),Xkt=o(" \u2014 "),ive=a("a"),Dkt=o("BioGptForSequenceClassification"),qkt=o(" (BioGpt model)"),Okt=l(),g8=a("li"),vOe=a("strong"),Qkt=o("bloom"),Ykt=o(" \u2014 "),dve=a("a"),zkt=o("BloomForSequenceClassification"),Hkt=o(" (BLOOM model)"),Kkt=l(),u8=a("li"),bOe=a("strong"),eSt=o("camembert"),oSt=o(" \u2014 "),cve=a("a"),rSt=o("CamembertForSequenceClassification"),tSt=o(" (CamemBERT model)"),aSt=l(),p8=a("li"),MOe=a("strong"),nSt=o("canine"),sSt=o(" \u2014 "),mve=a("a"),lSt=o("CanineForSequenceClassification"),iSt=o(" (CANINE model)"),dSt=l(),_8=a("li"),TOe=a("strong"),cSt=o("convbert"),mSt=o(" \u2014 "),fve=a("a"),fSt=o("ConvBertForSequenceClassification"),hSt=o(" (ConvBERT model)"),gSt=l(),v8=a("li"),FOe=a("strong"),uSt=o("ctrl"),pSt=o(" \u2014 "),hve=a("a"),_St=o("CTRLForSequenceClassification"),vSt=o(" (CTRL model)"),bSt=l(),b8=a("li"),EOe=a("strong"),MSt=o("data2vec-text"),TSt=o(" \u2014 "),gve=a("a"),FSt=o("Data2VecTextForSequenceClassification"),ESt=o(" (Data2VecText model)"),ySt=l(),M8=a("li"),yOe=a("strong"),CSt=o("deberta"),wSt=o(" \u2014 "),uve=a("a"),LSt=o("DebertaForSequenceClassification"),ASt=o(" (DeBERTa model)"),RSt=l(),T8=a("li"),COe=a("strong"),kSt=o("deberta-v2"),SSt=o(" \u2014 "),pve=a("a"),xSt=o("DebertaV2ForSequenceClassification"),GSt=o(" (DeBERTa-v2 model)"),BSt=l(),F8=a("li"),wOe=a("strong"),ISt=o("distilbert"),NSt=o(" \u2014 "),_ve=a("a"),$St=o("DistilBertForSequenceClassification"),jSt=o(" (DistilBERT model)"),PSt=l(),E8=a("li"),LOe=a("strong"),ZSt=o("electra"),VSt=o(" \u2014 "),vve=a("a"),WSt=o("ElectraForSequenceClassification"),JSt=o(" (ELECTRA model)"),USt=l(),y8=a("li"),AOe=a("strong"),XSt=o("ernie"),DSt=o(" \u2014 "),bve=a("a"),qSt=o("ErnieForSequenceClassification"),OSt=o(" (ERNIE model)"),QSt=l(),C8=a("li"),ROe=a("strong"),YSt=o("ernie_m"),zSt=o(" \u2014 "),Mve=a("a"),HSt=o("ErnieMForSequenceClassification"),KSt=o(" (ErnieM model)"),ext=l(),w8=a("li"),kOe=a("strong"),oxt=o("esm"),rxt=o(" \u2014 "),Tve=a("a"),txt=o("EsmForSequenceClassification"),axt=o(" (ESM 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falling back to using pattern matching on `),Jze=a("code"),dqt=o("pretrained_model_name_or_path"),cqt=o(":"),mqt=l(),Fe=a("ul"),hA=a("li"),Uze=a("strong"),fqt=o("albert"),hqt=o(" \u2014 "),n0e=a("a"),gqt=o("TFAlbertForMultipleChoice"),uqt=o(" (ALBERT model)"),pqt=l(),gA=a("li"),Xze=a("strong"),_qt=o("bert"),vqt=o(" \u2014 "),s0e=a("a"),bqt=o("TFBertForMultipleChoice"),Mqt=o(" (BERT model)"),Tqt=l(),uA=a("li"),Dze=a("strong"),Fqt=o("camembert"),Eqt=o(" \u2014 "),l0e=a("a"),yqt=o("TFCamembertForMultipleChoice"),Cqt=o(" (CamemBERT model)"),wqt=l(),pA=a("li"),qze=a("strong"),Lqt=o("convbert"),Aqt=o(" \u2014 "),i0e=a("a"),Rqt=o("TFConvBertForMultipleChoice"),kqt=o(" (ConvBERT model)"),Sqt=l(),_A=a("li"),Oze=a("strong"),xqt=o("distilbert"),Gqt=o(" \u2014 "),d0e=a("a"),Bqt=o("TFDistilBertForMultipleChoice"),Iqt=o(" (DistilBERT model)"),Nqt=l(),vA=a("li"),Qze=a("strong"),$qt=o("electra"),jqt=o(" \u2014 "),c0e=a("a"),Pqt=o("TFElectraForMultipleChoice"),Zqt=o(" (ELECTRA model)"),Vqt=l(),bA=a("li"),Yze=a("strong"),Wqt=o("flaubert"),Jqt=o(" \u2014 "),m0e=a("a"),Uqt=o("TFFlaubertForMultipleChoice"),Xqt=o(" (FlauBERT model)"),Dqt=l(),MA=a("li"),zze=a("strong"),qqt=o("funnel"),Oqt=o(" \u2014 "),f0e=a("a"),Qqt=o("TFFunnelForMultipleChoice"),Yqt=o(" (Funnel Transformer model)"),zqt=l(),TA=a("li"),Hze=a("strong"),Hqt=o("longformer"),Kqt=o(" \u2014 "),h0e=a("a"),eOt=o("TFLongformerForMultipleChoice"),oOt=o(" (Longformer model)"),rOt=l(),FA=a("li"),Kze=a("strong"),tOt=o("mobilebert"),aOt=o(" \u2014 "),g0e=a("a"),nOt=o("TFMobileBertForMultipleChoice"),sOt=o(" (MobileBERT model)"),lOt=l(),EA=a("li"),eHe=a("strong"),iOt=o("mpnet"),dOt=o(" \u2014 "),u0e=a("a"),cOt=o("TFMPNetForMultipleChoice"),mOt=o(" (MPNet model)"),fOt=l(),yA=a("li"),oHe=a("strong"),hOt=o("rembert"),gOt=o(" \u2014 "),p0e=a("a"),uOt=o("TFRemBertForMultipleChoice"),pOt=o(" (RemBERT model)"),_Ot=l(),CA=a("li"),rHe=a("strong"),vOt=o("roberta"),bOt=o(" \u2014 "),_0e=a("a"),MOt=o("TFRobertaForMultipleChoice"),TOt=o(" (RoBERTa model)"),FOt=l(),wA=a("li"),tHe=a("strong"),EOt=o("roberta-prelayernorm"),yOt=o(" \u2014 "),v0e=a("a"),COt=o("TFRobertaPreLayerNormForMultipleChoice"),wOt=o(" (RoBERTa-PreLayerNorm model)"),LOt=l(),LA=a("li"),aHe=a("strong"),AOt=o("roformer"),ROt=o(" \u2014 "),b0e=a("a"),kOt=o("TFRoFormerForMultipleChoice"),SOt=o(" (RoFormer model)"),xOt=l(),AA=a("li"),nHe=a("strong"),GOt=o("xlm"),BOt=o(" \u2014 "),M0e=a("a"),IOt=o("TFXLMForMultipleChoice"),NOt=o(" (XLM model)"),$Ot=l(),RA=a("li"),sHe=a("strong"),jOt=o("xlm-roberta"),POt=o(" \u2014 "),T0e=a("a"),ZOt=o("TFXLMRobertaForMultipleChoice"),VOt=o(" (XLM-RoBERTa model)"),WOt=l(),kA=a("li"),lHe=a("strong"),JOt=o("xlnet"),UOt=o(" \u2014 "),F0e=a("a"),XOt=o("TFXLNetForMultipleChoice"),DOt=o(" (XLNet model)"),qOt=l(),b(SA.$$.fragment),KGo=l(),gf=a("h3"),xA=a("a"),iHe=a("span"),b(wJ.$$.fragment),OOt=l(),dHe=a("span"),QOt=o("FlaxAutoModelForMultipleChoice"),eBo=l(),cr=a("div"),b(LJ.$$.fragment),YOt=l(),uf=a("p"),zOt=o(`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
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model\u2019s configuration. Use `),C0e=a("a"),hQt=o("from_pretrained()"),gQt=o(" to load the model weights."),uQt=l(),b(GA.$$.fragment),pQt=l(),pt=a("div"),b(kJ.$$.fragment),_Qt=l(),hHe=a("p"),vQt=o("Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),bQt=l(),ns=a("p"),MQt=o("The model class to instantiate is selected based on the "),gHe=a("code"),TQt=o("model_type"),FQt=o(` property of the config object (either
passed as an argument or loaded from `),uHe=a("code"),EQt=o("pretrained_model_name_or_path"),yQt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),pHe=a("code"),CQt=o("pretrained_model_name_or_path"),wQt=o(":"),LQt=l(),Ue=a("ul"),BA=a("li"),_He=a("strong"),AQt=o("albert"),RQt=o(" \u2014 "),w0e=a("a"),kQt=o("FlaxAlbertForMultipleChoice"),SQt=o(" (ALBERT model)"),xQt=l(),IA=a("li"),vHe=a("strong"),GQt=o("bert"),BQt=o(" \u2014 "),L0e=a("a"),IQt=o("FlaxBertForMultipleChoice"),NQt=o(" (BERT model)"),$Qt=l(),NA=a("li"),bHe=a("strong"),jQt=o("big_bird"),PQt=o(" \u2014 "),A0e=a("a"),ZQt=o("FlaxBigBirdForMultipleChoice"),VQt=o(" (BigBird model)"),WQt=l(),$A=a("li"),MHe=a("strong"),JQt=o("distilbert"),UQt=o(" \u2014 "),R0e=a("a"),XQt=o("FlaxDistilBertForMultipleChoice"),DQt=o(" (DistilBERT model)"),qQt=l(),jA=a("li"),THe=a("strong"),OQt=o("electra"),QQt=o(" \u2014 "),k0e=a("a"),YQt=o("FlaxElectraForMultipleChoice"),zQt=o(" (ELECTRA model)"),HQt=l(),PA=a("li"),FHe=a("strong"),KQt=o("roberta"),eYt=o(" \u2014 "),S0e=a("a"),oYt=o("FlaxRobertaForMultipleChoice"),rYt=o(" (RoBERTa model)"),tYt=l(),ZA=a("li"),EHe=a("strong"),aYt=o("roberta-prelayernorm"),nYt=o(" \u2014 "),x0e=a("a"),sYt=o("FlaxRobertaPreLayerNormForMultipleChoice"),lYt=o(" (RoBERTa-PreLayerNorm model)"),iYt=l(),VA=a("li"),yHe=a("strong"),dYt=o("roformer"),cYt=o(" \u2014 "),G0e=a("a"),mYt=o("FlaxRoFormerForMultipleChoice"),fYt=o(" (RoFormer model)"),hYt=l(),WA=a("li"),CHe=a("strong"),gYt=o("xlm-roberta"),uYt=o(" \u2014 "),B0e=a("a"),pYt=o("FlaxXLMRobertaForMultipleChoice"),_Yt=o(" (XLM-RoBERTa model)"),vYt=l(),b(JA.$$.fragment),oBo=l(),_f=a("h3"),UA=a("a"),wHe=a("span"),b(SJ.$$.fragment),bYt=l(),LHe=a("span"),MYt=o("AutoModelForNextSentencePrediction"),rBo=l(),mr=a("div"),b(xJ.$$.fragment),TYt=l(),vf=a("p"),FYt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
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passed as an argument or loaded from `),GHe=a("code"),OYt=o("pretrained_model_name_or_path"),QYt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),BHe=a("code"),YYt=o("pretrained_model_name_or_path"),zYt=o(":"),HYt=l(),eo=a("ul"),DA=a("li"),IHe=a("strong"),KYt=o("bert"),ezt=o(" \u2014 "),j0e=a("a"),ozt=o("BertForNextSentencePrediction"),rzt=o(" (BERT model)"),tzt=l(),qA=a("li"),NHe=a("strong"),azt=o("ernie"),nzt=o(" \u2014 "),P0e=a("a"),szt=o("ErnieForNextSentencePrediction"),lzt=o(" (ERNIE model)"),izt=l(),OA=a("li"),$He=a("strong"),dzt=o("fnet"),czt=o(" \u2014 "),Z0e=a("a"),mzt=o("FNetForNextSentencePrediction"),fzt=o(" (FNet model)"),hzt=l(),QA=a("li"),jHe=a("strong"),gzt=o("megatron-bert"),uzt=o(" \u2014 "),V0e=a("a"),pzt=o("MegatronBertForNextSentencePrediction"),_zt=o(" (Megatron-BERT model)"),vzt=l(),YA=a("li"),PHe=a("strong"),bzt=o("mobilebert"),Mzt=o(" \u2014 "),W0e=a("a"),Tzt=o("MobileBertForNextSentencePrediction"),Fzt=o(" (MobileBERT model)"),Ezt=l(),zA=a("li"),ZHe=a("strong"),yzt=o("nezha"),Czt=o(" \u2014 "),J0e=a("a"),wzt=o("NezhaForNextSentencePrediction"),Lzt=o(" (Nezha model)"),Azt=l(),HA=a("li"),VHe=a("strong"),Rzt=o("qdqbert"),kzt=o(" \u2014 "),U0e=a("a"),Szt=o("QDQBertForNextSentencePrediction"),xzt=o(" (QDQBert model)"),Gzt=l(),KA=a("p"),Bzt=o("The model is set in evaluation mode by default using "),WHe=a("code"),Izt=o("model.eval()"),Nzt=o(` (so for instance, dropout modules are
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method.`),qzt=l(),jJ=a("p"),Ozt=o("This class cannot be instantiated directly using "),DHe=a("code"),Qzt=o("__init__()"),Yzt=o(" (throws an error)."),zzt=l(),ca=a("div"),b(PJ.$$.fragment),Hzt=l(),qHe=a("p"),Kzt=o("Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),eHt=l(),Ff=a("p"),oHt=o(`Note:
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deactivated). To train the model, you should first set it back in training mode with `),Eeo=a("code"),zaa=o("model.train()"),Haa=l(),b(hk.$$.fragment),dBo=l(),Rf=a("h3"),gk=a("a"),yeo=a("span"),b(HJ.$$.fragment),Kaa=l(),Ceo=a("span"),ena=o("TFAutoModelForTokenClassification"),cBo=l(),ur=a("div"),b(KJ.$$.fragment),ona=l(),kf=a("p"),rna=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
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method.`),lna=l(),eU=a("p"),ina=o("This class cannot be instantiated directly using "),weo=a("code"),dna=o("__init__()"),cna=o(" (throws an error)."),mna=l(),ha=a("div"),b(oU.$$.fragment),fna=l(),Leo=a("p"),hna=o("Instantiates one of the model classes of the library (with a token classification head) from a configuration."),gna=l(),Sf=a("p"),una=o(`Note:
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falling back to using pattern matching on `),xeo=a("code"),kna=o("pretrained_model_name_or_path"),Sna=o(":"),xna=l(),ue=a("ul"),pk=a("li"),Geo=a("strong"),Gna=o("albert"),Bna=o(" \u2014 "),n4e=a("a"),Ina=o("TFAlbertForTokenClassification"),Nna=o(" (ALBERT model)"),$na=l(),_k=a("li"),Beo=a("strong"),jna=o("bert"),Pna=o(" \u2014 "),s4e=a("a"),Zna=o("TFBertForTokenClassification"),Vna=o(" (BERT model)"),Wna=l(),vk=a("li"),Ieo=a("strong"),Jna=o("camembert"),Una=o(" \u2014 "),l4e=a("a"),Xna=o("TFCamembertForTokenClassification"),Dna=o(" (CamemBERT model)"),qna=l(),bk=a("li"),Neo=a("strong"),Ona=o("convbert"),Qna=o(" \u2014 "),i4e=a("a"),Yna=o("TFConvBertForTokenClassification"),zna=o(" (ConvBERT model)"),Hna=l(),Mk=a("li"),$eo=a("strong"),Kna=o("deberta"),esa=o(" \u2014 "),d4e=a("a"),osa=o("TFDebertaForTokenClassification"),rsa=o(" (DeBERTa model)"),tsa=l(),Tk=a("li"),jeo=a("strong"),asa=o("deberta-v2"),nsa=o(" \u2014 "),c4e=a("a"),ssa=o("TFDebertaV2ForTokenClassification"),lsa=o(" (DeBERTa-v2 model)"),isa=l(),Fk=a("li"),Peo=a("strong"),dsa=o("distilbert"),csa=o(" \u2014 "),m4e=a("a"),msa=o("TFDistilBertForTokenClassification"),fsa=o(" (DistilBERT model)"),hsa=l(),Ek=a("li"),Zeo=a("strong"),gsa=o("electra"),usa=o(" \u2014 "),f4e=a("a"),psa=o("TFElectraForTokenClassification"),_sa=o(" (ELECTRA model)"),vsa=l(),yk=a("li"),Veo=a("strong"),bsa=o("esm"),Msa=o(" \u2014 "),h4e=a("a"),Tsa=o("TFEsmForTokenClassification"),Fsa=o(" (ESM model)"),Esa=l(),Ck=a("li"),Weo=a("strong"),ysa=o("flaubert"),Csa=o(" \u2014 "),g4e=a("a"),wsa=o("TFFlaubertForTokenClassification"),Lsa=o(" (FlauBERT model)"),Asa=l(),wk=a("li"),Jeo=a("strong"),Rsa=o("funnel"),ksa=o(" \u2014 "),u4e=a("a"),Ssa=o("TFFunnelForTokenClassification"),xsa=o(" (Funnel Transformer model)"),Gsa=l(),Lk=a("li"),Ueo=a("strong"),Bsa=o("layoutlm"),Isa=o(" \u2014 "),p4e=a("a"),Nsa=o("TFLayoutLMForTokenClassification"),$sa=o(" (LayoutLM model)"),jsa=l(),Ak=a("li"),Xeo=a("strong"),Psa=o("layoutlmv3"),Zsa=o(" \u2014 "),_4e=a("a"),Vsa=o("TFLayoutLMv3ForTokenClassification"),Wsa=o(" (LayoutLMv3 model)"),Jsa=l(),Rk=a("li"),Deo=a("strong"),Usa=o("longformer"),Xsa=o(" \u2014 "),v4e=a("a"),Dsa=o("TFLongformerForTokenClassification"),qsa=o(" (Longformer model)"),Osa=l(),kk=a("li"),qeo=a("strong"),Qsa=o("mobilebert"),Ysa=o(" \u2014 "),b4e=a("a"),zsa=o("TFMobileBertForTokenClassification"),Hsa=o(" (MobileBERT model)"),Ksa=l(),Sk=a("li"),Oeo=a("strong"),ela=o("mpnet"),ola=o(" \u2014 "),M4e=a("a"),rla=o("TFMPNetForTokenClassification"),tla=o(" (MPNet model)"),ala=l(),xk=a("li"),Qeo=a("strong"),nla=o("rembert"),sla=o(" \u2014 "),T4e=a("a"),lla=o("TFRemBertForTokenClassification"),ila=o(" (RemBERT model)"),dla=l(),Gk=a("li"),Yeo=a("strong"),cla=o("roberta"),mla=o(" \u2014 "),F4e=a("a"),fla=o("TFRobertaForTokenClassification"),hla=o(" (RoBERTa model)"),gla=l(),Bk=a("li"),zeo=a("strong"),ula=o("roberta-prelayernorm"),pla=o(" \u2014 "),E4e=a("a"),_la=o("TFRobertaPreLayerNormForTokenClassification"),vla=o(" (RoBERTa-PreLayerNorm model)"),bla=l(),Ik=a("li"),Heo=a("strong"),Mla=o("roformer"),Tla=o(" \u2014 "),y4e=a("a"),Fla=o("TFRoFormerForTokenClassification"),Ela=o(" (RoFormer model)"),yla=l(),Nk=a("li"),Keo=a("strong"),Cla=o("xlm"),wla=o(" \u2014 "),C4e=a("a"),Lla=o("TFXLMForTokenClassification"),Ala=o(" (XLM model)"),Rla=l(),$k=a("li"),eoo=a("strong"),kla=o("xlm-roberta"),Sla=o(" \u2014 "),w4e=a("a"),xla=o("TFXLMRobertaForTokenClassification"),Gla=o(" (XLM-RoBERTa model)"),Bla=l(),jk=a("li"),ooo=a("strong"),Ila=o("xlnet"),Nla=o(" \u2014 "),L4e=a("a"),$la=o("TFXLNetForTokenClassification"),jla=o(" (XLNet model)"),Pla=l(),b(Pk.$$.fragment),mBo=l(),xf=a("h3"),Zk=a("a"),roo=a("span"),b(tU.$$.fragment),Zla=l(),too=a("span"),Vla=o("FlaxAutoModelForTokenClassification"),fBo=l(),pr=a("div"),b(aU.$$.fragment),Wla=l(),Gf=a("p"),Jla=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
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method.`),bda=l(),cU=a("p"),Mda=o("This class cannot be instantiated directly using "),Foo=a("code"),Tda=o("__init__()"),Fda=o(" (throws an error)."),Eda=l(),ua=a("div"),b(mU.$$.fragment),yda=l(),Eoo=a("p"),Cda=o("Instantiates one of the model classes of the library (with a question answering head) from a configuration."),wda=l(),$f=a("p"),Lda=o(`Note:
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model\u2019s configuration. Use `),ETe=a("a"),$2a=o("from_pretrained()"),j2a=o(" to load the model weights."),P2a=l(),b(Nx.$$.fragment),Z2a=l(),Ft=a("div"),b(FU.$$.fragment),V2a=l(),Fto=a("p"),W2a=o("Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),J2a=l(),gs=a("p"),U2a=o("The model class to instantiate is selected based on the "),Eto=a("code"),X2a=o("model_type"),D2a=o(` property of the config object (either
passed as an argument or loaded from `),yto=a("code"),q2a=o("pretrained_model_name_or_path"),O2a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Cto=a("code"),Q2a=o("pretrained_model_name_or_path"),Y2a=o(":"),z2a=l(),xe=a("ul"),$x=a("li"),wto=a("strong"),H2a=o("albert"),K2a=o(" \u2014 "),yTe=a("a"),eva=o("FlaxAlbertForQuestionAnswering"),ova=o(" (ALBERT model)"),rva=l(),jx=a("li"),Lto=a("strong"),tva=o("bart"),ava=o(" \u2014 "),CTe=a("a"),nva=o("FlaxBartForQuestionAnswering"),sva=o(" (BART model)"),lva=l(),Px=a("li"),Ato=a("strong"),iva=o("bert"),dva=o(" \u2014 "),wTe=a("a"),cva=o("FlaxBertForQuestionAnswering"),mva=o(" (BERT model)"),fva=l(),Zx=a("li"),Rto=a("strong"),hva=o("big_bird"),gva=o(" \u2014 "),LTe=a("a"),uva=o("FlaxBigBirdForQuestionAnswering"),pva=o(" (BigBird model)"),_va=l(),Vx=a("li"),kto=a("strong"),vva=o("distilbert"),bva=o(" \u2014 "),ATe=a("a"),Mva=o("FlaxDistilBertForQuestionAnswering"),Tva=o(" (DistilBERT model)"),Fva=l(),Wx=a("li"),Sto=a("strong"),Eva=o("electra"),yva=o(" \u2014 "),RTe=a("a"),Cva=o("FlaxElectraForQuestionAnswering"),wva=o(" (ELECTRA model)"),Lva=l(),Jx=a("li"),xto=a("strong"),Ava=o("mbart"),Rva=o(" \u2014 "),kTe=a("a"),kva=o("FlaxMBartForQuestionAnswering"),Sva=o(" (mBART model)"),xva=l(),Ux=a("li"),Gto=a("strong"),Gva=o("roberta"),Bva=o(" \u2014 "),STe=a("a"),Iva=o("FlaxRobertaForQuestionAnswering"),Nva=o(" (RoBERTa model)"),$va=l(),Xx=a("li"),Bto=a("strong"),jva=o("roberta-prelayernorm"),Pva=o(" \u2014 "),xTe=a("a"),Zva=o("FlaxRobertaPreLayerNormForQuestionAnswering"),Vva=o(" (RoBERTa-PreLayerNorm model)"),Wva=l(),Dx=a("li"),Ito=a("strong"),Jva=o("roformer"),Uva=o(" \u2014 "),GTe=a("a"),Xva=o("FlaxRoFormerForQuestionAnswering"),Dva=o(" (RoFormer model)"),qva=l(),qx=a("li"),Nto=a("strong"),Ova=o("xlm-roberta"),Qva=o(" \u2014 "),BTe=a("a"),Yva=o("FlaxXLMRobertaForQuestionAnswering"),zva=o(" (XLM-RoBERTa model)"),Hva=l(),b(Ox.$$.fragment),bBo=l(),Uf=a("h3"),Qx=a("a"),$to=a("span"),b(EU.$$.fragment),Kva=l(),jto=a("span"),e1a=o("AutoModelForTextEncoding"),MBo=l(),yU=a("div"),b(CU.$$.fragment),TBo=l(),Xf=a("h3"),Yx=a("a"),Pto=a("span"),b(wU.$$.fragment),o1a=l(),Zto=a("span"),r1a=o("TFAutoModelForTextEncoding"),FBo=l(),LU=a("div"),b(AU.$$.fragment),EBo=l(),Df=a("h2"),zx=a("a"),Vto=a("span"),b(RU.$$.fragment),t1a=l(),Wto=a("span"),a1a=o("Computer vision"),yBo=l(),ITe=a("p"),n1a=o("The following auto classes are available for the following computer vision tasks."),CBo=l(),qf=a("h3"),Hx=a("a"),Jto=a("span"),b(kU.$$.fragment),s1a=l(),Uto=a("span"),l1a=o("AutoModelForDepthEstimation"),wBo=l(),Mr=a("div"),b(SU.$$.fragment),i1a=l(),Of=a("p"),d1a=o(`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 `),NTe=a("a"),c1a=o("from_pretrained()"),m1a=o(" class method or the "),$Te=a("a"),f1a=o("from_config()"),h1a=o(` class
method.`),g1a=l(),xU=a("p"),u1a=o("This class cannot be instantiated directly using "),Xto=a("code"),p1a=o("__init__()"),_1a=o(" (throws an error)."),v1a=l(),va=a("div"),b(GU.$$.fragment),b1a=l(),Dto=a("p"),M1a=o("Instantiates one of the model classes of the library (with a depth estimation head) from a configuration."),T1a=l(),Qf=a("p"),F1a=o(`Note:
Loading a model from its configuration file does `),qto=a("strong"),E1a=o("not"),y1a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),jTe=a("a"),C1a=o("from_pretrained()"),w1a=o(" to load the model weights."),L1a=l(),b(Kx.$$.fragment),A1a=l(),_o=a("div"),b(BU.$$.fragment),R1a=l(),Oto=a("p"),k1a=o("Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model."),S1a=l(),us=a("p"),x1a=o("The model class to instantiate is selected based on the "),Qto=a("code"),G1a=o("model_type"),B1a=o(` property of the config object (either
passed as an argument or loaded from `),Yto=a("code"),I1a=o("pretrained_model_name_or_path"),N1a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),zto=a("code"),$1a=o("pretrained_model_name_or_path"),j1a=o(":"),P1a=l(),IU=a("ul"),eG=a("li"),Hto=a("strong"),Z1a=o("dpt"),V1a=o(" \u2014 "),PTe=a("a"),W1a=o("DPTForDepthEstimation"),J1a=o(" (DPT model)"),U1a=l(),oG=a("li"),Kto=a("strong"),X1a=o("glpn"),D1a=o(" \u2014 "),ZTe=a("a"),q1a=o("GLPNForDepthEstimation"),O1a=o(" (GLPN model)"),Q1a=l(),rG=a("p"),Y1a=o("The model is set in evaluation mode by default using "),eao=a("code"),z1a=o("model.eval()"),H1a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),oao=a("code"),K1a=o("model.train()"),eba=l(),b(tG.$$.fragment),LBo=l(),Yf=a("h3"),aG=a("a"),rao=a("span"),b(NU.$$.fragment),oba=l(),tao=a("span"),rba=o("AutoModelForImageClassification"),ABo=l(),Tr=a("div"),b($U.$$.fragment),tba=l(),zf=a("p"),aba=o(`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 `),VTe=a("a"),nba=o("from_pretrained()"),sba=o(" class method or the "),WTe=a("a"),lba=o("from_config()"),iba=o(` class
method.`),dba=l(),jU=a("p"),cba=o("This class cannot be instantiated directly using "),aao=a("code"),mba=o("__init__()"),fba=o(" (throws an error)."),hba=l(),ba=a("div"),b(PU.$$.fragment),gba=l(),nao=a("p"),uba=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),pba=l(),Hf=a("p"),_ba=o(`Note:
Loading a model from its configuration file does `),sao=a("strong"),vba=o("not"),bba=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),JTe=a("a"),Mba=o("from_pretrained()"),Tba=o(" to load the model weights."),Fba=l(),b(nG.$$.fragment),Eba=l(),vo=a("div"),b(ZU.$$.fragment),yba=l(),lao=a("p"),Cba=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),wba=l(),ps=a("p"),Lba=o("The model class to instantiate is selected based on the "),iao=a("code"),Aba=o("model_type"),Rba=o(` property of the config object (either
passed as an argument or loaded from `),dao=a("code"),kba=o("pretrained_model_name_or_path"),Sba=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),cao=a("code"),xba=o("pretrained_model_name_or_path"),Gba=o(":"),Bba=l(),le=a("ul"),sG=a("li"),mao=a("strong"),Iba=o("beit"),Nba=o(" \u2014 "),UTe=a("a"),$ba=o("BeitForImageClassification"),jba=o(" (BEiT model)"),Pba=l(),lG=a("li"),fao=a("strong"),Zba=o("bit"),Vba=o(" \u2014 "),XTe=a("a"),Wba=o("BitForImageClassification"),Jba=o(" (BiT model)"),Uba=l(),iG=a("li"),hao=a("strong"),Xba=o("convnext"),Dba=o(" \u2014 "),DTe=a("a"),qba=o("ConvNextForImageClassification"),Oba=o(" (ConvNeXT model)"),Qba=l(),dG=a("li"),gao=a("strong"),Yba=o("convnextv2"),zba=o(" \u2014 "),qTe=a("a"),Hba=o("ConvNextV2ForImageClassification"),Kba=o(" (ConvNeXTV2 model)"),e0a=l(),cG=a("li"),uao=a("strong"),o0a=o("cvt"),r0a=o(" \u2014 "),OTe=a("a"),t0a=o("CvtForImageClassification"),a0a=o(" (CvT model)"),n0a=l(),mG=a("li"),pao=a("strong"),s0a=o("data2vec-vision"),l0a=o(" \u2014 "),QTe=a("a"),i0a=o("Data2VecVisionForImageClassification"),d0a=o(" (Data2VecVision model)"),c0a=l(),Wi=a("li"),_ao=a("strong"),m0a=o("deit"),f0a=o(" \u2014 "),YTe=a("a"),h0a=o("DeiTForImageClassification"),g0a=o(" or "),zTe=a("a"),u0a=o("DeiTForImageClassificationWithTeacher"),p0a=o(" (DeiT model)"),_0a=l(),fG=a("li"),vao=a("strong"),v0a=o("dinat"),b0a=o(" \u2014 "),HTe=a("a"),M0a=o("DinatForImageClassification"),T0a=o(" (DiNAT model)"),F0a=l(),hG=a("li"),bao=a("strong"),E0a=o("dinov2"),y0a=o(" \u2014 "),KTe=a("a"),C0a=o("Dinov2ForImageClassification"),w0a=o(" (DINOv2 model)"),L0a=l(),Ji=a("li"),Mao=a("strong"),A0a=o("efficientformer"),R0a=o(" \u2014 "),eFe=a("a"),k0a=o("EfficientFormerForImageClassification"),S0a=o(" or "),oFe=a("a"),x0a=o("EfficientFormerForImageClassificationWithTeacher"),G0a=o(" (EfficientFormer model)"),B0a=l(),gG=a("li"),Tao=a("strong"),I0a=o("efficientnet"),N0a=o(" \u2014 "),rFe=a("a"),$0a=o("EfficientNetForImageClassification"),j0a=o(" (EfficientNet model)"),P0a=l(),uG=a("li"),Fao=a("strong"),Z0a=o("focalnet"),V0a=o(" \u2014 "),tFe=a("a"),W0a=o("FocalNetForImageClassification"),J0a=o(" (FocalNet model)"),U0a=l(),pG=a("li"),Eao=a("strong"),X0a=o("imagegpt"),D0a=o(" \u2014 "),aFe=a("a"),q0a=o("ImageGPTForImageClassification"),O0a=o(" (ImageGPT model)"),Q0a=l(),Ui=a("li"),yao=a("strong"),Y0a=o("levit"),z0a=o(" \u2014 "),nFe=a("a"),H0a=o("LevitForImageClassification"),K0a=o(" or "),sFe=a("a"),e3a=o("LevitForImageClassificationWithTeacher"),o3a=o(" (LeViT model)"),r3a=l(),_G=a("li"),Cao=a("strong"),t3a=o("mobilenet_v1"),a3a=o(" \u2014 "),lFe=a("a"),n3a=o("MobileNetV1ForImageClassification"),s3a=o(" (MobileNetV1 model)"),l3a=l(),vG=a("li"),wao=a("strong"),i3a=o("mobilenet_v2"),d3a=o(" \u2014 "),iFe=a("a"),c3a=o("MobileNetV2ForImageClassification"),m3a=o(" (MobileNetV2 model)"),f3a=l(),bG=a("li"),Lao=a("strong"),h3a=o("mobilevit"),g3a=o(" \u2014 "),dFe=a("a"),u3a=o("MobileViTForImageClassification"),p3a=o(" (MobileViT model)"),_3a=l(),MG=a("li"),Aao=a("strong"),v3a=o("mobilevitv2"),b3a=o(" \u2014 "),cFe=a("a"),M3a=o("MobileViTV2ForImageClassification"),T3a=o(" (MobileViTV2 model)"),F3a=l(),TG=a("li"),Rao=a("strong"),E3a=o("nat"),y3a=o(" \u2014 "),mFe=a("a"),C3a=o("NatForImageClassification"),w3a=o(" (NAT model)"),L3a=l(),Ma=a("li"),kao=a("strong"),A3a=o("perceiver"),R3a=o(" \u2014 "),fFe=a("a"),k3a=o("PerceiverForImageClassificationLearned"),S3a=o(" or "),hFe=a("a"),x3a=o("PerceiverForImageClassificationFourier"),G3a=o(" or "),gFe=a("a"),B3a=o("PerceiverForImageClassificationConvProcessing"),I3a=o(" (Perceiver model)"),N3a=l(),FG=a("li"),Sao=a("strong"),$3a=o("poolformer"),j3a=o(" \u2014 "),uFe=a("a"),P3a=o("PoolFormerForImageClassification"),Z3a=o(" (PoolFormer model)"),V3a=l(),EG=a("li"),xao=a("strong"),W3a=o("pvt"),J3a=o(" \u2014 "),pFe=a("a"),U3a=o("PvtForImageClassification"),X3a=o(" (PVT model)"),D3a=l(),yG=a("li"),Gao=a("strong"),q3a=o("regnet"),O3a=o(" \u2014 "),_Fe=a("a"),Q3a=o("RegNetForImageClassification"),Y3a=o(" (RegNet 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deactivated). To train the model, you should first set it back in training mode with `),Uao=a("code"),D4a=o("model.train()"),q4a=l(),b(IG.$$.fragment),RBo=l(),Kf=a("h3"),NG=a("a"),Xao=a("span"),b(VU.$$.fragment),O4a=l(),Dao=a("span"),Q4a=o("TFAutoModelForImageClassification"),kBo=l(),Fr=a("div"),b(WU.$$.fragment),Y4a=l(),eh=a("p"),z4a=o(`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 `),LFe=a("a"),H4a=o("from_pretrained()"),K4a=o(" class method or the "),AFe=a("a"),eMa=o("from_config()"),oMa=o(` class
method.`),rMa=l(),JU=a("p"),tMa=o("This class cannot be instantiated directly using "),qao=a("code"),aMa=o("__init__()"),nMa=o(" (throws an error)."),sMa=l(),Ta=a("div"),b(UU.$$.fragment),lMa=l(),Oao=a("p"),iMa=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),dMa=l(),oh=a("p"),cMa=o(`Note:
Loading a model from its configuration file does `),Qao=a("strong"),mMa=o("not"),fMa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),RFe=a("a"),hMa=o("from_pretrained()"),gMa=o(" to load the model weights."),uMa=l(),b($G.$$.fragment),pMa=l(),Et=a("div"),b(XU.$$.fragment),_Ma=l(),Yao=a("p"),vMa=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),bMa=l(),_s=a("p"),MMa=o("The model class to instantiate is selected based on the "),zao=a("code"),TMa=o("model_type"),FMa=o(` property of the config object (either
passed as an argument or loaded from `),Hao=a("code"),EMa=o("pretrained_model_name_or_path"),yMa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Kao=a("code"),CMa=o("pretrained_model_name_or_path"),wMa=o(":"),LMa=l(),Ge=a("ul"),jG=a("li"),eno=a("strong"),AMa=o("convnext"),RMa=o(" \u2014 "),kFe=a("a"),kMa=o("TFConvNextForImageClassification"),SMa=o(" (ConvNeXT model)"),xMa=l(),PG=a("li"),ono=a("strong"),GMa=o("cvt"),BMa=o(" \u2014 "),SFe=a("a"),IMa=o("TFCvtForImageClassification"),NMa=o(" (CvT model)"),$Ma=l(),ZG=a("li"),rno=a("strong"),jMa=o("data2vec-vision"),PMa=o(" \u2014 "),xFe=a("a"),ZMa=o("TFData2VecVisionForImageClassification"),VMa=o(" (Data2VecVision model)"),WMa=l(),Xi=a("li"),tno=a("strong"),JMa=o("deit"),UMa=o(" \u2014 "),GFe=a("a"),XMa=o("TFDeiTForImageClassification"),DMa=o(" or "),BFe=a("a"),qMa=o("TFDeiTForImageClassificationWithTeacher"),OMa=o(" (DeiT model)"),QMa=l(),Di=a("li"),ano=a("strong"),YMa=o("efficientformer"),zMa=o(" \u2014 "),IFe=a("a"),HMa=o("TFEfficientFormerForImageClassification"),KMa=o(" or "),NFe=a("a"),eTa=o("TFEfficientFormerForImageClassificationWithTeacher"),oTa=o(" (EfficientFormer model)"),rTa=l(),VG=a("li"),nno=a("strong"),tTa=o("mobilevit"),aTa=o(" \u2014 "),$Fe=a("a"),nTa=o("TFMobileViTForImageClassification"),sTa=o(" (MobileViT model)"),lTa=l(),WG=a("li"),sno=a("strong"),iTa=o("regnet"),dTa=o(" \u2014 "),jFe=a("a"),cTa=o("TFRegNetForImageClassification"),mTa=o(" (RegNet model)"),fTa=l(),JG=a("li"),lno=a("strong"),hTa=o("resnet"),gTa=o(" \u2014 "),PFe=a("a"),uTa=o("TFResNetForImageClassification"),pTa=o(" (ResNet model)"),_Ta=l(),UG=a("li"),ino=a("strong"),vTa=o("segformer"),bTa=o(" \u2014 "),ZFe=a("a"),MTa=o("TFSegformerForImageClassification"),TTa=o(" (SegFormer model)"),FTa=l(),XG=a("li"),dno=a("strong"),ETa=o("swin"),yTa=o(" \u2014 "),VFe=a("a"),CTa=o("TFSwinForImageClassification"),wTa=o(" (Swin Transformer model)"),LTa=l(),DG=a("li"),cno=a("strong"),ATa=o("vit"),RTa=o(" \u2014 "),WFe=a("a"),kTa=o("TFViTForImageClassification"),STa=o(" (ViT model)"),xTa=l(),b(qG.$$.fragment),SBo=l(),rh=a("h3"),OG=a("a"),mno=a("span"),b(DU.$$.fragment),GTa=l(),fno=a("span"),BTa=o("FlaxAutoModelForImageClassification"),xBo=l(),Er=a("div"),b(qU.$$.fragment),ITa=l(),th=a("p"),NTa=o(`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
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method.`),VTa=l(),OU=a("p"),WTa=o("This class cannot be instantiated directly using "),hno=a("code"),JTa=o("__init__()"),UTa=o(" (throws an error)."),XTa=l(),Fa=a("div"),b(QU.$$.fragment),DTa=l(),gno=a("p"),qTa=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),OTa=l(),ah=a("p"),QTa=o(`Note:
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falling back to using pattern matching on `),bno=a("code"),cFa=o("pretrained_model_name_or_path"),mFa=o(":"),fFa=l(),bs=a("ul"),YG=a("li"),Mno=a("strong"),hFa=o("beit"),gFa=o(" \u2014 "),DFe=a("a"),uFa=o("FlaxBeitForImageClassification"),pFa=o(" (BEiT model)"),_Fa=l(),zG=a("li"),Tno=a("strong"),vFa=o("regnet"),bFa=o(" \u2014 "),qFe=a("a"),MFa=o("FlaxRegNetForImageClassification"),TFa=o(" (RegNet model)"),FFa=l(),HG=a("li"),Fno=a("strong"),EFa=o("resnet"),yFa=o(" \u2014 "),OFe=a("a"),CFa=o("FlaxResNetForImageClassification"),wFa=o(" (ResNet model)"),LFa=l(),KG=a("li"),Eno=a("strong"),AFa=o("vit"),RFa=o(" \u2014 "),QFe=a("a"),kFa=o("FlaxViTForImageClassification"),SFa=o(" (ViT model)"),xFa=l(),b(eB.$$.fragment),GBo=l(),nh=a("h3"),oB=a("a"),yno=a("span"),b(zU.$$.fragment),GFa=l(),Cno=a("span"),BFa=o("AutoModelForVideoClassification"),BBo=l(),yr=a("div"),b(HU.$$.fragment),IFa=l(),sh=a("p"),NFa=o(`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 `),YFe=a("a"),$Fa=o("from_pretrained()"),jFa=o(" class method or the "),zFe=a("a"),PFa=o("from_config()"),ZFa=o(` class
method.`),VFa=l(),KU=a("p"),WFa=o("This class cannot be instantiated directly using "),wno=a("code"),JFa=o("__init__()"),UFa=o(" (throws an error)."),XFa=l(),Ea=a("div"),b(eX.$$.fragment),DFa=l(),Lno=a("p"),qFa=o("Instantiates one of the model classes of the library (with a video classification head) from a configuration."),OFa=l(),lh=a("p"),QFa=o(`Note:
Loading a model from its configuration file does `),Ano=a("strong"),YFa=o("not"),zFa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),HFe=a("a"),HFa=o("from_pretrained()"),KFa=o(" to load the model weights."),eEa=l(),b(rB.$$.fragment),oEa=l(),bo=a("div"),b(oX.$$.fragment),rEa=l(),Rno=a("p"),tEa=o("Instantiate one of the model classes of the library (with a video classification head) from a pretrained model."),aEa=l(),Ms=a("p"),nEa=o("The model class to instantiate is selected based on the "),kno=a("code"),sEa=o("model_type"),lEa=o(` property of the config object (either
passed as an argument or loaded from `),Sno=a("code"),iEa=o("pretrained_model_name_or_path"),dEa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),xno=a("code"),cEa=o("pretrained_model_name_or_path"),mEa=o(":"),fEa=l(),ih=a("ul"),tB=a("li"),Gno=a("strong"),hEa=o("timesformer"),gEa=o(" \u2014 "),KFe=a("a"),uEa=o("TimesformerForVideoClassification"),pEa=o(" (TimeSformer model)"),_Ea=l(),aB=a("li"),Bno=a("strong"),vEa=o("videomae"),bEa=o(" \u2014 "),eEe=a("a"),MEa=o("VideoMAEForVideoClassification"),TEa=o(" (VideoMAE model)"),FEa=l(),nB=a("li"),Ino=a("strong"),EEa=o("vivit"),yEa=o(" \u2014 "),oEe=a("a"),CEa=o("VivitForVideoClassification"),wEa=o(" (ViViT model)"),LEa=l(),sB=a("p"),AEa=o("The model is set in evaluation mode by default using "),Nno=a("code"),REa=o("model.eval()"),kEa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),$no=a("code"),SEa=o("model.train()"),xEa=l(),b(lB.$$.fragment),IBo=l(),dh=a("h3"),iB=a("a"),jno=a("span"),b(rX.$$.fragment),GEa=l(),Pno=a("span"),BEa=o("AutoModelForMaskedImageModeling"),NBo=l(),Cr=a("div"),b(tX.$$.fragment),IEa=l(),ch=a("p"),NEa=o(`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 `),rEe=a("a"),$Ea=o("from_pretrained()"),jEa=o(" class method or the "),tEe=a("a"),PEa=o("from_config()"),ZEa=o(` class
method.`),VEa=l(),aX=a("p"),WEa=o("This class cannot be instantiated directly using "),Zno=a("code"),JEa=o("__init__()"),UEa=o(" (throws an error)."),XEa=l(),ya=a("div"),b(nX.$$.fragment),DEa=l(),Vno=a("p"),qEa=o("Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),OEa=l(),mh=a("p"),QEa=o(`Note:
Loading a model from its configuration file does `),Wno=a("strong"),YEa=o("not"),zEa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),aEe=a("a"),HEa=o("from_pretrained()"),KEa=o(" to load the model weights."),eya=l(),b(dB.$$.fragment),oya=l(),Mo=a("div"),b(sX.$$.fragment),rya=l(),Jno=a("p"),tya=o("Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),aya=l(),Ts=a("p"),nya=o("The model class to instantiate is selected based on the "),Uno=a("code"),sya=o("model_type"),lya=o(` property of the config object (either
passed as an argument or loaded from `),Xno=a("code"),iya=o("pretrained_model_name_or_path"),dya=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Dno=a("code"),cya=o("pretrained_model_name_or_path"),mya=o(":"),fya=l(),Nt=a("ul"),cB=a("li"),qno=a("strong"),hya=o("deit"),gya=o(" \u2014 "),nEe=a("a"),uya=o("DeiTForMaskedImageModeling"),pya=o(" (DeiT model)"),_ya=l(),mB=a("li"),Ono=a("strong"),vya=o("focalnet"),bya=o(" \u2014 "),sEe=a("a"),Mya=o("FocalNetForMaskedImageModeling"),Tya=o(" (FocalNet model)"),Fya=l(),fB=a("li"),Qno=a("strong"),Eya=o("swin"),yya=o(" \u2014 "),lEe=a("a"),Cya=o("SwinForMaskedImageModeling"),wya=o(" (Swin Transformer model)"),Lya=l(),hB=a("li"),Yno=a("strong"),Aya=o("swinv2"),Rya=o(" \u2014 "),iEe=a("a"),kya=o("Swinv2ForMaskedImageModeling"),Sya=o(" (Swin Transformer V2 model)"),xya=l(),gB=a("li"),zno=a("strong"),Gya=o("vit"),Bya=o(" \u2014 "),dEe=a("a"),Iya=o("ViTForMaskedImageModeling"),Nya=o(" (ViT model)"),$ya=l(),uB=a("p"),jya=o("The model is set in evaluation mode by default using "),Hno=a("code"),Pya=o("model.eval()"),Zya=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Kno=a("code"),Vya=o("model.train()"),Wya=l(),b(pB.$$.fragment),$Bo=l(),fh=a("h3"),_B=a("a"),eso=a("span"),b(lX.$$.fragment),Jya=l(),oso=a("span"),Uya=o("TFAutoModelForMaskedImageModeling"),jBo=l(),wr=a("div"),b(iX.$$.fragment),Xya=l(),hh=a("p"),Dya=o(`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 `),cEe=a("a"),qya=o("from_pretrained()"),Oya=o(" class method or the "),mEe=a("a"),Qya=o("from_config()"),Yya=o(` class
method.`),zya=l(),dX=a("p"),Hya=o("This class cannot be instantiated directly using "),rso=a("code"),Kya=o("__init__()"),e5a=o(" (throws an error)."),o5a=l(),Ca=a("div"),b(cX.$$.fragment),r5a=l(),tso=a("p"),t5a=o("Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),a5a=l(),gh=a("p"),n5a=o(`Note:
Loading a model from its configuration file does `),aso=a("strong"),s5a=o("not"),l5a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),fEe=a("a"),i5a=o("from_pretrained()"),d5a=o(" to load the model weights."),c5a=l(),b(vB.$$.fragment),m5a=l(),Ct=a("div"),b(mX.$$.fragment),f5a=l(),nso=a("p"),h5a=o("Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),g5a=l(),Fs=a("p"),u5a=o("The model class to instantiate is selected based on the "),sso=a("code"),p5a=o("model_type"),_5a=o(` property of the config object (either
passed as an argument or loaded from `),lso=a("code"),v5a=o("pretrained_model_name_or_path"),b5a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),iso=a("code"),M5a=o("pretrained_model_name_or_path"),T5a=o(":"),F5a=l(),fX=a("ul"),bB=a("li"),dso=a("strong"),E5a=o("deit"),y5a=o(" \u2014 "),hEe=a("a"),C5a=o("TFDeiTForMaskedImageModeling"),w5a=o(" (DeiT model)"),L5a=l(),MB=a("li"),cso=a("strong"),A5a=o("swin"),R5a=o(" \u2014 "),gEe=a("a"),k5a=o("TFSwinForMaskedImageModeling"),S5a=o(" (Swin Transformer model)"),x5a=l(),b(TB.$$.fragment),PBo=l(),uh=a("h3"),FB=a("a"),mso=a("span"),b(hX.$$.fragment),G5a=l(),fso=a("span"),B5a=o("AutoModelForObjectDetection"),ZBo=l(),Lr=a("div"),b(gX.$$.fragment),I5a=l(),ph=a("p"),N5a=o(`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 `),uEe=a("a"),$5a=o("from_pretrained()"),j5a=o(" class method or the "),pEe=a("a"),P5a=o("from_config()"),Z5a=o(` class
method.`),V5a=l(),uX=a("p"),W5a=o("This class cannot be instantiated directly using "),hso=a("code"),J5a=o("__init__()"),U5a=o(" (throws an error)."),X5a=l(),wa=a("div"),b(pX.$$.fragment),D5a=l(),gso=a("p"),q5a=o("Instantiates one of the model classes of the library (with a object detection head) from a configuration."),O5a=l(),_h=a("p"),Q5a=o(`Note:
Loading a model from its configuration file does `),uso=a("strong"),Y5a=o("not"),z5a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),_Ee=a("a"),H5a=o("from_pretrained()"),K5a=o(" to load the model weights."),e9a=l(),b(EB.$$.fragment),o9a=l(),To=a("div"),b(_X.$$.fragment),r9a=l(),pso=a("p"),t9a=o("Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),a9a=l(),Es=a("p"),n9a=o("The model class to instantiate is selected based on the "),_so=a("code"),s9a=o("model_type"),l9a=o(` property of the config object (either
passed as an argument or loaded from `),vso=a("code"),i9a=o("pretrained_model_name_or_path"),d9a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),bso=a("code"),c9a=o("pretrained_model_name_or_path"),m9a=o(":"),f9a=l(),Ar=a("ul"),yB=a("li"),Mso=a("strong"),h9a=o("conditional_detr"),g9a=o(" \u2014 "),vEe=a("a"),u9a=o("ConditionalDetrForObjectDetection"),p9a=o(" (Conditional DETR model)"),_9a=l(),CB=a("li"),Tso=a("strong"),v9a=o("deformable_detr"),b9a=o(" \u2014 "),bEe=a("a"),M9a=o("DeformableDetrForObjectDetection"),T9a=o(" (Deformable DETR model)"),F9a=l(),wB=a("li"),Fso=a("strong"),E9a=o("deta"),y9a=o(" \u2014 "),MEe=a("a"),C9a=o("DetaForObjectDetection"),w9a=o(" (DETA model)"),L9a=l(),LB=a("li"),Eso=a("strong"),A9a=o("detr"),R9a=o(" \u2014 "),TEe=a("a"),k9a=o("DetrForObjectDetection"),S9a=o(" (DETR model)"),x9a=l(),AB=a("li"),yso=a("strong"),G9a=o("table-transformer"),B9a=o(" \u2014 "),FEe=a("a"),I9a=o("TableTransformerForObjectDetection"),N9a=o(" (Table Transformer model)"),$9a=l(),RB=a("li"),Cso=a("strong"),j9a=o("yolos"),P9a=o(" \u2014 "),EEe=a("a"),Z9a=o("YolosForObjectDetection"),V9a=o(" (YOLOS model)"),W9a=l(),kB=a("p"),J9a=o("The model is set in evaluation mode by default using "),wso=a("code"),U9a=o("model.eval()"),X9a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Lso=a("code"),D9a=o("model.train()"),q9a=l(),b(SB.$$.fragment),VBo=l(),vh=a("h3"),xB=a("a"),Aso=a("span"),b(vX.$$.fragment),O9a=l(),Rso=a("span"),Q9a=o("AutoModelForImageSegmentation"),WBo=l(),Rr=a("div"),b(bX.$$.fragment),Y9a=l(),bh=a("p"),z9a=o(`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 `),yEe=a("a"),H9a=o("from_pretrained()"),K9a=o(" class method or the "),CEe=a("a"),eCa=o("from_config()"),oCa=o(` class
method.`),rCa=l(),MX=a("p"),tCa=o("This class cannot be instantiated directly using "),kso=a("code"),aCa=o("__init__()"),nCa=o(" (throws an error)."),sCa=l(),La=a("div"),b(TX.$$.fragment),lCa=l(),Sso=a("p"),iCa=o("Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),dCa=l(),Mh=a("p"),cCa=o(`Note:
Loading a model from its configuration file does `),xso=a("strong"),mCa=o("not"),fCa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),wEe=a("a"),hCa=o("from_pretrained()"),gCa=o(" to load the model weights."),uCa=l(),b(GB.$$.fragment),pCa=l(),Fo=a("div"),b(FX.$$.fragment),_Ca=l(),Gso=a("p"),vCa=o("Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),bCa=l(),ys=a("p"),MCa=o("The model class to instantiate is selected based on the "),Bso=a("code"),TCa=o("model_type"),FCa=o(` property of the config object (either
passed as an argument or loaded from `),Iso=a("code"),ECa=o("pretrained_model_name_or_path"),yCa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Nso=a("code"),CCa=o("pretrained_model_name_or_path"),wCa=o(":"),LCa=l(),$so=a("ul"),BB=a("li"),jso=a("strong"),ACa=o("detr"),RCa=o(" \u2014 "),LEe=a("a"),kCa=o("DetrForSegmentation"),SCa=o(" (DETR model)"),xCa=l(),IB=a("p"),GCa=o("The model is set in evaluation mode by default using "),Pso=a("code"),BCa=o("model.eval()"),ICa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Zso=a("code"),NCa=o("model.train()"),$Ca=l(),b(NB.$$.fragment),JBo=l(),Th=a("h3"),$B=a("a"),Vso=a("span"),b(EX.$$.fragment),jCa=l(),Wso=a("span"),PCa=o("AutoModelForSemanticSegmentation"),UBo=l(),kr=a("div"),b(yX.$$.fragment),ZCa=l(),Fh=a("p"),VCa=o(`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 `),AEe=a("a"),WCa=o("from_pretrained()"),JCa=o(" class method or the "),REe=a("a"),UCa=o("from_config()"),XCa=o(` class
method.`),DCa=l(),CX=a("p"),qCa=o("This class cannot be instantiated directly using "),Jso=a("code"),OCa=o("__init__()"),QCa=o(" (throws an error)."),YCa=l(),Aa=a("div"),b(wX.$$.fragment),zCa=l(),Uso=a("p"),HCa=o("Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),KCa=l(),Eh=a("p"),ewa=o(`Note:
Loading a model from its configuration file does `),Xso=a("strong"),owa=o("not"),rwa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),kEe=a("a"),twa=o("from_pretrained()"),awa=o(" to load the model weights."),nwa=l(),b(jB.$$.fragment),swa=l(),Eo=a("div"),b(LX.$$.fragment),lwa=l(),Dso=a("p"),iwa=o("Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),dwa=l(),Cs=a("p"),cwa=o("The model class to instantiate is selected based on the "),qso=a("code"),mwa=o("model_type"),fwa=o(` property of the config object (either
passed as an argument or loaded from `),Oso=a("code"),hwa=o("pretrained_model_name_or_path"),gwa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Qso=a("code"),uwa=o("pretrained_model_name_or_path"),pwa=o(":"),_wa=l(),Ye=a("ul"),PB=a("li"),Yso=a("strong"),vwa=o("beit"),bwa=o(" \u2014 "),SEe=a("a"),Mwa=o("BeitForSemanticSegmentation"),Twa=o(" (BEiT model)"),Fwa=l(),ZB=a("li"),zso=a("strong"),Ewa=o("data2vec-vision"),ywa=o(" \u2014 "),xEe=a("a"),Cwa=o("Data2VecVisionForSemanticSegmentation"),wwa=o(" (Data2VecVision model)"),Lwa=l(),VB=a("li"),Hso=a("strong"),Awa=o("dpt"),Rwa=o(" \u2014 "),GEe=a("a"),kwa=o("DPTForSemanticSegmentation"),Swa=o(" (DPT model)"),xwa=l(),WB=a("li"),Kso=a("strong"),Gwa=o("mobilenet_v2"),Bwa=o(" \u2014 "),BEe=a("a"),Iwa=o("MobileNetV2ForSemanticSegmentation"),Nwa=o(" (MobileNetV2 model)"),$wa=l(),JB=a("li"),elo=a("strong"),jwa=o("mobilevit"),Pwa=o(" \u2014 "),IEe=a("a"),Zwa=o("MobileViTForSemanticSegmentation"),Vwa=o(" (MobileViT model)"),Wwa=l(),UB=a("li"),olo=a("strong"),Jwa=o("mobilevitv2"),Uwa=o(" \u2014 "),NEe=a("a"),Xwa=o("MobileViTV2ForSemanticSegmentation"),Dwa=o(" (MobileViTV2 model)"),qwa=l(),XB=a("li"),rlo=a("strong"),Owa=o("segformer"),Qwa=o(" \u2014 "),$Ee=a("a"),Ywa=o("SegformerForSemanticSegmentation"),zwa=o(" (SegFormer model)"),Hwa=l(),DB=a("li"),tlo=a("strong"),Kwa=o("upernet"),e6a=o(" \u2014 "),jEe=a("a"),o6a=o("UperNetForSemanticSegmentation"),r6a=o(" (UPerNet model)"),t6a=l(),qB=a("p"),a6a=o("The model is set in evaluation mode by default using "),alo=a("code"),n6a=o("model.eval()"),s6a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),nlo=a("code"),l6a=o("model.train()"),i6a=l(),b(OB.$$.fragment),XBo=l(),yh=a("h3"),QB=a("a"),slo=a("span"),b(AX.$$.fragment),d6a=l(),llo=a("span"),c6a=o("TFAutoModelForSemanticSegmentation"),DBo=l(),Sr=a("div"),b(RX.$$.fragment),m6a=l(),Ch=a("p"),f6a=o(`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 `),PEe=a("a"),h6a=o("from_pretrained()"),g6a=o(" class method or the "),ZEe=a("a"),u6a=o("from_config()"),p6a=o(` class
method.`),_6a=l(),kX=a("p"),v6a=o("This class cannot be instantiated directly using "),ilo=a("code"),b6a=o("__init__()"),M6a=o(" (throws an error)."),T6a=l(),Ra=a("div"),b(SX.$$.fragment),F6a=l(),dlo=a("p"),E6a=o("Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),y6a=l(),wh=a("p"),C6a=o(`Note:
Loading a model from its configuration file does `),clo=a("strong"),w6a=o("not"),L6a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),VEe=a("a"),A6a=o("from_pretrained()"),R6a=o(" to load the model weights."),k6a=l(),b(YB.$$.fragment),S6a=l(),wt=a("div"),b(xX.$$.fragment),x6a=l(),mlo=a("p"),G6a=o("Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),B6a=l(),ws=a("p"),I6a=o("The model class to instantiate is selected based on the "),flo=a("code"),N6a=o("model_type"),$6a=o(` property of the config object (either
passed as an argument or loaded from `),hlo=a("code"),j6a=o("pretrained_model_name_or_path"),P6a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),glo=a("code"),Z6a=o("pretrained_model_name_or_path"),V6a=o(":"),W6a=l(),Lh=a("ul"),zB=a("li"),ulo=a("strong"),J6a=o("data2vec-vision"),U6a=o(" \u2014 "),WEe=a("a"),X6a=o("TFData2VecVisionForSemanticSegmentation"),D6a=o(" (Data2VecVision model)"),q6a=l(),HB=a("li"),plo=a("strong"),O6a=o("mobilevit"),Q6a=o(" \u2014 "),JEe=a("a"),Y6a=o("TFMobileViTForSemanticSegmentation"),z6a=o(" (MobileViT model)"),H6a=l(),KB=a("li"),_lo=a("strong"),K6a=o("segformer"),e8a=o(" \u2014 "),UEe=a("a"),o8a=o("TFSegformerForSemanticSegmentation"),r8a=o(" (SegFormer model)"),t8a=l(),b(eI.$$.fragment),qBo=l(),Ah=a("h3"),oI=a("a"),vlo=a("span"),b(GX.$$.fragment),a8a=l(),blo=a("span"),n8a=o("AutoModelForInstanceSegmentation"),OBo=l(),xr=a("div"),b(BX.$$.fragment),s8a=l(),Rh=a("p"),l8a=o(`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 `),XEe=a("a"),i8a=o("from_pretrained()"),d8a=o(" class method or the "),DEe=a("a"),c8a=o("from_config()"),m8a=o(` class
method.`),f8a=l(),IX=a("p"),h8a=o("This class cannot be instantiated directly using "),Mlo=a("code"),g8a=o("__init__()"),u8a=o(" (throws an error)."),p8a=l(),ka=a("div"),b(NX.$$.fragment),_8a=l(),Tlo=a("p"),v8a=o("Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),b8a=l(),kh=a("p"),M8a=o(`Note:
Loading a model from its configuration file does `),Flo=a("strong"),T8a=o("not"),F8a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),qEe=a("a"),E8a=o("from_pretrained()"),y8a=o(" to load the model weights."),C8a=l(),b(rI.$$.fragment),w8a=l(),yo=a("div"),b($X.$$.fragment),L8a=l(),Elo=a("p"),A8a=o("Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),R8a=l(),Ls=a("p"),k8a=o("The model class to instantiate is selected based on the "),ylo=a("code"),S8a=o("model_type"),x8a=o(` property of the config object (either
passed as an argument or loaded from `),Clo=a("code"),G8a=o("pretrained_model_name_or_path"),B8a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wlo=a("code"),I8a=o("pretrained_model_name_or_path"),N8a=o(":"),$8a=l(),Llo=a("ul"),tI=a("li"),Alo=a("strong"),j8a=o("maskformer"),P8a=o(" \u2014 "),OEe=a("a"),Z8a=o("MaskFormerForInstanceSegmentation"),V8a=o(" (MaskFormer model)"),W8a=l(),aI=a("p"),J8a=o("The model is set in evaluation mode by default using "),Rlo=a("code"),U8a=o("model.eval()"),X8a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),klo=a("code"),D8a=o("model.train()"),q8a=l(),b(nI.$$.fragment),QBo=l(),Sh=a("h3"),sI=a("a"),Slo=a("span"),b(jX.$$.fragment),O8a=l(),xlo=a("span"),Q8a=o("AutoModelForUniversalSegmentation"),YBo=l(),Gr=a("div"),b(PX.$$.fragment),Y8a=l(),xh=a("p"),z8a=o(`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 `),QEe=a("a"),H8a=o("from_pretrained()"),K8a=o(" class method or the "),YEe=a("a"),e7a=o("from_config()"),o7a=o(` class
method.`),r7a=l(),ZX=a("p"),t7a=o("This class cannot be instantiated directly using "),Glo=a("code"),a7a=o("__init__()"),n7a=o(" (throws an error)."),s7a=l(),Sa=a("div"),b(VX.$$.fragment),l7a=l(),Blo=a("p"),i7a=o("Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration."),d7a=l(),Gh=a("p"),c7a=o(`Note:
Loading a model from its configuration file does `),Ilo=a("strong"),m7a=o("not"),f7a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),zEe=a("a"),h7a=o("from_pretrained()"),g7a=o(" to load the model weights."),u7a=l(),b(lI.$$.fragment),p7a=l(),Co=a("div"),b(WX.$$.fragment),_7a=l(),Nlo=a("p"),v7a=o("Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model."),b7a=l(),As=a("p"),M7a=o("The model class to instantiate is selected based on the "),$lo=a("code"),T7a=o("model_type"),F7a=o(` property of the config object (either
passed as an argument or loaded from `),jlo=a("code"),E7a=o("pretrained_model_name_or_path"),y7a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Plo=a("code"),C7a=o("pretrained_model_name_or_path"),w7a=o(":"),L7a=l(),Rs=a("ul"),iI=a("li"),Zlo=a("strong"),A7a=o("detr"),R7a=o(" \u2014 "),HEe=a("a"),k7a=o("DetrForSegmentation"),S7a=o(" (DETR model)"),x7a=l(),dI=a("li"),Vlo=a("strong"),G7a=o("mask2former"),B7a=o(" \u2014 "),KEe=a("a"),I7a=o("Mask2FormerForUniversalSegmentation"),N7a=o(" (Mask2Former model)"),$7a=l(),cI=a("li"),Wlo=a("strong"),j7a=o("maskformer"),P7a=o(" \u2014 "),eye=a("a"),Z7a=o("MaskFormerForInstanceSegmentation"),V7a=o(" (MaskFormer model)"),W7a=l(),mI=a("li"),Jlo=a("strong"),J7a=o("oneformer"),U7a=o(" \u2014 "),oye=a("a"),X7a=o("OneFormerForUniversalSegmentation"),D7a=o(" (OneFormer model)"),q7a=l(),fI=a("p"),O7a=o("The model is set in evaluation mode by default using "),Ulo=a("code"),Q7a=o("model.eval()"),Y7a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Xlo=a("code"),z7a=o("model.train()"),H7a=l(),b(hI.$$.fragment),zBo=l(),Bh=a("h3"),gI=a("a"),Dlo=a("span"),b(JX.$$.fragment),K7a=l(),qlo=a("span"),eLa=o("AutoModelForZeroShotImageClassification"),HBo=l(),Br=a("div"),b(UX.$$.fragment),oLa=l(),Ih=a("p"),rLa=o(`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 `),rye=a("a"),tLa=o("from_pretrained()"),aLa=o(" class method or the "),tye=a("a"),nLa=o("from_config()"),sLa=o(` class
method.`),lLa=l(),XX=a("p"),iLa=o("This class cannot be instantiated directly using "),Olo=a("code"),dLa=o("__init__()"),cLa=o(" (throws an error)."),mLa=l(),xa=a("div"),b(DX.$$.fragment),fLa=l(),Qlo=a("p"),hLa=o("Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),gLa=l(),Nh=a("p"),uLa=o(`Note:
Loading a model from its configuration file does `),Ylo=a("strong"),pLa=o("not"),_La=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),aye=a("a"),vLa=o("from_pretrained()"),bLa=o(" to load the model weights."),MLa=l(),b(uI.$$.fragment),TLa=l(),wo=a("div"),b(qX.$$.fragment),FLa=l(),zlo=a("p"),ELa=o("Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),yLa=l(),ks=a("p"),CLa=o("The model class to instantiate is selected based on the "),Hlo=a("code"),wLa=o("model_type"),LLa=o(` property of the config object (either
passed as an argument or loaded from `),Klo=a("code"),ALa=o("pretrained_model_name_or_path"),RLa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),eio=a("code"),kLa=o("pretrained_model_name_or_path"),SLa=o(":"),xLa=l(),Ir=a("ul"),pI=a("li"),oio=a("strong"),GLa=o("align"),BLa=o(" \u2014 "),nye=a("a"),ILa=o("AlignModel"),NLa=o(" (ALIGN model)"),$La=l(),_I=a("li"),rio=a("strong"),jLa=o("altclip"),PLa=o(" \u2014 "),sye=a("a"),ZLa=o("AltCLIPModel"),VLa=o(" (AltCLIP model)"),WLa=l(),vI=a("li"),tio=a("strong"),JLa=o("blip"),ULa=o(" \u2014 "),lye=a("a"),XLa=o("BlipModel"),DLa=o(" (BLIP model)"),qLa=l(),bI=a("li"),aio=a("strong"),OLa=o("chinese_clip"),QLa=o(" \u2014 "),iye=a("a"),YLa=o("ChineseCLIPModel"),zLa=o(" (Chinese-CLIP model)"),HLa=l(),MI=a("li"),nio=a("strong"),KLa=o("clip"),eAa=o(" \u2014 "),dye=a("a"),oAa=o("CLIPModel"),rAa=o(" (CLIP model)"),tAa=l(),TI=a("li"),sio=a("strong"),aAa=o("clipseg"),nAa=o(" \u2014 "),cye=a("a"),sAa=o("CLIPSegModel"),lAa=o(" (CLIPSeg model)"),iAa=l(),FI=a("p"),dAa=o("The model is set in evaluation mode by default using "),lio=a("code"),cAa=o("model.eval()"),mAa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),iio=a("code"),fAa=o("model.train()"),hAa=l(),b(EI.$$.fragment),KBo=l(),$h=a("h3"),yI=a("a"),dio=a("span"),b(OX.$$.fragment),gAa=l(),cio=a("span"),uAa=o("TFAutoModelForZeroShotImageClassification"),eIo=l(),Nr=a("div"),b(QX.$$.fragment),pAa=l(),jh=a("p"),_Aa=o(`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 `),mye=a("a"),vAa=o("from_pretrained()"),bAa=o(" class method or the "),fye=a("a"),MAa=o("from_config()"),TAa=o(` class
method.`),FAa=l(),YX=a("p"),EAa=o("This class cannot be instantiated directly using "),mio=a("code"),yAa=o("__init__()"),CAa=o(" (throws an error)."),wAa=l(),Ga=a("div"),b(zX.$$.fragment),LAa=l(),fio=a("p"),AAa=o("Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),RAa=l(),Ph=a("p"),kAa=o(`Note:
Loading a model from its configuration file does `),hio=a("strong"),SAa=o("not"),xAa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),hye=a("a"),GAa=o("from_pretrained()"),BAa=o(" to load the model weights."),IAa=l(),b(CI.$$.fragment),NAa=l(),Lt=a("div"),b(HX.$$.fragment),$Aa=l(),gio=a("p"),jAa=o("Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),PAa=l(),Ss=a("p"),ZAa=o("The model class to instantiate is selected based on the "),uio=a("code"),VAa=o("model_type"),WAa=o(` property of the config object (either
passed as an argument or loaded from `),pio=a("code"),JAa=o("pretrained_model_name_or_path"),UAa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),_io=a("code"),XAa=o("pretrained_model_name_or_path"),DAa=o(":"),qAa=l(),KX=a("ul"),wI=a("li"),vio=a("strong"),OAa=o("blip"),QAa=o(" \u2014 "),gye=a("a"),YAa=o("TFBlipModel"),zAa=o(" (BLIP model)"),HAa=l(),LI=a("li"),bio=a("strong"),KAa=o("clip"),eRa=o(" \u2014 "),uye=a("a"),oRa=o("TFCLIPModel"),rRa=o(" (CLIP model)"),tRa=l(),b(AI.$$.fragment),oIo=l(),Zh=a("h3"),RI=a("a"),Mio=a("span"),b(eD.$$.fragment),aRa=l(),Tio=a("span"),nRa=o("AutoModelForZeroShotObjectDetection"),rIo=l(),$r=a("div"),b(oD.$$.fragment),sRa=l(),Vh=a("p"),lRa=o(`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 `),pye=a("a"),iRa=o("from_pretrained()"),dRa=o(" class method or the "),_ye=a("a"),cRa=o("from_config()"),mRa=o(` class
method.`),fRa=l(),rD=a("p"),hRa=o("This class cannot be instantiated directly using "),Fio=a("code"),gRa=o("__init__()"),uRa=o(" (throws an error)."),pRa=l(),Ba=a("div"),b(tD.$$.fragment),_Ra=l(),Eio=a("p"),vRa=o("Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration."),bRa=l(),Wh=a("p"),MRa=o(`Note:
Loading a model from its configuration file does `),yio=a("strong"),TRa=o("not"),FRa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),vye=a("a"),ERa=o("from_pretrained()"),yRa=o(" to load the model weights."),CRa=l(),b(kI.$$.fragment),wRa=l(),Lo=a("div"),b(aD.$$.fragment),LRa=l(),Cio=a("p"),ARa=o("Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model."),RRa=l(),xs=a("p"),kRa=o("The model class to instantiate is selected based on the "),wio=a("code"),SRa=o("model_type"),xRa=o(` property of the config object (either
passed as an argument or loaded from `),Lio=a("code"),GRa=o("pretrained_model_name_or_path"),BRa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Aio=a("code"),IRa=o("pretrained_model_name_or_path"),NRa=o(":"),$Ra=l(),Rio=a("ul"),SI=a("li"),kio=a("strong"),jRa=o("owlvit"),PRa=o(" \u2014 "),bye=a("a"),ZRa=o("OwlViTForObjectDetection"),VRa=o(" (OWL-ViT model)"),WRa=l(),xI=a("p"),JRa=o("The model is set in evaluation mode by default using "),Sio=a("code"),URa=o("model.eval()"),XRa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),xio=a("code"),DRa=o("model.train()"),qRa=l(),b(GI.$$.fragment),tIo=l(),Jh=a("h2"),BI=a("a"),Gio=a("span"),b(nD.$$.fragment),ORa=l(),Bio=a("span"),QRa=o("Audio"),aIo=l(),Mye=a("p"),YRa=o("The following auto classes are available for the following audio tasks."),nIo=l(),Uh=a("h3"),II=a("a"),Iio=a("span"),b(sD.$$.fragment),zRa=l(),Nio=a("span"),HRa=o("AutoModelForAudioClassification"),sIo=l(),jr=a("div"),b(lD.$$.fragment),KRa=l(),Xh=a("p"),eka=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the `),Tye=a("a"),oka=o("from_pretrained()"),rka=o(" class method or the "),Fye=a("a"),tka=o("from_config()"),aka=o(` class
method.`),nka=l(),iD=a("p"),ska=o("This class cannot be instantiated directly using "),$io=a("code"),lka=o("__init__()"),ika=o(" (throws an error)."),dka=l(),Ia=a("div"),b(dD.$$.fragment),cka=l(),jio=a("p"),mka=o("Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),fka=l(),Dh=a("p"),hka=o(`Note:
Loading a model from its configuration file does `),Pio=a("strong"),gka=o("not"),uka=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Eye=a("a"),pka=o("from_pretrained()"),_ka=o(" to load the model weights."),vka=l(),b(NI.$$.fragment),bka=l(),Ao=a("div"),b(cD.$$.fragment),Mka=l(),Zio=a("p"),Tka=o("Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),Fka=l(),Gs=a("p"),Eka=o("The model class to instantiate is selected based on the "),Vio=a("code"),yka=o("model_type"),Cka=o(` property of the config object (either
passed as an argument or loaded from `),Wio=a("code"),wka=o("pretrained_model_name_or_path"),Lka=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Jio=a("code"),Aka=o("pretrained_model_name_or_path"),Rka=o(":"),kka=l(),Be=a("ul"),$I=a("li"),Uio=a("strong"),Ska=o("audio-spectrogram-transformer"),xka=o(" \u2014 "),yye=a("a"),Gka=o("ASTForAudioClassification"),Bka=o(" (Audio Spectrogram Transformer model)"),Ika=l(),jI=a("li"),Xio=a("strong"),Nka=o("data2vec-audio"),$ka=o(" \u2014 "),Cye=a("a"),jka=o("Data2VecAudioForSequenceClassification"),Pka=o(" (Data2VecAudio model)"),Zka=l(),PI=a("li"),Dio=a("strong"),Vka=o("hubert"),Wka=o(" \u2014 "),wye=a("a"),Jka=o("HubertForSequenceClassification"),Uka=o(" (Hubert model)"),Xka=l(),ZI=a("li"),qio=a("strong"),Dka=o("sew"),qka=o(" \u2014 "),Lye=a("a"),Oka=o("SEWForSequenceClassification"),Qka=o(" (SEW model)"),Yka=l(),VI=a("li"),Oio=a("strong"),zka=o("sew-d"),Hka=o(" \u2014 "),Aye=a("a"),Kka=o("SEWDForSequenceClassification"),eSa=o(" (SEW-D model)"),oSa=l(),WI=a("li"),Qio=a("strong"),rSa=o("unispeech"),tSa=o(" \u2014 "),Rye=a("a"),aSa=o("UniSpeechForSequenceClassification"),nSa=o(" (UniSpeech model)"),sSa=l(),JI=a("li"),Yio=a("strong"),lSa=o("unispeech-sat"),iSa=o(" \u2014 "),kye=a("a"),dSa=o("UniSpeechSatForSequenceClassification"),cSa=o(" (UniSpeechSat model)"),mSa=l(),UI=a("li"),zio=a("strong"),fSa=o("wav2vec2"),hSa=o(" \u2014 "),Sye=a("a"),gSa=o("Wav2Vec2ForSequenceClassification"),uSa=o(" (Wav2Vec2 model)"),pSa=l(),XI=a("li"),Hio=a("strong"),_Sa=o("wav2vec2-conformer"),vSa=o(" \u2014 "),xye=a("a"),bSa=o("Wav2Vec2ConformerForSequenceClassification"),MSa=o(" (Wav2Vec2-Conformer model)"),TSa=l(),DI=a("li"),Kio=a("strong"),FSa=o("wavlm"),ESa=o(" \u2014 "),Gye=a("a"),ySa=o("WavLMForSequenceClassification"),CSa=o(" (WavLM model)"),wSa=l(),qI=a("li"),edo=a("strong"),LSa=o("whisper"),ASa=o(" \u2014 "),Bye=a("a"),RSa=o("WhisperForAudioClassification"),kSa=o(" (Whisper model)"),SSa=l(),OI=a("p"),xSa=o("The model is set in evaluation mode by default using "),odo=a("code"),GSa=o("model.eval()"),BSa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),rdo=a("code"),ISa=o("model.train()"),NSa=l(),b(QI.$$.fragment),lIo=l(),qh=a("h3"),YI=a("a"),tdo=a("span"),b(mD.$$.fragment),$Sa=l(),ado=a("span"),jSa=o("AutoModelForAudioFrameClassification"),iIo=l(),Pr=a("div"),b(fD.$$.fragment),PSa=l(),Oh=a("p"),ZSa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
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method.`),XSa=l(),hD=a("p"),DSa=o("This class cannot be instantiated directly using "),ndo=a("code"),qSa=o("__init__()"),OSa=o(" (throws an error)."),QSa=l(),Na=a("div"),b(gD.$$.fragment),YSa=l(),sdo=a("p"),zSa=o("Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),HSa=l(),Qh=a("p"),KSa=o(`Note:
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model\u2019s configuration. Use `),$ye=a("a"),rxa=o("from_pretrained()"),txa=o(" to load the model weights."),axa=l(),b(zI.$$.fragment),nxa=l(),At=a("div"),b(uD.$$.fragment),sxa=l(),ido=a("p"),lxa=o("Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),ixa=l(),Bs=a("p"),dxa=o("The model class to instantiate is selected based on the "),ddo=a("code"),cxa=o("model_type"),mxa=o(` property of the config object (either
passed as an argument or loaded from `),cdo=a("code"),fxa=o("pretrained_model_name_or_path"),hxa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mdo=a("code"),gxa=o("pretrained_model_name_or_path"),uxa=o(":"),pxa=l(),fdo=a("ul"),HI=a("li"),hdo=a("strong"),_xa=o("wav2vec2"),vxa=o(" \u2014 "),jye=a("a"),bxa=o("TFWav2Vec2ForSequenceClassification"),Mxa=o(" (Wav2Vec2 model)"),Txa=l(),b(KI.$$.fragment),dIo=l(),Yh=a("h3"),eN=a("a"),gdo=a("span"),b(pD.$$.fragment),Fxa=l(),udo=a("span"),Exa=o("TFAutoModelForAudioFrameClassification"),cIo=l(),Zr=a("div"),b(_D.$$.fragment),yxa=l(),zh=a("p"),Cxa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio frame (token) classification head) when created
with the `),Pye=a("a"),wxa=o("from_pretrained()"),Lxa=o(" class method or the "),Zye=a("a"),Axa=o("from_config()"),Rxa=o(` class
method.`),kxa=l(),vD=a("p"),Sxa=o("This class cannot be instantiated directly using "),pdo=a("code"),xxa=o("__init__()"),Gxa=o(" (throws an error)."),Bxa=l(),$a=a("div"),b(bD.$$.fragment),Ixa=l(),_do=a("p"),Nxa=o("Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),$xa=l(),Hh=a("p"),jxa=o(`Note:
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model\u2019s configuration. Use `),Vye=a("a"),Vxa=o("from_pretrained()"),Wxa=o(" to load the model weights."),Jxa=l(),b(oN.$$.fragment),Uxa=l(),Ro=a("div"),b(MD.$$.fragment),Xxa=l(),bdo=a("p"),Dxa=o("Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),qxa=l(),Is=a("p"),Oxa=o("The model class to instantiate is selected based on the "),Mdo=a("code"),Qxa=o("model_type"),Yxa=o(` property of the config object (either
passed as an argument or loaded from `),Tdo=a("code"),zxa=o("pretrained_model_name_or_path"),Hxa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Fdo=a("code"),Kxa=o("pretrained_model_name_or_path"),eGa=o(":"),oGa=l(),$t=a("ul"),rN=a("li"),Edo=a("strong"),rGa=o("data2vec-audio"),tGa=o(" \u2014 "),Wye=a("a"),aGa=o("Data2VecAudioForAudioFrameClassification"),nGa=o(" (Data2VecAudio model)"),sGa=l(),tN=a("li"),ydo=a("strong"),lGa=o("unispeech-sat"),iGa=o(" \u2014 "),Jye=a("a"),dGa=o("UniSpeechSatForAudioFrameClassification"),cGa=o(" (UniSpeechSat model)"),mGa=l(),aN=a("li"),Cdo=a("strong"),fGa=o("wav2vec2"),hGa=o(" \u2014 "),Uye=a("a"),gGa=o("Wav2Vec2ForAudioFrameClassification"),uGa=o(" (Wav2Vec2 model)"),pGa=l(),nN=a("li"),wdo=a("strong"),_Ga=o("wav2vec2-conformer"),vGa=o(" \u2014 "),Xye=a("a"),bGa=o("Wav2Vec2ConformerForAudioFrameClassification"),MGa=o(" (Wav2Vec2-Conformer model)"),TGa=l(),sN=a("li"),Ldo=a("strong"),FGa=o("wavlm"),EGa=o(" \u2014 "),Dye=a("a"),yGa=o("WavLMForAudioFrameClassification"),CGa=o(" (WavLM model)"),wGa=l(),lN=a("p"),LGa=o("The model is set in evaluation mode by default using "),Ado=a("code"),AGa=o("model.eval()"),RGa=o(` (so for instance, dropout modules are
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with the `),qye=a("a"),NGa=o("from_pretrained()"),$Ga=o(" class method or the "),Oye=a("a"),jGa=o("from_config()"),PGa=o(` class
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wHa=s(jLe);rar=r(wHa,"AutoTokenizer"),wHa.forEach(t),XIo.forEach(t),Yxo=i(m),Zo=n(m,"DIV",{class:!0});var qi=s(Zo);M(rV.$$.fragment,qi),tar=i(qi),tV=n(qi,"P",{});var DIo=s(tV);aar=r(DIo,`This is a generic tokenizer class that will be instantiated as one of the tokenizer classes of the library when
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"),Ite=n(RQ,"A",{href:!0});var dnn=s(Ite);o1r=r(dnn,"XLMRobertaTokenizer"),dnn.forEach(t),r1r=r(RQ," or "),Nte=n(RQ,"A",{href:!0});var cnn=s(Nte);t1r=r(cnn,"XLMRobertaTokenizerFast"),cnn.forEach(t),a1r=r(RQ," (XLM-RoBERTa model)"),RQ.forEach(t),n1r=i(S),Ni=n(S,"LI",{});var kQ=s(Ni);pke=n(kQ,"STRONG",{});var mnn=s(pke);s1r=r(mnn,"xlm-roberta-xl"),mnn.forEach(t),l1r=r(kQ," \u2014 "),$te=n(kQ,"A",{href:!0});var fnn=s($te);i1r=r(fnn,"XLMRobertaTokenizer"),fnn.forEach(t),d1r=r(kQ," or "),jte=n(kQ,"A",{href:!0});var hnn=s(jte);c1r=r(hnn,"XLMRobertaTokenizerFast"),hnn.forEach(t),m1r=r(kQ," (XLM-RoBERTa-XL model)"),kQ.forEach(t),f1r=i(S),$i=n(S,"LI",{});var SQ=s($i);_ke=n(SQ,"STRONG",{});var gnn=s(_ke);h1r=r(gnn,"xlnet"),gnn.forEach(t),g1r=r(SQ," \u2014 "),Pte=n(SQ,"A",{href:!0});var unn=s(Pte);u1r=r(unn,"XLNetTokenizer"),unn.forEach(t),p1r=r(SQ," or "),Zte=n(SQ,"A",{href:!0});var pnn=s(Zte);_1r=r(pnn,"XLNetTokenizerFast"),pnn.forEach(t),v1r=r(SQ," (XLNet model)"),SQ.forEach(t),b1r=i(S),ji=n(S,"LI",{});var xQ=s(ji);vke=n(xQ,"STRONG",{});var _nn=s(vke);M1r=r(_nn,"xmod"),_nn.forEach(t),T1r=r(xQ," \u2014 "),Vte=n(xQ,"A",{href:!0});var vnn=s(Vte);F1r=r(vnn,"XLMRobertaTokenizer"),vnn.forEach(t),E1r=r(xQ," or "),Wte=n(xQ,"A",{href:!0});var bnn=s(Wte);y1r=r(bnn,"XLMRobertaTokenizerFast"),bnn.forEach(t),C1r=r(xQ," (X-MOD model)"),xQ.forEach(t),w1r=i(S),Pi=n(S,"LI",{});var GQ=s(Pi);bke=n(GQ,"STRONG",{});var Mnn=s(bke);L1r=r(Mnn,"yoso"),Mnn.forEach(t),A1r=r(GQ," \u2014 "),Jte=n(GQ,"A",{href:!0});var Tnn=s(Jte);R1r=r(Tnn,"AlbertTokenizer"),Tnn.forEach(t),k1r=r(GQ," or "),Ute=n(GQ,"A",{href:!0});var Fnn=s(Ute);S1r=r(Fnn,"AlbertTokenizerFast"),Fnn.forEach(t),x1r=r(GQ," (YOSO model)"),GQ.forEach(t),S.forEach(t),G1r=i(Oi),M(xv.$$.fragment,Oi),Oi.forEach(t),B1r=i(qi),Gv=n(qi,"DIV",{class:!0});var OIo=s(Gv);M(sV.$$.fragment,OIo),I1r=i(OIo),Mke=n(OIo,"P",{});var Enn=s(Mke);N1r=r(Enn,"Register a new tokenizer in this mapping."),Enn.forEach(t),OIo.forEach(t),qi.forEach(t),zxo=i(m),Hc=n(m,"H2",{class:!0});var QIo=s(Hc);Bv=n(QIo,"A",{id:!0,class:!0,href:!0});var ynn=s(Bv);Tke=n(ynn,"SPAN",{});var Cnn=s(Tke);M(lV.$$.fragment,Cnn),Cnn.forEach(t),ynn.forEach(t),$1r=i(QIo),Fke=n(QIo,"SPAN",{});var wnn=s(Fke);j1r=r(wnn,"AutoFeatureExtractor"),wnn.forEach(t),QIo.forEach(t),Hxo=i(m),Vo=n(m,"DIV",{class:!0});var Qi=s(Vo);M(iV.$$.fragment,Qi),P1r=i(Qi),dV=n(Qi,"P",{});var YIo=s(dV);Z1r=r(YIo,`This is a generic feature extractor class that will be instantiated as one of the feature extractor classes of the
library when created with the `),Xte=n(YIo,"A",{href:!0});var Lnn=s(Xte);V1r=r(Lnn,"AutoFeatureExtractor.from_pretrained()"),Lnn.forEach(t),W1r=r(YIo," class method."),YIo.forEach(t),J1r=i(Qi),cV=n(Qi,"P",{});var zIo=s(cV);U1r=r(zIo,"This class cannot be instantiated directly using "),Eke=n(zIo,"CODE",{});var Ann=s(Eke);X1r=r(Ann,"__init__()"),Ann.forEach(t),D1r=r(zIo," (throws an error)."),zIo.forEach(t),q1r=i(Qi),to=n(Qi,"DIV",{class:!0});var za=s(to);M(mV.$$.fragment,za),O1r=i(za),yke=n(za,"P",{});var Rnn=s(yke);Q1r=r(Rnn,"Instantiate one of the feature extractor classes of the library from a pretrained model vocabulary."),Rnn.forEach(t),Y1r=i(za),$n=n(za,"P",{});var Fj=s($n);z1r=r(Fj,"The feature extractor class to instantiate is selected based on the "),Cke=n(Fj,"CODE",{});var knn=s(Cke);H1r=r(knn,"model_type"),knn.forEach(t),K1r=r(Fj,` property of the config object
(either passed as an argument or loaded from `),wke=n(Fj,"CODE",{});var Snn=s(wke);ebr=r(Snn,"pretrained_model_name_or_path"),Snn.forEach(t),obr=r(Fj,` if possible), or when it\u2019s
missing, by falling back to using pattern matching on `),Lke=n(Fj,"CODE",{});var xnn=s(Lke);rbr=r(xnn,"pretrained_model_name_or_path"),xnn.forEach(t),tbr=r(Fj,":"),Fj.forEach(t),abr=i(za),J=n(za,"UL",{});var D=s(J);Iv=n(D,"LI",{});var L2o=s(Iv);Ake=n(L2o,"STRONG",{});var Gnn=s(Ake);nbr=r(Gnn,"audio-spectrogram-transformer"),Gnn.forEach(t),sbr=r(L2o," \u2014 "),Dte=n(L2o,"A",{href:!0});var Bnn=s(Dte);lbr=r(Bnn,"ASTFeatureExtractor"),Bnn.forEach(t),ibr=r(L2o," (Audio Spectrogram Transformer model)"),L2o.forEach(t),dbr=i(D),Nv=n(D,"LI",{});var A2o=s(Nv);Rke=n(A2o,"STRONG",{});var Inn=s(Rke);cbr=r(Inn,"beit"),Inn.forEach(t),mbr=r(A2o," \u2014 "),qte=n(A2o,"A",{href:!0});var Nnn=s(qte);fbr=r(Nnn,"BeitFeatureExtractor"),Nnn.forEach(t),hbr=r(A2o," (BEiT model)"),A2o.forEach(t),gbr=i(D),$v=n(D,"LI",{});var R2o=s($v);kke=n(R2o,"STRONG",{});var $nn=s(kke);ubr=r($nn,"chinese_clip"),$nn.forEach(t),pbr=r(R2o," \u2014 "),Ote=n(R2o,"A",{href:!0});var jnn=s(Ote);_br=r(jnn,"ChineseCLIPFeatureExtractor"),jnn.forEach(t),vbr=r(R2o," (Chinese-CLIP model)"),R2o.forEach(t),bbr=i(D),jv=n(D,"LI",{});var k2o=s(jv);Ske=n(k2o,"STRONG",{});var Pnn=s(Ske);Mbr=r(Pnn,"clap"),Pnn.forEach(t),Tbr=r(k2o," \u2014 "),Qte=n(k2o,"A",{href:!0});var Znn=s(Qte);Fbr=r(Znn,"ClapFeatureExtractor"),Znn.forEach(t),Ebr=r(k2o," (CLAP model)"),k2o.forEach(t),ybr=i(D),Pv=n(D,"LI",{});var S2o=s(Pv);xke=n(S2o,"STRONG",{});var Vnn=s(xke);Cbr=r(Vnn,"clip"),Vnn.forEach(t),wbr=r(S2o," \u2014 "),Yte=n(S2o,"A",{href:!0});var Wnn=s(Yte);Lbr=r(Wnn,"CLIPFeatureExtractor"),Wnn.forEach(t),Abr=r(S2o," (CLIP model)"),S2o.forEach(t),Rbr=i(D),Zv=n(D,"LI",{});var x2o=s(Zv);Gke=n(x2o,"STRONG",{});var Jnn=s(Gke);kbr=r(Jnn,"clipseg"),Jnn.forEach(t),Sbr=r(x2o," \u2014 "),zte=n(x2o,"A",{href:!0});var Unn=s(zte);xbr=r(Unn,"ViTFeatureExtractor"),Unn.forEach(t),Gbr=r(x2o," (CLIPSeg model)"),x2o.forEach(t),Bbr=i(D),Vv=n(D,"LI",{});var G2o=s(Vv);Bke=n(G2o,"STRONG",{});var Xnn=s(Bke);Ibr=r(Xnn,"conditional_detr"),Xnn.forEach(t),Nbr=r(G2o," \u2014 "),Hte=n(G2o,"A",{href:!0});var Dnn=s(Hte);$br=r(Dnn,"ConditionalDetrFeatureExtractor"),Dnn.forEach(t),jbr=r(G2o," (Conditional DETR model)"),G2o.forEach(t),Pbr=i(D),Wv=n(D,"LI",{});var B2o=s(Wv);Ike=n(B2o,"STRONG",{});var qnn=s(Ike);Zbr=r(qnn,"convnext"),qnn.forEach(t),Vbr=r(B2o," \u2014 "),Kte=n(B2o,"A",{href:!0});var Onn=s(Kte);Wbr=r(Onn,"ConvNextFeatureExtractor"),Onn.forEach(t),Jbr=r(B2o," (ConvNeXT model)"),B2o.forEach(t),Ubr=i(D),Jv=n(D,"LI",{});var I2o=s(Jv);Nke=n(I2o,"STRONG",{});var Qnn=s(Nke);Xbr=r(Qnn,"cvt"),Qnn.forEach(t),Dbr=r(I2o," \u2014 "),eae=n(I2o,"A",{href:!0});var Ynn=s(eae);qbr=r(Ynn,"ConvNextFeatureExtractor"),Ynn.forEach(t),Obr=r(I2o," (CvT model)"),I2o.forEach(t),Qbr=i(D),Uv=n(D,"LI",{});var N2o=s(Uv);$ke=n(N2o,"STRONG",{});var znn=s($ke);Ybr=r(znn,"data2vec-audio"),znn.forEach(t),zbr=r(N2o," \u2014 "),oae=n(N2o,"A",{href:!0});var Hnn=s(oae);Hbr=r(Hnn,"Wav2Vec2FeatureExtractor"),Hnn.forEach(t),Kbr=r(N2o," (Data2VecAudio model)"),N2o.forEach(t),e0r=i(D),Xv=n(D,"LI",{});var $2o=s(Xv);jke=n($2o,"STRONG",{});var Knn=s(jke);o0r=r(Knn,"data2vec-vision"),Knn.forEach(t),r0r=r($2o," \u2014 "),rae=n($2o,"A",{href:!0});var esn=s(rae);t0r=r(esn,"BeitFeatureExtractor"),esn.forEach(t),a0r=r($2o," (Data2VecVision model)"),$2o.forEach(t),n0r=i(D),Dv=n(D,"LI",{});var j2o=s(Dv);Pke=n(j2o,"STRONG",{});var osn=s(Pke);s0r=r(osn,"deformable_detr"),osn.forEach(t),l0r=r(j2o," \u2014 "),tae=n(j2o,"A",{href:!0});var rsn=s(tae);i0r=r(rsn,"DeformableDetrFeatureExtractor"),rsn.forEach(t),d0r=r(j2o," (Deformable DETR model)"),j2o.forEach(t),c0r=i(D),qv=n(D,"LI",{});var P2o=s(qv);Zke=n(P2o,"STRONG",{});var tsn=s(Zke);m0r=r(tsn,"deit"),tsn.forEach(t),f0r=r(P2o," \u2014 "),aae=n(P2o,"A",{href:!0});var asn=s(aae);h0r=r(asn,"DeiTFeatureExtractor"),asn.forEach(t),g0r=r(P2o," (DeiT model)"),P2o.forEach(t),u0r=i(D),Ov=n(D,"LI",{});var Z2o=s(Ov);Vke=n(Z2o,"STRONG",{});var nsn=s(Vke);p0r=r(nsn,"detr"),nsn.forEach(t),_0r=r(Z2o," \u2014 "),nae=n(Z2o,"A",{href:!0});var ssn=s(nae);v0r=r(ssn,"DetrFeatureExtractor"),ssn.forEach(t),b0r=r(Z2o," (DETR model)"),Z2o.forEach(t),M0r=i(D),Qv=n(D,"LI",{});var V2o=s(Qv);Wke=n(V2o,"STRONG",{});var lsn=s(Wke);T0r=r(lsn,"dinat"),lsn.forEach(t),F0r=r(V2o," \u2014 "),sae=n(V2o,"A",{href:!0});var isn=s(sae);E0r=r(isn,"ViTFeatureExtractor"),isn.forEach(t),y0r=r(V2o," (DiNAT model)"),V2o.forEach(t),C0r=i(D),Yv=n(D,"LI",{});var W2o=s(Yv);Jke=n(W2o,"STRONG",{});var dsn=s(Jke);w0r=r(dsn,"donut-swin"),dsn.forEach(t),L0r=r(W2o," \u2014 "),lae=n(W2o,"A",{href:!0});var csn=s(lae);A0r=r(csn,"DonutFeatureExtractor"),csn.forEach(t),R0r=r(W2o," (DonutSwin model)"),W2o.forEach(t),k0r=i(D),zv=n(D,"LI",{});var J2o=s(zv);Uke=n(J2o,"STRONG",{});var msn=s(Uke);S0r=r(msn,"dpt"),msn.forEach(t),x0r=r(J2o," \u2014 "),iae=n(J2o,"A",{href:!0});var fsn=s(iae);G0r=r(fsn,"DPTFeatureExtractor"),fsn.forEach(t),B0r=r(J2o," (DPT model)"),J2o.forEach(t),I0r=i(D),Hv=n(D,"LI",{});var U2o=s(Hv);Xke=n(U2o,"STRONG",{});var hsn=s(Xke);N0r=r(hsn,"encodec"),hsn.forEach(t),$0r=r(U2o," \u2014 "),dae=n(U2o,"A",{href:!0});var gsn=s(dae);j0r=r(gsn,"EncodecFeatureExtractor"),gsn.forEach(t),P0r=r(U2o," (EnCodec model)"),U2o.forEach(t),Z0r=i(D),Kv=n(D,"LI",{});var X2o=s(Kv);Dke=n(X2o,"STRONG",{});var usn=s(Dke);V0r=r(usn,"flava"),usn.forEach(t),W0r=r(X2o," \u2014 "),cae=n(X2o,"A",{href:!0});var psn=s(cae);J0r=r(psn,"FlavaFeatureExtractor"),psn.forEach(t),U0r=r(X2o," (FLAVA model)"),X2o.forEach(t),X0r=i(D),e1=n(D,"LI",{});var D2o=s(e1);qke=n(D2o,"STRONG",{});var _sn=s(qke);D0r=r(_sn,"glpn"),_sn.forEach(t),q0r=r(D2o," \u2014 "),mae=n(D2o,"A",{href:!0});var vsn=s(mae);O0r=r(vsn,"GLPNFeatureExtractor"),vsn.forEach(t),Q0r=r(D2o," (GLPN model)"),D2o.forEach(t),Y0r=i(D),o1=n(D,"LI",{});var q2o=s(o1);Oke=n(q2o,"STRONG",{});var bsn=s(Oke);z0r=r(bsn,"groupvit"),bsn.forEach(t),H0r=r(q2o," \u2014 "),fae=n(q2o,"A",{href:!0});var Msn=s(fae);K0r=r(Msn,"CLIPFeatureExtractor"),Msn.forEach(t),e3r=r(q2o," (GroupViT model)"),q2o.forEach(t),o3r=i(D),r1=n(D,"LI",{});var O2o=s(r1);Qke=n(O2o,"STRONG",{});var Tsn=s(Qke);r3r=r(Tsn,"hubert"),Tsn.forEach(t),t3r=r(O2o," \u2014 "),hae=n(O2o,"A",{href:!0});var Fsn=s(hae);a3r=r(Fsn,"Wav2Vec2FeatureExtractor"),Fsn.forEach(t),n3r=r(O2o," (Hubert model)"),O2o.forEach(t),s3r=i(D),t1=n(D,"LI",{});var Q2o=s(t1);Yke=n(Q2o,"STRONG",{});var Esn=s(Yke);l3r=r(Esn,"imagegpt"),Esn.forEach(t),i3r=r(Q2o," \u2014 "),gae=n(Q2o,"A",{href:!0});var ysn=s(gae);d3r=r(ysn,"ImageGPTFeatureExtractor"),ysn.forEach(t),c3r=r(Q2o," (ImageGPT model)"),Q2o.forEach(t),m3r=i(D),a1=n(D,"LI",{});var Y2o=s(a1);zke=n(Y2o,"STRONG",{});var Csn=s(zke);f3r=r(Csn,"layoutlmv2"),Csn.forEach(t),h3r=r(Y2o," \u2014 "),uae=n(Y2o,"A",{href:!0});var wsn=s(uae);g3r=r(wsn,"LayoutLMv2FeatureExtractor"),wsn.forEach(t),u3r=r(Y2o," 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Uln=s(zae);xTr=r(Uln,"WhisperFeatureExtractor"),Uln.forEach(t),GTr=r(xvo," (Whisper model)"),xvo.forEach(t),BTr=i(D),V1=n(D,"LI",{});var Gvo=s(V1);BSe=n(Gvo,"STRONG",{});var Xln=s(BSe);ITr=r(Xln,"xclip"),Xln.forEach(t),NTr=r(Gvo," \u2014 "),Hae=n(Gvo,"A",{href:!0});var Dln=s(Hae);$Tr=r(Dln,"CLIPFeatureExtractor"),Dln.forEach(t),jTr=r(Gvo," (X-CLIP model)"),Gvo.forEach(t),PTr=i(D),W1=n(D,"LI",{});var Bvo=s(W1);ISe=n(Bvo,"STRONG",{});var qln=s(ISe);ZTr=r(qln,"yolos"),qln.forEach(t),VTr=r(Bvo," \u2014 "),Kae=n(Bvo,"A",{href:!0});var Oln=s(Kae);WTr=r(Oln,"YolosFeatureExtractor"),Oln.forEach(t),JTr=r(Bvo," (YOLOS model)"),Bvo.forEach(t),D.forEach(t),UTr=i(za),M(J1.$$.fragment,za),XTr=i(za),M(U1.$$.fragment,za),za.forEach(t),DTr=i(Qi),X1=n(Qi,"DIV",{class:!0});var HIo=s(X1);M(fV.$$.fragment,HIo),qTr=i(HIo),NSe=n(HIo,"P",{});var Qln=s(NSe);OTr=r(Qln,"Register a new feature extractor for this class."),Qln.forEach(t),HIo.forEach(t),Qi.forEach(t),Kxo=i(m),Kc=n(m,"H2",{class:!0});var KIo=s(Kc);D1=n(KIo,"A",{id:!0,class:!0,href:!0});var Yln=s(D1);$Se=n(Yln,"SPAN",{});var zln=s($Se);M(hV.$$.fragment,zln),zln.forEach(t),Yln.forEach(t),QTr=i(KIo),jSe=n(KIo,"SPAN",{});var Hln=s(jSe);YTr=r(Hln,"AutoImageProcessor"),Hln.forEach(t),KIo.forEach(t),eGo=i(m),Wo=n(m,"DIV",{class:!0});var Yi=s(Wo);M(gV.$$.fragment,Yi),zTr=i(Yi),uV=n(Yi,"P",{});var eNo=s(uV);HTr=r(eNo,`This is a generic image processor class that will be instantiated as one of the image processor classes of the
library when created with the `),ene=n(eNo,"A",{href:!0});var Kln=s(ene);KTr=r(Kln,"AutoImageProcessor.from_pretrained()"),Kln.forEach(t),eFr=r(eNo," class method."),eNo.forEach(t),oFr=i(Yi),pV=n(Yi,"P",{});var oNo=s(pV);rFr=r(oNo,"This class cannot be instantiated directly using "),PSe=n(oNo,"CODE",{});var ein=s(PSe);tFr=r(ein,"__init__()"),ein.forEach(t),aFr=r(oNo," (throws an error)."),oNo.forEach(t),nFr=i(Yi),ao=n(Yi,"DIV",{class:!0});var Ha=s(ao);M(_V.$$.fragment,Ha),sFr=i(Ha),ZSe=n(Ha,"P",{});var oin=s(ZSe);lFr=r(oin,"Instantiate one of the image processor classes of the library from a pretrained model vocabulary."),oin.forEach(t),iFr=i(Ha),jn=n(Ha,"P",{});var Ej=s(jn);dFr=r(Ej,"The image processor class to instantiate is selected based on the "),VSe=n(Ej,"CODE",{});var rin=s(VSe);cFr=r(rin,"model_type"),rin.forEach(t),mFr=r(Ej,` property of the config object
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xcn=s(gse);kwr=r(xcn,"ViTImageProcessor"),xcn.forEach(t),Swr=r(O1o," (ViTMSN model)"),O1o.forEach(t),xwr=i($),l0=n($,"LI",{});var Q1o=s(l0);aGe=n(Q1o,"STRONG",{});var Gcn=s(aGe);Gwr=r(Gcn,"xclip"),Gcn.forEach(t),Bwr=r(Q1o," \u2014 "),use=n(Q1o,"A",{href:!0});var Bcn=s(use);Iwr=r(Bcn,"CLIPImageProcessor"),Bcn.forEach(t),Nwr=r(Q1o," (X-CLIP model)"),Q1o.forEach(t),$wr=i($),i0=n($,"LI",{});var Y1o=s(i0);nGe=n(Y1o,"STRONG",{});var Icn=s(nGe);jwr=r(Icn,"yolos"),Icn.forEach(t),Pwr=r(Y1o," \u2014 "),pse=n(Y1o,"A",{href:!0});var Ncn=s(pse);Zwr=r(Ncn,"YolosImageProcessor"),Ncn.forEach(t),Vwr=r(Y1o," (YOLOS model)"),Y1o.forEach(t),$.forEach(t),Wwr=i(Ha),M(d0.$$.fragment,Ha),Jwr=i(Ha),M(c0.$$.fragment,Ha),Ha.forEach(t),Uwr=i(Yi),m0=n(Yi,"DIV",{class:!0});var rNo=s(m0);M(vV.$$.fragment,rNo),Xwr=i(rNo),sGe=n(rNo,"P",{});var $cn=s(sGe);Dwr=r($cn,"Register a new image processor for this class."),$cn.forEach(t),rNo.forEach(t),Yi.forEach(t),oGo=i(m),em=n(m,"H2",{class:!0});var tNo=s(em);f0=n(tNo,"A",{id:!0,class:!0,href:!0});var jcn=s(f0);lGe=n(jcn,"SPAN",{});var Pcn=s(lGe);M(bV.$$.fragment,Pcn),Pcn.forEach(t),jcn.forEach(t),qwr=i(tNo),iGe=n(tNo,"SPAN",{});var Zcn=s(iGe);Owr=r(Zcn,"AutoProcessor"),Zcn.forEach(t),tNo.forEach(t),rGo=i(m),Jo=n(m,"DIV",{class:!0});var zi=s(Jo);M(MV.$$.fragment,zi),Qwr=i(zi),TV=n(zi,"P",{});var aNo=s(TV);Ywr=r(aNo,`This is a generic processor class that will be instantiated as one of the processor classes of the library when
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nmn=s(yse);X6r=r(nmn,"ChineseCLIPProcessor"),nmn.forEach(t),D6r=r(tbo," (Chinese-CLIP model)"),tbo.forEach(t),q6r=i(re),M0=n(re,"LI",{});var abo=s(M0);MGe=n(abo,"STRONG",{});var smn=s(MGe);O6r=r(smn,"clap"),smn.forEach(t),Q6r=r(abo," \u2014 "),Cse=n(abo,"A",{href:!0});var lmn=s(Cse);Y6r=r(lmn,"ClapProcessor"),lmn.forEach(t),z6r=r(abo," (CLAP model)"),abo.forEach(t),H6r=i(re),T0=n(re,"LI",{});var nbo=s(T0);TGe=n(nbo,"STRONG",{});var imn=s(TGe);K6r=r(imn,"clip"),imn.forEach(t),e8r=r(nbo," \u2014 "),wse=n(nbo,"A",{href:!0});var dmn=s(wse);o8r=r(dmn,"CLIPProcessor"),dmn.forEach(t),r8r=r(nbo," (CLIP model)"),nbo.forEach(t),t8r=i(re),F0=n(re,"LI",{});var sbo=s(F0);FGe=n(sbo,"STRONG",{});var cmn=s(FGe);a8r=r(cmn,"clipseg"),cmn.forEach(t),n8r=r(sbo," \u2014 "),Lse=n(sbo,"A",{href:!0});var mmn=s(Lse);s8r=r(mmn,"CLIPSegProcessor"),mmn.forEach(t),l8r=r(sbo," (CLIPSeg model)"),sbo.forEach(t),i8r=i(re),E0=n(re,"LI",{});var lbo=s(E0);EGe=n(lbo,"STRONG",{});var 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vbo=s(B0);BGe=n(vbo,"STRONG",{});var Bmn=s(BGe);c7r=r(Bmn,"oneformer"),Bmn.forEach(t),m7r=r(vbo," \u2014 "),Pse=n(vbo,"A",{href:!0});var Imn=s(Pse);f7r=r(Imn,"OneFormerProcessor"),Imn.forEach(t),h7r=r(vbo," (OneFormer model)"),vbo.forEach(t),g7r=i(re),I0=n(re,"LI",{});var bbo=s(I0);IGe=n(bbo,"STRONG",{});var Nmn=s(IGe);u7r=r(Nmn,"owlvit"),Nmn.forEach(t),p7r=r(bbo," \u2014 "),Zse=n(bbo,"A",{href:!0});var $mn=s(Zse);_7r=r($mn,"OwlViTProcessor"),$mn.forEach(t),v7r=r(bbo," (OWL-ViT model)"),bbo.forEach(t),b7r=i(re),N0=n(re,"LI",{});var Mbo=s(N0);NGe=n(Mbo,"STRONG",{});var jmn=s(NGe);M7r=r(jmn,"pix2struct"),jmn.forEach(t),T7r=r(Mbo," \u2014 "),Vse=n(Mbo,"A",{href:!0});var Pmn=s(Vse);F7r=r(Pmn,"Pix2StructProcessor"),Pmn.forEach(t),E7r=r(Mbo," (Pix2Struct model)"),Mbo.forEach(t),y7r=i(re),$0=n(re,"LI",{});var Tbo=s($0);$Ge=n(Tbo,"STRONG",{});var Zmn=s($Ge);C7r=r(Zmn,"sam"),Zmn.forEach(t),w7r=r(Tbo," \u2014 "),Wse=n(Tbo,"A",{href:!0});var Vmn=s(Wse);L7r=r(Vmn,"SamProcessor"),Vmn.forEach(t),A7r=r(Tbo," (SAM model)"),Tbo.forEach(t),R7r=i(re),j0=n(re,"LI",{});var Fbo=s(j0);jGe=n(Fbo,"STRONG",{});var Wmn=s(jGe);k7r=r(Wmn,"sew"),Wmn.forEach(t),S7r=r(Fbo," \u2014 "),Jse=n(Fbo,"A",{href:!0});var Jmn=s(Jse);x7r=r(Jmn,"Wav2Vec2Processor"),Jmn.forEach(t),G7r=r(Fbo," (SEW model)"),Fbo.forEach(t),B7r=i(re),P0=n(re,"LI",{});var Ebo=s(P0);PGe=n(Ebo,"STRONG",{});var Umn=s(PGe);I7r=r(Umn,"sew-d"),Umn.forEach(t),N7r=r(Ebo," \u2014 "),Use=n(Ebo,"A",{href:!0});var Xmn=s(Use);$7r=r(Xmn,"Wav2Vec2Processor"),Xmn.forEach(t),j7r=r(Ebo," (SEW-D model)"),Ebo.forEach(t),P7r=i(re),Z0=n(re,"LI",{});var ybo=s(Z0);ZGe=n(ybo,"STRONG",{});var Dmn=s(ZGe);Z7r=r(Dmn,"speech_to_text"),Dmn.forEach(t),V7r=r(ybo," \u2014 "),Xse=n(ybo,"A",{href:!0});var qmn=s(Xse);W7r=r(qmn,"Speech2TextProcessor"),qmn.forEach(t),J7r=r(ybo," (Speech2Text model)"),ybo.forEach(t),U7r=i(re),V0=n(re,"LI",{});var Cbo=s(V0);VGe=n(Cbo,"STRONG",{});var Omn=s(VGe);X7r=r(Omn,"speech_to_text_2"),Omn.forEach(t),D7r=r(Cbo," \u2014 "),Dse=n(Cbo,"A",{href:!0});var Qmn=s(Dse);q7r=r(Qmn,"Speech2Text2Processor"),Qmn.forEach(t),O7r=r(Cbo," (Speech2Text2 model)"),Cbo.forEach(t),Q7r=i(re),W0=n(re,"LI",{});var wbo=s(W0);WGe=n(wbo,"STRONG",{});var Ymn=s(WGe);Y7r=r(Ymn,"speecht5"),Ymn.forEach(t),z7r=r(wbo," \u2014 "),qse=n(wbo,"A",{href:!0});var zmn=s(qse);H7r=r(zmn,"SpeechT5Processor"),zmn.forEach(t),K7r=r(wbo," (SpeechT5 model)"),wbo.forEach(t),eLr=i(re),J0=n(re,"LI",{});var Lbo=s(J0);JGe=n(Lbo,"STRONG",{});var Hmn=s(JGe);oLr=r(Hmn,"trocr"),Hmn.forEach(t),rLr=r(Lbo," \u2014 "),Ose=n(Lbo,"A",{href:!0});var Kmn=s(Ose);tLr=r(Kmn,"TrOCRProcessor"),Kmn.forEach(t),aLr=r(Lbo," (TrOCR model)"),Lbo.forEach(t),nLr=i(re),U0=n(re,"LI",{});var Abo=s(U0);UGe=n(Abo,"STRONG",{});var efn=s(UGe);sLr=r(efn,"tvlt"),efn.forEach(t),lLr=r(Abo," \u2014 "),Qse=n(Abo,"A",{href:!0});var ofn=s(Qse);iLr=r(ofn,"TvltProcessor"),ofn.forEach(t),dLr=r(Abo," (TVLT model)"),Abo.forEach(t),cLr=i(re),X0=n(re,"LI",{});var Rbo=s(X0);XGe=n(Rbo,"STRONG",{});var rfn=s(XGe);mLr=r(rfn,"unispeech"),rfn.forEach(t),fLr=r(Rbo," \u2014 "),Yse=n(Rbo,"A",{href:!0});var tfn=s(Yse);hLr=r(tfn,"Wav2Vec2Processor"),tfn.forEach(t),gLr=r(Rbo," (UniSpeech model)"),Rbo.forEach(t),uLr=i(re),D0=n(re,"LI",{});var kbo=s(D0);DGe=n(kbo,"STRONG",{});var afn=s(DGe);pLr=r(afn,"unispeech-sat"),afn.forEach(t),_Lr=r(kbo," \u2014 "),zse=n(kbo,"A",{href:!0});var nfn=s(zse);vLr=r(nfn,"Wav2Vec2Processor"),nfn.forEach(t),bLr=r(kbo," (UniSpeechSat model)"),kbo.forEach(t),MLr=i(re),q0=n(re,"LI",{});var Sbo=s(q0);qGe=n(Sbo,"STRONG",{});var sfn=s(qGe);TLr=r(sfn,"vilt"),sfn.forEach(t),FLr=r(Sbo," \u2014 "),Hse=n(Sbo,"A",{href:!0});var lfn=s(Hse);ELr=r(lfn,"ViltProcessor"),lfn.forEach(t),yLr=r(Sbo," (ViLT model)"),Sbo.forEach(t),CLr=i(re),O0=n(re,"LI",{});var xbo=s(O0);OGe=n(xbo,"STRONG",{});var ifn=s(OGe);wLr=r(ifn,"vision-text-dual-encoder"),ifn.forEach(t),LLr=r(xbo," \u2014 "),Kse=n(xbo,"A",{href:!0});var dfn=s(Kse);ALr=r(dfn,"VisionTextDualEncoderProcessor"),dfn.forEach(t),RLr=r(xbo," (VisionTextDualEncoder model)"),xbo.forEach(t),kLr=i(re),Q0=n(re,"LI",{});var Gbo=s(Q0);QGe=n(Gbo,"STRONG",{});var cfn=s(QGe);SLr=r(cfn,"wav2vec2"),cfn.forEach(t),xLr=r(Gbo," \u2014 "),ele=n(Gbo,"A",{href:!0});var mfn=s(ele);GLr=r(mfn,"Wav2Vec2Processor"),mfn.forEach(t),BLr=r(Gbo," (Wav2Vec2 model)"),Gbo.forEach(t),ILr=i(re),Y0=n(re,"LI",{});var Bbo=s(Y0);YGe=n(Bbo,"STRONG",{});var ffn=s(YGe);NLr=r(ffn,"wav2vec2-conformer"),ffn.forEach(t),$Lr=r(Bbo," \u2014 "),ole=n(Bbo,"A",{href:!0});var hfn=s(ole);jLr=r(hfn,"Wav2Vec2Processor"),hfn.forEach(t),PLr=r(Bbo," (Wav2Vec2-Conformer model)"),Bbo.forEach(t),ZLr=i(re),z0=n(re,"LI",{});var Ibo=s(z0);zGe=n(Ibo,"STRONG",{});var gfn=s(zGe);VLr=r(gfn,"wavlm"),gfn.forEach(t),WLr=r(Ibo," \u2014 "),rle=n(Ibo,"A",{href:!0});var ufn=s(rle);JLr=r(ufn,"Wav2Vec2Processor"),ufn.forEach(t),ULr=r(Ibo," (WavLM model)"),Ibo.forEach(t),XLr=i(re),H0=n(re,"LI",{});var Nbo=s(H0);HGe=n(Nbo,"STRONG",{});var pfn=s(HGe);DLr=r(pfn,"whisper"),pfn.forEach(t),qLr=r(Nbo," \u2014 "),tle=n(Nbo,"A",{href:!0});var _fn=s(tle);OLr=r(_fn,"WhisperProcessor"),_fn.forEach(t),QLr=r(Nbo," (Whisper model)"),Nbo.forEach(t),YLr=i(re),K0=n(re,"LI",{});var $bo=s(K0);KGe=n($bo,"STRONG",{});var vfn=s(KGe);zLr=r(vfn,"xclip"),vfn.forEach(t),HLr=r($bo," \u2014 "),ale=n($bo,"A",{href:!0});var bfn=s(ale);KLr=r(bfn,"XCLIPProcessor"),bfn.forEach(t),eAr=r($bo," (X-CLIP model)"),$bo.forEach(t),re.forEach(t),oAr=i(Ka),M(e3.$$.fragment,Ka),rAr=i(Ka),M(o3.$$.fragment,Ka),Ka.forEach(t),tAr=i(zi),r3=n(zi,"DIV",{class:!0});var sNo=s(r3);M(yV.$$.fragment,sNo),aAr=i(sNo),eBe=n(sNo,"P",{});var Mfn=s(eBe);nAr=r(Mfn,"Register a new processor for this class."),Mfn.forEach(t),sNo.forEach(t),zi.forEach(t),tGo=i(m),rm=n(m,"H2",{class:!0});var lNo=s(rm);t3=n(lNo,"A",{id:!0,class:!0,href:!0});var Tfn=s(t3);oBe=n(Tfn,"SPAN",{});var Ffn=s(oBe);M(CV.$$.fragment,Ffn),Ffn.forEach(t),Tfn.forEach(t),sAr=i(lNo),rBe=n(lNo,"SPAN",{});var Efn=s(rBe);lAr=r(Efn,"Generic model classes"),Efn.forEach(t),lNo.forEach(t),aGo=i(m),nle=n(m,"P",{});var yfn=s(nle);iAr=r(yfn,"The following auto classes are available for instantiating a base model class without a specific head."),yfn.forEach(t),nGo=i(m),tm=n(m,"H3",{class:!0});var iNo=s(tm);a3=n(iNo,"A",{id:!0,class:!0,href:!0});var Cfn=s(a3);tBe=n(Cfn,"SPAN",{});var wfn=s(tBe);M(wV.$$.fragment,wfn),wfn.forEach(t),Cfn.forEach(t),dAr=i(iNo),aBe=n(iNo,"SPAN",{});var Lfn=s(aBe);cAr=r(Lfn,"AutoModel"),Lfn.forEach(t),iNo.forEach(t),sGo=i(m),Uo=n(m,"DIV",{class:!0});var Hi=s(Uo);M(LV.$$.fragment,Hi),mAr=i(Hi),am=n(Hi,"P",{});var b9e=s(am);fAr=r(b9e,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),sle=n(b9e,"A",{href:!0});var Afn=s(sle);hAr=r(Afn,"from_pretrained()"),Afn.forEach(t),gAr=r(b9e," class method or the "),lle=n(b9e,"A",{href:!0});var Rfn=s(lle);uAr=r(Rfn,"from_config()"),Rfn.forEach(t),pAr=r(b9e,` class
method.`),b9e.forEach(t),_Ar=i(Hi),AV=n(Hi,"P",{});var dNo=s(AV);vAr=r(dNo,"This class cannot be instantiated directly using "),nBe=n(dNo,"CODE",{});var kfn=s(nBe);bAr=r(kfn,"__init__()"),kfn.forEach(t),MAr=r(dNo," (throws an error)."),dNo.forEach(t),TAr=i(Hi),Wt=n(Hi,"DIV",{class:!0});var yj=s(Wt);M(RV.$$.fragment,yj),FAr=i(yj),sBe=n(yj,"P",{});var Sfn=s(sBe);EAr=r(Sfn,"Instantiates one of the base model classes of the library from a configuration."),Sfn.forEach(t),yAr=i(yj),nm=n(yj,"P",{});var M9e=s(nm);CAr=r(M9e,`Note:
Loading a model from its configuration file does `),lBe=n(M9e,"STRONG",{});var xfn=s(lBe);wAr=r(xfn,"not"),xfn.forEach(t),LAr=r(M9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),ile=n(M9e,"A",{href:!0});var Gfn=s(ile);AAr=r(Gfn,"from_pretrained()"),Gfn.forEach(t),RAr=r(M9e," to load the model weights."),M9e.forEach(t),kAr=i(yj),M(n3.$$.fragment,yj),yj.forEach(t),SAr=i(Hi),so=n(Hi,"DIV",{class:!0});var en=s(so);M(kV.$$.fragment,en),xAr=i(en),iBe=n(en,"P",{});var Bfn=s(iBe);GAr=r(Bfn,"Instantiate one of the base model classes of the library from a pretrained model."),Bfn.forEach(t),BAr=i(en),Pn=n(en,"P",{});var Cj=s(Pn);IAr=r(Cj,"The model class to instantiate is selected based on the "),dBe=n(Cj,"CODE",{});var Ifn=s(dBe);NAr=r(Ifn,"model_type"),Ifn.forEach(t),$Ar=r(Cj,` property of the config object (either
passed as an argument or loaded from `),cBe=n(Cj,"CODE",{});var Nfn=s(cBe);jAr=r(Nfn,"pretrained_model_name_or_path"),Nfn.forEach(t),PAr=r(Cj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mBe=n(Cj,"CODE",{});var $fn=s(mBe);ZAr=r($fn,"pretrained_model_name_or_path"),$fn.forEach(t),VAr=r(Cj,":"),Cj.forEach(t),WAr=i(en),A=n(en,"UL",{});var R=s(A);s3=n(R,"LI",{});var jbo=s(s3);fBe=n(jbo,"STRONG",{});var jfn=s(fBe);JAr=r(jfn,"albert"),jfn.forEach(t),UAr=r(jbo," \u2014 "),dle=n(jbo,"A",{href:!0});var Pfn=s(dle);XAr=r(Pfn,"AlbertModel"),Pfn.forEach(t),DAr=r(jbo," (ALBERT model)"),jbo.forEach(t),qAr=i(R),l3=n(R,"LI",{});var Pbo=s(l3);hBe=n(Pbo,"STRONG",{});var Zfn=s(hBe);OAr=r(Zfn,"align"),Zfn.forEach(t),QAr=r(Pbo," \u2014 "),cle=n(Pbo,"A",{href:!0});var Vfn=s(cle);YAr=r(Vfn,"AlignModel"),Vfn.forEach(t),zAr=r(Pbo," (ALIGN model)"),Pbo.forEach(t),HAr=i(R),i3=n(R,"LI",{});var Zbo=s(i3);gBe=n(Zbo,"STRONG",{});var Wfn=s(gBe);KAr=r(Wfn,"altclip"),Wfn.forEach(t),eRr=r(Zbo," \u2014 "),mle=n(Zbo,"A",{href:!0});var Jfn=s(mle);oRr=r(Jfn,"AltCLIPModel"),Jfn.forEach(t),rRr=r(Zbo," (AltCLIP model)"),Zbo.forEach(t),tRr=i(R),d3=n(R,"LI",{});var Vbo=s(d3);uBe=n(Vbo,"STRONG",{});var Ufn=s(uBe);aRr=r(Ufn,"audio-spectrogram-transformer"),Ufn.forEach(t),nRr=r(Vbo," \u2014 "),fle=n(Vbo,"A",{href:!0});var Xfn=s(fle);sRr=r(Xfn,"ASTModel"),Xfn.forEach(t),lRr=r(Vbo," (Audio Spectrogram Transformer model)"),Vbo.forEach(t),iRr=i(R),c3=n(R,"LI",{});var Wbo=s(c3);pBe=n(Wbo,"STRONG",{});var Dfn=s(pBe);dRr=r(Dfn,"autoformer"),Dfn.forEach(t),cRr=r(Wbo," \u2014 "),hle=n(Wbo,"A",{href:!0});var qfn=s(hle);mRr=r(qfn,"AutoformerModel"),qfn.forEach(t),fRr=r(Wbo," (Autoformer model)"),Wbo.forEach(t),hRr=i(R),m3=n(R,"LI",{});var Jbo=s(m3);_Be=n(Jbo,"STRONG",{});var Ofn=s(_Be);gRr=r(Ofn,"bark"),Ofn.forEach(t),uRr=r(Jbo," \u2014 "),gle=n(Jbo,"A",{href:!0});var Qfn=s(gle);pRr=r(Qfn,"BarkModel"),Qfn.forEach(t),_Rr=r(Jbo," (Bark model)"),Jbo.forEach(t),vRr=i(R),f3=n(R,"LI",{});var Ubo=s(f3);vBe=n(Ubo,"STRONG",{});var Yfn=s(vBe);bRr=r(Yfn,"bart"),Yfn.forEach(t),MRr=r(Ubo," \u2014 "),ule=n(Ubo,"A",{href:!0});var 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iMo=s(kT);Z$e=n(iMo,"STRONG",{});var Ivn=s(Z$e);iDr=r(Ivn,"xmod"),Ivn.forEach(t),dDr=r(iMo," \u2014 "),xce=n(iMo,"A",{href:!0});var Nvn=s(xce);cDr=r(Nvn,"XmodModel"),Nvn.forEach(t),mDr=r(iMo," (X-MOD model)"),iMo.forEach(t),fDr=i(R),ST=n(R,"LI",{});var dMo=s(ST);V$e=n(dMo,"STRONG",{});var $vn=s(V$e);hDr=r($vn,"yolos"),$vn.forEach(t),gDr=r(dMo," \u2014 "),Gce=n(dMo,"A",{href:!0});var jvn=s(Gce);uDr=r(jvn,"YolosModel"),jvn.forEach(t),pDr=r(dMo," (YOLOS model)"),dMo.forEach(t),_Dr=i(R),xT=n(R,"LI",{});var cMo=s(xT);W$e=n(cMo,"STRONG",{});var Pvn=s(W$e);vDr=r(Pvn,"yoso"),Pvn.forEach(t),bDr=r(cMo," \u2014 "),Bce=n(cMo,"A",{href:!0});var Zvn=s(Bce);MDr=r(Zvn,"YosoModel"),Zvn.forEach(t),TDr=r(cMo," (YOSO model)"),cMo.forEach(t),R.forEach(t),FDr=i(en),GT=n(en,"P",{});var mMo=s(GT);EDr=r(mMo,"The model is set in evaluation mode by default using "),J$e=n(mMo,"CODE",{});var Vvn=s(J$e);yDr=r(Vvn,"model.eval()"),Vvn.forEach(t),CDr=r(mMo,` (so for instance, dropout modules are
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with the `),Ice=n(T9e,"A",{href:!0});var Dvn=s(Ice);xDr=r(Dvn,"from_pretrained()"),Dvn.forEach(t),GDr=r(T9e," class method or the "),Nce=n(T9e,"A",{href:!0});var qvn=s(Nce);BDr=r(qvn,"from_config()"),qvn.forEach(t),IDr=r(T9e,` class
method.`),T9e.forEach(t),NDr=i(Ki),GV=n(Ki,"P",{});var mNo=s(GV);$Dr=r(mNo,"This class cannot be instantiated directly using "),q$e=n(mNo,"CODE",{});var Ovn=s(q$e);jDr=r(Ovn,"__init__()"),Ovn.forEach(t),PDr=r(mNo," (throws an error)."),mNo.forEach(t),ZDr=i(Ki),Jt=n(Ki,"DIV",{class:!0});var wj=s(Jt);M(BV.$$.fragment,wj),VDr=i(wj),O$e=n(wj,"P",{});var Qvn=s(O$e);WDr=r(Qvn,"Instantiates one of the base model classes of the library from a configuration."),Qvn.forEach(t),JDr=i(wj),im=n(wj,"P",{});var F9e=s(im);UDr=r(F9e,`Note:
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model\u2019s configuration. Use `),$ce=n(F9e,"A",{href:!0});var zvn=s($ce);qDr=r(zvn,"from_pretrained()"),zvn.forEach(t),ODr=r(F9e," to load the model weights."),F9e.forEach(t),QDr=i(wj),M(NT.$$.fragment,wj),wj.forEach(t),YDr=i(Ki),tt=n(Ki,"DIV",{class:!0});var ed=s(tt);M(IV.$$.fragment,ed),zDr=i(ed),Y$e=n(ed,"P",{});var Hvn=s(Y$e);HDr=r(Hvn,"Instantiate one of the base model classes of the library from a pretrained model."),Hvn.forEach(t),KDr=i(ed),Zn=n(ed,"P",{});var Lj=s(Zn);eqr=r(Lj,"The model class to instantiate is selected based on the "),z$e=n(Lj,"CODE",{});var Kvn=s(z$e);oqr=r(Kvn,"model_type"),Kvn.forEach(t),rqr=r(Lj,` property of the config object (either
passed as an argument or loaded from `),H$e=n(Lj,"CODE",{});var e1n=s(H$e);tqr=r(e1n,"pretrained_model_name_or_path"),e1n.forEach(t),aqr=r(Lj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),K$e=n(Lj,"CODE",{});var o1n=s(K$e);nqr=r(o1n,"pretrained_model_name_or_path"),o1n.forEach(t),sqr=r(Lj,":"),Lj.forEach(t),lqr=i(ed),j=n(ed,"UL",{});var W=s(j);$T=n(W,"LI",{});var fMo=s($T);eje=n(fMo,"STRONG",{});var r1n=s(eje);iqr=r(r1n,"albert"),r1n.forEach(t),dqr=r(fMo," \u2014 "),jce=n(fMo,"A",{href:!0});var t1n=s(jce);cqr=r(t1n,"TFAlbertModel"),t1n.forEach(t),mqr=r(fMo," (ALBERT model)"),fMo.forEach(t),fqr=i(W),jT=n(W,"LI",{});var hMo=s(jT);oje=n(hMo,"STRONG",{});var a1n=s(oje);hqr=r(a1n,"bart"),a1n.forEach(t),gqr=r(hMo," \u2014 "),Pce=n(hMo,"A",{href:!0});var n1n=s(Pce);uqr=r(n1n,"TFBartModel"),n1n.forEach(t),pqr=r(hMo," (BART model)"),hMo.forEach(t),_qr=i(W),PT=n(W,"LI",{});var gMo=s(PT);rje=n(gMo,"STRONG",{});var s1n=s(rje);vqr=r(s1n,"bert"),s1n.forEach(t),bqr=r(gMo," \u2014 "),Zce=n(gMo,"A",{href:!0});var l1n=s(Zce);Mqr=r(l1n,"TFBertModel"),l1n.forEach(t),Tqr=r(gMo," (BERT model)"),gMo.forEach(t),Fqr=i(W),ZT=n(W,"LI",{});var uMo=s(ZT);tje=n(uMo,"STRONG",{});var i1n=s(tje);Eqr=r(i1n,"blenderbot"),i1n.forEach(t),yqr=r(uMo," \u2014 "),Vce=n(uMo,"A",{href:!0});var d1n=s(Vce);Cqr=r(d1n,"TFBlenderbotModel"),d1n.forEach(t),wqr=r(uMo," (Blenderbot model)"),uMo.forEach(t),Lqr=i(W),VT=n(W,"LI",{});var pMo=s(VT);aje=n(pMo,"STRONG",{});var c1n=s(aje);Aqr=r(c1n,"blenderbot-small"),c1n.forEach(t),Rqr=r(pMo," \u2014 "),Wce=n(pMo,"A",{href:!0});var m1n=s(Wce);kqr=r(m1n,"TFBlenderbotSmallModel"),m1n.forEach(t),Sqr=r(pMo," (BlenderbotSmall model)"),pMo.forEach(t),xqr=i(W),WT=n(W,"LI",{});var _Mo=s(WT);nje=n(_Mo,"STRONG",{});var f1n=s(nje);Gqr=r(f1n,"blip"),f1n.forEach(t),Bqr=r(_Mo," \u2014 "),Jce=n(_Mo,"A",{href:!0});var h1n=s(Jce);Iqr=r(h1n,"TFBlipModel"),h1n.forEach(t),Nqr=r(_Mo," (BLIP model)"),_Mo.forEach(t),$qr=i(W),JT=n(W,"LI",{});var vMo=s(JT);sje=n(vMo,"STRONG",{});var g1n=s(sje);jqr=r(g1n,"camembert"),g1n.forEach(t),Pqr=r(vMo," \u2014 "),Uce=n(vMo,"A",{href:!0});var u1n=s(Uce);Zqr=r(u1n,"TFCamembertModel"),u1n.forEach(t),Vqr=r(vMo," (CamemBERT model)"),vMo.forEach(t),Wqr=i(W),UT=n(W,"LI",{});var bMo=s(UT);lje=n(bMo,"STRONG",{});var p1n=s(lje);Jqr=r(p1n,"clip"),p1n.forEach(t),Uqr=r(bMo," \u2014 "),Xce=n(bMo,"A",{href:!0});var _1n=s(Xce);Xqr=r(_1n,"TFCLIPModel"),_1n.forEach(t),Dqr=r(bMo," (CLIP model)"),bMo.forEach(t),qqr=i(W),XT=n(W,"LI",{});var MMo=s(XT);ije=n(MMo,"STRONG",{});var v1n=s(ije);Oqr=r(v1n,"convbert"),v1n.forEach(t),Qqr=r(MMo," \u2014 "),Dce=n(MMo,"A",{href:!0});var b1n=s(Dce);Yqr=r(b1n,"TFConvBertModel"),b1n.forEach(t),zqr=r(MMo," (ConvBERT model)"),MMo.forEach(t),Hqr=i(W),DT=n(W,"LI",{});var TMo=s(DT);dje=n(TMo,"STRONG",{});var M1n=s(dje);Kqr=r(M1n,"convnext"),M1n.forEach(t),eOr=r(TMo," \u2014 "),qce=n(TMo,"A",{href:!0});var T1n=s(qce);oOr=r(T1n,"TFConvNextModel"),T1n.forEach(t),rOr=r(TMo," (ConvNeXT model)"),TMo.forEach(t),tOr=i(W),qT=n(W,"LI",{});var FMo=s(qT);cje=n(FMo,"STRONG",{});var F1n=s(cje);aOr=r(F1n,"ctrl"),F1n.forEach(t),nOr=r(FMo," \u2014 "),Oce=n(FMo,"A",{href:!0});var E1n=s(Oce);sOr=r(E1n,"TFCTRLModel"),E1n.forEach(t),lOr=r(FMo," (CTRL model)"),FMo.forEach(t),iOr=i(W),OT=n(W,"LI",{});var EMo=s(OT);mje=n(EMo,"STRONG",{});var y1n=s(mje);dOr=r(y1n,"cvt"),y1n.forEach(t),cOr=r(EMo," \u2014 "),Qce=n(EMo,"A",{href:!0});var C1n=s(Qce);mOr=r(C1n,"TFCvtModel"),C1n.forEach(t),fOr=r(EMo," (CvT model)"),EMo.forEach(t),hOr=i(W),QT=n(W,"LI",{});var yMo=s(QT);fje=n(yMo,"STRONG",{});var w1n=s(fje);gOr=r(w1n,"data2vec-vision"),w1n.forEach(t),uOr=r(yMo," \u2014 "),Yce=n(yMo,"A",{href:!0});var L1n=s(Yce);pOr=r(L1n,"TFData2VecVisionModel"),L1n.forEach(t),_Or=r(yMo," (Data2VecVision model)"),yMo.forEach(t),vOr=i(W),YT=n(W,"LI",{});var CMo=s(YT);hje=n(CMo,"STRONG",{});var A1n=s(hje);bOr=r(A1n,"deberta"),A1n.forEach(t),MOr=r(CMo," \u2014 "),zce=n(CMo,"A",{href:!0});var R1n=s(zce);TOr=r(R1n,"TFDebertaModel"),R1n.forEach(t),FOr=r(CMo," (DeBERTa 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$1n=s(ome);VOr=r($1n,"TFDPRQuestionEncoder"),$1n.forEach(t),WOr=r(RMo," (DPR model)"),RMo.forEach(t),JOr=i(W),oF=n(W,"LI",{});var kMo=s(oF);vje=n(kMo,"STRONG",{});var j1n=s(vje);UOr=r(j1n,"efficientformer"),j1n.forEach(t),XOr=r(kMo," \u2014 "),rme=n(kMo,"A",{href:!0});var P1n=s(rme);DOr=r(P1n,"TFEfficientFormerModel"),P1n.forEach(t),qOr=r(kMo," (EfficientFormer model)"),kMo.forEach(t),OOr=i(W),rF=n(W,"LI",{});var SMo=s(rF);bje=n(SMo,"STRONG",{});var Z1n=s(bje);QOr=r(Z1n,"electra"),Z1n.forEach(t),YOr=r(SMo," \u2014 "),tme=n(SMo,"A",{href:!0});var V1n=s(tme);zOr=r(V1n,"TFElectraModel"),V1n.forEach(t),HOr=r(SMo," (ELECTRA model)"),SMo.forEach(t),KOr=i(W),tF=n(W,"LI",{});var xMo=s(tF);Mje=n(xMo,"STRONG",{});var W1n=s(Mje);eQr=r(W1n,"esm"),W1n.forEach(t),oQr=r(xMo," \u2014 "),ame=n(xMo,"A",{href:!0});var J1n=s(ame);rQr=r(J1n,"TFEsmModel"),J1n.forEach(t),tQr=r(xMo," (ESM model)"),xMo.forEach(t),aQr=i(W),aF=n(W,"LI",{});var GMo=s(aF);Tje=n(GMo,"STRONG",{});var 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model\u2019s configuration. Use `),zme=n(y9e,"A",{href:!0});var S0n=s(zme);CKr=r(S0n,"from_pretrained()"),S0n.forEach(t),wKr=r(y9e," to load the model weights."),y9e.forEach(t),LKr=i(Aj),M(OF.$$.fragment,Aj),Aj.forEach(t),AKr=i(od),at=n(od,"DIV",{class:!0});var rd=s(at);M(ZV.$$.fragment,rd),RKr=i(rd),uPe=n(rd,"P",{});var x0n=s(uPe);kKr=r(x0n,"Instantiate one of the base model classes of the library from a pretrained model."),x0n.forEach(t),SKr=i(rd),Vn=n(rd,"P",{});var Rj=s(Vn);xKr=r(Rj,"The model class to instantiate is selected based on the "),pPe=n(Rj,"CODE",{});var G0n=s(pPe);GKr=r(G0n,"model_type"),G0n.forEach(t),BKr=r(Rj,` property of the config object (either
passed as an argument or loaded from `),_Pe=n(Rj,"CODE",{});var B0n=s(_Pe);IKr=r(B0n,"pretrained_model_name_or_path"),B0n.forEach(t),NKr=r(Rj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),vPe=n(Rj,"CODE",{});var I0n=s(vPe);$Kr=r(I0n,"pretrained_model_name_or_path"),I0n.forEach(t),jKr=r(Rj,":"),Rj.forEach(t),PKr=i(rd),se=n(rd,"UL",{});var ie=s(se);QF=n(ie,"LI",{});var TTo=s(QF);bPe=n(TTo,"STRONG",{});var N0n=s(bPe);ZKr=r(N0n,"albert"),N0n.forEach(t),VKr=r(TTo," \u2014 "),Hme=n(TTo,"A",{href:!0});var $0n=s(Hme);WKr=r($0n,"FlaxAlbertModel"),$0n.forEach(t),JKr=r(TTo," (ALBERT model)"),TTo.forEach(t),UKr=i(ie),YF=n(ie,"LI",{});var FTo=s(YF);MPe=n(FTo,"STRONG",{});var j0n=s(MPe);XKr=r(j0n,"bart"),j0n.forEach(t),DKr=r(FTo," \u2014 "),Kme=n(FTo,"A",{href:!0});var P0n=s(Kme);qKr=r(P0n,"FlaxBartModel"),P0n.forEach(t),OKr=r(FTo," (BART model)"),FTo.forEach(t),QKr=i(ie),zF=n(ie,"LI",{});var ETo=s(zF);TPe=n(ETo,"STRONG",{});var Z0n=s(TPe);YKr=r(Z0n,"beit"),Z0n.forEach(t),zKr=r(ETo," \u2014 "),efe=n(ETo,"A",{href:!0});var V0n=s(efe);HKr=r(V0n,"FlaxBeitModel"),V0n.forEach(t),KKr=r(ETo," (BEiT model)"),ETo.forEach(t),eet=i(ie),HF=n(ie,"LI",{});var yTo=s(HF);FPe=n(yTo,"STRONG",{});var W0n=s(FPe);oet=r(W0n,"bert"),W0n.forEach(t),ret=r(yTo," \u2014 "),ofe=n(yTo,"A",{href:!0});var J0n=s(ofe);tet=r(J0n,"FlaxBertModel"),J0n.forEach(t),aet=r(yTo," (BERT model)"),yTo.forEach(t),net=i(ie),KF=n(ie,"LI",{});var CTo=s(KF);EPe=n(CTo,"STRONG",{});var U0n=s(EPe);set=r(U0n,"big_bird"),U0n.forEach(t),iet=r(CTo," \u2014 "),rfe=n(CTo,"A",{href:!0});var X0n=s(rfe);det=r(X0n,"FlaxBigBirdModel"),X0n.forEach(t),cet=r(CTo," (BigBird model)"),CTo.forEach(t),met=i(ie),eE=n(ie,"LI",{});var wTo=s(eE);yPe=n(wTo,"STRONG",{});var D0n=s(yPe);fet=r(D0n,"blenderbot"),D0n.forEach(t),het=r(wTo," \u2014 "),tfe=n(wTo,"A",{href:!0});var q0n=s(tfe);get=r(q0n,"FlaxBlenderbotModel"),q0n.forEach(t),uet=r(wTo," (Blenderbot model)"),wTo.forEach(t),pet=i(ie),oE=n(ie,"LI",{});var LTo=s(oE);CPe=n(LTo,"STRONG",{});var O0n=s(CPe);_et=r(O0n,"blenderbot-small"),O0n.forEach(t),vet=r(LTo," \u2014 "),afe=n(LTo,"A",{href:!0});var Q0n=s(afe);bet=r(Q0n,"FlaxBlenderbotSmallModel"),Q0n.forEach(t),Met=r(LTo," (BlenderbotSmall model)"),LTo.forEach(t),Tet=i(ie),rE=n(ie,"LI",{});var ATo=s(rE);wPe=n(ATo,"STRONG",{});var Y0n=s(wPe);Fet=r(Y0n,"bloom"),Y0n.forEach(t),Eet=r(ATo," \u2014 "),nfe=n(ATo,"A",{href:!0});var z0n=s(nfe);yet=r(z0n,"FlaxBloomModel"),z0n.forEach(t),Cet=r(ATo," (BLOOM model)"),ATo.forEach(t),wet=i(ie),tE=n(ie,"LI",{});var RTo=s(tE);LPe=n(RTo,"STRONG",{});var H0n=s(LPe);Let=r(H0n,"clip"),H0n.forEach(t),Aet=r(RTo," \u2014 "),sfe=n(RTo,"A",{href:!0});var K0n=s(sfe);Ret=r(K0n,"FlaxCLIPModel"),K0n.forEach(t),ket=r(RTo," (CLIP model)"),RTo.forEach(t),Set=i(ie),aE=n(ie,"LI",{});var kTo=s(aE);APe=n(kTo,"STRONG",{});var e3n=s(APe);xet=r(e3n,"distilbert"),e3n.forEach(t),Get=r(kTo," \u2014 "),lfe=n(kTo,"A",{href:!0});var o3n=s(lfe);Bet=r(o3n,"FlaxDistilBertModel"),o3n.forEach(t),Iet=r(kTo," (DistilBERT model)"),kTo.forEach(t),Net=i(ie),nE=n(ie,"LI",{});var STo=s(nE);RPe=n(STo,"STRONG",{});var r3n=s(RPe);$et=r(r3n,"electra"),r3n.forEach(t),jet=r(STo," \u2014 "),ife=n(STo,"A",{href:!0});var t3n=s(ife);Pet=r(t3n,"FlaxElectraModel"),t3n.forEach(t),Zet=r(STo," (ELECTRA model)"),STo.forEach(t),Vet=i(ie),sE=n(ie,"LI",{});var xTo=s(sE);kPe=n(xTo,"STRONG",{});var a3n=s(kPe);Wet=r(a3n,"gpt-sw3"),a3n.forEach(t),Jet=r(xTo," \u2014 "),dfe=n(xTo,"A",{href:!0});var n3n=s(dfe);Uet=r(n3n,"FlaxGPT2Model"),n3n.forEach(t),Xet=r(xTo," (GPT-Sw3 model)"),xTo.forEach(t),Det=i(ie),lE=n(ie,"LI",{});var GTo=s(lE);SPe=n(GTo,"STRONG",{});var s3n=s(SPe);qet=r(s3n,"gpt2"),s3n.forEach(t),Oet=r(GTo," \u2014 "),cfe=n(GTo,"A",{href:!0});var l3n=s(cfe);Qet=r(l3n,"FlaxGPT2Model"),l3n.forEach(t),Yet=r(GTo," (OpenAI GPT-2 model)"),GTo.forEach(t),zet=i(ie),iE=n(ie,"LI",{});var BTo=s(iE);xPe=n(BTo,"STRONG",{});var i3n=s(xPe);Het=r(i3n,"gpt_neo"),i3n.forEach(t),Ket=r(BTo," \u2014 "),mfe=n(BTo,"A",{href:!0});var d3n=s(mfe);eot=r(d3n,"FlaxGPTNeoModel"),d3n.forEach(t),oot=r(BTo," (GPT Neo model)"),BTo.forEach(t),rot=i(ie),dE=n(ie,"LI",{});var ITo=s(dE);GPe=n(ITo,"STRONG",{});var c3n=s(GPe);tot=r(c3n,"gptj"),c3n.forEach(t),aot=r(ITo," \u2014 "),ffe=n(ITo,"A",{href:!0});var m3n=s(ffe);not=r(m3n,"FlaxGPTJModel"),m3n.forEach(t),sot=r(ITo," (GPT-J model)"),ITo.forEach(t),lot=i(ie),cE=n(ie,"LI",{});var NTo=s(cE);BPe=n(NTo,"STRONG",{});var f3n=s(BPe);iot=r(f3n,"longt5"),f3n.forEach(t),dot=r(NTo," \u2014 "),hfe=n(NTo,"A",{href:!0});var h3n=s(hfe);cot=r(h3n,"FlaxLongT5Model"),h3n.forEach(t),mot=r(NTo," (LongT5 model)"),NTo.forEach(t),fot=i(ie),mE=n(ie,"LI",{});var $To=s(mE);IPe=n($To,"STRONG",{});var g3n=s(IPe);hot=r(g3n,"marian"),g3n.forEach(t),got=r($To," \u2014 "),gfe=n($To,"A",{href:!0});var u3n=s(gfe);uot=r(u3n,"FlaxMarianModel"),u3n.forEach(t),pot=r($To," (Marian model)"),$To.forEach(t),_ot=i(ie),fE=n(ie,"LI",{});var jTo=s(fE);NPe=n(jTo,"STRONG",{});var p3n=s(NPe);vot=r(p3n,"mbart"),p3n.forEach(t),bot=r(jTo," \u2014 "),ufe=n(jTo,"A",{href:!0});var _3n=s(ufe);Mot=r(_3n,"FlaxMBartModel"),_3n.forEach(t),Tot=r(jTo," (mBART model)"),jTo.forEach(t),Fot=i(ie),hE=n(ie,"LI",{});var PTo=s(hE);$Pe=n(PTo,"STRONG",{});var v3n=s($Pe);Eot=r(v3n,"mt5"),v3n.forEach(t),yot=r(PTo," \u2014 "),pfe=n(PTo,"A",{href:!0});var b3n=s(pfe);Cot=r(b3n,"FlaxMT5Model"),b3n.forEach(t),wot=r(PTo," (MT5 model)"),PTo.forEach(t),Lot=i(ie),gE=n(ie,"LI",{});var ZTo=s(gE);jPe=n(ZTo,"STRONG",{});var M3n=s(jPe);Aot=r(M3n,"opt"),M3n.forEach(t),Rot=r(ZTo," \u2014 "),_fe=n(ZTo,"A",{href:!0});var T3n=s(_fe);kot=r(T3n,"FlaxOPTModel"),T3n.forEach(t),Sot=r(ZTo," (OPT model)"),ZTo.forEach(t),xot=i(ie),uE=n(ie,"LI",{});var VTo=s(uE);PPe=n(VTo,"STRONG",{});var F3n=s(PPe);Got=r(F3n,"pegasus"),F3n.forEach(t),Bot=r(VTo," \u2014 "),vfe=n(VTo,"A",{href:!0});var E3n=s(vfe);Iot=r(E3n,"FlaxPegasusModel"),E3n.forEach(t),Not=r(VTo," (Pegasus model)"),VTo.forEach(t),$ot=i(ie),pE=n(ie,"LI",{});var WTo=s(pE);ZPe=n(WTo,"STRONG",{});var y3n=s(ZPe);jot=r(y3n,"regnet"),y3n.forEach(t),Pot=r(WTo," \u2014 "),bfe=n(WTo,"A",{href:!0});var C3n=s(bfe);Zot=r(C3n,"FlaxRegNetModel"),C3n.forEach(t),Vot=r(WTo," (RegNet model)"),WTo.forEach(t),Wot=i(ie),_E=n(ie,"LI",{});var JTo=s(_E);VPe=n(JTo,"STRONG",{});var w3n=s(VPe);Jot=r(w3n,"resnet"),w3n.forEach(t),Uot=r(JTo," \u2014 "),Mfe=n(JTo,"A",{href:!0});var L3n=s(Mfe);Xot=r(L3n,"FlaxResNetModel"),L3n.forEach(t),Dot=r(JTo," (ResNet model)"),JTo.forEach(t),qot=i(ie),vE=n(ie,"LI",{});var UTo=s(vE);WPe=n(UTo,"STRONG",{});var A3n=s(WPe);Oot=r(A3n,"roberta"),A3n.forEach(t),Qot=r(UTo," \u2014 "),Tfe=n(UTo,"A",{href:!0});var R3n=s(Tfe);Yot=r(R3n,"FlaxRobertaModel"),R3n.forEach(t),zot=r(UTo," (RoBERTa model)"),UTo.forEach(t),Hot=i(ie),bE=n(ie,"LI",{});var XTo=s(bE);JPe=n(XTo,"STRONG",{});var k3n=s(JPe);Kot=r(k3n,"roberta-prelayernorm"),k3n.forEach(t),ert=r(XTo," \u2014 "),Ffe=n(XTo,"A",{href:!0});var S3n=s(Ffe);ort=r(S3n,"FlaxRobertaPreLayerNormModel"),S3n.forEach(t),rrt=r(XTo," (RoBERTa-PreLayerNorm model)"),XTo.forEach(t),trt=i(ie),ME=n(ie,"LI",{});var DTo=s(ME);UPe=n(DTo,"STRONG",{});var x3n=s(UPe);art=r(x3n,"roformer"),x3n.forEach(t),nrt=r(DTo," \u2014 "),Efe=n(DTo,"A",{href:!0});var G3n=s(Efe);srt=r(G3n,"FlaxRoFormerModel"),G3n.forEach(t),lrt=r(DTo," (RoFormer model)"),DTo.forEach(t),irt=i(ie),TE=n(ie,"LI",{});var qTo=s(TE);XPe=n(qTo,"STRONG",{});var B3n=s(XPe);drt=r(B3n,"t5"),B3n.forEach(t),crt=r(qTo," \u2014 "),yfe=n(qTo,"A",{href:!0});var I3n=s(yfe);mrt=r(I3n,"FlaxT5Model"),I3n.forEach(t),frt=r(qTo," (T5 model)"),qTo.forEach(t),hrt=i(ie),FE=n(ie,"LI",{});var OTo=s(FE);DPe=n(OTo,"STRONG",{});var N3n=s(DPe);grt=r(N3n,"vision-text-dual-encoder"),N3n.forEach(t),urt=r(OTo," \u2014 "),Cfe=n(OTo,"A",{href:!0});var $3n=s(Cfe);prt=r($3n,"FlaxVisionTextDualEncoderModel"),$3n.forEach(t),_rt=r(OTo," (VisionTextDualEncoder model)"),OTo.forEach(t),vrt=i(ie),EE=n(ie,"LI",{});var QTo=s(EE);qPe=n(QTo,"STRONG",{});var j3n=s(qPe);brt=r(j3n,"vit"),j3n.forEach(t),Mrt=r(QTo," \u2014 "),wfe=n(QTo,"A",{href:!0});var P3n=s(wfe);Trt=r(P3n,"FlaxViTModel"),P3n.forEach(t),Frt=r(QTo," (ViT model)"),QTo.forEach(t),Ert=i(ie),yE=n(ie,"LI",{});var YTo=s(yE);OPe=n(YTo,"STRONG",{});var Z3n=s(OPe);yrt=r(Z3n,"wav2vec2"),Z3n.forEach(t),Crt=r(YTo," \u2014 "),Lfe=n(YTo,"A",{href:!0});var V3n=s(Lfe);wrt=r(V3n,"FlaxWav2Vec2Model"),V3n.forEach(t),Lrt=r(YTo," (Wav2Vec2 model)"),YTo.forEach(t),Art=i(ie),CE=n(ie,"LI",{});var zTo=s(CE);QPe=n(zTo,"STRONG",{});var W3n=s(QPe);Rrt=r(W3n,"whisper"),W3n.forEach(t),krt=r(zTo," \u2014 "),Afe=n(zTo,"A",{href:!0});var J3n=s(Afe);Srt=r(J3n,"FlaxWhisperModel"),J3n.forEach(t),xrt=r(zTo," (Whisper model)"),zTo.forEach(t),Grt=i(ie),wE=n(ie,"LI",{});var HTo=s(wE);YPe=n(HTo,"STRONG",{});var U3n=s(YPe);Brt=r(U3n,"xglm"),U3n.forEach(t),Irt=r(HTo," \u2014 "),Rfe=n(HTo,"A",{href:!0});var X3n=s(Rfe);Nrt=r(X3n,"FlaxXGLMModel"),X3n.forEach(t),$rt=r(HTo," (XGLM model)"),HTo.forEach(t),jrt=i(ie),LE=n(ie,"LI",{});var KTo=s(LE);zPe=n(KTo,"STRONG",{});var D3n=s(zPe);Prt=r(D3n,"xlm-roberta"),D3n.forEach(t),Zrt=r(KTo," \u2014 "),kfe=n(KTo,"A",{href:!0});var q3n=s(kfe);Vrt=r(q3n,"FlaxXLMRobertaModel"),q3n.forEach(t),Wrt=r(KTo," (XLM-RoBERTa model)"),KTo.forEach(t),ie.forEach(t),Jrt=i(rd),M(AE.$$.fragment,rd),rd.forEach(t),od.forEach(t),mGo=i(m),fm=n(m,"H2",{class:!0});var gNo=s(fm);RE=n(gNo,"A",{id:!0,class:!0,href:!0});var O3n=s(RE);HPe=n(O3n,"SPAN",{});var Q3n=s(HPe);M(VV.$$.fragment,Q3n),Q3n.forEach(t),O3n.forEach(t),Urt=i(gNo),KPe=n(gNo,"SPAN",{});var Y3n=s(KPe);Xrt=r(Y3n,"Generic pretraining classes"),Y3n.forEach(t),gNo.forEach(t),fGo=i(m),Sfe=n(m,"P",{});var z3n=s(Sfe);Drt=r(z3n,"The following auto classes are available for instantiating a model with a pretraining head."),z3n.forEach(t),hGo=i(m),hm=n(m,"H3",{class:!0});var uNo=s(hm);kE=n(uNo,"A",{id:!0,class:!0,href:!0});var H3n=s(kE);eZe=n(H3n,"SPAN",{});var K3n=s(eZe);M(WV.$$.fragment,K3n),K3n.forEach(t),H3n.forEach(t),qrt=i(uNo),oZe=n(uNo,"SPAN",{});var e4n=s(oZe);Ort=r(e4n,"AutoModelForPreTraining"),e4n.forEach(t),uNo.forEach(t),gGo=i(m),qo=n(m,"DIV",{class:!0});var td=s(qo);M(JV.$$.fragment,td),Qrt=i(td),gm=n(td,"P",{});var C9e=s(gm);Yrt=r(C9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),xfe=n(C9e,"A",{href:!0});var o4n=s(xfe);zrt=r(o4n,"from_pretrained()"),o4n.forEach(t),Hrt=r(C9e," class method or the "),Gfe=n(C9e,"A",{href:!0});var r4n=s(Gfe);Krt=r(r4n,"from_config()"),r4n.forEach(t),ett=r(C9e,` class
method.`),C9e.forEach(t),ott=i(td),UV=n(td,"P",{});var pNo=s(UV);rtt=r(pNo,"This class cannot be instantiated directly using "),rZe=n(pNo,"CODE",{});var t4n=s(rZe);ttt=r(t4n,"__init__()"),t4n.forEach(t),att=r(pNo," (throws an error)."),pNo.forEach(t),ntt=i(td),Xt=n(td,"DIV",{class:!0});var kj=s(Xt);M(XV.$$.fragment,kj),stt=i(kj),tZe=n(kj,"P",{});var a4n=s(tZe);ltt=r(a4n,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),a4n.forEach(t),itt=i(kj),um=n(kj,"P",{});var w9e=s(um);dtt=r(w9e,`Note:
Loading a model from its configuration file does `),aZe=n(w9e,"STRONG",{});var n4n=s(aZe);ctt=r(n4n,"not"),n4n.forEach(t),mtt=r(w9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),Bfe=n(w9e,"A",{href:!0});var s4n=s(Bfe);ftt=r(s4n,"from_pretrained()"),s4n.forEach(t),htt=r(w9e," to load the model weights."),w9e.forEach(t),gtt=i(kj),M(SE.$$.fragment,kj),kj.forEach(t),utt=i(td),lo=n(td,"DIV",{class:!0});var on=s(lo);M(DV.$$.fragment,on),ptt=i(on),nZe=n(on,"P",{});var l4n=s(nZe);_tt=r(l4n,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),l4n.forEach(t),vtt=i(on),Wn=n(on,"P",{});var Sj=s(Wn);btt=r(Sj,"The model class to instantiate is selected based on the "),sZe=n(Sj,"CODE",{});var i4n=s(sZe);Mtt=r(i4n,"model_type"),i4n.forEach(t),Ttt=r(Sj,` property of the config object (either
passed as an argument or loaded from `),lZe=n(Sj,"CODE",{});var d4n=s(lZe);Ftt=r(d4n,"pretrained_model_name_or_path"),d4n.forEach(t),Ett=r(Sj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),iZe=n(Sj,"CODE",{});var c4n=s(iZe);ytt=r(c4n,"pretrained_model_name_or_path"),c4n.forEach(t),Ctt=r(Sj,":"),Sj.forEach(t),wtt=i(on),U=n(on,"UL",{});var q=s(U);xE=n(q,"LI",{});var eFo=s(xE);dZe=n(eFo,"STRONG",{});var m4n=s(dZe);Ltt=r(m4n,"albert"),m4n.forEach(t),Att=r(eFo," \u2014 "),Ife=n(eFo,"A",{href:!0});var f4n=s(Ife);Rtt=r(f4n,"AlbertForPreTraining"),f4n.forEach(t),ktt=r(eFo," (ALBERT model)"),eFo.forEach(t),Stt=i(q),GE=n(q,"LI",{});var oFo=s(GE);cZe=n(oFo,"STRONG",{});var h4n=s(cZe);xtt=r(h4n,"bart"),h4n.forEach(t),Gtt=r(oFo," \u2014 "),Nfe=n(oFo,"A",{href:!0});var g4n=s(Nfe);Btt=r(g4n,"BartForConditionalGeneration"),g4n.forEach(t),Itt=r(oFo," (BART model)"),oFo.forEach(t),Ntt=i(q),BE=n(q,"LI",{});var rFo=s(BE);mZe=n(rFo,"STRONG",{});var u4n=s(mZe);$tt=r(u4n,"bert"),u4n.forEach(t),jtt=r(rFo," \u2014 "),$fe=n(rFo,"A",{href:!0});var p4n=s($fe);Ptt=r(p4n,"BertForPreTraining"),p4n.forEach(t),Ztt=r(rFo," (BERT 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dTn=s(iVe);Fit=r(dTn,"visual_bert"),dTn.forEach(t),Eit=r(KFo," \u2014 "),Bhe=n(KFo,"A",{href:!0});var cTn=s(Bhe);yit=r(cTn,"VisualBertForPreTraining"),cTn.forEach(t),Cit=r(KFo," (VisualBERT model)"),KFo.forEach(t),wit=i(q),xy=n(q,"LI",{});var eEo=s(xy);dVe=n(eEo,"STRONG",{});var mTn=s(dVe);Lit=r(mTn,"vit_mae"),mTn.forEach(t),Ait=r(eEo," \u2014 "),Ihe=n(eEo,"A",{href:!0});var fTn=s(Ihe);Rit=r(fTn,"ViTMAEForPreTraining"),fTn.forEach(t),kit=r(eEo," (ViTMAE model)"),eEo.forEach(t),Sit=i(q),Gy=n(q,"LI",{});var oEo=s(Gy);cVe=n(oEo,"STRONG",{});var hTn=s(cVe);xit=r(hTn,"wav2vec2"),hTn.forEach(t),Git=r(oEo," \u2014 "),Nhe=n(oEo,"A",{href:!0});var gTn=s(Nhe);Bit=r(gTn,"Wav2Vec2ForPreTraining"),gTn.forEach(t),Iit=r(oEo," (Wav2Vec2 model)"),oEo.forEach(t),Nit=i(q),By=n(q,"LI",{});var rEo=s(By);mVe=n(rEo,"STRONG",{});var uTn=s(mVe);$it=r(uTn,"wav2vec2-conformer"),uTn.forEach(t),jit=r(rEo," \u2014 "),$he=n(rEo,"A",{href:!0});var pTn=s($he);Pit=r(pTn,"Wav2Vec2ConformerForPreTraining"),pTn.forEach(t),Zit=r(rEo," (Wav2Vec2-Conformer model)"),rEo.forEach(t),Vit=i(q),Iy=n(q,"LI",{});var tEo=s(Iy);fVe=n(tEo,"STRONG",{});var _Tn=s(fVe);Wit=r(_Tn,"xlm"),_Tn.forEach(t),Jit=r(tEo," \u2014 "),jhe=n(tEo,"A",{href:!0});var vTn=s(jhe);Uit=r(vTn,"XLMWithLMHeadModel"),vTn.forEach(t),Xit=r(tEo," (XLM model)"),tEo.forEach(t),Dit=i(q),Ny=n(q,"LI",{});var aEo=s(Ny);hVe=n(aEo,"STRONG",{});var bTn=s(hVe);qit=r(bTn,"xlm-roberta"),bTn.forEach(t),Oit=r(aEo," \u2014 "),Phe=n(aEo,"A",{href:!0});var MTn=s(Phe);Qit=r(MTn,"XLMRobertaForMaskedLM"),MTn.forEach(t),Yit=r(aEo," (XLM-RoBERTa model)"),aEo.forEach(t),zit=i(q),$y=n(q,"LI",{});var nEo=s($y);gVe=n(nEo,"STRONG",{});var TTn=s(gVe);Hit=r(TTn,"xlm-roberta-xl"),TTn.forEach(t),Kit=r(nEo," \u2014 "),Zhe=n(nEo,"A",{href:!0});var FTn=s(Zhe);edt=r(FTn,"XLMRobertaXLForMaskedLM"),FTn.forEach(t),odt=r(nEo," (XLM-RoBERTa-XL model)"),nEo.forEach(t),rdt=i(q),jy=n(q,"LI",{});var sEo=s(jy);uVe=n(sEo,"STRONG",{});var ETn=s(uVe);tdt=r(ETn,"xlnet"),ETn.forEach(t),adt=r(sEo," \u2014 "),Vhe=n(sEo,"A",{href:!0});var yTn=s(Vhe);ndt=r(yTn,"XLNetLMHeadModel"),yTn.forEach(t),sdt=r(sEo," (XLNet model)"),sEo.forEach(t),ldt=i(q),Py=n(q,"LI",{});var lEo=s(Py);pVe=n(lEo,"STRONG",{});var CTn=s(pVe);idt=r(CTn,"xmod"),CTn.forEach(t),ddt=r(lEo," \u2014 "),Whe=n(lEo,"A",{href:!0});var wTn=s(Whe);cdt=r(wTn,"XmodForMaskedLM"),wTn.forEach(t),mdt=r(lEo," (X-MOD model)"),lEo.forEach(t),q.forEach(t),fdt=i(on),Zy=n(on,"P",{});var iEo=s(Zy);hdt=r(iEo,"The model is set in evaluation mode by default using "),_Ve=n(iEo,"CODE",{});var LTn=s(_Ve);gdt=r(LTn,"model.eval()"),LTn.forEach(t),udt=r(iEo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),vVe=n(iEo,"CODE",{});var ATn=s(vVe);pdt=r(ATn,"model.train()"),ATn.forEach(t),iEo.forEach(t),_dt=i(on),M(Vy.$$.fragment,on),on.forEach(t),td.forEach(t),uGo=i(m),pm=n(m,"H3",{class:!0});var _No=s(pm);Wy=n(_No,"A",{id:!0,class:!0,href:!0});var RTn=s(Wy);bVe=n(RTn,"SPAN",{});var kTn=s(bVe);M(qV.$$.fragment,kTn),kTn.forEach(t),RTn.forEach(t),vdt=i(_No),MVe=n(_No,"SPAN",{});var STn=s(MVe);bdt=r(STn,"TFAutoModelForPreTraining"),STn.forEach(t),_No.forEach(t),pGo=i(m),Oo=n(m,"DIV",{class:!0});var ad=s(Oo);M(OV.$$.fragment,ad),Mdt=i(ad),_m=n(ad,"P",{});var L9e=s(_m);Tdt=r(L9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),Jhe=n(L9e,"A",{href:!0});var xTn=s(Jhe);Fdt=r(xTn,"from_pretrained()"),xTn.forEach(t),Edt=r(L9e," class method or the "),Uhe=n(L9e,"A",{href:!0});var GTn=s(Uhe);ydt=r(GTn,"from_config()"),GTn.forEach(t),Cdt=r(L9e,` class
method.`),L9e.forEach(t),wdt=i(ad),QV=n(ad,"P",{});var vNo=s(QV);Ldt=r(vNo,"This class cannot be instantiated directly using "),TVe=n(vNo,"CODE",{});var BTn=s(TVe);Adt=r(BTn,"__init__()"),BTn.forEach(t),Rdt=r(vNo," (throws an error)."),vNo.forEach(t),kdt=i(ad),Dt=n(ad,"DIV",{class:!0});var xj=s(Dt);M(YV.$$.fragment,xj),Sdt=i(xj),FVe=n(xj,"P",{});var ITn=s(FVe);xdt=r(ITn,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),ITn.forEach(t),Gdt=i(xj),vm=n(xj,"P",{});var A9e=s(vm);Bdt=r(A9e,`Note:
Loading a model from its configuration file does `),EVe=n(A9e,"STRONG",{});var NTn=s(EVe);Idt=r(NTn,"not"),NTn.forEach(t),Ndt=r(A9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),Xhe=n(A9e,"A",{href:!0});var $Tn=s(Xhe);$dt=r($Tn,"from_pretrained()"),$Tn.forEach(t),jdt=r(A9e," to load the model weights."),A9e.forEach(t),Pdt=i(xj),M(Jy.$$.fragment,xj),xj.forEach(t),Zdt=i(ad),nt=n(ad,"DIV",{class:!0});var nd=s(nt);M(zV.$$.fragment,nd),Vdt=i(nd),yVe=n(nd,"P",{});var jTn=s(yVe);Wdt=r(jTn,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),jTn.forEach(t),Jdt=i(nd),Jn=n(nd,"P",{});var Gj=s(Jn);Udt=r(Gj,"The model class to instantiate is selected based on the "),CVe=n(Gj,"CODE",{});var PTn=s(CVe);Xdt=r(PTn,"model_type"),PTn.forEach(t),Ddt=r(Gj,` property of the config object (either
passed as an argument or loaded from `),wVe=n(Gj,"CODE",{});var ZTn=s(wVe);qdt=r(ZTn,"pretrained_model_name_or_path"),ZTn.forEach(t),Odt=r(Gj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),LVe=n(Gj,"CODE",{});var VTn=s(LVe);Qdt=r(VTn,"pretrained_model_name_or_path"),VTn.forEach(t),Ydt=r(Gj,":"),Gj.forEach(t),zdt=i(nd),fe=n(nd,"UL",{});var he=s(fe);Uy=n(he,"LI",{});var dEo=s(Uy);AVe=n(dEo,"STRONG",{});var WTn=s(AVe);Hdt=r(WTn,"albert"),WTn.forEach(t),Kdt=r(dEo," \u2014 "),Dhe=n(dEo,"A",{href:!0});var JTn=s(Dhe);ect=r(JTn,"TFAlbertForPreTraining"),JTn.forEach(t),oct=r(dEo," (ALBERT model)"),dEo.forEach(t),rct=i(he),Xy=n(he,"LI",{});var cEo=s(Xy);RVe=n(cEo,"STRONG",{});var UTn=s(RVe);tct=r(UTn,"bart"),UTn.forEach(t),act=r(cEo," \u2014 "),qhe=n(cEo,"A",{href:!0});var XTn=s(qhe);nct=r(XTn,"TFBartForConditionalGeneration"),XTn.forEach(t),sct=r(cEo," (BART model)"),cEo.forEach(t),lct=i(he),Dy=n(he,"LI",{});var mEo=s(Dy);kVe=n(mEo,"STRONG",{});var DTn=s(kVe);ict=r(DTn,"bert"),DTn.forEach(t),dct=r(mEo," \u2014 "),Ohe=n(mEo,"A",{href:!0});var qTn=s(Ohe);cct=r(qTn,"TFBertForPreTraining"),qTn.forEach(t),mct=r(mEo," (BERT model)"),mEo.forEach(t),fct=i(he),qy=n(he,"LI",{});var fEo=s(qy);SVe=n(fEo,"STRONG",{});var OTn=s(SVe);hct=r(OTn,"camembert"),OTn.forEach(t),gct=r(fEo," \u2014 "),Qhe=n(fEo,"A",{href:!0});var QTn=s(Qhe);uct=r(QTn,"TFCamembertForMaskedLM"),QTn.forEach(t),pct=r(fEo," (CamemBERT model)"),fEo.forEach(t),_ct=i(he),Oy=n(he,"LI",{});var hEo=s(Oy);xVe=n(hEo,"STRONG",{});var YTn=s(xVe);vct=r(YTn,"ctrl"),YTn.forEach(t),bct=r(hEo," \u2014 "),Yhe=n(hEo,"A",{href:!0});var zTn=s(Yhe);Mct=r(zTn,"TFCTRLLMHeadModel"),zTn.forEach(t),Tct=r(hEo," (CTRL model)"),hEo.forEach(t),Fct=i(he),Qy=n(he,"LI",{});var gEo=s(Qy);GVe=n(gEo,"STRONG",{});var HTn=s(GVe);Ect=r(HTn,"distilbert"),HTn.forEach(t),yct=r(gEo," \u2014 "),zhe=n(gEo,"A",{href:!0});var KTn=s(zhe);Cct=r(KTn,"TFDistilBertForMaskedLM"),KTn.forEach(t),wct=r(gEo," (DistilBERT model)"),gEo.forEach(t),Lct=i(he),Yy=n(he,"LI",{});var uEo=s(Yy);BVe=n(uEo,"STRONG",{});var eFn=s(BVe);Act=r(eFn,"electra"),eFn.forEach(t),Rct=r(uEo," \u2014 "),Hhe=n(uEo,"A",{href:!0});var oFn=s(Hhe);kct=r(oFn,"TFElectraForPreTraining"),oFn.forEach(t),Sct=r(uEo," (ELECTRA model)"),uEo.forEach(t),xct=i(he),zy=n(he,"LI",{});var pEo=s(zy);IVe=n(pEo,"STRONG",{});var rFn=s(IVe);Gct=r(rFn,"flaubert"),rFn.forEach(t),Bct=r(pEo," \u2014 "),Khe=n(pEo,"A",{href:!0});var tFn=s(Khe);Ict=r(tFn,"TFFlaubertWithLMHeadModel"),tFn.forEach(t),Nct=r(pEo," (FlauBERT model)"),pEo.forEach(t),$ct=i(he),Hy=n(he,"LI",{});var _Eo=s(Hy);NVe=n(_Eo,"STRONG",{});var aFn=s(NVe);jct=r(aFn,"funnel"),aFn.forEach(t),Pct=r(_Eo," \u2014 "),ege=n(_Eo,"A",{href:!0});var nFn=s(ege);Zct=r(nFn,"TFFunnelForPreTraining"),nFn.forEach(t),Vct=r(_Eo," (Funnel Transformer model)"),_Eo.forEach(t),Wct=i(he),Ky=n(he,"LI",{});var vEo=s(Ky);$Ve=n(vEo,"STRONG",{});var sFn=s($Ve);Jct=r(sFn,"gpt-sw3"),sFn.forEach(t),Uct=r(vEo," \u2014 "),oge=n(vEo,"A",{href:!0});var lFn=s(oge);Xct=r(lFn,"TFGPT2LMHeadModel"),lFn.forEach(t),Dct=r(vEo," (GPT-Sw3 model)"),vEo.forEach(t),qct=i(he),e5=n(he,"LI",{});var bEo=s(e5);jVe=n(bEo,"STRONG",{});var iFn=s(jVe);Oct=r(iFn,"gpt2"),iFn.forEach(t),Qct=r(bEo," \u2014 "),rge=n(bEo,"A",{href:!0});var dFn=s(rge);Yct=r(dFn,"TFGPT2LMHeadModel"),dFn.forEach(t),zct=r(bEo," (OpenAI GPT-2 model)"),bEo.forEach(t),Hct=i(he),o5=n(he,"LI",{});var MEo=s(o5);PVe=n(MEo,"STRONG",{});var cFn=s(PVe);Kct=r(cFn,"layoutlm"),cFn.forEach(t),emt=r(MEo," \u2014 "),tge=n(MEo,"A",{href:!0});var mFn=s(tge);omt=r(mFn,"TFLayoutLMForMaskedLM"),mFn.forEach(t),rmt=r(MEo," (LayoutLM model)"),MEo.forEach(t),tmt=i(he),r5=n(he,"LI",{});var TEo=s(r5);ZVe=n(TEo,"STRONG",{});var fFn=s(ZVe);amt=r(fFn,"lxmert"),fFn.forEach(t),nmt=r(TEo," \u2014 "),age=n(TEo,"A",{href:!0});var hFn=s(age);smt=r(hFn,"TFLxmertForPreTraining"),hFn.forEach(t),lmt=r(TEo," (LXMERT model)"),TEo.forEach(t),imt=i(he),t5=n(he,"LI",{});var FEo=s(t5);VVe=n(FEo,"STRONG",{});var gFn=s(VVe);dmt=r(gFn,"mobilebert"),gFn.forEach(t),cmt=r(FEo," \u2014 "),nge=n(FEo,"A",{href:!0});var uFn=s(nge);mmt=r(uFn,"TFMobileBertForPreTraining"),uFn.forEach(t),fmt=r(FEo," (MobileBERT model)"),FEo.forEach(t),hmt=i(he),a5=n(he,"LI",{});var EEo=s(a5);WVe=n(EEo,"STRONG",{});var pFn=s(WVe);gmt=r(pFn,"mpnet"),pFn.forEach(t),umt=r(EEo," \u2014 "),sge=n(EEo,"A",{href:!0});var _Fn=s(sge);pmt=r(_Fn,"TFMPNetForMaskedLM"),_Fn.forEach(t),_mt=r(EEo," (MPNet model)"),EEo.forEach(t),vmt=i(he),n5=n(he,"LI",{});var yEo=s(n5);JVe=n(yEo,"STRONG",{});var vFn=s(JVe);bmt=r(vFn,"openai-gpt"),vFn.forEach(t),Mmt=r(yEo," \u2014 "),lge=n(yEo,"A",{href:!0});var bFn=s(lge);Tmt=r(bFn,"TFOpenAIGPTLMHeadModel"),bFn.forEach(t),Fmt=r(yEo," (OpenAI GPT model)"),yEo.forEach(t),Emt=i(he),s5=n(he,"LI",{});var CEo=s(s5);UVe=n(CEo,"STRONG",{});var MFn=s(UVe);ymt=r(MFn,"roberta"),MFn.forEach(t),Cmt=r(CEo," \u2014 "),ige=n(CEo,"A",{href:!0});var TFn=s(ige);wmt=r(TFn,"TFRobertaForMaskedLM"),TFn.forEach(t),Lmt=r(CEo," (RoBERTa model)"),CEo.forEach(t),Amt=i(he),l5=n(he,"LI",{});var wEo=s(l5);XVe=n(wEo,"STRONG",{});var FFn=s(XVe);Rmt=r(FFn,"roberta-prelayernorm"),FFn.forEach(t),kmt=r(wEo," \u2014 "),dge=n(wEo,"A",{href:!0});var EFn=s(dge);Smt=r(EFn,"TFRobertaPreLayerNormForMaskedLM"),EFn.forEach(t),xmt=r(wEo," (RoBERTa-PreLayerNorm model)"),wEo.forEach(t),Gmt=i(he),i5=n(he,"LI",{});var LEo=s(i5);DVe=n(LEo,"STRONG",{});var yFn=s(DVe);Bmt=r(yFn,"t5"),yFn.forEach(t),Imt=r(LEo," \u2014 "),cge=n(LEo,"A",{href:!0});var CFn=s(cge);Nmt=r(CFn,"TFT5ForConditionalGeneration"),CFn.forEach(t),$mt=r(LEo," (T5 model)"),LEo.forEach(t),jmt=i(he),d5=n(he,"LI",{});var AEo=s(d5);qVe=n(AEo,"STRONG",{});var wFn=s(qVe);Pmt=r(wFn,"tapas"),wFn.forEach(t),Zmt=r(AEo," \u2014 "),mge=n(AEo,"A",{href:!0});var LFn=s(mge);Vmt=r(LFn,"TFTapasForMaskedLM"),LFn.forEach(t),Wmt=r(AEo," (TAPAS model)"),AEo.forEach(t),Jmt=i(he),c5=n(he,"LI",{});var REo=s(c5);OVe=n(REo,"STRONG",{});var AFn=s(OVe);Umt=r(AFn,"transfo-xl"),AFn.forEach(t),Xmt=r(REo," \u2014 "),fge=n(REo,"A",{href:!0});var RFn=s(fge);Dmt=r(RFn,"TFTransfoXLLMHeadModel"),RFn.forEach(t),qmt=r(REo," (Transformer-XL model)"),REo.forEach(t),Omt=i(he),m5=n(he,"LI",{});var kEo=s(m5);QVe=n(kEo,"STRONG",{});var kFn=s(QVe);Qmt=r(kFn,"vit_mae"),kFn.forEach(t),Ymt=r(kEo," \u2014 "),hge=n(kEo,"A",{href:!0});var SFn=s(hge);zmt=r(SFn,"TFViTMAEForPreTraining"),SFn.forEach(t),Hmt=r(kEo," (ViTMAE model)"),kEo.forEach(t),Kmt=i(he),f5=n(he,"LI",{});var SEo=s(f5);YVe=n(SEo,"STRONG",{});var xFn=s(YVe);eft=r(xFn,"xlm"),xFn.forEach(t),oft=r(SEo," \u2014 "),gge=n(SEo,"A",{href:!0});var GFn=s(gge);rft=r(GFn,"TFXLMWithLMHeadModel"),GFn.forEach(t),tft=r(SEo," (XLM model)"),SEo.forEach(t),aft=i(he),h5=n(he,"LI",{});var xEo=s(h5);zVe=n(xEo,"STRONG",{});var BFn=s(zVe);nft=r(BFn,"xlm-roberta"),BFn.forEach(t),sft=r(xEo," \u2014 "),uge=n(xEo,"A",{href:!0});var IFn=s(uge);lft=r(IFn,"TFXLMRobertaForMaskedLM"),IFn.forEach(t),ift=r(xEo," (XLM-RoBERTa model)"),xEo.forEach(t),dft=i(he),g5=n(he,"LI",{});var GEo=s(g5);HVe=n(GEo,"STRONG",{});var NFn=s(HVe);cft=r(NFn,"xlnet"),NFn.forEach(t),mft=r(GEo," \u2014 "),pge=n(GEo,"A",{href:!0});var $Fn=s(pge);fft=r($Fn,"TFXLNetLMHeadModel"),$Fn.forEach(t),hft=r(GEo," (XLNet model)"),GEo.forEach(t),he.forEach(t),gft=i(nd),M(u5.$$.fragment,nd),nd.forEach(t),ad.forEach(t),_Go=i(m),bm=n(m,"H3",{class:!0});var bNo=s(bm);p5=n(bNo,"A",{id:!0,class:!0,href:!0});var jFn=s(p5);KVe=n(jFn,"SPAN",{});var PFn=s(KVe);M(HV.$$.fragment,PFn),PFn.forEach(t),jFn.forEach(t),uft=i(bNo),eWe=n(bNo,"SPAN",{});var ZFn=s(eWe);pft=r(ZFn,"FlaxAutoModelForPreTraining"),ZFn.forEach(t),bNo.forEach(t),vGo=i(m),Qo=n(m,"DIV",{class:!0});var sd=s(Qo);M(KV.$$.fragment,sd),_ft=i(sd),Mm=n(sd,"P",{});var R9e=s(Mm);vft=r(R9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),_ge=n(R9e,"A",{href:!0});var VFn=s(_ge);bft=r(VFn,"from_pretrained()"),VFn.forEach(t),Mft=r(R9e," class method or the "),vge=n(R9e,"A",{href:!0});var WFn=s(vge);Tft=r(WFn,"from_config()"),WFn.forEach(t),Fft=r(R9e,` class
method.`),R9e.forEach(t),Eft=i(sd),eW=n(sd,"P",{});var MNo=s(eW);yft=r(MNo,"This class cannot be instantiated directly using "),oWe=n(MNo,"CODE",{});var JFn=s(oWe);Cft=r(JFn,"__init__()"),JFn.forEach(t),wft=r(MNo," (throws an error)."),MNo.forEach(t),Lft=i(sd),qt=n(sd,"DIV",{class:!0});var Bj=s(qt);M(oW.$$.fragment,Bj),Aft=i(Bj),rWe=n(Bj,"P",{});var UFn=s(rWe);Rft=r(UFn,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),UFn.forEach(t),kft=i(Bj),Tm=n(Bj,"P",{});var k9e=s(Tm);Sft=r(k9e,`Note:
Loading a model from its configuration file does `),tWe=n(k9e,"STRONG",{});var XFn=s(tWe);xft=r(XFn,"not"),XFn.forEach(t),Gft=r(k9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),bge=n(k9e,"A",{href:!0});var DFn=s(bge);Bft=r(DFn,"from_pretrained()"),DFn.forEach(t),Ift=r(k9e," to load the model weights."),k9e.forEach(t),Nft=i(Bj),M(_5.$$.fragment,Bj),Bj.forEach(t),$ft=i(sd),st=n(sd,"DIV",{class:!0});var ld=s(st);M(rW.$$.fragment,ld),jft=i(ld),aWe=n(ld,"P",{});var qFn=s(aWe);Pft=r(qFn,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),qFn.forEach(t),Zft=i(ld),Un=n(ld,"P",{});var Ij=s(Un);Vft=r(Ij,"The model class to instantiate is selected based on the "),nWe=n(Ij,"CODE",{});var OFn=s(nWe);Wft=r(OFn,"model_type"),OFn.forEach(t),Jft=r(Ij,` property of the config object (either
passed as an argument or loaded from `),sWe=n(Ij,"CODE",{});var QFn=s(sWe);Uft=r(QFn,"pretrained_model_name_or_path"),QFn.forEach(t),Xft=r(Ij,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),lWe=n(Ij,"CODE",{});var YFn=s(lWe);Dft=r(YFn,"pretrained_model_name_or_path"),YFn.forEach(t),qft=r(Ij,":"),Ij.forEach(t),Oft=i(ld),we=n(ld,"UL",{});var Ae=s(we);v5=n(Ae,"LI",{});var BEo=s(v5);iWe=n(BEo,"STRONG",{});var zFn=s(iWe);Qft=r(zFn,"albert"),zFn.forEach(t),Yft=r(BEo," \u2014 "),Mge=n(BEo,"A",{href:!0});var HFn=s(Mge);zft=r(HFn,"FlaxAlbertForPreTraining"),HFn.forEach(t),Hft=r(BEo," (ALBERT model)"),BEo.forEach(t),Kft=i(Ae),b5=n(Ae,"LI",{});var IEo=s(b5);dWe=n(IEo,"STRONG",{});var KFn=s(dWe);eht=r(KFn,"bart"),KFn.forEach(t),oht=r(IEo," \u2014 "),Tge=n(IEo,"A",{href:!0});var eEn=s(Tge);rht=r(eEn,"FlaxBartForConditionalGeneration"),eEn.forEach(t),tht=r(IEo," (BART model)"),IEo.forEach(t),aht=i(Ae),M5=n(Ae,"LI",{});var NEo=s(M5);cWe=n(NEo,"STRONG",{});var oEn=s(cWe);nht=r(oEn,"bert"),oEn.forEach(t),sht=r(NEo," \u2014 "),Fge=n(NEo,"A",{href:!0});var rEn=s(Fge);lht=r(rEn,"FlaxBertForPreTraining"),rEn.forEach(t),iht=r(NEo," (BERT model)"),NEo.forEach(t),dht=i(Ae),T5=n(Ae,"LI",{});var $Eo=s(T5);mWe=n($Eo,"STRONG",{});var tEn=s(mWe);cht=r(tEn,"big_bird"),tEn.forEach(t),mht=r($Eo," \u2014 "),Ege=n($Eo,"A",{href:!0});var aEn=s(Ege);fht=r(aEn,"FlaxBigBirdForPreTraining"),aEn.forEach(t),hht=r($Eo," (BigBird model)"),$Eo.forEach(t),ght=i(Ae),F5=n(Ae,"LI",{});var jEo=s(F5);fWe=n(jEo,"STRONG",{});var nEn=s(fWe);uht=r(nEn,"electra"),nEn.forEach(t),pht=r(jEo," \u2014 "),yge=n(jEo,"A",{href:!0});var sEn=s(yge);_ht=r(sEn,"FlaxElectraForPreTraining"),sEn.forEach(t),vht=r(jEo," (ELECTRA model)"),jEo.forEach(t),bht=i(Ae),E5=n(Ae,"LI",{});var PEo=s(E5);hWe=n(PEo,"STRONG",{});var lEn=s(hWe);Mht=r(lEn,"longt5"),lEn.forEach(t),Tht=r(PEo," \u2014 "),Cge=n(PEo,"A",{href:!0});var iEn=s(Cge);Fht=r(iEn,"FlaxLongT5ForConditionalGeneration"),iEn.forEach(t),Eht=r(PEo," (LongT5 model)"),PEo.forEach(t),yht=i(Ae),y5=n(Ae,"LI",{});var ZEo=s(y5);gWe=n(ZEo,"STRONG",{});var dEn=s(gWe);Cht=r(dEn,"mbart"),dEn.forEach(t),wht=r(ZEo," \u2014 "),wge=n(ZEo,"A",{href:!0});var cEn=s(wge);Lht=r(cEn,"FlaxMBartForConditionalGeneration"),cEn.forEach(t),Aht=r(ZEo," (mBART model)"),ZEo.forEach(t),Rht=i(Ae),C5=n(Ae,"LI",{});var VEo=s(C5);uWe=n(VEo,"STRONG",{});var mEn=s(uWe);kht=r(mEn,"mt5"),mEn.forEach(t),Sht=r(VEo," \u2014 "),Lge=n(VEo,"A",{href:!0});var fEn=s(Lge);xht=r(fEn,"FlaxMT5ForConditionalGeneration"),fEn.forEach(t),Ght=r(VEo," (MT5 model)"),VEo.forEach(t),Bht=i(Ae),w5=n(Ae,"LI",{});var WEo=s(w5);pWe=n(WEo,"STRONG",{});var hEn=s(pWe);Iht=r(hEn,"roberta"),hEn.forEach(t),Nht=r(WEo," \u2014 "),Age=n(WEo,"A",{href:!0});var gEn=s(Age);$ht=r(gEn,"FlaxRobertaForMaskedLM"),gEn.forEach(t),jht=r(WEo," (RoBERTa model)"),WEo.forEach(t),Pht=i(Ae),L5=n(Ae,"LI",{});var JEo=s(L5);_We=n(JEo,"STRONG",{});var uEn=s(_We);Zht=r(uEn,"roberta-prelayernorm"),uEn.forEach(t),Vht=r(JEo," \u2014 "),Rge=n(JEo,"A",{href:!0});var pEn=s(Rge);Wht=r(pEn,"FlaxRobertaPreLayerNormForMaskedLM"),pEn.forEach(t),Jht=r(JEo," (RoBERTa-PreLayerNorm 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"),Gge=n(qEo,"A",{href:!0});var yEn=s(Gge);igt=r(yEn,"FlaxWhisperForConditionalGeneration"),yEn.forEach(t),dgt=r(qEo," (Whisper model)"),qEo.forEach(t),cgt=i(Ae),x5=n(Ae,"LI",{});var OEo=s(x5);FWe=n(OEo,"STRONG",{});var CEn=s(FWe);mgt=r(CEn,"xlm-roberta"),CEn.forEach(t),fgt=r(OEo," \u2014 "),Bge=n(OEo,"A",{href:!0});var wEn=s(Bge);hgt=r(wEn,"FlaxXLMRobertaForMaskedLM"),wEn.forEach(t),ggt=r(OEo," (XLM-RoBERTa model)"),OEo.forEach(t),Ae.forEach(t),ugt=i(ld),M(G5.$$.fragment,ld),ld.forEach(t),sd.forEach(t),bGo=i(m),Fm=n(m,"H2",{class:!0});var TNo=s(Fm);B5=n(TNo,"A",{id:!0,class:!0,href:!0});var LEn=s(B5);EWe=n(LEn,"SPAN",{});var AEn=s(EWe);M(tW.$$.fragment,AEn),AEn.forEach(t),LEn.forEach(t),pgt=i(TNo),yWe=n(TNo,"SPAN",{});var REn=s(yWe);_gt=r(REn,"Natural Language Processing"),REn.forEach(t),TNo.forEach(t),MGo=i(m),Ige=n(m,"P",{});var kEn=s(Ige);vgt=r(kEn,"The following auto classes are available for the following natural language processing tasks."),kEn.forEach(t),TGo=i(m),Em=n(m,"H3",{class:!0});var FNo=s(Em);I5=n(FNo,"A",{id:!0,class:!0,href:!0});var SEn=s(I5);CWe=n(SEn,"SPAN",{});var xEn=s(CWe);M(aW.$$.fragment,xEn),xEn.forEach(t),SEn.forEach(t),bgt=i(FNo),wWe=n(FNo,"SPAN",{});var GEn=s(wWe);Mgt=r(GEn,"AutoModelForCausalLM"),GEn.forEach(t),FNo.forEach(t),FGo=i(m),Yo=n(m,"DIV",{class:!0});var id=s(Yo);M(nW.$$.fragment,id),Tgt=i(id),ym=n(id,"P",{});var S9e=s(ym);Fgt=r(S9e,`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 `),Nge=n(S9e,"A",{href:!0});var BEn=s(Nge);Egt=r(BEn,"from_pretrained()"),BEn.forEach(t),ygt=r(S9e," class method or the "),$ge=n(S9e,"A",{href:!0});var IEn=s($ge);Cgt=r(IEn,"from_config()"),IEn.forEach(t),wgt=r(S9e,` class
method.`),S9e.forEach(t),Lgt=i(id),sW=n(id,"P",{});var ENo=s(sW);Agt=r(ENo,"This class cannot be instantiated directly using "),LWe=n(ENo,"CODE",{});var NEn=s(LWe);Rgt=r(NEn,"__init__()"),NEn.forEach(t),kgt=r(ENo," (throws an error)."),ENo.forEach(t),Sgt=i(id),Ot=n(id,"DIV",{class:!0});var Nj=s(Ot);M(lW.$$.fragment,Nj),xgt=i(Nj),AWe=n(Nj,"P",{});var $En=s(AWe);Ggt=r($En,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),$En.forEach(t),Bgt=i(Nj),Cm=n(Nj,"P",{});var x9e=s(Cm);Igt=r(x9e,`Note:
Loading a model from its configuration file does `),RWe=n(x9e,"STRONG",{});var jEn=s(RWe);Ngt=r(jEn,"not"),jEn.forEach(t),$gt=r(x9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),jge=n(x9e,"A",{href:!0});var PEn=s(jge);jgt=r(PEn,"from_pretrained()"),PEn.forEach(t),Pgt=r(x9e," to load the model weights."),x9e.forEach(t),Zgt=i(Nj),M(N5.$$.fragment,Nj),Nj.forEach(t),Vgt=i(id),io=n(id,"DIV",{class:!0});var rn=s(io);M(iW.$$.fragment,rn),Wgt=i(rn),kWe=n(rn,"P",{});var ZEn=s(kWe);Jgt=r(ZEn,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),ZEn.forEach(t),Ugt=i(rn),Xn=n(rn,"P",{});var $j=s(Xn);Xgt=r($j,"The model class to instantiate is selected based on the "),SWe=n($j,"CODE",{});var VEn=s(SWe);Dgt=r(VEn,"model_type"),VEn.forEach(t),qgt=r($j,` property of the config object (either
passed as an argument or loaded from `),xWe=n($j,"CODE",{});var WEn=s(xWe);Ogt=r(WEn,"pretrained_model_name_or_path"),WEn.forEach(t),Qgt=r($j,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),GWe=n($j,"CODE",{});var JEn=s(GWe);Ygt=r(JEn,"pretrained_model_name_or_path"),JEn.forEach(t),zgt=r($j,":"),$j.forEach(t),Hgt=i(rn),Q=n(rn,"UL",{});var Y=s(Q);$5=n(Y,"LI",{});var QEo=s($5);BWe=n(QEo,"STRONG",{});var UEn=s(BWe);Kgt=r(UEn,"bart"),UEn.forEach(t),eut=r(QEo," \u2014 "),Pge=n(QEo,"A",{href:!0});var XEn=s(Pge);out=r(XEn,"BartForCausalLM"),XEn.forEach(t),rut=r(QEo," (BART model)"),QEo.forEach(t),tut=i(Y),j5=n(Y,"LI",{});var YEo=s(j5);IWe=n(YEo,"STRONG",{});var DEn=s(IWe);aut=r(DEn,"bert"),DEn.forEach(t),nut=r(YEo," \u2014 "),Zge=n(YEo,"A",{href:!0});var qEn=s(Zge);sut=r(qEn,"BertLMHeadModel"),qEn.forEach(t),lut=r(YEo," (BERT model)"),YEo.forEach(t),iut=i(Y),P5=n(Y,"LI",{});var zEo=s(P5);NWe=n(zEo,"STRONG",{});var OEn=s(NWe);dut=r(OEn,"bert-generation"),OEn.forEach(t),cut=r(zEo," \u2014 "),Vge=n(zEo,"A",{href:!0});var QEn=s(Vge);mut=r(QEn,"BertGenerationDecoder"),QEn.forEach(t),fut=r(zEo," (Bert Generation model)"),zEo.forEach(t),hut=i(Y),Z5=n(Y,"LI",{});var HEo=s(Z5);$We=n(HEo,"STRONG",{});var YEn=s($We);gut=r(YEn,"big_bird"),YEn.forEach(t),uut=r(HEo," \u2014 "),Wge=n(HEo,"A",{href:!0});var zEn=s(Wge);put=r(zEn,"BigBirdForCausalLM"),zEn.forEach(t),_ut=r(HEo," (BigBird model)"),HEo.forEach(t),vut=i(Y),V5=n(Y,"LI",{});var KEo=s(V5);jWe=n(KEo,"STRONG",{});var HEn=s(jWe);but=r(HEn,"bigbird_pegasus"),HEn.forEach(t),Mut=r(KEo," \u2014 "),Jge=n(KEo,"A",{href:!0});var KEn=s(Jge);Tut=r(KEn,"BigBirdPegasusForCausalLM"),KEn.forEach(t),Fut=r(KEo," (BigBird-Pegasus model)"),KEo.forEach(t),Eut=i(Y),W5=n(Y,"LI",{});var eyo=s(W5);PWe=n(eyo,"STRONG",{});var eyn=s(PWe);yut=r(eyn,"biogpt"),eyn.forEach(t),Cut=r(eyo," \u2014 "),Uge=n(eyo,"A",{href:!0});var oyn=s(Uge);wut=r(oyn,"BioGptForCausalLM"),oyn.forEach(t),Lut=r(eyo," (BioGpt model)"),eyo.forEach(t),Aut=i(Y),J5=n(Y,"LI",{});var oyo=s(J5);ZWe=n(oyo,"STRONG",{});var ryn=s(ZWe);Rut=r(ryn,"blenderbot"),ryn.forEach(t),kut=r(oyo," \u2014 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J5n=s($ue);Yvt=r(J5n,"XLMRobertaForCausalLM"),J5n.forEach(t),zvt=r(Oyo," (XLM-RoBERTa model)"),Oyo.forEach(t),Hvt=i(Y),$9=n(Y,"LI",{});var Qyo=s($9);IJe=n(Qyo,"STRONG",{});var U5n=s(IJe);Kvt=r(U5n,"xlm-roberta-xl"),U5n.forEach(t),e1t=r(Qyo," \u2014 "),jue=n(Qyo,"A",{href:!0});var X5n=s(jue);o1t=r(X5n,"XLMRobertaXLForCausalLM"),X5n.forEach(t),r1t=r(Qyo," (XLM-RoBERTa-XL model)"),Qyo.forEach(t),t1t=i(Y),j9=n(Y,"LI",{});var Yyo=s(j9);NJe=n(Yyo,"STRONG",{});var D5n=s(NJe);a1t=r(D5n,"xlnet"),D5n.forEach(t),n1t=r(Yyo," \u2014 "),Pue=n(Yyo,"A",{href:!0});var q5n=s(Pue);s1t=r(q5n,"XLNetLMHeadModel"),q5n.forEach(t),l1t=r(Yyo," (XLNet model)"),Yyo.forEach(t),i1t=i(Y),P9=n(Y,"LI",{});var zyo=s(P9);$Je=n(zyo,"STRONG",{});var O5n=s($Je);d1t=r(O5n,"xmod"),O5n.forEach(t),c1t=r(zyo," \u2014 "),Zue=n(zyo,"A",{href:!0});var Q5n=s(Zue);m1t=r(Q5n,"XmodForCausalLM"),Q5n.forEach(t),f1t=r(zyo," (X-MOD model)"),zyo.forEach(t),Y.forEach(t),h1t=i(rn),Z9=n(rn,"P",{});var Hyo=s(Z9);g1t=r(Hyo,"The model is set in 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deactivated). To train the model, you should first set it back in training mode with `),PJe=n(Hyo,"CODE",{});var z5n=s(PJe);_1t=r(z5n,"model.train()"),z5n.forEach(t),Hyo.forEach(t),v1t=i(rn),M(V9.$$.fragment,rn),rn.forEach(t),id.forEach(t),EGo=i(m),wm=n(m,"H3",{class:!0});var yNo=s(wm);W9=n(yNo,"A",{id:!0,class:!0,href:!0});var H5n=s(W9);ZJe=n(H5n,"SPAN",{});var K5n=s(ZJe);M(dW.$$.fragment,K5n),K5n.forEach(t),H5n.forEach(t),b1t=i(yNo),VJe=n(yNo,"SPAN",{});var e9n=s(VJe);M1t=r(e9n,"TFAutoModelForCausalLM"),e9n.forEach(t),yNo.forEach(t),yGo=i(m),zo=n(m,"DIV",{class:!0});var dd=s(zo);M(cW.$$.fragment,dd),T1t=i(dd),Lm=n(dd,"P",{});var G9e=s(Lm);F1t=r(G9e,`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 `),Vue=n(G9e,"A",{href:!0});var o9n=s(Vue);E1t=r(o9n,"from_pretrained()"),o9n.forEach(t),y1t=r(G9e," class method or the "),Wue=n(G9e,"A",{href:!0});var r9n=s(Wue);C1t=r(r9n,"from_config()"),r9n.forEach(t),w1t=r(G9e,` class
method.`),G9e.forEach(t),L1t=i(dd),mW=n(dd,"P",{});var CNo=s(mW);A1t=r(CNo,"This class cannot be instantiated directly using "),WJe=n(CNo,"CODE",{});var t9n=s(WJe);R1t=r(t9n,"__init__()"),t9n.forEach(t),k1t=r(CNo," (throws an error)."),CNo.forEach(t),S1t=i(dd),Qt=n(dd,"DIV",{class:!0});var jj=s(Qt);M(fW.$$.fragment,jj),x1t=i(jj),JJe=n(jj,"P",{});var a9n=s(JJe);G1t=r(a9n,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),a9n.forEach(t),B1t=i(jj),Am=n(jj,"P",{});var B9e=s(Am);I1t=r(B9e,`Note:
Loading a model from its configuration file does `),UJe=n(B9e,"STRONG",{});var n9n=s(UJe);N1t=r(n9n,"not"),n9n.forEach(t),$1t=r(B9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),Jue=n(B9e,"A",{href:!0});var s9n=s(Jue);j1t=r(s9n,"from_pretrained()"),s9n.forEach(t),P1t=r(B9e," to load the model weights."),B9e.forEach(t),Z1t=i(jj),M(J9.$$.fragment,jj),jj.forEach(t),V1t=i(dd),lt=n(dd,"DIV",{class:!0});var cd=s(lt);M(hW.$$.fragment,cd),W1t=i(cd),XJe=n(cd,"P",{});var l9n=s(XJe);J1t=r(l9n,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),l9n.forEach(t),U1t=i(cd),Dn=n(cd,"P",{});var Pj=s(Dn);X1t=r(Pj,"The model class to instantiate is selected based on the "),DJe=n(Pj,"CODE",{});var i9n=s(DJe);D1t=r(i9n,"model_type"),i9n.forEach(t),q1t=r(Pj,` property of the config object (either
passed as an argument or loaded from `),qJe=n(Pj,"CODE",{});var d9n=s(qJe);O1t=r(d9n,"pretrained_model_name_or_path"),d9n.forEach(t),Q1t=r(Pj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),OJe=n(Pj,"CODE",{});var c9n=s(OJe);Y1t=r(c9n,"pretrained_model_name_or_path"),c9n.forEach(t),z1t=r(Pj,":"),Pj.forEach(t),H1t=i(cd),Ee=n(cd,"UL",{});var Ce=s(Ee);U9=n(Ce,"LI",{});var Kyo=s(U9);QJe=n(Kyo,"STRONG",{});var m9n=s(QJe);K1t=r(m9n,"bert"),m9n.forEach(t),ebt=r(Kyo," \u2014 "),Uue=n(Kyo,"A",{href:!0});var f9n=s(Uue);obt=r(f9n,"TFBertLMHeadModel"),f9n.forEach(t),rbt=r(Kyo," (BERT model)"),Kyo.forEach(t),tbt=i(Ce),X9=n(Ce,"LI",{});var e5o=s(X9);YJe=n(e5o,"STRONG",{});var h9n=s(YJe);abt=r(h9n,"camembert"),h9n.forEach(t),nbt=r(e5o," \u2014 "),Xue=n(e5o,"A",{href:!0});var g9n=s(Xue);sbt=r(g9n,"TFCamembertForCausalLM"),g9n.forEach(t),lbt=r(e5o," (CamemBERT model)"),e5o.forEach(t),ibt=i(Ce),D9=n(Ce,"LI",{});var o5o=s(D9);zJe=n(o5o,"STRONG",{});var u9n=s(zJe);dbt=r(u9n,"ctrl"),u9n.forEach(t),cbt=r(o5o," \u2014 "),Due=n(o5o,"A",{href:!0});var p9n=s(Due);mbt=r(p9n,"TFCTRLLMHeadModel"),p9n.forEach(t),fbt=r(o5o," (CTRL model)"),o5o.forEach(t),hbt=i(Ce),q9=n(Ce,"LI",{});var r5o=s(q9);HJe=n(r5o,"STRONG",{});var _9n=s(HJe);gbt=r(_9n,"gpt-sw3"),_9n.forEach(t),ubt=r(r5o," \u2014 "),que=n(r5o,"A",{href:!0});var v9n=s(que);pbt=r(v9n,"TFGPT2LMHeadModel"),v9n.forEach(t),_bt=r(r5o," (GPT-Sw3 model)"),r5o.forEach(t),vbt=i(Ce),O9=n(Ce,"LI",{});var t5o=s(O9);KJe=n(t5o,"STRONG",{});var b9n=s(KJe);bbt=r(b9n,"gpt2"),b9n.forEach(t),Mbt=r(t5o," \u2014 "),Oue=n(t5o,"A",{href:!0});var M9n=s(Oue);Tbt=r(M9n,"TFGPT2LMHeadModel"),M9n.forEach(t),Fbt=r(t5o," (OpenAI GPT-2 model)"),t5o.forEach(t),Ebt=i(Ce),Q9=n(Ce,"LI",{});var a5o=s(Q9);eUe=n(a5o,"STRONG",{});var T9n=s(eUe);ybt=r(T9n,"gptj"),T9n.forEach(t),Cbt=r(a5o," \u2014 "),Que=n(a5o,"A",{href:!0});var F9n=s(Que);wbt=r(F9n,"TFGPTJForCausalLM"),F9n.forEach(t),Lbt=r(a5o," (GPT-J model)"),a5o.forEach(t),Abt=i(Ce),Y9=n(Ce,"LI",{});var n5o=s(Y9);oUe=n(n5o,"STRONG",{});var E9n=s(oUe);Rbt=r(E9n,"openai-gpt"),E9n.forEach(t),kbt=r(n5o," \u2014 "),Yue=n(n5o,"A",{href:!0});var y9n=s(Yue);Sbt=r(y9n,"TFOpenAIGPTLMHeadModel"),y9n.forEach(t),xbt=r(n5o," (OpenAI GPT model)"),n5o.forEach(t),Gbt=i(Ce),z9=n(Ce,"LI",{});var s5o=s(z9);rUe=n(s5o,"STRONG",{});var C9n=s(rUe);Bbt=r(C9n,"opt"),C9n.forEach(t),Ibt=r(s5o," \u2014 "),zue=n(s5o,"A",{href:!0});var w9n=s(zue);Nbt=r(w9n,"TFOPTForCausalLM"),w9n.forEach(t),$bt=r(s5o," (OPT model)"),s5o.forEach(t),jbt=i(Ce),H9=n(Ce,"LI",{});var l5o=s(H9);tUe=n(l5o,"STRONG",{});var L9n=s(tUe);Pbt=r(L9n,"rembert"),L9n.forEach(t),Zbt=r(l5o," \u2014 "),Hue=n(l5o,"A",{href:!0});var A9n=s(Hue);Vbt=r(A9n,"TFRemBertForCausalLM"),A9n.forEach(t),Wbt=r(l5o," (RemBERT model)"),l5o.forEach(t),Jbt=i(Ce),K9=n(Ce,"LI",{});var i5o=s(K9);aUe=n(i5o,"STRONG",{});var R9n=s(aUe);Ubt=r(R9n,"roberta"),R9n.forEach(t),Xbt=r(i5o," \u2014 "),Kue=n(i5o,"A",{href:!0});var k9n=s(Kue);Dbt=r(k9n,"TFRobertaForCausalLM"),k9n.forEach(t),qbt=r(i5o," (RoBERTa model)"),i5o.forEach(t),Obt=i(Ce),eC=n(Ce,"LI",{});var d5o=s(eC);nUe=n(d5o,"STRONG",{});var S9n=s(nUe);Qbt=r(S9n,"roberta-prelayernorm"),S9n.forEach(t),Ybt=r(d5o," \u2014 "),epe=n(d5o,"A",{href:!0});var x9n=s(epe);zbt=r(x9n,"TFRobertaPreLayerNormForCausalLM"),x9n.forEach(t),Hbt=r(d5o," (RoBERTa-PreLayerNorm model)"),d5o.forEach(t),Kbt=i(Ce),oC=n(Ce,"LI",{});var c5o=s(oC);sUe=n(c5o,"STRONG",{});var G9n=s(sUe);e0t=r(G9n,"roformer"),G9n.forEach(t),o0t=r(c5o," \u2014 "),ope=n(c5o,"A",{href:!0});var B9n=s(ope);r0t=r(B9n,"TFRoFormerForCausalLM"),B9n.forEach(t),t0t=r(c5o," (RoFormer model)"),c5o.forEach(t),a0t=i(Ce),rC=n(Ce,"LI",{});var m5o=s(rC);lUe=n(m5o,"STRONG",{});var I9n=s(lUe);n0t=r(I9n,"transfo-xl"),I9n.forEach(t),s0t=r(m5o," \u2014 "),rpe=n(m5o,"A",{href:!0});var N9n=s(rpe);l0t=r(N9n,"TFTransfoXLLMHeadModel"),N9n.forEach(t),i0t=r(m5o," (Transformer-XL model)"),m5o.forEach(t),d0t=i(Ce),tC=n(Ce,"LI",{});var f5o=s(tC);iUe=n(f5o,"STRONG",{});var $9n=s(iUe);c0t=r($9n,"xglm"),$9n.forEach(t),m0t=r(f5o," \u2014 "),tpe=n(f5o,"A",{href:!0});var j9n=s(tpe);f0t=r(j9n,"TFXGLMForCausalLM"),j9n.forEach(t),h0t=r(f5o," (XGLM model)"),f5o.forEach(t),g0t=i(Ce),aC=n(Ce,"LI",{});var h5o=s(aC);dUe=n(h5o,"STRONG",{});var P9n=s(dUe);u0t=r(P9n,"xlm"),P9n.forEach(t),p0t=r(h5o," \u2014 "),ape=n(h5o,"A",{href:!0});var Z9n=s(ape);_0t=r(Z9n,"TFXLMWithLMHeadModel"),Z9n.forEach(t),v0t=r(h5o," (XLM model)"),h5o.forEach(t),b0t=i(Ce),nC=n(Ce,"LI",{});var g5o=s(nC);cUe=n(g5o,"STRONG",{});var V9n=s(cUe);M0t=r(V9n,"xlm-roberta"),V9n.forEach(t),T0t=r(g5o," \u2014 "),npe=n(g5o,"A",{href:!0});var W9n=s(npe);F0t=r(W9n,"TFXLMRobertaForCausalLM"),W9n.forEach(t),E0t=r(g5o," (XLM-RoBERTa model)"),g5o.forEach(t),y0t=i(Ce),sC=n(Ce,"LI",{});var u5o=s(sC);mUe=n(u5o,"STRONG",{});var J9n=s(mUe);C0t=r(J9n,"xlnet"),J9n.forEach(t),w0t=r(u5o," \u2014 "),spe=n(u5o,"A",{href:!0});var U9n=s(spe);L0t=r(U9n,"TFXLNetLMHeadModel"),U9n.forEach(t),A0t=r(u5o," (XLNet model)"),u5o.forEach(t),Ce.forEach(t),R0t=i(cd),M(lC.$$.fragment,cd),cd.forEach(t),dd.forEach(t),CGo=i(m),Rm=n(m,"H3",{class:!0});var wNo=s(Rm);iC=n(wNo,"A",{id:!0,class:!0,href:!0});var X9n=s(iC);fUe=n(X9n,"SPAN",{});var D9n=s(fUe);M(gW.$$.fragment,D9n),D9n.forEach(t),X9n.forEach(t),k0t=i(wNo),hUe=n(wNo,"SPAN",{});var q9n=s(hUe);S0t=r(q9n,"FlaxAutoModelForCausalLM"),q9n.forEach(t),wNo.forEach(t),wGo=i(m),Ho=n(m,"DIV",{class:!0});var md=s(Ho);M(uW.$$.fragment,md),x0t=i(md),km=n(md,"P",{});var I9e=s(km);G0t=r(I9e,`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 `),lpe=n(I9e,"A",{href:!0});var O9n=s(lpe);B0t=r(O9n,"from_pretrained()"),O9n.forEach(t),I0t=r(I9e," class method or the "),ipe=n(I9e,"A",{href:!0});var Q9n=s(ipe);N0t=r(Q9n,"from_config()"),Q9n.forEach(t),$0t=r(I9e,` class
method.`),I9e.forEach(t),j0t=i(md),pW=n(md,"P",{});var LNo=s(pW);P0t=r(LNo,"This class cannot be instantiated directly using "),gUe=n(LNo,"CODE",{});var Y9n=s(gUe);Z0t=r(Y9n,"__init__()"),Y9n.forEach(t),V0t=r(LNo," (throws an error)."),LNo.forEach(t),W0t=i(md),Yt=n(md,"DIV",{class:!0});var Zj=s(Yt);M(_W.$$.fragment,Zj),J0t=i(Zj),uUe=n(Zj,"P",{});var z9n=s(uUe);U0t=r(z9n,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),z9n.forEach(t),X0t=i(Zj),Sm=n(Zj,"P",{});var N9e=s(Sm);D0t=r(N9e,`Note:
Loading a model from its configuration file does `),pUe=n(N9e,"STRONG",{});var H9n=s(pUe);q0t=r(H9n,"not"),H9n.forEach(t),O0t=r(N9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),dpe=n(N9e,"A",{href:!0});var K9n=s(dpe);Q0t=r(K9n,"from_pretrained()"),K9n.forEach(t),Y0t=r(N9e," to load the model weights."),N9e.forEach(t),z0t=i(Zj),M(dC.$$.fragment,Zj),Zj.forEach(t),H0t=i(md),it=n(md,"DIV",{class:!0});var fd=s(it);M(vW.$$.fragment,fd),K0t=i(fd),_Ue=n(fd,"P",{});var eCn=s(_Ue);e3t=r(eCn,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),eCn.forEach(t),o3t=i(fd),qn=n(fd,"P",{});var Vj=s(qn);r3t=r(Vj,"The model class to instantiate is selected based on the "),vUe=n(Vj,"CODE",{});var oCn=s(vUe);t3t=r(oCn,"model_type"),oCn.forEach(t),a3t=r(Vj,` property of the config object (either
passed as an argument or loaded from `),bUe=n(Vj,"CODE",{});var rCn=s(bUe);n3t=r(rCn,"pretrained_model_name_or_path"),rCn.forEach(t),s3t=r(Vj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),MUe=n(Vj,"CODE",{});var tCn=s(MUe);l3t=r(tCn,"pretrained_model_name_or_path"),tCn.forEach(t),i3t=r(Vj,":"),Vj.forEach(t),d3t=i(fd),Le=n(fd,"UL",{});var Re=s(Le);cC=n(Re,"LI",{});var p5o=s(cC);TUe=n(p5o,"STRONG",{});var aCn=s(TUe);c3t=r(aCn,"bart"),aCn.forEach(t),m3t=r(p5o," \u2014 "),cpe=n(p5o,"A",{href:!0});var nCn=s(cpe);f3t=r(nCn,"FlaxBartForCausalLM"),nCn.forEach(t),h3t=r(p5o," (BART model)"),p5o.forEach(t),g3t=i(Re),mC=n(Re,"LI",{});var _5o=s(mC);FUe=n(_5o,"STRONG",{});var sCn=s(FUe);u3t=r(sCn,"bert"),sCn.forEach(t),p3t=r(_5o," \u2014 "),mpe=n(_5o,"A",{href:!0});var lCn=s(mpe);_3t=r(lCn,"FlaxBertForCausalLM"),lCn.forEach(t),v3t=r(_5o," (BERT model)"),_5o.forEach(t),b3t=i(Re),fC=n(Re,"LI",{});var v5o=s(fC);EUe=n(v5o,"STRONG",{});var iCn=s(EUe);M3t=r(iCn,"big_bird"),iCn.forEach(t),T3t=r(v5o," \u2014 "),fpe=n(v5o,"A",{href:!0});var dCn=s(fpe);F3t=r(dCn,"FlaxBigBirdForCausalLM"),dCn.forEach(t),E3t=r(v5o," (BigBird model)"),v5o.forEach(t),y3t=i(Re),hC=n(Re,"LI",{});var b5o=s(hC);yUe=n(b5o,"STRONG",{});var cCn=s(yUe);C3t=r(cCn,"bloom"),cCn.forEach(t),w3t=r(b5o," \u2014 "),hpe=n(b5o,"A",{href:!0});var mCn=s(hpe);L3t=r(mCn,"FlaxBloomForCausalLM"),mCn.forEach(t),A3t=r(b5o," (BLOOM model)"),b5o.forEach(t),R3t=i(Re),gC=n(Re,"LI",{});var M5o=s(gC);CUe=n(M5o,"STRONG",{});var fCn=s(CUe);k3t=r(fCn,"electra"),fCn.forEach(t),S3t=r(M5o," \u2014 "),gpe=n(M5o,"A",{href:!0});var hCn=s(gpe);x3t=r(hCn,"FlaxElectraForCausalLM"),hCn.forEach(t),G3t=r(M5o," (ELECTRA model)"),M5o.forEach(t),B3t=i(Re),uC=n(Re,"LI",{});var T5o=s(uC);wUe=n(T5o,"STRONG",{});var gCn=s(wUe);I3t=r(gCn,"gpt-sw3"),gCn.forEach(t),N3t=r(T5o," \u2014 "),upe=n(T5o,"A",{href:!0});var uCn=s(upe);$3t=r(uCn,"FlaxGPT2LMHeadModel"),uCn.forEach(t),j3t=r(T5o," (GPT-Sw3 model)"),T5o.forEach(t),P3t=i(Re),pC=n(Re,"LI",{});var F5o=s(pC);LUe=n(F5o,"STRONG",{});var pCn=s(LUe);Z3t=r(pCn,"gpt2"),pCn.forEach(t),V3t=r(F5o," \u2014 "),ppe=n(F5o,"A",{href:!0});var _Cn=s(ppe);W3t=r(_Cn,"FlaxGPT2LMHeadModel"),_Cn.forEach(t),J3t=r(F5o," (OpenAI GPT-2 model)"),F5o.forEach(t),U3t=i(Re),_C=n(Re,"LI",{});var E5o=s(_C);AUe=n(E5o,"STRONG",{});var vCn=s(AUe);X3t=r(vCn,"gpt_neo"),vCn.forEach(t),D3t=r(E5o," \u2014 "),_pe=n(E5o,"A",{href:!0});var bCn=s(_pe);q3t=r(bCn,"FlaxGPTNeoForCausalLM"),bCn.forEach(t),O3t=r(E5o," (GPT Neo model)"),E5o.forEach(t),Q3t=i(Re),vC=n(Re,"LI",{});var y5o=s(vC);RUe=n(y5o,"STRONG",{});var MCn=s(RUe);Y3t=r(MCn,"gptj"),MCn.forEach(t),z3t=r(y5o," \u2014 "),vpe=n(y5o,"A",{href:!0});var TCn=s(vpe);H3t=r(TCn,"FlaxGPTJForCausalLM"),TCn.forEach(t),K3t=r(y5o," (GPT-J model)"),y5o.forEach(t),e4t=i(Re),bC=n(Re,"LI",{});var C5o=s(bC);kUe=n(C5o,"STRONG",{});var FCn=s(kUe);o4t=r(FCn,"opt"),FCn.forEach(t),r4t=r(C5o," \u2014 "),bpe=n(C5o,"A",{href:!0});var ECn=s(bpe);t4t=r(ECn,"FlaxOPTForCausalLM"),ECn.forEach(t),a4t=r(C5o," (OPT model)"),C5o.forEach(t),n4t=i(Re),MC=n(Re,"LI",{});var w5o=s(MC);SUe=n(w5o,"STRONG",{});var yCn=s(SUe);s4t=r(yCn,"roberta"),yCn.forEach(t),l4t=r(w5o," \u2014 "),Mpe=n(w5o,"A",{href:!0});var CCn=s(Mpe);i4t=r(CCn,"FlaxRobertaForCausalLM"),CCn.forEach(t),d4t=r(w5o," (RoBERTa model)"),w5o.forEach(t),c4t=i(Re),TC=n(Re,"LI",{});var L5o=s(TC);xUe=n(L5o,"STRONG",{});var wCn=s(xUe);m4t=r(wCn,"roberta-prelayernorm"),wCn.forEach(t),f4t=r(L5o," \u2014 "),Tpe=n(L5o,"A",{href:!0});var LCn=s(Tpe);h4t=r(LCn,"FlaxRobertaPreLayerNormForCausalLM"),LCn.forEach(t),g4t=r(L5o," (RoBERTa-PreLayerNorm model)"),L5o.forEach(t),u4t=i(Re),FC=n(Re,"LI",{});var A5o=s(FC);GUe=n(A5o,"STRONG",{});var ACn=s(GUe);p4t=r(ACn,"xglm"),ACn.forEach(t),_4t=r(A5o," \u2014 "),Fpe=n(A5o,"A",{href:!0});var RCn=s(Fpe);v4t=r(RCn,"FlaxXGLMForCausalLM"),RCn.forEach(t),b4t=r(A5o," (XGLM model)"),A5o.forEach(t),M4t=i(Re),EC=n(Re,"LI",{});var R5o=s(EC);BUe=n(R5o,"STRONG",{});var kCn=s(BUe);T4t=r(kCn,"xlm-roberta"),kCn.forEach(t),F4t=r(R5o," \u2014 "),Epe=n(R5o,"A",{href:!0});var SCn=s(Epe);E4t=r(SCn,"FlaxXLMRobertaForCausalLM"),SCn.forEach(t),y4t=r(R5o," (XLM-RoBERTa model)"),R5o.forEach(t),Re.forEach(t),C4t=i(fd),M(yC.$$.fragment,fd),fd.forEach(t),md.forEach(t),LGo=i(m),xm=n(m,"H3",{class:!0});var ANo=s(xm);CC=n(ANo,"A",{id:!0,class:!0,href:!0});var xCn=s(CC);IUe=n(xCn,"SPAN",{});var GCn=s(IUe);M(bW.$$.fragment,GCn),GCn.forEach(t),xCn.forEach(t),w4t=i(ANo),NUe=n(ANo,"SPAN",{});var BCn=s(NUe);L4t=r(BCn,"AutoModelForMaskedLM"),BCn.forEach(t),ANo.forEach(t),AGo=i(m),Ko=n(m,"DIV",{class:!0});var hd=s(Ko);M(MW.$$.fragment,hd),A4t=i(hd),Gm=n(hd,"P",{});var $9e=s(Gm);R4t=r($9e,`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 `),ype=n($9e,"A",{href:!0});var ICn=s(ype);k4t=r(ICn,"from_pretrained()"),ICn.forEach(t),S4t=r($9e," class method or the "),Cpe=n($9e,"A",{href:!0});var NCn=s(Cpe);x4t=r(NCn,"from_config()"),NCn.forEach(t),G4t=r($9e,` class
method.`),$9e.forEach(t),B4t=i(hd),TW=n(hd,"P",{});var RNo=s(TW);I4t=r(RNo,"This class cannot be instantiated directly using "),$Ue=n(RNo,"CODE",{});var $Cn=s($Ue);N4t=r($Cn,"__init__()"),$Cn.forEach(t),$4t=r(RNo," (throws an error)."),RNo.forEach(t),j4t=i(hd),zt=n(hd,"DIV",{class:!0});var Wj=s(zt);M(FW.$$.fragment,Wj),P4t=i(Wj),jUe=n(Wj,"P",{});var jCn=s(jUe);Z4t=r(jCn,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),jCn.forEach(t),V4t=i(Wj),Bm=n(Wj,"P",{});var j9e=s(Bm);W4t=r(j9e,`Note:
Loading a model from its configuration file does `),PUe=n(j9e,"STRONG",{});var PCn=s(PUe);J4t=r(PCn,"not"),PCn.forEach(t),U4t=r(j9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),wpe=n(j9e,"A",{href:!0});var ZCn=s(wpe);X4t=r(ZCn,"from_pretrained()"),ZCn.forEach(t),D4t=r(j9e," to load the model weights."),j9e.forEach(t),q4t=i(Wj),M(wC.$$.fragment,Wj),Wj.forEach(t),O4t=i(hd),co=n(hd,"DIV",{class:!0});var tn=s(co);M(EW.$$.fragment,tn),Q4t=i(tn),ZUe=n(tn,"P",{});var VCn=s(ZUe);Y4t=r(VCn,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),VCn.forEach(t),z4t=i(tn),On=n(tn,"P",{});var Jj=s(On);H4t=r(Jj,"The model class to instantiate is selected based on the "),VUe=n(Jj,"CODE",{});var WCn=s(VUe);K4t=r(WCn,"model_type"),WCn.forEach(t),eMt=r(Jj,` property of the config object (either
passed as an argument or loaded from `),WUe=n(Jj,"CODE",{});var JCn=s(WUe);oMt=r(JCn,"pretrained_model_name_or_path"),JCn.forEach(t),rMt=r(Jj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),JUe=n(Jj,"CODE",{});var UCn=s(JUe);tMt=r(UCn,"pretrained_model_name_or_path"),UCn.forEach(t),aMt=r(Jj,":"),Jj.forEach(t),nMt=i(tn),K=n(tn,"UL",{});var ee=s(K);LC=n(ee,"LI",{});var k5o=s(LC);UUe=n(k5o,"STRONG",{});var XCn=s(UUe);sMt=r(XCn,"albert"),XCn.forEach(t),lMt=r(k5o," \u2014 "),Lpe=n(k5o,"A",{href:!0});var DCn=s(Lpe);iMt=r(DCn,"AlbertForMaskedLM"),DCn.forEach(t),dMt=r(k5o," (ALBERT model)"),k5o.forEach(t),cMt=i(ee),AC=n(ee,"LI",{});var S5o=s(AC);XUe=n(S5o,"STRONG",{});var qCn=s(XUe);mMt=r(qCn,"bart"),qCn.forEach(t),fMt=r(S5o," \u2014 "),Ape=n(S5o,"A",{href:!0});var OCn=s(Ape);hMt=r(OCn,"BartForConditionalGeneration"),OCn.forEach(t),gMt=r(S5o," (BART model)"),S5o.forEach(t),uMt=i(ee),RC=n(ee,"LI",{});var x5o=s(RC);DUe=n(x5o,"STRONG",{});var QCn=s(DUe);pMt=r(QCn,"bert"),QCn.forEach(t),_Mt=r(x5o," \u2014 "),Rpe=n(x5o,"A",{href:!0});var YCn=s(Rpe);vMt=r(YCn,"BertForMaskedLM"),YCn.forEach(t),bMt=r(x5o," (BERT model)"),x5o.forEach(t),MMt=i(ee),kC=n(ee,"LI",{});var G5o=s(kC);qUe=n(G5o,"STRONG",{});var zCn=s(qUe);TMt=r(zCn,"big_bird"),zCn.forEach(t),FMt=r(G5o," \u2014 "),kpe=n(G5o,"A",{href:!0});var HCn=s(kpe);EMt=r(HCn,"BigBirdForMaskedLM"),HCn.forEach(t),yMt=r(G5o," (BigBird model)"),G5o.forEach(t),CMt=i(ee),SC=n(ee,"LI",{});var B5o=s(SC);OUe=n(B5o,"STRONG",{});var KCn=s(OUe);wMt=r(KCn,"camembert"),KCn.forEach(t),LMt=r(B5o," \u2014 "),Spe=n(B5o,"A",{href:!0});var ewn=s(Spe);AMt=r(ewn,"CamembertForMaskedLM"),ewn.forEach(t),RMt=r(B5o," (CamemBERT model)"),B5o.forEach(t),kMt=i(ee),xC=n(ee,"LI",{});var I5o=s(xC);QUe=n(I5o,"STRONG",{});var own=s(QUe);SMt=r(own,"convbert"),own.forEach(t),xMt=r(I5o," \u2014 "),xpe=n(I5o,"A",{href:!0});var rwn=s(xpe);GMt=r(rwn,"ConvBertForMaskedLM"),rwn.forEach(t),BMt=r(I5o," (ConvBERT model)"),I5o.forEach(t),IMt=i(ee),GC=n(ee,"LI",{});var N5o=s(GC);YUe=n(N5o,"STRONG",{});var twn=s(YUe);NMt=r(twn,"data2vec-text"),twn.forEach(t),$Mt=r(N5o," \u2014 "),Gpe=n(N5o,"A",{href:!0});var awn=s(Gpe);jMt=r(awn,"Data2VecTextForMaskedLM"),awn.forEach(t),PMt=r(N5o," (Data2VecText model)"),N5o.forEach(t),ZMt=i(ee),BC=n(ee,"LI",{});var $5o=s(BC);zUe=n($5o,"STRONG",{});var nwn=s(zUe);VMt=r(nwn,"deberta"),nwn.forEach(t),WMt=r($5o," \u2014 "),Bpe=n($5o,"A",{href:!0});var swn=s(Bpe);JMt=r(swn,"DebertaForMaskedLM"),swn.forEach(t),UMt=r($5o," (DeBERTa model)"),$5o.forEach(t),XMt=i(ee),IC=n(ee,"LI",{});var j5o=s(IC);HUe=n(j5o,"STRONG",{});var lwn=s(HUe);DMt=r(lwn,"deberta-v2"),lwn.forEach(t),qMt=r(j5o," \u2014 "),Ipe=n(j5o,"A",{href:!0});var iwn=s(Ipe);OMt=r(iwn,"DebertaV2ForMaskedLM"),iwn.forEach(t),QMt=r(j5o," (DeBERTa-v2 model)"),j5o.forEach(t),YMt=i(ee),NC=n(ee,"LI",{});var P5o=s(NC);KUe=n(P5o,"STRONG",{});var dwn=s(KUe);zMt=r(dwn,"distilbert"),dwn.forEach(t),HMt=r(P5o," \u2014 "),Npe=n(P5o,"A",{href:!0});var cwn=s(Npe);KMt=r(cwn,"DistilBertForMaskedLM"),cwn.forEach(t),eTt=r(P5o," (DistilBERT model)"),P5o.forEach(t),oTt=i(ee),$C=n(ee,"LI",{});var Z5o=s($C);eXe=n(Z5o,"STRONG",{});var mwn=s(eXe);rTt=r(mwn,"electra"),mwn.forEach(t),tTt=r(Z5o," \u2014 "),$pe=n(Z5o,"A",{href:!0});var fwn=s($pe);aTt=r(fwn,"ElectraForMaskedLM"),fwn.forEach(t),nTt=r(Z5o," (ELECTRA model)"),Z5o.forEach(t),sTt=i(ee),jC=n(ee,"LI",{});var V5o=s(jC);oXe=n(V5o,"STRONG",{});var hwn=s(oXe);lTt=r(hwn,"ernie"),hwn.forEach(t),iTt=r(V5o," \u2014 "),jpe=n(V5o,"A",{href:!0});var gwn=s(jpe);dTt=r(gwn,"ErnieForMaskedLM"),gwn.forEach(t),cTt=r(V5o," (ERNIE model)"),V5o.forEach(t),mTt=i(ee),PC=n(ee,"LI",{});var W5o=s(PC);rXe=n(W5o,"STRONG",{});var uwn=s(rXe);fTt=r(uwn,"esm"),uwn.forEach(t),hTt=r(W5o," \u2014 "),Ppe=n(W5o,"A",{href:!0});var pwn=s(Ppe);gTt=r(pwn,"EsmForMaskedLM"),pwn.forEach(t),uTt=r(W5o," (ESM model)"),W5o.forEach(t),pTt=i(ee),ZC=n(ee,"LI",{});var J5o=s(ZC);tXe=n(J5o,"STRONG",{});var _wn=s(tXe);_Tt=r(_wn,"flaubert"),_wn.forEach(t),vTt=r(J5o," \u2014 "),Zpe=n(J5o,"A",{href:!0});var vwn=s(Zpe);bTt=r(vwn,"FlaubertWithLMHeadModel"),vwn.forEach(t),MTt=r(J5o," (FlauBERT model)"),J5o.forEach(t),TTt=i(ee),VC=n(ee,"LI",{});var U5o=s(VC);aXe=n(U5o,"STRONG",{});var bwn=s(aXe);FTt=r(bwn,"fnet"),bwn.forEach(t),ETt=r(U5o," \u2014 "),Vpe=n(U5o,"A",{href:!0});var Mwn=s(Vpe);yTt=r(Mwn,"FNetForMaskedLM"),Mwn.forEach(t),CTt=r(U5o," (FNet model)"),U5o.forEach(t),wTt=i(ee),WC=n(ee,"LI",{});var X5o=s(WC);nXe=n(X5o,"STRONG",{});var Twn=s(nXe);LTt=r(Twn,"funnel"),Twn.forEach(t),ATt=r(X5o," \u2014 "),Wpe=n(X5o,"A",{href:!0});var Fwn=s(Wpe);RTt=r(Fwn,"FunnelForMaskedLM"),Fwn.forEach(t),kTt=r(X5o," (Funnel Transformer model)"),X5o.forEach(t),STt=i(ee),JC=n(ee,"LI",{});var D5o=s(JC);sXe=n(D5o,"STRONG",{});var Ewn=s(sXe);xTt=r(Ewn,"ibert"),Ewn.forEach(t),GTt=r(D5o," \u2014 "),Jpe=n(D5o,"A",{href:!0});var ywn=s(Jpe);BTt=r(ywn,"IBertForMaskedLM"),ywn.forEach(t),ITt=r(D5o," (I-BERT model)"),D5o.forEach(t),NTt=i(ee),UC=n(ee,"LI",{});var q5o=s(UC);lXe=n(q5o,"STRONG",{});var Cwn=s(lXe);$Tt=r(Cwn,"layoutlm"),Cwn.forEach(t),jTt=r(q5o," \u2014 "),Upe=n(q5o,"A",{href:!0});var wwn=s(Upe);PTt=r(wwn,"LayoutLMForMaskedLM"),wwn.forEach(t),ZTt=r(q5o," (LayoutLM model)"),q5o.forEach(t),VTt=i(ee),XC=n(ee,"LI",{});var O5o=s(XC);iXe=n(O5o,"STRONG",{});var Lwn=s(iXe);WTt=r(Lwn,"longformer"),Lwn.forEach(t),JTt=r(O5o," \u2014 "),Xpe=n(O5o,"A",{href:!0});var Awn=s(Xpe);UTt=r(Awn,"LongformerForMaskedLM"),Awn.forEach(t),XTt=r(O5o," (Longformer model)"),O5o.forEach(t),DTt=i(ee),DC=n(ee,"LI",{});var Q5o=s(DC);dXe=n(Q5o,"STRONG",{});var Rwn=s(dXe);qTt=r(Rwn,"luke"),Rwn.forEach(t),OTt=r(Q5o," \u2014 "),Dpe=n(Q5o,"A",{href:!0});var kwn=s(Dpe);QTt=r(kwn,"LukeForMaskedLM"),kwn.forEach(t),YTt=r(Q5o," (LUKE model)"),Q5o.forEach(t),zTt=i(ee),qC=n(ee,"LI",{});var Y5o=s(qC);cXe=n(Y5o,"STRONG",{});var Swn=s(cXe);HTt=r(Swn,"mbart"),Swn.forEach(t),KTt=r(Y5o," \u2014 "),qpe=n(Y5o,"A",{href:!0});var xwn=s(qpe);eFt=r(xwn,"MBartForConditionalGeneration"),xwn.forEach(t),oFt=r(Y5o," (mBART model)"),Y5o.forEach(t),rFt=i(ee),OC=n(ee,"LI",{});var z5o=s(OC);mXe=n(z5o,"STRONG",{});var Gwn=s(mXe);tFt=r(Gwn,"mega"),Gwn.forEach(t),aFt=r(z5o," \u2014 "),Ope=n(z5o,"A",{href:!0});var Bwn=s(Ope);nFt=r(Bwn,"MegaForMaskedLM"),Bwn.forEach(t),sFt=r(z5o," (MEGA model)"),z5o.forEach(t),lFt=i(ee),QC=n(ee,"LI",{});var H5o=s(QC);fXe=n(H5o,"STRONG",{});var Iwn=s(fXe);iFt=r(Iwn,"megatron-bert"),Iwn.forEach(t),dFt=r(H5o," \u2014 "),Qpe=n(H5o,"A",{href:!0});var Nwn=s(Qpe);cFt=r(Nwn,"MegatronBertForMaskedLM"),Nwn.forEach(t),mFt=r(H5o," (Megatron-BERT model)"),H5o.forEach(t),fFt=i(ee),YC=n(ee,"LI",{});var K5o=s(YC);hXe=n(K5o,"STRONG",{});var $wn=s(hXe);hFt=r($wn,"mobilebert"),$wn.forEach(t),gFt=r(K5o," \u2014 "),Ype=n(K5o,"A",{href:!0});var jwn=s(Ype);uFt=r(jwn,"MobileBertForMaskedLM"),jwn.forEach(t),pFt=r(K5o," (MobileBERT model)"),K5o.forEach(t),_Ft=i(ee),zC=n(ee,"LI",{});var e9o=s(zC);gXe=n(e9o,"STRONG",{});var Pwn=s(gXe);vFt=r(Pwn,"mpnet"),Pwn.forEach(t),bFt=r(e9o," \u2014 "),zpe=n(e9o,"A",{href:!0});var 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method.`),P9e.forEach(t),Ayt=i(gd),wW=n(gd,"P",{});var SNo=s(wW);Ryt=r(SNo,"This class cannot be instantiated directly using "),ZXe=n(SNo,"CODE",{});var B6n=s(ZXe);kyt=r(B6n,"__init__()"),B6n.forEach(t),Syt=r(SNo," (throws an error)."),SNo.forEach(t),xyt=i(gd),Ht=n(gd,"DIV",{class:!0});var Uj=s(Ht);M(LW.$$.fragment,Uj),Gyt=i(Uj),VXe=n(Uj,"P",{});var I6n=s(VXe);Byt=r(I6n,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),I6n.forEach(t),Iyt=i(Uj),$m=n(Uj,"P",{});var Z9e=s($m);Nyt=r(Z9e,`Note:
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model\u2019s configuration. Use `),b_e=n(Z9e,"A",{href:!0});var $6n=s(b_e);Pyt=r($6n,"from_pretrained()"),$6n.forEach(t),Zyt=r(Z9e," to load the model weights."),Z9e.forEach(t),Vyt=i(Uj),M(Tw.$$.fragment,Uj),Uj.forEach(t),Wyt=i(gd),dt=n(gd,"DIV",{class:!0});var ud=s(dt);M(AW.$$.fragment,ud),Jyt=i(ud),JXe=n(ud,"P",{});var j6n=s(JXe);Uyt=r(j6n,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),j6n.forEach(t),Xyt=i(ud),Qn=n(ud,"P",{});var Xj=s(Qn);Dyt=r(Xj,"The model class to instantiate is selected based on the "),UXe=n(Xj,"CODE",{});var P6n=s(UXe);qyt=r(P6n,"model_type"),P6n.forEach(t),Oyt=r(Xj,` property of the config object (either
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falling back to using pattern matching on `),DXe=n(Xj,"CODE",{});var V6n=s(DXe);zyt=r(V6n,"pretrained_model_name_or_path"),V6n.forEach(t),Hyt=r(Xj,":"),Xj.forEach(t),Kyt=i(ud),pe=n(ud,"UL",{});var Me=s(pe);Fw=n(Me,"LI",{});var F9o=s(Fw);qXe=n(F9o,"STRONG",{});var W6n=s(qXe);e5t=r(W6n,"albert"),W6n.forEach(t),o5t=r(F9o," \u2014 "),M_e=n(F9o,"A",{href:!0});var J6n=s(M_e);r5t=r(J6n,"TFAlbertForMaskedLM"),J6n.forEach(t),t5t=r(F9o," (ALBERT model)"),F9o.forEach(t),a5t=i(Me),Ew=n(Me,"LI",{});var E9o=s(Ew);OXe=n(E9o,"STRONG",{});var U6n=s(OXe);n5t=r(U6n,"bert"),U6n.forEach(t),s5t=r(E9o," \u2014 "),T_e=n(E9o,"A",{href:!0});var X6n=s(T_e);l5t=r(X6n,"TFBertForMaskedLM"),X6n.forEach(t),i5t=r(E9o," (BERT model)"),E9o.forEach(t),d5t=i(Me),yw=n(Me,"LI",{});var y9o=s(yw);QXe=n(y9o,"STRONG",{});var D6n=s(QXe);c5t=r(D6n,"camembert"),D6n.forEach(t),m5t=r(y9o," \u2014 "),F_e=n(y9o,"A",{href:!0});var q6n=s(F_e);f5t=r(q6n,"TFCamembertForMaskedLM"),q6n.forEach(t),h5t=r(y9o," (CamemBERT 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x9o=s(xw);tDe=n(x9o,"STRONG",{});var i8n=s(tDe);Y5t=r(i8n,"funnel"),i8n.forEach(t),z5t=r(x9o," \u2014 "),k_e=n(x9o,"A",{href:!0});var d8n=s(k_e);H5t=r(d8n,"TFFunnelForMaskedLM"),d8n.forEach(t),K5t=r(x9o," (Funnel Transformer model)"),x9o.forEach(t),e9t=i(Me),Gw=n(Me,"LI",{});var G9o=s(Gw);aDe=n(G9o,"STRONG",{});var c8n=s(aDe);o9t=r(c8n,"layoutlm"),c8n.forEach(t),r9t=r(G9o," \u2014 "),S_e=n(G9o,"A",{href:!0});var m8n=s(S_e);t9t=r(m8n,"TFLayoutLMForMaskedLM"),m8n.forEach(t),a9t=r(G9o," (LayoutLM model)"),G9o.forEach(t),n9t=i(Me),Bw=n(Me,"LI",{});var B9o=s(Bw);nDe=n(B9o,"STRONG",{});var f8n=s(nDe);s9t=r(f8n,"longformer"),f8n.forEach(t),l9t=r(B9o," \u2014 "),x_e=n(B9o,"A",{href:!0});var h8n=s(x_e);i9t=r(h8n,"TFLongformerForMaskedLM"),h8n.forEach(t),d9t=r(B9o," (Longformer model)"),B9o.forEach(t),c9t=i(Me),Iw=n(Me,"LI",{});var I9o=s(Iw);sDe=n(I9o,"STRONG",{});var g8n=s(sDe);m9t=r(g8n,"mobilebert"),g8n.forEach(t),f9t=r(I9o," \u2014 "),G_e=n(I9o,"A",{href:!0});var u8n=s(G_e);h9t=r(u8n,"TFMobileBertForMaskedLM"),u8n.forEach(t),g9t=r(I9o," (MobileBERT model)"),I9o.forEach(t),u9t=i(Me),Nw=n(Me,"LI",{});var N9o=s(Nw);lDe=n(N9o,"STRONG",{});var p8n=s(lDe);p9t=r(p8n,"mpnet"),p8n.forEach(t),_9t=r(N9o," \u2014 "),B_e=n(N9o,"A",{href:!0});var _8n=s(B_e);v9t=r(_8n,"TFMPNetForMaskedLM"),_8n.forEach(t),b9t=r(N9o," (MPNet model)"),N9o.forEach(t),M9t=i(Me),$w=n(Me,"LI",{});var $9o=s($w);iDe=n($9o,"STRONG",{});var v8n=s(iDe);T9t=r(v8n,"rembert"),v8n.forEach(t),F9t=r($9o," \u2014 "),I_e=n($9o,"A",{href:!0});var b8n=s(I_e);E9t=r(b8n,"TFRemBertForMaskedLM"),b8n.forEach(t),y9t=r($9o," (RemBERT model)"),$9o.forEach(t),C9t=i(Me),jw=n(Me,"LI",{});var j9o=s(jw);dDe=n(j9o,"STRONG",{});var M8n=s(dDe);w9t=r(M8n,"roberta"),M8n.forEach(t),L9t=r(j9o," \u2014 "),N_e=n(j9o,"A",{href:!0});var T8n=s(N_e);A9t=r(T8n,"TFRobertaForMaskedLM"),T8n.forEach(t),R9t=r(j9o," (RoBERTa model)"),j9o.forEach(t),k9t=i(Me),Pw=n(Me,"LI",{});var P9o=s(Pw);cDe=n(P9o,"STRONG",{});var F8n=s(cDe);S9t=r(F8n,"roberta-prelayernorm"),F8n.forEach(t),x9t=r(P9o," \u2014 "),$_e=n(P9o,"A",{href:!0});var E8n=s($_e);G9t=r(E8n,"TFRobertaPreLayerNormForMaskedLM"),E8n.forEach(t),B9t=r(P9o," (RoBERTa-PreLayerNorm model)"),P9o.forEach(t),I9t=i(Me),Zw=n(Me,"LI",{});var Z9o=s(Zw);mDe=n(Z9o,"STRONG",{});var y8n=s(mDe);N9t=r(y8n,"roformer"),y8n.forEach(t),$9t=r(Z9o," \u2014 "),j_e=n(Z9o,"A",{href:!0});var C8n=s(j_e);j9t=r(C8n,"TFRoFormerForMaskedLM"),C8n.forEach(t),P9t=r(Z9o," (RoFormer model)"),Z9o.forEach(t),Z9t=i(Me),Vw=n(Me,"LI",{});var V9o=s(Vw);fDe=n(V9o,"STRONG",{});var w8n=s(fDe);V9t=r(w8n,"tapas"),w8n.forEach(t),W9t=r(V9o," \u2014 "),P_e=n(V9o,"A",{href:!0});var L8n=s(P_e);J9t=r(L8n,"TFTapasForMaskedLM"),L8n.forEach(t),U9t=r(V9o," (TAPAS model)"),V9o.forEach(t),X9t=i(Me),Ww=n(Me,"LI",{});var W9o=s(Ww);hDe=n(W9o,"STRONG",{});var A8n=s(hDe);D9t=r(A8n,"xlm"),A8n.forEach(t),q9t=r(W9o," \u2014 "),Z_e=n(W9o,"A",{href:!0});var 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with the `),W_e=n(V9e,"A",{href:!0});var I8n=s(W_e);sCt=r(I8n,"from_pretrained()"),I8n.forEach(t),lCt=r(V9e," class method or the "),J_e=n(V9e,"A",{href:!0});var N8n=s(J_e);iCt=r(N8n,"from_config()"),N8n.forEach(t),dCt=r(V9e,` class
method.`),V9e.forEach(t),cCt=i(pd),SW=n(pd,"P",{});var GNo=s(SW);mCt=r(GNo,"This class cannot be instantiated directly using "),_De=n(GNo,"CODE",{});var $8n=s(_De);fCt=r($8n,"__init__()"),$8n.forEach(t),hCt=r(GNo," (throws an error)."),GNo.forEach(t),gCt=i(pd),Kt=n(pd,"DIV",{class:!0});var Dj=s(Kt);M(xW.$$.fragment,Dj),uCt=i(Dj),vDe=n(Dj,"P",{});var j8n=s(vDe);pCt=r(j8n,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),j8n.forEach(t),_Ct=i(Dj),Zm=n(Dj,"P",{});var W9e=s(Zm);vCt=r(W9e,`Note:
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model\u2019s configuration. Use `),U_e=n(W9e,"A",{href:!0});var Z8n=s(U_e);TCt=r(Z8n,"from_pretrained()"),Z8n.forEach(t),FCt=r(W9e," to load the model weights."),W9e.forEach(t),ECt=i(Dj),M(Dw.$$.fragment,Dj),Dj.forEach(t),yCt=i(pd),ct=n(pd,"DIV",{class:!0});var _d=s(ct);M(GW.$$.fragment,_d),CCt=i(_d),MDe=n(_d,"P",{});var V8n=s(MDe);wCt=r(V8n,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),V8n.forEach(t),LCt=i(_d),Yn=n(_d,"P",{});var qj=s(Yn);ACt=r(qj,"The model class to instantiate is selected based on the "),TDe=n(qj,"CODE",{});var W8n=s(TDe);RCt=r(W8n,"model_type"),W8n.forEach(t),kCt=r(qj,` property of the config object (either
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falling back to using pattern matching on `),EDe=n(qj,"CODE",{});var U8n=s(EDe);GCt=r(U8n,"pretrained_model_name_or_path"),U8n.forEach(t),BCt=r(qj,":"),qj.forEach(t),ICt=i(_d),ke=n(_d,"UL",{});var Pe=s(ke);qw=n(Pe,"LI",{});var U9o=s(qw);yDe=n(U9o,"STRONG",{});var X8n=s(yDe);NCt=r(X8n,"albert"),X8n.forEach(t),$Ct=r(U9o," \u2014 "),X_e=n(U9o,"A",{href:!0});var D8n=s(X_e);jCt=r(D8n,"FlaxAlbertForMaskedLM"),D8n.forEach(t),PCt=r(U9o," (ALBERT model)"),U9o.forEach(t),ZCt=i(Pe),Ow=n(Pe,"LI",{});var X9o=s(Ow);CDe=n(X9o,"STRONG",{});var q8n=s(CDe);VCt=r(q8n,"bart"),q8n.forEach(t),WCt=r(X9o," \u2014 "),D_e=n(X9o,"A",{href:!0});var O8n=s(D_e);JCt=r(O8n,"FlaxBartForConditionalGeneration"),O8n.forEach(t),UCt=r(X9o," (BART model)"),X9o.forEach(t),XCt=i(Pe),Qw=n(Pe,"LI",{});var D9o=s(Qw);wDe=n(D9o,"STRONG",{});var Q8n=s(wDe);DCt=r(Q8n,"bert"),Q8n.forEach(t),qCt=r(D9o," \u2014 "),q_e=n(D9o,"A",{href:!0});var Y8n=s(q_e);OCt=r(Y8n,"FlaxBertForMaskedLM"),Y8n.forEach(t),QCt=r(D9o," (BERT model)"),D9o.forEach(t),YCt=i(Pe),Yw=n(Pe,"LI",{});var q9o=s(Yw);LDe=n(q9o,"STRONG",{});var z8n=s(LDe);zCt=r(z8n,"big_bird"),z8n.forEach(t),HCt=r(q9o," \u2014 "),O_e=n(q9o,"A",{href:!0});var H8n=s(O_e);KCt=r(H8n,"FlaxBigBirdForMaskedLM"),H8n.forEach(t),ewt=r(q9o," (BigBird model)"),q9o.forEach(t),owt=i(Pe),zw=n(Pe,"LI",{});var O9o=s(zw);ADe=n(O9o,"STRONG",{});var K8n=s(ADe);rwt=r(K8n,"distilbert"),K8n.forEach(t),twt=r(O9o," \u2014 "),Q_e=n(O9o,"A",{href:!0});var e7n=s(Q_e);awt=r(e7n,"FlaxDistilBertForMaskedLM"),e7n.forEach(t),nwt=r(O9o," (DistilBERT model)"),O9o.forEach(t),swt=i(Pe),Hw=n(Pe,"LI",{});var Q9o=s(Hw);RDe=n(Q9o,"STRONG",{});var o7n=s(RDe);lwt=r(o7n,"electra"),o7n.forEach(t),iwt=r(Q9o," \u2014 "),Y_e=n(Q9o,"A",{href:!0});var r7n=s(Y_e);dwt=r(r7n,"FlaxElectraForMaskedLM"),r7n.forEach(t),cwt=r(Q9o," (ELECTRA model)"),Q9o.forEach(t),mwt=i(Pe),Kw=n(Pe,"LI",{});var Y9o=s(Kw);kDe=n(Y9o,"STRONG",{});var t7n=s(kDe);fwt=r(t7n,"mbart"),t7n.forEach(t),hwt=r(Y9o," \u2014 "),z_e=n(Y9o,"A",{href:!0});var a7n=s(z_e);gwt=r(a7n,"FlaxMBartForConditionalGeneration"),a7n.forEach(t),uwt=r(Y9o," (mBART model)"),Y9o.forEach(t),pwt=i(Pe),e6=n(Pe,"LI",{});var z9o=s(e6);SDe=n(z9o,"STRONG",{});var n7n=s(SDe);_wt=r(n7n,"roberta"),n7n.forEach(t),vwt=r(z9o," \u2014 "),H_e=n(z9o,"A",{href:!0});var s7n=s(H_e);bwt=r(s7n,"FlaxRobertaForMaskedLM"),s7n.forEach(t),Mwt=r(z9o," (RoBERTa model)"),z9o.forEach(t),Twt=i(Pe),o6=n(Pe,"LI",{});var H9o=s(o6);xDe=n(H9o,"STRONG",{});var l7n=s(xDe);Fwt=r(l7n,"roberta-prelayernorm"),l7n.forEach(t),Ewt=r(H9o," \u2014 "),K_e=n(H9o,"A",{href:!0});var i7n=s(K_e);ywt=r(i7n,"FlaxRobertaPreLayerNormForMaskedLM"),i7n.forEach(t),Cwt=r(H9o," (RoBERTa-PreLayerNorm model)"),H9o.forEach(t),wwt=i(Pe),r6=n(Pe,"LI",{});var K9o=s(r6);GDe=n(K9o,"STRONG",{});var d7n=s(GDe);Lwt=r(d7n,"roformer"),d7n.forEach(t),Awt=r(K9o," \u2014 "),e2e=n(K9o,"A",{href:!0});var c7n=s(e2e);Rwt=r(c7n,"FlaxRoFormerForMaskedLM"),c7n.forEach(t),kwt=r(K9o," (RoFormer model)"),K9o.forEach(t),Swt=i(Pe),t6=n(Pe,"LI",{});var eCo=s(t6);BDe=n(eCo,"STRONG",{});var m7n=s(BDe);xwt=r(m7n,"xlm-roberta"),m7n.forEach(t),Gwt=r(eCo," \u2014 "),o2e=n(eCo,"A",{href:!0});var f7n=s(o2e);Bwt=r(f7n,"FlaxXLMRobertaForMaskedLM"),f7n.forEach(t),Iwt=r(eCo," (XLM-RoBERTa model)"),eCo.forEach(t),Pe.forEach(t),Nwt=i(_d),M(a6.$$.fragment,_d),_d.forEach(t),pd.forEach(t),GGo=i(m),Vm=n(m,"H3",{class:!0});var BNo=s(Vm);n6=n(BNo,"A",{id:!0,class:!0,href:!0});var h7n=s(n6);IDe=n(h7n,"SPAN",{});var g7n=s(IDe);M(BW.$$.fragment,g7n),g7n.forEach(t),h7n.forEach(t),$wt=i(BNo),NDe=n(BNo,"SPAN",{});var u7n=s(NDe);jwt=r(u7n,"AutoModelForMaskGeneration"),u7n.forEach(t),BNo.forEach(t),BGo=i(m),IW=n(m,"DIV",{class:!0});var p7n=s(IW);M(NW.$$.fragment,p7n),p7n.forEach(t),IGo=i(m),Wm=n(m,"H3",{class:!0});var INo=s(Wm);s6=n(INo,"A",{id:!0,class:!0,href:!0});var _7n=s(s6);$De=n(_7n,"SPAN",{});var v7n=s($De);M($W.$$.fragment,v7n),v7n.forEach(t),_7n.forEach(t),Pwt=i(INo),jDe=n(INo,"SPAN",{});var b7n=s(jDe);Zwt=r(b7n,"TFAutoModelForMaskGeneration"),b7n.forEach(t),INo.forEach(t),NGo=i(m),jW=n(m,"DIV",{class:!0});var M7n=s(jW);M(PW.$$.fragment,M7n),M7n.forEach(t),$Go=i(m),Jm=n(m,"H3",{class:!0});var NNo=s(Jm);l6=n(NNo,"A",{id:!0,class:!0,href:!0});var T7n=s(l6);PDe=n(T7n,"SPAN",{});var F7n=s(PDe);M(ZW.$$.fragment,F7n),F7n.forEach(t),T7n.forEach(t),Vwt=i(NNo),ZDe=n(NNo,"SPAN",{});var E7n=s(ZDe);Wwt=r(E7n,"AutoModelForSeq2SeqLM"),E7n.forEach(t),NNo.forEach(t),jGo=i(m),rr=n(m,"DIV",{class:!0});var vd=s(rr);M(VW.$$.fragment,vd),Jwt=i(vd),Um=n(vd,"P",{});var J9e=s(Um);Uwt=r(J9e,`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
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model\u2019s configuration. Use `),a2e=n(U9e,"A",{href:!0});var R7n=s(a2e);s6t=r(R7n,"from_pretrained()"),R7n.forEach(t),l6t=r(U9e," to load the model weights."),U9e.forEach(t),i6t=i(Oj),M(i6.$$.fragment,Oj),Oj.forEach(t),d6t=i(vd),mo=n(vd,"DIV",{class:!0});var an=s(mo);M(UW.$$.fragment,an),c6t=i(an),UDe=n(an,"P",{});var k7n=s(UDe);m6t=r(k7n,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),k7n.forEach(t),f6t=i(an),zn=n(an,"P",{});var Qj=s(zn);h6t=r(Qj,"The model class to instantiate is selected based on the "),XDe=n(Qj,"CODE",{});var S7n=s(XDe);g6t=r(S7n,"model_type"),S7n.forEach(t),u6t=r(Qj,` property of the config object (either
passed as an argument or loaded from `),DDe=n(Qj,"CODE",{});var x7n=s(DDe);p6t=r(x7n,"pretrained_model_name_or_path"),x7n.forEach(t),_6t=r(Qj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),qDe=n(Qj,"CODE",{});var G7n=s(qDe);v6t=r(G7n,"pretrained_model_name_or_path"),G7n.forEach(t),b6t=r(Qj,":"),Qj.forEach(t),M6t=i(an),ge=n(an,"UL",{});var ve=s(ge);d6=n(ve,"LI",{});var oCo=s(d6);ODe=n(oCo,"STRONG",{});var B7n=s(ODe);T6t=r(B7n,"bart"),B7n.forEach(t),F6t=r(oCo," \u2014 "),n2e=n(oCo,"A",{href:!0});var I7n=s(n2e);E6t=r(I7n,"BartForConditionalGeneration"),I7n.forEach(t),y6t=r(oCo," (BART model)"),oCo.forEach(t),C6t=i(ve),c6=n(ve,"LI",{});var rCo=s(c6);QDe=n(rCo,"STRONG",{});var N7n=s(QDe);w6t=r(N7n,"bigbird_pegasus"),N7n.forEach(t),L6t=r(rCo," \u2014 "),s2e=n(rCo,"A",{href:!0});var $7n=s(s2e);A6t=r($7n,"BigBirdPegasusForConditionalGeneration"),$7n.forEach(t),R6t=r(rCo," (BigBird-Pegasus model)"),rCo.forEach(t),k6t=i(ve),m6=n(ve,"LI",{});var tCo=s(m6);YDe=n(tCo,"STRONG",{});var j7n=s(YDe);S6t=r(j7n,"blenderbot"),j7n.forEach(t),x6t=r(tCo," \u2014 "),l2e=n(tCo,"A",{href:!0});var P7n=s(l2e);G6t=r(P7n,"BlenderbotForConditionalGeneration"),P7n.forEach(t),B6t=r(tCo," (Blenderbot model)"),tCo.forEach(t),I6t=i(ve),f6=n(ve,"LI",{});var aCo=s(f6);zDe=n(aCo,"STRONG",{});var Z7n=s(zDe);N6t=r(Z7n,"blenderbot-small"),Z7n.forEach(t),$6t=r(aCo," \u2014 "),i2e=n(aCo,"A",{href:!0});var V7n=s(i2e);j6t=r(V7n,"BlenderbotSmallForConditionalGeneration"),V7n.forEach(t),P6t=r(aCo," (BlenderbotSmall model)"),aCo.forEach(t),Z6t=i(ve),h6=n(ve,"LI",{});var nCo=s(h6);HDe=n(nCo,"STRONG",{});var W7n=s(HDe);V6t=r(W7n,"encoder-decoder"),W7n.forEach(t),W6t=r(nCo," \u2014 "),d2e=n(nCo,"A",{href:!0});var J7n=s(d2e);J6t=r(J7n,"EncoderDecoderModel"),J7n.forEach(t),U6t=r(nCo," (Encoder decoder model)"),nCo.forEach(t),X6t=i(ve),g6=n(ve,"LI",{});var sCo=s(g6);KDe=n(sCo,"STRONG",{});var U7n=s(KDe);D6t=r(U7n,"fsmt"),U7n.forEach(t),q6t=r(sCo," \u2014 "),c2e=n(sCo,"A",{href:!0});var X7n=s(c2e);O6t=r(X7n,"FSMTForConditionalGeneration"),X7n.forEach(t),Q6t=r(sCo," (FairSeq Machine-Translation model)"),sCo.forEach(t),Y6t=i(ve),u6=n(ve,"LI",{});var lCo=s(u6);eqe=n(lCo,"STRONG",{});var D7n=s(eqe);z6t=r(D7n,"gptsan-japanese"),D7n.forEach(t),H6t=r(lCo," \u2014 "),m2e=n(lCo,"A",{href:!0});var q7n=s(m2e);K6t=r(q7n,"GPTSanJapaneseForConditionalGeneration"),q7n.forEach(t),e8t=r(lCo," (GPTSAN-japanese model)"),lCo.forEach(t),o8t=i(ve),p6=n(ve,"LI",{});var iCo=s(p6);oqe=n(iCo,"STRONG",{});var O7n=s(oqe);r8t=r(O7n,"led"),O7n.forEach(t),t8t=r(iCo," \u2014 "),f2e=n(iCo,"A",{href:!0});var Q7n=s(f2e);a8t=r(Q7n,"LEDForConditionalGeneration"),Q7n.forEach(t),n8t=r(iCo," (LED model)"),iCo.forEach(t),s8t=i(ve),_6=n(ve,"LI",{});var dCo=s(_6);rqe=n(dCo,"STRONG",{});var Y7n=s(rqe);l8t=r(Y7n,"longt5"),Y7n.forEach(t),i8t=r(dCo," \u2014 "),h2e=n(dCo,"A",{href:!0});var z7n=s(h2e);d8t=r(z7n,"LongT5ForConditionalGeneration"),z7n.forEach(t),c8t=r(dCo," (LongT5 model)"),dCo.forEach(t),m8t=i(ve),v6=n(ve,"LI",{});var cCo=s(v6);tqe=n(cCo,"STRONG",{});var H7n=s(tqe);f8t=r(H7n,"m2m_100"),H7n.forEach(t),h8t=r(cCo," \u2014 "),g2e=n(cCo,"A",{href:!0});var K7n=s(g2e);g8t=r(K7n,"M2M100ForConditionalGeneration"),K7n.forEach(t),u8t=r(cCo," (M2M100 model)"),cCo.forEach(t),p8t=i(ve),b6=n(ve,"LI",{});var mCo=s(b6);aqe=n(mCo,"STRONG",{});var eLn=s(aqe);_8t=r(eLn,"marian"),eLn.forEach(t),v8t=r(mCo," \u2014 "),u2e=n(mCo,"A",{href:!0});var oLn=s(u2e);b8t=r(oLn,"MarianMTModel"),oLn.forEach(t),M8t=r(mCo," (Marian model)"),mCo.forEach(t),T8t=i(ve),M6=n(ve,"LI",{});var fCo=s(M6);nqe=n(fCo,"STRONG",{});var rLn=s(nqe);F8t=r(rLn,"mbart"),rLn.forEach(t),E8t=r(fCo," \u2014 "),p2e=n(fCo,"A",{href:!0});var tLn=s(p2e);y8t=r(tLn,"MBartForConditionalGeneration"),tLn.forEach(t),C8t=r(fCo," (mBART model)"),fCo.forEach(t),w8t=i(ve),T6=n(ve,"LI",{});var hCo=s(T6);sqe=n(hCo,"STRONG",{});var aLn=s(sqe);L8t=r(aLn,"mt5"),aLn.forEach(t),A8t=r(hCo," \u2014 "),_2e=n(hCo,"A",{href:!0});var nLn=s(_2e);R8t=r(nLn,"MT5ForConditionalGeneration"),nLn.forEach(t),k8t=r(hCo," (MT5 model)"),hCo.forEach(t),S8t=i(ve),F6=n(ve,"LI",{});var gCo=s(F6);lqe=n(gCo,"STRONG",{});var sLn=s(lqe);x8t=r(sLn,"mvp"),sLn.forEach(t),G8t=r(gCo," \u2014 "),v2e=n(gCo,"A",{href:!0});var lLn=s(v2e);B8t=r(lLn,"MvpForConditionalGeneration"),lLn.forEach(t),I8t=r(gCo," (MVP model)"),gCo.forEach(t),N8t=i(ve),E6=n(ve,"LI",{});var uCo=s(E6);iqe=n(uCo,"STRONG",{});var iLn=s(iqe);$8t=r(iLn,"nllb-moe"),iLn.forEach(t),j8t=r(uCo," \u2014 "),b2e=n(uCo,"A",{href:!0});var dLn=s(b2e);P8t=r(dLn,"NllbMoeForConditionalGeneration"),dLn.forEach(t),Z8t=r(uCo," (NLLB-MOE model)"),uCo.forEach(t),V8t=i(ve),y6=n(ve,"LI",{});var pCo=s(y6);dqe=n(pCo,"STRONG",{});var cLn=s(dqe);W8t=r(cLn,"pegasus"),cLn.forEach(t),J8t=r(pCo," \u2014 "),M2e=n(pCo,"A",{href:!0});var mLn=s(M2e);U8t=r(mLn,"PegasusForConditionalGeneration"),mLn.forEach(t),X8t=r(pCo," (Pegasus model)"),pCo.forEach(t),D8t=i(ve),C6=n(ve,"LI",{});var _Co=s(C6);cqe=n(_Co,"STRONG",{});var fLn=s(cqe);q8t=r(fLn,"pegasus_x"),fLn.forEach(t),O8t=r(_Co," \u2014 "),T2e=n(_Co,"A",{href:!0});var hLn=s(T2e);Q8t=r(hLn,"PegasusXForConditionalGeneration"),hLn.forEach(t),Y8t=r(_Co," (PEGASUS-X model)"),_Co.forEach(t),z8t=i(ve),w6=n(ve,"LI",{});var vCo=s(w6);mqe=n(vCo,"STRONG",{});var gLn=s(mqe);H8t=r(gLn,"plbart"),gLn.forEach(t),K8t=r(vCo," \u2014 "),F2e=n(vCo,"A",{href:!0});var uLn=s(F2e);e7t=r(uLn,"PLBartForConditionalGeneration"),uLn.forEach(t),o7t=r(vCo," (PLBart model)"),vCo.forEach(t),r7t=i(ve),L6=n(ve,"LI",{});var bCo=s(L6);fqe=n(bCo,"STRONG",{});var pLn=s(fqe);t7t=r(pLn,"prophetnet"),pLn.forEach(t),a7t=r(bCo," \u2014 "),E2e=n(bCo,"A",{href:!0});var _Ln=s(E2e);n7t=r(_Ln,"ProphetNetForConditionalGeneration"),_Ln.forEach(t),s7t=r(bCo," (ProphetNet model)"),bCo.forEach(t),l7t=i(ve),A6=n(ve,"LI",{});var MCo=s(A6);hqe=n(MCo,"STRONG",{});var vLn=s(hqe);i7t=r(vLn,"switch_transformers"),vLn.forEach(t),d7t=r(MCo," \u2014 "),y2e=n(MCo,"A",{href:!0});var bLn=s(y2e);c7t=r(bLn,"SwitchTransformersForConditionalGeneration"),bLn.forEach(t),m7t=r(MCo," (SwitchTransformers model)"),MCo.forEach(t),f7t=i(ve),R6=n(ve,"LI",{});var TCo=s(R6);gqe=n(TCo,"STRONG",{});var MLn=s(gqe);h7t=r(MLn,"t5"),MLn.forEach(t),g7t=r(TCo," \u2014 "),C2e=n(TCo,"A",{href:!0});var TLn=s(C2e);u7t=r(TLn,"T5ForConditionalGeneration"),TLn.forEach(t),p7t=r(TCo," (T5 model)"),TCo.forEach(t),_7t=i(ve),k6=n(ve,"LI",{});var FCo=s(k6);uqe=n(FCo,"STRONG",{});var FLn=s(uqe);v7t=r(FLn,"umt5"),FLn.forEach(t),b7t=r(FCo," \u2014 "),w2e=n(FCo,"A",{href:!0});var ELn=s(w2e);M7t=r(ELn,"UMT5ForConditionalGeneration"),ELn.forEach(t),T7t=r(FCo," (UMT5 model)"),FCo.forEach(t),F7t=i(ve),S6=n(ve,"LI",{});var ECo=s(S6);pqe=n(ECo,"STRONG",{});var yLn=s(pqe);E7t=r(yLn,"xlm-prophetnet"),yLn.forEach(t),y7t=r(ECo," \u2014 "),L2e=n(ECo,"A",{href:!0});var CLn=s(L2e);C7t=r(CLn,"XLMProphetNetForConditionalGeneration"),CLn.forEach(t),w7t=r(ECo," (XLM-ProphetNet model)"),ECo.forEach(t),ve.forEach(t),L7t=i(an),x6=n(an,"P",{});var yCo=s(x6);A7t=r(yCo,"The model is set in evaluation mode by default using "),_qe=n(yCo,"CODE",{});var wLn=s(_qe);R7t=r(wLn,"model.eval()"),wLn.forEach(t),k7t=r(yCo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),vqe=n(yCo,"CODE",{});var LLn=s(vqe);S7t=r(LLn,"model.train()"),LLn.forEach(t),yCo.forEach(t),x7t=i(an),M(G6.$$.fragment,an),an.forEach(t),vd.forEach(t),PGo=i(m),Dm=n(m,"H3",{class:!0});var jNo=s(Dm);B6=n(jNo,"A",{id:!0,class:!0,href:!0});var ALn=s(B6);bqe=n(ALn,"SPAN",{});var RLn=s(bqe);M(XW.$$.fragment,RLn),RLn.forEach(t),ALn.forEach(t),G7t=i(jNo),Mqe=n(jNo,"SPAN",{});var kLn=s(Mqe);B7t=r(kLn,"TFAutoModelForSeq2SeqLM"),kLn.forEach(t),jNo.forEach(t),ZGo=i(m),tr=n(m,"DIV",{class:!0});var bd=s(tr);M(DW.$$.fragment,bd),I7t=i(bd),qm=n(bd,"P",{});var X9e=s(qm);N7t=r(X9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),A2e=n(X9e,"A",{href:!0});var SLn=s(A2e);$7t=r(SLn,"from_pretrained()"),SLn.forEach(t),j7t=r(X9e," class method or the "),R2e=n(X9e,"A",{href:!0});var xLn=s(R2e);P7t=r(xLn,"from_config()"),xLn.forEach(t),Z7t=r(X9e,` class
method.`),X9e.forEach(t),V7t=i(bd),qW=n(bd,"P",{});var PNo=s(qW);W7t=r(PNo,"This class cannot be instantiated directly using "),Tqe=n(PNo,"CODE",{});var GLn=s(Tqe);J7t=r(GLn,"__init__()"),GLn.forEach(t),U7t=r(PNo," (throws an error)."),PNo.forEach(t),X7t=i(bd),oa=n(bd,"DIV",{class:!0});var Yj=s(oa);M(OW.$$.fragment,Yj),D7t=i(Yj),Fqe=n(Yj,"P",{});var BLn=s(Fqe);q7t=r(BLn,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),BLn.forEach(t),O7t=i(Yj),Om=n(Yj,"P",{});var D9e=s(Om);Q7t=r(D9e,`Note:
Loading a model from its configuration file does `),Eqe=n(D9e,"STRONG",{});var ILn=s(Eqe);Y7t=r(ILn,"not"),ILn.forEach(t),z7t=r(D9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),k2e=n(D9e,"A",{href:!0});var NLn=s(k2e);H7t=r(NLn,"from_pretrained()"),NLn.forEach(t),K7t=r(D9e," to load the model weights."),D9e.forEach(t),eLt=i(Yj),M(I6.$$.fragment,Yj),Yj.forEach(t),oLt=i(bd),mt=n(bd,"DIV",{class:!0});var Md=s(mt);M(QW.$$.fragment,Md),rLt=i(Md),yqe=n(Md,"P",{});var $Ln=s(yqe);tLt=r($Ln,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),$Ln.forEach(t),aLt=i(Md),Hn=n(Md,"P",{});var zj=s(Hn);nLt=r(zj,"The model class to instantiate is selected based on the "),Cqe=n(zj,"CODE",{});var jLn=s(Cqe);sLt=r(jLn,"model_type"),jLn.forEach(t),lLt=r(zj,` property of the config object (either
passed as an argument or loaded from `),wqe=n(zj,"CODE",{});var PLn=s(wqe);iLt=r(PLn,"pretrained_model_name_or_path"),PLn.forEach(t),dLt=r(zj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Lqe=n(zj,"CODE",{});var ZLn=s(Lqe);cLt=r(ZLn,"pretrained_model_name_or_path"),ZLn.forEach(t),mLt=r(zj,":"),zj.forEach(t),fLt=i(Md),Ne=n(Md,"UL",{});var De=s(Ne);N6=n(De,"LI",{});var CCo=s(N6);Aqe=n(CCo,"STRONG",{});var VLn=s(Aqe);hLt=r(VLn,"bart"),VLn.forEach(t),gLt=r(CCo," \u2014 "),S2e=n(CCo,"A",{href:!0});var WLn=s(S2e);uLt=r(WLn,"TFBartForConditionalGeneration"),WLn.forEach(t),pLt=r(CCo," (BART model)"),CCo.forEach(t),_Lt=i(De),$6=n(De,"LI",{});var wCo=s($6);Rqe=n(wCo,"STRONG",{});var JLn=s(Rqe);vLt=r(JLn,"blenderbot"),JLn.forEach(t),bLt=r(wCo," \u2014 "),x2e=n(wCo,"A",{href:!0});var ULn=s(x2e);MLt=r(ULn,"TFBlenderbotForConditionalGeneration"),ULn.forEach(t),TLt=r(wCo," (Blenderbot model)"),wCo.forEach(t),FLt=i(De),j6=n(De,"LI",{});var LCo=s(j6);kqe=n(LCo,"STRONG",{});var XLn=s(kqe);ELt=r(XLn,"blenderbot-small"),XLn.forEach(t),yLt=r(LCo," \u2014 "),G2e=n(LCo,"A",{href:!0});var DLn=s(G2e);CLt=r(DLn,"TFBlenderbotSmallForConditionalGeneration"),DLn.forEach(t),wLt=r(LCo," (BlenderbotSmall model)"),LCo.forEach(t),LLt=i(De),P6=n(De,"LI",{});var ACo=s(P6);Sqe=n(ACo,"STRONG",{});var qLn=s(Sqe);ALt=r(qLn,"encoder-decoder"),qLn.forEach(t),RLt=r(ACo," \u2014 "),B2e=n(ACo,"A",{href:!0});var OLn=s(B2e);kLt=r(OLn,"TFEncoderDecoderModel"),OLn.forEach(t),SLt=r(ACo," (Encoder decoder model)"),ACo.forEach(t),xLt=i(De),Z6=n(De,"LI",{});var RCo=s(Z6);xqe=n(RCo,"STRONG",{});var QLn=s(xqe);GLt=r(QLn,"led"),QLn.forEach(t),BLt=r(RCo," \u2014 "),I2e=n(RCo,"A",{href:!0});var YLn=s(I2e);ILt=r(YLn,"TFLEDForConditionalGeneration"),YLn.forEach(t),NLt=r(RCo," (LED model)"),RCo.forEach(t),$Lt=i(De),V6=n(De,"LI",{});var kCo=s(V6);Gqe=n(kCo,"STRONG",{});var zLn=s(Gqe);jLt=r(zLn,"marian"),zLn.forEach(t),PLt=r(kCo," \u2014 "),N2e=n(kCo,"A",{href:!0});var HLn=s(N2e);ZLt=r(HLn,"TFMarianMTModel"),HLn.forEach(t),VLt=r(kCo," (Marian model)"),kCo.forEach(t),WLt=i(De),W6=n(De,"LI",{});var SCo=s(W6);Bqe=n(SCo,"STRONG",{});var KLn=s(Bqe);JLt=r(KLn,"mbart"),KLn.forEach(t),ULt=r(SCo," \u2014 "),$2e=n(SCo,"A",{href:!0});var eAn=s($2e);XLt=r(eAn,"TFMBartForConditionalGeneration"),eAn.forEach(t),DLt=r(SCo," (mBART model)"),SCo.forEach(t),qLt=i(De),J6=n(De,"LI",{});var xCo=s(J6);Iqe=n(xCo,"STRONG",{});var oAn=s(Iqe);OLt=r(oAn,"mt5"),oAn.forEach(t),QLt=r(xCo," \u2014 "),j2e=n(xCo,"A",{href:!0});var rAn=s(j2e);YLt=r(rAn,"TFMT5ForConditionalGeneration"),rAn.forEach(t),zLt=r(xCo," (MT5 model)"),xCo.forEach(t),HLt=i(De),U6=n(De,"LI",{});var GCo=s(U6);Nqe=n(GCo,"STRONG",{});var tAn=s(Nqe);KLt=r(tAn,"pegasus"),tAn.forEach(t),eAt=r(GCo," \u2014 "),P2e=n(GCo,"A",{href:!0});var aAn=s(P2e);oAt=r(aAn,"TFPegasusForConditionalGeneration"),aAn.forEach(t),rAt=r(GCo," (Pegasus model)"),GCo.forEach(t),tAt=i(De),X6=n(De,"LI",{});var BCo=s(X6);$qe=n(BCo,"STRONG",{});var nAn=s($qe);aAt=r(nAn,"t5"),nAn.forEach(t),nAt=r(BCo," \u2014 "),Z2e=n(BCo,"A",{href:!0});var sAn=s(Z2e);sAt=r(sAn,"TFT5ForConditionalGeneration"),sAn.forEach(t),lAt=r(BCo," (T5 model)"),BCo.forEach(t),De.forEach(t),iAt=i(Md),M(D6.$$.fragment,Md),Md.forEach(t),bd.forEach(t),VGo=i(m),Qm=n(m,"H3",{class:!0});var ZNo=s(Qm);q6=n(ZNo,"A",{id:!0,class:!0,href:!0});var lAn=s(q6);jqe=n(lAn,"SPAN",{});var iAn=s(jqe);M(YW.$$.fragment,iAn),iAn.forEach(t),lAn.forEach(t),dAt=i(ZNo),Pqe=n(ZNo,"SPAN",{});var dAn=s(Pqe);cAt=r(dAn,"FlaxAutoModelForSeq2SeqLM"),dAn.forEach(t),ZNo.forEach(t),WGo=i(m),ar=n(m,"DIV",{class:!0});var Td=s(ar);M(zW.$$.fragment,Td),mAt=i(Td),Ym=n(Td,"P",{});var q9e=s(Ym);fAt=r(q9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),V2e=n(q9e,"A",{href:!0});var cAn=s(V2e);hAt=r(cAn,"from_pretrained()"),cAn.forEach(t),gAt=r(q9e," class method or the "),W2e=n(q9e,"A",{href:!0});var mAn=s(W2e);uAt=r(mAn,"from_config()"),mAn.forEach(t),pAt=r(q9e,` class
method.`),q9e.forEach(t),_At=i(Td),HW=n(Td,"P",{});var VNo=s(HW);vAt=r(VNo,"This class cannot be instantiated directly using "),Zqe=n(VNo,"CODE",{});var fAn=s(Zqe);bAt=r(fAn,"__init__()"),fAn.forEach(t),MAt=r(VNo," (throws an error)."),VNo.forEach(t),TAt=i(Td),ra=n(Td,"DIV",{class:!0});var Hj=s(ra);M(KW.$$.fragment,Hj),FAt=i(Hj),Vqe=n(Hj,"P",{});var hAn=s(Vqe);EAt=r(hAn,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),hAn.forEach(t),yAt=i(Hj),zm=n(Hj,"P",{});var O9e=s(zm);CAt=r(O9e,`Note:
Loading a model from its configuration file does `),Wqe=n(O9e,"STRONG",{});var gAn=s(Wqe);wAt=r(gAn,"not"),gAn.forEach(t),LAt=r(O9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),J2e=n(O9e,"A",{href:!0});var uAn=s(J2e);AAt=r(uAn,"from_pretrained()"),uAn.forEach(t),RAt=r(O9e," to load the model weights."),O9e.forEach(t),kAt=i(Hj),M(O6.$$.fragment,Hj),Hj.forEach(t),SAt=i(Td),ft=n(Td,"DIV",{class:!0});var Fd=s(ft);M(eJ.$$.fragment,Fd),xAt=i(Fd),Jqe=n(Fd,"P",{});var pAn=s(Jqe);GAt=r(pAn,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),pAn.forEach(t),BAt=i(Fd),Kn=n(Fd,"P",{});var Kj=s(Kn);IAt=r(Kj,"The model class to instantiate is selected based on the "),Uqe=n(Kj,"CODE",{});var _An=s(Uqe);NAt=r(_An,"model_type"),_An.forEach(t),$At=r(Kj,` property of the config object (either
passed as an argument or loaded from `),Xqe=n(Kj,"CODE",{});var vAn=s(Xqe);jAt=r(vAn,"pretrained_model_name_or_path"),vAn.forEach(t),PAt=r(Kj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Dqe=n(Kj,"CODE",{});var bAn=s(Dqe);ZAt=r(bAn,"pretrained_model_name_or_path"),bAn.forEach(t),VAt=r(Kj,":"),Kj.forEach(t),WAt=i(Fd),$e=n(Fd,"UL",{});var qe=s($e);Q6=n(qe,"LI",{});var ICo=s(Q6);qqe=n(ICo,"STRONG",{});var MAn=s(qqe);JAt=r(MAn,"bart"),MAn.forEach(t),UAt=r(ICo," \u2014 "),U2e=n(ICo,"A",{href:!0});var TAn=s(U2e);XAt=r(TAn,"FlaxBartForConditionalGeneration"),TAn.forEach(t),DAt=r(ICo," (BART model)"),ICo.forEach(t),qAt=i(qe),Y6=n(qe,"LI",{});var NCo=s(Y6);Oqe=n(NCo,"STRONG",{});var FAn=s(Oqe);OAt=r(FAn,"blenderbot"),FAn.forEach(t),QAt=r(NCo," \u2014 "),X2e=n(NCo,"A",{href:!0});var EAn=s(X2e);YAt=r(EAn,"FlaxBlenderbotForConditionalGeneration"),EAn.forEach(t),zAt=r(NCo," (Blenderbot model)"),NCo.forEach(t),HAt=i(qe),z6=n(qe,"LI",{});var $Co=s(z6);Qqe=n($Co,"STRONG",{});var yAn=s(Qqe);KAt=r(yAn,"blenderbot-small"),yAn.forEach(t),eRt=r($Co," \u2014 "),D2e=n($Co,"A",{href:!0});var CAn=s(D2e);oRt=r(CAn,"FlaxBlenderbotSmallForConditionalGeneration"),CAn.forEach(t),rRt=r($Co," (BlenderbotSmall model)"),$Co.forEach(t),tRt=i(qe),H6=n(qe,"LI",{});var jCo=s(H6);Yqe=n(jCo,"STRONG",{});var wAn=s(Yqe);aRt=r(wAn,"encoder-decoder"),wAn.forEach(t),nRt=r(jCo," \u2014 "),q2e=n(jCo,"A",{href:!0});var LAn=s(q2e);sRt=r(LAn,"FlaxEncoderDecoderModel"),LAn.forEach(t),lRt=r(jCo," (Encoder decoder model)"),jCo.forEach(t),iRt=i(qe),K6=n(qe,"LI",{});var PCo=s(K6);zqe=n(PCo,"STRONG",{});var AAn=s(zqe);dRt=r(AAn,"longt5"),AAn.forEach(t),cRt=r(PCo," \u2014 "),O2e=n(PCo,"A",{href:!0});var RAn=s(O2e);mRt=r(RAn,"FlaxLongT5ForConditionalGeneration"),RAn.forEach(t),fRt=r(PCo," (LongT5 model)"),PCo.forEach(t),hRt=i(qe),e8=n(qe,"LI",{});var ZCo=s(e8);Hqe=n(ZCo,"STRONG",{});var kAn=s(Hqe);gRt=r(kAn,"marian"),kAn.forEach(t),uRt=r(ZCo," \u2014 "),Q2e=n(ZCo,"A",{href:!0});var SAn=s(Q2e);pRt=r(SAn,"FlaxMarianMTModel"),SAn.forEach(t),_Rt=r(ZCo," (Marian model)"),ZCo.forEach(t),vRt=i(qe),o8=n(qe,"LI",{});var VCo=s(o8);Kqe=n(VCo,"STRONG",{});var xAn=s(Kqe);bRt=r(xAn,"mbart"),xAn.forEach(t),MRt=r(VCo," \u2014 "),Y2e=n(VCo,"A",{href:!0});var GAn=s(Y2e);TRt=r(GAn,"FlaxMBartForConditionalGeneration"),GAn.forEach(t),FRt=r(VCo," (mBART model)"),VCo.forEach(t),ERt=i(qe),r8=n(qe,"LI",{});var WCo=s(r8);eOe=n(WCo,"STRONG",{});var BAn=s(eOe);yRt=r(BAn,"mt5"),BAn.forEach(t),CRt=r(WCo," \u2014 "),z2e=n(WCo,"A",{href:!0});var IAn=s(z2e);wRt=r(IAn,"FlaxMT5ForConditionalGeneration"),IAn.forEach(t),LRt=r(WCo," (MT5 model)"),WCo.forEach(t),ARt=i(qe),t8=n(qe,"LI",{});var JCo=s(t8);oOe=n(JCo,"STRONG",{});var NAn=s(oOe);RRt=r(NAn,"pegasus"),NAn.forEach(t),kRt=r(JCo," \u2014 "),H2e=n(JCo,"A",{href:!0});var $An=s(H2e);SRt=r($An,"FlaxPegasusForConditionalGeneration"),$An.forEach(t),xRt=r(JCo," (Pegasus model)"),JCo.forEach(t),GRt=i(qe),a8=n(qe,"LI",{});var UCo=s(a8);rOe=n(UCo,"STRONG",{});var jAn=s(rOe);BRt=r(jAn,"t5"),jAn.forEach(t),IRt=r(UCo," \u2014 "),K2e=n(UCo,"A",{href:!0});var PAn=s(K2e);NRt=r(PAn,"FlaxT5ForConditionalGeneration"),PAn.forEach(t),$Rt=r(UCo," (T5 model)"),UCo.forEach(t),qe.forEach(t),jRt=i(Fd),M(n8.$$.fragment,Fd),Fd.forEach(t),Td.forEach(t),JGo=i(m),Hm=n(m,"H3",{class:!0});var WNo=s(Hm);s8=n(WNo,"A",{id:!0,class:!0,href:!0});var ZAn=s(s8);tOe=n(ZAn,"SPAN",{});var VAn=s(tOe);M(oJ.$$.fragment,VAn),VAn.forEach(t),ZAn.forEach(t),PRt=i(WNo),aOe=n(WNo,"SPAN",{});var WAn=s(aOe);ZRt=r(WAn,"AutoModelForSequenceClassification"),WAn.forEach(t),WNo.forEach(t),UGo=i(m),nr=n(m,"DIV",{class:!0});var Ed=s(nr);M(rJ.$$.fragment,Ed),VRt=i(Ed),Km=n(Ed,"P",{});var Q9e=s(Km);WRt=r(Q9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the `),eve=n(Q9e,"A",{href:!0});var JAn=s(eve);JRt=r(JAn,"from_pretrained()"),JAn.forEach(t),URt=r(Q9e," class method or the "),ove=n(Q9e,"A",{href:!0});var UAn=s(ove);XRt=r(UAn,"from_config()"),UAn.forEach(t),DRt=r(Q9e,` class
method.`),Q9e.forEach(t),qRt=i(Ed),tJ=n(Ed,"P",{});var JNo=s(tJ);ORt=r(JNo,"This class cannot be instantiated directly using "),nOe=n(JNo,"CODE",{});var XAn=s(nOe);QRt=r(XAn,"__init__()"),XAn.forEach(t),YRt=r(JNo," (throws an error)."),JNo.forEach(t),zRt=i(Ed),ta=n(Ed,"DIV",{class:!0});var eP=s(ta);M(aJ.$$.fragment,eP),HRt=i(eP),sOe=n(eP,"P",{});var DAn=s(sOe);KRt=r(DAn,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),DAn.forEach(t),ekt=i(eP),ef=n(eP,"P",{});var Y9e=s(ef);okt=r(Y9e,`Note:
Loading a model from its configuration file does `),lOe=n(Y9e,"STRONG",{});var qAn=s(lOe);rkt=r(qAn,"not"),qAn.forEach(t),tkt=r(Y9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),rve=n(Y9e,"A",{href:!0});var OAn=s(rve);akt=r(OAn,"from_pretrained()"),OAn.forEach(t),nkt=r(Y9e," to load the model weights."),Y9e.forEach(t),skt=i(eP),M(l8.$$.fragment,eP),eP.forEach(t),lkt=i(Ed),fo=n(Ed,"DIV",{class:!0});var nn=s(fo);M(nJ.$$.fragment,nn),ikt=i(nn),iOe=n(nn,"P",{});var QAn=s(iOe);dkt=r(QAn,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),QAn.forEach(t),ckt=i(nn),es=n(nn,"P",{});var oP=s(es);mkt=r(oP,"The model class to instantiate is selected based on the "),dOe=n(oP,"CODE",{});var YAn=s(dOe);fkt=r(YAn,"model_type"),YAn.forEach(t),hkt=r(oP,` property of the config object (either
passed as an argument or loaded from `),cOe=n(oP,"CODE",{});var zAn=s(cOe);gkt=r(zAn,"pretrained_model_name_or_path"),zAn.forEach(t),ukt=r(oP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mOe=n(oP,"CODE",{});var HAn=s(mOe);pkt=r(HAn,"pretrained_model_name_or_path"),HAn.forEach(t),_kt=r(oP,":"),oP.forEach(t),vkt=i(nn),B=n(nn,"UL",{});var I=s(B);i8=n(I,"LI",{});var XCo=s(i8);fOe=n(XCo,"STRONG",{});var KAn=s(fOe);bkt=r(KAn,"albert"),KAn.forEach(t),Mkt=r(XCo," \u2014 "),tve=n(XCo,"A",{href:!0});var eRn=s(tve);Tkt=r(eRn,"AlbertForSequenceClassification"),eRn.forEach(t),Fkt=r(XCo," (ALBERT model)"),XCo.forEach(t),Ekt=i(I),d8=n(I,"LI",{});var DCo=s(d8);hOe=n(DCo,"STRONG",{});var oRn=s(hOe);ykt=r(oRn,"bart"),oRn.forEach(t),Ckt=r(DCo," \u2014 "),ave=n(DCo,"A",{href:!0});var rRn=s(ave);wkt=r(rRn,"BartForSequenceClassification"),rRn.forEach(t),Lkt=r(DCo," (BART model)"),DCo.forEach(t),Akt=i(I),c8=n(I,"LI",{});var qCo=s(c8);gOe=n(qCo,"STRONG",{});var tRn=s(gOe);Rkt=r(tRn,"bert"),tRn.forEach(t),kkt=r(qCo," \u2014 "),nve=n(qCo,"A",{href:!0});var aRn=s(nve);Skt=r(aRn,"BertForSequenceClassification"),aRn.forEach(t),xkt=r(qCo," (BERT model)"),qCo.forEach(t),Gkt=i(I),m8=n(I,"LI",{});var OCo=s(m8);uOe=n(OCo,"STRONG",{});var nRn=s(uOe);Bkt=r(nRn,"big_bird"),nRn.forEach(t),Ikt=r(OCo," \u2014 "),sve=n(OCo,"A",{href:!0});var sRn=s(sve);Nkt=r(sRn,"BigBirdForSequenceClassification"),sRn.forEach(t),$kt=r(OCo," (BigBird model)"),OCo.forEach(t),jkt=i(I),f8=n(I,"LI",{});var QCo=s(f8);pOe=n(QCo,"STRONG",{});var lRn=s(pOe);Pkt=r(lRn,"bigbird_pegasus"),lRn.forEach(t),Zkt=r(QCo," \u2014 "),lve=n(QCo,"A",{href:!0});var iRn=s(lve);Vkt=r(iRn,"BigBirdPegasusForSequenceClassification"),iRn.forEach(t),Wkt=r(QCo," (BigBird-Pegasus model)"),QCo.forEach(t),Jkt=i(I),h8=n(I,"LI",{});var YCo=s(h8);_Oe=n(YCo,"STRONG",{});var dRn=s(_Oe);Ukt=r(dRn,"biogpt"),dRn.forEach(t),Xkt=r(YCo," \u2014 "),ive=n(YCo,"A",{href:!0});var cRn=s(ive);Dkt=r(cRn,"BioGptForSequenceClassification"),cRn.forEach(t),qkt=r(YCo," (BioGpt model)"),YCo.forEach(t),Okt=i(I),g8=n(I,"LI",{});var zCo=s(g8);vOe=n(zCo,"STRONG",{});var mRn=s(vOe);Qkt=r(mRn,"bloom"),mRn.forEach(t),Ykt=r(zCo," \u2014 "),dve=n(zCo,"A",{href:!0});var fRn=s(dve);zkt=r(fRn,"BloomForSequenceClassification"),fRn.forEach(t),Hkt=r(zCo," (BLOOM model)"),zCo.forEach(t),Kkt=i(I),u8=n(I,"LI",{});var HCo=s(u8);bOe=n(HCo,"STRONG",{});var hRn=s(bOe);eSt=r(hRn,"camembert"),hRn.forEach(t),oSt=r(HCo," \u2014 "),cve=n(HCo,"A",{href:!0});var gRn=s(cve);rSt=r(gRn,"CamembertForSequenceClassification"),gRn.forEach(t),tSt=r(HCo," (CamemBERT model)"),HCo.forEach(t),aSt=i(I),p8=n(I,"LI",{});var KCo=s(p8);MOe=n(KCo,"STRONG",{});var uRn=s(MOe);nSt=r(uRn,"canine"),uRn.forEach(t),sSt=r(KCo," \u2014 "),mve=n(KCo,"A",{href:!0});var pRn=s(mve);lSt=r(pRn,"CanineForSequenceClassification"),pRn.forEach(t),iSt=r(KCo," (CANINE model)"),KCo.forEach(t),dSt=i(I),_8=n(I,"LI",{});var ewo=s(_8);TOe=n(ewo,"STRONG",{});var _Rn=s(TOe);cSt=r(_Rn,"convbert"),_Rn.forEach(t),mSt=r(ewo," \u2014 "),fve=n(ewo,"A",{href:!0});var vRn=s(fve);fSt=r(vRn,"ConvBertForSequenceClassification"),vRn.forEach(t),hSt=r(ewo," (ConvBERT model)"),ewo.forEach(t),gSt=i(I),v8=n(I,"LI",{});var owo=s(v8);FOe=n(owo,"STRONG",{});var bRn=s(FOe);uSt=r(bRn,"ctrl"),bRn.forEach(t),pSt=r(owo," \u2014 "),hve=n(owo,"A",{href:!0});var MRn=s(hve);_St=r(MRn,"CTRLForSequenceClassification"),MRn.forEach(t),vSt=r(owo," (CTRL model)"),owo.forEach(t),bSt=i(I),b8=n(I,"LI",{});var rwo=s(b8);EOe=n(rwo,"STRONG",{});var TRn=s(EOe);MSt=r(TRn,"data2vec-text"),TRn.forEach(t),TSt=r(rwo," \u2014 "),gve=n(rwo,"A",{href:!0});var FRn=s(gve);FSt=r(FRn,"Data2VecTextForSequenceClassification"),FRn.forEach(t),ESt=r(rwo," (Data2VecText model)"),rwo.forEach(t),ySt=i(I),M8=n(I,"LI",{});var two=s(M8);yOe=n(two,"STRONG",{});var ERn=s(yOe);CSt=r(ERn,"deberta"),ERn.forEach(t),wSt=r(two," \u2014 "),uve=n(two,"A",{href:!0});var yRn=s(uve);LSt=r(yRn,"DebertaForSequenceClassification"),yRn.forEach(t),ASt=r(two," (DeBERTa model)"),two.forEach(t),RSt=i(I),T8=n(I,"LI",{});var awo=s(T8);COe=n(awo,"STRONG",{});var CRn=s(COe);kSt=r(CRn,"deberta-v2"),CRn.forEach(t),SSt=r(awo," \u2014 "),pve=n(awo,"A",{href:!0});var wRn=s(pve);xSt=r(wRn,"DebertaV2ForSequenceClassification"),wRn.forEach(t),GSt=r(awo," (DeBERTa-v2 model)"),awo.forEach(t),BSt=i(I),F8=n(I,"LI",{});var nwo=s(F8);wOe=n(nwo,"STRONG",{});var LRn=s(wOe);ISt=r(LRn,"distilbert"),LRn.forEach(t),NSt=r(nwo," \u2014 "),_ve=n(nwo,"A",{href:!0});var ARn=s(_ve);$St=r(ARn,"DistilBertForSequenceClassification"),ARn.forEach(t),jSt=r(nwo," (DistilBERT model)"),nwo.forEach(t),PSt=i(I),E8=n(I,"LI",{});var swo=s(E8);LOe=n(swo,"STRONG",{});var RRn=s(LOe);ZSt=r(RRn,"electra"),RRn.forEach(t),VSt=r(swo," \u2014 "),vve=n(swo,"A",{href:!0});var kRn=s(vve);WSt=r(kRn,"ElectraForSequenceClassification"),kRn.forEach(t),JSt=r(swo," (ELECTRA model)"),swo.forEach(t),USt=i(I),y8=n(I,"LI",{});var lwo=s(y8);AOe=n(lwo,"STRONG",{});var SRn=s(AOe);XSt=r(SRn,"ernie"),SRn.forEach(t),DSt=r(lwo," \u2014 "),bve=n(lwo,"A",{href:!0});var xRn=s(bve);qSt=r(xRn,"ErnieForSequenceClassification"),xRn.forEach(t),OSt=r(lwo," (ERNIE model)"),lwo.forEach(t),QSt=i(I),C8=n(I,"LI",{});var iwo=s(C8);ROe=n(iwo,"STRONG",{});var GRn=s(ROe);YSt=r(GRn,"ernie_m"),GRn.forEach(t),zSt=r(iwo," \u2014 "),Mve=n(iwo,"A",{href:!0});var BRn=s(Mve);HSt=r(BRn,"ErnieMForSequenceClassification"),BRn.forEach(t),KSt=r(iwo," (ErnieM model)"),iwo.forEach(t),ext=i(I),w8=n(I,"LI",{});var dwo=s(w8);kOe=n(dwo,"STRONG",{});var IRn=s(kOe);oxt=r(IRn,"esm"),IRn.forEach(t),rxt=r(dwo," \u2014 "),Tve=n(dwo,"A",{href:!0});var NRn=s(Tve);txt=r(NRn,"EsmForSequenceClassification"),NRn.forEach(t),axt=r(dwo," (ESM model)"),dwo.forEach(t),nxt=i(I),L8=n(I,"LI",{});var cwo=s(L8);SOe=n(cwo,"STRONG",{});var $Rn=s(SOe);sxt=r($Rn,"falcon"),$Rn.forEach(t),lxt=r(cwo," \u2014 "),xOe=n(cwo,"CODE",{});var jRn=s(xOe);ixt=r(jRn,"FalconForSequenceClassification"),jRn.forEach(t),dxt=r(cwo," (Falcon model)"),cwo.forEach(t),cxt=i(I),A8=n(I,"LI",{});var mwo=s(A8);GOe=n(mwo,"STRONG",{});var PRn=s(GOe);mxt=r(PRn,"flaubert"),PRn.forEach(t),fxt=r(mwo," \u2014 "),Fve=n(mwo,"A",{href:!0});var ZRn=s(Fve);hxt=r(ZRn,"FlaubertForSequenceClassification"),ZRn.forEach(t),gxt=r(mwo," (FlauBERT model)"),mwo.forEach(t),uxt=i(I),R8=n(I,"LI",{});var fwo=s(R8);BOe=n(fwo,"STRONG",{});var VRn=s(BOe);pxt=r(VRn,"fnet"),VRn.forEach(t),_xt=r(fwo," \u2014 "),Eve=n(fwo,"A",{href:!0});var WRn=s(Eve);vxt=r(WRn,"FNetForSequenceClassification"),WRn.forEach(t),bxt=r(fwo," (FNet model)"),fwo.forEach(t),Mxt=i(I),k8=n(I,"LI",{});var hwo=s(k8);IOe=n(hwo,"STRONG",{});var JRn=s(IOe);Txt=r(JRn,"funnel"),JRn.forEach(t),Fxt=r(hwo," \u2014 "),yve=n(hwo,"A",{href:!0});var URn=s(yve);Ext=r(URn,"FunnelForSequenceClassification"),URn.forEach(t),yxt=r(hwo," (Funnel Transformer model)"),hwo.forEach(t),Cxt=i(I),S8=n(I,"LI",{});var gwo=s(S8);NOe=n(gwo,"STRONG",{});var XRn=s(NOe);wxt=r(XRn,"gpt-sw3"),XRn.forEach(t),Lxt=r(gwo," \u2014 "),Cve=n(gwo,"A",{href:!0});var DRn=s(Cve);Axt=r(DRn,"GPT2ForSequenceClassification"),DRn.forEach(t),Rxt=r(gwo," (GPT-Sw3 model)"),gwo.forEach(t),kxt=i(I),x8=n(I,"LI",{});var uwo=s(x8);$Oe=n(uwo,"STRONG",{});var qRn=s($Oe);Sxt=r(qRn,"gpt2"),qRn.forEach(t),xxt=r(uwo," \u2014 "),wve=n(uwo,"A",{href:!0});var ORn=s(wve);Gxt=r(ORn,"GPT2ForSequenceClassification"),ORn.forEach(t),Bxt=r(uwo," (OpenAI GPT-2 model)"),uwo.forEach(t),Ixt=i(I),G8=n(I,"LI",{});var pwo=s(G8);jOe=n(pwo,"STRONG",{});var QRn=s(jOe);Nxt=r(QRn,"gpt_bigcode"),QRn.forEach(t),$xt=r(pwo," \u2014 "),Lve=n(pwo,"A",{href:!0});var YRn=s(Lve);jxt=r(YRn,"GPTBigCodeForSequenceClassification"),YRn.forEach(t),Pxt=r(pwo," (GPTBigCode model)"),pwo.forEach(t),Zxt=i(I),B8=n(I,"LI",{});var _wo=s(B8);POe=n(_wo,"STRONG",{});var zRn=s(POe);Vxt=r(zRn,"gpt_neo"),zRn.forEach(t),Wxt=r(_wo," \u2014 "),Ave=n(_wo,"A",{href:!0});var HRn=s(Ave);Jxt=r(HRn,"GPTNeoForSequenceClassification"),HRn.forEach(t),Uxt=r(_wo," (GPT Neo model)"),_wo.forEach(t),Xxt=i(I),I8=n(I,"LI",{});var 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"),M1e=n(d6o,"A",{href:!0});var NSn=s(M1e);t$t=r(NSn,"YosoForSequenceClassification"),NSn.forEach(t),a$t=r(d6o," (YOSO model)"),d6o.forEach(t),I.forEach(t),n$t=i(nn),L7=n(nn,"P",{});var c6o=s(L7);s$t=r(c6o,"The model is set in evaluation mode by default using "),xQe=n(c6o,"CODE",{});var $Sn=s(xQe);l$t=r($Sn,"model.eval()"),$Sn.forEach(t),i$t=r(c6o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),GQe=n(c6o,"CODE",{});var jSn=s(GQe);d$t=r(jSn,"model.train()"),jSn.forEach(t),c6o.forEach(t),c$t=i(nn),M(A7.$$.fragment,nn),nn.forEach(t),Ed.forEach(t),XGo=i(m),of=n(m,"H3",{class:!0});var UNo=s(of);R7=n(UNo,"A",{id:!0,class:!0,href:!0});var PSn=s(R7);BQe=n(PSn,"SPAN",{});var ZSn=s(BQe);M(sJ.$$.fragment,ZSn),ZSn.forEach(t),PSn.forEach(t),m$t=i(UNo),IQe=n(UNo,"SPAN",{});var VSn=s(IQe);f$t=r(VSn,"TFAutoModelForSequenceClassification"),VSn.forEach(t),UNo.forEach(t),DGo=i(m),sr=n(m,"DIV",{class:!0});var yd=s(sr);M(lJ.$$.fragment,yd),h$t=i(yd),rf=n(yd,"P",{});var z9e=s(rf);g$t=r(z9e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the `),T1e=n(z9e,"A",{href:!0});var WSn=s(T1e);u$t=r(WSn,"from_pretrained()"),WSn.forEach(t),p$t=r(z9e," class method or the "),F1e=n(z9e,"A",{href:!0});var JSn=s(F1e);_$t=r(JSn,"from_config()"),JSn.forEach(t),v$t=r(z9e,` class
method.`),z9e.forEach(t),b$t=i(yd),iJ=n(yd,"P",{});var XNo=s(iJ);M$t=r(XNo,"This class cannot be instantiated directly using "),NQe=n(XNo,"CODE",{});var USn=s(NQe);T$t=r(USn,"__init__()"),USn.forEach(t),F$t=r(XNo," (throws an error)."),XNo.forEach(t),E$t=i(yd),aa=n(yd,"DIV",{class:!0});var rP=s(aa);M(dJ.$$.fragment,rP),y$t=i(rP),$Qe=n(rP,"P",{});var XSn=s($Qe);C$t=r(XSn,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),XSn.forEach(t),w$t=i(rP),tf=n(rP,"P",{});var H9e=s(tf);L$t=r(H9e,`Note:
Loading a model from its configuration file does `),jQe=n(H9e,"STRONG",{});var DSn=s(jQe);A$t=r(DSn,"not"),DSn.forEach(t),R$t=r(H9e,` load the model weights. It only affects the
model\u2019s configuration. Use `),E1e=n(H9e,"A",{href:!0});var qSn=s(E1e);k$t=r(qSn,"from_pretrained()"),qSn.forEach(t),S$t=r(H9e," to load the model weights."),H9e.forEach(t),x$t=i(rP),M(k7.$$.fragment,rP),rP.forEach(t),G$t=i(yd),ht=n(yd,"DIV",{class:!0});var Cd=s(ht);M(cJ.$$.fragment,Cd),B$t=i(Cd),PQe=n(Cd,"P",{});var OSn=s(PQe);I$t=r(OSn,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),OSn.forEach(t),N$t=i(Cd),os=n(Cd,"P",{});var tP=s(os);$$t=r(tP,"The model class to instantiate is selected based on the "),ZQe=n(tP,"CODE",{});var QSn=s(ZQe);j$t=r(QSn,"model_type"),QSn.forEach(t),P$t=r(tP,` property of the config object (either
passed as an argument or loaded from `),VQe=n(tP,"CODE",{});var YSn=s(VQe);Z$t=r(YSn,"pretrained_model_name_or_path"),YSn.forEach(t),V$t=r(tP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),WQe=n(tP,"CODE",{});var zSn=s(WQe);W$t=r(zSn,"pretrained_model_name_or_path"),zSn.forEach(t),J$t=r(tP,":"),tP.forEach(t),U$t=i(Cd),de=n(Cd,"UL",{});var me=s(de);S7=n(me,"LI",{});var m6o=s(S7);JQe=n(m6o,"STRONG",{});var HSn=s(JQe);X$t=r(HSn,"albert"),HSn.forEach(t),D$t=r(m6o," \u2014 "),y1e=n(m6o,"A",{href:!0});var KSn=s(y1e);q$t=r(KSn,"TFAlbertForSequenceClassification"),KSn.forEach(t),O$t=r(m6o," (ALBERT model)"),m6o.forEach(t),Q$t=i(me),x7=n(me,"LI",{});var f6o=s(x7);UQe=n(f6o,"STRONG",{});var exn=s(UQe);Y$t=r(exn,"bart"),exn.forEach(t),z$t=r(f6o," \u2014 "),C1e=n(f6o,"A",{href:!0});var oxn=s(C1e);H$t=r(oxn,"TFBartForSequenceClassification"),oxn.forEach(t),K$t=r(f6o," (BART model)"),f6o.forEach(t),ejt=i(me),G7=n(me,"LI",{});var h6o=s(G7);XQe=n(h6o,"STRONG",{});var rxn=s(XQe);ojt=r(rxn,"bert"),rxn.forEach(t),rjt=r(h6o," \u2014 "),w1e=n(h6o,"A",{href:!0});var txn=s(w1e);tjt=r(txn,"TFBertForSequenceClassification"),txn.forEach(t),ajt=r(h6o," (BERT model)"),h6o.forEach(t),njt=i(me),B7=n(me,"LI",{});var g6o=s(B7);DQe=n(g6o,"STRONG",{});var axn=s(DQe);sjt=r(axn,"camembert"),axn.forEach(t),ljt=r(g6o," \u2014 "),L1e=n(g6o,"A",{href:!0});var nxn=s(L1e);ijt=r(nxn,"TFCamembertForSequenceClassification"),nxn.forEach(t),djt=r(g6o," (CamemBERT model)"),g6o.forEach(t),cjt=i(me),I7=n(me,"LI",{});var u6o=s(I7);qQe=n(u6o,"STRONG",{});var sxn=s(qQe);mjt=r(sxn,"convbert"),sxn.forEach(t),fjt=r(u6o," \u2014 "),A1e=n(u6o,"A",{href:!0});var lxn=s(A1e);hjt=r(lxn,"TFConvBertForSequenceClassification"),lxn.forEach(t),gjt=r(u6o," (ConvBERT model)"),u6o.forEach(t),ujt=i(me),N7=n(me,"LI",{});var p6o=s(N7);OQe=n(p6o,"STRONG",{});var ixn=s(OQe);pjt=r(ixn,"ctrl"),ixn.forEach(t),_jt=r(p6o," \u2014 "),R1e=n(p6o,"A",{href:!0});var dxn=s(R1e);vjt=r(dxn,"TFCTRLForSequenceClassification"),dxn.forEach(t),bjt=r(p6o," (CTRL model)"),p6o.forEach(t),Mjt=i(me),$7=n(me,"LI",{});var _6o=s($7);QQe=n(_6o,"STRONG",{});var cxn=s(QQe);Tjt=r(cxn,"deberta"),cxn.forEach(t),Fjt=r(_6o," \u2014 "),k1e=n(_6o,"A",{href:!0});var mxn=s(k1e);Ejt=r(mxn,"TFDebertaForSequenceClassification"),mxn.forEach(t),yjt=r(_6o," (DeBERTa model)"),_6o.forEach(t),Cjt=i(me),j7=n(me,"LI",{});var v6o=s(j7);YQe=n(v6o,"STRONG",{});var fxn=s(YQe);wjt=r(fxn,"deberta-v2"),fxn.forEach(t),Ljt=r(v6o," \u2014 "),S1e=n(v6o,"A",{href:!0});var hxn=s(S1e);Ajt=r(hxn,"TFDebertaV2ForSequenceClassification"),hxn.forEach(t),Rjt=r(v6o," (DeBERTa-v2 model)"),v6o.forEach(t),kjt=i(me),P7=n(me,"LI",{});var b6o=s(P7);zQe=n(b6o,"STRONG",{});var gxn=s(zQe);Sjt=r(gxn,"distilbert"),gxn.forEach(t),xjt=r(b6o," \u2014 "),x1e=n(b6o,"A",{href:!0});var uxn=s(x1e);Gjt=r(uxn,"TFDistilBertForSequenceClassification"),uxn.forEach(t),Bjt=r(b6o," (DistilBERT model)"),b6o.forEach(t),Ijt=i(me),Z7=n(me,"LI",{});var M6o=s(Z7);HQe=n(M6o,"STRONG",{});var pxn=s(HQe);Njt=r(pxn,"electra"),pxn.forEach(t),$jt=r(M6o," \u2014 "),G1e=n(M6o,"A",{href:!0});var _xn=s(G1e);jjt=r(_xn,"TFElectraForSequenceClassification"),_xn.forEach(t),Pjt=r(M6o," (ELECTRA model)"),M6o.forEach(t),Zjt=i(me),V7=n(me,"LI",{});var T6o=s(V7);KQe=n(T6o,"STRONG",{});var vxn=s(KQe);Vjt=r(vxn,"esm"),vxn.forEach(t),Wjt=r(T6o," \u2014 "),B1e=n(T6o,"A",{href:!0});var bxn=s(B1e);Jjt=r(bxn,"TFEsmForSequenceClassification"),bxn.forEach(t),Ujt=r(T6o," (ESM model)"),T6o.forEach(t),Xjt=i(me),W7=n(me,"LI",{});var F6o=s(W7);eYe=n(F6o,"STRONG",{});var Mxn=s(eYe);Djt=r(Mxn,"flaubert"),Mxn.forEach(t),qjt=r(F6o," \u2014 "),I1e=n(F6o,"A",{href:!0});var Txn=s(I1e);Ojt=r(Txn,"TFFlaubertForSequenceClassification"),Txn.forEach(t),Qjt=r(F6o," (FlauBERT model)"),F6o.forEach(t),Yjt=i(me),J7=n(me,"LI",{});var E6o=s(J7);oYe=n(E6o,"STRONG",{});var Fxn=s(oYe);zjt=r(Fxn,"funnel"),Fxn.forEach(t),Hjt=r(E6o," \u2014 "),N1e=n(E6o,"A",{href:!0});var Exn=s(N1e);Kjt=r(Exn,"TFFunnelForSequenceClassification"),Exn.forEach(t),ePt=r(E6o," (Funnel Transformer 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\u2014 "),Z1e=n(L6o,"A",{href:!0});var Sxn=s(Z1e);bPt=r(Sxn,"TFLayoutLMForSequenceClassification"),Sxn.forEach(t),MPt=r(L6o," (LayoutLM model)"),L6o.forEach(t),TPt=i(me),O7=n(me,"LI",{});var A6o=s(O7);sYe=n(A6o,"STRONG",{});var xxn=s(sYe);FPt=r(xxn,"layoutlmv3"),xxn.forEach(t),EPt=r(A6o," \u2014 "),V1e=n(A6o,"A",{href:!0});var Gxn=s(V1e);yPt=r(Gxn,"TFLayoutLMv3ForSequenceClassification"),Gxn.forEach(t),CPt=r(A6o," (LayoutLMv3 model)"),A6o.forEach(t),wPt=i(me),Q7=n(me,"LI",{});var R6o=s(Q7);lYe=n(R6o,"STRONG",{});var Bxn=s(lYe);LPt=r(Bxn,"longformer"),Bxn.forEach(t),APt=r(R6o," \u2014 "),W1e=n(R6o,"A",{href:!0});var Ixn=s(W1e);RPt=r(Ixn,"TFLongformerForSequenceClassification"),Ixn.forEach(t),kPt=r(R6o," (Longformer model)"),R6o.forEach(t),SPt=i(me),Y7=n(me,"LI",{});var k6o=s(Y7);iYe=n(k6o,"STRONG",{});var Nxn=s(iYe);xPt=r(Nxn,"mobilebert"),Nxn.forEach(t),GPt=r(k6o," \u2014 "),J1e=n(k6o,"A",{href:!0});var $xn=s(J1e);BPt=r($xn,"TFMobileBertForSequenceClassification"),$xn.forEach(t),IPt=r(k6o," (MobileBERT model)"),k6o.forEach(t),NPt=i(me),z7=n(me,"LI",{});var S6o=s(z7);dYe=n(S6o,"STRONG",{});var jxn=s(dYe);$Pt=r(jxn,"mpnet"),jxn.forEach(t),jPt=r(S6o," \u2014 "),U1e=n(S6o,"A",{href:!0});var Pxn=s(U1e);PPt=r(Pxn,"TFMPNetForSequenceClassification"),Pxn.forEach(t),ZPt=r(S6o," (MPNet model)"),S6o.forEach(t),VPt=i(me),H7=n(me,"LI",{});var x6o=s(H7);cYe=n(x6o,"STRONG",{});var Zxn=s(cYe);WPt=r(Zxn,"openai-gpt"),Zxn.forEach(t),JPt=r(x6o," \u2014 "),X1e=n(x6o,"A",{href:!0});var Vxn=s(X1e);UPt=r(Vxn,"TFOpenAIGPTForSequenceClassification"),Vxn.forEach(t),XPt=r(x6o," (OpenAI GPT model)"),x6o.forEach(t),DPt=i(me),K7=n(me,"LI",{});var G6o=s(K7);mYe=n(G6o,"STRONG",{});var Wxn=s(mYe);qPt=r(Wxn,"rembert"),Wxn.forEach(t),OPt=r(G6o," \u2014 "),D1e=n(G6o,"A",{href:!0});var Jxn=s(D1e);QPt=r(Jxn,"TFRemBertForSequenceClassification"),Jxn.forEach(t),YPt=r(G6o," (RemBERT model)"),G6o.forEach(t),zPt=i(me),eL=n(me,"LI",{});var B6o=s(eL);fYe=n(B6o,"STRONG",{});var Uxn=s(fYe);HPt=r(Uxn,"roberta"),Uxn.forEach(t),KPt=r(B6o," \u2014 "),q1e=n(B6o,"A",{href:!0});var Xxn=s(q1e);eZt=r(Xxn,"TFRobertaForSequenceClassification"),Xxn.forEach(t),oZt=r(B6o," (RoBERTa model)"),B6o.forEach(t),rZt=i(me),oL=n(me,"LI",{});var I6o=s(oL);hYe=n(I6o,"STRONG",{});var Dxn=s(hYe);tZt=r(Dxn,"roberta-prelayernorm"),Dxn.forEach(t),aZt=r(I6o," \u2014 "),O1e=n(I6o,"A",{href:!0});var qxn=s(O1e);nZt=r(qxn,"TFRobertaPreLayerNormForSequenceClassification"),qxn.forEach(t),sZt=r(I6o," (RoBERTa-PreLayerNorm model)"),I6o.forEach(t),lZt=i(me),rL=n(me,"LI",{});var N6o=s(rL);gYe=n(N6o,"STRONG",{});var Oxn=s(gYe);iZt=r(Oxn,"roformer"),Oxn.forEach(t),dZt=r(N6o," \u2014 "),Q1e=n(N6o,"A",{href:!0});var Qxn=s(Q1e);cZt=r(Qxn,"TFRoFormerForSequenceClassification"),Qxn.forEach(t),mZt=r(N6o," (RoFormer model)"),N6o.forEach(t),fZt=i(me),tL=n(me,"LI",{});var $6o=s(tL);uYe=n($6o,"STRONG",{});var Yxn=s(uYe);hZt=r(Yxn,"tapas"),Yxn.forEach(t),gZt=r($6o," \u2014 "),Y1e=n($6o,"A",{href:!0});var zxn=s(Y1e);uZt=r(zxn,"TFTapasForSequenceClassification"),zxn.forEach(t),pZt=r($6o," (TAPAS model)"),$6o.forEach(t),_Zt=i(me),aL=n(me,"LI",{});var j6o=s(aL);pYe=n(j6o,"STRONG",{});var Hxn=s(pYe);vZt=r(Hxn,"transfo-xl"),Hxn.forEach(t),bZt=r(j6o," \u2014 "),z1e=n(j6o,"A",{href:!0});var Kxn=s(z1e);MZt=r(Kxn,"TFTransfoXLForSequenceClassification"),Kxn.forEach(t),TZt=r(j6o," (Transformer-XL model)"),j6o.forEach(t),FZt=i(me),nL=n(me,"LI",{});var P6o=s(nL);_Ye=n(P6o,"STRONG",{});var eGn=s(_Ye);EZt=r(eGn,"xlm"),eGn.forEach(t),yZt=r(P6o," \u2014 "),H1e=n(P6o,"A",{href:!0});var oGn=s(H1e);CZt=r(oGn,"TFXLMForSequenceClassification"),oGn.forEach(t),wZt=r(P6o," (XLM model)"),P6o.forEach(t),LZt=i(me),sL=n(me,"LI",{});var Z6o=s(sL);vYe=n(Z6o,"STRONG",{});var rGn=s(vYe);AZt=r(rGn,"xlm-roberta"),rGn.forEach(t),RZt=r(Z6o," \u2014 "),K1e=n(Z6o,"A",{href:!0});var tGn=s(K1e);kZt=r(tGn,"TFXLMRobertaForSequenceClassification"),tGn.forEach(t),SZt=r(Z6o," (XLM-RoBERTa model)"),Z6o.forEach(t),xZt=i(me),lL=n(me,"LI",{});var V6o=s(lL);bYe=n(V6o,"STRONG",{});var aGn=s(bYe);GZt=r(aGn,"xlnet"),aGn.forEach(t),BZt=r(V6o," \u2014 "),ebe=n(V6o,"A",{href:!0});var nGn=s(ebe);IZt=r(nGn,"TFXLNetForSequenceClassification"),nGn.forEach(t),NZt=r(V6o," (XLNet model)"),V6o.forEach(t),me.forEach(t),$Zt=i(Cd),M(iL.$$.fragment,Cd),Cd.forEach(t),yd.forEach(t),qGo=i(m),af=n(m,"H3",{class:!0});var DNo=s(af);dL=n(DNo,"A",{id:!0,class:!0,href:!0});var sGn=s(dL);MYe=n(sGn,"SPAN",{});var lGn=s(MYe);M(mJ.$$.fragment,lGn),lGn.forEach(t),sGn.forEach(t),jZt=i(DNo),TYe=n(DNo,"SPAN",{});var iGn=s(TYe);PZt=r(iGn,"FlaxAutoModelForSequenceClassification"),iGn.forEach(t),DNo.forEach(t),OGo=i(m),lr=n(m,"DIV",{class:!0});var wd=s(lr);M(fJ.$$.fragment,wd),ZZt=i(wd),nf=n(wd,"P",{});var K9e=s(nf);VZt=r(K9e,`This is a generic model class that will be instantiated as one of the model classes of 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with the `),obe=n(K9e,"A",{href:!0});var dGn=s(obe);WZt=r(dGn,"from_pretrained()"),dGn.forEach(t),JZt=r(K9e," class method or the "),rbe=n(K9e,"A",{href:!0});var cGn=s(rbe);UZt=r(cGn,"from_config()"),cGn.forEach(t),XZt=r(K9e,` class
method.`),K9e.forEach(t),DZt=i(wd),hJ=n(wd,"P",{});var qNo=s(hJ);qZt=r(qNo,"This class cannot be instantiated directly using "),FYe=n(qNo,"CODE",{});var mGn=s(FYe);OZt=r(mGn,"__init__()"),mGn.forEach(t),QZt=r(qNo," (throws an error)."),qNo.forEach(t),YZt=i(wd),na=n(wd,"DIV",{class:!0});var aP=s(na);M(gJ.$$.fragment,aP),zZt=i(aP),EYe=n(aP,"P",{});var fGn=s(EYe);HZt=r(fGn,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),fGn.forEach(t),KZt=i(aP),sf=n(aP,"P",{});var eCe=s(sf);eVt=r(eCe,`Note:
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model\u2019s configuration. Use `),tbe=n(eCe,"A",{href:!0});var gGn=s(tbe);tVt=r(gGn,"from_pretrained()"),gGn.forEach(t),aVt=r(eCe," to load the model weights."),eCe.forEach(t),nVt=i(aP),M(cL.$$.fragment,aP),aP.forEach(t),sVt=i(wd),gt=n(wd,"DIV",{class:!0});var Ld=s(gt);M(uJ.$$.fragment,Ld),lVt=i(Ld),CYe=n(Ld,"P",{});var uGn=s(CYe);iVt=r(uGn,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),uGn.forEach(t),dVt=i(Ld),rs=n(Ld,"P",{});var nP=s(rs);cVt=r(nP,"The model class to instantiate is selected based on the "),wYe=n(nP,"CODE",{});var pGn=s(wYe);mVt=r(pGn,"model_type"),pGn.forEach(t),fVt=r(nP,` property of the config object (either
passed as an argument or loaded from `),LYe=n(nP,"CODE",{});var _Gn=s(LYe);hVt=r(_Gn,"pretrained_model_name_or_path"),_Gn.forEach(t),gVt=r(nP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),AYe=n(nP,"CODE",{});var vGn=s(AYe);uVt=r(vGn,"pretrained_model_name_or_path"),vGn.forEach(t),pVt=r(nP,":"),nP.forEach(t),_Vt=i(Ld),Se=n(Ld,"UL",{});var Ze=s(Se);mL=n(Ze,"LI",{});var W6o=s(mL);RYe=n(W6o,"STRONG",{});var bGn=s(RYe);vVt=r(bGn,"albert"),bGn.forEach(t),bVt=r(W6o," \u2014 "),abe=n(W6o,"A",{href:!0});var MGn=s(abe);MVt=r(MGn,"FlaxAlbertForSequenceClassification"),MGn.forEach(t),TVt=r(W6o," (ALBERT model)"),W6o.forEach(t),FVt=i(Ze),fL=n(Ze,"LI",{});var J6o=s(fL);kYe=n(J6o,"STRONG",{});var TGn=s(kYe);EVt=r(TGn,"bart"),TGn.forEach(t),yVt=r(J6o," \u2014 "),nbe=n(J6o,"A",{href:!0});var FGn=s(nbe);CVt=r(FGn,"FlaxBartForSequenceClassification"),FGn.forEach(t),wVt=r(J6o," (BART model)"),J6o.forEach(t),LVt=i(Ze),hL=n(Ze,"LI",{});var U6o=s(hL);SYe=n(U6o,"STRONG",{});var EGn=s(SYe);AVt=r(EGn,"bert"),EGn.forEach(t),RVt=r(U6o," \u2014 "),sbe=n(U6o,"A",{href:!0});var yGn=s(sbe);kVt=r(yGn,"FlaxBertForSequenceClassification"),yGn.forEach(t),SVt=r(U6o," (BERT model)"),U6o.forEach(t),xVt=i(Ze),gL=n(Ze,"LI",{});var X6o=s(gL);xYe=n(X6o,"STRONG",{});var CGn=s(xYe);GVt=r(CGn,"big_bird"),CGn.forEach(t),BVt=r(X6o," \u2014 "),lbe=n(X6o,"A",{href:!0});var wGn=s(lbe);IVt=r(wGn,"FlaxBigBirdForSequenceClassification"),wGn.forEach(t),NVt=r(X6o," (BigBird model)"),X6o.forEach(t),$Vt=i(Ze),uL=n(Ze,"LI",{});var D6o=s(uL);GYe=n(D6o,"STRONG",{});var LGn=s(GYe);jVt=r(LGn,"distilbert"),LGn.forEach(t),PVt=r(D6o," \u2014 "),ibe=n(D6o,"A",{href:!0});var AGn=s(ibe);ZVt=r(AGn,"FlaxDistilBertForSequenceClassification"),AGn.forEach(t),VVt=r(D6o," (DistilBERT model)"),D6o.forEach(t),WVt=i(Ze),pL=n(Ze,"LI",{});var q6o=s(pL);BYe=n(q6o,"STRONG",{});var RGn=s(BYe);JVt=r(RGn,"electra"),RGn.forEach(t),UVt=r(q6o," \u2014 "),dbe=n(q6o,"A",{href:!0});var kGn=s(dbe);XVt=r(kGn,"FlaxElectraForSequenceClassification"),kGn.forEach(t),DVt=r(q6o," (ELECTRA model)"),q6o.forEach(t),qVt=i(Ze),_L=n(Ze,"LI",{});var O6o=s(_L);IYe=n(O6o,"STRONG",{});var SGn=s(IYe);OVt=r(SGn,"mbart"),SGn.forEach(t),QVt=r(O6o," \u2014 "),cbe=n(O6o,"A",{href:!0});var xGn=s(cbe);YVt=r(xGn,"FlaxMBartForSequenceClassification"),xGn.forEach(t),zVt=r(O6o," (mBART model)"),O6o.forEach(t),HVt=i(Ze),vL=n(Ze,"LI",{});var Q6o=s(vL);NYe=n(Q6o,"STRONG",{});var GGn=s(NYe);KVt=r(GGn,"roberta"),GGn.forEach(t),eWt=r(Q6o," \u2014 "),mbe=n(Q6o,"A",{href:!0});var BGn=s(mbe);oWt=r(BGn,"FlaxRobertaForSequenceClassification"),BGn.forEach(t),rWt=r(Q6o," (RoBERTa model)"),Q6o.forEach(t),tWt=i(Ze),bL=n(Ze,"LI",{});var Y6o=s(bL);$Ye=n(Y6o,"STRONG",{});var IGn=s($Ye);aWt=r(IGn,"roberta-prelayernorm"),IGn.forEach(t),nWt=r(Y6o," \u2014 "),fbe=n(Y6o,"A",{href:!0});var NGn=s(fbe);sWt=r(NGn,"FlaxRobertaPreLayerNormForSequenceClassification"),NGn.forEach(t),lWt=r(Y6o," (RoBERTa-PreLayerNorm model)"),Y6o.forEach(t),iWt=i(Ze),ML=n(Ze,"LI",{});var z6o=s(ML);jYe=n(z6o,"STRONG",{});var $Gn=s(jYe);dWt=r($Gn,"roformer"),$Gn.forEach(t),cWt=r(z6o," \u2014 "),hbe=n(z6o,"A",{href:!0});var jGn=s(hbe);mWt=r(jGn,"FlaxRoFormerForSequenceClassification"),jGn.forEach(t),fWt=r(z6o," (RoFormer model)"),z6o.forEach(t),hWt=i(Ze),TL=n(Ze,"LI",{});var H6o=s(TL);PYe=n(H6o,"STRONG",{});var PGn=s(PYe);gWt=r(PGn,"xlm-roberta"),PGn.forEach(t),uWt=r(H6o," \u2014 "),gbe=n(H6o,"A",{href:!0});var ZGn=s(gbe);pWt=r(ZGn,"FlaxXLMRobertaForSequenceClassification"),ZGn.forEach(t),_Wt=r(H6o," (XLM-RoBERTa model)"),H6o.forEach(t),Ze.forEach(t),vWt=i(Ld),M(FL.$$.fragment,Ld),Ld.forEach(t),wd.forEach(t),QGo=i(m),lf=n(m,"H3",{class:!0});var ONo=s(lf);EL=n(ONo,"A",{id:!0,class:!0,href:!0});var VGn=s(EL);ZYe=n(VGn,"SPAN",{});var WGn=s(ZYe);M(pJ.$$.fragment,WGn),WGn.forEach(t),VGn.forEach(t),bWt=i(ONo),VYe=n(ONo,"SPAN",{});var JGn=s(VYe);MWt=r(JGn,"AutoModelForMultipleChoice"),JGn.forEach(t),ONo.forEach(t),YGo=i(m),ir=n(m,"DIV",{class:!0});var Ad=s(ir);M(_J.$$.fragment,Ad),TWt=i(Ad),df=n(Ad,"P",{});var oCe=s(df);FWt=r(oCe,`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
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method.`),oCe.forEach(t),LWt=i(Ad),vJ=n(Ad,"P",{});var QNo=s(vJ);AWt=r(QNo,"This class cannot be instantiated directly using "),WYe=n(QNo,"CODE",{});var DGn=s(WYe);RWt=r(DGn,"__init__()"),DGn.forEach(t),kWt=r(QNo," (throws an error)."),QNo.forEach(t),SWt=i(Ad),sa=n(Ad,"DIV",{class:!0});var sP=s(sa);M(bJ.$$.fragment,sP),xWt=i(sP),JYe=n(sP,"P",{});var qGn=s(JYe);GWt=r(qGn,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),qGn.forEach(t),BWt=i(sP),cf=n(sP,"P",{});var rCe=s(cf);IWt=r(rCe,`Note:
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model\u2019s configuration. Use `),_be=n(rCe,"A",{href:!0});var QGn=s(_be);jWt=r(QGn,"from_pretrained()"),QGn.forEach(t),PWt=r(rCe," to load the model weights."),rCe.forEach(t),ZWt=i(sP),M(yL.$$.fragment,sP),sP.forEach(t),VWt=i(Ad),ho=n(Ad,"DIV",{class:!0});var sn=s(ho);M(MJ.$$.fragment,sn),WWt=i(sn),XYe=n(sn,"P",{});var YGn=s(XYe);JWt=r(YGn,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),YGn.forEach(t),UWt=i(sn),ts=n(sn,"P",{});var lP=s(ts);XWt=r(lP,"The model class to instantiate is selected based on the "),DYe=n(lP,"CODE",{});var zGn=s(DYe);DWt=r(zGn,"model_type"),zGn.forEach(t),qWt=r(lP,` property of the config object (either
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falling back to using pattern matching on `),OYe=n(lP,"CODE",{});var KGn=s(OYe);YWt=r(KGn,"pretrained_model_name_or_path"),KGn.forEach(t),zWt=r(lP,":"),lP.forEach(t),HWt=i(sn),ae=n(sn,"UL",{});var ne=s(ae);CL=n(ne,"LI",{});var K6o=s(CL);QYe=n(K6o,"STRONG",{});var eBn=s(QYe);KWt=r(eBn,"albert"),eBn.forEach(t),eJt=r(K6o," \u2014 "),vbe=n(K6o,"A",{href:!0});var oBn=s(vbe);oJt=r(oBn,"AlbertForMultipleChoice"),oBn.forEach(t),rJt=r(K6o," (ALBERT model)"),K6o.forEach(t),tJt=i(ne),wL=n(ne,"LI",{});var e8o=s(wL);YYe=n(e8o,"STRONG",{});var rBn=s(YYe);aJt=r(rBn,"bert"),rBn.forEach(t),nJt=r(e8o," \u2014 "),bbe=n(e8o,"A",{href:!0});var tBn=s(bbe);sJt=r(tBn,"BertForMultipleChoice"),tBn.forEach(t),lJt=r(e8o," (BERT model)"),e8o.forEach(t),iJt=i(ne),LL=n(ne,"LI",{});var o8o=s(LL);zYe=n(o8o,"STRONG",{});var aBn=s(zYe);dJt=r(aBn,"big_bird"),aBn.forEach(t),cJt=r(o8o," \u2014 "),Mbe=n(o8o,"A",{href:!0});var nBn=s(Mbe);mJt=r(nBn,"BigBirdForMultipleChoice"),nBn.forEach(t),fJt=r(o8o," (BigBird 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"),v0e=n(z8o,"A",{href:!0});var cNn=s(v0e);COt=r(cNn,"TFRobertaPreLayerNormForMultipleChoice"),cNn.forEach(t),wOt=r(z8o," (RoBERTa-PreLayerNorm model)"),z8o.forEach(t),LOt=i(ye),LA=n(ye,"LI",{});var H8o=s(LA);aHe=n(H8o,"STRONG",{});var mNn=s(aHe);AOt=r(mNn,"roformer"),mNn.forEach(t),ROt=r(H8o," \u2014 "),b0e=n(H8o,"A",{href:!0});var fNn=s(b0e);kOt=r(fNn,"TFRoFormerForMultipleChoice"),fNn.forEach(t),SOt=r(H8o," (RoFormer model)"),H8o.forEach(t),xOt=i(ye),AA=n(ye,"LI",{});var K8o=s(AA);nHe=n(K8o,"STRONG",{});var hNn=s(nHe);GOt=r(hNn,"xlm"),hNn.forEach(t),BOt=r(K8o," \u2014 "),M0e=n(K8o,"A",{href:!0});var gNn=s(M0e);IOt=r(gNn,"TFXLMForMultipleChoice"),gNn.forEach(t),NOt=r(K8o," (XLM model)"),K8o.forEach(t),$Ot=i(ye),RA=n(ye,"LI",{});var e7o=s(RA);sHe=n(e7o,"STRONG",{});var uNn=s(sHe);jOt=r(uNn,"xlm-roberta"),uNn.forEach(t),POt=r(e7o," \u2014 "),T0e=n(e7o,"A",{href:!0});var pNn=s(T0e);ZOt=r(pNn,"TFXLMRobertaForMultipleChoice"),pNn.forEach(t),VOt=r(e7o," (XLM-RoBERTa 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with the `),E0e=n(nCe,"A",{href:!0});var FNn=s(E0e);HOt=r(FNn,"from_pretrained()"),FNn.forEach(t),KOt=r(nCe," class method or the "),y0e=n(nCe,"A",{href:!0});var ENn=s(y0e);eQt=r(ENn,"from_config()"),ENn.forEach(t),oQt=r(nCe,` class
method.`),nCe.forEach(t),rQt=i(Sd),AJ=n(Sd,"P",{});var KNo=s(AJ);tQt=r(KNo,"This class cannot be instantiated directly using "),cHe=n(KNo,"CODE",{});var yNn=s(cHe);aQt=r(yNn,"__init__()"),yNn.forEach(t),nQt=r(KNo," (throws an error)."),KNo.forEach(t),sQt=i(Sd),ia=n(Sd,"DIV",{class:!0});var cP=s(ia);M(RJ.$$.fragment,cP),lQt=i(cP),mHe=n(cP,"P",{});var CNn=s(mHe);iQt=r(CNn,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),CNn.forEach(t),dQt=i(cP),pf=n(cP,"P",{});var sCe=s(pf);cQt=r(sCe,`Note:
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model\u2019s configuration. Use `),C0e=n(sCe,"A",{href:!0});var LNn=s(C0e);hQt=r(LNn,"from_pretrained()"),LNn.forEach(t),gQt=r(sCe," to load the model weights."),sCe.forEach(t),uQt=i(cP),M(GA.$$.fragment,cP),cP.forEach(t),pQt=i(Sd),pt=n(Sd,"DIV",{class:!0});var xd=s(pt);M(kJ.$$.fragment,xd),_Qt=i(xd),hHe=n(xd,"P",{});var ANn=s(hHe);vQt=r(ANn,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),ANn.forEach(t),bQt=i(xd),ns=n(xd,"P",{});var mP=s(ns);MQt=r(mP,"The model class to instantiate is selected based on the "),gHe=n(mP,"CODE",{});var RNn=s(gHe);TQt=r(RNn,"model_type"),RNn.forEach(t),FQt=r(mP,` property of the config object (either
passed as an argument or loaded from `),uHe=n(mP,"CODE",{});var kNn=s(uHe);EQt=r(kNn,"pretrained_model_name_or_path"),kNn.forEach(t),yQt=r(mP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),pHe=n(mP,"CODE",{});var SNn=s(pHe);CQt=r(SNn,"pretrained_model_name_or_path"),SNn.forEach(t),wQt=r(mP,":"),mP.forEach(t),LQt=i(xd),Ue=n(xd,"UL",{});var He=s(Ue);BA=n(He,"LI",{});var r7o=s(BA);_He=n(r7o,"STRONG",{});var xNn=s(_He);AQt=r(xNn,"albert"),xNn.forEach(t),RQt=r(r7o," \u2014 "),w0e=n(r7o,"A",{href:!0});var GNn=s(w0e);kQt=r(GNn,"FlaxAlbertForMultipleChoice"),GNn.forEach(t),SQt=r(r7o," (ALBERT model)"),r7o.forEach(t),xQt=i(He),IA=n(He,"LI",{});var t7o=s(IA);vHe=n(t7o,"STRONG",{});var BNn=s(vHe);GQt=r(BNn,"bert"),BNn.forEach(t),BQt=r(t7o," \u2014 "),L0e=n(t7o,"A",{href:!0});var INn=s(L0e);IQt=r(INn,"FlaxBertForMultipleChoice"),INn.forEach(t),NQt=r(t7o," (BERT model)"),t7o.forEach(t),$Qt=i(He),NA=n(He,"LI",{});var a7o=s(NA);bHe=n(a7o,"STRONG",{});var NNn=s(bHe);jQt=r(NNn,"big_bird"),NNn.forEach(t),PQt=r(a7o," \u2014 "),A0e=n(a7o,"A",{href:!0});var $Nn=s(A0e);ZQt=r($Nn,"FlaxBigBirdForMultipleChoice"),$Nn.forEach(t),VQt=r(a7o," (BigBird model)"),a7o.forEach(t),WQt=i(He),$A=n(He,"LI",{});var n7o=s($A);MHe=n(n7o,"STRONG",{});var jNn=s(MHe);JQt=r(jNn,"distilbert"),jNn.forEach(t),UQt=r(n7o," \u2014 "),R0e=n(n7o,"A",{href:!0});var PNn=s(R0e);XQt=r(PNn,"FlaxDistilBertForMultipleChoice"),PNn.forEach(t),DQt=r(n7o," (DistilBERT model)"),n7o.forEach(t),qQt=i(He),jA=n(He,"LI",{});var s7o=s(jA);THe=n(s7o,"STRONG",{});var ZNn=s(THe);OQt=r(ZNn,"electra"),ZNn.forEach(t),QQt=r(s7o," \u2014 "),k0e=n(s7o,"A",{href:!0});var VNn=s(k0e);YQt=r(VNn,"FlaxElectraForMultipleChoice"),VNn.forEach(t),zQt=r(s7o," (ELECTRA model)"),s7o.forEach(t),HQt=i(He),PA=n(He,"LI",{});var l7o=s(PA);FHe=n(l7o,"STRONG",{});var WNn=s(FHe);KQt=r(WNn,"roberta"),WNn.forEach(t),eYt=r(l7o," \u2014 "),S0e=n(l7o,"A",{href:!0});var JNn=s(S0e);oYt=r(JNn,"FlaxRobertaForMultipleChoice"),JNn.forEach(t),rYt=r(l7o," (RoBERTa model)"),l7o.forEach(t),tYt=i(He),ZA=n(He,"LI",{});var i7o=s(ZA);EHe=n(i7o,"STRONG",{});var UNn=s(EHe);aYt=r(UNn,"roberta-prelayernorm"),UNn.forEach(t),nYt=r(i7o," \u2014 "),x0e=n(i7o,"A",{href:!0});var XNn=s(x0e);sYt=r(XNn,"FlaxRobertaPreLayerNormForMultipleChoice"),XNn.forEach(t),lYt=r(i7o," (RoBERTa-PreLayerNorm model)"),i7o.forEach(t),iYt=i(He),VA=n(He,"LI",{});var d7o=s(VA);yHe=n(d7o,"STRONG",{});var DNn=s(yHe);dYt=r(DNn,"roformer"),DNn.forEach(t),cYt=r(d7o," \u2014 "),G0e=n(d7o,"A",{href:!0});var qNn=s(G0e);mYt=r(qNn,"FlaxRoFormerForMultipleChoice"),qNn.forEach(t),fYt=r(d7o," (RoFormer model)"),d7o.forEach(t),hYt=i(He),WA=n(He,"LI",{});var c7o=s(WA);CHe=n(c7o,"STRONG",{});var ONn=s(CHe);gYt=r(ONn,"xlm-roberta"),ONn.forEach(t),uYt=r(c7o," \u2014 "),B0e=n(c7o,"A",{href:!0});var QNn=s(B0e);pYt=r(QNn,"FlaxXLMRobertaForMultipleChoice"),QNn.forEach(t),_Yt=r(c7o," (XLM-RoBERTa model)"),c7o.forEach(t),He.forEach(t),vYt=i(xd),M(JA.$$.fragment,xd),xd.forEach(t),Sd.forEach(t),oBo=i(m),_f=n(m,"H3",{class:!0});var e$o=s(_f);UA=n(e$o,"A",{id:!0,class:!0,href:!0});var YNn=s(UA);wHe=n(YNn,"SPAN",{});var zNn=s(wHe);M(SJ.$$.fragment,zNn),zNn.forEach(t),YNn.forEach(t),bYt=i(e$o),LHe=n(e$o,"SPAN",{});var HNn=s(LHe);MYt=r(HNn,"AutoModelForNextSentencePrediction"),HNn.forEach(t),e$o.forEach(t),rBo=i(m),mr=n(m,"DIV",{class:!0});var Gd=s(mr);M(xJ.$$.fragment,Gd),TYt=i(Gd),vf=n(Gd,"P",{});var lCe=s(vf);FYt=r(lCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),I0e=n(lCe,"A",{href:!0});var KNn=s(I0e);EYt=r(KNn,"from_pretrained()"),KNn.forEach(t),yYt=r(lCe," class method or the "),N0e=n(lCe,"A",{href:!0});var e$n=s(N0e);CYt=r(e$n,"from_config()"),e$n.forEach(t),wYt=r(lCe,` class
method.`),lCe.forEach(t),LYt=i(Gd),GJ=n(Gd,"P",{});var o$o=s(GJ);AYt=r(o$o,"This class cannot be instantiated directly using "),AHe=n(o$o,"CODE",{});var o$n=s(AHe);RYt=r(o$n,"__init__()"),o$n.forEach(t),kYt=r(o$o," (throws an error)."),o$o.forEach(t),SYt=i(Gd),da=n(Gd,"DIV",{class:!0});var fP=s(da);M(BJ.$$.fragment,fP),xYt=i(fP),RHe=n(fP,"P",{});var r$n=s(RHe);GYt=r(r$n,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),r$n.forEach(t),BYt=i(fP),bf=n(fP,"P",{});var iCe=s(bf);IYt=r(iCe,`Note:
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model\u2019s configuration. Use `),$0e=n(iCe,"A",{href:!0});var a$n=s($0e);jYt=r(a$n,"from_pretrained()"),a$n.forEach(t),PYt=r(iCe," to load the model weights."),iCe.forEach(t),ZYt=i(fP),M(XA.$$.fragment,fP),fP.forEach(t),VYt=i(Gd),go=n(Gd,"DIV",{class:!0});var ln=s(go);M(IJ.$$.fragment,ln),WYt=i(ln),SHe=n(ln,"P",{});var n$n=s(SHe);JYt=r(n$n,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),n$n.forEach(t),UYt=i(ln),ss=n(ln,"P",{});var hP=s(ss);XYt=r(hP,"The model class to instantiate is selected based on the "),xHe=n(hP,"CODE",{});var s$n=s(xHe);DYt=r(s$n,"model_type"),s$n.forEach(t),qYt=r(hP,` property of the config object (either
passed as an argument or loaded from `),GHe=n(hP,"CODE",{});var l$n=s(GHe);OYt=r(l$n,"pretrained_model_name_or_path"),l$n.forEach(t),QYt=r(hP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),BHe=n(hP,"CODE",{});var i$n=s(BHe);YYt=r(i$n,"pretrained_model_name_or_path"),i$n.forEach(t),zYt=r(hP,":"),hP.forEach(t),HYt=i(ln),eo=n(ln,"UL",{});var It=s(eo);DA=n(It,"LI",{});var m7o=s(DA);IHe=n(m7o,"STRONG",{});var d$n=s(IHe);KYt=r(d$n,"bert"),d$n.forEach(t),ezt=r(m7o," \u2014 "),j0e=n(m7o,"A",{href:!0});var c$n=s(j0e);ozt=r(c$n,"BertForNextSentencePrediction"),c$n.forEach(t),rzt=r(m7o," (BERT model)"),m7o.forEach(t),tzt=i(It),qA=n(It,"LI",{});var f7o=s(qA);NHe=n(f7o,"STRONG",{});var m$n=s(NHe);azt=r(m$n,"ernie"),m$n.forEach(t),nzt=r(f7o," \u2014 "),P0e=n(f7o,"A",{href:!0});var f$n=s(P0e);szt=r(f$n,"ErnieForNextSentencePrediction"),f$n.forEach(t),lzt=r(f7o," (ERNIE model)"),f7o.forEach(t),izt=i(It),OA=n(It,"LI",{});var h7o=s(OA);$He=n(h7o,"STRONG",{});var h$n=s($He);dzt=r(h$n,"fnet"),h$n.forEach(t),czt=r(h7o," \u2014 "),Z0e=n(h7o,"A",{href:!0});var g$n=s(Z0e);mzt=r(g$n,"FNetForNextSentencePrediction"),g$n.forEach(t),fzt=r(h7o," (FNet model)"),h7o.forEach(t),hzt=i(It),QA=n(It,"LI",{});var g7o=s(QA);jHe=n(g7o,"STRONG",{});var u$n=s(jHe);gzt=r(u$n,"megatron-bert"),u$n.forEach(t),uzt=r(g7o," \u2014 "),V0e=n(g7o,"A",{href:!0});var p$n=s(V0e);pzt=r(p$n,"MegatronBertForNextSentencePrediction"),p$n.forEach(t),_zt=r(g7o," (Megatron-BERT model)"),g7o.forEach(t),vzt=i(It),YA=n(It,"LI",{});var u7o=s(YA);PHe=n(u7o,"STRONG",{});var _$n=s(PHe);bzt=r(_$n,"mobilebert"),_$n.forEach(t),Mzt=r(u7o," \u2014 "),W0e=n(u7o,"A",{href:!0});var v$n=s(W0e);Tzt=r(v$n,"MobileBertForNextSentencePrediction"),v$n.forEach(t),Fzt=r(u7o," (MobileBERT model)"),u7o.forEach(t),Ezt=i(It),zA=n(It,"LI",{});var p7o=s(zA);ZHe=n(p7o,"STRONG",{});var b$n=s(ZHe);yzt=r(b$n,"nezha"),b$n.forEach(t),Czt=r(p7o," \u2014 "),J0e=n(p7o,"A",{href:!0});var M$n=s(J0e);wzt=r(M$n,"NezhaForNextSentencePrediction"),M$n.forEach(t),Lzt=r(p7o," (Nezha model)"),p7o.forEach(t),Azt=i(It),HA=n(It,"LI",{});var _7o=s(HA);VHe=n(_7o,"STRONG",{});var T$n=s(VHe);Rzt=r(T$n,"qdqbert"),T$n.forEach(t),kzt=r(_7o," \u2014 "),U0e=n(_7o,"A",{href:!0});var F$n=s(U0e);Szt=r(F$n,"QDQBertForNextSentencePrediction"),F$n.forEach(t),xzt=r(_7o," (QDQBert model)"),_7o.forEach(t),It.forEach(t),Gzt=i(ln),KA=n(ln,"P",{});var v7o=s(KA);Bzt=r(v7o,"The model is set in evaluation mode by default using "),WHe=n(v7o,"CODE",{});var E$n=s(WHe);Izt=r(E$n,"model.eval()"),E$n.forEach(t),Nzt=r(v7o,` (so for instance, dropout modules are
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with the `),X0e=n(dCe,"A",{href:!0});var A$n=s(X0e);Jzt=r(A$n,"from_pretrained()"),A$n.forEach(t),Uzt=r(dCe," class method or the "),D0e=n(dCe,"A",{href:!0});var R$n=s(D0e);Xzt=r(R$n,"from_config()"),R$n.forEach(t),Dzt=r(dCe,` class
method.`),dCe.forEach(t),qzt=i(Bd),jJ=n(Bd,"P",{});var t$o=s(jJ);Ozt=r(t$o,"This class cannot be instantiated directly using "),DHe=n(t$o,"CODE",{});var k$n=s(DHe);Qzt=r(k$n,"__init__()"),k$n.forEach(t),Yzt=r(t$o," (throws an error)."),t$o.forEach(t),zzt=i(Bd),ca=n(Bd,"DIV",{class:!0});var gP=s(ca);M(PJ.$$.fragment,gP),Hzt=i(gP),qHe=n(gP,"P",{});var S$n=s(qHe);Kzt=r(S$n,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),S$n.forEach(t),eHt=i(gP),Ff=n(gP,"P",{});var cCe=s(Ff);oHt=r(cCe,`Note:
Loading a model from its configuration file does `),OHe=n(cCe,"STRONG",{});var x$n=s(OHe);rHt=r(x$n,"not"),x$n.forEach(t),tHt=r(cCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),q0e=n(cCe,"A",{href:!0});var G$n=s(q0e);aHt=r(G$n,"from_pretrained()"),G$n.forEach(t),nHt=r(cCe," to load the model weights."),cCe.forEach(t),sHt=i(gP),M(rR.$$.fragment,gP),gP.forEach(t),lHt=i(Bd),_t=n(Bd,"DIV",{class:!0});var Id=s(_t);M(ZJ.$$.fragment,Id),iHt=i(Id),QHe=n(Id,"P",{});var B$n=s(QHe);dHt=r(B$n,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),B$n.forEach(t),cHt=i(Id),ls=n(Id,"P",{});var uP=s(ls);mHt=r(uP,"The model class to instantiate is selected based on the "),YHe=n(uP,"CODE",{});var I$n=s(YHe);fHt=r(I$n,"model_type"),I$n.forEach(t),hHt=r(uP,` property of the config object (either
passed as an argument or loaded from `),zHe=n(uP,"CODE",{});var N$n=s(zHe);gHt=r(N$n,"pretrained_model_name_or_path"),N$n.forEach(t),uHt=r(uP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),HHe=n(uP,"CODE",{});var $$n=s(HHe);pHt=r($$n,"pretrained_model_name_or_path"),$$n.forEach(t),_Ht=r(uP,":"),uP.forEach(t),vHt=i(Id),VJ=n(Id,"UL",{});var a$o=s(VJ);tR=n(a$o,"LI",{});var b7o=s(tR);KHe=n(b7o,"STRONG",{});var j$n=s(KHe);bHt=r(j$n,"bert"),j$n.forEach(t),MHt=r(b7o," \u2014 "),O0e=n(b7o,"A",{href:!0});var P$n=s(O0e);THt=r(P$n,"TFBertForNextSentencePrediction"),P$n.forEach(t),FHt=r(b7o," (BERT model)"),b7o.forEach(t),EHt=i(a$o),aR=n(a$o,"LI",{});var M7o=s(aR);eKe=n(M7o,"STRONG",{});var Z$n=s(eKe);yHt=r(Z$n,"mobilebert"),Z$n.forEach(t),CHt=r(M7o," \u2014 "),Q0e=n(M7o,"A",{href:!0});var V$n=s(Q0e);wHt=r(V$n,"TFMobileBertForNextSentencePrediction"),V$n.forEach(t),LHt=r(M7o," (MobileBERT model)"),M7o.forEach(t),a$o.forEach(t),AHt=i(Id),M(nR.$$.fragment,Id),Id.forEach(t),Bd.forEach(t),nBo=i(m),Ef=n(m,"H3",{class:!0});var n$o=s(Ef);sR=n(n$o,"A",{id:!0,class:!0,href:!0});var W$n=s(sR);oKe=n(W$n,"SPAN",{});var J$n=s(oKe);M(WJ.$$.fragment,J$n),J$n.forEach(t),W$n.forEach(t),RHt=i(n$o),rKe=n(n$o,"SPAN",{});var U$n=s(rKe);kHt=r(U$n,"FlaxAutoModelForNextSentencePrediction"),U$n.forEach(t),n$o.forEach(t),sBo=i(m),hr=n(m,"DIV",{class:!0});var Nd=s(hr);M(JJ.$$.fragment,Nd),SHt=i(Nd),yf=n(Nd,"P",{});var mCe=s(yf);xHt=r(mCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),Y0e=n(mCe,"A",{href:!0});var X$n=s(Y0e);GHt=r(X$n,"from_pretrained()"),X$n.forEach(t),BHt=r(mCe," class method or the "),z0e=n(mCe,"A",{href:!0});var D$n=s(z0e);IHt=r(D$n,"from_config()"),D$n.forEach(t),NHt=r(mCe,` class
method.`),mCe.forEach(t),$Ht=i(Nd),UJ=n(Nd,"P",{});var s$o=s(UJ);jHt=r(s$o,"This class cannot be instantiated directly using "),tKe=n(s$o,"CODE",{});var q$n=s(tKe);PHt=r(q$n,"__init__()"),q$n.forEach(t),ZHt=r(s$o," (throws an error)."),s$o.forEach(t),VHt=i(Nd),ma=n(Nd,"DIV",{class:!0});var pP=s(ma);M(XJ.$$.fragment,pP),WHt=i(pP),aKe=n(pP,"P",{});var O$n=s(aKe);JHt=r(O$n,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),O$n.forEach(t),UHt=i(pP),Cf=n(pP,"P",{});var fCe=s(Cf);XHt=r(fCe,`Note:
Loading a model from its configuration file does `),nKe=n(fCe,"STRONG",{});var Q$n=s(nKe);DHt=r(Q$n,"not"),Q$n.forEach(t),qHt=r(fCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),H0e=n(fCe,"A",{href:!0});var Y$n=s(H0e);OHt=r(Y$n,"from_pretrained()"),Y$n.forEach(t),QHt=r(fCe," to load the model weights."),fCe.forEach(t),YHt=i(pP),M(lR.$$.fragment,pP),pP.forEach(t),zHt=i(Nd),vt=n(Nd,"DIV",{class:!0});var $d=s(vt);M(DJ.$$.fragment,$d),HHt=i($d),sKe=n($d,"P",{});var z$n=s(sKe);KHt=r(z$n,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),z$n.forEach(t),eKt=i($d),is=n($d,"P",{});var _P=s(is);oKt=r(_P,"The model class to instantiate is selected based on the "),lKe=n(_P,"CODE",{});var H$n=s(lKe);rKt=r(H$n,"model_type"),H$n.forEach(t),tKt=r(_P,` property of the config object (either
passed as an argument or loaded from `),iKe=n(_P,"CODE",{});var K$n=s(iKe);aKt=r(K$n,"pretrained_model_name_or_path"),K$n.forEach(t),nKt=r(_P,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),dKe=n(_P,"CODE",{});var ejn=s(dKe);sKt=r(ejn,"pretrained_model_name_or_path"),ejn.forEach(t),lKt=r(_P,":"),_P.forEach(t),iKt=i($d),cKe=n($d,"UL",{});var ojn=s(cKe);iR=n(ojn,"LI",{});var T7o=s(iR);mKe=n(T7o,"STRONG",{});var rjn=s(mKe);dKt=r(rjn,"bert"),rjn.forEach(t),cKt=r(T7o," \u2014 "),K0e=n(T7o,"A",{href:!0});var tjn=s(K0e);mKt=r(tjn,"FlaxBertForNextSentencePrediction"),tjn.forEach(t),fKt=r(T7o," (BERT model)"),T7o.forEach(t),ojn.forEach(t),hKt=i($d),M(dR.$$.fragment,$d),$d.forEach(t),Nd.forEach(t),lBo=i(m),wf=n(m,"H3",{class:!0});var l$o=s(wf);cR=n(l$o,"A",{id:!0,class:!0,href:!0});var ajn=s(cR);fKe=n(ajn,"SPAN",{});var njn=s(fKe);M(qJ.$$.fragment,njn),njn.forEach(t),ajn.forEach(t),gKt=i(l$o),hKe=n(l$o,"SPAN",{});var sjn=s(hKe);uKt=r(sjn,"AutoModelForTokenClassification"),sjn.forEach(t),l$o.forEach(t),iBo=i(m),gr=n(m,"DIV",{class:!0});var jd=s(gr);M(OJ.$$.fragment,jd),pKt=i(jd),Lf=n(jd,"P",{});var hCe=s(Lf);_Kt=r(hCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the `),e3e=n(hCe,"A",{href:!0});var ljn=s(e3e);vKt=r(ljn,"from_pretrained()"),ljn.forEach(t),bKt=r(hCe," class method or the "),o3e=n(hCe,"A",{href:!0});var ijn=s(o3e);MKt=r(ijn,"from_config()"),ijn.forEach(t),TKt=r(hCe,` class
method.`),hCe.forEach(t),FKt=i(jd),QJ=n(jd,"P",{});var i$o=s(QJ);EKt=r(i$o,"This class cannot be instantiated directly using "),gKe=n(i$o,"CODE",{});var djn=s(gKe);yKt=r(djn,"__init__()"),djn.forEach(t),CKt=r(i$o," (throws an error)."),i$o.forEach(t),wKt=i(jd),fa=n(jd,"DIV",{class:!0});var vP=s(fa);M(YJ.$$.fragment,vP),LKt=i(vP),uKe=n(vP,"P",{});var cjn=s(uKe);AKt=r(cjn,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),cjn.forEach(t),RKt=i(vP),Af=n(vP,"P",{});var gCe=s(Af);kKt=r(gCe,`Note:
Loading a model from its configuration file does `),pKe=n(gCe,"STRONG",{});var mjn=s(pKe);SKt=r(mjn,"not"),mjn.forEach(t),xKt=r(gCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),r3e=n(gCe,"A",{href:!0});var fjn=s(r3e);GKt=r(fjn,"from_pretrained()"),fjn.forEach(t),BKt=r(gCe," to load the model weights."),gCe.forEach(t),IKt=i(vP),M(mR.$$.fragment,vP),vP.forEach(t),NKt=i(jd),uo=n(jd,"DIV",{class:!0});var dn=s(uo);M(zJ.$$.fragment,dn),$Kt=i(dn),_Ke=n(dn,"P",{});var hjn=s(_Ke);jKt=r(hjn,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),hjn.forEach(t),PKt=i(dn),ds=n(dn,"P",{});var bP=s(ds);ZKt=r(bP,"The model class to instantiate is selected based on the "),vKe=n(bP,"CODE",{});var gjn=s(vKe);VKt=r(gjn,"model_type"),gjn.forEach(t),WKt=r(bP,` property of the config object (either
passed as an argument or loaded from `),bKe=n(bP,"CODE",{});var ujn=s(bKe);JKt=r(ujn,"pretrained_model_name_or_path"),ujn.forEach(t),UKt=r(bP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),MKe=n(bP,"CODE",{});var pjn=s(MKe);XKt=r(pjn,"pretrained_model_name_or_path"),pjn.forEach(t),DKt=r(bP,":"),bP.forEach(t),qKt=i(dn),z=n(dn,"UL",{});var H=s(z);fR=n(H,"LI",{});var F7o=s(fR);TKe=n(F7o,"STRONG",{});var _jn=s(TKe);OKt=r(_jn,"albert"),_jn.forEach(t),QKt=r(F7o," \u2014 "),t3e=n(F7o,"A",{href:!0});var vjn=s(t3e);YKt=r(vjn,"AlbertForTokenClassification"),vjn.forEach(t),zKt=r(F7o," (ALBERT model)"),F7o.forEach(t),HKt=i(H),hR=n(H,"LI",{});var E7o=s(hR);FKe=n(E7o,"STRONG",{});var bjn=s(FKe);KKt=r(bjn,"bert"),bjn.forEach(t),eea=r(E7o," \u2014 "),a3e=n(E7o,"A",{href:!0});var Mjn=s(a3e);oea=r(Mjn,"BertForTokenClassification"),Mjn.forEach(t),rea=r(E7o," (BERT model)"),E7o.forEach(t),tea=i(H),gR=n(H,"LI",{});var y7o=s(gR);EKe=n(y7o,"STRONG",{});var Tjn=s(EKe);aea=r(Tjn,"big_bird"),Tjn.forEach(t),nea=r(y7o," \u2014 "),n3e=n(y7o,"A",{href:!0});var Fjn=s(n3e);sea=r(Fjn,"BigBirdForTokenClassification"),Fjn.forEach(t),lea=r(y7o," (BigBird model)"),y7o.forEach(t),iea=i(H),uR=n(H,"LI",{});var C7o=s(uR);yKe=n(C7o,"STRONG",{});var Ejn=s(yKe);dea=r(Ejn,"biogpt"),Ejn.forEach(t),cea=r(C7o," \u2014 "),s3e=n(C7o,"A",{href:!0});var yjn=s(s3e);mea=r(yjn,"BioGptForTokenClassification"),yjn.forEach(t),fea=r(C7o," (BioGpt model)"),C7o.forEach(t),hea=i(H),pR=n(H,"LI",{});var w7o=s(pR);CKe=n(w7o,"STRONG",{});var Cjn=s(CKe);gea=r(Cjn,"bloom"),Cjn.forEach(t),uea=r(w7o," \u2014 "),l3e=n(w7o,"A",{href:!0});var wjn=s(l3e);pea=r(wjn,"BloomForTokenClassification"),wjn.forEach(t),_ea=r(w7o," (BLOOM model)"),w7o.forEach(t),vea=i(H),_R=n(H,"LI",{});var L7o=s(_R);wKe=n(L7o,"STRONG",{});var Ljn=s(wKe);bea=r(Ljn,"camembert"),Ljn.forEach(t),Mea=r(L7o," \u2014 "),i3e=n(L7o,"A",{href:!0});var Ajn=s(i3e);Tea=r(Ajn,"CamembertForTokenClassification"),Ajn.forEach(t),Fea=r(L7o," (CamemBERT model)"),L7o.forEach(t),Eea=i(H),vR=n(H,"LI",{});var A7o=s(vR);LKe=n(A7o,"STRONG",{});var Rjn=s(LKe);yea=r(Rjn,"canine"),Rjn.forEach(t),Cea=r(A7o," \u2014 "),d3e=n(A7o,"A",{href:!0});var kjn=s(d3e);wea=r(kjn,"CanineForTokenClassification"),kjn.forEach(t),Lea=r(A7o," (CANINE model)"),A7o.forEach(t),Aea=i(H),bR=n(H,"LI",{});var R7o=s(bR);AKe=n(R7o,"STRONG",{});var Sjn=s(AKe);Rea=r(Sjn,"convbert"),Sjn.forEach(t),kea=r(R7o," \u2014 "),c3e=n(R7o,"A",{href:!0});var xjn=s(c3e);Sea=r(xjn,"ConvBertForTokenClassification"),xjn.forEach(t),xea=r(R7o," (ConvBERT model)"),R7o.forEach(t),Gea=i(H),MR=n(H,"LI",{});var k7o=s(MR);RKe=n(k7o,"STRONG",{});var Gjn=s(RKe);Bea=r(Gjn,"data2vec-text"),Gjn.forEach(t),Iea=r(k7o," \u2014 "),m3e=n(k7o,"A",{href:!0});var Bjn=s(m3e);Nea=r(Bjn,"Data2VecTextForTokenClassification"),Bjn.forEach(t),$ea=r(k7o," (Data2VecText model)"),k7o.forEach(t),jea=i(H),TR=n(H,"LI",{});var S7o=s(TR);kKe=n(S7o,"STRONG",{});var Ijn=s(kKe);Pea=r(Ijn,"deberta"),Ijn.forEach(t),Zea=r(S7o," \u2014 "),f3e=n(S7o,"A",{href:!0});var Njn=s(f3e);Vea=r(Njn,"DebertaForTokenClassification"),Njn.forEach(t),Wea=r(S7o," (DeBERTa 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\u2014 "),p3e=n(I7o,"A",{href:!0});var Ujn=s(p3e);loa=r(Ujn,"ErnieForTokenClassification"),Ujn.forEach(t),ioa=r(I7o," (ERNIE model)"),I7o.forEach(t),doa=i(H),wR=n(H,"LI",{});var N7o=s(wR);IKe=n(N7o,"STRONG",{});var Xjn=s(IKe);coa=r(Xjn,"ernie_m"),Xjn.forEach(t),moa=r(N7o," \u2014 "),_3e=n(N7o,"A",{href:!0});var Djn=s(_3e);foa=r(Djn,"ErnieMForTokenClassification"),Djn.forEach(t),hoa=r(N7o," (ErnieM model)"),N7o.forEach(t),goa=i(H),LR=n(H,"LI",{});var $7o=s(LR);NKe=n($7o,"STRONG",{});var qjn=s(NKe);uoa=r(qjn,"esm"),qjn.forEach(t),poa=r($7o," \u2014 "),v3e=n($7o,"A",{href:!0});var Ojn=s(v3e);_oa=r(Ojn,"EsmForTokenClassification"),Ojn.forEach(t),voa=r($7o," (ESM model)"),$7o.forEach(t),boa=i(H),AR=n(H,"LI",{});var j7o=s(AR);$Ke=n(j7o,"STRONG",{});var Qjn=s($Ke);Moa=r(Qjn,"falcon"),Qjn.forEach(t),Toa=r(j7o," \u2014 "),jKe=n(j7o,"CODE",{});var Yjn=s(jKe);Foa=r(Yjn,"FalconForTokenClassification"),Yjn.forEach(t),Eoa=r(j7o," (Falcon model)"),j7o.forEach(t),yoa=i(H),RR=n(H,"LI",{});var P7o=s(RR);PKe=n(P7o,"STRONG",{});var zjn=s(PKe);Coa=r(zjn,"flaubert"),zjn.forEach(t),woa=r(P7o," \u2014 "),b3e=n(P7o,"A",{href:!0});var Hjn=s(b3e);Loa=r(Hjn,"FlaubertForTokenClassification"),Hjn.forEach(t),Aoa=r(P7o," (FlauBERT model)"),P7o.forEach(t),Roa=i(H),kR=n(H,"LI",{});var Z7o=s(kR);ZKe=n(Z7o,"STRONG",{});var Kjn=s(ZKe);koa=r(Kjn,"fnet"),Kjn.forEach(t),Soa=r(Z7o," \u2014 "),M3e=n(Z7o,"A",{href:!0});var ePn=s(M3e);xoa=r(ePn,"FNetForTokenClassification"),ePn.forEach(t),Goa=r(Z7o," (FNet model)"),Z7o.forEach(t),Boa=i(H),SR=n(H,"LI",{});var V7o=s(SR);VKe=n(V7o,"STRONG",{});var oPn=s(VKe);Ioa=r(oPn,"funnel"),oPn.forEach(t),Noa=r(V7o," \u2014 "),T3e=n(V7o,"A",{href:!0});var rPn=s(T3e);$oa=r(rPn,"FunnelForTokenClassification"),rPn.forEach(t),joa=r(V7o," (Funnel Transformer model)"),V7o.forEach(t),Poa=i(H),xR=n(H,"LI",{});var W7o=s(xR);WKe=n(W7o,"STRONG",{});var tPn=s(WKe);Zoa=r(tPn,"gpt-sw3"),tPn.forEach(t),Voa=r(W7o," \u2014 "),F3e=n(W7o,"A",{href:!0});var aPn=s(F3e);Woa=r(aPn,"GPT2ForTokenClassification"),aPn.forEach(t),Joa=r(W7o," (GPT-Sw3 model)"),W7o.forEach(t),Uoa=i(H),GR=n(H,"LI",{});var J7o=s(GR);JKe=n(J7o,"STRONG",{});var nPn=s(JKe);Xoa=r(nPn,"gpt2"),nPn.forEach(t),Doa=r(J7o," \u2014 "),E3e=n(J7o,"A",{href:!0});var sPn=s(E3e);qoa=r(sPn,"GPT2ForTokenClassification"),sPn.forEach(t),Ooa=r(J7o," (OpenAI GPT-2 model)"),J7o.forEach(t),Qoa=i(H),BR=n(H,"LI",{});var U7o=s(BR);UKe=n(U7o,"STRONG",{});var lPn=s(UKe);Yoa=r(lPn,"gpt_bigcode"),lPn.forEach(t),zoa=r(U7o," \u2014 "),y3e=n(U7o,"A",{href:!0});var iPn=s(y3e);Hoa=r(iPn,"GPTBigCodeForTokenClassification"),iPn.forEach(t),Koa=r(U7o," (GPTBigCode model)"),U7o.forEach(t),era=i(H),IR=n(H,"LI",{});var X7o=s(IR);XKe=n(X7o,"STRONG",{});var dPn=s(XKe);ora=r(dPn,"gpt_neo"),dPn.forEach(t),rra=r(X7o," \u2014 "),C3e=n(X7o,"A",{href:!0});var cPn=s(C3e);tra=r(cPn,"GPTNeoForTokenClassification"),cPn.forEach(t),ara=r(X7o," (GPT Neo model)"),X7o.forEach(t),nra=i(H),NR=n(H,"LI",{});var D7o=s(NR);DKe=n(D7o,"STRONG",{});var mPn=s(DKe);sra=r(mPn,"gpt_neox"),mPn.forEach(t),lra=r(D7o," \u2014 "),w3e=n(D7o,"A",{href:!0});var fPn=s(w3e);ira=r(fPn,"GPTNeoXForTokenClassification"),fPn.forEach(t),dra=r(D7o," (GPT NeoX model)"),D7o.forEach(t),cra=i(H),$R=n(H,"LI",{});var q7o=s($R);qKe=n(q7o,"STRONG",{});var hPn=s(qKe);mra=r(hPn,"ibert"),hPn.forEach(t),fra=r(q7o," \u2014 "),L3e=n(q7o,"A",{href:!0});var gPn=s(L3e);hra=r(gPn,"IBertForTokenClassification"),gPn.forEach(t),gra=r(q7o," (I-BERT model)"),q7o.forEach(t),ura=i(H),jR=n(H,"LI",{});var O7o=s(jR);OKe=n(O7o,"STRONG",{});var uPn=s(OKe);pra=r(uPn,"layoutlm"),uPn.forEach(t),_ra=r(O7o," \u2014 "),A3e=n(O7o,"A",{href:!0});var pPn=s(A3e);vra=r(pPn,"LayoutLMForTokenClassification"),pPn.forEach(t),bra=r(O7o," (LayoutLM model)"),O7o.forEach(t),Mra=i(H),PR=n(H,"LI",{});var Q7o=s(PR);QKe=n(Q7o,"STRONG",{});var _Pn=s(QKe);Tra=r(_Pn,"layoutlmv2"),_Pn.forEach(t),Fra=r(Q7o," \u2014 "),R3e=n(Q7o,"A",{href:!0});var vPn=s(R3e);Era=r(vPn,"LayoutLMv2ForTokenClassification"),vPn.forEach(t),yra=r(Q7o," (LayoutLMv2 model)"),Q7o.forEach(t),Cra=i(H),ZR=n(H,"LI",{});var Y7o=s(ZR);YKe=n(Y7o,"STRONG",{});var bPn=s(YKe);wra=r(bPn,"layoutlmv3"),bPn.forEach(t),Lra=r(Y7o," \u2014 "),k3e=n(Y7o,"A",{href:!0});var MPn=s(k3e);Ara=r(MPn,"LayoutLMv3ForTokenClassification"),MPn.forEach(t),Rra=r(Y7o," (LayoutLMv3 model)"),Y7o.forEach(t),kra=i(H),VR=n(H,"LI",{});var z7o=s(VR);zKe=n(z7o,"STRONG",{});var TPn=s(zKe);Sra=r(TPn,"lilt"),TPn.forEach(t),xra=r(z7o," \u2014 "),S3e=n(z7o,"A",{href:!0});var FPn=s(S3e);Gra=r(FPn,"LiltForTokenClassification"),FPn.forEach(t),Bra=r(z7o," (LiLT model)"),z7o.forEach(t),Ira=i(H),WR=n(H,"LI",{});var H7o=s(WR);HKe=n(H7o,"STRONG",{});var EPn=s(HKe);Nra=r(EPn,"longformer"),EPn.forEach(t),$ra=r(H7o," \u2014 "),x3e=n(H7o,"A",{href:!0});var yPn=s(x3e);jra=r(yPn,"LongformerForTokenClassification"),yPn.forEach(t),Pra=r(H7o," (Longformer model)"),H7o.forEach(t),Zra=i(H),JR=n(H,"LI",{});var K7o=s(JR);KKe=n(K7o,"STRONG",{});var CPn=s(KKe);Vra=r(CPn,"luke"),CPn.forEach(t),Wra=r(K7o," \u2014 "),G3e=n(K7o,"A",{href:!0});var wPn=s(G3e);Jra=r(wPn,"LukeForTokenClassification"),wPn.forEach(t),Ura=r(K7o," (LUKE model)"),K7o.forEach(t),Xra=i(H),UR=n(H,"LI",{});var eLo=s(UR);eeo=n(eLo,"STRONG",{});var LPn=s(eeo);Dra=r(LPn,"markuplm"),LPn.forEach(t),qra=r(eLo," \u2014 "),B3e=n(eLo,"A",{href:!0});var APn=s(B3e);Ora=r(APn,"MarkupLMForTokenClassification"),APn.forEach(t),Qra=r(eLo," (MarkupLM model)"),eLo.forEach(t),Yra=i(H),XR=n(H,"LI",{});var oLo=s(XR);oeo=n(oLo,"STRONG",{});var RPn=s(oeo);zra=r(RPn,"mega"),RPn.forEach(t),Hra=r(oLo," \u2014 "),I3e=n(oLo,"A",{href:!0});var kPn=s(I3e);Kra=r(kPn,"MegaForTokenClassification"),kPn.forEach(t),eta=r(oLo," (MEGA model)"),oLo.forEach(t),ota=i(H),DR=n(H,"LI",{});var rLo=s(DR);reo=n(rLo,"STRONG",{});var SPn=s(reo);rta=r(SPn,"megatron-bert"),SPn.forEach(t),tta=r(rLo," \u2014 "),N3e=n(rLo,"A",{href:!0});var xPn=s(N3e);ata=r(xPn,"MegatronBertForTokenClassification"),xPn.forEach(t),nta=r(rLo," (Megatron-BERT model)"),rLo.forEach(t),sta=i(H),qR=n(H,"LI",{});var tLo=s(qR);teo=n(tLo,"STRONG",{});var GPn=s(teo);lta=r(GPn,"mobilebert"),GPn.forEach(t),ita=r(tLo," \u2014 "),$3e=n(tLo,"A",{href:!0});var BPn=s($3e);dta=r(BPn,"MobileBertForTokenClassification"),BPn.forEach(t),cta=r(tLo," (MobileBERT model)"),tLo.forEach(t),mta=i(H),OR=n(H,"LI",{});var aLo=s(OR);aeo=n(aLo,"STRONG",{});var IPn=s(aeo);fta=r(IPn,"mpnet"),IPn.forEach(t),hta=r(aLo," \u2014 "),j3e=n(aLo,"A",{href:!0});var NPn=s(j3e);gta=r(NPn,"MPNetForTokenClassification"),NPn.forEach(t),uta=r(aLo," (MPNet model)"),aLo.forEach(t),pta=i(H),QR=n(H,"LI",{});var nLo=s(QR);neo=n(nLo,"STRONG",{});var $Pn=s(neo);_ta=r($Pn,"mpt"),$Pn.forEach(t),vta=r(nLo," \u2014 "),P3e=n(nLo,"A",{href:!0});var jPn=s(P3e);bta=r(jPn,"MptForTokenClassification"),jPn.forEach(t),Mta=r(nLo," (MPT model)"),nLo.forEach(t),Tta=i(H),YR=n(H,"LI",{});var sLo=s(YR);seo=n(sLo,"STRONG",{});var PPn=s(seo);Fta=r(PPn,"mra"),PPn.forEach(t),Eta=r(sLo," \u2014 "),Z3e=n(sLo,"A",{href:!0});var ZPn=s(Z3e);yta=r(ZPn,"MraForTokenClassification"),ZPn.forEach(t),Cta=r(sLo," (MRA model)"),sLo.forEach(t),wta=i(H),zR=n(H,"LI",{});var lLo=s(zR);leo=n(lLo,"STRONG",{});var VPn=s(leo);Lta=r(VPn,"nezha"),VPn.forEach(t),Ata=r(lLo," \u2014 "),V3e=n(lLo,"A",{href:!0});var WPn=s(V3e);Rta=r(WPn,"NezhaForTokenClassification"),WPn.forEach(t),kta=r(lLo," (Nezha model)"),lLo.forEach(t),Sta=i(H),HR=n(H,"LI",{});var iLo=s(HR);ieo=n(iLo,"STRONG",{});var JPn=s(ieo);xta=r(JPn,"nystromformer"),JPn.forEach(t),Gta=r(iLo," \u2014 "),W3e=n(iLo,"A",{href:!0});var UPn=s(W3e);Bta=r(UPn,"NystromformerForTokenClassification"),UPn.forEach(t),Ita=r(iLo," (Nystr\xF6mformer model)"),iLo.forEach(t),Nta=i(H),KR=n(H,"LI",{});var dLo=s(KR);deo=n(dLo,"STRONG",{});var XPn=s(deo);$ta=r(XPn,"qdqbert"),XPn.forEach(t),jta=r(dLo," \u2014 "),J3e=n(dLo,"A",{href:!0});var DPn=s(J3e);Pta=r(DPn,"QDQBertForTokenClassification"),DPn.forEach(t),Zta=r(dLo," (QDQBert model)"),dLo.forEach(t),Vta=i(H),ek=n(H,"LI",{});var cLo=s(ek);ceo=n(cLo,"STRONG",{});var qPn=s(ceo);Wta=r(qPn,"rembert"),qPn.forEach(t),Jta=r(cLo," \u2014 "),U3e=n(cLo,"A",{href:!0});var OPn=s(U3e);Uta=r(OPn,"RemBertForTokenClassification"),OPn.forEach(t),Xta=r(cLo," (RemBERT model)"),cLo.forEach(t),Dta=i(H),ok=n(H,"LI",{});var mLo=s(ok);meo=n(mLo,"STRONG",{});var QPn=s(meo);qta=r(QPn,"roberta"),QPn.forEach(t),Ota=r(mLo," \u2014 "),X3e=n(mLo,"A",{href:!0});var YPn=s(X3e);Qta=r(YPn,"RobertaForTokenClassification"),YPn.forEach(t),Yta=r(mLo," (RoBERTa model)"),mLo.forEach(t),zta=i(H),rk=n(H,"LI",{});var fLo=s(rk);feo=n(fLo,"STRONG",{});var zPn=s(feo);Hta=r(zPn,"roberta-prelayernorm"),zPn.forEach(t),Kta=r(fLo," \u2014 "),D3e=n(fLo,"A",{href:!0});var HPn=s(D3e);eaa=r(HPn,"RobertaPreLayerNormForTokenClassification"),HPn.forEach(t),oaa=r(fLo," (RoBERTa-PreLayerNorm 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\u2014 "),Y3e=n(pLo,"A",{href:!0});var sZn=s(Y3e);Maa=r(sZn,"XLMForTokenClassification"),sZn.forEach(t),Taa=r(pLo," (XLM model)"),pLo.forEach(t),Faa=i(H),lk=n(H,"LI",{});var _Lo=s(lk);_eo=n(_Lo,"STRONG",{});var lZn=s(_eo);Eaa=r(lZn,"xlm-roberta"),lZn.forEach(t),yaa=r(_Lo," \u2014 "),z3e=n(_Lo,"A",{href:!0});var iZn=s(z3e);Caa=r(iZn,"XLMRobertaForTokenClassification"),iZn.forEach(t),waa=r(_Lo," (XLM-RoBERTa model)"),_Lo.forEach(t),Laa=i(H),ik=n(H,"LI",{});var vLo=s(ik);veo=n(vLo,"STRONG",{});var dZn=s(veo);Aaa=r(dZn,"xlm-roberta-xl"),dZn.forEach(t),Raa=r(vLo," \u2014 "),H3e=n(vLo,"A",{href:!0});var cZn=s(H3e);kaa=r(cZn,"XLMRobertaXLForTokenClassification"),cZn.forEach(t),Saa=r(vLo," (XLM-RoBERTa-XL model)"),vLo.forEach(t),xaa=i(H),dk=n(H,"LI",{});var bLo=s(dk);beo=n(bLo,"STRONG",{});var mZn=s(beo);Gaa=r(mZn,"xlnet"),mZn.forEach(t),Baa=r(bLo," \u2014 "),K3e=n(bLo,"A",{href:!0});var fZn=s(K3e);Iaa=r(fZn,"XLNetForTokenClassification"),fZn.forEach(t),Naa=r(bLo," (XLNet model)"),bLo.forEach(t),$aa=i(H),ck=n(H,"LI",{});var MLo=s(ck);Meo=n(MLo,"STRONG",{});var hZn=s(Meo);jaa=r(hZn,"xmod"),hZn.forEach(t),Paa=r(MLo," \u2014 "),e4e=n(MLo,"A",{href:!0});var gZn=s(e4e);Zaa=r(gZn,"XmodForTokenClassification"),gZn.forEach(t),Vaa=r(MLo," (X-MOD model)"),MLo.forEach(t),Waa=i(H),mk=n(H,"LI",{});var TLo=s(mk);Teo=n(TLo,"STRONG",{});var uZn=s(Teo);Jaa=r(uZn,"yoso"),uZn.forEach(t),Uaa=r(TLo," \u2014 "),o4e=n(TLo,"A",{href:!0});var pZn=s(o4e);Xaa=r(pZn,"YosoForTokenClassification"),pZn.forEach(t),Daa=r(TLo," (YOSO model)"),TLo.forEach(t),H.forEach(t),qaa=i(dn),fk=n(dn,"P",{});var FLo=s(fk);Oaa=r(FLo,"The model is set in evaluation mode by default using "),Feo=n(FLo,"CODE",{});var _Zn=s(Feo);Qaa=r(_Zn,"model.eval()"),_Zn.forEach(t),Yaa=r(FLo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Eeo=n(FLo,"CODE",{});var vZn=s(Eeo);zaa=r(vZn,"model.train()"),vZn.forEach(t),FLo.forEach(t),Haa=i(dn),M(hk.$$.fragment,dn),dn.forEach(t),jd.forEach(t),dBo=i(m),Rf=n(m,"H3",{class:!0});var d$o=s(Rf);gk=n(d$o,"A",{id:!0,class:!0,href:!0});var bZn=s(gk);yeo=n(bZn,"SPAN",{});var MZn=s(yeo);M(HJ.$$.fragment,MZn),MZn.forEach(t),bZn.forEach(t),Kaa=i(d$o),Ceo=n(d$o,"SPAN",{});var TZn=s(Ceo);ena=r(TZn,"TFAutoModelForTokenClassification"),TZn.forEach(t),d$o.forEach(t),cBo=i(m),ur=n(m,"DIV",{class:!0});var Pd=s(ur);M(KJ.$$.fragment,Pd),ona=i(Pd),kf=n(Pd,"P",{});var uCe=s(kf);rna=r(uCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
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method.`),uCe.forEach(t),lna=i(Pd),eU=n(Pd,"P",{});var c$o=s(eU);ina=r(c$o,"This class cannot be instantiated directly using "),weo=n(c$o,"CODE",{});var yZn=s(weo);dna=r(yZn,"__init__()"),yZn.forEach(t),cna=r(c$o," (throws an error)."),c$o.forEach(t),mna=i(Pd),ha=n(Pd,"DIV",{class:!0});var MP=s(ha);M(oU.$$.fragment,MP),fna=i(MP),Leo=n(MP,"P",{});var CZn=s(Leo);hna=r(CZn,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),CZn.forEach(t),gna=i(MP),Sf=n(MP,"P",{});var pCe=s(Sf);una=r(pCe,`Note:
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model\u2019s configuration. Use `),a4e=n(pCe,"A",{href:!0});var LZn=s(a4e);vna=r(LZn,"from_pretrained()"),LZn.forEach(t),bna=r(pCe," to load the model weights."),pCe.forEach(t),Mna=i(MP),M(uk.$$.fragment,MP),MP.forEach(t),Tna=i(Pd),bt=n(Pd,"DIV",{class:!0});var Zd=s(bt);M(rU.$$.fragment,Zd),Fna=i(Zd),Reo=n(Zd,"P",{});var AZn=s(Reo);Ena=r(AZn,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),AZn.forEach(t),yna=i(Zd),cs=n(Zd,"P",{});var TP=s(cs);Cna=r(TP,"The model class to instantiate is selected based on the "),keo=n(TP,"CODE",{});var RZn=s(keo);wna=r(RZn,"model_type"),RZn.forEach(t),Lna=r(TP,` property of the config object (either
passed as an argument or loaded from `),Seo=n(TP,"CODE",{});var kZn=s(Seo);Ana=r(kZn,"pretrained_model_name_or_path"),kZn.forEach(t),Rna=r(TP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),xeo=n(TP,"CODE",{});var SZn=s(xeo);kna=r(SZn,"pretrained_model_name_or_path"),SZn.forEach(t),Sna=r(TP,":"),TP.forEach(t),xna=i(Zd),ue=n(Zd,"UL",{});var be=s(ue);pk=n(be,"LI",{});var ELo=s(pk);Geo=n(ELo,"STRONG",{});var xZn=s(Geo);Gna=r(xZn,"albert"),xZn.forEach(t),Bna=r(ELo," \u2014 "),n4e=n(ELo,"A",{href:!0});var GZn=s(n4e);Ina=r(GZn,"TFAlbertForTokenClassification"),GZn.forEach(t),Nna=r(ELo," (ALBERT model)"),ELo.forEach(t),$na=i(be),_k=n(be,"LI",{});var yLo=s(_k);Beo=n(yLo,"STRONG",{});var BZn=s(Beo);jna=r(BZn,"bert"),BZn.forEach(t),Pna=r(yLo," \u2014 "),s4e=n(yLo,"A",{href:!0});var IZn=s(s4e);Zna=r(IZn,"TFBertForTokenClassification"),IZn.forEach(t),Vna=r(yLo," (BERT model)"),yLo.forEach(t),Wna=i(be),vk=n(be,"LI",{});var CLo=s(vk);Ieo=n(CLo,"STRONG",{});var NZn=s(Ieo);Jna=r(NZn,"camembert"),NZn.forEach(t),Una=r(CLo," \u2014 "),l4e=n(CLo,"A",{href:!0});var $Zn=s(l4e);Xna=r($Zn,"TFCamembertForTokenClassification"),$Zn.forEach(t),Dna=r(CLo," 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method.`),_Ce.forEach(t),Ola=i(Vd),nU=n(Vd,"P",{});var f$o=s(nU);Qla=r(f$o,"This class cannot be instantiated directly using "),aoo=n(f$o,"CODE",{});var RVn=s(aoo);Yla=r(RVn,"__init__()"),RVn.forEach(t),zla=r(f$o," (throws an error)."),f$o.forEach(t),Hla=i(Vd),ga=n(Vd,"DIV",{class:!0});var FP=s(ga);M(sU.$$.fragment,FP),Kla=i(FP),noo=n(FP,"P",{});var kVn=s(noo);eia=r(kVn,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),kVn.forEach(t),oia=i(FP),Bf=n(FP,"P",{});var vCe=s(Bf);ria=r(vCe,`Note:
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model\u2019s configuration. Use `),k4e=n(vCe,"A",{href:!0});var xVn=s(k4e);nia=r(xVn,"from_pretrained()"),xVn.forEach(t),sia=r(vCe," to load the model weights."),vCe.forEach(t),lia=i(FP),M(Vk.$$.fragment,FP),FP.forEach(t),iia=i(Vd),Mt=n(Vd,"DIV",{class:!0});var Wd=s(Mt);M(lU.$$.fragment,Wd),dia=i(Wd),loo=n(Wd,"P",{});var GVn=s(loo);cia=r(GVn,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),GVn.forEach(t),mia=i(Wd),ms=n(Wd,"P",{});var EP=s(ms);fia=r(EP,"The model class to instantiate is selected based on the "),ioo=n(EP,"CODE",{});var BVn=s(ioo);hia=r(BVn,"model_type"),BVn.forEach(t),gia=r(EP,` property of the config object (either
passed as an argument or loaded from `),doo=n(EP,"CODE",{});var IVn=s(doo);uia=r(IVn,"pretrained_model_name_or_path"),IVn.forEach(t),pia=r(EP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),coo=n(EP,"CODE",{});var NVn=s(coo);_ia=r(NVn,"pretrained_model_name_or_path"),NVn.forEach(t),via=r(EP,":"),EP.forEach(t),bia=i(Wd),Xe=n(Wd,"UL",{});var Ke=s(Xe);Wk=n(Ke,"LI",{});var DLo=s(Wk);moo=n(DLo,"STRONG",{});var $Vn=s(moo);Mia=r($Vn,"albert"),$Vn.forEach(t),Tia=r(DLo," \u2014 "),S4e=n(DLo,"A",{href:!0});var jVn=s(S4e);Fia=r(jVn,"FlaxAlbertForTokenClassification"),jVn.forEach(t),Eia=r(DLo," (ALBERT model)"),DLo.forEach(t),yia=i(Ke),Jk=n(Ke,"LI",{});var qLo=s(Jk);foo=n(qLo,"STRONG",{});var PVn=s(foo);Cia=r(PVn,"bert"),PVn.forEach(t),wia=r(qLo," \u2014 "),x4e=n(qLo,"A",{href:!0});var ZVn=s(x4e);Lia=r(ZVn,"FlaxBertForTokenClassification"),ZVn.forEach(t),Aia=r(qLo," (BERT model)"),qLo.forEach(t),Ria=i(Ke),Uk=n(Ke,"LI",{});var OLo=s(Uk);hoo=n(OLo,"STRONG",{});var VVn=s(hoo);kia=r(VVn,"big_bird"),VVn.forEach(t),Sia=r(OLo," \u2014 "),G4e=n(OLo,"A",{href:!0});var WVn=s(G4e);xia=r(WVn,"FlaxBigBirdForTokenClassification"),WVn.forEach(t),Gia=r(OLo," (BigBird model)"),OLo.forEach(t),Bia=i(Ke),Xk=n(Ke,"LI",{});var QLo=s(Xk);goo=n(QLo,"STRONG",{});var JVn=s(goo);Iia=r(JVn,"distilbert"),JVn.forEach(t),Nia=r(QLo," \u2014 "),B4e=n(QLo,"A",{href:!0});var UVn=s(B4e);$ia=r(UVn,"FlaxDistilBertForTokenClassification"),UVn.forEach(t),jia=r(QLo," (DistilBERT model)"),QLo.forEach(t),Pia=i(Ke),Dk=n(Ke,"LI",{});var YLo=s(Dk);uoo=n(YLo,"STRONG",{});var XVn=s(uoo);Zia=r(XVn,"electra"),XVn.forEach(t),Via=r(YLo," \u2014 "),I4e=n(YLo,"A",{href:!0});var DVn=s(I4e);Wia=r(DVn,"FlaxElectraForTokenClassification"),DVn.forEach(t),Jia=r(YLo," (ELECTRA model)"),YLo.forEach(t),Uia=i(Ke),qk=n(Ke,"LI",{});var zLo=s(qk);poo=n(zLo,"STRONG",{});var qVn=s(poo);Xia=r(qVn,"roberta"),qVn.forEach(t),Dia=r(zLo," \u2014 "),N4e=n(zLo,"A",{href:!0});var OVn=s(N4e);qia=r(OVn,"FlaxRobertaForTokenClassification"),OVn.forEach(t),Oia=r(zLo," (RoBERTa model)"),zLo.forEach(t),Qia=i(Ke),Ok=n(Ke,"LI",{});var HLo=s(Ok);_oo=n(HLo,"STRONG",{});var QVn=s(_oo);Yia=r(QVn,"roberta-prelayernorm"),QVn.forEach(t),zia=r(HLo," \u2014 "),$4e=n(HLo,"A",{href:!0});var YVn=s($4e);Hia=r(YVn,"FlaxRobertaPreLayerNormForTokenClassification"),YVn.forEach(t),Kia=r(HLo," (RoBERTa-PreLayerNorm model)"),HLo.forEach(t),eda=i(Ke),Qk=n(Ke,"LI",{});var KLo=s(Qk);voo=n(KLo,"STRONG",{});var zVn=s(voo);oda=r(zVn,"roformer"),zVn.forEach(t),rda=r(KLo," \u2014 "),j4e=n(KLo,"A",{href:!0});var HVn=s(j4e);tda=r(HVn,"FlaxRoFormerForTokenClassification"),HVn.forEach(t),ada=r(KLo," (RoFormer model)"),KLo.forEach(t),nda=i(Ke),Yk=n(Ke,"LI",{});var eAo=s(Yk);boo=n(eAo,"STRONG",{});var KVn=s(boo);sda=r(KVn,"xlm-roberta"),KVn.forEach(t),lda=r(eAo," \u2014 "),P4e=n(eAo,"A",{href:!0});var eWn=s(P4e);ida=r(eWn,"FlaxXLMRobertaForTokenClassification"),eWn.forEach(t),dda=r(eAo," (XLM-RoBERTa model)"),eAo.forEach(t),Ke.forEach(t),cda=i(Wd),M(zk.$$.fragment,Wd),Wd.forEach(t),Vd.forEach(t),hBo=i(m),If=n(m,"H3",{class:!0});var h$o=s(If);Hk=n(h$o,"A",{id:!0,class:!0,href:!0});var oWn=s(Hk);Moo=n(oWn,"SPAN",{});var rWn=s(Moo);M(iU.$$.fragment,rWn),rWn.forEach(t),oWn.forEach(t),mda=i(h$o),Too=n(h$o,"SPAN",{});var tWn=s(Too);fda=r(tWn,"AutoModelForQuestionAnswering"),tWn.forEach(t),h$o.forEach(t),gBo=i(m),_r=n(m,"DIV",{class:!0});var Jd=s(_r);M(dU.$$.fragment,Jd),hda=i(Jd),Nf=n(Jd,"P",{});var bCe=s(Nf);gda=r(bCe,`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
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with the `),YMe=n(TCe,"A",{href:!0});var IUn=s(YMe);Zua=r(IUn,"from_pretrained()"),IUn.forEach(t),Vua=r(TCe," class method or the "),zMe=n(TCe,"A",{href:!0});var NUn=s(zMe);Wua=r(NUn,"from_config()"),NUn.forEach(t),Jua=r(TCe,` class
method.`),TCe.forEach(t),Uua=i(Ud),uU=n(Ud,"P",{});var p$o=s(uU);Xua=r(p$o,"This class cannot be instantiated directly using "),Wro=n(p$o,"CODE",{});var $Un=s(Wro);Dua=r($Un,"__init__()"),$Un.forEach(t),qua=r(p$o," (throws an error)."),p$o.forEach(t),Oua=i(Ud),pa=n(Ud,"DIV",{class:!0});var wP=s(pa);M(pU.$$.fragment,wP),Qua=i(wP),Jro=n(wP,"P",{});var jUn=s(Jro);Yua=r(jUn,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),jUn.forEach(t),zua=i(wP),Zf=n(wP,"P",{});var FCe=s(Zf);Hua=r(FCe,`Note:
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model\u2019s configuration. Use `),HMe=n(FCe,"A",{href:!0});var ZUn=s(HMe);opa=r(ZUn,"from_pretrained()"),ZUn.forEach(t),rpa=r(FCe," to load the model weights."),FCe.forEach(t),tpa=i(wP),M(mx.$$.fragment,wP),wP.forEach(t),apa=i(Ud),Tt=n(Ud,"DIV",{class:!0});var Xd=s(Tt);M(_U.$$.fragment,Xd),npa=i(Xd),Xro=n(Xd,"P",{});var VUn=s(Xro);spa=r(VUn,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),VUn.forEach(t),lpa=i(Xd),hs=n(Xd,"P",{});var LP=s(hs);ipa=r(LP,"The model class to instantiate is selected based on the "),Dro=n(LP,"CODE",{});var WUn=s(Dro);dpa=r(WUn,"model_type"),WUn.forEach(t),cpa=r(LP,` property of the config object (either
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"),uTe=n(wRo,"A",{href:!0});var yXn=s(uTe);U_a=r(yXn,"TFRobertaForQuestionAnswering"),yXn.forEach(t),X_a=r(wRo," (RoBERTa model)"),wRo.forEach(t),D_a=i(Te),Rx=n(Te,"LI",{});var LRo=s(Rx);fto=n(LRo,"STRONG",{});var CXn=s(fto);q_a=r(CXn,"roberta-prelayernorm"),CXn.forEach(t),O_a=r(LRo," \u2014 "),pTe=n(LRo,"A",{href:!0});var wXn=s(pTe);Q_a=r(wXn,"TFRobertaPreLayerNormForQuestionAnswering"),wXn.forEach(t),Y_a=r(LRo," (RoBERTa-PreLayerNorm model)"),LRo.forEach(t),z_a=i(Te),kx=n(Te,"LI",{});var ARo=s(kx);hto=n(ARo,"STRONG",{});var LXn=s(hto);H_a=r(LXn,"roformer"),LXn.forEach(t),K_a=r(ARo," \u2014 "),_Te=n(ARo,"A",{href:!0});var AXn=s(_Te);e2a=r(AXn,"TFRoFormerForQuestionAnswering"),AXn.forEach(t),o2a=r(ARo," (RoFormer model)"),ARo.forEach(t),r2a=i(Te),Sx=n(Te,"LI",{});var RRo=s(Sx);gto=n(RRo,"STRONG",{});var RXn=s(gto);t2a=r(RXn,"xlm"),RXn.forEach(t),a2a=r(RRo," \u2014 "),vTe=n(RRo,"A",{href:!0});var kXn=s(vTe);n2a=r(kXn,"TFXLMForQuestionAnsweringSimple"),kXn.forEach(t),s2a=r(RRo," (XLM 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$Xn=s(vto);b2a=r($Xn,"FlaxAutoModelForQuestionAnswering"),$Xn.forEach(t),_$o.forEach(t),vBo=i(m),br=n(m,"DIV",{class:!0});var Dd=s(br);M(bU.$$.fragment,Dd),M2a=i(Dd),Wf=n(Dd,"P",{});var ECe=s(Wf);T2a=r(ECe,`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 `),TTe=n(ECe,"A",{href:!0});var jXn=s(TTe);F2a=r(jXn,"from_pretrained()"),jXn.forEach(t),E2a=r(ECe," class method or the "),FTe=n(ECe,"A",{href:!0});var PXn=s(FTe);y2a=r(PXn,"from_config()"),PXn.forEach(t),C2a=r(ECe,` class
method.`),ECe.forEach(t),w2a=i(Dd),MU=n(Dd,"P",{});var v$o=s(MU);L2a=r(v$o,"This class cannot be instantiated directly using "),bto=n(v$o,"CODE",{});var ZXn=s(bto);A2a=r(ZXn,"__init__()"),ZXn.forEach(t),R2a=r(v$o," (throws an error)."),v$o.forEach(t),k2a=i(Dd),_a=n(Dd,"DIV",{class:!0});var AP=s(_a);M(TU.$$.fragment,AP),S2a=i(AP),Mto=n(AP,"P",{});var VXn=s(Mto);x2a=r(VXn,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),VXn.forEach(t),G2a=i(AP),Jf=n(AP,"P",{});var yCe=s(Jf);B2a=r(yCe,`Note:
Loading a model from its configuration file does `),Tto=n(yCe,"STRONG",{});var WXn=s(Tto);I2a=r(WXn,"not"),WXn.forEach(t),N2a=r(yCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),ETe=n(yCe,"A",{href:!0});var JXn=s(ETe);$2a=r(JXn,"from_pretrained()"),JXn.forEach(t),j2a=r(yCe," to load the model weights."),yCe.forEach(t),P2a=i(AP),M(Nx.$$.fragment,AP),AP.forEach(t),Z2a=i(Dd),Ft=n(Dd,"DIV",{class:!0});var qd=s(Ft);M(FU.$$.fragment,qd),V2a=i(qd),Fto=n(qd,"P",{});var UXn=s(Fto);W2a=r(UXn,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),UXn.forEach(t),J2a=i(qd),gs=n(qd,"P",{});var RP=s(gs);U2a=r(RP,"The model class to instantiate is selected based on the "),Eto=n(RP,"CODE",{});var XXn=s(Eto);X2a=r(XXn,"model_type"),XXn.forEach(t),D2a=r(RP,` property of the config object (either
passed as an argument or loaded from `),yto=n(RP,"CODE",{});var DXn=s(yto);q2a=r(DXn,"pretrained_model_name_or_path"),DXn.forEach(t),O2a=r(RP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Cto=n(RP,"CODE",{});var qXn=s(Cto);Q2a=r(qXn,"pretrained_model_name_or_path"),qXn.forEach(t),Y2a=r(RP,":"),RP.forEach(t),z2a=i(qd),xe=n(qd,"UL",{});var Ve=s(xe);$x=n(Ve,"LI",{});var xRo=s($x);wto=n(xRo,"STRONG",{});var OXn=s(wto);H2a=r(OXn,"albert"),OXn.forEach(t),K2a=r(xRo," \u2014 "),yTe=n(xRo,"A",{href:!0});var QXn=s(yTe);eva=r(QXn,"FlaxAlbertForQuestionAnswering"),QXn.forEach(t),ova=r(xRo," (ALBERT model)"),xRo.forEach(t),rva=i(Ve),jx=n(Ve,"LI",{});var GRo=s(jx);Lto=n(GRo,"STRONG",{});var YXn=s(Lto);tva=r(YXn,"bart"),YXn.forEach(t),ava=r(GRo," \u2014 "),CTe=n(GRo,"A",{href:!0});var zXn=s(CTe);nva=r(zXn,"FlaxBartForQuestionAnswering"),zXn.forEach(t),sva=r(GRo," (BART model)"),GRo.forEach(t),lva=i(Ve),Px=n(Ve,"LI",{});var BRo=s(Px);Ato=n(BRo,"STRONG",{});var HXn=s(Ato);iva=r(HXn,"bert"),HXn.forEach(t),dva=r(BRo," \u2014 "),wTe=n(BRo,"A",{href:!0});var KXn=s(wTe);cva=r(KXn,"FlaxBertForQuestionAnswering"),KXn.forEach(t),mva=r(BRo," (BERT model)"),BRo.forEach(t),fva=i(Ve),Zx=n(Ve,"LI",{});var IRo=s(Zx);Rto=n(IRo,"STRONG",{});var eDn=s(Rto);hva=r(eDn,"big_bird"),eDn.forEach(t),gva=r(IRo," \u2014 "),LTe=n(IRo,"A",{href:!0});var oDn=s(LTe);uva=r(oDn,"FlaxBigBirdForQuestionAnswering"),oDn.forEach(t),pva=r(IRo," (BigBird model)"),IRo.forEach(t),_va=i(Ve),Vx=n(Ve,"LI",{});var NRo=s(Vx);kto=n(NRo,"STRONG",{});var rDn=s(kto);vva=r(rDn,"distilbert"),rDn.forEach(t),bva=r(NRo," \u2014 "),ATe=n(NRo,"A",{href:!0});var tDn=s(ATe);Mva=r(tDn,"FlaxDistilBertForQuestionAnswering"),tDn.forEach(t),Tva=r(NRo," (DistilBERT model)"),NRo.forEach(t),Fva=i(Ve),Wx=n(Ve,"LI",{});var $Ro=s(Wx);Sto=n($Ro,"STRONG",{});var aDn=s(Sto);Eva=r(aDn,"electra"),aDn.forEach(t),yva=r($Ro," \u2014 "),RTe=n($Ro,"A",{href:!0});var nDn=s(RTe);Cva=r(nDn,"FlaxElectraForQuestionAnswering"),nDn.forEach(t),wva=r($Ro," (ELECTRA model)"),$Ro.forEach(t),Lva=i(Ve),Jx=n(Ve,"LI",{});var jRo=s(Jx);xto=n(jRo,"STRONG",{});var sDn=s(xto);Ava=r(sDn,"mbart"),sDn.forEach(t),Rva=r(jRo," \u2014 "),kTe=n(jRo,"A",{href:!0});var lDn=s(kTe);kva=r(lDn,"FlaxMBartForQuestionAnswering"),lDn.forEach(t),Sva=r(jRo," (mBART model)"),jRo.forEach(t),xva=i(Ve),Ux=n(Ve,"LI",{});var PRo=s(Ux);Gto=n(PRo,"STRONG",{});var iDn=s(Gto);Gva=r(iDn,"roberta"),iDn.forEach(t),Bva=r(PRo," \u2014 "),STe=n(PRo,"A",{href:!0});var dDn=s(STe);Iva=r(dDn,"FlaxRobertaForQuestionAnswering"),dDn.forEach(t),Nva=r(PRo," (RoBERTa model)"),PRo.forEach(t),$va=i(Ve),Xx=n(Ve,"LI",{});var ZRo=s(Xx);Bto=n(ZRo,"STRONG",{});var cDn=s(Bto);jva=r(cDn,"roberta-prelayernorm"),cDn.forEach(t),Pva=r(ZRo," \u2014 "),xTe=n(ZRo,"A",{href:!0});var mDn=s(xTe);Zva=r(mDn,"FlaxRobertaPreLayerNormForQuestionAnswering"),mDn.forEach(t),Vva=r(ZRo," (RoBERTa-PreLayerNorm model)"),ZRo.forEach(t),Wva=i(Ve),Dx=n(Ve,"LI",{});var VRo=s(Dx);Ito=n(VRo,"STRONG",{});var fDn=s(Ito);Jva=r(fDn,"roformer"),fDn.forEach(t),Uva=r(VRo," \u2014 "),GTe=n(VRo,"A",{href:!0});var hDn=s(GTe);Xva=r(hDn,"FlaxRoFormerForQuestionAnswering"),hDn.forEach(t),Dva=r(VRo," (RoFormer model)"),VRo.forEach(t),qva=i(Ve),qx=n(Ve,"LI",{});var WRo=s(qx);Nto=n(WRo,"STRONG",{});var gDn=s(Nto);Ova=r(gDn,"xlm-roberta"),gDn.forEach(t),Qva=r(WRo," \u2014 "),BTe=n(WRo,"A",{href:!0});var uDn=s(BTe);Yva=r(uDn,"FlaxXLMRobertaForQuestionAnswering"),uDn.forEach(t),zva=r(WRo," (XLM-RoBERTa model)"),WRo.forEach(t),Ve.forEach(t),Hva=i(qd),M(Ox.$$.fragment,qd),qd.forEach(t),Dd.forEach(t),bBo=i(m),Uf=n(m,"H3",{class:!0});var b$o=s(Uf);Qx=n(b$o,"A",{id:!0,class:!0,href:!0});var pDn=s(Qx);$to=n(pDn,"SPAN",{});var _Dn=s($to);M(EU.$$.fragment,_Dn),_Dn.forEach(t),pDn.forEach(t),Kva=i(b$o),jto=n(b$o,"SPAN",{});var vDn=s(jto);e1a=r(vDn,"AutoModelForTextEncoding"),vDn.forEach(t),b$o.forEach(t),MBo=i(m),yU=n(m,"DIV",{class:!0});var bDn=s(yU);M(CU.$$.fragment,bDn),bDn.forEach(t),TBo=i(m),Xf=n(m,"H3",{class:!0});var M$o=s(Xf);Yx=n(M$o,"A",{id:!0,class:!0,href:!0});var MDn=s(Yx);Pto=n(MDn,"SPAN",{});var TDn=s(Pto);M(wU.$$.fragment,TDn),TDn.forEach(t),MDn.forEach(t),o1a=i(M$o),Zto=n(M$o,"SPAN",{});var FDn=s(Zto);r1a=r(FDn,"TFAutoModelForTextEncoding"),FDn.forEach(t),M$o.forEach(t),FBo=i(m),LU=n(m,"DIV",{class:!0});var EDn=s(LU);M(AU.$$.fragment,EDn),EDn.forEach(t),EBo=i(m),Df=n(m,"H2",{class:!0});var T$o=s(Df);zx=n(T$o,"A",{id:!0,class:!0,href:!0});var yDn=s(zx);Vto=n(yDn,"SPAN",{});var CDn=s(Vto);M(RU.$$.fragment,CDn),CDn.forEach(t),yDn.forEach(t),t1a=i(T$o),Wto=n(T$o,"SPAN",{});var wDn=s(Wto);a1a=r(wDn,"Computer vision"),wDn.forEach(t),T$o.forEach(t),yBo=i(m),ITe=n(m,"P",{});var LDn=s(ITe);n1a=r(LDn,"The following auto classes are available for the following computer vision tasks."),LDn.forEach(t),CBo=i(m),qf=n(m,"H3",{class:!0});var F$o=s(qf);Hx=n(F$o,"A",{id:!0,class:!0,href:!0});var ADn=s(Hx);Jto=n(ADn,"SPAN",{});var RDn=s(Jto);M(kU.$$.fragment,RDn),RDn.forEach(t),ADn.forEach(t),s1a=i(F$o),Uto=n(F$o,"SPAN",{});var kDn=s(Uto);l1a=r(kDn,"AutoModelForDepthEstimation"),kDn.forEach(t),F$o.forEach(t),wBo=i(m),Mr=n(m,"DIV",{class:!0});var Od=s(Mr);M(SU.$$.fragment,Od),i1a=i(Od),Of=n(Od,"P",{});var CCe=s(Of);d1a=r(CCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a depth estimation head) when created
with the `),NTe=n(CCe,"A",{href:!0});var SDn=s(NTe);c1a=r(SDn,"from_pretrained()"),SDn.forEach(t),m1a=r(CCe," class method or the "),$Te=n(CCe,"A",{href:!0});var xDn=s($Te);f1a=r(xDn,"from_config()"),xDn.forEach(t),h1a=r(CCe,` class
method.`),CCe.forEach(t),g1a=i(Od),xU=n(Od,"P",{});var E$o=s(xU);u1a=r(E$o,"This class cannot be instantiated directly using "),Xto=n(E$o,"CODE",{});var GDn=s(Xto);p1a=r(GDn,"__init__()"),GDn.forEach(t),_1a=r(E$o," (throws an error)."),E$o.forEach(t),v1a=i(Od),va=n(Od,"DIV",{class:!0});var kP=s(va);M(GU.$$.fragment,kP),b1a=i(kP),Dto=n(kP,"P",{});var BDn=s(Dto);M1a=r(BDn,"Instantiates one of the model classes of the library (with a depth estimation head) from a configuration."),BDn.forEach(t),T1a=i(kP),Qf=n(kP,"P",{});var wCe=s(Qf);F1a=r(wCe,`Note:
Loading a model from its configuration file does `),qto=n(wCe,"STRONG",{});var IDn=s(qto);E1a=r(IDn,"not"),IDn.forEach(t),y1a=r(wCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),jTe=n(wCe,"A",{href:!0});var NDn=s(jTe);C1a=r(NDn,"from_pretrained()"),NDn.forEach(t),w1a=r(wCe," to load the model weights."),wCe.forEach(t),L1a=i(kP),M(Kx.$$.fragment,kP),kP.forEach(t),A1a=i(Od),_o=n(Od,"DIV",{class:!0});var mn=s(_o);M(BU.$$.fragment,mn),R1a=i(mn),Oto=n(mn,"P",{});var $Dn=s(Oto);k1a=r($Dn,"Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model."),$Dn.forEach(t),S1a=i(mn),us=n(mn,"P",{});var SP=s(us);x1a=r(SP,"The model class to instantiate is selected based on the "),Qto=n(SP,"CODE",{});var jDn=s(Qto);G1a=r(jDn,"model_type"),jDn.forEach(t),B1a=r(SP,` property of the config object (either
passed as an argument or loaded from `),Yto=n(SP,"CODE",{});var PDn=s(Yto);I1a=r(PDn,"pretrained_model_name_or_path"),PDn.forEach(t),N1a=r(SP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),zto=n(SP,"CODE",{});var ZDn=s(zto);$1a=r(ZDn,"pretrained_model_name_or_path"),ZDn.forEach(t),j1a=r(SP,":"),SP.forEach(t),P1a=i(mn),IU=n(mn,"UL",{});var y$o=s(IU);eG=n(y$o,"LI",{});var JRo=s(eG);Hto=n(JRo,"STRONG",{});var VDn=s(Hto);Z1a=r(VDn,"dpt"),VDn.forEach(t),V1a=r(JRo," \u2014 "),PTe=n(JRo,"A",{href:!0});var WDn=s(PTe);W1a=r(WDn,"DPTForDepthEstimation"),WDn.forEach(t),J1a=r(JRo," (DPT model)"),JRo.forEach(t),U1a=i(y$o),oG=n(y$o,"LI",{});var URo=s(oG);Kto=n(URo,"STRONG",{});var JDn=s(Kto);X1a=r(JDn,"glpn"),JDn.forEach(t),D1a=r(URo," \u2014 "),ZTe=n(URo,"A",{href:!0});var UDn=s(ZTe);q1a=r(UDn,"GLPNForDepthEstimation"),UDn.forEach(t),O1a=r(URo," (GLPN model)"),URo.forEach(t),y$o.forEach(t),Q1a=i(mn),rG=n(mn,"P",{});var XRo=s(rG);Y1a=r(XRo,"The model is set in evaluation mode by default using "),eao=n(XRo,"CODE",{});var XDn=s(eao);z1a=r(XDn,"model.eval()"),XDn.forEach(t),H1a=r(XRo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),oao=n(XRo,"CODE",{});var DDn=s(oao);K1a=r(DDn,"model.train()"),DDn.forEach(t),XRo.forEach(t),eba=i(mn),M(tG.$$.fragment,mn),mn.forEach(t),Od.forEach(t),LBo=i(m),Yf=n(m,"H3",{class:!0});var C$o=s(Yf);aG=n(C$o,"A",{id:!0,class:!0,href:!0});var qDn=s(aG);rao=n(qDn,"SPAN",{});var ODn=s(rao);M(NU.$$.fragment,ODn),ODn.forEach(t),qDn.forEach(t),oba=i(C$o),tao=n(C$o,"SPAN",{});var QDn=s(tao);rba=r(QDn,"AutoModelForImageClassification"),QDn.forEach(t),C$o.forEach(t),ABo=i(m),Tr=n(m,"DIV",{class:!0});var Qd=s(Tr);M($U.$$.fragment,Qd),tba=i(Qd),zf=n(Qd,"P",{});var LCe=s(zf);aba=r(LCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),VTe=n(LCe,"A",{href:!0});var YDn=s(VTe);nba=r(YDn,"from_pretrained()"),YDn.forEach(t),sba=r(LCe," class method or the "),WTe=n(LCe,"A",{href:!0});var zDn=s(WTe);lba=r(zDn,"from_config()"),zDn.forEach(t),iba=r(LCe,` class
method.`),LCe.forEach(t),dba=i(Qd),jU=n(Qd,"P",{});var w$o=s(jU);cba=r(w$o,"This class cannot be instantiated directly using "),aao=n(w$o,"CODE",{});var HDn=s(aao);mba=r(HDn,"__init__()"),HDn.forEach(t),fba=r(w$o," (throws an error)."),w$o.forEach(t),hba=i(Qd),ba=n(Qd,"DIV",{class:!0});var xP=s(ba);M(PU.$$.fragment,xP),gba=i(xP),nao=n(xP,"P",{});var KDn=s(nao);uba=r(KDn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),KDn.forEach(t),pba=i(xP),Hf=n(xP,"P",{});var ACe=s(Hf);_ba=r(ACe,`Note:
Loading a model from its configuration file does `),sao=n(ACe,"STRONG",{});var eqn=s(sao);vba=r(eqn,"not"),eqn.forEach(t),bba=r(ACe,` load the model weights. It only affects the
model\u2019s configuration. Use `),JTe=n(ACe,"A",{href:!0});var oqn=s(JTe);Mba=r(oqn,"from_pretrained()"),oqn.forEach(t),Tba=r(ACe," to load the model weights."),ACe.forEach(t),Fba=i(xP),M(nG.$$.fragment,xP),xP.forEach(t),Eba=i(Qd),vo=n(Qd,"DIV",{class:!0});var fn=s(vo);M(ZU.$$.fragment,fn),yba=i(fn),lao=n(fn,"P",{});var rqn=s(lao);Cba=r(rqn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),rqn.forEach(t),wba=i(fn),ps=n(fn,"P",{});var GP=s(ps);Lba=r(GP,"The model class to instantiate is selected based on the "),iao=n(GP,"CODE",{});var tqn=s(iao);Aba=r(tqn,"model_type"),tqn.forEach(t),Rba=r(GP,` property of the config object (either
passed as an argument or loaded from `),dao=n(GP,"CODE",{});var aqn=s(dao);kba=r(aqn,"pretrained_model_name_or_path"),aqn.forEach(t),Sba=r(GP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),cao=n(GP,"CODE",{});var nqn=s(cao);xba=r(nqn,"pretrained_model_name_or_path"),nqn.forEach(t),Gba=r(GP,":"),GP.forEach(t),Bba=i(fn),le=n(fn,"UL",{});var ce=s(le);sG=n(ce,"LI",{});var DRo=s(sG);mao=n(DRo,"STRONG",{});var sqn=s(mao);Iba=r(sqn,"beit"),sqn.forEach(t),Nba=r(DRo," \u2014 "),UTe=n(DRo,"A",{href:!0});var lqn=s(UTe);$ba=r(lqn,"BeitForImageClassification"),lqn.forEach(t),jba=r(DRo," (BEiT model)"),DRo.forEach(t),Pba=i(ce),lG=n(ce,"LI",{});var qRo=s(lG);fao=n(qRo,"STRONG",{});var iqn=s(fao);Zba=r(iqn,"bit"),iqn.forEach(t),Vba=r(qRo," \u2014 "),XTe=n(qRo,"A",{href:!0});var dqn=s(XTe);Wba=r(dqn,"BitForImageClassification"),dqn.forEach(t),Jba=r(qRo," (BiT model)"),qRo.forEach(t),Uba=i(ce),iG=n(ce,"LI",{});var ORo=s(iG);hao=n(ORo,"STRONG",{});var cqn=s(hao);Xba=r(cqn,"convnext"),cqn.forEach(t),Dba=r(ORo," \u2014 "),DTe=n(ORo,"A",{href:!0});var mqn=s(DTe);qba=r(mqn,"ConvNextForImageClassification"),mqn.forEach(t),Oba=r(ORo," (ConvNeXT 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vqn=s(_ao);m0a=r(vqn,"deit"),vqn.forEach(t),f0a=r(NQ," \u2014 "),YTe=n(NQ,"A",{href:!0});var bqn=s(YTe);h0a=r(bqn,"DeiTForImageClassification"),bqn.forEach(t),g0a=r(NQ," or "),zTe=n(NQ,"A",{href:!0});var Mqn=s(zTe);u0a=r(Mqn,"DeiTForImageClassificationWithTeacher"),Mqn.forEach(t),p0a=r(NQ," (DeiT model)"),NQ.forEach(t),_0a=i(ce),fG=n(ce,"LI",{});var HRo=s(fG);vao=n(HRo,"STRONG",{});var Tqn=s(vao);v0a=r(Tqn,"dinat"),Tqn.forEach(t),b0a=r(HRo," \u2014 "),HTe=n(HRo,"A",{href:!0});var Fqn=s(HTe);M0a=r(Fqn,"DinatForImageClassification"),Fqn.forEach(t),T0a=r(HRo," (DiNAT model)"),HRo.forEach(t),F0a=i(ce),hG=n(ce,"LI",{});var KRo=s(hG);bao=n(KRo,"STRONG",{});var Eqn=s(bao);E0a=r(Eqn,"dinov2"),Eqn.forEach(t),y0a=r(KRo," \u2014 "),KTe=n(KRo,"A",{href:!0});var yqn=s(KTe);C0a=r(yqn,"Dinov2ForImageClassification"),yqn.forEach(t),w0a=r(KRo," (DINOv2 model)"),KRo.forEach(t),L0a=i(ce),Ji=n(ce,"LI",{});var $Q=s(Ji);Mao=n($Q,"STRONG",{});var Cqn=s(Mao);A0a=r(Cqn,"efficientformer"),Cqn.forEach(t),R0a=r($Q," \u2014 "),eFe=n($Q,"A",{href:!0});var wqn=s(eFe);k0a=r(wqn,"EfficientFormerForImageClassification"),wqn.forEach(t),S0a=r($Q," or "),oFe=n($Q,"A",{href:!0});var Lqn=s(oFe);x0a=r(Lqn,"EfficientFormerForImageClassificationWithTeacher"),Lqn.forEach(t),G0a=r($Q," (EfficientFormer model)"),$Q.forEach(t),B0a=i(ce),gG=n(ce,"LI",{});var eko=s(gG);Tao=n(eko,"STRONG",{});var Aqn=s(Tao);I0a=r(Aqn,"efficientnet"),Aqn.forEach(t),N0a=r(eko," \u2014 "),rFe=n(eko,"A",{href:!0});var Rqn=s(rFe);$0a=r(Rqn,"EfficientNetForImageClassification"),Rqn.forEach(t),j0a=r(eko," (EfficientNet model)"),eko.forEach(t),P0a=i(ce),uG=n(ce,"LI",{});var oko=s(uG);Fao=n(oko,"STRONG",{});var kqn=s(Fao);Z0a=r(kqn,"focalnet"),kqn.forEach(t),V0a=r(oko," \u2014 "),tFe=n(oko,"A",{href:!0});var Sqn=s(tFe);W0a=r(Sqn,"FocalNetForImageClassification"),Sqn.forEach(t),J0a=r(oko," (FocalNet model)"),oko.forEach(t),U0a=i(ce),pG=n(ce,"LI",{});var rko=s(pG);Eao=n(rko,"STRONG",{});var xqn=s(Eao);X0a=r(xqn,"imagegpt"),xqn.forEach(t),D0a=r(rko," \u2014 "),aFe=n(rko,"A",{href:!0});var Gqn=s(aFe);q0a=r(Gqn,"ImageGPTForImageClassification"),Gqn.forEach(t),O0a=r(rko," (ImageGPT model)"),rko.forEach(t),Q0a=i(ce),Ui=n(ce,"LI",{});var jQ=s(Ui);yao=n(jQ,"STRONG",{});var Bqn=s(yao);Y0a=r(Bqn,"levit"),Bqn.forEach(t),z0a=r(jQ," \u2014 "),nFe=n(jQ,"A",{href:!0});var Iqn=s(nFe);H0a=r(Iqn,"LevitForImageClassification"),Iqn.forEach(t),K0a=r(jQ," or "),sFe=n(jQ,"A",{href:!0});var Nqn=s(sFe);e3a=r(Nqn,"LevitForImageClassificationWithTeacher"),Nqn.forEach(t),o3a=r(jQ," (LeViT model)"),jQ.forEach(t),r3a=i(ce),_G=n(ce,"LI",{});var tko=s(_G);Cao=n(tko,"STRONG",{});var $qn=s(Cao);t3a=r($qn,"mobilenet_v1"),$qn.forEach(t),a3a=r(tko," \u2014 "),lFe=n(tko,"A",{href:!0});var jqn=s(lFe);n3a=r(jqn,"MobileNetV1ForImageClassification"),jqn.forEach(t),s3a=r(tko," (MobileNetV1 model)"),tko.forEach(t),l3a=i(ce),vG=n(ce,"LI",{});var ako=s(vG);wao=n(ako,"STRONG",{});var Pqn=s(wao);i3a=r(Pqn,"mobilenet_v2"),Pqn.forEach(t),d3a=r(ako," \u2014 "),iFe=n(ako,"A",{href:!0});var Zqn=s(iFe);c3a=r(Zqn,"MobileNetV2ForImageClassification"),Zqn.forEach(t),m3a=r(ako," (MobileNetV2 model)"),ako.forEach(t),f3a=i(ce),bG=n(ce,"LI",{});var nko=s(bG);Lao=n(nko,"STRONG",{});var Vqn=s(Lao);h3a=r(Vqn,"mobilevit"),Vqn.forEach(t),g3a=r(nko," \u2014 "),dFe=n(nko,"A",{href:!0});var Wqn=s(dFe);u3a=r(Wqn,"MobileViTForImageClassification"),Wqn.forEach(t),p3a=r(nko," (MobileViT model)"),nko.forEach(t),_3a=i(ce),MG=n(ce,"LI",{});var sko=s(MG);Aao=n(sko,"STRONG",{});var Jqn=s(Aao);v3a=r(Jqn,"mobilevitv2"),Jqn.forEach(t),b3a=r(sko," \u2014 "),cFe=n(sko,"A",{href:!0});var Uqn=s(cFe);M3a=r(Uqn,"MobileViTV2ForImageClassification"),Uqn.forEach(t),T3a=r(sko," (MobileViTV2 model)"),sko.forEach(t),F3a=i(ce),TG=n(ce,"LI",{});var lko=s(TG);Rao=n(lko,"STRONG",{});var Xqn=s(Rao);E3a=r(Xqn,"nat"),Xqn.forEach(t),y3a=r(lko," \u2014 "),mFe=n(lko,"A",{href:!0});var Dqn=s(mFe);C3a=r(Dqn,"NatForImageClassification"),Dqn.forEach(t),w3a=r(lko," (NAT model)"),lko.forEach(t),L3a=i(ce),Ma=n(ce,"LI",{});var Ug=s(Ma);kao=n(Ug,"STRONG",{});var qqn=s(kao);A3a=r(qqn,"perceiver"),qqn.forEach(t),R3a=r(Ug," \u2014 "),fFe=n(Ug,"A",{href:!0});var Oqn=s(fFe);k3a=r(Oqn,"PerceiverForImageClassificationLearned"),Oqn.forEach(t),S3a=r(Ug," or "),hFe=n(Ug,"A",{href:!0});var Qqn=s(hFe);x3a=r(Qqn,"PerceiverForImageClassificationFourier"),Qqn.forEach(t),G3a=r(Ug," or "),gFe=n(Ug,"A",{href:!0});var Yqn=s(gFe);B3a=r(Yqn,"PerceiverForImageClassificationConvProcessing"),Yqn.forEach(t),I3a=r(Ug," (Perceiver model)"),Ug.forEach(t),N3a=i(ce),FG=n(ce,"LI",{});var iko=s(FG);Sao=n(iko,"STRONG",{});var zqn=s(Sao);$3a=r(zqn,"poolformer"),zqn.forEach(t),j3a=r(iko," \u2014 "),uFe=n(iko,"A",{href:!0});var Hqn=s(uFe);P3a=r(Hqn,"PoolFormerForImageClassification"),Hqn.forEach(t),Z3a=r(iko," (PoolFormer model)"),iko.forEach(t),V3a=i(ce),EG=n(ce,"LI",{});var dko=s(EG);xao=n(dko,"STRONG",{});var Kqn=s(xao);W3a=r(Kqn,"pvt"),Kqn.forEach(t),J3a=r(dko," \u2014 "),pFe=n(dko,"A",{href:!0});var eOn=s(pFe);U3a=r(eOn,"PvtForImageClassification"),eOn.forEach(t),X3a=r(dko," (PVT model)"),dko.forEach(t),D3a=i(ce),yG=n(ce,"LI",{});var cko=s(yG);Gao=n(cko,"STRONG",{});var oOn=s(Gao);q3a=r(oOn,"regnet"),oOn.forEach(t),O3a=r(cko," \u2014 "),_Fe=n(cko,"A",{href:!0});var rOn=s(_Fe);Q3a=r(rOn,"RegNetForImageClassification"),rOn.forEach(t),Y3a=r(cko," (RegNet model)"),cko.forEach(t),z3a=i(ce),CG=n(ce,"LI",{});var mko=s(CG);Bao=n(mko,"STRONG",{});var tOn=s(Bao);H3a=r(tOn,"resnet"),tOn.forEach(t),K3a=r(mko," \u2014 "),vFe=n(mko,"A",{href:!0});var aOn=s(vFe);e4a=r(aOn,"ResNetForImageClassification"),aOn.forEach(t),o4a=r(mko," (ResNet model)"),mko.forEach(t),r4a=i(ce),wG=n(ce,"LI",{});var fko=s(wG);Iao=n(fko,"STRONG",{});var nOn=s(Iao);t4a=r(nOn,"segformer"),nOn.forEach(t),a4a=r(fko," \u2014 "),bFe=n(fko,"A",{href:!0});var sOn=s(bFe);n4a=r(sOn,"SegformerForImageClassification"),sOn.forEach(t),s4a=r(fko," (SegFormer model)"),fko.forEach(t),l4a=i(ce),LG=n(ce,"LI",{});var hko=s(LG);Nao=n(hko,"STRONG",{});var lOn=s(Nao);i4a=r(lOn,"swiftformer"),lOn.forEach(t),d4a=r(hko," \u2014 "),MFe=n(hko,"A",{href:!0});var iOn=s(MFe);c4a=r(iOn,"SwiftFormerForImageClassification"),iOn.forEach(t),m4a=r(hko," (SwiftFormer model)"),hko.forEach(t),f4a=i(ce),AG=n(ce,"LI",{});var gko=s(AG);$ao=n(gko,"STRONG",{});var dOn=s($ao);h4a=r(dOn,"swin"),dOn.forEach(t),g4a=r(gko," \u2014 "),TFe=n(gko,"A",{href:!0});var cOn=s(TFe);u4a=r(cOn,"SwinForImageClassification"),cOn.forEach(t),p4a=r(gko," (Swin Transformer model)"),gko.forEach(t),_4a=i(ce),RG=n(ce,"LI",{});var uko=s(RG);jao=n(uko,"STRONG",{});var mOn=s(jao);v4a=r(mOn,"swinv2"),mOn.forEach(t),b4a=r(uko," \u2014 "),FFe=n(uko,"A",{href:!0});var fOn=s(FFe);M4a=r(fOn,"Swinv2ForImageClassification"),fOn.forEach(t),T4a=r(uko," (Swin Transformer V2 model)"),uko.forEach(t),F4a=i(ce),kG=n(ce,"LI",{});var pko=s(kG);Pao=n(pko,"STRONG",{});var hOn=s(Pao);E4a=r(hOn,"van"),hOn.forEach(t),y4a=r(pko," \u2014 "),EFe=n(pko,"A",{href:!0});var gOn=s(EFe);C4a=r(gOn,"VanForImageClassification"),gOn.forEach(t),w4a=r(pko," (VAN model)"),pko.forEach(t),L4a=i(ce),SG=n(ce,"LI",{});var _ko=s(SG);Zao=n(_ko,"STRONG",{});var uOn=s(Zao);A4a=r(uOn,"vit"),uOn.forEach(t),R4a=r(_ko," \u2014 "),yFe=n(_ko,"A",{href:!0});var pOn=s(yFe);k4a=r(pOn,"ViTForImageClassification"),pOn.forEach(t),S4a=r(_ko," (ViT model)"),_ko.forEach(t),x4a=i(ce),xG=n(ce,"LI",{});var vko=s(xG);Vao=n(vko,"STRONG",{});var _On=s(Vao);G4a=r(_On,"vit_hybrid"),_On.forEach(t),B4a=r(vko," \u2014 "),CFe=n(vko,"A",{href:!0});var vOn=s(CFe);I4a=r(vOn,"ViTHybridForImageClassification"),vOn.forEach(t),N4a=r(vko," (ViT Hybrid model)"),vko.forEach(t),$4a=i(ce),GG=n(ce,"LI",{});var bko=s(GG);Wao=n(bko,"STRONG",{});var bOn=s(Wao);j4a=r(bOn,"vit_msn"),bOn.forEach(t),P4a=r(bko," \u2014 "),wFe=n(bko,"A",{href:!0});var MOn=s(wFe);Z4a=r(MOn,"ViTMSNForImageClassification"),MOn.forEach(t),V4a=r(bko," (ViTMSN model)"),bko.forEach(t),ce.forEach(t),W4a=i(fn),BG=n(fn,"P",{});var Mko=s(BG);J4a=r(Mko,"The model is set in evaluation mode by default using "),Jao=n(Mko,"CODE",{});var TOn=s(Jao);U4a=r(TOn,"model.eval()"),TOn.forEach(t),X4a=r(Mko,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Uao=n(Mko,"CODE",{});var FOn=s(Uao);D4a=r(FOn,"model.train()"),FOn.forEach(t),Mko.forEach(t),q4a=i(fn),M(IG.$$.fragment,fn),fn.forEach(t),Qd.forEach(t),RBo=i(m),Kf=n(m,"H3",{class:!0});var L$o=s(Kf);NG=n(L$o,"A",{id:!0,class:!0,href:!0});var EOn=s(NG);Xao=n(EOn,"SPAN",{});var yOn=s(Xao);M(VU.$$.fragment,yOn),yOn.forEach(t),EOn.forEach(t),O4a=i(L$o),Dao=n(L$o,"SPAN",{});var COn=s(Dao);Q4a=r(COn,"TFAutoModelForImageClassification"),COn.forEach(t),L$o.forEach(t),kBo=i(m),Fr=n(m,"DIV",{class:!0});var Yd=s(Fr);M(WU.$$.fragment,Yd),Y4a=i(Yd),eh=n(Yd,"P",{});var RCe=s(eh);z4a=r(RCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),LFe=n(RCe,"A",{href:!0});var wOn=s(LFe);H4a=r(wOn,"from_pretrained()"),wOn.forEach(t),K4a=r(RCe," class method or the "),AFe=n(RCe,"A",{href:!0});var LOn=s(AFe);eMa=r(LOn,"from_config()"),LOn.forEach(t),oMa=r(RCe,` class
method.`),RCe.forEach(t),rMa=i(Yd),JU=n(Yd,"P",{});var A$o=s(JU);tMa=r(A$o,"This class cannot be instantiated directly using "),qao=n(A$o,"CODE",{});var AOn=s(qao);aMa=r(AOn,"__init__()"),AOn.forEach(t),nMa=r(A$o," (throws an error)."),A$o.forEach(t),sMa=i(Yd),Ta=n(Yd,"DIV",{class:!0});var BP=s(Ta);M(UU.$$.fragment,BP),lMa=i(BP),Oao=n(BP,"P",{});var ROn=s(Oao);iMa=r(ROn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),ROn.forEach(t),dMa=i(BP),oh=n(BP,"P",{});var kCe=s(oh);cMa=r(kCe,`Note:
Loading a model from its configuration file does `),Qao=n(kCe,"STRONG",{});var kOn=s(Qao);mMa=r(kOn,"not"),kOn.forEach(t),fMa=r(kCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),RFe=n(kCe,"A",{href:!0});var SOn=s(RFe);hMa=r(SOn,"from_pretrained()"),SOn.forEach(t),gMa=r(kCe," to load the model weights."),kCe.forEach(t),uMa=i(BP),M($G.$$.fragment,BP),BP.forEach(t),pMa=i(Yd),Et=n(Yd,"DIV",{class:!0});var zd=s(Et);M(XU.$$.fragment,zd),_Ma=i(zd),Yao=n(zd,"P",{});var xOn=s(Yao);vMa=r(xOn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),xOn.forEach(t),bMa=i(zd),_s=n(zd,"P",{});var IP=s(_s);MMa=r(IP,"The model class to instantiate is selected based on the "),zao=n(IP,"CODE",{});var GOn=s(zao);TMa=r(GOn,"model_type"),GOn.forEach(t),FMa=r(IP,` property of the config object (either
passed as an argument or loaded from `),Hao=n(IP,"CODE",{});var BOn=s(Hao);EMa=r(BOn,"pretrained_model_name_or_path"),BOn.forEach(t),yMa=r(IP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Kao=n(IP,"CODE",{});var IOn=s(Kao);CMa=r(IOn,"pretrained_model_name_or_path"),IOn.forEach(t),wMa=r(IP,":"),IP.forEach(t),LMa=i(zd),Ge=n(zd,"UL",{});var We=s(Ge);jG=n(We,"LI",{});var Tko=s(jG);eno=n(Tko,"STRONG",{});var NOn=s(eno);AMa=r(NOn,"convnext"),NOn.forEach(t),RMa=r(Tko," \u2014 "),kFe=n(Tko,"A",{href:!0});var $On=s(kFe);kMa=r($On,"TFConvNextForImageClassification"),$On.forEach(t),SMa=r(Tko," (ConvNeXT model)"),Tko.forEach(t),xMa=i(We),PG=n(We,"LI",{});var Fko=s(PG);ono=n(Fko,"STRONG",{});var jOn=s(ono);GMa=r(jOn,"cvt"),jOn.forEach(t),BMa=r(Fko," \u2014 "),SFe=n(Fko,"A",{href:!0});var POn=s(SFe);IMa=r(POn,"TFCvtForImageClassification"),POn.forEach(t),NMa=r(Fko," (CvT model)"),Fko.forEach(t),$Ma=i(We),ZG=n(We,"LI",{});var Eko=s(ZG);rno=n(Eko,"STRONG",{});var ZOn=s(rno);jMa=r(ZOn,"data2vec-vision"),ZOn.forEach(t),PMa=r(Eko," \u2014 "),xFe=n(Eko,"A",{href:!0});var VOn=s(xFe);ZMa=r(VOn,"TFData2VecVisionForImageClassification"),VOn.forEach(t),VMa=r(Eko," (Data2VecVision model)"),Eko.forEach(t),WMa=i(We),Xi=n(We,"LI",{});var PQ=s(Xi);tno=n(PQ,"STRONG",{});var WOn=s(tno);JMa=r(WOn,"deit"),WOn.forEach(t),UMa=r(PQ," \u2014 "),GFe=n(PQ,"A",{href:!0});var JOn=s(GFe);XMa=r(JOn,"TFDeiTForImageClassification"),JOn.forEach(t),DMa=r(PQ," or "),BFe=n(PQ,"A",{href:!0});var UOn=s(BFe);qMa=r(UOn,"TFDeiTForImageClassificationWithTeacher"),UOn.forEach(t),OMa=r(PQ," (DeiT model)"),PQ.forEach(t),QMa=i(We),Di=n(We,"LI",{});var ZQ=s(Di);ano=n(ZQ,"STRONG",{});var XOn=s(ano);YMa=r(XOn,"efficientformer"),XOn.forEach(t),zMa=r(ZQ," \u2014 "),IFe=n(ZQ,"A",{href:!0});var DOn=s(IFe);HMa=r(DOn,"TFEfficientFormerForImageClassification"),DOn.forEach(t),KMa=r(ZQ," or "),NFe=n(ZQ,"A",{href:!0});var qOn=s(NFe);eTa=r(qOn,"TFEfficientFormerForImageClassificationWithTeacher"),qOn.forEach(t),oTa=r(ZQ," (EfficientFormer model)"),ZQ.forEach(t),rTa=i(We),VG=n(We,"LI",{});var yko=s(VG);nno=n(yko,"STRONG",{});var OOn=s(nno);tTa=r(OOn,"mobilevit"),OOn.forEach(t),aTa=r(yko," \u2014 "),$Fe=n(yko,"A",{href:!0});var QOn=s($Fe);nTa=r(QOn,"TFMobileViTForImageClassification"),QOn.forEach(t),sTa=r(yko," (MobileViT model)"),yko.forEach(t),lTa=i(We),WG=n(We,"LI",{});var Cko=s(WG);sno=n(Cko,"STRONG",{});var YOn=s(sno);iTa=r(YOn,"regnet"),YOn.forEach(t),dTa=r(Cko," \u2014 "),jFe=n(Cko,"A",{href:!0});var zOn=s(jFe);cTa=r(zOn,"TFRegNetForImageClassification"),zOn.forEach(t),mTa=r(Cko," (RegNet model)"),Cko.forEach(t),fTa=i(We),JG=n(We,"LI",{});var wko=s(JG);lno=n(wko,"STRONG",{});var HOn=s(lno);hTa=r(HOn,"resnet"),HOn.forEach(t),gTa=r(wko," \u2014 "),PFe=n(wko,"A",{href:!0});var KOn=s(PFe);uTa=r(KOn,"TFResNetForImageClassification"),KOn.forEach(t),pTa=r(wko," (ResNet model)"),wko.forEach(t),_Ta=i(We),UG=n(We,"LI",{});var Lko=s(UG);ino=n(Lko,"STRONG",{});var eQn=s(ino);vTa=r(eQn,"segformer"),eQn.forEach(t),bTa=r(Lko," \u2014 "),ZFe=n(Lko,"A",{href:!0});var oQn=s(ZFe);MTa=r(oQn,"TFSegformerForImageClassification"),oQn.forEach(t),TTa=r(Lko," (SegFormer model)"),Lko.forEach(t),FTa=i(We),XG=n(We,"LI",{});var Ako=s(XG);dno=n(Ako,"STRONG",{});var rQn=s(dno);ETa=r(rQn,"swin"),rQn.forEach(t),yTa=r(Ako," \u2014 "),VFe=n(Ako,"A",{href:!0});var tQn=s(VFe);CTa=r(tQn,"TFSwinForImageClassification"),tQn.forEach(t),wTa=r(Ako," (Swin Transformer model)"),Ako.forEach(t),LTa=i(We),DG=n(We,"LI",{});var Rko=s(DG);cno=n(Rko,"STRONG",{});var aQn=s(cno);ATa=r(aQn,"vit"),aQn.forEach(t),RTa=r(Rko," \u2014 "),WFe=n(Rko,"A",{href:!0});var nQn=s(WFe);kTa=r(nQn,"TFViTForImageClassification"),nQn.forEach(t),STa=r(Rko," (ViT model)"),Rko.forEach(t),We.forEach(t),xTa=i(zd),M(qG.$$.fragment,zd),zd.forEach(t),Yd.forEach(t),SBo=i(m),rh=n(m,"H3",{class:!0});var R$o=s(rh);OG=n(R$o,"A",{id:!0,class:!0,href:!0});var sQn=s(OG);mno=n(sQn,"SPAN",{});var lQn=s(mno);M(DU.$$.fragment,lQn),lQn.forEach(t),sQn.forEach(t),GTa=i(R$o),fno=n(R$o,"SPAN",{});var iQn=s(fno);BTa=r(iQn,"FlaxAutoModelForImageClassification"),iQn.forEach(t),R$o.forEach(t),xBo=i(m),Er=n(m,"DIV",{class:!0});var Hd=s(Er);M(qU.$$.fragment,Hd),ITa=i(Hd),th=n(Hd,"P",{});var SCe=s(th);NTa=r(SCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),JFe=n(SCe,"A",{href:!0});var dQn=s(JFe);$Ta=r(dQn,"from_pretrained()"),dQn.forEach(t),jTa=r(SCe," class method or the "),UFe=n(SCe,"A",{href:!0});var cQn=s(UFe);PTa=r(cQn,"from_config()"),cQn.forEach(t),ZTa=r(SCe,` class
method.`),SCe.forEach(t),VTa=i(Hd),OU=n(Hd,"P",{});var k$o=s(OU);WTa=r(k$o,"This class cannot be instantiated directly using "),hno=n(k$o,"CODE",{});var mQn=s(hno);JTa=r(mQn,"__init__()"),mQn.forEach(t),UTa=r(k$o," (throws an error)."),k$o.forEach(t),XTa=i(Hd),Fa=n(Hd,"DIV",{class:!0});var NP=s(Fa);M(QU.$$.fragment,NP),DTa=i(NP),gno=n(NP,"P",{});var fQn=s(gno);qTa=r(fQn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),fQn.forEach(t),OTa=i(NP),ah=n(NP,"P",{});var xCe=s(ah);QTa=r(xCe,`Note:
Loading a model from its configuration file does `),uno=n(xCe,"STRONG",{});var hQn=s(uno);YTa=r(hQn,"not"),hQn.forEach(t),zTa=r(xCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),XFe=n(xCe,"A",{href:!0});var gQn=s(XFe);HTa=r(gQn,"from_pretrained()"),gQn.forEach(t),KTa=r(xCe," to load the model weights."),xCe.forEach(t),eFa=i(NP),M(QG.$$.fragment,NP),NP.forEach(t),oFa=i(Hd),yt=n(Hd,"DIV",{class:!0});var Kd=s(yt);M(YU.$$.fragment,Kd),rFa=i(Kd),pno=n(Kd,"P",{});var uQn=s(pno);tFa=r(uQn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),uQn.forEach(t),aFa=i(Kd),vs=n(Kd,"P",{});var $P=s(vs);nFa=r($P,"The model class to instantiate is selected based on the "),_no=n($P,"CODE",{});var pQn=s(_no);sFa=r(pQn,"model_type"),pQn.forEach(t),lFa=r($P,` property of the config object (either
passed as an argument or loaded from `),vno=n($P,"CODE",{});var _Qn=s(vno);iFa=r(_Qn,"pretrained_model_name_or_path"),_Qn.forEach(t),dFa=r($P,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),bno=n($P,"CODE",{});var vQn=s(bno);cFa=r(vQn,"pretrained_model_name_or_path"),vQn.forEach(t),mFa=r($P,":"),$P.forEach(t),fFa=i(Kd),bs=n(Kd,"UL",{});var jP=s(bs);YG=n(jP,"LI",{});var kko=s(YG);Mno=n(kko,"STRONG",{});var bQn=s(Mno);hFa=r(bQn,"beit"),bQn.forEach(t),gFa=r(kko," \u2014 "),DFe=n(kko,"A",{href:!0});var MQn=s(DFe);uFa=r(MQn,"FlaxBeitForImageClassification"),MQn.forEach(t),pFa=r(kko," (BEiT model)"),kko.forEach(t),_Fa=i(jP),zG=n(jP,"LI",{});var Sko=s(zG);Tno=n(Sko,"STRONG",{});var TQn=s(Tno);vFa=r(TQn,"regnet"),TQn.forEach(t),bFa=r(Sko," \u2014 "),qFe=n(Sko,"A",{href:!0});var FQn=s(qFe);MFa=r(FQn,"FlaxRegNetForImageClassification"),FQn.forEach(t),TFa=r(Sko," (RegNet model)"),Sko.forEach(t),FFa=i(jP),HG=n(jP,"LI",{});var xko=s(HG);Fno=n(xko,"STRONG",{});var EQn=s(Fno);EFa=r(EQn,"resnet"),EQn.forEach(t),yFa=r(xko," \u2014 "),OFe=n(xko,"A",{href:!0});var yQn=s(OFe);CFa=r(yQn,"FlaxResNetForImageClassification"),yQn.forEach(t),wFa=r(xko," (ResNet model)"),xko.forEach(t),LFa=i(jP),KG=n(jP,"LI",{});var Gko=s(KG);Eno=n(Gko,"STRONG",{});var CQn=s(Eno);AFa=r(CQn,"vit"),CQn.forEach(t),RFa=r(Gko," \u2014 "),QFe=n(Gko,"A",{href:!0});var wQn=s(QFe);kFa=r(wQn,"FlaxViTForImageClassification"),wQn.forEach(t),SFa=r(Gko," (ViT model)"),Gko.forEach(t),jP.forEach(t),xFa=i(Kd),M(eB.$$.fragment,Kd),Kd.forEach(t),Hd.forEach(t),GBo=i(m),nh=n(m,"H3",{class:!0});var S$o=s(nh);oB=n(S$o,"A",{id:!0,class:!0,href:!0});var LQn=s(oB);yno=n(LQn,"SPAN",{});var AQn=s(yno);M(zU.$$.fragment,AQn),AQn.forEach(t),LQn.forEach(t),GFa=i(S$o),Cno=n(S$o,"SPAN",{});var RQn=s(Cno);BFa=r(RQn,"AutoModelForVideoClassification"),RQn.forEach(t),S$o.forEach(t),BBo=i(m),yr=n(m,"DIV",{class:!0});var ec=s(yr);M(HU.$$.fragment,ec),IFa=i(ec),sh=n(ec,"P",{});var GCe=s(sh);NFa=r(GCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a video classification head) when created
with the `),YFe=n(GCe,"A",{href:!0});var kQn=s(YFe);$Fa=r(kQn,"from_pretrained()"),kQn.forEach(t),jFa=r(GCe," class method or the "),zFe=n(GCe,"A",{href:!0});var SQn=s(zFe);PFa=r(SQn,"from_config()"),SQn.forEach(t),ZFa=r(GCe,` class
method.`),GCe.forEach(t),VFa=i(ec),KU=n(ec,"P",{});var x$o=s(KU);WFa=r(x$o,"This class cannot be instantiated directly using "),wno=n(x$o,"CODE",{});var xQn=s(wno);JFa=r(xQn,"__init__()"),xQn.forEach(t),UFa=r(x$o," (throws an error)."),x$o.forEach(t),XFa=i(ec),Ea=n(ec,"DIV",{class:!0});var PP=s(Ea);M(eX.$$.fragment,PP),DFa=i(PP),Lno=n(PP,"P",{});var GQn=s(Lno);qFa=r(GQn,"Instantiates one of the model classes of the library (with a video classification head) from a configuration."),GQn.forEach(t),OFa=i(PP),lh=n(PP,"P",{});var BCe=s(lh);QFa=r(BCe,`Note:
Loading a model from its configuration file does `),Ano=n(BCe,"STRONG",{});var BQn=s(Ano);YFa=r(BQn,"not"),BQn.forEach(t),zFa=r(BCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),HFe=n(BCe,"A",{href:!0});var IQn=s(HFe);HFa=r(IQn,"from_pretrained()"),IQn.forEach(t),KFa=r(BCe," to load the model weights."),BCe.forEach(t),eEa=i(PP),M(rB.$$.fragment,PP),PP.forEach(t),oEa=i(ec),bo=n(ec,"DIV",{class:!0});var hn=s(bo);M(oX.$$.fragment,hn),rEa=i(hn),Rno=n(hn,"P",{});var NQn=s(Rno);tEa=r(NQn,"Instantiate one of the model classes of the library (with a video classification head) from a pretrained model."),NQn.forEach(t),aEa=i(hn),Ms=n(hn,"P",{});var ZP=s(Ms);nEa=r(ZP,"The model class to instantiate is selected based on the "),kno=n(ZP,"CODE",{});var $Qn=s(kno);sEa=r($Qn,"model_type"),$Qn.forEach(t),lEa=r(ZP,` property of the config object (either
passed as an argument or loaded from `),Sno=n(ZP,"CODE",{});var jQn=s(Sno);iEa=r(jQn,"pretrained_model_name_or_path"),jQn.forEach(t),dEa=r(ZP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),xno=n(ZP,"CODE",{});var PQn=s(xno);cEa=r(PQn,"pretrained_model_name_or_path"),PQn.forEach(t),mEa=r(ZP,":"),ZP.forEach(t),fEa=i(hn),ih=n(hn,"UL",{});var ICe=s(ih);tB=n(ICe,"LI",{});var Bko=s(tB);Gno=n(Bko,"STRONG",{});var ZQn=s(Gno);hEa=r(ZQn,"timesformer"),ZQn.forEach(t),gEa=r(Bko," \u2014 "),KFe=n(Bko,"A",{href:!0});var VQn=s(KFe);uEa=r(VQn,"TimesformerForVideoClassification"),VQn.forEach(t),pEa=r(Bko," (TimeSformer model)"),Bko.forEach(t),_Ea=i(ICe),aB=n(ICe,"LI",{});var Iko=s(aB);Bno=n(Iko,"STRONG",{});var WQn=s(Bno);vEa=r(WQn,"videomae"),WQn.forEach(t),bEa=r(Iko," \u2014 "),eEe=n(Iko,"A",{href:!0});var JQn=s(eEe);MEa=r(JQn,"VideoMAEForVideoClassification"),JQn.forEach(t),TEa=r(Iko," (VideoMAE model)"),Iko.forEach(t),FEa=i(ICe),nB=n(ICe,"LI",{});var Nko=s(nB);Ino=n(Nko,"STRONG",{});var UQn=s(Ino);EEa=r(UQn,"vivit"),UQn.forEach(t),yEa=r(Nko," \u2014 "),oEe=n(Nko,"A",{href:!0});var XQn=s(oEe);CEa=r(XQn,"VivitForVideoClassification"),XQn.forEach(t),wEa=r(Nko," (ViViT model)"),Nko.forEach(t),ICe.forEach(t),LEa=i(hn),sB=n(hn,"P",{});var $ko=s(sB);AEa=r($ko,"The model is set in evaluation mode by default using "),Nno=n($ko,"CODE",{});var DQn=s(Nno);REa=r(DQn,"model.eval()"),DQn.forEach(t),kEa=r($ko,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),$no=n($ko,"CODE",{});var qQn=s($no);SEa=r(qQn,"model.train()"),qQn.forEach(t),$ko.forEach(t),xEa=i(hn),M(lB.$$.fragment,hn),hn.forEach(t),ec.forEach(t),IBo=i(m),dh=n(m,"H3",{class:!0});var G$o=s(dh);iB=n(G$o,"A",{id:!0,class:!0,href:!0});var OQn=s(iB);jno=n(OQn,"SPAN",{});var QQn=s(jno);M(rX.$$.fragment,QQn),QQn.forEach(t),OQn.forEach(t),GEa=i(G$o),Pno=n(G$o,"SPAN",{});var YQn=s(Pno);BEa=r(YQn,"AutoModelForMaskedImageModeling"),YQn.forEach(t),G$o.forEach(t),NBo=i(m),Cr=n(m,"DIV",{class:!0});var oc=s(Cr);M(tX.$$.fragment,oc),IEa=i(oc),ch=n(oc,"P",{});var NCe=s(ch);NEa=r(NCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),rEe=n(NCe,"A",{href:!0});var zQn=s(rEe);$Ea=r(zQn,"from_pretrained()"),zQn.forEach(t),jEa=r(NCe," class method or the "),tEe=n(NCe,"A",{href:!0});var HQn=s(tEe);PEa=r(HQn,"from_config()"),HQn.forEach(t),ZEa=r(NCe,` class
method.`),NCe.forEach(t),VEa=i(oc),aX=n(oc,"P",{});var B$o=s(aX);WEa=r(B$o,"This class cannot be instantiated directly using "),Zno=n(B$o,"CODE",{});var KQn=s(Zno);JEa=r(KQn,"__init__()"),KQn.forEach(t),UEa=r(B$o," (throws an error)."),B$o.forEach(t),XEa=i(oc),ya=n(oc,"DIV",{class:!0});var VP=s(ya);M(nX.$$.fragment,VP),DEa=i(VP),Vno=n(VP,"P",{});var eYn=s(Vno);qEa=r(eYn,"Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),eYn.forEach(t),OEa=i(VP),mh=n(VP,"P",{});var $Ce=s(mh);QEa=r($Ce,`Note:
Loading a model from its configuration file does `),Wno=n($Ce,"STRONG",{});var oYn=s(Wno);YEa=r(oYn,"not"),oYn.forEach(t),zEa=r($Ce,` load the model weights. It only affects the
model\u2019s configuration. Use `),aEe=n($Ce,"A",{href:!0});var rYn=s(aEe);HEa=r(rYn,"from_pretrained()"),rYn.forEach(t),KEa=r($Ce," to load the model weights."),$Ce.forEach(t),eya=i(VP),M(dB.$$.fragment,VP),VP.forEach(t),oya=i(oc),Mo=n(oc,"DIV",{class:!0});var gn=s(Mo);M(sX.$$.fragment,gn),rya=i(gn),Jno=n(gn,"P",{});var tYn=s(Jno);tya=r(tYn,"Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),tYn.forEach(t),aya=i(gn),Ts=n(gn,"P",{});var WP=s(Ts);nya=r(WP,"The model class to instantiate is selected based on the "),Uno=n(WP,"CODE",{});var aYn=s(Uno);sya=r(aYn,"model_type"),aYn.forEach(t),lya=r(WP,` property of the config object (either
passed as an argument or loaded from `),Xno=n(WP,"CODE",{});var nYn=s(Xno);iya=r(nYn,"pretrained_model_name_or_path"),nYn.forEach(t),dya=r(WP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Dno=n(WP,"CODE",{});var sYn=s(Dno);cya=r(sYn,"pretrained_model_name_or_path"),sYn.forEach(t),mya=r(WP,":"),WP.forEach(t),fya=i(gn),Nt=n(gn,"UL",{});var rc=s(Nt);cB=n(rc,"LI",{});var jko=s(cB);qno=n(jko,"STRONG",{});var lYn=s(qno);hya=r(lYn,"deit"),lYn.forEach(t),gya=r(jko," \u2014 "),nEe=n(jko,"A",{href:!0});var iYn=s(nEe);uya=r(iYn,"DeiTForMaskedImageModeling"),iYn.forEach(t),pya=r(jko," (DeiT model)"),jko.forEach(t),_ya=i(rc),mB=n(rc,"LI",{});var Pko=s(mB);Ono=n(Pko,"STRONG",{});var dYn=s(Ono);vya=r(dYn,"focalnet"),dYn.forEach(t),bya=r(Pko," \u2014 "),sEe=n(Pko,"A",{href:!0});var cYn=s(sEe);Mya=r(cYn,"FocalNetForMaskedImageModeling"),cYn.forEach(t),Tya=r(Pko," (FocalNet model)"),Pko.forEach(t),Fya=i(rc),fB=n(rc,"LI",{});var Zko=s(fB);Qno=n(Zko,"STRONG",{});var mYn=s(Qno);Eya=r(mYn,"swin"),mYn.forEach(t),yya=r(Zko," \u2014 "),lEe=n(Zko,"A",{href:!0});var fYn=s(lEe);Cya=r(fYn,"SwinForMaskedImageModeling"),fYn.forEach(t),wya=r(Zko," (Swin Transformer model)"),Zko.forEach(t),Lya=i(rc),hB=n(rc,"LI",{});var Vko=s(hB);Yno=n(Vko,"STRONG",{});var hYn=s(Yno);Aya=r(hYn,"swinv2"),hYn.forEach(t),Rya=r(Vko," \u2014 "),iEe=n(Vko,"A",{href:!0});var gYn=s(iEe);kya=r(gYn,"Swinv2ForMaskedImageModeling"),gYn.forEach(t),Sya=r(Vko," (Swin Transformer V2 model)"),Vko.forEach(t),xya=i(rc),gB=n(rc,"LI",{});var Wko=s(gB);zno=n(Wko,"STRONG",{});var uYn=s(zno);Gya=r(uYn,"vit"),uYn.forEach(t),Bya=r(Wko," \u2014 "),dEe=n(Wko,"A",{href:!0});var pYn=s(dEe);Iya=r(pYn,"ViTForMaskedImageModeling"),pYn.forEach(t),Nya=r(Wko," (ViT model)"),Wko.forEach(t),rc.forEach(t),$ya=i(gn),uB=n(gn,"P",{});var Jko=s(uB);jya=r(Jko,"The model is set in evaluation mode by default using "),Hno=n(Jko,"CODE",{});var _Yn=s(Hno);Pya=r(_Yn,"model.eval()"),_Yn.forEach(t),Zya=r(Jko,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Kno=n(Jko,"CODE",{});var vYn=s(Kno);Vya=r(vYn,"model.train()"),vYn.forEach(t),Jko.forEach(t),Wya=i(gn),M(pB.$$.fragment,gn),gn.forEach(t),oc.forEach(t),$Bo=i(m),fh=n(m,"H3",{class:!0});var I$o=s(fh);_B=n(I$o,"A",{id:!0,class:!0,href:!0});var bYn=s(_B);eso=n(bYn,"SPAN",{});var MYn=s(eso);M(lX.$$.fragment,MYn),MYn.forEach(t),bYn.forEach(t),Jya=i(I$o),oso=n(I$o,"SPAN",{});var TYn=s(oso);Uya=r(TYn,"TFAutoModelForMaskedImageModeling"),TYn.forEach(t),I$o.forEach(t),jBo=i(m),wr=n(m,"DIV",{class:!0});var tc=s(wr);M(iX.$$.fragment,tc),Xya=i(tc),hh=n(tc,"P",{});var jCe=s(hh);Dya=r(jCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),cEe=n(jCe,"A",{href:!0});var FYn=s(cEe);qya=r(FYn,"from_pretrained()"),FYn.forEach(t),Oya=r(jCe," class method or the "),mEe=n(jCe,"A",{href:!0});var EYn=s(mEe);Qya=r(EYn,"from_config()"),EYn.forEach(t),Yya=r(jCe,` class
method.`),jCe.forEach(t),zya=i(tc),dX=n(tc,"P",{});var N$o=s(dX);Hya=r(N$o,"This class cannot be instantiated directly using "),rso=n(N$o,"CODE",{});var yYn=s(rso);Kya=r(yYn,"__init__()"),yYn.forEach(t),e5a=r(N$o," (throws an error)."),N$o.forEach(t),o5a=i(tc),Ca=n(tc,"DIV",{class:!0});var JP=s(Ca);M(cX.$$.fragment,JP),r5a=i(JP),tso=n(JP,"P",{});var CYn=s(tso);t5a=r(CYn,"Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),CYn.forEach(t),a5a=i(JP),gh=n(JP,"P",{});var PCe=s(gh);n5a=r(PCe,`Note:
Loading a model from its configuration file does `),aso=n(PCe,"STRONG",{});var wYn=s(aso);s5a=r(wYn,"not"),wYn.forEach(t),l5a=r(PCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),fEe=n(PCe,"A",{href:!0});var LYn=s(fEe);i5a=r(LYn,"from_pretrained()"),LYn.forEach(t),d5a=r(PCe," to load the model weights."),PCe.forEach(t),c5a=i(JP),M(vB.$$.fragment,JP),JP.forEach(t),m5a=i(tc),Ct=n(tc,"DIV",{class:!0});var ac=s(Ct);M(mX.$$.fragment,ac),f5a=i(ac),nso=n(ac,"P",{});var AYn=s(nso);h5a=r(AYn,"Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),AYn.forEach(t),g5a=i(ac),Fs=n(ac,"P",{});var UP=s(Fs);u5a=r(UP,"The model class to instantiate is selected based on the "),sso=n(UP,"CODE",{});var RYn=s(sso);p5a=r(RYn,"model_type"),RYn.forEach(t),_5a=r(UP,` property of the config object (either
passed as an argument or loaded from `),lso=n(UP,"CODE",{});var kYn=s(lso);v5a=r(kYn,"pretrained_model_name_or_path"),kYn.forEach(t),b5a=r(UP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),iso=n(UP,"CODE",{});var SYn=s(iso);M5a=r(SYn,"pretrained_model_name_or_path"),SYn.forEach(t),T5a=r(UP,":"),UP.forEach(t),F5a=i(ac),fX=n(ac,"UL",{});var $$o=s(fX);bB=n($$o,"LI",{});var Uko=s(bB);dso=n(Uko,"STRONG",{});var xYn=s(dso);E5a=r(xYn,"deit"),xYn.forEach(t),y5a=r(Uko," \u2014 "),hEe=n(Uko,"A",{href:!0});var GYn=s(hEe);C5a=r(GYn,"TFDeiTForMaskedImageModeling"),GYn.forEach(t),w5a=r(Uko," (DeiT model)"),Uko.forEach(t),L5a=i($$o),MB=n($$o,"LI",{});var Xko=s(MB);cso=n(Xko,"STRONG",{});var BYn=s(cso);A5a=r(BYn,"swin"),BYn.forEach(t),R5a=r(Xko," \u2014 "),gEe=n(Xko,"A",{href:!0});var IYn=s(gEe);k5a=r(IYn,"TFSwinForMaskedImageModeling"),IYn.forEach(t),S5a=r(Xko," (Swin Transformer model)"),Xko.forEach(t),$$o.forEach(t),x5a=i(ac),M(TB.$$.fragment,ac),ac.forEach(t),tc.forEach(t),PBo=i(m),uh=n(m,"H3",{class:!0});var j$o=s(uh);FB=n(j$o,"A",{id:!0,class:!0,href:!0});var NYn=s(FB);mso=n(NYn,"SPAN",{});var $Yn=s(mso);M(hX.$$.fragment,$Yn),$Yn.forEach(t),NYn.forEach(t),G5a=i(j$o),fso=n(j$o,"SPAN",{});var jYn=s(fso);B5a=r(jYn,"AutoModelForObjectDetection"),jYn.forEach(t),j$o.forEach(t),ZBo=i(m),Lr=n(m,"DIV",{class:!0});var nc=s(Lr);M(gX.$$.fragment,nc),I5a=i(nc),ph=n(nc,"P",{});var ZCe=s(ph);N5a=r(ZCe,`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 `),uEe=n(ZCe,"A",{href:!0});var PYn=s(uEe);$5a=r(PYn,"from_pretrained()"),PYn.forEach(t),j5a=r(ZCe," class method or the "),pEe=n(ZCe,"A",{href:!0});var ZYn=s(pEe);P5a=r(ZYn,"from_config()"),ZYn.forEach(t),Z5a=r(ZCe,` class
method.`),ZCe.forEach(t),V5a=i(nc),uX=n(nc,"P",{});var P$o=s(uX);W5a=r(P$o,"This class cannot be instantiated directly using "),hso=n(P$o,"CODE",{});var VYn=s(hso);J5a=r(VYn,"__init__()"),VYn.forEach(t),U5a=r(P$o," (throws an error)."),P$o.forEach(t),X5a=i(nc),wa=n(nc,"DIV",{class:!0});var XP=s(wa);M(pX.$$.fragment,XP),D5a=i(XP),gso=n(XP,"P",{});var WYn=s(gso);q5a=r(WYn,"Instantiates one of the model classes of the library (with a object detection head) from a configuration."),WYn.forEach(t),O5a=i(XP),_h=n(XP,"P",{});var VCe=s(_h);Q5a=r(VCe,`Note:
Loading a model from its configuration file does `),uso=n(VCe,"STRONG",{});var JYn=s(uso);Y5a=r(JYn,"not"),JYn.forEach(t),z5a=r(VCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),_Ee=n(VCe,"A",{href:!0});var UYn=s(_Ee);H5a=r(UYn,"from_pretrained()"),UYn.forEach(t),K5a=r(VCe," to load the model weights."),VCe.forEach(t),e9a=i(XP),M(EB.$$.fragment,XP),XP.forEach(t),o9a=i(nc),To=n(nc,"DIV",{class:!0});var un=s(To);M(_X.$$.fragment,un),r9a=i(un),pso=n(un,"P",{});var XYn=s(pso);t9a=r(XYn,"Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),XYn.forEach(t),a9a=i(un),Es=n(un,"P",{});var DP=s(Es);n9a=r(DP,"The model class to instantiate is selected based on the "),_so=n(DP,"CODE",{});var DYn=s(_so);s9a=r(DYn,"model_type"),DYn.forEach(t),l9a=r(DP,` property of the config object (either
passed as an argument or loaded from `),vso=n(DP,"CODE",{});var qYn=s(vso);i9a=r(qYn,"pretrained_model_name_or_path"),qYn.forEach(t),d9a=r(DP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),bso=n(DP,"CODE",{});var OYn=s(bso);c9a=r(OYn,"pretrained_model_name_or_path"),OYn.forEach(t),m9a=r(DP,":"),DP.forEach(t),f9a=i(un),Ar=n(un,"UL",{});var pn=s(Ar);yB=n(pn,"LI",{});var Dko=s(yB);Mso=n(Dko,"STRONG",{});var QYn=s(Mso);h9a=r(QYn,"conditional_detr"),QYn.forEach(t),g9a=r(Dko," \u2014 "),vEe=n(Dko,"A",{href:!0});var YYn=s(vEe);u9a=r(YYn,"ConditionalDetrForObjectDetection"),YYn.forEach(t),p9a=r(Dko," (Conditional DETR model)"),Dko.forEach(t),_9a=i(pn),CB=n(pn,"LI",{});var qko=s(CB);Tso=n(qko,"STRONG",{});var zYn=s(Tso);v9a=r(zYn,"deformable_detr"),zYn.forEach(t),b9a=r(qko," \u2014 "),bEe=n(qko,"A",{href:!0});var HYn=s(bEe);M9a=r(HYn,"DeformableDetrForObjectDetection"),HYn.forEach(t),T9a=r(qko," (Deformable DETR model)"),qko.forEach(t),F9a=i(pn),wB=n(pn,"LI",{});var Oko=s(wB);Fso=n(Oko,"STRONG",{});var KYn=s(Fso);E9a=r(KYn,"deta"),KYn.forEach(t),y9a=r(Oko," \u2014 "),MEe=n(Oko,"A",{href:!0});var ezn=s(MEe);C9a=r(ezn,"DetaForObjectDetection"),ezn.forEach(t),w9a=r(Oko," (DETA model)"),Oko.forEach(t),L9a=i(pn),LB=n(pn,"LI",{});var Qko=s(LB);Eso=n(Qko,"STRONG",{});var ozn=s(Eso);A9a=r(ozn,"detr"),ozn.forEach(t),R9a=r(Qko," \u2014 "),TEe=n(Qko,"A",{href:!0});var rzn=s(TEe);k9a=r(rzn,"DetrForObjectDetection"),rzn.forEach(t),S9a=r(Qko," (DETR model)"),Qko.forEach(t),x9a=i(pn),AB=n(pn,"LI",{});var Yko=s(AB);yso=n(Yko,"STRONG",{});var tzn=s(yso);G9a=r(tzn,"table-transformer"),tzn.forEach(t),B9a=r(Yko," \u2014 "),FEe=n(Yko,"A",{href:!0});var azn=s(FEe);I9a=r(azn,"TableTransformerForObjectDetection"),azn.forEach(t),N9a=r(Yko," (Table Transformer model)"),Yko.forEach(t),$9a=i(pn),RB=n(pn,"LI",{});var zko=s(RB);Cso=n(zko,"STRONG",{});var nzn=s(Cso);j9a=r(nzn,"yolos"),nzn.forEach(t),P9a=r(zko," \u2014 "),EEe=n(zko,"A",{href:!0});var szn=s(EEe);Z9a=r(szn,"YolosForObjectDetection"),szn.forEach(t),V9a=r(zko," (YOLOS model)"),zko.forEach(t),pn.forEach(t),W9a=i(un),kB=n(un,"P",{});var Hko=s(kB);J9a=r(Hko,"The model is set in evaluation mode by default using "),wso=n(Hko,"CODE",{});var lzn=s(wso);U9a=r(lzn,"model.eval()"),lzn.forEach(t),X9a=r(Hko,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Lso=n(Hko,"CODE",{});var izn=s(Lso);D9a=r(izn,"model.train()"),izn.forEach(t),Hko.forEach(t),q9a=i(un),M(SB.$$.fragment,un),un.forEach(t),nc.forEach(t),VBo=i(m),vh=n(m,"H3",{class:!0});var Z$o=s(vh);xB=n(Z$o,"A",{id:!0,class:!0,href:!0});var dzn=s(xB);Aso=n(dzn,"SPAN",{});var czn=s(Aso);M(vX.$$.fragment,czn),czn.forEach(t),dzn.forEach(t),O9a=i(Z$o),Rso=n(Z$o,"SPAN",{});var mzn=s(Rso);Q9a=r(mzn,"AutoModelForImageSegmentation"),mzn.forEach(t),Z$o.forEach(t),WBo=i(m),Rr=n(m,"DIV",{class:!0});var sc=s(Rr);M(bX.$$.fragment,sc),Y9a=i(sc),bh=n(sc,"P",{});var WCe=s(bh);z9a=r(WCe,`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 `),yEe=n(WCe,"A",{href:!0});var fzn=s(yEe);H9a=r(fzn,"from_pretrained()"),fzn.forEach(t),K9a=r(WCe," class method or the "),CEe=n(WCe,"A",{href:!0});var hzn=s(CEe);eCa=r(hzn,"from_config()"),hzn.forEach(t),oCa=r(WCe,` class
method.`),WCe.forEach(t),rCa=i(sc),MX=n(sc,"P",{});var V$o=s(MX);tCa=r(V$o,"This class cannot be instantiated directly using "),kso=n(V$o,"CODE",{});var gzn=s(kso);aCa=r(gzn,"__init__()"),gzn.forEach(t),nCa=r(V$o," (throws an error)."),V$o.forEach(t),sCa=i(sc),La=n(sc,"DIV",{class:!0});var qP=s(La);M(TX.$$.fragment,qP),lCa=i(qP),Sso=n(qP,"P",{});var uzn=s(Sso);iCa=r(uzn,"Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),uzn.forEach(t),dCa=i(qP),Mh=n(qP,"P",{});var JCe=s(Mh);cCa=r(JCe,`Note:
Loading a model from its configuration file does `),xso=n(JCe,"STRONG",{});var pzn=s(xso);mCa=r(pzn,"not"),pzn.forEach(t),fCa=r(JCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),wEe=n(JCe,"A",{href:!0});var _zn=s(wEe);hCa=r(_zn,"from_pretrained()"),_zn.forEach(t),gCa=r(JCe," to load the model weights."),JCe.forEach(t),uCa=i(qP),M(GB.$$.fragment,qP),qP.forEach(t),pCa=i(sc),Fo=n(sc,"DIV",{class:!0});var _n=s(Fo);M(FX.$$.fragment,_n),_Ca=i(_n),Gso=n(_n,"P",{});var vzn=s(Gso);vCa=r(vzn,"Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),vzn.forEach(t),bCa=i(_n),ys=n(_n,"P",{});var OP=s(ys);MCa=r(OP,"The model class to instantiate is selected based on the "),Bso=n(OP,"CODE",{});var bzn=s(Bso);TCa=r(bzn,"model_type"),bzn.forEach(t),FCa=r(OP,` property of the config object (either
passed as an argument or loaded from `),Iso=n(OP,"CODE",{});var Mzn=s(Iso);ECa=r(Mzn,"pretrained_model_name_or_path"),Mzn.forEach(t),yCa=r(OP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Nso=n(OP,"CODE",{});var Tzn=s(Nso);CCa=r(Tzn,"pretrained_model_name_or_path"),Tzn.forEach(t),wCa=r(OP,":"),OP.forEach(t),LCa=i(_n),$so=n(_n,"UL",{});var Fzn=s($so);BB=n(Fzn,"LI",{});var Kko=s(BB);jso=n(Kko,"STRONG",{});var Ezn=s(jso);ACa=r(Ezn,"detr"),Ezn.forEach(t),RCa=r(Kko," \u2014 "),LEe=n(Kko,"A",{href:!0});var yzn=s(LEe);kCa=r(yzn,"DetrForSegmentation"),yzn.forEach(t),SCa=r(Kko," (DETR model)"),Kko.forEach(t),Fzn.forEach(t),xCa=i(_n),IB=n(_n,"P",{});var eSo=s(IB);GCa=r(eSo,"The model is set in evaluation mode by default using "),Pso=n(eSo,"CODE",{});var Czn=s(Pso);BCa=r(Czn,"model.eval()"),Czn.forEach(t),ICa=r(eSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Zso=n(eSo,"CODE",{});var wzn=s(Zso);NCa=r(wzn,"model.train()"),wzn.forEach(t),eSo.forEach(t),$Ca=i(_n),M(NB.$$.fragment,_n),_n.forEach(t),sc.forEach(t),JBo=i(m),Th=n(m,"H3",{class:!0});var W$o=s(Th);$B=n(W$o,"A",{id:!0,class:!0,href:!0});var Lzn=s($B);Vso=n(Lzn,"SPAN",{});var Azn=s(Vso);M(EX.$$.fragment,Azn),Azn.forEach(t),Lzn.forEach(t),jCa=i(W$o),Wso=n(W$o,"SPAN",{});var Rzn=s(Wso);PCa=r(Rzn,"AutoModelForSemanticSegmentation"),Rzn.forEach(t),W$o.forEach(t),UBo=i(m),kr=n(m,"DIV",{class:!0});var lc=s(kr);M(yX.$$.fragment,lc),ZCa=i(lc),Fh=n(lc,"P",{});var UCe=s(Fh);VCa=r(UCe,`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 `),AEe=n(UCe,"A",{href:!0});var kzn=s(AEe);WCa=r(kzn,"from_pretrained()"),kzn.forEach(t),JCa=r(UCe," class method or the "),REe=n(UCe,"A",{href:!0});var Szn=s(REe);UCa=r(Szn,"from_config()"),Szn.forEach(t),XCa=r(UCe,` class
method.`),UCe.forEach(t),DCa=i(lc),CX=n(lc,"P",{});var J$o=s(CX);qCa=r(J$o,"This class cannot be instantiated directly using "),Jso=n(J$o,"CODE",{});var xzn=s(Jso);OCa=r(xzn,"__init__()"),xzn.forEach(t),QCa=r(J$o," (throws an error)."),J$o.forEach(t),YCa=i(lc),Aa=n(lc,"DIV",{class:!0});var QP=s(Aa);M(wX.$$.fragment,QP),zCa=i(QP),Uso=n(QP,"P",{});var Gzn=s(Uso);HCa=r(Gzn,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),Gzn.forEach(t),KCa=i(QP),Eh=n(QP,"P",{});var XCe=s(Eh);ewa=r(XCe,`Note:
Loading a model from its configuration file does `),Xso=n(XCe,"STRONG",{});var Bzn=s(Xso);owa=r(Bzn,"not"),Bzn.forEach(t),rwa=r(XCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),kEe=n(XCe,"A",{href:!0});var Izn=s(kEe);twa=r(Izn,"from_pretrained()"),Izn.forEach(t),awa=r(XCe," to load the model weights."),XCe.forEach(t),nwa=i(QP),M(jB.$$.fragment,QP),QP.forEach(t),swa=i(lc),Eo=n(lc,"DIV",{class:!0});var vn=s(Eo);M(LX.$$.fragment,vn),lwa=i(vn),Dso=n(vn,"P",{});var Nzn=s(Dso);iwa=r(Nzn,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),Nzn.forEach(t),dwa=i(vn),Cs=n(vn,"P",{});var YP=s(Cs);cwa=r(YP,"The model class to instantiate is selected based on the "),qso=n(YP,"CODE",{});var $zn=s(qso);mwa=r($zn,"model_type"),$zn.forEach(t),fwa=r(YP,` property of the config object (either
passed as an argument or loaded from `),Oso=n(YP,"CODE",{});var jzn=s(Oso);hwa=r(jzn,"pretrained_model_name_or_path"),jzn.forEach(t),gwa=r(YP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Qso=n(YP,"CODE",{});var Pzn=s(Qso);uwa=r(Pzn,"pretrained_model_name_or_path"),Pzn.forEach(t),pwa=r(YP,":"),YP.forEach(t),_wa=i(vn),Ye=n(vn,"UL",{});var $o=s(Ye);PB=n($o,"LI",{});var oSo=s(PB);Yso=n(oSo,"STRONG",{});var Zzn=s(Yso);vwa=r(Zzn,"beit"),Zzn.forEach(t),bwa=r(oSo," \u2014 "),SEe=n(oSo,"A",{href:!0});var Vzn=s(SEe);Mwa=r(Vzn,"BeitForSemanticSegmentation"),Vzn.forEach(t),Twa=r(oSo," (BEiT model)"),oSo.forEach(t),Fwa=i($o),ZB=n($o,"LI",{});var rSo=s(ZB);zso=n(rSo,"STRONG",{});var Wzn=s(zso);Ewa=r(Wzn,"data2vec-vision"),Wzn.forEach(t),ywa=r(rSo," \u2014 "),xEe=n(rSo,"A",{href:!0});var Jzn=s(xEe);Cwa=r(Jzn,"Data2VecVisionForSemanticSegmentation"),Jzn.forEach(t),wwa=r(rSo," (Data2VecVision model)"),rSo.forEach(t),Lwa=i($o),VB=n($o,"LI",{});var tSo=s(VB);Hso=n(tSo,"STRONG",{});var Uzn=s(Hso);Awa=r(Uzn,"dpt"),Uzn.forEach(t),Rwa=r(tSo," \u2014 "),GEe=n(tSo,"A",{href:!0});var Xzn=s(GEe);kwa=r(Xzn,"DPTForSemanticSegmentation"),Xzn.forEach(t),Swa=r(tSo," (DPT model)"),tSo.forEach(t),xwa=i($o),WB=n($o,"LI",{});var aSo=s(WB);Kso=n(aSo,"STRONG",{});var Dzn=s(Kso);Gwa=r(Dzn,"mobilenet_v2"),Dzn.forEach(t),Bwa=r(aSo," \u2014 "),BEe=n(aSo,"A",{href:!0});var qzn=s(BEe);Iwa=r(qzn,"MobileNetV2ForSemanticSegmentation"),qzn.forEach(t),Nwa=r(aSo," (MobileNetV2 model)"),aSo.forEach(t),$wa=i($o),JB=n($o,"LI",{});var nSo=s(JB);elo=n(nSo,"STRONG",{});var Ozn=s(elo);jwa=r(Ozn,"mobilevit"),Ozn.forEach(t),Pwa=r(nSo," \u2014 "),IEe=n(nSo,"A",{href:!0});var Qzn=s(IEe);Zwa=r(Qzn,"MobileViTForSemanticSegmentation"),Qzn.forEach(t),Vwa=r(nSo," (MobileViT model)"),nSo.forEach(t),Wwa=i($o),UB=n($o,"LI",{});var sSo=s(UB);olo=n(sSo,"STRONG",{});var Yzn=s(olo);Jwa=r(Yzn,"mobilevitv2"),Yzn.forEach(t),Uwa=r(sSo," \u2014 "),NEe=n(sSo,"A",{href:!0});var zzn=s(NEe);Xwa=r(zzn,"MobileViTV2ForSemanticSegmentation"),zzn.forEach(t),Dwa=r(sSo," (MobileViTV2 model)"),sSo.forEach(t),qwa=i($o),XB=n($o,"LI",{});var lSo=s(XB);rlo=n(lSo,"STRONG",{});var Hzn=s(rlo);Owa=r(Hzn,"segformer"),Hzn.forEach(t),Qwa=r(lSo," \u2014 "),$Ee=n(lSo,"A",{href:!0});var Kzn=s($Ee);Ywa=r(Kzn,"SegformerForSemanticSegmentation"),Kzn.forEach(t),zwa=r(lSo," (SegFormer model)"),lSo.forEach(t),Hwa=i($o),DB=n($o,"LI",{});var iSo=s(DB);tlo=n(iSo,"STRONG",{});var eHn=s(tlo);Kwa=r(eHn,"upernet"),eHn.forEach(t),e6a=r(iSo," \u2014 "),jEe=n(iSo,"A",{href:!0});var oHn=s(jEe);o6a=r(oHn,"UperNetForSemanticSegmentation"),oHn.forEach(t),r6a=r(iSo," (UPerNet model)"),iSo.forEach(t),$o.forEach(t),t6a=i(vn),qB=n(vn,"P",{});var dSo=s(qB);a6a=r(dSo,"The model is set in evaluation mode by default using "),alo=n(dSo,"CODE",{});var rHn=s(alo);n6a=r(rHn,"model.eval()"),rHn.forEach(t),s6a=r(dSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),nlo=n(dSo,"CODE",{});var tHn=s(nlo);l6a=r(tHn,"model.train()"),tHn.forEach(t),dSo.forEach(t),i6a=i(vn),M(OB.$$.fragment,vn),vn.forEach(t),lc.forEach(t),XBo=i(m),yh=n(m,"H3",{class:!0});var U$o=s(yh);QB=n(U$o,"A",{id:!0,class:!0,href:!0});var aHn=s(QB);slo=n(aHn,"SPAN",{});var nHn=s(slo);M(AX.$$.fragment,nHn),nHn.forEach(t),aHn.forEach(t),d6a=i(U$o),llo=n(U$o,"SPAN",{});var sHn=s(llo);c6a=r(sHn,"TFAutoModelForSemanticSegmentation"),sHn.forEach(t),U$o.forEach(t),DBo=i(m),Sr=n(m,"DIV",{class:!0});var ic=s(Sr);M(RX.$$.fragment,ic),m6a=i(ic),Ch=n(ic,"P",{});var DCe=s(Ch);f6a=r(DCe,`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 `),PEe=n(DCe,"A",{href:!0});var lHn=s(PEe);h6a=r(lHn,"from_pretrained()"),lHn.forEach(t),g6a=r(DCe," class method or the "),ZEe=n(DCe,"A",{href:!0});var iHn=s(ZEe);u6a=r(iHn,"from_config()"),iHn.forEach(t),p6a=r(DCe,` class
method.`),DCe.forEach(t),_6a=i(ic),kX=n(ic,"P",{});var X$o=s(kX);v6a=r(X$o,"This class cannot be instantiated directly using "),ilo=n(X$o,"CODE",{});var dHn=s(ilo);b6a=r(dHn,"__init__()"),dHn.forEach(t),M6a=r(X$o," (throws an error)."),X$o.forEach(t),T6a=i(ic),Ra=n(ic,"DIV",{class:!0});var zP=s(Ra);M(SX.$$.fragment,zP),F6a=i(zP),dlo=n(zP,"P",{});var cHn=s(dlo);E6a=r(cHn,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),cHn.forEach(t),y6a=i(zP),wh=n(zP,"P",{});var qCe=s(wh);C6a=r(qCe,`Note:
Loading a model from its configuration file does `),clo=n(qCe,"STRONG",{});var mHn=s(clo);w6a=r(mHn,"not"),mHn.forEach(t),L6a=r(qCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),VEe=n(qCe,"A",{href:!0});var fHn=s(VEe);A6a=r(fHn,"from_pretrained()"),fHn.forEach(t),R6a=r(qCe," to load the model weights."),qCe.forEach(t),k6a=i(zP),M(YB.$$.fragment,zP),zP.forEach(t),S6a=i(ic),wt=n(ic,"DIV",{class:!0});var dc=s(wt);M(xX.$$.fragment,dc),x6a=i(dc),mlo=n(dc,"P",{});var hHn=s(mlo);G6a=r(hHn,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),hHn.forEach(t),B6a=i(dc),ws=n(dc,"P",{});var HP=s(ws);I6a=r(HP,"The model class to instantiate is selected based on the "),flo=n(HP,"CODE",{});var gHn=s(flo);N6a=r(gHn,"model_type"),gHn.forEach(t),$6a=r(HP,` property of the config object (either
passed as an argument or loaded from `),hlo=n(HP,"CODE",{});var uHn=s(hlo);j6a=r(uHn,"pretrained_model_name_or_path"),uHn.forEach(t),P6a=r(HP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),glo=n(HP,"CODE",{});var pHn=s(glo);Z6a=r(pHn,"pretrained_model_name_or_path"),pHn.forEach(t),V6a=r(HP,":"),HP.forEach(t),W6a=i(dc),Lh=n(dc,"UL",{});var OCe=s(Lh);zB=n(OCe,"LI",{});var cSo=s(zB);ulo=n(cSo,"STRONG",{});var _Hn=s(ulo);J6a=r(_Hn,"data2vec-vision"),_Hn.forEach(t),U6a=r(cSo," \u2014 "),WEe=n(cSo,"A",{href:!0});var vHn=s(WEe);X6a=r(vHn,"TFData2VecVisionForSemanticSegmentation"),vHn.forEach(t),D6a=r(cSo," (Data2VecVision model)"),cSo.forEach(t),q6a=i(OCe),HB=n(OCe,"LI",{});var mSo=s(HB);plo=n(mSo,"STRONG",{});var bHn=s(plo);O6a=r(bHn,"mobilevit"),bHn.forEach(t),Q6a=r(mSo," \u2014 "),JEe=n(mSo,"A",{href:!0});var MHn=s(JEe);Y6a=r(MHn,"TFMobileViTForSemanticSegmentation"),MHn.forEach(t),z6a=r(mSo," (MobileViT model)"),mSo.forEach(t),H6a=i(OCe),KB=n(OCe,"LI",{});var fSo=s(KB);_lo=n(fSo,"STRONG",{});var THn=s(_lo);K6a=r(THn,"segformer"),THn.forEach(t),e8a=r(fSo," \u2014 "),UEe=n(fSo,"A",{href:!0});var FHn=s(UEe);o8a=r(FHn,"TFSegformerForSemanticSegmentation"),FHn.forEach(t),r8a=r(fSo," (SegFormer model)"),fSo.forEach(t),OCe.forEach(t),t8a=i(dc),M(eI.$$.fragment,dc),dc.forEach(t),ic.forEach(t),qBo=i(m),Ah=n(m,"H3",{class:!0});var D$o=s(Ah);oI=n(D$o,"A",{id:!0,class:!0,href:!0});var EHn=s(oI);vlo=n(EHn,"SPAN",{});var yHn=s(vlo);M(GX.$$.fragment,yHn),yHn.forEach(t),EHn.forEach(t),a8a=i(D$o),blo=n(D$o,"SPAN",{});var CHn=s(blo);n8a=r(CHn,"AutoModelForInstanceSegmentation"),CHn.forEach(t),D$o.forEach(t),OBo=i(m),xr=n(m,"DIV",{class:!0});var cc=s(xr);M(BX.$$.fragment,cc),s8a=i(cc),Rh=n(cc,"P",{});var QCe=s(Rh);l8a=r(QCe,`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 `),XEe=n(QCe,"A",{href:!0});var wHn=s(XEe);i8a=r(wHn,"from_pretrained()"),wHn.forEach(t),d8a=r(QCe," class method or the "),DEe=n(QCe,"A",{href:!0});var LHn=s(DEe);c8a=r(LHn,"from_config()"),LHn.forEach(t),m8a=r(QCe,` class
method.`),QCe.forEach(t),f8a=i(cc),IX=n(cc,"P",{});var q$o=s(IX);h8a=r(q$o,"This class cannot be instantiated directly using "),Mlo=n(q$o,"CODE",{});var AHn=s(Mlo);g8a=r(AHn,"__init__()"),AHn.forEach(t),u8a=r(q$o," (throws an error)."),q$o.forEach(t),p8a=i(cc),ka=n(cc,"DIV",{class:!0});var KP=s(ka);M(NX.$$.fragment,KP),_8a=i(KP),Tlo=n(KP,"P",{});var RHn=s(Tlo);v8a=r(RHn,"Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),RHn.forEach(t),b8a=i(KP),kh=n(KP,"P",{});var YCe=s(kh);M8a=r(YCe,`Note:
Loading a model from its configuration file does `),Flo=n(YCe,"STRONG",{});var kHn=s(Flo);T8a=r(kHn,"not"),kHn.forEach(t),F8a=r(YCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),qEe=n(YCe,"A",{href:!0});var SHn=s(qEe);E8a=r(SHn,"from_pretrained()"),SHn.forEach(t),y8a=r(YCe," to load the model weights."),YCe.forEach(t),C8a=i(KP),M(rI.$$.fragment,KP),KP.forEach(t),w8a=i(cc),yo=n(cc,"DIV",{class:!0});var bn=s(yo);M($X.$$.fragment,bn),L8a=i(bn),Elo=n(bn,"P",{});var xHn=s(Elo);A8a=r(xHn,"Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),xHn.forEach(t),R8a=i(bn),Ls=n(bn,"P",{});var eZ=s(Ls);k8a=r(eZ,"The model class to instantiate is selected based on the "),ylo=n(eZ,"CODE",{});var GHn=s(ylo);S8a=r(GHn,"model_type"),GHn.forEach(t),x8a=r(eZ,` property of the config object (either
passed as an argument or loaded from `),Clo=n(eZ,"CODE",{});var BHn=s(Clo);G8a=r(BHn,"pretrained_model_name_or_path"),BHn.forEach(t),B8a=r(eZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wlo=n(eZ,"CODE",{});var IHn=s(wlo);I8a=r(IHn,"pretrained_model_name_or_path"),IHn.forEach(t),N8a=r(eZ,":"),eZ.forEach(t),$8a=i(bn),Llo=n(bn,"UL",{});var NHn=s(Llo);tI=n(NHn,"LI",{});var hSo=s(tI);Alo=n(hSo,"STRONG",{});var $Hn=s(Alo);j8a=r($Hn,"maskformer"),$Hn.forEach(t),P8a=r(hSo," \u2014 "),OEe=n(hSo,"A",{href:!0});var jHn=s(OEe);Z8a=r(jHn,"MaskFormerForInstanceSegmentation"),jHn.forEach(t),V8a=r(hSo," (MaskFormer model)"),hSo.forEach(t),NHn.forEach(t),W8a=i(bn),aI=n(bn,"P",{});var gSo=s(aI);J8a=r(gSo,"The model is set in evaluation mode by default using "),Rlo=n(gSo,"CODE",{});var PHn=s(Rlo);U8a=r(PHn,"model.eval()"),PHn.forEach(t),X8a=r(gSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),klo=n(gSo,"CODE",{});var ZHn=s(klo);D8a=r(ZHn,"model.train()"),ZHn.forEach(t),gSo.forEach(t),q8a=i(bn),M(nI.$$.fragment,bn),bn.forEach(t),cc.forEach(t),QBo=i(m),Sh=n(m,"H3",{class:!0});var O$o=s(Sh);sI=n(O$o,"A",{id:!0,class:!0,href:!0});var VHn=s(sI);Slo=n(VHn,"SPAN",{});var WHn=s(Slo);M(jX.$$.fragment,WHn),WHn.forEach(t),VHn.forEach(t),O8a=i(O$o),xlo=n(O$o,"SPAN",{});var JHn=s(xlo);Q8a=r(JHn,"AutoModelForUniversalSegmentation"),JHn.forEach(t),O$o.forEach(t),YBo=i(m),Gr=n(m,"DIV",{class:!0});var mc=s(Gr);M(PX.$$.fragment,mc),Y8a=i(mc),xh=n(mc,"P",{});var zCe=s(xh);z8a=r(zCe,`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 `),QEe=n(zCe,"A",{href:!0});var UHn=s(QEe);H8a=r(UHn,"from_pretrained()"),UHn.forEach(t),K8a=r(zCe," class method or the "),YEe=n(zCe,"A",{href:!0});var XHn=s(YEe);e7a=r(XHn,"from_config()"),XHn.forEach(t),o7a=r(zCe,` class
method.`),zCe.forEach(t),r7a=i(mc),ZX=n(mc,"P",{});var Q$o=s(ZX);t7a=r(Q$o,"This class cannot be instantiated directly using "),Glo=n(Q$o,"CODE",{});var DHn=s(Glo);a7a=r(DHn,"__init__()"),DHn.forEach(t),n7a=r(Q$o," (throws an error)."),Q$o.forEach(t),s7a=i(mc),Sa=n(mc,"DIV",{class:!0});var oZ=s(Sa);M(VX.$$.fragment,oZ),l7a=i(oZ),Blo=n(oZ,"P",{});var qHn=s(Blo);i7a=r(qHn,"Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration."),qHn.forEach(t),d7a=i(oZ),Gh=n(oZ,"P",{});var HCe=s(Gh);c7a=r(HCe,`Note:
Loading a model from its configuration file does `),Ilo=n(HCe,"STRONG",{});var OHn=s(Ilo);m7a=r(OHn,"not"),OHn.forEach(t),f7a=r(HCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),zEe=n(HCe,"A",{href:!0});var QHn=s(zEe);h7a=r(QHn,"from_pretrained()"),QHn.forEach(t),g7a=r(HCe," to load the model weights."),HCe.forEach(t),u7a=i(oZ),M(lI.$$.fragment,oZ),oZ.forEach(t),p7a=i(mc),Co=n(mc,"DIV",{class:!0});var Mn=s(Co);M(WX.$$.fragment,Mn),_7a=i(Mn),Nlo=n(Mn,"P",{});var YHn=s(Nlo);v7a=r(YHn,"Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model."),YHn.forEach(t),b7a=i(Mn),As=n(Mn,"P",{});var rZ=s(As);M7a=r(rZ,"The model class to instantiate is selected based on the "),$lo=n(rZ,"CODE",{});var zHn=s($lo);T7a=r(zHn,"model_type"),zHn.forEach(t),F7a=r(rZ,` property of the config object (either
passed as an argument or loaded from `),jlo=n(rZ,"CODE",{});var HHn=s(jlo);E7a=r(HHn,"pretrained_model_name_or_path"),HHn.forEach(t),y7a=r(rZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Plo=n(rZ,"CODE",{});var KHn=s(Plo);C7a=r(KHn,"pretrained_model_name_or_path"),KHn.forEach(t),w7a=r(rZ,":"),rZ.forEach(t),L7a=i(Mn),Rs=n(Mn,"UL",{});var tZ=s(Rs);iI=n(tZ,"LI",{});var uSo=s(iI);Zlo=n(uSo,"STRONG",{});var eKn=s(Zlo);A7a=r(eKn,"detr"),eKn.forEach(t),R7a=r(uSo," \u2014 "),HEe=n(uSo,"A",{href:!0});var oKn=s(HEe);k7a=r(oKn,"DetrForSegmentation"),oKn.forEach(t),S7a=r(uSo," (DETR model)"),uSo.forEach(t),x7a=i(tZ),dI=n(tZ,"LI",{});var pSo=s(dI);Vlo=n(pSo,"STRONG",{});var rKn=s(Vlo);G7a=r(rKn,"mask2former"),rKn.forEach(t),B7a=r(pSo," \u2014 "),KEe=n(pSo,"A",{href:!0});var tKn=s(KEe);I7a=r(tKn,"Mask2FormerForUniversalSegmentation"),tKn.forEach(t),N7a=r(pSo," (Mask2Former model)"),pSo.forEach(t),$7a=i(tZ),cI=n(tZ,"LI",{});var _So=s(cI);Wlo=n(_So,"STRONG",{});var aKn=s(Wlo);j7a=r(aKn,"maskformer"),aKn.forEach(t),P7a=r(_So," \u2014 "),eye=n(_So,"A",{href:!0});var nKn=s(eye);Z7a=r(nKn,"MaskFormerForInstanceSegmentation"),nKn.forEach(t),V7a=r(_So," (MaskFormer model)"),_So.forEach(t),W7a=i(tZ),mI=n(tZ,"LI",{});var vSo=s(mI);Jlo=n(vSo,"STRONG",{});var sKn=s(Jlo);J7a=r(sKn,"oneformer"),sKn.forEach(t),U7a=r(vSo," \u2014 "),oye=n(vSo,"A",{href:!0});var lKn=s(oye);X7a=r(lKn,"OneFormerForUniversalSegmentation"),lKn.forEach(t),D7a=r(vSo," (OneFormer model)"),vSo.forEach(t),tZ.forEach(t),q7a=i(Mn),fI=n(Mn,"P",{});var bSo=s(fI);O7a=r(bSo,"The model is set in evaluation mode by default using "),Ulo=n(bSo,"CODE",{});var iKn=s(Ulo);Q7a=r(iKn,"model.eval()"),iKn.forEach(t),Y7a=r(bSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Xlo=n(bSo,"CODE",{});var dKn=s(Xlo);z7a=r(dKn,"model.train()"),dKn.forEach(t),bSo.forEach(t),H7a=i(Mn),M(hI.$$.fragment,Mn),Mn.forEach(t),mc.forEach(t),zBo=i(m),Bh=n(m,"H3",{class:!0});var Y$o=s(Bh);gI=n(Y$o,"A",{id:!0,class:!0,href:!0});var cKn=s(gI);Dlo=n(cKn,"SPAN",{});var mKn=s(Dlo);M(JX.$$.fragment,mKn),mKn.forEach(t),cKn.forEach(t),K7a=i(Y$o),qlo=n(Y$o,"SPAN",{});var fKn=s(qlo);eLa=r(fKn,"AutoModelForZeroShotImageClassification"),fKn.forEach(t),Y$o.forEach(t),HBo=i(m),Br=n(m,"DIV",{class:!0});var fc=s(Br);M(UX.$$.fragment,fc),oLa=i(fc),Ih=n(fc,"P",{});var KCe=s(Ih);rLa=r(KCe,`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 `),rye=n(KCe,"A",{href:!0});var hKn=s(rye);tLa=r(hKn,"from_pretrained()"),hKn.forEach(t),aLa=r(KCe," class method or the "),tye=n(KCe,"A",{href:!0});var gKn=s(tye);nLa=r(gKn,"from_config()"),gKn.forEach(t),sLa=r(KCe,` class
method.`),KCe.forEach(t),lLa=i(fc),XX=n(fc,"P",{});var z$o=s(XX);iLa=r(z$o,"This class cannot be instantiated directly using "),Olo=n(z$o,"CODE",{});var uKn=s(Olo);dLa=r(uKn,"__init__()"),uKn.forEach(t),cLa=r(z$o," (throws an error)."),z$o.forEach(t),mLa=i(fc),xa=n(fc,"DIV",{class:!0});var aZ=s(xa);M(DX.$$.fragment,aZ),fLa=i(aZ),Qlo=n(aZ,"P",{});var pKn=s(Qlo);hLa=r(pKn,"Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),pKn.forEach(t),gLa=i(aZ),Nh=n(aZ,"P",{});var ewe=s(Nh);uLa=r(ewe,`Note:
Loading a model from its configuration file does `),Ylo=n(ewe,"STRONG",{});var _Kn=s(Ylo);pLa=r(_Kn,"not"),_Kn.forEach(t),_La=r(ewe,` load the model weights. It only affects the
model\u2019s configuration. Use `),aye=n(ewe,"A",{href:!0});var vKn=s(aye);vLa=r(vKn,"from_pretrained()"),vKn.forEach(t),bLa=r(ewe," to load the model weights."),ewe.forEach(t),MLa=i(aZ),M(uI.$$.fragment,aZ),aZ.forEach(t),TLa=i(fc),wo=n(fc,"DIV",{class:!0});var Tn=s(wo);M(qX.$$.fragment,Tn),FLa=i(Tn),zlo=n(Tn,"P",{});var bKn=s(zlo);ELa=r(bKn,"Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),bKn.forEach(t),yLa=i(Tn),ks=n(Tn,"P",{});var nZ=s(ks);CLa=r(nZ,"The model class to instantiate is selected based on the "),Hlo=n(nZ,"CODE",{});var MKn=s(Hlo);wLa=r(MKn,"model_type"),MKn.forEach(t),LLa=r(nZ,` property of the config object (either
passed as an argument or loaded from `),Klo=n(nZ,"CODE",{});var TKn=s(Klo);ALa=r(TKn,"pretrained_model_name_or_path"),TKn.forEach(t),RLa=r(nZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),eio=n(nZ,"CODE",{});var FKn=s(eio);kLa=r(FKn,"pretrained_model_name_or_path"),FKn.forEach(t),SLa=r(nZ,":"),nZ.forEach(t),xLa=i(Tn),Ir=n(Tn,"UL",{});var Fn=s(Ir);pI=n(Fn,"LI",{});var MSo=s(pI);oio=n(MSo,"STRONG",{});var EKn=s(oio);GLa=r(EKn,"align"),EKn.forEach(t),BLa=r(MSo," \u2014 "),nye=n(MSo,"A",{href:!0});var yKn=s(nye);ILa=r(yKn,"AlignModel"),yKn.forEach(t),NLa=r(MSo," (ALIGN model)"),MSo.forEach(t),$La=i(Fn),_I=n(Fn,"LI",{});var TSo=s(_I);rio=n(TSo,"STRONG",{});var CKn=s(rio);jLa=r(CKn,"altclip"),CKn.forEach(t),PLa=r(TSo," \u2014 "),sye=n(TSo,"A",{href:!0});var wKn=s(sye);ZLa=r(wKn,"AltCLIPModel"),wKn.forEach(t),VLa=r(TSo," (AltCLIP model)"),TSo.forEach(t),WLa=i(Fn),vI=n(Fn,"LI",{});var FSo=s(vI);tio=n(FSo,"STRONG",{});var LKn=s(tio);JLa=r(LKn,"blip"),LKn.forEach(t),ULa=r(FSo," \u2014 "),lye=n(FSo,"A",{href:!0});var AKn=s(lye);XLa=r(AKn,"BlipModel"),AKn.forEach(t),DLa=r(FSo," (BLIP model)"),FSo.forEach(t),qLa=i(Fn),bI=n(Fn,"LI",{});var ESo=s(bI);aio=n(ESo,"STRONG",{});var RKn=s(aio);OLa=r(RKn,"chinese_clip"),RKn.forEach(t),QLa=r(ESo," \u2014 "),iye=n(ESo,"A",{href:!0});var kKn=s(iye);YLa=r(kKn,"ChineseCLIPModel"),kKn.forEach(t),zLa=r(ESo," (Chinese-CLIP model)"),ESo.forEach(t),HLa=i(Fn),MI=n(Fn,"LI",{});var ySo=s(MI);nio=n(ySo,"STRONG",{});var SKn=s(nio);KLa=r(SKn,"clip"),SKn.forEach(t),eAa=r(ySo," \u2014 "),dye=n(ySo,"A",{href:!0});var xKn=s(dye);oAa=r(xKn,"CLIPModel"),xKn.forEach(t),rAa=r(ySo," (CLIP model)"),ySo.forEach(t),tAa=i(Fn),TI=n(Fn,"LI",{});var CSo=s(TI);sio=n(CSo,"STRONG",{});var GKn=s(sio);aAa=r(GKn,"clipseg"),GKn.forEach(t),nAa=r(CSo," \u2014 "),cye=n(CSo,"A",{href:!0});var BKn=s(cye);sAa=r(BKn,"CLIPSegModel"),BKn.forEach(t),lAa=r(CSo," (CLIPSeg model)"),CSo.forEach(t),Fn.forEach(t),iAa=i(Tn),FI=n(Tn,"P",{});var wSo=s(FI);dAa=r(wSo,"The model is set in evaluation mode by default using "),lio=n(wSo,"CODE",{});var IKn=s(lio);cAa=r(IKn,"model.eval()"),IKn.forEach(t),mAa=r(wSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),iio=n(wSo,"CODE",{});var NKn=s(iio);fAa=r(NKn,"model.train()"),NKn.forEach(t),wSo.forEach(t),hAa=i(Tn),M(EI.$$.fragment,Tn),Tn.forEach(t),fc.forEach(t),KBo=i(m),$h=n(m,"H3",{class:!0});var H$o=s($h);yI=n(H$o,"A",{id:!0,class:!0,href:!0});var $Kn=s(yI);dio=n($Kn,"SPAN",{});var jKn=s(dio);M(OX.$$.fragment,jKn),jKn.forEach(t),$Kn.forEach(t),gAa=i(H$o),cio=n(H$o,"SPAN",{});var PKn=s(cio);uAa=r(PKn,"TFAutoModelForZeroShotImageClassification"),PKn.forEach(t),H$o.forEach(t),eIo=i(m),Nr=n(m,"DIV",{class:!0});var hc=s(Nr);M(QX.$$.fragment,hc),pAa=i(hc),jh=n(hc,"P",{});var owe=s(jh);_Aa=r(owe,`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 `),mye=n(owe,"A",{href:!0});var ZKn=s(mye);vAa=r(ZKn,"from_pretrained()"),ZKn.forEach(t),bAa=r(owe," class method or the "),fye=n(owe,"A",{href:!0});var VKn=s(fye);MAa=r(VKn,"from_config()"),VKn.forEach(t),TAa=r(owe,` class
method.`),owe.forEach(t),FAa=i(hc),YX=n(hc,"P",{});var K$o=s(YX);EAa=r(K$o,"This class cannot be instantiated directly using "),mio=n(K$o,"CODE",{});var WKn=s(mio);yAa=r(WKn,"__init__()"),WKn.forEach(t),CAa=r(K$o," (throws an error)."),K$o.forEach(t),wAa=i(hc),Ga=n(hc,"DIV",{class:!0});var sZ=s(Ga);M(zX.$$.fragment,sZ),LAa=i(sZ),fio=n(sZ,"P",{});var JKn=s(fio);AAa=r(JKn,"Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),JKn.forEach(t),RAa=i(sZ),Ph=n(sZ,"P",{});var rwe=s(Ph);kAa=r(rwe,`Note:
Loading a model from its configuration file does `),hio=n(rwe,"STRONG",{});var UKn=s(hio);SAa=r(UKn,"not"),UKn.forEach(t),xAa=r(rwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),hye=n(rwe,"A",{href:!0});var XKn=s(hye);GAa=r(XKn,"from_pretrained()"),XKn.forEach(t),BAa=r(rwe," to load the model weights."),rwe.forEach(t),IAa=i(sZ),M(CI.$$.fragment,sZ),sZ.forEach(t),NAa=i(hc),Lt=n(hc,"DIV",{class:!0});var gc=s(Lt);M(HX.$$.fragment,gc),$Aa=i(gc),gio=n(gc,"P",{});var DKn=s(gio);jAa=r(DKn,"Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),DKn.forEach(t),PAa=i(gc),Ss=n(gc,"P",{});var lZ=s(Ss);ZAa=r(lZ,"The model class to instantiate is selected based on the "),uio=n(lZ,"CODE",{});var qKn=s(uio);VAa=r(qKn,"model_type"),qKn.forEach(t),WAa=r(lZ,` property of the config object (either
passed as an argument or loaded from `),pio=n(lZ,"CODE",{});var OKn=s(pio);JAa=r(OKn,"pretrained_model_name_or_path"),OKn.forEach(t),UAa=r(lZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),_io=n(lZ,"CODE",{});var QKn=s(_io);XAa=r(QKn,"pretrained_model_name_or_path"),QKn.forEach(t),DAa=r(lZ,":"),lZ.forEach(t),qAa=i(gc),KX=n(gc,"UL",{});var ejo=s(KX);wI=n(ejo,"LI",{});var LSo=s(wI);vio=n(LSo,"STRONG",{});var YKn=s(vio);OAa=r(YKn,"blip"),YKn.forEach(t),QAa=r(LSo," \u2014 "),gye=n(LSo,"A",{href:!0});var zKn=s(gye);YAa=r(zKn,"TFBlipModel"),zKn.forEach(t),zAa=r(LSo," (BLIP model)"),LSo.forEach(t),HAa=i(ejo),LI=n(ejo,"LI",{});var ASo=s(LI);bio=n(ASo,"STRONG",{});var HKn=s(bio);KAa=r(HKn,"clip"),HKn.forEach(t),eRa=r(ASo," \u2014 "),uye=n(ASo,"A",{href:!0});var KKn=s(uye);oRa=r(KKn,"TFCLIPModel"),KKn.forEach(t),rRa=r(ASo," (CLIP model)"),ASo.forEach(t),ejo.forEach(t),tRa=i(gc),M(AI.$$.fragment,gc),gc.forEach(t),hc.forEach(t),oIo=i(m),Zh=n(m,"H3",{class:!0});var ojo=s(Zh);RI=n(ojo,"A",{id:!0,class:!0,href:!0});var ees=s(RI);Mio=n(ees,"SPAN",{});var oes=s(Mio);M(eD.$$.fragment,oes),oes.forEach(t),ees.forEach(t),aRa=i(ojo),Tio=n(ojo,"SPAN",{});var res=s(Tio);nRa=r(res,"AutoModelForZeroShotObjectDetection"),res.forEach(t),ojo.forEach(t),rIo=i(m),$r=n(m,"DIV",{class:!0});var uc=s($r);M(oD.$$.fragment,uc),sRa=i(uc),Vh=n(uc,"P",{});var twe=s(Vh);lRa=r(twe,`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 `),pye=n(twe,"A",{href:!0});var tes=s(pye);iRa=r(tes,"from_pretrained()"),tes.forEach(t),dRa=r(twe," class method or the "),_ye=n(twe,"A",{href:!0});var aes=s(_ye);cRa=r(aes,"from_config()"),aes.forEach(t),mRa=r(twe,` class
method.`),twe.forEach(t),fRa=i(uc),rD=n(uc,"P",{});var rjo=s(rD);hRa=r(rjo,"This class cannot be instantiated directly using "),Fio=n(rjo,"CODE",{});var nes=s(Fio);gRa=r(nes,"__init__()"),nes.forEach(t),uRa=r(rjo," (throws an error)."),rjo.forEach(t),pRa=i(uc),Ba=n(uc,"DIV",{class:!0});var iZ=s(Ba);M(tD.$$.fragment,iZ),_Ra=i(iZ),Eio=n(iZ,"P",{});var ses=s(Eio);vRa=r(ses,"Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration."),ses.forEach(t),bRa=i(iZ),Wh=n(iZ,"P",{});var awe=s(Wh);MRa=r(awe,`Note:
Loading a model from its configuration file does `),yio=n(awe,"STRONG",{});var les=s(yio);TRa=r(les,"not"),les.forEach(t),FRa=r(awe,` load the model weights. It only affects the
model\u2019s configuration. Use `),vye=n(awe,"A",{href:!0});var ies=s(vye);ERa=r(ies,"from_pretrained()"),ies.forEach(t),yRa=r(awe," to load the model weights."),awe.forEach(t),CRa=i(iZ),M(kI.$$.fragment,iZ),iZ.forEach(t),wRa=i(uc),Lo=n(uc,"DIV",{class:!0});var En=s(Lo);M(aD.$$.fragment,En),LRa=i(En),Cio=n(En,"P",{});var des=s(Cio);ARa=r(des,"Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model."),des.forEach(t),RRa=i(En),xs=n(En,"P",{});var dZ=s(xs);kRa=r(dZ,"The model class to instantiate is selected based on the "),wio=n(dZ,"CODE",{});var ces=s(wio);SRa=r(ces,"model_type"),ces.forEach(t),xRa=r(dZ,` property of the config object (either
passed as an argument or loaded from `),Lio=n(dZ,"CODE",{});var mes=s(Lio);GRa=r(mes,"pretrained_model_name_or_path"),mes.forEach(t),BRa=r(dZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Aio=n(dZ,"CODE",{});var fes=s(Aio);IRa=r(fes,"pretrained_model_name_or_path"),fes.forEach(t),NRa=r(dZ,":"),dZ.forEach(t),$Ra=i(En),Rio=n(En,"UL",{});var hes=s(Rio);SI=n(hes,"LI",{});var RSo=s(SI);kio=n(RSo,"STRONG",{});var ges=s(kio);jRa=r(ges,"owlvit"),ges.forEach(t),PRa=r(RSo," \u2014 "),bye=n(RSo,"A",{href:!0});var ues=s(bye);ZRa=r(ues,"OwlViTForObjectDetection"),ues.forEach(t),VRa=r(RSo," (OWL-ViT model)"),RSo.forEach(t),hes.forEach(t),WRa=i(En),xI=n(En,"P",{});var kSo=s(xI);JRa=r(kSo,"The model is set in evaluation mode by default using "),Sio=n(kSo,"CODE",{});var pes=s(Sio);URa=r(pes,"model.eval()"),pes.forEach(t),XRa=r(kSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),xio=n(kSo,"CODE",{});var _es=s(xio);DRa=r(_es,"model.train()"),_es.forEach(t),kSo.forEach(t),qRa=i(En),M(GI.$$.fragment,En),En.forEach(t),uc.forEach(t),tIo=i(m),Jh=n(m,"H2",{class:!0});var tjo=s(Jh);BI=n(tjo,"A",{id:!0,class:!0,href:!0});var ves=s(BI);Gio=n(ves,"SPAN",{});var bes=s(Gio);M(nD.$$.fragment,bes),bes.forEach(t),ves.forEach(t),ORa=i(tjo),Bio=n(tjo,"SPAN",{});var Mes=s(Bio);QRa=r(Mes,"Audio"),Mes.forEach(t),tjo.forEach(t),aIo=i(m),Mye=n(m,"P",{});var Tes=s(Mye);YRa=r(Tes,"The following auto classes are available for the following audio tasks."),Tes.forEach(t),nIo=i(m),Uh=n(m,"H3",{class:!0});var ajo=s(Uh);II=n(ajo,"A",{id:!0,class:!0,href:!0});var Fes=s(II);Iio=n(Fes,"SPAN",{});var Ees=s(Iio);M(sD.$$.fragment,Ees),Ees.forEach(t),Fes.forEach(t),zRa=i(ajo),Nio=n(ajo,"SPAN",{});var yes=s(Nio);HRa=r(yes,"AutoModelForAudioClassification"),yes.forEach(t),ajo.forEach(t),sIo=i(m),jr=n(m,"DIV",{class:!0});var pc=s(jr);M(lD.$$.fragment,pc),KRa=i(pc),Xh=n(pc,"P",{});var nwe=s(Xh);eka=r(nwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the `),Tye=n(nwe,"A",{href:!0});var Ces=s(Tye);oka=r(Ces,"from_pretrained()"),Ces.forEach(t),rka=r(nwe," class method or the "),Fye=n(nwe,"A",{href:!0});var wes=s(Fye);tka=r(wes,"from_config()"),wes.forEach(t),aka=r(nwe,` class
method.`),nwe.forEach(t),nka=i(pc),iD=n(pc,"P",{});var njo=s(iD);ska=r(njo,"This class cannot be instantiated directly using "),$io=n(njo,"CODE",{});var Les=s($io);lka=r(Les,"__init__()"),Les.forEach(t),ika=r(njo," (throws an error)."),njo.forEach(t),dka=i(pc),Ia=n(pc,"DIV",{class:!0});var cZ=s(Ia);M(dD.$$.fragment,cZ),cka=i(cZ),jio=n(cZ,"P",{});var Aes=s(jio);mka=r(Aes,"Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),Aes.forEach(t),fka=i(cZ),Dh=n(cZ,"P",{});var swe=s(Dh);hka=r(swe,`Note:
Loading a model from its configuration file does `),Pio=n(swe,"STRONG",{});var Res=s(Pio);gka=r(Res,"not"),Res.forEach(t),uka=r(swe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Eye=n(swe,"A",{href:!0});var kes=s(Eye);pka=r(kes,"from_pretrained()"),kes.forEach(t),_ka=r(swe," to load the model weights."),swe.forEach(t),vka=i(cZ),M(NI.$$.fragment,cZ),cZ.forEach(t),bka=i(pc),Ao=n(pc,"DIV",{class:!0});var yn=s(Ao);M(cD.$$.fragment,yn),Mka=i(yn),Zio=n(yn,"P",{});var Ses=s(Zio);Tka=r(Ses,"Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),Ses.forEach(t),Fka=i(yn),Gs=n(yn,"P",{});var mZ=s(Gs);Eka=r(mZ,"The model class to instantiate is selected based on the "),Vio=n(mZ,"CODE",{});var xes=s(Vio);yka=r(xes,"model_type"),xes.forEach(t),Cka=r(mZ,` property of the config object (either
passed as an argument or loaded from `),Wio=n(mZ,"CODE",{});var Ges=s(Wio);wka=r(Ges,"pretrained_model_name_or_path"),Ges.forEach(t),Lka=r(mZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Jio=n(mZ,"CODE",{});var Bes=s(Jio);Aka=r(Bes,"pretrained_model_name_or_path"),Bes.forEach(t),Rka=r(mZ,":"),mZ.forEach(t),kka=i(yn),Be=n(yn,"UL",{});var Je=s(Be);$I=n(Je,"LI",{});var SSo=s($I);Uio=n(SSo,"STRONG",{});var Ies=s(Uio);Ska=r(Ies,"audio-spectrogram-transformer"),Ies.forEach(t),xka=r(SSo," \u2014 "),yye=n(SSo,"A",{href:!0});var Nes=s(yye);Gka=r(Nes,"ASTForAudioClassification"),Nes.forEach(t),Bka=r(SSo," (Audio Spectrogram Transformer model)"),SSo.forEach(t),Ika=i(Je),jI=n(Je,"LI",{});var xSo=s(jI);Xio=n(xSo,"STRONG",{});var $es=s(Xio);Nka=r($es,"data2vec-audio"),$es.forEach(t),$ka=r(xSo," \u2014 "),Cye=n(xSo,"A",{href:!0});var jes=s(Cye);jka=r(jes,"Data2VecAudioForSequenceClassification"),jes.forEach(t),Pka=r(xSo," (Data2VecAudio model)"),xSo.forEach(t),Zka=i(Je),PI=n(Je,"LI",{});var GSo=s(PI);Dio=n(GSo,"STRONG",{});var Pes=s(Dio);Vka=r(Pes,"hubert"),Pes.forEach(t),Wka=r(GSo," \u2014 "),wye=n(GSo,"A",{href:!0});var Zes=s(wye);Jka=r(Zes,"HubertForSequenceClassification"),Zes.forEach(t),Uka=r(GSo," (Hubert model)"),GSo.forEach(t),Xka=i(Je),ZI=n(Je,"LI",{});var BSo=s(ZI);qio=n(BSo,"STRONG",{});var Ves=s(qio);Dka=r(Ves,"sew"),Ves.forEach(t),qka=r(BSo," \u2014 "),Lye=n(BSo,"A",{href:!0});var Wes=s(Lye);Oka=r(Wes,"SEWForSequenceClassification"),Wes.forEach(t),Qka=r(BSo," (SEW model)"),BSo.forEach(t),Yka=i(Je),VI=n(Je,"LI",{});var ISo=s(VI);Oio=n(ISo,"STRONG",{});var Jes=s(Oio);zka=r(Jes,"sew-d"),Jes.forEach(t),Hka=r(ISo," \u2014 "),Aye=n(ISo,"A",{href:!0});var Ues=s(Aye);Kka=r(Ues,"SEWDForSequenceClassification"),Ues.forEach(t),eSa=r(ISo," (SEW-D model)"),ISo.forEach(t),oSa=i(Je),WI=n(Je,"LI",{});var NSo=s(WI);Qio=n(NSo,"STRONG",{});var Xes=s(Qio);rSa=r(Xes,"unispeech"),Xes.forEach(t),tSa=r(NSo," \u2014 "),Rye=n(NSo,"A",{href:!0});var Des=s(Rye);aSa=r(Des,"UniSpeechForSequenceClassification"),Des.forEach(t),nSa=r(NSo," (UniSpeech model)"),NSo.forEach(t),sSa=i(Je),JI=n(Je,"LI",{});var $So=s(JI);Yio=n($So,"STRONG",{});var qes=s(Yio);lSa=r(qes,"unispeech-sat"),qes.forEach(t),iSa=r($So," \u2014 "),kye=n($So,"A",{href:!0});var Oes=s(kye);dSa=r(Oes,"UniSpeechSatForSequenceClassification"),Oes.forEach(t),cSa=r($So," (UniSpeechSat model)"),$So.forEach(t),mSa=i(Je),UI=n(Je,"LI",{});var jSo=s(UI);zio=n(jSo,"STRONG",{});var Qes=s(zio);fSa=r(Qes,"wav2vec2"),Qes.forEach(t),hSa=r(jSo," \u2014 "),Sye=n(jSo,"A",{href:!0});var Yes=s(Sye);gSa=r(Yes,"Wav2Vec2ForSequenceClassification"),Yes.forEach(t),uSa=r(jSo," (Wav2Vec2 model)"),jSo.forEach(t),pSa=i(Je),XI=n(Je,"LI",{});var PSo=s(XI);Hio=n(PSo,"STRONG",{});var zes=s(Hio);_Sa=r(zes,"wav2vec2-conformer"),zes.forEach(t),vSa=r(PSo," \u2014 "),xye=n(PSo,"A",{href:!0});var Hes=s(xye);bSa=r(Hes,"Wav2Vec2ConformerForSequenceClassification"),Hes.forEach(t),MSa=r(PSo," (Wav2Vec2-Conformer model)"),PSo.forEach(t),TSa=i(Je),DI=n(Je,"LI",{});var ZSo=s(DI);Kio=n(ZSo,"STRONG",{});var Kes=s(Kio);FSa=r(Kes,"wavlm"),Kes.forEach(t),ESa=r(ZSo," \u2014 "),Gye=n(ZSo,"A",{href:!0});var eos=s(Gye);ySa=r(eos,"WavLMForSequenceClassification"),eos.forEach(t),CSa=r(ZSo," (WavLM model)"),ZSo.forEach(t),wSa=i(Je),qI=n(Je,"LI",{});var VSo=s(qI);edo=n(VSo,"STRONG",{});var oos=s(edo);LSa=r(oos,"whisper"),oos.forEach(t),ASa=r(VSo," \u2014 "),Bye=n(VSo,"A",{href:!0});var ros=s(Bye);RSa=r(ros,"WhisperForAudioClassification"),ros.forEach(t),kSa=r(VSo," (Whisper model)"),VSo.forEach(t),Je.forEach(t),SSa=i(yn),OI=n(yn,"P",{});var WSo=s(OI);xSa=r(WSo,"The model is set in evaluation mode by default using "),odo=n(WSo,"CODE",{});var tos=s(odo);GSa=r(tos,"model.eval()"),tos.forEach(t),BSa=r(WSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),rdo=n(WSo,"CODE",{});var aos=s(rdo);ISa=r(aos,"model.train()"),aos.forEach(t),WSo.forEach(t),NSa=i(yn),M(QI.$$.fragment,yn),yn.forEach(t),pc.forEach(t),lIo=i(m),qh=n(m,"H3",{class:!0});var sjo=s(qh);YI=n(sjo,"A",{id:!0,class:!0,href:!0});var nos=s(YI);tdo=n(nos,"SPAN",{});var sos=s(tdo);M(mD.$$.fragment,sos),sos.forEach(t),nos.forEach(t),$Sa=i(sjo),ado=n(sjo,"SPAN",{});var los=s(ado);jSa=r(los,"AutoModelForAudioFrameClassification"),los.forEach(t),sjo.forEach(t),iIo=i(m),Pr=n(m,"DIV",{class:!0});var _c=s(Pr);M(fD.$$.fragment,_c),PSa=i(_c),Oh=n(_c,"P",{});var lwe=s(Oh);ZSa=r(lwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the `),Iye=n(lwe,"A",{href:!0});var ios=s(Iye);VSa=r(ios,"from_pretrained()"),ios.forEach(t),WSa=r(lwe," class method or the "),Nye=n(lwe,"A",{href:!0});var dos=s(Nye);JSa=r(dos,"from_config()"),dos.forEach(t),USa=r(lwe,` class
method.`),lwe.forEach(t),XSa=i(_c),hD=n(_c,"P",{});var ljo=s(hD);DSa=r(ljo,"This class cannot be instantiated directly using "),ndo=n(ljo,"CODE",{});var cos=s(ndo);qSa=r(cos,"__init__()"),cos.forEach(t),OSa=r(ljo," (throws an error)."),ljo.forEach(t),QSa=i(_c),Na=n(_c,"DIV",{class:!0});var fZ=s(Na);M(gD.$$.fragment,fZ),YSa=i(fZ),sdo=n(fZ,"P",{});var mos=s(sdo);zSa=r(mos,"Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),mos.forEach(t),HSa=i(fZ),Qh=n(fZ,"P",{});var iwe=s(Qh);KSa=r(iwe,`Note:
Loading a model from its configuration file does `),ldo=n(iwe,"STRONG",{});var fos=s(ldo);exa=r(fos,"not"),fos.forEach(t),oxa=r(iwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),$ye=n(iwe,"A",{href:!0});var hos=s($ye);rxa=r(hos,"from_pretrained()"),hos.forEach(t),txa=r(iwe," to load the model weights."),iwe.forEach(t),axa=i(fZ),M(zI.$$.fragment,fZ),fZ.forEach(t),nxa=i(_c),At=n(_c,"DIV",{class:!0});var vc=s(At);M(uD.$$.fragment,vc),sxa=i(vc),ido=n(vc,"P",{});var gos=s(ido);lxa=r(gos,"Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),gos.forEach(t),ixa=i(vc),Bs=n(vc,"P",{});var hZ=s(Bs);dxa=r(hZ,"The model class to instantiate is selected based on the "),ddo=n(hZ,"CODE",{});var uos=s(ddo);cxa=r(uos,"model_type"),uos.forEach(t),mxa=r(hZ,` property of the config object (either
passed as an argument or loaded from `),cdo=n(hZ,"CODE",{});var pos=s(cdo);fxa=r(pos,"pretrained_model_name_or_path"),pos.forEach(t),hxa=r(hZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mdo=n(hZ,"CODE",{});var _os=s(mdo);gxa=r(_os,"pretrained_model_name_or_path"),_os.forEach(t),uxa=r(hZ,":"),hZ.forEach(t),pxa=i(vc),fdo=n(vc,"UL",{});var vos=s(fdo);HI=n(vos,"LI",{});var JSo=s(HI);hdo=n(JSo,"STRONG",{});var bos=s(hdo);_xa=r(bos,"wav2vec2"),bos.forEach(t),vxa=r(JSo," \u2014 "),jye=n(JSo,"A",{href:!0});var Mos=s(jye);bxa=r(Mos,"TFWav2Vec2ForSequenceClassification"),Mos.forEach(t),Mxa=r(JSo," (Wav2Vec2 model)"),JSo.forEach(t),vos.forEach(t),Txa=i(vc),M(KI.$$.fragment,vc),vc.forEach(t),_c.forEach(t),dIo=i(m),Yh=n(m,"H3",{class:!0});var ijo=s(Yh);eN=n(ijo,"A",{id:!0,class:!0,href:!0});var Tos=s(eN);gdo=n(Tos,"SPAN",{});var Fos=s(gdo);M(pD.$$.fragment,Fos),Fos.forEach(t),Tos.forEach(t),Fxa=i(ijo),udo=n(ijo,"SPAN",{});var Eos=s(udo);Exa=r(Eos,"TFAutoModelForAudioFrameClassification"),Eos.forEach(t),ijo.forEach(t),cIo=i(m),Zr=n(m,"DIV",{class:!0});var bc=s(Zr);M(_D.$$.fragment,bc),yxa=i(bc),zh=n(bc,"P",{});var dwe=s(zh);Cxa=r(dwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio frame (token) classification head) when created
with the `),Pye=n(dwe,"A",{href:!0});var yos=s(Pye);wxa=r(yos,"from_pretrained()"),yos.forEach(t),Lxa=r(dwe," class method or the "),Zye=n(dwe,"A",{href:!0});var Cos=s(Zye);Axa=r(Cos,"from_config()"),Cos.forEach(t),Rxa=r(dwe,` class
method.`),dwe.forEach(t),kxa=i(bc),vD=n(bc,"P",{});var djo=s(vD);Sxa=r(djo,"This class cannot be instantiated directly using "),pdo=n(djo,"CODE",{});var wos=s(pdo);xxa=r(wos,"__init__()"),wos.forEach(t),Gxa=r(djo," (throws an error)."),djo.forEach(t),Bxa=i(bc),$a=n(bc,"DIV",{class:!0});var gZ=s($a);M(bD.$$.fragment,gZ),Ixa=i(gZ),_do=n(gZ,"P",{});var Los=s(_do);Nxa=r(Los,"Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),Los.forEach(t),$xa=i(gZ),Hh=n(gZ,"P",{});var cwe=s(Hh);jxa=r(cwe,`Note:
Loading a model from its configuration file does `),vdo=n(cwe,"STRONG",{});var Aos=s(vdo);Pxa=r(Aos,"not"),Aos.forEach(t),Zxa=r(cwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Vye=n(cwe,"A",{href:!0});var Ros=s(Vye);Vxa=r(Ros,"from_pretrained()"),Ros.forEach(t),Wxa=r(cwe," to load the model weights."),cwe.forEach(t),Jxa=i(gZ),M(oN.$$.fragment,gZ),gZ.forEach(t),Uxa=i(bc),Ro=n(bc,"DIV",{class:!0});var Cn=s(Ro);M(MD.$$.fragment,Cn),Xxa=i(Cn),bdo=n(Cn,"P",{});var kos=s(bdo);Dxa=r(kos,"Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),kos.forEach(t),qxa=i(Cn),Is=n(Cn,"P",{});var uZ=s(Is);Oxa=r(uZ,"The model class to instantiate is selected based on the "),Mdo=n(uZ,"CODE",{});var Sos=s(Mdo);Qxa=r(Sos,"model_type"),Sos.forEach(t),Yxa=r(uZ,` property of the config object (either
passed as an argument or loaded from `),Tdo=n(uZ,"CODE",{});var xos=s(Tdo);zxa=r(xos,"pretrained_model_name_or_path"),xos.forEach(t),Hxa=r(uZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Fdo=n(uZ,"CODE",{});var Gos=s(Fdo);Kxa=r(Gos,"pretrained_model_name_or_path"),Gos.forEach(t),eGa=r(uZ,":"),uZ.forEach(t),oGa=i(Cn),$t=n(Cn,"UL",{});var Mc=s($t);rN=n(Mc,"LI",{});var USo=s(rN);Edo=n(USo,"STRONG",{});var Bos=s(Edo);rGa=r(Bos,"data2vec-audio"),Bos.forEach(t),tGa=r(USo," \u2014 "),Wye=n(USo,"A",{href:!0});var Ios=s(Wye);aGa=r(Ios,"Data2VecAudioForAudioFrameClassification"),Ios.forEach(t),nGa=r(USo," (Data2VecAudio model)"),USo.forEach(t),sGa=i(Mc),tN=n(Mc,"LI",{});var XSo=s(tN);ydo=n(XSo,"STRONG",{});var Nos=s(ydo);lGa=r(Nos,"unispeech-sat"),Nos.forEach(t),iGa=r(XSo," \u2014 "),Jye=n(XSo,"A",{href:!0});var $os=s(Jye);dGa=r($os,"UniSpeechSatForAudioFrameClassification"),$os.forEach(t),cGa=r(XSo," (UniSpeechSat model)"),XSo.forEach(t),mGa=i(Mc),aN=n(Mc,"LI",{});var DSo=s(aN);Cdo=n(DSo,"STRONG",{});var jos=s(Cdo);fGa=r(jos,"wav2vec2"),jos.forEach(t),hGa=r(DSo," \u2014 "),Uye=n(DSo,"A",{href:!0});var Pos=s(Uye);gGa=r(Pos,"Wav2Vec2ForAudioFrameClassification"),Pos.forEach(t),uGa=r(DSo," (Wav2Vec2 model)"),DSo.forEach(t),pGa=i(Mc),nN=n(Mc,"LI",{});var qSo=s(nN);wdo=n(qSo,"STRONG",{});var Zos=s(wdo);_Ga=r(Zos,"wav2vec2-conformer"),Zos.forEach(t),vGa=r(qSo," \u2014 "),Xye=n(qSo,"A",{href:!0});var Vos=s(Xye);bGa=r(Vos,"Wav2Vec2ConformerForAudioFrameClassification"),Vos.forEach(t),MGa=r(qSo," (Wav2Vec2-Conformer model)"),qSo.forEach(t),TGa=i(Mc),sN=n(Mc,"LI",{});var OSo=s(sN);Ldo=n(OSo,"STRONG",{});var Wos=s(Ldo);FGa=r(Wos,"wavlm"),Wos.forEach(t),EGa=r(OSo," \u2014 "),Dye=n(OSo,"A",{href:!0});var Jos=s(Dye);yGa=r(Jos,"WavLMForAudioFrameClassification"),Jos.forEach(t),CGa=r(OSo," (WavLM model)"),OSo.forEach(t),Mc.forEach(t),wGa=i(Cn),lN=n(Cn,"P",{});var QSo=s(lN);LGa=r(QSo,"The model is set in evaluation mode by default using "),Ado=n(QSo,"CODE",{});var Uos=s(Ado);AGa=r(Uos,"model.eval()"),Uos.forEach(t),RGa=r(QSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Rdo=n(QSo,"CODE",{});var Xos=s(Rdo);kGa=r(Xos,"model.train()"),Xos.forEach(t),QSo.forEach(t),SGa=i(Cn),M(iN.$$.fragment,Cn),Cn.forEach(t),bc.forEach(t),mIo=i(m),Kh=n(m,"H3",{class:!0});var cjo=s(Kh);dN=n(cjo,"A",{id:!0,class:!0,href:!0});var Dos=s(dN);kdo=n(Dos,"SPAN",{});var qos=s(kdo);M(TD.$$.fragment,qos),qos.forEach(t),Dos.forEach(t),xGa=i(cjo),Sdo=n(cjo,"SPAN",{});var Oos=s(Sdo);GGa=r(Oos,"AutoModelForCTC"),Oos.forEach(t),cjo.forEach(t),fIo=i(m),Vr=n(m,"DIV",{class:!0});var Tc=s(Vr);M(FD.$$.fragment,Tc),BGa=i(Tc),eg=n(Tc,"P",{});var mwe=s(eg);IGa=r(mwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a connectionist temporal classification head) when created
with the `),qye=n(mwe,"A",{href:!0});var Qos=s(qye);NGa=r(Qos,"from_pretrained()"),Qos.forEach(t),$Ga=r(mwe," class method or the "),Oye=n(mwe,"A",{href:!0});var Yos=s(Oye);jGa=r(Yos,"from_config()"),Yos.forEach(t),PGa=r(mwe,` class
method.`),mwe.forEach(t),ZGa=i(Tc),ED=n(Tc,"P",{});var mjo=s(ED);VGa=r(mjo,"This class cannot be instantiated directly using "),xdo=n(mjo,"CODE",{});var zos=s(xdo);WGa=r(zos,"__init__()"),zos.forEach(t),JGa=r(mjo," (throws an error)."),mjo.forEach(t),UGa=i(Tc),ja=n(Tc,"DIV",{class:!0});var pZ=s(ja);M(yD.$$.fragment,pZ),XGa=i(pZ),Gdo=n(pZ,"P",{});var Hos=s(Gdo);DGa=r(Hos,"Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),Hos.forEach(t),qGa=i(pZ),og=n(pZ,"P",{});var fwe=s(og);OGa=r(fwe,`Note:
Loading a model from its configuration file does `),Bdo=n(fwe,"STRONG",{});var Kos=s(Bdo);QGa=r(Kos,"not"),Kos.forEach(t),YGa=r(fwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Qye=n(fwe,"A",{href:!0});var ers=s(Qye);zGa=r(ers,"from_pretrained()"),ers.forEach(t),HGa=r(fwe," to load the model weights."),fwe.forEach(t),KGa=i(pZ),M(cN.$$.fragment,pZ),pZ.forEach(t),eBa=i(Tc),ko=n(Tc,"DIV",{class:!0});var wn=s(ko);M(CD.$$.fragment,wn),oBa=i(wn),Ido=n(wn,"P",{});var ors=s(Ido);rBa=r(ors,"Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model."),ors.forEach(t),tBa=i(wn),Ns=n(wn,"P",{});var _Z=s(Ns);aBa=r(_Z,"The model class to instantiate is selected based on the "),Ndo=n(_Z,"CODE",{});var rrs=s(Ndo);nBa=r(rrs,"model_type"),rrs.forEach(t),sBa=r(_Z,` property of the config object (either
passed as an argument or loaded from `),$do=n(_Z,"CODE",{});var trs=s($do);lBa=r(trs,"pretrained_model_name_or_path"),trs.forEach(t),iBa=r(_Z,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),jdo=n(_Z,"CODE",{});var ars=s(jdo);dBa=r(ars,"pretrained_model_name_or_path"),ars.forEach(t),cBa=r(_Z,":"),_Z.forEach(t),mBa=i(wn),je=n(wn,"UL",{});var Oe=s(je);mN=n(Oe,"LI",{});var YSo=s(mN);Pdo=n(YSo,"STRONG",{});var nrs=s(Pdo);fBa=r(nrs,"data2vec-audio"),nrs.forEach(t),hBa=r(YSo," \u2014 "),Yye=n(YSo,"A",{href:!0});var srs=s(Yye);gBa=r(srs,"Data2VecAudioForCTC"),srs.forEach(t),uBa=r(YSo," (Data2VecAudio model)"),YSo.forEach(t),pBa=i(Oe),fN=n(Oe,"LI",{});var zSo=s(fN);Zdo=n(zSo,"STRONG",{});var lrs=s(Zdo);_Ba=r(lrs,"hubert"),lrs.forEach(t),vBa=r(zSo," \u2014 "),zye=n(zSo,"A",{href:!0});var irs=s(zye);bBa=r(irs,"HubertForCTC"),irs.forEach(t),MBa=r(zSo," (Hubert model)"),zSo.forEach(t),TBa=i(Oe),hN=n(Oe,"LI",{});var HSo=s(hN);Vdo=n(HSo,"STRONG",{});var drs=s(Vdo);FBa=r(drs,"mctct"),drs.forEach(t),EBa=r(HSo," \u2014 "),Hye=n(HSo,"A",{href:!0});var crs=s(Hye);yBa=r(crs,"MCTCTForCTC"),crs.forEach(t),CBa=r(HSo," (M-CTC-T model)"),HSo.forEach(t),wBa=i(Oe),gN=n(Oe,"LI",{});var KSo=s(gN);Wdo=n(KSo,"STRONG",{});var mrs=s(Wdo);LBa=r(mrs,"sew"),mrs.forEach(t),ABa=r(KSo," \u2014 "),Kye=n(KSo,"A",{href:!0});var frs=s(Kye);RBa=r(frs,"SEWForCTC"),frs.forEach(t),kBa=r(KSo," (SEW model)"),KSo.forEach(t),SBa=i(Oe),uN=n(Oe,"LI",{});var exo=s(uN);Jdo=n(exo,"STRONG",{});var hrs=s(Jdo);xBa=r(hrs,"sew-d"),hrs.forEach(t),GBa=r(exo," \u2014 "),e5e=n(exo,"A",{href:!0});var grs=s(e5e);BBa=r(grs,"SEWDForCTC"),grs.forEach(t),IBa=r(exo," (SEW-D model)"),exo.forEach(t),NBa=i(Oe),pN=n(Oe,"LI",{});var oxo=s(pN);Udo=n(oxo,"STRONG",{});var urs=s(Udo);$Ba=r(urs,"unispeech"),urs.forEach(t),jBa=r(oxo," \u2014 "),o5e=n(oxo,"A",{href:!0});var prs=s(o5e);PBa=r(prs,"UniSpeechForCTC"),prs.forEach(t),ZBa=r(oxo," (UniSpeech model)"),oxo.forEach(t),VBa=i(Oe),_N=n(Oe,"LI",{});var rxo=s(_N);Xdo=n(rxo,"STRONG",{});var _rs=s(Xdo);WBa=r(_rs,"unispeech-sat"),_rs.forEach(t),JBa=r(rxo," \u2014 "),r5e=n(rxo,"A",{href:!0});var vrs=s(r5e);UBa=r(vrs,"UniSpeechSatForCTC"),vrs.forEach(t),XBa=r(rxo," (UniSpeechSat model)"),rxo.forEach(t),DBa=i(Oe),vN=n(Oe,"LI",{});var txo=s(vN);Ddo=n(txo,"STRONG",{});var brs=s(Ddo);qBa=r(brs,"wav2vec2"),brs.forEach(t),OBa=r(txo," \u2014 "),t5e=n(txo,"A",{href:!0});var Mrs=s(t5e);QBa=r(Mrs,"Wav2Vec2ForCTC"),Mrs.forEach(t),YBa=r(txo," (Wav2Vec2 model)"),txo.forEach(t),zBa=i(Oe),bN=n(Oe,"LI",{});var axo=s(bN);qdo=n(axo,"STRONG",{});var Trs=s(qdo);HBa=r(Trs,"wav2vec2-conformer"),Trs.forEach(t),KBa=r(axo," \u2014 "),a5e=n(axo,"A",{href:!0});var Frs=s(a5e);eIa=r(Frs,"Wav2Vec2ConformerForCTC"),Frs.forEach(t),oIa=r(axo," (Wav2Vec2-Conformer model)"),axo.forEach(t),rIa=i(Oe),MN=n(Oe,"LI",{});var nxo=s(MN);Odo=n(nxo,"STRONG",{});var Ers=s(Odo);tIa=r(Ers,"wavlm"),Ers.forEach(t),aIa=r(nxo," \u2014 "),n5e=n(nxo,"A",{href:!0});var yrs=s(n5e);nIa=r(yrs,"WavLMForCTC"),yrs.forEach(t),sIa=r(nxo," (WavLM model)"),nxo.forEach(t),Oe.forEach(t),lIa=i(wn),TN=n(wn,"P",{});var sxo=s(TN);iIa=r(sxo,"The model is set in evaluation mode by default using "),Qdo=n(sxo,"CODE",{});var Crs=s(Qdo);dIa=r(Crs,"model.eval()"),Crs.forEach(t),cIa=r(sxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Ydo=n(sxo,"CODE",{});var wrs=s(Ydo);mIa=r(wrs,"model.train()"),wrs.forEach(t),sxo.forEach(t),fIa=i(wn),M(FN.$$.fragment,wn),wn.forEach(t),Tc.forEach(t),hIo=i(m),rg=n(m,"H3",{class:!0});var fjo=s(rg);EN=n(fjo,"A",{id:!0,class:!0,href:!0});var Lrs=s(EN);zdo=n(Lrs,"SPAN",{});var Ars=s(zdo);M(wD.$$.fragment,Ars),Ars.forEach(t),Lrs.forEach(t),hIa=i(fjo),Hdo=n(fjo,"SPAN",{});var Rrs=s(Hdo);gIa=r(Rrs,"AutoModelForSpeechSeq2Seq"),Rrs.forEach(t),fjo.forEach(t),gIo=i(m),Wr=n(m,"DIV",{class:!0});var Fc=s(Wr);M(LD.$$.fragment,Fc),uIa=i(Fc),tg=n(Fc,"P",{});var hwe=s(tg);pIa=r(hwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),s5e=n(hwe,"A",{href:!0});var krs=s(s5e);_Ia=r(krs,"from_pretrained()"),krs.forEach(t),vIa=r(hwe," class method or the "),l5e=n(hwe,"A",{href:!0});var Srs=s(l5e);bIa=r(Srs,"from_config()"),Srs.forEach(t),MIa=r(hwe,` class
method.`),hwe.forEach(t),TIa=i(Fc),AD=n(Fc,"P",{});var hjo=s(AD);FIa=r(hjo,"This class cannot be instantiated directly using "),Kdo=n(hjo,"CODE",{});var xrs=s(Kdo);EIa=r(xrs,"__init__()"),xrs.forEach(t),yIa=r(hjo," (throws an error)."),hjo.forEach(t),CIa=i(Fc),Pa=n(Fc,"DIV",{class:!0});var vZ=s(Pa);M(RD.$$.fragment,vZ),wIa=i(vZ),eco=n(vZ,"P",{});var Grs=s(eco);LIa=r(Grs,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),Grs.forEach(t),AIa=i(vZ),ag=n(vZ,"P",{});var gwe=s(ag);RIa=r(gwe,`Note:
Loading a model from its configuration file does `),oco=n(gwe,"STRONG",{});var Brs=s(oco);kIa=r(Brs,"not"),Brs.forEach(t),SIa=r(gwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),i5e=n(gwe,"A",{href:!0});var Irs=s(i5e);xIa=r(Irs,"from_pretrained()"),Irs.forEach(t),GIa=r(gwe," to load the model weights."),gwe.forEach(t),BIa=i(vZ),M(yN.$$.fragment,vZ),vZ.forEach(t),IIa=i(Fc),So=n(Fc,"DIV",{class:!0});var Ln=s(So);M(kD.$$.fragment,Ln),NIa=i(Ln),rco=n(Ln,"P",{});var Nrs=s(rco);$Ia=r(Nrs,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),Nrs.forEach(t),jIa=i(Ln),$s=n(Ln,"P",{});var bZ=s($s);PIa=r(bZ,"The model class to instantiate is selected based on the "),tco=n(bZ,"CODE",{});var $rs=s(tco);ZIa=r($rs,"model_type"),$rs.forEach(t),VIa=r(bZ,` property of the config object (either
passed as an argument or loaded from `),aco=n(bZ,"CODE",{});var jrs=s(aco);WIa=r(jrs,"pretrained_model_name_or_path"),jrs.forEach(t),JIa=r(bZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nco=n(bZ,"CODE",{});var Prs=s(nco);UIa=r(Prs,"pretrained_model_name_or_path"),Prs.forEach(t),XIa=r(bZ,":"),bZ.forEach(t),DIa=i(Ln),js=n(Ln,"UL",{});var MZ=s(js);CN=n(MZ,"LI",{});var lxo=s(CN);sco=n(lxo,"STRONG",{});var Zrs=s(sco);qIa=r(Zrs,"speech-encoder-decoder"),Zrs.forEach(t),OIa=r(lxo," \u2014 "),d5e=n(lxo,"A",{href:!0});var Vrs=s(d5e);QIa=r(Vrs,"SpeechEncoderDecoderModel"),Vrs.forEach(t),YIa=r(lxo," (Speech Encoder decoder model)"),lxo.forEach(t),zIa=i(MZ),wN=n(MZ,"LI",{});var ixo=s(wN);lco=n(ixo,"STRONG",{});var Wrs=s(lco);HIa=r(Wrs,"speech_to_text"),Wrs.forEach(t),KIa=r(ixo," \u2014 "),c5e=n(ixo,"A",{href:!0});var Jrs=s(c5e);eNa=r(Jrs,"Speech2TextForConditionalGeneration"),Jrs.forEach(t),oNa=r(ixo," (Speech2Text model)"),ixo.forEach(t),rNa=i(MZ),LN=n(MZ,"LI",{});var dxo=s(LN);ico=n(dxo,"STRONG",{});var Urs=s(ico);tNa=r(Urs,"speecht5"),Urs.forEach(t),aNa=r(dxo," \u2014 "),m5e=n(dxo,"A",{href:!0});var Xrs=s(m5e);nNa=r(Xrs,"SpeechT5ForSpeechToText"),Xrs.forEach(t),sNa=r(dxo," (SpeechT5 model)"),dxo.forEach(t),lNa=i(MZ),AN=n(MZ,"LI",{});var cxo=s(AN);dco=n(cxo,"STRONG",{});var Drs=s(dco);iNa=r(Drs,"whisper"),Drs.forEach(t),dNa=r(cxo," \u2014 "),f5e=n(cxo,"A",{href:!0});var qrs=s(f5e);cNa=r(qrs,"WhisperForConditionalGeneration"),qrs.forEach(t),mNa=r(cxo," (Whisper model)"),cxo.forEach(t),MZ.forEach(t),fNa=i(Ln),RN=n(Ln,"P",{});var mxo=s(RN);hNa=r(mxo,"The model is set in evaluation mode by default using "),cco=n(mxo,"CODE",{});var Ors=s(cco);gNa=r(Ors,"model.eval()"),Ors.forEach(t),uNa=r(mxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),mco=n(mxo,"CODE",{});var Qrs=s(mco);pNa=r(Qrs,"model.train()"),Qrs.forEach(t),mxo.forEach(t),_Na=i(Ln),M(kN.$$.fragment,Ln),Ln.forEach(t),Fc.forEach(t),uIo=i(m),ng=n(m,"H3",{class:!0});var gjo=s(ng);SN=n(gjo,"A",{id:!0,class:!0,href:!0});var Yrs=s(SN);fco=n(Yrs,"SPAN",{});var zrs=s(fco);M(SD.$$.fragment,zrs),zrs.forEach(t),Yrs.forEach(t),vNa=i(gjo),hco=n(gjo,"SPAN",{});var Hrs=s(hco);bNa=r(Hrs,"TFAutoModelForSpeechSeq2Seq"),Hrs.forEach(t),gjo.forEach(t),pIo=i(m),Jr=n(m,"DIV",{class:!0});var Ec=s(Jr);M(xD.$$.fragment,Ec),MNa=i(Ec),sg=n(Ec,"P",{});var uwe=s(sg);TNa=r(uwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),h5e=n(uwe,"A",{href:!0});var Krs=s(h5e);FNa=r(Krs,"from_pretrained()"),Krs.forEach(t),ENa=r(uwe," class method or the "),g5e=n(uwe,"A",{href:!0});var ets=s(g5e);yNa=r(ets,"from_config()"),ets.forEach(t),CNa=r(uwe,` class
method.`),uwe.forEach(t),wNa=i(Ec),GD=n(Ec,"P",{});var ujo=s(GD);LNa=r(ujo,"This class cannot be instantiated directly using "),gco=n(ujo,"CODE",{});var ots=s(gco);ANa=r(ots,"__init__()"),ots.forEach(t),RNa=r(ujo," (throws an error)."),ujo.forEach(t),kNa=i(Ec),Za=n(Ec,"DIV",{class:!0});var TZ=s(Za);M(BD.$$.fragment,TZ),SNa=i(TZ),uco=n(TZ,"P",{});var rts=s(uco);xNa=r(rts,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),rts.forEach(t),GNa=i(TZ),lg=n(TZ,"P",{});var pwe=s(lg);BNa=r(pwe,`Note:
Loading a model from its configuration file does `),pco=n(pwe,"STRONG",{});var tts=s(pco);INa=r(tts,"not"),tts.forEach(t),NNa=r(pwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),u5e=n(pwe,"A",{href:!0});var ats=s(u5e);$Na=r(ats,"from_pretrained()"),ats.forEach(t),jNa=r(pwe," to load the model weights."),pwe.forEach(t),PNa=i(TZ),M(xN.$$.fragment,TZ),TZ.forEach(t),ZNa=i(Ec),Rt=n(Ec,"DIV",{class:!0});var yc=s(Rt);M(ID.$$.fragment,yc),VNa=i(yc),_co=n(yc,"P",{});var nts=s(_co);WNa=r(nts,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),nts.forEach(t),JNa=i(yc),Ps=n(yc,"P",{});var FZ=s(Ps);UNa=r(FZ,"The model class to instantiate is selected based on the "),vco=n(FZ,"CODE",{});var sts=s(vco);XNa=r(sts,"model_type"),sts.forEach(t),DNa=r(FZ,` property of the config object (either
passed as an argument or loaded from `),bco=n(FZ,"CODE",{});var lts=s(bco);qNa=r(lts,"pretrained_model_name_or_path"),lts.forEach(t),ONa=r(FZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Mco=n(FZ,"CODE",{});var its=s(Mco);QNa=r(its,"pretrained_model_name_or_path"),its.forEach(t),YNa=r(FZ,":"),FZ.forEach(t),zNa=i(yc),ND=n(yc,"UL",{});var pjo=s(ND);GN=n(pjo,"LI",{});var fxo=s(GN);Tco=n(fxo,"STRONG",{});var dts=s(Tco);HNa=r(dts,"speech_to_text"),dts.forEach(t),KNa=r(fxo," \u2014 "),p5e=n(fxo,"A",{href:!0});var cts=s(p5e);e$a=r(cts,"TFSpeech2TextForConditionalGeneration"),cts.forEach(t),o$a=r(fxo," (Speech2Text model)"),fxo.forEach(t),r$a=i(pjo),BN=n(pjo,"LI",{});var hxo=s(BN);Fco=n(hxo,"STRONG",{});var mts=s(Fco);t$a=r(mts,"whisper"),mts.forEach(t),a$a=r(hxo," \u2014 "),_5e=n(hxo,"A",{href:!0});var fts=s(_5e);n$a=r(fts,"TFWhisperForConditionalGeneration"),fts.forEach(t),s$a=r(hxo," (Whisper model)"),hxo.forEach(t),pjo.forEach(t),l$a=i(yc),M(IN.$$.fragment,yc),yc.forEach(t),Ec.forEach(t),_Io=i(m),ig=n(m,"H3",{class:!0});var _jo=s(ig);NN=n(_jo,"A",{id:!0,class:!0,href:!0});var hts=s(NN);Eco=n(hts,"SPAN",{});var gts=s(Eco);M($D.$$.fragment,gts),gts.forEach(t),hts.forEach(t),i$a=i(_jo),yco=n(_jo,"SPAN",{});var uts=s(yco);d$a=r(uts,"FlaxAutoModelForSpeechSeq2Seq"),uts.forEach(t),_jo.forEach(t),vIo=i(m),Ur=n(m,"DIV",{class:!0});var Cc=s(Ur);M(jD.$$.fragment,Cc),c$a=i(Cc),dg=n(Cc,"P",{});var _we=s(dg);m$a=r(_we,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),v5e=n(_we,"A",{href:!0});var pts=s(v5e);f$a=r(pts,"from_pretrained()"),pts.forEach(t),h$a=r(_we," class method or the "),b5e=n(_we,"A",{href:!0});var _ts=s(b5e);g$a=r(_ts,"from_config()"),_ts.forEach(t),u$a=r(_we,` class
method.`),_we.forEach(t),p$a=i(Cc),PD=n(Cc,"P",{});var vjo=s(PD);_$a=r(vjo,"This class cannot be instantiated directly using "),Cco=n(vjo,"CODE",{});var vts=s(Cco);v$a=r(vts,"__init__()"),vts.forEach(t),b$a=r(vjo," (throws an error)."),vjo.forEach(t),M$a=i(Cc),Va=n(Cc,"DIV",{class:!0});var EZ=s(Va);M(ZD.$$.fragment,EZ),T$a=i(EZ),wco=n(EZ,"P",{});var bts=s(wco);F$a=r(bts,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),bts.forEach(t),E$a=i(EZ),cg=n(EZ,"P",{});var vwe=s(cg);y$a=r(vwe,`Note:
Loading a model from its configuration file does `),Lco=n(vwe,"STRONG",{});var Mts=s(Lco);C$a=r(Mts,"not"),Mts.forEach(t),w$a=r(vwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),M5e=n(vwe,"A",{href:!0});var Tts=s(M5e);L$a=r(Tts,"from_pretrained()"),Tts.forEach(t),A$a=r(vwe," to load the model weights."),vwe.forEach(t),R$a=i(EZ),M($N.$$.fragment,EZ),EZ.forEach(t),k$a=i(Cc),kt=n(Cc,"DIV",{class:!0});var wc=s(kt);M(VD.$$.fragment,wc),S$a=i(wc),Aco=n(wc,"P",{});var Fts=s(Aco);x$a=r(Fts,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),Fts.forEach(t),G$a=i(wc),Zs=n(wc,"P",{});var yZ=s(Zs);B$a=r(yZ,"The model class to instantiate is selected based on the "),Rco=n(yZ,"CODE",{});var Ets=s(Rco);I$a=r(Ets,"model_type"),Ets.forEach(t),N$a=r(yZ,` property of the config object (either
passed as an argument or loaded from `),kco=n(yZ,"CODE",{});var yts=s(kco);$$a=r(yts,"pretrained_model_name_or_path"),yts.forEach(t),j$a=r(yZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Sco=n(yZ,"CODE",{});var Cts=s(Sco);P$a=r(Cts,"pretrained_model_name_or_path"),Cts.forEach(t),Z$a=r(yZ,":"),yZ.forEach(t),V$a=i(wc),WD=n(wc,"UL",{});var bjo=s(WD);jN=n(bjo,"LI",{});var gxo=s(jN);xco=n(gxo,"STRONG",{});var wts=s(xco);W$a=r(wts,"speech-encoder-decoder"),wts.forEach(t),J$a=r(gxo," \u2014 "),T5e=n(gxo,"A",{href:!0});var Lts=s(T5e);U$a=r(Lts,"FlaxSpeechEncoderDecoderModel"),Lts.forEach(t),X$a=r(gxo," (Speech Encoder decoder model)"),gxo.forEach(t),D$a=i(bjo),PN=n(bjo,"LI",{});var uxo=s(PN);Gco=n(uxo,"STRONG",{});var Ats=s(Gco);q$a=r(Ats,"whisper"),Ats.forEach(t),O$a=r(uxo," \u2014 "),F5e=n(uxo,"A",{href:!0});var Rts=s(F5e);Q$a=r(Rts,"FlaxWhisperForConditionalGeneration"),Rts.forEach(t),Y$a=r(uxo," (Whisper model)"),uxo.forEach(t),bjo.forEach(t),z$a=i(wc),M(ZN.$$.fragment,wc),wc.forEach(t),Cc.forEach(t),bIo=i(m),mg=n(m,"H3",{class:!0});var Mjo=s(mg);VN=n(Mjo,"A",{id:!0,class:!0,href:!0});var kts=s(VN);Bco=n(kts,"SPAN",{});var Sts=s(Bco);M(JD.$$.fragment,Sts),Sts.forEach(t),kts.forEach(t),H$a=i(Mjo),Ico=n(Mjo,"SPAN",{});var xts=s(Ico);K$a=r(xts,"AutoModelForAudioXVector"),xts.forEach(t),Mjo.forEach(t),MIo=i(m),Xr=n(m,"DIV",{class:!0});var Lc=s(Xr);M(UD.$$.fragment,Lc),eja=i(Lc),fg=n(Lc,"P",{});var bwe=s(fg);oja=r(bwe,`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 `),E5e=n(bwe,"A",{href:!0});var Gts=s(E5e);rja=r(Gts,"from_pretrained()"),Gts.forEach(t),tja=r(bwe," class method or the "),y5e=n(bwe,"A",{href:!0});var Bts=s(y5e);aja=r(Bts,"from_config()"),Bts.forEach(t),nja=r(bwe,` class
method.`),bwe.forEach(t),sja=i(Lc),XD=n(Lc,"P",{});var Tjo=s(XD);lja=r(Tjo,"This class cannot be instantiated directly using "),Nco=n(Tjo,"CODE",{});var Its=s(Nco);ija=r(Its,"__init__()"),Its.forEach(t),dja=r(Tjo," (throws an error)."),Tjo.forEach(t),cja=i(Lc),Wa=n(Lc,"DIV",{class:!0});var CZ=s(Wa);M(DD.$$.fragment,CZ),mja=i(CZ),$co=n(CZ,"P",{});var Nts=s($co);fja=r(Nts,"Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration."),Nts.forEach(t),hja=i(CZ),hg=n(CZ,"P",{});var Mwe=s(hg);gja=r(Mwe,`Note:
Loading a model from its configuration file does `),jco=n(Mwe,"STRONG",{});var $ts=s(jco);uja=r($ts,"not"),$ts.forEach(t),pja=r(Mwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),C5e=n(Mwe,"A",{href:!0});var jts=s(C5e);_ja=r(jts,"from_pretrained()"),jts.forEach(t),vja=r(Mwe," to load the model weights."),Mwe.forEach(t),bja=i(CZ),M(WN.$$.fragment,CZ),CZ.forEach(t),Mja=i(Lc),xo=n(Lc,"DIV",{class:!0});var An=s(xo);M(qD.$$.fragment,An),Tja=i(An),Pco=n(An,"P",{});var Pts=s(Pco);Fja=r(Pts,"Instantiate one of the model classes of the library (with a audio retrieval via x-vector head) from a pretrained model."),Pts.forEach(t),Eja=i(An),Vs=n(An,"P",{});var wZ=s(Vs);yja=r(wZ,"The model class to instantiate is selected based on the "),Zco=n(wZ,"CODE",{});var Zts=s(Zco);Cja=r(Zts,"model_type"),Zts.forEach(t),wja=r(wZ,` property of the config object (either
passed as an argument or loaded from `),Vco=n(wZ,"CODE",{});var Vts=s(Vco);Lja=r(Vts,"pretrained_model_name_or_path"),Vts.forEach(t),Aja=r(wZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Wco=n(wZ,"CODE",{});var Wts=s(Wco);Rja=r(Wts,"pretrained_model_name_or_path"),Wts.forEach(t),kja=r(wZ,":"),wZ.forEach(t),Sja=i(An),jt=n(An,"UL",{});var Ac=s(jt);JN=n(Ac,"LI",{});var pxo=s(JN);Jco=n(pxo,"STRONG",{});var Jts=s(Jco);xja=r(Jts,"data2vec-audio"),Jts.forEach(t),Gja=r(pxo," \u2014 "),w5e=n(pxo,"A",{href:!0});var Uts=s(w5e);Bja=r(Uts,"Data2VecAudioForXVector"),Uts.forEach(t),Ija=r(pxo," (Data2VecAudio model)"),pxo.forEach(t),Nja=i(Ac),UN=n(Ac,"LI",{});var _xo=s(UN);Uco=n(_xo,"STRONG",{});var Xts=s(Uco);$ja=r(Xts,"unispeech-sat"),Xts.forEach(t),jja=r(_xo," \u2014 "),L5e=n(_xo,"A",{href:!0});var Dts=s(L5e);Pja=r(Dts,"UniSpeechSatForXVector"),Dts.forEach(t),Zja=r(_xo," (UniSpeechSat model)"),_xo.forEach(t),Vja=i(Ac),XN=n(Ac,"LI",{});var vxo=s(XN);Xco=n(vxo,"STRONG",{});var qts=s(Xco);Wja=r(qts,"wav2vec2"),qts.forEach(t),Jja=r(vxo," \u2014 "),A5e=n(vxo,"A",{href:!0});var Ots=s(A5e);Uja=r(Ots,"Wav2Vec2ForXVector"),Ots.forEach(t),Xja=r(vxo," (Wav2Vec2 model)"),vxo.forEach(t),Dja=i(Ac),DN=n(Ac,"LI",{});var bxo=s(DN);Dco=n(bxo,"STRONG",{});var Qts=s(Dco);qja=r(Qts,"wav2vec2-conformer"),Qts.forEach(t),Oja=r(bxo," \u2014 "),R5e=n(bxo,"A",{href:!0});var Yts=s(R5e);Qja=r(Yts,"Wav2Vec2ConformerForXVector"),Yts.forEach(t),Yja=r(bxo," (Wav2Vec2-Conformer model)"),bxo.forEach(t),zja=i(Ac),qN=n(Ac,"LI",{});var Mxo=s(qN);qco=n(Mxo,"STRONG",{});var zts=s(qco);Hja=r(zts,"wavlm"),zts.forEach(t),Kja=r(Mxo," \u2014 "),k5e=n(Mxo,"A",{href:!0});var Hts=s(k5e);ePa=r(Hts,"WavLMForXVector"),Hts.forEach(t),oPa=r(Mxo," (WavLM model)"),Mxo.forEach(t),Ac.forEach(t),rPa=i(An),ON=n(An,"P",{});var Txo=s(ON);tPa=r(Txo,"The model is set in evaluation mode by default using "),Oco=n(Txo,"CODE",{});var Kts=s(Oco);aPa=r(Kts,"model.eval()"),Kts.forEach(t),nPa=r(Txo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Qco=n(Txo,"CODE",{});var eas=s(Qco);sPa=r(eas,"model.train()"),eas.forEach(t),Txo.forEach(t),lPa=i(An),M(QN.$$.fragment,An),An.forEach(t),Lc.forEach(t),TIo=i(m),gg=n(m,"H3",{class:!0});var Fjo=s(gg);YN=n(Fjo,"A",{id:!0,class:!0,href:!0});var oas=s(YN);Yco=n(oas,"SPAN",{});var ras=s(Yco);M(OD.$$.fragment,ras),ras.forEach(t),oas.forEach(t),iPa=i(Fjo),zco=n(Fjo,"SPAN",{});var tas=s(zco);dPa=r(tas,"AutoModelForTextToSpectrogram"),tas.forEach(t),Fjo.forEach(t),FIo=i(m),QD=n(m,"DIV",{class:!0});var aas=s(QD);M(YD.$$.fragment,aas),aas.forEach(t),EIo=i(m),ug=n(m,"H3",{class:!0});var Ejo=s(ug);zN=n(Ejo,"A",{id:!0,class:!0,href:!0});var nas=s(zN);Hco=n(nas,"SPAN",{});var sas=s(Hco);M(zD.$$.fragment,sas),sas.forEach(t),nas.forEach(t),cPa=i(Ejo),Kco=n(Ejo,"SPAN",{});var las=s(Kco);mPa=r(las,"AutoModelForTextToWaveform"),las.forEach(t),Ejo.forEach(t),yIo=i(m),HD=n(m,"DIV",{class:!0});var ias=s(HD);M(KD.$$.fragment,ias),ias.forEach(t),CIo=i(m),pg=n(m,"H2",{class:!0});var yjo=s(pg);HN=n(yjo,"A",{id:!0,class:!0,href:!0});var das=s(HN);emo=n(das,"SPAN",{});var cas=s(emo);M(eq.$$.fragment,cas),cas.forEach(t),das.forEach(t),fPa=i(yjo),omo=n(yjo,"SPAN",{});var mas=s(omo);hPa=r(mas,"Multimodal"),mas.forEach(t),yjo.forEach(t),wIo=i(m),S5e=n(m,"P",{});var fas=s(S5e);gPa=r(fas,"The following auto classes are available for the following multimodal tasks."),fas.forEach(t),LIo=i(m),_g=n(m,"H3",{class:!0});var Cjo=s(_g);KN=n(Cjo,"A",{id:!0,class:!0,href:!0});var has=s(KN);rmo=n(has,"SPAN",{});var gas=s(rmo);M(oq.$$.fragment,gas),gas.forEach(t),has.forEach(t),uPa=i(Cjo),tmo=n(Cjo,"SPAN",{});var uas=s(tmo);pPa=r(uas,"AutoModelForTableQuestionAnswering"),uas.forEach(t),Cjo.forEach(t),AIo=i(m),Dr=n(m,"DIV",{class:!0});var Rc=s(Dr);M(rq.$$.fragment,Rc),_Pa=i(Rc),vg=n(Rc,"P",{});var Twe=s(vg);vPa=r(Twe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the `),x5e=n(Twe,"A",{href:!0});var pas=s(x5e);bPa=r(pas,"from_pretrained()"),pas.forEach(t),MPa=r(Twe," class method or the "),G5e=n(Twe,"A",{href:!0});var _as=s(G5e);TPa=r(_as,"from_config()"),_as.forEach(t),FPa=r(Twe,` class
method.`),Twe.forEach(t),EPa=i(Rc),tq=n(Rc,"P",{});var wjo=s(tq);yPa=r(wjo,"This class cannot be instantiated directly using "),amo=n(wjo,"CODE",{});var vas=s(amo);CPa=r(vas,"__init__()"),vas.forEach(t),wPa=r(wjo," (throws an error)."),wjo.forEach(t),LPa=i(Rc),Ja=n(Rc,"DIV",{class:!0});var LZ=s(Ja);M(aq.$$.fragment,LZ),APa=i(LZ),nmo=n(LZ,"P",{});var bas=s(nmo);RPa=r(bas,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),bas.forEach(t),kPa=i(LZ),bg=n(LZ,"P",{});var Fwe=s(bg);SPa=r(Fwe,`Note:
Loading a model from its configuration file does `),smo=n(Fwe,"STRONG",{});var Mas=s(smo);xPa=r(Mas,"not"),Mas.forEach(t),GPa=r(Fwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),B5e=n(Fwe,"A",{href:!0});var Tas=s(B5e);BPa=r(Tas,"from_pretrained()"),Tas.forEach(t),IPa=r(Fwe," to load the model weights."),Fwe.forEach(t),NPa=i(LZ),M(e$.$$.fragment,LZ),LZ.forEach(t),$Pa=i(Rc),Go=n(Rc,"DIV",{class:!0});var Rn=s(Go);M(nq.$$.fragment,Rn),jPa=i(Rn),lmo=n(Rn,"P",{});var Fas=s(lmo);PPa=r(Fas,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),Fas.forEach(t),ZPa=i(Rn),Ws=n(Rn,"P",{});var AZ=s(Ws);VPa=r(AZ,"The model class to instantiate is selected based on the "),imo=n(AZ,"CODE",{});var Eas=s(imo);WPa=r(Eas,"model_type"),Eas.forEach(t),JPa=r(AZ,` property of the config object (either
passed as an argument or loaded from `),dmo=n(AZ,"CODE",{});var yas=s(dmo);UPa=r(yas,"pretrained_model_name_or_path"),yas.forEach(t),XPa=r(AZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),cmo=n(AZ,"CODE",{});var Cas=s(cmo);DPa=r(Cas,"pretrained_model_name_or_path"),Cas.forEach(t),qPa=r(AZ,":"),AZ.forEach(t),OPa=i(Rn),mmo=n(Rn,"UL",{});var was=s(mmo);o$=n(was,"LI",{});var Fxo=s(o$);fmo=n(Fxo,"STRONG",{});var Las=s(fmo);QPa=r(Las,"tapas"),Las.forEach(t),YPa=r(Fxo," \u2014 "),I5e=n(Fxo,"A",{href:!0});var Aas=s(I5e);zPa=r(Aas,"TapasForQuestionAnswering"),Aas.forEach(t),HPa=r(Fxo," (TAPAS model)"),Fxo.forEach(t),was.forEach(t),KPa=i(Rn),r$=n(Rn,"P",{});var Exo=s(r$);eZa=r(Exo,"The model is set in evaluation mode by default using "),hmo=n(Exo,"CODE",{});var Ras=s(hmo);oZa=r(Ras,"model.eval()"),Ras.forEach(t),rZa=r(Exo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),gmo=n(Exo,"CODE",{});var kas=s(gmo);tZa=r(kas,"model.train()"),kas.forEach(t),Exo.forEach(t),aZa=i(Rn),M(t$.$$.fragment,Rn),Rn.forEach(t),Rc.forEach(t),RIo=i(m),Mg=n(m,"H3",{class:!0});var Ljo=s(Mg);a$=n(Ljo,"A",{id:!0,class:!0,href:!0});var Sas=s(a$);umo=n(Sas,"SPAN",{});var xas=s(umo);M(sq.$$.fragment,xas),xas.forEach(t),Sas.forEach(t),nZa=i(Ljo),pmo=n(Ljo,"SPAN",{});var Gas=s(pmo);sZa=r(Gas,"TFAutoModelForTableQuestionAnswering"),Gas.forEach(t),Ljo.forEach(t),kIo=i(m),qr=n(m,"DIV",{class:!0});var kc=s(qr);M(lq.$$.fragment,kc),lZa=i(kc),Tg=n(kc,"P",{});var Ewe=s(Tg);iZa=r(Ewe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the `),N5e=n(Ewe,"A",{href:!0});var Bas=s(N5e);dZa=r(Bas,"from_pretrained()"),Bas.forEach(t),cZa=r(Ewe," class method or the "),$5e=n(Ewe,"A",{href:!0});var Ias=s($5e);mZa=r(Ias,"from_config()"),Ias.forEach(t),fZa=r(Ewe,` class
method.`),Ewe.forEach(t),hZa=i(kc),iq=n(kc,"P",{});var Ajo=s(iq);gZa=r(Ajo,"This class cannot be instantiated directly using "),_mo=n(Ajo,"CODE",{});var Nas=s(_mo);uZa=r(Nas,"__init__()"),Nas.forEach(t),pZa=r(Ajo," (throws an error)."),Ajo.forEach(t),_Za=i(kc),Ua=n(kc,"DIV",{class:!0});var RZ=s(Ua);M(dq.$$.fragment,RZ),vZa=i(RZ),vmo=n(RZ,"P",{});var $as=s(vmo);bZa=r($as,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),$as.forEach(t),MZa=i(RZ),Fg=n(RZ,"P",{});var ywe=s(Fg);TZa=r(ywe,`Note:
Loading a model from its configuration file does `),bmo=n(ywe,"STRONG",{});var jas=s(bmo);FZa=r(jas,"not"),jas.forEach(t),EZa=r(ywe,` load the model weights. It only affects the
model\u2019s configuration. Use `),j5e=n(ywe,"A",{href:!0});var Pas=s(j5e);yZa=r(Pas,"from_pretrained()"),Pas.forEach(t),CZa=r(ywe," to load the model weights."),ywe.forEach(t),wZa=i(RZ),M(n$.$$.fragment,RZ),RZ.forEach(t),LZa=i(kc),St=n(kc,"DIV",{class:!0});var Sc=s(St);M(cq.$$.fragment,Sc),AZa=i(Sc),Mmo=n(Sc,"P",{});var Zas=s(Mmo);RZa=r(Zas,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),Zas.forEach(t),kZa=i(Sc),Js=n(Sc,"P",{});var kZ=s(Js);SZa=r(kZ,"The model class to instantiate is selected based on the "),Tmo=n(kZ,"CODE",{});var Vas=s(Tmo);xZa=r(Vas,"model_type"),Vas.forEach(t),GZa=r(kZ,` property of the config object (either
passed as an argument or loaded from `),Fmo=n(kZ,"CODE",{});var Was=s(Fmo);BZa=r(Was,"pretrained_model_name_or_path"),Was.forEach(t),IZa=r(kZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Emo=n(kZ,"CODE",{});var Jas=s(Emo);NZa=r(Jas,"pretrained_model_name_or_path"),Jas.forEach(t),$Za=r(kZ,":"),kZ.forEach(t),jZa=i(Sc),ymo=n(Sc,"UL",{});var Uas=s(ymo);s$=n(Uas,"LI",{});var yxo=s(s$);Cmo=n(yxo,"STRONG",{});var Xas=s(Cmo);PZa=r(Xas,"tapas"),Xas.forEach(t),ZZa=r(yxo," \u2014 "),P5e=n(yxo,"A",{href:!0});var Das=s(P5e);VZa=r(Das,"TFTapasForQuestionAnswering"),Das.forEach(t),WZa=r(yxo," (TAPAS model)"),yxo.forEach(t),Uas.forEach(t),JZa=i(Sc),M(l$.$$.fragment,Sc),Sc.forEach(t),kc.forEach(t),SIo=i(m),Eg=n(m,"H3",{class:!0});var Rjo=s(Eg);i$=n(Rjo,"A",{id:!0,class:!0,href:!0});var qas=s(i$);wmo=n(qas,"SPAN",{});var Oas=s(wmo);M(mq.$$.fragment,Oas),Oas.forEach(t),qas.forEach(t),UZa=i(Rjo),Lmo=n(Rjo,"SPAN",{});var Qas=s(Lmo);XZa=r(Qas,"AutoModelForDocumentQuestionAnswering"),Qas.forEach(t),Rjo.forEach(t),xIo=i(m),Or=n(m,"DIV",{class:!0});var xc=s(Or);M(fq.$$.fragment,xc),DZa=i(xc),yg=n(xc,"P",{});var Cwe=s(yg);qZa=r(Cwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created
with the `),Z5e=n(Cwe,"A",{href:!0});var Yas=s(Z5e);OZa=r(Yas,"from_pretrained()"),Yas.forEach(t),QZa=r(Cwe," class method or the "),V5e=n(Cwe,"A",{href:!0});var zas=s(V5e);YZa=r(zas,"from_config()"),zas.forEach(t),zZa=r(Cwe,` class
method.`),Cwe.forEach(t),HZa=i(xc),hq=n(xc,"P",{});var kjo=s(hq);KZa=r(kjo,"This class cannot be instantiated directly using "),Amo=n(kjo,"CODE",{});var Has=s(Amo);eVa=r(Has,"__init__()"),Has.forEach(t),oVa=r(kjo," (throws an error)."),kjo.forEach(t),rVa=i(xc),Xa=n(xc,"DIV",{class:!0});var SZ=s(Xa);M(gq.$$.fragment,SZ),tVa=i(SZ),Rmo=n(SZ,"P",{});var Kas=s(Rmo);aVa=r(Kas,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),Kas.forEach(t),nVa=i(SZ),Cg=n(SZ,"P",{});var wwe=s(Cg);sVa=r(wwe,`Note:
Loading a model from its configuration file does `),kmo=n(wwe,"STRONG",{});var ens=s(kmo);lVa=r(ens,"not"),ens.forEach(t),iVa=r(wwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),W5e=n(wwe,"A",{href:!0});var ons=s(W5e);dVa=r(ons,"from_pretrained()"),ons.forEach(t),cVa=r(wwe," to load the model weights."),wwe.forEach(t),mVa=i(SZ),M(d$.$$.fragment,SZ),SZ.forEach(t),fVa=i(xc),Bo=n(xc,"DIV",{class:!0});var kn=s(Bo);M(uq.$$.fragment,kn),hVa=i(kn),Smo=n(kn,"P",{});var rns=s(Smo);gVa=r(rns,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),rns.forEach(t),uVa=i(kn),Us=n(kn,"P",{});var xZ=s(Us);pVa=r(xZ,"The model class to instantiate is selected based on the "),xmo=n(xZ,"CODE",{});var tns=s(xmo);_Va=r(tns,"model_type"),tns.forEach(t),vVa=r(xZ,` property of the config object (either
passed as an argument or loaded from `),Gmo=n(xZ,"CODE",{});var ans=s(Gmo);bVa=r(ans,"pretrained_model_name_or_path"),ans.forEach(t),MVa=r(xZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Bmo=n(xZ,"CODE",{});var nns=s(Bmo);TVa=r(nns,"pretrained_model_name_or_path"),nns.forEach(t),FVa=r(xZ,":"),xZ.forEach(t),EVa=i(kn),wg=n(kn,"UL",{});var Lwe=s(wg);c$=n(Lwe,"LI",{});var Cxo=s(c$);Imo=n(Cxo,"STRONG",{});var sns=s(Imo);yVa=r(sns,"layoutlm"),sns.forEach(t),CVa=r(Cxo," \u2014 "),J5e=n(Cxo,"A",{href:!0});var lns=s(J5e);wVa=r(lns,"LayoutLMForQuestionAnswering"),lns.forEach(t),LVa=r(Cxo," (LayoutLM model)"),Cxo.forEach(t),AVa=i(Lwe),m$=n(Lwe,"LI",{});var wxo=s(m$);Nmo=n(wxo,"STRONG",{});var ins=s(Nmo);RVa=r(ins,"layoutlmv2"),ins.forEach(t),kVa=r(wxo," \u2014 "),U5e=n(wxo,"A",{href:!0});var dns=s(U5e);SVa=r(dns,"LayoutLMv2ForQuestionAnswering"),dns.forEach(t),xVa=r(wxo," (LayoutLMv2 model)"),wxo.forEach(t),GVa=i(Lwe),f$=n(Lwe,"LI",{});var Lxo=s(f$);$mo=n(Lxo,"STRONG",{});var cns=s($mo);BVa=r(cns,"layoutlmv3"),cns.forEach(t),IVa=r(Lxo," \u2014 "),X5e=n(Lxo,"A",{href:!0});var mns=s(X5e);NVa=r(mns,"LayoutLMv3ForQuestionAnswering"),mns.forEach(t),$Va=r(Lxo," (LayoutLMv3 model)"),Lxo.forEach(t),Lwe.forEach(t),jVa=i(kn),h$=n(kn,"P",{});var Axo=s(h$);PVa=r(Axo,"The model is set in evaluation mode by default using "),jmo=n(Axo,"CODE",{});var fns=s(jmo);ZVa=r(fns,"model.eval()"),fns.forEach(t),VVa=r(Axo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Pmo=n(Axo,"CODE",{});var hns=s(Pmo);WVa=r(hns,"model.train()"),hns.forEach(t),Axo.forEach(t),JVa=i(kn),M(g$.$$.fragment,kn),kn.forEach(t),xc.forEach(t),GIo=i(m),Lg=n(m,"H3",{class:!0});var Sjo=s(Lg);u$=n(Sjo,"A",{id:!0,class:!0,href:!0});var gns=s(u$);Zmo=n(gns,"SPAN",{});var uns=s(Zmo);M(pq.$$.fragment,uns),uns.forEach(t),gns.forEach(t),UVa=i(Sjo),Vmo=n(Sjo,"SPAN",{});var pns=s(Vmo);XVa=r(pns,"TFAutoModelForDocumentQuestionAnswering"),pns.forEach(t),Sjo.forEach(t),BIo=i(m),Qr=n(m,"DIV",{class:!0});var Gc=s(Qr);M(_q.$$.fragment,Gc),DVa=i(Gc),Ag=n(Gc,"P",{});var Awe=s(Ag);qVa=r(Awe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created
with the `),D5e=n(Awe,"A",{href:!0});var _ns=s(D5e);OVa=r(_ns,"from_pretrained()"),_ns.forEach(t),QVa=r(Awe," class method or the "),q5e=n(Awe,"A",{href:!0});var vns=s(q5e);YVa=r(vns,"from_config()"),vns.forEach(t),zVa=r(Awe,` class
method.`),Awe.forEach(t),HVa=i(Gc),vq=n(Gc,"P",{});var xjo=s(vq);KVa=r(xjo,"This class cannot be instantiated directly using "),Wmo=n(xjo,"CODE",{});var bns=s(Wmo);eWa=r(bns,"__init__()"),bns.forEach(t),oWa=r(xjo," (throws an error)."),xjo.forEach(t),rWa=i(Gc),Da=n(Gc,"DIV",{class:!0});var GZ=s(Da);M(bq.$$.fragment,GZ),tWa=i(GZ),Jmo=n(GZ,"P",{});var Mns=s(Jmo);aWa=r(Mns,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),Mns.forEach(t),nWa=i(GZ),Rg=n(GZ,"P",{});var Rwe=s(Rg);sWa=r(Rwe,`Note:
Loading a model from its configuration file does `),Umo=n(Rwe,"STRONG",{});var Tns=s(Umo);lWa=r(Tns,"not"),Tns.forEach(t),iWa=r(Rwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),O5e=n(Rwe,"A",{href:!0});var Fns=s(O5e);dWa=r(Fns,"from_pretrained()"),Fns.forEach(t),cWa=r(Rwe," to load the model weights."),Rwe.forEach(t),mWa=i(GZ),M(p$.$$.fragment,GZ),GZ.forEach(t),fWa=i(Gc),xt=n(Gc,"DIV",{class:!0});var Bc=s(xt);M(Mq.$$.fragment,Bc),hWa=i(Bc),Xmo=n(Bc,"P",{});var Ens=s(Xmo);gWa=r(Ens,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),Ens.forEach(t),uWa=i(Bc),Xs=n(Bc,"P",{});var BZ=s(Xs);pWa=r(BZ,"The model class to instantiate is selected based on the "),Dmo=n(BZ,"CODE",{});var yns=s(Dmo);_Wa=r(yns,"model_type"),yns.forEach(t),vWa=r(BZ,` property of the config object (either
passed as an argument or loaded from `),qmo=n(BZ,"CODE",{});var Cns=s(qmo);bWa=r(Cns,"pretrained_model_name_or_path"),Cns.forEach(t),MWa=r(BZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Omo=n(BZ,"CODE",{});var wns=s(Omo);TWa=r(wns,"pretrained_model_name_or_path"),wns.forEach(t),FWa=r(BZ,":"),BZ.forEach(t),EWa=i(Bc),Qmo=n(Bc,"UL",{});var Lns=s(Qmo);_$=n(Lns,"LI",{});var Rxo=s(_$);Ymo=n(Rxo,"STRONG",{});var Ans=s(Ymo);yWa=r(Ans,"layoutlm"),Ans.forEach(t),CWa=r(Rxo," \u2014 "),Q5e=n(Rxo,"A",{href:!0});var Rns=s(Q5e);wWa=r(Rns,"TFLayoutLMForQuestionAnswering"),Rns.forEach(t),LWa=r(Rxo," (LayoutLM model)"),Rxo.forEach(t),Lns.forEach(t),AWa=i(Bc),M(v$.$$.fragment,Bc),Bc.forEach(t),Gc.forEach(t),IIo=i(m),kg=n(m,"H3",{class:!0});var Gjo=s(kg);b$=n(Gjo,"A",{id:!0,class:!0,href:!0});var kns=s(b$);zmo=n(kns,"SPAN",{});var Sns=s(zmo);M(Tq.$$.fragment,Sns),Sns.forEach(t),kns.forEach(t),RWa=i(Gjo),Hmo=n(Gjo,"SPAN",{});var xns=s(Hmo);kWa=r(xns,"AutoModelForVisualQuestionAnswering"),xns.forEach(t),Gjo.forEach(t),NIo=i(m),Yr=n(m,"DIV",{class:!0});var Ic=s(Yr);M(Fq.$$.fragment,Ic),SWa=i(Ic),Sg=n(Ic,"P",{});var kwe=s(Sg);xWa=r(kwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a visual question answering head) when created
with the `),Y5e=n(kwe,"A",{href:!0});var Gns=s(Y5e);GWa=r(Gns,"from_pretrained()"),Gns.forEach(t),BWa=r(kwe," class method or the "),z5e=n(kwe,"A",{href:!0});var Bns=s(z5e);IWa=r(Bns,"from_config()"),Bns.forEach(t),NWa=r(kwe,` class
method.`),kwe.forEach(t),$Wa=i(Ic),Eq=n(Ic,"P",{});var Bjo=s(Eq);jWa=r(Bjo,"This class cannot be instantiated directly using "),Kmo=n(Bjo,"CODE",{});var Ins=s(Kmo);PWa=r(Ins,"__init__()"),Ins.forEach(t),ZWa=r(Bjo," (throws an error)."),Bjo.forEach(t),VWa=i(Ic),qa=n(Ic,"DIV",{class:!0});var IZ=s(qa);M(yq.$$.fragment,IZ),WWa=i(IZ),efo=n(IZ,"P",{});var Nns=s(efo);JWa=r(Nns,"Instantiates one of the model classes of the library (with a visual question answering head) from a configuration."),Nns.forEach(t),UWa=i(IZ),xg=n(IZ,"P",{});var Swe=s(xg);XWa=r(Swe,`Note:
Loading a model from its configuration file does `),ofo=n(Swe,"STRONG",{});var $ns=s(ofo);DWa=r($ns,"not"),$ns.forEach(t),qWa=r(Swe,` load the model weights. It only affects the
model\u2019s configuration. Use `),H5e=n(Swe,"A",{href:!0});var jns=s(H5e);OWa=r(jns,"from_pretrained()"),jns.forEach(t),QWa=r(Swe," to load the model weights."),Swe.forEach(t),YWa=i(IZ),M(M$.$$.fragment,IZ),IZ.forEach(t),zWa=i(Ic),Io=n(Ic,"DIV",{class:!0});var Sn=s(Io);M(Cq.$$.fragment,Sn),HWa=i(Sn),rfo=n(Sn,"P",{});var Pns=s(rfo);KWa=r(Pns,"Instantiate one of the model classes of the library (with a visual question answering head) from a pretrained model."),Pns.forEach(t),eJa=i(Sn),Ds=n(Sn,"P",{});var NZ=s(Ds);oJa=r(NZ,"The model class to instantiate is selected based on the "),tfo=n(NZ,"CODE",{});var Zns=s(tfo);rJa=r(Zns,"model_type"),Zns.forEach(t),tJa=r(NZ,` property of the config object (either
passed as an argument or loaded from `),afo=n(NZ,"CODE",{});var Vns=s(afo);aJa=r(Vns,"pretrained_model_name_or_path"),Vns.forEach(t),nJa=r(NZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nfo=n(NZ,"CODE",{});var Wns=s(nfo);sJa=r(Wns,"pretrained_model_name_or_path"),Wns.forEach(t),lJa=r(NZ,":"),NZ.forEach(t),iJa=i(Sn),sfo=n(Sn,"UL",{});var Jns=s(sfo);T$=n(Jns,"LI",{});var kxo=s(T$);lfo=n(kxo,"STRONG",{});var Uns=s(lfo);dJa=r(Uns,"vilt"),Uns.forEach(t),cJa=r(kxo," \u2014 "),K5e=n(kxo,"A",{href:!0});var Xns=s(K5e);mJa=r(Xns,"ViltForQuestionAnswering"),Xns.forEach(t),fJa=r(kxo," (ViLT model)"),kxo.forEach(t),Jns.forEach(t),hJa=i(Sn),F$=n(Sn,"P",{});var Sxo=s(F$);gJa=r(Sxo,"The model is set in evaluation mode by default using "),ifo=n(Sxo,"CODE",{});var Dns=s(ifo);uJa=r(Dns,"model.eval()"),Dns.forEach(t),pJa=r(Sxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),dfo=n(Sxo,"CODE",{});var qns=s(dfo);_Ja=r(qns,"model.train()"),qns.forEach(t),Sxo.forEach(t),vJa=i(Sn),M(E$.$$.fragment,Sn),Sn.forEach(t),Ic.forEach(t),$Io=i(m),Gg=n(m,"H3",{class:!0});var Ijo=s(Gg);y$=n(Ijo,"A",{id:!0,class:!0,href:!0});var Ons=s(y$);cfo=n(Ons,"SPAN",{});var Qns=s(cfo);M(wq.$$.fragment,Qns),Qns.forEach(t),Ons.forEach(t),bJa=i(Ijo),mfo=n(Ijo,"SPAN",{});var Yns=s(mfo);MJa=r(Yns,"AutoModelForVision2Seq"),Yns.forEach(t),Ijo.forEach(t),jIo=i(m),zr=n(m,"DIV",{class:!0});var Nc=s(zr);M(Lq.$$.fragment,Nc),TJa=i(Nc),Bg=n(Nc,"P",{});var xwe=s(Bg);FJa=r(xwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
with the `),e9e=n(xwe,"A",{href:!0});var zns=s(e9e);EJa=r(zns,"from_pretrained()"),zns.forEach(t),yJa=r(xwe," class method or the "),o9e=n(xwe,"A",{href:!0});var Hns=s(o9e);CJa=r(Hns,"from_config()"),Hns.forEach(t),wJa=r(xwe,` class
method.`),xwe.forEach(t),LJa=i(Nc),Aq=n(Nc,"P",{});var Njo=s(Aq);AJa=r(Njo,"This class cannot be instantiated directly using "),ffo=n(Njo,"CODE",{});var Kns=s(ffo);RJa=r(Kns,"__init__()"),Kns.forEach(t),kJa=r(Njo," (throws an error)."),Njo.forEach(t),SJa=i(Nc),Oa=n(Nc,"DIV",{class:!0});var $Z=s(Oa);M(Rq.$$.fragment,$Z),xJa=i($Z),hfo=n($Z,"P",{});var ess=s(hfo);GJa=r(ess,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),ess.forEach(t),BJa=i($Z),Ig=n($Z,"P",{});var Gwe=s(Ig);IJa=r(Gwe,`Note:
Loading a model from its configuration file does `),gfo=n(Gwe,"STRONG",{});var oss=s(gfo);NJa=r(oss,"not"),oss.forEach(t),$Ja=r(Gwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),r9e=n(Gwe,"A",{href:!0});var rss=s(r9e);jJa=r(rss,"from_pretrained()"),rss.forEach(t),PJa=r(Gwe," to load the model weights."),Gwe.forEach(t),ZJa=i($Z),M(C$.$$.fragment,$Z),$Z.forEach(t),VJa=i(Nc),No=n(Nc,"DIV",{class:!0});var xn=s(No);M(kq.$$.fragment,xn),WJa=i(xn),ufo=n(xn,"P",{});var tss=s(ufo);JJa=r(tss,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),tss.forEach(t),UJa=i(xn),qs=n(xn,"P",{});var jZ=s(qs);XJa=r(jZ,"The model class to instantiate is selected based on the "),pfo=n(jZ,"CODE",{});var ass=s(pfo);DJa=r(ass,"model_type"),ass.forEach(t),qJa=r(jZ,` property of the config object (either
passed as an argument or loaded from `),_fo=n(jZ,"CODE",{});var nss=s(_fo);OJa=r(nss,"pretrained_model_name_or_path"),nss.forEach(t),QJa=r(jZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),vfo=n(jZ,"CODE",{});var sss=s(vfo);YJa=r(sss,"pretrained_model_name_or_path"),sss.forEach(t),zJa=r(jZ,":"),jZ.forEach(t),HJa=i(xn),Hr=n(xn,"UL",{});var Gn=s(Hr);w$=n(Gn,"LI",{});var xxo=s(w$);bfo=n(xxo,"STRONG",{});var lss=s(bfo);KJa=r(lss,"blip"),lss.forEach(t),eUa=r(xxo," \u2014 "),t9e=n(xxo,"A",{href:!0});var iss=s(t9e);oUa=r(iss,"BlipForConditionalGeneration"),iss.forEach(t),rUa=r(xxo," (BLIP model)"),xxo.forEach(t),tUa=i(Gn),L$=n(Gn,"LI",{});var Gxo=s(L$);Mfo=n(Gxo,"STRONG",{});var dss=s(Mfo);aUa=r(dss,"blip-2"),dss.forEach(t),nUa=r(Gxo," \u2014 "),a9e=n(Gxo,"A",{href:!0});var css=s(a9e);sUa=r(css,"Blip2ForConditionalGeneration"),css.forEach(t),lUa=r(Gxo," (BLIP-2 model)"),Gxo.forEach(t),iUa=i(Gn),A$=n(Gn,"LI",{});var Bxo=s(A$);Tfo=n(Bxo,"STRONG",{});var mss=s(Tfo);dUa=r(mss,"git"),mss.forEach(t),cUa=r(Bxo," \u2014 "),n9e=n(Bxo,"A",{href:!0});var fss=s(n9e);mUa=r(fss,"GitForCausalLM"),fss.forEach(t),fUa=r(Bxo," (GIT model)"),Bxo.forEach(t),hUa=i(Gn),R$=n(Gn,"LI",{});var Ixo=s(R$);Ffo=n(Ixo,"STRONG",{});var hss=s(Ffo);gUa=r(hss,"instructblip"),hss.forEach(t),uUa=r(Ixo," \u2014 "),s9e=n(Ixo,"A",{href:!0});var gss=s(s9e);pUa=r(gss,"InstructBlipForConditionalGeneration"),gss.forEach(t),_Ua=r(Ixo," (InstructBLIP model)"),Ixo.forEach(t),vUa=i(Gn),k$=n(Gn,"LI",{});var Nxo=s(k$);Efo=n(Nxo,"STRONG",{});var uss=s(Efo);bUa=r(uss,"pix2struct"),uss.forEach(t),MUa=r(Nxo," \u2014 "),l9e=n(Nxo,"A",{href:!0});var pss=s(l9e);TUa=r(pss,"Pix2StructForConditionalGeneration"),pss.forEach(t),FUa=r(Nxo," (Pix2Struct model)"),Nxo.forEach(t),EUa=i(Gn),S$=n(Gn,"LI",{});var $xo=s(S$);yfo=n($xo,"STRONG",{});var _ss=s(yfo);yUa=r(_ss,"vision-encoder-decoder"),_ss.forEach(t),CUa=r($xo," \u2014 "),i9e=n($xo,"A",{href:!0});var vss=s(i9e);wUa=r(vss,"VisionEncoderDecoderModel"),vss.forEach(t),LUa=r($xo," (Vision Encoder decoder model)"),$xo.forEach(t),Gn.forEach(t),AUa=i(xn),x$=n(xn,"P",{});var jxo=s(x$);RUa=r(jxo,"The model is set in evaluation mode by default using "),Cfo=n(jxo,"CODE",{});var bss=s(Cfo);kUa=r(bss,"model.eval()"),bss.forEach(t),SUa=r(jxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),wfo=n(jxo,"CODE",{});var Mss=s(wfo);xUa=r(Mss,"model.train()"),Mss.forEach(t),jxo.forEach(t),GUa=i(xn),M(G$.$$.fragment,xn),xn.forEach(t),Nc.forEach(t),PIo=i(m),Ng=n(m,"H3",{class:!0});var $jo=s(Ng);B$=n($jo,"A",{id:!0,class:!0,href:!0});var Tss=s(B$);Lfo=n(Tss,"SPAN",{});var Fss=s(Lfo);M(Sq.$$.fragment,Fss),Fss.forEach(t),Tss.forEach(t),BUa=i($jo),Afo=n($jo,"SPAN",{});var Ess=s(Afo);IUa=r(Ess,"TFAutoModelForVision2Seq"),Ess.forEach(t),$jo.forEach(t),ZIo=i(m),Kr=n(m,"DIV",{class:!0});var $c=s(Kr);M(xq.$$.fragment,$c),NUa=i($c),$g=n($c,"P",{});var Bwe=s($g);$Ua=r(Bwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
with the `),d9e=n(Bwe,"A",{href:!0});var yss=s(d9e);jUa=r(yss,"from_pretrained()"),yss.forEach(t),PUa=r(Bwe," class method or the "),c9e=n(Bwe,"A",{href:!0});var Css=s(c9e);ZUa=r(Css,"from_config()"),Css.forEach(t),VUa=r(Bwe,` class
method.`),Bwe.forEach(t),WUa=i($c),Gq=n($c,"P",{});var jjo=s(Gq);JUa=r(jjo,"This class cannot be instantiated directly using "),Rfo=n(jjo,"CODE",{});var wss=s(Rfo);UUa=r(wss,"__init__()"),wss.forEach(t),XUa=r(jjo," (throws an error)."),jjo.forEach(t),DUa=i($c),Qa=n($c,"DIV",{class:!0});var PZ=s(Qa);M(Bq.$$.fragment,PZ),qUa=i(PZ),kfo=n(PZ,"P",{});var Lss=s(kfo);OUa=r(Lss,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),Lss.forEach(t),QUa=i(PZ),jg=n(PZ,"P",{});var Iwe=s(jg);YUa=r(Iwe,`Note:
Loading a model from its configuration file does `),Sfo=n(Iwe,"STRONG",{});var Ass=s(Sfo);zUa=r(Ass,"not"),Ass.forEach(t),HUa=r(Iwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),m9e=n(Iwe,"A",{href:!0});var Rss=s(m9e);KUa=r(Rss,"from_pretrained()"),Rss.forEach(t),eXa=r(Iwe," to load the model weights."),Iwe.forEach(t),oXa=i(PZ),M(I$.$$.fragment,PZ),PZ.forEach(t),rXa=i($c),Gt=n($c,"DIV",{class:!0});var jc=s(Gt);M(Iq.$$.fragment,jc),tXa=i(jc),xfo=n(jc,"P",{});var kss=s(xfo);aXa=r(kss,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),kss.forEach(t),nXa=i(jc),Os=n(jc,"P",{});var ZZ=s(Os);sXa=r(ZZ,"The model class to instantiate is selected based on the "),Gfo=n(ZZ,"CODE",{});var Sss=s(Gfo);lXa=r(Sss,"model_type"),Sss.forEach(t),iXa=r(ZZ,` property of the config object (either
passed as an argument or loaded from `),Bfo=n(ZZ,"CODE",{});var xss=s(Bfo);dXa=r(xss,"pretrained_model_name_or_path"),xss.forEach(t),cXa=r(ZZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ifo=n(ZZ,"CODE",{});var Gss=s(Ifo);mXa=r(Gss,"pretrained_model_name_or_path"),Gss.forEach(t),fXa=r(ZZ,":"),ZZ.forEach(t),hXa=i(jc),Nq=n(jc,"UL",{});var Pjo=s(Nq);N$=n(Pjo,"LI",{});var Pxo=s(N$);Nfo=n(Pxo,"STRONG",{});var Bss=s(Nfo);gXa=r(Bss,"blip"),Bss.forEach(t),uXa=r(Pxo," \u2014 "),f9e=n(Pxo,"A",{href:!0});var Iss=s(f9e);pXa=r(Iss,"TFBlipForConditionalGeneration"),Iss.forEach(t),_Xa=r(Pxo," (BLIP model)"),Pxo.forEach(t),vXa=i(Pjo),$$=n(Pjo,"LI",{});var Zxo=s($$);$fo=n(Zxo,"STRONG",{});var Nss=s($fo);bXa=r(Nss,"vision-encoder-decoder"),Nss.forEach(t),MXa=r(Zxo," \u2014 "),h9e=n(Zxo,"A",{href:!0});var $ss=s(h9e);TXa=r($ss,"TFVisionEncoderDecoderModel"),$ss.forEach(t),FXa=r(Zxo," (Vision Encoder decoder model)"),Zxo.forEach(t),Pjo.forEach(t),EXa=i(jc),M(j$.$$.fragment,jc),jc.forEach(t),$c.forEach(t),VIo=i(m),Pg=n(m,"H3",{class:!0});var Zjo=s(Pg);P$=n(Zjo,"A",{id:!0,class:!0,href:!0});var jss=s(P$);jfo=n(jss,"SPAN",{});var Pss=s(jfo);M($q.$$.fragment,Pss),Pss.forEach(t),jss.forEach(t),yXa=i(Zjo),Pfo=n(Zjo,"SPAN",{});var Zss=s(Pfo);CXa=r(Zss,"FlaxAutoModelForVision2Seq"),Zss.forEach(t),Zjo.forEach(t),WIo=i(m),et=n(m,"DIV",{class:!0});var Pc=s(et);M(jq.$$.fragment,Pc),wXa=i(Pc),Zg=n(Pc,"P",{});var Nwe=s(Zg);LXa=r(Nwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
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method.`),Nwe.forEach(t),xXa=i(Pc),Pq=n(Pc,"P",{});var Vjo=s(Pq);GXa=r(Vjo,"This class cannot be instantiated directly using "),Zfo=n(Vjo,"CODE",{});var Jss=s(Zfo);BXa=r(Jss,"__init__()"),Jss.forEach(t),IXa=r(Vjo," (throws an error)."),Vjo.forEach(t),NXa=i(Pc),Ya=n(Pc,"DIV",{class:!0});var VZ=s(Ya);M(Zq.$$.fragment,VZ),$Xa=i(VZ),Vfo=n(VZ,"P",{});var Uss=s(Vfo);jXa=r(Uss,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),Uss.forEach(t),PXa=i(VZ),Vg=n(VZ,"P",{});var $we=s(Vg);ZXa=r($we,`Note:
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