import{S as els,i as ols,s as rls,e as a,k as l,w as v,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 b,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,m,g,ze,Vm,Xg,Vt,Wm,Jm,JZ,Dg,Qe,oo,Um,zs,UZ,Hs,Ks,XZ,Xm,el,DZ,Dm,qg,Bn;return{c(){h=a("p"),C=o("If your "),u=a("code"),f=o("NewModelConfig"),p=o(" is a subclass of "),m=a("code"),g=o("~transformer.PretrainedConfig"),ze=o(`, make sure its
`),Vm=a("code"),Xg=o("model_type"),Vt=o(" attribute is set to the same key you use when registering the config (here "),Wm=a("code"),Jm=o('"new-model"'),JZ=o(")."),Dg=l(),Qe=a("p"),oo=o("Likewise, if your "),Um=a("code"),zs=o("NewModel"),UZ=o(" is a subclass of "),Hs=a("a"),Ks=o("PreTrainedModel"),XZ=o(`, make sure its
`),Xm=a("code"),el=o("config_class"),DZ=o(` attribute is set to the same class you use when registering the model (here
`),Dm=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 "),m=n(Ie,"CODE",{});var qm=s(m);g=r(qm,"~transformer.PretrainedConfig"),qm.forEach(t),ze=r(Ie,`, make sure its
`),Vm=n(Ie,"CODE",{});var WQ=s(Vm);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 "),Wm=n(Ie,"CODE",{});var JQ=s(Wm);Jm=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 "),Um=n(jo,"CODE",{});var In=s(Um);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
`),Xm=n(jo,"CODE",{});var Og=s(Xm);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
`),Dm=n(jo,"CODE",{});var XQ=s(Dm);qg=r(XQ,"NewModelConfig"),XQ.forEach(t),Bn=r(jo,")."),jo.forEach(t),this.h()},h(){d(Hs,"href","/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel")},m(ro,Ie){b(ro,h,Ie),e(h,C),e(h,u),e(u,f),e(h,p),e(h,m),e(m,g),e(h,ze),e(h,Vm),e(Vm,Xg),e(h,Vt),e(h,Wm),e(Wm,Jm),e(h,JZ),b(ro,Dg,Ie),b(ro,Qe,Ie),e(Qe,oo),e(Qe,Um),e(Um,zs),e(Qe,UZ),e(Qe,Hs),e(Hs,Ks),e(Qe,XZ),e(Qe,Xm),e(Xm,el),e(Qe,DZ),e(Qe,Dm),e(Dm,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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(m){h=n(m,"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(m,g){b(m,h,g),e(h,C),e(h,u),e(u,f),e(h,p)},d(m){m&&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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function mls(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(m){h=n(m,"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(m,g){b(m,h,g),e(h,C),e(h,u),e(u,f),e(h,p)},d(m){m&&t(h)}}}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> 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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(m){h=n(m,"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(m,g){b(m,h,g),e(h,C),e(h,u),e(u,f),e(h,p)},d(m){m&&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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function bls(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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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, 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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Jls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yU2VxMlNlcUxNJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JTZXEyU2VxTE0uZnJvbV9wcmV0cmFpbmVkKCUyMnQ1LWJhc2UlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JTZXEyU2VxTE0uZnJvbV9wcmV0cmFpbmVkKCUyMnQ1LWJhc2UlMjIlMkMlMjBvdXRwdXRfYXR0ZW50aW9ucyUzRFRydWUpJTBBbW9kZWwuY29uZmlnLm91dHB1dF9hdHRlbnRpb25zJTBBJTBBJTIzJTIwTG9hZGluZyUyMGZyb20lMjBhJTIwUHlUb3JjaCUyMGNoZWNrcG9pbnQlMjBmaWxlJTIwaW5zdGVhZCUyMG9mJTIwYSUyMFRlbnNvckZsb3clMjBtb2RlbCUyMChzbG93ZXIpJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyLiUyRnB0X21vZGVsJTJGdDVfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yU2VxMlNlcUxNLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGcHRfbW9kZWwlMkZ0NV9weXRvcmNoX21vZGVsLmJpbiUyMiUyQyUyMGZyb21fcHQlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Xls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFRGJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwUHlUb3JjaCUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnRmX21vZGVsJTJGYmVydF90Zl9jaGVja3BvaW50LmNrcHQuaW5kZXglMjIlMkMlMjBmcm9tX3RmJTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function zls(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JNdWx0aXBsZUNob2ljZSUwQSUwQSUyMyUyMERvd25sb2FkJTIwbW9kZWwlMjBhbmQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JNdWx0aXBsZUNob2ljZS5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQSUwQSUyMyUyMFVwZGF0ZSUyMGNvbmZpZ3VyYXRpb24lMjBkdXJpbmclMjBsb2FkaW5nJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JNdWx0aXBsZUNob2ljZS5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFRGJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwUHlUb3JjaCUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yTXVsdGlwbGVDaG9pY2UuZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMi4lMkZ0Zl9tb2RlbCUyRmJlcnRfdGZfY2hlY2twb2ludC5ja3B0LmluZGV4JTIyJTJDJTIwZnJvbV90ZiUzRFRydWUlMkMlMjBjb25maWclM0Rjb25maWclMEEp",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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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, 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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function nis(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, 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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function lis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwbW9kZWwlMjBhbmQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQSUwQSUyMyUyMFVwZGF0ZSUyMGNvbmZpZ3VyYXRpb24lMjBkdXJpbmclMjBsb2FkaW5nJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbi5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnB0X21vZGVsJTJGYmVydF9weXRvcmNoX21vZGVsLmJpbiUyMiUyQyUyMGZyb21fcHQlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function mis(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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function cis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHl0b3JjaF9tb2RlbC5iaW4lMjIlMkMlMjBmcm9tX3B0JTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function his(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwbW9kZWwlMjBhbmQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQSUwQSUyMyUyMFVwZGF0ZSUyMGNvbmZpZ3VyYXRpb24lMjBkdXJpbmclMjBsb2FkaW5nJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnB0X21vZGVsJTJGYmVydF9weXRvcmNoX21vZGVsLmJpbiUyMiUyQyUyMGZyb21fcHQlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function bis(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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function vis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yUXVlc3Rpb25BbnN3ZXJpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbEZvclF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JRdWVzdGlvbkFuc3dlcmluZy5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9yUXVlc3Rpb25BbnN3ZXJpbmcuZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHl0b3JjaF9tb2RlbC5iaW4lMjIlMkMlMjBmcm9tX3B0JTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Tis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JEZXB0aEVzdGltYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yRGVwdGhFc3RpbWF0aW9uLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckRlcHRoRXN0aW1hdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFRGJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwUHlUb3JjaCUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yRGVwdGhFc3RpbWF0aW9uLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX2NoZWNrcG9pbnQuY2twdC5pbmRleCUyMiUyQyUyMGZyb21fdGYlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Ris(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JWaWRlb0NsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclZpZGVvQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVmlkZW9DbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFRGJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwUHlUb3JjaCUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVmlkZW9DbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnRmX21vZGVsJTJGYmVydF90Zl9jaGVja3BvaW50LmNrcHQuaW5kZXglMjIlMkMlMjBmcm9tX3RmJTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Xis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JVbml2ZXJzYWxTZWdtZW50YXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVW5pdmVyc2FsU2VnbWVudGF0aW9uLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclVuaXZlcnNhbFNlZ21lbnRhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFRGJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwUHlUb3JjaCUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVW5pdmVyc2FsU2VnbWVudGF0aW9uLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX2NoZWNrcG9pbnQuY2twdC5pbmRleCUyMiUyQyUyMGZyb21fdGYlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function Qis(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclplcm9TaG90SW1hZ2VDbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwbW9kZWwlMjBhbmQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclplcm9TaG90SW1hZ2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQSUwQSUyMyUyMFVwZGF0ZSUyMGNvbmZpZ3VyYXRpb24lMjBkdXJpbmclMjBsb2FkaW5nJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclplcm9TaG90SW1hZ2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclplcm9TaG90SW1hZ2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnB0X21vZGVsJTJGYmVydF9weXRvcmNoX21vZGVsLmJpbiUyMiUyQyUyMGZyb21fcHQlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function dds(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, 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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function mds(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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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, 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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function hds(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, 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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function bds(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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function vds(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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}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(),v(f.$$.fragment)},l(m){h=n(m,"P",{});var g=s(h);C=r(g,"Examples:"),g.forEach(t),u=i(m),M(f.$$.fragment,m)},m(m,g){b(m,h,g),e(h,C),b(m,u,g),T(f,m,g),p=!0},p:V,i(m){p||(F(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(h),m&&t(u),y(f,m)}}}function kds(x){let 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m=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/main/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/main/en/main_classes/configuration#transformers.PretrainedConfig.save_pretrained">save_pretrained()</a> method, or the <a href="/docs/transformers/main/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
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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
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<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/main/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.,
<code>./my_model_directory/preprocessor_config.json</code>.</li>
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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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<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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Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
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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/main/src/transformers/models/auto/processing_auto.py#L131"}}),eT=new Wjo({props:{$$slots:{default:[fls]},$$scope:{ctx:x}}}),oT=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/main/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/main/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/main/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/main/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/main/en/model_doc/audio-spectrogram-transformer#transformers.ASTConfig">ASTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/audio-spectrogram-transformer#transformers.ASTModel">ASTModel</a> (Audio Spectrogram Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertModel">AlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/align#transformers.AlignConfig">AlignConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/align#transformers.AlignModel">AlignModel</a> (ALIGN model)</li>
<li><a href="/docs/transformers/main/en/model_doc/altclip#transformers.AltCLIPConfig">AltCLIPConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/autoformer#transformers.AutoformerConfig">AutoformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/autoformer#transformers.AutoformerModel">AutoformerModel</a> (Autoformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bark#transformers.BarkConfig">BarkConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bark#transformers.BarkModel">BarkModel</a> (Bark model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.BartModel">BartModel</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/beit#transformers.BeitModel">BeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertModel">BertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert-generation#transformers.BertGenerationEncoder">BertGenerationEncoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdModel">BigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusModel">BigBirdPegasusModel</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/biogpt#transformers.BioGptModel">BioGptModel</a> (BioGpt model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bit#transformers.BitConfig">BitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bit#transformers.BitModel">BitModel</a> (BiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotModel">BlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallModel">BlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2Model">Blip2Model</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomModel">BloomModel</a> (BLOOM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bridgetower#transformers.BridgeTowerConfig">BridgeTowerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bridgetower#transformers.BridgeTowerModel">BridgeTowerModel</a> (BridgeTower model)</li>
<li><a href="/docs/transformers/main/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/clipseg#transformers.CLIPSegConfig">CLIPSegConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/clipseg#transformers.CLIPSegModel">CLIPSegModel</a> (CLIPSeg model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLModel">CTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertModel">CamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineModel">CanineModel</a> (CANINE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/chinese_clip#transformers.ChineseCLIPConfig">ChineseCLIPConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/chinese_clip#transformers.ChineseCLIPModel">ChineseCLIPModel</a> (Chinese-CLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/clap#transformers.ClapConfig">ClapConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/clap#transformers.ClapModel">ClapModel</a> (CLAP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/codegen#transformers.CodeGenConfig">CodeGenConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/codegen#transformers.CodeGenModel">CodeGenModel</a> (CodeGen model)</li>
<li><a href="/docs/transformers/main/en/model_doc/conditional_detr#transformers.ConditionalDetrConfig">ConditionalDetrConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/conditional_detr#transformers.ConditionalDetrModel">ConditionalDetrModel</a> (Conditional DETR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertModel">ConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convnext#transformers.ConvNextModel">ConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convnextv2#transformers.ConvNextV2Config">ConvNextV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convnextv2#transformers.ConvNextV2Model">ConvNextV2Model</a> (ConvNeXTV2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/cpmant#transformers.CpmAntConfig">CpmAntConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/cpmant#transformers.CpmAntModel">CpmAntModel</a> (CPM-Ant model)</li>
<li><a href="/docs/transformers/main/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/cvt#transformers.CvtModel">CvtModel</a> (CvT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/dpt#transformers.DPTModel">DPTModel</a> (DPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioModel">Data2VecAudioModel</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextModel">Data2VecTextModel</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecVisionModel">Data2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaModel">DebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Model">DebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/decision_transformer#transformers.DecisionTransformerConfig">DecisionTransformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/decision_transformer#transformers.DecisionTransformerModel">DecisionTransformerModel</a> (Decision Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deformable_detr#transformers.DeformableDetrConfig">DeformableDetrConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deformable_detr#transformers.DeformableDetrModel">DeformableDetrModel</a> (Deformable DETR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deit#transformers.DeiTModel">DeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deta#transformers.DetaConfig">DetaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deta#transformers.DetaModel">DetaModel</a> (DETA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/detr#transformers.DetrModel">DetrModel</a> (DETR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/dinat#transformers.DinatConfig">DinatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/dinat#transformers.DinatModel">DinatModel</a> (DiNAT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/dinov2#transformers.Dinov2Config">Dinov2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/dinov2#transformers.Dinov2Model">Dinov2Model</a> (DINOv2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/donut#transformers.DonutSwinConfig">DonutSwinConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/donut#transformers.DonutSwinModel">DonutSwinModel</a> (DonutSwin model)</li>
<li><a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.EfficientFormerModel">EfficientFormerModel</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/efficientnet#transformers.EfficientNetConfig">EfficientNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/efficientnet#transformers.EfficientNetModel">EfficientNetModel</a> (EfficientNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraModel">ElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/encodec#transformers.EncodecConfig">EncodecConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/encodec#transformers.EncodecModel">EncodecModel</a> (EnCodec model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieModel">ErnieModel</a> (ERNIE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMModel">ErnieMModel</a> (ErnieM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmModel">EsmModel</a> (ESM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetModel">FNetModel</a> (FNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/main/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/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertModel">FlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaModel">FlavaModel</a> (FLAVA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/focalnet#transformers.FocalNetConfig">FocalNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/focalnet#transformers.FocalNetModel">FocalNetModel</a> (FocalNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelModel">FunnelModel</a> or <a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelBaseModel">FunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/glpn#transformers.GLPNModel">GLPNModel</a> (GLPN model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Model">GPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeModel">GPTBigCodeModel</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJModel">GPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoModel">GPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXModel">GPTNeoXModel</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseModel">GPTNeoXJapaneseModel</a> (GPT NeoX Japanese model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/main/en/model_doc/git#transformers.GitConfig">GitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/git#transformers.GitModel">GitModel</a> (GIT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/graphormer#transformers.GraphormerConfig">GraphormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/graphormer#transformers.GraphormerModel">GraphormerModel</a> (Graphormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/groupvit#transformers.GroupViTModel">GroupViTModel</a> (GroupViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/hubert#transformers.HubertModel">HubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertModel">IBertModel</a> (I-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/idefics#transformers.IdeficsConfig">IdeficsConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/idefics#transformers.IdeficsModel">IdeficsModel</a> (IDEFICS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/imagegpt#transformers.ImageGPTModel">ImageGPTModel</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/informer#transformers.InformerConfig">InformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/informer#transformers.InformerModel">InformerModel</a> (Informer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/jukebox#transformers.JukeboxConfig">JukeboxConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/jukebox#transformers.JukeboxModel">JukeboxModel</a> (Jukebox model)</li>
<li><a href="/docs/transformers/main/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/led#transformers.LEDModel">LEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMModel">LayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2Model">LayoutLMv2Model</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Model">LayoutLMv3Model</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/levit#transformers.LevitModel">LevitModel</a> (LeViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/lilt#transformers.LiltModel">LiltModel</a> (LiLT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/llama2#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/llama2#transformers.LlamaModel">LlamaModel</a> (LLaMA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longt5#transformers.LongT5Model">LongT5Model</a> (LongT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerModel">LongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeModel">LukeModel</a> (LUKE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/lxmert#transformers.LxmertModel">LxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/m2m_100#transformers.M2M100Model">M2M100Model</a> (M2M100 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartModel">MBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mctct#transformers.MCTCTModel">MCTCTModel</a> (M-CTC-T model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetModel">MPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Model">MT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianModel">MarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/main/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/markuplm#transformers.MarkupLMModel">MarkupLMModel</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mask2former#transformers.Mask2FormerConfig">Mask2FormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mask2former#transformers.Mask2FormerModel">Mask2FormerModel</a> (Mask2Former model)</li>
<li><a href="/docs/transformers/main/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/main/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/main/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaModel">MegaModel</a> (MEGA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertModel">MegatronBertModel</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mgp-str#transformers.MgpstrConfig">MgpstrConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mgp-str#transformers.MgpstrForSceneTextRecognition">MgpstrForSceneTextRecognition</a> (MGP-STR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertModel">MobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config">MobileNetV1Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilenet_v1#transformers.MobileNetV1Model">MobileNetV1Model</a> (MobileNetV1 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilenet_v2#transformers.MobileNetV2Model">MobileNetV2Model</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTModel">MobileViTModel</a> (MobileViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilevitv2#transformers.MobileViTV2Config">MobileViTV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilevitv2#transformers.MobileViTV2Model">MobileViTV2Model</a> (MobileViTV2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptModel">MptModel</a> (MPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mra#transformers.MraModel">MraModel</a> (MRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpModel">MvpModel</a> (MVP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nat#transformers.NatConfig">NatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nat#transformers.NatModel">NatModel</a> (NAT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaModel">NezhaModel</a> (Nezha model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nllb-moe#transformers.NllbMoeConfig">NllbMoeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nllb-moe#transformers.NllbMoeModel">NllbMoeModel</a> (NLLB-MOE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerModel">NystromformerModel</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTModel">OPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/oneformer#transformers.OneFormerConfig">OneFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/oneformer#transformers.OneFormerModel">OneFormerModel</a> (OneFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTModel">OpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/open-llama#transformers.OpenLlamaModel">OpenLlamaModel</a> (OpenLlama model)</li>
<li><a href="/docs/transformers/main/en/model_doc/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/owlvit#transformers.OwlViTModel">OwlViTModel</a> (OWL-ViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/plbart#transformers.PLBartModel">PLBartModel</a> (PLBart model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusModel">PegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pegasus_x#transformers.PegasusXConfig">PegasusXConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pegasus_x#transformers.PegasusXModel">PegasusXModel</a> (PEGASUS-X model)</li>
<li><a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverModel">PerceiverModel</a> (Perceiver model)</li>
<li><a href="/docs/transformers/main/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/poolformer#transformers.PoolFormerModel">PoolFormerModel</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetModel">ProphetNetModel</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pvt#transformers.PvtConfig">PvtConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pvt#transformers.PvtModel">PvtModel</a> (PVT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertModel">QDQBertModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerModel">ReformerModel</a> (Reformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetModel">RegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertModel">RemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/resnet#transformers.ResNetModel">ResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertModel">RoCBertModel</a> (RoCBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerModel">RoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaModel">RobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormModel">RobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rwkv#transformers.RwkvConfig">RwkvConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rwkv#transformers.RwkvModel">RwkvModel</a> (RWKV model)</li>
<li><a href="/docs/transformers/main/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/sew#transformers.SEWModel">SEWModel</a> (SEW model)</li>
<li><a href="/docs/transformers/main/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/sew-d#transformers.SEWDModel">SEWDModel</a> (SEW-D model)</li>
<li><a href="/docs/transformers/main/en/model_doc/sam#transformers.SamConfig">SamConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/sam#transformers.SamModel">SamModel</a> (SAM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/segformer#transformers.SegformerModel">SegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/speech_to_text#transformers.Speech2TextModel">Speech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/main/en/model_doc/speecht5#transformers.SpeechT5Config">SpeechT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/speecht5#transformers.SpeechT5Model">SpeechT5Model</a> (SpeechT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/splinter#transformers.SplinterModel">SplinterModel</a> (Splinter model)</li>
<li><a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertModel">SqueezeBertModel</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swiftformer#transformers.SwiftFormerConfig">SwiftFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swiftformer#transformers.SwiftFormerModel">SwiftFormerModel</a> (SwiftFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swin2sr#transformers.Swin2SRConfig">Swin2SRConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swin2sr#transformers.Swin2SRModel">Swin2SRModel</a> (Swin2SR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swin#transformers.SwinModel">SwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swinv2#transformers.Swinv2Model">Swinv2Model</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/switch_transformers#transformers.SwitchTransformersModel">SwitchTransformersModel</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Model">T5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/table-transformer#transformers.TableTransformerConfig">TableTransformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/table-transformer#transformers.TableTransformerModel">TableTransformerModel</a> (Table Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasModel">TapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerConfig">TimeSeriesTransformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerModel">TimeSeriesTransformerModel</a> (Time Series Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a> configuration class: <a href="/docs/transformers/main/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/main/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerConfig">TrajectoryTransformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerModel">TrajectoryTransformerModel</a> (Trajectory Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLModel">TransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tvlt#transformers.TvltConfig">TvltConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tvlt#transformers.TvltModel">TvltModel</a> (TVLT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/umt5#transformers.UMT5Model">UMT5Model</a> (UMT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech#transformers.UniSpeechModel">UniSpeechModel</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel">UniSpeechSatModel</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/main/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/van#transformers.VanModel">VanModel</a> (VAN model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTModel">ViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit_hybrid#transformers.ViTHybridConfig">ViTHybridConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit_hybrid#transformers.ViTHybridModel">ViTHybridModel</a> (ViT Hybrid model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit_mae#transformers.ViTMAEModel">ViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit_msn#transformers.ViTMSNModel">ViTMSNModel</a> (ViTMSN model)</li>
<li><a href="/docs/transformers/main/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/videomae#transformers.VideoMAEModel">VideoMAEModel</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vilt#transformers.ViltModel">ViltModel</a> (ViLT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderModel">VisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/main/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/visual_bert#transformers.VisualBertModel">VisualBertModel</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vivit#transformers.VivitConfig">VivitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vivit#transformers.VivitModel">VivitModel</a> (ViViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Model">Wav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerModel">Wav2Vec2ConformerModel</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMModel">WavLMModel</a> (WavLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperModel">WhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xclip#transformers.XCLIPConfig">XCLIPConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xclip#transformers.XCLIPModel">XCLIPModel</a> (X-CLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xglm#transformers.XGLMModel">XGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMModel">XLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetModel">XLMProphetNetModel</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaModel">XLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLModel">XLMRobertaXLModel</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetModel">XLNetModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodModel">XmodModel</a> (X-MOD model)</li>
<li><a href="/docs/transformers/main/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/yolos#transformers.YolosModel">YolosModel</a> (YOLOS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoModel">YosoModel</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),nT=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/main/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/main/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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),By=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/main/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/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.TFAlbertModel">TFAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.TFBartModel">TFBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.TFBertModel">TFBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.TFBlenderbotModel">TFBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallModel">TFBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blip#transformers.TFBlipModel">TFBlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ctrl#transformers.TFCTRLModel">TFCTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.TFCamembertModel">TFCamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.TFConvBertModel">TFConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convnext#transformers.TFConvNextModel">TFConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/cvt#transformers.TFCvtModel">TFCvtModel</a> (CvT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/dpr#transformers.TFDPRQuestionEncoder">TFDPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.TFData2VecVisionModel">TFData2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.TFDebertaModel">TFDebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.TFDebertaV2Model">TFDebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deit#transformers.TFDeiTModel">TFDeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertModel">TFDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.TFEfficientFormerModel">TFEfficientFormerModel</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.TFElectraModel">TFElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/esm#transformers.TFEsmModel">TFEsmModel</a> (ESM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.TFFlaubertModel">TFFlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.TFFunnelModel">TFFunnelModel</a> or <a href="/docs/transformers/main/en/model_doc/funnel#transformers.TFFunnelBaseModel">TFFunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.TFGPT2Model">TFGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.TFGPTJModel">TFGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/groupvit#transformers.TFGroupViTModel">TFGroupViTModel</a> (GroupViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/hubert#transformers.TFHubertModel">TFHubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/led#transformers.TFLEDModel">TFLEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.TFLayoutLMModel">TFLayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3Model">TFLayoutLMv3Model</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.TFLongformerModel">TFLongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/lxmert#transformers.TFLxmertModel">TFLxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.TFMBartModel">TFMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.TFMPNetModel">TFMPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mt5#transformers.TFMT5Model">TFMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/marian#transformers.TFMarianModel">TFMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.TFMobileBertModel">TFMobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.TFMobileViTModel">TFMobileViTModel</a> (MobileViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/opt#transformers.TFOPTModel">TFOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.TFOpenAIGPTModel">TFOpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pegasus#transformers.TFPegasusModel">TFPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/regnet#transformers.TFRegNetModel">TFRegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.TFRemBertModel">TFRemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/resnet#transformers.TFResNetModel">TFResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.TFRoFormerModel">TFRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.TFRobertaModel">TFRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormModel">TFRobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/sam#transformers.SamConfig">SamConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/sam#transformers.TFSamModel">TFSamModel</a> (SAM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/segformer#transformers.TFSegformerModel">TFSegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/speech_to_text#transformers.TFSpeech2TextModel">TFSpeech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swin#transformers.TFSwinModel">TFSwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.TFT5Model">TFT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TFTapasModel">TFTapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TFTransfoXLModel">TFTransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit#transformers.TFViTModel">TFViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit_mae#transformers.TFViTMAEModel">TFViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vision-text-dual-encoder#transformers.TFVisionTextDualEncoderModel">TFVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.TFWav2Vec2Model">TFWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/whisper#transformers.TFWhisperModel">TFWhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xglm#transformers.TFXGLMModel">TFXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.TFXLMModel">TFXLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.TFXLMRobertaModel">TFXLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.TFXLNetModel">TFXLNetModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),Ny=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/main/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/main/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/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),D0=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/main/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/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.FlaxAlbertModel">FlaxAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.FlaxBartModel">FlaxBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/beit#transformers.FlaxBeitModel">FlaxBeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.FlaxBertModel">FlaxBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.FlaxBigBirdModel">FlaxBigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.FlaxBlenderbotModel">FlaxBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallModel">FlaxBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bloom#transformers.FlaxBloomModel">FlaxBloomModel</a> (BLOOM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/clip#transformers.FlaxCLIPModel">FlaxCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.FlaxDistilBertModel">FlaxDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.FlaxElectraModel">FlaxElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.FlaxGPT2Model">FlaxGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.FlaxGPTJModel">FlaxGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.FlaxGPTNeoModel">FlaxGPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longt5#transformers.FlaxLongT5Model">FlaxLongT5Model</a> (LongT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.FlaxMBartModel">FlaxMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mt5#transformers.FlaxMT5Model">FlaxMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/marian#transformers.FlaxMarianModel">FlaxMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/opt#transformers.FlaxOPTModel">FlaxOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pegasus#transformers.FlaxPegasusModel">FlaxPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/regnet#transformers.FlaxRegNetModel">FlaxRegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/resnet#transformers.FlaxResNetModel">FlaxResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.FlaxRoFormerModel">FlaxRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.FlaxRobertaModel">FlaxRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormModel">FlaxRobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.FlaxT5Model">FlaxT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit#transformers.FlaxViTModel">FlaxViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vision-text-dual-encoder#transformers.FlaxVisionTextDualEncoderModel">FlaxVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2Model">FlaxWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/whisper#transformers.FlaxWhisperModel">FlaxWhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xglm#transformers.FlaxXGLMModel">FlaxXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaModel">FlaxXLMRobertaModel</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),O0=new Z({props:{anchor:"transformers.FlaxAutoModel.from_config.example",$$slots:{default:[bls]},$$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/main/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/main/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/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),A5=new Z({props:{anchor:"transformers.FlaxAutoModel.from_pretrained.example",$$slots:{default:[vls]},$$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/main/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/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertForPreTraining">AlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertForPreTraining">BertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdForPreTraining">BigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraForPreTraining">ElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieForPreTraining">ErnieForPreTraining</a> (ERNIE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetForPreTraining">FNetForPreTraining</a> (FNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaForPreTraining">FlavaForPreTraining</a> (FLAVA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelForPreTraining">FunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForCausalLM">GPTBigCodeForCausalLM</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/idefics#transformers.IdeficsConfig">IdeficsConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/idefics#transformers.IdeficsForVisionText2Text">IdeficsForVisionText2Text</a> (IDEFICS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/lxmert#transformers.LxmertForPreTraining">LxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaForMaskedLM">MegaForMaskedLM</a> (MEGA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertForPreTraining">MegatronBertForPreTraining</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertForPreTraining">MobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptForCausalLM">MptForCausalLM</a> (MPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mra#transformers.MraForMaskedLM">MraForMaskedLM</a> (MRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaForPreTraining">NezhaForPreTraining</a> (Nezha model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nllb-moe#transformers.NllbMoeConfig">NllbMoeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nllb-moe#transformers.NllbMoeForConditionalGeneration">NllbMoeForConditionalGeneration</a> (NLLB-MOE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertForPreTraining">RoCBertForPreTraining</a> (RoCBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMaskedLM">RobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rwkv#transformers.RwkvConfig">RwkvConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rwkv#transformers.RwkvForCausalLM">RwkvForCausalLM</a> (RWKV model)</li>
<li><a href="/docs/transformers/main/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/splinter#transformers.SplinterForPreTraining">SplinterForPreTraining</a> (Splinter model)</li>
<li><a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/switch_transformers#transformers.SwitchTransformersForConditionalGeneration">SwitchTransformersForConditionalGeneration</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tvlt#transformers.TvltConfig">TvltConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tvlt#transformers.TvltForPreTraining">TvltForPreTraining</a> (TVLT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech#transformers.UniSpeechForPreTraining">UniSpeechForPreTraining</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatForPreTraining">UniSpeechSatForPreTraining</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit_mae#transformers.ViTMAEForPreTraining">ViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/videomae#transformers.VideoMAEForPreTraining">VideoMAEForPreTraining</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/visual_bert#transformers.VisualBertForPreTraining">VisualBertForPreTraining</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2ForPreTraining">Wav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForPreTraining">Wav2Vec2ConformerForPreTraining</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodForMaskedLM">XmodForMaskedLM</a> (X-MOD model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),S5=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/main/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/main/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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),V3=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/main/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/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.TFAlbertForPreTraining">TFAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.TFBertForPreTraining">TFBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.TFElectraForPreTraining">TFElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.TFFunnelForPreTraining">TFFunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/lxmert#transformers.TFLxmertForPreTraining">TFLxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.TFMobileBertForPreTraining">TFMobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMaskedLM">TFRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit_mae#transformers.TFViTMAEForPreTraining">TFViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),J3=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/main/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/main/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/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),u9=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/main/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/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.FlaxAlbertForPreTraining">FlaxAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.FlaxBertForPreTraining">FlaxBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.FlaxBigBirdForPreTraining">FlaxBigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.FlaxElectraForPreTraining">FlaxElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMaskedLM">FlaxRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2ForPreTraining">FlaxWav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),_9=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/main/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/main/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/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),G9=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/main/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/main/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/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.BartForCausalLM">BartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertLMHeadModel">BertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert-generation#transformers.BertGenerationDecoder">BertGenerationDecoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdForCausalLM">BigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForCausalLM">BigBirdPegasusForCausalLM</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/biogpt#transformers.BioGptForCausalLM">BioGptForCausalLM</a> (BioGpt model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotForCausalLM">BlenderbotForCausalLM</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForCausalLM">BlenderbotSmallForCausalLM</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertForCausalLM">CamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/codegen#transformers.CodeGenConfig">CodeGenConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/codegen#transformers.CodeGenForCausalLM">CodeGenForCausalLM</a> (CodeGen model)</li>
<li><a href="/docs/transformers/main/en/model_doc/cpmant#transformers.CpmAntConfig">CpmAntConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/cpmant#transformers.CpmAntForCausalLM">CpmAntForCausalLM</a> (CPM-Ant model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextForCausalLM">Data2VecTextForCausalLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraForCausalLM">ElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/main/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/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForCausalLM">GPTBigCodeForCausalLM</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJForCausalLM">GPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoForCausalLM">GPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXForCausalLM">GPTNeoXForCausalLM</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseForCausalLM">GPTNeoXJapaneseForCausalLM</a> (GPT NeoX Japanese model)</li>
<li><a href="/docs/transformers/main/en/model_doc/git#transformers.GitConfig">GitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/git#transformers.GitForCausalLM">GitForCausalLM</a> (GIT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/llama2#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/llama2#transformers.LlamaForCausalLM">LlamaForCausalLM</a> (LLaMA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartForCausalLM">MBartForCausalLM</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianForCausalLM">MarianForCausalLM</a> (Marian model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaForCausalLM">MegaForCausalLM</a> (MEGA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertForCausalLM">MegatronBertForCausalLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptForCausalLM">MptForCausalLM</a> (MPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/musicgen#transformers.MusicgenConfig">MusicgenConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/musicgen#transformers.MusicgenForCausalLM">MusicgenForCausalLM</a> (MusicGen model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpForCausalLM">MvpForCausalLM</a> (MVP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTForCausalLM">OPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/open-llama#transformers.OpenLlamaForCausalLM">OpenLlamaForCausalLM</a> (OpenLlama model)</li>
<li><a href="/docs/transformers/main/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/plbart#transformers.PLBartForCausalLM">PLBartForCausalLM</a> (PLBart model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusForCausalLM">PegasusForCausalLM</a> (Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetForCausalLM">ProphetNetForCausalLM</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertLMHeadModel">QDQBertLMHeadModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerModelWithLMHead">ReformerModelWithLMHead</a> (Reformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertForCausalLM">RemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertForCausalLM">RoCBertForCausalLM</a> (RoCBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerForCausalLM">RoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaForCausalLM">RobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForCausalLM">RobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rwkv#transformers.RwkvConfig">RwkvConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rwkv#transformers.RwkvForCausalLM">RwkvForCausalLM</a> (RWKV model)</li>
<li><a href="/docs/transformers/main/en/model_doc/speech_to_text_2#transformers.Speech2Text2Config">Speech2Text2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/speech_to_text_2#transformers.Speech2Text2ForCausalLM">Speech2Text2ForCausalLM</a> (Speech2Text2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/trocr#transformers.TrOCRConfig">TrOCRConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/trocr#transformers.TrOCRForCausalLM">TrOCRForCausalLM</a> (TrOCR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xglm#transformers.XGLMForCausalLM">XGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForCausalLM">XLMProphetNetForCausalLM</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaForCausalLM">XLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForCausalLM">XLMRobertaXLForCausalLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodForCausalLM">XmodForCausalLM</a> (X-MOD model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),N9=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/main/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/main/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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),V4=new Z({props:{anchor:"transformers.AutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Lls]},$$scope:{ctx:x}}}),dW=new te({}),mW=new G({props:{name:"class transformers.TFAutoModelForCausalLM",anchor:"transformers.TFAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#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/main/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/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.TFBertLMHeadModel">TFBertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.TFCamembertForCausalLM">TFCamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.TFGPTJForCausalLM">TFGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/opt#transformers.TFOPTForCausalLM">TFOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.TFRemBertForCausalLM">TFRemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.TFRoFormerForCausalLM">TFRoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.TFRobertaForCausalLM">TFRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForCausalLM">TFRobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xglm#transformers.TFXGLMForCausalLM">TFXGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForCausalLM">TFXLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),J4=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/main/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/main/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/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.FlaxBartForCausalLM">FlaxBartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.FlaxBertForCausalLM">FlaxBertForCausalLM</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.FlaxBigBirdForCausalLM">FlaxBigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bloom#transformers.FlaxBloomForCausalLM">FlaxBloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.FlaxElectraForCausalLM">FlaxElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.FlaxGPT2LMHeadModel">FlaxGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.FlaxGPTJForCausalLM">FlaxGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.FlaxGPTNeoForCausalLM">FlaxGPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/opt#transformers.FlaxOPTForCausalLM">FlaxOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.FlaxRobertaForCausalLM">FlaxRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForCausalLM">FlaxRobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xglm#transformers.FlaxXGLMForCausalLM">FlaxXGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForCausalLM">FlaxXLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),dC=new Z({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.example",$$slots:{default:[kls]},$$scope:{ctx:x}}}),bW=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/main/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/main/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/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),yC=new Z({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Sls]},$$scope:{ctx:x}}}),vW=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/main/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/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertForMaskedLM">AlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertForMaskedLM">BertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdForMaskedLM">BigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertForMaskedLM">ConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraForMaskedLM">ElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieForMaskedLM">ErnieForMaskedLM</a> (ERNIE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmForMaskedLM">EsmForMaskedLM</a> (ESM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetForMaskedLM">FNetForMaskedLM</a> (FNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelForMaskedLM">FunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaForMaskedLM">MegaForMaskedLM</a> (MEGA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertForMaskedLM">MegatronBertForMaskedLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertForMaskedLM">MobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mra#transformers.MraForMaskedLM">MraForMaskedLM</a> (MRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaForMaskedLM">NezhaForMaskedLM</a> (Nezha model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerForMaskedLM">NystromformerForMaskedLM</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverForMaskedLM">PerceiverForMaskedLM</a> (Perceiver model)</li>
<li><a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertForMaskedLM">QDQBertForMaskedLM</a> (QDQBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerForMaskedLM">ReformerForMaskedLM</a> (Reformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertForMaskedLM">RemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertForMaskedLM">RoCBertForMaskedLM</a> (RoCBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerForMaskedLM">RoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMaskedLM">RobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <code>Wav2Vec2ForMaskedLM</code> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodForMaskedLM">XmodForMaskedLM</a> (X-MOD model)</li>
<li><a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoForMaskedLM">YosoForMaskedLM</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),vw=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/main/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/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.TFAlbertForMaskedLM">TFAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.TFBertForMaskedLM">TFBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.TFConvBertForMaskedLM">TFConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.TFDebertaForMaskedLM">TFDebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.TFDebertaV2ForMaskedLM">TFDebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.TFElectraForMaskedLM">TFElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/esm#transformers.TFEsmForMaskedLM">TFEsmForMaskedLM</a> (ESM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.TFFunnelForMaskedLM">TFFunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.TFLongformerForMaskedLM">TFLongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.TFMobileBertForMaskedLM">TFMobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.TFRemBertForMaskedLM">TFRemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.TFRoFormerForMaskedLM">TFRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMaskedLM">TFRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.FlaxAlbertForMaskedLM">FlaxAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.FlaxBertForMaskedLM">FlaxBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.FlaxBigBirdForMaskedLM">FlaxBigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.FlaxDistilBertForMaskedLM">FlaxDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.FlaxElectraForMaskedLM">FlaxElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMaskedLM">FlaxRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/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/main/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/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForConditionalGeneration">BigBirdPegasusForConditionalGeneration</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotForConditionalGeneration">BlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForConditionalGeneration">BlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/main/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/encoder-decoder#transformers.EncoderDecoderModel">EncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/main/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/led#transformers.LEDForConditionalGeneration">LEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longt5#transformers.LongT5ForConditionalGeneration">LongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/m2m_100#transformers.M2M100ForConditionalGeneration">M2M100ForConditionalGeneration</a> (M2M100 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5ForConditionalGeneration">MT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianMTModel">MarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nllb-moe#transformers.NllbMoeConfig">NllbMoeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nllb-moe#transformers.NllbMoeForConditionalGeneration">NllbMoeForConditionalGeneration</a> (NLLB-MOE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/plbart#transformers.PLBartForConditionalGeneration">PLBartForConditionalGeneration</a> (PLBart model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusForConditionalGeneration">PegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pegasus_x#transformers.PegasusXConfig">PegasusXConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pegasus_x#transformers.PegasusXForConditionalGeneration">PegasusXForConditionalGeneration</a> (PEGASUS-X model)</li>
<li><a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/prophetnet#transformers.ProphetNetForConditionalGeneration">ProphetNetForConditionalGeneration</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/switch_transformers#transformers.SwitchTransformersForConditionalGeneration">SwitchTransformersForConditionalGeneration</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/umt5#transformers.UMT5ForConditionalGeneration">UMT5ForConditionalGeneration</a> (UMT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForConditionalGeneration">XLMProphetNetForConditionalGeneration</a> (XLM-ProphetNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.TFBlenderbotForConditionalGeneration">TFBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallForConditionalGeneration">TFBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/main/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/encoder-decoder#transformers.TFEncoderDecoderModel">TFEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/main/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/led#transformers.TFLEDForConditionalGeneration">TFLEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.TFMBartForConditionalGeneration">TFMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mt5#transformers.TFMT5ForConditionalGeneration">TFMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/marian#transformers.TFMarianMTModel">TFMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pegasus#transformers.TFPegasusForConditionalGeneration">TFPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot#transformers.FlaxBlenderbotForConditionalGeneration">FlaxBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallForConditionalGeneration">FlaxBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/main/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/encoder-decoder#transformers.FlaxEncoderDecoderModel">FlaxEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/marian#transformers.FlaxMarianMTModel">FlaxMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pegasus#transformers.FlaxPegasusForConditionalGeneration">FlaxPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertForSequenceClassification">AlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.BartForSequenceClassification">BartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertForSequenceClassification">BertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdForSequenceClassification">BigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForSequenceClassification">BigBirdPegasusForSequenceClassification</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/biogpt#transformers.BioGptForSequenceClassification">BioGptForSequenceClassification</a> (BioGpt model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomForSequenceClassification">BloomForSequenceClassification</a> (BLOOM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLForSequenceClassification">CTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertForSequenceClassification">CamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineForSequenceClassification">CanineForSequenceClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertForSequenceClassification">ConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextForSequenceClassification">Data2VecTextForSequenceClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaForSequenceClassification">DebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2ForSequenceClassification">DebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraForSequenceClassification">ElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieForSequenceClassification">ErnieForSequenceClassification</a> (ERNIE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMForSequenceClassification">ErnieMForSequenceClassification</a> (ErnieM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmForSequenceClassification">EsmForSequenceClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/main/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/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertForSequenceClassification">FlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelForSequenceClassification">FunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2ForSequenceClassification">GPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForSequenceClassification">GPTBigCodeForSequenceClassification</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJForSequenceClassification">GPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoForSequenceClassification">GPTNeoForSequenceClassification</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXForSequenceClassification">GPTNeoXForSequenceClassification</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertForSequenceClassification">IBertForSequenceClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/led#transformers.LEDForSequenceClassification">LEDForSequenceClassification</a> (LED model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMForSequenceClassification">LayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForSequenceClassification">LayoutLMv2ForSequenceClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForSequenceClassification">LayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/lilt#transformers.LiltForSequenceClassification">LiltForSequenceClassification</a> (LiLT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/llama2#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/llama2#transformers.LlamaForSequenceClassification">LlamaForSequenceClassification</a> (LLaMA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerForSequenceClassification">LongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeForSequenceClassification">LukeForSequenceClassification</a> (LUKE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartForSequenceClassification">MBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetForSequenceClassification">MPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5ForSequenceClassification">MT5ForSequenceClassification</a> (MT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/markuplm#transformers.MarkupLMForSequenceClassification">MarkupLMForSequenceClassification</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaForSequenceClassification">MegaForSequenceClassification</a> (MEGA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertForSequenceClassification">MegatronBertForSequenceClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertForSequenceClassification">MobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptForSequenceClassification">MptForSequenceClassification</a> (MPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mra#transformers.MraForSequenceClassification">MraForSequenceClassification</a> (MRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpForSequenceClassification">MvpForSequenceClassification</a> (MVP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaForSequenceClassification">NezhaForSequenceClassification</a> (Nezha model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerForSequenceClassification">NystromformerForSequenceClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTForSequenceClassification">OPTForSequenceClassification</a> (OPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTForSequenceClassification">OpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/open-llama#transformers.OpenLlamaForSequenceClassification">OpenLlamaForSequenceClassification</a> (OpenLlama model)</li>
<li><a href="/docs/transformers/main/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/plbart#transformers.PLBartForSequenceClassification">PLBartForSequenceClassification</a> (PLBart model)</li>
<li><a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverForSequenceClassification">PerceiverForSequenceClassification</a> (Perceiver model)</li>
<li><a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertForSequenceClassification">QDQBertForSequenceClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerForSequenceClassification">ReformerForSequenceClassification</a> (Reformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertForSequenceClassification">RemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertForSequenceClassification">RoCBertForSequenceClassification</a> (RoCBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerForSequenceClassification">RoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaForSequenceClassification">RobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForSequenceClassification">RobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertForSequenceClassification">SqueezeBertForSequenceClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.T5ForSequenceClassification">T5ForSequenceClassification</a> (T5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasForSequenceClassification">TapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLForSequenceClassification">TransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/umt5#transformers.UMT5ForSequenceClassification">UMT5ForSequenceClassification</a> (UMT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMForSequenceClassification">XLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaForSequenceClassification">XLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForSequenceClassification">XLMRobertaXLForSequenceClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetForSequenceClassification">XLNetForSequenceClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodForSequenceClassification">XmodForSequenceClassification</a> (X-MOD model)</li>
<li><a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoForSequenceClassification">YosoForSequenceClassification</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.TFAlbertForSequenceClassification">TFAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.TFBartForSequenceClassification">TFBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.TFBertForSequenceClassification">TFBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ctrl#transformers.TFCTRLForSequenceClassification">TFCTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.TFCamembertForSequenceClassification">TFCamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.TFConvBertForSequenceClassification">TFConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.TFDebertaForSequenceClassification">TFDebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.TFDebertaV2ForSequenceClassification">TFDebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForSequenceClassification">TFDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.TFElectraForSequenceClassification">TFElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/esm#transformers.TFEsmForSequenceClassification">TFEsmForSequenceClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.TFFlaubertForSequenceClassification">TFFlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.TFFunnelForSequenceClassification">TFFunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.TFGPT2ForSequenceClassification">TFGPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.TFGPTJForSequenceClassification">TFGPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.TFLayoutLMForSequenceClassification">TFLayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForSequenceClassification">TFLayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.TFLongformerForSequenceClassification">TFLongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.TFMPNetForSequenceClassification">TFMPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.TFMobileBertForSequenceClassification">TFMobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/openai-gpt#transformers.TFOpenAIGPTForSequenceClassification">TFOpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.TFRemBertForSequenceClassification">TFRemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.TFRoFormerForSequenceClassification">TFRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.TFRobertaForSequenceClassification">TFRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForSequenceClassification">TFRobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TFTapasForSequenceClassification">TFTapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/transfo-xl#transformers.TFTransfoXLForSequenceClassification">TFTransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.TFXLMForSequenceClassification">TFXLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForSequenceClassification">TFXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.TFXLNetForSequenceClassification">TFXLNetForSequenceClassification</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),k7=new Z({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.example",$$slots:{default:[Dls]},$$scope:{ctx:x}}}),mJ=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/main/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/main/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/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),iL=new Z({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[qls]},$$scope:{ctx:x}}}),cJ=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/main/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/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.FlaxAlbertForSequenceClassification">FlaxAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.FlaxBartForSequenceClassification">FlaxBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.FlaxBertForSequenceClassification">FlaxBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.FlaxBigBirdForSequenceClassification">FlaxBigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.FlaxDistilBertForSequenceClassification">FlaxDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.FlaxElectraForSequenceClassification">FlaxElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.FlaxMBartForSequenceClassification">FlaxMBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.FlaxRoFormerForSequenceClassification">FlaxRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.FlaxRobertaForSequenceClassification">FlaxRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForSequenceClassification">FlaxRobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForSequenceClassification">FlaxXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),mL=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/main/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/main/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/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/src/transformers/models/auto/modeling_auto.py#L1294"}}),vJ=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/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertForMultipleChoice">AlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertForMultipleChoice">BertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdForMultipleChoice">BigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertForMultipleChoice">CamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineForMultipleChoice">CanineForMultipleChoice</a> (CANINE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertForMultipleChoice">ConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextForMultipleChoice">Data2VecTextForMultipleChoice</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2ForMultipleChoice">DebertaV2ForMultipleChoice</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForMultipleChoice">DistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraForMultipleChoice">ElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieForMultipleChoice">ErnieForMultipleChoice</a> (ERNIE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMForMultipleChoice">ErnieMForMultipleChoice</a> (ErnieM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetForMultipleChoice">FNetForMultipleChoice</a> (FNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertForMultipleChoice">FlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelForMultipleChoice">FunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertForMultipleChoice">IBertForMultipleChoice</a> (I-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerForMultipleChoice">LongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeForMultipleChoice">LukeForMultipleChoice</a> (LUKE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetForMultipleChoice">MPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaForMultipleChoice">MegaForMultipleChoice</a> (MEGA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertForMultipleChoice">MegatronBertForMultipleChoice</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertForMultipleChoice">MobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mra#transformers.MraForMultipleChoice">MraForMultipleChoice</a> (MRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaForMultipleChoice">NezhaForMultipleChoice</a> (Nezha model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerForMultipleChoice">NystromformerForMultipleChoice</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertForMultipleChoice">QDQBertForMultipleChoice</a> (QDQBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertForMultipleChoice">RemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertForMultipleChoice">RoCBertForMultipleChoice</a> (RoCBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerForMultipleChoice">RoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaForMultipleChoice">RobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMultipleChoice">RobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertForMultipleChoice">SqueezeBertForMultipleChoice</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMForMultipleChoice">XLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaForMultipleChoice">XLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMultipleChoice">XLMRobertaXLForMultipleChoice</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetForMultipleChoice">XLNetForMultipleChoice</a> (XLNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodForMultipleChoice">XmodForMultipleChoice</a> (X-MOD model)</li>
<li><a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoForMultipleChoice">YosoForMultipleChoice</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),mA=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/main/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/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.TFAlbertForMultipleChoice">TFAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.TFBertForMultipleChoice">TFBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.TFCamembertForMultipleChoice">TFCamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.TFConvBertForMultipleChoice">TFConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForMultipleChoice">TFDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.TFElectraForMultipleChoice">TFElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.TFFlaubertForMultipleChoice">TFFlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.TFFunnelForMultipleChoice">TFFunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.TFLongformerForMultipleChoice">TFLongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.TFMPNetForMultipleChoice">TFMPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.TFMobileBertForMultipleChoice">TFMobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.TFRemBertForMultipleChoice">TFRemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.TFRoFormerForMultipleChoice">TFRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.TFRobertaForMultipleChoice">TFRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMultipleChoice">TFRobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.TFXLMForMultipleChoice">TFXLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMultipleChoice">TFXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.TFXLNetForMultipleChoice">TFXLNetForMultipleChoice</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.FlaxAlbertForMultipleChoice">FlaxAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.FlaxBertForMultipleChoice">FlaxBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.FlaxBigBirdForMultipleChoice">FlaxBigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.FlaxDistilBertForMultipleChoice">FlaxDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.FlaxElectraForMultipleChoice">FlaxElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.FlaxRoFormerForMultipleChoice">FlaxRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.FlaxRobertaForMultipleChoice">FlaxRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMultipleChoice">FlaxRobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMultipleChoice">FlaxXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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 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.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;
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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.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
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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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Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
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automatically loaded:</p>
<ul>
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already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertForNextSentencePrediction">BertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieForNextSentencePrediction">ErnieForNextSentencePrediction</a> (ERNIE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetForNextSentencePrediction">FNetForNextSentencePrediction</a> (FNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertForNextSentencePrediction">MegatronBertForNextSentencePrediction</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertForNextSentencePrediction">MobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaForNextSentencePrediction">NezhaForNextSentencePrediction</a> (Nezha model)</li>
<li><a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertForNextSentencePrediction">QDQBertForNextSentencePrediction</a> (QDQBert model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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.AutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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
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<li>The model was saved using <a href="/docs/transformers/main/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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A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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
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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.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;
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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.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
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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.AutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.TFBertForNextSentencePrediction">TFBertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.TFMobileBertForNextSentencePrediction">TFMobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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;
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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.TFAutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.FlaxBertForNextSentencePrediction">FlaxBertForNextSentencePrediction</a> (BERT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertForTokenClassification">AlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertForTokenClassification">BertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdForTokenClassification">BigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/biogpt#transformers.BioGptForTokenClassification">BioGptForTokenClassification</a> (BioGpt model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomForTokenClassification">BloomForTokenClassification</a> (BLOOM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertForTokenClassification">CamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineForTokenClassification">CanineForTokenClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertForTokenClassification">ConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextForTokenClassification">Data2VecTextForTokenClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaForTokenClassification">DebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2ForTokenClassification">DebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForTokenClassification">DistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraForTokenClassification">ElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieForTokenClassification">ErnieForTokenClassification</a> (ERNIE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMForTokenClassification">ErnieMForTokenClassification</a> (ErnieM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmForTokenClassification">EsmForTokenClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/main/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/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertForTokenClassification">FlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelForTokenClassification">FunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2ForTokenClassification">GPT2ForTokenClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForTokenClassification">GPTBigCodeForTokenClassification</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoForTokenClassification">GPTNeoForTokenClassification</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXForTokenClassification">GPTNeoXForTokenClassification</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertForTokenClassification">IBertForTokenClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMForTokenClassification">LayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForTokenClassification">LayoutLMv2ForTokenClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForTokenClassification">LayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/lilt#transformers.LiltForTokenClassification">LiltForTokenClassification</a> (LiLT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerForTokenClassification">LongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeForTokenClassification">LukeForTokenClassification</a> (LUKE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetForTokenClassification">MPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/markuplm#transformers.MarkupLMForTokenClassification">MarkupLMForTokenClassification</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaForTokenClassification">MegaForTokenClassification</a> (MEGA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertForTokenClassification">MegatronBertForTokenClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertForTokenClassification">MobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptForTokenClassification">MptForTokenClassification</a> (MPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mra#transformers.MraForTokenClassification">MraForTokenClassification</a> (MRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaForTokenClassification">NezhaForTokenClassification</a> (Nezha model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerForTokenClassification">NystromformerForTokenClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertForTokenClassification">QDQBertForTokenClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertForTokenClassification">RemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertForTokenClassification">RoCBertForTokenClassification</a> (RoCBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerForTokenClassification">RoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaForTokenClassification">RobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForTokenClassification">RobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertForTokenClassification">SqueezeBertForTokenClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMForTokenClassification">XLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaForTokenClassification">XLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForTokenClassification">XLMRobertaXLForTokenClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetForTokenClassification">XLNetForTokenClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodForTokenClassification">XmodForTokenClassification</a> (X-MOD model)</li>
<li><a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoForTokenClassification">YosoForTokenClassification</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),cR=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/main/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/main/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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.TFAlbertForTokenClassification">TFAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.TFBertForTokenClassification">TFBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.TFCamembertForTokenClassification">TFCamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.TFConvBertForTokenClassification">TFConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.TFDebertaForTokenClassification">TFDebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.TFDebertaV2ForTokenClassification">TFDebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForTokenClassification">TFDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.TFElectraForTokenClassification">TFElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/esm#transformers.TFEsmForTokenClassification">TFEsmForTokenClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.TFFlaubertForTokenClassification">TFFlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.TFFunnelForTokenClassification">TFFunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.TFLayoutLMForTokenClassification">TFLayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForTokenClassification">TFLayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.TFLongformerForTokenClassification">TFLongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.TFMPNetForTokenClassification">TFMPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.TFMobileBertForTokenClassification">TFMobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.TFRemBertForTokenClassification">TFRemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.TFRoFormerForTokenClassification">TFRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.TFRobertaForTokenClassification">TFRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForTokenClassification">TFRobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.TFXLMForTokenClassification">TFXLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForTokenClassification">TFXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.TFXLNetForTokenClassification">TFXLNetForTokenClassification</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),uk=new Z({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_config.example",$$slots:{default:[mis]},$$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/main/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/main/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/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),Pk=new Z({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[cis]},$$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/main/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/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.FlaxAlbertForTokenClassification">FlaxAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.FlaxBertForTokenClassification">FlaxBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.FlaxBigBirdForTokenClassification">FlaxBigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.FlaxDistilBertForTokenClassification">FlaxDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.FlaxElectraForTokenClassification">FlaxElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.FlaxRoFormerForTokenClassification">FlaxRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.FlaxRobertaForTokenClassification">FlaxRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForTokenClassification">FlaxRobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForTokenClassification">FlaxXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/src/transformers/models/auto/modeling_auto.py#L1247"}}),cU=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/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.AlbertForQuestionAnswering">AlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.BartForQuestionAnswering">BartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertForQuestionAnswering">BertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdForQuestionAnswering">BigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForQuestionAnswering">BigBirdPegasusForQuestionAnswering</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bloom#transformers.BloomForQuestionAnswering">BloomForQuestionAnswering</a> (BLOOM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertForQuestionAnswering">CamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/canine#transformers.CanineForQuestionAnswering">CanineForQuestionAnswering</a> (CANINE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertForQuestionAnswering">ConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecTextForQuestionAnswering">Data2VecTextForQuestionAnswering</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaForQuestionAnswering">DebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2ForQuestionAnswering">DebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraForQuestionAnswering">ElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie#transformers.ErnieForQuestionAnswering">ErnieForQuestionAnswering</a> (ERNIE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ernie_m#transformers.ErnieMForQuestionAnswering">ErnieMForQuestionAnswering</a> (ErnieM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/main/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/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertForQuestionAnsweringSimple">FlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelForQuestionAnswering">FunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2ForQuestionAnswering">GPT2ForQuestionAnswering</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJForQuestionAnswering">GPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neo#transformers.GPTNeoForQuestionAnswering">GPTNeoForQuestionAnswering</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gpt_neox#transformers.GPTNeoXForQuestionAnswering">GPTNeoXForQuestionAnswering</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/ibert#transformers.IBertForQuestionAnswering">IBertForQuestionAnswering</a> (I-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/led#transformers.LEDForQuestionAnswering">LEDForQuestionAnswering</a> (LED model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/lilt#transformers.LiltForQuestionAnswering">LiltForQuestionAnswering</a> (LiLT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerForQuestionAnswering">LongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/luke#transformers.LukeForQuestionAnswering">LukeForQuestionAnswering</a> (LUKE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/lxmert#transformers.LxmertForQuestionAnswering">LxmertForQuestionAnswering</a> (LXMERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartForQuestionAnswering">MBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetForQuestionAnswering">MPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mt5#transformers.MT5ForQuestionAnswering">MT5ForQuestionAnswering</a> (MT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/markuplm#transformers.MarkupLMForQuestionAnswering">MarkupLMForQuestionAnswering</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mega#transformers.MegaForQuestionAnswering">MegaForQuestionAnswering</a> (MEGA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/megatron-bert#transformers.MegatronBertForQuestionAnswering">MegatronBertForQuestionAnswering</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertForQuestionAnswering">MobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpt#transformers.MptForQuestionAnswering">MptForQuestionAnswering</a> (MPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mra#transformers.MraForQuestionAnswering">MraForQuestionAnswering</a> (MRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mvp#transformers.MvpForQuestionAnswering">MvpForQuestionAnswering</a> (MVP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nezha#transformers.NezhaForQuestionAnswering">NezhaForQuestionAnswering</a> (Nezha model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nystromformer#transformers.NystromformerForQuestionAnswering">NystromformerForQuestionAnswering</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/opt#transformers.OPTForQuestionAnswering">OPTForQuestionAnswering</a> (OPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/qdqbert#transformers.QDQBertForQuestionAnswering">QDQBertForQuestionAnswering</a> (QDQBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/reformer#transformers.ReformerForQuestionAnswering">ReformerForQuestionAnswering</a> (Reformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertForQuestionAnswering">RemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roc_bert#transformers.RoCBertForQuestionAnswering">RoCBertForQuestionAnswering</a> (RoCBert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerForQuestionAnswering">RoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaForQuestionAnswering">RobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForQuestionAnswering">RobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/splinter#transformers.SplinterForQuestionAnswering">SplinterForQuestionAnswering</a> (Splinter model)</li>
<li><a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/squeezebert#transformers.SqueezeBertForQuestionAnswering">SqueezeBertForQuestionAnswering</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/t5#transformers.T5ForQuestionAnswering">T5ForQuestionAnswering</a> (T5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/umt5#transformers.UMT5ForQuestionAnswering">UMT5ForQuestionAnswering</a> (UMT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMForQuestionAnsweringSimple">XLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaForQuestionAnswering">XLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForQuestionAnswering">XLMRobertaXLForQuestionAnswering</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetForQuestionAnsweringSimple">XLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xmod#transformers.XmodForQuestionAnswering">XmodForQuestionAnswering</a> (X-MOD model)</li>
<li><a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/yoso#transformers.YosoForQuestionAnswering">YosoForQuestionAnswering</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.TFAlbertForQuestionAnswering">TFAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.TFBertForQuestionAnswering">TFBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/camembert#transformers.TFCamembertForQuestionAnswering">TFCamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convbert#transformers.TFConvBertForQuestionAnswering">TFConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta#transformers.TFDebertaForQuestionAnswering">TFDebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deberta-v2#transformers.TFDebertaV2ForQuestionAnswering">TFDebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForQuestionAnswering">TFDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.TFElectraForQuestionAnswering">TFElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/flaubert#transformers.TFFlaubertForQuestionAnsweringSimple">TFFlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/funnel#transformers.TFFunnelForQuestionAnswering">TFFunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/gptj#transformers.TFGPTJForQuestionAnswering">TFGPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForQuestionAnswering">TFLayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/longformer#transformers.TFLongformerForQuestionAnswering">TFLongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mpnet#transformers.TFMPNetForQuestionAnswering">TFMPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilebert#transformers.TFMobileBertForQuestionAnswering">TFMobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/rembert#transformers.TFRemBertForQuestionAnswering">TFRemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.TFRoFormerForQuestionAnswering">TFRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.TFRobertaForQuestionAnswering">TFRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForQuestionAnswering">TFRobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm#transformers.TFXLMForQuestionAnsweringSimple">TFXLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForQuestionAnswering">TFXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlnet#transformers.TFXLNetForQuestionAnsweringSimple">TFXLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),cx=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/main/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/main/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/main/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
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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.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;
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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.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.,
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automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/albert#transformers.FlaxAlbertForQuestionAnswering">FlaxAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bart#transformers.FlaxBartForQuestionAnswering">FlaxBartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bert#transformers.FlaxBertForQuestionAnswering">FlaxBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/big_bird#transformers.FlaxBigBirdForQuestionAnswering">FlaxBigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.FlaxDistilBertForQuestionAnswering">FlaxDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/electra#transformers.FlaxElectraForQuestionAnswering">FlaxElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mbart#transformers.FlaxMBartForQuestionAnswering">FlaxMBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roformer#transformers.FlaxRoFormerForQuestionAnswering">FlaxRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta#transformers.FlaxRobertaForQuestionAnswering">FlaxRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForQuestionAnswering">FlaxRobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForQuestionAnswering">FlaxXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),Nx=new Z({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[bis]},$$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/main/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/main/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/main/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
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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.FlaxAutoModelForQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),Ox=new Z({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[vis]},$$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/main/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/main/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/main/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/main/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/main/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/dpt#transformers.DPTForDepthEstimation">DPTForDepthEstimation</a> (DPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/glpn#transformers.GLPNForDepthEstimation">GLPNForDepthEstimation</a> (GLPN model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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
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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.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
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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.AutoModelForDepthEstimation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/beit#transformers.BeitForImageClassification">BeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/bit#transformers.BitConfig">BitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/bit#transformers.BitForImageClassification">BitForImageClassification</a> (BiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convnext#transformers.ConvNextForImageClassification">ConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/convnextv2#transformers.ConvNextV2Config">ConvNextV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convnextv2#transformers.ConvNextV2ForImageClassification">ConvNextV2ForImageClassification</a> (ConvNeXTV2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/cvt#transformers.CvtForImageClassification">CvtForImageClassification</a> (CvT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecVisionForImageClassification">Data2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deit#transformers.DeiTForImageClassification">DeiTForImageClassification</a> or <a href="/docs/transformers/main/en/model_doc/deit#transformers.DeiTForImageClassificationWithTeacher">DeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/dinat#transformers.DinatConfig">DinatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/dinat#transformers.DinatForImageClassification">DinatForImageClassification</a> (DiNAT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/dinov2#transformers.Dinov2Config">Dinov2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/dinov2#transformers.Dinov2ForImageClassification">Dinov2ForImageClassification</a> (DINOv2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassification">EfficientFormerForImageClassification</a> or <a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassificationWithTeacher">EfficientFormerForImageClassificationWithTeacher</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/efficientnet#transformers.EfficientNetConfig">EfficientNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/efficientnet#transformers.EfficientNetForImageClassification">EfficientNetForImageClassification</a> (EfficientNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/focalnet#transformers.FocalNetConfig">FocalNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/focalnet#transformers.FocalNetForImageClassification">FocalNetForImageClassification</a> (FocalNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/imagegpt#transformers.ImageGPTForImageClassification">ImageGPTForImageClassification</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/levit#transformers.LevitForImageClassification">LevitForImageClassification</a> or <a href="/docs/transformers/main/en/model_doc/levit#transformers.LevitForImageClassificationWithTeacher">LevitForImageClassificationWithTeacher</a> (LeViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config">MobileNetV1Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilenet_v1#transformers.MobileNetV1ForImageClassification">MobileNetV1ForImageClassification</a> (MobileNetV1 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForImageClassification">MobileNetV2ForImageClassification</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTForImageClassification">MobileViTForImageClassification</a> (MobileViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilevitv2#transformers.MobileViTV2Config">MobileViTV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilevitv2#transformers.MobileViTV2ForImageClassification">MobileViTV2ForImageClassification</a> (MobileViTV2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/nat#transformers.NatConfig">NatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/nat#transformers.NatForImageClassification">NatForImageClassification</a> (NAT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverForImageClassificationLearned">PerceiverForImageClassificationLearned</a> or <a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverForImageClassificationFourier">PerceiverForImageClassificationFourier</a> or <a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverForImageClassificationConvProcessing">PerceiverForImageClassificationConvProcessing</a> (Perceiver model)</li>
<li><a href="/docs/transformers/main/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/poolformer#transformers.PoolFormerForImageClassification">PoolFormerForImageClassification</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pvt#transformers.PvtConfig">PvtConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pvt#transformers.PvtForImageClassification">PvtForImageClassification</a> (PVT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetForImageClassification">RegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/resnet#transformers.ResNetForImageClassification">ResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/segformer#transformers.SegformerForImageClassification">SegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swiftformer#transformers.SwiftFormerConfig">SwiftFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swiftformer#transformers.SwiftFormerForImageClassification">SwiftFormerForImageClassification</a> (SwiftFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swin#transformers.SwinForImageClassification">SwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swinv2#transformers.Swinv2ForImageClassification">Swinv2ForImageClassification</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/van#transformers.VanForImageClassification">VanForImageClassification</a> (VAN model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTForImageClassification">ViTForImageClassification</a> (ViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit_hybrid#transformers.ViTHybridConfig">ViTHybridConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit_hybrid#transformers.ViTHybridForImageClassification">ViTHybridForImageClassification</a> (ViT Hybrid model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit_msn#transformers.ViTMSNForImageClassification">ViTMSNForImageClassification</a> (ViTMSN model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/convnext#transformers.TFConvNextForImageClassification">TFConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/cvt#transformers.TFCvtForImageClassification">TFCvtForImageClassification</a> (CvT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.TFData2VecVisionForImageClassification">TFData2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deit#transformers.TFDeiTForImageClassification">TFDeiTForImageClassification</a> or <a href="/docs/transformers/main/en/model_doc/deit#transformers.TFDeiTForImageClassificationWithTeacher">TFDeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.TFEfficientFormerForImageClassification">TFEfficientFormerForImageClassification</a> or <a href="/docs/transformers/main/en/model_doc/efficientformer#transformers.TFEfficientFormerForImageClassificationWithTeacher">TFEfficientFormerForImageClassificationWithTeacher</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.TFMobileViTForImageClassification">TFMobileViTForImageClassification</a> (MobileViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/regnet#transformers.TFRegNetForImageClassification">TFRegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/resnet#transformers.TFResNetForImageClassification">TFResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/segformer#transformers.TFSegformerForImageClassification">TFSegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swin#transformers.TFSwinForImageClassification">TFSwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit#transformers.TFViTForImageClassification">TFViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),$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/main/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>
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Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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/main/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.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
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Load the model weights from a PyTorch checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,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
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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.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;
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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.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
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execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/beit#transformers.FlaxBeitForImageClassification">FlaxBeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/regnet#transformers.FlaxRegNetForImageClassification">FlaxRegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/resnet#transformers.FlaxResNetForImageClassification">FlaxResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit#transformers.FlaxViTForImageClassification">FlaxViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<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.FlaxAutoModelForImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),eB=new Z({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[Lis]},$$scope:{ctx:x}}}),zU=new te({}),HU=new G({props:{name:"class transformers.AutoModelForVideoClassification",anchor:"transformers.AutoModelForVideoClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1383"}}),eX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForVideoClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVideoClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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/main/en/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/timesformer#transformers.TimesformerForVideoClassification">TimesformerForVideoClassification</a> (TimeSformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/videomae#transformers.VideoMAEForVideoClassification">VideoMAEForVideoClassification</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vivit#transformers.VivitConfig">VivitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vivit#transformers.VivitForVideoClassification">VivitForVideoClassification</a> (ViViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),rB=new Z({props:{anchor:"transformers.AutoModelForVideoClassification.from_config.example",$$slots:{default:[Ais]},$$scope:{ctx:x}}}),oX=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForVideoClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
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/main/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.AutoModelForVideoClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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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.AutoModelForVideoClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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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
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),lB=new Z({props:{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.example",$$slots:{default:[Ris]},$$scope:{ctx:x}}}),rX=new te({}),tX=new G({props:{name:"class transformers.AutoModelForMaskedImageModeling",anchor:"transformers.AutoModelForMaskedImageModeling",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1448"}}),nX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForMaskedImageModeling.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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/main/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deit#transformers.DeiTForMaskedImageModeling">DeiTForMaskedImageModeling</a> (DeiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/focalnet#transformers.FocalNetConfig">FocalNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/focalnet#transformers.FocalNetForMaskedImageModeling">FocalNetForMaskedImageModeling</a> (FocalNet model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swin#transformers.SwinForMaskedImageModeling">SwinForMaskedImageModeling</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swinv2#transformers.Swinv2ForMaskedImageModeling">Swinv2ForMaskedImageModeling</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTForMaskedImageModeling">ViTForMaskedImageModeling</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<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.AutoModelForMaskedImageModeling.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/src/transformers/models/auto/modeling_tf_auto.py#L565"}}),mX=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/main/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/main/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deit#transformers.TFDeiTForMaskedImageModeling">TFDeiTForMaskedImageModeling</a> (DeiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/swin#transformers.TFSwinForMaskedImageModeling">TFSwinForMaskedImageModeling</a> (Swin Transformer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),bB=new Z({props:{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[xis]},$$scope:{ctx:x}}}),cX=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/main/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/main/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/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/en/model_doc/conditional_detr#transformers.ConditionalDetrConfig">ConditionalDetrConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/conditional_detr#transformers.ConditionalDetrForObjectDetection">ConditionalDetrForObjectDetection</a> (Conditional DETR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deformable_detr#transformers.DeformableDetrConfig">DeformableDetrConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deformable_detr#transformers.DeformableDetrForObjectDetection">DeformableDetrForObjectDetection</a> (Deformable DETR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/deta#transformers.DetaConfig">DetaConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/deta#transformers.DetaForObjectDetection">DetaForObjectDetection</a> (DETA model)</li>
<li><a href="/docs/transformers/main/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/detr#transformers.DetrForObjectDetection">DetrForObjectDetection</a> (DETR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/table-transformer#transformers.TableTransformerConfig">TableTransformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/table-transformer#transformers.TableTransformerForObjectDetection">TableTransformerForObjectDetection</a> (Table Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/yolos#transformers.YolosForObjectDetection">YolosForObjectDetection</a> (YOLOS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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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<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.AutoModelForObjectDetection.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Whether or not to 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.AutoModelForObjectDetection.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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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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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/main/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/main/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR 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/main/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
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/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.AutoModelForImageSegmentation.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/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForImageSegmentation.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.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
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForImageSegmentation.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.AutoModelForImageSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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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.,
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automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/beit#transformers.BeitForSemanticSegmentation">BeitForSemanticSegmentation</a> (BEiT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/dpt#transformers.DPTForSemanticSegmentation">DPTForSemanticSegmentation</a> (DPT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecVisionForSemanticSegmentation">Data2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForSemanticSegmentation">MobileNetV2ForSemanticSegmentation</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTForSemanticSegmentation">MobileViTForSemanticSegmentation</a> (MobileViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mobilevitv2#transformers.MobileViTV2Config">MobileViTV2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilevitv2#transformers.MobileViTV2ForSemanticSegmentation">MobileViTV2ForSemanticSegmentation</a> (MobileViTV2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/segformer#transformers.SegformerForSemanticSegmentation">SegformerForSemanticSegmentation</a> (SegFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/upernet#transformers.UperNetConfig">UperNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation">UperNetForSemanticSegmentation</a> (UPerNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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;
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save 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 TensorFlow checkpoint save file (see docstring of
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<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
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<li><a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.TFMobileViTForSemanticSegmentation">TFMobileViTForSemanticSegmentation</a> (MobileViT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/segformer#transformers.TFSegformerForSemanticSegmentation">TFSegformerForSemanticSegmentation</a> (SegFormer 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/main/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
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argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<li>The model was saved using <a href="/docs/transformers/main/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
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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
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
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automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),rI=new Z({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.example",$$slots:{default:[Wis]},$$scope:{ctx:x}}}),$X=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
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/main/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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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/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.AutoModelForInstanceSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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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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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.AutoModelForInstanceSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),nI=new Z({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.example",$$slots:{default:[Jis]},$$scope:{ctx:x}}}),jX=new te({}),PX=new G({props:{name:"class transformers.AutoModelForUniversalSegmentation",anchor:"transformers.AutoModelForUniversalSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1342"}}),VX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForUniversalSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForUniversalSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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/main/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mask2former#transformers.Mask2FormerConfig">Mask2FormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mask2former#transformers.Mask2FormerForUniversalSegmentation">Mask2FormerForUniversalSegmentation</a> (Mask2Former model)</li>
<li><a href="/docs/transformers/main/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/oneformer#transformers.OneFormerConfig">OneFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/oneformer#transformers.OneFormerForUniversalSegmentation">OneFormerForUniversalSegmentation</a> (OneFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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.AutoModelForUniversalSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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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.AutoModelForUniversalSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/en/model_doc/align#transformers.AlignConfig">AlignConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/align#transformers.AlignModel">AlignModel</a> (ALIGN model)</li>
<li><a href="/docs/transformers/main/en/model_doc/altclip#transformers.AltCLIPConfig">AltCLIPConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/clipseg#transformers.CLIPSegConfig">CLIPSegConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/clipseg#transformers.CLIPSegModel">CLIPSegModel</a> (CLIPSeg model)</li>
<li><a href="/docs/transformers/main/en/model_doc/chinese_clip#transformers.ChineseCLIPConfig">ChineseCLIPConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/chinese_clip#transformers.ChineseCLIPModel">ChineseCLIPModel</a> (Chinese-CLIP model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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
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<li>The model was saved using <a href="/docs/transformers/main/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.AutoModelForZeroShotImageClassification.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/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForZeroShotImageClassification.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.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 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.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
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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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.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.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blip#transformers.TFBlipModel">TFBlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/main/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/main/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/main/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>
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
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<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/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/main/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
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Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Whether or not to 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
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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.,
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automatically loaded:</p>
<ul>
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already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/en/model_doc/audio-spectrogram-transformer#transformers.ASTConfig">ASTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/audio-spectrogram-transformer#transformers.ASTForAudioClassification">ASTForAudioClassification</a> (Audio Spectrogram Transformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioForSequenceClassification">Data2VecAudioForSequenceClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/main/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/hubert#transformers.HubertForSequenceClassification">HubertForSequenceClassification</a> (Hubert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/sew#transformers.SEWForSequenceClassification">SEWForSequenceClassification</a> (SEW model)</li>
<li><a href="/docs/transformers/main/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/sew-d#transformers.SEWDForSequenceClassification">SEWDForSequenceClassification</a> (SEW-D model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech#transformers.UniSpeechForSequenceClassification">UniSpeechForSequenceClassification</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatForSequenceClassification">UniSpeechSatForSequenceClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2ForSequenceClassification">Wav2Vec2ForSequenceClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForSequenceClassification">Wav2Vec2ConformerForSequenceClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMForSequenceClassification">WavLMForSequenceClassification</a> (WavLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperForAudioClassification">WhisperForAudioClassification</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),NI=new Z({props:{anchor:"transformers.AutoModelForAudioClassification.from_config.example",$$slots:{default:[His]},$$scope:{ctx:x}}}),mD=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;
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<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
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/main/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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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
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<li>The model was saved using <a href="/docs/transformers/main/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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<p>This option can be used if you want to create a model from a pretrained configuration but load your own
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
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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
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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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Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),QI=new Z({props:{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[Kis]},$$scope:{ctx:x}}}),cD=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/main/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/main/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/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.TFWav2Vec2ForSequenceClassification">TFWav2Vec2ForSequenceClassification</a> (Wav2Vec2 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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;
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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.TFAutoModelForAudioClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),KI=new Z({props:{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[ods]},$$scope:{ctx:x}}}),pD=new te({}),_D=new G({props:{name:"class transformers.AutoModelForAudioFrameClassification",anchor:"transformers.AutoModelForAudioFrameClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1420"}}),vD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForAudioFrameClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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/main/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioForAudioFrameClassification">Data2VecAudioForAudioFrameClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatForAudioFrameClassification">UniSpeechSatForAudioFrameClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2ForAudioFrameClassification">Wav2Vec2ForAudioFrameClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForAudioFrameClassification">Wav2Vec2ConformerForAudioFrameClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMForAudioFrameClassification">WavLMForAudioFrameClassification</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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.AutoModelForAudioFrameClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<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.AutoModelForAudioFrameClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioForCTC">Data2VecAudioForCTC</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/main/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/hubert#transformers.HubertForCTC">HubertForCTC</a> (Hubert model)</li>
<li><a href="/docs/transformers/main/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/mctct#transformers.MCTCTForCTC">MCTCTForCTC</a> (M-CTC-T model)</li>
<li><a href="/docs/transformers/main/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/sew#transformers.SEWForCTC">SEWForCTC</a> (SEW model)</li>
<li><a href="/docs/transformers/main/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/sew-d#transformers.SEWDForCTC">SEWDForCTC</a> (SEW-D model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech#transformers.UniSpeechForCTC">UniSpeechForCTC</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatForCTC">UniSpeechSatForCTC</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC">Wav2Vec2ForCTC</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForCTC">Wav2Vec2ConformerForCTC</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMForCTC">WavLMForCTC</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),mN=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;
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<li>A path to a <em>directory</em> containing model weights saved using
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<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
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<li><a href="/docs/transformers/main/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderConfig">SpeechEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderModel">SpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li>
<li><a href="/docs/transformers/main/en/model_doc/speecht5#transformers.SpeechT5Config">SpeechT5Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/speecht5#transformers.SpeechT5ForSpeechToText">SpeechT5ForSpeechToText</a> (SpeechT5 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperForConditionalGeneration">WhisperForConditionalGeneration</a> (Whisper model)</li>
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<ul>
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<li>A path to a <em>directory</em> containing model weights saved using
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<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
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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<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
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<p>This option can be used if you want to create a model from a pretrained configuration but load your own
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Path to a directory in which a downloaded pretrained model configuration should be cached if the
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
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<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/speech_to_text#transformers.TFSpeech2TextForConditionalGeneration">TFSpeech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/whisper#transformers.TFWhisperForConditionalGeneration">TFWhisperForConditionalGeneration</a> (Whisper model)</li>
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<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
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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/main/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>
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/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
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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.TFAutoModelForSpeechSeq2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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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.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
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already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderConfig">SpeechEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/speech-encoder-decoder#transformers.FlaxSpeechEncoderDecoderModel">FlaxSpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li>
<li><a href="/docs/transformers/main/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),$N=new Z({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[mds]},$$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/main/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/main/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/main/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.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<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.FlaxAutoModelForSpeechSeq2Seq.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.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;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.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.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),ZN=new Z({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[cds]},$$scope:{ctx:x}}}),JD=new te({}),UD=new G({props:{name:"class transformers.AutoModelForAudioXVector",anchor:"transformers.AutoModelForAudioXVector",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1429"}}),DD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForAudioXVector.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioXVector.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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/main/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioForXVector">Data2VecAudioForXVector</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/unispeech-sat#transformers.UniSpeechSatForXVector">UniSpeechSatForXVector</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2#transformers.Wav2Vec2ForXVector">Wav2Vec2ForXVector</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForXVector">Wav2Vec2ConformerForXVector</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/wavlm#transformers.WavLMForXVector">WavLMForXVector</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<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;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForAudioXVector.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.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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/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/main/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/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TapasForQuestionAnswering">TapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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.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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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
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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.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;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForTableQuestionAnswering.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.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
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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.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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/tapas#transformers.TFTapasForQuestionAnswering">TFTapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),n$=new Z({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[pds]},$$scope:{ctx:x}}}),mq=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/main/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/main/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/main/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.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;
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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.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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),l$=new Z({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[_ds]},$$scope:{ctx:x}}}),cq=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/main/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/main/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/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.LayoutLMForQuestionAnswering">LayoutLMForQuestionAnswering</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),d$=new Z({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[bds]},$$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/main/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/main/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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),g$=new Z({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[vds]},$$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/main/src/transformers/models/auto/modeling_tf_auto.py#L640"}}),vq=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/main/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/main/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/layoutlm#transformers.TFLayoutLMForQuestionAnswering">TFLayoutLMForQuestionAnswering</a> (LayoutLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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.TFAutoModelForDocumentQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<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.TFAutoModelForDocumentQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.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.TFAutoModelForDocumentQuestionAnswering.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.TFAutoModelForDocumentQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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.TFAutoModelForDocumentQuestionAnswering.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.TFAutoModelForDocumentQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),b$=new Z({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[Tds]},$$scope:{ctx:x}}}),Tq=new te({}),Fq=new G({props:{name:"class transformers.AutoModelForVisualQuestionAnswering",anchor:"transformers.AutoModelForVisualQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1265"}}),yq=new G({props:{name:"from_config",anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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/main/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vilt#transformers.ViltForQuestionAnswering">ViltForQuestionAnswering</a> (ViLT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<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.AutoModelForVisualQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),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/main/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/main/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/main/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration">Blip2ForConditionalGeneration</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/main/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blip#transformers.BlipForConditionalGeneration">BlipForConditionalGeneration</a> (BLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/git#transformers.GitConfig">GitConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/git#transformers.GitForCausalLM">GitForCausalLM</a> (GIT model)</li>
<li><a href="/docs/transformers/main/en/model_doc/instructblip#transformers.InstructBlipConfig">InstructBlipConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/instructblip#transformers.InstructBlipForConditionalGeneration">InstructBlipForConditionalGeneration</a> (InstructBLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructConfig">Pix2StructConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pix2struct#transformers.Pix2StructForConditionalGeneration">Pix2StructForConditionalGeneration</a> (Pix2Struct model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/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/main/src/transformers/models/auto/auto_factory.py#L408"}}),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/main/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/main/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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/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
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForVision2Seq.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.AutoModelForVision2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForVision2Seq.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.AutoModelForVision2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),G$=new Z({props:{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[Cds]},$$scope:{ctx:x}}}),Sq=new te({}),xq=new G({props:{name:"class transformers.TFAutoModelForVision2Seq",anchor:"transformers.TFAutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L601"}}),Bq=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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/main/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blip#transformers.TFBlipForConditionalGeneration">TFBlipForConditionalGeneration</a> (BLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vision-encoder-decoder#transformers.TFVisionEncoderDecoderModel">TFVisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),I$=new Z({props:{anchor:"transformers.TFAutoModelForVision2Seq.from_config.example",$$slots:{default:[wds]},$$scope:{ctx:x}}}),Iq=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForVision2Seq.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/main/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.TFAutoModelForVision2Seq.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.TFAutoModelForVision2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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/main/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.TFAutoModelForVision2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForVision2Seq.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.TFAutoModelForVision2Seq.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.TFAutoModelForVision2Seq.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.TFAutoModelForVision2Seq.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.TFAutoModelForVision2Seq.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.TFAutoModelForVision2Seq.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.TFAutoModelForVision2Seq.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.TFAutoModelForVision2Seq.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.TFAutoModelForVision2Seq.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.TFAutoModelForVision2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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/main/src/transformers/models/auto/auto_factory.py#L439"}}),j$=new Z({props:{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[Lds]},$$scope:{ctx:x}}}),$q=new te({}),jq=new G({props:{name:"class transformers.FlaxAutoModelForVision2Seq",anchor:"transformers.FlaxAutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L361"}}),Zq=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vision-encoder-decoder#transformers.FlaxVisionEncoderDecoderModel">FlaxVisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L408"}}),Z$=new Z({props:{anchor:"transformers.FlaxAutoModelForVision2Seq.from_config.example",$$slots:{default:[Ads]},$$scope:{ctx:x}}}),Vq=new G({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForVision2Seq.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/main/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.FlaxAutoModelForVision2Seq.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.FlaxAutoModelForVision2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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/main/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.FlaxAutoModelForVision2Seq.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.FlaxAutoModelForVision2Seq.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.FlaxAutoModelForVision2Seq.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.FlaxAutoModelForVision2Seq.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.FlaxAutoModelForVision2Seq.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.FlaxAutoModelForVision2Seq.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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identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
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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.FlaxAutoModelForVision2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
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<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
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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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will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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model)"),aQr=l(),a0=a("li"),Tje=a("strong"),nQr=o("flaubert"),sQr=o(" \u2014 "),nce=a("a"),lQr=o("TFFlaubertModel"),iQr=o(" (FlauBERT model)"),dQr=l(),Vi=a("li"),Fje=a("strong"),mQr=o("funnel"),cQr=o(" \u2014 "),sce=a("a"),fQr=o("TFFunnelModel"),hQr=o(" or "),lce=a("a"),gQr=o("TFFunnelBaseModel"),uQr=o(" (Funnel Transformer model)"),pQr=l(),n0=a("li"),Eje=a("strong"),_Qr=o("gpt-sw3"),bQr=o(" \u2014 "),ice=a("a"),vQr=o("TFGPT2Model"),MQr=o(" (GPT-Sw3 model)"),TQr=l(),s0=a("li"),yje=a("strong"),FQr=o("gpt2"),EQr=o(" \u2014 "),dce=a("a"),yQr=o("TFGPT2Model"),CQr=o(" (OpenAI GPT-2 model)"),wQr=l(),l0=a("li"),Cje=a("strong"),LQr=o("gptj"),AQr=o(" \u2014 "),mce=a("a"),RQr=o("TFGPTJModel"),kQr=o(" (GPT-J model)"),SQr=l(),i0=a("li"),wje=a("strong"),xQr=o("groupvit"),GQr=o(" \u2014 "),cce=a("a"),BQr=o("TFGroupViTModel"),IQr=o(" (GroupViT model)"),NQr=l(),d0=a("li"),Lje=a("strong"),$Qr=o("hubert"),jQr=o(" \u2014 "),fce=a("a"),PQr=o("TFHubertModel"),ZQr=o(" (Hubert 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"),Mce=a("a"),CYr=o("TFMobileBertModel"),wYr=o(" (MobileBERT model)"),LYr=l(),b0=a("li"),Nje=a("strong"),AYr=o("mobilevit"),RYr=o(" \u2014 "),Tce=a("a"),kYr=o("TFMobileViTModel"),SYr=o(" (MobileViT model)"),xYr=l(),v0=a("li"),$je=a("strong"),GYr=o("mpnet"),BYr=o(" \u2014 "),Fce=a("a"),IYr=o("TFMPNetModel"),NYr=o(" (MPNet model)"),$Yr=l(),M0=a("li"),jje=a("strong"),jYr=o("mt5"),PYr=o(" \u2014 "),Ece=a("a"),ZYr=o("TFMT5Model"),VYr=o(" (MT5 model)"),WYr=l(),T0=a("li"),Pje=a("strong"),JYr=o("openai-gpt"),UYr=o(" \u2014 "),yce=a("a"),XYr=o("TFOpenAIGPTModel"),DYr=o(" (OpenAI GPT model)"),qYr=l(),F0=a("li"),Zje=a("strong"),OYr=o("opt"),QYr=o(" \u2014 "),Cce=a("a"),YYr=o("TFOPTModel"),zYr=o(" (OPT model)"),HYr=l(),E0=a("li"),Vje=a("strong"),KYr=o("pegasus"),ezr=o(" \u2014 "),wce=a("a"),ozr=o("TFPegasusModel"),rzr=o(" (Pegasus model)"),tzr=l(),y0=a("li"),Wje=a("strong"),azr=o("regnet"),nzr=o(" \u2014 "),Lce=a("a"),szr=o("TFRegNetModel"),lzr=o(" (RegNet model)"),izr=l(),C0=a("li"),Jje=a("strong"),dzr=o("rembert"),mzr=o(" \u2014 "),Ace=a("a"),czr=o("TFRemBertModel"),fzr=o(" (RemBERT model)"),hzr=l(),w0=a("li"),Uje=a("strong"),gzr=o("resnet"),uzr=o(" \u2014 "),Rce=a("a"),pzr=o("TFResNetModel"),_zr=o(" (ResNet model)"),bzr=l(),L0=a("li"),Xje=a("strong"),vzr=o("roberta"),Mzr=o(" \u2014 "),kce=a("a"),Tzr=o("TFRobertaModel"),Fzr=o(" (RoBERTa model)"),Ezr=l(),A0=a("li"),Dje=a("strong"),yzr=o("roberta-prelayernorm"),Czr=o(" \u2014 "),Sce=a("a"),wzr=o("TFRobertaPreLayerNormModel"),Lzr=o(" (RoBERTa-PreLayerNorm model)"),Azr=l(),R0=a("li"),qje=a("strong"),Rzr=o("roformer"),kzr=o(" \u2014 "),xce=a("a"),Szr=o("TFRoFormerModel"),xzr=o(" (RoFormer model)"),Gzr=l(),k0=a("li"),Oje=a("strong"),Bzr=o("sam"),Izr=o(" \u2014 "),Gce=a("a"),Nzr=o("TFSamModel"),$zr=o(" (SAM model)"),jzr=l(),S0=a("li"),Qje=a("strong"),Pzr=o("segformer"),Zzr=o(" \u2014 "),Bce=a("a"),Vzr=o("TFSegformerModel"),Wzr=o(" (SegFormer model)"),Jzr=l(),x0=a("li"),Yje=a("strong"),Uzr=o("speech_to_text"),Xzr=o(" \u2014 "),Ice=a("a"),Dzr=o("TFSpeech2TextModel"),qzr=o(" (Speech2Text model)"),Ozr=l(),G0=a("li"),zje=a("strong"),Qzr=o("swin"),Yzr=o(" \u2014 "),Nce=a("a"),zzr=o("TFSwinModel"),Hzr=o(" (Swin Transformer model)"),Kzr=l(),B0=a("li"),Hje=a("strong"),eHr=o("t5"),oHr=o(" \u2014 "),$ce=a("a"),rHr=o("TFT5Model"),tHr=o(" (T5 model)"),aHr=l(),I0=a("li"),Kje=a("strong"),nHr=o("tapas"),sHr=o(" \u2014 "),jce=a("a"),lHr=o("TFTapasModel"),iHr=o(" (TAPAS model)"),dHr=l(),N0=a("li"),ePe=a("strong"),mHr=o("transfo-xl"),cHr=o(" \u2014 "),Pce=a("a"),fHr=o("TFTransfoXLModel"),hHr=o(" (Transformer-XL model)"),gHr=l(),$0=a("li"),oPe=a("strong"),uHr=o("vision-text-dual-encoder"),pHr=o(" \u2014 "),Zce=a("a"),_Hr=o("TFVisionTextDualEncoderModel"),bHr=o(" (VisionTextDualEncoder model)"),vHr=l(),j0=a("li"),rPe=a("strong"),MHr=o("vit"),THr=o(" \u2014 "),Vce=a("a"),FHr=o("TFViTModel"),EHr=o(" (ViT 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with the `),Qce=a("a"),mKr=o("from_pretrained()"),cKr=o(" class method or the "),Yce=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)."),bKr=l(),Ut=a("div"),v(PV.$$.fragment),vKr=l(),hPe=a("p"),MKr=o("Instantiates one of the base model classes of the library from a configuration."),TKr=l(),cc=a("p"),FKr=o(`Note:
Loading a model from its configuration file does `),gPe=a("strong"),EKr=o("not"),yKr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),zce=a("a"),CKr=o("from_pretrained()"),wKr=o(" to load the model weights."),LKr=l(),v(O0.$$.fragment),AKr=l(),at=a("div"),v(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 `),bPe=a("code"),$Kr=o("pretrained_model_name_or_path"),jKr=o(":"),PKr=l(),se=a("ul"),Q0=a("li"),vPe=a("strong"),ZKr=o("albert"),VKr=o(" \u2014 "),Hce=a("a"),WKr=o("FlaxAlbertModel"),JKr=o(" (ALBERT model)"),UKr=l(),Y0=a("li"),MPe=a("strong"),XKr=o("bart"),DKr=o(" \u2014 "),Kce=a("a"),qKr=o("FlaxBartModel"),OKr=o(" (BART model)"),QKr=l(),z0=a("li"),TPe=a("strong"),YKr=o("beit"),zKr=o(" \u2014 "),efe=a("a"),HKr=o("FlaxBeitModel"),KKr=o(" (BEiT model)"),eet=l(),H0=a("li"),FPe=a("strong"),oet=o("bert"),ret=o(" \u2014 "),ofe=a("a"),tet=o("FlaxBertModel"),aet=o(" (BERT model)"),net=l(),K0=a("li"),EPe=a("strong"),set=o("big_bird"),iet=o(" \u2014 "),rfe=a("a"),det=o("FlaxBigBirdModel"),met=o(" (BigBird model)"),cet=l(),e5=a("li"),yPe=a("strong"),fet=o("blenderbot"),het=o(" \u2014 "),tfe=a("a"),get=o("FlaxBlenderbotModel"),uet=o(" (Blenderbot model)"),pet=l(),o5=a("li"),CPe=a("strong"),_et=o("blenderbot-small"),bet=o(" \u2014 "),afe=a("a"),vet=o("FlaxBlenderbotSmallModel"),Met=o(" (BlenderbotSmall model)"),Tet=l(),r5=a("li"),wPe=a("strong"),Fet=o("bloom"),Eet=o(" \u2014 "),nfe=a("a"),yet=o("FlaxBloomModel"),Cet=o(" (BLOOM model)"),wet=l(),t5=a("li"),LPe=a("strong"),Let=o("clip"),Aet=o(" \u2014 "),sfe=a("a"),Ret=o("FlaxCLIPModel"),ket=o(" (CLIP model)"),Set=l(),a5=a("li"),APe=a("strong"),xet=o("distilbert"),Get=o(" \u2014 "),lfe=a("a"),Bet=o("FlaxDistilBertModel"),Iet=o(" (DistilBERT model)"),Net=l(),n5=a("li"),RPe=a("strong"),$et=o("electra"),jet=o(" \u2014 "),ife=a("a"),Pet=o("FlaxElectraModel"),Zet=o(" (ELECTRA model)"),Vet=l(),s5=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(),l5=a("li"),SPe=a("strong"),qet=o("gpt2"),Oet=o(" \u2014 "),mfe=a("a"),Qet=o("FlaxGPT2Model"),Yet=o(" (OpenAI GPT-2 model)"),zet=l(),i5=a("li"),xPe=a("strong"),Het=o("gpt_neo"),Ket=o(" \u2014 "),cfe=a("a"),eot=o("FlaxGPTNeoModel"),oot=o(" (GPT Neo 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"),vfe=a("a"),Zot=o("FlaxRegNetModel"),Vot=o(" (RegNet model)"),Wot=l(),_5=a("li"),VPe=a("strong"),Jot=o("resnet"),Uot=o(" \u2014 "),Mfe=a("a"),Xot=o("FlaxResNetModel"),Dot=o(" (ResNet model)"),qot=l(),b5=a("li"),WPe=a("strong"),Oot=o("roberta"),Qot=o(" \u2014 "),Tfe=a("a"),Yot=o("FlaxRobertaModel"),zot=o(" (RoBERTa model)"),Hot=l(),v5=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(),M5=a("li"),UPe=a("strong"),art=o("roformer"),nrt=o(" \u2014 "),Efe=a("a"),srt=o("FlaxRoFormerModel"),lrt=o(" (RoFormer model)"),irt=l(),T5=a("li"),XPe=a("strong"),drt=o("t5"),mrt=o(" \u2014 "),yfe=a("a"),crt=o("FlaxT5Model"),frt=o(" (T5 model)"),hrt=l(),F5=a("li"),DPe=a("strong"),grt=o("vision-text-dual-encoder"),urt=o(" \u2014 "),Cfe=a("a"),prt=o("FlaxVisionTextDualEncoderModel"),_rt=o(" (VisionTextDualEncoder model)"),brt=l(),E5=a("li"),qPe=a("strong"),vrt=o("vit"),Mrt=o(" \u2014 "),wfe=a("a"),Trt=o("FlaxViTModel"),Frt=o(" (ViT model)"),Ert=l(),y5=a("li"),OPe=a("strong"),yrt=o("wav2vec2"),Crt=o(" \u2014 "),Lfe=a("a"),wrt=o("FlaxWav2Vec2Model"),Lrt=o(" (Wav2Vec2 model)"),Art=l(),C5=a("li"),QPe=a("strong"),Rrt=o("whisper"),krt=o(" \u2014 "),Afe=a("a"),Srt=o("FlaxWhisperModel"),xrt=o(" (Whisper model)"),Grt=l(),w5=a("li"),YPe=a("strong"),Brt=o("xglm"),Irt=o(" \u2014 "),Rfe=a("a"),Nrt=o("FlaxXGLMModel"),$rt=o(" (XGLM model)"),jrt=l(),L5=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(),v(A5.$$.fragment),cGo=l(),fc=a("h2"),R5=a("a"),HPe=a("span"),v(VV.$$.fragment),Urt=l(),KPe=a("span"),Xrt=o("Generic pretraining classes"),fGo=l(),Sfe=a("p"),Drt=o("The following auto classes are available for instantiating a model with a pretraining head."),hGo=l(),hc=a("h3"),k5=a("a"),eZe=a("span"),v(WV.$$.fragment),qrt=l(),oZe=a("span"),Ort=o("AutoModelForPreTraining"),gGo=l(),qo=a("div"),v(JV.$$.fragment),Qrt=l(),gc=a("p"),Yrt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),xfe=a("a"),zrt=o("from_pretrained()"),Hrt=o(" class method or the "),Gfe=a("a"),Krt=o("from_config()"),ett=o(` class
method.`),ott=l(),UV=a("p"),rtt=o("This class cannot be instantiated directly using "),rZe=a("code"),ttt=o("__init__()"),att=o(" (throws an error)."),ntt=l(),Xt=a("div"),v(XV.$$.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(),uc=a("p"),dtt=o(`Note:
Loading a model from its configuration file does `),aZe=a("strong"),mtt=o("not"),ctt=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(),v(S5.$$.fragment),utt=l(),lo=a("div"),v(DV.$$.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."),btt=l(),Wn=a("p"),vtt=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"),x5=a("li"),dZe=a("strong"),Ltt=o("albert"),Att=o(" \u2014 "),Ife=a("a"),Rtt=o("AlbertForPreTraining"),ktt=o(" (ALBERT model)"),Stt=l(),G5=a("li"),mZe=a("strong"),xtt=o("bart"),Gtt=o(" \u2014 "),Nfe=a("a"),Btt=o("BartForConditionalGeneration"),Itt=o(" (BART model)"),Ntt=l(),B5=a("li"),cZe=a("strong"),$tt=o("bert"),jtt=o(" \u2014 "),$fe=a("a"),Ptt=o("BertForPreTraining"),Ztt=o(" (BERT model)"),Vtt=l(),I5=a("li"),fZe=a("strong"),Wtt=o("big_bird"),Jtt=o(" \u2014 "),jfe=a("a"),Utt=o("BigBirdForPreTraining"),Xtt=o(" (BigBird model)"),Dtt=l(),N5=a("li"),hZe=a("strong"),qtt=o("bloom"),Ott=o(" \u2014 "),Pfe=a("a"),Qtt=o("BloomForCausalLM"),Ytt=o(" (BLOOM model)"),ztt=l(),$5=a("li"),gZe=a("strong"),Htt=o("camembert"),Ktt=o(" \u2014 "),Zfe=a("a"),eat=o("CamembertForMaskedLM"),oat=o(" (CamemBERT model)"),rat=l(),j5=a("li"),uZe=a("strong"),tat=o("ctrl"),aat=o(" \u2014 "),Vfe=a("a"),nat=o("CTRLLMHeadModel"),sat=o(" (CTRL model)"),lat=l(),P5=a("li"),pZe=a("strong"),iat=o("data2vec-text"),dat=o(" \u2014 "),Wfe=a("a"),mat=o("Data2VecTextForMaskedLM"),cat=o(" (Data2VecText model)"),fat=l(),Z5=a("li"),_Ze=a("strong"),hat=o("deberta"),gat=o(" \u2014 "),Jfe=a("a"),uat=o("DebertaForMaskedLM"),pat=o(" (DeBERTa model)"),_at=l(),V5=a("li"),bZe=a("strong"),bat=o("deberta-v2"),vat=o(" \u2014 "),Ufe=a("a"),Mat=o("DebertaV2ForMaskedLM"),Tat=o(" (DeBERTa-v2 model)"),Fat=l(),W5=a("li"),vZe=a("strong"),Eat=o("distilbert"),yat=o(" \u2014 "),Xfe=a("a"),Cat=o("DistilBertForMaskedLM"),wat=o(" (DistilBERT model)"),Lat=l(),J5=a("li"),MZe=a("strong"),Aat=o("electra"),Rat=o(" \u2014 "),Dfe=a("a"),kat=o("ElectraForPreTraining"),Sat=o(" (ELECTRA model)"),xat=l(),U5=a("li"),TZe=a("strong"),Gat=o("ernie"),Bat=o(" \u2014 "),qfe=a("a"),Iat=o("ErnieForPreTraining"),Nat=o(" (ERNIE model)"),$at=l(),X5=a("li"),FZe=a("strong"),jat=o("flaubert"),Pat=o(" \u2014 "),Ofe=a("a"),Zat=o("FlaubertWithLMHeadModel"),Vat=o(" (FlauBERT model)"),Wat=l(),D5=a("li"),EZe=a("strong"),Jat=o("flava"),Uat=o(" \u2014 "),Qfe=a("a"),Xat=o("FlavaForPreTraining"),Dat=o(" (FLAVA model)"),qat=l(),q5=a("li"),yZe=a("strong"),Oat=o("fnet"),Qat=o(" \u2014 "),Yfe=a("a"),Yat=o("FNetForPreTraining"),zat=o(" (FNet model)"),Hat=l(),O5=a("li"),CZe=a("strong"),Kat=o("fsmt"),ent=o(" \u2014 "),zfe=a("a"),ont=o("FSMTForConditionalGeneration"),rnt=o(" (FairSeq Machine-Translation model)"),tnt=l(),Q5=a("li"),wZe=a("strong"),ant=o("funnel"),nnt=o(" \u2014 "),Hfe=a("a"),snt=o("FunnelForPreTraining"),lnt=o(" (Funnel Transformer model)"),int=l(),Y5=a("li"),LZe=a("strong"),dnt=o("gpt-sw3"),mnt=o(" \u2014 "),Kfe=a("a"),cnt=o("GPT2LMHeadModel"),fnt=o(" (GPT-Sw3 model)"),hnt=l(),z5=a("li"),AZe=a("strong"),gnt=o("gpt2"),unt=o(" \u2014 "),ehe=a("a"),pnt=o("GPT2LMHeadModel"),_nt=o(" (OpenAI GPT-2 model)"),bnt=l(),H5=a("li"),RZe=a("strong"),vnt=o("gpt_bigcode"),Mnt=o(" \u2014 "),ohe=a("a"),Tnt=o("GPTBigCodeForCausalLM"),Fnt=o(" (GPTBigCode model)"),Ent=l(),K5=a("li"),kZe=a("strong"),ynt=o("gptsan-japanese"),Cnt=o(" \u2014 "),rhe=a("a"),wnt=o("GPTSanJapaneseForConditionalGeneration"),Lnt=o(" (GPTSAN-japanese model)"),Ant=l(),e3=a("li"),SZe=a("strong"),Rnt=o("ibert"),knt=o(" \u2014 "),the=a("a"),Snt=o("IBertForMaskedLM"),xnt=o(" (I-BERT model)"),Gnt=l(),o3=a("li"),xZe=a("strong"),Bnt=o("idefics"),Int=o(" \u2014 "),ahe=a("a"),Nnt=o("IdeficsForVisionText2Text"),$nt=o(" (IDEFICS model)"),jnt=l(),r3=a("li"),GZe=a("strong"),Pnt=o("layoutlm"),Znt=o(" \u2014 "),nhe=a("a"),Vnt=o("LayoutLMForMaskedLM"),Wnt=o(" (LayoutLM model)"),Jnt=l(),t3=a("li"),BZe=a("strong"),Unt=o("longformer"),Xnt=o(" \u2014 "),she=a("a"),Dnt=o("LongformerForMaskedLM"),qnt=o(" (Longformer model)"),Ont=l(),a3=a("li"),IZe=a("strong"),Qnt=o("luke"),Ynt=o(" \u2014 "),lhe=a("a"),znt=o("LukeForMaskedLM"),Hnt=o(" (LUKE model)"),Knt=l(),n3=a("li"),NZe=a("strong"),est=o("lxmert"),ost=o(" \u2014 "),ihe=a("a"),rst=o("LxmertForPreTraining"),tst=o(" (LXMERT model)"),ast=l(),s3=a("li"),$Ze=a("strong"),nst=o("mega"),sst=o(" \u2014 "),dhe=a("a"),lst=o("MegaForMaskedLM"),ist=o(" (MEGA model)"),dst=l(),l3=a("li"),jZe=a("strong"),mst=o("megatron-bert"),cst=o(" \u2014 "),mhe=a("a"),fst=o("MegatronBertForPreTraining"),hst=o(" (Megatron-BERT model)"),gst=l(),i3=a("li"),PZe=a("strong"),ust=o("mobilebert"),pst=o(" \u2014 "),che=a("a"),_st=o("MobileBertForPreTraining"),bst=o(" (MobileBERT model)"),vst=l(),d3=a("li"),ZZe=a("strong"),Mst=o("mpnet"),Tst=o(" \u2014 "),fhe=a("a"),Fst=o("MPNetForMaskedLM"),Est=o(" (MPNet model)"),yst=l(),m3=a("li"),VZe=a("strong"),Cst=o("mpt"),wst=o(" \u2014 "),hhe=a("a"),Lst=o("MptForCausalLM"),Ast=o(" (MPT model)"),Rst=l(),c3=a("li"),WZe=a("strong"),kst=o("mra"),Sst=o(" \u2014 "),ghe=a("a"),xst=o("MraForMaskedLM"),Gst=o(" (MRA model)"),Bst=l(),f3=a("li"),JZe=a("strong"),Ist=o("mvp"),Nst=o(" \u2014 "),uhe=a("a"),$st=o("MvpForConditionalGeneration"),jst=o(" (MVP model)"),Pst=l(),h3=a("li"),UZe=a("strong"),Zst=o("nezha"),Vst=o(" \u2014 "),phe=a("a"),Wst=o("NezhaForPreTraining"),Jst=o(" (Nezha model)"),Ust=l(),g3=a("li"),XZe=a("strong"),Xst=o("nllb-moe"),Dst=o(" \u2014 "),_he=a("a"),qst=o("NllbMoeForConditionalGeneration"),Ost=o(" (NLLB-MOE model)"),Qst=l(),u3=a("li"),DZe=a("strong"),Yst=o("openai-gpt"),zst=o(" \u2014 "),bhe=a("a"),Hst=o("OpenAIGPTLMHeadModel"),Kst=o(" (OpenAI GPT model)"),elt=l(),p3=a("li"),qZe=a("strong"),olt=o("retribert"),rlt=o(" \u2014 "),vhe=a("a"),tlt=o("RetriBertModel"),alt=o(" (RetriBERT model)"),nlt=l(),_3=a("li"),OZe=a("strong"),slt=o("roberta"),llt=o(" \u2014 "),Mhe=a("a"),ilt=o("RobertaForMaskedLM"),dlt=o(" (RoBERTa model)"),mlt=l(),b3=a("li"),QZe=a("strong"),clt=o("roberta-prelayernorm"),flt=o(" \u2014 "),The=a("a"),hlt=o("RobertaPreLayerNormForMaskedLM"),glt=o(" (RoBERTa-PreLayerNorm model)"),ult=l(),v3=a("li"),YZe=a("strong"),plt=o("roc_bert"),_lt=o(" \u2014 "),Fhe=a("a"),blt=o("RoCBertForPreTraining"),vlt=o(" (RoCBert model)"),Mlt=l(),M3=a("li"),zZe=a("strong"),Tlt=o("rwkv"),Flt=o(" \u2014 "),Ehe=a("a"),Elt=o("RwkvForCausalLM"),ylt=o(" (RWKV model)"),Clt=l(),T3=a("li"),HZe=a("strong"),wlt=o("splinter"),Llt=o(" \u2014 "),yhe=a("a"),Alt=o("SplinterForPreTraining"),Rlt=o(" (Splinter model)"),klt=l(),F3=a("li"),KZe=a("strong"),Slt=o("squeezebert"),xlt=o(" \u2014 "),Che=a("a"),Glt=o("SqueezeBertForMaskedLM"),Blt=o(" (SqueezeBERT model)"),Ilt=l(),E3=a("li"),eVe=a("strong"),Nlt=o("switch_transformers"),$lt=o(" \u2014 "),whe=a("a"),jlt=o("SwitchTransformersForConditionalGeneration"),Plt=o(" (SwitchTransformers model)"),Zlt=l(),y3=a("li"),oVe=a("strong"),Vlt=o("t5"),Wlt=o(" \u2014 "),Lhe=a("a"),Jlt=o("T5ForConditionalGeneration"),Ult=o(" (T5 model)"),Xlt=l(),C3=a("li"),rVe=a("strong"),Dlt=o("tapas"),qlt=o(" \u2014 "),Ahe=a("a"),Olt=o("TapasForMaskedLM"),Qlt=o(" (TAPAS model)"),Ylt=l(),w3=a("li"),tVe=a("strong"),zlt=o("transfo-xl"),Hlt=o(" \u2014 "),Rhe=a("a"),Klt=o("TransfoXLLMHeadModel"),eit=o(" (Transformer-XL model)"),oit=l(),L3=a("li"),aVe=a("strong"),rit=o("tvlt"),tit=o(" \u2014 "),khe=a("a"),ait=o("TvltForPreTraining"),nit=o(" (TVLT model)"),sit=l(),A3=a("li"),nVe=a("strong"),lit=o("unispeech"),iit=o(" \u2014 "),She=a("a"),dit=o("UniSpeechForPreTraining"),mit=o(" (UniSpeech model)"),cit=l(),R3=a("li"),sVe=a("strong"),fit=o("unispeech-sat"),hit=o(" \u2014 "),xhe=a("a"),git=o("UniSpeechSatForPreTraining"),uit=o(" (UniSpeechSat model)"),pit=l(),k3=a("li"),lVe=a("strong"),_it=o("videomae"),bit=o(" \u2014 "),Ghe=a("a"),vit=o("VideoMAEForPreTraining"),Mit=o(" (VideoMAE model)"),Tit=l(),S3=a("li"),iVe=a("strong"),Fit=o("visual_bert"),Eit=o(" \u2014 "),Bhe=a("a"),yit=o("VisualBertForPreTraining"),Cit=o(" (VisualBERT model)"),wit=l(),x3=a("li"),dVe=a("strong"),Lit=o("vit_mae"),Ait=o(" \u2014 "),Ihe=a("a"),Rit=o("ViTMAEForPreTraining"),kit=o(" (ViTMAE model)"),Sit=l(),G3=a("li"),mVe=a("strong"),xit=o("wav2vec2"),Git=o(" \u2014 "),Nhe=a("a"),Bit=o("Wav2Vec2ForPreTraining"),Iit=o(" (Wav2Vec2 model)"),Nit=l(),B3=a("li"),cVe=a("strong"),$it=o("wav2vec2-conformer"),jit=o(" \u2014 "),$he=a("a"),Pit=o("Wav2Vec2ConformerForPreTraining"),Zit=o(" (Wav2Vec2-Conformer model)"),Vit=l(),I3=a("li"),fVe=a("strong"),Wit=o("xlm"),Jit=o(" \u2014 "),jhe=a("a"),Uit=o("XLMWithLMHeadModel"),Xit=o(" (XLM model)"),Dit=l(),N3=a("li"),hVe=a("strong"),qit=o("xlm-roberta"),Oit=o(" \u2014 "),Phe=a("a"),Qit=o("XLMRobertaForMaskedLM"),Yit=o(" (XLM-RoBERTa model)"),zit=l(),$3=a("li"),gVe=a("strong"),Hit=o("xlm-roberta-xl"),Kit=o(" \u2014 "),Zhe=a("a"),edt=o("XLMRobertaXLForMaskedLM"),odt=o(" (XLM-RoBERTa-XL model)"),rdt=l(),j3=a("li"),uVe=a("strong"),tdt=o("xlnet"),adt=o(" \u2014 "),Vhe=a("a"),ndt=o("XLNetLMHeadModel"),sdt=o(" (XLNet model)"),ldt=l(),P3=a("li"),pVe=a("strong"),idt=o("xmod"),ddt=o(" \u2014 "),Whe=a("a"),mdt=o("XmodForMaskedLM"),cdt=o(" (X-MOD model)"),fdt=l(),Z3=a("p"),hdt=o("The model is set in evaluation mode by default using "),_Ve=a("code"),gdt=o("model.eval()"),udt=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),bVe=a("code"),pdt=o("model.train()"),_dt=l(),v(V3.$$.fragment),uGo=l(),pc=a("h3"),W3=a("a"),vVe=a("span"),v(qV.$$.fragment),bdt=l(),MVe=a("span"),vdt=o("TFAutoModelForPreTraining"),pGo=l(),Oo=a("div"),v(OV.$$.fragment),Mdt=l(),_c=a("p"),Tdt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
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method.`),wdt=l(),QV=a("p"),Ldt=o("This class cannot be instantiated directly using "),TVe=a("code"),Adt=o("__init__()"),Rdt=o(" (throws an error)."),kdt=l(),Dt=a("div"),v(YV.$$.fragment),Sdt=l(),FVe=a("p"),xdt=o("Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),Gdt=l(),bc=a("p"),Bdt=o(`Note:
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model\u2019s configuration. Use `),Xhe=a("a"),$dt=o("from_pretrained()"),jdt=o(" to load the model weights."),Pdt=l(),v(J3.$$.fragment),Zdt=l(),nt=a("div"),v(zV.$$.fragment),Vdt=l(),yVe=a("p"),Wdt=o("Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),Jdt=l(),Jn=a("p"),Udt=o("The model class to instantiate is selected based on the "),CVe=a("code"),Xdt=o("model_type"),Ddt=o(` property of the config object (either
passed as an argument or loaded from `),wVe=a("code"),qdt=o("pretrained_model_name_or_path"),Odt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),LVe=a("code"),Qdt=o("pretrained_model_name_or_path"),Ydt=o(":"),zdt=l(),fe=a("ul"),U3=a("li"),AVe=a("strong"),Hdt=o("albert"),Kdt=o(" \u2014 "),Dhe=a("a"),emt=o("TFAlbertForPreTraining"),omt=o(" (ALBERT model)"),rmt=l(),X3=a("li"),RVe=a("strong"),tmt=o("bart"),amt=o(" \u2014 "),qhe=a("a"),nmt=o("TFBartForConditionalGeneration"),smt=o(" (BART model)"),lmt=l(),D3=a("li"),kVe=a("strong"),imt=o("bert"),dmt=o(" \u2014 "),Ohe=a("a"),mmt=o("TFBertForPreTraining"),cmt=o(" (BERT model)"),fmt=l(),q3=a("li"),SVe=a("strong"),hmt=o("camembert"),gmt=o(" \u2014 "),Qhe=a("a"),umt=o("TFCamembertForMaskedLM"),pmt=o(" (CamemBERT model)"),_mt=l(),O3=a("li"),xVe=a("strong"),bmt=o("ctrl"),vmt=o(" \u2014 "),Yhe=a("a"),Mmt=o("TFCTRLLMHeadModel"),Tmt=o(" (CTRL model)"),Fmt=l(),Q3=a("li"),GVe=a("strong"),Emt=o("distilbert"),ymt=o(" \u2014 "),zhe=a("a"),Cmt=o("TFDistilBertForMaskedLM"),wmt=o(" (DistilBERT model)"),Lmt=l(),Y3=a("li"),BVe=a("strong"),Amt=o("electra"),Rmt=o(" \u2014 "),Hhe=a("a"),kmt=o("TFElectraForPreTraining"),Smt=o(" (ELECTRA model)"),xmt=l(),z3=a("li"),IVe=a("strong"),Gmt=o("flaubert"),Bmt=o(" \u2014 "),Khe=a("a"),Imt=o("TFFlaubertWithLMHeadModel"),Nmt=o(" (FlauBERT model)"),$mt=l(),H3=a("li"),NVe=a("strong"),jmt=o("funnel"),Pmt=o(" \u2014 "),ege=a("a"),Zmt=o("TFFunnelForPreTraining"),Vmt=o(" (Funnel Transformer model)"),Wmt=l(),K3=a("li"),$Ve=a("strong"),Jmt=o("gpt-sw3"),Umt=o(" \u2014 "),oge=a("a"),Xmt=o("TFGPT2LMHeadModel"),Dmt=o(" (GPT-Sw3 model)"),qmt=l(),e9=a("li"),jVe=a("strong"),Omt=o("gpt2"),Qmt=o(" \u2014 "),rge=a("a"),Ymt=o("TFGPT2LMHeadModel"),zmt=o(" (OpenAI GPT-2 model)"),Hmt=l(),o9=a("li"),PVe=a("strong"),Kmt=o("layoutlm"),ect=o(" \u2014 "),tge=a("a"),oct=o("TFLayoutLMForMaskedLM"),rct=o(" (LayoutLM model)"),tct=l(),r9=a("li"),ZVe=a("strong"),act=o("lxmert"),nct=o(" \u2014 "),age=a("a"),sct=o("TFLxmertForPreTraining"),lct=o(" (LXMERT model)"),ict=l(),t9=a("li"),VVe=a("strong"),dct=o("mobilebert"),mct=o(" \u2014 "),nge=a("a"),cct=o("TFMobileBertForPreTraining"),fct=o(" (MobileBERT model)"),hct=l(),a9=a("li"),WVe=a("strong"),gct=o("mpnet"),uct=o(" \u2014 "),sge=a("a"),pct=o("TFMPNetForMaskedLM"),_ct=o(" (MPNet model)"),bct=l(),n9=a("li"),JVe=a("strong"),vct=o("openai-gpt"),Mct=o(" \u2014 "),lge=a("a"),Tct=o("TFOpenAIGPTLMHeadModel"),Fct=o(" (OpenAI GPT model)"),Ect=l(),s9=a("li"),UVe=a("strong"),yct=o("roberta"),Cct=o(" \u2014 "),ige=a("a"),wct=o("TFRobertaForMaskedLM"),Lct=o(" (RoBERTa model)"),Act=l(),l9=a("li"),XVe=a("strong"),Rct=o("roberta-prelayernorm"),kct=o(" \u2014 "),dge=a("a"),Sct=o("TFRobertaPreLayerNormForMaskedLM"),xct=o(" (RoBERTa-PreLayerNorm model)"),Gct=l(),i9=a("li"),DVe=a("strong"),Bct=o("t5"),Ict=o(" \u2014 "),mge=a("a"),Nct=o("TFT5ForConditionalGeneration"),$ct=o(" (T5 model)"),jct=l(),d9=a("li"),qVe=a("strong"),Pct=o("tapas"),Zct=o(" \u2014 "),cge=a("a"),Vct=o("TFTapasForMaskedLM"),Wct=o(" (TAPAS model)"),Jct=l(),m9=a("li"),OVe=a("strong"),Uct=o("transfo-xl"),Xct=o(" \u2014 "),fge=a("a"),Dct=o("TFTransfoXLLMHeadModel"),qct=o(" (Transformer-XL model)"),Oct=l(),c9=a("li"),QVe=a("strong"),Qct=o("vit_mae"),Yct=o(" \u2014 "),hge=a("a"),zct=o("TFViTMAEForPreTraining"),Hct=o(" (ViTMAE model)"),Kct=l(),f9=a("li"),YVe=a("strong"),eft=o("xlm"),oft=o(" \u2014 "),gge=a("a"),rft=o("TFXLMWithLMHeadModel"),tft=o(" (XLM model)"),aft=l(),h9=a("li"),zVe=a("strong"),nft=o("xlm-roberta"),sft=o(" \u2014 "),uge=a("a"),lft=o("TFXLMRobertaForMaskedLM"),ift=o(" (XLM-RoBERTa model)"),dft=l(),g9=a("li"),HVe=a("strong"),mft=o("xlnet"),cft=o(" \u2014 "),pge=a("a"),fft=o("TFXLNetLMHeadModel"),hft=o(" (XLNet model)"),gft=l(),v(u9.$$.fragment),_Go=l(),vc=a("h3"),p9=a("a"),KVe=a("span"),v(HV.$$.fragment),uft=l(),eWe=a("span"),pft=o("FlaxAutoModelForPreTraining"),bGo=l(),Qo=a("div"),v(KV.$$.fragment),_ft=l(),Mc=a("p"),bft=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
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model\u2019s configuration. Use `),vge=a("a"),Bft=o("from_pretrained()"),Ift=o(" to load the model weights."),Nft=l(),v(_9.$$.fragment),$ft=l(),st=a("div"),v(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"),b9=a("li"),iWe=a("strong"),Qft=o("albert"),Yft=o(" \u2014 "),Mge=a("a"),zft=o("FlaxAlbertForPreTraining"),Hft=o(" (ALBERT model)"),Kft=l(),v9=a("li"),dWe=a("strong"),eht=o("bart"),oht=o(" \u2014 "),Tge=a("a"),rht=o("FlaxBartForConditionalGeneration"),tht=o(" (BART model)"),aht=l(),M9=a("li"),mWe=a("strong"),nht=o("bert"),sht=o(" \u2014 "),Fge=a("a"),lht=o("FlaxBertForPreTraining"),iht=o(" (BERT model)"),dht=l(),T9=a("li"),cWe=a("strong"),mht=o("big_bird"),cht=o(" \u2014 "),Ege=a("a"),fht=o("FlaxBigBirdForPreTraining"),hht=o(" (BigBird model)"),ght=l(),F9=a("li"),fWe=a("strong"),uht=o("electra"),pht=o(" \u2014 "),yge=a("a"),_ht=o("FlaxElectraForPreTraining"),bht=o(" (ELECTRA model)"),vht=l(),E9=a("li"),hWe=a("strong"),Mht=o("longt5"),Tht=o(" \u2014 "),Cge=a("a"),Fht=o("FlaxLongT5ForConditionalGeneration"),Eht=o(" (LongT5 model)"),yht=l(),y9=a("li"),gWe=a("strong"),Cht=o("mbart"),wht=o(" \u2014 "),wge=a("a"),Lht=o("FlaxMBartForConditionalGeneration"),Aht=o(" (mBART model)"),Rht=l(),C9=a("li"),uWe=a("strong"),kht=o("mt5"),Sht=o(" \u2014 "),Lge=a("a"),xht=o("FlaxMT5ForConditionalGeneration"),Ght=o(" (MT5 model)"),Bht=l(),w9=a("li"),pWe=a("strong"),Iht=o("roberta"),Nht=o(" \u2014 "),Age=a("a"),$ht=o("FlaxRobertaForMaskedLM"),jht=o(" (RoBERTa model)"),Pht=l(),L9=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(),A9=a("li"),bWe=a("strong"),Xht=o("roformer"),Dht=o(" \u2014 "),kge=a("a"),qht=o("FlaxRoFormerForMaskedLM"),Oht=o(" (RoFormer model)"),Qht=l(),R9=a("li"),vWe=a("strong"),Yht=o("t5"),zht=o(" \u2014 "),Sge=a("a"),Hht=o("FlaxT5ForConditionalGeneration"),Kht=o(" (T5 model)"),egt=l(),k9=a("li"),MWe=a("strong"),ogt=o("wav2vec2"),rgt=o(" \u2014 "),xge=a("a"),tgt=o("FlaxWav2Vec2ForPreTraining"),agt=o(" (Wav2Vec2 model)"),ngt=l(),S9=a("li"),TWe=a("strong"),sgt=o("whisper"),lgt=o(" \u2014 "),Gge=a("a"),igt=o("FlaxWhisperForConditionalGeneration"),dgt=o(" (Whisper model)"),mgt=l(),x9=a("li"),FWe=a("strong"),cgt=o("xlm-roberta"),fgt=o(" \u2014 "),Bge=a("a"),hgt=o("FlaxXLMRobertaForMaskedLM"),ggt=o(" (XLM-RoBERTa model)"),ugt=l(),v(G9.$$.fragment),vGo=l(),Fc=a("h2"),B9=a("a"),EWe=a("span"),v(tW.$$.fragment),pgt=l(),yWe=a("span"),_gt=o("Natural Language Processing"),MGo=l(),Ige=a("p"),bgt=o("The following auto classes are available for the following natural language processing tasks."),TGo=l(),Ec=a("h3"),I9=a("a"),CWe=a("span"),v(aW.$$.fragment),vgt=l(),wWe=a("span"),Mgt=o("AutoModelForCausalLM"),FGo=l(),Yo=a("div"),v(nW.$$.fragment),Tgt=l(),yc=a("p"),Fgt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),Nge=a("a"),Egt=o("from_pretrained()"),ygt=o(" class method or the "),$ge=a("a"),Cgt=o("from_config()"),wgt=o(` class
method.`),Lgt=l(),sW=a("p"),Agt=o("This class cannot be instantiated directly using "),LWe=a("code"),Rgt=o("__init__()"),kgt=o(" (throws an error)."),Sgt=l(),Ot=a("div"),v(lW.$$.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(),Cc=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(),v(N9.$$.fragment),Vgt=l(),io=a("div"),v(iW.$$.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"),$9=a("li"),BWe=a("strong"),Kgt=o("bart"),eut=o(" \u2014 "),Pge=a("a"),out=o("BartForCausalLM"),rut=o(" (BART model)"),tut=l(),j9=a("li"),IWe=a("strong"),aut=o("bert"),nut=o(" \u2014 "),Zge=a("a"),sut=o("BertLMHeadModel"),lut=o(" (BERT model)"),iut=l(),P9=a("li"),NWe=a("strong"),dut=o("bert-generation"),mut=o(" \u2014 "),Vge=a("a"),cut=o("BertGenerationDecoder"),fut=o(" (Bert Generation model)"),hut=l(),Z9=a("li"),$We=a("strong"),gut=o("big_bird"),uut=o(" \u2014 "),Wge=a("a"),put=o("BigBirdForCausalLM"),_ut=o(" (BigBird model)"),but=l(),V9=a("li"),jWe=a("strong"),vut=o("bigbird_pegasus"),Mut=o(" \u2014 "),Jge=a("a"),Tut=o("BigBirdPegasusForCausalLM"),Fut=o(" (BigBird-Pegasus model)"),Eut=l(),W9=a("li"),PWe=a("strong"),yut=o("biogpt"),Cut=o(" \u2014 "),Uge=a("a"),wut=o("BioGptForCausalLM"),Lut=o(" (BioGpt model)"),Aut=l(),J9=a("li"),ZWe=a("strong"),Rut=o("blenderbot"),kut=o(" \u2014 "),Xge=a("a"),Sut=o("BlenderbotForCausalLM"),xut=o(" (Blenderbot model)"),Gut=l(),U9=a("li"),VWe=a("strong"),But=o("blenderbot-small"),Iut=o(" \u2014 "),Dge=a("a"),Nut=o("BlenderbotSmallForCausalLM"),$ut=o(" (BlenderbotSmall model)"),jut=l(),X9=a("li"),WWe=a("strong"),Put=o("bloom"),Zut=o(" \u2014 "),qge=a("a"),Vut=o("BloomForCausalLM"),Wut=o(" (BLOOM model)"),Jut=l(),D9=a("li"),JWe=a("strong"),Uut=o("camembert"),Xut=o(" \u2014 "),Oge=a("a"),Dut=o("CamembertForCausalLM"),qut=o(" (CamemBERT model)"),Out=l(),q9=a("li"),UWe=a("strong"),Qut=o("codegen"),Yut=o(" \u2014 "),Qge=a("a"),zut=o("CodeGenForCausalLM"),Hut=o(" (CodeGen model)"),Kut=l(),O9=a("li"),XWe=a("strong"),ept=o("cpmant"),opt=o(" \u2014 "),Yge=a("a"),rpt=o("CpmAntForCausalLM"),tpt=o(" (CPM-Ant model)"),apt=l(),Q9=a("li"),DWe=a("strong"),npt=o("ctrl"),spt=o(" \u2014 "),zge=a("a"),lpt=o("CTRLLMHeadModel"),ipt=o(" (CTRL model)"),dpt=l(),Y9=a("li"),qWe=a("strong"),mpt=o("data2vec-text"),cpt=o(" \u2014 "),Hge=a("a"),fpt=o("Data2VecTextForCausalLM"),hpt=o(" (Data2VecText model)"),gpt=l(),z9=a("li"),OWe=a("strong"),upt=o("electra"),ppt=o(" \u2014 "),Kge=a("a"),_pt=o("ElectraForCausalLM"),bpt=o(" (ELECTRA model)"),vpt=l(),H9=a("li"),QWe=a("strong"),Mpt=o("ernie"),Tpt=o(" \u2014 "),eue=a("a"),Fpt=o("ErnieForCausalLM"),Ept=o(" (ERNIE model)"),ypt=l(),K9=a("li"),YWe=a("strong"),Cpt=o("falcon"),wpt=o(" \u2014 "),zWe=a("code"),Lpt=o("FalconForCausalLM"),Apt=o(" (Falcon model)"),Rpt=l(),e4=a("li"),HWe=a("strong"),kpt=o("git"),Spt=o(" \u2014 "),oue=a("a"),xpt=o("GitForCausalLM"),Gpt=o(" (GIT model)"),Bpt=l(),o4=a("li"),KWe=a("strong"),Ipt=o("gpt-sw3"),Npt=o(" \u2014 "),rue=a("a"),$pt=o("GPT2LMHeadModel"),jpt=o(" (GPT-Sw3 model)"),Ppt=l(),r4=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(),t4=a("li"),oJe=a("strong"),Xpt=o("gpt_bigcode"),Dpt=o(" \u2014 "),aue=a("a"),qpt=o("GPTBigCodeForCausalLM"),Opt=o(" (GPTBigCode model)"),Qpt=l(),a4=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(),n4=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(),s4=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)"),m_t=l(),l4=a("li"),nJe=a("strong"),c_t=o("gptj"),f_t=o(" \u2014 "),iue=a("a"),h_t=o("GPTJForCausalLM"),g_t=o(" (GPT-J model)"),u_t=l(),i4=a("li"),sJe=a("strong"),p_t=o("llama"),__t=o(" \u2014 "),due=a("a"),b_t=o("LlamaForCausalLM"),v_t=o(" (LLaMA model)"),M_t=l(),d4=a("li"),lJe=a("strong"),T_t=o("marian"),F_t=o(" \u2014 "),mue=a("a"),E_t=o("MarianForCausalLM"),y_t=o(" (Marian model)"),C_t=l(),m4=a("li"),iJe=a("strong"),w_t=o("mbart"),L_t=o(" \u2014 "),cue=a("a"),A_t=o("MBartForCausalLM"),R_t=o(" (mBART model)"),k_t=l(),c4=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(),f4=a("li"),mJe=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(),h4=a("li"),cJe=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(),g4=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(),u4=a("li"),hJe=a("strong"),z_t=o("mvp"),H_t=o(" \u2014 "),pue=a("a"),K_t=o("MvpForCausalLM"),e1t=o(" (MVP model)"),o1t=l(),p4=a("li"),gJe=a("strong"),r1t=o("open-llama"),t1t=o(" \u2014 "),_ue=a("a"),a1t=o("OpenLlamaForCausalLM"),n1t=o(" (OpenLlama model)"),s1t=l(),_4=a("li"),uJe=a("strong"),l1t=o("openai-gpt"),i1t=o(" \u2014 "),bue=a("a"),d1t=o("OpenAIGPTLMHeadModel"),m1t=o(" (OpenAI GPT model)"),c1t=l(),b4=a("li"),pJe=a("strong"),f1t=o("opt"),h1t=o(" \u2014 "),vue=a("a"),g1t=o("OPTForCausalLM"),u1t=o(" (OPT model)"),p1t=l(),v4=a("li"),_Je=a("strong"),_1t=o("pegasus"),b1t=o(" \u2014 "),Mue=a("a"),v1t=o("PegasusForCausalLM"),M1t=o(" (Pegasus model)"),T1t=l(),M4=a("li"),bJe=a("strong"),F1t=o("plbart"),E1t=o(" \u2014 "),Tue=a("a"),y1t=o("PLBartForCausalLM"),C1t=o(" (PLBart model)"),w1t=l(),T4=a("li"),vJe=a("strong"),L1t=o("prophetnet"),A1t=o(" \u2014 "),Fue=a("a"),R1t=o("ProphetNetForCausalLM"),k1t=o(" (ProphetNet model)"),S1t=l(),F4=a("li"),MJe=a("strong"),x1t=o("qdqbert"),G1t=o(" \u2014 "),Eue=a("a"),B1t=o("QDQBertLMHeadModel"),I1t=o(" (QDQBert model)"),N1t=l(),E4=a("li"),TJe=a("strong"),$1t=o("reformer"),j1t=o(" \u2014 "),yue=a("a"),P1t=o("ReformerModelWithLMHead"),Z1t=o(" (Reformer model)"),V1t=l(),y4=a("li"),FJe=a("strong"),W1t=o("rembert"),J1t=o(" \u2014 "),Cue=a("a"),U1t=o("RemBertForCausalLM"),X1t=o(" (RemBERT model)"),D1t=l(),C4=a("li"),EJe=a("strong"),q1t=o("roberta"),O1t=o(" \u2014 "),wue=a("a"),Q1t=o("RobertaForCausalLM"),Y1t=o(" (RoBERTa model)"),z1t=l(),w4=a("li"),yJe=a("strong"),H1t=o("roberta-prelayernorm"),K1t=o(" \u2014 "),Lue=a("a"),ebt=o("RobertaPreLayerNormForCausalLM"),obt=o(" (RoBERTa-PreLayerNorm model)"),rbt=l(),L4=a("li"),CJe=a("strong"),tbt=o("roc_bert"),abt=o(" \u2014 "),Aue=a("a"),nbt=o("RoCBertForCausalLM"),sbt=o(" (RoCBert model)"),lbt=l(),A4=a("li"),wJe=a("strong"),ibt=o("roformer"),dbt=o(" \u2014 "),Rue=a("a"),mbt=o("RoFormerForCausalLM"),cbt=o(" (RoFormer model)"),fbt=l(),R4=a("li"),LJe=a("strong"),hbt=o("rwkv"),gbt=o(" \u2014 "),kue=a("a"),ubt=o("RwkvForCausalLM"),pbt=o(" (RWKV model)"),_bt=l(),k4=a("li"),AJe=a("strong"),bbt=o("speech_to_text_2"),vbt=o(" \u2014 "),Sue=a("a"),Mbt=o("Speech2Text2ForCausalLM"),Tbt=o(" (Speech2Text2 model)"),Fbt=l(),S4=a("li"),RJe=a("strong"),Ebt=o("transfo-xl"),ybt=o(" \u2014 "),xue=a("a"),Cbt=o("TransfoXLLMHeadModel"),wbt=o(" (Transformer-XL model)"),Lbt=l(),x4=a("li"),kJe=a("strong"),Abt=o("trocr"),Rbt=o(" \u2014 "),Gue=a("a"),kbt=o("TrOCRForCausalLM"),Sbt=o(" (TrOCR model)"),xbt=l(),G4=a("li"),SJe=a("strong"),Gbt=o("xglm"),Bbt=o(" \u2014 "),Bue=a("a"),Ibt=o("XGLMForCausalLM"),Nbt=o(" (XGLM model)"),$bt=l(),B4=a("li"),xJe=a("strong"),jbt=o("xlm"),Pbt=o(" \u2014 "),Iue=a("a"),Zbt=o("XLMWithLMHeadModel"),Vbt=o(" (XLM model)"),Wbt=l(),I4=a("li"),GJe=a("strong"),Jbt=o("xlm-prophetnet"),Ubt=o(" \u2014 "),Nue=a("a"),Xbt=o("XLMProphetNetForCausalLM"),Dbt=o(" (XLM-ProphetNet model)"),qbt=l(),N4=a("li"),BJe=a("strong"),Obt=o("xlm-roberta"),Qbt=o(" \u2014 "),$ue=a("a"),Ybt=o("XLMRobertaForCausalLM"),zbt=o(" (XLM-RoBERTa model)"),Hbt=l(),$4=a("li"),IJe=a("strong"),Kbt=o("xlm-roberta-xl"),e2t=o(" \u2014 "),jue=a("a"),o2t=o("XLMRobertaXLForCausalLM"),r2t=o(" (XLM-RoBERTa-XL model)"),t2t=l(),j4=a("li"),NJe=a("strong"),a2t=o("xlnet"),n2t=o(" \u2014 "),Pue=a("a"),s2t=o("XLNetLMHeadModel"),l2t=o(" (XLNet model)"),i2t=l(),P4=a("li"),$Je=a("strong"),d2t=o("xmod"),m2t=o(" \u2014 "),Zue=a("a"),c2t=o("XmodForCausalLM"),f2t=o(" (X-MOD model)"),h2t=l(),Z4=a("p"),g2t=o("The model is set in evaluation mode by default using "),jJe=a("code"),u2t=o("model.eval()"),p2t=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"),_2t=o("model.train()"),b2t=l(),v(V4.$$.fragment),EGo=l(),wc=a("h3"),W4=a("a"),ZJe=a("span"),v(dW.$$.fragment),v2t=l(),VJe=a("span"),M2t=o("TFAutoModelForCausalLM"),yGo=l(),zo=a("div"),v(mW.$$.fragment),T2t=l(),Lc=a("p"),F2t=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),Vue=a("a"),E2t=o("from_pretrained()"),y2t=o(" class method or the "),Wue=a("a"),C2t=o("from_config()"),w2t=o(` class
method.`),L2t=l(),cW=a("p"),A2t=o("This class cannot be instantiated directly using "),WJe=a("code"),R2t=o("__init__()"),k2t=o(" (throws an error)."),S2t=l(),Qt=a("div"),v(fW.$$.fragment),x2t=l(),JJe=a("p"),G2t=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),B2t=l(),Ac=a("p"),I2t=o(`Note:
Loading a model from its configuration file does `),UJe=a("strong"),N2t=o("not"),$2t=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Jue=a("a"),j2t=o("from_pretrained()"),P2t=o(" to load the model weights."),Z2t=l(),v(J4.$$.fragment),V2t=l(),lt=a("div"),v(hW.$$.fragment),W2t=l(),XJe=a("p"),J2t=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),U2t=l(),Dn=a("p"),X2t=o("The model class to instantiate is selected based on the "),DJe=a("code"),D2t=o("model_type"),q2t=o(` property of the config object (either
passed as an argument or loaded from `),qJe=a("code"),O2t=o("pretrained_model_name_or_path"),Q2t=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),OJe=a("code"),Y2t=o("pretrained_model_name_or_path"),z2t=o(":"),H2t=l(),Ee=a("ul"),U4=a("li"),QJe=a("strong"),K2t=o("bert"),evt=o(" \u2014 "),Uue=a("a"),ovt=o("TFBertLMHeadModel"),rvt=o(" (BERT model)"),tvt=l(),X4=a("li"),YJe=a("strong"),avt=o("camembert"),nvt=o(" \u2014 "),Xue=a("a"),svt=o("TFCamembertForCausalLM"),lvt=o(" (CamemBERT model)"),ivt=l(),D4=a("li"),zJe=a("strong"),dvt=o("ctrl"),mvt=o(" \u2014 "),Due=a("a"),cvt=o("TFCTRLLMHeadModel"),fvt=o(" (CTRL model)"),hvt=l(),q4=a("li"),HJe=a("strong"),gvt=o("gpt-sw3"),uvt=o(" \u2014 "),que=a("a"),pvt=o("TFGPT2LMHeadModel"),_vt=o(" (GPT-Sw3 model)"),bvt=l(),O4=a("li"),KJe=a("strong"),vvt=o("gpt2"),Mvt=o(" \u2014 "),Oue=a("a"),Tvt=o("TFGPT2LMHeadModel"),Fvt=o(" (OpenAI GPT-2 model)"),Evt=l(),Q4=a("li"),eUe=a("strong"),yvt=o("gptj"),Cvt=o(" \u2014 "),Que=a("a"),wvt=o("TFGPTJForCausalLM"),Lvt=o(" (GPT-J model)"),Avt=l(),Y4=a("li"),oUe=a("strong"),Rvt=o("openai-gpt"),kvt=o(" \u2014 "),Yue=a("a"),Svt=o("TFOpenAIGPTLMHeadModel"),xvt=o(" (OpenAI GPT model)"),Gvt=l(),z4=a("li"),rUe=a("strong"),Bvt=o("opt"),Ivt=o(" \u2014 "),zue=a("a"),Nvt=o("TFOPTForCausalLM"),$vt=o(" (OPT model)"),jvt=l(),H4=a("li"),tUe=a("strong"),Pvt=o("rembert"),Zvt=o(" \u2014 "),Hue=a("a"),Vvt=o("TFRemBertForCausalLM"),Wvt=o(" (RemBERT model)"),Jvt=l(),K4=a("li"),aUe=a("strong"),Uvt=o("roberta"),Xvt=o(" \u2014 "),Kue=a("a"),Dvt=o("TFRobertaForCausalLM"),qvt=o(" (RoBERTa model)"),Ovt=l(),eC=a("li"),nUe=a("strong"),Qvt=o("roberta-prelayernorm"),Yvt=o(" \u2014 "),epe=a("a"),zvt=o("TFRobertaPreLayerNormForCausalLM"),Hvt=o(" (RoBERTa-PreLayerNorm model)"),Kvt=l(),oC=a("li"),sUe=a("strong"),eMt=o("roformer"),oMt=o(" \u2014 "),ope=a("a"),rMt=o("TFRoFormerForCausalLM"),tMt=o(" (RoFormer model)"),aMt=l(),rC=a("li"),lUe=a("strong"),nMt=o("transfo-xl"),sMt=o(" \u2014 "),rpe=a("a"),lMt=o("TFTransfoXLLMHeadModel"),iMt=o(" (Transformer-XL model)"),dMt=l(),tC=a("li"),iUe=a("strong"),mMt=o("xglm"),cMt=o(" \u2014 "),tpe=a("a"),fMt=o("TFXGLMForCausalLM"),hMt=o(" (XGLM model)"),gMt=l(),aC=a("li"),dUe=a("strong"),uMt=o("xlm"),pMt=o(" \u2014 "),ape=a("a"),_Mt=o("TFXLMWithLMHeadModel"),bMt=o(" (XLM model)"),vMt=l(),nC=a("li"),mUe=a("strong"),MMt=o("xlm-roberta"),TMt=o(" \u2014 "),npe=a("a"),FMt=o("TFXLMRobertaForCausalLM"),EMt=o(" (XLM-RoBERTa model)"),yMt=l(),sC=a("li"),cUe=a("strong"),CMt=o("xlnet"),wMt=o(" \u2014 "),spe=a("a"),LMt=o("TFXLNetLMHeadModel"),AMt=o(" (XLNet model)"),RMt=l(),v(lC.$$.fragment),CGo=l(),Rc=a("h3"),iC=a("a"),fUe=a("span"),v(gW.$$.fragment),kMt=l(),hUe=a("span"),SMt=o("FlaxAutoModelForCausalLM"),wGo=l(),Ho=a("div"),v(uW.$$.fragment),xMt=l(),kc=a("p"),GMt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),lpe=a("a"),BMt=o("from_pretrained()"),IMt=o(" class method or the "),ipe=a("a"),NMt=o("from_config()"),$Mt=o(` class
method.`),jMt=l(),pW=a("p"),PMt=o("This class cannot be instantiated directly using "),gUe=a("code"),ZMt=o("__init__()"),VMt=o(" (throws an error)."),WMt=l(),Yt=a("div"),v(_W.$$.fragment),JMt=l(),uUe=a("p"),UMt=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),XMt=l(),Sc=a("p"),DMt=o(`Note:
Loading a model from its configuration file does `),pUe=a("strong"),qMt=o("not"),OMt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),dpe=a("a"),QMt=o("from_pretrained()"),YMt=o(" to load the model weights."),zMt=l(),v(dC.$$.fragment),HMt=l(),it=a("div"),v(bW.$$.fragment),KMt=l(),_Ue=a("p"),eTt=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),oTt=l(),qn=a("p"),rTt=o("The model class to instantiate is selected based on the "),bUe=a("code"),tTt=o("model_type"),aTt=o(` property of the config object (either
passed as an argument or loaded from `),vUe=a("code"),nTt=o("pretrained_model_name_or_path"),sTt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),MUe=a("code"),lTt=o("pretrained_model_name_or_path"),iTt=o(":"),dTt=l(),Le=a("ul"),mC=a("li"),TUe=a("strong"),mTt=o("bart"),cTt=o(" \u2014 "),mpe=a("a"),fTt=o("FlaxBartForCausalLM"),hTt=o(" (BART model)"),gTt=l(),cC=a("li"),FUe=a("strong"),uTt=o("bert"),pTt=o(" \u2014 "),cpe=a("a"),_Tt=o("FlaxBertForCausalLM"),bTt=o(" (BERT model)"),vTt=l(),fC=a("li"),EUe=a("strong"),MTt=o("big_bird"),TTt=o(" \u2014 "),fpe=a("a"),FTt=o("FlaxBigBirdForCausalLM"),ETt=o(" (BigBird model)"),yTt=l(),hC=a("li"),yUe=a("strong"),CTt=o("bloom"),wTt=o(" \u2014 "),hpe=a("a"),LTt=o("FlaxBloomForCausalLM"),ATt=o(" (BLOOM model)"),RTt=l(),gC=a("li"),CUe=a("strong"),kTt=o("electra"),STt=o(" \u2014 "),gpe=a("a"),xTt=o("FlaxElectraForCausalLM"),GTt=o(" (ELECTRA model)"),BTt=l(),uC=a("li"),wUe=a("strong"),ITt=o("gpt-sw3"),NTt=o(" \u2014 "),upe=a("a"),$Tt=o("FlaxGPT2LMHeadModel"),jTt=o(" (GPT-Sw3 model)"),PTt=l(),pC=a("li"),LUe=a("strong"),ZTt=o("gpt2"),VTt=o(" \u2014 "),ppe=a("a"),WTt=o("FlaxGPT2LMHeadModel"),JTt=o(" (OpenAI GPT-2 model)"),UTt=l(),_C=a("li"),AUe=a("strong"),XTt=o("gpt_neo"),DTt=o(" \u2014 "),_pe=a("a"),qTt=o("FlaxGPTNeoForCausalLM"),OTt=o(" (GPT Neo model)"),QTt=l(),bC=a("li"),RUe=a("strong"),YTt=o("gptj"),zTt=o(" \u2014 "),bpe=a("a"),HTt=o("FlaxGPTJForCausalLM"),KTt=o(" (GPT-J model)"),eFt=l(),vC=a("li"),kUe=a("strong"),oFt=o("opt"),rFt=o(" \u2014 "),vpe=a("a"),tFt=o("FlaxOPTForCausalLM"),aFt=o(" (OPT model)"),nFt=l(),MC=a("li"),SUe=a("strong"),sFt=o("roberta"),lFt=o(" \u2014 "),Mpe=a("a"),iFt=o("FlaxRobertaForCausalLM"),dFt=o(" (RoBERTa model)"),mFt=l(),TC=a("li"),xUe=a("strong"),cFt=o("roberta-prelayernorm"),fFt=o(" \u2014 "),Tpe=a("a"),hFt=o("FlaxRobertaPreLayerNormForCausalLM"),gFt=o(" (RoBERTa-PreLayerNorm model)"),uFt=l(),FC=a("li"),GUe=a("strong"),pFt=o("xglm"),_Ft=o(" \u2014 "),Fpe=a("a"),bFt=o("FlaxXGLMForCausalLM"),vFt=o(" (XGLM model)"),MFt=l(),EC=a("li"),BUe=a("strong"),TFt=o("xlm-roberta"),FFt=o(" \u2014 "),Epe=a("a"),EFt=o("FlaxXLMRobertaForCausalLM"),yFt=o(" (XLM-RoBERTa model)"),CFt=l(),v(yC.$$.fragment),LGo=l(),xc=a("h3"),CC=a("a"),IUe=a("span"),v(vW.$$.fragment),wFt=l(),NUe=a("span"),LFt=o("AutoModelForMaskedLM"),AGo=l(),Ko=a("div"),v(MW.$$.fragment),AFt=l(),Gc=a("p"),RFt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the `),ype=a("a"),kFt=o("from_pretrained()"),SFt=o(" class method or the "),Cpe=a("a"),xFt=o("from_config()"),GFt=o(` class
method.`),BFt=l(),TW=a("p"),IFt=o("This class cannot be instantiated directly using "),$Ue=a("code"),NFt=o("__init__()"),$Ft=o(" (throws an error)."),jFt=l(),zt=a("div"),v(FW.$$.fragment),PFt=l(),jUe=a("p"),ZFt=o("Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),VFt=l(),Bc=a("p"),WFt=o(`Note:
Loading a model from its configuration file does `),PUe=a("strong"),JFt=o("not"),UFt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),wpe=a("a"),XFt=o("from_pretrained()"),DFt=o(" to load the model weights."),qFt=l(),v(wC.$$.fragment),OFt=l(),mo=a("div"),v(EW.$$.fragment),QFt=l(),ZUe=a("p"),YFt=o("Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),zFt=l(),On=a("p"),HFt=o("The model class to instantiate is selected based on the "),VUe=a("code"),KFt=o("model_type"),eEt=o(` property of the config object (either
passed as an argument or loaded from `),WUe=a("code"),oEt=o("pretrained_model_name_or_path"),rEt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),JUe=a("code"),tEt=o("pretrained_model_name_or_path"),aEt=o(":"),nEt=l(),K=a("ul"),LC=a("li"),UUe=a("strong"),sEt=o("albert"),lEt=o(" \u2014 "),Lpe=a("a"),iEt=o("AlbertForMaskedLM"),dEt=o(" (ALBERT model)"),mEt=l(),AC=a("li"),XUe=a("strong"),cEt=o("bart"),fEt=o(" \u2014 "),Ape=a("a"),hEt=o("BartForConditionalGeneration"),gEt=o(" (BART model)"),uEt=l(),RC=a("li"),DUe=a("strong"),pEt=o("bert"),_Et=o(" \u2014 "),Rpe=a("a"),bEt=o("BertForMaskedLM"),vEt=o(" (BERT model)"),MEt=l(),kC=a("li"),qUe=a("strong"),TEt=o("big_bird"),FEt=o(" \u2014 "),kpe=a("a"),EEt=o("BigBirdForMaskedLM"),yEt=o(" (BigBird model)"),CEt=l(),SC=a("li"),OUe=a("strong"),wEt=o("camembert"),LEt=o(" \u2014 "),Spe=a("a"),AEt=o("CamembertForMaskedLM"),REt=o(" (CamemBERT model)"),kEt=l(),xC=a("li"),QUe=a("strong"),SEt=o("convbert"),xEt=o(" \u2014 "),xpe=a("a"),GEt=o("ConvBertForMaskedLM"),BEt=o(" (ConvBERT model)"),IEt=l(),GC=a("li"),YUe=a("strong"),NEt=o("data2vec-text"),$Et=o(" \u2014 "),Gpe=a("a"),jEt=o("Data2VecTextForMaskedLM"),PEt=o(" (Data2VecText model)"),ZEt=l(),BC=a("li"),zUe=a("strong"),VEt=o("deberta"),WEt=o(" \u2014 "),Bpe=a("a"),JEt=o("DebertaForMaskedLM"),UEt=o(" (DeBERTa model)"),XEt=l(),IC=a("li"),HUe=a("strong"),DEt=o("deberta-v2"),qEt=o(" \u2014 "),Ipe=a("a"),OEt=o("DebertaV2ForMaskedLM"),QEt=o(" (DeBERTa-v2 model)"),YEt=l(),NC=a("li"),KUe=a("strong"),zEt=o("distilbert"),HEt=o(" \u2014 "),Npe=a("a"),KEt=o("DistilBertForMaskedLM"),eyt=o(" (DistilBERT model)"),oyt=l(),$C=a("li"),eXe=a("strong"),ryt=o("electra"),tyt=o(" \u2014 "),$pe=a("a"),ayt=o("ElectraForMaskedLM"),nyt=o(" (ELECTRA model)"),syt=l(),jC=a("li"),oXe=a("strong"),lyt=o("ernie"),iyt=o(" \u2014 "),jpe=a("a"),dyt=o("ErnieForMaskedLM"),myt=o(" (ERNIE model)"),cyt=l(),PC=a("li"),rXe=a("strong"),fyt=o("esm"),hyt=o(" \u2014 "),Ppe=a("a"),gyt=o("EsmForMaskedLM"),uyt=o(" (ESM model)"),pyt=l(),ZC=a("li"),tXe=a("strong"),_yt=o("flaubert"),byt=o(" \u2014 "),Zpe=a("a"),vyt=o("FlaubertWithLMHeadModel"),Myt=o(" (FlauBERT model)"),Tyt=l(),VC=a("li"),aXe=a("strong"),Fyt=o("fnet"),Eyt=o(" \u2014 "),Vpe=a("a"),yyt=o("FNetForMaskedLM"),Cyt=o(" (FNet model)"),wyt=l(),WC=a("li"),nXe=a("strong"),Lyt=o("funnel"),Ayt=o(" \u2014 "),Wpe=a("a"),Ryt=o("FunnelForMaskedLM"),kyt=o(" (Funnel Transformer model)"),Syt=l(),JC=a("li"),sXe=a("strong"),xyt=o("ibert"),Gyt=o(" \u2014 "),Jpe=a("a"),Byt=o("IBertForMaskedLM"),Iyt=o(" (I-BERT model)"),Nyt=l(),UC=a("li"),lXe=a("strong"),$yt=o("layoutlm"),jyt=o(" \u2014 "),Upe=a("a"),Pyt=o("LayoutLMForMaskedLM"),Zyt=o(" (LayoutLM model)"),Vyt=l(),XC=a("li"),iXe=a("strong"),Wyt=o("longformer"),Jyt=o(" \u2014 "),Xpe=a("a"),Uyt=o("LongformerForMaskedLM"),Xyt=o(" (Longformer model)"),Dyt=l(),DC=a("li"),dXe=a("strong"),qyt=o("luke"),Oyt=o(" \u2014 "),Dpe=a("a"),Qyt=o("LukeForMaskedLM"),Yyt=o(" (LUKE model)"),zyt=l(),qC=a("li"),mXe=a("strong"),Hyt=o("mbart"),Kyt=o(" \u2014 "),qpe=a("a"),e0t=o("MBartForConditionalGeneration"),o0t=o(" (mBART model)"),r0t=l(),OC=a("li"),cXe=a("strong"),t0t=o("mega"),a0t=o(" \u2014 "),Ope=a("a"),n0t=o("MegaForMaskedLM"),s0t=o(" (MEGA model)"),l0t=l(),QC=a("li"),fXe=a("strong"),i0t=o("megatron-bert"),d0t=o(" \u2014 "),Qpe=a("a"),m0t=o("MegatronBertForMaskedLM"),c0t=o(" (Megatron-BERT model)"),f0t=l(),YC=a("li"),hXe=a("strong"),h0t=o("mobilebert"),g0t=o(" \u2014 "),Ype=a("a"),u0t=o("MobileBertForMaskedLM"),p0t=o(" (MobileBERT model)"),_0t=l(),zC=a("li"),gXe=a("strong"),b0t=o("mpnet"),v0t=o(" \u2014 "),zpe=a("a"),M0t=o("MPNetForMaskedLM"),T0t=o(" (MPNet model)"),F0t=l(),HC=a("li"),uXe=a("strong"),E0t=o("mra"),y0t=o(" \u2014 "),Hpe=a("a"),C0t=o("MraForMaskedLM"),w0t=o(" (MRA model)"),L0t=l(),KC=a("li"),pXe=a("strong"),A0t=o("mvp"),R0t=o(" \u2014 "),Kpe=a("a"),k0t=o("MvpForConditionalGeneration"),S0t=o(" (MVP model)"),x0t=l(),ew=a("li"),_Xe=a("strong"),G0t=o("nezha"),B0t=o(" \u2014 "),e_e=a("a"),I0t=o("NezhaForMaskedLM"),N0t=o(" 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(RoBERTa-PreLayerNorm model)"),b5t=l(),iw=a("li"),CXe=a("strong"),v5t=o("roc_bert"),M5t=o(" \u2014 "),i_e=a("a"),T5t=o("RoCBertForMaskedLM"),F5t=o(" (RoCBert model)"),E5t=l(),dw=a("li"),wXe=a("strong"),y5t=o("roformer"),C5t=o(" \u2014 "),d_e=a("a"),w5t=o("RoFormerForMaskedLM"),L5t=o(" (RoFormer model)"),A5t=l(),mw=a("li"),LXe=a("strong"),R5t=o("squeezebert"),k5t=o(" \u2014 "),m_e=a("a"),S5t=o("SqueezeBertForMaskedLM"),x5t=o(" (SqueezeBERT model)"),G5t=l(),cw=a("li"),AXe=a("strong"),B5t=o("tapas"),I5t=o(" \u2014 "),c_e=a("a"),N5t=o("TapasForMaskedLM"),$5t=o(" (TAPAS model)"),j5t=l(),fw=a("li"),RXe=a("strong"),P5t=o("wav2vec2"),Z5t=o(" \u2014 "),kXe=a("code"),V5t=o("Wav2Vec2ForMaskedLM"),W5t=o(" (Wav2Vec2 model)"),J5t=l(),hw=a("li"),SXe=a("strong"),U5t=o("xlm"),X5t=o(" \u2014 "),f_e=a("a"),D5t=o("XLMWithLMHeadModel"),q5t=o(" (XLM model)"),O5t=l(),gw=a("li"),xXe=a("strong"),Q5t=o("xlm-roberta"),Y5t=o(" \u2014 "),h_e=a("a"),z5t=o("XLMRobertaForMaskedLM"),H5t=o(" (XLM-RoBERTa 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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"),v(aJ.$$.fragment),HRt=l(),sOe=a("p"),KRt=o("Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),ekt=l(),ef=a("p"),okt=o(`Note:
Loading a model from its configuration file does `),lOe=a("strong"),rkt=o("not"),tkt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),rbe=a("a"),akt=o("from_pretrained()"),nkt=o(" to load the model weights."),skt=l(),v(l8.$$.fragment),lkt=l(),fo=a("div"),v(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."),mkt=l(),es=a("p"),ckt=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 `),mOe=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 `),cOe=a("code"),pkt=o("pretrained_model_name_or_path"),_kt=o(":"),bkt=l(),B=a("ul"),i8=a("li"),fOe=a("strong"),vkt=o("albert"),Mkt=o(" \u2014 "),tbe=a("a"),Tkt=o("AlbertForSequenceClassification"),Fkt=o(" (ALBERT model)"),Ekt=l(),d8=a("li"),hOe=a("strong"),ykt=o("bart"),Ckt=o(" \u2014 "),abe=a("a"),wkt=o("BartForSequenceClassification"),Lkt=o(" (BART model)"),Akt=l(),m8=a("li"),gOe=a("strong"),Rkt=o("bert"),kkt=o(" \u2014 "),nbe=a("a"),Skt=o("BertForSequenceClassification"),xkt=o(" (BERT model)"),Gkt=l(),c8=a("li"),uOe=a("strong"),Bkt=o("big_bird"),Ikt=o(" \u2014 "),sbe=a("a"),Nkt=o("BigBirdForSequenceClassification"),$kt=o(" (BigBird model)"),jkt=l(),f8=a("li"),pOe=a("strong"),Pkt=o("bigbird_pegasus"),Zkt=o(" \u2014 "),lbe=a("a"),Vkt=o("BigBirdPegasusForSequenceClassification"),Wkt=o(" (BigBird-Pegasus model)"),Jkt=l(),h8=a("li"),_Oe=a("strong"),Ukt=o("biogpt"),Xkt=o(" \u2014 "),ibe=a("a"),Dkt=o("BioGptForSequenceClassification"),qkt=o(" 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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 "),wMe=a("a"),kQt=o("FlaxAlbertForMultipleChoice"),SQt=o(" (ALBERT model)"),xQt=l(),IA=a("li"),bHe=a("strong"),GQt=o("bert"),BQt=o(" \u2014 "),LMe=a("a"),IQt=o("FlaxBertForMultipleChoice"),NQt=o(" (BERT model)"),$Qt=l(),NA=a("li"),vHe=a("strong"),jQt=o("big_bird"),PQt=o(" \u2014 "),AMe=a("a"),ZQt=o("FlaxBigBirdForMultipleChoice"),VQt=o(" (BigBird model)"),WQt=l(),$A=a("li"),MHe=a("strong"),JQt=o("distilbert"),UQt=o(" \u2014 "),RMe=a("a"),XQt=o("FlaxDistilBertForMultipleChoice"),DQt=o(" (DistilBERT model)"),qQt=l(),jA=a("li"),THe=a("strong"),OQt=o("electra"),QQt=o(" \u2014 "),kMe=a("a"),YQt=o("FlaxElectraForMultipleChoice"),zQt=o(" (ELECTRA model)"),HQt=l(),PA=a("li"),FHe=a("strong"),KQt=o("roberta"),eYt=o(" \u2014 "),SMe=a("a"),oYt=o("FlaxRobertaForMultipleChoice"),rYt=o(" (RoBERTa model)"),tYt=l(),ZA=a("li"),EHe=a("strong"),aYt=o("roberta-prelayernorm"),nYt=o(" \u2014 "),xMe=a("a"),sYt=o("FlaxRobertaPreLayerNormForMultipleChoice"),lYt=o(" (RoBERTa-PreLayerNorm model)"),iYt=l(),VA=a("li"),yHe=a("strong"),dYt=o("roformer"),mYt=o(" \u2014 "),GMe=a("a"),cYt=o("FlaxRoFormerForMultipleChoice"),fYt=o(" (RoFormer model)"),hYt=l(),WA=a("li"),CHe=a("strong"),gYt=o("xlm-roberta"),uYt=o(" \u2014 "),BMe=a("a"),pYt=o("FlaxXLMRobertaForMultipleChoice"),_Yt=o(" (XLM-RoBERTa model)"),bYt=l(),v(JA.$$.fragment),oBo=l(),_f=a("h3"),UA=a("a"),wHe=a("span"),v(SJ.$$.fragment),vYt=l(),LHe=a("span"),MYt=o("AutoModelForNextSentencePrediction"),rBo=l(),cr=a("div"),v(xJ.$$.fragment),TYt=l(),bf=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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model\u2019s configuration. Use `),Eye=a("a"),$1a=o("from_pretrained()"),j1a=o(" to load the model weights."),P1a=l(),v(Nx.$$.fragment),Z1a=l(),Ft=a("div"),v(FU.$$.fragment),V1a=l(),Fto=a("p"),W1a=o("Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),J1a=l(),gs=a("p"),U1a=o("The model class to instantiate is selected based on the "),Eto=a("code"),X1a=o("model_type"),D1a=o(` property of the config object (either
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falling back to using pattern matching on `),Cto=a("code"),Q1a=o("pretrained_model_name_or_path"),Y1a=o(":"),z1a=l(),xe=a("ul"),$x=a("li"),wto=a("strong"),H1a=o("albert"),K1a=o(" \u2014 "),yye=a("a"),eba=o("FlaxAlbertForQuestionAnswering"),oba=o(" (ALBERT model)"),rba=l(),jx=a("li"),Lto=a("strong"),tba=o("bart"),aba=o(" \u2014 "),Cye=a("a"),nba=o("FlaxBartForQuestionAnswering"),sba=o(" (BART model)"),lba=l(),Px=a("li"),Ato=a("strong"),iba=o("bert"),dba=o(" \u2014 "),wye=a("a"),mba=o("FlaxBertForQuestionAnswering"),cba=o(" (BERT model)"),fba=l(),Zx=a("li"),Rto=a("strong"),hba=o("big_bird"),gba=o(" \u2014 "),Lye=a("a"),uba=o("FlaxBigBirdForQuestionAnswering"),pba=o(" (BigBird model)"),_ba=l(),Vx=a("li"),kto=a("strong"),bba=o("distilbert"),vba=o(" \u2014 "),Aye=a("a"),Mba=o("FlaxDistilBertForQuestionAnswering"),Tba=o(" (DistilBERT model)"),Fba=l(),Wx=a("li"),Sto=a("strong"),Eba=o("electra"),yba=o(" \u2014 "),Rye=a("a"),Cba=o("FlaxElectraForQuestionAnswering"),wba=o(" (ELECTRA model)"),Lba=l(),Jx=a("li"),xto=a("strong"),Aba=o("mbart"),Rba=o(" \u2014 "),kye=a("a"),kba=o("FlaxMBartForQuestionAnswering"),Sba=o(" (mBART model)"),xba=l(),Ux=a("li"),Gto=a("strong"),Gba=o("roberta"),Bba=o(" \u2014 "),Sye=a("a"),Iba=o("FlaxRobertaForQuestionAnswering"),Nba=o(" (RoBERTa model)"),$ba=l(),Xx=a("li"),Bto=a("strong"),jba=o("roberta-prelayernorm"),Pba=o(" \u2014 "),xye=a("a"),Zba=o("FlaxRobertaPreLayerNormForQuestionAnswering"),Vba=o(" (RoBERTa-PreLayerNorm model)"),Wba=l(),Dx=a("li"),Ito=a("strong"),Jba=o("roformer"),Uba=o(" \u2014 "),Gye=a("a"),Xba=o("FlaxRoFormerForQuestionAnswering"),Dba=o(" (RoFormer model)"),qba=l(),qx=a("li"),Nto=a("strong"),Oba=o("xlm-roberta"),Qba=o(" \u2014 "),Bye=a("a"),Yba=o("FlaxXLMRobertaForQuestionAnswering"),zba=o(" (XLM-RoBERTa model)"),Hba=l(),v(Ox.$$.fragment),vBo=l(),Uf=a("h3"),Qx=a("a"),$to=a("span"),v(EU.$$.fragment),Kba=l(),jto=a("span"),e2a=o("AutoModelForTextEncoding"),MBo=l(),yU=a("div"),v(CU.$$.fragment),TBo=l(),Xf=a("h3"),Yx=a("a"),Pto=a("span"),v(wU.$$.fragment),o2a=l(),Zto=a("span"),r2a=o("TFAutoModelForTextEncoding"),FBo=l(),LU=a("div"),v(AU.$$.fragment),EBo=l(),Df=a("h2"),zx=a("a"),Vto=a("span"),v(RU.$$.fragment),t2a=l(),Wto=a("span"),a2a=o("Computer vision"),yBo=l(),Iye=a("p"),n2a=o("The following auto classes are available for the following computer vision tasks."),CBo=l(),qf=a("h3"),Hx=a("a"),Jto=a("span"),v(kU.$$.fragment),s2a=l(),Uto=a("span"),l2a=o("AutoModelForDepthEstimation"),wBo=l(),Mr=a("div"),v(SU.$$.fragment),i2a=l(),Of=a("p"),d2a=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 `),Nye=a("a"),m2a=o("from_pretrained()"),c2a=o(" class method or the "),$ye=a("a"),f2a=o("from_config()"),h2a=o(` class
method.`),g2a=l(),xU=a("p"),u2a=o("This class cannot be instantiated directly using "),Xto=a("code"),p2a=o("__init__()"),_2a=o(" (throws an error)."),b2a=l(),ba=a("div"),v(GU.$$.fragment),v2a=l(),Dto=a("p"),M2a=o("Instantiates one of the model classes of the library (with a depth estimation head) from a configuration."),T2a=l(),Qf=a("p"),F2a=o(`Note:
Loading a model from its configuration file does `),qto=a("strong"),E2a=o("not"),y2a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),jye=a("a"),C2a=o("from_pretrained()"),w2a=o(" to load the model weights."),L2a=l(),v(Kx.$$.fragment),A2a=l(),_o=a("div"),v(BU.$$.fragment),R2a=l(),Oto=a("p"),k2a=o("Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model."),S2a=l(),us=a("p"),x2a=o("The model class to instantiate is selected based on the "),Qto=a("code"),G2a=o("model_type"),B2a=o(` property of the config object (either
passed as an argument or loaded from `),Yto=a("code"),I2a=o("pretrained_model_name_or_path"),N2a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),zto=a("code"),$2a=o("pretrained_model_name_or_path"),j2a=o(":"),P2a=l(),IU=a("ul"),eG=a("li"),Hto=a("strong"),Z2a=o("dpt"),V2a=o(" \u2014 "),Pye=a("a"),W2a=o("DPTForDepthEstimation"),J2a=o(" (DPT model)"),U2a=l(),oG=a("li"),Kto=a("strong"),X2a=o("glpn"),D2a=o(" \u2014 "),Zye=a("a"),q2a=o("GLPNForDepthEstimation"),O2a=o(" (GLPN model)"),Q2a=l(),rG=a("p"),Y2a=o("The model is set in evaluation mode by default using "),eao=a("code"),z2a=o("model.eval()"),H2a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),oao=a("code"),K2a=o("model.train()"),eva=l(),v(tG.$$.fragment),LBo=l(),Yf=a("h3"),aG=a("a"),rao=a("span"),v(NU.$$.fragment),ova=l(),tao=a("span"),rva=o("AutoModelForImageClassification"),ABo=l(),Tr=a("div"),v($U.$$.fragment),tva=l(),zf=a("p"),ava=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 `),Vye=a("a"),nva=o("from_pretrained()"),sva=o(" class method or the "),Wye=a("a"),lva=o("from_config()"),iva=o(` class
method.`),dva=l(),jU=a("p"),mva=o("This class cannot be instantiated directly using "),aao=a("code"),cva=o("__init__()"),fva=o(" (throws an error)."),hva=l(),va=a("div"),v(PU.$$.fragment),gva=l(),nao=a("p"),uva=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),pva=l(),Hf=a("p"),_va=o(`Note:
Loading a model from its configuration file does `),sao=a("strong"),bva=o("not"),vva=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Jye=a("a"),Mva=o("from_pretrained()"),Tva=o(" to load the model weights."),Fva=l(),v(nG.$$.fragment),Eva=l(),bo=a("div"),v(ZU.$$.fragment),yva=l(),lao=a("p"),Cva=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),wva=l(),ps=a("p"),Lva=o("The model class to instantiate is selected based on the "),iao=a("code"),Ava=o("model_type"),Rva=o(` property of the config object (either
passed as an argument or loaded from `),dao=a("code"),kva=o("pretrained_model_name_or_path"),Sva=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mao=a("code"),xva=o("pretrained_model_name_or_path"),Gva=o(":"),Bva=l(),le=a("ul"),sG=a("li"),cao=a("strong"),Iva=o("beit"),Nva=o(" \u2014 "),Uye=a("a"),$va=o("BeitForImageClassification"),jva=o(" (BEiT model)"),Pva=l(),lG=a("li"),fao=a("strong"),Zva=o("bit"),Vva=o(" \u2014 "),Xye=a("a"),Wva=o("BitForImageClassification"),Jva=o(" (BiT model)"),Uva=l(),iG=a("li"),hao=a("strong"),Xva=o("convnext"),Dva=o(" \u2014 "),Dye=a("a"),qva=o("ConvNextForImageClassification"),Ova=o(" (ConvNeXT model)"),Qva=l(),dG=a("li"),gao=a("strong"),Yva=o("convnextv2"),zva=o(" \u2014 "),qye=a("a"),Hva=o("ConvNextV2ForImageClassification"),Kva=o(" (ConvNeXTV2 model)"),eMa=l(),mG=a("li"),uao=a("strong"),oMa=o("cvt"),rMa=o(" \u2014 "),Oye=a("a"),tMa=o("CvtForImageClassification"),aMa=o(" (CvT model)"),nMa=l(),cG=a("li"),pao=a("strong"),sMa=o("data2vec-vision"),lMa=o(" \u2014 "),Qye=a("a"),iMa=o("Data2VecVisionForImageClassification"),dMa=o(" (Data2VecVision model)"),mMa=l(),Wi=a("li"),_ao=a("strong"),cMa=o("deit"),fMa=o(" \u2014 "),Yye=a("a"),hMa=o("DeiTForImageClassification"),gMa=o(" or "),zye=a("a"),uMa=o("DeiTForImageClassificationWithTeacher"),pMa=o(" (DeiT model)"),_Ma=l(),fG=a("li"),bao=a("strong"),bMa=o("dinat"),vMa=o(" \u2014 "),Hye=a("a"),MMa=o("DinatForImageClassification"),TMa=o(" (DiNAT model)"),FMa=l(),hG=a("li"),vao=a("strong"),EMa=o("dinov2"),yMa=o(" \u2014 "),Kye=a("a"),CMa=o("Dinov2ForImageClassification"),wMa=o(" (DINOv2 model)"),LMa=l(),Ji=a("li"),Mao=a("strong"),AMa=o("efficientformer"),RMa=o(" \u2014 "),e0e=a("a"),kMa=o("EfficientFormerForImageClassification"),SMa=o(" or "),o0e=a("a"),xMa=o("EfficientFormerForImageClassificationWithTeacher"),GMa=o(" (EfficientFormer model)"),BMa=l(),gG=a("li"),Tao=a("strong"),IMa=o("efficientnet"),NMa=o(" \u2014 "),r0e=a("a"),$Ma=o("EfficientNetForImageClassification"),jMa=o(" (EfficientNet model)"),PMa=l(),uG=a("li"),Fao=a("strong"),ZMa=o("focalnet"),VMa=o(" \u2014 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"),m0e=a("a"),MTa=o("MobileViTV2ForImageClassification"),TTa=o(" (MobileViTV2 model)"),FTa=l(),TG=a("li"),Rao=a("strong"),ETa=o("nat"),yTa=o(" \u2014 "),c0e=a("a"),CTa=o("NatForImageClassification"),wTa=o(" (NAT model)"),LTa=l(),Ma=a("li"),kao=a("strong"),ATa=o("perceiver"),RTa=o(" \u2014 "),f0e=a("a"),kTa=o("PerceiverForImageClassificationLearned"),STa=o(" or "),h0e=a("a"),xTa=o("PerceiverForImageClassificationFourier"),GTa=o(" or "),g0e=a("a"),BTa=o("PerceiverForImageClassificationConvProcessing"),ITa=o(" (Perceiver model)"),NTa=l(),FG=a("li"),Sao=a("strong"),$Ta=o("poolformer"),jTa=o(" \u2014 "),u0e=a("a"),PTa=o("PoolFormerForImageClassification"),ZTa=o(" (PoolFormer model)"),VTa=l(),EG=a("li"),xao=a("strong"),WTa=o("pvt"),JTa=o(" \u2014 "),p0e=a("a"),UTa=o("PvtForImageClassification"),XTa=o(" (PVT model)"),DTa=l(),yG=a("li"),Gao=a("strong"),qTa=o("regnet"),OTa=o(" \u2014 "),_0e=a("a"),QTa=o("RegNetForImageClassification"),YTa=o(" (RegNet 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with the `),Y0e=a("a"),$0a=o("from_pretrained()"),j0a=o(" class method or the "),z0e=a("a"),P0a=o("from_config()"),Z0a=o(` class
method.`),V0a=l(),KU=a("p"),W0a=o("This class cannot be instantiated directly using "),wno=a("code"),J0a=o("__init__()"),U0a=o(" (throws an error)."),X0a=l(),Ea=a("div"),v(eX.$$.fragment),D0a=l(),Lno=a("p"),q0a=o("Instantiates one of the model classes of the library (with a video classification head) from a configuration."),O0a=l(),lh=a("p"),Q0a=o(`Note:
Loading a model from its configuration file does `),Ano=a("strong"),Y0a=o("not"),z0a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),H0e=a("a"),H0a=o("from_pretrained()"),K0a=o(" to load the model weights."),e5a=l(),v(rB.$$.fragment),o5a=l(),vo=a("div"),v(oX.$$.fragment),r5a=l(),Rno=a("p"),t5a=o("Instantiate one of the model classes of the library (with a video classification head) from a pretrained model."),a5a=l(),Ms=a("p"),n5a=o("The model class to instantiate is selected based on the "),kno=a("code"),s5a=o("model_type"),l5a=o(` property of the config object (either
passed as an argument or loaded from `),Sno=a("code"),i5a=o("pretrained_model_name_or_path"),d5a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),xno=a("code"),m5a=o("pretrained_model_name_or_path"),c5a=o(":"),f5a=l(),ih=a("ul"),tB=a("li"),Gno=a("strong"),h5a=o("timesformer"),g5a=o(" \u2014 "),K0e=a("a"),u5a=o("TimesformerForVideoClassification"),p5a=o(" (TimeSformer model)"),_5a=l(),aB=a("li"),Bno=a("strong"),b5a=o("videomae"),v5a=o(" \u2014 "),e5e=a("a"),M5a=o("VideoMAEForVideoClassification"),T5a=o(" (VideoMAE model)"),F5a=l(),nB=a("li"),Ino=a("strong"),E5a=o("vivit"),y5a=o(" \u2014 "),o5e=a("a"),C5a=o("VivitForVideoClassification"),w5a=o(" (ViViT model)"),L5a=l(),sB=a("p"),A5a=o("The model is set in evaluation mode by default using "),Nno=a("code"),R5a=o("model.eval()"),k5a=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"),S5a=o("model.train()"),x5a=l(),v(lB.$$.fragment),IBo=l(),dh=a("h3"),iB=a("a"),jno=a("span"),v(rX.$$.fragment),G5a=l(),Pno=a("span"),B5a=o("AutoModelForMaskedImageModeling"),NBo=l(),Cr=a("div"),v(tX.$$.fragment),I5a=l(),mh=a("p"),N5a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),r5e=a("a"),$5a=o("from_pretrained()"),j5a=o(" class method or the "),t5e=a("a"),P5a=o("from_config()"),Z5a=o(` class
method.`),V5a=l(),aX=a("p"),W5a=o("This class cannot be instantiated directly using "),Zno=a("code"),J5a=o("__init__()"),U5a=o(" (throws an error)."),X5a=l(),ya=a("div"),v(nX.$$.fragment),D5a=l(),Vno=a("p"),q5a=o("Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),O5a=l(),ch=a("p"),Q5a=o(`Note:
Loading a model from its configuration file does `),Wno=a("strong"),Y5a=o("not"),z5a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),a5e=a("a"),H5a=o("from_pretrained()"),K5a=o(" to load the model weights."),e3a=l(),v(dB.$$.fragment),o3a=l(),Mo=a("div"),v(sX.$$.fragment),r3a=l(),Jno=a("p"),t3a=o("Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),a3a=l(),Ts=a("p"),n3a=o("The model class to instantiate is selected based on the "),Uno=a("code"),s3a=o("model_type"),l3a=o(` property of the config object (either
passed as an argument or loaded from `),Xno=a("code"),i3a=o("pretrained_model_name_or_path"),d3a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Dno=a("code"),m3a=o("pretrained_model_name_or_path"),c3a=o(":"),f3a=l(),Nt=a("ul"),mB=a("li"),qno=a("strong"),h3a=o("deit"),g3a=o(" \u2014 "),n5e=a("a"),u3a=o("DeiTForMaskedImageModeling"),p3a=o(" (DeiT model)"),_3a=l(),cB=a("li"),Ono=a("strong"),b3a=o("focalnet"),v3a=o(" \u2014 "),s5e=a("a"),M3a=o("FocalNetForMaskedImageModeling"),T3a=o(" (FocalNet model)"),F3a=l(),fB=a("li"),Qno=a("strong"),E3a=o("swin"),y3a=o(" \u2014 "),l5e=a("a"),C3a=o("SwinForMaskedImageModeling"),w3a=o(" (Swin Transformer model)"),L3a=l(),hB=a("li"),Yno=a("strong"),A3a=o("swinv2"),R3a=o(" \u2014 "),i5e=a("a"),k3a=o("Swinv2ForMaskedImageModeling"),S3a=o(" (Swin Transformer V2 model)"),x3a=l(),gB=a("li"),zno=a("strong"),G3a=o("vit"),B3a=o(" \u2014 "),d5e=a("a"),I3a=o("ViTForMaskedImageModeling"),N3a=o(" (ViT model)"),$3a=l(),uB=a("p"),j3a=o("The model is set in evaluation mode by default using "),Hno=a("code"),P3a=o("model.eval()"),Z3a=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"),V3a=o("model.train()"),W3a=l(),v(pB.$$.fragment),$Bo=l(),fh=a("h3"),_B=a("a"),eso=a("span"),v(lX.$$.fragment),J3a=l(),oso=a("span"),U3a=o("TFAutoModelForMaskedImageModeling"),jBo=l(),wr=a("div"),v(iX.$$.fragment),X3a=l(),hh=a("p"),D3a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),m5e=a("a"),q3a=o("from_pretrained()"),O3a=o(" class method or the "),c5e=a("a"),Q3a=o("from_config()"),Y3a=o(` class
method.`),z3a=l(),dX=a("p"),H3a=o("This class cannot be instantiated directly using "),rso=a("code"),K3a=o("__init__()"),e9a=o(" (throws an error)."),o9a=l(),Ca=a("div"),v(mX.$$.fragment),r9a=l(),tso=a("p"),t9a=o("Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),a9a=l(),gh=a("p"),n9a=o(`Note:
Loading a model from its configuration file does `),aso=a("strong"),s9a=o("not"),l9a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),f5e=a("a"),i9a=o("from_pretrained()"),d9a=o(" to load the model weights."),m9a=l(),v(bB.$$.fragment),c9a=l(),Ct=a("div"),v(cX.$$.fragment),f9a=l(),nso=a("p"),h9a=o("Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),g9a=l(),Fs=a("p"),u9a=o("The model class to instantiate is selected based on the "),sso=a("code"),p9a=o("model_type"),_9a=o(` property of the config object (either
passed as an argument or loaded from `),lso=a("code"),b9a=o("pretrained_model_name_or_path"),v9a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),iso=a("code"),M9a=o("pretrained_model_name_or_path"),T9a=o(":"),F9a=l(),fX=a("ul"),vB=a("li"),dso=a("strong"),E9a=o("deit"),y9a=o(" \u2014 "),h5e=a("a"),C9a=o("TFDeiTForMaskedImageModeling"),w9a=o(" (DeiT model)"),L9a=l(),MB=a("li"),mso=a("strong"),A9a=o("swin"),R9a=o(" \u2014 "),g5e=a("a"),k9a=o("TFSwinForMaskedImageModeling"),S9a=o(" (Swin Transformer model)"),x9a=l(),v(TB.$$.fragment),PBo=l(),uh=a("h3"),FB=a("a"),cso=a("span"),v(hX.$$.fragment),G9a=l(),fso=a("span"),B9a=o("AutoModelForObjectDetection"),ZBo=l(),Lr=a("div"),v(gX.$$.fragment),I9a=l(),ph=a("p"),N9a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created
with the `),u5e=a("a"),$9a=o("from_pretrained()"),j9a=o(" class method or the "),p5e=a("a"),P9a=o("from_config()"),Z9a=o(` class
method.`),V9a=l(),uX=a("p"),W9a=o("This class cannot be instantiated directly using "),hso=a("code"),J9a=o("__init__()"),U9a=o(" (throws an error)."),X9a=l(),wa=a("div"),v(pX.$$.fragment),D9a=l(),gso=a("p"),q9a=o("Instantiates one of the model classes of the library (with a object detection head) from a configuration."),O9a=l(),_h=a("p"),Q9a=o(`Note:
Loading a model from its configuration file does `),uso=a("strong"),Y9a=o("not"),z9a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),_5e=a("a"),H9a=o("from_pretrained()"),K9a=o(" to load the model weights."),e4a=l(),v(EB.$$.fragment),o4a=l(),To=a("div"),v(_X.$$.fragment),r4a=l(),pso=a("p"),t4a=o("Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),a4a=l(),Es=a("p"),n4a=o("The model class to instantiate is selected based on the "),_so=a("code"),s4a=o("model_type"),l4a=o(` property of the config object (either
passed as an argument or loaded from `),bso=a("code"),i4a=o("pretrained_model_name_or_path"),d4a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),vso=a("code"),m4a=o("pretrained_model_name_or_path"),c4a=o(":"),f4a=l(),Ar=a("ul"),yB=a("li"),Mso=a("strong"),h4a=o("conditional_detr"),g4a=o(" \u2014 "),b5e=a("a"),u4a=o("ConditionalDetrForObjectDetection"),p4a=o(" (Conditional DETR model)"),_4a=l(),CB=a("li"),Tso=a("strong"),b4a=o("deformable_detr"),v4a=o(" \u2014 "),v5e=a("a"),M4a=o("DeformableDetrForObjectDetection"),T4a=o(" (Deformable DETR model)"),F4a=l(),wB=a("li"),Fso=a("strong"),E4a=o("deta"),y4a=o(" \u2014 "),M5e=a("a"),C4a=o("DetaForObjectDetection"),w4a=o(" (DETA model)"),L4a=l(),LB=a("li"),Eso=a("strong"),A4a=o("detr"),R4a=o(" \u2014 "),T5e=a("a"),k4a=o("DetrForObjectDetection"),S4a=o(" (DETR model)"),x4a=l(),AB=a("li"),yso=a("strong"),G4a=o("table-transformer"),B4a=o(" \u2014 "),F5e=a("a"),I4a=o("TableTransformerForObjectDetection"),N4a=o(" (Table Transformer model)"),$4a=l(),RB=a("li"),Cso=a("strong"),j4a=o("yolos"),P4a=o(" \u2014 "),E5e=a("a"),Z4a=o("YolosForObjectDetection"),V4a=o(" (YOLOS model)"),W4a=l(),kB=a("p"),J4a=o("The model is set in evaluation mode by default using "),wso=a("code"),U4a=o("model.eval()"),X4a=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"),D4a=o("model.train()"),q4a=l(),v(SB.$$.fragment),VBo=l(),bh=a("h3"),xB=a("a"),Aso=a("span"),v(bX.$$.fragment),O4a=l(),Rso=a("span"),Q4a=o("AutoModelForImageSegmentation"),WBo=l(),Rr=a("div"),v(vX.$$.fragment),Y4a=l(),vh=a("p"),z4a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
with the `),y5e=a("a"),H4a=o("from_pretrained()"),K4a=o(" class method or the "),C5e=a("a"),eCa=o("from_config()"),oCa=o(` class
method.`),rCa=l(),MX=a("p"),tCa=o("This class cannot be instantiated directly using "),kso=a("code"),aCa=o("__init__()"),nCa=o(" (throws an error)."),sCa=l(),La=a("div"),v(TX.$$.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"),mCa=o(`Note:
Loading a model from its configuration file does `),xso=a("strong"),cCa=o("not"),fCa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),w5e=a("a"),hCa=o("from_pretrained()"),gCa=o(" to load the model weights."),uCa=l(),v(GB.$$.fragment),pCa=l(),Fo=a("div"),v(FX.$$.fragment),_Ca=l(),Gso=a("p"),bCa=o("Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),vCa=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(),$so=a("ul"),BB=a("li"),jso=a("strong"),ACa=o("detr"),RCa=o(" \u2014 "),L5e=a("a"),kCa=o("DetrForSegmentation"),SCa=o(" (DETR model)"),xCa=l(),IB=a("p"),GCa=o("The model is set in evaluation mode by default using "),Pso=a("code"),BCa=o("model.eval()"),ICa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Zso=a("code"),NCa=o("model.train()"),$Ca=l(),v(NB.$$.fragment),JBo=l(),Th=a("h3"),$B=a("a"),Vso=a("span"),v(EX.$$.fragment),jCa=l(),Wso=a("span"),PCa=o("AutoModelForSemanticSegmentation"),UBo=l(),kr=a("div"),v(yX.$$.fragment),ZCa=l(),Fh=a("p"),VCa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),A5e=a("a"),WCa=o("from_pretrained()"),JCa=o(" class method or the "),R5e=a("a"),UCa=o("from_config()"),XCa=o(` class
method.`),DCa=l(),CX=a("p"),qCa=o("This class cannot be instantiated directly using "),Jso=a("code"),OCa=o("__init__()"),QCa=o(" (throws an error)."),YCa=l(),Aa=a("div"),v(wX.$$.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 `),k5e=a("a"),twa=o("from_pretrained()"),awa=o(" to load the model weights."),nwa=l(),v(jB.$$.fragment),swa=l(),Eo=a("div"),v(LX.$$.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"),mwa=o("The model class to instantiate is selected based on the "),qso=a("code"),cwa=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"),bwa=o("beit"),vwa=o(" \u2014 "),S5e=a("a"),Mwa=o("BeitForSemanticSegmentation"),Twa=o(" (BEiT model)"),Fwa=l(),ZB=a("li"),zso=a("strong"),Ewa=o("data2vec-vision"),ywa=o(" \u2014 "),x5e=a("a"),Cwa=o("Data2VecVisionForSemanticSegmentation"),wwa=o(" (Data2VecVision model)"),Lwa=l(),VB=a("li"),Hso=a("strong"),Awa=o("dpt"),Rwa=o(" \u2014 "),G5e=a("a"),kwa=o("DPTForSemanticSegmentation"),Swa=o(" (DPT model)"),xwa=l(),WB=a("li"),Kso=a("strong"),Gwa=o("mobilenet_v2"),Bwa=o(" \u2014 "),B5e=a("a"),Iwa=o("MobileNetV2ForSemanticSegmentation"),Nwa=o(" (MobileNetV2 model)"),$wa=l(),JB=a("li"),elo=a("strong"),jwa=o("mobilevit"),Pwa=o(" \u2014 "),I5e=a("a"),Zwa=o("MobileViTForSemanticSegmentation"),Vwa=o(" (MobileViT model)"),Wwa=l(),UB=a("li"),olo=a("strong"),Jwa=o("mobilevitv2"),Uwa=o(" \u2014 "),N5e=a("a"),Xwa=o("MobileViTV2ForSemanticSegmentation"),Dwa=o(" (MobileViTV2 model)"),qwa=l(),XB=a("li"),rlo=a("strong"),Owa=o("segformer"),Qwa=o(" \u2014 "),$5e=a("a"),Ywa=o("SegformerForSemanticSegmentation"),zwa=o(" (SegFormer model)"),Hwa=l(),DB=a("li"),tlo=a("strong"),Kwa=o("upernet"),e6a=o(" \u2014 "),j5e=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(),v(OB.$$.fragment),XBo=l(),yh=a("h3"),QB=a("a"),slo=a("span"),v(AX.$$.fragment),d6a=l(),llo=a("span"),m6a=o("TFAutoModelForSemanticSegmentation"),DBo=l(),Sr=a("div"),v(RX.$$.fragment),c6a=l(),Ch=a("p"),f6a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),P5e=a("a"),h6a=o("from_pretrained()"),g6a=o(" class method or the "),Z5e=a("a"),u6a=o("from_config()"),p6a=o(` class
method.`),_6a=l(),kX=a("p"),b6a=o("This class cannot be instantiated directly using "),ilo=a("code"),v6a=o("__init__()"),M6a=o(" (throws an error)."),T6a=l(),Ra=a("div"),v(SX.$$.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 `),mlo=a("strong"),w6a=o("not"),L6a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),V5e=a("a"),A6a=o("from_pretrained()"),R6a=o(" to load the model weights."),k6a=l(),v(YB.$$.fragment),S6a=l(),wt=a("div"),v(xX.$$.fragment),x6a=l(),clo=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 "),W5e=a("a"),X6a=o("TFData2VecVisionForSemanticSegmentation"),D6a=o(" (Data2VecVision model)"),q6a=l(),HB=a("li"),plo=a("strong"),O6a=o("mobilevit"),Q6a=o(" \u2014 "),J5e=a("a"),Y6a=o("TFMobileViTForSemanticSegmentation"),z6a=o(" (MobileViT model)"),H6a=l(),KB=a("li"),_lo=a("strong"),K6a=o("segformer"),e8a=o(" \u2014 "),U5e=a("a"),o8a=o("TFSegformerForSemanticSegmentation"),r8a=o(" (SegFormer model)"),t8a=l(),v(eI.$$.fragment),qBo=l(),Ah=a("h3"),oI=a("a"),blo=a("span"),v(GX.$$.fragment),a8a=l(),vlo=a("span"),n8a=o("AutoModelForInstanceSegmentation"),OBo=l(),xr=a("div"),v(BX.$$.fragment),s8a=l(),Rh=a("p"),l8a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created
with the `),X5e=a("a"),i8a=o("from_pretrained()"),d8a=o(" class method or the "),D5e=a("a"),m8a=o("from_config()"),c8a=o(` class
method.`),f8a=l(),IX=a("p"),h8a=o("This class cannot be instantiated directly using "),Mlo=a("code"),g8a=o("__init__()"),u8a=o(" (throws an error)."),p8a=l(),ka=a("div"),v(NX.$$.fragment),_8a=l(),Tlo=a("p"),b8a=o("Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),v8a=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 `),q5e=a("a"),E8a=o("from_pretrained()"),y8a=o(" to load the model weights."),C8a=l(),v(rI.$$.fragment),w8a=l(),yo=a("div"),v($X.$$.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 "),O5e=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(),v(nI.$$.fragment),QBo=l(),Sh=a("h3"),sI=a("a"),Slo=a("span"),v(jX.$$.fragment),O8a=l(),xlo=a("span"),Q8a=o("AutoModelForUniversalSegmentation"),YBo=l(),Gr=a("div"),v(PX.$$.fragment),Y8a=l(),xh=a("p"),z8a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a universal image segmentation head) when created
with the `),Q5e=a("a"),H8a=o("from_pretrained()"),K8a=o(" class method or the "),Y5e=a("a"),e7a=o("from_config()"),o7a=o(` class
method.`),r7a=l(),ZX=a("p"),t7a=o("This class cannot be instantiated directly using "),Glo=a("code"),a7a=o("__init__()"),n7a=o(" (throws an error)."),s7a=l(),Sa=a("div"),v(VX.$$.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"),m7a=o(`Note:
Loading a model from its configuration file does `),Ilo=a("strong"),c7a=o("not"),f7a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),z5e=a("a"),h7a=o("from_pretrained()"),g7a=o(" to load the model weights."),u7a=l(),v(lI.$$.fragment),p7a=l(),Co=a("div"),v(WX.$$.fragment),_7a=l(),Nlo=a("p"),b7a=o("Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model."),v7a=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 "),H5e=a("a"),k7a=o("DetrForSegmentation"),S7a=o(" (DETR model)"),x7a=l(),dI=a("li"),Vlo=a("strong"),G7a=o("mask2former"),B7a=o(" \u2014 "),K5e=a("a"),I7a=o("Mask2FormerForUniversalSegmentation"),N7a=o(" (Mask2Former model)"),$7a=l(),mI=a("li"),Wlo=a("strong"),j7a=o("maskformer"),P7a=o(" \u2014 "),e3e=a("a"),Z7a=o("MaskFormerForInstanceSegmentation"),V7a=o(" (MaskFormer model)"),W7a=l(),cI=a("li"),Jlo=a("strong"),J7a=o("oneformer"),U7a=o(" \u2014 "),o3e=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(),v(hI.$$.fragment),zBo=l(),Bh=a("h3"),gI=a("a"),Dlo=a("span"),v(JX.$$.fragment),K7a=l(),qlo=a("span"),eLa=o("AutoModelForZeroShotImageClassification"),HBo=l(),Br=a("div"),v(UX.$$.fragment),oLa=l(),Ih=a("p"),rLa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created
with the `),r3e=a("a"),tLa=o("from_pretrained()"),aLa=o(" class method or the "),t3e=a("a"),nLa=o("from_config()"),sLa=o(` class
method.`),lLa=l(),XX=a("p"),iLa=o("This class cannot be instantiated directly using "),Olo=a("code"),dLa=o("__init__()"),mLa=o(" (throws an error)."),cLa=l(),xa=a("div"),v(DX.$$.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 `),a3e=a("a"),bLa=o("from_pretrained()"),vLa=o(" to load the model weights."),MLa=l(),v(uI.$$.fragment),TLa=l(),wo=a("div"),v(qX.$$.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 "),n3e=a("a"),ILa=o("AlignModel"),NLa=o(" (ALIGN model)"),$La=l(),_I=a("li"),rio=a("strong"),jLa=o("altclip"),PLa=o(" \u2014 "),s3e=a("a"),ZLa=o("AltCLIPModel"),VLa=o(" (AltCLIP model)"),WLa=l(),bI=a("li"),tio=a("strong"),JLa=o("blip"),ULa=o(" \u2014 "),l3e=a("a"),XLa=o("BlipModel"),DLa=o(" (BLIP model)"),qLa=l(),vI=a("li"),aio=a("strong"),OLa=o("chinese_clip"),QLa=o(" \u2014 "),i3e=a("a"),YLa=o("ChineseCLIPModel"),zLa=o(" (Chinese-CLIP model)"),HLa=l(),MI=a("li"),nio=a("strong"),KLa=o("clip"),eAa=o(" \u2014 "),d3e=a("a"),oAa=o("CLIPModel"),rAa=o(" (CLIP model)"),tAa=l(),TI=a("li"),sio=a("strong"),aAa=o("clipseg"),nAa=o(" \u2014 "),m3e=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"),mAa=o("model.eval()"),cAa=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(),v(EI.$$.fragment),KBo=l(),$h=a("h3"),yI=a("a"),dio=a("span"),v(OX.$$.fragment),gAa=l(),mio=a("span"),uAa=o("TFAutoModelForZeroShotImageClassification"),eIo=l(),Nr=a("div"),v(QX.$$.fragment),pAa=l(),jh=a("p"),_Aa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created
with the `),c3e=a("a"),bAa=o("from_pretrained()"),vAa=o(" class method or the "),f3e=a("a"),MAa=o("from_config()"),TAa=o(` class
method.`),FAa=l(),YX=a("p"),EAa=o("This class cannot be instantiated directly using "),cio=a("code"),yAa=o("__init__()"),CAa=o(" (throws an error)."),wAa=l(),Ga=a("div"),v(zX.$$.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 `),h3e=a("a"),GAa=o("from_pretrained()"),BAa=o(" to load the model weights."),IAa=l(),v(CI.$$.fragment),NAa=l(),Lt=a("div"),v(HX.$$.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"),bio=a("strong"),OAa=o("blip"),QAa=o(" \u2014 "),g3e=a("a"),YAa=o("TFBlipModel"),zAa=o(" (BLIP model)"),HAa=l(),LI=a("li"),vio=a("strong"),KAa=o("clip"),eRa=o(" \u2014 "),u3e=a("a"),oRa=o("TFCLIPModel"),rRa=o(" (CLIP model)"),tRa=l(),v(AI.$$.fragment),oIo=l(),Zh=a("h3"),RI=a("a"),Mio=a("span"),v(eD.$$.fragment),aRa=l(),Tio=a("span"),nRa=o("AutoModelForZeroShotObjectDetection"),rIo=l(),$r=a("div"),v(oD.$$.fragment),sRa=l(),Vh=a("p"),lRa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot object detection head) when created
with the `),p3e=a("a"),iRa=o("from_pretrained()"),dRa=o(" class method or the "),_3e=a("a"),mRa=o("from_config()"),cRa=o(` class
method.`),fRa=l(),rD=a("p"),hRa=o("This class cannot be instantiated directly using "),Fio=a("code"),gRa=o("__init__()"),uRa=o(" (throws an error)."),pRa=l(),Ba=a("div"),v(tD.$$.fragment),_Ra=l(),Eio=a("p"),bRa=o("Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration."),vRa=l(),Wh=a("p"),MRa=o(`Note:
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model\u2019s configuration. Use `),b3e=a("a"),ERa=o("from_pretrained()"),yRa=o(" to load the model weights."),CRa=l(),v(kI.$$.fragment),wRa=l(),Lo=a("div"),v(aD.$$.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 "),v3e=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(),v(GI.$$.fragment),tIo=l(),Jh=a("h2"),BI=a("a"),Gio=a("span"),v(nD.$$.fragment),ORa=l(),Bio=a("span"),QRa=o("Audio"),aIo=l(),M3e=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"),v(sD.$$.fragment),zRa=l(),Nio=a("span"),HRa=o("AutoModelForAudioClassification"),sIo=l(),jr=a("div"),v(lD.$$.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 `),T3e=a("a"),oka=o("from_pretrained()"),rka=o(" class method or the "),F3e=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"),v(dD.$$.fragment),mka=l(),jio=a("p"),cka=o("Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),fka=l(),Dh=a("p"),hka=o(`Note:
Loading a model from its configuration file does `),Pio=a("strong"),gka=o("not"),uka=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),E3e=a("a"),pka=o("from_pretrained()"),_ka=o(" to load the model weights."),bka=l(),v(NI.$$.fragment),vka=l(),Ao=a("div"),v(mD.$$.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 "),y3e=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 "),C3e=a("a"),jka=o("Data2VecAudioForSequenceClassification"),Pka=o(" (Data2VecAudio model)"),Zka=l(),PI=a("li"),Dio=a("strong"),Vka=o("hubert"),Wka=o(" \u2014 "),w3e=a("a"),Jka=o("HubertForSequenceClassification"),Uka=o(" (Hubert model)"),Xka=l(),ZI=a("li"),qio=a("strong"),Dka=o("sew"),qka=o(" \u2014 "),L3e=a("a"),Oka=o("SEWForSequenceClassification"),Qka=o(" (SEW model)"),Yka=l(),VI=a("li"),Oio=a("strong"),zka=o("sew-d"),Hka=o(" \u2014 "),A3e=a("a"),Kka=o("SEWDForSequenceClassification"),eSa=o(" (SEW-D model)"),oSa=l(),WI=a("li"),Qio=a("strong"),rSa=o("unispeech"),tSa=o(" \u2014 "),R3e=a("a"),aSa=o("UniSpeechForSequenceClassification"),nSa=o(" (UniSpeech model)"),sSa=l(),JI=a("li"),Yio=a("strong"),lSa=o("unispeech-sat"),iSa=o(" \u2014 "),k3e=a("a"),dSa=o("UniSpeechSatForSequenceClassification"),mSa=o(" (UniSpeechSat model)"),cSa=l(),UI=a("li"),zio=a("strong"),fSa=o("wav2vec2"),hSa=o(" \u2014 "),S3e=a("a"),gSa=o("Wav2Vec2ForSequenceClassification"),uSa=o(" (Wav2Vec2 model)"),pSa=l(),XI=a("li"),Hio=a("strong"),_Sa=o("wav2vec2-conformer"),bSa=o(" \u2014 "),x3e=a("a"),vSa=o("Wav2Vec2ConformerForSequenceClassification"),MSa=o(" (Wav2Vec2-Conformer model)"),TSa=l(),DI=a("li"),Kio=a("strong"),FSa=o("wavlm"),ESa=o(" \u2014 "),G3e=a("a"),ySa=o("WavLMForSequenceClassification"),CSa=o(" (WavLM model)"),wSa=l(),qI=a("li"),edo=a("strong"),LSa=o("whisper"),ASa=o(" \u2014 "),B3e=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(),v(QI.$$.fragment),lIo=l(),qh=a("h3"),YI=a("a"),tdo=a("span"),v(cD.$$.fragment),$Sa=l(),ado=a("span"),jSa=o("AutoModelForAudioFrameClassification"),iIo=l(),Pr=a("div"),v(fD.$$.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"),v(gD.$$.fragment),YSa=l(),sdo=a("p"),zSa=o("Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),HSa=l(),Qh=a("p"),KSa=o(`Note:
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model\u2019s configuration. Use `),$3e=a("a"),rxa=o("from_pretrained()"),txa=o(" to load the model weights."),axa=l(),v(zI.$$.fragment),nxa=l(),At=a("div"),v(uD.$$.fragment),sxa=l(),ido=a("p"),lxa=o("Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),ixa=l(),Bs=a("p"),dxa=o("The model class to instantiate is selected based on the "),ddo=a("code"),mxa=o("model_type"),cxa=o(` property of the config object (either
passed as an argument or loaded from `),mdo=a("code"),fxa=o("pretrained_model_name_or_path"),hxa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),cdo=a("code"),gxa=o("pretrained_model_name_or_path"),uxa=o(":"),pxa=l(),fdo=a("ul"),HI=a("li"),hdo=a("strong"),_xa=o("wav2vec2"),bxa=o(" \u2014 "),j3e=a("a"),vxa=o("TFWav2Vec2ForSequenceClassification"),Mxa=o(" (Wav2Vec2 model)"),Txa=l(),v(KI.$$.fragment),dIo=l(),Yh=a("h3"),eN=a("a"),gdo=a("span"),v(pD.$$.fragment),Fxa=l(),udo=a("span"),Exa=o("TFAutoModelForAudioFrameClassification"),mIo=l(),Zr=a("div"),v(_D.$$.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
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method.`),kxa=l(),bD=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"),v(vD.$$.fragment),Ixa=l(),_do=a("p"),Nxa=o("Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),$xa=l(),Hh=a("p"),jxa=o(`Note:
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model\u2019s configuration. Use `),V3e=a("a"),Vxa=o("from_pretrained()"),Wxa=o(" to load the model weights."),Jxa=l(),v(oN.$$.fragment),Uxa=l(),Ro=a("div"),v(MD.$$.fragment),Xxa=l(),vdo=a("p"),Dxa=o("Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),qxa=l(),Is=a("p"),Oxa=o("The model class to instantiate is selected based on the "),Mdo=a("code"),Qxa=o("model_type"),Yxa=o(` property of the config object (either
passed as an argument or loaded from `),Tdo=a("code"),zxa=o("pretrained_model_name_or_path"),Hxa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Fdo=a("code"),Kxa=o("pretrained_model_name_or_path"),eGa=o(":"),oGa=l(),$t=a("ul"),rN=a("li"),Edo=a("strong"),rGa=o("data2vec-audio"),tGa=o(" \u2014 "),W3e=a("a"),aGa=o("Data2VecAudioForAudioFrameClassification"),nGa=o(" (Data2VecAudio model)"),sGa=l(),tN=a("li"),ydo=a("strong"),lGa=o("unispeech-sat"),iGa=o(" \u2014 "),J3e=a("a"),dGa=o("UniSpeechSatForAudioFrameClassification"),mGa=o(" (UniSpeechSat model)"),cGa=l(),aN=a("li"),Cdo=a("strong"),fGa=o("wav2vec2"),hGa=o(" \u2014 "),U3e=a("a"),gGa=o("Wav2Vec2ForAudioFrameClassification"),uGa=o(" (Wav2Vec2 model)"),pGa=l(),nN=a("li"),wdo=a("strong"),_Ga=o("wav2vec2-conformer"),bGa=o(" \u2014 "),X3e=a("a"),vGa=o("Wav2Vec2ConformerForAudioFrameClassification"),MGa=o(" (Wav2Vec2-Conformer model)"),TGa=l(),sN=a("li"),Ldo=a("strong"),FGa=o("wavlm"),EGa=o(" \u2014 "),D3e=a("a"),yGa=o("WavLMForAudioFrameClassification"),CGa=o(" (WavLM model)"),wGa=l(),lN=a("p"),LGa=o("The model is set in evaluation mode by default using "),Ado=a("code"),AGa=o("model.eval()"),RGa=o(` (so for instance, dropout modules are
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method.`),ZGa=l(),ED=a("p"),VGa=o("This class cannot be instantiated directly using "),xdo=a("code"),WGa=o("__init__()"),JGa=o(" (throws an error)."),UGa=l(),ja=a("div"),v(yD.$$.fragment),XGa=l(),Gdo=a("p"),DGa=o("Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),qGa=l(),og=a("p"),OGa=o(`Note:
Loading a model from its configuration file does `),Bdo=a("strong"),QGa=o("not"),YGa=o(` load the model weights. It only affects the
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wHa=s(jLe);rar=r(wHa,"AutoTokenizer"),wHa.forEach(t),XIo.forEach(t),Yxo=i(c),Zo=n(c,"DIV",{class:!0});var qi=s(Zo);M(rV.$$.fragment,qi),tar=i(qi),tV=n(qi,"P",{});var DIo=s(tV);aar=r(DIo,`This is a generic tokenizer class that will be instantiated as one of the tokenizer classes of the library when
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model)"),SQ.forEach(t),v2r=i(S),ji=n(S,"LI",{});var xQ=s(ji);bke=n(xQ,"STRONG",{});var _nn=s(bke);M2r=r(_nn,"xmod"),_nn.forEach(t),T2r=r(xQ," \u2014 "),Vte=n(xQ,"A",{href:!0});var bnn=s(Vte);F2r=r(bnn,"XLMRobertaTokenizer"),bnn.forEach(t),E2r=r(xQ," or "),Wte=n(xQ,"A",{href:!0});var vnn=s(Wte);y2r=r(vnn,"XLMRobertaTokenizerFast"),vnn.forEach(t),C2r=r(xQ," (X-MOD model)"),xQ.forEach(t),w2r=i(S),Pi=n(S,"LI",{});var GQ=s(Pi);vke=n(GQ,"STRONG",{});var Mnn=s(vke);L2r=r(Mnn,"yoso"),Mnn.forEach(t),A2r=r(GQ," \u2014 "),Jte=n(GQ,"A",{href:!0});var Tnn=s(Jte);R2r=r(Tnn,"AlbertTokenizer"),Tnn.forEach(t),k2r=r(GQ," or "),Ute=n(GQ,"A",{href:!0});var Fnn=s(Ute);S2r=r(Fnn,"AlbertTokenizerFast"),Fnn.forEach(t),x2r=r(GQ," (YOSO model)"),GQ.forEach(t),S.forEach(t),G2r=i(Oi),M(xb.$$.fragment,Oi),Oi.forEach(t),B2r=i(qi),Gb=n(qi,"DIV",{class:!0});var OIo=s(Gb);M(sV.$$.fragment,OIo),I2r=i(OIo),Mke=n(OIo,"P",{});var Enn=s(Mke);N2r=r(Enn,"Register a new tokenizer in this mapping."),Enn.forEach(t),OIo.forEach(t),qi.forEach(t),zxo=i(c),Hm=n(c,"H2",{class:!0});var QIo=s(Hm);Bb=n(QIo,"A",{id:!0,class:!0,href:!0});var ynn=s(Bb);Tke=n(ynn,"SPAN",{});var Cnn=s(Tke);M(lV.$$.fragment,Cnn),Cnn.forEach(t),ynn.forEach(t),$2r=i(QIo),Fke=n(QIo,"SPAN",{});var wnn=s(Fke);j2r=r(wnn,"AutoFeatureExtractor"),wnn.forEach(t),QIo.forEach(t),Hxo=i(c),Vo=n(c,"DIV",{class:!0});var Qi=s(Vo);M(iV.$$.fragment,Qi),P2r=i(Qi),dV=n(Qi,"P",{});var YIo=s(dV);Z2r=r(YIo,`This is a generic feature extractor class that will be instantiated as one of the feature extractor classes of the
library when created with the `),Xte=n(YIo,"A",{href:!0});var Lnn=s(Xte);V2r=r(Lnn,"AutoFeatureExtractor.from_pretrained()"),Lnn.forEach(t),W2r=r(YIo," class method."),YIo.forEach(t),J2r=i(Qi),mV=n(Qi,"P",{});var zIo=s(mV);U2r=r(zIo,"This class cannot be instantiated directly using "),Eke=n(zIo,"CODE",{});var Ann=s(Eke);X2r=r(Ann,"__init__()"),Ann.forEach(t),D2r=r(zIo," (throws an error)."),zIo.forEach(t),q2r=i(Qi),to=n(Qi,"DIV",{class:!0});var za=s(to);M(cV.$$.fragment,za),O2r=i(za),yke=n(za,"P",{});var Rnn=s(yke);Q2r=r(Rnn,"Instantiate one of the feature extractor classes of the library from a pretrained model vocabulary."),Rnn.forEach(t),Y2r=i(za),$n=n(za,"P",{});var Fj=s($n);z2r=r(Fj,"The feature extractor class to instantiate is selected based on the "),Cke=n(Fj,"CODE",{});var knn=s(Cke);H2r=r(knn,"model_type"),knn.forEach(t),K2r=r(Fj,` property of the config object
(either passed as an argument or loaded from `),wke=n(Fj,"CODE",{});var Snn=s(wke);evr=r(Snn,"pretrained_model_name_or_path"),Snn.forEach(t),ovr=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);rvr=r(xnn,"pretrained_model_name_or_path"),xnn.forEach(t),tvr=r(Fj,":"),Fj.forEach(t),avr=i(za),J=n(za,"UL",{});var D=s(J);Ib=n(D,"LI",{});var L1o=s(Ib);Ake=n(L1o,"STRONG",{});var Gnn=s(Ake);nvr=r(Gnn,"audio-spectrogram-transformer"),Gnn.forEach(t),svr=r(L1o," \u2014 "),Dte=n(L1o,"A",{href:!0});var Bnn=s(Dte);lvr=r(Bnn,"ASTFeatureExtractor"),Bnn.forEach(t),ivr=r(L1o," (Audio Spectrogram Transformer model)"),L1o.forEach(t),dvr=i(D),Nb=n(D,"LI",{});var A1o=s(Nb);Rke=n(A1o,"STRONG",{});var Inn=s(Rke);mvr=r(Inn,"beit"),Inn.forEach(t),cvr=r(A1o," \u2014 "),qte=n(A1o,"A",{href:!0});var Nnn=s(qte);fvr=r(Nnn,"BeitFeatureExtractor"),Nnn.forEach(t),hvr=r(A1o," (BEiT model)"),A1o.forEach(t),gvr=i(D),$b=n(D,"LI",{});var R1o=s($b);kke=n(R1o,"STRONG",{});var $nn=s(kke);uvr=r($nn,"chinese_clip"),$nn.forEach(t),pvr=r(R1o," \u2014 "),Ote=n(R1o,"A",{href:!0});var jnn=s(Ote);_vr=r(jnn,"ChineseCLIPFeatureExtractor"),jnn.forEach(t),bvr=r(R1o," (Chinese-CLIP model)"),R1o.forEach(t),vvr=i(D),jb=n(D,"LI",{});var k1o=s(jb);Ske=n(k1o,"STRONG",{});var Pnn=s(Ske);Mvr=r(Pnn,"clap"),Pnn.forEach(t),Tvr=r(k1o," \u2014 "),Qte=n(k1o,"A",{href:!0});var Znn=s(Qte);Fvr=r(Znn,"ClapFeatureExtractor"),Znn.forEach(t),Evr=r(k1o," (CLAP model)"),k1o.forEach(t),yvr=i(D),Pb=n(D,"LI",{});var S1o=s(Pb);xke=n(S1o,"STRONG",{});var Vnn=s(xke);Cvr=r(Vnn,"clip"),Vnn.forEach(t),wvr=r(S1o," \u2014 "),Yte=n(S1o,"A",{href:!0});var Wnn=s(Yte);Lvr=r(Wnn,"CLIPFeatureExtractor"),Wnn.forEach(t),Avr=r(S1o," (CLIP model)"),S1o.forEach(t),Rvr=i(D),Zb=n(D,"LI",{});var x1o=s(Zb);Gke=n(x1o,"STRONG",{});var Jnn=s(Gke);kvr=r(Jnn,"clipseg"),Jnn.forEach(t),Svr=r(x1o," \u2014 "),zte=n(x1o,"A",{href:!0});var Unn=s(zte);xvr=r(Unn,"ViTFeatureExtractor"),Unn.forEach(t),Gvr=r(x1o," (CLIPSeg model)"),x1o.forEach(t),Bvr=i(D),Vb=n(D,"LI",{});var G1o=s(Vb);Bke=n(G1o,"STRONG",{});var Xnn=s(Bke);Ivr=r(Xnn,"conditional_detr"),Xnn.forEach(t),Nvr=r(G1o," \u2014 "),Hte=n(G1o,"A",{href:!0});var Dnn=s(Hte);$vr=r(Dnn,"ConditionalDetrFeatureExtractor"),Dnn.forEach(t),jvr=r(G1o," (Conditional DETR model)"),G1o.forEach(t),Pvr=i(D),Wb=n(D,"LI",{});var B1o=s(Wb);Ike=n(B1o,"STRONG",{});var qnn=s(Ike);Zvr=r(qnn,"convnext"),qnn.forEach(t),Vvr=r(B1o," \u2014 "),Kte=n(B1o,"A",{href:!0});var Onn=s(Kte);Wvr=r(Onn,"ConvNextFeatureExtractor"),Onn.forEach(t),Jvr=r(B1o," (ConvNeXT model)"),B1o.forEach(t),Uvr=i(D),Jb=n(D,"LI",{});var I1o=s(Jb);Nke=n(I1o,"STRONG",{});var Qnn=s(Nke);Xvr=r(Qnn,"cvt"),Qnn.forEach(t),Dvr=r(I1o," \u2014 "),eae=n(I1o,"A",{href:!0});var Ynn=s(eae);qvr=r(Ynn,"ConvNextFeatureExtractor"),Ynn.forEach(t),Ovr=r(I1o," (CvT model)"),I1o.forEach(t),Qvr=i(D),Ub=n(D,"LI",{});var N1o=s(Ub);$ke=n(N1o,"STRONG",{});var znn=s($ke);Yvr=r(znn,"data2vec-audio"),znn.forEach(t),zvr=r(N1o," \u2014 "),oae=n(N1o,"A",{href:!0});var Hnn=s(oae);Hvr=r(Hnn,"Wav2Vec2FeatureExtractor"),Hnn.forEach(t),Kvr=r(N1o," (Data2VecAudio model)"),N1o.forEach(t),eMr=i(D),Xb=n(D,"LI",{});var $1o=s(Xb);jke=n($1o,"STRONG",{});var Knn=s(jke);oMr=r(Knn,"data2vec-vision"),Knn.forEach(t),rMr=r($1o," \u2014 "),rae=n($1o,"A",{href:!0});var esn=s(rae);tMr=r(esn,"BeitFeatureExtractor"),esn.forEach(t),aMr=r($1o," (Data2VecVision model)"),$1o.forEach(t),nMr=i(D),Db=n(D,"LI",{});var j1o=s(Db);Pke=n(j1o,"STRONG",{});var osn=s(Pke);sMr=r(osn,"deformable_detr"),osn.forEach(t),lMr=r(j1o," \u2014 "),tae=n(j1o,"A",{href:!0});var rsn=s(tae);iMr=r(rsn,"DeformableDetrFeatureExtractor"),rsn.forEach(t),dMr=r(j1o," (Deformable DETR model)"),j1o.forEach(t),mMr=i(D),qb=n(D,"LI",{});var P1o=s(qb);Zke=n(P1o,"STRONG",{});var tsn=s(Zke);cMr=r(tsn,"deit"),tsn.forEach(t),fMr=r(P1o," \u2014 "),aae=n(P1o,"A",{href:!0});var asn=s(aae);hMr=r(asn,"DeiTFeatureExtractor"),asn.forEach(t),gMr=r(P1o," (DeiT model)"),P1o.forEach(t),uMr=i(D),Ob=n(D,"LI",{});var 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fsn=s(iae);GMr=r(fsn,"DPTFeatureExtractor"),fsn.forEach(t),BMr=r(J1o," (DPT model)"),J1o.forEach(t),IMr=i(D),Hb=n(D,"LI",{});var U1o=s(Hb);Xke=n(U1o,"STRONG",{});var hsn=s(Xke);NMr=r(hsn,"encodec"),hsn.forEach(t),$Mr=r(U1o," \u2014 "),dae=n(U1o,"A",{href:!0});var gsn=s(dae);jMr=r(gsn,"EncodecFeatureExtractor"),gsn.forEach(t),PMr=r(U1o," (EnCodec model)"),U1o.forEach(t),ZMr=i(D),Kb=n(D,"LI",{});var X1o=s(Kb);Dke=n(X1o,"STRONG",{});var usn=s(Dke);VMr=r(usn,"flava"),usn.forEach(t),WMr=r(X1o," \u2014 "),mae=n(X1o,"A",{href:!0});var psn=s(mae);JMr=r(psn,"FlavaFeatureExtractor"),psn.forEach(t),UMr=r(X1o," (FLAVA model)"),X1o.forEach(t),XMr=i(D),e2=n(D,"LI",{});var D1o=s(e2);qke=n(D1o,"STRONG",{});var _sn=s(qke);DMr=r(_sn,"glpn"),_sn.forEach(t),qMr=r(D1o," \u2014 "),cae=n(D1o,"A",{href:!0});var bsn=s(cae);OMr=r(bsn,"GLPNFeatureExtractor"),bsn.forEach(t),QMr=r(D1o," (GLPN model)"),D1o.forEach(t),YMr=i(D),o2=n(D,"LI",{});var q1o=s(o2);Oke=n(q1o,"STRONG",{});var vsn=s(Oke);zMr=r(vsn,"groupvit"),vsn.forEach(t),HMr=r(q1o," \u2014 "),fae=n(q1o,"A",{href:!0});var Msn=s(fae);KMr=r(Msn,"CLIPFeatureExtractor"),Msn.forEach(t),eTr=r(q1o," (GroupViT model)"),q1o.forEach(t),oTr=i(D),r2=n(D,"LI",{});var O1o=s(r2);Qke=n(O1o,"STRONG",{});var Tsn=s(Qke);rTr=r(Tsn,"hubert"),Tsn.forEach(t),tTr=r(O1o," \u2014 "),hae=n(O1o,"A",{href:!0});var Fsn=s(hae);aTr=r(Fsn,"Wav2Vec2FeatureExtractor"),Fsn.forEach(t),nTr=r(O1o," (Hubert model)"),O1o.forEach(t),sTr=i(D),t2=n(D,"LI",{});var Q1o=s(t2);Yke=n(Q1o,"STRONG",{});var Esn=s(Yke);lTr=r(Esn,"imagegpt"),Esn.forEach(t),iTr=r(Q1o," \u2014 "),gae=n(Q1o,"A",{href:!0});var ysn=s(gae);dTr=r(ysn,"ImageGPTFeatureExtractor"),ysn.forEach(t),mTr=r(Q1o," (ImageGPT model)"),Q1o.forEach(t),cTr=i(D),a2=n(D,"LI",{});var Y1o=s(a2);zke=n(Y1o,"STRONG",{});var Csn=s(zke);fTr=r(Csn,"layoutlmv2"),Csn.forEach(t),hTr=r(Y1o," \u2014 "),uae=n(Y1o,"A",{href:!0});var wsn=s(uae);gTr=r(wsn,"LayoutLMv2FeatureExtractor"),wsn.forEach(t),uTr=r(Y1o," 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Uln=s(zae);xyr=r(Uln,"WhisperFeatureExtractor"),Uln.forEach(t),Gyr=r(xbo," (Whisper model)"),xbo.forEach(t),Byr=i(D),V2=n(D,"LI",{});var Gbo=s(V2);BSe=n(Gbo,"STRONG",{});var Xln=s(BSe);Iyr=r(Xln,"xclip"),Xln.forEach(t),Nyr=r(Gbo," \u2014 "),Hae=n(Gbo,"A",{href:!0});var Dln=s(Hae);$yr=r(Dln,"CLIPFeatureExtractor"),Dln.forEach(t),jyr=r(Gbo," (X-CLIP model)"),Gbo.forEach(t),Pyr=i(D),W2=n(D,"LI",{});var Bbo=s(W2);ISe=n(Bbo,"STRONG",{});var qln=s(ISe);Zyr=r(qln,"yolos"),qln.forEach(t),Vyr=r(Bbo," \u2014 "),Kae=n(Bbo,"A",{href:!0});var Oln=s(Kae);Wyr=r(Oln,"YolosFeatureExtractor"),Oln.forEach(t),Jyr=r(Bbo," (YOLOS model)"),Bbo.forEach(t),D.forEach(t),Uyr=i(za),M(J2.$$.fragment,za),Xyr=i(za),M(U2.$$.fragment,za),za.forEach(t),Dyr=i(Qi),X2=n(Qi,"DIV",{class:!0});var HIo=s(X2);M(fV.$$.fragment,HIo),qyr=i(HIo),NSe=n(HIo,"P",{});var Qln=s(NSe);Oyr=r(Qln,"Register a new feature extractor for this class."),Qln.forEach(t),HIo.forEach(t),Qi.forEach(t),Kxo=i(c),Km=n(c,"H2",{class:!0});var KIo=s(Km);D2=n(KIo,"A",{id:!0,class:!0,href:!0});var Yln=s(D2);$Se=n(Yln,"SPAN",{});var zln=s($Se);M(hV.$$.fragment,zln),zln.forEach(t),Yln.forEach(t),Qyr=i(KIo),jSe=n(KIo,"SPAN",{});var Hln=s(jSe);Yyr=r(Hln,"AutoImageProcessor"),Hln.forEach(t),KIo.forEach(t),eGo=i(c),Wo=n(c,"DIV",{class:!0});var Yi=s(Wo);M(gV.$$.fragment,Yi),zyr=i(Yi),uV=n(Yi,"P",{});var eNo=s(uV);Hyr=r(eNo,`This is a generic image processor class that will be instantiated as one of the image processor classes of the
library when created with the `),ene=n(eNo,"A",{href:!0});var Kln=s(ene);Kyr=r(Kln,"AutoImageProcessor.from_pretrained()"),Kln.forEach(t),e0r=r(eNo," class method."),eNo.forEach(t),o0r=i(Yi),pV=n(Yi,"P",{});var oNo=s(pV);r0r=r(oNo,"This class cannot be instantiated directly using "),PSe=n(oNo,"CODE",{});var ein=s(PSe);t0r=r(ein,"__init__()"),ein.forEach(t),a0r=r(oNo," (throws an error)."),oNo.forEach(t),n0r=i(Yi),ao=n(Yi,"DIV",{class:!0});var Ha=s(ao);M(_V.$$.fragment,Ha),s0r=i(Ha),ZSe=n(Ha,"P",{});var oin=s(ZSe);l0r=r(oin,"Instantiate one of the image processor classes of the library from a pretrained model vocabulary."),oin.forEach(t),i0r=i(Ha),jn=n(Ha,"P",{});var Ej=s(jn);d0r=r(Ej,"The image processor class to instantiate is selected based on the "),VSe=n(Ej,"CODE",{});var rin=s(VSe);m0r=r(rin,"model_type"),rin.forEach(t),c0r=r(Ej,` property of the config object
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xmn=s(gse);kwr=r(xmn,"ViTImageProcessor"),xmn.forEach(t),Swr=r(O2o," (ViTMSN model)"),O2o.forEach(t),xwr=i($),lM=n($,"LI",{});var Q2o=s(lM);aGe=n(Q2o,"STRONG",{});var Gmn=s(aGe);Gwr=r(Gmn,"xclip"),Gmn.forEach(t),Bwr=r(Q2o," \u2014 "),use=n(Q2o,"A",{href:!0});var Bmn=s(use);Iwr=r(Bmn,"CLIPImageProcessor"),Bmn.forEach(t),Nwr=r(Q2o," (X-CLIP model)"),Q2o.forEach(t),$wr=i($),iM=n($,"LI",{});var Y2o=s(iM);nGe=n(Y2o,"STRONG",{});var Imn=s(nGe);jwr=r(Imn,"yolos"),Imn.forEach(t),Pwr=r(Y2o," \u2014 "),pse=n(Y2o,"A",{href:!0});var Nmn=s(pse);Zwr=r(Nmn,"YolosImageProcessor"),Nmn.forEach(t),Vwr=r(Y2o," (YOLOS model)"),Y2o.forEach(t),$.forEach(t),Wwr=i(Ha),M(dM.$$.fragment,Ha),Jwr=i(Ha),M(mM.$$.fragment,Ha),Ha.forEach(t),Uwr=i(Yi),cM=n(Yi,"DIV",{class:!0});var rNo=s(cM);M(bV.$$.fragment,rNo),Xwr=i(rNo),sGe=n(rNo,"P",{});var $mn=s(sGe);Dwr=r($mn,"Register a new image processor for this class."),$mn.forEach(t),rNo.forEach(t),Yi.forEach(t),oGo=i(c),ec=n(c,"H2",{class:!0});var 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bvo=s(BM);BGe=n(bvo,"STRONG",{});var Bcn=s(BGe);m7r=r(Bcn,"oneformer"),Bcn.forEach(t),c7r=r(bvo," \u2014 "),Pse=n(bvo,"A",{href:!0});var Icn=s(Pse);f7r=r(Icn,"OneFormerProcessor"),Icn.forEach(t),h7r=r(bvo," (OneFormer model)"),bvo.forEach(t),g7r=i(re),IM=n(re,"LI",{});var vvo=s(IM);IGe=n(vvo,"STRONG",{});var Ncn=s(IGe);u7r=r(Ncn,"owlvit"),Ncn.forEach(t),p7r=r(vvo," \u2014 "),Zse=n(vvo,"A",{href:!0});var $cn=s(Zse);_7r=r($cn,"OwlViTProcessor"),$cn.forEach(t),b7r=r(vvo," (OWL-ViT model)"),vvo.forEach(t),v7r=i(re),NM=n(re,"LI",{});var Mvo=s(NM);NGe=n(Mvo,"STRONG",{});var jcn=s(NGe);M7r=r(jcn,"pix2struct"),jcn.forEach(t),T7r=r(Mvo," \u2014 "),Vse=n(Mvo,"A",{href:!0});var Pcn=s(Vse);F7r=r(Pcn,"Pix2StructProcessor"),Pcn.forEach(t),E7r=r(Mvo," (Pix2Struct model)"),Mvo.forEach(t),y7r=i(re),$M=n(re,"LI",{});var Tvo=s($M);$Ge=n(Tvo,"STRONG",{});var Zcn=s($Ge);C7r=r(Zcn,"sam"),Zcn.forEach(t),w7r=r(Tvo," \u2014 "),Wse=n(Tvo,"A",{href:!0});var Vcn=s(Wse);L7r=r(Vcn,"SamProcessor"),Vcn.forEach(t),A7r=r(Tvo," (SAM model)"),Tvo.forEach(t),R7r=i(re),jM=n(re,"LI",{});var Fvo=s(jM);jGe=n(Fvo,"STRONG",{});var Wcn=s(jGe);k7r=r(Wcn,"sew"),Wcn.forEach(t),S7r=r(Fvo," \u2014 "),Jse=n(Fvo,"A",{href:!0});var Jcn=s(Jse);x7r=r(Jcn,"Wav2Vec2Processor"),Jcn.forEach(t),G7r=r(Fvo," (SEW model)"),Fvo.forEach(t),B7r=i(re),PM=n(re,"LI",{});var Evo=s(PM);PGe=n(Evo,"STRONG",{});var Ucn=s(PGe);I7r=r(Ucn,"sew-d"),Ucn.forEach(t),N7r=r(Evo," \u2014 "),Use=n(Evo,"A",{href:!0});var Xcn=s(Use);$7r=r(Xcn,"Wav2Vec2Processor"),Xcn.forEach(t),j7r=r(Evo," (SEW-D model)"),Evo.forEach(t),P7r=i(re),ZM=n(re,"LI",{});var yvo=s(ZM);ZGe=n(yvo,"STRONG",{});var Dcn=s(ZGe);Z7r=r(Dcn,"speech_to_text"),Dcn.forEach(t),V7r=r(yvo," \u2014 "),Xse=n(yvo,"A",{href:!0});var qcn=s(Xse);W7r=r(qcn,"Speech2TextProcessor"),qcn.forEach(t),J7r=r(yvo," (Speech2Text model)"),yvo.forEach(t),U7r=i(re),VM=n(re,"LI",{});var Cvo=s(VM);VGe=n(Cvo,"STRONG",{});var Ocn=s(VGe);X7r=r(Ocn,"speech_to_text_2"),Ocn.forEach(t),D7r=r(Cvo," \u2014 "),Dse=n(Cvo,"A",{href:!0});var Qcn=s(Dse);q7r=r(Qcn,"Speech2Text2Processor"),Qcn.forEach(t),O7r=r(Cvo," (Speech2Text2 model)"),Cvo.forEach(t),Q7r=i(re),WM=n(re,"LI",{});var wvo=s(WM);WGe=n(wvo,"STRONG",{});var Ycn=s(WGe);Y7r=r(Ycn,"speecht5"),Ycn.forEach(t),z7r=r(wvo," \u2014 "),qse=n(wvo,"A",{href:!0});var zcn=s(qse);H7r=r(zcn,"SpeechT5Processor"),zcn.forEach(t),K7r=r(wvo," (SpeechT5 model)"),wvo.forEach(t),eLr=i(re),JM=n(re,"LI",{});var Lvo=s(JM);JGe=n(Lvo,"STRONG",{});var Hcn=s(JGe);oLr=r(Hcn,"trocr"),Hcn.forEach(t),rLr=r(Lvo," \u2014 "),Ose=n(Lvo,"A",{href:!0});var Kcn=s(Ose);tLr=r(Kcn,"TrOCRProcessor"),Kcn.forEach(t),aLr=r(Lvo," (TrOCR model)"),Lvo.forEach(t),nLr=i(re),UM=n(re,"LI",{});var Avo=s(UM);UGe=n(Avo,"STRONG",{});var efn=s(UGe);sLr=r(efn,"tvlt"),efn.forEach(t),lLr=r(Avo," \u2014 "),Qse=n(Avo,"A",{href:!0});var ofn=s(Qse);iLr=r(ofn,"TvltProcessor"),ofn.forEach(t),dLr=r(Avo," (TVLT model)"),Avo.forEach(t),mLr=i(re),XM=n(re,"LI",{});var Rvo=s(XM);XGe=n(Rvo,"STRONG",{});var rfn=s(XGe);cLr=r(rfn,"unispeech"),rfn.forEach(t),fLr=r(Rvo," \u2014 "),Yse=n(Rvo,"A",{href:!0});var tfn=s(Yse);hLr=r(tfn,"Wav2Vec2Processor"),tfn.forEach(t),gLr=r(Rvo," (UniSpeech model)"),Rvo.forEach(t),uLr=i(re),DM=n(re,"LI",{});var kvo=s(DM);DGe=n(kvo,"STRONG",{});var afn=s(DGe);pLr=r(afn,"unispeech-sat"),afn.forEach(t),_Lr=r(kvo," \u2014 "),zse=n(kvo,"A",{href:!0});var nfn=s(zse);bLr=r(nfn,"Wav2Vec2Processor"),nfn.forEach(t),vLr=r(kvo," (UniSpeechSat model)"),kvo.forEach(t),MLr=i(re),qM=n(re,"LI",{});var Svo=s(qM);qGe=n(Svo,"STRONG",{});var sfn=s(qGe);TLr=r(sfn,"vilt"),sfn.forEach(t),FLr=r(Svo," \u2014 "),Hse=n(Svo,"A",{href:!0});var lfn=s(Hse);ELr=r(lfn,"ViltProcessor"),lfn.forEach(t),yLr=r(Svo," (ViLT model)"),Svo.forEach(t),CLr=i(re),OM=n(re,"LI",{});var xvo=s(OM);OGe=n(xvo,"STRONG",{});var ifn=s(OGe);wLr=r(ifn,"vision-text-dual-encoder"),ifn.forEach(t),LLr=r(xvo," \u2014 "),Kse=n(xvo,"A",{href:!0});var dfn=s(Kse);ALr=r(dfn,"VisionTextDualEncoderProcessor"),dfn.forEach(t),RLr=r(xvo," (VisionTextDualEncoder model)"),xvo.forEach(t),kLr=i(re),QM=n(re,"LI",{});var Gvo=s(QM);QGe=n(Gvo,"STRONG",{});var mfn=s(QGe);SLr=r(mfn,"wav2vec2"),mfn.forEach(t),xLr=r(Gvo," \u2014 "),ele=n(Gvo,"A",{href:!0});var cfn=s(ele);GLr=r(cfn,"Wav2Vec2Processor"),cfn.forEach(t),BLr=r(Gvo," (Wav2Vec2 model)"),Gvo.forEach(t),ILr=i(re),YM=n(re,"LI",{});var Bvo=s(YM);YGe=n(Bvo,"STRONG",{});var ffn=s(YGe);NLr=r(ffn,"wav2vec2-conformer"),ffn.forEach(t),$Lr=r(Bvo," \u2014 "),ole=n(Bvo,"A",{href:!0});var hfn=s(ole);jLr=r(hfn,"Wav2Vec2Processor"),hfn.forEach(t),PLr=r(Bvo," (Wav2Vec2-Conformer model)"),Bvo.forEach(t),ZLr=i(re),zM=n(re,"LI",{});var Ivo=s(zM);zGe=n(Ivo,"STRONG",{});var gfn=s(zGe);VLr=r(gfn,"wavlm"),gfn.forEach(t),WLr=r(Ivo," \u2014 "),rle=n(Ivo,"A",{href:!0});var ufn=s(rle);JLr=r(ufn,"Wav2Vec2Processor"),ufn.forEach(t),ULr=r(Ivo," (WavLM model)"),Ivo.forEach(t),XLr=i(re),HM=n(re,"LI",{});var Nvo=s(HM);HGe=n(Nvo,"STRONG",{});var pfn=s(HGe);DLr=r(pfn,"whisper"),pfn.forEach(t),qLr=r(Nvo," \u2014 "),tle=n(Nvo,"A",{href:!0});var _fn=s(tle);OLr=r(_fn,"WhisperProcessor"),_fn.forEach(t),QLr=r(Nvo," (Whisper model)"),Nvo.forEach(t),YLr=i(re),KM=n(re,"LI",{});var $vo=s(KM);KGe=n($vo,"STRONG",{});var bfn=s(KGe);zLr=r(bfn,"xclip"),bfn.forEach(t),HLr=r($vo," \u2014 "),ale=n($vo,"A",{href:!0});var vfn=s(ale);KLr=r(vfn,"XCLIPProcessor"),vfn.forEach(t),eAr=r($vo," (X-CLIP model)"),$vo.forEach(t),re.forEach(t),oAr=i(Ka),M(eT.$$.fragment,Ka),rAr=i(Ka),M(oT.$$.fragment,Ka),Ka.forEach(t),tAr=i(zi),rT=n(zi,"DIV",{class:!0});var sNo=s(rT);M(yV.$$.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(c),rc=n(c,"H2",{class:!0});var lNo=s(rc);tT=n(lNo,"A",{id:!0,class:!0,href:!0});var Tfn=s(tT);oBe=n(Tfn,"SPAN",{});var Ffn=s(oBe);M(CV.$$.fragment,Ffn),Ffn.forEach(t),Tfn.forEach(t),sAr=i(lNo),rBe=n(lNo,"SPAN",{});var Efn=s(rBe);lAr=r(Efn,"Generic model classes"),Efn.forEach(t),lNo.forEach(t),aGo=i(c),nle=n(c,"P",{});var yfn=s(nle);iAr=r(yfn,"The following auto classes are available for instantiating a base model class without a specific head."),yfn.forEach(t),nGo=i(c),tc=n(c,"H3",{class:!0});var iNo=s(tc);aT=n(iNo,"A",{id:!0,class:!0,href:!0});var Cfn=s(aT);tBe=n(Cfn,"SPAN",{});var wfn=s(tBe);M(wV.$$.fragment,wfn),wfn.forEach(t),Cfn.forEach(t),dAr=i(iNo),aBe=n(iNo,"SPAN",{});var Lfn=s(aBe);mAr=r(Lfn,"AutoModel"),Lfn.forEach(t),iNo.forEach(t),sGo=i(c),Uo=n(c,"DIV",{class:!0});var Hi=s(Uo);M(LV.$$.fragment,Hi),cAr=i(Hi),ac=n(Hi,"P",{});var v4e=s(ac);fAr=r(v4e,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),sle=n(v4e,"A",{href:!0});var Afn=s(sle);hAr=r(Afn,"from_pretrained()"),Afn.forEach(t),gAr=r(v4e," class method or the "),lle=n(v4e,"A",{href:!0});var Rfn=s(lle);uAr=r(Rfn,"from_config()"),Rfn.forEach(t),pAr=r(v4e,` class
method.`),v4e.forEach(t),_Ar=i(Hi),AV=n(Hi,"P",{});var dNo=s(AV);bAr=r(dNo,"This class cannot be instantiated directly using "),nBe=n(dNo,"CODE",{});var kfn=s(nBe);vAr=r(kfn,"__init__()"),kfn.forEach(t),MAr=r(dNo," (throws an error)."),dNo.forEach(t),TAr=i(Hi),Wt=n(Hi,"DIV",{class:!0});var yj=s(Wt);M(RV.$$.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),nc=n(yj,"P",{});var M4e=s(nc);CAr=r(M4e,`Note:
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model\u2019s configuration. Use `),ile=n(M4e,"A",{href:!0});var Gfn=s(ile);AAr=r(Gfn,"from_pretrained()"),Gfn.forEach(t),RAr=r(M4e," to load the model weights."),M4e.forEach(t),kAr=i(yj),M(nT.$$.fragment,yj),yj.forEach(t),SAr=i(Hi),so=n(Hi,"DIV",{class:!0});var en=s(so);M(kV.$$.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 `),mBe=n(Cj,"CODE",{});var Nfn=s(mBe);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 `),cBe=n(Cj,"CODE",{});var $fn=s(cBe);ZAr=r($fn,"pretrained_model_name_or_path"),$fn.forEach(t),VAr=r(Cj,":"),Cj.forEach(t),WAr=i(en),A=n(en,"UL",{});var R=s(A);sT=n(R,"LI",{});var jvo=s(sT);fBe=n(jvo,"STRONG",{});var jfn=s(fBe);JAr=r(jfn,"albert"),jfn.forEach(t),UAr=r(jvo," \u2014 "),dle=n(jvo,"A",{href:!0});var Pfn=s(dle);XAr=r(Pfn,"AlbertModel"),Pfn.forEach(t),DAr=r(jvo," (ALBERT model)"),jvo.forEach(t),qAr=i(R),lT=n(R,"LI",{});var Pvo=s(lT);hBe=n(Pvo,"STRONG",{});var Zfn=s(hBe);OAr=r(Zfn,"align"),Zfn.forEach(t),QAr=r(Pvo," \u2014 "),mle=n(Pvo,"A",{href:!0});var Vfn=s(mle);YAr=r(Vfn,"AlignModel"),Vfn.forEach(t),zAr=r(Pvo," (ALIGN model)"),Pvo.forEach(t),HAr=i(R),iT=n(R,"LI",{});var Zvo=s(iT);gBe=n(Zvo,"STRONG",{});var Wfn=s(gBe);KAr=r(Wfn,"altclip"),Wfn.forEach(t),eRr=r(Zvo," \u2014 "),cle=n(Zvo,"A",{href:!0});var Jfn=s(cle);oRr=r(Jfn,"AltCLIPModel"),Jfn.forEach(t),rRr=r(Zvo," (AltCLIP model)"),Zvo.forEach(t),tRr=i(R),dT=n(R,"LI",{});var 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QMn=s(afe);vet=r(QMn,"FlaxBlenderbotSmallModel"),QMn.forEach(t),Met=r(Lyo," (BlenderbotSmall model)"),Lyo.forEach(t),Tet=i(ie),r5=n(ie,"LI",{});var Ayo=s(r5);wPe=n(Ayo,"STRONG",{});var YMn=s(wPe);Fet=r(YMn,"bloom"),YMn.forEach(t),Eet=r(Ayo," \u2014 "),nfe=n(Ayo,"A",{href:!0});var zMn=s(nfe);yet=r(zMn,"FlaxBloomModel"),zMn.forEach(t),Cet=r(Ayo," (BLOOM model)"),Ayo.forEach(t),wet=i(ie),t5=n(ie,"LI",{});var Ryo=s(t5);LPe=n(Ryo,"STRONG",{});var HMn=s(LPe);Let=r(HMn,"clip"),HMn.forEach(t),Aet=r(Ryo," \u2014 "),sfe=n(Ryo,"A",{href:!0});var KMn=s(sfe);Ret=r(KMn,"FlaxCLIPModel"),KMn.forEach(t),ket=r(Ryo," (CLIP model)"),Ryo.forEach(t),Set=i(ie),a5=n(ie,"LI",{});var kyo=s(a5);APe=n(kyo,"STRONG",{});var eTn=s(APe);xet=r(eTn,"distilbert"),eTn.forEach(t),Get=r(kyo," \u2014 "),lfe=n(kyo,"A",{href:!0});var oTn=s(lfe);Bet=r(oTn,"FlaxDistilBertModel"),oTn.forEach(t),Iet=r(kyo," (DistilBERT model)"),kyo.forEach(t),Net=i(ie),n5=n(ie,"LI",{});var Syo=s(n5);RPe=n(Syo,"STRONG",{});var rTn=s(RPe);$et=r(rTn,"electra"),rTn.forEach(t),jet=r(Syo," \u2014 "),ife=n(Syo,"A",{href:!0});var tTn=s(ife);Pet=r(tTn,"FlaxElectraModel"),tTn.forEach(t),Zet=r(Syo," (ELECTRA model)"),Syo.forEach(t),Vet=i(ie),s5=n(ie,"LI",{});var xyo=s(s5);kPe=n(xyo,"STRONG",{});var aTn=s(kPe);Wet=r(aTn,"gpt-sw3"),aTn.forEach(t),Jet=r(xyo," \u2014 "),dfe=n(xyo,"A",{href:!0});var nTn=s(dfe);Uet=r(nTn,"FlaxGPT2Model"),nTn.forEach(t),Xet=r(xyo," (GPT-Sw3 model)"),xyo.forEach(t),Det=i(ie),l5=n(ie,"LI",{});var Gyo=s(l5);SPe=n(Gyo,"STRONG",{});var sTn=s(SPe);qet=r(sTn,"gpt2"),sTn.forEach(t),Oet=r(Gyo," \u2014 "),mfe=n(Gyo,"A",{href:!0});var lTn=s(mfe);Qet=r(lTn,"FlaxGPT2Model"),lTn.forEach(t),Yet=r(Gyo," (OpenAI GPT-2 model)"),Gyo.forEach(t),zet=i(ie),i5=n(ie,"LI",{});var Byo=s(i5);xPe=n(Byo,"STRONG",{});var iTn=s(xPe);Het=r(iTn,"gpt_neo"),iTn.forEach(t),Ket=r(Byo," \u2014 "),cfe=n(Byo,"A",{href:!0});var dTn=s(cfe);eot=r(dTn,"FlaxGPTNeoModel"),dTn.forEach(t),oot=r(Byo," (GPT Neo 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_Tn=s(ufe);Mot=r(_Tn,"FlaxMBartModel"),_Tn.forEach(t),Tot=r(jyo," (mBART model)"),jyo.forEach(t),Fot=i(ie),h5=n(ie,"LI",{});var Pyo=s(h5);$Pe=n(Pyo,"STRONG",{});var bTn=s($Pe);Eot=r(bTn,"mt5"),bTn.forEach(t),yot=r(Pyo," \u2014 "),pfe=n(Pyo,"A",{href:!0});var vTn=s(pfe);Cot=r(vTn,"FlaxMT5Model"),vTn.forEach(t),wot=r(Pyo," (MT5 model)"),Pyo.forEach(t),Lot=i(ie),g5=n(ie,"LI",{});var Zyo=s(g5);jPe=n(Zyo,"STRONG",{});var MTn=s(jPe);Aot=r(MTn,"opt"),MTn.forEach(t),Rot=r(Zyo," \u2014 "),_fe=n(Zyo,"A",{href:!0});var TTn=s(_fe);kot=r(TTn,"FlaxOPTModel"),TTn.forEach(t),Sot=r(Zyo," (OPT model)"),Zyo.forEach(t),xot=i(ie),u5=n(ie,"LI",{});var Vyo=s(u5);PPe=n(Vyo,"STRONG",{});var FTn=s(PPe);Got=r(FTn,"pegasus"),FTn.forEach(t),Bot=r(Vyo," \u2014 "),bfe=n(Vyo,"A",{href:!0});var ETn=s(bfe);Iot=r(ETn,"FlaxPegasusModel"),ETn.forEach(t),Not=r(Vyo," (Pegasus model)"),Vyo.forEach(t),$ot=i(ie),p5=n(ie,"LI",{});var Wyo=s(p5);ZPe=n(Wyo,"STRONG",{});var yTn=s(ZPe);jot=r(yTn,"regnet"),yTn.forEach(t),Pot=r(Wyo," \u2014 "),vfe=n(Wyo,"A",{href:!0});var CTn=s(vfe);Zot=r(CTn,"FlaxRegNetModel"),CTn.forEach(t),Vot=r(Wyo," (RegNet model)"),Wyo.forEach(t),Wot=i(ie),_5=n(ie,"LI",{});var Jyo=s(_5);VPe=n(Jyo,"STRONG",{});var wTn=s(VPe);Jot=r(wTn,"resnet"),wTn.forEach(t),Uot=r(Jyo," \u2014 "),Mfe=n(Jyo,"A",{href:!0});var LTn=s(Mfe);Xot=r(LTn,"FlaxResNetModel"),LTn.forEach(t),Dot=r(Jyo," (ResNet model)"),Jyo.forEach(t),qot=i(ie),b5=n(ie,"LI",{});var Uyo=s(b5);WPe=n(Uyo,"STRONG",{});var ATn=s(WPe);Oot=r(ATn,"roberta"),ATn.forEach(t),Qot=r(Uyo," \u2014 "),Tfe=n(Uyo,"A",{href:!0});var RTn=s(Tfe);Yot=r(RTn,"FlaxRobertaModel"),RTn.forEach(t),zot=r(Uyo," (RoBERTa model)"),Uyo.forEach(t),Hot=i(ie),v5=n(ie,"LI",{});var Xyo=s(v5);JPe=n(Xyo,"STRONG",{});var kTn=s(JPe);Kot=r(kTn,"roberta-prelayernorm"),kTn.forEach(t),ert=r(Xyo," \u2014 "),Ffe=n(Xyo,"A",{href:!0});var STn=s(Ffe);ort=r(STn,"FlaxRobertaPreLayerNormModel"),STn.forEach(t),rrt=r(Xyo," (RoBERTa-PreLayerNorm model)"),Xyo.forEach(t),trt=i(ie),M5=n(ie,"LI",{});var Dyo=s(M5);UPe=n(Dyo,"STRONG",{});var xTn=s(UPe);art=r(xTn,"roformer"),xTn.forEach(t),nrt=r(Dyo," \u2014 "),Efe=n(Dyo,"A",{href:!0});var GTn=s(Efe);srt=r(GTn,"FlaxRoFormerModel"),GTn.forEach(t),lrt=r(Dyo," (RoFormer model)"),Dyo.forEach(t),irt=i(ie),T5=n(ie,"LI",{});var qyo=s(T5);XPe=n(qyo,"STRONG",{});var BTn=s(XPe);drt=r(BTn,"t5"),BTn.forEach(t),mrt=r(qyo," \u2014 "),yfe=n(qyo,"A",{href:!0});var ITn=s(yfe);crt=r(ITn,"FlaxT5Model"),ITn.forEach(t),frt=r(qyo," (T5 model)"),qyo.forEach(t),hrt=i(ie),F5=n(ie,"LI",{});var Oyo=s(F5);DPe=n(Oyo,"STRONG",{});var NTn=s(DPe);grt=r(NTn,"vision-text-dual-encoder"),NTn.forEach(t),urt=r(Oyo," \u2014 "),Cfe=n(Oyo,"A",{href:!0});var $Tn=s(Cfe);prt=r($Tn,"FlaxVisionTextDualEncoderModel"),$Tn.forEach(t),_rt=r(Oyo," (VisionTextDualEncoder model)"),Oyo.forEach(t),brt=i(ie),E5=n(ie,"LI",{});var Qyo=s(E5);qPe=n(Qyo,"STRONG",{});var jTn=s(qPe);vrt=r(jTn,"vit"),jTn.forEach(t),Mrt=r(Qyo," \u2014 "),wfe=n(Qyo,"A",{href:!0});var PTn=s(wfe);Trt=r(PTn,"FlaxViTModel"),PTn.forEach(t),Frt=r(Qyo," (ViT model)"),Qyo.forEach(t),Ert=i(ie),y5=n(ie,"LI",{});var Yyo=s(y5);OPe=n(Yyo,"STRONG",{});var ZTn=s(OPe);yrt=r(ZTn,"wav2vec2"),ZTn.forEach(t),Crt=r(Yyo," \u2014 "),Lfe=n(Yyo,"A",{href:!0});var VTn=s(Lfe);wrt=r(VTn,"FlaxWav2Vec2Model"),VTn.forEach(t),Lrt=r(Yyo," (Wav2Vec2 model)"),Yyo.forEach(t),Art=i(ie),C5=n(ie,"LI",{});var zyo=s(C5);QPe=n(zyo,"STRONG",{});var WTn=s(QPe);Rrt=r(WTn,"whisper"),WTn.forEach(t),krt=r(zyo," \u2014 "),Afe=n(zyo,"A",{href:!0});var JTn=s(Afe);Srt=r(JTn,"FlaxWhisperModel"),JTn.forEach(t),xrt=r(zyo," (Whisper model)"),zyo.forEach(t),Grt=i(ie),w5=n(ie,"LI",{});var Hyo=s(w5);YPe=n(Hyo,"STRONG",{});var UTn=s(YPe);Brt=r(UTn,"xglm"),UTn.forEach(t),Irt=r(Hyo," \u2014 "),Rfe=n(Hyo,"A",{href:!0});var XTn=s(Rfe);Nrt=r(XTn,"FlaxXGLMModel"),XTn.forEach(t),$rt=r(Hyo," (XGLM model)"),Hyo.forEach(t),jrt=i(ie),L5=n(ie,"LI",{});var Kyo=s(L5);zPe=n(Kyo,"STRONG",{});var DTn=s(zPe);Prt=r(DTn,"xlm-roberta"),DTn.forEach(t),Zrt=r(Kyo," \u2014 "),kfe=n(Kyo,"A",{href:!0});var qTn=s(kfe);Vrt=r(qTn,"FlaxXLMRobertaModel"),qTn.forEach(t),Wrt=r(Kyo," (XLM-RoBERTa model)"),Kyo.forEach(t),ie.forEach(t),Jrt=i(rd),M(A5.$$.fragment,rd),rd.forEach(t),od.forEach(t),cGo=i(c),fc=n(c,"H2",{class:!0});var gNo=s(fc);R5=n(gNo,"A",{id:!0,class:!0,href:!0});var OTn=s(R5);HPe=n(OTn,"SPAN",{});var QTn=s(HPe);M(VV.$$.fragment,QTn),QTn.forEach(t),OTn.forEach(t),Urt=i(gNo),KPe=n(gNo,"SPAN",{});var YTn=s(KPe);Xrt=r(YTn,"Generic pretraining classes"),YTn.forEach(t),gNo.forEach(t),fGo=i(c),Sfe=n(c,"P",{});var zTn=s(Sfe);Drt=r(zTn,"The following auto classes are available for instantiating a model with a pretraining head."),zTn.forEach(t),hGo=i(c),hc=n(c,"H3",{class:!0});var uNo=s(hc);k5=n(uNo,"A",{id:!0,class:!0,href:!0});var HTn=s(k5);eZe=n(HTn,"SPAN",{});var KTn=s(eZe);M(WV.$$.fragment,KTn),KTn.forEach(t),HTn.forEach(t),qrt=i(uNo),oZe=n(uNo,"SPAN",{});var eFn=s(oZe);Ort=r(eFn,"AutoModelForPreTraining"),eFn.forEach(t),uNo.forEach(t),gGo=i(c),qo=n(c,"DIV",{class:!0});var td=s(qo);M(JV.$$.fragment,td),Qrt=i(td),gc=n(td,"P",{});var C4e=s(gc);Yrt=r(C4e,`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(C4e,"A",{href:!0});var oFn=s(xfe);zrt=r(oFn,"from_pretrained()"),oFn.forEach(t),Hrt=r(C4e," class method or the "),Gfe=n(C4e,"A",{href:!0});var rFn=s(Gfe);Krt=r(rFn,"from_config()"),rFn.forEach(t),ett=r(C4e,` class
method.`),C4e.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 tFn=s(rZe);ttt=r(tFn,"__init__()"),tFn.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.$$.fragment,kj),stt=i(kj),tZe=n(kj,"P",{});var aFn=s(tZe);ltt=r(aFn,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),aFn.forEach(t),itt=i(kj),uc=n(kj,"P",{});var w4e=s(uc);dtt=r(w4e,`Note:
Loading a model from its configuration file does `),aZe=n(w4e,"STRONG",{});var nFn=s(aZe);mtt=r(nFn,"not"),nFn.forEach(t),ctt=r(w4e,` load the model weights. It only affects the
model\u2019s configuration. Use `),Bfe=n(w4e,"A",{href:!0});var sFn=s(Bfe);ftt=r(sFn,"from_pretrained()"),sFn.forEach(t),htt=r(w4e," to load the model weights."),w4e.forEach(t),gtt=i(kj),M(S5.$$.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 lFn=s(nZe);_tt=r(lFn,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),lFn.forEach(t),btt=i(on),Wn=n(on,"P",{});var Sj=s(Wn);vtt=r(Sj,"The model class to instantiate is selected based on the "),sZe=n(Sj,"CODE",{});var iFn=s(sZe);Mtt=r(iFn,"model_type"),iFn.forEach(t),Ttt=r(Sj,` property of the config object (either
passed as an argument or loaded from `),lZe=n(Sj,"CODE",{});var dFn=s(lZe);Ftt=r(dFn,"pretrained_model_name_or_path"),dFn.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 mFn=s(iZe);ytt=r(mFn,"pretrained_model_name_or_path"),mFn.forEach(t),Ctt=r(Sj,":"),Sj.forEach(t),wtt=i(on),U=n(on,"UL",{});var q=s(U);x5=n(q,"LI",{});var e0o=s(x5);dZe=n(e0o,"STRONG",{});var cFn=s(dZe);Ltt=r(cFn,"albert"),cFn.forEach(t),Att=r(e0o," \u2014 "),Ife=n(e0o,"A",{href:!0});var fFn=s(Ife);Rtt=r(fFn,"AlbertForPreTraining"),fFn.forEach(t),ktt=r(e0o," (ALBERT model)"),e0o.forEach(t),Stt=i(q),G5=n(q,"LI",{});var o0o=s(G5);mZe=n(o0o,"STRONG",{});var hFn=s(mZe);xtt=r(hFn,"bart"),hFn.forEach(t),Gtt=r(o0o," \u2014 "),Nfe=n(o0o,"A",{href:!0});var gFn=s(Nfe);Btt=r(gFn,"BartForConditionalGeneration"),gFn.forEach(t),Itt=r(o0o," (BART model)"),o0o.forEach(t),Ntt=i(q),B5=n(q,"LI",{});var r0o=s(B5);cZe=n(r0o,"STRONG",{});var uFn=s(cZe);$tt=r(uFn,"bert"),uFn.forEach(t),jtt=r(r0o," \u2014 "),$fe=n(r0o,"A",{href:!0});var pFn=s($fe);Ptt=r(pFn,"BertForPreTraining"),pFn.forEach(t),Ztt=r(r0o," (BERT 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yFn=s(Vfe);nat=r(yFn,"CTRLLMHeadModel"),yFn.forEach(t),sat=r(s0o," (CTRL model)"),s0o.forEach(t),lat=i(q),P5=n(q,"LI",{});var l0o=s(P5);pZe=n(l0o,"STRONG",{});var CFn=s(pZe);iat=r(CFn,"data2vec-text"),CFn.forEach(t),dat=r(l0o," \u2014 "),Wfe=n(l0o,"A",{href:!0});var wFn=s(Wfe);mat=r(wFn,"Data2VecTextForMaskedLM"),wFn.forEach(t),cat=r(l0o," (Data2VecText model)"),l0o.forEach(t),fat=i(q),Z5=n(q,"LI",{});var i0o=s(Z5);_Ze=n(i0o,"STRONG",{});var LFn=s(_Ze);hat=r(LFn,"deberta"),LFn.forEach(t),gat=r(i0o," \u2014 "),Jfe=n(i0o,"A",{href:!0});var AFn=s(Jfe);uat=r(AFn,"DebertaForMaskedLM"),AFn.forEach(t),pat=r(i0o," (DeBERTa model)"),i0o.forEach(t),_at=i(q),V5=n(q,"LI",{});var d0o=s(V5);bZe=n(d0o,"STRONG",{});var RFn=s(bZe);bat=r(RFn,"deberta-v2"),RFn.forEach(t),vat=r(d0o," \u2014 "),Ufe=n(d0o,"A",{href:!0});var kFn=s(Ufe);Mat=r(kFn,"DebertaV2ForMaskedLM"),kFn.forEach(t),Tat=r(d0o," (DeBERTa-v2 model)"),d0o.forEach(t),Fat=i(q),W5=n(q,"LI",{});var m0o=s(W5);vZe=n(m0o,"STRONG",{});var 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D0o=s(y3);oVe=n(D0o,"STRONG",{});var QEn=s(oVe);Vlt=r(QEn,"t5"),QEn.forEach(t),Wlt=r(D0o," \u2014 "),Lhe=n(D0o,"A",{href:!0});var YEn=s(Lhe);Jlt=r(YEn,"T5ForConditionalGeneration"),YEn.forEach(t),Ult=r(D0o," (T5 model)"),D0o.forEach(t),Xlt=i(q),C3=n(q,"LI",{});var q0o=s(C3);rVe=n(q0o,"STRONG",{});var zEn=s(rVe);Dlt=r(zEn,"tapas"),zEn.forEach(t),qlt=r(q0o," \u2014 "),Ahe=n(q0o,"A",{href:!0});var HEn=s(Ahe);Olt=r(HEn,"TapasForMaskedLM"),HEn.forEach(t),Qlt=r(q0o," (TAPAS model)"),q0o.forEach(t),Ylt=i(q),w3=n(q,"LI",{});var O0o=s(w3);tVe=n(O0o,"STRONG",{});var KEn=s(tVe);zlt=r(KEn,"transfo-xl"),KEn.forEach(t),Hlt=r(O0o," \u2014 "),Rhe=n(O0o,"A",{href:!0});var eyn=s(Rhe);Klt=r(eyn,"TransfoXLLMHeadModel"),eyn.forEach(t),eit=r(O0o," (Transformer-XL model)"),O0o.forEach(t),oit=i(q),L3=n(q,"LI",{});var Q0o=s(L3);aVe=n(Q0o,"STRONG",{});var oyn=s(aVe);rit=r(oyn,"tvlt"),oyn.forEach(t),tit=r(Q0o," \u2014 "),khe=n(Q0o,"A",{href:!0});var ryn=s(khe);ait=r(ryn,"TvltForPreTraining"),ryn.forEach(t),nit=r(Q0o," (TVLT model)"),Q0o.forEach(t),sit=i(q),A3=n(q,"LI",{});var Y0o=s(A3);nVe=n(Y0o,"STRONG",{});var tyn=s(nVe);lit=r(tyn,"unispeech"),tyn.forEach(t),iit=r(Y0o," \u2014 "),She=n(Y0o,"A",{href:!0});var ayn=s(She);dit=r(ayn,"UniSpeechForPreTraining"),ayn.forEach(t),mit=r(Y0o," (UniSpeech model)"),Y0o.forEach(t),cit=i(q),R3=n(q,"LI",{});var z0o=s(R3);sVe=n(z0o,"STRONG",{});var nyn=s(sVe);fit=r(nyn,"unispeech-sat"),nyn.forEach(t),hit=r(z0o," \u2014 "),xhe=n(z0o,"A",{href:!0});var syn=s(xhe);git=r(syn,"UniSpeechSatForPreTraining"),syn.forEach(t),uit=r(z0o," (UniSpeechSat model)"),z0o.forEach(t),pit=i(q),k3=n(q,"LI",{});var H0o=s(k3);lVe=n(H0o,"STRONG",{});var lyn=s(lVe);_it=r(lyn,"videomae"),lyn.forEach(t),bit=r(H0o," \u2014 "),Ghe=n(H0o,"A",{href:!0});var iyn=s(Ghe);vit=r(iyn,"VideoMAEForPreTraining"),iyn.forEach(t),Mit=r(H0o," (VideoMAE model)"),H0o.forEach(t),Tit=i(q),S3=n(q,"LI",{});var K0o=s(S3);iVe=n(K0o,"STRONG",{});var dyn=s(iVe);Fit=r(dyn,"visual_bert"),dyn.forEach(t),Eit=r(K0o," \u2014 "),Bhe=n(K0o,"A",{href:!0});var myn=s(Bhe);yit=r(myn,"VisualBertForPreTraining"),myn.forEach(t),Cit=r(K0o," (VisualBERT model)"),K0o.forEach(t),wit=i(q),x3=n(q,"LI",{});var e5o=s(x3);dVe=n(e5o,"STRONG",{});var cyn=s(dVe);Lit=r(cyn,"vit_mae"),cyn.forEach(t),Ait=r(e5o," \u2014 "),Ihe=n(e5o,"A",{href:!0});var fyn=s(Ihe);Rit=r(fyn,"ViTMAEForPreTraining"),fyn.forEach(t),kit=r(e5o," (ViTMAE model)"),e5o.forEach(t),Sit=i(q),G3=n(q,"LI",{});var o5o=s(G3);mVe=n(o5o,"STRONG",{});var hyn=s(mVe);xit=r(hyn,"wav2vec2"),hyn.forEach(t),Git=r(o5o," \u2014 "),Nhe=n(o5o,"A",{href:!0});var gyn=s(Nhe);Bit=r(gyn,"Wav2Vec2ForPreTraining"),gyn.forEach(t),Iit=r(o5o," (Wav2Vec2 model)"),o5o.forEach(t),Nit=i(q),B3=n(q,"LI",{});var r5o=s(B3);cVe=n(r5o,"STRONG",{});var uyn=s(cVe);$it=r(uyn,"wav2vec2-conformer"),uyn.forEach(t),jit=r(r5o," \u2014 "),$he=n(r5o,"A",{href:!0});var pyn=s($he);Pit=r(pyn,"Wav2Vec2ConformerForPreTraining"),pyn.forEach(t),Zit=r(r5o," (Wav2Vec2-Conformer model)"),r5o.forEach(t),Vit=i(q),I3=n(q,"LI",{});var t5o=s(I3);fVe=n(t5o,"STRONG",{});var _yn=s(fVe);Wit=r(_yn,"xlm"),_yn.forEach(t),Jit=r(t5o," \u2014 "),jhe=n(t5o,"A",{href:!0});var byn=s(jhe);Uit=r(byn,"XLMWithLMHeadModel"),byn.forEach(t),Xit=r(t5o," (XLM model)"),t5o.forEach(t),Dit=i(q),N3=n(q,"LI",{});var a5o=s(N3);hVe=n(a5o,"STRONG",{});var vyn=s(hVe);qit=r(vyn,"xlm-roberta"),vyn.forEach(t),Oit=r(a5o," \u2014 "),Phe=n(a5o,"A",{href:!0});var Myn=s(Phe);Qit=r(Myn,"XLMRobertaForMaskedLM"),Myn.forEach(t),Yit=r(a5o," (XLM-RoBERTa model)"),a5o.forEach(t),zit=i(q),$3=n(q,"LI",{});var n5o=s($3);gVe=n(n5o,"STRONG",{});var Tyn=s(gVe);Hit=r(Tyn,"xlm-roberta-xl"),Tyn.forEach(t),Kit=r(n5o," \u2014 "),Zhe=n(n5o,"A",{href:!0});var Fyn=s(Zhe);edt=r(Fyn,"XLMRobertaXLForMaskedLM"),Fyn.forEach(t),odt=r(n5o," (XLM-RoBERTa-XL model)"),n5o.forEach(t),rdt=i(q),j3=n(q,"LI",{});var s5o=s(j3);uVe=n(s5o,"STRONG",{});var Eyn=s(uVe);tdt=r(Eyn,"xlnet"),Eyn.forEach(t),adt=r(s5o," \u2014 "),Vhe=n(s5o,"A",{href:!0});var yyn=s(Vhe);ndt=r(yyn,"XLNetLMHeadModel"),yyn.forEach(t),sdt=r(s5o," (XLNet model)"),s5o.forEach(t),ldt=i(q),P3=n(q,"LI",{});var l5o=s(P3);pVe=n(l5o,"STRONG",{});var Cyn=s(pVe);idt=r(Cyn,"xmod"),Cyn.forEach(t),ddt=r(l5o," \u2014 "),Whe=n(l5o,"A",{href:!0});var wyn=s(Whe);mdt=r(wyn,"XmodForMaskedLM"),wyn.forEach(t),cdt=r(l5o," (X-MOD model)"),l5o.forEach(t),q.forEach(t),fdt=i(on),Z3=n(on,"P",{});var i5o=s(Z3);hdt=r(i5o,"The model is set in evaluation mode by default using "),_Ve=n(i5o,"CODE",{});var Lyn=s(_Ve);gdt=r(Lyn,"model.eval()"),Lyn.forEach(t),udt=r(i5o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),bVe=n(i5o,"CODE",{});var Ayn=s(bVe);pdt=r(Ayn,"model.train()"),Ayn.forEach(t),i5o.forEach(t),_dt=i(on),M(V3.$$.fragment,on),on.forEach(t),td.forEach(t),uGo=i(c),pc=n(c,"H3",{class:!0});var _No=s(pc);W3=n(_No,"A",{id:!0,class:!0,href:!0});var Ryn=s(W3);vVe=n(Ryn,"SPAN",{});var kyn=s(vVe);M(qV.$$.fragment,kyn),kyn.forEach(t),Ryn.forEach(t),bdt=i(_No),MVe=n(_No,"SPAN",{});var Syn=s(MVe);vdt=r(Syn,"TFAutoModelForPreTraining"),Syn.forEach(t),_No.forEach(t),pGo=i(c),Oo=n(c,"DIV",{class:!0});var ad=s(Oo);M(OV.$$.fragment,ad),Mdt=i(ad),_c=n(ad,"P",{});var L4e=s(_c);Tdt=r(L4e,`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(L4e,"A",{href:!0});var xyn=s(Jhe);Fdt=r(xyn,"from_pretrained()"),xyn.forEach(t),Edt=r(L4e," class method or the "),Uhe=n(L4e,"A",{href:!0});var Gyn=s(Uhe);ydt=r(Gyn,"from_config()"),Gyn.forEach(t),Cdt=r(L4e,` class
method.`),L4e.forEach(t),wdt=i(ad),QV=n(ad,"P",{});var bNo=s(QV);Ldt=r(bNo,"This class cannot be instantiated directly using "),TVe=n(bNo,"CODE",{});var Byn=s(TVe);Adt=r(Byn,"__init__()"),Byn.forEach(t),Rdt=r(bNo," (throws an error)."),bNo.forEach(t),kdt=i(ad),Dt=n(ad,"DIV",{class:!0});var xj=s(Dt);M(YV.$$.fragment,xj),Sdt=i(xj),FVe=n(xj,"P",{});var Iyn=s(FVe);xdt=r(Iyn,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),Iyn.forEach(t),Gdt=i(xj),bc=n(xj,"P",{});var A4e=s(bc);Bdt=r(A4e,`Note:
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model\u2019s configuration. Use `),Xhe=n(A4e,"A",{href:!0});var $yn=s(Xhe);$dt=r($yn,"from_pretrained()"),$yn.forEach(t),jdt=r(A4e," to load the model weights."),A4e.forEach(t),Pdt=i(xj),M(J3.$$.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 jyn=s(yVe);Wdt=r(jyn,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),jyn.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 Pyn=s(CVe);Xdt=r(Pyn,"model_type"),Pyn.forEach(t),Ddt=r(Gj,` property of the config object (either
passed as an argument or loaded from `),wVe=n(Gj,"CODE",{});var Zyn=s(wVe);qdt=r(Zyn,"pretrained_model_name_or_path"),Zyn.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 Vyn=s(LVe);Qdt=r(Vyn,"pretrained_model_name_or_path"),Vyn.forEach(t),Ydt=r(Gj,":"),Gj.forEach(t),zdt=i(nd),fe=n(nd,"UL",{});var he=s(fe);U3=n(he,"LI",{});var d5o=s(U3);AVe=n(d5o,"STRONG",{});var Wyn=s(AVe);Hdt=r(Wyn,"albert"),Wyn.forEach(t),Kdt=r(d5o," \u2014 "),Dhe=n(d5o,"A",{href:!0});var Jyn=s(Dhe);emt=r(Jyn,"TFAlbertForPreTraining"),Jyn.forEach(t),omt=r(d5o," (ALBERT model)"),d5o.forEach(t),rmt=i(he),X3=n(he,"LI",{});var m5o=s(X3);RVe=n(m5o,"STRONG",{});var Uyn=s(RVe);tmt=r(Uyn,"bart"),Uyn.forEach(t),amt=r(m5o," \u2014 "),qhe=n(m5o,"A",{href:!0});var Xyn=s(qhe);nmt=r(Xyn,"TFBartForConditionalGeneration"),Xyn.forEach(t),smt=r(m5o," (BART model)"),m5o.forEach(t),lmt=i(he),D3=n(he,"LI",{});var c5o=s(D3);kVe=n(c5o,"STRONG",{});var Dyn=s(kVe);imt=r(Dyn,"bert"),Dyn.forEach(t),dmt=r(c5o," \u2014 "),Ohe=n(c5o,"A",{href:!0});var qyn=s(Ohe);mmt=r(qyn,"TFBertForPreTraining"),qyn.forEach(t),cmt=r(c5o," (BERT model)"),c5o.forEach(t),fmt=i(he),q3=n(he,"LI",{});var f5o=s(q3);SVe=n(f5o,"STRONG",{});var Oyn=s(SVe);hmt=r(Oyn,"camembert"),Oyn.forEach(t),gmt=r(f5o," \u2014 "),Qhe=n(f5o,"A",{href:!0});var Qyn=s(Qhe);umt=r(Qyn,"TFCamembertForMaskedLM"),Qyn.forEach(t),pmt=r(f5o," (CamemBERT model)"),f5o.forEach(t),_mt=i(he),O3=n(he,"LI",{});var h5o=s(O3);xVe=n(h5o,"STRONG",{});var Yyn=s(xVe);bmt=r(Yyn,"ctrl"),Yyn.forEach(t),vmt=r(h5o," \u2014 "),Yhe=n(h5o,"A",{href:!0});var zyn=s(Yhe);Mmt=r(zyn,"TFCTRLLMHeadModel"),zyn.forEach(t),Tmt=r(h5o," (CTRL model)"),h5o.forEach(t),Fmt=i(he),Q3=n(he,"LI",{});var g5o=s(Q3);GVe=n(g5o,"STRONG",{});var Hyn=s(GVe);Emt=r(Hyn,"distilbert"),Hyn.forEach(t),ymt=r(g5o," \u2014 "),zhe=n(g5o,"A",{href:!0});var Kyn=s(zhe);Cmt=r(Kyn,"TFDistilBertForMaskedLM"),Kyn.forEach(t),wmt=r(g5o," (DistilBERT model)"),g5o.forEach(t),Lmt=i(he),Y3=n(he,"LI",{});var u5o=s(Y3);BVe=n(u5o,"STRONG",{});var e0n=s(BVe);Amt=r(e0n,"electra"),e0n.forEach(t),Rmt=r(u5o," \u2014 "),Hhe=n(u5o,"A",{href:!0});var o0n=s(Hhe);kmt=r(o0n,"TFElectraForPreTraining"),o0n.forEach(t),Smt=r(u5o," (ELECTRA model)"),u5o.forEach(t),xmt=i(he),z3=n(he,"LI",{});var p5o=s(z3);IVe=n(p5o,"STRONG",{});var r0n=s(IVe);Gmt=r(r0n,"flaubert"),r0n.forEach(t),Bmt=r(p5o," \u2014 "),Khe=n(p5o,"A",{href:!0});var t0n=s(Khe);Imt=r(t0n,"TFFlaubertWithLMHeadModel"),t0n.forEach(t),Nmt=r(p5o," (FlauBERT model)"),p5o.forEach(t),$mt=i(he),H3=n(he,"LI",{});var _5o=s(H3);NVe=n(_5o,"STRONG",{});var a0n=s(NVe);jmt=r(a0n,"funnel"),a0n.forEach(t),Pmt=r(_5o," \u2014 "),ege=n(_5o,"A",{href:!0});var n0n=s(ege);Zmt=r(n0n,"TFFunnelForPreTraining"),n0n.forEach(t),Vmt=r(_5o," (Funnel Transformer model)"),_5o.forEach(t),Wmt=i(he),K3=n(he,"LI",{});var b5o=s(K3);$Ve=n(b5o,"STRONG",{});var s0n=s($Ve);Jmt=r(s0n,"gpt-sw3"),s0n.forEach(t),Umt=r(b5o," \u2014 "),oge=n(b5o,"A",{href:!0});var l0n=s(oge);Xmt=r(l0n,"TFGPT2LMHeadModel"),l0n.forEach(t),Dmt=r(b5o," (GPT-Sw3 model)"),b5o.forEach(t),qmt=i(he),e9=n(he,"LI",{});var v5o=s(e9);jVe=n(v5o,"STRONG",{});var i0n=s(jVe);Omt=r(i0n,"gpt2"),i0n.forEach(t),Qmt=r(v5o," \u2014 "),rge=n(v5o,"A",{href:!0});var d0n=s(rge);Ymt=r(d0n,"TFGPT2LMHeadModel"),d0n.forEach(t),zmt=r(v5o," (OpenAI GPT-2 model)"),v5o.forEach(t),Hmt=i(he),o9=n(he,"LI",{});var M5o=s(o9);PVe=n(M5o,"STRONG",{});var m0n=s(PVe);Kmt=r(m0n,"layoutlm"),m0n.forEach(t),ect=r(M5o," \u2014 "),tge=n(M5o,"A",{href:!0});var c0n=s(tge);oct=r(c0n,"TFLayoutLMForMaskedLM"),c0n.forEach(t),rct=r(M5o," (LayoutLM model)"),M5o.forEach(t),tct=i(he),r9=n(he,"LI",{});var T5o=s(r9);ZVe=n(T5o,"STRONG",{});var f0n=s(ZVe);act=r(f0n,"lxmert"),f0n.forEach(t),nct=r(T5o," \u2014 "),age=n(T5o,"A",{href:!0});var h0n=s(age);sct=r(h0n,"TFLxmertForPreTraining"),h0n.forEach(t),lct=r(T5o," (LXMERT model)"),T5o.forEach(t),ict=i(he),t9=n(he,"LI",{});var F5o=s(t9);VVe=n(F5o,"STRONG",{});var g0n=s(VVe);dct=r(g0n,"mobilebert"),g0n.forEach(t),mct=r(F5o," \u2014 "),nge=n(F5o,"A",{href:!0});var u0n=s(nge);cct=r(u0n,"TFMobileBertForPreTraining"),u0n.forEach(t),fct=r(F5o," (MobileBERT model)"),F5o.forEach(t),hct=i(he),a9=n(he,"LI",{});var E5o=s(a9);WVe=n(E5o,"STRONG",{});var p0n=s(WVe);gct=r(p0n,"mpnet"),p0n.forEach(t),uct=r(E5o," \u2014 "),sge=n(E5o,"A",{href:!0});var _0n=s(sge);pct=r(_0n,"TFMPNetForMaskedLM"),_0n.forEach(t),_ct=r(E5o," (MPNet model)"),E5o.forEach(t),bct=i(he),n9=n(he,"LI",{});var y5o=s(n9);JVe=n(y5o,"STRONG",{});var b0n=s(JVe);vct=r(b0n,"openai-gpt"),b0n.forEach(t),Mct=r(y5o," \u2014 "),lge=n(y5o,"A",{href:!0});var v0n=s(lge);Tct=r(v0n,"TFOpenAIGPTLMHeadModel"),v0n.forEach(t),Fct=r(y5o," (OpenAI GPT model)"),y5o.forEach(t),Ect=i(he),s9=n(he,"LI",{});var C5o=s(s9);UVe=n(C5o,"STRONG",{});var M0n=s(UVe);yct=r(M0n,"roberta"),M0n.forEach(t),Cct=r(C5o," \u2014 "),ige=n(C5o,"A",{href:!0});var T0n=s(ige);wct=r(T0n,"TFRobertaForMaskedLM"),T0n.forEach(t),Lct=r(C5o," (RoBERTa model)"),C5o.forEach(t),Act=i(he),l9=n(he,"LI",{});var w5o=s(l9);XVe=n(w5o,"STRONG",{});var F0n=s(XVe);Rct=r(F0n,"roberta-prelayernorm"),F0n.forEach(t),kct=r(w5o," \u2014 "),dge=n(w5o,"A",{href:!0});var E0n=s(dge);Sct=r(E0n,"TFRobertaPreLayerNormForMaskedLM"),E0n.forEach(t),xct=r(w5o," (RoBERTa-PreLayerNorm model)"),w5o.forEach(t),Gct=i(he),i9=n(he,"LI",{});var L5o=s(i9);DVe=n(L5o,"STRONG",{});var y0n=s(DVe);Bct=r(y0n,"t5"),y0n.forEach(t),Ict=r(L5o," \u2014 "),mge=n(L5o,"A",{href:!0});var C0n=s(mge);Nct=r(C0n,"TFT5ForConditionalGeneration"),C0n.forEach(t),$ct=r(L5o," (T5 model)"),L5o.forEach(t),jct=i(he),d9=n(he,"LI",{});var A5o=s(d9);qVe=n(A5o,"STRONG",{});var w0n=s(qVe);Pct=r(w0n,"tapas"),w0n.forEach(t),Zct=r(A5o," \u2014 "),cge=n(A5o,"A",{href:!0});var L0n=s(cge);Vct=r(L0n,"TFTapasForMaskedLM"),L0n.forEach(t),Wct=r(A5o," (TAPAS model)"),A5o.forEach(t),Jct=i(he),m9=n(he,"LI",{});var R5o=s(m9);OVe=n(R5o,"STRONG",{});var A0n=s(OVe);Uct=r(A0n,"transfo-xl"),A0n.forEach(t),Xct=r(R5o," \u2014 "),fge=n(R5o,"A",{href:!0});var R0n=s(fge);Dct=r(R0n,"TFTransfoXLLMHeadModel"),R0n.forEach(t),qct=r(R5o," (Transformer-XL model)"),R5o.forEach(t),Oct=i(he),c9=n(he,"LI",{});var k5o=s(c9);QVe=n(k5o,"STRONG",{});var k0n=s(QVe);Qct=r(k0n,"vit_mae"),k0n.forEach(t),Yct=r(k5o," \u2014 "),hge=n(k5o,"A",{href:!0});var S0n=s(hge);zct=r(S0n,"TFViTMAEForPreTraining"),S0n.forEach(t),Hct=r(k5o," (ViTMAE model)"),k5o.forEach(t),Kct=i(he),f9=n(he,"LI",{});var S5o=s(f9);YVe=n(S5o,"STRONG",{});var x0n=s(YVe);eft=r(x0n,"xlm"),x0n.forEach(t),oft=r(S5o," \u2014 "),gge=n(S5o,"A",{href:!0});var G0n=s(gge);rft=r(G0n,"TFXLMWithLMHeadModel"),G0n.forEach(t),tft=r(S5o," (XLM model)"),S5o.forEach(t),aft=i(he),h9=n(he,"LI",{});var x5o=s(h9);zVe=n(x5o,"STRONG",{});var B0n=s(zVe);nft=r(B0n,"xlm-roberta"),B0n.forEach(t),sft=r(x5o," \u2014 "),uge=n(x5o,"A",{href:!0});var I0n=s(uge);lft=r(I0n,"TFXLMRobertaForMaskedLM"),I0n.forEach(t),ift=r(x5o," (XLM-RoBERTa model)"),x5o.forEach(t),dft=i(he),g9=n(he,"LI",{});var G5o=s(g9);HVe=n(G5o,"STRONG",{});var N0n=s(HVe);mft=r(N0n,"xlnet"),N0n.forEach(t),cft=r(G5o," \u2014 "),pge=n(G5o,"A",{href:!0});var $0n=s(pge);fft=r($0n,"TFXLNetLMHeadModel"),$0n.forEach(t),hft=r(G5o," (XLNet model)"),G5o.forEach(t),he.forEach(t),gft=i(nd),M(u9.$$.fragment,nd),nd.forEach(t),ad.forEach(t),_Go=i(c),vc=n(c,"H3",{class:!0});var vNo=s(vc);p9=n(vNo,"A",{id:!0,class:!0,href:!0});var j0n=s(p9);KVe=n(j0n,"SPAN",{});var P0n=s(KVe);M(HV.$$.fragment,P0n),P0n.forEach(t),j0n.forEach(t),uft=i(vNo),eWe=n(vNo,"SPAN",{});var Z0n=s(eWe);pft=r(Z0n,"FlaxAutoModelForPreTraining"),Z0n.forEach(t),vNo.forEach(t),bGo=i(c),Qo=n(c,"DIV",{class:!0});var sd=s(Qo);M(KV.$$.fragment,sd),_ft=i(sd),Mc=n(sd,"P",{});var R4e=s(Mc);bft=r(R4e,`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(R4e,"A",{href:!0});var V0n=s(_ge);vft=r(V0n,"from_pretrained()"),V0n.forEach(t),Mft=r(R4e," class method or the "),bge=n(R4e,"A",{href:!0});var W0n=s(bge);Tft=r(W0n,"from_config()"),W0n.forEach(t),Fft=r(R4e,` class
method.`),R4e.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 J0n=s(oWe);Cft=r(J0n,"__init__()"),J0n.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.$$.fragment,Bj),Aft=i(Bj),rWe=n(Bj,"P",{});var U0n=s(rWe);Rft=r(U0n,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),U0n.forEach(t),kft=i(Bj),Tc=n(Bj,"P",{});var k4e=s(Tc);Sft=r(k4e,`Note:
Loading a model from its configuration file does `),tWe=n(k4e,"STRONG",{});var X0n=s(tWe);xft=r(X0n,"not"),X0n.forEach(t),Gft=r(k4e,` load the model weights. It only affects the
model\u2019s configuration. Use `),vge=n(k4e,"A",{href:!0});var D0n=s(vge);Bft=r(D0n,"from_pretrained()"),D0n.forEach(t),Ift=r(k4e," to load the model weights."),k4e.forEach(t),Nft=i(Bj),M(_9.$$.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 q0n=s(aWe);Pft=r(q0n,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),q0n.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 O0n=s(nWe);Wft=r(O0n,"model_type"),O0n.forEach(t),Jft=r(Ij,` property of the config object (either
passed as an argument or loaded from `),sWe=n(Ij,"CODE",{});var Q0n=s(sWe);Uft=r(Q0n,"pretrained_model_name_or_path"),Q0n.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 Y0n=s(lWe);Dft=r(Y0n,"pretrained_model_name_or_path"),Y0n.forEach(t),qft=r(Ij,":"),Ij.forEach(t),Oft=i(ld),we=n(ld,"UL",{});var Ae=s(we);b9=n(Ae,"LI",{});var B5o=s(b9);iWe=n(B5o,"STRONG",{});var z0n=s(iWe);Qft=r(z0n,"albert"),z0n.forEach(t),Yft=r(B5o," \u2014 "),Mge=n(B5o,"A",{href:!0});var H0n=s(Mge);zft=r(H0n,"FlaxAlbertForPreTraining"),H0n.forEach(t),Hft=r(B5o," (ALBERT model)"),B5o.forEach(t),Kft=i(Ae),v9=n(Ae,"LI",{});var I5o=s(v9);dWe=n(I5o,"STRONG",{});var K0n=s(dWe);eht=r(K0n,"bart"),K0n.forEach(t),oht=r(I5o," \u2014 "),Tge=n(I5o,"A",{href:!0});var e5n=s(Tge);rht=r(e5n,"FlaxBartForConditionalGeneration"),e5n.forEach(t),tht=r(I5o," (BART model)"),I5o.forEach(t),aht=i(Ae),M9=n(Ae,"LI",{});var N5o=s(M9);mWe=n(N5o,"STRONG",{});var o5n=s(mWe);nht=r(o5n,"bert"),o5n.forEach(t),sht=r(N5o," \u2014 "),Fge=n(N5o,"A",{href:!0});var r5n=s(Fge);lht=r(r5n,"FlaxBertForPreTraining"),r5n.forEach(t),iht=r(N5o," (BERT 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"),wge=n(Z5o,"A",{href:!0});var m5n=s(wge);Lht=r(m5n,"FlaxMBartForConditionalGeneration"),m5n.forEach(t),Aht=r(Z5o," (mBART model)"),Z5o.forEach(t),Rht=i(Ae),C9=n(Ae,"LI",{});var V5o=s(C9);uWe=n(V5o,"STRONG",{});var c5n=s(uWe);kht=r(c5n,"mt5"),c5n.forEach(t),Sht=r(V5o," \u2014 "),Lge=n(V5o,"A",{href:!0});var f5n=s(Lge);xht=r(f5n,"FlaxMT5ForConditionalGeneration"),f5n.forEach(t),Ght=r(V5o," (MT5 model)"),V5o.forEach(t),Bht=i(Ae),w9=n(Ae,"LI",{});var W5o=s(w9);pWe=n(W5o,"STRONG",{});var h5n=s(pWe);Iht=r(h5n,"roberta"),h5n.forEach(t),Nht=r(W5o," \u2014 "),Age=n(W5o,"A",{href:!0});var g5n=s(Age);$ht=r(g5n,"FlaxRobertaForMaskedLM"),g5n.forEach(t),jht=r(W5o," (RoBERTa model)"),W5o.forEach(t),Pht=i(Ae),L9=n(Ae,"LI",{});var J5o=s(L9);_We=n(J5o,"STRONG",{});var u5n=s(_We);Zht=r(u5n,"roberta-prelayernorm"),u5n.forEach(t),Vht=r(J5o," \u2014 "),Rge=n(J5o,"A",{href:!0});var p5n=s(Rge);Wht=r(p5n,"FlaxRobertaPreLayerNormForMaskedLM"),p5n.forEach(t),Jht=r(J5o," (RoBERTa-PreLayerNorm 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tasks."),k5n.forEach(t),TGo=i(c),Ec=n(c,"H3",{class:!0});var FNo=s(Ec);I9=n(FNo,"A",{id:!0,class:!0,href:!0});var S5n=s(I9);CWe=n(S5n,"SPAN",{});var x5n=s(CWe);M(aW.$$.fragment,x5n),x5n.forEach(t),S5n.forEach(t),vgt=i(FNo),wWe=n(FNo,"SPAN",{});var G5n=s(wWe);Mgt=r(G5n,"AutoModelForCausalLM"),G5n.forEach(t),FNo.forEach(t),FGo=i(c),Yo=n(c,"DIV",{class:!0});var id=s(Yo);M(nW.$$.fragment,id),Tgt=i(id),yc=n(id,"P",{});var S4e=s(yc);Fgt=r(S4e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),Nge=n(S4e,"A",{href:!0});var B5n=s(Nge);Egt=r(B5n,"from_pretrained()"),B5n.forEach(t),ygt=r(S4e," class method or the "),$ge=n(S4e,"A",{href:!0});var I5n=s($ge);Cgt=r(I5n,"from_config()"),I5n.forEach(t),wgt=r(S4e,` class
method.`),S4e.forEach(t),Lgt=i(id),sW=n(id,"P",{});var ENo=s(sW);Agt=r(ENo,"This class cannot be instantiated directly using "),LWe=n(ENo,"CODE",{});var N5n=s(LWe);Rgt=r(N5n,"__init__()"),N5n.forEach(t),kgt=r(ENo," (throws an error)."),ENo.forEach(t),Sgt=i(id),Ot=n(id,"DIV",{class:!0});var Nj=s(Ot);M(lW.$$.fragment,Nj),xgt=i(Nj),AWe=n(Nj,"P",{});var $5n=s(AWe);Ggt=r($5n,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),$5n.forEach(t),Bgt=i(Nj),Cc=n(Nj,"P",{});var x4e=s(Cc);Igt=r(x4e,`Note:
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model\u2019s configuration. Use `),jge=n(x4e,"A",{href:!0});var P5n=s(jge);jgt=r(P5n,"from_pretrained()"),P5n.forEach(t),Pgt=r(x4e," to load the model weights."),x4e.forEach(t),Zgt=i(Nj),M(N9.$$.fragment,Nj),Nj.forEach(t),Vgt=i(id),io=n(id,"DIV",{class:!0});var rn=s(io);M(iW.$$.fragment,rn),Wgt=i(rn),kWe=n(rn,"P",{});var Z5n=s(kWe);Jgt=r(Z5n,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),Z5n.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($j,"CODE",{});var V5n=s(SWe);Dgt=r(V5n,"model_type"),V5n.forEach(t),qgt=r($j,` property of the config object (either
passed as an argument or loaded from `),xWe=n($j,"CODE",{});var W5n=s(xWe);Ogt=r(W5n,"pretrained_model_name_or_path"),W5n.forEach(t),Qgt=r($j,` if possible), or when it\u2019s missing, by
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"),kue=n(V3o,"A",{href:!0});var R9n=s(kue);ubt=r(R9n,"RwkvForCausalLM"),R9n.forEach(t),pbt=r(V3o," (RWKV model)"),V3o.forEach(t),_bt=i(Y),k4=n(Y,"LI",{});var W3o=s(k4);AJe=n(W3o,"STRONG",{});var k9n=s(AJe);bbt=r(k9n,"speech_to_text_2"),k9n.forEach(t),vbt=r(W3o," \u2014 "),Sue=n(W3o,"A",{href:!0});var S9n=s(Sue);Mbt=r(S9n,"Speech2Text2ForCausalLM"),S9n.forEach(t),Tbt=r(W3o," (Speech2Text2 model)"),W3o.forEach(t),Fbt=i(Y),S4=n(Y,"LI",{});var J3o=s(S4);RJe=n(J3o,"STRONG",{});var x9n=s(RJe);Ebt=r(x9n,"transfo-xl"),x9n.forEach(t),ybt=r(J3o," \u2014 "),xue=n(J3o,"A",{href:!0});var G9n=s(xue);Cbt=r(G9n,"TransfoXLLMHeadModel"),G9n.forEach(t),wbt=r(J3o," (Transformer-XL model)"),J3o.forEach(t),Lbt=i(Y),x4=n(Y,"LI",{});var U3o=s(x4);kJe=n(U3o,"STRONG",{});var B9n=s(kJe);Abt=r(B9n,"trocr"),B9n.forEach(t),Rbt=r(U3o," \u2014 "),Gue=n(U3o,"A",{href:!0});var I9n=s(Gue);kbt=r(I9n,"TrOCRForCausalLM"),I9n.forEach(t),Sbt=r(U3o," (TrOCR model)"),U3o.forEach(t),xbt=i(Y),G4=n(Y,"LI",{});var X3o=s(G4);SJe=n(X3o,"STRONG",{});var N9n=s(SJe);Gbt=r(N9n,"xglm"),N9n.forEach(t),Bbt=r(X3o," \u2014 "),Bue=n(X3o,"A",{href:!0});var $9n=s(Bue);Ibt=r($9n,"XGLMForCausalLM"),$9n.forEach(t),Nbt=r(X3o," (XGLM model)"),X3o.forEach(t),$bt=i(Y),B4=n(Y,"LI",{});var D3o=s(B4);xJe=n(D3o,"STRONG",{});var j9n=s(xJe);jbt=r(j9n,"xlm"),j9n.forEach(t),Pbt=r(D3o," \u2014 "),Iue=n(D3o,"A",{href:!0});var P9n=s(Iue);Zbt=r(P9n,"XLMWithLMHeadModel"),P9n.forEach(t),Vbt=r(D3o," (XLM model)"),D3o.forEach(t),Wbt=i(Y),I4=n(Y,"LI",{});var q3o=s(I4);GJe=n(q3o,"STRONG",{});var Z9n=s(GJe);Jbt=r(Z9n,"xlm-prophetnet"),Z9n.forEach(t),Ubt=r(q3o," \u2014 "),Nue=n(q3o,"A",{href:!0});var V9n=s(Nue);Xbt=r(V9n,"XLMProphetNetForCausalLM"),V9n.forEach(t),Dbt=r(q3o," (XLM-ProphetNet model)"),q3o.forEach(t),qbt=i(Y),N4=n(Y,"LI",{});var O3o=s(N4);BJe=n(O3o,"STRONG",{});var W9n=s(BJe);Obt=r(W9n,"xlm-roberta"),W9n.forEach(t),Qbt=r(O3o," \u2014 "),$ue=n(O3o,"A",{href:!0});var J9n=s($ue);Ybt=r(J9n,"XLMRobertaForCausalLM"),J9n.forEach(t),zbt=r(O3o," (XLM-RoBERTa model)"),O3o.forEach(t),Hbt=i(Y),$4=n(Y,"LI",{});var Q3o=s($4);IJe=n(Q3o,"STRONG",{});var U9n=s(IJe);Kbt=r(U9n,"xlm-roberta-xl"),U9n.forEach(t),e2t=r(Q3o," \u2014 "),jue=n(Q3o,"A",{href:!0});var X9n=s(jue);o2t=r(X9n,"XLMRobertaXLForCausalLM"),X9n.forEach(t),r2t=r(Q3o," (XLM-RoBERTa-XL model)"),Q3o.forEach(t),t2t=i(Y),j4=n(Y,"LI",{});var Y3o=s(j4);NJe=n(Y3o,"STRONG",{});var D9n=s(NJe);a2t=r(D9n,"xlnet"),D9n.forEach(t),n2t=r(Y3o," \u2014 "),Pue=n(Y3o,"A",{href:!0});var q9n=s(Pue);s2t=r(q9n,"XLNetLMHeadModel"),q9n.forEach(t),l2t=r(Y3o," (XLNet model)"),Y3o.forEach(t),i2t=i(Y),P4=n(Y,"LI",{});var z3o=s(P4);$Je=n(z3o,"STRONG",{});var O9n=s($Je);d2t=r(O9n,"xmod"),O9n.forEach(t),m2t=r(z3o," \u2014 "),Zue=n(z3o,"A",{href:!0});var Q9n=s(Zue);c2t=r(Q9n,"XmodForCausalLM"),Q9n.forEach(t),f2t=r(z3o," (X-MOD model)"),z3o.forEach(t),Y.forEach(t),h2t=i(rn),Z4=n(rn,"P",{});var H3o=s(Z4);g2t=r(H3o,"The model is set in evaluation mode by default using "),jJe=n(H3o,"CODE",{});var Y9n=s(jJe);u2t=r(Y9n,"model.eval()"),Y9n.forEach(t),p2t=r(H3o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),PJe=n(H3o,"CODE",{});var z9n=s(PJe);_2t=r(z9n,"model.train()"),z9n.forEach(t),H3o.forEach(t),b2t=i(rn),M(V4.$$.fragment,rn),rn.forEach(t),id.forEach(t),EGo=i(c),wc=n(c,"H3",{class:!0});var yNo=s(wc);W4=n(yNo,"A",{id:!0,class:!0,href:!0});var H9n=s(W4);ZJe=n(H9n,"SPAN",{});var K9n=s(ZJe);M(dW.$$.fragment,K9n),K9n.forEach(t),H9n.forEach(t),v2t=i(yNo),VJe=n(yNo,"SPAN",{});var e4n=s(VJe);M2t=r(e4n,"TFAutoModelForCausalLM"),e4n.forEach(t),yNo.forEach(t),yGo=i(c),zo=n(c,"DIV",{class:!0});var dd=s(zo);M(mW.$$.fragment,dd),T2t=i(dd),Lc=n(dd,"P",{});var G4e=s(Lc);F2t=r(G4e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),Vue=n(G4e,"A",{href:!0});var o4n=s(Vue);E2t=r(o4n,"from_pretrained()"),o4n.forEach(t),y2t=r(G4e," class method or the "),Wue=n(G4e,"A",{href:!0});var r4n=s(Wue);C2t=r(r4n,"from_config()"),r4n.forEach(t),w2t=r(G4e,` class
method.`),G4e.forEach(t),L2t=i(dd),cW=n(dd,"P",{});var CNo=s(cW);A2t=r(CNo,"This class cannot be instantiated directly using "),WJe=n(CNo,"CODE",{});var t4n=s(WJe);R2t=r(t4n,"__init__()"),t4n.forEach(t),k2t=r(CNo," (throws an error)."),CNo.forEach(t),S2t=i(dd),Qt=n(dd,"DIV",{class:!0});var jj=s(Qt);M(fW.$$.fragment,jj),x2t=i(jj),JJe=n(jj,"P",{});var a4n=s(JJe);G2t=r(a4n,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),a4n.forEach(t),B2t=i(jj),Ac=n(jj,"P",{});var B4e=s(Ac);I2t=r(B4e,`Note:
Loading a model from its configuration file does `),UJe=n(B4e,"STRONG",{});var n4n=s(UJe);N2t=r(n4n,"not"),n4n.forEach(t),$2t=r(B4e,` load the model weights. It only affects the
model\u2019s configuration. Use `),Jue=n(B4e,"A",{href:!0});var s4n=s(Jue);j2t=r(s4n,"from_pretrained()"),s4n.forEach(t),P2t=r(B4e," to load the model weights."),B4e.forEach(t),Z2t=i(jj),M(J4.$$.fragment,jj),jj.forEach(t),V2t=i(dd),lt=n(dd,"DIV",{class:!0});var md=s(lt);M(hW.$$.fragment,md),W2t=i(md),XJe=n(md,"P",{});var l4n=s(XJe);J2t=r(l4n,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),l4n.forEach(t),U2t=i(md),Dn=n(md,"P",{});var Pj=s(Dn);X2t=r(Pj,"The model class to instantiate is selected based on the "),DJe=n(Pj,"CODE",{});var i4n=s(DJe);D2t=r(i4n,"model_type"),i4n.forEach(t),q2t=r(Pj,` property of the config object (either
passed as an argument or loaded from `),qJe=n(Pj,"CODE",{});var d4n=s(qJe);O2t=r(d4n,"pretrained_model_name_or_path"),d4n.forEach(t),Q2t=r(Pj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),OJe=n(Pj,"CODE",{});var m4n=s(OJe);Y2t=r(m4n,"pretrained_model_name_or_path"),m4n.forEach(t),z2t=r(Pj,":"),Pj.forEach(t),H2t=i(md),Ee=n(md,"UL",{});var Ce=s(Ee);U4=n(Ce,"LI",{});var K3o=s(U4);QJe=n(K3o,"STRONG",{});var c4n=s(QJe);K2t=r(c4n,"bert"),c4n.forEach(t),evt=r(K3o," \u2014 "),Uue=n(K3o,"A",{href:!0});var f4n=s(Uue);ovt=r(f4n,"TFBertLMHeadModel"),f4n.forEach(t),rvt=r(K3o," (BERT model)"),K3o.forEach(t),tvt=i(Ce),X4=n(Ce,"LI",{});var e9o=s(X4);YJe=n(e9o,"STRONG",{});var h4n=s(YJe);avt=r(h4n,"camembert"),h4n.forEach(t),nvt=r(e9o," \u2014 "),Xue=n(e9o,"A",{href:!0});var g4n=s(Xue);svt=r(g4n,"TFCamembertForCausalLM"),g4n.forEach(t),lvt=r(e9o," (CamemBERT model)"),e9o.forEach(t),ivt=i(Ce),D4=n(Ce,"LI",{});var o9o=s(D4);zJe=n(o9o,"STRONG",{});var u4n=s(zJe);dvt=r(u4n,"ctrl"),u4n.forEach(t),mvt=r(o9o," \u2014 "),Due=n(o9o,"A",{href:!0});var p4n=s(Due);cvt=r(p4n,"TFCTRLLMHeadModel"),p4n.forEach(t),fvt=r(o9o," (CTRL 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y4n=s(Yue);Svt=r(y4n,"TFOpenAIGPTLMHeadModel"),y4n.forEach(t),xvt=r(n9o," (OpenAI GPT model)"),n9o.forEach(t),Gvt=i(Ce),z4=n(Ce,"LI",{});var s9o=s(z4);rUe=n(s9o,"STRONG",{});var C4n=s(rUe);Bvt=r(C4n,"opt"),C4n.forEach(t),Ivt=r(s9o," \u2014 "),zue=n(s9o,"A",{href:!0});var w4n=s(zue);Nvt=r(w4n,"TFOPTForCausalLM"),w4n.forEach(t),$vt=r(s9o," (OPT model)"),s9o.forEach(t),jvt=i(Ce),H4=n(Ce,"LI",{});var l9o=s(H4);tUe=n(l9o,"STRONG",{});var L4n=s(tUe);Pvt=r(L4n,"rembert"),L4n.forEach(t),Zvt=r(l9o," \u2014 "),Hue=n(l9o,"A",{href:!0});var A4n=s(Hue);Vvt=r(A4n,"TFRemBertForCausalLM"),A4n.forEach(t),Wvt=r(l9o," (RemBERT model)"),l9o.forEach(t),Jvt=i(Ce),K4=n(Ce,"LI",{});var i9o=s(K4);aUe=n(i9o,"STRONG",{});var R4n=s(aUe);Uvt=r(R4n,"roberta"),R4n.forEach(t),Xvt=r(i9o," \u2014 "),Kue=n(i9o,"A",{href:!0});var k4n=s(Kue);Dvt=r(k4n,"TFRobertaForCausalLM"),k4n.forEach(t),qvt=r(i9o," (RoBERTa model)"),i9o.forEach(t),Ovt=i(Ce),eC=n(Ce,"LI",{});var d9o=s(eC);nUe=n(d9o,"STRONG",{});var S4n=s(nUe);Qvt=r(S4n,"roberta-prelayernorm"),S4n.forEach(t),Yvt=r(d9o," \u2014 "),epe=n(d9o,"A",{href:!0});var x4n=s(epe);zvt=r(x4n,"TFRobertaPreLayerNormForCausalLM"),x4n.forEach(t),Hvt=r(d9o," (RoBERTa-PreLayerNorm model)"),d9o.forEach(t),Kvt=i(Ce),oC=n(Ce,"LI",{});var m9o=s(oC);sUe=n(m9o,"STRONG",{});var G4n=s(sUe);eMt=r(G4n,"roformer"),G4n.forEach(t),oMt=r(m9o," \u2014 "),ope=n(m9o,"A",{href:!0});var B4n=s(ope);rMt=r(B4n,"TFRoFormerForCausalLM"),B4n.forEach(t),tMt=r(m9o," (RoFormer model)"),m9o.forEach(t),aMt=i(Ce),rC=n(Ce,"LI",{});var c9o=s(rC);lUe=n(c9o,"STRONG",{});var I4n=s(lUe);nMt=r(I4n,"transfo-xl"),I4n.forEach(t),sMt=r(c9o," \u2014 "),rpe=n(c9o,"A",{href:!0});var N4n=s(rpe);lMt=r(N4n,"TFTransfoXLLMHeadModel"),N4n.forEach(t),iMt=r(c9o," (Transformer-XL model)"),c9o.forEach(t),dMt=i(Ce),tC=n(Ce,"LI",{});var f9o=s(tC);iUe=n(f9o,"STRONG",{});var $4n=s(iUe);mMt=r($4n,"xglm"),$4n.forEach(t),cMt=r(f9o," \u2014 "),tpe=n(f9o,"A",{href:!0});var j4n=s(tpe);fMt=r(j4n,"TFXGLMForCausalLM"),j4n.forEach(t),hMt=r(f9o," (XGLM model)"),f9o.forEach(t),gMt=i(Ce),aC=n(Ce,"LI",{});var h9o=s(aC);dUe=n(h9o,"STRONG",{});var P4n=s(dUe);uMt=r(P4n,"xlm"),P4n.forEach(t),pMt=r(h9o," \u2014 "),ape=n(h9o,"A",{href:!0});var Z4n=s(ape);_Mt=r(Z4n,"TFXLMWithLMHeadModel"),Z4n.forEach(t),bMt=r(h9o," (XLM model)"),h9o.forEach(t),vMt=i(Ce),nC=n(Ce,"LI",{});var g9o=s(nC);mUe=n(g9o,"STRONG",{});var V4n=s(mUe);MMt=r(V4n,"xlm-roberta"),V4n.forEach(t),TMt=r(g9o," \u2014 "),npe=n(g9o,"A",{href:!0});var W4n=s(npe);FMt=r(W4n,"TFXLMRobertaForCausalLM"),W4n.forEach(t),EMt=r(g9o," (XLM-RoBERTa model)"),g9o.forEach(t),yMt=i(Ce),sC=n(Ce,"LI",{});var u9o=s(sC);cUe=n(u9o,"STRONG",{});var J4n=s(cUe);CMt=r(J4n,"xlnet"),J4n.forEach(t),wMt=r(u9o," \u2014 "),spe=n(u9o,"A",{href:!0});var U4n=s(spe);LMt=r(U4n,"TFXLNetLMHeadModel"),U4n.forEach(t),AMt=r(u9o," (XLNet 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with the `),lpe=n(I4e,"A",{href:!0});var O4n=s(lpe);BMt=r(O4n,"from_pretrained()"),O4n.forEach(t),IMt=r(I4e," class method or the "),ipe=n(I4e,"A",{href:!0});var Q4n=s(ipe);NMt=r(Q4n,"from_config()"),Q4n.forEach(t),$Mt=r(I4e,` class
method.`),I4e.forEach(t),jMt=i(cd),pW=n(cd,"P",{});var LNo=s(pW);PMt=r(LNo,"This class cannot be instantiated directly using "),gUe=n(LNo,"CODE",{});var Y4n=s(gUe);ZMt=r(Y4n,"__init__()"),Y4n.forEach(t),VMt=r(LNo," (throws an error)."),LNo.forEach(t),WMt=i(cd),Yt=n(cd,"DIV",{class:!0});var Zj=s(Yt);M(_W.$$.fragment,Zj),JMt=i(Zj),uUe=n(Zj,"P",{});var z4n=s(uUe);UMt=r(z4n,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),z4n.forEach(t),XMt=i(Zj),Sc=n(Zj,"P",{});var N4e=s(Sc);DMt=r(N4e,`Note:
Loading a model from its configuration file does `),pUe=n(N4e,"STRONG",{});var H4n=s(pUe);qMt=r(H4n,"not"),H4n.forEach(t),OMt=r(N4e,` load the model weights. It only affects the
model\u2019s configuration. Use `),dpe=n(N4e,"A",{href:!0});var K4n=s(dpe);QMt=r(K4n,"from_pretrained()"),K4n.forEach(t),YMt=r(N4e," to load the model weights."),N4e.forEach(t),zMt=i(Zj),M(dC.$$.fragment,Zj),Zj.forEach(t),HMt=i(cd),it=n(cd,"DIV",{class:!0});var fd=s(it);M(bW.$$.fragment,fd),KMt=i(fd),_Ue=n(fd,"P",{});var eCn=s(_Ue);eTt=r(eCn,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),eCn.forEach(t),oTt=i(fd),qn=n(fd,"P",{});var Vj=s(qn);rTt=r(Vj,"The model class to instantiate is selected based on the "),bUe=n(Vj,"CODE",{});var oCn=s(bUe);tTt=r(oCn,"model_type"),oCn.forEach(t),aTt=r(Vj,` property of the config object (either
passed as an argument or loaded from `),vUe=n(Vj,"CODE",{});var rCn=s(vUe);nTt=r(rCn,"pretrained_model_name_or_path"),rCn.forEach(t),sTt=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);lTt=r(tCn,"pretrained_model_name_or_path"),tCn.forEach(t),iTt=r(Vj,":"),Vj.forEach(t),dTt=i(fd),Le=n(fd,"UL",{});var Re=s(Le);mC=n(Re,"LI",{});var p9o=s(mC);TUe=n(p9o,"STRONG",{});var aCn=s(TUe);mTt=r(aCn,"bart"),aCn.forEach(t),cTt=r(p9o," \u2014 "),mpe=n(p9o,"A",{href:!0});var nCn=s(mpe);fTt=r(nCn,"FlaxBartForCausalLM"),nCn.forEach(t),hTt=r(p9o," (BART model)"),p9o.forEach(t),gTt=i(Re),cC=n(Re,"LI",{});var _9o=s(cC);FUe=n(_9o,"STRONG",{});var sCn=s(FUe);uTt=r(sCn,"bert"),sCn.forEach(t),pTt=r(_9o," \u2014 "),cpe=n(_9o,"A",{href:!0});var lCn=s(cpe);_Tt=r(lCn,"FlaxBertForCausalLM"),lCn.forEach(t),bTt=r(_9o," (BERT model)"),_9o.forEach(t),vTt=i(Re),fC=n(Re,"LI",{});var b9o=s(fC);EUe=n(b9o,"STRONG",{});var iCn=s(EUe);MTt=r(iCn,"big_bird"),iCn.forEach(t),TTt=r(b9o," \u2014 "),fpe=n(b9o,"A",{href:!0});var dCn=s(fpe);FTt=r(dCn,"FlaxBigBirdForCausalLM"),dCn.forEach(t),ETt=r(b9o," (BigBird model)"),b9o.forEach(t),yTt=i(Re),hC=n(Re,"LI",{});var v9o=s(hC);yUe=n(v9o,"STRONG",{});var mCn=s(yUe);CTt=r(mCn,"bloom"),mCn.forEach(t),wTt=r(v9o," \u2014 "),hpe=n(v9o,"A",{href:!0});var cCn=s(hpe);LTt=r(cCn,"FlaxBloomForCausalLM"),cCn.forEach(t),ATt=r(v9o," (BLOOM model)"),v9o.forEach(t),RTt=i(Re),gC=n(Re,"LI",{});var M9o=s(gC);CUe=n(M9o,"STRONG",{});var fCn=s(CUe);kTt=r(fCn,"electra"),fCn.forEach(t),STt=r(M9o," \u2014 "),gpe=n(M9o,"A",{href:!0});var hCn=s(gpe);xTt=r(hCn,"FlaxElectraForCausalLM"),hCn.forEach(t),GTt=r(M9o," (ELECTRA model)"),M9o.forEach(t),BTt=i(Re),uC=n(Re,"LI",{});var T9o=s(uC);wUe=n(T9o,"STRONG",{});var gCn=s(wUe);ITt=r(gCn,"gpt-sw3"),gCn.forEach(t),NTt=r(T9o," \u2014 "),upe=n(T9o,"A",{href:!0});var uCn=s(upe);$Tt=r(uCn,"FlaxGPT2LMHeadModel"),uCn.forEach(t),jTt=r(T9o," (GPT-Sw3 model)"),T9o.forEach(t),PTt=i(Re),pC=n(Re,"LI",{});var F9o=s(pC);LUe=n(F9o,"STRONG",{});var pCn=s(LUe);ZTt=r(pCn,"gpt2"),pCn.forEach(t),VTt=r(F9o," \u2014 "),ppe=n(F9o,"A",{href:!0});var _Cn=s(ppe);WTt=r(_Cn,"FlaxGPT2LMHeadModel"),_Cn.forEach(t),JTt=r(F9o," (OpenAI GPT-2 model)"),F9o.forEach(t),UTt=i(Re),_C=n(Re,"LI",{});var E9o=s(_C);AUe=n(E9o,"STRONG",{});var bCn=s(AUe);XTt=r(bCn,"gpt_neo"),bCn.forEach(t),DTt=r(E9o," \u2014 "),_pe=n(E9o,"A",{href:!0});var vCn=s(_pe);qTt=r(vCn,"FlaxGPTNeoForCausalLM"),vCn.forEach(t),OTt=r(E9o," (GPT Neo model)"),E9o.forEach(t),QTt=i(Re),bC=n(Re,"LI",{});var y9o=s(bC);RUe=n(y9o,"STRONG",{});var MCn=s(RUe);YTt=r(MCn,"gptj"),MCn.forEach(t),zTt=r(y9o," \u2014 "),bpe=n(y9o,"A",{href:!0});var TCn=s(bpe);HTt=r(TCn,"FlaxGPTJForCausalLM"),TCn.forEach(t),KTt=r(y9o," (GPT-J model)"),y9o.forEach(t),eFt=i(Re),vC=n(Re,"LI",{});var C9o=s(vC);kUe=n(C9o,"STRONG",{});var FCn=s(kUe);oFt=r(FCn,"opt"),FCn.forEach(t),rFt=r(C9o," \u2014 "),vpe=n(C9o,"A",{href:!0});var ECn=s(vpe);tFt=r(ECn,"FlaxOPTForCausalLM"),ECn.forEach(t),aFt=r(C9o," (OPT model)"),C9o.forEach(t),nFt=i(Re),MC=n(Re,"LI",{});var w9o=s(MC);SUe=n(w9o,"STRONG",{});var yCn=s(SUe);sFt=r(yCn,"roberta"),yCn.forEach(t),lFt=r(w9o," \u2014 "),Mpe=n(w9o,"A",{href:!0});var CCn=s(Mpe);iFt=r(CCn,"FlaxRobertaForCausalLM"),CCn.forEach(t),dFt=r(w9o," (RoBERTa model)"),w9o.forEach(t),mFt=i(Re),TC=n(Re,"LI",{});var L9o=s(TC);xUe=n(L9o,"STRONG",{});var wCn=s(xUe);cFt=r(wCn,"roberta-prelayernorm"),wCn.forEach(t),fFt=r(L9o," \u2014 "),Tpe=n(L9o,"A",{href:!0});var LCn=s(Tpe);hFt=r(LCn,"FlaxRobertaPreLayerNormForCausalLM"),LCn.forEach(t),gFt=r(L9o," (RoBERTa-PreLayerNorm model)"),L9o.forEach(t),uFt=i(Re),FC=n(Re,"LI",{});var A9o=s(FC);GUe=n(A9o,"STRONG",{});var ACn=s(GUe);pFt=r(ACn,"xglm"),ACn.forEach(t),_Ft=r(A9o," \u2014 "),Fpe=n(A9o,"A",{href:!0});var RCn=s(Fpe);bFt=r(RCn,"FlaxXGLMForCausalLM"),RCn.forEach(t),vFt=r(A9o," (XGLM model)"),A9o.forEach(t),MFt=i(Re),EC=n(Re,"LI",{});var R9o=s(EC);BUe=n(R9o,"STRONG",{});var kCn=s(BUe);TFt=r(kCn,"xlm-roberta"),kCn.forEach(t),FFt=r(R9o," \u2014 "),Epe=n(R9o,"A",{href:!0});var SCn=s(Epe);EFt=r(SCn,"FlaxXLMRobertaForCausalLM"),SCn.forEach(t),yFt=r(R9o," (XLM-RoBERTa model)"),R9o.forEach(t),Re.forEach(t),CFt=i(fd),M(yC.$$.fragment,fd),fd.forEach(t),cd.forEach(t),LGo=i(c),xc=n(c,"H3",{class:!0});var ANo=s(xc);CC=n(ANo,"A",{id:!0,class:!0,href:!0});var xCn=s(CC);IUe=n(xCn,"SPAN",{});var GCn=s(IUe);M(vW.$$.fragment,GCn),GCn.forEach(t),xCn.forEach(t),wFt=i(ANo),NUe=n(ANo,"SPAN",{});var BCn=s(NUe);LFt=r(BCn,"AutoModelForMaskedLM"),BCn.forEach(t),ANo.forEach(t),AGo=i(c),Ko=n(c,"DIV",{class:!0});var hd=s(Ko);M(MW.$$.fragment,hd),AFt=i(hd),Gc=n(hd,"P",{});var $4e=s(Gc);RFt=r($4e,`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($4e,"A",{href:!0});var ICn=s(ype);kFt=r(ICn,"from_pretrained()"),ICn.forEach(t),SFt=r($4e," class method or the "),Cpe=n($4e,"A",{href:!0});var NCn=s(Cpe);xFt=r(NCn,"from_config()"),NCn.forEach(t),GFt=r($4e,` class
method.`),$4e.forEach(t),BFt=i(hd),TW=n(hd,"P",{});var RNo=s(TW);IFt=r(RNo,"This class cannot be instantiated directly using "),$Ue=n(RNo,"CODE",{});var $Cn=s($Ue);NFt=r($Cn,"__init__()"),$Cn.forEach(t),$Ft=r(RNo," (throws an error)."),RNo.forEach(t),jFt=i(hd),zt=n(hd,"DIV",{class:!0});var Wj=s(zt);M(FW.$$.fragment,Wj),PFt=i(Wj),jUe=n(Wj,"P",{});var jCn=s(jUe);ZFt=r(jCn,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),jCn.forEach(t),VFt=i(Wj),Bc=n(Wj,"P",{});var j4e=s(Bc);WFt=r(j4e,`Note:
Loading a model from its configuration file does `),PUe=n(j4e,"STRONG",{});var PCn=s(PUe);JFt=r(PCn,"not"),PCn.forEach(t),UFt=r(j4e,` load the model weights. It only affects the
model\u2019s configuration. Use `),wpe=n(j4e,"A",{href:!0});var ZCn=s(wpe);XFt=r(ZCn,"from_pretrained()"),ZCn.forEach(t),DFt=r(j4e," to load the model weights."),j4e.forEach(t),qFt=i(Wj),M(wC.$$.fragment,Wj),Wj.forEach(t),OFt=i(hd),mo=n(hd,"DIV",{class:!0});var tn=s(mo);M(EW.$$.fragment,tn),QFt=i(tn),ZUe=n(tn,"P",{});var VCn=s(ZUe);YFt=r(VCn,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),VCn.forEach(t),zFt=i(tn),On=n(tn,"P",{});var Jj=s(On);HFt=r(Jj,"The model class to instantiate is selected based on the "),VUe=n(Jj,"CODE",{});var WCn=s(VUe);KFt=r(WCn,"model_type"),WCn.forEach(t),eEt=r(Jj,` property of the config object (either
passed as an argument or loaded from `),WUe=n(Jj,"CODE",{});var JCn=s(WUe);oEt=r(JCn,"pretrained_model_name_or_path"),JCn.forEach(t),rEt=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);tEt=r(UCn,"pretrained_model_name_or_path"),UCn.forEach(t),aEt=r(Jj,":"),Jj.forEach(t),nEt=i(tn),K=n(tn,"UL",{});var ee=s(K);LC=n(ee,"LI",{});var k9o=s(LC);UUe=n(k9o,"STRONG",{});var XCn=s(UUe);sEt=r(XCn,"albert"),XCn.forEach(t),lEt=r(k9o," \u2014 "),Lpe=n(k9o,"A",{href:!0});var DCn=s(Lpe);iEt=r(DCn,"AlbertForMaskedLM"),DCn.forEach(t),dEt=r(k9o," (ALBERT model)"),k9o.forEach(t),mEt=i(ee),AC=n(ee,"LI",{});var S9o=s(AC);XUe=n(S9o,"STRONG",{});var qCn=s(XUe);cEt=r(qCn,"bart"),qCn.forEach(t),fEt=r(S9o," \u2014 "),Ape=n(S9o,"A",{href:!0});var OCn=s(Ape);hEt=r(OCn,"BartForConditionalGeneration"),OCn.forEach(t),gEt=r(S9o," (BART model)"),S9o.forEach(t),uEt=i(ee),RC=n(ee,"LI",{});var x9o=s(RC);DUe=n(x9o,"STRONG",{});var QCn=s(DUe);pEt=r(QCn,"bert"),QCn.forEach(t),_Et=r(x9o," \u2014 "),Rpe=n(x9o,"A",{href:!0});var YCn=s(Rpe);bEt=r(YCn,"BertForMaskedLM"),YCn.forEach(t),vEt=r(x9o," (BERT model)"),x9o.forEach(t),MEt=i(ee),kC=n(ee,"LI",{});var G9o=s(kC);qUe=n(G9o,"STRONG",{});var zCn=s(qUe);TEt=r(zCn,"big_bird"),zCn.forEach(t),FEt=r(G9o," \u2014 "),kpe=n(G9o,"A",{href:!0});var HCn=s(kpe);EEt=r(HCn,"BigBirdForMaskedLM"),HCn.forEach(t),yEt=r(G9o," (BigBird model)"),G9o.forEach(t),CEt=i(ee),SC=n(ee,"LI",{});var B9o=s(SC);OUe=n(B9o,"STRONG",{});var KCn=s(OUe);wEt=r(KCn,"camembert"),KCn.forEach(t),LEt=r(B9o," \u2014 "),Spe=n(B9o,"A",{href:!0});var ewn=s(Spe);AEt=r(ewn,"CamembertForMaskedLM"),ewn.forEach(t),REt=r(B9o," (CamemBERT model)"),B9o.forEach(t),kEt=i(ee),xC=n(ee,"LI",{});var I9o=s(xC);QUe=n(I9o,"STRONG",{});var own=s(QUe);SEt=r(own,"convbert"),own.forEach(t),xEt=r(I9o," \u2014 "),xpe=n(I9o,"A",{href:!0});var rwn=s(xpe);GEt=r(rwn,"ConvBertForMaskedLM"),rwn.forEach(t),BEt=r(I9o," (ConvBERT model)"),I9o.forEach(t),IEt=i(ee),GC=n(ee,"LI",{});var N9o=s(GC);YUe=n(N9o,"STRONG",{});var twn=s(YUe);NEt=r(twn,"data2vec-text"),twn.forEach(t),$Et=r(N9o," \u2014 "),Gpe=n(N9o,"A",{href:!0});var awn=s(Gpe);jEt=r(awn,"Data2VecTextForMaskedLM"),awn.forEach(t),PEt=r(N9o," (Data2VecText model)"),N9o.forEach(t),ZEt=i(ee),BC=n(ee,"LI",{});var $9o=s(BC);zUe=n($9o,"STRONG",{});var nwn=s(zUe);VEt=r(nwn,"deberta"),nwn.forEach(t),WEt=r($9o," \u2014 "),Bpe=n($9o,"A",{href:!0});var swn=s(Bpe);JEt=r(swn,"DebertaForMaskedLM"),swn.forEach(t),UEt=r($9o," (DeBERTa model)"),$9o.forEach(t),XEt=i(ee),IC=n(ee,"LI",{});var j9o=s(IC);HUe=n(j9o,"STRONG",{});var lwn=s(HUe);DEt=r(lwn,"deberta-v2"),lwn.forEach(t),qEt=r(j9o," \u2014 "),Ipe=n(j9o,"A",{href:!0});var iwn=s(Ipe);OEt=r(iwn,"DebertaV2ForMaskedLM"),iwn.forEach(t),QEt=r(j9o," (DeBERTa-v2 model)"),j9o.forEach(t),YEt=i(ee),NC=n(ee,"LI",{});var P9o=s(NC);KUe=n(P9o,"STRONG",{});var dwn=s(KUe);zEt=r(dwn,"distilbert"),dwn.forEach(t),HEt=r(P9o," \u2014 "),Npe=n(P9o,"A",{href:!0});var mwn=s(Npe);KEt=r(mwn,"DistilBertForMaskedLM"),mwn.forEach(t),eyt=r(P9o," (DistilBERT model)"),P9o.forEach(t),oyt=i(ee),$C=n(ee,"LI",{});var Z9o=s($C);eXe=n(Z9o,"STRONG",{});var cwn=s(eXe);ryt=r(cwn,"electra"),cwn.forEach(t),tyt=r(Z9o," \u2014 "),$pe=n(Z9o,"A",{href:!0});var fwn=s($pe);ayt=r(fwn,"ElectraForMaskedLM"),fwn.forEach(t),nyt=r(Z9o," (ELECTRA model)"),Z9o.forEach(t),syt=i(ee),jC=n(ee,"LI",{});var V9o=s(jC);oXe=n(V9o,"STRONG",{});var hwn=s(oXe);lyt=r(hwn,"ernie"),hwn.forEach(t),iyt=r(V9o," \u2014 "),jpe=n(V9o,"A",{href:!0});var gwn=s(jpe);dyt=r(gwn,"ErnieForMaskedLM"),gwn.forEach(t),myt=r(V9o," (ERNIE model)"),V9o.forEach(t),cyt=i(ee),PC=n(ee,"LI",{});var W9o=s(PC);rXe=n(W9o,"STRONG",{});var uwn=s(rXe);fyt=r(uwn,"esm"),uwn.forEach(t),hyt=r(W9o," \u2014 "),Ppe=n(W9o,"A",{href:!0});var pwn=s(Ppe);gyt=r(pwn,"EsmForMaskedLM"),pwn.forEach(t),uyt=r(W9o," (ESM model)"),W9o.forEach(t),pyt=i(ee),ZC=n(ee,"LI",{});var J9o=s(ZC);tXe=n(J9o,"STRONG",{});var _wn=s(tXe);_yt=r(_wn,"flaubert"),_wn.forEach(t),byt=r(J9o," \u2014 "),Zpe=n(J9o,"A",{href:!0});var bwn=s(Zpe);vyt=r(bwn,"FlaubertWithLMHeadModel"),bwn.forEach(t),Myt=r(J9o," 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with the `),__e=n(P4e,"A",{href:!0});var x6n=s(__e);y3t=r(x6n,"from_pretrained()"),x6n.forEach(t),C3t=r(P4e," class method or the "),b_e=n(P4e,"A",{href:!0});var G6n=s(b_e);w3t=r(G6n,"from_config()"),G6n.forEach(t),L3t=r(P4e,` class
method.`),P4e.forEach(t),A3t=i(gd),wW=n(gd,"P",{});var SNo=s(wW);R3t=r(SNo,"This class cannot be instantiated directly using "),ZXe=n(SNo,"CODE",{});var B6n=s(ZXe);k3t=r(B6n,"__init__()"),B6n.forEach(t),S3t=r(SNo," (throws an error)."),SNo.forEach(t),x3t=i(gd),Ht=n(gd,"DIV",{class:!0});var Uj=s(Ht);M(LW.$$.fragment,Uj),G3t=i(Uj),VXe=n(Uj,"P",{});var I6n=s(VXe);B3t=r(I6n,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),I6n.forEach(t),I3t=i(Uj),$c=n(Uj,"P",{});var Z4e=s($c);N3t=r(Z4e,`Note:
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model\u2019s configuration. Use `),v_e=n(Z4e,"A",{href:!0});var $6n=s(v_e);P3t=r($6n,"from_pretrained()"),$6n.forEach(t),Z3t=r(Z4e," to load the model weights."),Z4e.forEach(t),V3t=i(Uj),M(Tw.$$.fragment,Uj),Uj.forEach(t),W3t=i(gd),dt=n(gd,"DIV",{class:!0});var ud=s(dt);M(AW.$$.fragment,ud),J3t=i(ud),JXe=n(ud,"P",{});var j6n=s(JXe);U3t=r(j6n,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),j6n.forEach(t),X3t=i(ud),Qn=n(ud,"P",{});var Xj=s(Qn);D3t=r(Xj,"The model class to instantiate is selected based on the "),UXe=n(Xj,"CODE",{});var P6n=s(UXe);q3t=r(P6n,"model_type"),P6n.forEach(t),O3t=r(Xj,` property of the config object (either
passed as an argument or loaded from `),XXe=n(Xj,"CODE",{});var Z6n=s(XXe);Q3t=r(Z6n,"pretrained_model_name_or_path"),Z6n.forEach(t),Y3t=r(Xj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),DXe=n(Xj,"CODE",{});var V6n=s(DXe);z3t=r(V6n,"pretrained_model_name_or_path"),V6n.forEach(t),H3t=r(Xj,":"),Xj.forEach(t),K3t=i(ud),pe=n(ud,"UL",{});var Me=s(pe);Fw=n(Me,"LI",{});var F4o=s(Fw);qXe=n(F4o,"STRONG",{});var W6n=s(qXe);e9t=r(W6n,"albert"),W6n.forEach(t),o9t=r(F4o," \u2014 "),M_e=n(F4o,"A",{href:!0});var J6n=s(M_e);r9t=r(J6n,"TFAlbertForMaskedLM"),J6n.forEach(t),t9t=r(F4o," (ALBERT model)"),F4o.forEach(t),a9t=i(Me),Ew=n(Me,"LI",{});var E4o=s(Ew);OXe=n(E4o,"STRONG",{});var U6n=s(OXe);n9t=r(U6n,"bert"),U6n.forEach(t),s9t=r(E4o," \u2014 "),T_e=n(E4o,"A",{href:!0});var X6n=s(T_e);l9t=r(X6n,"TFBertForMaskedLM"),X6n.forEach(t),i9t=r(E4o," (BERT model)"),E4o.forEach(t),d9t=i(Me),yw=n(Me,"LI",{});var y4o=s(yw);QXe=n(y4o,"STRONG",{});var D6n=s(QXe);m9t=r(D6n,"camembert"),D6n.forEach(t),c9t=r(y4o," \u2014 "),F_e=n(y4o,"A",{href:!0});var q6n=s(F_e);f9t=r(q6n,"TFCamembertForMaskedLM"),q6n.forEach(t),h9t=r(y4o," (CamemBERT 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u8n=s(G_e);h4t=r(u8n,"TFMobileBertForMaskedLM"),u8n.forEach(t),g4t=r(I4o," (MobileBERT model)"),I4o.forEach(t),u4t=i(Me),Nw=n(Me,"LI",{});var N4o=s(Nw);lDe=n(N4o,"STRONG",{});var p8n=s(lDe);p4t=r(p8n,"mpnet"),p8n.forEach(t),_4t=r(N4o," \u2014 "),B_e=n(N4o,"A",{href:!0});var _8n=s(B_e);b4t=r(_8n,"TFMPNetForMaskedLM"),_8n.forEach(t),v4t=r(N4o," (MPNet model)"),N4o.forEach(t),M4t=i(Me),$w=n(Me,"LI",{});var $4o=s($w);iDe=n($4o,"STRONG",{});var b8n=s(iDe);T4t=r(b8n,"rembert"),b8n.forEach(t),F4t=r($4o," \u2014 "),I_e=n($4o,"A",{href:!0});var v8n=s(I_e);E4t=r(v8n,"TFRemBertForMaskedLM"),v8n.forEach(t),y4t=r($4o," (RemBERT model)"),$4o.forEach(t),C4t=i(Me),jw=n(Me,"LI",{});var j4o=s(jw);dDe=n(j4o,"STRONG",{});var M8n=s(dDe);w4t=r(M8n,"roberta"),M8n.forEach(t),L4t=r(j4o," \u2014 "),N_e=n(j4o,"A",{href:!0});var T8n=s(N_e);A4t=r(T8n,"TFRobertaForMaskedLM"),T8n.forEach(t),R4t=r(j4o," (RoBERTa model)"),j4o.forEach(t),k4t=i(Me),Pw=n(Me,"LI",{});var P4o=s(Pw);mDe=n(P4o,"STRONG",{});var F8n=s(mDe);S4t=r(F8n,"roberta-prelayernorm"),F8n.forEach(t),x4t=r(P4o," \u2014 "),$_e=n(P4o,"A",{href:!0});var E8n=s($_e);G4t=r(E8n,"TFRobertaPreLayerNormForMaskedLM"),E8n.forEach(t),B4t=r(P4o," (RoBERTa-PreLayerNorm model)"),P4o.forEach(t),I4t=i(Me),Zw=n(Me,"LI",{});var Z4o=s(Zw);cDe=n(Z4o,"STRONG",{});var y8n=s(cDe);N4t=r(y8n,"roformer"),y8n.forEach(t),$4t=r(Z4o," \u2014 "),j_e=n(Z4o,"A",{href:!0});var C8n=s(j_e);j4t=r(C8n,"TFRoFormerForMaskedLM"),C8n.forEach(t),P4t=r(Z4o," (RoFormer model)"),Z4o.forEach(t),Z4t=i(Me),Vw=n(Me,"LI",{});var V4o=s(Vw);fDe=n(V4o,"STRONG",{});var w8n=s(fDe);V4t=r(w8n,"tapas"),w8n.forEach(t),W4t=r(V4o," \u2014 "),P_e=n(V4o,"A",{href:!0});var L8n=s(P_e);J4t=r(L8n,"TFTapasForMaskedLM"),L8n.forEach(t),U4t=r(V4o," (TAPAS model)"),V4o.forEach(t),X4t=i(Me),Ww=n(Me,"LI",{});var W4o=s(Ww);hDe=n(W4o,"STRONG",{});var A8n=s(hDe);D4t=r(A8n,"xlm"),A8n.forEach(t),q4t=r(W4o," \u2014 "),Z_e=n(W4o,"A",{href:!0});var 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with the `),W_e=n(V4e,"A",{href:!0});var I8n=s(W_e);sCt=r(I8n,"from_pretrained()"),I8n.forEach(t),lCt=r(V4e," class method or the "),J_e=n(V4e,"A",{href:!0});var N8n=s(J_e);iCt=r(N8n,"from_config()"),N8n.forEach(t),dCt=r(V4e,` class
method.`),V4e.forEach(t),mCt=i(pd),SW=n(pd,"P",{});var GNo=s(SW);cCt=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),bDe=n(Dj,"P",{});var j8n=s(bDe);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),Zc=n(Dj,"P",{});var W4e=s(Zc);bCt=r(W4e,`Note:
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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 U4o=s(qw);yDe=n(U4o,"STRONG",{});var X8n=s(yDe);NCt=r(X8n,"albert"),X8n.forEach(t),$Ct=r(U4o," \u2014 "),X_e=n(U4o,"A",{href:!0});var D8n=s(X_e);jCt=r(D8n,"FlaxAlbertForMaskedLM"),D8n.forEach(t),PCt=r(U4o," (ALBERT model)"),U4o.forEach(t),ZCt=i(Pe),Ow=n(Pe,"LI",{});var X4o=s(Ow);CDe=n(X4o,"STRONG",{});var q8n=s(CDe);VCt=r(q8n,"bart"),q8n.forEach(t),WCt=r(X4o," \u2014 "),D_e=n(X4o,"A",{href:!0});var O8n=s(D_e);JCt=r(O8n,"FlaxBartForConditionalGeneration"),O8n.forEach(t),UCt=r(X4o," (BART model)"),X4o.forEach(t),XCt=i(Pe),Qw=n(Pe,"LI",{});var D4o=s(Qw);wDe=n(D4o,"STRONG",{});var Q8n=s(wDe);DCt=r(Q8n,"bert"),Q8n.forEach(t),qCt=r(D4o," \u2014 "),q_e=n(D4o,"A",{href:!0});var Y8n=s(q_e);OCt=r(Y8n,"FlaxBertForMaskedLM"),Y8n.forEach(t),QCt=r(D4o," (BERT model)"),D4o.forEach(t),YCt=i(Pe),Yw=n(Pe,"LI",{});var q4o=s(Yw);LDe=n(q4o,"STRONG",{});var z8n=s(LDe);zCt=r(z8n,"big_bird"),z8n.forEach(t),HCt=r(q4o," \u2014 "),O_e=n(q4o,"A",{href:!0});var H8n=s(O_e);KCt=r(H8n,"FlaxBigBirdForMaskedLM"),H8n.forEach(t),ewt=r(q4o," (BigBird model)"),q4o.forEach(t),owt=i(Pe),zw=n(Pe,"LI",{});var O4o=s(zw);ADe=n(O4o,"STRONG",{});var K8n=s(ADe);rwt=r(K8n,"distilbert"),K8n.forEach(t),twt=r(O4o," \u2014 "),Q_e=n(O4o,"A",{href:!0});var e7n=s(Q_e);awt=r(e7n,"FlaxDistilBertForMaskedLM"),e7n.forEach(t),nwt=r(O4o," (DistilBERT model)"),O4o.forEach(t),swt=i(Pe),Hw=n(Pe,"LI",{});var Q4o=s(Hw);RDe=n(Q4o,"STRONG",{});var o7n=s(RDe);lwt=r(o7n,"electra"),o7n.forEach(t),iwt=r(Q4o," \u2014 "),Y_e=n(Q4o,"A",{href:!0});var r7n=s(Y_e);dwt=r(r7n,"FlaxElectraForMaskedLM"),r7n.forEach(t),mwt=r(Q4o," (ELECTRA model)"),Q4o.forEach(t),cwt=i(Pe),Kw=n(Pe,"LI",{});var Y4o=s(Kw);kDe=n(Y4o,"STRONG",{});var t7n=s(kDe);fwt=r(t7n,"mbart"),t7n.forEach(t),hwt=r(Y4o," \u2014 "),z_e=n(Y4o,"A",{href:!0});var a7n=s(z_e);gwt=r(a7n,"FlaxMBartForConditionalGeneration"),a7n.forEach(t),uwt=r(Y4o," (mBART model)"),Y4o.forEach(t),pwt=i(Pe),e6=n(Pe,"LI",{});var z4o=s(e6);SDe=n(z4o,"STRONG",{});var n7n=s(SDe);_wt=r(n7n,"roberta"),n7n.forEach(t),bwt=r(z4o," \u2014 "),H_e=n(z4o,"A",{href:!0});var s7n=s(H_e);vwt=r(s7n,"FlaxRobertaForMaskedLM"),s7n.forEach(t),Mwt=r(z4o," (RoBERTa model)"),z4o.forEach(t),Twt=i(Pe),o6=n(Pe,"LI",{});var H4o=s(o6);xDe=n(H4o,"STRONG",{});var l7n=s(xDe);Fwt=r(l7n,"roberta-prelayernorm"),l7n.forEach(t),Ewt=r(H4o," \u2014 "),K_e=n(H4o,"A",{href:!0});var i7n=s(K_e);ywt=r(i7n,"FlaxRobertaPreLayerNormForMaskedLM"),i7n.forEach(t),Cwt=r(H4o," (RoBERTa-PreLayerNorm model)"),H4o.forEach(t),wwt=i(Pe),r6=n(Pe,"LI",{});var K4o=s(r6);GDe=n(K4o,"STRONG",{});var d7n=s(GDe);Lwt=r(d7n,"roformer"),d7n.forEach(t),Awt=r(K4o," \u2014 "),e1e=n(K4o,"A",{href:!0});var m7n=s(e1e);Rwt=r(m7n,"FlaxRoFormerForMaskedLM"),m7n.forEach(t),kwt=r(K4o," (RoFormer model)"),K4o.forEach(t),Swt=i(Pe),t6=n(Pe,"LI",{});var eCo=s(t6);BDe=n(eCo,"STRONG",{});var c7n=s(BDe);xwt=r(c7n,"xlm-roberta"),c7n.forEach(t),Gwt=r(eCo," \u2014 "),o1e=n(eCo,"A",{href:!0});var f7n=s(o1e);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(c),Vc=n(c,"H3",{class:!0});var BNo=s(Vc);n6=n(BNo,"A",{id:!0,class:!0,href:!0});var h7n=s(n6);IDe=n(h7n,"SPAN",{});var g7n=s(IDe);M(BW.$$.fragment,g7n),g7n.forEach(t),h7n.forEach(t),$wt=i(BNo),NDe=n(BNo,"SPAN",{});var u7n=s(NDe);jwt=r(u7n,"AutoModelForMaskGeneration"),u7n.forEach(t),BNo.forEach(t),BGo=i(c),IW=n(c,"DIV",{class:!0});var p7n=s(IW);M(NW.$$.fragment,p7n),p7n.forEach(t),IGo=i(c),Wc=n(c,"H3",{class:!0});var INo=s(Wc);s6=n(INo,"A",{id:!0,class:!0,href:!0});var _7n=s(s6);$De=n(_7n,"SPAN",{});var b7n=s($De);M($W.$$.fragment,b7n),b7n.forEach(t),_7n.forEach(t),Pwt=i(INo),jDe=n(INo,"SPAN",{});var v7n=s(jDe);Zwt=r(v7n,"TFAutoModelForMaskGeneration"),v7n.forEach(t),INo.forEach(t),NGo=i(c),jW=n(c,"DIV",{class:!0});var M7n=s(jW);M(PW.$$.fragment,M7n),M7n.forEach(t),$Go=i(c),Jc=n(c,"H3",{class:!0});var NNo=s(Jc);l6=n(NNo,"A",{id:!0,class:!0,href:!0});var T7n=s(l6);PDe=n(T7n,"SPAN",{});var F7n=s(PDe);M(ZW.$$.fragment,F7n),F7n.forEach(t),T7n.forEach(t),Vwt=i(NNo),ZDe=n(NNo,"SPAN",{});var E7n=s(ZDe);Wwt=r(E7n,"AutoModelForSeq2SeqLM"),E7n.forEach(t),NNo.forEach(t),jGo=i(c),rr=n(c,"DIV",{class:!0});var bd=s(rr);M(VW.$$.fragment,bd),Jwt=i(bd),Uc=n(bd,"P",{});var J4e=s(Uc);Uwt=r(J4e,`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 `),r1e=n(J4e,"A",{href:!0});var y7n=s(r1e);Xwt=r(y7n,"from_pretrained()"),y7n.forEach(t),Dwt=r(J4e," class method or the "),t1e=n(J4e,"A",{href:!0});var C7n=s(t1e);qwt=r(C7n,"from_config()"),C7n.forEach(t),Owt=r(J4e,` class
method.`),J4e.forEach(t),Qwt=i(bd),WW=n(bd,"P",{});var $No=s(WW);Ywt=r($No,"This class cannot be instantiated directly using "),VDe=n($No,"CODE",{});var w7n=s(VDe);zwt=r(w7n,"__init__()"),w7n.forEach(t),Hwt=r($No," (throws an error)."),$No.forEach(t),Kwt=i(bd),ea=n(bd,"DIV",{class:!0});var Oj=s(ea);M(JW.$$.fragment,Oj),e6t=i(Oj),WDe=n(Oj,"P",{});var L7n=s(WDe);o6t=r(L7n,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),L7n.forEach(t),r6t=i(Oj),Xc=n(Oj,"P",{});var U4e=s(Xc);t6t=r(U4e,`Note:
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model\u2019s configuration. Use `),a1e=n(U4e,"A",{href:!0});var R7n=s(a1e);s6t=r(R7n,"from_pretrained()"),R7n.forEach(t),l6t=r(U4e," to load the model weights."),U4e.forEach(t),i6t=i(Oj),M(i6.$$.fragment,Oj),Oj.forEach(t),d6t=i(bd),co=n(bd,"DIV",{class:!0});var an=s(co);M(UW.$$.fragment,an),m6t=i(an),UDe=n(an,"P",{});var k7n=s(UDe);c6t=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);b6t=r(G7n,"pretrained_model_name_or_path"),G7n.forEach(t),v6t=r(Qj,":"),Qj.forEach(t),M6t=i(an),ge=n(an,"UL",{});var be=s(ge);d6=n(be,"LI",{});var oCo=s(d6);ODe=n(oCo,"STRONG",{});var B7n=s(ODe);T6t=r(B7n,"bart"),B7n.forEach(t),F6t=r(oCo," \u2014 "),n1e=n(oCo,"A",{href:!0});var I7n=s(n1e);E6t=r(I7n,"BartForConditionalGeneration"),I7n.forEach(t),y6t=r(oCo," (BART model)"),oCo.forEach(t),C6t=i(be),m6=n(be,"LI",{});var rCo=s(m6);QDe=n(rCo,"STRONG",{});var N7n=s(QDe);w6t=r(N7n,"bigbird_pegasus"),N7n.forEach(t),L6t=r(rCo," \u2014 "),s1e=n(rCo,"A",{href:!0});var $7n=s(s1e);A6t=r($7n,"BigBirdPegasusForConditionalGeneration"),$7n.forEach(t),R6t=r(rCo," (BigBird-Pegasus model)"),rCo.forEach(t),k6t=i(be),c6=n(be,"LI",{});var tCo=s(c6);YDe=n(tCo,"STRONG",{});var j7n=s(YDe);S6t=r(j7n,"blenderbot"),j7n.forEach(t),x6t=r(tCo," \u2014 "),l1e=n(tCo,"A",{href:!0});var P7n=s(l1e);G6t=r(P7n,"BlenderbotForConditionalGeneration"),P7n.forEach(t),B6t=r(tCo," (Blenderbot model)"),tCo.forEach(t),I6t=i(be),f6=n(be,"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 "),i1e=n(aCo,"A",{href:!0});var V7n=s(i1e);j6t=r(V7n,"BlenderbotSmallForConditionalGeneration"),V7n.forEach(t),P6t=r(aCo," (BlenderbotSmall model)"),aCo.forEach(t),Z6t=i(be),h6=n(be,"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 "),d1e=n(nCo,"A",{href:!0});var J7n=s(d1e);J6t=r(J7n,"EncoderDecoderModel"),J7n.forEach(t),U6t=r(nCo," (Encoder decoder model)"),nCo.forEach(t),X6t=i(be),g6=n(be,"LI",{});var sCo=s(g6);KDe=n(sCo,"STRONG",{});var U7n=s(KDe);D6t=r(U7n,"fsmt"),U7n.forEach(t),q6t=r(sCo," \u2014 "),m1e=n(sCo,"A",{href:!0});var X7n=s(m1e);O6t=r(X7n,"FSMTForConditionalGeneration"),X7n.forEach(t),Q6t=r(sCo," (FairSeq Machine-Translation 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H7n=s(tqe);f8t=r(H7n,"m2m_100"),H7n.forEach(t),h8t=r(mCo," \u2014 "),g1e=n(mCo,"A",{href:!0});var K7n=s(g1e);g8t=r(K7n,"M2M100ForConditionalGeneration"),K7n.forEach(t),u8t=r(mCo," (M2M100 model)"),mCo.forEach(t),p8t=i(be),v6=n(be,"LI",{});var cCo=s(v6);aqe=n(cCo,"STRONG",{});var eLn=s(aqe);_8t=r(eLn,"marian"),eLn.forEach(t),b8t=r(cCo," \u2014 "),u1e=n(cCo,"A",{href:!0});var oLn=s(u1e);v8t=r(oLn,"MarianMTModel"),oLn.forEach(t),M8t=r(cCo," (Marian model)"),cCo.forEach(t),T8t=i(be),M6=n(be,"LI",{});var fCo=s(M6);nqe=n(fCo,"STRONG",{});var rLn=s(nqe);F8t=r(rLn,"mbart"),rLn.forEach(t),E8t=r(fCo," \u2014 "),p1e=n(fCo,"A",{href:!0});var tLn=s(p1e);y8t=r(tLn,"MBartForConditionalGeneration"),tLn.forEach(t),C8t=r(fCo," (mBART model)"),fCo.forEach(t),w8t=i(be),T6=n(be,"LI",{});var hCo=s(T6);sqe=n(hCo,"STRONG",{});var aLn=s(sqe);L8t=r(aLn,"mt5"),aLn.forEach(t),A8t=r(hCo," \u2014 "),_1e=n(hCo,"A",{href:!0});var nLn=s(_1e);R8t=r(nLn,"MT5ForConditionalGeneration"),nLn.forEach(t),k8t=r(hCo," (MT5 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deactivated). To train the model, you should first set it back in training mode with `),bqe=n(yCo,"CODE",{});var LLn=s(bqe);S7t=r(LLn,"model.train()"),LLn.forEach(t),yCo.forEach(t),x7t=i(an),M(G6.$$.fragment,an),an.forEach(t),bd.forEach(t),PGo=i(c),Dc=n(c,"H3",{class:!0});var jNo=s(Dc);B6=n(jNo,"A",{id:!0,class:!0,href:!0});var ALn=s(B6);vqe=n(ALn,"SPAN",{});var RLn=s(vqe);M(XW.$$.fragment,RLn),RLn.forEach(t),ALn.forEach(t),G7t=i(jNo),Mqe=n(jNo,"SPAN",{});var kLn=s(Mqe);B7t=r(kLn,"TFAutoModelForSeq2SeqLM"),kLn.forEach(t),jNo.forEach(t),ZGo=i(c),tr=n(c,"DIV",{class:!0});var vd=s(tr);M(DW.$$.fragment,vd),I7t=i(vd),qc=n(vd,"P",{});var X4e=s(qc);N7t=r(X4e,`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 `),A1e=n(X4e,"A",{href:!0});var SLn=s(A1e);$7t=r(SLn,"from_pretrained()"),SLn.forEach(t),j7t=r(X4e," class method or the "),R1e=n(X4e,"A",{href:!0});var xLn=s(R1e);P7t=r(xLn,"from_config()"),xLn.forEach(t),Z7t=r(X4e,` class
method.`),X4e.forEach(t),V7t=i(vd),qW=n(vd,"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(vd),oa=n(vd,"DIV",{class:!0});var Yj=s(oa);M(OW.$$.fragment,Yj),D7t=i(Yj),Fqe=n(Yj,"P",{});var BLn=s(Fqe);q7t=r(BLn,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),BLn.forEach(t),O7t=i(Yj),Oc=n(Yj,"P",{});var D4e=s(Oc);Q7t=r(D4e,`Note:
Loading a model from its configuration file does `),Eqe=n(D4e,"STRONG",{});var ILn=s(Eqe);Y7t=r(ILn,"not"),ILn.forEach(t),z7t=r(D4e,` load the model weights. It only affects the
model\u2019s configuration. Use `),k1e=n(D4e,"A",{href:!0});var NLn=s(k1e);H7t=r(NLn,"from_pretrained()"),NLn.forEach(t),K7t=r(D4e," to load the model weights."),D4e.forEach(t),eLt=i(Yj),M(I6.$$.fragment,Yj),Yj.forEach(t),oLt=i(vd),ct=n(vd,"DIV",{class:!0});var Md=s(ct);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);mLt=r(ZLn,"pretrained_model_name_or_path"),ZLn.forEach(t),cLt=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 "),S1e=n(CCo,"A",{href:!0});var WLn=s(S1e);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);bLt=r(JLn,"blenderbot"),JLn.forEach(t),vLt=r(wCo," \u2014 "),x1e=n(wCo,"A",{href:!0});var ULn=s(x1e);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 "),G1e=n(LCo,"A",{href:!0});var DLn=s(G1e);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 "),B1e=n(ACo,"A",{href:!0});var OLn=s(B1e);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 "),I1e=n(RCo,"A",{href:!0});var YLn=s(I1e);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 "),N1e=n(kCo,"A",{href:!0});var HLn=s(N1e);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 "),$1e=n(SCo,"A",{href:!0});var eAn=s($1e);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 "),j1e=n(xCo,"A",{href:!0});var rAn=s(j1e);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 "),P1e=n(GCo,"A",{href:!0});var aAn=s(P1e);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 "),Z1e=n(BCo,"A",{href:!0});var sAn=s(Z1e);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),vd.forEach(t),VGo=i(c),Qc=n(c,"H3",{class:!0});var ZNo=s(Qc);q6=n(ZNo,"A",{id:!0,class:!0,href:!0});var lAn=s(q6);jqe=n(lAn,"SPAN",{});var iAn=s(jqe);M(YW.$$.fragment,iAn),iAn.forEach(t),lAn.forEach(t),dAt=i(ZNo),Pqe=n(ZNo,"SPAN",{});var dAn=s(Pqe);mAt=r(dAn,"FlaxAutoModelForSeq2SeqLM"),dAn.forEach(t),ZNo.forEach(t),WGo=i(c),ar=n(c,"DIV",{class:!0});var Td=s(ar);M(zW.$$.fragment,Td),cAt=i(Td),Yc=n(Td,"P",{});var q4e=s(Yc);fAt=r(q4e,`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 `),V1e=n(q4e,"A",{href:!0});var mAn=s(V1e);hAt=r(mAn,"from_pretrained()"),mAn.forEach(t),gAt=r(q4e," class method or the "),W1e=n(q4e,"A",{href:!0});var cAn=s(W1e);uAt=r(cAn,"from_config()"),cAn.forEach(t),pAt=r(q4e,` class
method.`),q4e.forEach(t),_At=i(Td),HW=n(Td,"P",{});var VNo=s(HW);bAt=r(VNo,"This class cannot be instantiated directly using "),Zqe=n(VNo,"CODE",{});var fAn=s(Zqe);vAt=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.$$.fragment,Hj),FAt=i(Hj),Vqe=n(Hj,"P",{});var hAn=s(Vqe);EAt=r(hAn,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),hAn.forEach(t),yAt=i(Hj),zc=n(Hj,"P",{});var O4e=s(zc);CAt=r(O4e,`Note:
Loading a model from its configuration file does `),Wqe=n(O4e,"STRONG",{});var gAn=s(Wqe);wAt=r(gAn,"not"),gAn.forEach(t),LAt=r(O4e,` load the model weights. It only affects the
model\u2019s configuration. Use `),J1e=n(O4e,"A",{href:!0});var uAn=s(J1e);AAt=r(uAn,"from_pretrained()"),uAn.forEach(t),RAt=r(O4e," to load the model weights."),O4e.forEach(t),kAt=i(Hj),M(O6.$$.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 bAn=s(Xqe);jAt=r(bAn,"pretrained_model_name_or_path"),bAn.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 vAn=s(Dqe);ZAt=r(vAn,"pretrained_model_name_or_path"),vAn.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 "),U1e=n(ICo,"A",{href:!0});var TAn=s(U1e);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 "),X1e=n(NCo,"A",{href:!0});var EAn=s(X1e);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 "),D1e=n($Co,"A",{href:!0});var CAn=s(D1e);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 "),q1e=n(jCo,"A",{href:!0});var LAn=s(q1e);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),mRt=r(PCo," \u2014 "),O1e=n(PCo,"A",{href:!0});var RAn=s(O1e);cRt=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 "),Q1e=n(ZCo,"A",{href:!0});var SAn=s(Q1e);pRt=r(SAn,"FlaxMarianMTModel"),SAn.forEach(t),_Rt=r(ZCo," (Marian model)"),ZCo.forEach(t),bRt=i(qe),o8=n(qe,"LI",{});var VCo=s(o8);Kqe=n(VCo,"STRONG",{});var xAn=s(Kqe);vRt=r(xAn,"mbart"),xAn.forEach(t),MRt=r(VCo," \u2014 "),Y1e=n(VCo,"A",{href:!0});var GAn=s(Y1e);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 "),z1e=n(WCo,"A",{href:!0});var IAn=s(z1e);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 "),H1e=n(JCo,"A",{href:!0});var $An=s(H1e);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 "),K1e=n(UCo,"A",{href:!0});var PAn=s(K1e);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(c),Hc=n(c,"H3",{class:!0});var WNo=s(Hc);s8=n(WNo,"A",{id:!0,class:!0,href:!0});var ZAn=s(s8);tOe=n(ZAn,"SPAN",{});var VAn=s(tOe);M(oJ.$$.fragment,VAn),VAn.forEach(t),ZAn.forEach(t),PRt=i(WNo),aOe=n(WNo,"SPAN",{});var WAn=s(aOe);ZRt=r(WAn,"AutoModelForSequenceClassification"),WAn.forEach(t),WNo.forEach(t),UGo=i(c),nr=n(c,"DIV",{class:!0});var Ed=s(nr);M(rJ.$$.fragment,Ed),VRt=i(Ed),Kc=n(Ed,"P",{});var Q4e=s(Kc);WRt=r(Q4e,`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 `),ebe=n(Q4e,"A",{href:!0});var JAn=s(ebe);JRt=r(JAn,"from_pretrained()"),JAn.forEach(t),URt=r(Q4e," class method or the "),obe=n(Q4e,"A",{href:!0});var UAn=s(obe);XRt=r(UAn,"from_config()"),UAn.forEach(t),DRt=r(Q4e,` class
method.`),Q4e.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.$$.fragment,eP),HRt=i(eP),sOe=n(eP,"P",{});var DAn=s(sOe);KRt=r(DAn,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),DAn.forEach(t),ekt=i(eP),ef=n(eP,"P",{});var Y4e=s(ef);okt=r(Y4e,`Note:
Loading a model from its configuration file does `),lOe=n(Y4e,"STRONG",{});var qAn=s(lOe);rkt=r(qAn,"not"),qAn.forEach(t),tkt=r(Y4e,` load the model weights. It only affects the
model\u2019s configuration. Use `),rbe=n(Y4e,"A",{href:!0});var OAn=s(rbe);akt=r(OAn,"from_pretrained()"),OAn.forEach(t),nkt=r(Y4e," to load the model weights."),Y4e.forEach(t),skt=i(eP),M(l8.$$.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),mkt=i(nn),es=n(nn,"P",{});var oP=s(es);ckt=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 `),mOe=n(oP,"CODE",{});var zAn=s(mOe);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 `),cOe=n(oP,"CODE",{});var HAn=s(cOe);pkt=r(HAn,"pretrained_model_name_or_path"),HAn.forEach(t),_kt=r(oP,":"),oP.forEach(t),bkt=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);vkt=r(KAn,"albert"),KAn.forEach(t),Mkt=r(XCo," \u2014 "),tbe=n(XCo,"A",{href:!0});var eRn=s(tbe);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 "),abe=n(DCo,"A",{href:!0});var rRn=s(abe);wkt=r(rRn,"BartForSequenceClassification"),rRn.forEach(t),Lkt=r(DCo," (BART model)"),DCo.forEach(t),Akt=i(I),m8=n(I,"LI",{});var qCo=s(m8);gOe=n(qCo,"STRONG",{});var tRn=s(gOe);Rkt=r(tRn,"bert"),tRn.forEach(t),kkt=r(qCo," \u2014 "),nbe=n(qCo,"A",{href:!0});var aRn=s(nbe);Skt=r(aRn,"BertForSequenceClassification"),aRn.forEach(t),xkt=r(qCo," (BERT 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Okn=s(Kbe);sIt=r(Okn,"OpenAIGPTForSequenceClassification"),Okn.forEach(t),lIt=r(Wwo," (OpenAI GPT model)"),Wwo.forEach(t),iIt=i(I),s7=n(I,"LI",{});var Jwo=s(s7);cQe=n(Jwo,"STRONG",{});var Qkn=s(cQe);dIt=r(Qkn,"opt"),Qkn.forEach(t),mIt=r(Jwo," \u2014 "),e2e=n(Jwo,"A",{href:!0});var Ykn=s(e2e);cIt=r(Ykn,"OPTForSequenceClassification"),Ykn.forEach(t),fIt=r(Jwo," (OPT model)"),Jwo.forEach(t),hIt=i(I),l7=n(I,"LI",{});var Uwo=s(l7);fQe=n(Uwo,"STRONG",{});var zkn=s(fQe);gIt=r(zkn,"perceiver"),zkn.forEach(t),uIt=r(Uwo," \u2014 "),o2e=n(Uwo,"A",{href:!0});var Hkn=s(o2e);pIt=r(Hkn,"PerceiverForSequenceClassification"),Hkn.forEach(t),_It=r(Uwo," (Perceiver model)"),Uwo.forEach(t),bIt=i(I),i7=n(I,"LI",{});var Xwo=s(i7);hQe=n(Xwo,"STRONG",{});var Kkn=s(hQe);vIt=r(Kkn,"plbart"),Kkn.forEach(t),MIt=r(Xwo," \u2014 "),r2e=n(Xwo,"A",{href:!0});var eSn=s(r2e);TIt=r(eSn,"PLBartForSequenceClassification"),eSn.forEach(t),FIt=r(Xwo," (PLBart model)"),Xwo.forEach(t),EIt=i(I),d7=n(I,"LI",{});var Dwo=s(d7);gQe=n(Dwo,"STRONG",{});var oSn=s(gQe);yIt=r(oSn,"qdqbert"),oSn.forEach(t),CIt=r(Dwo," \u2014 "),t2e=n(Dwo,"A",{href:!0});var rSn=s(t2e);wIt=r(rSn,"QDQBertForSequenceClassification"),rSn.forEach(t),LIt=r(Dwo," (QDQBert model)"),Dwo.forEach(t),AIt=i(I),m7=n(I,"LI",{});var qwo=s(m7);uQe=n(qwo,"STRONG",{});var tSn=s(uQe);RIt=r(tSn,"reformer"),tSn.forEach(t),kIt=r(qwo," \u2014 "),a2e=n(qwo,"A",{href:!0});var aSn=s(a2e);SIt=r(aSn,"ReformerForSequenceClassification"),aSn.forEach(t),xIt=r(qwo," (Reformer model)"),qwo.forEach(t),GIt=i(I),c7=n(I,"LI",{});var Owo=s(c7);pQe=n(Owo,"STRONG",{});var nSn=s(pQe);BIt=r(nSn,"rembert"),nSn.forEach(t),IIt=r(Owo," \u2014 "),n2e=n(Owo,"A",{href:!0});var sSn=s(n2e);NIt=r(sSn,"RemBertForSequenceClassification"),sSn.forEach(t),$It=r(Owo," (RemBERT model)"),Owo.forEach(t),jIt=i(I),f7=n(I,"LI",{});var Qwo=s(f7);_Qe=n(Qwo,"STRONG",{});var lSn=s(_Qe);PIt=r(lSn,"roberta"),lSn.forEach(t),ZIt=r(Qwo," \u2014 "),s2e=n(Qwo,"A",{href:!0});var iSn=s(s2e);VIt=r(iSn,"RobertaForSequenceClassification"),iSn.forEach(t),WIt=r(Qwo," (RoBERTa model)"),Qwo.forEach(t),JIt=i(I),h7=n(I,"LI",{});var Ywo=s(h7);bQe=n(Ywo,"STRONG",{});var dSn=s(bQe);UIt=r(dSn,"roberta-prelayernorm"),dSn.forEach(t),XIt=r(Ywo," \u2014 "),l2e=n(Ywo,"A",{href:!0});var mSn=s(l2e);DIt=r(mSn,"RobertaPreLayerNormForSequenceClassification"),mSn.forEach(t),qIt=r(Ywo," (RoBERTa-PreLayerNorm model)"),Ywo.forEach(t),OIt=i(I),g7=n(I,"LI",{});var zwo=s(g7);vQe=n(zwo,"STRONG",{});var cSn=s(vQe);QIt=r(cSn,"roc_bert"),cSn.forEach(t),YIt=r(zwo," \u2014 "),i2e=n(zwo,"A",{href:!0});var fSn=s(i2e);zIt=r(fSn,"RoCBertForSequenceClassification"),fSn.forEach(t),HIt=r(zwo," (RoCBert model)"),zwo.forEach(t),KIt=i(I),u7=n(I,"LI",{});var Hwo=s(u7);MQe=n(Hwo,"STRONG",{});var hSn=s(MQe);eNt=r(hSn,"roformer"),hSn.forEach(t),oNt=r(Hwo," \u2014 "),d2e=n(Hwo,"A",{href:!0});var gSn=s(d2e);rNt=r(gSn,"RoFormerForSequenceClassification"),gSn.forEach(t),tNt=r(Hwo," (RoFormer 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deactivated). To train the model, you should first set it back in training mode with `),GQe=n(m6o,"CODE",{});var jSn=s(GQe);d$t=r(jSn,"model.train()"),jSn.forEach(t),m6o.forEach(t),m$t=i(nn),M(A7.$$.fragment,nn),nn.forEach(t),Ed.forEach(t),XGo=i(c),of=n(c,"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.$$.fragment,ZSn),ZSn.forEach(t),PSn.forEach(t),c$t=i(UNo),IQe=n(UNo,"SPAN",{});var VSn=s(IQe);f$t=r(VSn,"TFAutoModelForSequenceClassification"),VSn.forEach(t),UNo.forEach(t),DGo=i(c),sr=n(c,"DIV",{class:!0});var yd=s(sr);M(lJ.$$.fragment,yd),h$t=i(yd),rf=n(yd,"P",{});var z4e=s(rf);g$t=r(z4e,`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 `),T2e=n(z4e,"A",{href:!0});var WSn=s(T2e);u$t=r(WSn,"from_pretrained()"),WSn.forEach(t),p$t=r(z4e," class method or the "),F2e=n(z4e,"A",{href:!0});var JSn=s(F2e);_$t=r(JSn,"from_config()"),JSn.forEach(t),b$t=r(z4e,` class
method.`),z4e.forEach(t),v$t=i(yd),iJ=n(yd,"P",{});var XNo=s(iJ);M$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.$$.fragment,rP),y$t=i(rP),$Qe=n(rP,"P",{});var XSn=s($Qe);C$t=r(XSn,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),XSn.forEach(t),w$t=i(rP),tf=n(rP,"P",{});var H4e=s(tf);L$t=r(H4e,`Note:
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model\u2019s configuration. Use `),E2e=n(H4e,"A",{href:!0});var qSn=s(E2e);k$t=r(qSn,"from_pretrained()"),qSn.forEach(t),S$t=r(H4e," to load the model weights."),H4e.forEach(t),x$t=i(rP),M(k7.$$.fragment,rP),rP.forEach(t),G$t=i(yd),ht=n(yd,"DIV",{class:!0});var Cd=s(ht);M(mJ.$$.fragment,Cd),B$t=i(Cd),PQe=n(Cd,"P",{});var OSn=s(PQe);I$t=r(OSn,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),OSn.forEach(t),N$t=i(Cd),os=n(Cd,"P",{});var tP=s(os);$$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$t=i(Cd),de=n(Cd,"UL",{});var ce=s(de);S7=n(ce,"LI",{});var c6o=s(S7);JQe=n(c6o,"STRONG",{});var HSn=s(JQe);X$t=r(HSn,"albert"),HSn.forEach(t),D$t=r(c6o," \u2014 "),y2e=n(c6o,"A",{href:!0});var KSn=s(y2e);q$t=r(KSn,"TFAlbertForSequenceClassification"),KSn.forEach(t),O$t=r(c6o," (ALBERT model)"),c6o.forEach(t),Q$t=i(ce),x7=n(ce,"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 "),C2e=n(f6o,"A",{href:!0});var oxn=s(C2e);H$t=r(oxn,"TFBartForSequenceClassification"),oxn.forEach(t),K$t=r(f6o," (BART model)"),f6o.forEach(t),ejt=i(ce),G7=n(ce,"LI",{});var h6o=s(G7);XQe=n(h6o,"STRONG",{});var rxn=s(XQe);ojt=r(rxn,"bert"),rxn.forEach(t),rjt=r(h6o," \u2014 "),w2e=n(h6o,"A",{href:!0});var txn=s(w2e);tjt=r(txn,"TFBertForSequenceClassification"),txn.forEach(t),ajt=r(h6o," (BERT model)"),h6o.forEach(t),njt=i(ce),B7=n(ce,"LI",{});var g6o=s(B7);DQe=n(g6o,"STRONG",{});var axn=s(DQe);sjt=r(axn,"camembert"),axn.forEach(t),ljt=r(g6o," \u2014 "),L2e=n(g6o,"A",{href:!0});var nxn=s(L2e);ijt=r(nxn,"TFCamembertForSequenceClassification"),nxn.forEach(t),djt=r(g6o," (CamemBERT model)"),g6o.forEach(t),mjt=i(ce),I7=n(ce,"LI",{});var u6o=s(I7);qQe=n(u6o,"STRONG",{});var sxn=s(qQe);cjt=r(sxn,"convbert"),sxn.forEach(t),fjt=r(u6o," \u2014 "),A2e=n(u6o,"A",{href:!0});var lxn=s(A2e);hjt=r(lxn,"TFConvBertForSequenceClassification"),lxn.forEach(t),gjt=r(u6o," (ConvBERT model)"),u6o.forEach(t),ujt=i(ce),N7=n(ce,"LI",{});var p6o=s(N7);OQe=n(p6o,"STRONG",{});var ixn=s(OQe);pjt=r(ixn,"ctrl"),ixn.forEach(t),_jt=r(p6o," \u2014 "),R2e=n(p6o,"A",{href:!0});var dxn=s(R2e);bjt=r(dxn,"TFCTRLForSequenceClassification"),dxn.forEach(t),vjt=r(p6o," (CTRL model)"),p6o.forEach(t),Mjt=i(ce),$7=n(ce,"LI",{});var _6o=s($7);QQe=n(_6o,"STRONG",{});var mxn=s(QQe);Tjt=r(mxn,"deberta"),mxn.forEach(t),Fjt=r(_6o," \u2014 "),k2e=n(_6o,"A",{href:!0});var cxn=s(k2e);Ejt=r(cxn,"TFDebertaForSequenceClassification"),cxn.forEach(t),yjt=r(_6o," (DeBERTa model)"),_6o.forEach(t),Cjt=i(ce),j7=n(ce,"LI",{});var b6o=s(j7);YQe=n(b6o,"STRONG",{});var fxn=s(YQe);wjt=r(fxn,"deberta-v2"),fxn.forEach(t),Ljt=r(b6o," \u2014 "),S2e=n(b6o,"A",{href:!0});var hxn=s(S2e);Ajt=r(hxn,"TFDebertaV2ForSequenceClassification"),hxn.forEach(t),Rjt=r(b6o," (DeBERTa-v2 model)"),b6o.forEach(t),kjt=i(ce),P7=n(ce,"LI",{});var v6o=s(P7);zQe=n(v6o,"STRONG",{});var gxn=s(zQe);Sjt=r(gxn,"distilbert"),gxn.forEach(t),xjt=r(v6o," \u2014 "),x2e=n(v6o,"A",{href:!0});var uxn=s(x2e);Gjt=r(uxn,"TFDistilBertForSequenceClassification"),uxn.forEach(t),Bjt=r(v6o," (DistilBERT model)"),v6o.forEach(t),Ijt=i(ce),Z7=n(ce,"LI",{});var M6o=s(Z7);HQe=n(M6o,"STRONG",{});var pxn=s(HQe);Njt=r(pxn,"electra"),pxn.forEach(t),$jt=r(M6o," \u2014 "),G2e=n(M6o,"A",{href:!0});var _xn=s(G2e);jjt=r(_xn,"TFElectraForSequenceClassification"),_xn.forEach(t),Pjt=r(M6o," (ELECTRA model)"),M6o.forEach(t),Zjt=i(ce),V7=n(ce,"LI",{});var T6o=s(V7);KQe=n(T6o,"STRONG",{});var bxn=s(KQe);Vjt=r(bxn,"esm"),bxn.forEach(t),Wjt=r(T6o," \u2014 "),B2e=n(T6o,"A",{href:!0});var vxn=s(B2e);Jjt=r(vxn,"TFEsmForSequenceClassification"),vxn.forEach(t),Ujt=r(T6o," (ESM model)"),T6o.forEach(t),Xjt=i(ce),W7=n(ce,"LI",{});var F6o=s(W7);eYe=n(F6o,"STRONG",{});var Mxn=s(eYe);Djt=r(Mxn,"flaubert"),Mxn.forEach(t),qjt=r(F6o," \u2014 "),I2e=n(F6o,"A",{href:!0});var Txn=s(I2e);Ojt=r(Txn,"TFFlaubertForSequenceClassification"),Txn.forEach(t),Qjt=r(F6o," (FlauBERT model)"),F6o.forEach(t),Yjt=i(ce),J7=n(ce,"LI",{});var E6o=s(J7);oYe=n(E6o,"STRONG",{});var Fxn=s(oYe);zjt=r(Fxn,"funnel"),Fxn.forEach(t),Hjt=r(E6o," \u2014 "),N2e=n(E6o,"A",{href:!0});var Exn=s(N2e);Kjt=r(Exn,"TFFunnelForSequenceClassification"),Exn.forEach(t),ePt=r(E6o," (Funnel Transformer 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$xn=s(J2e);BPt=r($xn,"TFMobileBertForSequenceClassification"),$xn.forEach(t),IPt=r(k6o," (MobileBERT model)"),k6o.forEach(t),NPt=i(ce),z7=n(ce,"LI",{});var S6o=s(z7);dYe=n(S6o,"STRONG",{});var jxn=s(dYe);$Pt=r(jxn,"mpnet"),jxn.forEach(t),jPt=r(S6o," \u2014 "),U2e=n(S6o,"A",{href:!0});var Pxn=s(U2e);PPt=r(Pxn,"TFMPNetForSequenceClassification"),Pxn.forEach(t),ZPt=r(S6o," (MPNet model)"),S6o.forEach(t),VPt=i(ce),H7=n(ce,"LI",{});var x6o=s(H7);mYe=n(x6o,"STRONG",{});var Zxn=s(mYe);WPt=r(Zxn,"openai-gpt"),Zxn.forEach(t),JPt=r(x6o," \u2014 "),X2e=n(x6o,"A",{href:!0});var Vxn=s(X2e);UPt=r(Vxn,"TFOpenAIGPTForSequenceClassification"),Vxn.forEach(t),XPt=r(x6o," (OpenAI GPT model)"),x6o.forEach(t),DPt=i(ce),K7=n(ce,"LI",{});var G6o=s(K7);cYe=n(G6o,"STRONG",{});var Wxn=s(cYe);qPt=r(Wxn,"rembert"),Wxn.forEach(t),OPt=r(G6o," \u2014 "),D2e=n(G6o,"A",{href:!0});var Jxn=s(D2e);QPt=r(Jxn,"TFRemBertForSequenceClassification"),Jxn.forEach(t),YPt=r(G6o," (RemBERT 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tGn=s(K2e);kZt=r(tGn,"TFXLMRobertaForSequenceClassification"),tGn.forEach(t),SZt=r(Z6o," (XLM-RoBERTa model)"),Z6o.forEach(t),xZt=i(ce),lL=n(ce,"LI",{});var V6o=s(lL);vYe=n(V6o,"STRONG",{});var aGn=s(vYe);GZt=r(aGn,"xlnet"),aGn.forEach(t),BZt=r(V6o," \u2014 "),eve=n(V6o,"A",{href:!0});var nGn=s(eve);IZt=r(nGn,"TFXLNetForSequenceClassification"),nGn.forEach(t),NZt=r(V6o," (XLNet model)"),V6o.forEach(t),ce.forEach(t),$Zt=i(Cd),M(iL.$$.fragment,Cd),Cd.forEach(t),yd.forEach(t),qGo=i(c),af=n(c,"H3",{class:!0});var DNo=s(af);dL=n(DNo,"A",{id:!0,class:!0,href:!0});var sGn=s(dL);MYe=n(sGn,"SPAN",{});var lGn=s(MYe);M(cJ.$$.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(c),lr=n(c,"DIV",{class:!0});var wd=s(lr);M(fJ.$$.fragment,wd),ZZt=i(wd),nf=n(wd,"P",{});var K4e=s(nf);VZt=r(K4e,`This is a generic model class that will be instantiated as one of the model classes of 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method.`),K4e.forEach(t),DZt=i(wd),hJ=n(wd,"P",{});var qNo=s(hJ);qZt=r(qNo,"This class cannot be instantiated directly using "),FYe=n(qNo,"CODE",{});var cGn=s(FYe);OZt=r(cGn,"__init__()"),cGn.forEach(t),QZt=r(qNo," (throws an error)."),qNo.forEach(t),YZt=i(wd),na=n(wd,"DIV",{class:!0});var aP=s(na);M(gJ.$$.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 `),tve=n(eCe,"A",{href:!0});var gGn=s(tve);tVt=r(gGn,"from_pretrained()"),gGn.forEach(t),aVt=r(eCe," to load the model weights."),eCe.forEach(t),nVt=i(aP),M(mL.$$.fragment,aP),aP.forEach(t),sVt=i(wd),gt=n(wd,"DIV",{class:!0});var Ld=s(gt);M(uJ.$$.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);mVt=r(nP,"The model class to instantiate is selected based on the "),wYe=n(nP,"CODE",{});var pGn=s(wYe);cVt=r(pGn,"model_type"),pGn.forEach(t),fVt=r(nP,` property of the config object (either
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falling back to using pattern matching on `),AYe=n(nP,"CODE",{});var bGn=s(AYe);uVt=r(bGn,"pretrained_model_name_or_path"),bGn.forEach(t),pVt=r(nP,":"),nP.forEach(t),_Vt=i(Ld),Se=n(Ld,"UL",{});var Ze=s(Se);cL=n(Ze,"LI",{});var W6o=s(cL);RYe=n(W6o,"STRONG",{});var vGn=s(RYe);bVt=r(vGn,"albert"),vGn.forEach(t),vVt=r(W6o," \u2014 "),ave=n(W6o,"A",{href:!0});var MGn=s(ave);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 "),nve=n(J6o,"A",{href:!0});var FGn=s(nve);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 "),sve=n(U6o,"A",{href:!0});var yGn=s(sve);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 "),lve=n(X6o,"A",{href:!0});var wGn=s(lve);IVt=r(wGn,"FlaxBigBirdForSequenceClassification"),wGn.forEach(t),NVt=r(X6o," (BigBird model)"),X6o.forEach(t),$Vt=i(Ze),uL=n(Ze,"LI",{});var D6o=s(uL);GYe=n(D6o,"STRONG",{});var LGn=s(GYe);jVt=r(LGn,"distilbert"),LGn.forEach(t),PVt=r(D6o," \u2014 "),ive=n(D6o,"A",{href:!0});var AGn=s(ive);ZVt=r(AGn,"FlaxDistilBertForSequenceClassification"),AGn.forEach(t),VVt=r(D6o," (DistilBERT model)"),D6o.forEach(t),WVt=i(Ze),pL=n(Ze,"LI",{});var q6o=s(pL);BYe=n(q6o,"STRONG",{});var RGn=s(BYe);JVt=r(RGn,"electra"),RGn.forEach(t),UVt=r(q6o," \u2014 "),dve=n(q6o,"A",{href:!0});var kGn=s(dve);XVt=r(kGn,"FlaxElectraForSequenceClassification"),kGn.forEach(t),DVt=r(q6o," (ELECTRA model)"),q6o.forEach(t),qVt=i(Ze),_L=n(Ze,"LI",{});var O6o=s(_L);IYe=n(O6o,"STRONG",{});var SGn=s(IYe);OVt=r(SGn,"mbart"),SGn.forEach(t),QVt=r(O6o," \u2014 "),mve=n(O6o,"A",{href:!0});var xGn=s(mve);YVt=r(xGn,"FlaxMBartForSequenceClassification"),xGn.forEach(t),zVt=r(O6o," (mBART model)"),O6o.forEach(t),HVt=i(Ze),bL=n(Ze,"LI",{});var Q6o=s(bL);NYe=n(Q6o,"STRONG",{});var GGn=s(NYe);KVt=r(GGn,"roberta"),GGn.forEach(t),eWt=r(Q6o," \u2014 "),cve=n(Q6o,"A",{href:!0});var BGn=s(cve);oWt=r(BGn,"FlaxRobertaForSequenceClassification"),BGn.forEach(t),rWt=r(Q6o," (RoBERTa model)"),Q6o.forEach(t),tWt=i(Ze),vL=n(Ze,"LI",{});var Y6o=s(vL);$Ye=n(Y6o,"STRONG",{});var IGn=s($Ye);aWt=r(IGn,"roberta-prelayernorm"),IGn.forEach(t),nWt=r(Y6o," \u2014 "),fve=n(Y6o,"A",{href:!0});var NGn=s(fve);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),mWt=r(z6o," \u2014 "),hve=n(z6o,"A",{href:!0});var jGn=s(hve);cWt=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 "),gve=n(H6o,"A",{href:!0});var ZGn=s(gve);pWt=r(ZGn,"FlaxXLMRobertaForSequenceClassification"),ZGn.forEach(t),_Wt=r(H6o," (XLM-RoBERTa model)"),H6o.forEach(t),Ze.forEach(t),bWt=i(Ld),M(FL.$$.fragment,Ld),Ld.forEach(t),wd.forEach(t),QGo=i(c),lf=n(c,"H3",{class:!0});var ONo=s(lf);EL=n(ONo,"A",{id:!0,class:!0,href:!0});var VGn=s(EL);ZYe=n(VGn,"SPAN",{});var WGn=s(ZYe);M(pJ.$$.fragment,WGn),WGn.forEach(t),VGn.forEach(t),vWt=i(ONo),VYe=n(ONo,"SPAN",{});var JGn=s(VYe);MWt=r(JGn,"AutoModelForMultipleChoice"),JGn.forEach(t),ONo.forEach(t),YGo=i(c),ir=n(c,"DIV",{class:!0});var Ad=s(ir);M(_J.$$.fragment,Ad),TWt=i(Ad),df=n(Ad,"P",{});var oCe=s(df);FWt=r(oCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
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method.`),oCe.forEach(t),LWt=i(Ad),bJ=n(Ad,"P",{});var QNo=s(bJ);AWt=r(QNo,"This class cannot be instantiated directly using "),WYe=n(QNo,"CODE",{});var DGn=s(WYe);RWt=r(DGn,"__init__()"),DGn.forEach(t),kWt=r(QNo," (throws an error)."),QNo.forEach(t),SWt=i(Ad),sa=n(Ad,"DIV",{class:!0});var sP=s(sa);M(vJ.$$.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),mf=n(sP,"P",{});var rCe=s(mf);IWt=r(rCe,`Note:
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model\u2019s configuration. Use `),_ve=n(rCe,"A",{href:!0});var QGn=s(_ve);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 "),bve=n(K6o,"A",{href:!0});var oBn=s(bve);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 "),vve=n(e8o,"A",{href:!0});var tBn=s(vve);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),mJt=r(o8o," \u2014 "),Mve=n(o8o,"A",{href:!0});var nBn=s(Mve);cJt=r(nBn,"BigBirdForMultipleChoice"),nBn.forEach(t),fJt=r(o8o," (BigBird 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method.`),tCe.forEach(t),ZDt=i(Rd),EJ=n(Rd,"P",{});var zNo=s(EJ);VDt=r(zNo,"This class cannot be instantiated directly using "),$ze=n(zNo,"CODE",{});var RIn=s($ze);WDt=r(RIn,"__init__()"),RIn.forEach(t),JDt=r(zNo," (throws an error)."),zNo.forEach(t),UDt=i(Rd),la=n(Rd,"DIV",{class:!0});var iP=s(la);M(yJ.$$.fragment,iP),XDt=i(iP),jze=n(iP,"P",{});var kIn=s(jze);DDt=r(kIn,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),kIn.forEach(t),qDt=i(iP),hf=n(iP,"P",{});var aCe=s(hf);ODt=r(aCe,`Note:
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model\u2019s configuration. Use `),aMe=n(aCe,"A",{href:!0});var xIn=s(aMe);zDt=r(xIn,"from_pretrained()"),xIn.forEach(t),HDt=r(aCe," to load the model weights."),aCe.forEach(t),KDt=i(iP),M(fA.$$.fragment,iP),iP.forEach(t),eqt=i(Rd),ut=n(Rd,"DIV",{class:!0});var kd=s(ut);M(CJ.$$.fragment,kd),oqt=i(kd),Zze=n(kd,"P",{});var GIn=s(Zze);rqt=r(GIn,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),GIn.forEach(t),tqt=i(kd),as=n(kd,"P",{});var dP=s(as);aqt=r(dP,"The model class to instantiate is selected based on the "),Vze=n(dP,"CODE",{});var BIn=s(Vze);nqt=r(BIn,"model_type"),BIn.forEach(t),sqt=r(dP,` property of the config object (either
passed as an argument or loaded from `),Wze=n(dP,"CODE",{});var IIn=s(Wze);lqt=r(IIn,"pretrained_model_name_or_path"),IIn.forEach(t),iqt=r(dP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Jze=n(dP,"CODE",{});var NIn=s(Jze);dqt=r(NIn,"pretrained_model_name_or_path"),NIn.forEach(t),mqt=r(dP,":"),dP.forEach(t),cqt=i(kd),Fe=n(kd,"UL",{});var ye=s(Fe);hA=n(ye,"LI",{});var j8o=s(hA);Uze=n(j8o,"STRONG",{});var $In=s(Uze);fqt=r($In,"albert"),$In.forEach(t),hqt=r(j8o," \u2014 "),nMe=n(j8o,"A",{href:!0});var jIn=s(nMe);gqt=r(jIn,"TFAlbertForMultipleChoice"),jIn.forEach(t),uqt=r(j8o," (ALBERT model)"),j8o.forEach(t),pqt=i(ye),gA=n(ye,"LI",{});var P8o=s(gA);Xze=n(P8o,"STRONG",{});var PIn=s(Xze);_qt=r(PIn,"bert"),PIn.forEach(t),bqt=r(P8o," \u2014 "),sMe=n(P8o,"A",{href:!0});var ZIn=s(sMe);vqt=r(ZIn,"TFBertForMultipleChoice"),ZIn.forEach(t),Mqt=r(P8o," (BERT model)"),P8o.forEach(t),Tqt=i(ye),uA=n(ye,"LI",{});var Z8o=s(uA);Dze=n(Z8o,"STRONG",{});var VIn=s(Dze);Fqt=r(VIn,"camembert"),VIn.forEach(t),Eqt=r(Z8o," \u2014 "),lMe=n(Z8o,"A",{href:!0});var WIn=s(lMe);yqt=r(WIn,"TFCamembertForMultipleChoice"),WIn.forEach(t),Cqt=r(Z8o," (CamemBERT 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\u2014 "),cMe=n(U8o,"A",{href:!0});var YIn=s(cMe);Uqt=r(YIn,"TFFlaubertForMultipleChoice"),YIn.forEach(t),Xqt=r(U8o," (FlauBERT model)"),U8o.forEach(t),Dqt=i(ye),MA=n(ye,"LI",{});var X8o=s(MA);zze=n(X8o,"STRONG",{});var zIn=s(zze);qqt=r(zIn,"funnel"),zIn.forEach(t),Oqt=r(X8o," \u2014 "),fMe=n(X8o,"A",{href:!0});var HIn=s(fMe);Qqt=r(HIn,"TFFunnelForMultipleChoice"),HIn.forEach(t),Yqt=r(X8o," (Funnel Transformer model)"),X8o.forEach(t),zqt=i(ye),TA=n(ye,"LI",{});var D8o=s(TA);Hze=n(D8o,"STRONG",{});var KIn=s(Hze);Hqt=r(KIn,"longformer"),KIn.forEach(t),Kqt=r(D8o," \u2014 "),hMe=n(D8o,"A",{href:!0});var eNn=s(hMe);eOt=r(eNn,"TFLongformerForMultipleChoice"),eNn.forEach(t),oOt=r(D8o," (Longformer model)"),D8o.forEach(t),rOt=i(ye),FA=n(ye,"LI",{});var q8o=s(FA);Kze=n(q8o,"STRONG",{});var oNn=s(Kze);tOt=r(oNn,"mobilebert"),oNn.forEach(t),aOt=r(q8o," \u2014 "),gMe=n(q8o,"A",{href:!0});var rNn=s(gMe);nOt=r(rNn,"TFMobileBertForMultipleChoice"),rNn.forEach(t),sOt=r(q8o," (MobileBERT 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"),bMe=n(z8o,"A",{href:!0});var mNn=s(bMe);COt=r(mNn,"TFRobertaPreLayerNormForMultipleChoice"),mNn.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 cNn=s(aHe);AOt=r(cNn,"roformer"),cNn.forEach(t),ROt=r(H8o," \u2014 "),vMe=n(H8o,"A",{href:!0});var fNn=s(vMe);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 "),MMe=n(K8o,"A",{href:!0});var gNn=s(MMe);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 "),TMe=n(e7o,"A",{href:!0});var pNn=s(TMe);ZOt=r(pNn,"TFXLMRobertaForMultipleChoice"),pNn.forEach(t),VOt=r(e7o," (XLM-RoBERTa 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with the `),EMe=n(nCe,"A",{href:!0});var FNn=s(EMe);HOt=r(FNn,"from_pretrained()"),FNn.forEach(t),KOt=r(nCe," class method or the "),yMe=n(nCe,"A",{href:!0});var ENn=s(yMe);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 "),mHe=n(KNo,"CODE",{});var yNn=s(mHe);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 mP=s(ia);M(RJ.$$.fragment,mP),lQt=i(mP),cHe=n(mP,"P",{});var CNn=s(cHe);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(mP),pf=n(mP,"P",{});var sCe=s(pf);mQt=r(sCe,`Note:
Loading a model from its configuration file does `),fHe=n(sCe,"STRONG",{});var wNn=s(fHe);cQt=r(wNn,"not"),wNn.forEach(t),fQt=r(sCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),CMe=n(sCe,"A",{href:!0});var LNn=s(CMe);hQt=r(LNn,"from_pretrained()"),LNn.forEach(t),gQt=r(sCe," to load the model weights."),sCe.forEach(t),uQt=i(mP),M(GA.$$.fragment,mP),mP.forEach(t),pQt=i(Sd),pt=n(Sd,"DIV",{class:!0});var xd=s(pt);M(kJ.$$.fragment,xd),_Qt=i(xd),hHe=n(xd,"P",{});var ANn=s(hHe);bQt=r(ANn,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),ANn.forEach(t),vQt=i(xd),ns=n(xd,"P",{});var cP=s(ns);MQt=r(cP,"The model class to instantiate is selected based on the "),gHe=n(cP,"CODE",{});var RNn=s(gHe);TQt=r(RNn,"model_type"),RNn.forEach(t),FQt=r(cP,` property of the config object (either
passed as an argument or loaded from `),uHe=n(cP,"CODE",{});var kNn=s(uHe);EQt=r(kNn,"pretrained_model_name_or_path"),kNn.forEach(t),yQt=r(cP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),pHe=n(cP,"CODE",{});var SNn=s(pHe);CQt=r(SNn,"pretrained_model_name_or_path"),SNn.forEach(t),wQt=r(cP,":"),cP.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 "),wMe=n(r7o,"A",{href:!0});var GNn=s(wMe);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);bHe=n(t7o,"STRONG",{});var BNn=s(bHe);GQt=r(BNn,"bert"),BNn.forEach(t),BQt=r(t7o," \u2014 "),LMe=n(t7o,"A",{href:!0});var INn=s(LMe);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);vHe=n(a7o,"STRONG",{});var NNn=s(vHe);jQt=r(NNn,"big_bird"),NNn.forEach(t),PQt=r(a7o," \u2014 "),AMe=n(a7o,"A",{href:!0});var $Nn=s(AMe);ZQt=r($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 "),RMe=n(n7o,"A",{href:!0});var PNn=s(RMe);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 "),kMe=n(s7o,"A",{href:!0});var VNn=s(kMe);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 "),SMe=n(l7o,"A",{href:!0});var JNn=s(SMe);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 "),xMe=n(i7o,"A",{href:!0});var XNn=s(xMe);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),mYt=r(d7o," \u2014 "),GMe=n(d7o,"A",{href:!0});var qNn=s(GMe);cYt=r(qNn,"FlaxRoFormerForMultipleChoice"),qNn.forEach(t),fYt=r(d7o," (RoFormer model)"),d7o.forEach(t),hYt=i(He),WA=n(He,"LI",{});var m7o=s(WA);CHe=n(m7o,"STRONG",{});var ONn=s(CHe);gYt=r(ONn,"xlm-roberta"),ONn.forEach(t),uYt=r(m7o," \u2014 "),BMe=n(m7o,"A",{href:!0});var QNn=s(BMe);pYt=r(QNn,"FlaxXLMRobertaForMultipleChoice"),QNn.forEach(t),_Yt=r(m7o," (XLM-RoBERTa model)"),m7o.forEach(t),He.forEach(t),bYt=i(xd),M(JA.$$.fragment,xd),xd.forEach(t),Sd.forEach(t),oBo=i(c),_f=n(c,"H3",{class:!0});var e$o=s(_f);UA=n(e$o,"A",{id:!0,class:!0,href:!0});var YNn=s(UA);wHe=n(YNn,"SPAN",{});var zNn=s(wHe);M(SJ.$$.fragment,zNn),zNn.forEach(t),YNn.forEach(t),vYt=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(c),cr=n(c,"DIV",{class:!0});var Gd=s(cr);M(xJ.$$.fragment,Gd),TYt=i(Gd),bf=n(Gd,"P",{});var lCe=s(bf);FYt=r(lCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),IMe=n(lCe,"A",{href:!0});var KNn=s(IMe);EYt=r(KNn,"from_pretrained()"),KNn.forEach(t),yYt=r(lCe," class method or the "),NMe=n(lCe,"A",{href:!0});var e$n=s(NMe);CYt=r(e$n,"from_config()"),e$n.forEach(t),wYt=r(lCe,` class
method.`),lCe.forEach(t),LYt=i(Gd),GJ=n(Gd,"P",{});var o$o=s(GJ);AYt=r(o$o,"This class cannot be instantiated directly using "),AHe=n(o$o,"CODE",{});var o$n=s(AHe);RYt=r(o$n,"__init__()"),o$n.forEach(t),kYt=r(o$o," (throws an error)."),o$o.forEach(t),SYt=i(Gd),da=n(Gd,"DIV",{class:!0});var fP=s(da);M(BJ.$$.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$n.forEach(t),BYt=i(fP),vf=n(fP,"P",{});var iCe=s(vf);IYt=r(iCe,`Note:
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model\u2019s configuration. Use `),$Me=n(iCe,"A",{href:!0});var a$n=s($Me);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 c7o=s(DA);IHe=n(c7o,"STRONG",{});var d$n=s(IHe);KYt=r(d$n,"bert"),d$n.forEach(t),ezt=r(c7o," \u2014 "),jMe=n(c7o,"A",{href:!0});var m$n=s(jMe);ozt=r(m$n,"BertForNextSentencePrediction"),m$n.forEach(t),rzt=r(c7o," (BERT model)"),c7o.forEach(t),tzt=i(It),qA=n(It,"LI",{});var f7o=s(qA);NHe=n(f7o,"STRONG",{});var c$n=s(NHe);azt=r(c$n,"ernie"),c$n.forEach(t),nzt=r(f7o," \u2014 "),PMe=n(f7o,"A",{href:!0});var f$n=s(PMe);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$n.forEach(t),mzt=r(h7o," \u2014 "),ZMe=n(h7o,"A",{href:!0});var g$n=s(ZMe);czt=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$n.forEach(t),uzt=r(g7o," \u2014 "),VMe=n(g7o,"A",{href:!0});var p$n=s(VMe);pzt=r(p$n,"MegatronBertForNextSentencePrediction"),p$n.forEach(t),_zt=r(g7o," (Megatron-BERT model)"),g7o.forEach(t),bzt=i(It),YA=n(It,"LI",{});var u7o=s(YA);PHe=n(u7o,"STRONG",{});var _$n=s(PHe);vzt=r(_$n,"mobilebert"),_$n.forEach(t),Mzt=r(u7o," \u2014 "),WMe=n(u7o,"A",{href:!0});var b$n=s(WMe);Tzt=r(b$n,"MobileBertForNextSentencePrediction"),b$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 v$n=s(ZHe);yzt=r(v$n,"nezha"),v$n.forEach(t),Czt=r(p7o," \u2014 "),JMe=n(p7o,"A",{href:!0});var M$n=s(JMe);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$n.forEach(t),kzt=r(_7o," \u2014 "),UMe=n(_7o,"A",{href:!0});var F$n=s(UMe);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 b7o=s(KA);Bzt=r(b7o,"The model is set in evaluation mode by default using "),WHe=n(b7o,"CODE",{});var E$n=s(WHe);Izt=r(E$n,"model.eval()"),E$n.forEach(t),Nzt=r(b7o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),JHe=n(b7o,"CODE",{});var y$n=s(JHe);$zt=r(y$n,"model.train()"),y$n.forEach(t),b7o.forEach(t),jzt=i(ln),M(eR.$$.fragment,ln),ln.forEach(t),Gd.forEach(t),tBo=i(c),Mf=n(c,"H3",{class:!0});var r$o=s(Mf);oR=n(r$o,"A",{id:!0,class:!0,href:!0});var C$n=s(oR);UHe=n(C$n,"SPAN",{});var w$n=s(UHe);M(NJ.$$.fragment,w$n),w$n.forEach(t),C$n.forEach(t),Pzt=i(r$o),XHe=n(r$o,"SPAN",{});var L$n=s(XHe);Zzt=r(L$n,"TFAutoModelForNextSentencePrediction"),L$n.forEach(t),r$o.forEach(t),aBo=i(c),fr=n(c,"DIV",{class:!0});var Bd=s(fr);M($J.$$.fragment,Bd),Vzt=i(Bd),Tf=n(Bd,"P",{});var dCe=s(Tf);Wzt=r(dCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),XMe=n(dCe,"A",{href:!0});var A$n=s(XMe);Jzt=r(A$n,"from_pretrained()"),A$n.forEach(t),Uzt=r(dCe," class method or the "),DMe=n(dCe,"A",{href:!0});var R$n=s(DMe);Xzt=r(R$n,"from_config()"),R$n.forEach(t),Dzt=r(dCe,` class
method.`),dCe.forEach(t),qzt=i(Bd),jJ=n(Bd,"P",{});var t$o=s(jJ);Ozt=r(t$o,"This class cannot be instantiated directly using "),DHe=n(t$o,"CODE",{});var k$n=s(DHe);Qzt=r(k$n,"__init__()"),k$n.forEach(t),Yzt=r(t$o," (throws an error)."),t$o.forEach(t),zzt=i(Bd),ma=n(Bd,"DIV",{class:!0});var gP=s(ma);M(PJ.$$.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$n.forEach(t),eHt=i(gP),Ff=n(gP,"P",{});var mCe=s(Ff);oHt=r(mCe,`Note:
Loading a model from its configuration file does `),OHe=n(mCe,"STRONG",{});var x$n=s(OHe);rHt=r(x$n,"not"),x$n.forEach(t),tHt=r(mCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),qMe=n(mCe,"A",{href:!0});var G$n=s(qMe);aHt=r(G$n,"from_pretrained()"),G$n.forEach(t),nHt=r(mCe," to load the model weights."),mCe.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$n.forEach(t),mHt=i(Id),ls=n(Id,"P",{});var uP=s(ls);cHt=r(uP,"The model class to instantiate is selected based on the "),YHe=n(uP,"CODE",{});var I$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"),$$n.forEach(t),_Ht=r(uP,":"),uP.forEach(t),bHt=i(Id),VJ=n(Id,"UL",{});var a$o=s(VJ);tR=n(a$o,"LI",{});var v7o=s(tR);KHe=n(v7o,"STRONG",{});var j$n=s(KHe);vHt=r(j$n,"bert"),j$n.forEach(t),MHt=r(v7o," \u2014 "),OMe=n(v7o,"A",{href:!0});var P$n=s(OMe);THt=r(P$n,"TFBertForNextSentencePrediction"),P$n.forEach(t),FHt=r(v7o," (BERT model)"),v7o.forEach(t),EHt=i(a$o),aR=n(a$o,"LI",{});var M7o=s(aR);eKe=n(M7o,"STRONG",{});var Z$n=s(eKe);yHt=r(Z$n,"mobilebert"),Z$n.forEach(t),CHt=r(M7o," \u2014 "),QMe=n(M7o,"A",{href:!0});var V$n=s(QMe);wHt=r(V$n,"TFMobileBertForNextSentencePrediction"),V$n.forEach(t),LHt=r(M7o," (MobileBERT model)"),M7o.forEach(t),a$o.forEach(t),AHt=i(Id),M(nR.$$.fragment,Id),Id.forEach(t),Bd.forEach(t),nBo=i(c),Ef=n(c,"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.$$.fragment,J$n),J$n.forEach(t),W$n.forEach(t),RHt=i(n$o),rKe=n(n$o,"SPAN",{});var U$n=s(rKe);kHt=r(U$n,"FlaxAutoModelForNextSentencePrediction"),U$n.forEach(t),n$o.forEach(t),sBo=i(c),hr=n(c,"DIV",{class:!0});var Nd=s(hr);M(JJ.$$.fragment,Nd),SHt=i(Nd),yf=n(Nd,"P",{});var cCe=s(yf);xHt=r(cCe,`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 `),YMe=n(cCe,"A",{href:!0});var X$n=s(YMe);GHt=r(X$n,"from_pretrained()"),X$n.forEach(t),BHt=r(cCe," class method or the "),zMe=n(cCe,"A",{href:!0});var D$n=s(zMe);IHt=r(D$n,"from_config()"),D$n.forEach(t),NHt=r(cCe,` class
method.`),cCe.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),ca=n(Nd,"DIV",{class:!0});var pP=s(ca);M(XJ.$$.fragment,pP),WHt=i(pP),aKe=n(pP,"P",{});var O$n=s(aKe);JHt=r(O$n,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),O$n.forEach(t),UHt=i(pP),Cf=n(pP,"P",{});var fCe=s(Cf);XHt=r(fCe,`Note:
Loading a model from its configuration file does `),nKe=n(fCe,"STRONG",{});var Q$n=s(nKe);DHt=r(Q$n,"not"),Q$n.forEach(t),qHt=r(fCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),HMe=n(fCe,"A",{href:!0});var Y$n=s(HMe);OHt=r(Y$n,"from_pretrained()"),Y$n.forEach(t),QHt=r(fCe," to load the model weights."),fCe.forEach(t),YHt=i(pP),M(lR.$$.fragment,pP),pP.forEach(t),zHt=i(Nd),bt=n(Nd,"DIV",{class:!0});var $d=s(bt);M(DJ.$$.fragment,$d),HHt=i($d),sKe=n($d,"P",{});var z$n=s(sKe);KHt=r(z$n,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),z$n.forEach(t),eKt=i($d),is=n($d,"P",{});var _P=s(is);oKt=r(_P,"The model class to instantiate is selected based on the "),lKe=n(_P,"CODE",{});var H$n=s(lKe);rKt=r(H$n,"model_type"),H$n.forEach(t),tKt=r(_P,` property of the config object (either
passed as an argument or loaded from `),iKe=n(_P,"CODE",{});var K$n=s(iKe);aKt=r(K$n,"pretrained_model_name_or_path"),K$n.forEach(t),nKt=r(_P,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),dKe=n(_P,"CODE",{});var ejn=s(dKe);sKt=r(ejn,"pretrained_model_name_or_path"),ejn.forEach(t),lKt=r(_P,":"),_P.forEach(t),iKt=i($d),mKe=n($d,"UL",{});var ojn=s(mKe);iR=n(ojn,"LI",{});var T7o=s(iR);cKe=n(T7o,"STRONG",{});var rjn=s(cKe);dKt=r(rjn,"bert"),rjn.forEach(t),mKt=r(T7o," \u2014 "),KMe=n(T7o,"A",{href:!0});var tjn=s(KMe);cKt=r(tjn,"FlaxBertForNextSentencePrediction"),tjn.forEach(t),fKt=r(T7o," (BERT model)"),T7o.forEach(t),ojn.forEach(t),hKt=i($d),M(dR.$$.fragment,$d),$d.forEach(t),Nd.forEach(t),lBo=i(c),wf=n(c,"H3",{class:!0});var l$o=s(wf);mR=n(l$o,"A",{id:!0,class:!0,href:!0});var ajn=s(mR);fKe=n(ajn,"SPAN",{});var njn=s(fKe);M(qJ.$$.fragment,njn),njn.forEach(t),ajn.forEach(t),gKt=i(l$o),hKe=n(l$o,"SPAN",{});var sjn=s(hKe);uKt=r(sjn,"AutoModelForTokenClassification"),sjn.forEach(t),l$o.forEach(t),iBo=i(c),gr=n(c,"DIV",{class:!0});var jd=s(gr);M(OJ.$$.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 `),eTe=n(hCe,"A",{href:!0});var ljn=s(eTe);bKt=r(ljn,"from_pretrained()"),ljn.forEach(t),vKt=r(hCe," class method or the "),oTe=n(hCe,"A",{href:!0});var ijn=s(oTe);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 bP=s(fa);M(YJ.$$.fragment,bP),LKt=i(bP),uKe=n(bP,"P",{});var mjn=s(uKe);AKt=r(mjn,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),mjn.forEach(t),RKt=i(bP),Af=n(bP,"P",{});var gCe=s(Af);kKt=r(gCe,`Note:
Loading a model from its configuration file does `),pKe=n(gCe,"STRONG",{});var cjn=s(pKe);SKt=r(cjn,"not"),cjn.forEach(t),xKt=r(gCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),rTe=n(gCe,"A",{href:!0});var fjn=s(rTe);GKt=r(fjn,"from_pretrained()"),fjn.forEach(t),BKt=r(gCe," to load the model weights."),gCe.forEach(t),IKt=i(bP),M(cR.$$.fragment,bP),bP.forEach(t),NKt=i(jd),uo=n(jd,"DIV",{class:!0});var dn=s(uo);M(zJ.$$.fragment,dn),$Kt=i(dn),_Ke=n(dn,"P",{});var hjn=s(_Ke);jKt=r(hjn,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),hjn.forEach(t),PKt=i(dn),ds=n(dn,"P",{});var vP=s(ds);ZKt=r(vP,"The model class to instantiate is selected based on the "),bKe=n(vP,"CODE",{});var gjn=s(bKe);VKt=r(gjn,"model_type"),gjn.forEach(t),WKt=r(vP,` property of the config object (either
passed as an argument or loaded from `),vKe=n(vP,"CODE",{});var ujn=s(vKe);JKt=r(ujn,"pretrained_model_name_or_path"),ujn.forEach(t),UKt=r(vP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),MKe=n(vP,"CODE",{});var pjn=s(MKe);XKt=r(pjn,"pretrained_model_name_or_path"),pjn.forEach(t),DKt=r(vP,":"),vP.forEach(t),qKt=i(dn),z=n(dn,"UL",{});var H=s(z);fR=n(H,"LI",{});var F7o=s(fR);TKe=n(F7o,"STRONG",{});var _jn=s(TKe);OKt=r(_jn,"albert"),_jn.forEach(t),QKt=r(F7o," \u2014 "),tTe=n(F7o,"A",{href:!0});var bjn=s(tTe);YKt=r(bjn,"AlbertForTokenClassification"),bjn.forEach(t),zKt=r(F7o," (ALBERT model)"),F7o.forEach(t),HKt=i(H),hR=n(H,"LI",{});var E7o=s(hR);FKe=n(E7o,"STRONG",{});var vjn=s(FKe);KKt=r(vjn,"bert"),vjn.forEach(t),eea=r(E7o," \u2014 "),aTe=n(E7o,"A",{href:!0});var Mjn=s(aTe);oea=r(Mjn,"BertForTokenClassification"),Mjn.forEach(t),rea=r(E7o," (BERT model)"),E7o.forEach(t),tea=i(H),gR=n(H,"LI",{});var y7o=s(gR);EKe=n(y7o,"STRONG",{});var Tjn=s(EKe);aea=r(Tjn,"big_bird"),Tjn.forEach(t),nea=r(y7o," \u2014 "),nTe=n(y7o,"A",{href:!0});var Fjn=s(nTe);sea=r(Fjn,"BigBirdForTokenClassification"),Fjn.forEach(t),lea=r(y7o," (BigBird 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deactivated). To train the model, you should first set it back in training mode with `),Eeo=n(FLo,"CODE",{});var bZn=s(Eeo);zaa=r(bZn,"model.train()"),bZn.forEach(t),FLo.forEach(t),Haa=i(dn),M(hk.$$.fragment,dn),dn.forEach(t),jd.forEach(t),dBo=i(c),Rf=n(c,"H3",{class:!0});var d$o=s(Rf);gk=n(d$o,"A",{id:!0,class:!0,href:!0});var vZn=s(gk);yeo=n(vZn,"SPAN",{});var MZn=s(yeo);M(HJ.$$.fragment,MZn),MZn.forEach(t),vZn.forEach(t),Kaa=i(d$o),Ceo=n(d$o,"SPAN",{});var TZn=s(Ceo);ena=r(TZn,"TFAutoModelForTokenClassification"),TZn.forEach(t),d$o.forEach(t),mBo=i(c),ur=n(c,"DIV",{class:!0});var Pd=s(ur);M(KJ.$$.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
with the `),rFe=n(uCe,"A",{href:!0});var FZn=s(rFe);tna=r(FZn,"from_pretrained()"),FZn.forEach(t),ana=r(uCe," class method or the "),tFe=n(uCe,"A",{href:!0});var EZn=s(tFe);nna=r(EZn,"from_config()"),EZn.forEach(t),sna=r(uCe,` class
method.`),uCe.forEach(t),lna=i(Pd),eU=n(Pd,"P",{});var m$o=s(eU);ina=r(m$o,"This class cannot be instantiated directly using "),weo=n(m$o,"CODE",{});var yZn=s(weo);dna=r(yZn,"__init__()"),yZn.forEach(t),mna=r(m$o," (throws an error)."),m$o.forEach(t),cna=i(Pd),ha=n(Pd,"DIV",{class:!0});var MP=s(ha);M(oU.$$.fragment,MP),fna=i(MP),Leo=n(MP,"P",{});var CZn=s(Leo);hna=r(CZn,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),CZn.forEach(t),gna=i(MP),Sf=n(MP,"P",{});var pCe=s(Sf);una=r(pCe,`Note:
Loading a model from its configuration file does `),Aeo=n(pCe,"STRONG",{});var wZn=s(Aeo);pna=r(wZn,"not"),wZn.forEach(t),_na=r(pCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),aFe=n(pCe,"A",{href:!0});var LZn=s(aFe);bna=r(LZn,"from_pretrained()"),LZn.forEach(t),vna=r(pCe," to load the model weights."),pCe.forEach(t),Mna=i(MP),M(uk.$$.fragment,MP),MP.forEach(t),Tna=i(Pd),vt=n(Pd,"DIV",{class:!0});var Zd=s(vt);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),ms=n(Zd,"P",{});var TP=s(ms);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 ve=s(ue);pk=n(ve,"LI",{});var ELo=s(pk);Geo=n(ELo,"STRONG",{});var xZn=s(Geo);Gna=r(xZn,"albert"),xZn.forEach(t),Bna=r(ELo," \u2014 "),nFe=n(ELo,"A",{href:!0});var GZn=s(nFe);Ina=r(GZn,"TFAlbertForTokenClassification"),GZn.forEach(t),Nna=r(ELo," (ALBERT model)"),ELo.forEach(t),$na=i(ve),_k=n(ve,"LI",{});var yLo=s(_k);Beo=n(yLo,"STRONG",{});var BZn=s(Beo);jna=r(BZn,"bert"),BZn.forEach(t),Pna=r(yLo," \u2014 "),sFe=n(yLo,"A",{href:!0});var IZn=s(sFe);Zna=r(IZn,"TFBertForTokenClassification"),IZn.forEach(t),Vna=r(yLo," (BERT model)"),yLo.forEach(t),Wna=i(ve),bk=n(ve,"LI",{});var CLo=s(bk);Ieo=n(CLo,"STRONG",{});var NZn=s(Ieo);Jna=r(NZn,"camembert"),NZn.forEach(t),Una=r(CLo," \u2014 "),lFe=n(CLo,"A",{href:!0});var $Zn=s(lFe);Xna=r($Zn,"TFCamembertForTokenClassification"),$Zn.forEach(t),Dna=r(CLo," (CamemBERT model)"),CLo.forEach(t),qna=i(ve),vk=n(ve,"LI",{});var wLo=s(vk);Neo=n(wLo,"STRONG",{});var jZn=s(Neo);Ona=r(jZn,"convbert"),jZn.forEach(t),Qna=r(wLo," \u2014 "),iFe=n(wLo,"A",{href:!0});var PZn=s(iFe);Yna=r(PZn,"TFConvBertForTokenClassification"),PZn.forEach(t),zna=r(wLo," (ConvBERT model)"),wLo.forEach(t),Hna=i(ve),Mk=n(ve,"LI",{});var LLo=s(Mk);$eo=n(LLo,"STRONG",{});var ZZn=s($eo);Kna=r(ZZn,"deberta"),ZZn.forEach(t),esa=r(LLo," \u2014 "),dFe=n(LLo,"A",{href:!0});var VZn=s(dFe);osa=r(VZn,"TFDebertaForTokenClassification"),VZn.forEach(t),rsa=r(LLo," (DeBERTa model)"),LLo.forEach(t),tsa=i(ve),Tk=n(ve,"LI",{});var ALo=s(Tk);jeo=n(ALo,"STRONG",{});var WZn=s(jeo);asa=r(WZn,"deberta-v2"),WZn.forEach(t),nsa=r(ALo," \u2014 "),mFe=n(ALo,"A",{href:!0});var JZn=s(mFe);ssa=r(JZn,"TFDebertaV2ForTokenClassification"),JZn.forEach(t),lsa=r(ALo," (DeBERTa-v2 model)"),ALo.forEach(t),isa=i(ve),Fk=n(ve,"LI",{});var RLo=s(Fk);Peo=n(RLo,"STRONG",{});var UZn=s(Peo);dsa=r(UZn,"distilbert"),UZn.forEach(t),msa=r(RLo," \u2014 "),cFe=n(RLo,"A",{href:!0});var XZn=s(cFe);csa=r(XZn,"TFDistilBertForTokenClassification"),XZn.forEach(t),fsa=r(RLo," (DistilBERT model)"),RLo.forEach(t),hsa=i(ve),Ek=n(ve,"LI",{});var kLo=s(Ek);Zeo=n(kLo,"STRONG",{});var DZn=s(Zeo);gsa=r(DZn,"electra"),DZn.forEach(t),usa=r(kLo," \u2014 "),fFe=n(kLo,"A",{href:!0});var qZn=s(fFe);psa=r(qZn,"TFElectraForTokenClassification"),qZn.forEach(t),_sa=r(kLo," (ELECTRA model)"),kLo.forEach(t),bsa=i(ve),yk=n(ve,"LI",{});var SLo=s(yk);Veo=n(SLo,"STRONG",{});var OZn=s(Veo);vsa=r(OZn,"esm"),OZn.forEach(t),Msa=r(SLo," \u2014 "),hFe=n(SLo,"A",{href:!0});var QZn=s(hFe);Tsa=r(QZn,"TFEsmForTokenClassification"),QZn.forEach(t),Fsa=r(SLo," (ESM model)"),SLo.forEach(t),Esa=i(ve),Ck=n(ve,"LI",{});var xLo=s(Ck);Weo=n(xLo,"STRONG",{});var YZn=s(Weo);ysa=r(YZn,"flaubert"),YZn.forEach(t),Csa=r(xLo," \u2014 "),gFe=n(xLo,"A",{href:!0});var zZn=s(gFe);wsa=r(zZn,"TFFlaubertForTokenClassification"),zZn.forEach(t),Lsa=r(xLo," (FlauBERT model)"),xLo.forEach(t),Asa=i(ve),wk=n(ve,"LI",{});var GLo=s(wk);Jeo=n(GLo,"STRONG",{});var HZn=s(Jeo);Rsa=r(HZn,"funnel"),HZn.forEach(t),ksa=r(GLo," \u2014 "),uFe=n(GLo,"A",{href:!0});var KZn=s(uFe);Ssa=r(KZn,"TFFunnelForTokenClassification"),KZn.forEach(t),xsa=r(GLo," (Funnel Transformer model)"),GLo.forEach(t),Gsa=i(ve),Lk=n(ve,"LI",{});var BLo=s(Lk);Ueo=n(BLo,"STRONG",{});var eVn=s(Ueo);Bsa=r(eVn,"layoutlm"),eVn.forEach(t),Isa=r(BLo," \u2014 "),pFe=n(BLo,"A",{href:!0});var oVn=s(pFe);Nsa=r(oVn,"TFLayoutLMForTokenClassification"),oVn.forEach(t),$sa=r(BLo," (LayoutLM model)"),BLo.forEach(t),jsa=i(ve),Ak=n(ve,"LI",{});var ILo=s(Ak);Xeo=n(ILo,"STRONG",{});var rVn=s(Xeo);Psa=r(rVn,"layoutlmv3"),rVn.forEach(t),Zsa=r(ILo," \u2014 "),_Fe=n(ILo,"A",{href:!0});var tVn=s(_Fe);Vsa=r(tVn,"TFLayoutLMv3ForTokenClassification"),tVn.forEach(t),Wsa=r(ILo," (LayoutLMv3 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mVn=s(Qeo);nla=r(mVn,"rembert"),mVn.forEach(t),sla=r(PLo," \u2014 "),TFe=n(PLo,"A",{href:!0});var cVn=s(TFe);lla=r(cVn,"TFRemBertForTokenClassification"),cVn.forEach(t),ila=r(PLo," (RemBERT model)"),PLo.forEach(t),dla=i(ve),Gk=n(ve,"LI",{});var ZLo=s(Gk);Yeo=n(ZLo,"STRONG",{});var fVn=s(Yeo);mla=r(fVn,"roberta"),fVn.forEach(t),cla=r(ZLo," \u2014 "),FFe=n(ZLo,"A",{href:!0});var hVn=s(FFe);fla=r(hVn,"TFRobertaForTokenClassification"),hVn.forEach(t),hla=r(ZLo," (RoBERTa model)"),ZLo.forEach(t),gla=i(ve),Bk=n(ve,"LI",{});var VLo=s(Bk);zeo=n(VLo,"STRONG",{});var gVn=s(zeo);ula=r(gVn,"roberta-prelayernorm"),gVn.forEach(t),pla=r(VLo," \u2014 "),EFe=n(VLo,"A",{href:!0});var uVn=s(EFe);_la=r(uVn,"TFRobertaPreLayerNormForTokenClassification"),uVn.forEach(t),bla=r(VLo," (RoBERTa-PreLayerNorm model)"),VLo.forEach(t),vla=i(ve),Ik=n(ve,"LI",{});var WLo=s(Ik);Heo=n(WLo,"STRONG",{});var pVn=s(Heo);Mla=r(pVn,"roformer"),pVn.forEach(t),Tla=r(WLo," \u2014 "),yFe=n(WLo,"A",{href:!0});var _Vn=s(yFe);Fla=r(_Vn,"TFRoFormerForTokenClassification"),_Vn.forEach(t),Ela=r(WLo," (RoFormer model)"),WLo.forEach(t),yla=i(ve),Nk=n(ve,"LI",{});var JLo=s(Nk);Keo=n(JLo,"STRONG",{});var bVn=s(Keo);Cla=r(bVn,"xlm"),bVn.forEach(t),wla=r(JLo," \u2014 "),CFe=n(JLo,"A",{href:!0});var vVn=s(CFe);Lla=r(vVn,"TFXLMForTokenClassification"),vVn.forEach(t),Ala=r(JLo," (XLM model)"),JLo.forEach(t),Rla=i(ve),$k=n(ve,"LI",{});var ULo=s($k);eoo=n(ULo,"STRONG",{});var MVn=s(eoo);kla=r(MVn,"xlm-roberta"),MVn.forEach(t),Sla=r(ULo," \u2014 "),wFe=n(ULo,"A",{href:!0});var TVn=s(wFe);xla=r(TVn,"TFXLMRobertaForTokenClassification"),TVn.forEach(t),Gla=r(ULo," (XLM-RoBERTa model)"),ULo.forEach(t),Bla=i(ve),jk=n(ve,"LI",{});var XLo=s(jk);ooo=n(XLo,"STRONG",{});var FVn=s(ooo);Ila=r(FVn,"xlnet"),FVn.forEach(t),Nla=r(XLo," \u2014 "),LFe=n(XLo,"A",{href:!0});var EVn=s(LFe);$la=r(EVn,"TFXLNetForTokenClassification"),EVn.forEach(t),jla=r(XLo," (XLNet model)"),XLo.forEach(t),ve.forEach(t),Pla=i(Zd),M(Pk.$$.fragment,Zd),Zd.forEach(t),Pd.forEach(t),cBo=i(c),xf=n(c,"H3",{class:!0});var c$o=s(xf);Zk=n(c$o,"A",{id:!0,class:!0,href:!0});var yVn=s(Zk);roo=n(yVn,"SPAN",{});var CVn=s(roo);M(tU.$$.fragment,CVn),CVn.forEach(t),yVn.forEach(t),Zla=i(c$o),too=n(c$o,"SPAN",{});var wVn=s(too);Vla=r(wVn,"FlaxAutoModelForTokenClassification"),wVn.forEach(t),c$o.forEach(t),fBo=i(c),pr=n(c,"DIV",{class:!0});var Vd=s(pr);M(aU.$$.fragment,Vd),Wla=i(Vd),Gf=n(Vd,"P",{});var _Ce=s(Gf);Jla=r(_Ce,`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 `),AFe=n(_Ce,"A",{href:!0});var LVn=s(AFe);Ula=r(LVn,"from_pretrained()"),LVn.forEach(t),Xla=r(_Ce," class method or the "),RFe=n(_Ce,"A",{href:!0});var AVn=s(RFe);Dla=r(AVn,"from_config()"),AVn.forEach(t),qla=r(_Ce,` class
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.$$.fragment,FP),Kla=i(FP),noo=n(FP,"P",{});var kVn=s(noo);eia=r(kVn,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),kVn.forEach(t),oia=i(FP),Bf=n(FP,"P",{});var bCe=s(Bf);ria=r(bCe,`Note:
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model\u2019s configuration. Use `),kFe=n(bCe,"A",{href:!0});var xVn=s(kFe);nia=r(xVn,"from_pretrained()"),xVn.forEach(t),sia=r(bCe," to load the model weights."),bCe.forEach(t),lia=i(FP),M(Vk.$$.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);mia=r(GVn,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),GVn.forEach(t),cia=i(Wd),cs=n(Wd,"P",{});var EP=s(cs);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 `),moo=n(EP,"CODE",{});var NVn=s(moo);_ia=r(NVn,"pretrained_model_name_or_path"),NVn.forEach(t),bia=r(EP,":"),EP.forEach(t),via=i(Wd),Xe=n(Wd,"UL",{});var Ke=s(Xe);Wk=n(Ke,"LI",{});var DLo=s(Wk);coo=n(DLo,"STRONG",{});var $Vn=s(coo);Mia=r($Vn,"albert"),$Vn.forEach(t),Tia=r(DLo," \u2014 "),SFe=n(DLo,"A",{href:!0});var jVn=s(SFe);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 "),xFe=n(qLo,"A",{href:!0});var ZVn=s(xFe);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 "),GFe=n(OLo,"A",{href:!0});var WVn=s(GFe);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 "),BFe=n(QLo,"A",{href:!0});var UVn=s(BFe);$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 "),IFe=n(YLo,"A",{href:!0});var DVn=s(IFe);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 "),NFe=n(zLo,"A",{href:!0});var OVn=s(NFe);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 "),$Fe=n(HLo,"A",{href:!0});var YVn=s($Fe);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);boo=n(KLo,"STRONG",{});var zVn=s(boo);oda=r(zVn,"roformer"),zVn.forEach(t),rda=r(KLo," \u2014 "),jFe=n(KLo,"A",{href:!0});var HVn=s(jFe);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);voo=n(eAo,"STRONG",{});var KVn=s(voo);sda=r(KVn,"xlm-roberta"),KVn.forEach(t),lda=r(eAo," \u2014 "),PFe=n(eAo,"A",{href:!0});var eWn=s(PFe);ida=r(eWn,"FlaxXLMRobertaForTokenClassification"),eWn.forEach(t),dda=r(eAo," (XLM-RoBERTa model)"),eAo.forEach(t),Ke.forEach(t),mda=i(Wd),M(zk.$$.fragment,Wd),Wd.forEach(t),Vd.forEach(t),hBo=i(c),If=n(c,"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.$$.fragment,rWn),rWn.forEach(t),oWn.forEach(t),cda=i(h$o),Too=n(h$o,"SPAN",{});var tWn=s(Too);fda=r(tWn,"AutoModelForQuestionAnswering"),tWn.forEach(t),h$o.forEach(t),gBo=i(c),_r=n(c,"DIV",{class:!0});var Jd=s(_r);M(dU.$$.fragment,Jd),hda=i(Jd),Nf=n(Jd,"P",{});var vCe=s(Nf);gda=r(vCe,`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 `),ZFe=n(vCe,"A",{href:!0});var aWn=s(ZFe);uda=r(aWn,"from_pretrained()"),aWn.forEach(t),pda=r(vCe," class method or the "),VFe=n(vCe,"A",{href:!0});var nWn=s(VFe);_da=r(nWn,"from_config()"),nWn.forEach(t),bda=r(vCe,` class
method.`),vCe.forEach(t),vda=i(Jd),mU=n(Jd,"P",{});var g$o=s(mU);Mda=r(g$o,"This class cannot be instantiated directly using "),Foo=n(g$o,"CODE",{});var sWn=s(Foo);Tda=r(sWn,"__init__()"),sWn.forEach(t),Fda=r(g$o," (throws an error)."),g$o.forEach(t),Eda=i(Jd),ua=n(Jd,"DIV",{class:!0});var yP=s(ua);M(cU.$$.fragment,yP),yda=i(yP),Eoo=n(yP,"P",{});var lWn=s(Eoo);Cda=r(lWn,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),lWn.forEach(t),wda=i(yP),$f=n(yP,"P",{});var MCe=s($f);Lda=r(MCe,`Note:
Loading a model from its configuration file does `),yoo=n(MCe,"STRONG",{});var iWn=s(yoo);Ada=r(iWn,"not"),iWn.forEach(t),Rda=r(MCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),WFe=n(MCe,"A",{href:!0});var dWn=s(WFe);kda=r(dWn,"from_pretrained()"),dWn.forEach(t),Sda=r(MCe," to load the model weights."),MCe.forEach(t),xda=i(yP),M(Kk.$$.fragment,yP),yP.forEach(t),Gda=i(Jd),po=n(Jd,"DIV",{class:!0});var mn=s(po);M(fU.$$.fragment,mn),Bda=i(mn),Coo=n(mn,"P",{});var mWn=s(Coo);Ida=r(mWn,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),mWn.forEach(t),Nda=i(mn),fs=n(mn,"P",{});var CP=s(fs);$da=r(CP,"The model class to instantiate is selected based on the "),woo=n(CP,"CODE",{});var cWn=s(woo);jda=r(cWn,"model_type"),cWn.forEach(t),Pda=r(CP,` property of the config object (either
passed as an argument or loaded from `),Loo=n(CP,"CODE",{});var fWn=s(Loo);Zda=r(fWn,"pretrained_model_name_or_path"),fWn.forEach(t),Vda=r(CP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Aoo=n(CP,"CODE",{});var hWn=s(Aoo);Wda=r(hWn,"pretrained_model_name_or_path"),hWn.forEach(t),Jda=r(CP,":"),CP.forEach(t),Uda=i(mn),X=n(mn,"UL",{});var O=s(X);eS=n(O,"LI",{});var oAo=s(eS);Roo=n(oAo,"STRONG",{});var gWn=s(Roo);Xda=r(gWn,"albert"),gWn.forEach(t),Dda=r(oAo," \u2014 "),JFe=n(oAo,"A",{href:!0});var uWn=s(JFe);qda=r(uWn,"AlbertForQuestionAnswering"),uWn.forEach(t),Oda=r(oAo," (ALBERT model)"),oAo.forEach(t),Qda=i(O),oS=n(O,"LI",{});var rAo=s(oS);koo=n(rAo,"STRONG",{});var pWn=s(koo);Yda=r(pWn,"bart"),pWn.forEach(t),zda=r(rAo," \u2014 "),UFe=n(rAo,"A",{href:!0});var _Wn=s(UFe);Hda=r(_Wn,"BartForQuestionAnswering"),_Wn.forEach(t),Kda=r(rAo," (BART model)"),rAo.forEach(t),ema=i(O),rS=n(O,"LI",{});var tAo=s(rS);Soo=n(tAo,"STRONG",{});var bWn=s(Soo);oma=r(bWn,"bert"),bWn.forEach(t),rma=r(tAo," \u2014 "),XFe=n(tAo,"A",{href:!0});var vWn=s(XFe);tma=r(vWn,"BertForQuestionAnswering"),vWn.forEach(t),ama=r(tAo," (BERT model)"),tAo.forEach(t),nma=i(O),tS=n(O,"LI",{});var aAo=s(tS);xoo=n(aAo,"STRONG",{});var MWn=s(xoo);sma=r(MWn,"big_bird"),MWn.forEach(t),lma=r(aAo," \u2014 "),DFe=n(aAo,"A",{href:!0});var TWn=s(DFe);ima=r(TWn,"BigBirdForQuestionAnswering"),TWn.forEach(t),dma=r(aAo," (BigBird model)"),aAo.forEach(t),mma=i(O),aS=n(O,"LI",{});var nAo=s(aS);Goo=n(nAo,"STRONG",{});var FWn=s(Goo);cma=r(FWn,"bigbird_pegasus"),FWn.forEach(t),fma=r(nAo," \u2014 "),qFe=n(nAo,"A",{href:!0});var EWn=s(qFe);hma=r(EWn,"BigBirdPegasusForQuestionAnswering"),EWn.forEach(t),gma=r(nAo," (BigBird-Pegasus model)"),nAo.forEach(t),uma=i(O),nS=n(O,"LI",{});var sAo=s(nS);Boo=n(sAo,"STRONG",{});var yWn=s(Boo);pma=r(yWn,"bloom"),yWn.forEach(t),_ma=r(sAo," \u2014 "),OFe=n(sAo,"A",{href:!0});var CWn=s(OFe);bma=r(CWn,"BloomForQuestionAnswering"),CWn.forEach(t),vma=r(sAo," (BLOOM model)"),sAo.forEach(t),Mma=i(O),sS=n(O,"LI",{});var lAo=s(sS);Ioo=n(lAo,"STRONG",{});var wWn=s(Ioo);Tma=r(wWn,"camembert"),wWn.forEach(t),Fma=r(lAo," \u2014 "),QFe=n(lAo,"A",{href:!0});var LWn=s(QFe);Ema=r(LWn,"CamembertForQuestionAnswering"),LWn.forEach(t),yma=r(lAo," (CamemBERT model)"),lAo.forEach(t),Cma=i(O),lS=n(O,"LI",{});var iAo=s(lS);Noo=n(iAo,"STRONG",{});var AWn=s(Noo);wma=r(AWn,"canine"),AWn.forEach(t),Lma=r(iAo," \u2014 "),YFe=n(iAo,"A",{href:!0});var RWn=s(YFe);Ama=r(RWn,"CanineForQuestionAnswering"),RWn.forEach(t),Rma=r(iAo," (CANINE model)"),iAo.forEach(t),kma=i(O),iS=n(O,"LI",{});var dAo=s(iS);$oo=n(dAo,"STRONG",{});var kWn=s($oo);Sma=r(kWn,"convbert"),kWn.forEach(t),xma=r(dAo," \u2014 "),zFe=n(dAo,"A",{href:!0});var SWn=s(zFe);Gma=r(SWn,"ConvBertForQuestionAnswering"),SWn.forEach(t),Bma=r(dAo," (ConvBERT model)"),dAo.forEach(t),Ima=i(O),dS=n(O,"LI",{});var mAo=s(dS);joo=n(mAo,"STRONG",{});var xWn=s(joo);Nma=r(xWn,"data2vec-text"),xWn.forEach(t),$ma=r(mAo," \u2014 "),HFe=n(mAo,"A",{href:!0});var GWn=s(HFe);jma=r(GWn,"Data2VecTextForQuestionAnswering"),GWn.forEach(t),Pma=r(mAo," (Data2VecText model)"),mAo.forEach(t),Zma=i(O),mS=n(O,"LI",{});var cAo=s(mS);Poo=n(cAo,"STRONG",{});var BWn=s(Poo);Vma=r(BWn,"deberta"),BWn.forEach(t),Wma=r(cAo," \u2014 "),KFe=n(cAo,"A",{href:!0});var IWn=s(KFe);Jma=r(IWn,"DebertaForQuestionAnswering"),IWn.forEach(t),Uma=r(cAo," (DeBERTa model)"),cAo.forEach(t),Xma=i(O),cS=n(O,"LI",{});var fAo=s(cS);Zoo=n(fAo,"STRONG",{});var NWn=s(Zoo);Dma=r(NWn,"deberta-v2"),NWn.forEach(t),qma=r(fAo," \u2014 "),eEe=n(fAo,"A",{href:!0});var $Wn=s(eEe);Oma=r($Wn,"DebertaV2ForQuestionAnswering"),$Wn.forEach(t),Qma=r(fAo," (DeBERTa-v2 model)"),fAo.forEach(t),Yma=i(O),fS=n(O,"LI",{});var hAo=s(fS);Voo=n(hAo,"STRONG",{});var jWn=s(Voo);zma=r(jWn,"distilbert"),jWn.forEach(t),Hma=r(hAo," \u2014 "),oEe=n(hAo,"A",{href:!0});var PWn=s(oEe);Kma=r(PWn,"DistilBertForQuestionAnswering"),PWn.forEach(t),eca=r(hAo," (DistilBERT model)"),hAo.forEach(t),oca=i(O),hS=n(O,"LI",{});var gAo=s(hS);Woo=n(gAo,"STRONG",{});var ZWn=s(Woo);rca=r(ZWn,"electra"),ZWn.forEach(t),tca=r(gAo," \u2014 "),rEe=n(gAo,"A",{href:!0});var VWn=s(rEe);aca=r(VWn,"ElectraForQuestionAnswering"),VWn.forEach(t),nca=r(gAo," (ELECTRA model)"),gAo.forEach(t),sca=i(O),gS=n(O,"LI",{});var uAo=s(gS);Joo=n(uAo,"STRONG",{});var WWn=s(Joo);lca=r(WWn,"ernie"),WWn.forEach(t),ica=r(uAo," \u2014 "),tEe=n(uAo,"A",{href:!0});var JWn=s(tEe);dca=r(JWn,"ErnieForQuestionAnswering"),JWn.forEach(t),mca=r(uAo," (ERNIE model)"),uAo.forEach(t),cca=i(O),uS=n(O,"LI",{});var pAo=s(uS);Uoo=n(pAo,"STRONG",{});var UWn=s(Uoo);fca=r(UWn,"ernie_m"),UWn.forEach(t),hca=r(pAo," \u2014 "),aEe=n(pAo,"A",{href:!0});var XWn=s(aEe);gca=r(XWn,"ErnieMForQuestionAnswering"),XWn.forEach(t),uca=r(pAo," (ErnieM model)"),pAo.forEach(t),pca=i(O),pS=n(O,"LI",{});var _Ao=s(pS);Xoo=n(_Ao,"STRONG",{});var DWn=s(Xoo);_ca=r(DWn,"falcon"),DWn.forEach(t),bca=r(_Ao," \u2014 "),Doo=n(_Ao,"CODE",{});var qWn=s(Doo);vca=r(qWn,"FalconForQuestionAnswering"),qWn.forEach(t),Mca=r(_Ao," (Falcon model)"),_Ao.forEach(t),Tca=i(O),_S=n(O,"LI",{});var bAo=s(_S);qoo=n(bAo,"STRONG",{});var OWn=s(qoo);Fca=r(OWn,"flaubert"),OWn.forEach(t),Eca=r(bAo," \u2014 "),nEe=n(bAo,"A",{href:!0});var QWn=s(nEe);yca=r(QWn,"FlaubertForQuestionAnsweringSimple"),QWn.forEach(t),Cca=r(bAo," (FlauBERT model)"),bAo.forEach(t),wca=i(O),bS=n(O,"LI",{});var vAo=s(bS);Ooo=n(vAo,"STRONG",{});var YWn=s(Ooo);Lca=r(YWn,"fnet"),YWn.forEach(t),Aca=r(vAo," \u2014 "),sEe=n(vAo,"A",{href:!0});var zWn=s(sEe);Rca=r(zWn,"FNetForQuestionAnswering"),zWn.forEach(t),kca=r(vAo," (FNet model)"),vAo.forEach(t),Sca=i(O),vS=n(O,"LI",{});var MAo=s(vS);Qoo=n(MAo,"STRONG",{});var HWn=s(Qoo);xca=r(HWn,"funnel"),HWn.forEach(t),Gca=r(MAo," \u2014 "),lEe=n(MAo,"A",{href:!0});var KWn=s(lEe);Bca=r(KWn,"FunnelForQuestionAnswering"),KWn.forEach(t),Ica=r(MAo," (Funnel Transformer model)"),MAo.forEach(t),Nca=i(O),MS=n(O,"LI",{});var TAo=s(MS);Yoo=n(TAo,"STRONG",{});var eJn=s(Yoo);$ca=r(eJn,"gpt2"),eJn.forEach(t),jca=r(TAo," \u2014 "),iEe=n(TAo,"A",{href:!0});var oJn=s(iEe);Pca=r(oJn,"GPT2ForQuestionAnswering"),oJn.forEach(t),Zca=r(TAo," (OpenAI GPT-2 model)"),TAo.forEach(t),Vca=i(O),TS=n(O,"LI",{});var FAo=s(TS);zoo=n(FAo,"STRONG",{});var rJn=s(zoo);Wca=r(rJn,"gpt_neo"),rJn.forEach(t),Jca=r(FAo," \u2014 "),dEe=n(FAo,"A",{href:!0});var tJn=s(dEe);Uca=r(tJn,"GPTNeoForQuestionAnswering"),tJn.forEach(t),Xca=r(FAo," (GPT Neo model)"),FAo.forEach(t),Dca=i(O),FS=n(O,"LI",{});var EAo=s(FS);Hoo=n(EAo,"STRONG",{});var aJn=s(Hoo);qca=r(aJn,"gpt_neox"),aJn.forEach(t),Oca=r(EAo," \u2014 "),mEe=n(EAo,"A",{href:!0});var nJn=s(mEe);Qca=r(nJn,"GPTNeoXForQuestionAnswering"),nJn.forEach(t),Yca=r(EAo," (GPT NeoX model)"),EAo.forEach(t),zca=i(O),ES=n(O,"LI",{});var yAo=s(ES);Koo=n(yAo,"STRONG",{});var sJn=s(Koo);Hca=r(sJn,"gptj"),sJn.forEach(t),Kca=r(yAo," \u2014 "),cEe=n(yAo,"A",{href:!0});var lJn=s(cEe);efa=r(lJn,"GPTJForQuestionAnswering"),lJn.forEach(t),ofa=r(yAo," (GPT-J model)"),yAo.forEach(t),rfa=i(O),yS=n(O,"LI",{});var CAo=s(yS);ero=n(CAo,"STRONG",{});var iJn=s(ero);tfa=r(iJn,"ibert"),iJn.forEach(t),afa=r(CAo," \u2014 "),fEe=n(CAo,"A",{href:!0});var dJn=s(fEe);nfa=r(dJn,"IBertForQuestionAnswering"),dJn.forEach(t),sfa=r(CAo," (I-BERT model)"),CAo.forEach(t),lfa=i(O),CS=n(O,"LI",{});var wAo=s(CS);oro=n(wAo,"STRONG",{});var mJn=s(oro);ifa=r(mJn,"layoutlmv2"),mJn.forEach(t),dfa=r(wAo," \u2014 "),hEe=n(wAo,"A",{href:!0});var cJn=s(hEe);mfa=r(cJn,"LayoutLMv2ForQuestionAnswering"),cJn.forEach(t),cfa=r(wAo," (LayoutLMv2 model)"),wAo.forEach(t),ffa=i(O),wS=n(O,"LI",{});var LAo=s(wS);rro=n(LAo,"STRONG",{});var fJn=s(rro);hfa=r(fJn,"layoutlmv3"),fJn.forEach(t),gfa=r(LAo," \u2014 "),gEe=n(LAo,"A",{href:!0});var hJn=s(gEe);ufa=r(hJn,"LayoutLMv3ForQuestionAnswering"),hJn.forEach(t),pfa=r(LAo," (LayoutLMv3 model)"),LAo.forEach(t),_fa=i(O),LS=n(O,"LI",{});var AAo=s(LS);tro=n(AAo,"STRONG",{});var gJn=s(tro);bfa=r(gJn,"led"),gJn.forEach(t),vfa=r(AAo," \u2014 "),uEe=n(AAo,"A",{href:!0});var 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model\u2019s configuration. Use `),HEe=n(FCe,"A",{href:!0});var ZUn=s(HEe);opa=r(ZUn,"from_pretrained()"),ZUn.forEach(t),rpa=r(FCe," to load the model weights."),FCe.forEach(t),tpa=i(wP),M(cx.$$.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),mpa=r(LP,` property of the config object (either
passed as an argument or loaded from `),qro=n(LP,"CODE",{});var JUn=s(qro);cpa=r(JUn,"pretrained_model_name_or_path"),JUn.forEach(t),fpa=r(LP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Oro=n(LP,"CODE",{});var UUn=s(Oro);hpa=r(UUn,"pretrained_model_name_or_path"),UUn.forEach(t),gpa=r(LP,":"),LP.forEach(t),upa=i(Xd),_e=n(Xd,"UL",{});var Te=s(_e);fx=n(Te,"LI",{});var mRo=s(fx);Qro=n(mRo,"STRONG",{});var XUn=s(Qro);ppa=r(XUn,"albert"),XUn.forEach(t),_pa=r(mRo," \u2014 "),KEe=n(mRo,"A",{href:!0});var DUn=s(KEe);bpa=r(DUn,"TFAlbertForQuestionAnswering"),DUn.forEach(t),vpa=r(mRo," (ALBERT model)"),mRo.forEach(t),Mpa=i(Te),hx=n(Te,"LI",{});var cRo=s(hx);Yro=n(cRo,"STRONG",{});var qUn=s(Yro);Tpa=r(qUn,"bert"),qUn.forEach(t),Fpa=r(cRo," \u2014 "),eye=n(cRo,"A",{href:!0});var OUn=s(eye);Epa=r(OUn,"TFBertForQuestionAnswering"),OUn.forEach(t),ypa=r(cRo," (BERT model)"),cRo.forEach(t),Cpa=i(Te),gx=n(Te,"LI",{});var fRo=s(gx);zro=n(fRo,"STRONG",{});var QUn=s(zro);wpa=r(QUn,"camembert"),QUn.forEach(t),Lpa=r(fRo," \u2014 "),oye=n(fRo,"A",{href:!0});var YUn=s(oye);Apa=r(YUn,"TFCamembertForQuestionAnswering"),YUn.forEach(t),Rpa=r(fRo," (CamemBERT model)"),fRo.forEach(t),kpa=i(Te),ux=n(Te,"LI",{});var hRo=s(ux);Hro=n(hRo,"STRONG",{});var zUn=s(Hro);Spa=r(zUn,"convbert"),zUn.forEach(t),xpa=r(hRo," \u2014 "),rye=n(hRo,"A",{href:!0});var HUn=s(rye);Gpa=r(HUn,"TFConvBertForQuestionAnswering"),HUn.forEach(t),Bpa=r(hRo," (ConvBERT model)"),hRo.forEach(t),Ipa=i(Te),px=n(Te,"LI",{});var gRo=s(px);Kro=n(gRo,"STRONG",{});var KUn=s(Kro);Npa=r(KUn,"deberta"),KUn.forEach(t),$pa=r(gRo," \u2014 "),tye=n(gRo,"A",{href:!0});var eXn=s(tye);jpa=r(eXn,"TFDebertaForQuestionAnswering"),eXn.forEach(t),Ppa=r(gRo," (DeBERTa model)"),gRo.forEach(t),Zpa=i(Te),_x=n(Te,"LI",{});var uRo=s(_x);eto=n(uRo,"STRONG",{});var oXn=s(eto);Vpa=r(oXn,"deberta-v2"),oXn.forEach(t),Wpa=r(uRo," \u2014 "),aye=n(uRo,"A",{href:!0});var rXn=s(aye);Jpa=r(rXn,"TFDebertaV2ForQuestionAnswering"),rXn.forEach(t),Upa=r(uRo," (DeBERTa-v2 model)"),uRo.forEach(t),Xpa=i(Te),bx=n(Te,"LI",{});var pRo=s(bx);oto=n(pRo,"STRONG",{});var tXn=s(oto);Dpa=r(tXn,"distilbert"),tXn.forEach(t),qpa=r(pRo," \u2014 "),nye=n(pRo,"A",{href:!0});var aXn=s(nye);Opa=r(aXn,"TFDistilBertForQuestionAnswering"),aXn.forEach(t),Qpa=r(pRo," (DistilBERT model)"),pRo.forEach(t),Ypa=i(Te),vx=n(Te,"LI",{});var _Ro=s(vx);rto=n(_Ro,"STRONG",{});var nXn=s(rto);zpa=r(nXn,"electra"),nXn.forEach(t),Hpa=r(_Ro," \u2014 "),sye=n(_Ro,"A",{href:!0});var sXn=s(sye);Kpa=r(sXn,"TFElectraForQuestionAnswering"),sXn.forEach(t),e_a=r(_Ro," (ELECTRA model)"),_Ro.forEach(t),o_a=i(Te),Mx=n(Te,"LI",{});var bRo=s(Mx);tto=n(bRo,"STRONG",{});var lXn=s(tto);r_a=r(lXn,"flaubert"),lXn.forEach(t),t_a=r(bRo," \u2014 "),lye=n(bRo,"A",{href:!0});var iXn=s(lye);a_a=r(iXn,"TFFlaubertForQuestionAnsweringSimple"),iXn.forEach(t),n_a=r(bRo," (FlauBERT model)"),bRo.forEach(t),s_a=i(Te),Tx=n(Te,"LI",{});var vRo=s(Tx);ato=n(vRo,"STRONG",{});var dXn=s(ato);l_a=r(dXn,"funnel"),dXn.forEach(t),i_a=r(vRo," \u2014 "),iye=n(vRo,"A",{href:!0});var mXn=s(iye);d_a=r(mXn,"TFFunnelForQuestionAnswering"),mXn.forEach(t),m_a=r(vRo," (Funnel Transformer model)"),vRo.forEach(t),c_a=i(Te),Fx=n(Te,"LI",{});var MRo=s(Fx);nto=n(MRo,"STRONG",{});var cXn=s(nto);f_a=r(cXn,"gptj"),cXn.forEach(t),h_a=r(MRo," \u2014 "),dye=n(MRo,"A",{href:!0});var fXn=s(dye);g_a=r(fXn,"TFGPTJForQuestionAnswering"),fXn.forEach(t),u_a=r(MRo," (GPT-J model)"),MRo.forEach(t),p_a=i(Te),Ex=n(Te,"LI",{});var TRo=s(Ex);sto=n(TRo,"STRONG",{});var hXn=s(sto);__a=r(hXn,"layoutlmv3"),hXn.forEach(t),b_a=r(TRo," \u2014 "),mye=n(TRo,"A",{href:!0});var gXn=s(mye);v_a=r(gXn,"TFLayoutLMv3ForQuestionAnswering"),gXn.forEach(t),M_a=r(TRo," (LayoutLMv3 model)"),TRo.forEach(t),T_a=i(Te),yx=n(Te,"LI",{});var FRo=s(yx);lto=n(FRo,"STRONG",{});var uXn=s(lto);F_a=r(uXn,"longformer"),uXn.forEach(t),E_a=r(FRo," \u2014 "),cye=n(FRo,"A",{href:!0});var pXn=s(cye);y_a=r(pXn,"TFLongformerForQuestionAnswering"),pXn.forEach(t),C_a=r(FRo," (Longformer model)"),FRo.forEach(t),w_a=i(Te),Cx=n(Te,"LI",{});var ERo=s(Cx);ito=n(ERo,"STRONG",{});var _Xn=s(ito);L_a=r(_Xn,"mobilebert"),_Xn.forEach(t),A_a=r(ERo," \u2014 "),fye=n(ERo,"A",{href:!0});var bXn=s(fye);R_a=r(bXn,"TFMobileBertForQuestionAnswering"),bXn.forEach(t),k_a=r(ERo," (MobileBERT model)"),ERo.forEach(t),S_a=i(Te),wx=n(Te,"LI",{});var yRo=s(wx);dto=n(yRo,"STRONG",{});var vXn=s(dto);x_a=r(vXn,"mpnet"),vXn.forEach(t),G_a=r(yRo," \u2014 "),hye=n(yRo,"A",{href:!0});var MXn=s(hye);B_a=r(MXn,"TFMPNetForQuestionAnswering"),MXn.forEach(t),I_a=r(yRo," (MPNet model)"),yRo.forEach(t),N_a=i(Te),Lx=n(Te,"LI",{});var CRo=s(Lx);mto=n(CRo,"STRONG",{});var TXn=s(mto);$_a=r(TXn,"rembert"),TXn.forEach(t),j_a=r(CRo," \u2014 "),gye=n(CRo,"A",{href:!0});var FXn=s(gye);P_a=r(FXn,"TFRemBertForQuestionAnswering"),FXn.forEach(t),Z_a=r(CRo," (RemBERT model)"),CRo.forEach(t),V_a=i(Te),Ax=n(Te,"LI",{});var wRo=s(Ax);cto=n(wRo,"STRONG",{});var EXn=s(cto);W_a=r(EXn,"roberta"),EXn.forEach(t),J_a=r(wRo," \u2014 "),uye=n(wRo,"A",{href:!0});var yXn=s(uye);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 "),pye=n(LRo,"A",{href:!0});var wXn=s(pye);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 "),_ye=n(ARo,"A",{href:!0});var AXn=s(_ye);e1a=r(AXn,"TFRoFormerForQuestionAnswering"),AXn.forEach(t),o1a=r(ARo," (RoFormer model)"),ARo.forEach(t),r1a=i(Te),Sx=n(Te,"LI",{});var RRo=s(Sx);gto=n(RRo,"STRONG",{});var RXn=s(gto);t1a=r(RXn,"xlm"),RXn.forEach(t),a1a=r(RRo," \u2014 "),bye=n(RRo,"A",{href:!0});var kXn=s(bye);n1a=r(kXn,"TFXLMForQuestionAnsweringSimple"),kXn.forEach(t),s1a=r(RRo," (XLM model)"),RRo.forEach(t),l1a=i(Te),xx=n(Te,"LI",{});var kRo=s(xx);uto=n(kRo,"STRONG",{});var SXn=s(uto);i1a=r(SXn,"xlm-roberta"),SXn.forEach(t),d1a=r(kRo," \u2014 "),vye=n(kRo,"A",{href:!0});var xXn=s(vye);m1a=r(xXn,"TFXLMRobertaForQuestionAnswering"),xXn.forEach(t),c1a=r(kRo," (XLM-RoBERTa model)"),kRo.forEach(t),f1a=i(Te),Gx=n(Te,"LI",{});var SRo=s(Gx);pto=n(SRo,"STRONG",{});var GXn=s(pto);h1a=r(GXn,"xlnet"),GXn.forEach(t),g1a=r(SRo," \u2014 "),Mye=n(SRo,"A",{href:!0});var BXn=s(Mye);u1a=r(BXn,"TFXLNetForQuestionAnsweringSimple"),BXn.forEach(t),p1a=r(SRo," (XLNet model)"),SRo.forEach(t),Te.forEach(t),_1a=i(Xd),M(Bx.$$.fragment,Xd),Xd.forEach(t),Ud.forEach(t),_Bo=i(c),Vf=n(c,"H3",{class:!0});var _$o=s(Vf);Ix=n(_$o,"A",{id:!0,class:!0,href:!0});var IXn=s(Ix);_to=n(IXn,"SPAN",{});var NXn=s(_to);M(bU.$$.fragment,NXn),NXn.forEach(t),IXn.forEach(t),b1a=i(_$o),bto=n(_$o,"SPAN",{});var $Xn=s(bto);v1a=r($Xn,"FlaxAutoModelForQuestionAnswering"),$Xn.forEach(t),_$o.forEach(t),bBo=i(c),vr=n(c,"DIV",{class:!0});var Dd=s(vr);M(vU.$$.fragment,Dd),M1a=i(Dd),Wf=n(Dd,"P",{});var ECe=s(Wf);T1a=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 `),Tye=n(ECe,"A",{href:!0});var jXn=s(Tye);F1a=r(jXn,"from_pretrained()"),jXn.forEach(t),E1a=r(ECe," class method or the "),Fye=n(ECe,"A",{href:!0});var PXn=s(Fye);y1a=r(PXn,"from_config()"),PXn.forEach(t),C1a=r(ECe,` class
method.`),ECe.forEach(t),w1a=i(Dd),MU=n(Dd,"P",{});var b$o=s(MU);L1a=r(b$o,"This class cannot be instantiated directly using "),vto=n(b$o,"CODE",{});var ZXn=s(vto);A1a=r(ZXn,"__init__()"),ZXn.forEach(t),R1a=r(b$o," (throws an error)."),b$o.forEach(t),k1a=i(Dd),_a=n(Dd,"DIV",{class:!0});var AP=s(_a);M(TU.$$.fragment,AP),S1a=i(AP),Mto=n(AP,"P",{});var VXn=s(Mto);x1a=r(VXn,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),VXn.forEach(t),G1a=i(AP),Jf=n(AP,"P",{});var yCe=s(Jf);B1a=r(yCe,`Note:
Loading a model from its configuration file does `),Tto=n(yCe,"STRONG",{});var WXn=s(Tto);I1a=r(WXn,"not"),WXn.forEach(t),N1a=r(yCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Eye=n(yCe,"A",{href:!0});var JXn=s(Eye);$1a=r(JXn,"from_pretrained()"),JXn.forEach(t),j1a=r(yCe," to load the model weights."),yCe.forEach(t),P1a=i(AP),M(Nx.$$.fragment,AP),AP.forEach(t),Z1a=i(Dd),Ft=n(Dd,"DIV",{class:!0});var qd=s(Ft);M(FU.$$.fragment,qd),V1a=i(qd),Fto=n(qd,"P",{});var UXn=s(Fto);W1a=r(UXn,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),UXn.forEach(t),J1a=i(qd),gs=n(qd,"P",{});var RP=s(gs);U1a=r(RP,"The model class to instantiate is selected based on the "),Eto=n(RP,"CODE",{});var XXn=s(Eto);X1a=r(XXn,"model_type"),XXn.forEach(t),D1a=r(RP,` property of the config object (either
passed as an argument or loaded from `),yto=n(RP,"CODE",{});var DXn=s(yto);q1a=r(DXn,"pretrained_model_name_or_path"),DXn.forEach(t),O1a=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);Q1a=r(qXn,"pretrained_model_name_or_path"),qXn.forEach(t),Y1a=r(RP,":"),RP.forEach(t),z1a=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);H1a=r(OXn,"albert"),OXn.forEach(t),K1a=r(xRo," \u2014 "),yye=n(xRo,"A",{href:!0});var QXn=s(yye);eba=r(QXn,"FlaxAlbertForQuestionAnswering"),QXn.forEach(t),oba=r(xRo," (ALBERT model)"),xRo.forEach(t),rba=i(Ve),jx=n(Ve,"LI",{});var GRo=s(jx);Lto=n(GRo,"STRONG",{});var YXn=s(Lto);tba=r(YXn,"bart"),YXn.forEach(t),aba=r(GRo," \u2014 "),Cye=n(GRo,"A",{href:!0});var zXn=s(Cye);nba=r(zXn,"FlaxBartForQuestionAnswering"),zXn.forEach(t),sba=r(GRo," (BART model)"),GRo.forEach(t),lba=i(Ve),Px=n(Ve,"LI",{});var BRo=s(Px);Ato=n(BRo,"STRONG",{});var HXn=s(Ato);iba=r(HXn,"bert"),HXn.forEach(t),dba=r(BRo," \u2014 "),wye=n(BRo,"A",{href:!0});var KXn=s(wye);mba=r(KXn,"FlaxBertForQuestionAnswering"),KXn.forEach(t),cba=r(BRo," (BERT model)"),BRo.forEach(t),fba=i(Ve),Zx=n(Ve,"LI",{});var IRo=s(Zx);Rto=n(IRo,"STRONG",{});var eDn=s(Rto);hba=r(eDn,"big_bird"),eDn.forEach(t),gba=r(IRo," \u2014 "),Lye=n(IRo,"A",{href:!0});var oDn=s(Lye);uba=r(oDn,"FlaxBigBirdForQuestionAnswering"),oDn.forEach(t),pba=r(IRo," (BigBird model)"),IRo.forEach(t),_ba=i(Ve),Vx=n(Ve,"LI",{});var NRo=s(Vx);kto=n(NRo,"STRONG",{});var rDn=s(kto);bba=r(rDn,"distilbert"),rDn.forEach(t),vba=r(NRo," \u2014 "),Aye=n(NRo,"A",{href:!0});var tDn=s(Aye);Mba=r(tDn,"FlaxDistilBertForQuestionAnswering"),tDn.forEach(t),Tba=r(NRo," (DistilBERT model)"),NRo.forEach(t),Fba=i(Ve),Wx=n(Ve,"LI",{});var $Ro=s(Wx);Sto=n($Ro,"STRONG",{});var aDn=s(Sto);Eba=r(aDn,"electra"),aDn.forEach(t),yba=r($Ro," \u2014 "),Rye=n($Ro,"A",{href:!0});var nDn=s(Rye);Cba=r(nDn,"FlaxElectraForQuestionAnswering"),nDn.forEach(t),wba=r($Ro," (ELECTRA model)"),$Ro.forEach(t),Lba=i(Ve),Jx=n(Ve,"LI",{});var jRo=s(Jx);xto=n(jRo,"STRONG",{});var sDn=s(xto);Aba=r(sDn,"mbart"),sDn.forEach(t),Rba=r(jRo," \u2014 "),kye=n(jRo,"A",{href:!0});var lDn=s(kye);kba=r(lDn,"FlaxMBartForQuestionAnswering"),lDn.forEach(t),Sba=r(jRo," (mBART model)"),jRo.forEach(t),xba=i(Ve),Ux=n(Ve,"LI",{});var PRo=s(Ux);Gto=n(PRo,"STRONG",{});var iDn=s(Gto);Gba=r(iDn,"roberta"),iDn.forEach(t),Bba=r(PRo," \u2014 "),Sye=n(PRo,"A",{href:!0});var dDn=s(Sye);Iba=r(dDn,"FlaxRobertaForQuestionAnswering"),dDn.forEach(t),Nba=r(PRo," (RoBERTa model)"),PRo.forEach(t),$ba=i(Ve),Xx=n(Ve,"LI",{});var ZRo=s(Xx);Bto=n(ZRo,"STRONG",{});var mDn=s(Bto);jba=r(mDn,"roberta-prelayernorm"),mDn.forEach(t),Pba=r(ZRo," \u2014 "),xye=n(ZRo,"A",{href:!0});var cDn=s(xye);Zba=r(cDn,"FlaxRobertaPreLayerNormForQuestionAnswering"),cDn.forEach(t),Vba=r(ZRo," (RoBERTa-PreLayerNorm model)"),ZRo.forEach(t),Wba=i(Ve),Dx=n(Ve,"LI",{});var VRo=s(Dx);Ito=n(VRo,"STRONG",{});var fDn=s(Ito);Jba=r(fDn,"roformer"),fDn.forEach(t),Uba=r(VRo," \u2014 "),Gye=n(VRo,"A",{href:!0});var hDn=s(Gye);Xba=r(hDn,"FlaxRoFormerForQuestionAnswering"),hDn.forEach(t),Dba=r(VRo," (RoFormer model)"),VRo.forEach(t),qba=i(Ve),qx=n(Ve,"LI",{});var WRo=s(qx);Nto=n(WRo,"STRONG",{});var gDn=s(Nto);Oba=r(gDn,"xlm-roberta"),gDn.forEach(t),Qba=r(WRo," \u2014 "),Bye=n(WRo,"A",{href:!0});var uDn=s(Bye);Yba=r(uDn,"FlaxXLMRobertaForQuestionAnswering"),uDn.forEach(t),zba=r(WRo," (XLM-RoBERTa model)"),WRo.forEach(t),Ve.forEach(t),Hba=i(qd),M(Ox.$$.fragment,qd),qd.forEach(t),Dd.forEach(t),vBo=i(c),Uf=n(c,"H3",{class:!0});var v$o=s(Uf);Qx=n(v$o,"A",{id:!0,class:!0,href:!0});var pDn=s(Qx);$to=n(pDn,"SPAN",{});var _Dn=s($to);M(EU.$$.fragment,_Dn),_Dn.forEach(t),pDn.forEach(t),Kba=i(v$o),jto=n(v$o,"SPAN",{});var bDn=s(jto);e2a=r(bDn,"AutoModelForTextEncoding"),bDn.forEach(t),v$o.forEach(t),MBo=i(c),yU=n(c,"DIV",{class:!0});var vDn=s(yU);M(CU.$$.fragment,vDn),vDn.forEach(t),TBo=i(c),Xf=n(c,"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.$$.fragment,TDn),TDn.forEach(t),MDn.forEach(t),o2a=i(M$o),Zto=n(M$o,"SPAN",{});var FDn=s(Zto);r2a=r(FDn,"TFAutoModelForTextEncoding"),FDn.forEach(t),M$o.forEach(t),FBo=i(c),LU=n(c,"DIV",{class:!0});var EDn=s(LU);M(AU.$$.fragment,EDn),EDn.forEach(t),EBo=i(c),Df=n(c,"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.$$.fragment,CDn),CDn.forEach(t),yDn.forEach(t),t2a=i(T$o),Wto=n(T$o,"SPAN",{});var wDn=s(Wto);a2a=r(wDn,"Computer vision"),wDn.forEach(t),T$o.forEach(t),yBo=i(c),Iye=n(c,"P",{});var LDn=s(Iye);n2a=r(LDn,"The following auto classes are available for the following computer vision tasks."),LDn.forEach(t),CBo=i(c),qf=n(c,"H3",{class:!0});var F$o=s(qf);Hx=n(F$o,"A",{id:!0,class:!0,href:!0});var ADn=s(Hx);Jto=n(ADn,"SPAN",{});var RDn=s(Jto);M(kU.$$.fragment,RDn),RDn.forEach(t),ADn.forEach(t),s2a=i(F$o),Uto=n(F$o,"SPAN",{});var kDn=s(Uto);l2a=r(kDn,"AutoModelForDepthEstimation"),kDn.forEach(t),F$o.forEach(t),wBo=i(c),Mr=n(c,"DIV",{class:!0});var Od=s(Mr);M(SU.$$.fragment,Od),i2a=i(Od),Of=n(Od,"P",{});var CCe=s(Of);d2a=r(CCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a depth estimation head) when created
with the `),Nye=n(CCe,"A",{href:!0});var SDn=s(Nye);m2a=r(SDn,"from_pretrained()"),SDn.forEach(t),c2a=r(CCe," class method or the "),$ye=n(CCe,"A",{href:!0});var xDn=s($ye);f2a=r(xDn,"from_config()"),xDn.forEach(t),h2a=r(CCe,` class
method.`),CCe.forEach(t),g2a=i(Od),xU=n(Od,"P",{});var E$o=s(xU);u2a=r(E$o,"This class cannot be instantiated directly using "),Xto=n(E$o,"CODE",{});var GDn=s(Xto);p2a=r(GDn,"__init__()"),GDn.forEach(t),_2a=r(E$o," (throws an error)."),E$o.forEach(t),b2a=i(Od),ba=n(Od,"DIV",{class:!0});var kP=s(ba);M(GU.$$.fragment,kP),v2a=i(kP),Dto=n(kP,"P",{});var BDn=s(Dto);M2a=r(BDn,"Instantiates one of the model classes of the library (with a depth estimation head) from a configuration."),BDn.forEach(t),T2a=i(kP),Qf=n(kP,"P",{});var wCe=s(Qf);F2a=r(wCe,`Note:
Loading a model from its configuration file does `),qto=n(wCe,"STRONG",{});var IDn=s(qto);E2a=r(IDn,"not"),IDn.forEach(t),y2a=r(wCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),jye=n(wCe,"A",{href:!0});var NDn=s(jye);C2a=r(NDn,"from_pretrained()"),NDn.forEach(t),w2a=r(wCe," to load the model weights."),wCe.forEach(t),L2a=i(kP),M(Kx.$$.fragment,kP),kP.forEach(t),A2a=i(Od),_o=n(Od,"DIV",{class:!0});var cn=s(_o);M(BU.$$.fragment,cn),R2a=i(cn),Oto=n(cn,"P",{});var $Dn=s(Oto);k2a=r($Dn,"Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model."),$Dn.forEach(t),S2a=i(cn),us=n(cn,"P",{});var SP=s(us);x2a=r(SP,"The model class to instantiate is selected based on the "),Qto=n(SP,"CODE",{});var jDn=s(Qto);G2a=r(jDn,"model_type"),jDn.forEach(t),B2a=r(SP,` property of the config object (either
passed as an argument or loaded from `),Yto=n(SP,"CODE",{});var PDn=s(Yto);I2a=r(PDn,"pretrained_model_name_or_path"),PDn.forEach(t),N2a=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);$2a=r(ZDn,"pretrained_model_name_or_path"),ZDn.forEach(t),j2a=r(SP,":"),SP.forEach(t),P2a=i(cn),IU=n(cn,"UL",{});var y$o=s(IU);eG=n(y$o,"LI",{});var JRo=s(eG);Hto=n(JRo,"STRONG",{});var VDn=s(Hto);Z2a=r(VDn,"dpt"),VDn.forEach(t),V2a=r(JRo," \u2014 "),Pye=n(JRo,"A",{href:!0});var WDn=s(Pye);W2a=r(WDn,"DPTForDepthEstimation"),WDn.forEach(t),J2a=r(JRo," (DPT model)"),JRo.forEach(t),U2a=i(y$o),oG=n(y$o,"LI",{});var URo=s(oG);Kto=n(URo,"STRONG",{});var JDn=s(Kto);X2a=r(JDn,"glpn"),JDn.forEach(t),D2a=r(URo," \u2014 "),Zye=n(URo,"A",{href:!0});var UDn=s(Zye);q2a=r(UDn,"GLPNForDepthEstimation"),UDn.forEach(t),O2a=r(URo," (GLPN model)"),URo.forEach(t),y$o.forEach(t),Q2a=i(cn),rG=n(cn,"P",{});var XRo=s(rG);Y2a=r(XRo,"The model is set in evaluation mode by default using "),eao=n(XRo,"CODE",{});var XDn=s(eao);z2a=r(XDn,"model.eval()"),XDn.forEach(t),H2a=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);K2a=r(DDn,"model.train()"),DDn.forEach(t),XRo.forEach(t),eva=i(cn),M(tG.$$.fragment,cn),cn.forEach(t),Od.forEach(t),LBo=i(c),Yf=n(c,"H3",{class:!0});var C$o=s(Yf);aG=n(C$o,"A",{id:!0,class:!0,href:!0});var qDn=s(aG);rao=n(qDn,"SPAN",{});var ODn=s(rao);M(NU.$$.fragment,ODn),ODn.forEach(t),qDn.forEach(t),ova=i(C$o),tao=n(C$o,"SPAN",{});var QDn=s(tao);rva=r(QDn,"AutoModelForImageClassification"),QDn.forEach(t),C$o.forEach(t),ABo=i(c),Tr=n(c,"DIV",{class:!0});var Qd=s(Tr);M($U.$$.fragment,Qd),tva=i(Qd),zf=n(Qd,"P",{});var LCe=s(zf);ava=r(LCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),Vye=n(LCe,"A",{href:!0});var YDn=s(Vye);nva=r(YDn,"from_pretrained()"),YDn.forEach(t),sva=r(LCe," class method or the "),Wye=n(LCe,"A",{href:!0});var zDn=s(Wye);lva=r(zDn,"from_config()"),zDn.forEach(t),iva=r(LCe,` class
method.`),LCe.forEach(t),dva=i(Qd),jU=n(Qd,"P",{});var w$o=s(jU);mva=r(w$o,"This class cannot be instantiated directly using "),aao=n(w$o,"CODE",{});var HDn=s(aao);cva=r(HDn,"__init__()"),HDn.forEach(t),fva=r(w$o," (throws an error)."),w$o.forEach(t),hva=i(Qd),va=n(Qd,"DIV",{class:!0});var xP=s(va);M(PU.$$.fragment,xP),gva=i(xP),nao=n(xP,"P",{});var KDn=s(nao);uva=r(KDn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),KDn.forEach(t),pva=i(xP),Hf=n(xP,"P",{});var ACe=s(Hf);_va=r(ACe,`Note:
Loading a model from its configuration file does `),sao=n(ACe,"STRONG",{});var eqn=s(sao);bva=r(eqn,"not"),eqn.forEach(t),vva=r(ACe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Jye=n(ACe,"A",{href:!0});var oqn=s(Jye);Mva=r(oqn,"from_pretrained()"),oqn.forEach(t),Tva=r(ACe," to load the model weights."),ACe.forEach(t),Fva=i(xP),M(nG.$$.fragment,xP),xP.forEach(t),Eva=i(Qd),bo=n(Qd,"DIV",{class:!0});var fn=s(bo);M(ZU.$$.fragment,fn),yva=i(fn),lao=n(fn,"P",{});var rqn=s(lao);Cva=r(rqn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),rqn.forEach(t),wva=i(fn),ps=n(fn,"P",{});var GP=s(ps);Lva=r(GP,"The model class to instantiate is selected based on the "),iao=n(GP,"CODE",{});var tqn=s(iao);Ava=r(tqn,"model_type"),tqn.forEach(t),Rva=r(GP,` property of the config object (either
passed as an argument or loaded from `),dao=n(GP,"CODE",{});var aqn=s(dao);kva=r(aqn,"pretrained_model_name_or_path"),aqn.forEach(t),Sva=r(GP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mao=n(GP,"CODE",{});var nqn=s(mao);xva=r(nqn,"pretrained_model_name_or_path"),nqn.forEach(t),Gva=r(GP,":"),GP.forEach(t),Bva=i(fn),le=n(fn,"UL",{});var me=s(le);sG=n(me,"LI",{});var DRo=s(sG);cao=n(DRo,"STRONG",{});var sqn=s(cao);Iva=r(sqn,"beit"),sqn.forEach(t),Nva=r(DRo," \u2014 "),Uye=n(DRo,"A",{href:!0});var lqn=s(Uye);$va=r(lqn,"BeitForImageClassification"),lqn.forEach(t),jva=r(DRo," (BEiT model)"),DRo.forEach(t),Pva=i(me),lG=n(me,"LI",{});var qRo=s(lG);fao=n(qRo,"STRONG",{});var iqn=s(fao);Zva=r(iqn,"bit"),iqn.forEach(t),Vva=r(qRo," \u2014 "),Xye=n(qRo,"A",{href:!0});var dqn=s(Xye);Wva=r(dqn,"BitForImageClassification"),dqn.forEach(t),Jva=r(qRo," (BiT model)"),qRo.forEach(t),Uva=i(me),iG=n(me,"LI",{});var ORo=s(iG);hao=n(ORo,"STRONG",{});var mqn=s(hao);Xva=r(mqn,"convnext"),mqn.forEach(t),Dva=r(ORo," \u2014 "),Dye=n(ORo,"A",{href:!0});var cqn=s(Dye);qva=r(cqn,"ConvNextForImageClassification"),cqn.forEach(t),Ova=r(ORo," (ConvNeXT model)"),ORo.forEach(t),Qva=i(me),dG=n(me,"LI",{});var QRo=s(dG);gao=n(QRo,"STRONG",{});var fqn=s(gao);Yva=r(fqn,"convnextv2"),fqn.forEach(t),zva=r(QRo," \u2014 "),qye=n(QRo,"A",{href:!0});var hqn=s(qye);Hva=r(hqn,"ConvNextV2ForImageClassification"),hqn.forEach(t),Kva=r(QRo," (ConvNeXTV2 model)"),QRo.forEach(t),eMa=i(me),mG=n(me,"LI",{});var YRo=s(mG);uao=n(YRo,"STRONG",{});var gqn=s(uao);oMa=r(gqn,"cvt"),gqn.forEach(t),rMa=r(YRo," \u2014 "),Oye=n(YRo,"A",{href:!0});var uqn=s(Oye);tMa=r(uqn,"CvtForImageClassification"),uqn.forEach(t),aMa=r(YRo," (CvT model)"),YRo.forEach(t),nMa=i(me),cG=n(me,"LI",{});var zRo=s(cG);pao=n(zRo,"STRONG",{});var pqn=s(pao);sMa=r(pqn,"data2vec-vision"),pqn.forEach(t),lMa=r(zRo," \u2014 "),Qye=n(zRo,"A",{href:!0});var _qn=s(Qye);iMa=r(_qn,"Data2VecVisionForImageClassification"),_qn.forEach(t),dMa=r(zRo," (Data2VecVision model)"),zRo.forEach(t),mMa=i(me),Wi=n(me,"LI",{});var NQ=s(Wi);_ao=n(NQ,"STRONG",{});var bqn=s(_ao);cMa=r(bqn,"deit"),bqn.forEach(t),fMa=r(NQ," \u2014 "),Yye=n(NQ,"A",{href:!0});var vqn=s(Yye);hMa=r(vqn,"DeiTForImageClassification"),vqn.forEach(t),gMa=r(NQ," or "),zye=n(NQ,"A",{href:!0});var Mqn=s(zye);uMa=r(Mqn,"DeiTForImageClassificationWithTeacher"),Mqn.forEach(t),pMa=r(NQ," (DeiT model)"),NQ.forEach(t),_Ma=i(me),fG=n(me,"LI",{});var HRo=s(fG);bao=n(HRo,"STRONG",{});var Tqn=s(bao);bMa=r(Tqn,"dinat"),Tqn.forEach(t),vMa=r(HRo," \u2014 "),Hye=n(HRo,"A",{href:!0});var Fqn=s(Hye);MMa=r(Fqn,"DinatForImageClassification"),Fqn.forEach(t),TMa=r(HRo," (DiNAT model)"),HRo.forEach(t),FMa=i(me),hG=n(me,"LI",{});var KRo=s(hG);vao=n(KRo,"STRONG",{});var Eqn=s(vao);EMa=r(Eqn,"dinov2"),Eqn.forEach(t),yMa=r(KRo," \u2014 "),Kye=n(KRo,"A",{href:!0});var yqn=s(Kye);CMa=r(yqn,"Dinov2ForImageClassification"),yqn.forEach(t),wMa=r(KRo," (DINOv2 model)"),KRo.forEach(t),LMa=i(me),Ji=n(me,"LI",{});var $Q=s(Ji);Mao=n($Q,"STRONG",{});var Cqn=s(Mao);AMa=r(Cqn,"efficientformer"),Cqn.forEach(t),RMa=r($Q," \u2014 "),e0e=n($Q,"A",{href:!0});var wqn=s(e0e);kMa=r(wqn,"EfficientFormerForImageClassification"),wqn.forEach(t),SMa=r($Q," or "),o0e=n($Q,"A",{href:!0});var Lqn=s(o0e);xMa=r(Lqn,"EfficientFormerForImageClassificationWithTeacher"),Lqn.forEach(t),GMa=r($Q," (EfficientFormer model)"),$Q.forEach(t),BMa=i(me),gG=n(me,"LI",{});var eko=s(gG);Tao=n(eko,"STRONG",{});var Aqn=s(Tao);IMa=r(Aqn,"efficientnet"),Aqn.forEach(t),NMa=r(eko," \u2014 "),r0e=n(eko,"A",{href:!0});var Rqn=s(r0e);$Ma=r(Rqn,"EfficientNetForImageClassification"),Rqn.forEach(t),jMa=r(eko," (EfficientNet model)"),eko.forEach(t),PMa=i(me),uG=n(me,"LI",{});var oko=s(uG);Fao=n(oko,"STRONG",{});var kqn=s(Fao);ZMa=r(kqn,"focalnet"),kqn.forEach(t),VMa=r(oko," \u2014 "),t0e=n(oko,"A",{href:!0});var Sqn=s(t0e);WMa=r(Sqn,"FocalNetForImageClassification"),Sqn.forEach(t),JMa=r(oko," (FocalNet model)"),oko.forEach(t),UMa=i(me),pG=n(me,"LI",{});var rko=s(pG);Eao=n(rko,"STRONG",{});var xqn=s(Eao);XMa=r(xqn,"imagegpt"),xqn.forEach(t),DMa=r(rko," \u2014 "),a0e=n(rko,"A",{href:!0});var Gqn=s(a0e);qMa=r(Gqn,"ImageGPTForImageClassification"),Gqn.forEach(t),OMa=r(rko," (ImageGPT model)"),rko.forEach(t),QMa=i(me),Ui=n(me,"LI",{});var jQ=s(Ui);yao=n(jQ,"STRONG",{});var Bqn=s(yao);YMa=r(Bqn,"levit"),Bqn.forEach(t),zMa=r(jQ," \u2014 "),n0e=n(jQ,"A",{href:!0});var Iqn=s(n0e);HMa=r(Iqn,"LevitForImageClassification"),Iqn.forEach(t),KMa=r(jQ," or "),s0e=n(jQ,"A",{href:!0});var Nqn=s(s0e);eTa=r(Nqn,"LevitForImageClassificationWithTeacher"),Nqn.forEach(t),oTa=r(jQ," (LeViT model)"),jQ.forEach(t),rTa=i(me),_G=n(me,"LI",{});var tko=s(_G);Cao=n(tko,"STRONG",{});var $qn=s(Cao);tTa=r($qn,"mobilenet_v1"),$qn.forEach(t),aTa=r(tko," \u2014 "),l0e=n(tko,"A",{href:!0});var jqn=s(l0e);nTa=r(jqn,"MobileNetV1ForImageClassification"),jqn.forEach(t),sTa=r(tko," (MobileNetV1 model)"),tko.forEach(t),lTa=i(me),bG=n(me,"LI",{});var ako=s(bG);wao=n(ako,"STRONG",{});var Pqn=s(wao);iTa=r(Pqn,"mobilenet_v2"),Pqn.forEach(t),dTa=r(ako," \u2014 "),i0e=n(ako,"A",{href:!0});var Zqn=s(i0e);mTa=r(Zqn,"MobileNetV2ForImageClassification"),Zqn.forEach(t),cTa=r(ako," (MobileNetV2 model)"),ako.forEach(t),fTa=i(me),vG=n(me,"LI",{});var nko=s(vG);Lao=n(nko,"STRONG",{});var Vqn=s(Lao);hTa=r(Vqn,"mobilevit"),Vqn.forEach(t),gTa=r(nko," \u2014 "),d0e=n(nko,"A",{href:!0});var Wqn=s(d0e);uTa=r(Wqn,"MobileViTForImageClassification"),Wqn.forEach(t),pTa=r(nko," (MobileViT model)"),nko.forEach(t),_Ta=i(me),MG=n(me,"LI",{});var sko=s(MG);Aao=n(sko,"STRONG",{});var Jqn=s(Aao);bTa=r(Jqn,"mobilevitv2"),Jqn.forEach(t),vTa=r(sko," \u2014 "),m0e=n(sko,"A",{href:!0});var Uqn=s(m0e);MTa=r(Uqn,"MobileViTV2ForImageClassification"),Uqn.forEach(t),TTa=r(sko," (MobileViTV2 model)"),sko.forEach(t),FTa=i(me),TG=n(me,"LI",{});var lko=s(TG);Rao=n(lko,"STRONG",{});var Xqn=s(Rao);ETa=r(Xqn,"nat"),Xqn.forEach(t),yTa=r(lko," \u2014 "),c0e=n(lko,"A",{href:!0});var Dqn=s(c0e);CTa=r(Dqn,"NatForImageClassification"),Dqn.forEach(t),wTa=r(lko," (NAT model)"),lko.forEach(t),LTa=i(me),Ma=n(me,"LI",{});var Ug=s(Ma);kao=n(Ug,"STRONG",{});var qqn=s(kao);ATa=r(qqn,"perceiver"),qqn.forEach(t),RTa=r(Ug," \u2014 "),f0e=n(Ug,"A",{href:!0});var Oqn=s(f0e);kTa=r(Oqn,"PerceiverForImageClassificationLearned"),Oqn.forEach(t),STa=r(Ug," or "),h0e=n(Ug,"A",{href:!0});var Qqn=s(h0e);xTa=r(Qqn,"PerceiverForImageClassificationFourier"),Qqn.forEach(t),GTa=r(Ug," or "),g0e=n(Ug,"A",{href:!0});var Yqn=s(g0e);BTa=r(Yqn,"PerceiverForImageClassificationConvProcessing"),Yqn.forEach(t),ITa=r(Ug," (Perceiver model)"),Ug.forEach(t),NTa=i(me),FG=n(me,"LI",{});var iko=s(FG);Sao=n(iko,"STRONG",{});var zqn=s(Sao);$Ta=r(zqn,"poolformer"),zqn.forEach(t),jTa=r(iko," \u2014 "),u0e=n(iko,"A",{href:!0});var Hqn=s(u0e);PTa=r(Hqn,"PoolFormerForImageClassification"),Hqn.forEach(t),ZTa=r(iko," (PoolFormer model)"),iko.forEach(t),VTa=i(me),EG=n(me,"LI",{});var dko=s(EG);xao=n(dko,"STRONG",{});var Kqn=s(xao);WTa=r(Kqn,"pvt"),Kqn.forEach(t),JTa=r(dko," \u2014 "),p0e=n(dko,"A",{href:!0});var eOn=s(p0e);UTa=r(eOn,"PvtForImageClassification"),eOn.forEach(t),XTa=r(dko," (PVT model)"),dko.forEach(t),DTa=i(me),yG=n(me,"LI",{});var mko=s(yG);Gao=n(mko,"STRONG",{});var oOn=s(Gao);qTa=r(oOn,"regnet"),oOn.forEach(t),OTa=r(mko," \u2014 "),_0e=n(mko,"A",{href:!0});var rOn=s(_0e);QTa=r(rOn,"RegNetForImageClassification"),rOn.forEach(t),YTa=r(mko," (RegNet model)"),mko.forEach(t),zTa=i(me),CG=n(me,"LI",{});var cko=s(CG);Bao=n(cko,"STRONG",{});var tOn=s(Bao);HTa=r(tOn,"resnet"),tOn.forEach(t),KTa=r(cko," \u2014 "),b0e=n(cko,"A",{href:!0});var aOn=s(b0e);eFa=r(aOn,"ResNetForImageClassification"),aOn.forEach(t),oFa=r(cko," (ResNet model)"),cko.forEach(t),rFa=i(me),wG=n(me,"LI",{});var fko=s(wG);Iao=n(fko,"STRONG",{});var nOn=s(Iao);tFa=r(nOn,"segformer"),nOn.forEach(t),aFa=r(fko," \u2014 "),v0e=n(fko,"A",{href:!0});var sOn=s(v0e);nFa=r(sOn,"SegformerForImageClassification"),sOn.forEach(t),sFa=r(fko," (SegFormer model)"),fko.forEach(t),lFa=i(me),LG=n(me,"LI",{});var hko=s(LG);Nao=n(hko,"STRONG",{});var lOn=s(Nao);iFa=r(lOn,"swiftformer"),lOn.forEach(t),dFa=r(hko," \u2014 "),M0e=n(hko,"A",{href:!0});var iOn=s(M0e);mFa=r(iOn,"SwiftFormerForImageClassification"),iOn.forEach(t),cFa=r(hko," (SwiftFormer model)"),hko.forEach(t),fFa=i(me),AG=n(me,"LI",{});var gko=s(AG);$ao=n(gko,"STRONG",{});var dOn=s($ao);hFa=r(dOn,"swin"),dOn.forEach(t),gFa=r(gko," \u2014 "),T0e=n(gko,"A",{href:!0});var mOn=s(T0e);uFa=r(mOn,"SwinForImageClassification"),mOn.forEach(t),pFa=r(gko," (Swin Transformer model)"),gko.forEach(t),_Fa=i(me),RG=n(me,"LI",{});var uko=s(RG);jao=n(uko,"STRONG",{});var cOn=s(jao);bFa=r(cOn,"swinv2"),cOn.forEach(t),vFa=r(uko," \u2014 "),F0e=n(uko,"A",{href:!0});var fOn=s(F0e);MFa=r(fOn,"Swinv2ForImageClassification"),fOn.forEach(t),TFa=r(uko," (Swin Transformer V2 model)"),uko.forEach(t),FFa=i(me),kG=n(me,"LI",{});var pko=s(kG);Pao=n(pko,"STRONG",{});var hOn=s(Pao);EFa=r(hOn,"van"),hOn.forEach(t),yFa=r(pko," \u2014 "),E0e=n(pko,"A",{href:!0});var gOn=s(E0e);CFa=r(gOn,"VanForImageClassification"),gOn.forEach(t),wFa=r(pko," (VAN model)"),pko.forEach(t),LFa=i(me),SG=n(me,"LI",{});var _ko=s(SG);Zao=n(_ko,"STRONG",{});var uOn=s(Zao);AFa=r(uOn,"vit"),uOn.forEach(t),RFa=r(_ko," \u2014 "),y0e=n(_ko,"A",{href:!0});var pOn=s(y0e);kFa=r(pOn,"ViTForImageClassification"),pOn.forEach(t),SFa=r(_ko," (ViT model)"),_ko.forEach(t),xFa=i(me),xG=n(me,"LI",{});var bko=s(xG);Vao=n(bko,"STRONG",{});var _On=s(Vao);GFa=r(_On,"vit_hybrid"),_On.forEach(t),BFa=r(bko," \u2014 "),C0e=n(bko,"A",{href:!0});var bOn=s(C0e);IFa=r(bOn,"ViTHybridForImageClassification"),bOn.forEach(t),NFa=r(bko," (ViT Hybrid model)"),bko.forEach(t),$Fa=i(me),GG=n(me,"LI",{});var vko=s(GG);Wao=n(vko,"STRONG",{});var vOn=s(Wao);jFa=r(vOn,"vit_msn"),vOn.forEach(t),PFa=r(vko," \u2014 "),w0e=n(vko,"A",{href:!0});var MOn=s(w0e);ZFa=r(MOn,"ViTMSNForImageClassification"),MOn.forEach(t),VFa=r(vko," (ViTMSN model)"),vko.forEach(t),me.forEach(t),WFa=i(fn),BG=n(fn,"P",{});var Mko=s(BG);JFa=r(Mko,"The model is set in evaluation mode by default using "),Jao=n(Mko,"CODE",{});var TOn=s(Jao);UFa=r(TOn,"model.eval()"),TOn.forEach(t),XFa=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);DFa=r(FOn,"model.train()"),FOn.forEach(t),Mko.forEach(t),qFa=i(fn),M(IG.$$.fragment,fn),fn.forEach(t),Qd.forEach(t),RBo=i(c),Kf=n(c,"H3",{class:!0});var L$o=s(Kf);NG=n(L$o,"A",{id:!0,class:!0,href:!0});var EOn=s(NG);Xao=n(EOn,"SPAN",{});var yOn=s(Xao);M(VU.$$.fragment,yOn),yOn.forEach(t),EOn.forEach(t),OFa=i(L$o),Dao=n(L$o,"SPAN",{});var COn=s(Dao);QFa=r(COn,"TFAutoModelForImageClassification"),COn.forEach(t),L$o.forEach(t),kBo=i(c),Fr=n(c,"DIV",{class:!0});var Yd=s(Fr);M(WU.$$.fragment,Yd),YFa=i(Yd),eh=n(Yd,"P",{});var RCe=s(eh);zFa=r(RCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),L0e=n(RCe,"A",{href:!0});var wOn=s(L0e);HFa=r(wOn,"from_pretrained()"),wOn.forEach(t),KFa=r(RCe," class method or the "),A0e=n(RCe,"A",{href:!0});var LOn=s(A0e);eEa=r(LOn,"from_config()"),LOn.forEach(t),oEa=r(RCe,` class
method.`),RCe.forEach(t),rEa=i(Yd),JU=n(Yd,"P",{});var A$o=s(JU);tEa=r(A$o,"This class cannot be instantiated directly using "),qao=n(A$o,"CODE",{});var AOn=s(qao);aEa=r(AOn,"__init__()"),AOn.forEach(t),nEa=r(A$o," (throws an error)."),A$o.forEach(t),sEa=i(Yd),Ta=n(Yd,"DIV",{class:!0});var BP=s(Ta);M(UU.$$.fragment,BP),lEa=i(BP),Oao=n(BP,"P",{});var ROn=s(Oao);iEa=r(ROn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),ROn.forEach(t),dEa=i(BP),oh=n(BP,"P",{});var kCe=s(oh);mEa=r(kCe,`Note:
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model\u2019s configuration. Use `),R0e=n(kCe,"A",{href:!0});var SOn=s(R0e);hEa=r(SOn,"from_pretrained()"),SOn.forEach(t),gEa=r(kCe," to load the model weights."),kCe.forEach(t),uEa=i(BP),M($G.$$.fragment,BP),BP.forEach(t),pEa=i(Yd),Et=n(Yd,"DIV",{class:!0});var zd=s(Et);M(XU.$$.fragment,zd),_Ea=i(zd),Yao=n(zd,"P",{});var xOn=s(Yao);bEa=r(xOn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),xOn.forEach(t),vEa=i(zd),_s=n(zd,"P",{});var IP=s(_s);MEa=r(IP,"The model class to instantiate is selected based on the "),zao=n(IP,"CODE",{});var GOn=s(zao);TEa=r(GOn,"model_type"),GOn.forEach(t),FEa=r(IP,` property of the config object (either
passed as an argument or loaded from `),Hao=n(IP,"CODE",{});var BOn=s(Hao);EEa=r(BOn,"pretrained_model_name_or_path"),BOn.forEach(t),yEa=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);CEa=r(IOn,"pretrained_model_name_or_path"),IOn.forEach(t),wEa=r(IP,":"),IP.forEach(t),LEa=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);AEa=r(NOn,"convnext"),NOn.forEach(t),REa=r(Tko," \u2014 "),k0e=n(Tko,"A",{href:!0});var $On=s(k0e);kEa=r($On,"TFConvNextForImageClassification"),$On.forEach(t),SEa=r(Tko," (ConvNeXT model)"),Tko.forEach(t),xEa=i(We),PG=n(We,"LI",{});var Fko=s(PG);ono=n(Fko,"STRONG",{});var jOn=s(ono);GEa=r(jOn,"cvt"),jOn.forEach(t),BEa=r(Fko," \u2014 "),S0e=n(Fko,"A",{href:!0});var POn=s(S0e);IEa=r(POn,"TFCvtForImageClassification"),POn.forEach(t),NEa=r(Fko," (CvT model)"),Fko.forEach(t),$Ea=i(We),ZG=n(We,"LI",{});var Eko=s(ZG);rno=n(Eko,"STRONG",{});var ZOn=s(rno);jEa=r(ZOn,"data2vec-vision"),ZOn.forEach(t),PEa=r(Eko," \u2014 "),x0e=n(Eko,"A",{href:!0});var VOn=s(x0e);ZEa=r(VOn,"TFData2VecVisionForImageClassification"),VOn.forEach(t),VEa=r(Eko," (Data2VecVision model)"),Eko.forEach(t),WEa=i(We),Xi=n(We,"LI",{});var PQ=s(Xi);tno=n(PQ,"STRONG",{});var WOn=s(tno);JEa=r(WOn,"deit"),WOn.forEach(t),UEa=r(PQ," \u2014 "),G0e=n(PQ,"A",{href:!0});var JOn=s(G0e);XEa=r(JOn,"TFDeiTForImageClassification"),JOn.forEach(t),DEa=r(PQ," or "),B0e=n(PQ,"A",{href:!0});var UOn=s(B0e);qEa=r(UOn,"TFDeiTForImageClassificationWithTeacher"),UOn.forEach(t),OEa=r(PQ," (DeiT model)"),PQ.forEach(t),QEa=i(We),Di=n(We,"LI",{});var ZQ=s(Di);ano=n(ZQ,"STRONG",{});var XOn=s(ano);YEa=r(XOn,"efficientformer"),XOn.forEach(t),zEa=r(ZQ," \u2014 "),I0e=n(ZQ,"A",{href:!0});var DOn=s(I0e);HEa=r(DOn,"TFEfficientFormerForImageClassification"),DOn.forEach(t),KEa=r(ZQ," or "),N0e=n(ZQ,"A",{href:!0});var qOn=s(N0e);eya=r(qOn,"TFEfficientFormerForImageClassificationWithTeacher"),qOn.forEach(t),oya=r(ZQ," (EfficientFormer model)"),ZQ.forEach(t),rya=i(We),VG=n(We,"LI",{});var yko=s(VG);nno=n(yko,"STRONG",{});var OOn=s(nno);tya=r(OOn,"mobilevit"),OOn.forEach(t),aya=r(yko," \u2014 "),$0e=n(yko,"A",{href:!0});var QOn=s($0e);nya=r(QOn,"TFMobileViTForImageClassification"),QOn.forEach(t),sya=r(yko," (MobileViT model)"),yko.forEach(t),lya=i(We),WG=n(We,"LI",{});var Cko=s(WG);sno=n(Cko,"STRONG",{});var YOn=s(sno);iya=r(YOn,"regnet"),YOn.forEach(t),dya=r(Cko," \u2014 "),j0e=n(Cko,"A",{href:!0});var zOn=s(j0e);mya=r(zOn,"TFRegNetForImageClassification"),zOn.forEach(t),cya=r(Cko," (RegNet model)"),Cko.forEach(t),fya=i(We),JG=n(We,"LI",{});var wko=s(JG);lno=n(wko,"STRONG",{});var HOn=s(lno);hya=r(HOn,"resnet"),HOn.forEach(t),gya=r(wko," \u2014 "),P0e=n(wko,"A",{href:!0});var KOn=s(P0e);uya=r(KOn,"TFResNetForImageClassification"),KOn.forEach(t),pya=r(wko," (ResNet model)"),wko.forEach(t),_ya=i(We),UG=n(We,"LI",{});var Lko=s(UG);ino=n(Lko,"STRONG",{});var eQn=s(ino);bya=r(eQn,"segformer"),eQn.forEach(t),vya=r(Lko," \u2014 "),Z0e=n(Lko,"A",{href:!0});var oQn=s(Z0e);Mya=r(oQn,"TFSegformerForImageClassification"),oQn.forEach(t),Tya=r(Lko," (SegFormer model)"),Lko.forEach(t),Fya=i(We),XG=n(We,"LI",{});var Ako=s(XG);dno=n(Ako,"STRONG",{});var rQn=s(dno);Eya=r(rQn,"swin"),rQn.forEach(t),yya=r(Ako," \u2014 "),V0e=n(Ako,"A",{href:!0});var tQn=s(V0e);Cya=r(tQn,"TFSwinForImageClassification"),tQn.forEach(t),wya=r(Ako," (Swin Transformer model)"),Ako.forEach(t),Lya=i(We),DG=n(We,"LI",{});var Rko=s(DG);mno=n(Rko,"STRONG",{});var aQn=s(mno);Aya=r(aQn,"vit"),aQn.forEach(t),Rya=r(Rko," \u2014 "),W0e=n(Rko,"A",{href:!0});var nQn=s(W0e);kya=r(nQn,"TFViTForImageClassification"),nQn.forEach(t),Sya=r(Rko," (ViT model)"),Rko.forEach(t),We.forEach(t),xya=i(zd),M(qG.$$.fragment,zd),zd.forEach(t),Yd.forEach(t),SBo=i(c),rh=n(c,"H3",{class:!0});var R$o=s(rh);OG=n(R$o,"A",{id:!0,class:!0,href:!0});var sQn=s(OG);cno=n(sQn,"SPAN",{});var lQn=s(cno);M(DU.$$.fragment,lQn),lQn.forEach(t),sQn.forEach(t),Gya=i(R$o),fno=n(R$o,"SPAN",{});var iQn=s(fno);Bya=r(iQn,"FlaxAutoModelForImageClassification"),iQn.forEach(t),R$o.forEach(t),xBo=i(c),Er=n(c,"DIV",{class:!0});var Hd=s(Er);M(qU.$$.fragment,Hd),Iya=i(Hd),th=n(Hd,"P",{});var SCe=s(th);Nya=r(SCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),J0e=n(SCe,"A",{href:!0});var dQn=s(J0e);$ya=r(dQn,"from_pretrained()"),dQn.forEach(t),jya=r(SCe," class method or the "),U0e=n(SCe,"A",{href:!0});var mQn=s(U0e);Pya=r(mQn,"from_config()"),mQn.forEach(t),Zya=r(SCe,` class
method.`),SCe.forEach(t),Vya=i(Hd),OU=n(Hd,"P",{});var k$o=s(OU);Wya=r(k$o,"This class cannot be instantiated directly using "),hno=n(k$o,"CODE",{});var cQn=s(hno);Jya=r(cQn,"__init__()"),cQn.forEach(t),Uya=r(k$o," (throws an error)."),k$o.forEach(t),Xya=i(Hd),Fa=n(Hd,"DIV",{class:!0});var NP=s(Fa);M(QU.$$.fragment,NP),Dya=i(NP),gno=n(NP,"P",{});var fQn=s(gno);qya=r(fQn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),fQn.forEach(t),Oya=i(NP),ah=n(NP,"P",{});var xCe=s(ah);Qya=r(xCe,`Note:
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model\u2019s configuration. Use `),X0e=n(xCe,"A",{href:!0});var gQn=s(X0e);Hya=r(gQn,"from_pretrained()"),gQn.forEach(t),Kya=r(xCe," to load the model weights."),xCe.forEach(t),e0a=i(NP),M(QG.$$.fragment,NP),NP.forEach(t),o0a=i(Hd),yt=n(Hd,"DIV",{class:!0});var Kd=s(yt);M(YU.$$.fragment,Kd),r0a=i(Kd),pno=n(Kd,"P",{});var uQn=s(pno);t0a=r(uQn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),uQn.forEach(t),a0a=i(Kd),bs=n(Kd,"P",{});var $P=s(bs);n0a=r($P,"The model class to instantiate is selected based on the "),_no=n($P,"CODE",{});var pQn=s(_no);s0a=r(pQn,"model_type"),pQn.forEach(t),l0a=r($P,` property of the config object (either
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falling back to using pattern matching on `),vno=n($P,"CODE",{});var bQn=s(vno);m0a=r(bQn,"pretrained_model_name_or_path"),bQn.forEach(t),c0a=r($P,":"),$P.forEach(t),f0a=i(Kd),vs=n(Kd,"UL",{});var jP=s(vs);YG=n(jP,"LI",{});var kko=s(YG);Mno=n(kko,"STRONG",{});var vQn=s(Mno);h0a=r(vQn,"beit"),vQn.forEach(t),g0a=r(kko," \u2014 "),D0e=n(kko,"A",{href:!0});var MQn=s(D0e);u0a=r(MQn,"FlaxBeitForImageClassification"),MQn.forEach(t),p0a=r(kko," (BEiT model)"),kko.forEach(t),_0a=i(jP),zG=n(jP,"LI",{});var Sko=s(zG);Tno=n(Sko,"STRONG",{});var TQn=s(Tno);b0a=r(TQn,"regnet"),TQn.forEach(t),v0a=r(Sko," \u2014 "),q0e=n(Sko,"A",{href:!0});var FQn=s(q0e);M0a=r(FQn,"FlaxRegNetForImageClassification"),FQn.forEach(t),T0a=r(Sko," (RegNet model)"),Sko.forEach(t),F0a=i(jP),HG=n(jP,"LI",{});var xko=s(HG);Fno=n(xko,"STRONG",{});var EQn=s(Fno);E0a=r(EQn,"resnet"),EQn.forEach(t),y0a=r(xko," \u2014 "),O0e=n(xko,"A",{href:!0});var yQn=s(O0e);C0a=r(yQn,"FlaxResNetForImageClassification"),yQn.forEach(t),w0a=r(xko," (ResNet model)"),xko.forEach(t),L0a=i(jP),KG=n(jP,"LI",{});var Gko=s(KG);Eno=n(Gko,"STRONG",{});var CQn=s(Eno);A0a=r(CQn,"vit"),CQn.forEach(t),R0a=r(Gko," \u2014 "),Q0e=n(Gko,"A",{href:!0});var wQn=s(Q0e);k0a=r(wQn,"FlaxViTForImageClassification"),wQn.forEach(t),S0a=r(Gko," (ViT model)"),Gko.forEach(t),jP.forEach(t),x0a=i(Kd),M(eB.$$.fragment,Kd),Kd.forEach(t),Hd.forEach(t),GBo=i(c),nh=n(c,"H3",{class:!0});var S$o=s(nh);oB=n(S$o,"A",{id:!0,class:!0,href:!0});var LQn=s(oB);yno=n(LQn,"SPAN",{});var AQn=s(yno);M(zU.$$.fragment,AQn),AQn.forEach(t),LQn.forEach(t),G0a=i(S$o),Cno=n(S$o,"SPAN",{});var RQn=s(Cno);B0a=r(RQn,"AutoModelForVideoClassification"),RQn.forEach(t),S$o.forEach(t),BBo=i(c),yr=n(c,"DIV",{class:!0});var em=s(yr);M(HU.$$.fragment,em),I0a=i(em),sh=n(em,"P",{});var GCe=s(sh);N0a=r(GCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a video classification head) when created
with the `),Y0e=n(GCe,"A",{href:!0});var kQn=s(Y0e);$0a=r(kQn,"from_pretrained()"),kQn.forEach(t),j0a=r(GCe," class method or the "),z0e=n(GCe,"A",{href:!0});var SQn=s(z0e);P0a=r(SQn,"from_config()"),SQn.forEach(t),Z0a=r(GCe,` class
method.`),GCe.forEach(t),V0a=i(em),KU=n(em,"P",{});var x$o=s(KU);W0a=r(x$o,"This class cannot be instantiated directly using "),wno=n(x$o,"CODE",{});var xQn=s(wno);J0a=r(xQn,"__init__()"),xQn.forEach(t),U0a=r(x$o," (throws an error)."),x$o.forEach(t),X0a=i(em),Ea=n(em,"DIV",{class:!0});var PP=s(Ea);M(eX.$$.fragment,PP),D0a=i(PP),Lno=n(PP,"P",{});var GQn=s(Lno);q0a=r(GQn,"Instantiates one of the model classes of the library (with a video classification head) from a configuration."),GQn.forEach(t),O0a=i(PP),lh=n(PP,"P",{});var BCe=s(lh);Q0a=r(BCe,`Note:
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model\u2019s configuration. Use `),H0e=n(BCe,"A",{href:!0});var IQn=s(H0e);H0a=r(IQn,"from_pretrained()"),IQn.forEach(t),K0a=r(BCe," to load the model weights."),BCe.forEach(t),e5a=i(PP),M(rB.$$.fragment,PP),PP.forEach(t),o5a=i(em),vo=n(em,"DIV",{class:!0});var hn=s(vo);M(oX.$$.fragment,hn),r5a=i(hn),Rno=n(hn,"P",{});var NQn=s(Rno);t5a=r(NQn,"Instantiate one of the model classes of the library (with a video classification head) from a pretrained model."),NQn.forEach(t),a5a=i(hn),Ms=n(hn,"P",{});var ZP=s(Ms);n5a=r(ZP,"The model class to instantiate is selected based on the "),kno=n(ZP,"CODE",{});var $Qn=s(kno);s5a=r($Qn,"model_type"),$Qn.forEach(t),l5a=r(ZP,` property of the config object (either
passed as an argument or loaded from `),Sno=n(ZP,"CODE",{});var jQn=s(Sno);i5a=r(jQn,"pretrained_model_name_or_path"),jQn.forEach(t),d5a=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);m5a=r(PQn,"pretrained_model_name_or_path"),PQn.forEach(t),c5a=r(ZP,":"),ZP.forEach(t),f5a=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);h5a=r(ZQn,"timesformer"),ZQn.forEach(t),g5a=r(Bko," \u2014 "),K0e=n(Bko,"A",{href:!0});var VQn=s(K0e);u5a=r(VQn,"TimesformerForVideoClassification"),VQn.forEach(t),p5a=r(Bko," (TimeSformer model)"),Bko.forEach(t),_5a=i(ICe),aB=n(ICe,"LI",{});var Iko=s(aB);Bno=n(Iko,"STRONG",{});var WQn=s(Bno);b5a=r(WQn,"videomae"),WQn.forEach(t),v5a=r(Iko," \u2014 "),e5e=n(Iko,"A",{href:!0});var JQn=s(e5e);M5a=r(JQn,"VideoMAEForVideoClassification"),JQn.forEach(t),T5a=r(Iko," (VideoMAE model)"),Iko.forEach(t),F5a=i(ICe),nB=n(ICe,"LI",{});var Nko=s(nB);Ino=n(Nko,"STRONG",{});var UQn=s(Ino);E5a=r(UQn,"vivit"),UQn.forEach(t),y5a=r(Nko," \u2014 "),o5e=n(Nko,"A",{href:!0});var XQn=s(o5e);C5a=r(XQn,"VivitForVideoClassification"),XQn.forEach(t),w5a=r(Nko," (ViViT model)"),Nko.forEach(t),ICe.forEach(t),L5a=i(hn),sB=n(hn,"P",{});var $ko=s(sB);A5a=r($ko,"The model is set in evaluation mode by default using "),Nno=n($ko,"CODE",{});var DQn=s(Nno);R5a=r(DQn,"model.eval()"),DQn.forEach(t),k5a=r($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($no);S5a=r(qQn,"model.train()"),qQn.forEach(t),$ko.forEach(t),x5a=i(hn),M(lB.$$.fragment,hn),hn.forEach(t),em.forEach(t),IBo=i(c),dh=n(c,"H3",{class:!0});var G$o=s(dh);iB=n(G$o,"A",{id:!0,class:!0,href:!0});var OQn=s(iB);jno=n(OQn,"SPAN",{});var QQn=s(jno);M(rX.$$.fragment,QQn),QQn.forEach(t),OQn.forEach(t),G5a=i(G$o),Pno=n(G$o,"SPAN",{});var YQn=s(Pno);B5a=r(YQn,"AutoModelForMaskedImageModeling"),YQn.forEach(t),G$o.forEach(t),NBo=i(c),Cr=n(c,"DIV",{class:!0});var om=s(Cr);M(tX.$$.fragment,om),I5a=i(om),mh=n(om,"P",{});var NCe=s(mh);N5a=r(NCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),r5e=n(NCe,"A",{href:!0});var zQn=s(r5e);$5a=r(zQn,"from_pretrained()"),zQn.forEach(t),j5a=r(NCe," class method or the "),t5e=n(NCe,"A",{href:!0});var HQn=s(t5e);P5a=r(HQn,"from_config()"),HQn.forEach(t),Z5a=r(NCe,` class
method.`),NCe.forEach(t),V5a=i(om),aX=n(om,"P",{});var B$o=s(aX);W5a=r(B$o,"This class cannot be instantiated directly using "),Zno=n(B$o,"CODE",{});var KQn=s(Zno);J5a=r(KQn,"__init__()"),KQn.forEach(t),U5a=r(B$o," (throws an error)."),B$o.forEach(t),X5a=i(om),ya=n(om,"DIV",{class:!0});var VP=s(ya);M(nX.$$.fragment,VP),D5a=i(VP),Vno=n(VP,"P",{});var eYn=s(Vno);q5a=r(eYn,"Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),eYn.forEach(t),O5a=i(VP),ch=n(VP,"P",{});var $Ce=s(ch);Q5a=r($Ce,`Note:
Loading a model from its configuration file does `),Wno=n($Ce,"STRONG",{});var oYn=s(Wno);Y5a=r(oYn,"not"),oYn.forEach(t),z5a=r($Ce,` load the model weights. It only affects the
model\u2019s configuration. Use `),a5e=n($Ce,"A",{href:!0});var rYn=s(a5e);H5a=r(rYn,"from_pretrained()"),rYn.forEach(t),K5a=r($Ce," to load the model weights."),$Ce.forEach(t),e3a=i(VP),M(dB.$$.fragment,VP),VP.forEach(t),o3a=i(om),Mo=n(om,"DIV",{class:!0});var gn=s(Mo);M(sX.$$.fragment,gn),r3a=i(gn),Jno=n(gn,"P",{});var tYn=s(Jno);t3a=r(tYn,"Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),tYn.forEach(t),a3a=i(gn),Ts=n(gn,"P",{});var WP=s(Ts);n3a=r(WP,"The model class to instantiate is selected based on the "),Uno=n(WP,"CODE",{});var aYn=s(Uno);s3a=r(aYn,"model_type"),aYn.forEach(t),l3a=r(WP,` property of the config object (either
passed as an argument or loaded from `),Xno=n(WP,"CODE",{});var nYn=s(Xno);i3a=r(nYn,"pretrained_model_name_or_path"),nYn.forEach(t),d3a=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);m3a=r(sYn,"pretrained_model_name_or_path"),sYn.forEach(t),c3a=r(WP,":"),WP.forEach(t),f3a=i(gn),Nt=n(gn,"UL",{});var rm=s(Nt);mB=n(rm,"LI",{});var jko=s(mB);qno=n(jko,"STRONG",{});var lYn=s(qno);h3a=r(lYn,"deit"),lYn.forEach(t),g3a=r(jko," \u2014 "),n5e=n(jko,"A",{href:!0});var iYn=s(n5e);u3a=r(iYn,"DeiTForMaskedImageModeling"),iYn.forEach(t),p3a=r(jko," (DeiT model)"),jko.forEach(t),_3a=i(rm),cB=n(rm,"LI",{});var Pko=s(cB);Ono=n(Pko,"STRONG",{});var dYn=s(Ono);b3a=r(dYn,"focalnet"),dYn.forEach(t),v3a=r(Pko," \u2014 "),s5e=n(Pko,"A",{href:!0});var mYn=s(s5e);M3a=r(mYn,"FocalNetForMaskedImageModeling"),mYn.forEach(t),T3a=r(Pko," (FocalNet model)"),Pko.forEach(t),F3a=i(rm),fB=n(rm,"LI",{});var Zko=s(fB);Qno=n(Zko,"STRONG",{});var cYn=s(Qno);E3a=r(cYn,"swin"),cYn.forEach(t),y3a=r(Zko," \u2014 "),l5e=n(Zko,"A",{href:!0});var fYn=s(l5e);C3a=r(fYn,"SwinForMaskedImageModeling"),fYn.forEach(t),w3a=r(Zko," (Swin Transformer model)"),Zko.forEach(t),L3a=i(rm),hB=n(rm,"LI",{});var Vko=s(hB);Yno=n(Vko,"STRONG",{});var hYn=s(Yno);A3a=r(hYn,"swinv2"),hYn.forEach(t),R3a=r(Vko," \u2014 "),i5e=n(Vko,"A",{href:!0});var gYn=s(i5e);k3a=r(gYn,"Swinv2ForMaskedImageModeling"),gYn.forEach(t),S3a=r(Vko," (Swin Transformer V2 model)"),Vko.forEach(t),x3a=i(rm),gB=n(rm,"LI",{});var Wko=s(gB);zno=n(Wko,"STRONG",{});var uYn=s(zno);G3a=r(uYn,"vit"),uYn.forEach(t),B3a=r(Wko," \u2014 "),d5e=n(Wko,"A",{href:!0});var pYn=s(d5e);I3a=r(pYn,"ViTForMaskedImageModeling"),pYn.forEach(t),N3a=r(Wko," (ViT model)"),Wko.forEach(t),rm.forEach(t),$3a=i(gn),uB=n(gn,"P",{});var Jko=s(uB);j3a=r(Jko,"The model is set in evaluation mode by default using "),Hno=n(Jko,"CODE",{});var _Yn=s(Hno);P3a=r(_Yn,"model.eval()"),_Yn.forEach(t),Z3a=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 bYn=s(Kno);V3a=r(bYn,"model.train()"),bYn.forEach(t),Jko.forEach(t),W3a=i(gn),M(pB.$$.fragment,gn),gn.forEach(t),om.forEach(t),$Bo=i(c),fh=n(c,"H3",{class:!0});var I$o=s(fh);_B=n(I$o,"A",{id:!0,class:!0,href:!0});var vYn=s(_B);eso=n(vYn,"SPAN",{});var MYn=s(eso);M(lX.$$.fragment,MYn),MYn.forEach(t),vYn.forEach(t),J3a=i(I$o),oso=n(I$o,"SPAN",{});var TYn=s(oso);U3a=r(TYn,"TFAutoModelForMaskedImageModeling"),TYn.forEach(t),I$o.forEach(t),jBo=i(c),wr=n(c,"DIV",{class:!0});var tm=s(wr);M(iX.$$.fragment,tm),X3a=i(tm),hh=n(tm,"P",{});var jCe=s(hh);D3a=r(jCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),m5e=n(jCe,"A",{href:!0});var FYn=s(m5e);q3a=r(FYn,"from_pretrained()"),FYn.forEach(t),O3a=r(jCe," class method or the "),c5e=n(jCe,"A",{href:!0});var EYn=s(c5e);Q3a=r(EYn,"from_config()"),EYn.forEach(t),Y3a=r(jCe,` class
method.`),jCe.forEach(t),z3a=i(tm),dX=n(tm,"P",{});var N$o=s(dX);H3a=r(N$o,"This class cannot be instantiated directly using "),rso=n(N$o,"CODE",{});var yYn=s(rso);K3a=r(yYn,"__init__()"),yYn.forEach(t),e9a=r(N$o," (throws an error)."),N$o.forEach(t),o9a=i(tm),Ca=n(tm,"DIV",{class:!0});var JP=s(Ca);M(mX.$$.fragment,JP),r9a=i(JP),tso=n(JP,"P",{});var CYn=s(tso);t9a=r(CYn,"Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),CYn.forEach(t),a9a=i(JP),gh=n(JP,"P",{});var PCe=s(gh);n9a=r(PCe,`Note:
Loading a model from its configuration file does `),aso=n(PCe,"STRONG",{});var wYn=s(aso);s9a=r(wYn,"not"),wYn.forEach(t),l9a=r(PCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),f5e=n(PCe,"A",{href:!0});var LYn=s(f5e);i9a=r(LYn,"from_pretrained()"),LYn.forEach(t),d9a=r(PCe," to load the model weights."),PCe.forEach(t),m9a=i(JP),M(bB.$$.fragment,JP),JP.forEach(t),c9a=i(tm),Ct=n(tm,"DIV",{class:!0});var am=s(Ct);M(cX.$$.fragment,am),f9a=i(am),nso=n(am,"P",{});var AYn=s(nso);h9a=r(AYn,"Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),AYn.forEach(t),g9a=i(am),Fs=n(am,"P",{});var UP=s(Fs);u9a=r(UP,"The model class to instantiate is selected based on the "),sso=n(UP,"CODE",{});var RYn=s(sso);p9a=r(RYn,"model_type"),RYn.forEach(t),_9a=r(UP,` property of the config object (either
passed as an argument or loaded from `),lso=n(UP,"CODE",{});var kYn=s(lso);b9a=r(kYn,"pretrained_model_name_or_path"),kYn.forEach(t),v9a=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);M9a=r(SYn,"pretrained_model_name_or_path"),SYn.forEach(t),T9a=r(UP,":"),UP.forEach(t),F9a=i(am),fX=n(am,"UL",{});var $$o=s(fX);vB=n($$o,"LI",{});var Uko=s(vB);dso=n(Uko,"STRONG",{});var xYn=s(dso);E9a=r(xYn,"deit"),xYn.forEach(t),y9a=r(Uko," \u2014 "),h5e=n(Uko,"A",{href:!0});var GYn=s(h5e);C9a=r(GYn,"TFDeiTForMaskedImageModeling"),GYn.forEach(t),w9a=r(Uko," (DeiT model)"),Uko.forEach(t),L9a=i($$o),MB=n($$o,"LI",{});var Xko=s(MB);mso=n(Xko,"STRONG",{});var BYn=s(mso);A9a=r(BYn,"swin"),BYn.forEach(t),R9a=r(Xko," \u2014 "),g5e=n(Xko,"A",{href:!0});var IYn=s(g5e);k9a=r(IYn,"TFSwinForMaskedImageModeling"),IYn.forEach(t),S9a=r(Xko," (Swin Transformer model)"),Xko.forEach(t),$$o.forEach(t),x9a=i(am),M(TB.$$.fragment,am),am.forEach(t),tm.forEach(t),PBo=i(c),uh=n(c,"H3",{class:!0});var j$o=s(uh);FB=n(j$o,"A",{id:!0,class:!0,href:!0});var NYn=s(FB);cso=n(NYn,"SPAN",{});var $Yn=s(cso);M(hX.$$.fragment,$Yn),$Yn.forEach(t),NYn.forEach(t),G9a=i(j$o),fso=n(j$o,"SPAN",{});var jYn=s(fso);B9a=r(jYn,"AutoModelForObjectDetection"),jYn.forEach(t),j$o.forEach(t),ZBo=i(c),Lr=n(c,"DIV",{class:!0});var nm=s(Lr);M(gX.$$.fragment,nm),I9a=i(nm),ph=n(nm,"P",{});var ZCe=s(ph);N9a=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 `),u5e=n(ZCe,"A",{href:!0});var PYn=s(u5e);$9a=r(PYn,"from_pretrained()"),PYn.forEach(t),j9a=r(ZCe," class method or the "),p5e=n(ZCe,"A",{href:!0});var ZYn=s(p5e);P9a=r(ZYn,"from_config()"),ZYn.forEach(t),Z9a=r(ZCe,` class
method.`),ZCe.forEach(t),V9a=i(nm),uX=n(nm,"P",{});var P$o=s(uX);W9a=r(P$o,"This class cannot be instantiated directly using "),hso=n(P$o,"CODE",{});var VYn=s(hso);J9a=r(VYn,"__init__()"),VYn.forEach(t),U9a=r(P$o," (throws an error)."),P$o.forEach(t),X9a=i(nm),wa=n(nm,"DIV",{class:!0});var XP=s(wa);M(pX.$$.fragment,XP),D9a=i(XP),gso=n(XP,"P",{});var WYn=s(gso);q9a=r(WYn,"Instantiates one of the model classes of the library (with a object detection head) from a configuration."),WYn.forEach(t),O9a=i(XP),_h=n(XP,"P",{});var VCe=s(_h);Q9a=r(VCe,`Note:
Loading a model from its configuration file does `),uso=n(VCe,"STRONG",{});var JYn=s(uso);Y9a=r(JYn,"not"),JYn.forEach(t),z9a=r(VCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),_5e=n(VCe,"A",{href:!0});var UYn=s(_5e);H9a=r(UYn,"from_pretrained()"),UYn.forEach(t),K9a=r(VCe," to load the model weights."),VCe.forEach(t),e4a=i(XP),M(EB.$$.fragment,XP),XP.forEach(t),o4a=i(nm),To=n(nm,"DIV",{class:!0});var un=s(To);M(_X.$$.fragment,un),r4a=i(un),pso=n(un,"P",{});var XYn=s(pso);t4a=r(XYn,"Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),XYn.forEach(t),a4a=i(un),Es=n(un,"P",{});var DP=s(Es);n4a=r(DP,"The model class to instantiate is selected based on the "),_so=n(DP,"CODE",{});var DYn=s(_so);s4a=r(DYn,"model_type"),DYn.forEach(t),l4a=r(DP,` property of the config object (either
passed as an argument or loaded from `),bso=n(DP,"CODE",{});var qYn=s(bso);i4a=r(qYn,"pretrained_model_name_or_path"),qYn.forEach(t),d4a=r(DP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),vso=n(DP,"CODE",{});var OYn=s(vso);m4a=r(OYn,"pretrained_model_name_or_path"),OYn.forEach(t),c4a=r(DP,":"),DP.forEach(t),f4a=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);h4a=r(QYn,"conditional_detr"),QYn.forEach(t),g4a=r(Dko," \u2014 "),b5e=n(Dko,"A",{href:!0});var YYn=s(b5e);u4a=r(YYn,"ConditionalDetrForObjectDetection"),YYn.forEach(t),p4a=r(Dko," (Conditional DETR model)"),Dko.forEach(t),_4a=i(pn),CB=n(pn,"LI",{});var qko=s(CB);Tso=n(qko,"STRONG",{});var zYn=s(Tso);b4a=r(zYn,"deformable_detr"),zYn.forEach(t),v4a=r(qko," \u2014 "),v5e=n(qko,"A",{href:!0});var HYn=s(v5e);M4a=r(HYn,"DeformableDetrForObjectDetection"),HYn.forEach(t),T4a=r(qko," (Deformable DETR model)"),qko.forEach(t),F4a=i(pn),wB=n(pn,"LI",{});var Oko=s(wB);Fso=n(Oko,"STRONG",{});var KYn=s(Fso);E4a=r(KYn,"deta"),KYn.forEach(t),y4a=r(Oko," \u2014 "),M5e=n(Oko,"A",{href:!0});var ezn=s(M5e);C4a=r(ezn,"DetaForObjectDetection"),ezn.forEach(t),w4a=r(Oko," (DETA model)"),Oko.forEach(t),L4a=i(pn),LB=n(pn,"LI",{});var Qko=s(LB);Eso=n(Qko,"STRONG",{});var ozn=s(Eso);A4a=r(ozn,"detr"),ozn.forEach(t),R4a=r(Qko," \u2014 "),T5e=n(Qko,"A",{href:!0});var rzn=s(T5e);k4a=r(rzn,"DetrForObjectDetection"),rzn.forEach(t),S4a=r(Qko," (DETR model)"),Qko.forEach(t),x4a=i(pn),AB=n(pn,"LI",{});var Yko=s(AB);yso=n(Yko,"STRONG",{});var tzn=s(yso);G4a=r(tzn,"table-transformer"),tzn.forEach(t),B4a=r(Yko," \u2014 "),F5e=n(Yko,"A",{href:!0});var azn=s(F5e);I4a=r(azn,"TableTransformerForObjectDetection"),azn.forEach(t),N4a=r(Yko," (Table Transformer model)"),Yko.forEach(t),$4a=i(pn),RB=n(pn,"LI",{});var zko=s(RB);Cso=n(zko,"STRONG",{});var nzn=s(Cso);j4a=r(nzn,"yolos"),nzn.forEach(t),P4a=r(zko," \u2014 "),E5e=n(zko,"A",{href:!0});var szn=s(E5e);Z4a=r(szn,"YolosForObjectDetection"),szn.forEach(t),V4a=r(zko," (YOLOS model)"),zko.forEach(t),pn.forEach(t),W4a=i(un),kB=n(un,"P",{});var Hko=s(kB);J4a=r(Hko,"The model is set in evaluation mode by default using "),wso=n(Hko,"CODE",{});var lzn=s(wso);U4a=r(lzn,"model.eval()"),lzn.forEach(t),X4a=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);D4a=r(izn,"model.train()"),izn.forEach(t),Hko.forEach(t),q4a=i(un),M(SB.$$.fragment,un),un.forEach(t),nm.forEach(t),VBo=i(c),bh=n(c,"H3",{class:!0});var Z$o=s(bh);xB=n(Z$o,"A",{id:!0,class:!0,href:!0});var dzn=s(xB);Aso=n(dzn,"SPAN",{});var mzn=s(Aso);M(bX.$$.fragment,mzn),mzn.forEach(t),dzn.forEach(t),O4a=i(Z$o),Rso=n(Z$o,"SPAN",{});var czn=s(Rso);Q4a=r(czn,"AutoModelForImageSegmentation"),czn.forEach(t),Z$o.forEach(t),WBo=i(c),Rr=n(c,"DIV",{class:!0});var sm=s(Rr);M(vX.$$.fragment,sm),Y4a=i(sm),vh=n(sm,"P",{});var WCe=s(vh);z4a=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 `),y5e=n(WCe,"A",{href:!0});var fzn=s(y5e);H4a=r(fzn,"from_pretrained()"),fzn.forEach(t),K4a=r(WCe," class method or the "),C5e=n(WCe,"A",{href:!0});var hzn=s(C5e);eCa=r(hzn,"from_config()"),hzn.forEach(t),oCa=r(WCe,` class
method.`),WCe.forEach(t),rCa=i(sm),MX=n(sm,"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(sm),La=n(sm,"DIV",{class:!0});var qP=s(La);M(TX.$$.fragment,qP),lCa=i(qP),Sso=n(qP,"P",{});var uzn=s(Sso);iCa=r(uzn,"Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),uzn.forEach(t),dCa=i(qP),Mh=n(qP,"P",{});var JCe=s(Mh);mCa=r(JCe,`Note:
Loading a model from its configuration file does `),xso=n(JCe,"STRONG",{});var pzn=s(xso);cCa=r(pzn,"not"),pzn.forEach(t),fCa=r(JCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),w5e=n(JCe,"A",{href:!0});var _zn=s(w5e);hCa=r(_zn,"from_pretrained()"),_zn.forEach(t),gCa=r(JCe," to load the model weights."),JCe.forEach(t),uCa=i(qP),M(GB.$$.fragment,qP),qP.forEach(t),pCa=i(sm),Fo=n(sm,"DIV",{class:!0});var _n=s(Fo);M(FX.$$.fragment,_n),_Ca=i(_n),Gso=n(_n,"P",{});var bzn=s(Gso);bCa=r(bzn,"Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),bzn.forEach(t),vCa=i(_n),ys=n(_n,"P",{});var OP=s(ys);MCa=r(OP,"The model class to instantiate is selected based on the "),Bso=n(OP,"CODE",{});var vzn=s(Bso);TCa=r(vzn,"model_type"),vzn.forEach(t),FCa=r(OP,` property of the config object (either
passed as an argument or loaded from `),Iso=n(OP,"CODE",{});var Mzn=s(Iso);ECa=r(Mzn,"pretrained_model_name_or_path"),Mzn.forEach(t),yCa=r(OP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Nso=n(OP,"CODE",{});var Tzn=s(Nso);CCa=r(Tzn,"pretrained_model_name_or_path"),Tzn.forEach(t),wCa=r(OP,":"),OP.forEach(t),LCa=i(_n),$so=n(_n,"UL",{});var Fzn=s($so);BB=n(Fzn,"LI",{});var Kko=s(BB);jso=n(Kko,"STRONG",{});var Ezn=s(jso);ACa=r(Ezn,"detr"),Ezn.forEach(t),RCa=r(Kko," \u2014 "),L5e=n(Kko,"A",{href:!0});var yzn=s(L5e);kCa=r(yzn,"DetrForSegmentation"),yzn.forEach(t),SCa=r(Kko," (DETR model)"),Kko.forEach(t),Fzn.forEach(t),xCa=i(_n),IB=n(_n,"P",{});var eSo=s(IB);GCa=r(eSo,"The model is set in evaluation mode by default using "),Pso=n(eSo,"CODE",{});var Czn=s(Pso);BCa=r(Czn,"model.eval()"),Czn.forEach(t),ICa=r(eSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Zso=n(eSo,"CODE",{});var wzn=s(Zso);NCa=r(wzn,"model.train()"),wzn.forEach(t),eSo.forEach(t),$Ca=i(_n),M(NB.$$.fragment,_n),_n.forEach(t),sm.forEach(t),JBo=i(c),Th=n(c,"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.$$.fragment,Azn),Azn.forEach(t),Lzn.forEach(t),jCa=i(W$o),Wso=n(W$o,"SPAN",{});var Rzn=s(Wso);PCa=r(Rzn,"AutoModelForSemanticSegmentation"),Rzn.forEach(t),W$o.forEach(t),UBo=i(c),kr=n(c,"DIV",{class:!0});var lm=s(kr);M(yX.$$.fragment,lm),ZCa=i(lm),Fh=n(lm,"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 `),A5e=n(UCe,"A",{href:!0});var kzn=s(A5e);WCa=r(kzn,"from_pretrained()"),kzn.forEach(t),JCa=r(UCe," class method or the "),R5e=n(UCe,"A",{href:!0});var Szn=s(R5e);UCa=r(Szn,"from_config()"),Szn.forEach(t),XCa=r(UCe,` class
method.`),UCe.forEach(t),DCa=i(lm),CX=n(lm,"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(lm),Aa=n(lm,"DIV",{class:!0});var QP=s(Aa);M(wX.$$.fragment,QP),zCa=i(QP),Uso=n(QP,"P",{});var Gzn=s(Uso);HCa=r(Gzn,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),Gzn.forEach(t),KCa=i(QP),Eh=n(QP,"P",{});var XCe=s(Eh);ewa=r(XCe,`Note:
Loading a model from its configuration file does `),Xso=n(XCe,"STRONG",{});var Bzn=s(Xso);owa=r(Bzn,"not"),Bzn.forEach(t),rwa=r(XCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),k5e=n(XCe,"A",{href:!0});var Izn=s(k5e);twa=r(Izn,"from_pretrained()"),Izn.forEach(t),awa=r(XCe," to load the model weights."),XCe.forEach(t),nwa=i(QP),M(jB.$$.fragment,QP),QP.forEach(t),swa=i(lm),Eo=n(lm,"DIV",{class:!0});var bn=s(Eo);M(LX.$$.fragment,bn),lwa=i(bn),Dso=n(bn,"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(bn),Cs=n(bn,"P",{});var YP=s(Cs);mwa=r(YP,"The model class to instantiate is selected based on the "),qso=n(YP,"CODE",{});var $zn=s(qso);cwa=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(bn),Ye=n(bn,"UL",{});var $o=s(Ye);PB=n($o,"LI",{});var oSo=s(PB);Yso=n(oSo,"STRONG",{});var Zzn=s(Yso);bwa=r(Zzn,"beit"),Zzn.forEach(t),vwa=r(oSo," \u2014 "),S5e=n(oSo,"A",{href:!0});var Vzn=s(S5e);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 "),x5e=n(rSo,"A",{href:!0});var Jzn=s(x5e);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 "),G5e=n(tSo,"A",{href:!0});var Xzn=s(G5e);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 "),B5e=n(aSo,"A",{href:!0});var qzn=s(B5e);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 "),I5e=n(nSo,"A",{href:!0});var Qzn=s(I5e);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 "),N5e=n(sSo,"A",{href:!0});var zzn=s(N5e);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 "),$5e=n(lSo,"A",{href:!0});var Kzn=s($5e);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 "),j5e=n(iSo,"A",{href:!0});var oHn=s(j5e);o6a=r(oHn,"UperNetForSemanticSegmentation"),oHn.forEach(t),r6a=r(iSo," (UPerNet model)"),iSo.forEach(t),$o.forEach(t),t6a=i(bn),qB=n(bn,"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(bn),M(OB.$$.fragment,bn),bn.forEach(t),lm.forEach(t),XBo=i(c),yh=n(c,"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.$$.fragment,nHn),nHn.forEach(t),aHn.forEach(t),d6a=i(U$o),llo=n(U$o,"SPAN",{});var sHn=s(llo);m6a=r(sHn,"TFAutoModelForSemanticSegmentation"),sHn.forEach(t),U$o.forEach(t),DBo=i(c),Sr=n(c,"DIV",{class:!0});var im=s(Sr);M(RX.$$.fragment,im),c6a=i(im),Ch=n(im,"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 `),P5e=n(DCe,"A",{href:!0});var lHn=s(P5e);h6a=r(lHn,"from_pretrained()"),lHn.forEach(t),g6a=r(DCe," class method or the "),Z5e=n(DCe,"A",{href:!0});var iHn=s(Z5e);u6a=r(iHn,"from_config()"),iHn.forEach(t),p6a=r(DCe,` class
method.`),DCe.forEach(t),_6a=i(im),kX=n(im,"P",{});var X$o=s(kX);b6a=r(X$o,"This class cannot be instantiated directly using "),ilo=n(X$o,"CODE",{});var dHn=s(ilo);v6a=r(dHn,"__init__()"),dHn.forEach(t),M6a=r(X$o," (throws an error)."),X$o.forEach(t),T6a=i(im),Ra=n(im,"DIV",{class:!0});var zP=s(Ra);M(SX.$$.fragment,zP),F6a=i(zP),dlo=n(zP,"P",{});var mHn=s(dlo);E6a=r(mHn,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),mHn.forEach(t),y6a=i(zP),wh=n(zP,"P",{});var qCe=s(wh);C6a=r(qCe,`Note:
Loading a model from its configuration file does `),mlo=n(qCe,"STRONG",{});var cHn=s(mlo);w6a=r(cHn,"not"),cHn.forEach(t),L6a=r(qCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),V5e=n(qCe,"A",{href:!0});var fHn=s(V5e);A6a=r(fHn,"from_pretrained()"),fHn.forEach(t),R6a=r(qCe," to load the model weights."),qCe.forEach(t),k6a=i(zP),M(YB.$$.fragment,zP),zP.forEach(t),S6a=i(im),wt=n(im,"DIV",{class:!0});var dm=s(wt);M(xX.$$.fragment,dm),x6a=i(dm),clo=n(dm,"P",{});var hHn=s(clo);G6a=r(hHn,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),hHn.forEach(t),B6a=i(dm),ws=n(dm,"P",{});var HP=s(ws);I6a=r(HP,"The model class to instantiate is selected based on the "),flo=n(HP,"CODE",{});var gHn=s(flo);N6a=r(gHn,"model_type"),gHn.forEach(t),$6a=r(HP,` property of the config object (either
passed as an argument or loaded from `),hlo=n(HP,"CODE",{});var uHn=s(hlo);j6a=r(uHn,"pretrained_model_name_or_path"),uHn.forEach(t),P6a=r(HP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),glo=n(HP,"CODE",{});var pHn=s(glo);Z6a=r(pHn,"pretrained_model_name_or_path"),pHn.forEach(t),V6a=r(HP,":"),HP.forEach(t),W6a=i(dm),Lh=n(dm,"UL",{});var OCe=s(Lh);zB=n(OCe,"LI",{});var mSo=s(zB);ulo=n(mSo,"STRONG",{});var _Hn=s(ulo);J6a=r(_Hn,"data2vec-vision"),_Hn.forEach(t),U6a=r(mSo," \u2014 "),W5e=n(mSo,"A",{href:!0});var bHn=s(W5e);X6a=r(bHn,"TFData2VecVisionForSemanticSegmentation"),bHn.forEach(t),D6a=r(mSo," (Data2VecVision model)"),mSo.forEach(t),q6a=i(OCe),HB=n(OCe,"LI",{});var cSo=s(HB);plo=n(cSo,"STRONG",{});var vHn=s(plo);O6a=r(vHn,"mobilevit"),vHn.forEach(t),Q6a=r(cSo," \u2014 "),J5e=n(cSo,"A",{href:!0});var MHn=s(J5e);Y6a=r(MHn,"TFMobileViTForSemanticSegmentation"),MHn.forEach(t),z6a=r(cSo," (MobileViT model)"),cSo.forEach(t),H6a=i(OCe),KB=n(OCe,"LI",{});var fSo=s(KB);_lo=n(fSo,"STRONG",{});var THn=s(_lo);K6a=r(THn,"segformer"),THn.forEach(t),e8a=r(fSo," \u2014 "),U5e=n(fSo,"A",{href:!0});var FHn=s(U5e);o8a=r(FHn,"TFSegformerForSemanticSegmentation"),FHn.forEach(t),r8a=r(fSo," (SegFormer model)"),fSo.forEach(t),OCe.forEach(t),t8a=i(dm),M(eI.$$.fragment,dm),dm.forEach(t),im.forEach(t),qBo=i(c),Ah=n(c,"H3",{class:!0});var D$o=s(Ah);oI=n(D$o,"A",{id:!0,class:!0,href:!0});var EHn=s(oI);blo=n(EHn,"SPAN",{});var yHn=s(blo);M(GX.$$.fragment,yHn),yHn.forEach(t),EHn.forEach(t),a8a=i(D$o),vlo=n(D$o,"SPAN",{});var CHn=s(vlo);n8a=r(CHn,"AutoModelForInstanceSegmentation"),CHn.forEach(t),D$o.forEach(t),OBo=i(c),xr=n(c,"DIV",{class:!0});var mm=s(xr);M(BX.$$.fragment,mm),s8a=i(mm),Rh=n(mm,"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 `),X5e=n(QCe,"A",{href:!0});var wHn=s(X5e);i8a=r(wHn,"from_pretrained()"),wHn.forEach(t),d8a=r(QCe," class method or the "),D5e=n(QCe,"A",{href:!0});var LHn=s(D5e);m8a=r(LHn,"from_config()"),LHn.forEach(t),c8a=r(QCe,` class
method.`),QCe.forEach(t),f8a=i(mm),IX=n(mm,"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(mm),ka=n(mm,"DIV",{class:!0});var KP=s(ka);M(NX.$$.fragment,KP),_8a=i(KP),Tlo=n(KP,"P",{});var RHn=s(Tlo);b8a=r(RHn,"Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),RHn.forEach(t),v8a=i(KP),kh=n(KP,"P",{});var YCe=s(kh);M8a=r(YCe,`Note:
Loading a model from its configuration file does `),Flo=n(YCe,"STRONG",{});var kHn=s(Flo);T8a=r(kHn,"not"),kHn.forEach(t),F8a=r(YCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),q5e=n(YCe,"A",{href:!0});var SHn=s(q5e);E8a=r(SHn,"from_pretrained()"),SHn.forEach(t),y8a=r(YCe," to load the model weights."),YCe.forEach(t),C8a=i(KP),M(rI.$$.fragment,KP),KP.forEach(t),w8a=i(mm),yo=n(mm,"DIV",{class:!0});var vn=s(yo);M($X.$$.fragment,vn),L8a=i(vn),Elo=n(vn,"P",{});var xHn=s(Elo);A8a=r(xHn,"Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),xHn.forEach(t),R8a=i(vn),Ls=n(vn,"P",{});var eZ=s(Ls);k8a=r(eZ,"The model class to instantiate is selected based on the "),ylo=n(eZ,"CODE",{});var GHn=s(ylo);S8a=r(GHn,"model_type"),GHn.forEach(t),x8a=r(eZ,` property of the config object (either
passed as an argument or loaded from `),Clo=n(eZ,"CODE",{});var BHn=s(Clo);G8a=r(BHn,"pretrained_model_name_or_path"),BHn.forEach(t),B8a=r(eZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wlo=n(eZ,"CODE",{});var IHn=s(wlo);I8a=r(IHn,"pretrained_model_name_or_path"),IHn.forEach(t),N8a=r(eZ,":"),eZ.forEach(t),$8a=i(vn),Llo=n(vn,"UL",{});var NHn=s(Llo);tI=n(NHn,"LI",{});var hSo=s(tI);Alo=n(hSo,"STRONG",{});var $Hn=s(Alo);j8a=r($Hn,"maskformer"),$Hn.forEach(t),P8a=r(hSo," \u2014 "),O5e=n(hSo,"A",{href:!0});var jHn=s(O5e);Z8a=r(jHn,"MaskFormerForInstanceSegmentation"),jHn.forEach(t),V8a=r(hSo," (MaskFormer model)"),hSo.forEach(t),NHn.forEach(t),W8a=i(vn),aI=n(vn,"P",{});var gSo=s(aI);J8a=r(gSo,"The model is set in evaluation mode by default using "),Rlo=n(gSo,"CODE",{});var PHn=s(Rlo);U8a=r(PHn,"model.eval()"),PHn.forEach(t),X8a=r(gSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),klo=n(gSo,"CODE",{});var ZHn=s(klo);D8a=r(ZHn,"model.train()"),ZHn.forEach(t),gSo.forEach(t),q8a=i(vn),M(nI.$$.fragment,vn),vn.forEach(t),mm.forEach(t),QBo=i(c),Sh=n(c,"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.$$.fragment,WHn),WHn.forEach(t),VHn.forEach(t),O8a=i(O$o),xlo=n(O$o,"SPAN",{});var JHn=s(xlo);Q8a=r(JHn,"AutoModelForUniversalSegmentation"),JHn.forEach(t),O$o.forEach(t),YBo=i(c),Gr=n(c,"DIV",{class:!0});var cm=s(Gr);M(PX.$$.fragment,cm),Y8a=i(cm),xh=n(cm,"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 `),Q5e=n(zCe,"A",{href:!0});var UHn=s(Q5e);H8a=r(UHn,"from_pretrained()"),UHn.forEach(t),K8a=r(zCe," class method or the "),Y5e=n(zCe,"A",{href:!0});var XHn=s(Y5e);e7a=r(XHn,"from_config()"),XHn.forEach(t),o7a=r(zCe,` class
method.`),zCe.forEach(t),r7a=i(cm),ZX=n(cm,"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(cm),Sa=n(cm,"DIV",{class:!0});var oZ=s(Sa);M(VX.$$.fragment,oZ),l7a=i(oZ),Blo=n(oZ,"P",{});var qHn=s(Blo);i7a=r(qHn,"Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration."),qHn.forEach(t),d7a=i(oZ),Gh=n(oZ,"P",{});var HCe=s(Gh);m7a=r(HCe,`Note:
Loading a model from its configuration file does `),Ilo=n(HCe,"STRONG",{});var OHn=s(Ilo);c7a=r(OHn,"not"),OHn.forEach(t),f7a=r(HCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),z5e=n(HCe,"A",{href:!0});var QHn=s(z5e);h7a=r(QHn,"from_pretrained()"),QHn.forEach(t),g7a=r(HCe," to load the model weights."),HCe.forEach(t),u7a=i(oZ),M(lI.$$.fragment,oZ),oZ.forEach(t),p7a=i(cm),Co=n(cm,"DIV",{class:!0});var Mn=s(Co);M(WX.$$.fragment,Mn),_7a=i(Mn),Nlo=n(Mn,"P",{});var YHn=s(Nlo);b7a=r(YHn,"Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model."),YHn.forEach(t),v7a=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 "),H5e=n(uSo,"A",{href:!0});var oKn=s(H5e);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 "),K5e=n(pSo,"A",{href:!0});var tKn=s(K5e);I7a=r(tKn,"Mask2FormerForUniversalSegmentation"),tKn.forEach(t),N7a=r(pSo," (Mask2Former model)"),pSo.forEach(t),$7a=i(tZ),mI=n(tZ,"LI",{});var _So=s(mI);Wlo=n(_So,"STRONG",{});var aKn=s(Wlo);j7a=r(aKn,"maskformer"),aKn.forEach(t),P7a=r(_So," \u2014 "),e3e=n(_So,"A",{href:!0});var nKn=s(e3e);Z7a=r(nKn,"MaskFormerForInstanceSegmentation"),nKn.forEach(t),V7a=r(_So," (MaskFormer model)"),_So.forEach(t),W7a=i(tZ),cI=n(tZ,"LI",{});var bSo=s(cI);Jlo=n(bSo,"STRONG",{});var sKn=s(Jlo);J7a=r(sKn,"oneformer"),sKn.forEach(t),U7a=r(bSo," \u2014 "),o3e=n(bSo,"A",{href:!0});var lKn=s(o3e);X7a=r(lKn,"OneFormerForUniversalSegmentation"),lKn.forEach(t),D7a=r(bSo," (OneFormer model)"),bSo.forEach(t),tZ.forEach(t),q7a=i(Mn),fI=n(Mn,"P",{});var vSo=s(fI);O7a=r(vSo,"The model is set in evaluation mode by default using "),Ulo=n(vSo,"CODE",{});var iKn=s(Ulo);Q7a=r(iKn,"model.eval()"),iKn.forEach(t),Y7a=r(vSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Xlo=n(vSo,"CODE",{});var dKn=s(Xlo);z7a=r(dKn,"model.train()"),dKn.forEach(t),vSo.forEach(t),H7a=i(Mn),M(hI.$$.fragment,Mn),Mn.forEach(t),cm.forEach(t),zBo=i(c),Bh=n(c,"H3",{class:!0});var Y$o=s(Bh);gI=n(Y$o,"A",{id:!0,class:!0,href:!0});var mKn=s(gI);Dlo=n(mKn,"SPAN",{});var cKn=s(Dlo);M(JX.$$.fragment,cKn),cKn.forEach(t),mKn.forEach(t),K7a=i(Y$o),qlo=n(Y$o,"SPAN",{});var fKn=s(qlo);eLa=r(fKn,"AutoModelForZeroShotImageClassification"),fKn.forEach(t),Y$o.forEach(t),HBo=i(c),Br=n(c,"DIV",{class:!0});var fm=s(Br);M(UX.$$.fragment,fm),oLa=i(fm),Ih=n(fm,"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 `),r3e=n(KCe,"A",{href:!0});var hKn=s(r3e);tLa=r(hKn,"from_pretrained()"),hKn.forEach(t),aLa=r(KCe," class method or the "),t3e=n(KCe,"A",{href:!0});var gKn=s(t3e);nLa=r(gKn,"from_config()"),gKn.forEach(t),sLa=r(KCe,` class
method.`),KCe.forEach(t),lLa=i(fm),XX=n(fm,"P",{});var z$o=s(XX);iLa=r(z$o,"This class cannot be instantiated directly using "),Olo=n(z$o,"CODE",{});var uKn=s(Olo);dLa=r(uKn,"__init__()"),uKn.forEach(t),mLa=r(z$o," (throws an error)."),z$o.forEach(t),cLa=i(fm),xa=n(fm,"DIV",{class:!0});var aZ=s(xa);M(DX.$$.fragment,aZ),fLa=i(aZ),Qlo=n(aZ,"P",{});var pKn=s(Qlo);hLa=r(pKn,"Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),pKn.forEach(t),gLa=i(aZ),Nh=n(aZ,"P",{});var ewe=s(Nh);uLa=r(ewe,`Note:
Loading a model from its configuration file does `),Ylo=n(ewe,"STRONG",{});var _Kn=s(Ylo);pLa=r(_Kn,"not"),_Kn.forEach(t),_La=r(ewe,` load the model weights. It only affects the
model\u2019s configuration. Use `),a3e=n(ewe,"A",{href:!0});var bKn=s(a3e);bLa=r(bKn,"from_pretrained()"),bKn.forEach(t),vLa=r(ewe," to load the model weights."),ewe.forEach(t),MLa=i(aZ),M(uI.$$.fragment,aZ),aZ.forEach(t),TLa=i(fm),wo=n(fm,"DIV",{class:!0});var Tn=s(wo);M(qX.$$.fragment,Tn),FLa=i(Tn),zlo=n(Tn,"P",{});var vKn=s(zlo);ELa=r(vKn,"Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),vKn.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 "),n3e=n(MSo,"A",{href:!0});var yKn=s(n3e);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 "),s3e=n(TSo,"A",{href:!0});var wKn=s(s3e);ZLa=r(wKn,"AltCLIPModel"),wKn.forEach(t),VLa=r(TSo," (AltCLIP model)"),TSo.forEach(t),WLa=i(Fn),bI=n(Fn,"LI",{});var FSo=s(bI);tio=n(FSo,"STRONG",{});var LKn=s(tio);JLa=r(LKn,"blip"),LKn.forEach(t),ULa=r(FSo," \u2014 "),l3e=n(FSo,"A",{href:!0});var AKn=s(l3e);XLa=r(AKn,"BlipModel"),AKn.forEach(t),DLa=r(FSo," (BLIP model)"),FSo.forEach(t),qLa=i(Fn),vI=n(Fn,"LI",{});var ESo=s(vI);aio=n(ESo,"STRONG",{});var RKn=s(aio);OLa=r(RKn,"chinese_clip"),RKn.forEach(t),QLa=r(ESo," \u2014 "),i3e=n(ESo,"A",{href:!0});var kKn=s(i3e);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 "),d3e=n(ySo,"A",{href:!0});var xKn=s(d3e);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 "),m3e=n(CSo,"A",{href:!0});var BKn=s(m3e);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);mAa=r(IKn,"model.eval()"),IKn.forEach(t),cAa=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),fm.forEach(t),KBo=i(c),$h=n(c,"H3",{class:!0});var H$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.$$.fragment,jKn),jKn.forEach(t),$Kn.forEach(t),gAa=i(H$o),mio=n(H$o,"SPAN",{});var PKn=s(mio);uAa=r(PKn,"TFAutoModelForZeroShotImageClassification"),PKn.forEach(t),H$o.forEach(t),eIo=i(c),Nr=n(c,"DIV",{class:!0});var hm=s(Nr);M(QX.$$.fragment,hm),pAa=i(hm),jh=n(hm,"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 `),c3e=n(owe,"A",{href:!0});var ZKn=s(c3e);bAa=r(ZKn,"from_pretrained()"),ZKn.forEach(t),vAa=r(owe," class method or the "),f3e=n(owe,"A",{href:!0});var VKn=s(f3e);MAa=r(VKn,"from_config()"),VKn.forEach(t),TAa=r(owe,` class
method.`),owe.forEach(t),FAa=i(hm),YX=n(hm,"P",{});var K$o=s(YX);EAa=r(K$o,"This class cannot be instantiated directly using "),cio=n(K$o,"CODE",{});var WKn=s(cio);yAa=r(WKn,"__init__()"),WKn.forEach(t),CAa=r(K$o," (throws an error)."),K$o.forEach(t),wAa=i(hm),Ga=n(hm,"DIV",{class:!0});var sZ=s(Ga);M(zX.$$.fragment,sZ),LAa=i(sZ),fio=n(sZ,"P",{});var JKn=s(fio);AAa=r(JKn,"Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),JKn.forEach(t),RAa=i(sZ),Ph=n(sZ,"P",{});var rwe=s(Ph);kAa=r(rwe,`Note:
Loading a model from its configuration file does `),hio=n(rwe,"STRONG",{});var UKn=s(hio);SAa=r(UKn,"not"),UKn.forEach(t),xAa=r(rwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),h3e=n(rwe,"A",{href:!0});var XKn=s(h3e);GAa=r(XKn,"from_pretrained()"),XKn.forEach(t),BAa=r(rwe," to load the model weights."),rwe.forEach(t),IAa=i(sZ),M(CI.$$.fragment,sZ),sZ.forEach(t),NAa=i(hm),Lt=n(hm,"DIV",{class:!0});var gm=s(Lt);M(HX.$$.fragment,gm),$Aa=i(gm),gio=n(gm,"P",{});var DKn=s(gio);jAa=r(DKn,"Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),DKn.forEach(t),PAa=i(gm),Ss=n(gm,"P",{});var lZ=s(Ss);ZAa=r(lZ,"The model class to instantiate is selected based on the "),uio=n(lZ,"CODE",{});var qKn=s(uio);VAa=r(qKn,"model_type"),qKn.forEach(t),WAa=r(lZ,` property of the config object (either
passed as an argument or loaded from `),pio=n(lZ,"CODE",{});var OKn=s(pio);JAa=r(OKn,"pretrained_model_name_or_path"),OKn.forEach(t),UAa=r(lZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),_io=n(lZ,"CODE",{});var QKn=s(_io);XAa=r(QKn,"pretrained_model_name_or_path"),QKn.forEach(t),DAa=r(lZ,":"),lZ.forEach(t),qAa=i(gm),KX=n(gm,"UL",{});var ejo=s(KX);wI=n(ejo,"LI",{});var LSo=s(wI);bio=n(LSo,"STRONG",{});var YKn=s(bio);OAa=r(YKn,"blip"),YKn.forEach(t),QAa=r(LSo," \u2014 "),g3e=n(LSo,"A",{href:!0});var zKn=s(g3e);YAa=r(zKn,"TFBlipModel"),zKn.forEach(t),zAa=r(LSo," (BLIP model)"),LSo.forEach(t),HAa=i(ejo),LI=n(ejo,"LI",{});var ASo=s(LI);vio=n(ASo,"STRONG",{});var HKn=s(vio);KAa=r(HKn,"clip"),HKn.forEach(t),eRa=r(ASo," \u2014 "),u3e=n(ASo,"A",{href:!0});var KKn=s(u3e);oRa=r(KKn,"TFCLIPModel"),KKn.forEach(t),rRa=r(ASo," (CLIP model)"),ASo.forEach(t),ejo.forEach(t),tRa=i(gm),M(AI.$$.fragment,gm),gm.forEach(t),hm.forEach(t),oIo=i(c),Zh=n(c,"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.$$.fragment,oes),oes.forEach(t),ees.forEach(t),aRa=i(ojo),Tio=n(ojo,"SPAN",{});var res=s(Tio);nRa=r(res,"AutoModelForZeroShotObjectDetection"),res.forEach(t),ojo.forEach(t),rIo=i(c),$r=n(c,"DIV",{class:!0});var um=s($r);M(oD.$$.fragment,um),sRa=i(um),Vh=n(um,"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 `),p3e=n(twe,"A",{href:!0});var tes=s(p3e);iRa=r(tes,"from_pretrained()"),tes.forEach(t),dRa=r(twe," class method or the "),_3e=n(twe,"A",{href:!0});var aes=s(_3e);mRa=r(aes,"from_config()"),aes.forEach(t),cRa=r(twe,` class
method.`),twe.forEach(t),fRa=i(um),rD=n(um,"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(um),Ba=n(um,"DIV",{class:!0});var iZ=s(Ba);M(tD.$$.fragment,iZ),_Ra=i(iZ),Eio=n(iZ,"P",{});var ses=s(Eio);bRa=r(ses,"Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration."),ses.forEach(t),vRa=i(iZ),Wh=n(iZ,"P",{});var awe=s(Wh);MRa=r(awe,`Note:
Loading a model from its configuration file does `),yio=n(awe,"STRONG",{});var les=s(yio);TRa=r(les,"not"),les.forEach(t),FRa=r(awe,` load the model weights. It only affects the
model\u2019s configuration. Use `),b3e=n(awe,"A",{href:!0});var ies=s(b3e);ERa=r(ies,"from_pretrained()"),ies.forEach(t),yRa=r(awe," to load the model weights."),awe.forEach(t),CRa=i(iZ),M(kI.$$.fragment,iZ),iZ.forEach(t),wRa=i(um),Lo=n(um,"DIV",{class:!0});var En=s(Lo);M(aD.$$.fragment,En),LRa=i(En),Cio=n(En,"P",{});var des=s(Cio);ARa=r(des,"Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model."),des.forEach(t),RRa=i(En),xs=n(En,"P",{});var dZ=s(xs);kRa=r(dZ,"The model class to instantiate is selected based on the "),wio=n(dZ,"CODE",{});var mes=s(wio);SRa=r(mes,"model_type"),mes.forEach(t),xRa=r(dZ,` property of the config object (either
passed as an argument or loaded from `),Lio=n(dZ,"CODE",{});var ces=s(Lio);GRa=r(ces,"pretrained_model_name_or_path"),ces.forEach(t),BRa=r(dZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Aio=n(dZ,"CODE",{});var fes=s(Aio);IRa=r(fes,"pretrained_model_name_or_path"),fes.forEach(t),NRa=r(dZ,":"),dZ.forEach(t),$Ra=i(En),Rio=n(En,"UL",{});var hes=s(Rio);SI=n(hes,"LI",{});var RSo=s(SI);kio=n(RSo,"STRONG",{});var ges=s(kio);jRa=r(ges,"owlvit"),ges.forEach(t),PRa=r(RSo," \u2014 "),v3e=n(RSo,"A",{href:!0});var ues=s(v3e);ZRa=r(ues,"OwlViTForObjectDetection"),ues.forEach(t),VRa=r(RSo," (OWL-ViT model)"),RSo.forEach(t),hes.forEach(t),WRa=i(En),xI=n(En,"P",{});var kSo=s(xI);JRa=r(kSo,"The model is set in evaluation mode by default using "),Sio=n(kSo,"CODE",{});var pes=s(Sio);URa=r(pes,"model.eval()"),pes.forEach(t),XRa=r(kSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),xio=n(kSo,"CODE",{});var _es=s(xio);DRa=r(_es,"model.train()"),_es.forEach(t),kSo.forEach(t),qRa=i(En),M(GI.$$.fragment,En),En.forEach(t),um.forEach(t),tIo=i(c),Jh=n(c,"H2",{class:!0});var tjo=s(Jh);BI=n(tjo,"A",{id:!0,class:!0,href:!0});var bes=s(BI);Gio=n(bes,"SPAN",{});var ves=s(Gio);M(nD.$$.fragment,ves),ves.forEach(t),bes.forEach(t),ORa=i(tjo),Bio=n(tjo,"SPAN",{});var Mes=s(Bio);QRa=r(Mes,"Audio"),Mes.forEach(t),tjo.forEach(t),aIo=i(c),M3e=n(c,"P",{});var Tes=s(M3e);YRa=r(Tes,"The following auto classes are available for the following audio tasks."),Tes.forEach(t),nIo=i(c),Uh=n(c,"H3",{class:!0});var ajo=s(Uh);II=n(ajo,"A",{id:!0,class:!0,href:!0});var Fes=s(II);Iio=n(Fes,"SPAN",{});var Ees=s(Iio);M(sD.$$.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(c),jr=n(c,"DIV",{class:!0});var pm=s(jr);M(lD.$$.fragment,pm),KRa=i(pm),Xh=n(pm,"P",{});var nwe=s(Xh);eka=r(nwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the `),T3e=n(nwe,"A",{href:!0});var Ces=s(T3e);oka=r(Ces,"from_pretrained()"),Ces.forEach(t),rka=r(nwe," class method or the "),F3e=n(nwe,"A",{href:!0});var wes=s(F3e);tka=r(wes,"from_config()"),wes.forEach(t),aka=r(nwe,` class
method.`),nwe.forEach(t),nka=i(pm),iD=n(pm,"P",{});var njo=s(iD);ska=r(njo,"This class cannot be instantiated directly using "),$io=n(njo,"CODE",{});var Les=s($io);lka=r(Les,"__init__()"),Les.forEach(t),ika=r(njo," (throws an error)."),njo.forEach(t),dka=i(pm),Ia=n(pm,"DIV",{class:!0});var mZ=s(Ia);M(dD.$$.fragment,mZ),mka=i(mZ),jio=n(mZ,"P",{});var Aes=s(jio);cka=r(Aes,"Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),Aes.forEach(t),fka=i(mZ),Dh=n(mZ,"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 `),E3e=n(swe,"A",{href:!0});var kes=s(E3e);pka=r(kes,"from_pretrained()"),kes.forEach(t),_ka=r(swe," to load the model weights."),swe.forEach(t),bka=i(mZ),M(NI.$$.fragment,mZ),mZ.forEach(t),vka=i(pm),Ao=n(pm,"DIV",{class:!0});var yn=s(Ao);M(mD.$$.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 cZ=s(Gs);Eka=r(cZ,"The model class to instantiate is selected based on the "),Vio=n(cZ,"CODE",{});var xes=s(Vio);yka=r(xes,"model_type"),xes.forEach(t),Cka=r(cZ,` property of the config object (either
passed as an argument or loaded from `),Wio=n(cZ,"CODE",{});var Ges=s(Wio);wka=r(Ges,"pretrained_model_name_or_path"),Ges.forEach(t),Lka=r(cZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Jio=n(cZ,"CODE",{});var Bes=s(Jio);Aka=r(Bes,"pretrained_model_name_or_path"),Bes.forEach(t),Rka=r(cZ,":"),cZ.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 "),y3e=n(SSo,"A",{href:!0});var Nes=s(y3e);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 "),C3e=n(xSo,"A",{href:!0});var jes=s(C3e);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 "),w3e=n(GSo,"A",{href:!0});var Zes=s(w3e);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 "),L3e=n(BSo,"A",{href:!0});var Wes=s(L3e);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 "),A3e=n(ISo,"A",{href:!0});var Ues=s(A3e);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 "),R3e=n(NSo,"A",{href:!0});var Des=s(R3e);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($So," \u2014 "),k3e=n($So,"A",{href:!0});var Oes=s(k3e);dSa=r(Oes,"UniSpeechSatForSequenceClassification"),Oes.forEach(t),mSa=r($So," (UniSpeechSat model)"),$So.forEach(t),cSa=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 "),S3e=n(jSo,"A",{href:!0});var Yes=s(S3e);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),bSa=r(PSo," \u2014 "),x3e=n(PSo,"A",{href:!0});var Hes=s(x3e);vSa=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 "),G3e=n(ZSo,"A",{href:!0});var eos=s(G3e);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 "),B3e=n(VSo,"A",{href:!0});var ros=s(B3e);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.$$.fragment,yn),yn.forEach(t),pm.forEach(t),lIo=i(c),qh=n(c,"H3",{class:!0});var sjo=s(qh);YI=n(sjo,"A",{id:!0,class:!0,href:!0});var nos=s(YI);tdo=n(nos,"SPAN",{});var sos=s(tdo);M(cD.$$.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(c),Pr=n(c,"DIV",{class:!0});var _m=s(Pr);M(fD.$$.fragment,_m),PSa=i(_m),Oh=n(_m,"P",{});var lwe=s(Oh);ZSa=r(lwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the `),I3e=n(lwe,"A",{href:!0});var ios=s(I3e);VSa=r(ios,"from_pretrained()"),ios.forEach(t),WSa=r(lwe," class method or the "),N3e=n(lwe,"A",{href:!0});var dos=s(N3e);JSa=r(dos,"from_config()"),dos.forEach(t),USa=r(lwe,` class
method.`),lwe.forEach(t),XSa=i(_m),hD=n(_m,"P",{});var ljo=s(hD);DSa=r(ljo,"This class cannot be instantiated directly using "),ndo=n(ljo,"CODE",{});var mos=s(ndo);qSa=r(mos,"__init__()"),mos.forEach(t),OSa=r(ljo," (throws an error)."),ljo.forEach(t),QSa=i(_m),Na=n(_m,"DIV",{class:!0});var fZ=s(Na);M(gD.$$.fragment,fZ),YSa=i(fZ),sdo=n(fZ,"P",{});var cos=s(sdo);zSa=r(cos,"Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),cos.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 `),$3e=n(iwe,"A",{href:!0});var hos=s($3e);rxa=r(hos,"from_pretrained()"),hos.forEach(t),txa=r(iwe," to load the model weights."),iwe.forEach(t),axa=i(fZ),M(zI.$$.fragment,fZ),fZ.forEach(t),nxa=i(_m),At=n(_m,"DIV",{class:!0});var bm=s(At);M(uD.$$.fragment,bm),sxa=i(bm),ido=n(bm,"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(bm),Bs=n(bm,"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);mxa=r(uos,"model_type"),uos.forEach(t),cxa=r(hZ,` property of the config object (either
passed as an argument or loaded from `),mdo=n(hZ,"CODE",{});var pos=s(mdo);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 `),cdo=n(hZ,"CODE",{});var _os=s(cdo);gxa=r(_os,"pretrained_model_name_or_path"),_os.forEach(t),uxa=r(hZ,":"),hZ.forEach(t),pxa=i(bm),fdo=n(bm,"UL",{});var bos=s(fdo);HI=n(bos,"LI",{});var JSo=s(HI);hdo=n(JSo,"STRONG",{});var vos=s(hdo);_xa=r(vos,"wav2vec2"),vos.forEach(t),bxa=r(JSo," \u2014 "),j3e=n(JSo,"A",{href:!0});var Mos=s(j3e);vxa=r(Mos,"TFWav2Vec2ForSequenceClassification"),Mos.forEach(t),Mxa=r(JSo," (Wav2Vec2 model)"),JSo.forEach(t),bos.forEach(t),Txa=i(bm),M(KI.$$.fragment,bm),bm.forEach(t),_m.forEach(t),dIo=i(c),Yh=n(c,"H3",{class:!0});var ijo=s(Yh);eN=n(ijo,"A",{id:!0,class:!0,href:!0});var Tos=s(eN);gdo=n(Tos,"SPAN",{});var Fos=s(gdo);M(pD.$$.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),mIo=i(c),Zr=n(c,"DIV",{class:!0});var vm=s(Zr);M(_D.$$.fragment,vm),yxa=i(vm),zh=n(vm,"P",{});var dwe=s(zh);Cxa=r(dwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio frame (token) classification head) when created
with the `),P3e=n(dwe,"A",{href:!0});var yos=s(P3e);wxa=r(yos,"from_pretrained()"),yos.forEach(t),Lxa=r(dwe," class method or the "),Z3e=n(dwe,"A",{href:!0});var Cos=s(Z3e);Axa=r(Cos,"from_config()"),Cos.forEach(t),Rxa=r(dwe,` class
method.`),dwe.forEach(t),kxa=i(vm),bD=n(vm,"P",{});var djo=s(bD);Sxa=r(djo,"This class cannot be instantiated directly using "),pdo=n(djo,"CODE",{});var wos=s(pdo);xxa=r(wos,"__init__()"),wos.forEach(t),Gxa=r(djo," (throws an error)."),djo.forEach(t),Bxa=i(vm),$a=n(vm,"DIV",{class:!0});var gZ=s($a);M(vD.$$.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 mwe=s(Hh);jxa=r(mwe,`Note:
Loading a model from its configuration file does `),bdo=n(mwe,"STRONG",{});var Aos=s(bdo);Pxa=r(Aos,"not"),Aos.forEach(t),Zxa=r(mwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),V3e=n(mwe,"A",{href:!0});var Ros=s(V3e);Vxa=r(Ros,"from_pretrained()"),Ros.forEach(t),Wxa=r(mwe," to load the model weights."),mwe.forEach(t),Jxa=i(gZ),M(oN.$$.fragment,gZ),gZ.forEach(t),Uxa=i(vm),Ro=n(vm,"DIV",{class:!0});var Cn=s(Ro);M(MD.$$.fragment,Cn),Xxa=i(Cn),vdo=n(Cn,"P",{});var kos=s(vdo);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),$t=n(Cn,"UL",{});var Mm=s($t);rN=n(Mm,"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 "),W3e=n(USo,"A",{href:!0});var Ios=s(W3e);aGa=r(Ios,"Data2VecAudioForAudioFrameClassification"),Ios.forEach(t),nGa=r(USo," (Data2VecAudio model)"),USo.forEach(t),sGa=i(Mm),tN=n(Mm,"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 "),J3e=n(XSo,"A",{href:!0});var $os=s(J3e);dGa=r($os,"UniSpeechSatForAudioFrameClassification"),$os.forEach(t),mGa=r(XSo," (UniSpeechSat model)"),XSo.forEach(t),cGa=i(Mm),aN=n(Mm,"LI",{});var DSo=s(aN);Cdo=n(DSo,"STRONG",{});var jos=s(Cdo);fGa=r(jos,"wav2vec2"),jos.forEach(t),hGa=r(DSo," \u2014 "),U3e=n(DSo,"A",{href:!0});var Pos=s(U3e);gGa=r(Pos,"Wav2Vec2ForAudioFrameClassification"),Pos.forEach(t),uGa=r(DSo," (Wav2Vec2 model)"),DSo.forEach(t),pGa=i(Mm),nN=n(Mm,"LI",{});var qSo=s(nN);wdo=n(qSo,"STRONG",{});var Zos=s(wdo);_Ga=r(Zos,"wav2vec2-conformer"),Zos.forEach(t),bGa=r(qSo," \u2014 "),X3e=n(qSo,"A",{href:!0});var Vos=s(X3e);vGa=r(Vos,"Wav2Vec2ConformerForAudioFrameClassification"),Vos.forEach(t),MGa=r(qSo," (Wav2Vec2-Conformer model)"),qSo.forEach(t),TGa=i(Mm),sN=n(Mm,"LI",{});var OSo=s(sN);Ldo=n(OSo,"STRONG",{});var Wos=s(Ldo);FGa=r(Wos,"wavlm"),Wos.forEach(t),EGa=r(OSo," \u2014 "),D3e=n(OSo,"A",{href:!0});var Jos=s(D3e);yGa=r(Jos,"WavLMForAudioFrameClassification"),Jos.forEach(t),CGa=r(OSo," (WavLM model)"),OSo.forEach(t),Mm.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.$$.fragment,Cn),Cn.forEach(t),vm.forEach(t),cIo=i(c),Kh=n(c,"H3",{class:!0});var mjo=s(Kh);dN=n(mjo,"A",{id:!0,class:!0,href:!0});var Dos=s(dN);kdo=n(Dos,"SPAN",{});var qos=s(kdo);M(TD.$$.fragment,qos),qos.forEach(t),Dos.forEach(t),xGa=i(mjo),Sdo=n(mjo,"SPAN",{});var Oos=s(Sdo);GGa=r(Oos,"AutoModelForCTC"),Oos.forEach(t),mjo.forEach(t),fIo=i(c),Vr=n(c,"DIV",{class:!0});var Tm=s(Vr);M(FD.$$.fragment,Tm),BGa=i(Tm),eg=n(Tm,"P",{});var cwe=s(eg);IGa=r(cwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a connectionist temporal classification head) when created
with the `),q3e=n(cwe,"A",{href:!0});var Qos=s(q3e);NGa=r(Qos,"from_pretrained()"),Qos.forEach(t),$Ga=r(cwe," class method or the "),O3e=n(cwe,"A",{href:!0});var Yos=s(O3e);jGa=r(Yos,"from_config()"),Yos.forEach(t),PGa=r(cwe,` class
method.`),cwe.forEach(t),ZGa=i(Tm),ED=n(Tm,"P",{});var cjo=s(ED);VGa=r(cjo,"This class cannot be instantiated directly using "),xdo=n(cjo,"CODE",{});var zos=s(xdo);WGa=r(zos,"__init__()"),zos.forEach(t),JGa=r(cjo," (throws an error)."),cjo.forEach(t),UGa=i(Tm),ja=n(Tm,"DIV",{class:!0});var pZ=s(ja);M(yD.$$.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 `),Q3e=n(fwe,"A",{href:!0});var ers=s(Q3e);zGa=r(ers,"from_pretrained()"),ers.forEach(t),HGa=r(fwe," to load the model weights."),fwe.forEach(t),KGa=i(pZ),M(mN.$$.fragment,pZ),pZ.forEach(t),eBa=i(Tm),ko=n(Tm,"DIV",{class:!0});var wn=s(ko);M(CD.$$.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),mBa=r(_Z,":"),_Z.forEach(t),cBa=i(wn),je=n(wn,"UL",{});var Oe=s(je);cN=n(Oe,"LI",{});var YSo=s(cN);Pdo=n(YSo,"STRONG",{});var nrs=s(Pdo);fBa=r(nrs,"data2vec-audio"),nrs.forEach(t),hBa=r(YSo," \u2014 "),Y3e=n(YSo,"A",{href:!0});var srs=s(Y3e);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),bBa=r(zSo," \u2014 "),z3e=n(zSo,"A",{href:!0});var irs=s(z3e);vBa=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 "),H3e=n(HSo,"A",{href:!0});var mrs=s(H3e);yBa=r(mrs,"MCTCTForCTC"),mrs.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 crs=s(Wdo);LBa=r(crs,"sew"),crs.forEach(t),ABa=r(KSo," \u2014 "),K3e=n(KSo,"A",{href:!0});var frs=s(K3e);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 "),e9e=n(exo,"A",{href:!0});var grs=s(e9e);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 "),o9e=n(oxo,"A",{href:!0});var prs=s(o9e);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 "),r9e=n(rxo,"A",{href:!0});var brs=s(r9e);UBa=r(brs,"UniSpeechSatForCTC"),brs.forEach(t),XBa=r(rxo," (UniSpeechSat model)"),rxo.forEach(t),DBa=i(Oe),bN=n(Oe,"LI",{});var txo=s(bN);Ddo=n(txo,"STRONG",{});var vrs=s(Ddo);qBa=r(vrs,"wav2vec2"),vrs.forEach(t),OBa=r(txo," \u2014 "),t9e=n(txo,"A",{href:!0});var Mrs=s(t9e);QBa=r(Mrs,"Wav2Vec2ForCTC"),Mrs.forEach(t),YBa=r(txo," (Wav2Vec2 model)"),txo.forEach(t),zBa=i(Oe),vN=n(Oe,"LI",{});var axo=s(vN);qdo=n(axo,"STRONG",{});var Trs=s(qdo);HBa=r(Trs,"wav2vec2-conformer"),Trs.forEach(t),KBa=r(axo," \u2014 "),a9e=n(axo,"A",{href:!0});var Frs=s(a9e);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 "),n9e=n(nxo,"A",{href:!0});var yrs=s(n9e);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),mIa=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);cIa=r(wrs,"model.train()"),wrs.forEach(t),sxo.forEach(t),fIa=i(wn),M(FN.$$.fragment,wn),wn.forEach(t),Tm.forEach(t),hIo=i(c),rg=n(c,"H3",{class:!0});var fjo=s(rg);EN=n(fjo,"A",{id:!0,class:!0,href:!0});var Lrs=s(EN);zdo=n(Lrs,"SPAN",{});var Ars=s(zdo);M(wD.$$.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(c),Wr=n(c,"DIV",{class:!0});var Fm=s(Wr);M(LD.$$.fragment,Fm),uIa=i(Fm),tg=n(Fm,"P",{});var hwe=s(tg);pIa=r(hwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),s9e=n(hwe,"A",{href:!0});var krs=s(s9e);_Ia=r(krs,"from_pretrained()"),krs.forEach(t),bIa=r(hwe," class method or the "),l9e=n(hwe,"A",{href:!0});var Srs=s(l9e);vIa=r(Srs,"from_config()"),Srs.forEach(t),MIa=r(hwe,` class
method.`),hwe.forEach(t),TIa=i(Fm),AD=n(Fm,"P",{});var hjo=s(AD);FIa=r(hjo,"This class cannot be instantiated directly using "),Kdo=n(hjo,"CODE",{});var xrs=s(Kdo);EIa=r(xrs,"__init__()"),xrs.forEach(t),yIa=r(hjo," (throws an error)."),hjo.forEach(t),CIa=i(Fm),Pa=n(Fm,"DIV",{class:!0});var bZ=s(Pa);M(RD.$$.fragment,bZ),wIa=i(bZ),emo=n(bZ,"P",{});var Grs=s(emo);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(bZ),ag=n(bZ,"P",{});var gwe=s(ag);RIa=r(gwe,`Note:
Loading a model from its configuration file does `),omo=n(gwe,"STRONG",{});var Brs=s(omo);kIa=r(Brs,"not"),Brs.forEach(t),SIa=r(gwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),i9e=n(gwe,"A",{href:!0});var Irs=s(i9e);xIa=r(Irs,"from_pretrained()"),Irs.forEach(t),GIa=r(gwe," to load the model weights."),gwe.forEach(t),BIa=i(bZ),M(yN.$$.fragment,bZ),bZ.forEach(t),IIa=i(Fm),So=n(Fm,"DIV",{class:!0});var Ln=s(So);M(kD.$$.fragment,Ln),NIa=i(Ln),rmo=n(Ln,"P",{});var Nrs=s(rmo);$Ia=r(Nrs,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),Nrs.forEach(t),jIa=i(Ln),$s=n(Ln,"P",{});var vZ=s($s);PIa=r(vZ,"The model class to instantiate is selected based on the "),tmo=n(vZ,"CODE",{});var $rs=s(tmo);ZIa=r($rs,"model_type"),$rs.forEach(t),VIa=r(vZ,` property of the config object (either
passed as an argument or loaded from `),amo=n(vZ,"CODE",{});var jrs=s(amo);WIa=r(jrs,"pretrained_model_name_or_path"),jrs.forEach(t),JIa=r(vZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nmo=n(vZ,"CODE",{});var Prs=s(nmo);UIa=r(Prs,"pretrained_model_name_or_path"),Prs.forEach(t),XIa=r(vZ,":"),vZ.forEach(t),DIa=i(Ln),js=n(Ln,"UL",{});var MZ=s(js);CN=n(MZ,"LI",{});var lxo=s(CN);smo=n(lxo,"STRONG",{});var Zrs=s(smo);qIa=r(Zrs,"speech-encoder-decoder"),Zrs.forEach(t),OIa=r(lxo," \u2014 "),d9e=n(lxo,"A",{href:!0});var Vrs=s(d9e);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);lmo=n(ixo,"STRONG",{});var Wrs=s(lmo);HIa=r(Wrs,"speech_to_text"),Wrs.forEach(t),KIa=r(ixo," \u2014 "),m9e=n(ixo,"A",{href:!0});var Jrs=s(m9e);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);imo=n(dxo,"STRONG",{});var Urs=s(imo);tNa=r(Urs,"speecht5"),Urs.forEach(t),aNa=r(dxo," \u2014 "),c9e=n(dxo,"A",{href:!0});var Xrs=s(c9e);nNa=r(Xrs,"SpeechT5ForSpeechToText"),Xrs.forEach(t),sNa=r(dxo," (SpeechT5 model)"),dxo.forEach(t),lNa=i(MZ),AN=n(MZ,"LI",{});var mxo=s(AN);dmo=n(mxo,"STRONG",{});var Drs=s(dmo);iNa=r(Drs,"whisper"),Drs.forEach(t),dNa=r(mxo," \u2014 "),f9e=n(mxo,"A",{href:!0});var qrs=s(f9e);mNa=r(qrs,"WhisperForConditionalGeneration"),qrs.forEach(t),cNa=r(mxo," (Whisper model)"),mxo.forEach(t),MZ.forEach(t),fNa=i(Ln),RN=n(Ln,"P",{});var cxo=s(RN);hNa=r(cxo,"The model is set in evaluation mode by default using "),mmo=n(cxo,"CODE",{});var Ors=s(mmo);gNa=r(Ors,"model.eval()"),Ors.forEach(t),uNa=r(cxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),cmo=n(cxo,"CODE",{});var Qrs=s(cmo);pNa=r(Qrs,"model.train()"),Qrs.forEach(t),cxo.forEach(t),_Na=i(Ln),M(kN.$$.fragment,Ln),Ln.forEach(t),Fm.forEach(t),uIo=i(c),ng=n(c,"H3",{class:!0});var gjo=s(ng);SN=n(gjo,"A",{id:!0,class:!0,href:!0});var Yrs=s(SN);fmo=n(Yrs,"SPAN",{});var zrs=s(fmo);M(SD.$$.fragment,zrs),zrs.forEach(t),Yrs.forEach(t),bNa=i(gjo),hmo=n(gjo,"SPAN",{});var Hrs=s(hmo);vNa=r(Hrs,"TFAutoModelForSpeechSeq2Seq"),Hrs.forEach(t),gjo.forEach(t),pIo=i(c),Jr=n(c,"DIV",{class:!0});var Em=s(Jr);M(xD.$$.fragment,Em),MNa=i(Em),sg=n(Em,"P",{});var uwe=s(sg);TNa=r(uwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),h9e=n(uwe,"A",{href:!0});var Krs=s(h9e);FNa=r(Krs,"from_pretrained()"),Krs.forEach(t),ENa=r(uwe," class method or the "),g9e=n(uwe,"A",{href:!0});var ets=s(g9e);yNa=r(ets,"from_config()"),ets.forEach(t),CNa=r(uwe,` class
method.`),uwe.forEach(t),wNa=i(Em),GD=n(Em,"P",{});var ujo=s(GD);LNa=r(ujo,"This class cannot be instantiated directly using "),gmo=n(ujo,"CODE",{});var ots=s(gmo);ANa=r(ots,"__init__()"),ots.forEach(t),RNa=r(ujo," (throws an error)."),ujo.forEach(t),kNa=i(Em),Za=n(Em,"DIV",{class:!0});var TZ=s(Za);M(BD.$$.fragment,TZ),SNa=i(TZ),umo=n(TZ,"P",{});var rts=s(umo);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 `),pmo=n(pwe,"STRONG",{});var tts=s(pmo);INa=r(tts,"not"),tts.forEach(t),NNa=r(pwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),u9e=n(pwe,"A",{href:!0});var ats=s(u9e);$Na=r(ats,"from_pretrained()"),ats.forEach(t),jNa=r(pwe," to load the model weights."),pwe.forEach(t),PNa=i(TZ),M(xN.$$.fragment,TZ),TZ.forEach(t),ZNa=i(Em),Rt=n(Em,"DIV",{class:!0});var ym=s(Rt);M(ID.$$.fragment,ym),VNa=i(ym),_mo=n(ym,"P",{});var nts=s(_mo);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(ym),Ps=n(ym,"P",{});var FZ=s(Ps);UNa=r(FZ,"The model class to instantiate is selected based on the "),bmo=n(FZ,"CODE",{});var sts=s(bmo);XNa=r(sts,"model_type"),sts.forEach(t),DNa=r(FZ,` property of the config object (either
passed as an argument or loaded from `),vmo=n(FZ,"CODE",{});var lts=s(vmo);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 `),Mmo=n(FZ,"CODE",{});var its=s(Mmo);QNa=r(its,"pretrained_model_name_or_path"),its.forEach(t),YNa=r(FZ,":"),FZ.forEach(t),zNa=i(ym),ND=n(ym,"UL",{});var pjo=s(ND);GN=n(pjo,"LI",{});var fxo=s(GN);Tmo=n(fxo,"STRONG",{});var dts=s(Tmo);HNa=r(dts,"speech_to_text"),dts.forEach(t),KNa=r(fxo," \u2014 "),p9e=n(fxo,"A",{href:!0});var mts=s(p9e);e$a=r(mts,"TFSpeech2TextForConditionalGeneration"),mts.forEach(t),o$a=r(fxo," (Speech2Text model)"),fxo.forEach(t),r$a=i(pjo),BN=n(pjo,"LI",{});var hxo=s(BN);Fmo=n(hxo,"STRONG",{});var cts=s(Fmo);t$a=r(cts,"whisper"),cts.forEach(t),a$a=r(hxo," \u2014 "),_9e=n(hxo,"A",{href:!0});var fts=s(_9e);n$a=r(fts,"TFWhisperForConditionalGeneration"),fts.forEach(t),s$a=r(hxo," (Whisper model)"),hxo.forEach(t),pjo.forEach(t),l$a=i(ym),M(IN.$$.fragment,ym),ym.forEach(t),Em.forEach(t),_Io=i(c),ig=n(c,"H3",{class:!0});var _jo=s(ig);NN=n(_jo,"A",{id:!0,class:!0,href:!0});var hts=s(NN);Emo=n(hts,"SPAN",{});var gts=s(Emo);M($D.$$.fragment,gts),gts.forEach(t),hts.forEach(t),i$a=i(_jo),ymo=n(_jo,"SPAN",{});var uts=s(ymo);d$a=r(uts,"FlaxAutoModelForSpeechSeq2Seq"),uts.forEach(t),_jo.forEach(t),bIo=i(c),Ur=n(c,"DIV",{class:!0});var Cm=s(Ur);M(jD.$$.fragment,Cm),m$a=i(Cm),dg=n(Cm,"P",{});var _we=s(dg);c$a=r(_we,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),b9e=n(_we,"A",{href:!0});var pts=s(b9e);f$a=r(pts,"from_pretrained()"),pts.forEach(t),h$a=r(_we," class method or the "),v9e=n(_we,"A",{href:!0});var _ts=s(v9e);g$a=r(_ts,"from_config()"),_ts.forEach(t),u$a=r(_we,` class
method.`),_we.forEach(t),p$a=i(Cm),PD=n(Cm,"P",{});var bjo=s(PD);_$a=r(bjo,"This class cannot be instantiated directly using "),Cmo=n(bjo,"CODE",{});var bts=s(Cmo);b$a=r(bts,"__init__()"),bts.forEach(t),v$a=r(bjo," (throws an error)."),bjo.forEach(t),M$a=i(Cm),Va=n(Cm,"DIV",{class:!0});var EZ=s(Va);M(ZD.$$.fragment,EZ),T$a=i(EZ),wmo=n(EZ,"P",{});var vts=s(wmo);F$a=r(vts,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),vts.forEach(t),E$a=i(EZ),mg=n(EZ,"P",{});var bwe=s(mg);y$a=r(bwe,`Note:
Loading a model from its configuration file does `),Lmo=n(bwe,"STRONG",{});var Mts=s(Lmo);C$a=r(Mts,"not"),Mts.forEach(t),w$a=r(bwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),M9e=n(bwe,"A",{href:!0});var Tts=s(M9e);L$a=r(Tts,"from_pretrained()"),Tts.forEach(t),A$a=r(bwe," to load the model weights."),bwe.forEach(t),R$a=i(EZ),M($N.$$.fragment,EZ),EZ.forEach(t),k$a=i(Cm),kt=n(Cm,"DIV",{class:!0});var wm=s(kt);M(VD.$$.fragment,wm),S$a=i(wm),Amo=n(wm,"P",{});var Fts=s(Amo);x$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$a=i(wm),Zs=n(wm,"P",{});var yZ=s(Zs);B$a=r(yZ,"The model class to instantiate is selected based on the "),Rmo=n(yZ,"CODE",{});var Ets=s(Rmo);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 `),kmo=n(yZ,"CODE",{});var yts=s(kmo);$$a=r(yts,"pretrained_model_name_or_path"),yts.forEach(t),j$a=r(yZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Smo=n(yZ,"CODE",{});var Cts=s(Smo);P$a=r(Cts,"pretrained_model_name_or_path"),Cts.forEach(t),Z$a=r(yZ,":"),yZ.forEach(t),V$a=i(wm),WD=n(wm,"UL",{});var vjo=s(WD);jN=n(vjo,"LI",{});var gxo=s(jN);xmo=n(gxo,"STRONG",{});var wts=s(xmo);W$a=r(wts,"speech-encoder-decoder"),wts.forEach(t),J$a=r(gxo," \u2014 "),T9e=n(gxo,"A",{href:!0});var Lts=s(T9e);U$a=r(Lts,"FlaxSpeechEncoderDecoderModel"),Lts.forEach(t),X$a=r(gxo," (Speech Encoder decoder model)"),gxo.forEach(t),D$a=i(vjo),PN=n(vjo,"LI",{});var uxo=s(PN);Gmo=n(uxo,"STRONG",{});var Ats=s(Gmo);q$a=r(Ats,"whisper"),Ats.forEach(t),O$a=r(uxo," \u2014 "),F9e=n(uxo,"A",{href:!0});var Rts=s(F9e);Q$a=r(Rts,"FlaxWhisperForConditionalGeneration"),Rts.forEach(t),Y$a=r(uxo," (Whisper model)"),uxo.forEach(t),vjo.forEach(t),z$a=i(wm),M(ZN.$$.fragment,wm),wm.forEach(t),Cm.forEach(t),vIo=i(c),cg=n(c,"H3",{class:!0});var Mjo=s(cg);VN=n(Mjo,"A",{id:!0,class:!0,href:!0});var kts=s(VN);Bmo=n(kts,"SPAN",{});var Sts=s(Bmo);M(JD.$$.fragment,Sts),Sts.forEach(t),kts.forEach(t),H$a=i(Mjo),Imo=n(Mjo,"SPAN",{});var xts=s(Imo);K$a=r(xts,"AutoModelForAudioXVector"),xts.forEach(t),Mjo.forEach(t),MIo=i(c),Xr=n(c,"DIV",{class:!0});var Lm=s(Xr);M(UD.$$.fragment,Lm),eja=i(Lm),fg=n(Lm,"P",{});var vwe=s(fg);oja=r(vwe,`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 `),E9e=n(vwe,"A",{href:!0});var Gts=s(E9e);rja=r(Gts,"from_pretrained()"),Gts.forEach(t),tja=r(vwe," class method or the "),y9e=n(vwe,"A",{href:!0});var Bts=s(y9e);aja=r(Bts,"from_config()"),Bts.forEach(t),nja=r(vwe,` class
method.`),vwe.forEach(t),sja=i(Lm),XD=n(Lm,"P",{});var Tjo=s(XD);lja=r(Tjo,"This class cannot be instantiated directly using "),Nmo=n(Tjo,"CODE",{});var Its=s(Nmo);ija=r(Its,"__init__()"),Its.forEach(t),dja=r(Tjo," (throws an error)."),Tjo.forEach(t),mja=i(Lm),Wa=n(Lm,"DIV",{class:!0});var CZ=s(Wa);M(DD.$$.fragment,CZ),cja=i(CZ),$mo=n(CZ,"P",{});var Nts=s($mo);fja=r(Nts,"Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration."),Nts.forEach(t),hja=i(CZ),hg=n(CZ,"P",{});var Mwe=s(hg);gja=r(Mwe,`Note:
Loading a model from its configuration file does `),jmo=n(Mwe,"STRONG",{});var $ts=s(jmo);uja=r($ts,"not"),$ts.forEach(t),pja=r(Mwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),C9e=n(Mwe,"A",{href:!0});var jts=s(C9e);_ja=r(jts,"from_pretrained()"),jts.forEach(t),bja=r(Mwe," to load the model weights."),Mwe.forEach(t),vja=i(CZ),M(WN.$$.fragment,CZ),CZ.forEach(t),Mja=i(Lm),xo=n(Lm,"DIV",{class:!0});var An=s(xo);M(qD.$$.fragment,An),Tja=i(An),Pmo=n(An,"P",{});var Pts=s(Pmo);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 "),Zmo=n(wZ,"CODE",{});var Zts=s(Zmo);Cja=r(Zts,"model_type"),Zts.forEach(t),wja=r(wZ,` property of the config object (either
passed as an argument or loaded from `),Vmo=n(wZ,"CODE",{});var Vts=s(Vmo);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 `),Wmo=n(wZ,"CODE",{});var Wts=s(Wmo);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 Am=s(jt);JN=n(Am,"LI",{});var pxo=s(JN);Jmo=n(pxo,"STRONG",{});var Jts=s(Jmo);xja=r(Jts,"data2vec-audio"),Jts.forEach(t),Gja=r(pxo," \u2014 "),w9e=n(pxo,"A",{href:!0});var Uts=s(w9e);Bja=r(Uts,"Data2VecAudioForXVector"),Uts.forEach(t),Ija=r(pxo," (Data2VecAudio model)"),pxo.forEach(t),Nja=i(Am),UN=n(Am,"LI",{});var _xo=s(UN);Umo=n(_xo,"STRONG",{});var Xts=s(Umo);$ja=r(Xts,"unispeech-sat"),Xts.forEach(t),jja=r(_xo," \u2014 "),L9e=n(_xo,"A",{href:!0});var Dts=s(L9e);Pja=r(Dts,"UniSpeechSatForXVector"),Dts.forEach(t),Zja=r(_xo," (UniSpeechSat model)"),_xo.forEach(t),Vja=i(Am),XN=n(Am,"LI",{});var bxo=s(XN);Xmo=n(bxo,"STRONG",{});var qts=s(Xmo);Wja=r(qts,"wav2vec2"),qts.forEach(t),Jja=r(bxo," \u2014 "),A9e=n(bxo,"A",{href:!0});var Ots=s(A9e);Uja=r(Ots,"Wav2Vec2ForXVector"),Ots.forEach(t),Xja=r(bxo," (Wav2Vec2 model)"),bxo.forEach(t),Dja=i(Am),DN=n(Am,"LI",{});var vxo=s(DN);Dmo=n(vxo,"STRONG",{});var Qts=s(Dmo);qja=r(Qts,"wav2vec2-conformer"),Qts.forEach(t),Oja=r(vxo," \u2014 "),R9e=n(vxo,"A",{href:!0});var Yts=s(R9e);Qja=r(Yts,"Wav2Vec2ConformerForXVector"),Yts.forEach(t),Yja=r(vxo," (Wav2Vec2-Conformer model)"),vxo.forEach(t),zja=i(Am),qN=n(Am,"LI",{});var Mxo=s(qN);qmo=n(Mxo,"STRONG",{});var zts=s(qmo);Hja=r(zts,"wavlm"),zts.forEach(t),Kja=r(Mxo," \u2014 "),k9e=n(Mxo,"A",{href:!0});var Hts=s(k9e);ePa=r(Hts,"WavLMForXVector"),Hts.forEach(t),oPa=r(Mxo," (WavLM model)"),Mxo.forEach(t),Am.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 "),Omo=n(Txo,"CODE",{});var Kts=s(Omo);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 `),Qmo=n(Txo,"CODE",{});var eas=s(Qmo);sPa=r(eas,"model.train()"),eas.forEach(t),Txo.forEach(t),lPa=i(An),M(QN.$$.fragment,An),An.forEach(t),Lm.forEach(t),TIo=i(c),gg=n(c,"H3",{class:!0});var Fjo=s(gg);YN=n(Fjo,"A",{id:!0,class:!0,href:!0});var oas=s(YN);Ymo=n(oas,"SPAN",{});var ras=s(Ymo);M(OD.$$.fragment,ras),ras.forEach(t),oas.forEach(t),iPa=i(Fjo),zmo=n(Fjo,"SPAN",{});var tas=s(zmo);dPa=r(tas,"AutoModelForTextToSpectrogram"),tas.forEach(t),Fjo.forEach(t),FIo=i(c),QD=n(c,"DIV",{class:!0});var aas=s(QD);M(YD.$$.fragment,aas),aas.forEach(t),EIo=i(c),ug=n(c,"H3",{class:!0});var Ejo=s(ug);zN=n(Ejo,"A",{id:!0,class:!0,href:!0});var nas=s(zN);Hmo=n(nas,"SPAN",{});var sas=s(Hmo);M(zD.$$.fragment,sas),sas.forEach(t),nas.forEach(t),mPa=i(Ejo),Kmo=n(Ejo,"SPAN",{});var las=s(Kmo);cPa=r(las,"AutoModelForTextToWaveform"),las.forEach(t),Ejo.forEach(t),yIo=i(c),HD=n(c,"DIV",{class:!0});var ias=s(HD);M(KD.$$.fragment,ias),ias.forEach(t),CIo=i(c),pg=n(c,"H2",{class:!0});var yjo=s(pg);HN=n(yjo,"A",{id:!0,class:!0,href:!0});var das=s(HN);eco=n(das,"SPAN",{});var mas=s(eco);M(eq.$$.fragment,mas),mas.forEach(t),das.forEach(t),fPa=i(yjo),oco=n(yjo,"SPAN",{});var cas=s(oco);hPa=r(cas,"Multimodal"),cas.forEach(t),yjo.forEach(t),wIo=i(c),S9e=n(c,"P",{});var fas=s(S9e);gPa=r(fas,"The following auto classes are available for the following multimodal tasks."),fas.forEach(t),LIo=i(c),_g=n(c,"H3",{class:!0});var Cjo=s(_g);KN=n(Cjo,"A",{id:!0,class:!0,href:!0});var has=s(KN);rco=n(has,"SPAN",{});var gas=s(rco);M(oq.$$.fragment,gas),gas.forEach(t),has.forEach(t),uPa=i(Cjo),tco=n(Cjo,"SPAN",{});var uas=s(tco);pPa=r(uas,"AutoModelForTableQuestionAnswering"),uas.forEach(t),Cjo.forEach(t),AIo=i(c),Dr=n(c,"DIV",{class:!0});var Rm=s(Dr);M(rq.$$.fragment,Rm),_Pa=i(Rm),bg=n(Rm,"P",{});var Twe=s(bg);bPa=r(Twe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the `),x9e=n(Twe,"A",{href:!0});var pas=s(x9e);vPa=r(pas,"from_pretrained()"),pas.forEach(t),MPa=r(Twe," class method or the "),G9e=n(Twe,"A",{href:!0});var _as=s(G9e);TPa=r(_as,"from_config()"),_as.forEach(t),FPa=r(Twe,` class
method.`),Twe.forEach(t),EPa=i(Rm),tq=n(Rm,"P",{});var wjo=s(tq);yPa=r(wjo,"This class cannot be instantiated directly using "),aco=n(wjo,"CODE",{});var bas=s(aco);CPa=r(bas,"__init__()"),bas.forEach(t),wPa=r(wjo," (throws an error)."),wjo.forEach(t),LPa=i(Rm),Ja=n(Rm,"DIV",{class:!0});var LZ=s(Ja);M(aq.$$.fragment,LZ),APa=i(LZ),nco=n(LZ,"P",{});var vas=s(nco);RPa=r(vas,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),vas.forEach(t),kPa=i(LZ),vg=n(LZ,"P",{});var Fwe=s(vg);SPa=r(Fwe,`Note:
Loading a model from its configuration file does `),sco=n(Fwe,"STRONG",{});var Mas=s(sco);xPa=r(Mas,"not"),Mas.forEach(t),GPa=r(Fwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),B9e=n(Fwe,"A",{href:!0});var Tas=s(B9e);BPa=r(Tas,"from_pretrained()"),Tas.forEach(t),IPa=r(Fwe," to load the model weights."),Fwe.forEach(t),NPa=i(LZ),M(e$.$$.fragment,LZ),LZ.forEach(t),$Pa=i(Rm),Go=n(Rm,"DIV",{class:!0});var Rn=s(Go);M(nq.$$.fragment,Rn),jPa=i(Rn),lco=n(Rn,"P",{});var Fas=s(lco);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 "),ico=n(AZ,"CODE",{});var Eas=s(ico);WPa=r(Eas,"model_type"),Eas.forEach(t),JPa=r(AZ,` property of the config object (either
passed as an argument or loaded from `),dco=n(AZ,"CODE",{});var yas=s(dco);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 `),mco=n(AZ,"CODE",{});var Cas=s(mco);DPa=r(Cas,"pretrained_model_name_or_path"),Cas.forEach(t),qPa=r(AZ,":"),AZ.forEach(t),OPa=i(Rn),cco=n(Rn,"UL",{});var was=s(cco);o$=n(was,"LI",{});var Fxo=s(o$);fco=n(Fxo,"STRONG",{});var Las=s(fco);QPa=r(Las,"tapas"),Las.forEach(t),YPa=r(Fxo," \u2014 "),I9e=n(Fxo,"A",{href:!0});var Aas=s(I9e);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 "),hco=n(Exo,"CODE",{});var Ras=s(hco);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 `),gco=n(Exo,"CODE",{});var kas=s(gco);tZa=r(kas,"model.train()"),kas.forEach(t),Exo.forEach(t),aZa=i(Rn),M(t$.$$.fragment,Rn),Rn.forEach(t),Rm.forEach(t),RIo=i(c),Mg=n(c,"H3",{class:!0});var Ljo=s(Mg);a$=n(Ljo,"A",{id:!0,class:!0,href:!0});var Sas=s(a$);uco=n(Sas,"SPAN",{});var xas=s(uco);M(sq.$$.fragment,xas),xas.forEach(t),Sas.forEach(t),nZa=i(Ljo),pco=n(Ljo,"SPAN",{});var Gas=s(pco);sZa=r(Gas,"TFAutoModelForTableQuestionAnswering"),Gas.forEach(t),Ljo.forEach(t),kIo=i(c),qr=n(c,"DIV",{class:!0});var km=s(qr);M(lq.$$.fragment,km),lZa=i(km),Tg=n(km,"P",{});var Ewe=s(Tg);iZa=r(Ewe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the `),N9e=n(Ewe,"A",{href:!0});var Bas=s(N9e);dZa=r(Bas,"from_pretrained()"),Bas.forEach(t),mZa=r(Ewe," class method or the "),$9e=n(Ewe,"A",{href:!0});var Ias=s($9e);cZa=r(Ias,"from_config()"),Ias.forEach(t),fZa=r(Ewe,` class
method.`),Ewe.forEach(t),hZa=i(km),iq=n(km,"P",{});var Ajo=s(iq);gZa=r(Ajo,"This class cannot be instantiated directly using "),_co=n(Ajo,"CODE",{});var Nas=s(_co);uZa=r(Nas,"__init__()"),Nas.forEach(t),pZa=r(Ajo," (throws an error)."),Ajo.forEach(t),_Za=i(km),Ua=n(km,"DIV",{class:!0});var RZ=s(Ua);M(dq.$$.fragment,RZ),bZa=i(RZ),bco=n(RZ,"P",{});var $as=s(bco);vZa=r($as,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),$as.forEach(t),MZa=i(RZ),Fg=n(RZ,"P",{});var ywe=s(Fg);TZa=r(ywe,`Note:
Loading a model from its configuration file does `),vco=n(ywe,"STRONG",{});var jas=s(vco);FZa=r(jas,"not"),jas.forEach(t),EZa=r(ywe,` load the model weights. It only affects the
model\u2019s configuration. Use `),j9e=n(ywe,"A",{href:!0});var Pas=s(j9e);yZa=r(Pas,"from_pretrained()"),Pas.forEach(t),CZa=r(ywe," to load the model weights."),ywe.forEach(t),wZa=i(RZ),M(n$.$$.fragment,RZ),RZ.forEach(t),LZa=i(km),St=n(km,"DIV",{class:!0});var Sm=s(St);M(mq.$$.fragment,Sm),AZa=i(Sm),Mco=n(Sm,"P",{});var Zas=s(Mco);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(Sm),Js=n(Sm,"P",{});var kZ=s(Js);SZa=r(kZ,"The model class to instantiate is selected based on the "),Tco=n(kZ,"CODE",{});var Vas=s(Tco);xZa=r(Vas,"model_type"),Vas.forEach(t),GZa=r(kZ,` property of the config object (either
passed as an argument or loaded from `),Fco=n(kZ,"CODE",{});var Was=s(Fco);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 `),Eco=n(kZ,"CODE",{});var Jas=s(Eco);NZa=r(Jas,"pretrained_model_name_or_path"),Jas.forEach(t),$Za=r(kZ,":"),kZ.forEach(t),jZa=i(Sm),yco=n(Sm,"UL",{});var Uas=s(yco);s$=n(Uas,"LI",{});var yxo=s(s$);Cco=n(yxo,"STRONG",{});var Xas=s(Cco);PZa=r(Xas,"tapas"),Xas.forEach(t),ZZa=r(yxo," \u2014 "),P9e=n(yxo,"A",{href:!0});var Das=s(P9e);VZa=r(Das,"TFTapasForQuestionAnswering"),Das.forEach(t),WZa=r(yxo," (TAPAS model)"),yxo.forEach(t),Uas.forEach(t),JZa=i(Sm),M(l$.$$.fragment,Sm),Sm.forEach(t),km.forEach(t),SIo=i(c),Eg=n(c,"H3",{class:!0});var Rjo=s(Eg);i$=n(Rjo,"A",{id:!0,class:!0,href:!0});var qas=s(i$);wco=n(qas,"SPAN",{});var Oas=s(wco);M(cq.$$.fragment,Oas),Oas.forEach(t),qas.forEach(t),UZa=i(Rjo),Lco=n(Rjo,"SPAN",{});var Qas=s(Lco);XZa=r(Qas,"AutoModelForDocumentQuestionAnswering"),Qas.forEach(t),Rjo.forEach(t),xIo=i(c),Or=n(c,"DIV",{class:!0});var xm=s(Or);M(fq.$$.fragment,xm),DZa=i(xm),yg=n(xm,"P",{});var Cwe=s(yg);qZa=r(Cwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created
with the `),Z9e=n(Cwe,"A",{href:!0});var Yas=s(Z9e);OZa=r(Yas,"from_pretrained()"),Yas.forEach(t),QZa=r(Cwe," class method or the "),V9e=n(Cwe,"A",{href:!0});var zas=s(V9e);YZa=r(zas,"from_config()"),zas.forEach(t),zZa=r(Cwe,` class
method.`),Cwe.forEach(t),HZa=i(xm),hq=n(xm,"P",{});var kjo=s(hq);KZa=r(kjo,"This class cannot be instantiated directly using "),Aco=n(kjo,"CODE",{});var Has=s(Aco);eVa=r(Has,"__init__()"),Has.forEach(t),oVa=r(kjo," (throws an error)."),kjo.forEach(t),rVa=i(xm),Xa=n(xm,"DIV",{class:!0});var SZ=s(Xa);M(gq.$$.fragment,SZ),tVa=i(SZ),Rco=n(SZ,"P",{});var Kas=s(Rco);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 `),kco=n(wwe,"STRONG",{});var ens=s(kco);lVa=r(ens,"not"),ens.forEach(t),iVa=r(wwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),W9e=n(wwe,"A",{href:!0});var ons=s(W9e);dVa=r(ons,"from_pretrained()"),ons.forEach(t),mVa=r(wwe," to load the model weights."),wwe.forEach(t),cVa=i(SZ),M(d$.$$.fragment,SZ),SZ.forEach(t),fVa=i(xm),Bo=n(xm,"DIV",{class:!0});var kn=s(Bo);M(uq.$$.fragment,kn),hVa=i(kn),Sco=n(kn,"P",{});var rns=s(Sco);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 "),xco=n(xZ,"CODE",{});var tns=s(xco);_Va=r(tns,"model_type"),tns.forEach(t),bVa=r(xZ,` property of the config object (either
passed as an argument or loaded from `),Gco=n(xZ,"CODE",{});var ans=s(Gco);vVa=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 `),Bco=n(xZ,"CODE",{});var nns=s(Bco);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);m$=n(Lwe,"LI",{});var Cxo=s(m$);Ico=n(Cxo,"STRONG",{});var sns=s(Ico);yVa=r(sns,"layoutlm"),sns.forEach(t),CVa=r(Cxo," \u2014 "),J9e=n(Cxo,"A",{href:!0});var lns=s(J9e);wVa=r(lns,"LayoutLMForQuestionAnswering"),lns.forEach(t),LVa=r(Cxo," (LayoutLM model)"),Cxo.forEach(t),AVa=i(Lwe),c$=n(Lwe,"LI",{});var wxo=s(c$);Nco=n(wxo,"STRONG",{});var ins=s(Nco);RVa=r(ins,"layoutlmv2"),ins.forEach(t),kVa=r(wxo," \u2014 "),U9e=n(wxo,"A",{href:!0});var dns=s(U9e);SVa=r(dns,"LayoutLMv2ForQuestionAnswering"),dns.forEach(t),xVa=r(wxo," (LayoutLMv2 model)"),wxo.forEach(t),GVa=i(Lwe),f$=n(Lwe,"LI",{});var Lxo=s(f$);$co=n(Lxo,"STRONG",{});var mns=s($co);BVa=r(mns,"layoutlmv3"),mns.forEach(t),IVa=r(Lxo," \u2014 "),X9e=n(Lxo,"A",{href:!0});var cns=s(X9e);NVa=r(cns,"LayoutLMv3ForQuestionAnswering"),cns.forEach(t),$Va=r(Lxo," (LayoutLMv3 model)"),Lxo.forEach(t),Lwe.forEach(t),jVa=i(kn),h$=n(kn,"P",{});var Axo=s(h$);PVa=r(Axo,"The model is set in evaluation mode by default using "),jco=n(Axo,"CODE",{});var fns=s(jco);ZVa=r(fns,"model.eval()"),fns.forEach(t),VVa=r(Axo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Pco=n(Axo,"CODE",{});var hns=s(Pco);WVa=r(hns,"model.train()"),hns.forEach(t),Axo.forEach(t),JVa=i(kn),M(g$.$$.fragment,kn),kn.forEach(t),xm.forEach(t),GIo=i(c),Lg=n(c,"H3",{class:!0});var Sjo=s(Lg);u$=n(Sjo,"A",{id:!0,class:!0,href:!0});var gns=s(u$);Zco=n(gns,"SPAN",{});var uns=s(Zco);M(pq.$$.fragment,uns),uns.forEach(t),gns.forEach(t),UVa=i(Sjo),Vco=n(Sjo,"SPAN",{});var pns=s(Vco);XVa=r(pns,"TFAutoModelForDocumentQuestionAnswering"),pns.forEach(t),Sjo.forEach(t),BIo=i(c),Qr=n(c,"DIV",{class:!0});var Gm=s(Qr);M(_q.$$.fragment,Gm),DVa=i(Gm),Ag=n(Gm,"P",{});var Awe=s(Ag);qVa=r(Awe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created
with the `),D9e=n(Awe,"A",{href:!0});var _ns=s(D9e);OVa=r(_ns,"from_pretrained()"),_ns.forEach(t),QVa=r(Awe," class method or the "),q9e=n(Awe,"A",{href:!0});var bns=s(q9e);YVa=r(bns,"from_config()"),bns.forEach(t),zVa=r(Awe,` class
method.`),Awe.forEach(t),HVa=i(Gm),bq=n(Gm,"P",{});var xjo=s(bq);KVa=r(xjo,"This class cannot be instantiated directly using "),Wco=n(xjo,"CODE",{});var vns=s(Wco);eWa=r(vns,"__init__()"),vns.forEach(t),oWa=r(xjo," (throws an error)."),xjo.forEach(t),rWa=i(Gm),Da=n(Gm,"DIV",{class:!0});var GZ=s(Da);M(vq.$$.fragment,GZ),tWa=i(GZ),Jco=n(GZ,"P",{});var Mns=s(Jco);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 `),Uco=n(Rwe,"STRONG",{});var Tns=s(Uco);lWa=r(Tns,"not"),Tns.forEach(t),iWa=r(Rwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),O9e=n(Rwe,"A",{href:!0});var Fns=s(O9e);dWa=r(Fns,"from_pretrained()"),Fns.forEach(t),mWa=r(Rwe," to load the model weights."),Rwe.forEach(t),cWa=i(GZ),M(p$.$$.fragment,GZ),GZ.forEach(t),fWa=i(Gm),xt=n(Gm,"DIV",{class:!0});var Bm=s(xt);M(Mq.$$.fragment,Bm),hWa=i(Bm),Xco=n(Bm,"P",{});var Ens=s(Xco);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(Bm),Xs=n(Bm,"P",{});var BZ=s(Xs);pWa=r(BZ,"The model class to instantiate is selected based on the "),Dco=n(BZ,"CODE",{});var yns=s(Dco);_Wa=r(yns,"model_type"),yns.forEach(t),bWa=r(BZ,` property of the config object (either
passed as an argument or loaded from `),qco=n(BZ,"CODE",{});var Cns=s(qco);vWa=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 `),Oco=n(BZ,"CODE",{});var wns=s(Oco);TWa=r(wns,"pretrained_model_name_or_path"),wns.forEach(t),FWa=r(BZ,":"),BZ.forEach(t),EWa=i(Bm),Qco=n(Bm,"UL",{});var Lns=s(Qco);_$=n(Lns,"LI",{});var Rxo=s(_$);Yco=n(Rxo,"STRONG",{});var Ans=s(Yco);yWa=r(Ans,"layoutlm"),Ans.forEach(t),CWa=r(Rxo," \u2014 "),Q9e=n(Rxo,"A",{href:!0});var Rns=s(Q9e);wWa=r(Rns,"TFLayoutLMForQuestionAnswering"),Rns.forEach(t),LWa=r(Rxo," (LayoutLM model)"),Rxo.forEach(t),Lns.forEach(t),AWa=i(Bm),M(b$.$$.fragment,Bm),Bm.forEach(t),Gm.forEach(t),IIo=i(c),kg=n(c,"H3",{class:!0});var Gjo=s(kg);v$=n(Gjo,"A",{id:!0,class:!0,href:!0});var kns=s(v$);zco=n(kns,"SPAN",{});var Sns=s(zco);M(Tq.$$.fragment,Sns),Sns.forEach(t),kns.forEach(t),RWa=i(Gjo),Hco=n(Gjo,"SPAN",{});var xns=s(Hco);kWa=r(xns,"AutoModelForVisualQuestionAnswering"),xns.forEach(t),Gjo.forEach(t),NIo=i(c),Yr=n(c,"DIV",{class:!0});var Im=s(Yr);M(Fq.$$.fragment,Im),SWa=i(Im),Sg=n(Im,"P",{});var kwe=s(Sg);xWa=r(kwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a visual question answering head) when created
with the `),Y9e=n(kwe,"A",{href:!0});var Gns=s(Y9e);GWa=r(Gns,"from_pretrained()"),Gns.forEach(t),BWa=r(kwe," class method or the "),z9e=n(kwe,"A",{href:!0});var Bns=s(z9e);IWa=r(Bns,"from_config()"),Bns.forEach(t),NWa=r(kwe,` class
method.`),kwe.forEach(t),$Wa=i(Im),Eq=n(Im,"P",{});var Bjo=s(Eq);jWa=r(Bjo,"This class cannot be instantiated directly using "),Kco=n(Bjo,"CODE",{});var Ins=s(Kco);PWa=r(Ins,"__init__()"),Ins.forEach(t),ZWa=r(Bjo," (throws an error)."),Bjo.forEach(t),VWa=i(Im),qa=n(Im,"DIV",{class:!0});var IZ=s(qa);M(yq.$$.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"),$ns.forEach(t),qWa=r(Swe,` load the model weights. It only affects the
model\u2019s configuration. Use `),H9e=n(Swe,"A",{href:!0});var jns=s(H9e);OWa=r(jns,"from_pretrained()"),jns.forEach(t),QWa=r(Swe," to load the model weights."),Swe.forEach(t),YWa=i(IZ),M(M$.$$.fragment,IZ),IZ.forEach(t),zWa=i(Im),Io=n(Im,"DIV",{class:!0});var Sn=s(Io);M(Cq.$$.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),mJa=r(kxo," \u2014 "),K9e=n(kxo,"A",{href:!0});var Xns=s(K9e);cJa=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),bJa=i(Sn),M(E$.$$.fragment,Sn),Sn.forEach(t),Im.forEach(t),$Io=i(c),Gg=n(c,"H3",{class:!0});var Ijo=s(Gg);y$=n(Ijo,"A",{id:!0,class:!0,href:!0});var Ons=s(y$);mfo=n(Ons,"SPAN",{});var Qns=s(mfo);M(wq.$$.fragment,Qns),Qns.forEach(t),Ons.forEach(t),vJa=i(Ijo),cfo=n(Ijo,"SPAN",{});var Yns=s(cfo);MJa=r(Yns,"AutoModelForVision2Seq"),Yns.forEach(t),Ijo.forEach(t),jIo=i(c),zr=n(c,"DIV",{class:!0});var Nm=s(zr);M(Lq.$$.fragment,Nm),TJa=i(Nm),Bg=n(Nm,"P",{});var xwe=s(Bg);FJa=r(xwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
with the `),e4e=n(xwe,"A",{href:!0});var zns=s(e4e);EJa=r(zns,"from_pretrained()"),zns.forEach(t),yJa=r(xwe," class method or the "),o4e=n(xwe,"A",{href:!0});var Hns=s(o4e);CJa=r(Hns,"from_config()"),Hns.forEach(t),wJa=r(xwe,` class
method.`),xwe.forEach(t),LJa=i(Nm),Aq=n(Nm,"P",{});var Njo=s(Aq);AJa=r(Njo,"This class cannot be instantiated directly using "),ffo=n(Njo,"CODE",{});var Kns=s(ffo);RJa=r(Kns,"__init__()"),Kns.forEach(t),kJa=r(Njo," (throws an error)."),Njo.forEach(t),SJa=i(Nm),Oa=n(Nm,"DIV",{class:!0});var $Z=s(Oa);M(Rq.$$.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 `),r4e=n(Gwe,"A",{href:!0});var rss=s(r4e);jJa=r(rss,"from_pretrained()"),rss.forEach(t),PJa=r(Gwe," to load the model weights."),Gwe.forEach(t),ZJa=i($Z),M(C$.$$.fragment,$Z),$Z.forEach(t),VJa=i(Nm),No=n(Nm,"DIV",{class:!0});var xn=s(No);M(kq.$$.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 `),bfo=n(jZ,"CODE",{});var sss=s(bfo);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$=n(Gn,"LI",{});var xxo=s(w$);vfo=n(xxo,"STRONG",{});var lss=s(vfo);KJa=r(lss,"blip"),lss.forEach(t),eUa=r(xxo," \u2014 "),t4e=n(xxo,"A",{href:!0});var iss=s(t4e);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 "),a4e=n(Gxo,"A",{href:!0});var mss=s(a4e);sUa=r(mss,"Blip2ForConditionalGeneration"),mss.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 css=s(Tfo);dUa=r(css,"git"),css.forEach(t),mUa=r(Bxo," \u2014 "),n4e=n(Bxo,"A",{href:!0});var fss=s(n4e);cUa=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 "),s4e=n(Ixo,"A",{href:!0});var gss=s(s4e);pUa=r(gss,"InstructBlipForConditionalGeneration"),gss.forEach(t),_Ua=r(Ixo," (InstructBLIP model)"),Ixo.forEach(t),bUa=i(Gn),k$=n(Gn,"LI",{});var Nxo=s(k$);Efo=n(Nxo,"STRONG",{});var uss=s(Efo);vUa=r(uss,"pix2struct"),uss.forEach(t),MUa=r(Nxo," \u2014 "),l4e=n(Nxo,"A",{href:!0});var pss=s(l4e);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 "),i4e=n($xo,"A",{href:!0});var bss=s(i4e);wUa=r(bss,"VisionEncoderDecoderModel"),bss.forEach(t),LUa=r($xo," (Vision Encoder decoder model)"),$xo.forEach(t),Gn.forEach(t),AUa=i(xn),x$=n(xn,"P",{});var jxo=s(x$);RUa=r(jxo,"The model is set in evaluation mode by default using "),Cfo=n(jxo,"CODE",{});var vss=s(Cfo);kUa=r(vss,"model.eval()"),vss.forEach(t),SUa=r(jxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),wfo=n(jxo,"CODE",{});var Mss=s(wfo);xUa=r(Mss,"model.train()"),Mss.forEach(t),jxo.forEach(t),GUa=i(xn),M(G$.$$.fragment,xn),xn.forEach(t),Nm.forEach(t),PIo=i(c),Ng=n(c,"H3",{class:!0});var $jo=s(Ng);B$=n($jo,"A",{id:!0,class:!0,href:!0});var Tss=s(B$);Lfo=n(Tss,"SPAN",{});var Fss=s(Lfo);M(Sq.$$.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(c),Kr=n(c,"DIV",{class:!0});var $m=s(Kr);M(xq.$$.fragment,$m),NUa=i($m),$g=n($m,"P",{});var Bwe=s($g);$Ua=r(Bwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
with the `),d4e=n(Bwe,"A",{href:!0});var yss=s(d4e);jUa=r(yss,"from_pretrained()"),yss.forEach(t),PUa=r(Bwe," class method or the "),m4e=n(Bwe,"A",{href:!0});var Css=s(m4e);ZUa=r(Css,"from_config()"),Css.forEach(t),VUa=r(Bwe,` class
method.`),Bwe.forEach(t),WUa=i($m),Gq=n($m,"P",{});var jjo=s(Gq);JUa=r(jjo,"This class cannot be instantiated directly using "),Rfo=n(jjo,"CODE",{});var wss=s(Rfo);UUa=r(wss,"__init__()"),wss.forEach(t),XUa=r(jjo," (throws an error)."),jjo.forEach(t),DUa=i($m),Qa=n($m,"DIV",{class:!0});var PZ=s(Qa);M(Bq.$$.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 `),c4e=n(Iwe,"A",{href:!0});var Rss=s(c4e);KUa=r(Rss,"from_pretrained()"),Rss.forEach(t),eXa=r(Iwe," to load the model weights."),Iwe.forEach(t),oXa=i(PZ),M(I$.$$.fragment,PZ),PZ.forEach(t),rXa=i($m),Gt=n($m,"DIV",{class:!0});var jm=s(Gt);M(Iq.$$.fragment,jm),tXa=i(jm),xfo=n(jm,"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(jm),Os=n(jm,"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),mXa=r(ZZ,` if possible), or when it\u2019s missing, by
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method.`),Nwe.forEach(t),xXa=i(Pm),Pq=n(Pm,"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(Pm),Ya=n(Pm,"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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NextImageProcessor"),d(Hne,"href","/docs/transformers/main/en/model_doc/sam#transformers.SamImageProcessor"),d(Kne,"href","/docs/transformers/main/en/model_doc/segformer#transformers.SegformerImageProcessor"),d(ese,"href","/docs/transformers/main/en/model_doc/vit#transformers.ViTImageProcessor"),d(ose,"href","/docs/transformers/main/en/model_doc/vit#transformers.ViTImageProcessor"),d(rse,"href","/docs/transformers/main/en/model_doc/swin2sr#transformers.Swin2SRImageProcessor"),d(tse,"href","/docs/transformers/main/en/model_doc/vit#transformers.ViTImageProcessor"),d(ase,"href","/docs/transformers/main/en/model_doc/detr#transformers.DetrImageProcessor"),d(nse,"href","/docs/transformers/main/en/model_doc/videomae#transformers.VideoMAEImageProcessor"),d(sse,"href","/docs/transformers/main/en/model_doc/tvlt#transformers.TvltImageProcessor"),d(lse,"href","/docs/transformers/main/en/model_doc/segformer#transformers.SegformerImageProcessor"),d(ise,"href","/docs/transformers/main/en/model_doc/convnext#transformers.ConvNextImageProcessor"),d(dse,"href","/docs/transformers/main/en/model_doc/videomae#transformers.VideoMAEImageProcessor"),d(mse,"href","/docs/transformers/main/en/model_doc/vilt#transformers.ViltImageProcessor"),d(cse,"href","/docs/transformers/main/en/model_doc/vit#transformers.ViTImageProcessor"),d(fse,"href","/docs/transformers/main/en/model_doc/vit_hybrid#transformers.ViTHybridImageProcessor"),d(hse,"href","/docs/transformers/main/en/model_doc/vit#transformers.ViTImageProcessor"),d(gse,"href","/docs/transformers/main/en/model_doc/vit#transformers.ViTImageProcessor"),d(use,"href","/docs/transformers/main/en/model_doc/clip#transformers.CLIPImageProcessor"),d(pse,"href","/docs/transformers/main/en/model_doc/yolos#transformers.YolosImageProcessor"),d(ao,"class","docstring 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