import{S as _ds,i as bds,s as vds,e as a,k as l,w as v,t as o,M as Mds,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 Tds,L as W}from"../../chunks/vendor-hf-doc-builder.js";import{T as GPo}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 V}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Fds(x){let h,C,u,f,p,m,g,ze,Jm,qg,Wt,Um,Xm,eV,Og,Qe,oo,Dm,zs,oV,Hs,Ks,rV,qm,el,tV,Om,Qg,In;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
`),Jm=a("code"),qg=o("model_type"),Wt=o(" attribute is set to the same key you use when registering the config (here "),Um=a("code"),Xm=o('"new-model"'),eV=o(")."),Og=l(),Qe=a("p"),oo=o("Likewise, if your "),Dm=a("code"),zs=o("NewModel"),oV=o(" is a subclass of "),Hs=a("a"),Ks=o("PreTrainedModel"),rV=o(`, make sure its
`),qm=a("code"),el=o("config_class"),tV=o(` attribute is set to the same class you use when registering the model (here
`),Om=a("code"),Qg=o("NewModelConfig"),In=o(")."),this.h()},l(ro){h=n(ro,"P",{});var Ie=s(h);C=r(Ie,"If your "),u=n(Ie,"CODE",{});var KQ=s(u);f=r(KQ,"NewModelConfig"),KQ.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
`),Jm=n(Ie,"CODE",{});var eY=s(Jm);qg=r(eY,"model_type"),eY.forEach(t),Wt=r(Ie," attribute is set to the same key you use when registering the config (here "),Um=n(Ie,"CODE",{});var oY=s(Um);Xm=r(oY,'"new-model"'),oY.forEach(t),eV=r(Ie,")."),Ie.forEach(t),Og=i(ro),Qe=n(ro,"P",{});var jo=s(Qe);oo=r(jo,"Likewise, if your "),Dm=n(jo,"CODE",{});var Nn=s(Dm);zs=r(Nn,"NewModel"),Nn.forEach(t),oV=r(jo," is a subclass of "),Hs=n(jo,"A",{href:!0});var rY=s(Hs);Ks=r(rY,"PreTrainedModel"),rY.forEach(t),rV=r(jo,`, make sure its
`),qm=n(jo,"CODE",{});var Yg=s(qm);el=r(Yg,"config_class"),Yg.forEach(t),tV=r(jo,` attribute is set to the same class you use when registering the model (here
`),Om=n(jo,"CODE",{});var tY=s(Om);Qg=r(tY,"NewModelConfig"),tY.forEach(t),In=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,Jm),e(Jm,qg),e(h,Wt),e(h,Um),e(Um,Xm),e(h,eV),b(ro,Og,Ie),b(ro,Qe,Ie),e(Qe,oo),e(Qe,Dm),e(Dm,zs),e(Qe,oV),e(Qe,Hs),e(Hs,Ks),e(Qe,rV),e(Qe,qm),e(qm,el),e(Qe,tV),e(Qe,Om),e(Om,Qg),e(Qe,In)},d(ro){ro&&t(h),ro&&t(Og),ro&&t(Qe)}}}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

<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:W,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:"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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:W,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{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 wds(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:W,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{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 Ads(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:W,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{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 kds(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:W,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:"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:W,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 xds(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:W,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:"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:W,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:"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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:W,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:"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:W,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:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbC5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGcHRfbW9kZWwlMkZiZXJ0X3B5dG9yY2hfbW9kZWwuYmluJTIyJTJDJTIwZnJvbV9wdCUzRFRydWUlMkMlMjBjb25maWclM0Rjb25maWclMEEp",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:W,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:"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:W,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 jds(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:W,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:"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:W,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 Zds(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:W,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:"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:W,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:"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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:W,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 Jds(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:W,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, 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:W,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 Xds(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:W,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, 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:W,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 qds(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:W,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, 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:W,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 Qds(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:W,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:"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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:W,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 zds(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:W,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, 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:W,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 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:W,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 ems(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:W,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 oms(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:W,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 rms(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:W,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 tms(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:W,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 ams(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:W,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 nms(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:W,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 sms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSeq2SeqLM

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/t5_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/t5_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),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:W,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 lms(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:W,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 ims(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSequenceClassification

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),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:W,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 dms(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:W,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 mms(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:W,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 cms(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:W,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 fms(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:W,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 hms(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:W,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 gms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMultipleChoice

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMultipleChoice.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),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:W,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 ums(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:W,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 pms(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:W,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 _ms(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:W,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 bms(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:W,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 vms(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:W,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 Mms(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:W,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 Tms(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:W,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 Fms(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:W,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 Ems(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:W,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 yms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForNextSentencePrediction

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForNextSentencePrediction.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),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:W,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 Cms(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:W,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 wms(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:W,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 Lms(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:W,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 Ams(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForTokenClassification

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),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:W,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 Rms(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:W,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 kms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForTokenClassification

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForTokenClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),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:W,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 Sms(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:W,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 xms(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:W,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 Gms(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:W,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 Bms(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:W,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 Ims(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:W,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 Nms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForQuestionAnswering

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),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:W,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 $ms(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:W,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 jms(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:W,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 Pms(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:W,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 Zms(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:W,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 Vms(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:W,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 Wms(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:W,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 Jms(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:W,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 Ums(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9ySW1hZ2VDbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwbW9kZWwlMjBhbmQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9ySW1hZ2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyKSUwQSUwQSUyMyUyMFVwZGF0ZSUyMGNvbmZpZ3VyYXRpb24lMjBkdXJpbmclMjBsb2FkaW5nJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9ySW1hZ2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFB5VG9yY2glMjBjaGVja3BvaW50JTIwZmlsZSUyMGluc3RlYWQlMjBvZiUyMGElMjBUZW5zb3JGbG93JTIwbW9kZWwlMjAoc2xvd2VyKSUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZwdF9tb2RlbCUyRmJlcnRfcHRfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsRm9ySW1hZ2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnB0X21vZGVsJTJGYmVydF9weXRvcmNoX21vZGVsLmJpbiUyMiUyQyUyMGZyb21fcHQlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",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:W,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 Xms(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:W,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 Dms(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVideoClassification

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVideoClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),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:W,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 qms(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:W,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 Oms(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:W,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 Qms(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:W,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 Yms(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:W,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 zms(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:W,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 Hms(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:W,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 Kms(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:W,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 ecs(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:W,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 ocs(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:W,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 rcs(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:W,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 tcs(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:W,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 acs(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:W,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 ncs(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:W,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 scs(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:W,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 lcs(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:W,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 ics(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForUniversalSegmentation

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForUniversalSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),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:W,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 dcs(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:W,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 mcs(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:W,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 ccs(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:W,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 fcs(x){let h,C,u,f,p;return f=new P({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForZeroShotImageClassification

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForZeroShotImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`}}),{c(){h=a("p"),C=o("Examples:"),u=l(),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:W,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 hcs(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:W,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 gcs(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:W,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 ucs(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:W,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 pcs(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:W,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 _cs(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:W,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 bcs(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:W,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 vcs(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:W,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 Mcs(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:W,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 Tcs(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:W,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 Fcs(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:W,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 Ecs(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:W,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 ycs(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:W,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 Ccs(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:W,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 wcs(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:W,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 Lcs(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:W,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 Acs(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:W,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 Rcs(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:W,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 kcs(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:W,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 Scs(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:W,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 xcs(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:W,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 Gcs(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:W,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 Bcs(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:W,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 Ics(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:W,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 Ncs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JEb2N1bWVudFF1ZXN0aW9uQW5zd2VyaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckRvY3VtZW50UXVlc3Rpb25BbnN3ZXJpbmcuZnJvbV9wcmV0cmFpbmVkKCUyMmltcGlyYSUyRmxheW91dGxtLWRvY3VtZW50LXFhJTIyJTJDJTIwcmV2aXNpb24lM0QlMjI1MmUwMWIzJTIyKSUwQSUwQSUyMyUyMFVwZGF0ZSUyMGNvbmZpZ3VyYXRpb24lMjBkdXJpbmclMjBsb2FkaW5nJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JEb2N1bWVudFF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fcHJldHJhaW5lZCglMjJpbXBpcmElMkZsYXlvdXRsbS1kb2N1bWVudC1xYSUyMiUyQyUyMHJldmlzaW9uJTNEJTIyNTJlMDFiMyUyMiUyQyUyMG91dHB1dF9hdHRlbnRpb25zJTNEVHJ1ZSklMEFtb2RlbC5jb25maWcub3V0cHV0X2F0dGVudGlvbnMlMEElMEElMjMlMjBMb2FkaW5nJTIwZnJvbSUyMGElMjBURiUyMGNoZWNrcG9pbnQlMjBmaWxlJTIwaW5zdGVhZCUyMG9mJTIwYSUyMFB5VG9yY2glMjBtb2RlbCUyMChzbG93ZXIpJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyLiUyRnRmX21vZGVsJTJGbGF5b3V0bG1fdGZfbW9kZWxfY29uZmlnLmpzb24lMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JEb2N1bWVudFF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGdGZfbW9kZWwlMkZsYXlvdXRsbV90Zl9jaGVja3BvaW50LmNrcHQuaW5kZXglMjIlMkMlMjBmcm9tX3RmJTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",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:W,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 $cs(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:W,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 jcs(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:W,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 Pcs(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:W,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 Zcs(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:W,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 Vcs(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:W,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 Wcs(x){let h,C,u,f,p;return f=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JWaXNpb24yU2VxJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBtb2RlbCUyMGFuZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclZpc2lvbjJTZXEuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVmlzaW9uMlNlcS5mcm9tX3ByZXRyYWluZWQoJTIyYmVydC1iYXNlLWNhc2VkJTIyJTJDJTIwb3V0cHV0X2F0dGVudGlvbnMlM0RUcnVlKSUwQW1vZGVsLmNvbmZpZy5vdXRwdXRfYXR0ZW50aW9ucyUwQSUwQSUyMyUyMExvYWRpbmclMjBmcm9tJTIwYSUyMFRGJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwUHlUb3JjaCUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVmlzaW9uMlNlcS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnRmX21vZGVsJTJGYmVydF90Zl9jaGVja3BvaW50LmNrcHQuaW5kZXglMjIlMkMlMjBmcm9tX3RmJTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",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:W,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 Jcs(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:W,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 Ucs(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:W,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 Xcs(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:W,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 Dcs(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:W,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 qcs(x){let 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m=new te({}),In=new P({props:{code:"bW9kZWwlMjAlM0QlMjBBdXRvTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS1jYXNlZCUyMik=",highlighted:'model = AutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)'}}),aV=new te({}),nV=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)`}}),Hg=new GPo({props:{warning:!0,$$slots:{default:[Fds]},$$scope:{ctx:x}}}),sV=new te({}),lV=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#L893"}}),mV=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
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoConfig.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download the model weights and configuration files and override the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoConfig.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<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.AutoConfig.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoConfig.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoConfig.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final configuration object.</p>
<p>If <code>True</code>, then this functions returns a <code>Tuple(config, unused_kwargs)</code> where <em>unused_kwargs</em> is a
dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
part of <code>kwargs</code> which has not been used to update <code>config</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoConfig.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoConfig.from_pretrained.kwargs(additional",description:`<strong>kwargs(additional</strong> keyword arguments, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are configuration attributes will be used to override the loaded
values. Behavior concerning key/value pairs whose keys are <em>not</em> configuration attributes is controlled
by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs(additional"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/configuration_auto.py#L916"}}),W1=new V({props:{anchor:"transformers.AutoConfig.from_pretrained.example",$$slots:{default:[Eds]},$$scope:{ctx:x}}}),cV=new G({props:{name:"register",anchor:"transformers.AutoConfig.register",parameters:[{name:"model_type",val:""},{name:"config",val:""}],parametersDescription:[{anchor:"transformers.AutoConfig.register.model_type",description:"<strong>model_type</strong> (<code>str</code>) &#x2014; The model type like &#x201C;bert&#x201D; or &#x201C;gpt&#x201D;.",name:"model_type"},{anchor:"transformers.AutoConfig.register.config",description:'<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014; The config to register.',name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/configuration_auto.py#L1048"}}),fV=new te({}),hV=new G({props:{name:"class transformers.AutoTokenizer",anchor:"transformers.AutoTokenizer",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/tokenization_auto.py#L543"}}),pV=new G({props:{name:"from_pretrained",anchor:"transformers.AutoTokenizer.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"*inputs",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoTokenizer.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a predefined tokenizer hosted inside a model repo on huggingface.co.
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 vocabulary files required by the tokenizer, for instance saved
using the <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.save_pretrained">save_pretrained()</a> method, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a single saved vocabulary file if and only if the tokenizer only requires a
single vocabulary file (like Bert or XLNet), e.g.: <code>./my_model_directory/vocab.txt</code>. (Not
applicable to all derived classes)</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoTokenizer.from_pretrained.inputs",description:`<strong>inputs</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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The configuration object used to determine the tokenizer class to instantiate.`,name:"config"},{anchor:"transformers.AutoTokenizer.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoTokenizer.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download the model weights and configuration files and override the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoTokenizer.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.AutoTokenizer.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoTokenizer.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoTokenizer.from_pretrained.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>) &#x2014;
In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for
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Use a <a href="https://huggingface.co/docs/tokenizers/index" rel="nofollow">fast Rust-based tokenizer</a> if it is supported for
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Tokenizer type to be loaded.`,name:"tokenizer_type"},{anchor:"transformers.AutoTokenizer.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoTokenizer.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Will be passed to the Tokenizer <code>__init__()</code> method. Can be used to set special tokens like
<code>bos_token</code>, <code>eos_token</code>, <code>unk_token</code>, <code>sep_token</code>, <code>pad_token</code>, <code>cls_token</code>, <code>mask_token</code>,
<code>additional_special_tokens</code>. See parameters in the <code>__init__()</code> for more details.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/tokenization_auto.py#L557"}}),$b=new V({props:{anchor:"transformers.AutoTokenizer.from_pretrained.example",$$slots:{default:[yds]},$$scope:{ctx:x}}}),_V=new G({props:{name:"register",anchor:"transformers.AutoTokenizer.register",parameters:[{name:"config_class",val:""},{name:"slow_tokenizer_class",val:" = None"},{name:"fast_tokenizer_class",val:" = None"}],parametersDescription:[{anchor:"transformers.AutoTokenizer.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.AutoTokenizer.register.slow_tokenizer_class",description:`<strong>slow_tokenizer_class</strong> (<code>PretrainedTokenizer</code>, <em>optional</em>) &#x2014;
The slow tokenizer to register.`,name:"slow_tokenizer_class"},{anchor:"transformers.AutoTokenizer.register.slow_tokenizer_class",description:`<strong>slow_tokenizer_class</strong> (<code>PretrainedTokenizerFast</code>, <em>optional</em>) &#x2014;
The fast tokenizer to register.`,name:"slow_tokenizer_class"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/tokenization_auto.py#L767"}}),bV=new te({}),vV=new G({props:{name:"class transformers.AutoFeatureExtractor",anchor:"transformers.AutoFeatureExtractor",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/feature_extraction_auto.py#L233"}}),FV=new G({props:{name:"from_pretrained",anchor:"transformers.AutoFeatureExtractor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoFeatureExtractor.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. 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 feature extractor file saved using the
<a href="/docs/transformers/main/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained">save_pretrained()</a> method, e.g.,
<code>./my_model_directory/</code>.</li>
<li>a path or url to a saved feature extractor JSON <em>file</em>, e.g.,
<code>./my_model_directory/preprocessor_config.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the feature extractor files and override the cached versions
if they exist.`,name:"force_download"},{anchor:"transformers.AutoFeatureExtractor.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.AutoFeatureExtractor.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final feature extractor object. If <code>True</code>, then this
functions returns a <code>Tuple(feature_extractor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
<code>kwargs</code> which has not been used to update <code>feature_extractor</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are feature extractor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are <em>not</em> feature extractor attributes is
controlled by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/feature_extraction_auto.py#L247"}}),Q2=new GPo({props:{$$slots:{default:[Cds]},$$scope:{ctx:x}}}),Y2=new V({props:{anchor:"transformers.AutoFeatureExtractor.from_pretrained.example",$$slots:{default:[wds]},$$scope:{ctx:x}}}),EV=new G({props:{name:"register",anchor:"transformers.AutoFeatureExtractor.register",parameters:[{name:"config_class",val:""},{name:"feature_extractor_class",val:""}],parametersDescription:[{anchor:"transformers.AutoFeatureExtractor.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.AutoFeatureExtractor.register.feature_extractor_class",description:"<strong>feature_extractor_class</strong> (<code>FeatureExtractorMixin</code>) &#x2014; The feature extractor to register.",name:"feature_extractor_class"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/feature_extraction_auto.py#L381"}}),yV=new te({}),CV=new G({props:{name:"class transformers.AutoImageProcessor",anchor:"transformers.AutoImageProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/image_processing_auto.py#L241"}}),AV=new G({props:{name:"from_pretrained",anchor:"transformers.AutoImageProcessor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoImageProcessor.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained image_processor hosted inside a model repo on
huggingface.co. 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 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>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoImageProcessor.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model image processor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoImageProcessor.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the image processor files and override the cached versions if
they exist.`,name:"force_download"},{anchor:"transformers.AutoImageProcessor.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<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.AutoImageProcessor.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoImageProcessor.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"transformers.AutoImageProcessor.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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If <code>False</code>, then this function returns just the final image processor object. If <code>True</code>, then this
functions returns a <code>Tuple(image_processor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
consisting of the key/value pairs whose keys are not image processor attributes: i.e., the part of
<code>kwargs</code> which has not been used to update <code>image_processor</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoImageProcessor.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoImageProcessor.from_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are image processor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are <em>not</em> image processor attributes is
controlled by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/image_processing_auto.py#L255"}}),uM=new GPo({props:{$$slots:{default:[Lds]},$$scope:{ctx:x}}}),pM=new V({props:{anchor:"transformers.AutoImageProcessor.from_pretrained.example",$$slots:{default:[Ads]},$$scope:{ctx:x}}}),RV=new G({props:{name:"register",anchor:"transformers.AutoImageProcessor.register",parameters:[{name:"config_class",val:""},{name:"image_processor_class",val:""}],parametersDescription:[{anchor:"transformers.AutoImageProcessor.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.AutoImageProcessor.register.image_processor_class",description:'<strong>image_processor_class</strong> (<a href="/docs/transformers/main/en/internal/image_processing_utils#transformers.ImageProcessingMixin">ImageProcessingMixin</a>) &#x2014; The image processor to register.',name:"image_processor_class"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/image_processing_auto.py#L407"}}),kV=new te({}),SV=new G({props:{name:"class transformers.AutoProcessor",anchor:"transformers.AutoProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/processing_auto.py#L118"}}),BV=new G({props:{name:"from_pretrained",anchor:"transformers.AutoProcessor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoProcessor.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. 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 processor files saved using the <code>save_pretrained()</code> method,
e.g., <code>./my_model_directory/</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoProcessor.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoProcessor.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the feature extractor files and override the cached versions
if they exist.`,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#L132"}}),lT=new GPo({props:{$$slots:{default:[Rds]},$$scope:{ctx:x}}}),iT=new V({props:{anchor:"transformers.AutoProcessor.from_pretrained.example",$$slots:{default:[kds]},$$scope:{ctx:x}}}),IV=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#L321"}}),NV=new te({}),$V=new te({}),jV=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#L1201"}}),ZV=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/vitdet#transformers.VitDetConfig">VitDetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vitdet#transformers.VitDetModel">VitDetModel</a> (VitDet 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#L417"}}),fT=new V({props:{anchor:"transformers.AutoModel.from_config.example",$$slots:{default:[Sds]},$$scope:{ctx:x}}}),VV=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#L448"}}),Jy=new V({props:{anchor:"transformers.AutoModel.from_pretrained.example",$$slots:{default:[xds]},$$scope:{ctx:x}}}),WV=new te({}),JV=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"}}),XV=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#L417"}}),Xy=new V({props:{anchor:"transformers.TFAutoModel.from_config.example",$$slots:{default:[Gds]},$$scope:{ctx:x}}}),DV=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#L448"}}),o5=new V({props:{anchor:"transformers.TFAutoModel.from_pretrained.example",$$slots:{default:[Bds]},$$scope:{ctx:x}}}),qV=new te({}),OV=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"}}),YV=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#L417"}}),t5=new V({props:{anchor:"transformers.FlaxAutoModel.from_config.example",$$slots:{default:[Ids]},$$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#L448"}}),$5=new V({props:{anchor:"transformers.FlaxAutoModel.from_pretrained.example",$$slots:{default:[Nds]},$$scope:{ctx:x}}}),HV=new te({}),KV=new te({}),eW=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#L1208"}}),rW=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#L417"}}),Z5=new V({props:{anchor:"transformers.AutoModelForPreTraining.from_config.example",$$slots:{default:[$ds]},$$scope:{ctx:x}}}),tW=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#L448"}}),Y3=new V({props:{anchor:"transformers.AutoModelForPreTraining.from_pretrained.example",$$slots:{default:[jds]},$$scope:{ctx:x}}}),aW=new te({}),nW=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"}}),lW=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#L417"}}),H3=new V({props:{anchor:"transformers.TFAutoModelForPreTraining.from_config.example",$$slots:{default:[Pds]},$$scope:{ctx:x}}}),iW=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#L448"}}),y9=new V({props:{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[Zds]},$$scope:{ctx:x}}}),dW=new te({}),mW=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"}}),fW=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#L417"}}),w9=new V({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.example",$$slots:{default:[Vds]},$$scope:{ctx:x}}}),hW=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#L448"}}),W9=new V({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[Wds]},$$scope:{ctx:x}}}),gW=new te({}),uW=new te({}),pW=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#L1223"}}),bW=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#L417"}}),X9=new V({props:{anchor:"transformers.AutoModelForCausalLM.from_config.example",$$slots:{default:[Jds]},$$scope:{ctx:x}}}),vW=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#L448"}}),z4=new V({props:{anchor:"transformers.AutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Uds]},$$scope:{ctx:x}}}),MW=new te({}),TW=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"}}),EW=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#L417"}}),K4=new V({props:{anchor:"transformers.TFAutoModelForCausalLM.from_config.example",$$slots:{default:[Xds]},$$scope:{ctx:x}}}),yW=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#L448"}}),_C=new V({props:{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Dds]},$$scope:{ctx:x}}}),CW=new te({}),wW=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"}}),AW=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#L417"}}),vC=new V({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.example",$$slots:{default:[qds]},$$scope:{ctx:x}}}),RW=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#L448"}}),BC=new V({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Ods]},$$scope:{ctx:x}}}),kW=new te({}),SW=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#L1230"}}),GW=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#L417"}}),NC=new V({props:{anchor:"transformers.AutoModelForMaskedLM.from_config.example",$$slots:{default:[Qds]},$$scope:{ctx:x}}}),BW=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#L448"}}),Rw=new V({props:{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[Yds]},$$scope:{ctx:x}}}),IW=new te({}),NW=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"}}),jW=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#L417"}}),Sw=new V({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_config.example",$$slots:{default:[zds]},$$scope:{ctx:x}}}),PW=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#L448"}}),e6=new V({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[Hds]},$$scope:{ctx:x}}}),ZW=new te({}),VW=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"}}),JW=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#L417"}}),r6=new V({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.example",$$slots:{default:[Kds]},$$scope:{ctx:x}}}),UW=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#L448"}}),g6=new V({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[ems]},$$scope:{ctx:x}}}),XW=new te({}),qW=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#L1193"}}),OW=new te({}),YW=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({}),HW=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#L1237"}}),eJ=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#L417"}}),b6=new V({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[oms]},$$scope:{ctx:x}}}),oJ=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#L448"}}),J6=new V({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[rms]},$$scope:{ctx:x}}}),rJ=new te({}),tJ=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"}}),nJ=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#L417"}}),X6=new V({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[tms]},$$scope:{ctx:x}}}),sJ=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#L448"}}),r8=new V({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[ams]},$$scope:{ctx:x}}}),lJ=new te({}),iJ=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"}}),mJ=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#L417"}}),a8=new V({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[nms]},$$scope:{ctx:x}}}),cJ=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#L448"}}),u8=new V({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[sms]},$$scope:{ctx:x}}}),fJ=new te({}),hJ=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#L1246"}}),uJ=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#L417"}}),_8=new V({props:{anchor:"transformers.AutoModelForSequenceClassification.from_config.example",$$slots:{default:[lms]},$$scope:{ctx:x}}}),pJ=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#L448"}}),P7=new V({props:{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[ims]},$$scope:{ctx:x}}}),_J=new te({}),bJ=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"}}),MJ=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#L417"}}),V7=new V({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.example",$$slots:{default:[dms]},$$scope:{ctx:x}}}),TJ=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#L448"}}),vL=new V({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[mms]},$$scope:{ctx:x}}}),FJ=new te({}),EJ=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"}}),CJ=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#L417"}}),TL=new V({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.example",$$slots:{default:[cms]},$$scope:{ctx:x}}}),wJ=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#L448"}}),GL=new V({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[fms]},$$scope:{ctx:x}}}),LJ=new te({}),AJ=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#L1302"}}),kJ=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#L417"}}),IL=new V({props:{anchor:"transformers.AutoModelForMultipleChoice.from_config.example",$$slots:{default:[hms]},$$scope:{ctx:x}}}),SJ=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#L448"}}),TA=new V({props:{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[gms]},$$scope:{ctx:x}}}),xJ=new te({}),GJ=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"}}),IJ=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#L417"}}),EA=new V({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.example",$$slots:{default:[ums]},$$scope:{ctx:x}}}),NJ=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#L448"}}),WA=new V({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[pms]},$$scope:{ctx:x}}}),$J=new te({}),jJ=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"}}),ZJ=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#L417"}}),UA=new V({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.example",$$slots:{default:[_ms]},$$scope:{ctx:x}}}),VJ=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>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
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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.FlaxAutoModelForMultipleChoice.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/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#L448"}}),eR=new V({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[bms]},$$scope:{ctx:x}}}),WJ=new te({}),JJ=new G({props:{name:"class transformers.AutoModelForNextSentencePrediction",anchor:"transformers.AutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1309"}}),XJ=new G({props:{name:"from_config",anchor:"transformers.AutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/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.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#L417"}}),rR=new V({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[vms]},$$scope:{ctx:x}}}),DJ=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>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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.AutoModelForNextSentencePrediction.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/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#L448"}}),cR=new V({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[Mms]},$$scope:{ctx:x}}}),qJ=new te({}),OJ=new G({props:{name:"class transformers.TFAutoModelForNextSentencePrediction",anchor:"transformers.TFAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L678"}}),YJ=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/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.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#L417"}}),hR=new V({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[Tms]},$$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;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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#L448"}}),pR=new V({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[Fms]},$$scope:{ctx:x}}}),KJ=new te({}),eU=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"}}),rU=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#L417"}}),bR=new V({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[Ems]},$$scope:{ctx:x}}}),tU=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#L448"}}),MR=new V({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[yms]},$$scope:{ctx:x}}}),aU=new te({}),nU=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#L1295"}}),lU=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#L417"}}),FR=new V({props:{anchor:"transformers.AutoModelForTokenClassification.from_config.example",$$slots:{default:[Cms]},$$scope:{ctx:x}}}),iU=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#L448"}}),yk=new V({props:{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[wms]},$$scope:{ctx:x}}}),dU=new te({}),mU=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"}}),fU=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#L417"}}),wk=new V({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_config.example",$$slots:{default:[Lms]},$$scope:{ctx:x}}}),hU=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#L448"}}),Yk=new V({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[Ams]},$$scope:{ctx:x}}}),gU=new te({}),uU=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"}}),_U=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#L417"}}),Hk=new V({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.example",$$slots:{default:[Rms]},$$scope:{ctx:x}}}),bU=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#L448"}}),iS=new V({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[kms]},$$scope:{ctx:x}}}),vU=new te({}),MU=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#L1255"}}),FU=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#L417"}}),mS=new V({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_config.example",$$slots:{default:[Sms]},$$scope:{ctx:x}}}),EU=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#L448"}}),Mx=new V({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[xms]},$$scope:{ctx:x}}}),yU=new te({}),CU=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"}}),LU=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#L417"}}),Fx=new V({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[Gms]},$$scope:{ctx:x}}}),AU=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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/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#L448"}}),Xx=new V({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[Bms]},$$scope:{ctx:x}}}),RU=new te({}),kU=new G({props:{name:"class transformers.FlaxAutoModelForQuestionAnswering",anchor:"transformers.FlaxAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L320"}}),xU=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/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.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#L417"}}),qx=new V({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[Ims]},$$scope:{ctx:x}}}),GU=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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/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#L448"}}),nG=new V({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[Nms]},$$scope:{ctx:x}}}),BU=new te({}),NU=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#L1197"}}),$U=new te({}),PU=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"}}),ZU=new te({}),VU=new te({}),WU=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#L1384"}}),UU=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#L417"}}),mG=new V({props:{anchor:"transformers.AutoModelForDepthEstimation.from_config.example",$$slots:{default:[$ms]},$$scope:{ctx:x}}}),XU=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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/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#L448"}}),gG=new V({props:{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.example",$$slots:{default:[jms]},$$scope:{ctx:x}}}),qU=new te({}),OU=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#L1318"}}),YU=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#L417"}}),pG=new V({props:{anchor:"transformers.AutoModelForImageClassification.from_config.example",$$slots:{default:[Pms]},$$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#L448"}}),DG=new V({props:{anchor:"transformers.AutoModelForImageClassification.from_pretrained.example",$$slots:{default:[Zms]},$$scope:{ctx:x}}}),HU=new te({}),KU=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"}}),oX=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#L417"}}),OG=new V({props:{anchor:"transformers.TFAutoModelForImageClassification.from_config.example",$$slots:{default:[Vms]},$$scope:{ctx:x}}}),rX=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>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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.TFAutoModelForImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/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#L448"}}),aB=new V({props:{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[Wms]},$$scope:{ctx:x}}}),tX=new te({}),aX=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"}}),sX=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#L417"}}),sB=new V({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_config.example",$$slots:{default:[Jms]},$$scope:{ctx:x}}}),lX=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
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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#L448"}}),cB=new V({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[Ums]},$$scope:{ctx:x}}}),iX=new te({}),dX=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#L1391"}}),cX=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#L417"}}),hB=new V({props:{anchor:"transformers.AutoModelForVideoClassification.from_config.example",$$slots:{default:[Xms]},$$scope:{ctx:x}}}),fX=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
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForVideoClassification.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.AutoModelForVideoClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/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#L448"}}),bB=new V({props:{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.example",$$slots:{default:[Dms]},$$scope:{ctx:x}}}),hX=new te({}),gX=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#L1456"}}),pX=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#L417"}}),MB=new V({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[qms]},$$scope:{ctx:x}}}),_X=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#L448"}}),LB=new V({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.example",$$slots:{default:[Oms]},$$scope:{ctx:x}}}),bX=new te({}),vX=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"}}),TX=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#L417"}}),RB=new V({props:{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[Qms]},$$scope:{ctx:x}}}),FX=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#L448"}}),xB=new V({props:{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.example",$$slots:{default:[Yms]},$$scope:{ctx:x}}}),yX=new te({}),CX=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#L1368"}}),LX=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#L417"}}),BB=new V({props:{anchor:"transformers.AutoModelForObjectDetection.from_config.example",$$slots:{default:[zms]},$$scope:{ctx:x}}}),AX=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
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/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.AutoModelForObjectDetection.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/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.AutoModelForObjectDetection.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/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#L448"}}),WB=new V({props:{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.example",$$slots:{default:[Hms]},$$scope:{ctx:x}}}),RX=new te({}),kX=new G({props:{name:"class transformers.AutoModelForImageSegmentation",anchor:"transformers.AutoModelForImageSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1334"}}),xX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForImageSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/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>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L417"}}),UB=new V({props:{anchor:"transformers.AutoModelForImageSegmentation.from_config.example",$$slots:{default:[Kms]},$$scope:{ctx:x}}}),GX=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForImageSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/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;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForImageSegmentation.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.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
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.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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<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.AutoModelForImageSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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#L448"}}),qB=new V({props:{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.example",$$slots:{default:[ecs]},$$scope:{ctx:x}}}),BX=new te({}),IX=new G({props:{name:"class transformers.AutoModelForSemanticSegmentation",anchor:"transformers.AutoModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1341"}}),$X=new G({props:{name:"from_config",anchor:"transformers.AutoModelForSemanticSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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.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#L417"}}),QB=new V({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[ocs]},$$scope:{ctx:x}}}),jX=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>
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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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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.AutoModelForSemanticSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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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>
<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/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/data2vec#transformers.TFData2VecVisionForSemanticSegmentation">TFData2VecVisionForSemanticSegmentation</a> (Data2VecVision 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.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>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L417"}}),lI=new V({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[tcs]},$$scope:{ctx:x}}}),JX=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/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.TFAutoModelForSemanticSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
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<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/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.TFAutoModelForSemanticSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<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.TFAutoModelForSemanticSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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#L448"}}),cI=new V({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[acs]},$$scope:{ctx:x}}}),UX=new te({}),XX=new G({props:{name:"class transformers.AutoModelForInstanceSegmentation",anchor:"transformers.AutoModelForInstanceSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1359"}}),qX=new G({props:{name:"from_config",anchor:"transformers.AutoModelForInstanceSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForInstanceSegmentation.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/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#L417"}}),hI=new V({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.example",$$slots:{default:[ncs]},$$scope:{ctx:x}}}),OX=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>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForInstanceSegmentation.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.AutoModelForInstanceSegmentation.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.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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForInstanceSegmentation.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.AutoModelForInstanceSegmentation.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.AutoModelForInstanceSegmentation.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.AutoModelForInstanceSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.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
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.AutoModelForInstanceSegmentation.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.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#L448"}}),pI=new V({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.example",$$slots:{default:[scs]},$$scope:{ctx:x}}}),QX=new te({}),YX=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#L1350"}}),HX=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#L417"}}),bI=new V({props:{anchor:"transformers.AutoModelForUniversalSegmentation.from_config.example",$$slots:{default:[lcs]},$$scope:{ctx:x}}}),KX=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;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForUniversalSegmentation.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.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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<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.AutoModelForUniversalSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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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>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L448"}}),yI=new V({props:{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.example",$$slots:{default:[ics]},$$scope:{ctx:x}}}),eD=new te({}),oD=new G({props:{name:"class transformers.AutoModelForZeroShotImageClassification",anchor:"transformers.AutoModelForZeroShotImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1325"}}),tD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForZeroShotImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/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/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#L417"}}),wI=new V({props:{anchor:"transformers.AutoModelForZeroShotImageClassification.from_config.example",$$slots:{default:[dcs]},$$scope:{ctx:x}}}),aD=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
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.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
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.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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForZeroShotImageClassification.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.AutoModelForZeroShotImageClassification.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.AutoModelForZeroShotImageClassification.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.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;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForZeroShotImageClassification.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.AutoModelForZeroShotImageClassification.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.AutoModelForZeroShotImageClassification.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.AutoModelForZeroShotImageClassification.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.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>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L448"}}),BI=new V({props:{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.example",$$slots:{default:[mcs]},$$scope:{ctx:x}}}),nD=new te({}),sD=new G({props:{name:"class transformers.TFAutoModelForZeroShotImageClassification",anchor:"transformers.TFAutoModelForZeroShotImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L583"}}),iD=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForZeroShotImageClassification.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.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>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L417"}}),NI=new V({props:{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_config.example",$$slots:{default:[ccs]},$$scope:{ctx:x}}}),dD=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForZeroShotImageClassification.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
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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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<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 PyTorch checkpoint save file (see docstring of
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
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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
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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/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/owlvit#transformers.OwlViTForObjectDetection">OwlViTForObjectDetection</a> (OWL-ViT 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
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Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/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.AutoModelForZeroShotObjectDetection.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
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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.AutoModelForZeroShotObjectDetection.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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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.AutoModelForZeroShotObjectDetection.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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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.AutoModelForZeroShotObjectDetection.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<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#L448"}}),UI=new V({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.example",$$slots:{default:[gcs]},$$scope:{ctx:x}}}),pD=new te({}),_D=new te({}),bD=new G({props:{name:"class transformers.AutoModelForAudioClassification",anchor:"transformers.AutoModelForAudioClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1405"}}),MD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForAudioClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/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.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#L417"}}),qI=new V({props:{anchor:"transformers.AutoModelForAudioClassification.from_config.example",$$slots:{default:[ucs]},$$scope:{ctx:x}}}),TD=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForAudioClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/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.AutoModelForAudioClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <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.AutoModelForAudioClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/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.AutoModelForAudioClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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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.AutoModelForAudioClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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#L448"}}),sN=new V({props:{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[pcs]},$$scope:{ctx:x}}}),FD=new te({}),ED=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"}}),CD=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#L417"}}),iN=new V({props:{anchor:"transformers.TFAutoModelForAudioClassification.from_config.example",$$slots:{default:[_cs]},$$scope:{ctx:x}}}),wD=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;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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#L448"}}),mN=new V({props:{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[bcs]},$$scope:{ctx:x}}}),LD=new te({}),AD=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#L1428"}}),kD=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#L417"}}),fN=new V({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.example",$$slots:{default:[vcs]},$$scope:{ctx:x}}}),SD=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#L448"}}),vN=new V({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.example",$$slots:{default:[Mcs]},$$scope:{ctx:x}}}),xD=new te({}),GD=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#L1412"}}),ID=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#L417"}}),TN=new V({props:{anchor:"transformers.AutoModelForCTC.from_config.example",$$slots:{default:[Tcs]},$$scope:{ctx:x}}}),ND=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForCTC.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCTC.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/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.AutoModelForCTC.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForCTC.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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.AutoModelForCTC.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/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.AutoModelForCTC.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForCTC.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForCTC.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForCTC.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<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.AutoModelForCTC.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForCTC.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForCTC.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForCTC.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForCTC.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForCTC.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForCTC.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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#L448"}}),GN=new V({props:{anchor:"transformers.AutoModelForCTC.from_pretrained.example",$$slots:{default:[Fcs]},$$scope:{ctx:x}}}),$D=new te({}),jD=new G({props:{name:"class transformers.AutoModelForSpeechSeq2Seq",anchor:"transformers.AutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1419"}}),ZD=new G({props:{name:"from_config",anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/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/pop2piano#transformers.Pop2PianoConfig">Pop2PianoConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/pop2piano#transformers.Pop2PianoForConditionalGeneration">Pop2PianoForConditionalGeneration</a> (Pop2Piano 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.Speech2TextForConditionalGeneration">Speech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<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>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L417"}}),IN=new V({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[Ecs]},$$scope:{ctx:x}}}),VD=new G({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/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.AutoModelForSpeechSeq2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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.AutoModelForSpeechSeq2Seq.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/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.AutoModelForSpeechSeq2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<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.AutoModelForSpeechSeq2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/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#L448"}}),WN=new V({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[ycs]},$$scope:{ctx:x}}}),WD=new te({}),JD=new G({props:{name:"class transformers.TFAutoModelForSpeechSeq2Seq",anchor:"transformers.TFAutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L687"}}),XD=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/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/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>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L417"}}),UN=new V({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[Ccs]},$$scope:{ctx:x}}}),DD=new G({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/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.TFAutoModelForSpeechSeq2Seq.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.TFAutoModelForSpeechSeq2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
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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;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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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.,
<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#L448"}}),qN=new V({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[wcs]},$$scope:{ctx:x}}}),OD=new te({}),QD=new G({props:{name:"class transformers.FlaxAutoModelForSpeechSeq2Seq",anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L368"}}),zD=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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/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#L417"}}),QN=new V({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[Lcs]},$$scope:{ctx:x}}}),HD=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#L448"}}),HN=new V({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[Acs]},$$scope:{ctx:x}}}),eq=new te({}),oq=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#L1437"}}),tq=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#L417"}}),e$=new V({props:{anchor:"transformers.AutoModelForAudioXVector.from_config.example",$$slots:{default:[Rcs]},$$scope:{ctx:x}}}),aq=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#L448"}}),l$=new V({props:{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.example",$$slots:{default:[kcs]},$$scope:{ctx:x}}}),nq=new te({}),lq=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#L1441"}}),iq=new te({}),mq=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#L1445"}}),cq=new te({}),fq=new te({}),hq=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#L1262"}}),uq=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#L417"}}),f$=new V({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[Scs]},$$scope:{ctx:x}}}),pq=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
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
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.AutoModelForTableQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/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#L448"}}),u$=new V({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[xcs]},$$scope:{ctx:x}}}),_q=new te({}),bq=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"}}),Mq=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#L417"}}),_$=new V({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[Gcs]},$$scope:{ctx:x}}}),Tq=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
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/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#L448"}}),v$=new V({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[Bcs]},$$scope:{ctx:x}}}),Fq=new te({}),Eq=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#L1284"}}),Cq=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#L417"}}),T$=new V({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[Ics]},$$scope:{ctx:x}}}),wq=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#L448"}}),w$=new V({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[Ncs]},$$scope:{ctx:x}}}),Lq=new te({}),Aq=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"}}),kq=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#L417"}}),A$=new V({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[$cs]},$$scope:{ctx:x}}}),Sq=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#L448"}}),k$=new V({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[jcs]},$$scope:{ctx:x}}}),xq=new te({}),Gq=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#L1273"}}),Iq=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#L417"}}),x$=new V({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.example",$$slots:{default:[Pcs]},$$scope:{ctx:x}}}),Nq=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#L448"}}),I$=new V({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.example",$$slots:{default:[Zcs]},$$scope:{ctx:x}}}),$q=new te({}),jq=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#L1398"}}),Zq=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#L417"}}),$$=new V({props:{anchor:"transformers.AutoModelForVision2Seq.from_config.example",$$slots:{default:[Vcs]},$$scope:{ctx:x}}}),Vq=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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForVision2Seq.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.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#L448"}}),X$=new V({props:{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[Wcs]},$$scope:{ctx:x}}}),Wq=new te({}),Jq=new G({props:{name:"class transformers.TFAutoModelForVision2Seq",anchor:"transformers.TFAutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L601"}}),Xq=new G({props:{name:"from_config",anchor:"transformers.TFAutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/blip#transformers.TFBlipForConditionalGeneration">TFBlipForConditionalGeneration</a> (BLIP model)</li>
<li><a href="/docs/transformers/main/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vision-encoder-decoder#transformers.TFVisionEncoderDecoderModel">TFVisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L417"}}),q$=new V({props:{anchor:"transformers.TFAutoModelForVision2Seq.from_config.example",$$slots:{default:[Jcs]},$$scope:{ctx:x}}}),Dq=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#L448"}}),Y$=new V({props:{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[Ucs]},$$scope:{ctx:x}}}),Oq=new te({}),Qq=new G({props:{name:"class transformers.FlaxAutoModelForVision2Seq",anchor:"transformers.FlaxAutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_flax_auto.py#L361"}}),zq=new G({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/vision-encoder-decoder#transformers.FlaxVisionEncoderDecoderModel">FlaxVisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L417"}}),H$=new V({props:{anchor:"transformers.FlaxAutoModelForVision2Seq.from_config.example",$$slots:{default:[Xcs]},$$scope:{ctx:x}}}),Hq=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;
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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.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\u2019s configuration. Use `),Lle=a("a"),zRr=o("from_pretrained()"),HRr=o(" to load the model weights."),KRr=l(),v(fT.$$.fragment),ekr=l(),so=a("div"),v(VV.$$.fragment),okr=l(),jBe=a("p"),rkr=o("Instantiate one of the base model classes of the library from a pretrained model."),tkr=l(),Zn=a("p"),akr=o("The model class to instantiate is selected based on the "),PBe=a("code"),nkr=o("model_type"),skr=o(` property of the config object (either
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with the `),pfe=a("a"),Uet=o("from_pretrained()"),Xet=o(" class method or the "),_fe=a("a"),Det=o("from_config()"),qet=o(` class
method.`),Oet=l(),QV=a("p"),Qet=o("This class cannot be instantiated directly using "),UPe=a("code"),Yet=o("__init__()"),zet=o(" (throws an error)."),Het=l(),Xt=a("div"),v(YV.$$.fragment),Ket=l(),XPe=a("p"),eot=o("Instantiates one of the base model classes of the library from a configuration."),oot=l(),hc=a("p"),rot=o(`Note:
Loading a model from its configuration file does `),DPe=a("strong"),tot=o("not"),aot=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),bfe=a("a"),not=o("from_pretrained()"),sot=o(" to load the model weights."),lot=l(),v(t5.$$.fragment),iot=l(),at=a("div"),v(zV.$$.fragment),dot=l(),qPe=a("p"),mot=o("Instantiate one of the base model classes of the library from a pretrained model."),cot=l(),Wn=a("p"),fot=o("The model class to instantiate is selected based on the "),OPe=a("code"),hot=o("model_type"),got=o(` property of the config object (either
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falling back to using pattern matching on `),YPe=a("code"),_ot=o("pretrained_model_name_or_path"),bot=o(":"),vot=l(),se=a("ul"),a5=a("li"),zPe=a("strong"),Mot=o("albert"),Tot=o(" \u2014 "),vfe=a("a"),Fot=o("FlaxAlbertModel"),Eot=o(" (ALBERT model)"),yot=l(),n5=a("li"),HPe=a("strong"),Cot=o("bart"),wot=o(" \u2014 "),Mfe=a("a"),Lot=o("FlaxBartModel"),Aot=o(" (BART model)"),Rot=l(),s5=a("li"),KPe=a("strong"),kot=o("beit"),Sot=o(" \u2014 "),Tfe=a("a"),xot=o("FlaxBeitModel"),Got=o(" (BEiT model)"),Bot=l(),l5=a("li"),eZe=a("strong"),Iot=o("bert"),Not=o(" \u2014 "),Ffe=a("a"),$ot=o("FlaxBertModel"),jot=o(" (BERT model)"),Pot=l(),i5=a("li"),oZe=a("strong"),Zot=o("big_bird"),Vot=o(" \u2014 "),Efe=a("a"),Wot=o("FlaxBigBirdModel"),Jot=o(" (BigBird model)"),Uot=l(),d5=a("li"),rZe=a("strong"),Xot=o("blenderbot"),Dot=o(" \u2014 "),yfe=a("a"),qot=o("FlaxBlenderbotModel"),Oot=o(" (Blenderbot model)"),Qot=l(),m5=a("li"),tZe=a("strong"),Yot=o("blenderbot-small"),zot=o(" \u2014 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"),Zfe=a("a"),vtt=o("FlaxRegNetModel"),Mtt=o(" (RegNet model)"),Ttt=l(),w5=a("li"),vZe=a("strong"),Ftt=o("resnet"),Ett=o(" \u2014 "),Vfe=a("a"),ytt=o("FlaxResNetModel"),Ctt=o(" (ResNet model)"),wtt=l(),L5=a("li"),MZe=a("strong"),Ltt=o("roberta"),Att=o(" \u2014 "),Wfe=a("a"),Rtt=o("FlaxRobertaModel"),ktt=o(" (RoBERTa model)"),Stt=l(),A5=a("li"),TZe=a("strong"),xtt=o("roberta-prelayernorm"),Gtt=o(" \u2014 "),Jfe=a("a"),Btt=o("FlaxRobertaPreLayerNormModel"),Itt=o(" (RoBERTa-PreLayerNorm model)"),Ntt=l(),R5=a("li"),FZe=a("strong"),$tt=o("roformer"),jtt=o(" \u2014 "),Ufe=a("a"),Ptt=o("FlaxRoFormerModel"),Ztt=o(" (RoFormer model)"),Vtt=l(),k5=a("li"),EZe=a("strong"),Wtt=o("t5"),Jtt=o(" \u2014 "),Xfe=a("a"),Utt=o("FlaxT5Model"),Xtt=o(" (T5 model)"),Dtt=l(),S5=a("li"),yZe=a("strong"),qtt=o("vision-text-dual-encoder"),Ott=o(" \u2014 "),Dfe=a("a"),Qtt=o("FlaxVisionTextDualEncoderModel"),Ytt=o(" (VisionTextDualEncoder model)"),ztt=l(),x5=a("li"),CZe=a("strong"),Htt=o("vit"),Ktt=o(" \u2014 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method.`),Bat=l(),oW=a("p"),Iat=o("This class cannot be instantiated directly using "),BZe=a("code"),Nat=o("__init__()"),$at=o(" (throws an error)."),jat=l(),Dt=a("div"),v(rW.$$.fragment),Pat=l(),IZe=a("p"),Zat=o("Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),Vat=l(),_c=a("p"),Wat=o(`Note:
Loading a model from its configuration file does `),NZe=a("strong"),Jat=o("not"),Uat=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),ohe=a("a"),Xat=o("from_pretrained()"),Dat=o(" to load the model weights."),qat=l(),v(Z5.$$.fragment),Oat=l(),lo=a("div"),v(tW.$$.fragment),Qat=l(),$Ze=a("p"),Yat=o("Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),zat=l(),Jn=a("p"),Hat=o("The model class to instantiate is selected based on the "),jZe=a("code"),Kat=o("model_type"),ent=o(` property of the config object (either
passed as an argument or loaded from `),PZe=a("code"),ont=o("pretrained_model_name_or_path"),rnt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ZZe=a("code"),tnt=o("pretrained_model_name_or_path"),ant=o(":"),nnt=l(),U=a("ul"),V5=a("li"),VZe=a("strong"),snt=o("albert"),lnt=o(" \u2014 "),rhe=a("a"),int=o("AlbertForPreTraining"),dnt=o(" (ALBERT model)"),mnt=l(),W5=a("li"),WZe=a("strong"),cnt=o("bart"),fnt=o(" \u2014 "),the=a("a"),hnt=o("BartForConditionalGeneration"),gnt=o(" (BART model)"),unt=l(),J5=a("li"),JZe=a("strong"),pnt=o("bert"),_nt=o(" \u2014 "),ahe=a("a"),bnt=o("BertForPreTraining"),vnt=o(" (BERT model)"),Mnt=l(),U5=a("li"),UZe=a("strong"),Tnt=o("big_bird"),Fnt=o(" \u2014 "),nhe=a("a"),Ent=o("BigBirdForPreTraining"),ynt=o(" (BigBird model)"),Cnt=l(),X5=a("li"),XZe=a("strong"),wnt=o("bloom"),Lnt=o(" \u2014 "),she=a("a"),Ant=o("BloomForCausalLM"),Rnt=o(" (BLOOM model)"),knt=l(),D5=a("li"),DZe=a("strong"),Snt=o("camembert"),xnt=o(" \u2014 "),lhe=a("a"),Gnt=o("CamembertForMaskedLM"),Bnt=o(" (CamemBERT model)"),Int=l(),q5=a("li"),qZe=a("strong"),Nnt=o("ctrl"),$nt=o(" \u2014 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"),Fhe=a("a"),elt=o("GPTBigCodeForCausalLM"),olt=o(" (GPTBigCode model)"),rlt=l(),i3=a("li"),iVe=a("strong"),tlt=o("gptsan-japanese"),alt=o(" \u2014 "),Ehe=a("a"),nlt=o("GPTSanJapaneseForConditionalGeneration"),slt=o(" (GPTSAN-japanese model)"),llt=l(),d3=a("li"),dVe=a("strong"),ilt=o("ibert"),dlt=o(" \u2014 "),yhe=a("a"),mlt=o("IBertForMaskedLM"),clt=o(" (I-BERT model)"),flt=l(),m3=a("li"),mVe=a("strong"),hlt=o("idefics"),glt=o(" \u2014 "),Che=a("a"),ult=o("IdeficsForVisionText2Text"),plt=o(" (IDEFICS model)"),_lt=l(),c3=a("li"),cVe=a("strong"),blt=o("layoutlm"),vlt=o(" \u2014 "),whe=a("a"),Mlt=o("LayoutLMForMaskedLM"),Tlt=o(" (LayoutLM model)"),Flt=l(),f3=a("li"),fVe=a("strong"),Elt=o("longformer"),ylt=o(" \u2014 "),Lhe=a("a"),Clt=o("LongformerForMaskedLM"),wlt=o(" (Longformer model)"),Llt=l(),h3=a("li"),hVe=a("strong"),Alt=o("luke"),Rlt=o(" \u2014 "),Ahe=a("a"),klt=o("LukeForMaskedLM"),Slt=o(" (LUKE model)"),xlt=l(),g3=a("li"),gVe=a("strong"),Glt=o("lxmert"),Blt=o(" \u2014 "),Rhe=a("a"),Ilt=o("LxmertForPreTraining"),Nlt=o(" (LXMERT model)"),$lt=l(),u3=a("li"),uVe=a("strong"),jlt=o("mega"),Plt=o(" \u2014 "),khe=a("a"),Zlt=o("MegaForMaskedLM"),Vlt=o(" (MEGA model)"),Wlt=l(),p3=a("li"),pVe=a("strong"),Jlt=o("megatron-bert"),Ult=o(" \u2014 "),She=a("a"),Xlt=o("MegatronBertForPreTraining"),Dlt=o(" (Megatron-BERT model)"),qlt=l(),_3=a("li"),_Ve=a("strong"),Olt=o("mobilebert"),Qlt=o(" \u2014 "),xhe=a("a"),Ylt=o("MobileBertForPreTraining"),zlt=o(" (MobileBERT model)"),Hlt=l(),b3=a("li"),bVe=a("strong"),Klt=o("mpnet"),eit=o(" \u2014 "),Ghe=a("a"),oit=o("MPNetForMaskedLM"),rit=o(" (MPNet model)"),tit=l(),v3=a("li"),vVe=a("strong"),ait=o("mpt"),nit=o(" \u2014 "),Bhe=a("a"),sit=o("MptForCausalLM"),lit=o(" (MPT model)"),iit=l(),M3=a("li"),MVe=a("strong"),dit=o("mra"),mit=o(" \u2014 "),Ihe=a("a"),cit=o("MraForMaskedLM"),fit=o(" (MRA model)"),hit=l(),T3=a("li"),TVe=a("strong"),git=o("mvp"),uit=o(" \u2014 "),Nhe=a("a"),pit=o("MvpForConditionalGeneration"),_it=o(" (MVP 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(RoCBert model)"),Kit=l(),R3=a("li"),RVe=a("strong"),edt=o("rwkv"),odt=o(" \u2014 "),Uhe=a("a"),rdt=o("RwkvForCausalLM"),tdt=o(" (RWKV model)"),adt=l(),k3=a("li"),kVe=a("strong"),ndt=o("splinter"),sdt=o(" \u2014 "),Xhe=a("a"),ldt=o("SplinterForPreTraining"),idt=o(" (Splinter model)"),ddt=l(),S3=a("li"),SVe=a("strong"),mdt=o("squeezebert"),cdt=o(" \u2014 "),Dhe=a("a"),fdt=o("SqueezeBertForMaskedLM"),hdt=o(" (SqueezeBERT model)"),gdt=l(),x3=a("li"),xVe=a("strong"),udt=o("switch_transformers"),pdt=o(" \u2014 "),qhe=a("a"),_dt=o("SwitchTransformersForConditionalGeneration"),bdt=o(" (SwitchTransformers model)"),vdt=l(),G3=a("li"),GVe=a("strong"),Mdt=o("t5"),Tdt=o(" \u2014 "),Ohe=a("a"),Fdt=o("T5ForConditionalGeneration"),Edt=o(" (T5 model)"),ydt=l(),B3=a("li"),BVe=a("strong"),Cdt=o("tapas"),wdt=o(" \u2014 "),Qhe=a("a"),Ldt=o("TapasForMaskedLM"),Adt=o(" (TAPAS model)"),Rdt=l(),I3=a("li"),IVe=a("strong"),kdt=o("transfo-xl"),Sdt=o(" \u2014 "),Yhe=a("a"),xdt=o("TransfoXLLMHeadModel"),Gdt=o(" (Transformer-XL model)"),Bdt=l(),N3=a("li"),NVe=a("strong"),Idt=o("tvlt"),Ndt=o(" \u2014 "),zhe=a("a"),$dt=o("TvltForPreTraining"),jdt=o(" (TVLT model)"),Pdt=l(),$3=a("li"),$Ve=a("strong"),Zdt=o("unispeech"),Vdt=o(" \u2014 "),Hhe=a("a"),Wdt=o("UniSpeechForPreTraining"),Jdt=o(" (UniSpeech model)"),Udt=l(),j3=a("li"),jVe=a("strong"),Xdt=o("unispeech-sat"),Ddt=o(" \u2014 "),Khe=a("a"),qdt=o("UniSpeechSatForPreTraining"),Odt=o(" (UniSpeechSat model)"),Qdt=l(),P3=a("li"),PVe=a("strong"),Ydt=o("videomae"),zdt=o(" \u2014 "),ege=a("a"),Hdt=o("VideoMAEForPreTraining"),Kdt=o(" (VideoMAE model)"),emt=l(),Z3=a("li"),ZVe=a("strong"),omt=o("visual_bert"),rmt=o(" \u2014 "),oge=a("a"),tmt=o("VisualBertForPreTraining"),amt=o(" (VisualBERT model)"),nmt=l(),V3=a("li"),VVe=a("strong"),smt=o("vit_mae"),lmt=o(" \u2014 "),rge=a("a"),imt=o("ViTMAEForPreTraining"),dmt=o(" (ViTMAE model)"),mmt=l(),W3=a("li"),WVe=a("strong"),cmt=o("wav2vec2"),fmt=o(" \u2014 "),tge=a("a"),hmt=o("Wav2Vec2ForPreTraining"),gmt=o(" 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model\u2019s configuration. Use `),Zge=a("a"),hgt=o("from_pretrained()"),ggt=o(" to load the model weights."),ugt=l(),v(w9.$$.fragment),pgt=l(),st=a("div"),v(hW.$$.fragment),_gt=l(),NWe=a("p"),bgt=o("Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),vgt=l(),Xn=a("p"),Mgt=o("The model class to instantiate is selected based on the "),$We=a("code"),Tgt=o("model_type"),Fgt=o(` property of the config object (either
passed as an argument or loaded from `),jWe=a("code"),Egt=o("pretrained_model_name_or_path"),ygt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),PWe=a("code"),Cgt=o("pretrained_model_name_or_path"),wgt=o(":"),Lgt=l(),we=a("ul"),L9=a("li"),ZWe=a("strong"),Agt=o("albert"),Rgt=o(" \u2014 "),Vge=a("a"),kgt=o("FlaxAlbertForPreTraining"),Sgt=o(" (ALBERT model)"),xgt=l(),A9=a("li"),VWe=a("strong"),Ggt=o("bart"),Bgt=o(" \u2014 "),Wge=a("a"),Igt=o("FlaxBartForConditionalGeneration"),Ngt=o(" (BART model)"),$gt=l(),R9=a("li"),WWe=a("strong"),jgt=o("bert"),Pgt=o(" \u2014 "),Jge=a("a"),Zgt=o("FlaxBertForPreTraining"),Vgt=o(" (BERT model)"),Wgt=l(),k9=a("li"),JWe=a("strong"),Jgt=o("big_bird"),Ugt=o(" \u2014 "),Uge=a("a"),Xgt=o("FlaxBigBirdForPreTraining"),Dgt=o(" (BigBird model)"),qgt=l(),S9=a("li"),UWe=a("strong"),Ogt=o("electra"),Qgt=o(" \u2014 "),Xge=a("a"),Ygt=o("FlaxElectraForPreTraining"),zgt=o(" (ELECTRA model)"),Hgt=l(),x9=a("li"),XWe=a("strong"),Kgt=o("longt5"),eut=o(" \u2014 "),Dge=a("a"),out=o("FlaxLongT5ForConditionalGeneration"),rut=o(" (LongT5 model)"),tut=l(),G9=a("li"),DWe=a("strong"),aut=o("mbart"),nut=o(" \u2014 "),qge=a("a"),sut=o("FlaxMBartForConditionalGeneration"),lut=o(" (mBART model)"),iut=l(),B9=a("li"),qWe=a("strong"),dut=o("mt5"),mut=o(" \u2014 "),Oge=a("a"),cut=o("FlaxMT5ForConditionalGeneration"),fut=o(" (MT5 model)"),hut=l(),I9=a("li"),OWe=a("strong"),gut=o("roberta"),uut=o(" \u2014 "),Qge=a("a"),put=o("FlaxRobertaForMaskedLM"),_ut=o(" (RoBERTa model)"),but=l(),N9=a("li"),QWe=a("strong"),vut=o("roberta-prelayernorm"),Mut=o(" \u2014 "),Yge=a("a"),Tut=o("FlaxRobertaPreLayerNormForMaskedLM"),Fut=o(" (RoBERTa-PreLayerNorm model)"),Eut=l(),$9=a("li"),YWe=a("strong"),yut=o("roformer"),Cut=o(" \u2014 "),zge=a("a"),wut=o("FlaxRoFormerForMaskedLM"),Lut=o(" (RoFormer model)"),Aut=l(),j9=a("li"),zWe=a("strong"),Rut=o("t5"),kut=o(" \u2014 "),Hge=a("a"),Sut=o("FlaxT5ForConditionalGeneration"),xut=o(" (T5 model)"),Gut=l(),P9=a("li"),HWe=a("strong"),But=o("wav2vec2"),Iut=o(" \u2014 "),Kge=a("a"),Nut=o("FlaxWav2Vec2ForPreTraining"),$ut=o(" (Wav2Vec2 model)"),jut=l(),Z9=a("li"),KWe=a("strong"),Put=o("whisper"),Zut=o(" \u2014 "),eue=a("a"),Vut=o("FlaxWhisperForConditionalGeneration"),Wut=o(" (Whisper model)"),Jut=l(),V9=a("li"),eJe=a("strong"),Uut=o("xlm-roberta"),Xut=o(" \u2014 "),oue=a("a"),Dut=o("FlaxXLMRobertaForMaskedLM"),qut=o(" (XLM-RoBERTa model)"),Out=l(),v(W9.$$.fragment),mBo=l(),yc=a("h2"),J9=a("a"),oJe=a("span"),v(gW.$$.fragment),Qut=l(),rJe=a("span"),Yut=o("Natural Language Processing"),cBo=l(),rue=a("p"),zut=o("The following auto classes are available for the following natural language processing tasks."),fBo=l(),Cc=a("h3"),U9=a("a"),tJe=a("span"),v(uW.$$.fragment),Hut=l(),aJe=a("span"),Kut=o("AutoModelForCausalLM"),hBo=l(),Yo=a("div"),v(pW.$$.fragment),ept=l(),wc=a("p"),opt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),tue=a("a"),rpt=o("from_pretrained()"),tpt=o(" class method or the "),aue=a("a"),apt=o("from_config()"),npt=o(` class
method.`),spt=l(),_W=a("p"),lpt=o("This class cannot be instantiated directly using "),nJe=a("code"),ipt=o("__init__()"),dpt=o(" (throws an error)."),mpt=l(),Qt=a("div"),v(bW.$$.fragment),cpt=l(),sJe=a("p"),fpt=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),hpt=l(),Lc=a("p"),gpt=o(`Note:
Loading a model from its configuration file does `),lJe=a("strong"),upt=o("not"),ppt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),nue=a("a"),_pt=o("from_pretrained()"),bpt=o(" to load the model weights."),vpt=l(),v(X9.$$.fragment),Mpt=l(),io=a("div"),v(vW.$$.fragment),Tpt=l(),iJe=a("p"),Fpt=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),Ept=l(),Dn=a("p"),ypt=o("The model class to instantiate is selected based on the "),dJe=a("code"),Cpt=o("model_type"),wpt=o(` property of the config object (either
passed as an argument or loaded from `),mJe=a("code"),Lpt=o("pretrained_model_name_or_path"),Apt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),cJe=a("code"),Rpt=o("pretrained_model_name_or_path"),kpt=o(":"),Spt=l(),Q=a("ul"),D9=a("li"),fJe=a("strong"),xpt=o("bart"),Gpt=o(" \u2014 "),sue=a("a"),Bpt=o("BartForCausalLM"),Ipt=o(" (BART model)"),Npt=l(),q9=a("li"),hJe=a("strong"),$pt=o("bert"),jpt=o(" \u2014 "),lue=a("a"),Ppt=o("BertLMHeadModel"),Zpt=o(" (BERT model)"),Vpt=l(),O9=a("li"),gJe=a("strong"),Wpt=o("bert-generation"),Jpt=o(" \u2014 "),iue=a("a"),Upt=o("BertGenerationDecoder"),Xpt=o(" (Bert Generation model)"),Dpt=l(),Q9=a("li"),uJe=a("strong"),qpt=o("big_bird"),Opt=o(" \u2014 "),due=a("a"),Qpt=o("BigBirdForCausalLM"),Ypt=o(" (BigBird model)"),zpt=l(),Y9=a("li"),pJe=a("strong"),Hpt=o("bigbird_pegasus"),Kpt=o(" \u2014 "),mue=a("a"),e_t=o("BigBirdPegasusForCausalLM"),o_t=o(" (BigBird-Pegasus model)"),r_t=l(),z9=a("li"),_Je=a("strong"),t_t=o("biogpt"),a_t=o(" \u2014 "),cue=a("a"),n_t=o("BioGptForCausalLM"),s_t=o(" (BioGpt model)"),l_t=l(),H9=a("li"),bJe=a("strong"),i_t=o("blenderbot"),d_t=o(" \u2014 "),fue=a("a"),m_t=o("BlenderbotForCausalLM"),c_t=o(" (Blenderbot model)"),f_t=l(),K9=a("li"),vJe=a("strong"),h_t=o("blenderbot-small"),g_t=o(" \u2014 "),hue=a("a"),u_t=o("BlenderbotSmallForCausalLM"),p_t=o(" (BlenderbotSmall model)"),__t=l(),e4=a("li"),MJe=a("strong"),b_t=o("bloom"),v_t=o(" \u2014 "),gue=a("a"),M_t=o("BloomForCausalLM"),T_t=o(" (BLOOM model)"),F_t=l(),o4=a("li"),TJe=a("strong"),E_t=o("camembert"),y_t=o(" \u2014 "),uue=a("a"),C_t=o("CamembertForCausalLM"),w_t=o(" (CamemBERT model)"),L_t=l(),r4=a("li"),FJe=a("strong"),A_t=o("code_llama"),R_t=o(" \u2014 "),pue=a("a"),k_t=o("LlamaForCausalLM"),S_t=o(" (CodeLlama model)"),x_t=l(),t4=a("li"),EJe=a("strong"),G_t=o("codegen"),B_t=o(" \u2014 "),_ue=a("a"),I_t=o("CodeGenForCausalLM"),N_t=o(" (CodeGen model)"),$_t=l(),a4=a("li"),yJe=a("strong"),j_t=o("cpmant"),P_t=o(" \u2014 "),bue=a("a"),Z_t=o("CpmAntForCausalLM"),V_t=o(" (CPM-Ant model)"),W_t=l(),n4=a("li"),CJe=a("strong"),J_t=o("ctrl"),U_t=o(" \u2014 "),vue=a("a"),X_t=o("CTRLLMHeadModel"),D_t=o(" (CTRL model)"),q_t=l(),s4=a("li"),wJe=a("strong"),O_t=o("data2vec-text"),Q_t=o(" \u2014 "),Mue=a("a"),Y_t=o("Data2VecTextForCausalLM"),z_t=o(" (Data2VecText model)"),H_t=l(),l4=a("li"),LJe=a("strong"),K_t=o("electra"),e1t=o(" \u2014 "),Tue=a("a"),o1t=o("ElectraForCausalLM"),r1t=o(" (ELECTRA model)"),t1t=l(),i4=a("li"),AJe=a("strong"),a1t=o("ernie"),n1t=o(" \u2014 "),Fue=a("a"),s1t=o("ErnieForCausalLM"),l1t=o(" (ERNIE model)"),i1t=l(),d4=a("li"),RJe=a("strong"),d1t=o("falcon"),m1t=o(" \u2014 "),kJe=a("code"),c1t=o("FalconForCausalLM"),f1t=o(" (Falcon model)"),h1t=l(),m4=a("li"),SJe=a("strong"),g1t=o("git"),u1t=o(" \u2014 "),Eue=a("a"),p1t=o("GitForCausalLM"),_1t=o(" (GIT model)"),b1t=l(),c4=a("li"),xJe=a("strong"),v1t=o("gpt-sw3"),M1t=o(" \u2014 "),yue=a("a"),T1t=o("GPT2LMHeadModel"),F1t=o(" (GPT-Sw3 model)"),E1t=l(),f4=a("li"),GJe=a("strong"),y1t=o("gpt2"),C1t=o(" \u2014 "),Cue=a("a"),w1t=o("GPT2LMHeadModel"),L1t=o(" (OpenAI GPT-2 model)"),A1t=l(),h4=a("li"),BJe=a("strong"),R1t=o("gpt_bigcode"),k1t=o(" \u2014 "),wue=a("a"),S1t=o("GPTBigCodeForCausalLM"),x1t=o(" (GPTBigCode model)"),G1t=l(),g4=a("li"),IJe=a("strong"),B1t=o("gpt_neo"),I1t=o(" \u2014 "),Lue=a("a"),N1t=o("GPTNeoForCausalLM"),$1t=o(" (GPT Neo model)"),j1t=l(),u4=a("li"),NJe=a("strong"),P1t=o("gpt_neox"),Z1t=o(" \u2014 "),Aue=a("a"),V1t=o("GPTNeoXForCausalLM"),W1t=o(" (GPT NeoX model)"),J1t=l(),p4=a("li"),$Je=a("strong"),U1t=o("gpt_neox_japanese"),X1t=o(" \u2014 "),Rue=a("a"),D1t=o("GPTNeoXJapaneseForCausalLM"),q1t=o(" (GPT NeoX Japanese model)"),O1t=l(),_4=a("li"),jJe=a("strong"),Q1t=o("gptj"),Y1t=o(" \u2014 "),kue=a("a"),z1t=o("GPTJForCausalLM"),H1t=o(" (GPT-J model)"),K1t=l(),b4=a("li"),PJe=a("strong"),ebt=o("llama"),obt=o(" \u2014 "),Sue=a("a"),rbt=o("LlamaForCausalLM"),tbt=o(" (LLaMA model)"),abt=l(),v4=a("li"),ZJe=a("strong"),nbt=o("marian"),sbt=o(" \u2014 "),xue=a("a"),lbt=o("MarianForCausalLM"),ibt=o(" (Marian model)"),dbt=l(),M4=a("li"),VJe=a("strong"),mbt=o("mbart"),cbt=o(" \u2014 "),Gue=a("a"),fbt=o("MBartForCausalLM"),hbt=o(" (mBART model)"),gbt=l(),T4=a("li"),WJe=a("strong"),ubt=o("mega"),pbt=o(" \u2014 "),Bue=a("a"),_bt=o("MegaForCausalLM"),bbt=o(" (MEGA model)"),vbt=l(),F4=a("li"),JJe=a("strong"),Mbt=o("megatron-bert"),Tbt=o(" \u2014 "),Iue=a("a"),Fbt=o("MegatronBertForCausalLM"),Ebt=o(" (Megatron-BERT model)"),ybt=l(),E4=a("li"),UJe=a("strong"),Cbt=o("mpt"),wbt=o(" \u2014 "),Nue=a("a"),Lbt=o("MptForCausalLM"),Abt=o(" (MPT model)"),Rbt=l(),y4=a("li"),XJe=a("strong"),kbt=o("musicgen"),Sbt=o(" \u2014 "),$ue=a("a"),xbt=o("MusicgenForCausalLM"),Gbt=o(" (MusicGen model)"),Bbt=l(),C4=a("li"),DJe=a("strong"),Ibt=o("mvp"),Nbt=o(" \u2014 "),jue=a("a"),$bt=o("MvpForCausalLM"),jbt=o(" (MVP model)"),Pbt=l(),w4=a("li"),qJe=a("strong"),Zbt=o("open-llama"),Vbt=o(" \u2014 "),Pue=a("a"),Wbt=o("OpenLlamaForCausalLM"),Jbt=o(" (OpenLlama model)"),Ubt=l(),L4=a("li"),OJe=a("strong"),Xbt=o("openai-gpt"),Dbt=o(" \u2014 "),Zue=a("a"),qbt=o("OpenAIGPTLMHeadModel"),Obt=o(" (OpenAI GPT model)"),Qbt=l(),A4=a("li"),QJe=a("strong"),Ybt=o("opt"),zbt=o(" \u2014 "),Vue=a("a"),Hbt=o("OPTForCausalLM"),Kbt=o(" (OPT model)"),e2t=l(),R4=a("li"),YJe=a("strong"),o2t=o("pegasus"),r2t=o(" \u2014 "),Wue=a("a"),t2t=o("PegasusForCausalLM"),a2t=o(" (Pegasus model)"),n2t=l(),k4=a("li"),zJe=a("strong"),s2t=o("plbart"),l2t=o(" \u2014 "),Jue=a("a"),i2t=o("PLBartForCausalLM"),d2t=o(" (PLBart model)"),m2t=l(),S4=a("li"),HJe=a("strong"),c2t=o("prophetnet"),f2t=o(" \u2014 "),Uue=a("a"),h2t=o("ProphetNetForCausalLM"),g2t=o(" (ProphetNet model)"),u2t=l(),x4=a("li"),KJe=a("strong"),p2t=o("qdqbert"),_2t=o(" \u2014 "),Xue=a("a"),b2t=o("QDQBertLMHeadModel"),v2t=o(" (QDQBert model)"),M2t=l(),G4=a("li"),eUe=a("strong"),T2t=o("reformer"),F2t=o(" \u2014 "),Due=a("a"),E2t=o("ReformerModelWithLMHead"),y2t=o(" (Reformer model)"),C2t=l(),B4=a("li"),oUe=a("strong"),w2t=o("rembert"),L2t=o(" \u2014 "),que=a("a"),A2t=o("RemBertForCausalLM"),R2t=o(" (RemBERT model)"),k2t=l(),I4=a("li"),rUe=a("strong"),S2t=o("roberta"),x2t=o(" \u2014 "),Oue=a("a"),G2t=o("RobertaForCausalLM"),B2t=o(" (RoBERTa model)"),I2t=l(),N4=a("li"),tUe=a("strong"),N2t=o("roberta-prelayernorm"),$2t=o(" \u2014 "),Que=a("a"),j2t=o("RobertaPreLayerNormForCausalLM"),P2t=o(" (RoBERTa-PreLayerNorm model)"),Z2t=l(),$4=a("li"),aUe=a("strong"),V2t=o("roc_bert"),W2t=o(" \u2014 "),Yue=a("a"),J2t=o("RoCBertForCausalLM"),U2t=o(" (RoCBert model)"),X2t=l(),j4=a("li"),nUe=a("strong"),D2t=o("roformer"),q2t=o(" \u2014 "),zue=a("a"),O2t=o("RoFormerForCausalLM"),Q2t=o(" (RoFormer model)"),Y2t=l(),P4=a("li"),sUe=a("strong"),z2t=o("rwkv"),H2t=o(" \u2014 "),Hue=a("a"),K2t=o("RwkvForCausalLM"),evt=o(" (RWKV model)"),ovt=l(),Z4=a("li"),lUe=a("strong"),rvt=o("speech_to_text_2"),tvt=o(" \u2014 "),Kue=a("a"),avt=o("Speech2Text2ForCausalLM"),nvt=o(" (Speech2Text2 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deactivated). To train the model, you should first set it back in training mode with `),bUe=a("code"),oMt=o("model.train()"),rMt=l(),v(z4.$$.fragment),gBo=l(),Ac=a("h3"),H4=a("a"),vUe=a("span"),v(MW.$$.fragment),tMt=l(),MUe=a("span"),aMt=o("TFAutoModelForCausalLM"),uBo=l(),zo=a("div"),v(TW.$$.fragment),nMt=l(),Rc=a("p"),sMt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),dpe=a("a"),lMt=o("from_pretrained()"),iMt=o(" class method or the "),mpe=a("a"),dMt=o("from_config()"),mMt=o(` class
method.`),cMt=l(),FW=a("p"),fMt=o("This class cannot be instantiated directly using "),TUe=a("code"),hMt=o("__init__()"),gMt=o(" (throws an error)."),uMt=l(),Yt=a("div"),v(EW.$$.fragment),pMt=l(),FUe=a("p"),_Mt=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),bMt=l(),kc=a("p"),vMt=o(`Note:
Loading a model from its configuration file does `),EUe=a("strong"),MMt=o("not"),TMt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),cpe=a("a"),FMt=o("from_pretrained()"),EMt=o(" to load the model weights."),yMt=l(),v(K4.$$.fragment),CMt=l(),lt=a("div"),v(yW.$$.fragment),wMt=l(),yUe=a("p"),LMt=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),AMt=l(),qn=a("p"),RMt=o("The model class to instantiate is selected based on the "),CUe=a("code"),kMt=o("model_type"),SMt=o(` property of the config object (either
passed as an argument or loaded from `),wUe=a("code"),xMt=o("pretrained_model_name_or_path"),GMt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),LUe=a("code"),BMt=o("pretrained_model_name_or_path"),IMt=o(":"),NMt=l(),Ee=a("ul"),eC=a("li"),AUe=a("strong"),$Mt=o("bert"),jMt=o(" \u2014 "),fpe=a("a"),PMt=o("TFBertLMHeadModel"),ZMt=o(" (BERT model)"),VMt=l(),oC=a("li"),RUe=a("strong"),WMt=o("camembert"),JMt=o(" \u2014 "),hpe=a("a"),UMt=o("TFCamembertForCausalLM"),XMt=o(" (CamemBERT model)"),DMt=l(),rC=a("li"),kUe=a("strong"),qMt=o("ctrl"),OMt=o(" \u2014 "),gpe=a("a"),QMt=o("TFCTRLLMHeadModel"),YMt=o(" (CTRL model)"),zMt=l(),tC=a("li"),SUe=a("strong"),HMt=o("gpt-sw3"),KMt=o(" \u2014 "),upe=a("a"),eTt=o("TFGPT2LMHeadModel"),oTt=o(" (GPT-Sw3 model)"),rTt=l(),aC=a("li"),xUe=a("strong"),tTt=o("gpt2"),aTt=o(" \u2014 "),ppe=a("a"),nTt=o("TFGPT2LMHeadModel"),sTt=o(" (OpenAI GPT-2 model)"),lTt=l(),nC=a("li"),GUe=a("strong"),iTt=o("gptj"),dTt=o(" \u2014 "),_pe=a("a"),mTt=o("TFGPTJForCausalLM"),cTt=o(" (GPT-J model)"),fTt=l(),sC=a("li"),BUe=a("strong"),hTt=o("openai-gpt"),gTt=o(" \u2014 "),bpe=a("a"),uTt=o("TFOpenAIGPTLMHeadModel"),pTt=o(" (OpenAI GPT model)"),_Tt=l(),lC=a("li"),IUe=a("strong"),bTt=o("opt"),vTt=o(" \u2014 "),vpe=a("a"),MTt=o("TFOPTForCausalLM"),TTt=o(" (OPT model)"),FTt=l(),iC=a("li"),NUe=a("strong"),ETt=o("rembert"),yTt=o(" \u2014 "),Mpe=a("a"),CTt=o("TFRemBertForCausalLM"),wTt=o(" (RemBERT model)"),LTt=l(),dC=a("li"),$Ue=a("strong"),ATt=o("roberta"),RTt=o(" \u2014 "),Tpe=a("a"),kTt=o("TFRobertaForCausalLM"),STt=o(" (RoBERTa model)"),xTt=l(),mC=a("li"),jUe=a("strong"),GTt=o("roberta-prelayernorm"),BTt=o(" \u2014 "),Fpe=a("a"),ITt=o("TFRobertaPreLayerNormForCausalLM"),NTt=o(" (RoBERTa-PreLayerNorm model)"),$Tt=l(),cC=a("li"),PUe=a("strong"),jTt=o("roformer"),PTt=o(" \u2014 "),Epe=a("a"),ZTt=o("TFRoFormerForCausalLM"),VTt=o(" (RoFormer model)"),WTt=l(),fC=a("li"),ZUe=a("strong"),JTt=o("transfo-xl"),UTt=o(" \u2014 "),ype=a("a"),XTt=o("TFTransfoXLLMHeadModel"),DTt=o(" (Transformer-XL model)"),qTt=l(),hC=a("li"),VUe=a("strong"),OTt=o("xglm"),QTt=o(" \u2014 "),Cpe=a("a"),YTt=o("TFXGLMForCausalLM"),zTt=o(" (XGLM model)"),HTt=l(),gC=a("li"),WUe=a("strong"),KTt=o("xlm"),eFt=o(" \u2014 "),wpe=a("a"),oFt=o("TFXLMWithLMHeadModel"),rFt=o(" (XLM model)"),tFt=l(),uC=a("li"),JUe=a("strong"),aFt=o("xlm-roberta"),nFt=o(" \u2014 "),Lpe=a("a"),sFt=o("TFXLMRobertaForCausalLM"),lFt=o(" (XLM-RoBERTa model)"),iFt=l(),pC=a("li"),UUe=a("strong"),dFt=o("xlnet"),mFt=o(" \u2014 "),Ape=a("a"),cFt=o("TFXLNetLMHeadModel"),fFt=o(" (XLNet model)"),hFt=l(),v(_C.$$.fragment),pBo=l(),Sc=a("h3"),bC=a("a"),XUe=a("span"),v(CW.$$.fragment),gFt=l(),DUe=a("span"),uFt=o("FlaxAutoModelForCausalLM"),_Bo=l(),Ho=a("div"),v(wW.$$.fragment),pFt=l(),xc=a("p"),_Ft=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),Rpe=a("a"),bFt=o("from_pretrained()"),vFt=o(" class method or the "),kpe=a("a"),MFt=o("from_config()"),TFt=o(` class
method.`),FFt=l(),LW=a("p"),EFt=o("This class cannot be instantiated directly using "),qUe=a("code"),yFt=o("__init__()"),CFt=o(" (throws an error)."),wFt=l(),zt=a("div"),v(AW.$$.fragment),LFt=l(),OUe=a("p"),AFt=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),RFt=l(),Gc=a("p"),kFt=o(`Note:
Loading a model from its configuration file does `),QUe=a("strong"),SFt=o("not"),xFt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Spe=a("a"),GFt=o("from_pretrained()"),BFt=o(" to load the model weights."),IFt=l(),v(vC.$$.fragment),NFt=l(),it=a("div"),v(RW.$$.fragment),$Ft=l(),YUe=a("p"),jFt=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),PFt=l(),On=a("p"),ZFt=o("The model class to instantiate is selected based on the "),zUe=a("code"),VFt=o("model_type"),WFt=o(` property of the config object (either
passed as an argument or loaded from `),HUe=a("code"),JFt=o("pretrained_model_name_or_path"),UFt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),KUe=a("code"),XFt=o("pretrained_model_name_or_path"),DFt=o(":"),qFt=l(),Le=a("ul"),MC=a("li"),eXe=a("strong"),OFt=o("bart"),QFt=o(" \u2014 "),xpe=a("a"),YFt=o("FlaxBartForCausalLM"),zFt=o(" (BART model)"),HFt=l(),TC=a("li"),oXe=a("strong"),KFt=o("bert"),eEt=o(" \u2014 "),Gpe=a("a"),oEt=o("FlaxBertForCausalLM"),rEt=o(" (BERT model)"),tEt=l(),FC=a("li"),rXe=a("strong"),aEt=o("big_bird"),nEt=o(" \u2014 "),Bpe=a("a"),sEt=o("FlaxBigBirdForCausalLM"),lEt=o(" (BigBird model)"),iEt=l(),EC=a("li"),tXe=a("strong"),dEt=o("bloom"),mEt=o(" \u2014 "),Ipe=a("a"),cEt=o("FlaxBloomForCausalLM"),fEt=o(" (BLOOM model)"),hEt=l(),yC=a("li"),aXe=a("strong"),gEt=o("electra"),uEt=o(" \u2014 "),Npe=a("a"),pEt=o("FlaxElectraForCausalLM"),_Et=o(" (ELECTRA model)"),bEt=l(),CC=a("li"),nXe=a("strong"),vEt=o("gpt-sw3"),MEt=o(" \u2014 "),$pe=a("a"),TEt=o("FlaxGPT2LMHeadModel"),FEt=o(" (GPT-Sw3 model)"),EEt=l(),wC=a("li"),sXe=a("strong"),yEt=o("gpt2"),CEt=o(" \u2014 "),jpe=a("a"),wEt=o("FlaxGPT2LMHeadModel"),LEt=o(" (OpenAI GPT-2 model)"),AEt=l(),LC=a("li"),lXe=a("strong"),REt=o("gpt_neo"),kEt=o(" \u2014 "),Ppe=a("a"),SEt=o("FlaxGPTNeoForCausalLM"),xEt=o(" (GPT Neo model)"),GEt=l(),AC=a("li"),iXe=a("strong"),BEt=o("gptj"),IEt=o(" \u2014 "),Zpe=a("a"),NEt=o("FlaxGPTJForCausalLM"),$Et=o(" (GPT-J model)"),jEt=l(),RC=a("li"),dXe=a("strong"),PEt=o("opt"),ZEt=o(" \u2014 "),Vpe=a("a"),VEt=o("FlaxOPTForCausalLM"),WEt=o(" (OPT model)"),JEt=l(),kC=a("li"),mXe=a("strong"),UEt=o("roberta"),XEt=o(" \u2014 "),Wpe=a("a"),DEt=o("FlaxRobertaForCausalLM"),qEt=o(" (RoBERTa model)"),OEt=l(),SC=a("li"),cXe=a("strong"),QEt=o("roberta-prelayernorm"),YEt=o(" \u2014 "),Jpe=a("a"),zEt=o("FlaxRobertaPreLayerNormForCausalLM"),HEt=o(" (RoBERTa-PreLayerNorm model)"),KEt=l(),xC=a("li"),fXe=a("strong"),eyt=o("xglm"),oyt=o(" \u2014 "),Upe=a("a"),ryt=o("FlaxXGLMForCausalLM"),tyt=o(" (XGLM model)"),ayt=l(),GC=a("li"),hXe=a("strong"),nyt=o("xlm-roberta"),syt=o(" \u2014 "),Xpe=a("a"),lyt=o("FlaxXLMRobertaForCausalLM"),iyt=o(" (XLM-RoBERTa model)"),dyt=l(),v(BC.$$.fragment),bBo=l(),Bc=a("h3"),IC=a("a"),gXe=a("span"),v(kW.$$.fragment),myt=l(),uXe=a("span"),cyt=o("AutoModelForMaskedLM"),vBo=l(),Ko=a("div"),v(SW.$$.fragment),fyt=l(),Ic=a("p"),hyt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
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method.`),byt=l(),xW=a("p"),vyt=o("This class cannot be instantiated directly using "),pXe=a("code"),Myt=o("__init__()"),Tyt=o(" (throws an error)."),Fyt=l(),Ht=a("div"),v(GW.$$.fragment),Eyt=l(),_Xe=a("p"),yyt=o("Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),Cyt=l(),Nc=a("p"),wyt=o(`Note:
Loading a model from its configuration file does `),bXe=a("strong"),Lyt=o("not"),Ayt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Ope=a("a"),Ryt=o("from_pretrained()"),kyt=o(" to load the model weights."),Syt=l(),v(NC.$$.fragment),xyt=l(),mo=a("div"),v(BW.$$.fragment),Gyt=l(),vXe=a("p"),Byt=o("Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),Iyt=l(),Qn=a("p"),Nyt=o("The model class to instantiate is selected based on the "),MXe=a("code"),$yt=o("model_type"),jyt=o(` property of the config object (either
passed as an argument or loaded from `),TXe=a("code"),Pyt=o("pretrained_model_name_or_path"),Zyt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),FXe=a("code"),Vyt=o("pretrained_model_name_or_path"),Wyt=o(":"),Jyt=l(),K=a("ul"),$C=a("li"),EXe=a("strong"),Uyt=o("albert"),Xyt=o(" \u2014 "),Qpe=a("a"),Dyt=o("AlbertForMaskedLM"),qyt=o(" (ALBERT model)"),Oyt=l(),jC=a("li"),yXe=a("strong"),Qyt=o("bart"),Yyt=o(" \u2014 "),Ype=a("a"),zyt=o("BartForConditionalGeneration"),Hyt=o(" (BART model)"),Kyt=l(),PC=a("li"),CXe=a("strong"),e0t=o("bert"),o0t=o(" \u2014 "),zpe=a("a"),r0t=o("BertForMaskedLM"),t0t=o(" (BERT model)"),a0t=l(),ZC=a("li"),wXe=a("strong"),n0t=o("big_bird"),s0t=o(" \u2014 "),Hpe=a("a"),l0t=o("BigBirdForMaskedLM"),i0t=o(" (BigBird model)"),d0t=l(),VC=a("li"),LXe=a("strong"),m0t=o("camembert"),c0t=o(" \u2014 "),Kpe=a("a"),f0t=o("CamembertForMaskedLM"),h0t=o(" (CamemBERT model)"),g0t=l(),WC=a("li"),AXe=a("strong"),u0t=o("convbert"),p0t=o(" \u2014 "),e_e=a("a"),_0t=o("ConvBertForMaskedLM"),b0t=o(" (ConvBERT model)"),v0t=l(),JC=a("li"),RXe=a("strong"),M0t=o("data2vec-text"),T0t=o(" \u2014 "),o_e=a("a"),F0t=o("Data2VecTextForMaskedLM"),E0t=o(" (Data2VecText model)"),y0t=l(),UC=a("li"),kXe=a("strong"),C0t=o("deberta"),w0t=o(" \u2014 "),r_e=a("a"),L0t=o("DebertaForMaskedLM"),A0t=o(" (DeBERTa model)"),R0t=l(),XC=a("li"),SXe=a("strong"),k0t=o("deberta-v2"),S0t=o(" \u2014 "),t_e=a("a"),x0t=o("DebertaV2ForMaskedLM"),G0t=o(" (DeBERTa-v2 model)"),B0t=l(),DC=a("li"),xXe=a("strong"),I0t=o("distilbert"),N0t=o(" \u2014 "),a_e=a("a"),$0t=o("DistilBertForMaskedLM"),j0t=o(" (DistilBERT model)"),P0t=l(),qC=a("li"),GXe=a("strong"),Z0t=o("electra"),V0t=o(" \u2014 "),n_e=a("a"),W0t=o("ElectraForMaskedLM"),J0t=o(" (ELECTRA model)"),U0t=l(),OC=a("li"),BXe=a("strong"),X0t=o("ernie"),D0t=o(" \u2014 "),s_e=a("a"),q0t=o("ErnieForMaskedLM"),O0t=o(" (ERNIE model)"),Q0t=l(),QC=a("li"),IXe=a("strong"),Y0t=o("esm"),z0t=o(" \u2014 "),l_e=a("a"),H0t=o("EsmForMaskedLM"),K0t=o(" (ESM model)"),e5t=l(),YC=a("li"),NXe=a("strong"),o5t=o("flaubert"),r5t=o(" \u2014 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"),u_e=a("a"),j5t=o("MBartForConditionalGeneration"),P5t=o(" (mBART model)"),Z5t=l(),aw=a("li"),UXe=a("strong"),V5t=o("mega"),W5t=o(" \u2014 "),p_e=a("a"),J5t=o("MegaForMaskedLM"),U5t=o(" (MEGA model)"),X5t=l(),nw=a("li"),XXe=a("strong"),D5t=o("megatron-bert"),q5t=o(" \u2014 "),__e=a("a"),O5t=o("MegatronBertForMaskedLM"),Q5t=o(" (Megatron-BERT model)"),Y5t=l(),sw=a("li"),DXe=a("strong"),z5t=o("mobilebert"),H5t=o(" \u2014 "),b_e=a("a"),K5t=o("MobileBertForMaskedLM"),e3t=o(" (MobileBERT model)"),o3t=l(),lw=a("li"),qXe=a("strong"),r3t=o("mpnet"),t3t=o(" \u2014 "),v_e=a("a"),a3t=o("MPNetForMaskedLM"),n3t=o(" (MPNet model)"),s3t=l(),iw=a("li"),OXe=a("strong"),l3t=o("mra"),i3t=o(" \u2014 "),M_e=a("a"),d3t=o("MraForMaskedLM"),m3t=o(" (MRA model)"),c3t=l(),dw=a("li"),QXe=a("strong"),f3t=o("mvp"),h3t=o(" \u2014 "),T_e=a("a"),g3t=o("MvpForConditionalGeneration"),u3t=o(" (MVP model)"),p3t=l(),mw=a("li"),YXe=a("strong"),_3t=o("nezha"),b3t=o(" \u2014 "),F_e=a("a"),v3t=o("NezhaForMaskedLM"),M3t=o(" 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(RoBERTa-PreLayerNorm model)"),r9t=l(),bw=a("li"),aDe=a("strong"),t9t=o("roc_bert"),a9t=o(" \u2014 "),k_e=a("a"),n9t=o("RoCBertForMaskedLM"),s9t=o(" (RoCBert model)"),l9t=l(),vw=a("li"),nDe=a("strong"),i9t=o("roformer"),d9t=o(" \u2014 "),S_e=a("a"),m9t=o("RoFormerForMaskedLM"),c9t=o(" (RoFormer model)"),f9t=l(),Mw=a("li"),sDe=a("strong"),h9t=o("squeezebert"),g9t=o(" \u2014 "),x_e=a("a"),u9t=o("SqueezeBertForMaskedLM"),p9t=o(" (SqueezeBERT model)"),_9t=l(),Tw=a("li"),lDe=a("strong"),b9t=o("tapas"),v9t=o(" \u2014 "),G_e=a("a"),M9t=o("TapasForMaskedLM"),T9t=o(" (TAPAS model)"),F9t=l(),Fw=a("li"),iDe=a("strong"),E9t=o("wav2vec2"),y9t=o(" \u2014 "),dDe=a("code"),C9t=o("Wav2Vec2ForMaskedLM"),w9t=o(" (Wav2Vec2 model)"),L9t=l(),Ew=a("li"),mDe=a("strong"),A9t=o("xlm"),R9t=o(" \u2014 "),B_e=a("a"),k9t=o("XLMWithLMHeadModel"),S9t=o(" (XLM model)"),x9t=l(),yw=a("li"),cDe=a("strong"),G9t=o("xlm-roberta"),B9t=o(" \u2014 "),I_e=a("a"),I9t=o("XLMRobertaForMaskedLM"),N9t=o(" (XLM-RoBERTa 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model\u2019s configuration. Use `),V_e=a("a"),E4t=o("from_pretrained()"),y4t=o(" to load the model weights."),C4t=l(),v(Sw.$$.fragment),w4t=l(),dt=a("div"),v(PW.$$.fragment),L4t=l(),FDe=a("p"),A4t=o("Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),R4t=l(),Yn=a("p"),k4t=o("The model class to instantiate is selected based on the "),EDe=a("code"),S4t=o("model_type"),x4t=o(` property of the config object (either
passed as an argument or loaded from `),yDe=a("code"),G4t=o("pretrained_model_name_or_path"),B4t=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),CDe=a("code"),I4t=o("pretrained_model_name_or_path"),N4t=o(":"),$4t=l(),pe=a("ul"),xw=a("li"),wDe=a("strong"),j4t=o("albert"),P4t=o(" \u2014 "),W_e=a("a"),Z4t=o("TFAlbertForMaskedLM"),V4t=o(" (ALBERT model)"),W4t=l(),Gw=a("li"),LDe=a("strong"),J4t=o("bert"),U4t=o(" \u2014 "),J_e=a("a"),X4t=o("TFBertForMaskedLM"),D4t=o(" (BERT model)"),q4t=l(),Bw=a("li"),ADe=a("strong"),O4t=o("camembert"),Q4t=o(" \u2014 "),U_e=a("a"),Y4t=o("TFCamembertForMaskedLM"),z4t=o(" (CamemBERT model)"),H4t=l(),Iw=a("li"),RDe=a("strong"),K4t=o("convbert"),eCt=o(" \u2014 "),X_e=a("a"),oCt=o("TFConvBertForMaskedLM"),rCt=o(" (ConvBERT model)"),tCt=l(),Nw=a("li"),kDe=a("strong"),aCt=o("deberta"),nCt=o(" \u2014 "),D_e=a("a"),sCt=o("TFDebertaForMaskedLM"),lCt=o(" (DeBERTa model)"),iCt=l(),$w=a("li"),SDe=a("strong"),dCt=o("deberta-v2"),mCt=o(" \u2014 "),q_e=a("a"),cCt=o("TFDebertaV2ForMaskedLM"),fCt=o(" (DeBERTa-v2 model)"),hCt=l(),jw=a("li"),xDe=a("strong"),gCt=o("distilbert"),uCt=o(" \u2014 "),O_e=a("a"),pCt=o("TFDistilBertForMaskedLM"),_Ct=o(" (DistilBERT model)"),bCt=l(),Pw=a("li"),GDe=a("strong"),vCt=o("electra"),MCt=o(" \u2014 "),Q_e=a("a"),TCt=o("TFElectraForMaskedLM"),FCt=o(" (ELECTRA model)"),ECt=l(),Zw=a("li"),BDe=a("strong"),yCt=o("esm"),CCt=o(" \u2014 "),Y_e=a("a"),wCt=o("TFEsmForMaskedLM"),LCt=o(" (ESM model)"),ACt=l(),Vw=a("li"),IDe=a("strong"),RCt=o("flaubert"),kCt=o(" \u2014 "),z_e=a("a"),SCt=o("TFFlaubertWithLMHeadModel"),xCt=o(" (FlauBERT model)"),GCt=l(),Ww=a("li"),NDe=a("strong"),BCt=o("funnel"),ICt=o(" \u2014 "),H_e=a("a"),NCt=o("TFFunnelForMaskedLM"),$Ct=o(" (Funnel Transformer model)"),jCt=l(),Jw=a("li"),$De=a("strong"),PCt=o("layoutlm"),ZCt=o(" \u2014 "),K_e=a("a"),VCt=o("TFLayoutLMForMaskedLM"),WCt=o(" (LayoutLM model)"),JCt=l(),Uw=a("li"),jDe=a("strong"),UCt=o("longformer"),XCt=o(" \u2014 "),e1e=a("a"),DCt=o("TFLongformerForMaskedLM"),qCt=o(" (Longformer model)"),OCt=l(),Xw=a("li"),PDe=a("strong"),QCt=o("mobilebert"),YCt=o(" \u2014 "),o1e=a("a"),zCt=o("TFMobileBertForMaskedLM"),HCt=o(" (MobileBERT model)"),KCt=l(),Dw=a("li"),ZDe=a("strong"),ewt=o("mpnet"),owt=o(" \u2014 "),r1e=a("a"),rwt=o("TFMPNetForMaskedLM"),twt=o(" (MPNet model)"),awt=l(),qw=a("li"),VDe=a("strong"),nwt=o("rembert"),swt=o(" \u2014 "),t1e=a("a"),lwt=o("TFRemBertForMaskedLM"),iwt=o(" (RemBERT model)"),dwt=l(),Ow=a("li"),WDe=a("strong"),mwt=o("roberta"),cwt=o(" \u2014 "),a1e=a("a"),fwt=o("TFRobertaForMaskedLM"),hwt=o(" (RoBERTa model)"),gwt=l(),Qw=a("li"),JDe=a("strong"),uwt=o("roberta-prelayernorm"),pwt=o(" \u2014 "),n1e=a("a"),_wt=o("TFRobertaPreLayerNormForMaskedLM"),bwt=o(" (RoBERTa-PreLayerNorm model)"),vwt=l(),Yw=a("li"),UDe=a("strong"),Mwt=o("roformer"),Twt=o(" \u2014 "),s1e=a("a"),Fwt=o("TFRoFormerForMaskedLM"),Ewt=o(" (RoFormer model)"),ywt=l(),zw=a("li"),XDe=a("strong"),Cwt=o("tapas"),wwt=o(" \u2014 "),l1e=a("a"),Lwt=o("TFTapasForMaskedLM"),Awt=o(" (TAPAS model)"),Rwt=l(),Hw=a("li"),DDe=a("strong"),kwt=o("xlm"),Swt=o(" \u2014 "),i1e=a("a"),xwt=o("TFXLMWithLMHeadModel"),Gwt=o(" (XLM model)"),Bwt=l(),Kw=a("li"),qDe=a("strong"),Iwt=o("xlm-roberta"),Nwt=o(" \u2014 "),d1e=a("a"),$wt=o("TFXLMRobertaForMaskedLM"),jwt=o(" (XLM-RoBERTa model)"),Pwt=l(),v(e6.$$.fragment),FBo=l(),Zc=a("h3"),o6=a("a"),ODe=a("span"),v(ZW.$$.fragment),Zwt=l(),QDe=a("span"),Vwt=o("FlaxAutoModelForMaskedLM"),EBo=l(),or=a("div"),v(VW.$$.fragment),Wwt=l(),Vc=a("p"),Jwt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
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method.`),Owt=l(),WW=a("p"),Qwt=o("This class cannot be instantiated directly using "),YDe=a("code"),Ywt=o("__init__()"),zwt=o(" (throws an error)."),Hwt=l(),ea=a("div"),v(JW.$$.fragment),Kwt=l(),zDe=a("p"),e6t=o("Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),o6t=l(),Wc=a("p"),r6t=o(`Note:
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passed as an argument or loaded from `),oqe=a("code"),u6t=o("pretrained_model_name_or_path"),p6t=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),rqe=a("code"),_6t=o("pretrained_model_name_or_path"),b6t=o(":"),v6t=l(),ke=a("ul"),t6=a("li"),tqe=a("strong"),M6t=o("albert"),T6t=o(" \u2014 "),h1e=a("a"),F6t=o("FlaxAlbertForMaskedLM"),E6t=o(" (ALBERT model)"),y6t=l(),a6=a("li"),aqe=a("strong"),C6t=o("bart"),w6t=o(" \u2014 "),g1e=a("a"),L6t=o("FlaxBartForConditionalGeneration"),A6t=o(" (BART model)"),R6t=l(),n6=a("li"),nqe=a("strong"),k6t=o("bert"),S6t=o(" \u2014 "),u1e=a("a"),x6t=o("FlaxBertForMaskedLM"),G6t=o(" (BERT model)"),B6t=l(),s6=a("li"),sqe=a("strong"),I6t=o("big_bird"),N6t=o(" \u2014 "),p1e=a("a"),$6t=o("FlaxBigBirdForMaskedLM"),j6t=o(" (BigBird model)"),P6t=l(),l6=a("li"),lqe=a("strong"),Z6t=o("distilbert"),V6t=o(" \u2014 "),_1e=a("a"),W6t=o("FlaxDistilBertForMaskedLM"),J6t=o(" (DistilBERT model)"),U6t=l(),i6=a("li"),iqe=a("strong"),X6t=o("electra"),D6t=o(" \u2014 "),b1e=a("a"),q6t=o("FlaxElectraForMaskedLM"),O6t=o(" (ELECTRA model)"),Q6t=l(),d6=a("li"),dqe=a("strong"),Y6t=o("mbart"),z6t=o(" \u2014 "),v1e=a("a"),H6t=o("FlaxMBartForConditionalGeneration"),K6t=o(" (mBART model)"),e8t=l(),m6=a("li"),mqe=a("strong"),o8t=o("roberta"),r8t=o(" \u2014 "),M1e=a("a"),t8t=o("FlaxRobertaForMaskedLM"),a8t=o(" (RoBERTa model)"),n8t=l(),c6=a("li"),cqe=a("strong"),s8t=o("roberta-prelayernorm"),l8t=o(" \u2014 "),T1e=a("a"),i8t=o("FlaxRobertaPreLayerNormForMaskedLM"),d8t=o(" (RoBERTa-PreLayerNorm model)"),m8t=l(),f6=a("li"),fqe=a("strong"),c8t=o("roformer"),f8t=o(" \u2014 "),F1e=a("a"),h8t=o("FlaxRoFormerForMaskedLM"),g8t=o(" (RoFormer model)"),u8t=l(),h6=a("li"),hqe=a("strong"),p8t=o("xlm-roberta"),_8t=o(" \u2014 "),E1e=a("a"),b8t=o("FlaxXLMRobertaForMaskedLM"),v8t=o(" (XLM-RoBERTa model)"),M8t=l(),v(g6.$$.fragment),yBo=l(),Jc=a("h3"),u6=a("a"),gqe=a("span"),v(XW.$$.fragment),T8t=l(),uqe=a("span"),F8t=o("AutoModelForMaskGeneration"),CBo=l(),DW=a("div"),v(qW.$$.fragment),wBo=l(),Uc=a("h3"),p6=a("a"),pqe=a("span"),v(OW.$$.fragment),E8t=l(),_qe=a("span"),y8t=o("TFAutoModelForMaskGeneration"),LBo=l(),QW=a("div"),v(YW.$$.fragment),ABo=l(),Xc=a("h3"),_6=a("a"),bqe=a("span"),v(zW.$$.fragment),C8t=l(),vqe=a("span"),w8t=o("AutoModelForSeq2SeqLM"),RBo=l(),rr=a("div"),v(HW.$$.fragment),L8t=l(),Dc=a("p"),A8t=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
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method.`),G8t=l(),KW=a("p"),B8t=o("This class cannot be instantiated directly using "),Mqe=a("code"),I8t=o("__init__()"),N8t=o(" (throws an error)."),$8t=l(),oa=a("div"),v(eJ.$$.fragment),j8t=l(),Tqe=a("p"),P8t=o("Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),Z8t=l(),qc=a("p"),V8t=o(`Note:
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passed as an argument or loaded from `),Cqe=a("code"),e7t=o("pretrained_model_name_or_path"),o7t=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wqe=a("code"),r7t=o("pretrained_model_name_or_path"),t7t=o(":"),a7t=l(),ge=a("ul"),v6=a("li"),Lqe=a("strong"),n7t=o("bart"),s7t=o(" \u2014 "),L1e=a("a"),l7t=o("BartForConditionalGeneration"),i7t=o(" (BART model)"),d7t=l(),M6=a("li"),Aqe=a("strong"),m7t=o("bigbird_pegasus"),c7t=o(" \u2014 "),A1e=a("a"),f7t=o("BigBirdPegasusForConditionalGeneration"),h7t=o(" (BigBird-Pegasus model)"),g7t=l(),T6=a("li"),Rqe=a("strong"),u7t=o("blenderbot"),p7t=o(" \u2014 "),R1e=a("a"),_7t=o("BlenderbotForConditionalGeneration"),b7t=o(" (Blenderbot model)"),v7t=l(),F6=a("li"),kqe=a("strong"),M7t=o("blenderbot-small"),T7t=o(" \u2014 "),k1e=a("a"),F7t=o("BlenderbotSmallForConditionalGeneration"),E7t=o(" (BlenderbotSmall model)"),y7t=l(),E6=a("li"),Sqe=a("strong"),C7t=o("encoder-decoder"),w7t=o(" \u2014 "),S1e=a("a"),L7t=o("EncoderDecoderModel"),A7t=o(" (Encoder decoder model)"),R7t=l(),y6=a("li"),xqe=a("strong"),k7t=o("fsmt"),S7t=o(" \u2014 "),x1e=a("a"),x7t=o("FSMTForConditionalGeneration"),G7t=o(" (FairSeq Machine-Translation model)"),B7t=l(),C6=a("li"),Gqe=a("strong"),I7t=o("gptsan-japanese"),N7t=o(" \u2014 "),G1e=a("a"),$7t=o("GPTSanJapaneseForConditionalGeneration"),j7t=o(" (GPTSAN-japanese model)"),P7t=l(),w6=a("li"),Bqe=a("strong"),Z7t=o("led"),V7t=o(" \u2014 "),B1e=a("a"),W7t=o("LEDForConditionalGeneration"),J7t=o(" (LED model)"),U7t=l(),L6=a("li"),Iqe=a("strong"),X7t=o("longt5"),D7t=o(" \u2014 "),I1e=a("a"),q7t=o("LongT5ForConditionalGeneration"),O7t=o(" (LongT5 model)"),Q7t=l(),A6=a("li"),Nqe=a("strong"),Y7t=o("m2m_100"),z7t=o(" \u2014 "),N1e=a("a"),H7t=o("M2M100ForConditionalGeneration"),K7t=o(" (M2M100 model)"),eLt=l(),R6=a("li"),$qe=a("strong"),oLt=o("marian"),rLt=o(" \u2014 "),$1e=a("a"),tLt=o("MarianMTModel"),aLt=o(" (Marian model)"),nLt=l(),k6=a("li"),jqe=a("strong"),sLt=o("mbart"),lLt=o(" \u2014 "),j1e=a("a"),iLt=o("MBartForConditionalGeneration"),dLt=o(" (mBART model)"),mLt=l(),S6=a("li"),Pqe=a("strong"),cLt=o("mt5"),fLt=o(" \u2014 "),P1e=a("a"),hLt=o("MT5ForConditionalGeneration"),gLt=o(" (MT5 model)"),uLt=l(),x6=a("li"),Zqe=a("strong"),pLt=o("mvp"),_Lt=o(" \u2014 "),Z1e=a("a"),bLt=o("MvpForConditionalGeneration"),vLt=o(" (MVP model)"),MLt=l(),G6=a("li"),Vqe=a("strong"),TLt=o("nllb-moe"),FLt=o(" \u2014 "),V1e=a("a"),ELt=o("NllbMoeForConditionalGeneration"),yLt=o(" (NLLB-MOE model)"),CLt=l(),B6=a("li"),Wqe=a("strong"),wLt=o("pegasus"),LLt=o(" \u2014 "),W1e=a("a"),ALt=o("PegasusForConditionalGeneration"),RLt=o(" (Pegasus model)"),kLt=l(),I6=a("li"),Jqe=a("strong"),SLt=o("pegasus_x"),xLt=o(" \u2014 "),J1e=a("a"),GLt=o("PegasusXForConditionalGeneration"),BLt=o(" (PEGASUS-X model)"),ILt=l(),N6=a("li"),Uqe=a("strong"),NLt=o("plbart"),$Lt=o(" \u2014 "),U1e=a("a"),jLt=o("PLBartForConditionalGeneration"),PLt=o(" (PLBart model)"),ZLt=l(),$6=a("li"),Xqe=a("strong"),VLt=o("prophetnet"),WLt=o(" \u2014 "),X1e=a("a"),JLt=o("ProphetNetForConditionalGeneration"),ULt=o(" (ProphetNet model)"),XLt=l(),j6=a("li"),Dqe=a("strong"),DLt=o("switch_transformers"),qLt=o(" \u2014 "),D1e=a("a"),OLt=o("SwitchTransformersForConditionalGeneration"),QLt=o(" (SwitchTransformers model)"),YLt=l(),P6=a("li"),qqe=a("strong"),zLt=o("t5"),HLt=o(" \u2014 "),q1e=a("a"),KLt=o("T5ForConditionalGeneration"),eAt=o(" (T5 model)"),oAt=l(),Z6=a("li"),Oqe=a("strong"),rAt=o("umt5"),tAt=o(" \u2014 "),O1e=a("a"),aAt=o("UMT5ForConditionalGeneration"),nAt=o(" (UMT5 model)"),sAt=l(),V6=a("li"),Qqe=a("strong"),lAt=o("xlm-prophetnet"),iAt=o(" \u2014 "),Q1e=a("a"),dAt=o("XLMProphetNetForConditionalGeneration"),mAt=o(" (XLM-ProphetNet model)"),cAt=l(),W6=a("p"),fAt=o("The model is set in evaluation mode by default using "),Yqe=a("code"),hAt=o("model.eval()"),gAt=o(` (so for instance, dropout modules are
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model\u2019s configuration. Use `),cbe=a("a"),fkt=o("from_pretrained()"),hkt=o(" to load the model weights."),gkt=l(),v(a8.$$.fragment),ukt=l(),ft=a("div"),v(cJ.$$.fragment),pkt=l(),FOe=a("p"),_kt=o("Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),bkt=l(),es=a("p"),vkt=o("The model class to instantiate is selected based on the "),EOe=a("code"),Mkt=o("model_type"),Tkt=o(` property of the config object (either
passed as an argument or loaded from `),yOe=a("code"),Fkt=o("pretrained_model_name_or_path"),Ekt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),COe=a("code"),ykt=o("pretrained_model_name_or_path"),Ckt=o(":"),wkt=l(),$e=a("ul"),n8=a("li"),wOe=a("strong"),Lkt=o("bart"),Akt=o(" \u2014 "),fbe=a("a"),Rkt=o("FlaxBartForConditionalGeneration"),kkt=o(" (BART model)"),Skt=l(),s8=a("li"),LOe=a("strong"),xkt=o("blenderbot"),Gkt=o(" \u2014 "),hbe=a("a"),Bkt=o("FlaxBlenderbotForConditionalGeneration"),Ikt=o(" (Blenderbot model)"),Nkt=l(),l8=a("li"),AOe=a("strong"),$kt=o("blenderbot-small"),jkt=o(" \u2014 "),gbe=a("a"),Pkt=o("FlaxBlenderbotSmallForConditionalGeneration"),Zkt=o(" (BlenderbotSmall model)"),Vkt=l(),i8=a("li"),ROe=a("strong"),Wkt=o("encoder-decoder"),Jkt=o(" \u2014 "),ube=a("a"),Ukt=o("FlaxEncoderDecoderModel"),Xkt=o(" (Encoder decoder model)"),Dkt=l(),d8=a("li"),kOe=a("strong"),qkt=o("longt5"),Okt=o(" \u2014 "),pbe=a("a"),Qkt=o("FlaxLongT5ForConditionalGeneration"),Ykt=o(" (LongT5 model)"),zkt=l(),m8=a("li"),SOe=a("strong"),Hkt=o("marian"),Kkt=o(" \u2014 "),_be=a("a"),eSt=o("FlaxMarianMTModel"),oSt=o(" (Marian model)"),rSt=l(),c8=a("li"),xOe=a("strong"),tSt=o("mbart"),aSt=o(" \u2014 "),bbe=a("a"),nSt=o("FlaxMBartForConditionalGeneration"),sSt=o(" (mBART model)"),lSt=l(),f8=a("li"),GOe=a("strong"),iSt=o("mt5"),dSt=o(" \u2014 "),vbe=a("a"),mSt=o("FlaxMT5ForConditionalGeneration"),cSt=o(" (MT5 model)"),fSt=l(),h8=a("li"),BOe=a("strong"),hSt=o("pegasus"),gSt=o(" \u2014 "),Mbe=a("a"),uSt=o("FlaxPegasusForConditionalGeneration"),pSt=o(" (Pegasus model)"),_St=l(),g8=a("li"),IOe=a("strong"),bSt=o("t5"),vSt=o(" \u2014 "),Tbe=a("a"),MSt=o("FlaxT5ForConditionalGeneration"),TSt=o(" (T5 model)"),FSt=l(),v(u8.$$.fragment),BBo=l(),ef=a("h3"),p8=a("a"),NOe=a("span"),v(fJ.$$.fragment),ESt=l(),$Oe=a("span"),ySt=o("AutoModelForSequenceClassification"),IBo=l(),nr=a("div"),v(hJ.$$.fragment),CSt=l(),of=a("p"),wSt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the `),Fbe=a("a"),LSt=o("from_pretrained()"),ASt=o(" class method or the "),Ebe=a("a"),RSt=o("from_config()"),kSt=o(` class
method.`),SSt=l(),gJ=a("p"),xSt=o("This class cannot be instantiated directly using "),jOe=a("code"),GSt=o("__init__()"),BSt=o(" (throws an error)."),ISt=l(),aa=a("div"),v(uJ.$$.fragment),NSt=l(),POe=a("p"),$St=o("Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),jSt=l(),rf=a("p"),PSt=o(`Note:
Loading a model from its configuration file does `),ZOe=a("strong"),ZSt=o("not"),VSt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),ybe=a("a"),WSt=o("from_pretrained()"),JSt=o(" to load the model weights."),USt=l(),v(_8.$$.fragment),XSt=l(),fo=a("div"),v(pJ.$$.fragment),DSt=l(),VOe=a("p"),qSt=o("Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),OSt=l(),os=a("p"),QSt=o("The model class to instantiate is selected based on the "),WOe=a("code"),YSt=o("model_type"),zSt=o(` property of the config object (either
passed as an argument or loaded from `),JOe=a("code"),HSt=o("pretrained_model_name_or_path"),KSt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),UOe=a("code"),ext=o("pretrained_model_name_or_path"),oxt=o(":"),rxt=l(),B=a("ul"),b8=a("li"),XOe=a("strong"),txt=o("albert"),axt=o(" \u2014 "),Cbe=a("a"),nxt=o("AlbertForSequenceClassification"),sxt=o(" (ALBERT model)"),lxt=l(),v8=a("li"),DOe=a("strong"),ixt=o("bart"),dxt=o(" \u2014 "),wbe=a("a"),mxt=o("BartForSequenceClassification"),cxt=o(" (BART model)"),fxt=l(),M8=a("li"),qOe=a("strong"),hxt=o("bert"),gxt=o(" \u2014 "),Lbe=a("a"),uxt=o("BertForSequenceClassification"),pxt=o(" (BERT model)"),_xt=l(),T8=a("li"),OOe=a("strong"),bxt=o("big_bird"),vxt=o(" \u2014 "),Abe=a("a"),Mxt=o("BigBirdForSequenceClassification"),Txt=o(" (BigBird model)"),Fxt=l(),F8=a("li"),QOe=a("strong"),Ext=o("bigbird_pegasus"),yxt=o(" \u2014 "),Rbe=a("a"),Cxt=o("BigBirdPegasusForSequenceClassification"),wxt=o(" (BigBird-Pegasus model)"),Lxt=l(),E8=a("li"),YOe=a("strong"),Axt=o("biogpt"),Rxt=o(" \u2014 "),kbe=a("a"),kxt=o("BioGptForSequenceClassification"),Sxt=o(" (BioGpt model)"),xxt=l(),y8=a("li"),zOe=a("strong"),Gxt=o("bloom"),Bxt=o(" \u2014 "),Sbe=a("a"),Ixt=o("BloomForSequenceClassification"),Nxt=o(" (BLOOM model)"),$xt=l(),C8=a("li"),HOe=a("strong"),jxt=o("camembert"),Pxt=o(" \u2014 "),xbe=a("a"),Zxt=o("CamembertForSequenceClassification"),Vxt=o(" (CamemBERT model)"),Wxt=l(),w8=a("li"),KOe=a("strong"),Jxt=o("canine"),Uxt=o(" \u2014 "),Gbe=a("a"),Xxt=o("CanineForSequenceClassification"),Dxt=o(" (CANINE model)"),qxt=l(),L8=a("li"),eQe=a("strong"),Oxt=o("code_llama"),Qxt=o(" \u2014 "),Bbe=a("a"),Yxt=o("LlamaForSequenceClassification"),zxt=o(" (CodeLlama model)"),Hxt=l(),A8=a("li"),oQe=a("strong"),Kxt=o("convbert"),eGt=o(" \u2014 "),Ibe=a("a"),oGt=o("ConvBertForSequenceClassification"),rGt=o(" (ConvBERT model)"),tGt=l(),R8=a("li"),rQe=a("strong"),aGt=o("ctrl"),nGt=o(" \u2014 "),Nbe=a("a"),sGt=o("CTRLForSequenceClassification"),lGt=o(" (CTRL model)"),iGt=l(),k8=a("li"),tQe=a("strong"),dGt=o("data2vec-text"),mGt=o(" \u2014 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model)"),JGt=l(),$8=a("li"),mQe=a("strong"),UGt=o("esm"),XGt=o(" \u2014 "),Ube=a("a"),DGt=o("EsmForSequenceClassification"),qGt=o(" (ESM model)"),OGt=l(),j8=a("li"),cQe=a("strong"),QGt=o("falcon"),YGt=o(" \u2014 "),fQe=a("code"),zGt=o("FalconForSequenceClassification"),HGt=o(" (Falcon model)"),KGt=l(),P8=a("li"),hQe=a("strong"),eBt=o("flaubert"),oBt=o(" \u2014 "),Xbe=a("a"),rBt=o("FlaubertForSequenceClassification"),tBt=o(" (FlauBERT model)"),aBt=l(),Z8=a("li"),gQe=a("strong"),nBt=o("fnet"),sBt=o(" \u2014 "),Dbe=a("a"),lBt=o("FNetForSequenceClassification"),iBt=o(" (FNet model)"),dBt=l(),V8=a("li"),uQe=a("strong"),mBt=o("funnel"),cBt=o(" \u2014 "),qbe=a("a"),fBt=o("FunnelForSequenceClassification"),hBt=o(" (Funnel Transformer model)"),gBt=l(),W8=a("li"),pQe=a("strong"),uBt=o("gpt-sw3"),pBt=o(" \u2014 "),Obe=a("a"),_Bt=o("GPT2ForSequenceClassification"),bBt=o(" (GPT-Sw3 model)"),vBt=l(),J8=a("li"),_Qe=a("strong"),MBt=o("gpt2"),TBt=o(" \u2014 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method.`),iPt=l(),vJ=a("p"),dPt=o("This class cannot be instantiated directly using "),pYe=a("code"),mPt=o("__init__()"),cPt=o(" (throws an error)."),fPt=l(),na=a("div"),v(MJ.$$.fragment),hPt=l(),_Ye=a("p"),gPt=o("Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),uPt=l(),nf=a("p"),pPt=o(`Note:
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falling back to using pattern matching on `),EHe=a("code"),HOt=o("pretrained_model_name_or_path"),KOt=o(":"),eQt=l(),Fe=a("ul"),yA=a("li"),yHe=a("strong"),oQt=o("albert"),rQt=o(" \u2014 "),AMe=a("a"),tQt=o("TFAlbertForMultipleChoice"),aQt=o(" (ALBERT model)"),nQt=l(),CA=a("li"),CHe=a("strong"),sQt=o("bert"),lQt=o(" \u2014 "),RMe=a("a"),iQt=o("TFBertForMultipleChoice"),dQt=o(" (BERT model)"),mQt=l(),wA=a("li"),wHe=a("strong"),cQt=o("camembert"),fQt=o(" \u2014 "),kMe=a("a"),hQt=o("TFCamembertForMultipleChoice"),gQt=o(" (CamemBERT model)"),uQt=l(),LA=a("li"),LHe=a("strong"),pQt=o("convbert"),_Qt=o(" \u2014 "),SMe=a("a"),bQt=o("TFConvBertForMultipleChoice"),vQt=o(" (ConvBERT model)"),MQt=l(),AA=a("li"),AHe=a("strong"),TQt=o("distilbert"),FQt=o(" \u2014 "),xMe=a("a"),EQt=o("TFDistilBertForMultipleChoice"),yQt=o(" (DistilBERT model)"),CQt=l(),RA=a("li"),RHe=a("strong"),wQt=o("electra"),LQt=o(" \u2014 "),GMe=a("a"),AQt=o("TFElectraForMultipleChoice"),RQt=o(" (ELECTRA model)"),kQt=l(),kA=a("li"),kHe=a("strong"),SQt=o("flaubert"),xQt=o(" \u2014 "),BMe=a("a"),GQt=o("TFFlaubertForMultipleChoice"),BQt=o(" (FlauBERT model)"),IQt=l(),SA=a("li"),SHe=a("strong"),NQt=o("funnel"),$Qt=o(" \u2014 "),IMe=a("a"),jQt=o("TFFunnelForMultipleChoice"),PQt=o(" (Funnel Transformer model)"),ZQt=l(),xA=a("li"),xHe=a("strong"),VQt=o("longformer"),WQt=o(" \u2014 "),NMe=a("a"),JQt=o("TFLongformerForMultipleChoice"),UQt=o(" (Longformer model)"),XQt=l(),GA=a("li"),GHe=a("strong"),DQt=o("mobilebert"),qQt=o(" \u2014 "),$Me=a("a"),OQt=o("TFMobileBertForMultipleChoice"),QQt=o(" (MobileBERT model)"),YQt=l(),BA=a("li"),BHe=a("strong"),zQt=o("mpnet"),HQt=o(" \u2014 "),jMe=a("a"),KQt=o("TFMPNetForMultipleChoice"),eYt=o(" (MPNet model)"),oYt=l(),IA=a("li"),IHe=a("strong"),rYt=o("rembert"),tYt=o(" \u2014 "),PMe=a("a"),aYt=o("TFRemBertForMultipleChoice"),nYt=o(" (RemBERT model)"),sYt=l(),NA=a("li"),NHe=a("strong"),lYt=o("roberta"),iYt=o(" \u2014 "),ZMe=a("a"),dYt=o("TFRobertaForMultipleChoice"),mYt=o(" (RoBERTa model)"),cYt=l(),$A=a("li"),$He=a("strong"),fYt=o("roberta-prelayernorm"),hYt=o(" \u2014 "),VMe=a("a"),gYt=o("TFRobertaPreLayerNormForMultipleChoice"),uYt=o(" (RoBERTa-PreLayerNorm model)"),pYt=l(),jA=a("li"),jHe=a("strong"),_Yt=o("roformer"),bYt=o(" \u2014 "),WMe=a("a"),vYt=o("TFRoFormerForMultipleChoice"),MYt=o(" (RoFormer model)"),TYt=l(),PA=a("li"),PHe=a("strong"),FYt=o("xlm"),EYt=o(" \u2014 "),JMe=a("a"),yYt=o("TFXLMForMultipleChoice"),CYt=o(" (XLM model)"),wYt=l(),ZA=a("li"),ZHe=a("strong"),LYt=o("xlm-roberta"),AYt=o(" \u2014 "),UMe=a("a"),RYt=o("TFXLMRobertaForMultipleChoice"),kYt=o(" (XLM-RoBERTa model)"),SYt=l(),VA=a("li"),VHe=a("strong"),xYt=o("xlnet"),GYt=o(" \u2014 "),XMe=a("a"),BYt=o("TFXLNetForMultipleChoice"),IYt=o(" (XLNet model)"),NYt=l(),v(WA.$$.fragment),UBo=l(),pf=a("h3"),JA=a("a"),WHe=a("span"),v($J.$$.fragment),$Yt=l(),JHe=a("span"),jYt=o("FlaxAutoModelForMultipleChoice"),XBo=l(),mr=a("div"),v(jJ.$$.fragment),PYt=l(),_f=a("p"),ZYt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
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passed as an argument or loaded from `),QHe=a("code"),fzt=o("pretrained_model_name_or_path"),hzt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),YHe=a("code"),gzt=o("pretrained_model_name_or_path"),uzt=o(":"),pzt=l(),Ue=a("ul"),XA=a("li"),zHe=a("strong"),_zt=o("albert"),bzt=o(" \u2014 "),QMe=a("a"),vzt=o("FlaxAlbertForMultipleChoice"),Mzt=o(" (ALBERT model)"),Tzt=l(),DA=a("li"),HHe=a("strong"),Fzt=o("bert"),Ezt=o(" \u2014 "),YMe=a("a"),yzt=o("FlaxBertForMultipleChoice"),Czt=o(" (BERT model)"),wzt=l(),qA=a("li"),KHe=a("strong"),Lzt=o("big_bird"),Azt=o(" \u2014 "),zMe=a("a"),Rzt=o("FlaxBigBirdForMultipleChoice"),kzt=o(" (BigBird model)"),Szt=l(),OA=a("li"),eKe=a("strong"),xzt=o("distilbert"),Gzt=o(" \u2014 "),HMe=a("a"),Bzt=o("FlaxDistilBertForMultipleChoice"),Izt=o(" (DistilBERT model)"),Nzt=l(),QA=a("li"),oKe=a("strong"),$zt=o("electra"),jzt=o(" \u2014 "),KMe=a("a"),Pzt=o("FlaxElectraForMultipleChoice"),Zzt=o(" (ELECTRA model)"),Vzt=l(),YA=a("li"),rKe=a("strong"),Wzt=o("roberta"),Jzt=o(" \u2014 "),eTe=a("a"),Uzt=o("FlaxRobertaForMultipleChoice"),Xzt=o(" (RoBERTa model)"),Dzt=l(),zA=a("li"),tKe=a("strong"),qzt=o("roberta-prelayernorm"),Ozt=o(" \u2014 "),oTe=a("a"),Qzt=o("FlaxRobertaPreLayerNormForMultipleChoice"),Yzt=o(" (RoBERTa-PreLayerNorm model)"),zzt=l(),HA=a("li"),aKe=a("strong"),Hzt=o("roformer"),Kzt=o(" \u2014 "),rTe=a("a"),eHt=o("FlaxRoFormerForMultipleChoice"),oHt=o(" (RoFormer model)"),rHt=l(),KA=a("li"),nKe=a("strong"),tHt=o("xlm-roberta"),aHt=o(" \u2014 "),tTe=a("a"),nHt=o("FlaxXLMRobertaForMultipleChoice"),sHt=o(" (XLM-RoBERTa model)"),lHt=l(),v(eR.$$.fragment),DBo=l(),vf=a("h3"),oR=a("a"),sKe=a("span"),v(WJ.$$.fragment),iHt=l(),lKe=a("span"),dHt=o("AutoModelForNextSentencePrediction"),qBo=l(),cr=a("div"),v(JJ.$$.fragment),mHt=l(),Mf=a("p"),cHt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
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method.`),pHt=l(),UJ=a("p"),_Ht=o("This class cannot be instantiated directly using "),iKe=a("code"),bHt=o("__init__()"),vHt=o(" (throws an error)."),MHt=l(),ma=a("div"),v(XJ.$$.fragment),THt=l(),dKe=a("p"),FHt=o("Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),EHt=l(),Tf=a("p"),yHt=o(`Note:
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model\u2019s configuration. Use `),sTe=a("a"),LHt=o("from_pretrained()"),AHt=o(" to load the model weights."),RHt=l(),v(rR.$$.fragment),kHt=l(),go=a("div"),v(DJ.$$.fragment),SHt=l(),cKe=a("p"),xHt=o("Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),GHt=l(),ls=a("p"),BHt=o("The model class to instantiate is selected based on the "),fKe=a("code"),IHt=o("model_type"),NHt=o(` property of the config object (either
passed as an argument or loaded from `),hKe=a("code"),$Ht=o("pretrained_model_name_or_path"),jHt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),gKe=a("code"),PHt=o("pretrained_model_name_or_path"),ZHt=o(":"),VHt=l(),eo=a("ul"),tR=a("li"),uKe=a("strong"),WHt=o("bert"),JHt=o(" \u2014 "),lTe=a("a"),UHt=o("BertForNextSentencePrediction"),XHt=o(" (BERT model)"),DHt=l(),aR=a("li"),pKe=a("strong"),qHt=o("ernie"),OHt=o(" \u2014 "),iTe=a("a"),QHt=o("ErnieForNextSentencePrediction"),YHt=o(" (ERNIE model)"),zHt=l(),nR=a("li"),_Ke=a("strong"),HHt=o("fnet"),KHt=o(" \u2014 "),dTe=a("a"),eKt=o("FNetForNextSentencePrediction"),oKt=o(" (FNet model)"),rKt=l(),sR=a("li"),bKe=a("strong"),tKt=o("megatron-bert"),aKt=o(" \u2014 "),mTe=a("a"),nKt=o("MegatronBertForNextSentencePrediction"),sKt=o(" (Megatron-BERT model)"),lKt=l(),lR=a("li"),vKe=a("strong"),iKt=o("mobilebert"),dKt=o(" \u2014 "),cTe=a("a"),mKt=o("MobileBertForNextSentencePrediction"),cKt=o(" (MobileBERT model)"),fKt=l(),iR=a("li"),MKe=a("strong"),hKt=o("nezha"),gKt=o(" \u2014 "),fTe=a("a"),uKt=o("NezhaForNextSentencePrediction"),pKt=o(" (Nezha model)"),_Kt=l(),dR=a("li"),TKe=a("strong"),bKt=o("qdqbert"),vKt=o(" \u2014 "),hTe=a("a"),MKt=o("QDQBertForNextSentencePrediction"),TKt=o(" (QDQBert model)"),FKt=l(),mR=a("p"),EKt=o("The model is set in evaluation mode by default using "),FKe=a("code"),yKt=o("model.eval()"),CKt=o(` (so for instance, dropout modules are
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method.`),NKt=l(),QJ=a("p"),$Kt=o("This class cannot be instantiated directly using "),wKe=a("code"),jKt=o("__init__()"),PKt=o(" (throws an error)."),ZKt=l(),ca=a("div"),v(YJ.$$.fragment),VKt=l(),LKe=a("p"),WKt=o("Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),JKt=l(),yf=a("p"),UKt=o(`Note:
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model\u2019s configuration. Use `),pTe=a("a"),qKt=o("from_pretrained()"),OKt=o(" to load the model weights."),QKt=l(),v(hR.$$.fragment),YKt=l(),_t=a("div"),v(zJ.$$.fragment),zKt=l(),RKe=a("p"),HKt=o("Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),KKt=l(),is=a("p"),eea=o("The model class to instantiate is selected based on the "),kKe=a("code"),oea=o("model_type"),rea=o(` property of the config object (either
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falling back to using pattern matching on `),xKe=a("code"),nea=o("pretrained_model_name_or_path"),sea=o(":"),lea=l(),HJ=a("ul"),gR=a("li"),GKe=a("strong"),iea=o("bert"),dea=o(" \u2014 "),_Te=a("a"),mea=o("TFBertForNextSentencePrediction"),cea=o(" (BERT model)"),fea=l(),uR=a("li"),BKe=a("strong"),hea=o("mobilebert"),gea=o(" \u2014 "),bTe=a("a"),uea=o("TFMobileBertForNextSentencePrediction"),pea=o(" (MobileBERT model)"),_ea=l(),v(pR.$$.fragment),YBo=l(),Cf=a("h3"),_R=a("a"),IKe=a("span"),v(KJ.$$.fragment),bea=l(),NKe=a("span"),vea=o("FlaxAutoModelForNextSentencePrediction"),zBo=l(),hr=a("div"),v(eU.$$.fragment),Mea=l(),wf=a("p"),Tea=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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falling back to using pattern matching on `),JKe=a("code"),Qea=o("pretrained_model_name_or_path"),Yea=o(":"),zea=l(),UKe=a("ul"),vR=a("li"),XKe=a("strong"),Hea=o("bert"),Kea=o(" \u2014 "),FTe=a("a"),eoa=o("FlaxBertForNextSentencePrediction"),ooa=o(" (BERT model)"),roa=l(),v(MR.$$.fragment),HBo=l(),Af=a("h3"),TR=a("a"),DKe=a("span"),v(aU.$$.fragment),toa=l(),qKe=a("span"),aoa=o("AutoModelForTokenClassification"),KBo=l(),gr=a("div"),v(nU.$$.fragment),noa=l(),Rf=a("p"),soa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
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model\u2019s configuration. Use `),Dye=a("a"),w2a=o("from_pretrained()"),L2a=o(" to load the model weights."),A2a=l(),v(qx.$$.fragment),R2a=l(),Ft=a("div"),v(GU.$$.fragment),k2a=l(),rao=a("p"),S2a=o("Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),x2a=l(),us=a("p"),G2a=o("The model class to instantiate is selected based on the "),tao=a("code"),B2a=o("model_type"),I2a=o(` property of the config object (either
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falling back to using pattern matching on `),nao=a("code"),j2a=o("pretrained_model_name_or_path"),P2a=o(":"),Z2a=l(),xe=a("ul"),Ox=a("li"),sao=a("strong"),V2a=o("albert"),W2a=o(" \u2014 "),qye=a("a"),J2a=o("FlaxAlbertForQuestionAnswering"),U2a=o(" (ALBERT model)"),X2a=l(),Qx=a("li"),lao=a("strong"),D2a=o("bart"),q2a=o(" \u2014 "),Oye=a("a"),O2a=o("FlaxBartForQuestionAnswering"),Q2a=o(" (BART model)"),Y2a=l(),Yx=a("li"),iao=a("strong"),z2a=o("bert"),H2a=o(" \u2014 "),Qye=a("a"),K2a=o("FlaxBertForQuestionAnswering"),eva=o(" (BERT model)"),ova=l(),zx=a("li"),dao=a("strong"),rva=o("big_bird"),tva=o(" \u2014 "),Yye=a("a"),ava=o("FlaxBigBirdForQuestionAnswering"),nva=o(" (BigBird model)"),sva=l(),Hx=a("li"),mao=a("strong"),lva=o("distilbert"),iva=o(" \u2014 "),zye=a("a"),dva=o("FlaxDistilBertForQuestionAnswering"),mva=o(" (DistilBERT model)"),cva=l(),Kx=a("li"),cao=a("strong"),fva=o("electra"),hva=o(" \u2014 "),Hye=a("a"),gva=o("FlaxElectraForQuestionAnswering"),uva=o(" (ELECTRA model)"),pva=l(),eG=a("li"),fao=a("strong"),_va=o("mbart"),bva=o(" \u2014 "),Kye=a("a"),vva=o("FlaxMBartForQuestionAnswering"),Mva=o(" (mBART model)"),Tva=l(),oG=a("li"),hao=a("strong"),Fva=o("roberta"),Eva=o(" \u2014 "),e0e=a("a"),yva=o("FlaxRobertaForQuestionAnswering"),Cva=o(" (RoBERTa model)"),wva=l(),rG=a("li"),gao=a("strong"),Lva=o("roberta-prelayernorm"),Ava=o(" \u2014 "),o0e=a("a"),Rva=o("FlaxRobertaPreLayerNormForQuestionAnswering"),kva=o(" (RoBERTa-PreLayerNorm model)"),Sva=l(),tG=a("li"),uao=a("strong"),xva=o("roformer"),Gva=o(" \u2014 "),r0e=a("a"),Bva=o("FlaxRoFormerForQuestionAnswering"),Iva=o(" (RoFormer model)"),Nva=l(),aG=a("li"),pao=a("strong"),$va=o("xlm-roberta"),jva=o(" \u2014 "),t0e=a("a"),Pva=o("FlaxXLMRobertaForQuestionAnswering"),Zva=o(" (XLM-RoBERTa model)"),Vva=l(),v(nG.$$.fragment),mIo=l(),Df=a("h3"),sG=a("a"),_ao=a("span"),v(BU.$$.fragment),Wva=l(),bao=a("span"),Jva=o("AutoModelForTextEncoding"),cIo=l(),IU=a("div"),v(NU.$$.fragment),fIo=l(),qf=a("h3"),lG=a("a"),vao=a("span"),v($U.$$.fragment),Uva=l(),Mao=a("span"),Xva=o("TFAutoModelForTextEncoding"),hIo=l(),jU=a("div"),v(PU.$$.fragment),gIo=l(),Of=a("h2"),iG=a("a"),Tao=a("span"),v(ZU.$$.fragment),Dva=l(),Fao=a("span"),qva=o("Computer vision"),uIo=l(),a0e=a("p"),Ova=o("The following auto classes are available for the following computer vision tasks."),pIo=l(),Qf=a("h3"),dG=a("a"),Eao=a("span"),v(VU.$$.fragment),Qva=l(),yao=a("span"),Yva=o("AutoModelForDepthEstimation"),_Io=l(),Mr=a("div"),v(WU.$$.fragment),zva=l(),Yf=a("p"),Hva=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a depth estimation head) when created
with the `),n0e=a("a"),Kva=o("from_pretrained()"),eMa=o(" class method or the "),s0e=a("a"),oMa=o("from_config()"),rMa=o(` class
method.`),tMa=l(),JU=a("p"),aMa=o("This class cannot be instantiated directly using "),Cao=a("code"),nMa=o("__init__()"),sMa=o(" (throws an error)."),lMa=l(),va=a("div"),v(UU.$$.fragment),iMa=l(),wao=a("p"),dMa=o("Instantiates one of the model classes of the library (with a depth estimation head) from a configuration."),mMa=l(),zf=a("p"),cMa=o(`Note:
Loading a model from its configuration file does `),Lao=a("strong"),fMa=o("not"),hMa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),l0e=a("a"),gMa=o("from_pretrained()"),uMa=o(" to load the model weights."),pMa=l(),v(mG.$$.fragment),_Ma=l(),_o=a("div"),v(XU.$$.fragment),bMa=l(),Aao=a("p"),vMa=o("Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model."),MMa=l(),ps=a("p"),TMa=o("The model class to instantiate is selected based on the "),Rao=a("code"),FMa=o("model_type"),EMa=o(` property of the config object (either
passed as an argument or loaded from `),kao=a("code"),yMa=o("pretrained_model_name_or_path"),CMa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Sao=a("code"),wMa=o("pretrained_model_name_or_path"),LMa=o(":"),AMa=l(),DU=a("ul"),cG=a("li"),xao=a("strong"),RMa=o("dpt"),kMa=o(" \u2014 "),i0e=a("a"),SMa=o("DPTForDepthEstimation"),xMa=o(" (DPT model)"),GMa=l(),fG=a("li"),Gao=a("strong"),BMa=o("glpn"),IMa=o(" \u2014 "),d0e=a("a"),NMa=o("GLPNForDepthEstimation"),$Ma=o(" (GLPN model)"),jMa=l(),hG=a("p"),PMa=o("The model is set in evaluation mode by default using "),Bao=a("code"),ZMa=o("model.eval()"),VMa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Iao=a("code"),WMa=o("model.train()"),JMa=l(),v(gG.$$.fragment),bIo=l(),Hf=a("h3"),uG=a("a"),Nao=a("span"),v(qU.$$.fragment),UMa=l(),$ao=a("span"),XMa=o("AutoModelForImageClassification"),vIo=l(),Tr=a("div"),v(OU.$$.fragment),DMa=l(),Kf=a("p"),qMa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),m0e=a("a"),OMa=o("from_pretrained()"),QMa=o(" class method or the "),c0e=a("a"),YMa=o("from_config()"),zMa=o(` class
method.`),HMa=l(),QU=a("p"),KMa=o("This class cannot be instantiated directly using "),jao=a("code"),eTa=o("__init__()"),oTa=o(" (throws an error)."),rTa=l(),Ma=a("div"),v(YU.$$.fragment),tTa=l(),Pao=a("p"),aTa=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),nTa=l(),eh=a("p"),sTa=o(`Note:
Loading a model from its configuration file does `),Zao=a("strong"),lTa=o("not"),iTa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),f0e=a("a"),dTa=o("from_pretrained()"),mTa=o(" to load the model weights."),cTa=l(),v(pG.$$.fragment),fTa=l(),bo=a("div"),v(zU.$$.fragment),hTa=l(),Vao=a("p"),gTa=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),uTa=l(),_s=a("p"),pTa=o("The model class to instantiate is selected based on the "),Wao=a("code"),_Ta=o("model_type"),bTa=o(` property of the config object (either
passed as an argument or loaded from `),Jao=a("code"),vTa=o("pretrained_model_name_or_path"),MTa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Uao=a("code"),TTa=o("pretrained_model_name_or_path"),FTa=o(":"),ETa=l(),le=a("ul"),_G=a("li"),Xao=a("strong"),yTa=o("beit"),CTa=o(" \u2014 "),h0e=a("a"),wTa=o("BeitForImageClassification"),LTa=o(" (BEiT model)"),ATa=l(),bG=a("li"),Dao=a("strong"),RTa=o("bit"),kTa=o(" \u2014 "),g0e=a("a"),STa=o("BitForImageClassification"),xTa=o(" (BiT model)"),GTa=l(),vG=a("li"),qao=a("strong"),BTa=o("convnext"),ITa=o(" \u2014 "),u0e=a("a"),NTa=o("ConvNextForImageClassification"),$Ta=o(" (ConvNeXT model)"),jTa=l(),MG=a("li"),Oao=a("strong"),PTa=o("convnextv2"),ZTa=o(" \u2014 "),p0e=a("a"),VTa=o("ConvNextV2ForImageClassification"),WTa=o(" (ConvNeXTV2 model)"),JTa=l(),TG=a("li"),Qao=a("strong"),UTa=o("cvt"),XTa=o(" \u2014 "),_0e=a("a"),DTa=o("CvtForImageClassification"),qTa=o(" (CvT model)"),OTa=l(),FG=a("li"),Yao=a("strong"),QTa=o("data2vec-vision"),YTa=o(" \u2014 "),b0e=a("a"),zTa=o("Data2VecVisionForImageClassification"),HTa=o(" (Data2VecVision model)"),KTa=l(),Ji=a("li"),zao=a("strong"),eFa=o("deit"),oFa=o(" \u2014 "),v0e=a("a"),rFa=o("DeiTForImageClassification"),tFa=o(" or "),M0e=a("a"),aFa=o("DeiTForImageClassificationWithTeacher"),nFa=o(" (DeiT model)"),sFa=l(),EG=a("li"),Hao=a("strong"),lFa=o("dinat"),iFa=o(" \u2014 "),T0e=a("a"),dFa=o("DinatForImageClassification"),mFa=o(" (DiNAT model)"),cFa=l(),yG=a("li"),Kao=a("strong"),fFa=o("dinov2"),hFa=o(" \u2014 "),F0e=a("a"),gFa=o("Dinov2ForImageClassification"),uFa=o(" (DINOv2 model)"),pFa=l(),Ui=a("li"),eno=a("strong"),_Fa=o("efficientformer"),bFa=o(" \u2014 "),E0e=a("a"),vFa=o("EfficientFormerForImageClassification"),MFa=o(" or "),y0e=a("a"),TFa=o("EfficientFormerForImageClassificationWithTeacher"),FFa=o(" (EfficientFormer model)"),EFa=l(),CG=a("li"),ono=a("strong"),yFa=o("efficientnet"),CFa=o(" \u2014 "),C0e=a("a"),wFa=o("EfficientNetForImageClassification"),LFa=o(" (EfficientNet model)"),AFa=l(),wG=a("li"),rno=a("strong"),RFa=o("focalnet"),kFa=o(" \u2014 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deactivated). To train the model, you should first set it back in training mode with `),yno=a("code"),Iya=o("model.train()"),Nya=l(),v(DG.$$.fragment),MIo=l(),oh=a("h3"),qG=a("a"),Cno=a("span"),v(HU.$$.fragment),$ya=l(),wno=a("span"),jya=o("TFAutoModelForImageClassification"),TIo=l(),Fr=a("div"),v(KU.$$.fragment),Pya=l(),rh=a("p"),Zya=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
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method.`),Xya=l(),eX=a("p"),Dya=o("This class cannot be instantiated directly using "),Lno=a("code"),qya=o("__init__()"),Oya=o(" (throws an error)."),Qya=l(),Fa=a("div"),v(oX.$$.fragment),Yya=l(),Ano=a("p"),zya=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),Hya=l(),th=a("p"),Kya=o(`Note:
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passed as an argument or loaded from `),xno=a("code"),f0a=o("pretrained_model_name_or_path"),h0a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Gno=a("code"),g0a=o("pretrained_model_name_or_path"),u0a=o(":"),p0a=l(),Ge=a("ul"),QG=a("li"),Bno=a("strong"),_0a=o("convnext"),b0a=o(" \u2014 "),K0e=a("a"),v0a=o("TFConvNextForImageClassification"),M0a=o(" (ConvNeXT model)"),T0a=l(),YG=a("li"),Ino=a("strong"),F0a=o("cvt"),E0a=o(" \u2014 "),e5e=a("a"),y0a=o("TFCvtForImageClassification"),C0a=o(" (CvT model)"),w0a=l(),zG=a("li"),Nno=a("strong"),L0a=o("data2vec-vision"),A0a=o(" \u2014 "),o5e=a("a"),R0a=o("TFData2VecVisionForImageClassification"),k0a=o(" (Data2VecVision model)"),S0a=l(),Di=a("li"),$no=a("strong"),x0a=o("deit"),G0a=o(" \u2014 "),r5e=a("a"),B0a=o("TFDeiTForImageClassification"),I0a=o(" or "),t5e=a("a"),N0a=o("TFDeiTForImageClassificationWithTeacher"),$0a=o(" (DeiT model)"),j0a=l(),qi=a("li"),jno=a("strong"),P0a=o("efficientformer"),Z0a=o(" \u2014 "),a5e=a("a"),V0a=o("TFEfficientFormerForImageClassification"),W0a=o(" or "),n5e=a("a"),J0a=o("TFEfficientFormerForImageClassificationWithTeacher"),U0a=o(" (EfficientFormer model)"),X0a=l(),HG=a("li"),Pno=a("strong"),D0a=o("mobilevit"),q0a=o(" \u2014 "),s5e=a("a"),O0a=o("TFMobileViTForImageClassification"),Q0a=o(" (MobileViT model)"),Y0a=l(),KG=a("li"),Zno=a("strong"),z0a=o("regnet"),H0a=o(" \u2014 "),l5e=a("a"),K0a=o("TFRegNetForImageClassification"),e5a=o(" (RegNet model)"),o5a=l(),eB=a("li"),Vno=a("strong"),r5a=o("resnet"),t5a=o(" \u2014 "),i5e=a("a"),a5a=o("TFResNetForImageClassification"),n5a=o(" (ResNet model)"),s5a=l(),oB=a("li"),Wno=a("strong"),l5a=o("segformer"),i5a=o(" \u2014 "),d5e=a("a"),d5a=o("TFSegformerForImageClassification"),m5a=o(" (SegFormer model)"),c5a=l(),rB=a("li"),Jno=a("strong"),f5a=o("swin"),h5a=o(" \u2014 "),m5e=a("a"),g5a=o("TFSwinForImageClassification"),u5a=o(" (Swin Transformer model)"),p5a=l(),tB=a("li"),Uno=a("strong"),_5a=o("vit"),b5a=o(" \u2014 "),c5e=a("a"),v5a=o("TFViTForImageClassification"),M5a=o(" (ViT model)"),T5a=l(),v(aB.$$.fragment),FIo=l(),ah=a("h3"),nB=a("a"),Xno=a("span"),v(tX.$$.fragment),F5a=l(),Dno=a("span"),E5a=o("FlaxAutoModelForImageClassification"),EIo=l(),Er=a("div"),v(aX.$$.fragment),y5a=l(),nh=a("p"),C5a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
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method.`),k5a=l(),nX=a("p"),S5a=o("This class cannot be instantiated directly using "),qno=a("code"),x5a=o("__init__()"),G5a=o(" (throws an error)."),B5a=l(),Ea=a("div"),v(sX.$$.fragment),I5a=l(),Ono=a("p"),N5a=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),$5a=l(),sh=a("p"),j5a=o(`Note:
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passed as an argument or loaded from `),Hno=a("code"),z5a=o("pretrained_model_name_or_path"),H5a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Kno=a("code"),K5a=o("pretrained_model_name_or_path"),e3a=o(":"),o3a=l(),Ms=a("ul"),lB=a("li"),eso=a("strong"),r3a=o("beit"),t3a=o(" \u2014 "),u5e=a("a"),a3a=o("FlaxBeitForImageClassification"),n3a=o(" (BEiT model)"),s3a=l(),iB=a("li"),oso=a("strong"),l3a=o("regnet"),i3a=o(" \u2014 "),p5e=a("a"),d3a=o("FlaxRegNetForImageClassification"),m3a=o(" (RegNet model)"),c3a=l(),dB=a("li"),rso=a("strong"),f3a=o("resnet"),h3a=o(" \u2014 "),_5e=a("a"),g3a=o("FlaxResNetForImageClassification"),u3a=o(" (ResNet model)"),p3a=l(),mB=a("li"),tso=a("strong"),_3a=o("vit"),b3a=o(" \u2014 "),b5e=a("a"),v3a=o("FlaxViTForImageClassification"),M3a=o(" (ViT model)"),T3a=l(),v(cB.$$.fragment),yIo=l(),lh=a("h3"),fB=a("a"),aso=a("span"),v(iX.$$.fragment),F3a=l(),nso=a("span"),E3a=o("AutoModelForVideoClassification"),CIo=l(),yr=a("div"),v(dX.$$.fragment),y3a=l(),ih=a("p"),C3a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a video classification head) when created
with the `),v5e=a("a"),w3a=o("from_pretrained()"),L3a=o(" class method or the "),M5e=a("a"),A3a=o("from_config()"),R3a=o(` class
method.`),k3a=l(),mX=a("p"),S3a=o("This class cannot be instantiated directly using "),sso=a("code"),x3a=o("__init__()"),G3a=o(" (throws an error)."),B3a=l(),ya=a("div"),v(cX.$$.fragment),I3a=l(),lso=a("p"),N3a=o("Instantiates one of the model classes of the library (with a video classification head) from a configuration."),$3a=l(),dh=a("p"),j3a=o(`Note:
Loading a model from its configuration file does `),iso=a("strong"),P3a=o("not"),Z3a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),T5e=a("a"),V3a=o("from_pretrained()"),W3a=o(" to load the model weights."),J3a=l(),v(hB.$$.fragment),U3a=l(),vo=a("div"),v(fX.$$.fragment),X3a=l(),dso=a("p"),D3a=o("Instantiate one of the model classes of the library (with a video classification head) from a pretrained model."),q3a=l(),Ts=a("p"),O3a=o("The model class to instantiate is selected based on the "),mso=a("code"),Q3a=o("model_type"),Y3a=o(` property of the config object (either
passed as an argument or loaded from `),cso=a("code"),z3a=o("pretrained_model_name_or_path"),H3a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),fso=a("code"),K3a=o("pretrained_model_name_or_path"),e9a=o(":"),o9a=l(),mh=a("ul"),gB=a("li"),hso=a("strong"),r9a=o("timesformer"),t9a=o(" \u2014 "),F5e=a("a"),a9a=o("TimesformerForVideoClassification"),n9a=o(" (TimeSformer model)"),s9a=l(),uB=a("li"),gso=a("strong"),l9a=o("videomae"),i9a=o(" \u2014 "),E5e=a("a"),d9a=o("VideoMAEForVideoClassification"),m9a=o(" (VideoMAE model)"),c9a=l(),pB=a("li"),uso=a("strong"),f9a=o("vivit"),h9a=o(" \u2014 "),y5e=a("a"),g9a=o("VivitForVideoClassification"),u9a=o(" (ViViT model)"),p9a=l(),_B=a("p"),_9a=o("The model is set in evaluation mode by default using "),pso=a("code"),b9a=o("model.eval()"),v9a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),_so=a("code"),M9a=o("model.train()"),T9a=l(),v(bB.$$.fragment),wIo=l(),ch=a("h3"),vB=a("a"),bso=a("span"),v(hX.$$.fragment),F9a=l(),vso=a("span"),E9a=o("AutoModelForMaskedImageModeling"),LIo=l(),Cr=a("div"),v(gX.$$.fragment),y9a=l(),fh=a("p"),C9a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),C5e=a("a"),w9a=o("from_pretrained()"),L9a=o(" class method or the "),w5e=a("a"),A9a=o("from_config()"),R9a=o(` class
method.`),k9a=l(),uX=a("p"),S9a=o("This class cannot be instantiated directly using "),Mso=a("code"),x9a=o("__init__()"),G9a=o(" (throws an error)."),B9a=l(),Ca=a("div"),v(pX.$$.fragment),I9a=l(),Tso=a("p"),N9a=o("Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),$9a=l(),hh=a("p"),j9a=o(`Note:
Loading a model from its configuration file does `),Fso=a("strong"),P9a=o("not"),Z9a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),L5e=a("a"),V9a=o("from_pretrained()"),W9a=o(" to load the model weights."),J9a=l(),v(MB.$$.fragment),U9a=l(),Mo=a("div"),v(_X.$$.fragment),X9a=l(),Eso=a("p"),D9a=o("Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),q9a=l(),Fs=a("p"),O9a=o("The model class to instantiate is selected based on the "),yso=a("code"),Q9a=o("model_type"),Y9a=o(` property of the config object (either
passed as an argument or loaded from `),Cso=a("code"),z9a=o("pretrained_model_name_or_path"),H9a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wso=a("code"),K9a=o("pretrained_model_name_or_path"),e4a=o(":"),o4a=l(),Nt=a("ul"),TB=a("li"),Lso=a("strong"),r4a=o("deit"),t4a=o(" \u2014 "),A5e=a("a"),a4a=o("DeiTForMaskedImageModeling"),n4a=o(" (DeiT model)"),s4a=l(),FB=a("li"),Aso=a("strong"),l4a=o("focalnet"),i4a=o(" \u2014 "),R5e=a("a"),d4a=o("FocalNetForMaskedImageModeling"),m4a=o(" (FocalNet model)"),c4a=l(),EB=a("li"),Rso=a("strong"),f4a=o("swin"),h4a=o(" \u2014 "),k5e=a("a"),g4a=o("SwinForMaskedImageModeling"),u4a=o(" (Swin Transformer model)"),p4a=l(),yB=a("li"),kso=a("strong"),_4a=o("swinv2"),b4a=o(" \u2014 "),S5e=a("a"),v4a=o("Swinv2ForMaskedImageModeling"),M4a=o(" (Swin Transformer V2 model)"),T4a=l(),CB=a("li"),Sso=a("strong"),F4a=o("vit"),E4a=o(" \u2014 "),x5e=a("a"),y4a=o("ViTForMaskedImageModeling"),C4a=o(" (ViT model)"),w4a=l(),wB=a("p"),L4a=o("The model is set in evaluation mode by default using "),xso=a("code"),A4a=o("model.eval()"),R4a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Gso=a("code"),k4a=o("model.train()"),S4a=l(),v(LB.$$.fragment),AIo=l(),gh=a("h3"),AB=a("a"),Bso=a("span"),v(bX.$$.fragment),x4a=l(),Iso=a("span"),G4a=o("TFAutoModelForMaskedImageModeling"),RIo=l(),wr=a("div"),v(vX.$$.fragment),B4a=l(),uh=a("p"),I4a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),G5e=a("a"),N4a=o("from_pretrained()"),$4a=o(" class method or the "),B5e=a("a"),j4a=o("from_config()"),P4a=o(` class
method.`),Z4a=l(),MX=a("p"),V4a=o("This class cannot be instantiated directly using "),Nso=a("code"),W4a=o("__init__()"),J4a=o(" (throws an error)."),U4a=l(),wa=a("div"),v(TX.$$.fragment),X4a=l(),$so=a("p"),D4a=o("Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),q4a=l(),ph=a("p"),O4a=o(`Note:
Loading a model from its configuration file does `),jso=a("strong"),Q4a=o("not"),Y4a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),I5e=a("a"),z4a=o("from_pretrained()"),H4a=o(" to load the model weights."),K4a=l(),v(RB.$$.fragment),eCa=l(),Ct=a("div"),v(FX.$$.fragment),oCa=l(),Pso=a("p"),rCa=o("Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),tCa=l(),Es=a("p"),aCa=o("The model class to instantiate is selected based on the "),Zso=a("code"),nCa=o("model_type"),sCa=o(` property of the config object (either
passed as an argument or loaded from `),Vso=a("code"),lCa=o("pretrained_model_name_or_path"),iCa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Wso=a("code"),dCa=o("pretrained_model_name_or_path"),mCa=o(":"),cCa=l(),EX=a("ul"),kB=a("li"),Jso=a("strong"),fCa=o("deit"),hCa=o(" \u2014 "),N5e=a("a"),gCa=o("TFDeiTForMaskedImageModeling"),uCa=o(" (DeiT model)"),pCa=l(),SB=a("li"),Uso=a("strong"),_Ca=o("swin"),bCa=o(" \u2014 "),$5e=a("a"),vCa=o("TFSwinForMaskedImageModeling"),MCa=o(" (Swin Transformer model)"),TCa=l(),v(xB.$$.fragment),kIo=l(),_h=a("h3"),GB=a("a"),Xso=a("span"),v(yX.$$.fragment),FCa=l(),Dso=a("span"),ECa=o("AutoModelForObjectDetection"),SIo=l(),Lr=a("div"),v(CX.$$.fragment),yCa=l(),bh=a("p"),CCa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created
with the `),j5e=a("a"),wCa=o("from_pretrained()"),LCa=o(" class method or the "),P5e=a("a"),ACa=o("from_config()"),RCa=o(` class
method.`),kCa=l(),wX=a("p"),SCa=o("This class cannot be instantiated directly using "),qso=a("code"),xCa=o("__init__()"),GCa=o(" (throws an error)."),BCa=l(),La=a("div"),v(LX.$$.fragment),ICa=l(),Oso=a("p"),NCa=o("Instantiates one of the model classes of the library (with a object detection head) from a configuration."),$Ca=l(),vh=a("p"),jCa=o(`Note:
Loading a model from its configuration file does `),Qso=a("strong"),PCa=o("not"),ZCa=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Z5e=a("a"),VCa=o("from_pretrained()"),WCa=o(" to load the model weights."),JCa=l(),v(BB.$$.fragment),UCa=l(),To=a("div"),v(AX.$$.fragment),XCa=l(),Yso=a("p"),DCa=o("Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),qCa=l(),ys=a("p"),OCa=o("The model class to instantiate is selected based on the "),zso=a("code"),QCa=o("model_type"),YCa=o(` property of the config object (either
passed as an argument or loaded from `),Hso=a("code"),zCa=o("pretrained_model_name_or_path"),HCa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Kso=a("code"),KCa=o("pretrained_model_name_or_path"),ewa=o(":"),owa=l(),Ar=a("ul"),IB=a("li"),elo=a("strong"),rwa=o("conditional_detr"),twa=o(" \u2014 "),V5e=a("a"),awa=o("ConditionalDetrForObjectDetection"),nwa=o(" (Conditional DETR model)"),swa=l(),NB=a("li"),olo=a("strong"),lwa=o("deformable_detr"),iwa=o(" \u2014 "),W5e=a("a"),dwa=o("DeformableDetrForObjectDetection"),mwa=o(" (Deformable DETR model)"),cwa=l(),$B=a("li"),rlo=a("strong"),fwa=o("deta"),hwa=o(" \u2014 "),J5e=a("a"),gwa=o("DetaForObjectDetection"),uwa=o(" (DETA model)"),pwa=l(),jB=a("li"),tlo=a("strong"),_wa=o("detr"),bwa=o(" \u2014 "),U5e=a("a"),vwa=o("DetrForObjectDetection"),Mwa=o(" (DETR model)"),Twa=l(),PB=a("li"),alo=a("strong"),Fwa=o("table-transformer"),Ewa=o(" \u2014 "),X5e=a("a"),ywa=o("TableTransformerForObjectDetection"),Cwa=o(" (Table Transformer model)"),wwa=l(),ZB=a("li"),nlo=a("strong"),Lwa=o("yolos"),Awa=o(" \u2014 "),D5e=a("a"),Rwa=o("YolosForObjectDetection"),kwa=o(" (YOLOS model)"),Swa=l(),VB=a("p"),xwa=o("The model is set in evaluation mode by default using "),slo=a("code"),Gwa=o("model.eval()"),Bwa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),llo=a("code"),Iwa=o("model.train()"),Nwa=l(),v(WB.$$.fragment),xIo=l(),Mh=a("h3"),JB=a("a"),ilo=a("span"),v(RX.$$.fragment),$wa=l(),dlo=a("span"),jwa=o("AutoModelForImageSegmentation"),GIo=l(),Rr=a("div"),v(kX.$$.fragment),Pwa=l(),Th=a("p"),Zwa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
with the `),q5e=a("a"),Vwa=o("from_pretrained()"),Wwa=o(" class method or the "),O5e=a("a"),Jwa=o("from_config()"),Uwa=o(` class
method.`),Xwa=l(),SX=a("p"),Dwa=o("This class cannot be instantiated directly using "),mlo=a("code"),qwa=o("__init__()"),Owa=o(" (throws an error)."),Qwa=l(),Aa=a("div"),v(xX.$$.fragment),Ywa=l(),clo=a("p"),zwa=o("Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),Hwa=l(),Fh=a("p"),Kwa=o(`Note:
Loading a model from its configuration file does `),flo=a("strong"),e6a=o("not"),o6a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Q5e=a("a"),r6a=o("from_pretrained()"),t6a=o(" to load the model weights."),a6a=l(),v(UB.$$.fragment),n6a=l(),Fo=a("div"),v(GX.$$.fragment),s6a=l(),hlo=a("p"),l6a=o("Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),i6a=l(),Cs=a("p"),d6a=o("The model class to instantiate is selected based on the "),glo=a("code"),m6a=o("model_type"),c6a=o(` property of the config object (either
passed as an argument or loaded from `),ulo=a("code"),f6a=o("pretrained_model_name_or_path"),h6a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),plo=a("code"),g6a=o("pretrained_model_name_or_path"),u6a=o(":"),p6a=l(),_lo=a("ul"),XB=a("li"),blo=a("strong"),_6a=o("detr"),b6a=o(" \u2014 "),Y5e=a("a"),v6a=o("DetrForSegmentation"),M6a=o(" (DETR model)"),T6a=l(),DB=a("p"),F6a=o("The model is set in evaluation mode by default using "),vlo=a("code"),E6a=o("model.eval()"),y6a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Mlo=a("code"),C6a=o("model.train()"),w6a=l(),v(qB.$$.fragment),BIo=l(),Eh=a("h3"),OB=a("a"),Tlo=a("span"),v(BX.$$.fragment),L6a=l(),Flo=a("span"),A6a=o("AutoModelForSemanticSegmentation"),IIo=l(),kr=a("div"),v(IX.$$.fragment),R6a=l(),yh=a("p"),k6a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),z5e=a("a"),S6a=o("from_pretrained()"),x6a=o(" class method or the "),H5e=a("a"),G6a=o("from_config()"),B6a=o(` class
method.`),I6a=l(),NX=a("p"),N6a=o("This class cannot be instantiated directly using "),Elo=a("code"),$6a=o("__init__()"),j6a=o(" (throws an error)."),P6a=l(),Ra=a("div"),v($X.$$.fragment),Z6a=l(),ylo=a("p"),V6a=o("Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),W6a=l(),Ch=a("p"),J6a=o(`Note:
Loading a model from its configuration file does `),Clo=a("strong"),U6a=o("not"),X6a=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),K5e=a("a"),D6a=o("from_pretrained()"),q6a=o(" to load the model weights."),O6a=l(),v(QB.$$.fragment),Q6a=l(),Eo=a("div"),v(jX.$$.fragment),Y6a=l(),wlo=a("p"),z6a=o("Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),H6a=l(),ws=a("p"),K6a=o("The model class to instantiate is selected based on the "),Llo=a("code"),e8a=o("model_type"),o8a=o(` property of the config object (either
passed as an argument or loaded from `),Alo=a("code"),r8a=o("pretrained_model_name_or_path"),t8a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Rlo=a("code"),a8a=o("pretrained_model_name_or_path"),n8a=o(":"),s8a=l(),Ye=a("ul"),YB=a("li"),klo=a("strong"),l8a=o("beit"),i8a=o(" \u2014 "),e3e=a("a"),d8a=o("BeitForSemanticSegmentation"),m8a=o(" (BEiT model)"),c8a=l(),zB=a("li"),Slo=a("strong"),f8a=o("data2vec-vision"),h8a=o(" \u2014 "),o3e=a("a"),g8a=o("Data2VecVisionForSemanticSegmentation"),u8a=o(" (Data2VecVision model)"),p8a=l(),HB=a("li"),xlo=a("strong"),_8a=o("dpt"),b8a=o(" \u2014 "),r3e=a("a"),v8a=o("DPTForSemanticSegmentation"),M8a=o(" (DPT model)"),T8a=l(),KB=a("li"),Glo=a("strong"),F8a=o("mobilenet_v2"),E8a=o(" \u2014 "),t3e=a("a"),y8a=o("MobileNetV2ForSemanticSegmentation"),C8a=o(" (MobileNetV2 model)"),w8a=l(),eI=a("li"),Blo=a("strong"),L8a=o("mobilevit"),A8a=o(" \u2014 "),a3e=a("a"),R8a=o("MobileViTForSemanticSegmentation"),k8a=o(" (MobileViT model)"),S8a=l(),oI=a("li"),Ilo=a("strong"),x8a=o("mobilevitv2"),G8a=o(" \u2014 "),n3e=a("a"),B8a=o("MobileViTV2ForSemanticSegmentation"),I8a=o(" (MobileViTV2 model)"),N8a=l(),rI=a("li"),Nlo=a("strong"),$8a=o("segformer"),j8a=o(" \u2014 "),s3e=a("a"),P8a=o("SegformerForSemanticSegmentation"),Z8a=o(" (SegFormer model)"),V8a=l(),tI=a("li"),$lo=a("strong"),W8a=o("upernet"),J8a=o(" \u2014 "),l3e=a("a"),U8a=o("UperNetForSemanticSegmentation"),X8a=o(" (UPerNet model)"),D8a=l(),aI=a("p"),q8a=o("The model is set in evaluation mode by default using "),jlo=a("code"),O8a=o("model.eval()"),Q8a=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Plo=a("code"),Y8a=o("model.train()"),z8a=l(),v(nI.$$.fragment),NIo=l(),wh=a("h3"),sI=a("a"),Zlo=a("span"),v(PX.$$.fragment),H8a=l(),Vlo=a("span"),K8a=o("TFAutoModelForSemanticSegmentation"),$Io=l(),Sr=a("div"),v(ZX.$$.fragment),e7a=l(),Lh=a("p"),o7a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),i3e=a("a"),r7a=o("from_pretrained()"),t7a=o(" class method or the "),d3e=a("a"),a7a=o("from_config()"),n7a=o(` class
method.`),s7a=l(),VX=a("p"),l7a=o("This class cannot be instantiated directly using "),Wlo=a("code"),i7a=o("__init__()"),d7a=o(" (throws an error)."),m7a=l(),ka=a("div"),v(WX.$$.fragment),c7a=l(),Jlo=a("p"),f7a=o("Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),h7a=l(),Ah=a("p"),g7a=o(`Note:
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model\u2019s configuration. Use `),m3e=a("a"),_7a=o("from_pretrained()"),b7a=o(" to load the model weights."),v7a=l(),v(lI.$$.fragment),M7a=l(),wt=a("div"),v(JX.$$.fragment),T7a=l(),Xlo=a("p"),F7a=o("Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),E7a=l(),Ls=a("p"),y7a=o("The model class to instantiate is selected based on the "),Dlo=a("code"),C7a=o("model_type"),w7a=o(` property of the config object (either
passed as an argument or loaded from `),qlo=a("code"),L7a=o("pretrained_model_name_or_path"),A7a=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Olo=a("code"),R7a=o("pretrained_model_name_or_path"),k7a=o(":"),S7a=l(),Rh=a("ul"),iI=a("li"),Qlo=a("strong"),x7a=o("data2vec-vision"),G7a=o(" \u2014 "),c3e=a("a"),B7a=o("TFData2VecVisionForSemanticSegmentation"),I7a=o(" (Data2VecVision model)"),N7a=l(),dI=a("li"),Ylo=a("strong"),$7a=o("mobilevit"),j7a=o(" \u2014 "),f3e=a("a"),P7a=o("TFMobileViTForSemanticSegmentation"),Z7a=o(" (MobileViT model)"),V7a=l(),mI=a("li"),zlo=a("strong"),W7a=o("segformer"),J7a=o(" \u2014 "),h3e=a("a"),U7a=o("TFSegformerForSemanticSegmentation"),X7a=o(" (SegFormer model)"),D7a=l(),v(cI.$$.fragment),jIo=l(),kh=a("h3"),fI=a("a"),Hlo=a("span"),v(UX.$$.fragment),q7a=l(),Klo=a("span"),O7a=o("AutoModelForInstanceSegmentation"),PIo=l(),xr=a("div"),v(XX.$$.fragment),Q7a=l(),Sh=a("p"),Y7a=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created
with the `),g3e=a("a"),z7a=o("from_pretrained()"),H7a=o(" class method or the "),u3e=a("a"),K7a=o("from_config()"),eLa=o(` class
method.`),oLa=l(),DX=a("p"),rLa=o("This class cannot be instantiated directly using "),eio=a("code"),tLa=o("__init__()"),aLa=o(" (throws an error)."),nLa=l(),Sa=a("div"),v(qX.$$.fragment),sLa=l(),oio=a("p"),lLa=o("Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),iLa=l(),xh=a("p"),dLa=o(`Note:
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passed as an argument or loaded from `),nio=a("code"),FLa=o("pretrained_model_name_or_path"),ELa=o(` if possible), or when it\u2019s missing, by
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passed as an argument or loaded from `),bio=a("code"),fAa=o("pretrained_model_name_or_path"),hAa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),vio=a("code"),gAa=o("pretrained_model_name_or_path"),uAa=o(":"),pAa=l(),ks=a("ul"),vI=a("li"),Mio=a("strong"),_Aa=o("detr"),bAa=o(" \u2014 "),T3e=a("a"),vAa=o("DetrForSegmentation"),MAa=o(" (DETR model)"),TAa=l(),MI=a("li"),Tio=a("strong"),FAa=o("mask2former"),EAa=o(" \u2014 "),F3e=a("a"),yAa=o("Mask2FormerForUniversalSegmentation"),CAa=o(" (Mask2Former model)"),wAa=l(),TI=a("li"),Fio=a("strong"),LAa=o("maskformer"),AAa=o(" \u2014 "),E3e=a("a"),RAa=o("MaskFormerForInstanceSegmentation"),kAa=o(" (MaskFormer model)"),SAa=l(),FI=a("li"),Eio=a("strong"),xAa=o("oneformer"),GAa=o(" \u2014 "),y3e=a("a"),BAa=o("OneFormerForUniversalSegmentation"),IAa=o(" (OneFormer model)"),NAa=l(),EI=a("p"),$Aa=o("The model is set in evaluation mode by default using "),yio=a("code"),jAa=o("model.eval()"),PAa=o(` (so for instance, dropout modules are
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method.`),YAa=l(),rD=a("p"),zAa=o("This class cannot be instantiated directly using "),Aio=a("code"),HAa=o("__init__()"),KAa=o(" (throws an error)."),eRa=l(),Ga=a("div"),v(tD.$$.fragment),oRa=l(),Rio=a("p"),rRa=o("Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),tRa=l(),jh=a("p"),aRa=o(`Note:
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passed as an argument or loaded from `),Gio=a("code"),_Ra=o("pretrained_model_name_or_path"),bRa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Bio=a("code"),vRa=o("pretrained_model_name_or_path"),MRa=o(":"),TRa=l(),Ir=a("ul"),LI=a("li"),Iio=a("strong"),FRa=o("align"),ERa=o(" \u2014 "),A3e=a("a"),yRa=o("AlignModel"),CRa=o(" (ALIGN model)"),wRa=l(),AI=a("li"),Nio=a("strong"),LRa=o("altclip"),ARa=o(" \u2014 "),R3e=a("a"),RRa=o("AltCLIPModel"),kRa=o(" (AltCLIP model)"),SRa=l(),RI=a("li"),$io=a("strong"),xRa=o("blip"),GRa=o(" \u2014 "),k3e=a("a"),BRa=o("BlipModel"),IRa=o(" (BLIP model)"),NRa=l(),kI=a("li"),jio=a("strong"),$Ra=o("chinese_clip"),jRa=o(" \u2014 "),S3e=a("a"),PRa=o("ChineseCLIPModel"),ZRa=o(" (Chinese-CLIP model)"),VRa=l(),SI=a("li"),Pio=a("strong"),WRa=o("clip"),JRa=o(" \u2014 "),x3e=a("a"),URa=o("CLIPModel"),XRa=o(" (CLIP model)"),DRa=l(),xI=a("li"),Zio=a("strong"),qRa=o("clipseg"),ORa=o(" \u2014 "),G3e=a("a"),QRa=o("CLIPSegModel"),YRa=o(" (CLIPSeg model)"),zRa=l(),GI=a("p"),HRa=o("The model is set in evaluation mode by default using "),Vio=a("code"),KRa=o("model.eval()"),eka=o(` (so for instance, dropout modules are
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method.`),cka=l(),lD=a("p"),fka=o("This class cannot be instantiated directly using "),Xio=a("code"),hka=o("__init__()"),gka=o(" (throws an error)."),uka=l(),Ba=a("div"),v(iD.$$.fragment),pka=l(),Dio=a("p"),_ka=o("Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),bka=l(),Vh=a("p"),vka=o(`Note:
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falling back to using pattern matching on `),zio=a("code"),Bka=o("pretrained_model_name_or_path"),Ika=o(":"),Nka=l(),mD=a("ul"),$I=a("li"),Hio=a("strong"),$ka=o("blip"),jka=o(" \u2014 "),$3e=a("a"),Pka=o("TFBlipModel"),Zka=o(" (BLIP model)"),Vka=l(),jI=a("li"),Kio=a("strong"),Wka=o("clip"),Jka=o(" \u2014 "),j3e=a("a"),Uka=o("TFCLIPModel"),Xka=o(" (CLIP model)"),Dka=l(),v(PI.$$.fragment),DIo=l(),Wh=a("h3"),ZI=a("a"),edo=a("span"),v(cD.$$.fragment),qka=l(),odo=a("span"),Oka=o("AutoModelForZeroShotObjectDetection"),qIo=l(),$r=a("div"),v(fD.$$.fragment),Qka=l(),Jh=a("p"),Yka=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot object detection head) when created
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falling back to using pattern matching on `),ido=a("code"),ySa=o("pretrained_model_name_or_path"),CSa=o(":"),wSa=l(),ddo=a("ul"),WI=a("li"),mdo=a("strong"),LSa=o("owlvit"),ASa=o(" \u2014 "),W3e=a("a"),RSa=o("OwlViTForObjectDetection"),kSa=o(" (OWL-ViT model)"),SSa=l(),JI=a("p"),xSa=o("The model is set in evaluation mode by default using "),cdo=a("code"),GSa=o("model.eval()"),BSa=o(` (so for instance, dropout modules are
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method.`),OSa=l(),vD=a("p"),QSa=o("This class cannot be instantiated directly using "),_do=a("code"),YSa=o("__init__()"),zSa=o(" (throws an error)."),HSa=l(),Na=a("div"),v(MD.$$.fragment),KSa=l(),bdo=a("p"),exa=o("Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),oxa=l(),Oh=a("p"),rxa=o(`Note:
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falling back to using pattern matching on `),Edo=a("code"),_xa=o("pretrained_model_name_or_path"),bxa=o(":"),vxa=l(),Be=a("ul"),OI=a("li"),ydo=a("strong"),Mxa=o("audio-spectrogram-transformer"),Txa=o(" \u2014 "),q3e=a("a"),Fxa=o("ASTForAudioClassification"),Exa=o(" (Audio Spectrogram Transformer model)"),yxa=l(),QI=a("li"),Cdo=a("strong"),Cxa=o("data2vec-audio"),wxa=o(" \u2014 "),O3e=a("a"),Lxa=o("Data2VecAudioForSequenceClassification"),Axa=o(" (Data2VecAudio model)"),Rxa=l(),YI=a("li"),wdo=a("strong"),kxa=o("hubert"),Sxa=o(" \u2014 "),Q3e=a("a"),xxa=o("HubertForSequenceClassification"),Gxa=o(" (Hubert model)"),Bxa=l(),zI=a("li"),Ldo=a("strong"),Ixa=o("sew"),Nxa=o(" \u2014 "),Y3e=a("a"),$xa=o("SEWForSequenceClassification"),jxa=o(" (SEW model)"),Pxa=l(),HI=a("li"),Ado=a("strong"),Zxa=o("sew-d"),Vxa=o(" \u2014 "),z3e=a("a"),Wxa=o("SEWDForSequenceClassification"),Jxa=o(" (SEW-D model)"),Uxa=l(),KI=a("li"),Rdo=a("strong"),Xxa=o("unispeech"),Dxa=o(" \u2014 "),H3e=a("a"),qxa=o("UniSpeechForSequenceClassification"),Oxa=o(" (UniSpeech model)"),Qxa=l(),eN=a("li"),kdo=a("strong"),Yxa=o("unispeech-sat"),zxa=o(" \u2014 "),K3e=a("a"),Hxa=o("UniSpeechSatForSequenceClassification"),Kxa=o(" (UniSpeechSat model)"),eGa=l(),oN=a("li"),Sdo=a("strong"),oGa=o("wav2vec2"),rGa=o(" \u2014 "),e9e=a("a"),tGa=o("Wav2Vec2ForSequenceClassification"),aGa=o(" (Wav2Vec2 model)"),nGa=l(),rN=a("li"),xdo=a("strong"),sGa=o("wav2vec2-conformer"),lGa=o(" \u2014 "),o9e=a("a"),iGa=o("Wav2Vec2ConformerForSequenceClassification"),dGa=o(" (Wav2Vec2-Conformer model)"),mGa=l(),tN=a("li"),Gdo=a("strong"),cGa=o("wavlm"),fGa=o(" \u2014 "),r9e=a("a"),hGa=o("WavLMForSequenceClassification"),gGa=o(" (WavLM model)"),uGa=l(),aN=a("li"),Bdo=a("strong"),pGa=o("whisper"),_Ga=o(" \u2014 "),t9e=a("a"),bGa=o("WhisperForAudioClassification"),vGa=o(" (Whisper model)"),MGa=l(),nN=a("p"),TGa=o("The model is set in evaluation mode by default using "),Ido=a("code"),FGa=o("model.eval()"),EGa=o(` (so for instance, dropout modules are
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falling back to using pattern matching on `),ufo=a("code"),uJa=o("pretrained_model_name_or_path"),pJa=o(":"),_Ja=l(),Ag=a("ul"),F$=a("li"),pfo=a("strong"),bJa=o("layoutlm"),vJa=o(" \u2014 "),h4e=a("a"),MJa=o("LayoutLMForQuestionAnswering"),TJa=o(" (LayoutLM model)"),FJa=l(),E$=a("li"),_fo=a("strong"),EJa=o("layoutlmv2"),yJa=o(" \u2014 "),g4e=a("a"),CJa=o("LayoutLMv2ForQuestionAnswering"),wJa=o(" (LayoutLMv2 model)"),LJa=l(),y$=a("li"),bfo=a("strong"),AJa=o("layoutlmv3"),RJa=o(" \u2014 "),u4e=a("a"),kJa=o("LayoutLMv3ForQuestionAnswering"),SJa=o(" (LayoutLMv3 model)"),xJa=l(),C$=a("p"),GJa=o("The model is set in evaluation mode by default using "),vfo=a("code"),BJa=o("model.eval()"),IJa=o(` (so for instance, dropout modules are
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passed as an argument or loaded from `),Pfo=a("code"),HUa=o("pretrained_model_name_or_path"),KUa=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Zfo=a("code"),eXa=o("pretrained_model_name_or_path"),oXa=o(":"),rXa=l(),Vfo=a("ul"),G$=a("li"),Wfo=a("strong"),tXa=o("vilt"),aXa=o(" \u2014 "),E4e=a("a"),nXa=o("ViltForQuestionAnswering"),sXa=o(" (ViLT model)"),lXa=l(),B$=a("p"),iXa=o("The model is set in evaluation mode by default using "),Jfo=a("code"),dXa=o("model.eval()"),mXa=o(` (so for instance, dropout modules are
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iln=s(Yte);avr=r(iln,"XLMTokenizer"),iln.forEach(t),nvr=r(dbo," (XLM model)"),dbo.forEach(t),svr=i(S),Nb=n(S,"LI",{});var mbo=s(Nb);Wke=n(mbo,"STRONG",{});var dln=s(Wke);lvr=r(dln,"xlm-prophetnet"),dln.forEach(t),ivr=r(mbo," \u2014 "),zte=n(mbo,"A",{href:!0});var mln=s(zte);dvr=r(mln,"XLMProphetNetTokenizer"),mln.forEach(t),mvr=r(mbo," (XLM-ProphetNet model)"),mbo.forEach(t),cvr=i(S),Ni=n(S,"LI",{});var VQ=s(Ni);Jke=n(VQ,"STRONG",{});var cln=s(Jke);fvr=r(cln,"xlm-roberta"),cln.forEach(t),hvr=r(VQ," \u2014 "),Hte=n(VQ,"A",{href:!0});var fln=s(Hte);gvr=r(fln,"XLMRobertaTokenizer"),fln.forEach(t),uvr=r(VQ," or "),Kte=n(VQ,"A",{href:!0});var hln=s(Kte);pvr=r(hln,"XLMRobertaTokenizerFast"),hln.forEach(t),_vr=r(VQ," (XLM-RoBERTa model)"),VQ.forEach(t),bvr=i(S),$i=n(S,"LI",{});var WQ=s($i);Uke=n(WQ,"STRONG",{});var gln=s(Uke);vvr=r(gln,"xlm-roberta-xl"),gln.forEach(t),Mvr=r(WQ," \u2014 "),eae=n(WQ,"A",{href:!0});var uln=s(eae);Tvr=r(uln,"XLMRobertaTokenizer"),uln.forEach(t),Fvr=r(WQ," or 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library when created with the `),iae=n(VNo,"A",{href:!0});var kln=s(iae);eMr=r(kln,"AutoFeatureExtractor.from_pretrained()"),kln.forEach(t),oMr=r(VNo," class method."),VNo.forEach(t),rMr=i(Yi),TV=n(Yi,"P",{});var WNo=s(TV);tMr=r(WNo,"This class cannot be instantiated directly using "),zke=n(WNo,"CODE",{});var Sln=s(zke);aMr=r(Sln,"__init__()"),Sln.forEach(t),nMr=r(WNo," (throws an error)."),WNo.forEach(t),sMr=i(Yi),to=n(Yi,"DIV",{class:!0});var Ha=s(to);M(FV.$$.fragment,Ha),lMr=i(Ha),Hke=n(Ha,"P",{});var xln=s(Hke);iMr=r(xln,"Instantiate one of the feature extractor classes of the library from a pretrained model vocabulary."),xln.forEach(t),dMr=i(Ha),jn=n(Ha,"P",{});var Bj=s(jn);mMr=r(Bj,"The feature extractor class to instantiate is selected based on the "),Kke=n(Bj,"CODE",{});var Gln=s(Kke);cMr=r(Gln,"model_type"),Gln.forEach(t),fMr=r(Bj,` property of the config object
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oin=s(bae);cTr=r(oin,"Wav2Vec2FeatureExtractor"),oin.forEach(t),fTr=r(Mbo," (Data2VecAudio model)"),Mbo.forEach(t),hTr=i(D),Yb=n(D,"LI",{});var Tbo=s(Yb);fSe=n(Tbo,"STRONG",{});var rin=s(fSe);gTr=r(rin,"data2vec-vision"),rin.forEach(t),uTr=r(Tbo," \u2014 "),vae=n(Tbo,"A",{href:!0});var tin=s(vae);pTr=r(tin,"BeitFeatureExtractor"),tin.forEach(t),_Tr=r(Tbo," (Data2VecVision model)"),Tbo.forEach(t),bTr=i(D),zb=n(D,"LI",{});var Fbo=s(zb);hSe=n(Fbo,"STRONG",{});var ain=s(hSe);vTr=r(ain,"deformable_detr"),ain.forEach(t),MTr=r(Fbo," \u2014 "),Mae=n(Fbo,"A",{href:!0});var nin=s(Mae);TTr=r(nin,"DeformableDetrFeatureExtractor"),nin.forEach(t),FTr=r(Fbo," (Deformable DETR model)"),Fbo.forEach(t),ETr=i(D),Hb=n(D,"LI",{});var Ebo=s(Hb);gSe=n(Ebo,"STRONG",{});var sin=s(gSe);yTr=r(sin,"deit"),sin.forEach(t),CTr=r(Ebo," \u2014 "),Tae=n(Ebo,"A",{href:!0});var lin=s(Tae);wTr=r(lin,"DeiTFeatureExtractor"),lin.forEach(t),LTr=r(Ebo," (DeiT model)"),Ebo.forEach(t),ATr=i(D),Kb=n(D,"LI",{});var 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library when created with the `),bne=n(XNo,"A",{href:!0});var amn=s(bne);_5r=r(amn,"AutoImageProcessor.from_pretrained()"),amn.forEach(t),b5r=r(XNo," class method."),XNo.forEach(t),v5r=i(zi),LV=n(zi,"P",{});var DNo=s(LV);M5r=r(DNo,"This class cannot be instantiated directly using "),gxe=n(DNo,"CODE",{});var nmn=s(gxe);T5r=r(nmn,"__init__()"),nmn.forEach(t),F5r=r(DNo," (throws an error)."),DNo.forEach(t),E5r=i(zi),ao=n(zi,"DIV",{class:!0});var Ka=s(ao);M(AV.$$.fragment,Ka),y5r=i(Ka),uxe=n(Ka,"P",{});var smn=s(uxe);C5r=r(smn,"Instantiate one of the image processor classes of the library from a pretrained model vocabulary."),smn.forEach(t),w5r=i(Ka),Pn=n(Ka,"P",{});var Ij=s(Pn);L5r=r(Ij,"The image processor class to instantiate is selected based on the "),pxe=n(Ij,"CODE",{});var lmn=s(pxe);A5r=r(lmn,"model_type"),lmn.forEach(t),R5r=r(Ij,` property of the config object
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missing, by falling back to using pattern matching on `),bxe=n(Ij,"CODE",{});var dmn=s(bxe);x5r=r(dmn,"pretrained_model_name_or_path"),dmn.forEach(t),G5r=r(Ij,":"),Ij.forEach(t),B5r=i(Ka),N=n(Ka,"UL",{});var $=s(N);K2=n($,"LI",{});var M2o=s(K2);vxe=n(M2o,"STRONG",{});var mmn=s(vxe);I5r=r(mmn,"align"),mmn.forEach(t),N5r=r(M2o," \u2014 "),vne=n(M2o,"A",{href:!0});var cmn=s(vne);$5r=r(cmn,"EfficientNetImageProcessor"),cmn.forEach(t),j5r=r(M2o," (ALIGN model)"),M2o.forEach(t),P5r=i($),ev=n($,"LI",{});var T2o=s(ev);Mxe=n(T2o,"STRONG",{});var fmn=s(Mxe);Z5r=r(fmn,"beit"),fmn.forEach(t),V5r=r(T2o," \u2014 "),Mne=n(T2o,"A",{href:!0});var hmn=s(Mne);W5r=r(hmn,"BeitImageProcessor"),hmn.forEach(t),J5r=r(T2o," (BEiT model)"),T2o.forEach(t),U5r=i($),ov=n($,"LI",{});var F2o=s(ov);Txe=n(F2o,"STRONG",{});var gmn=s(Txe);X5r=r(gmn,"bit"),gmn.forEach(t),D5r=r(F2o," \u2014 "),Tne=n(F2o,"A",{href:!0});var umn=s(Tne);q5r=r(umn,"BitImageProcessor"),umn.forEach(t),O5r=r(F2o," (BiT 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Emn=s(Cne);h3r=r(Emn,"ChineseCLIPImageProcessor"),Emn.forEach(t),g3r=r(w2o," (Chinese-CLIP model)"),w2o.forEach(t),u3r=i($),sv=n($,"LI",{});var L2o=s(sv);wxe=n(L2o,"STRONG",{});var ymn=s(wxe);p3r=r(ymn,"clip"),ymn.forEach(t),_3r=r(L2o," \u2014 "),wne=n(L2o,"A",{href:!0});var Cmn=s(wne);b3r=r(Cmn,"CLIPImageProcessor"),Cmn.forEach(t),v3r=r(L2o," (CLIP model)"),L2o.forEach(t),M3r=i($),lv=n($,"LI",{});var A2o=s(lv);Lxe=n(A2o,"STRONG",{});var wmn=s(Lxe);T3r=r(wmn,"clipseg"),wmn.forEach(t),F3r=r(A2o," \u2014 "),Lne=n(A2o,"A",{href:!0});var Lmn=s(Lne);E3r=r(Lmn,"ViTImageProcessor"),Lmn.forEach(t),y3r=r(A2o," (CLIPSeg model)"),A2o.forEach(t),C3r=i($),iv=n($,"LI",{});var R2o=s(iv);Axe=n(R2o,"STRONG",{});var Amn=s(Axe);w3r=r(Amn,"conditional_detr"),Amn.forEach(t),L3r=r(R2o," \u2014 "),Ane=n(R2o,"A",{href:!0});var Rmn=s(Ane);A3r=r(Rmn,"ConditionalDetrImageProcessor"),Rmn.forEach(t),R3r=r(R2o," (Conditional DETR model)"),R2o.forEach(t),k3r=i($),dv=n($,"LI",{});var k2o=s(dv);Rxe=n(k2o,"STRONG",{});var kmn=s(Rxe);S3r=r(kmn,"convnext"),kmn.forEach(t),x3r=r(k2o," \u2014 "),Rne=n(k2o,"A",{href:!0});var Smn=s(Rne);G3r=r(Smn,"ConvNextImageProcessor"),Smn.forEach(t),B3r=r(k2o," (ConvNeXT model)"),k2o.forEach(t),I3r=i($),mv=n($,"LI",{});var S2o=s(mv);kxe=n(S2o,"STRONG",{});var xmn=s(kxe);N3r=r(xmn,"convnextv2"),xmn.forEach(t),$3r=r(S2o," \u2014 "),kne=n(S2o,"A",{href:!0});var Gmn=s(kne);j3r=r(Gmn,"ConvNextImageProcessor"),Gmn.forEach(t),P3r=r(S2o," (ConvNeXTV2 model)"),S2o.forEach(t),Z3r=i($),cv=n($,"LI",{});var x2o=s(cv);Sxe=n(x2o,"STRONG",{});var Bmn=s(Sxe);V3r=r(Bmn,"cvt"),Bmn.forEach(t),W3r=r(x2o," \u2014 "),Sne=n(x2o,"A",{href:!0});var Imn=s(Sne);J3r=r(Imn,"ConvNextImageProcessor"),Imn.forEach(t),U3r=r(x2o," (CvT model)"),x2o.forEach(t),X3r=i($),fv=n($,"LI",{});var G2o=s(fv);xxe=n(G2o,"STRONG",{});var Nmn=s(xxe);D3r=r(Nmn,"data2vec-vision"),Nmn.forEach(t),q3r=r(G2o," \u2014 "),xne=n(G2o,"A",{href:!0});var $mn=s(xne);O3r=r($mn,"BeitImageProcessor"),$mn.forEach(t),Q3r=r(G2o," (Data2VecVision model)"),G2o.forEach(t),Y3r=i($),hv=n($,"LI",{});var B2o=s(hv);Gxe=n(B2o,"STRONG",{});var jmn=s(Gxe);z3r=r(jmn,"deformable_detr"),jmn.forEach(t),H3r=r(B2o," \u2014 "),Gne=n(B2o,"A",{href:!0});var Pmn=s(Gne);K3r=r(Pmn,"DeformableDetrImageProcessor"),Pmn.forEach(t),e9r=r(B2o," (Deformable DETR model)"),B2o.forEach(t),o9r=i($),gv=n($,"LI",{});var I2o=s(gv);Bxe=n(I2o,"STRONG",{});var Zmn=s(Bxe);r9r=r(Zmn,"deit"),Zmn.forEach(t),t9r=r(I2o," \u2014 "),Bne=n(I2o,"A",{href:!0});var Vmn=s(Bne);a9r=r(Vmn,"DeiTImageProcessor"),Vmn.forEach(t),n9r=r(I2o," (DeiT model)"),I2o.forEach(t),s9r=i($),uv=n($,"LI",{});var N2o=s(uv);Ixe=n(N2o,"STRONG",{});var Wmn=s(Ixe);l9r=r(Wmn,"deta"),Wmn.forEach(t),i9r=r(N2o," \u2014 "),Ine=n(N2o,"A",{href:!0});var Jmn=s(Ine);d9r=r(Jmn,"DetaImageProcessor"),Jmn.forEach(t),m9r=r(N2o," (DETA model)"),N2o.forEach(t),c9r=i($),pv=n($,"LI",{});var $2o=s(pv);Nxe=n($2o,"STRONG",{});var Umn=s(Nxe);f9r=r(Umn,"detr"),Umn.forEach(t),h9r=r($2o," \u2014 "),Nne=n($2o,"A",{href:!0});var Xmn=s(Nne);g9r=r(Xmn,"DetrImageProcessor"),Xmn.forEach(t),u9r=r($2o," (DETR model)"),$2o.forEach(t),p9r=i($),_v=n($,"LI",{});var j2o=s(_v);$xe=n(j2o,"STRONG",{});var Dmn=s($xe);_9r=r(Dmn,"dinat"),Dmn.forEach(t),b9r=r(j2o," \u2014 "),$ne=n(j2o,"A",{href:!0});var qmn=s($ne);v9r=r(qmn,"ViTImageProcessor"),qmn.forEach(t),M9r=r(j2o," (DiNAT model)"),j2o.forEach(t),T9r=i($),bv=n($,"LI",{});var P2o=s(bv);jxe=n(P2o,"STRONG",{});var Omn=s(jxe);F9r=r(Omn,"dinov2"),Omn.forEach(t),E9r=r(P2o," \u2014 "),jne=n(P2o,"A",{href:!0});var Qmn=s(jne);y9r=r(Qmn,"BitImageProcessor"),Qmn.forEach(t),C9r=r(P2o," (DINOv2 model)"),P2o.forEach(t),w9r=i($),vv=n($,"LI",{});var Z2o=s(vv);Pxe=n(Z2o,"STRONG",{});var Ymn=s(Pxe);L9r=r(Ymn,"donut-swin"),Ymn.forEach(t),A9r=r(Z2o," \u2014 "),Pne=n(Z2o,"A",{href:!0});var zmn=s(Pne);R9r=r(zmn,"DonutImageProcessor"),zmn.forEach(t),k9r=r(Z2o," (DonutSwin 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tMo=s(ZM);dBe=n(tMo,"STRONG",{});var Phn=s(dBe);ALr=r(Phn,"oneformer"),Phn.forEach(t),RLr=r(tMo," \u2014 "),tle=n(tMo,"A",{href:!0});var Zhn=s(tle);kLr=r(Zhn,"OneFormerProcessor"),Zhn.forEach(t),SLr=r(tMo," (OneFormer model)"),tMo.forEach(t),xLr=i(re),VM=n(re,"LI",{});var aMo=s(VM);mBe=n(aMo,"STRONG",{});var Vhn=s(mBe);GLr=r(Vhn,"owlvit"),Vhn.forEach(t),BLr=r(aMo," \u2014 "),ale=n(aMo,"A",{href:!0});var Whn=s(ale);ILr=r(Whn,"OwlViTProcessor"),Whn.forEach(t),NLr=r(aMo," (OWL-ViT model)"),aMo.forEach(t),$Lr=i(re),WM=n(re,"LI",{});var nMo=s(WM);cBe=n(nMo,"STRONG",{});var Jhn=s(cBe);jLr=r(Jhn,"pix2struct"),Jhn.forEach(t),PLr=r(nMo," \u2014 "),nle=n(nMo,"A",{href:!0});var Uhn=s(nle);ZLr=r(Uhn,"Pix2StructProcessor"),Uhn.forEach(t),VLr=r(nMo," (Pix2Struct model)"),nMo.forEach(t),WLr=i(re),JM=n(re,"LI",{});var sMo=s(JM);fBe=n(sMo,"STRONG",{});var Xhn=s(fBe);JLr=r(Xhn,"pop2piano"),Xhn.forEach(t),ULr=r(sMo," \u2014 "),sle=n(sMo,"A",{href:!0});var Dhn=s(sle);XLr=r(Dhn,"Pop2PianoProcessor"),Dhn.forEach(t),DLr=r(sMo," (Pop2Piano model)"),sMo.forEach(t),qLr=i(re),UM=n(re,"LI",{});var lMo=s(UM);hBe=n(lMo,"STRONG",{});var qhn=s(hBe);OLr=r(qhn,"sam"),qhn.forEach(t),QLr=r(lMo," \u2014 "),lle=n(lMo,"A",{href:!0});var Ohn=s(lle);YLr=r(Ohn,"SamProcessor"),Ohn.forEach(t),zLr=r(lMo," (SAM model)"),lMo.forEach(t),HLr=i(re),XM=n(re,"LI",{});var iMo=s(XM);gBe=n(iMo,"STRONG",{});var Qhn=s(gBe);KLr=r(Qhn,"sew"),Qhn.forEach(t),eAr=r(iMo," \u2014 "),ile=n(iMo,"A",{href:!0});var Yhn=s(ile);oAr=r(Yhn,"Wav2Vec2Processor"),Yhn.forEach(t),rAr=r(iMo," (SEW model)"),iMo.forEach(t),tAr=i(re),DM=n(re,"LI",{});var dMo=s(DM);uBe=n(dMo,"STRONG",{});var zhn=s(uBe);aAr=r(zhn,"sew-d"),zhn.forEach(t),nAr=r(dMo," \u2014 "),dle=n(dMo,"A",{href:!0});var Hhn=s(dle);sAr=r(Hhn,"Wav2Vec2Processor"),Hhn.forEach(t),lAr=r(dMo," (SEW-D model)"),dMo.forEach(t),iAr=i(re),qM=n(re,"LI",{});var mMo=s(qM);pBe=n(mMo,"STRONG",{});var Khn=s(pBe);dAr=r(Khn,"speech_to_text"),Khn.forEach(t),mAr=r(mMo," \u2014 "),mle=n(mMo,"A",{href:!0});var egn=s(mle);cAr=r(egn,"Speech2TextProcessor"),egn.forEach(t),fAr=r(mMo," (Speech2Text model)"),mMo.forEach(t),hAr=i(re),OM=n(re,"LI",{});var cMo=s(OM);_Be=n(cMo,"STRONG",{});var ogn=s(_Be);gAr=r(ogn,"speech_to_text_2"),ogn.forEach(t),uAr=r(cMo," \u2014 "),cle=n(cMo,"A",{href:!0});var rgn=s(cle);pAr=r(rgn,"Speech2Text2Processor"),rgn.forEach(t),_Ar=r(cMo," (Speech2Text2 model)"),cMo.forEach(t),bAr=i(re),QM=n(re,"LI",{});var fMo=s(QM);bBe=n(fMo,"STRONG",{});var tgn=s(bBe);vAr=r(tgn,"speecht5"),tgn.forEach(t),MAr=r(fMo," \u2014 "),fle=n(fMo,"A",{href:!0});var agn=s(fle);TAr=r(agn,"SpeechT5Processor"),agn.forEach(t),FAr=r(fMo," (SpeechT5 model)"),fMo.forEach(t),EAr=i(re),YM=n(re,"LI",{});var hMo=s(YM);vBe=n(hMo,"STRONG",{});var ngn=s(vBe);yAr=r(ngn,"trocr"),ngn.forEach(t),CAr=r(hMo," \u2014 "),hle=n(hMo,"A",{href:!0});var sgn=s(hle);wAr=r(sgn,"TrOCRProcessor"),sgn.forEach(t),LAr=r(hMo," (TrOCR model)"),hMo.forEach(t),AAr=i(re),zM=n(re,"LI",{});var gMo=s(zM);MBe=n(gMo,"STRONG",{});var lgn=s(MBe);RAr=r(lgn,"tvlt"),lgn.forEach(t),kAr=r(gMo," \u2014 "),gle=n(gMo,"A",{href:!0});var ign=s(gle);SAr=r(ign,"TvltProcessor"),ign.forEach(t),xAr=r(gMo," (TVLT model)"),gMo.forEach(t),GAr=i(re),HM=n(re,"LI",{});var uMo=s(HM);TBe=n(uMo,"STRONG",{});var dgn=s(TBe);BAr=r(dgn,"unispeech"),dgn.forEach(t),IAr=r(uMo," \u2014 "),ule=n(uMo,"A",{href:!0});var mgn=s(ule);NAr=r(mgn,"Wav2Vec2Processor"),mgn.forEach(t),$Ar=r(uMo," (UniSpeech model)"),uMo.forEach(t),jAr=i(re),KM=n(re,"LI",{});var pMo=s(KM);FBe=n(pMo,"STRONG",{});var cgn=s(FBe);PAr=r(cgn,"unispeech-sat"),cgn.forEach(t),ZAr=r(pMo," \u2014 "),ple=n(pMo,"A",{href:!0});var fgn=s(ple);VAr=r(fgn,"Wav2Vec2Processor"),fgn.forEach(t),WAr=r(pMo," (UniSpeechSat model)"),pMo.forEach(t),JAr=i(re),eT=n(re,"LI",{});var _Mo=s(eT);EBe=n(_Mo,"STRONG",{});var hgn=s(EBe);UAr=r(hgn,"vilt"),hgn.forEach(t),XAr=r(_Mo," \u2014 "),_le=n(_Mo,"A",{href:!0});var ggn=s(_le);DAr=r(ggn,"ViltProcessor"),ggn.forEach(t),qAr=r(_Mo," (ViLT model)"),_Mo.forEach(t),OAr=i(re),oT=n(re,"LI",{});var bMo=s(oT);yBe=n(bMo,"STRONG",{});var ugn=s(yBe);QAr=r(ugn,"vision-text-dual-encoder"),ugn.forEach(t),YAr=r(bMo," \u2014 "),ble=n(bMo,"A",{href:!0});var pgn=s(ble);zAr=r(pgn,"VisionTextDualEncoderProcessor"),pgn.forEach(t),HAr=r(bMo," (VisionTextDualEncoder model)"),bMo.forEach(t),KAr=i(re),rT=n(re,"LI",{});var vMo=s(rT);CBe=n(vMo,"STRONG",{});var _gn=s(CBe);eRr=r(_gn,"wav2vec2"),_gn.forEach(t),oRr=r(vMo," \u2014 "),vle=n(vMo,"A",{href:!0});var bgn=s(vle);rRr=r(bgn,"Wav2Vec2Processor"),bgn.forEach(t),tRr=r(vMo," (Wav2Vec2 model)"),vMo.forEach(t),aRr=i(re),tT=n(re,"LI",{});var MMo=s(tT);wBe=n(MMo,"STRONG",{});var vgn=s(wBe);nRr=r(vgn,"wav2vec2-conformer"),vgn.forEach(t),sRr=r(MMo," \u2014 "),Mle=n(MMo,"A",{href:!0});var Mgn=s(Mle);lRr=r(Mgn,"Wav2Vec2Processor"),Mgn.forEach(t),iRr=r(MMo," (Wav2Vec2-Conformer model)"),MMo.forEach(t),dRr=i(re),aT=n(re,"LI",{});var TMo=s(aT);LBe=n(TMo,"STRONG",{});var Tgn=s(LBe);mRr=r(Tgn,"wavlm"),Tgn.forEach(t),cRr=r(TMo," \u2014 "),Tle=n(TMo,"A",{href:!0});var Fgn=s(Tle);fRr=r(Fgn,"Wav2Vec2Processor"),Fgn.forEach(t),hRr=r(TMo," (WavLM model)"),TMo.forEach(t),gRr=i(re),nT=n(re,"LI",{});var FMo=s(nT);ABe=n(FMo,"STRONG",{});var Egn=s(ABe);uRr=r(Egn,"whisper"),Egn.forEach(t),pRr=r(FMo," \u2014 "),Fle=n(FMo,"A",{href:!0});var ygn=s(Fle);_Rr=r(ygn,"WhisperProcessor"),ygn.forEach(t),bRr=r(FMo," (Whisper model)"),FMo.forEach(t),vRr=i(re),sT=n(re,"LI",{});var EMo=s(sT);RBe=n(EMo,"STRONG",{});var Cgn=s(RBe);MRr=r(Cgn,"xclip"),Cgn.forEach(t),TRr=r(EMo," \u2014 "),Ele=n(EMo,"A",{href:!0});var wgn=s(Ele);FRr=r(wgn,"XCLIPProcessor"),wgn.forEach(t),ERr=r(EMo," (X-CLIP model)"),EMo.forEach(t),re.forEach(t),yRr=i(en),M(lT.$$.fragment,en),CRr=i(en),M(iT.$$.fragment,en),en.forEach(t),wRr=i(Hi),dT=n(Hi,"DIV",{class:!0});var zNo=s(dT);M(IV.$$.fragment,zNo),LRr=i(zNo),kBe=n(zNo,"P",{});var Lgn=s(kBe);ARr=r(Lgn,"Register a new processor for this class."),Lgn.forEach(t),zNo.forEach(t),Hi.forEach(t),OGo=i(c),ac=n(c,"H2",{class:!0});var HNo=s(ac);mT=n(HNo,"A",{id:!0,class:!0,href:!0});var Agn=s(mT);SBe=n(Agn,"SPAN",{});var Rgn=s(SBe);M(NV.$$.fragment,Rgn),Rgn.forEach(t),Agn.forEach(t),RRr=i(HNo),xBe=n(HNo,"SPAN",{});var kgn=s(xBe);kRr=r(kgn,"Generic model classes"),kgn.forEach(t),HNo.forEach(t),QGo=i(c),yle=n(c,"P",{});var Sgn=s(yle);SRr=r(Sgn,"The following auto classes are available for instantiating a base model class without a specific head."),Sgn.forEach(t),YGo=i(c),nc=n(c,"H3",{class:!0});var KNo=s(nc);cT=n(KNo,"A",{id:!0,class:!0,href:!0});var xgn=s(cT);GBe=n(xgn,"SPAN",{});var Ggn=s(GBe);M($V.$$.fragment,Ggn),Ggn.forEach(t),xgn.forEach(t),xRr=i(KNo),BBe=n(KNo,"SPAN",{});var Bgn=s(BBe);GRr=r(Bgn,"AutoModel"),Bgn.forEach(t),KNo.forEach(t),zGo=i(c),Uo=n(c,"DIV",{class:!0});var Ki=s(Uo);M(jV.$$.fragment,Ki),BRr=i(Ki),sc=n(Ki,"P",{});var J4e=s(sc);IRr=r(J4e,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),Cle=n(J4e,"A",{href:!0});var Ign=s(Cle);NRr=r(Ign,"from_pretrained()"),Ign.forEach(t),$Rr=r(J4e," class method or the "),wle=n(J4e,"A",{href:!0});var Ngn=s(wle);jRr=r(Ngn,"from_config()"),Ngn.forEach(t),PRr=r(J4e,` class
method.`),J4e.forEach(t),ZRr=i(Ki),PV=n(Ki,"P",{});var e$o=s(PV);VRr=r(e$o,"This class cannot be instantiated directly using "),IBe=n(e$o,"CODE",{});var $gn=s(IBe);WRr=r($gn,"__init__()"),$gn.forEach(t),JRr=r(e$o," (throws an error)."),e$o.forEach(t),URr=i(Ki),Jt=n(Ki,"DIV",{class:!0});var Nj=s(Jt);M(ZV.$$.fragment,Nj),XRr=i(Nj),NBe=n(Nj,"P",{});var jgn=s(NBe);DRr=r(jgn,"Instantiates one of the base model classes of the library from a configuration."),jgn.forEach(t),qRr=i(Nj),lc=n(Nj,"P",{});var U4e=s(lc);ORr=r(U4e,`Note:
Loading a model from its configuration file does `),$Be=n(U4e,"STRONG",{});var Pgn=s($Be);QRr=r(Pgn,"not"),Pgn.forEach(t),YRr=r(U4e,` load the model weights. It only affects the
model\u2019s configuration. Use `),Lle=n(U4e,"A",{href:!0});var Zgn=s(Lle);zRr=r(Zgn,"from_pretrained()"),Zgn.forEach(t),HRr=r(U4e," to load the model weights."),U4e.forEach(t),KRr=i(Nj),M(fT.$$.fragment,Nj),Nj.forEach(t),ekr=i(Ki),so=n(Ki,"DIV",{class:!0});var on=s(so);M(VV.$$.fragment,on),okr=i(on),jBe=n(on,"P",{});var Vgn=s(jBe);rkr=r(Vgn,"Instantiate one of the base model classes of the library from a pretrained model."),Vgn.forEach(t),tkr=i(on),Zn=n(on,"P",{});var $j=s(Zn);akr=r($j,"The model class to instantiate is selected based on the "),PBe=n($j,"CODE",{});var Wgn=s(PBe);nkr=r(Wgn,"model_type"),Wgn.forEach(t),skr=r($j,` property of the config object (either
passed as an argument or loaded from `),ZBe=n($j,"CODE",{});var Jgn=s(ZBe);lkr=r(Jgn,"pretrained_model_name_or_path"),Jgn.forEach(t),ikr=r($j,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),VBe=n($j,"CODE",{});var Ugn=s(VBe);dkr=r(Ugn,"pretrained_model_name_or_path"),Ugn.forEach(t),mkr=r($j,":"),$j.forEach(t),ckr=i(on),A=n(on,"UL",{});var R=s(A);hT=n(R,"LI",{});var yMo=s(hT);WBe=n(yMo,"STRONG",{});var Xgn=s(WBe);fkr=r(Xgn,"albert"),Xgn.forEach(t),hkr=r(yMo," \u2014 "),Ale=n(yMo,"A",{href:!0});var Dgn=s(Ale);gkr=r(Dgn,"AlbertModel"),Dgn.forEach(t),ukr=r(yMo," (ALBERT model)"),yMo.forEach(t),pkr=i(R),gT=n(R,"LI",{});var CMo=s(gT);JBe=n(CMo,"STRONG",{});var qgn=s(JBe);_kr=r(qgn,"align"),qgn.forEach(t),bkr=r(CMo," \u2014 "),Rle=n(CMo,"A",{href:!0});var Ogn=s(Rle);vkr=r(Ogn,"AlignModel"),Ogn.forEach(t),Mkr=r(CMo," (ALIGN model)"),CMo.forEach(t),Tkr=i(R),uT=n(R,"LI",{});var wMo=s(uT);UBe=n(wMo,"STRONG",{});var Qgn=s(UBe);Fkr=r(Qgn,"altclip"),Qgn.forEach(t),Ekr=r(wMo," \u2014 "),kle=n(wMo,"A",{href:!0});var Ygn=s(kle);ykr=r(Ygn,"AltCLIPModel"),Ygn.forEach(t),Ckr=r(wMo," (AltCLIP model)"),wMo.forEach(t),wkr=i(R),pT=n(R,"LI",{});var LMo=s(pT);XBe=n(LMo,"STRONG",{});var zgn=s(XBe);Lkr=r(zgn,"audio-spectrogram-transformer"),zgn.forEach(t),Akr=r(LMo," \u2014 "),Sle=n(LMo,"A",{href:!0});var Hgn=s(Sle);Rkr=r(Hgn,"ASTModel"),Hgn.forEach(t),kkr=r(LMo," (Audio Spectrogram Transformer model)"),LMo.forEach(t),Skr=i(R),_T=n(R,"LI",{});var AMo=s(_T);DBe=n(AMo,"STRONG",{});var Kgn=s(DBe);xkr=r(Kgn,"autoformer"),Kgn.forEach(t),Gkr=r(AMo," \u2014 "),xle=n(AMo,"A",{href:!0});var eun=s(xle);Bkr=r(eun,"AutoformerModel"),eun.forEach(t),Ikr=r(AMo," (Autoformer model)"),AMo.forEach(t),Nkr=i(R),bT=n(R,"LI",{});var RMo=s(bT);qBe=n(RMo,"STRONG",{});var oun=s(qBe);$kr=r(oun,"bark"),oun.forEach(t),jkr=r(RMo," \u2014 "),Gle=n(RMo,"A",{href:!0});var run=s(Gle);Pkr=r(run,"BarkModel"),run.forEach(t),Zkr=r(RMo," (Bark model)"),RMo.forEach(t),Vkr=i(R),vT=n(R,"LI",{});var kMo=s(vT);OBe=n(kMo,"STRONG",{});var tun=s(OBe);Wkr=r(tun,"bart"),tun.forEach(t),Jkr=r(kMo," \u2014 "),Ble=n(kMo,"A",{href:!0});var 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cun=s(HBe);iSr=r(cun,"big_bird"),cun.forEach(t),dSr=r(BMo," \u2014 "),jle=n(BMo,"A",{href:!0});var fun=s(jle);mSr=r(fun,"BigBirdModel"),fun.forEach(t),cSr=r(BMo," (BigBird model)"),BMo.forEach(t),fSr=i(R),yT=n(R,"LI",{});var IMo=s(yT);KBe=n(IMo,"STRONG",{});var hun=s(KBe);hSr=r(hun,"bigbird_pegasus"),hun.forEach(t),gSr=r(IMo," \u2014 "),Ple=n(IMo,"A",{href:!0});var gun=s(Ple);uSr=r(gun,"BigBirdPegasusModel"),gun.forEach(t),pSr=r(IMo," (BigBird-Pegasus model)"),IMo.forEach(t),_Sr=i(R),CT=n(R,"LI",{});var NMo=s(CT);eIe=n(NMo,"STRONG",{});var uun=s(eIe);bSr=r(uun,"biogpt"),uun.forEach(t),vSr=r(NMo," \u2014 "),Zle=n(NMo,"A",{href:!0});var pun=s(Zle);MSr=r(pun,"BioGptModel"),pun.forEach(t),TSr=r(NMo," (BioGpt model)"),NMo.forEach(t),FSr=i(R),wT=n(R,"LI",{});var $Mo=s(wT);oIe=n($Mo,"STRONG",{});var _un=s(oIe);ESr=r(_un,"bit"),_un.forEach(t),ySr=r($Mo," \u2014 "),Vle=n($Mo,"A",{href:!0});var bun=s(Vle);CSr=r(bun,"BitModel"),bun.forEach(t),wSr=r($Mo," (BiT 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sEn=s(Cfe);Hot=r(sEn,"FlaxBlenderbotSmallModel"),sEn.forEach(t),Kot=r(u0o," (BlenderbotSmall model)"),u0o.forEach(t),ert=i(ie),c5=n(ie,"LI",{});var p0o=s(c5);aZe=n(p0o,"STRONG",{});var lEn=s(aZe);ort=r(lEn,"bloom"),lEn.forEach(t),rrt=r(p0o," \u2014 "),wfe=n(p0o,"A",{href:!0});var iEn=s(wfe);trt=r(iEn,"FlaxBloomModel"),iEn.forEach(t),art=r(p0o," (BLOOM model)"),p0o.forEach(t),nrt=i(ie),f5=n(ie,"LI",{});var _0o=s(f5);nZe=n(_0o,"STRONG",{});var dEn=s(nZe);srt=r(dEn,"clip"),dEn.forEach(t),lrt=r(_0o," \u2014 "),Lfe=n(_0o,"A",{href:!0});var mEn=s(Lfe);irt=r(mEn,"FlaxCLIPModel"),mEn.forEach(t),drt=r(_0o," (CLIP model)"),_0o.forEach(t),mrt=i(ie),h5=n(ie,"LI",{});var b0o=s(h5);sZe=n(b0o,"STRONG",{});var cEn=s(sZe);crt=r(cEn,"distilbert"),cEn.forEach(t),frt=r(b0o," \u2014 "),Afe=n(b0o,"A",{href:!0});var fEn=s(Afe);hrt=r(fEn,"FlaxDistilBertModel"),fEn.forEach(t),grt=r(b0o," (DistilBERT model)"),b0o.forEach(t),urt=i(ie),g5=n(ie,"LI",{});var v0o=s(g5);lZe=n(v0o,"STRONG",{});var hEn=s(lZe);prt=r(hEn,"electra"),hEn.forEach(t),_rt=r(v0o," \u2014 "),Rfe=n(v0o,"A",{href:!0});var gEn=s(Rfe);brt=r(gEn,"FlaxElectraModel"),gEn.forEach(t),vrt=r(v0o," (ELECTRA model)"),v0o.forEach(t),Mrt=i(ie),u5=n(ie,"LI",{});var M0o=s(u5);iZe=n(M0o,"STRONG",{});var uEn=s(iZe);Trt=r(uEn,"gpt-sw3"),uEn.forEach(t),Frt=r(M0o," \u2014 "),kfe=n(M0o,"A",{href:!0});var pEn=s(kfe);Ert=r(pEn,"FlaxGPT2Model"),pEn.forEach(t),yrt=r(M0o," (GPT-Sw3 model)"),M0o.forEach(t),Crt=i(ie),p5=n(ie,"LI",{});var T0o=s(p5);dZe=n(T0o,"STRONG",{});var _En=s(dZe);wrt=r(_En,"gpt2"),_En.forEach(t),Lrt=r(T0o," \u2014 "),Sfe=n(T0o,"A",{href:!0});var bEn=s(Sfe);Art=r(bEn,"FlaxGPT2Model"),bEn.forEach(t),Rrt=r(T0o," (OpenAI GPT-2 model)"),T0o.forEach(t),krt=i(ie),_5=n(ie,"LI",{});var F0o=s(_5);mZe=n(F0o,"STRONG",{});var vEn=s(mZe);Srt=r(vEn,"gpt_neo"),vEn.forEach(t),xrt=r(F0o," \u2014 "),xfe=n(F0o,"A",{href:!0});var MEn=s(xfe);Grt=r(MEn,"FlaxGPTNeoModel"),MEn.forEach(t),Brt=r(F0o," (GPT Neo model)"),F0o.forEach(t),Irt=i(ie),b5=n(ie,"LI",{});var E0o=s(b5);cZe=n(E0o,"STRONG",{});var TEn=s(cZe);Nrt=r(TEn,"gptj"),TEn.forEach(t),$rt=r(E0o," \u2014 "),Gfe=n(E0o,"A",{href:!0});var FEn=s(Gfe);jrt=r(FEn,"FlaxGPTJModel"),FEn.forEach(t),Prt=r(E0o," (GPT-J model)"),E0o.forEach(t),Zrt=i(ie),v5=n(ie,"LI",{});var y0o=s(v5);fZe=n(y0o,"STRONG",{});var EEn=s(fZe);Vrt=r(EEn,"longt5"),EEn.forEach(t),Wrt=r(y0o," \u2014 "),Bfe=n(y0o,"A",{href:!0});var yEn=s(Bfe);Jrt=r(yEn,"FlaxLongT5Model"),yEn.forEach(t),Urt=r(y0o," (LongT5 model)"),y0o.forEach(t),Xrt=i(ie),M5=n(ie,"LI",{});var C0o=s(M5);hZe=n(C0o,"STRONG",{});var CEn=s(hZe);Drt=r(CEn,"marian"),CEn.forEach(t),qrt=r(C0o," \u2014 "),Ife=n(C0o,"A",{href:!0});var wEn=s(Ife);Ort=r(wEn,"FlaxMarianModel"),wEn.forEach(t),Qrt=r(C0o," (Marian model)"),C0o.forEach(t),Yrt=i(ie),T5=n(ie,"LI",{});var w0o=s(T5);gZe=n(w0o,"STRONG",{});var LEn=s(gZe);zrt=r(LEn,"mbart"),LEn.forEach(t),Hrt=r(w0o," \u2014 "),Nfe=n(w0o,"A",{href:!0});var AEn=s(Nfe);Krt=r(AEn,"FlaxMBartModel"),AEn.forEach(t),ett=r(w0o," (mBART model)"),w0o.forEach(t),ott=i(ie),F5=n(ie,"LI",{});var L0o=s(F5);uZe=n(L0o,"STRONG",{});var REn=s(uZe);rtt=r(REn,"mt5"),REn.forEach(t),ttt=r(L0o," \u2014 "),$fe=n(L0o,"A",{href:!0});var kEn=s($fe);att=r(kEn,"FlaxMT5Model"),kEn.forEach(t),ntt=r(L0o," (MT5 model)"),L0o.forEach(t),stt=i(ie),E5=n(ie,"LI",{});var A0o=s(E5);pZe=n(A0o,"STRONG",{});var SEn=s(pZe);ltt=r(SEn,"opt"),SEn.forEach(t),itt=r(A0o," \u2014 "),jfe=n(A0o,"A",{href:!0});var xEn=s(jfe);dtt=r(xEn,"FlaxOPTModel"),xEn.forEach(t),mtt=r(A0o," (OPT model)"),A0o.forEach(t),ctt=i(ie),y5=n(ie,"LI",{});var R0o=s(y5);_Ze=n(R0o,"STRONG",{});var GEn=s(_Ze);ftt=r(GEn,"pegasus"),GEn.forEach(t),htt=r(R0o," \u2014 "),Pfe=n(R0o,"A",{href:!0});var BEn=s(Pfe);gtt=r(BEn,"FlaxPegasusModel"),BEn.forEach(t),utt=r(R0o," (Pegasus model)"),R0o.forEach(t),ptt=i(ie),C5=n(ie,"LI",{});var k0o=s(C5);bZe=n(k0o,"STRONG",{});var IEn=s(bZe);_tt=r(IEn,"regnet"),IEn.forEach(t),btt=r(k0o," \u2014 "),Zfe=n(k0o,"A",{href:!0});var NEn=s(Zfe);vtt=r(NEn,"FlaxRegNetModel"),NEn.forEach(t),Mtt=r(k0o," (RegNet model)"),k0o.forEach(t),Ttt=i(ie),w5=n(ie,"LI",{});var S0o=s(w5);vZe=n(S0o,"STRONG",{});var $En=s(vZe);Ftt=r($En,"resnet"),$En.forEach(t),Ett=r(S0o," \u2014 "),Vfe=n(S0o,"A",{href:!0});var jEn=s(Vfe);ytt=r(jEn,"FlaxResNetModel"),jEn.forEach(t),Ctt=r(S0o," (ResNet model)"),S0o.forEach(t),wtt=i(ie),L5=n(ie,"LI",{});var x0o=s(L5);MZe=n(x0o,"STRONG",{});var PEn=s(MZe);Ltt=r(PEn,"roberta"),PEn.forEach(t),Att=r(x0o," \u2014 "),Wfe=n(x0o,"A",{href:!0});var ZEn=s(Wfe);Rtt=r(ZEn,"FlaxRobertaModel"),ZEn.forEach(t),ktt=r(x0o," (RoBERTa model)"),x0o.forEach(t),Stt=i(ie),A5=n(ie,"LI",{});var G0o=s(A5);TZe=n(G0o,"STRONG",{});var VEn=s(TZe);xtt=r(VEn,"roberta-prelayernorm"),VEn.forEach(t),Gtt=r(G0o," \u2014 "),Jfe=n(G0o,"A",{href:!0});var WEn=s(Jfe);Btt=r(WEn,"FlaxRobertaPreLayerNormModel"),WEn.forEach(t),Itt=r(G0o," (RoBERTa-PreLayerNorm model)"),G0o.forEach(t),Ntt=i(ie),R5=n(ie,"LI",{});var B0o=s(R5);FZe=n(B0o,"STRONG",{});var JEn=s(FZe);$tt=r(JEn,"roformer"),JEn.forEach(t),jtt=r(B0o," \u2014 "),Ufe=n(B0o,"A",{href:!0});var UEn=s(Ufe);Ptt=r(UEn,"FlaxRoFormerModel"),UEn.forEach(t),Ztt=r(B0o," (RoFormer model)"),B0o.forEach(t),Vtt=i(ie),k5=n(ie,"LI",{});var I0o=s(k5);EZe=n(I0o,"STRONG",{});var XEn=s(EZe);Wtt=r(XEn,"t5"),XEn.forEach(t),Jtt=r(I0o," \u2014 "),Xfe=n(I0o,"A",{href:!0});var DEn=s(Xfe);Utt=r(DEn,"FlaxT5Model"),DEn.forEach(t),Xtt=r(I0o," (T5 model)"),I0o.forEach(t),Dtt=i(ie),S5=n(ie,"LI",{});var N0o=s(S5);yZe=n(N0o,"STRONG",{});var qEn=s(yZe);qtt=r(qEn,"vision-text-dual-encoder"),qEn.forEach(t),Ott=r(N0o," \u2014 "),Dfe=n(N0o,"A",{href:!0});var OEn=s(Dfe);Qtt=r(OEn,"FlaxVisionTextDualEncoderModel"),OEn.forEach(t),Ytt=r(N0o," (VisionTextDualEncoder model)"),N0o.forEach(t),ztt=i(ie),x5=n(ie,"LI",{});var $0o=s(x5);CZe=n($0o,"STRONG",{});var QEn=s(CZe);Htt=r(QEn,"vit"),QEn.forEach(t),Ktt=r($0o," \u2014 "),qfe=n($0o,"A",{href:!0});var YEn=s(qfe);eat=r(YEn,"FlaxViTModel"),YEn.forEach(t),oat=r($0o," (ViT model)"),$0o.forEach(t),rat=i(ie),G5=n(ie,"LI",{});var j0o=s(G5);wZe=n(j0o,"STRONG",{});var zEn=s(wZe);tat=r(zEn,"wav2vec2"),zEn.forEach(t),aat=r(j0o," \u2014 "),Ofe=n(j0o,"A",{href:!0});var HEn=s(Ofe);nat=r(HEn,"FlaxWav2Vec2Model"),HEn.forEach(t),sat=r(j0o," (Wav2Vec2 model)"),j0o.forEach(t),lat=i(ie),B5=n(ie,"LI",{});var P0o=s(B5);LZe=n(P0o,"STRONG",{});var KEn=s(LZe);iat=r(KEn,"whisper"),KEn.forEach(t),dat=r(P0o," \u2014 "),Qfe=n(P0o,"A",{href:!0});var eyn=s(Qfe);mat=r(eyn,"FlaxWhisperModel"),eyn.forEach(t),cat=r(P0o," (Whisper model)"),P0o.forEach(t),fat=i(ie),I5=n(ie,"LI",{});var Z0o=s(I5);AZe=n(Z0o,"STRONG",{});var oyn=s(AZe);hat=r(oyn,"xglm"),oyn.forEach(t),gat=r(Z0o," \u2014 "),Yfe=n(Z0o,"A",{href:!0});var ryn=s(Yfe);uat=r(ryn,"FlaxXGLMModel"),ryn.forEach(t),pat=r(Z0o," (XGLM model)"),Z0o.forEach(t),_at=i(ie),N5=n(ie,"LI",{});var V0o=s(N5);RZe=n(V0o,"STRONG",{});var tyn=s(RZe);bat=r(tyn,"xlm-roberta"),tyn.forEach(t),vat=r(V0o," \u2014 "),zfe=n(V0o,"A",{href:!0});var ayn=s(zfe);Mat=r(ayn,"FlaxXLMRobertaModel"),ayn.forEach(t),Tat=r(V0o," (XLM-RoBERTa model)"),V0o.forEach(t),ie.forEach(t),Fat=i(td),M($5.$$.fragment,td),td.forEach(t),rd.forEach(t),rBo=i(c),gc=n(c,"H2",{class:!0});var n$o=s(gc);j5=n(n$o,"A",{id:!0,class:!0,href:!0});var nyn=s(j5);kZe=n(nyn,"SPAN",{});var syn=s(kZe);M(HV.$$.fragment,syn),syn.forEach(t),nyn.forEach(t),Eat=i(n$o),SZe=n(n$o,"SPAN",{});var lyn=s(SZe);yat=r(lyn,"Generic pretraining classes"),lyn.forEach(t),n$o.forEach(t),tBo=i(c),Hfe=n(c,"P",{});var iyn=s(Hfe);Cat=r(iyn,"The following auto classes are available for instantiating a model with a pretraining head."),iyn.forEach(t),aBo=i(c),uc=n(c,"H3",{class:!0});var s$o=s(uc);P5=n(s$o,"A",{id:!0,class:!0,href:!0});var dyn=s(P5);xZe=n(dyn,"SPAN",{});var myn=s(xZe);M(KV.$$.fragment,myn),myn.forEach(t),dyn.forEach(t),wat=i(s$o),GZe=n(s$o,"SPAN",{});var cyn=s(GZe);Lat=r(cyn,"AutoModelForPreTraining"),cyn.forEach(t),s$o.forEach(t),nBo=i(c),qo=n(c,"DIV",{class:!0});var ad=s(qo);M(eW.$$.fragment,ad),Aat=i(ad),pc=n(ad,"P",{});var Q4e=s(pc);Rat=r(Q4e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),Kfe=n(Q4e,"A",{href:!0});var fyn=s(Kfe);kat=r(fyn,"from_pretrained()"),fyn.forEach(t),Sat=r(Q4e," class method or the "),ehe=n(Q4e,"A",{href:!0});var hyn=s(ehe);xat=r(hyn,"from_config()"),hyn.forEach(t),Gat=r(Q4e,` class
method.`),Q4e.forEach(t),Bat=i(ad),oW=n(ad,"P",{});var l$o=s(oW);Iat=r(l$o,"This class cannot be instantiated directly using "),BZe=n(l$o,"CODE",{});var gyn=s(BZe);Nat=r(gyn,"__init__()"),gyn.forEach(t),$at=r(l$o," (throws an error)."),l$o.forEach(t),jat=i(ad),Dt=n(ad,"DIV",{class:!0});var Wj=s(Dt);M(rW.$$.fragment,Wj),Pat=i(Wj),IZe=n(Wj,"P",{});var uyn=s(IZe);Zat=r(uyn,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),uyn.forEach(t),Vat=i(Wj),_c=n(Wj,"P",{});var Y4e=s(_c);Wat=r(Y4e,`Note:
Loading a model from its configuration file does `),NZe=n(Y4e,"STRONG",{});var pyn=s(NZe);Jat=r(pyn,"not"),pyn.forEach(t),Uat=r(Y4e,` load the model weights. It only affects the
model\u2019s configuration. Use `),ohe=n(Y4e,"A",{href:!0});var _yn=s(ohe);Xat=r(_yn,"from_pretrained()"),_yn.forEach(t),Dat=r(Y4e," to load the model weights."),Y4e.forEach(t),qat=i(Wj),M(Z5.$$.fragment,Wj),Wj.forEach(t),Oat=i(ad),lo=n(ad,"DIV",{class:!0});var rn=s(lo);M(tW.$$.fragment,rn),Qat=i(rn),$Ze=n(rn,"P",{});var byn=s($Ze);Yat=r(byn,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),byn.forEach(t),zat=i(rn),Jn=n(rn,"P",{});var Jj=s(Jn);Hat=r(Jj,"The model class to instantiate is selected based on the "),jZe=n(Jj,"CODE",{});var vyn=s(jZe);Kat=r(vyn,"model_type"),vyn.forEach(t),ent=r(Jj,` property of the config object (either
passed as an argument or loaded from `),PZe=n(Jj,"CODE",{});var Myn=s(PZe);ont=r(Myn,"pretrained_model_name_or_path"),Myn.forEach(t),rnt=r(Jj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ZZe=n(Jj,"CODE",{});var Tyn=s(ZZe);tnt=r(Tyn,"pretrained_model_name_or_path"),Tyn.forEach(t),ant=r(Jj,":"),Jj.forEach(t),nnt=i(rn),U=n(rn,"UL",{});var q=s(U);V5=n(q,"LI",{});var W0o=s(V5);VZe=n(W0o,"STRONG",{});var Fyn=s(VZe);snt=r(Fyn,"albert"),Fyn.forEach(t),lnt=r(W0o," \u2014 "),rhe=n(W0o,"A",{href:!0});var Eyn=s(rhe);int=r(Eyn,"AlbertForPreTraining"),Eyn.forEach(t),dnt=r(W0o," (ALBERT model)"),W0o.forEach(t),mnt=i(q),W5=n(q,"LI",{});var J0o=s(W5);WZe=n(J0o,"STRONG",{});var yyn=s(WZe);cnt=r(yyn,"bart"),yyn.forEach(t),fnt=r(J0o," \u2014 "),the=n(J0o,"A",{href:!0});var Cyn=s(the);hnt=r(Cyn,"BartForConditionalGeneration"),Cyn.forEach(t),gnt=r(J0o," (BART model)"),J0o.forEach(t),unt=i(q),J5=n(q,"LI",{});var U0o=s(J5);JZe=n(U0o,"STRONG",{});var wyn=s(JZe);pnt=r(wyn,"bert"),wyn.forEach(t),_nt=r(U0o," \u2014 "),ahe=n(U0o,"A",{href:!0});var Lyn=s(ahe);bnt=r(Lyn,"BertForPreTraining"),Lyn.forEach(t),vnt=r(U0o," (BERT 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O5o=s(q3);qVe=n(O5o,"STRONG",{});var B5n=s(qVe);Nmt=r(B5n,"xlnet"),B5n.forEach(t),$mt=r(O5o," \u2014 "),ige=n(O5o,"A",{href:!0});var I5n=s(ige);jmt=r(I5n,"XLNetLMHeadModel"),I5n.forEach(t),Pmt=r(O5o," (XLNet model)"),O5o.forEach(t),Zmt=i(q),O3=n(q,"LI",{});var Q5o=s(O3);OVe=n(Q5o,"STRONG",{});var N5n=s(OVe);Vmt=r(N5n,"xmod"),N5n.forEach(t),Wmt=r(Q5o," \u2014 "),dge=n(Q5o,"A",{href:!0});var $5n=s(dge);Jmt=r($5n,"XmodForMaskedLM"),$5n.forEach(t),Umt=r(Q5o," (X-MOD model)"),Q5o.forEach(t),q.forEach(t),Xmt=i(rn),Q3=n(rn,"P",{});var Y5o=s(Q3);Dmt=r(Y5o,"The model is set in evaluation mode by default using "),QVe=n(Y5o,"CODE",{});var j5n=s(QVe);qmt=r(j5n,"model.eval()"),j5n.forEach(t),Omt=r(Y5o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),YVe=n(Y5o,"CODE",{});var P5n=s(YVe);Qmt=r(P5n,"model.train()"),P5n.forEach(t),Y5o.forEach(t),Ymt=i(rn),M(Y3.$$.fragment,rn),rn.forEach(t),ad.forEach(t),sBo=i(c),bc=n(c,"H3",{class:!0});var i$o=s(bc);z3=n(i$o,"A",{id:!0,class:!0,href:!0});var Z5n=s(z3);zVe=n(Z5n,"SPAN",{});var V5n=s(zVe);M(aW.$$.fragment,V5n),V5n.forEach(t),Z5n.forEach(t),zmt=i(i$o),HVe=n(i$o,"SPAN",{});var W5n=s(HVe);Hmt=r(W5n,"TFAutoModelForPreTraining"),W5n.forEach(t),i$o.forEach(t),lBo=i(c),Oo=n(c,"DIV",{class:!0});var nd=s(Oo);M(nW.$$.fragment,nd),Kmt=i(nd),vc=n(nd,"P",{});var z4e=s(vc);ect=r(z4e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),mge=n(z4e,"A",{href:!0});var J5n=s(mge);oct=r(J5n,"from_pretrained()"),J5n.forEach(t),rct=r(z4e," class method or the "),cge=n(z4e,"A",{href:!0});var U5n=s(cge);tct=r(U5n,"from_config()"),U5n.forEach(t),act=r(z4e,` class
method.`),z4e.forEach(t),nct=i(nd),sW=n(nd,"P",{});var d$o=s(sW);sct=r(d$o,"This class cannot be instantiated directly using "),KVe=n(d$o,"CODE",{});var X5n=s(KVe);lct=r(X5n,"__init__()"),X5n.forEach(t),ict=r(d$o," (throws an error)."),d$o.forEach(t),dct=i(nd),qt=n(nd,"DIV",{class:!0});var Uj=s(qt);M(lW.$$.fragment,Uj),mct=i(Uj),eWe=n(Uj,"P",{});var D5n=s(eWe);cct=r(D5n,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),D5n.forEach(t),fct=i(Uj),Mc=n(Uj,"P",{});var H4e=s(Mc);hct=r(H4e,`Note:
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model\u2019s configuration. Use `),fge=n(H4e,"A",{href:!0});var O5n=s(fge);pct=r(O5n,"from_pretrained()"),O5n.forEach(t),_ct=r(H4e," to load the model weights."),H4e.forEach(t),bct=i(Uj),M(H3.$$.fragment,Uj),Uj.forEach(t),vct=i(nd),nt=n(nd,"DIV",{class:!0});var sd=s(nt);M(iW.$$.fragment,sd),Mct=i(sd),rWe=n(sd,"P",{});var Q5n=s(rWe);Tct=r(Q5n,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),Q5n.forEach(t),Fct=i(sd),Un=n(sd,"P",{});var Xj=s(Un);Ect=r(Xj,"The model class to instantiate is selected based on the "),tWe=n(Xj,"CODE",{});var Y5n=s(tWe);yct=r(Y5n,"model_type"),Y5n.forEach(t),Cct=r(Xj,` property of the config object (either
passed as an argument or loaded from `),aWe=n(Xj,"CODE",{});var z5n=s(aWe);wct=r(z5n,"pretrained_model_name_or_path"),z5n.forEach(t),Lct=r(Xj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nWe=n(Xj,"CODE",{});var H5n=s(nWe);Act=r(H5n,"pretrained_model_name_or_path"),H5n.forEach(t),Rct=r(Xj,":"),Xj.forEach(t),kct=i(sd),fe=n(sd,"UL",{});var he=s(fe);K3=n(he,"LI",{});var z5o=s(K3);sWe=n(z5o,"STRONG",{});var K5n=s(sWe);Sct=r(K5n,"albert"),K5n.forEach(t),xct=r(z5o," \u2014 "),hge=n(z5o,"A",{href:!0});var e3n=s(hge);Gct=r(e3n,"TFAlbertForPreTraining"),e3n.forEach(t),Bct=r(z5o," (ALBERT model)"),z5o.forEach(t),Ict=i(he),e9=n(he,"LI",{});var H5o=s(e9);lWe=n(H5o,"STRONG",{});var o3n=s(lWe);Nct=r(o3n,"bart"),o3n.forEach(t),$ct=r(H5o," \u2014 "),gge=n(H5o,"A",{href:!0});var r3n=s(gge);jct=r(r3n,"TFBartForConditionalGeneration"),r3n.forEach(t),Pct=r(H5o," (BART model)"),H5o.forEach(t),Zct=i(he),o9=n(he,"LI",{});var K5o=s(o9);iWe=n(K5o,"STRONG",{});var t3n=s(iWe);Vct=r(t3n,"bert"),t3n.forEach(t),Wct=r(K5o," \u2014 "),uge=n(K5o,"A",{href:!0});var a3n=s(uge);Jct=r(a3n,"TFBertForPreTraining"),a3n.forEach(t),Uct=r(K5o," (BERT 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G3n=s(EWe);iht=r(G3n,"roberta-prelayernorm"),G3n.forEach(t),dht=r(g3o," \u2014 "),kge=n(g3o,"A",{href:!0});var B3n=s(kge);mht=r(B3n,"TFRobertaPreLayerNormForMaskedLM"),B3n.forEach(t),cht=r(g3o," (RoBERTa-PreLayerNorm model)"),g3o.forEach(t),fht=i(he),_9=n(he,"LI",{});var u3o=s(_9);yWe=n(u3o,"STRONG",{});var I3n=s(yWe);hht=r(I3n,"t5"),I3n.forEach(t),ght=r(u3o," \u2014 "),Sge=n(u3o,"A",{href:!0});var N3n=s(Sge);uht=r(N3n,"TFT5ForConditionalGeneration"),N3n.forEach(t),pht=r(u3o," (T5 model)"),u3o.forEach(t),_ht=i(he),b9=n(he,"LI",{});var p3o=s(b9);CWe=n(p3o,"STRONG",{});var $3n=s(CWe);bht=r($3n,"tapas"),$3n.forEach(t),vht=r(p3o," \u2014 "),xge=n(p3o,"A",{href:!0});var j3n=s(xge);Mht=r(j3n,"TFTapasForMaskedLM"),j3n.forEach(t),Tht=r(p3o," (TAPAS model)"),p3o.forEach(t),Fht=i(he),v9=n(he,"LI",{});var _3o=s(v9);wWe=n(_3o,"STRONG",{});var P3n=s(wWe);Eht=r(P3n,"transfo-xl"),P3n.forEach(t),yht=r(_3o," \u2014 "),Gge=n(_3o,"A",{href:!0});var Z3n=s(Gge);Cht=r(Z3n,"TFTransfoXLLMHeadModel"),Z3n.forEach(t),wht=r(_3o," (Transformer-XL model)"),_3o.forEach(t),Lht=i(he),M9=n(he,"LI",{});var b3o=s(M9);LWe=n(b3o,"STRONG",{});var V3n=s(LWe);Aht=r(V3n,"vit_mae"),V3n.forEach(t),Rht=r(b3o," \u2014 "),Bge=n(b3o,"A",{href:!0});var W3n=s(Bge);kht=r(W3n,"TFViTMAEForPreTraining"),W3n.forEach(t),Sht=r(b3o," (ViTMAE model)"),b3o.forEach(t),xht=i(he),T9=n(he,"LI",{});var v3o=s(T9);AWe=n(v3o,"STRONG",{});var J3n=s(AWe);Ght=r(J3n,"xlm"),J3n.forEach(t),Bht=r(v3o," \u2014 "),Ige=n(v3o,"A",{href:!0});var U3n=s(Ige);Iht=r(U3n,"TFXLMWithLMHeadModel"),U3n.forEach(t),Nht=r(v3o," (XLM model)"),v3o.forEach(t),$ht=i(he),F9=n(he,"LI",{});var M3o=s(F9);RWe=n(M3o,"STRONG",{});var X3n=s(RWe);jht=r(X3n,"xlm-roberta"),X3n.forEach(t),Pht=r(M3o," \u2014 "),Nge=n(M3o,"A",{href:!0});var D3n=s(Nge);Zht=r(D3n,"TFXLMRobertaForMaskedLM"),D3n.forEach(t),Vht=r(M3o," (XLM-RoBERTa model)"),M3o.forEach(t),Wht=i(he),E9=n(he,"LI",{});var T3o=s(E9);kWe=n(T3o,"STRONG",{});var q3n=s(kWe);Jht=r(q3n,"xlnet"),q3n.forEach(t),Uht=r(T3o," \u2014 "),$ge=n(T3o,"A",{href:!0});var O3n=s($ge);Xht=r(O3n,"TFXLNetLMHeadModel"),O3n.forEach(t),Dht=r(T3o," (XLNet model)"),T3o.forEach(t),he.forEach(t),qht=i(sd),M(y9.$$.fragment,sd),sd.forEach(t),nd.forEach(t),iBo=i(c),Tc=n(c,"H3",{class:!0});var m$o=s(Tc);C9=n(m$o,"A",{id:!0,class:!0,href:!0});var Q3n=s(C9);SWe=n(Q3n,"SPAN",{});var Y3n=s(SWe);M(dW.$$.fragment,Y3n),Y3n.forEach(t),Q3n.forEach(t),Oht=i(m$o),xWe=n(m$o,"SPAN",{});var z3n=s(xWe);Qht=r(z3n,"FlaxAutoModelForPreTraining"),z3n.forEach(t),m$o.forEach(t),dBo=i(c),Qo=n(c,"DIV",{class:!0});var ld=s(Qo);M(mW.$$.fragment,ld),Yht=i(ld),Fc=n(ld,"P",{});var K4e=s(Fc);zht=r(K4e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),jge=n(K4e,"A",{href:!0});var H3n=s(jge);Hht=r(H3n,"from_pretrained()"),H3n.forEach(t),Kht=r(K4e," class method or the "),Pge=n(K4e,"A",{href:!0});var K3n=s(Pge);egt=r(K3n,"from_config()"),K3n.forEach(t),ogt=r(K4e,` class
method.`),K4e.forEach(t),rgt=i(ld),cW=n(ld,"P",{});var c$o=s(cW);tgt=r(c$o,"This class cannot be instantiated directly using "),GWe=n(c$o,"CODE",{});var e9n=s(GWe);agt=r(e9n,"__init__()"),e9n.forEach(t),ngt=r(c$o," (throws an error)."),c$o.forEach(t),sgt=i(ld),Ot=n(ld,"DIV",{class:!0});var Dj=s(Ot);M(fW.$$.fragment,Dj),lgt=i(Dj),BWe=n(Dj,"P",{});var o9n=s(BWe);igt=r(o9n,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),o9n.forEach(t),dgt=i(Dj),Ec=n(Dj,"P",{});var eCe=s(Ec);mgt=r(eCe,`Note:
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model\u2019s configuration. Use `),Zge=n(eCe,"A",{href:!0});var t9n=s(Zge);hgt=r(t9n,"from_pretrained()"),t9n.forEach(t),ggt=r(eCe," to load the model weights."),eCe.forEach(t),ugt=i(Dj),M(w9.$$.fragment,Dj),Dj.forEach(t),pgt=i(ld),st=n(ld,"DIV",{class:!0});var id=s(st);M(hW.$$.fragment,id),_gt=i(id),NWe=n(id,"P",{});var a9n=s(NWe);bgt=r(a9n,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),a9n.forEach(t),vgt=i(id),Xn=n(id,"P",{});var qj=s(Xn);Mgt=r(qj,"The model class to instantiate is selected based on the "),$We=n(qj,"CODE",{});var n9n=s($We);Tgt=r(n9n,"model_type"),n9n.forEach(t),Fgt=r(qj,` property of the config object (either
passed as an argument or loaded from `),jWe=n(qj,"CODE",{});var s9n=s(jWe);Egt=r(s9n,"pretrained_model_name_or_path"),s9n.forEach(t),ygt=r(qj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),PWe=n(qj,"CODE",{});var l9n=s(PWe);Cgt=r(l9n,"pretrained_model_name_or_path"),l9n.forEach(t),wgt=r(qj,":"),qj.forEach(t),Lgt=i(id),we=n(id,"UL",{});var Ae=s(we);L9=n(Ae,"LI",{});var F3o=s(L9);ZWe=n(F3o,"STRONG",{});var i9n=s(ZWe);Agt=r(i9n,"albert"),i9n.forEach(t),Rgt=r(F3o," \u2014 "),Vge=n(F3o,"A",{href:!0});var d9n=s(Vge);kgt=r(d9n,"FlaxAlbertForPreTraining"),d9n.forEach(t),Sgt=r(F3o," (ALBERT model)"),F3o.forEach(t),xgt=i(Ae),A9=n(Ae,"LI",{});var E3o=s(A9);VWe=n(E3o,"STRONG",{});var m9n=s(VWe);Ggt=r(m9n,"bart"),m9n.forEach(t),Bgt=r(E3o," \u2014 "),Wge=n(E3o,"A",{href:!0});var c9n=s(Wge);Igt=r(c9n,"FlaxBartForConditionalGeneration"),c9n.forEach(t),Ngt=r(E3o," (BART model)"),E3o.forEach(t),$gt=i(Ae),R9=n(Ae,"LI",{});var y3o=s(R9);WWe=n(y3o,"STRONG",{});var f9n=s(WWe);jgt=r(f9n,"bert"),f9n.forEach(t),Pgt=r(y3o," \u2014 "),Jge=n(y3o,"A",{href:!0});var h9n=s(Jge);Zgt=r(h9n,"FlaxBertForPreTraining"),h9n.forEach(t),Vgt=r(y3o," (BERT 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with the `),tue=n(oCe,"A",{href:!0});var X9n=s(tue);rpt=r(X9n,"from_pretrained()"),X9n.forEach(t),tpt=r(oCe," class method or the "),aue=n(oCe,"A",{href:!0});var D9n=s(aue);apt=r(D9n,"from_config()"),D9n.forEach(t),npt=r(oCe,` class
method.`),oCe.forEach(t),spt=i(dd),_W=n(dd,"P",{});var g$o=s(_W);lpt=r(g$o,"This class cannot be instantiated directly using "),nJe=n(g$o,"CODE",{});var q9n=s(nJe);ipt=r(q9n,"__init__()"),q9n.forEach(t),dpt=r(g$o," (throws an error)."),g$o.forEach(t),mpt=i(dd),Qt=n(dd,"DIV",{class:!0});var Oj=s(Qt);M(bW.$$.fragment,Oj),cpt=i(Oj),sJe=n(Oj,"P",{});var O9n=s(sJe);fpt=r(O9n,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),O9n.forEach(t),hpt=i(Oj),Lc=n(Oj,"P",{});var rCe=s(Lc);gpt=r(rCe,`Note:
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model\u2019s configuration. Use `),nue=n(rCe,"A",{href:!0});var Y9n=s(nue);_pt=r(Y9n,"from_pretrained()"),Y9n.forEach(t),bpt=r(rCe," to load the model weights."),rCe.forEach(t),vpt=i(Oj),M(X9.$$.fragment,Oj),Oj.forEach(t),Mpt=i(dd),io=n(dd,"DIV",{class:!0});var tn=s(io);M(vW.$$.fragment,tn),Tpt=i(tn),iJe=n(tn,"P",{});var z9n=s(iJe);Fpt=r(z9n,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),z9n.forEach(t),Ept=i(tn),Dn=n(tn,"P",{});var Qj=s(Dn);ypt=r(Qj,"The model class to instantiate is selected based on the "),dJe=n(Qj,"CODE",{});var H9n=s(dJe);Cpt=r(H9n,"model_type"),H9n.forEach(t),wpt=r(Qj,` property of the config object (either
passed as an argument or loaded from `),mJe=n(Qj,"CODE",{});var K9n=s(mJe);Lpt=r(K9n,"pretrained_model_name_or_path"),K9n.forEach(t),Apt=r(Qj,` if possible), or when it\u2019s missing, by
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ZCn=s(zue);O2t=r(ZCn,"RoFormerForCausalLM"),ZCn.forEach(t),Q2t=r(R9o," (RoFormer model)"),R9o.forEach(t),Y2t=i(Y),P4=n(Y,"LI",{});var k9o=s(P4);sUe=n(k9o,"STRONG",{});var VCn=s(sUe);z2t=r(VCn,"rwkv"),VCn.forEach(t),H2t=r(k9o," \u2014 "),Hue=n(k9o,"A",{href:!0});var WCn=s(Hue);K2t=r(WCn,"RwkvForCausalLM"),WCn.forEach(t),evt=r(k9o," (RWKV model)"),k9o.forEach(t),ovt=i(Y),Z4=n(Y,"LI",{});var S9o=s(Z4);lUe=n(S9o,"STRONG",{});var JCn=s(lUe);rvt=r(JCn,"speech_to_text_2"),JCn.forEach(t),tvt=r(S9o," \u2014 "),Kue=n(S9o,"A",{href:!0});var UCn=s(Kue);avt=r(UCn,"Speech2Text2ForCausalLM"),UCn.forEach(t),nvt=r(S9o," (Speech2Text2 model)"),S9o.forEach(t),svt=i(Y),V4=n(Y,"LI",{});var x9o=s(V4);iUe=n(x9o,"STRONG",{});var XCn=s(iUe);lvt=r(XCn,"transfo-xl"),XCn.forEach(t),ivt=r(x9o," \u2014 "),epe=n(x9o,"A",{href:!0});var DCn=s(epe);dvt=r(DCn,"TransfoXLLMHeadModel"),DCn.forEach(t),mvt=r(x9o," (Transformer-XL model)"),x9o.forEach(t),cvt=i(Y),W4=n(Y,"LI",{});var G9o=s(W4);dUe=n(G9o,"STRONG",{});var 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with the `),dpe=n(tCe,"A",{href:!0});var gwn=s(dpe);lMt=r(gwn,"from_pretrained()"),gwn.forEach(t),iMt=r(tCe," class method or the "),mpe=n(tCe,"A",{href:!0});var uwn=s(mpe);dMt=r(uwn,"from_config()"),uwn.forEach(t),mMt=r(tCe,` class
method.`),tCe.forEach(t),cMt=i(md),FW=n(md,"P",{});var p$o=s(FW);fMt=r(p$o,"This class cannot be instantiated directly using "),TUe=n(p$o,"CODE",{});var pwn=s(TUe);hMt=r(pwn,"__init__()"),pwn.forEach(t),gMt=r(p$o," (throws an error)."),p$o.forEach(t),uMt=i(md),Yt=n(md,"DIV",{class:!0});var Yj=s(Yt);M(EW.$$.fragment,Yj),pMt=i(Yj),FUe=n(Yj,"P",{});var _wn=s(FUe);_Mt=r(_wn,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),_wn.forEach(t),bMt=i(Yj),kc=n(Yj,"P",{});var aCe=s(kc);vMt=r(aCe,`Note:
Loading a model from its configuration file does `),EUe=n(aCe,"STRONG",{});var bwn=s(EUe);MMt=r(bwn,"not"),bwn.forEach(t),TMt=r(aCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),cpe=n(aCe,"A",{href:!0});var vwn=s(cpe);FMt=r(vwn,"from_pretrained()"),vwn.forEach(t),EMt=r(aCe," to load the model weights."),aCe.forEach(t),yMt=i(Yj),M(K4.$$.fragment,Yj),Yj.forEach(t),CMt=i(md),lt=n(md,"DIV",{class:!0});var cd=s(lt);M(yW.$$.fragment,cd),wMt=i(cd),yUe=n(cd,"P",{});var Mwn=s(yUe);LMt=r(Mwn,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),Mwn.forEach(t),AMt=i(cd),qn=n(cd,"P",{});var zj=s(qn);RMt=r(zj,"The model class to instantiate is selected based on the "),CUe=n(zj,"CODE",{});var Twn=s(CUe);kMt=r(Twn,"model_type"),Twn.forEach(t),SMt=r(zj,` property of the config object (either
passed as an argument or loaded from `),wUe=n(zj,"CODE",{});var Fwn=s(wUe);xMt=r(Fwn,"pretrained_model_name_or_path"),Fwn.forEach(t),GMt=r(zj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),LUe=n(zj,"CODE",{});var Ewn=s(LUe);BMt=r(Ewn,"pretrained_model_name_or_path"),Ewn.forEach(t),IMt=r(zj,":"),zj.forEach(t),NMt=i(cd),Ee=n(cd,"UL",{});var Ce=s(Ee);eC=n(Ce,"LI",{});var W9o=s(eC);AUe=n(W9o,"STRONG",{});var ywn=s(AUe);$Mt=r(ywn,"bert"),ywn.forEach(t),jMt=r(W9o," \u2014 "),fpe=n(W9o,"A",{href:!0});var Cwn=s(fpe);PMt=r(Cwn,"TFBertLMHeadModel"),Cwn.forEach(t),ZMt=r(W9o," (BERT model)"),W9o.forEach(t),VMt=i(Ce),oC=n(Ce,"LI",{});var J9o=s(oC);RUe=n(J9o,"STRONG",{});var wwn=s(RUe);WMt=r(wwn,"camembert"),wwn.forEach(t),JMt=r(J9o," \u2014 "),hpe=n(J9o,"A",{href:!0});var Lwn=s(hpe);UMt=r(Lwn,"TFCamembertForCausalLM"),Lwn.forEach(t),XMt=r(J9o," (CamemBERT model)"),J9o.forEach(t),DMt=i(Ce),rC=n(Ce,"LI",{});var U9o=s(rC);kUe=n(U9o,"STRONG",{});var Awn=s(kUe);qMt=r(Awn,"ctrl"),Awn.forEach(t),OMt=r(U9o," \u2014 "),gpe=n(U9o,"A",{href:!0});var Rwn=s(gpe);QMt=r(Rwn,"TFCTRLLMHeadModel"),Rwn.forEach(t),YMt=r(U9o," (CTRL 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$wn=s(bpe);uTt=r($wn,"TFOpenAIGPTLMHeadModel"),$wn.forEach(t),pTt=r(O9o," (OpenAI GPT model)"),O9o.forEach(t),_Tt=i(Ce),lC=n(Ce,"LI",{});var Q9o=s(lC);IUe=n(Q9o,"STRONG",{});var jwn=s(IUe);bTt=r(jwn,"opt"),jwn.forEach(t),vTt=r(Q9o," \u2014 "),vpe=n(Q9o,"A",{href:!0});var Pwn=s(vpe);MTt=r(Pwn,"TFOPTForCausalLM"),Pwn.forEach(t),TTt=r(Q9o," (OPT model)"),Q9o.forEach(t),FTt=i(Ce),iC=n(Ce,"LI",{});var Y9o=s(iC);NUe=n(Y9o,"STRONG",{});var Zwn=s(NUe);ETt=r(Zwn,"rembert"),Zwn.forEach(t),yTt=r(Y9o," \u2014 "),Mpe=n(Y9o,"A",{href:!0});var Vwn=s(Mpe);CTt=r(Vwn,"TFRemBertForCausalLM"),Vwn.forEach(t),wTt=r(Y9o," (RemBERT model)"),Y9o.forEach(t),LTt=i(Ce),dC=n(Ce,"LI",{});var z9o=s(dC);$Ue=n(z9o,"STRONG",{});var Wwn=s($Ue);ATt=r(Wwn,"roberta"),Wwn.forEach(t),RTt=r(z9o," \u2014 "),Tpe=n(z9o,"A",{href:!0});var Jwn=s(Tpe);kTt=r(Jwn,"TFRobertaForCausalLM"),Jwn.forEach(t),STt=r(z9o," (RoBERTa model)"),z9o.forEach(t),xTt=i(Ce),mC=n(Ce,"LI",{});var H9o=s(mC);jUe=n(H9o,"STRONG",{});var 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method.`),nCe.forEach(t),FFt=i(fd),LW=n(fd,"P",{});var b$o=s(LW);EFt=r(b$o,"This class cannot be instantiated directly using "),qUe=n(b$o,"CODE",{});var d6n=s(qUe);yFt=r(d6n,"__init__()"),d6n.forEach(t),CFt=r(b$o," (throws an error)."),b$o.forEach(t),wFt=i(fd),zt=n(fd,"DIV",{class:!0});var Hj=s(zt);M(AW.$$.fragment,Hj),LFt=i(Hj),OUe=n(Hj,"P",{});var m6n=s(OUe);AFt=r(m6n,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),m6n.forEach(t),RFt=i(Hj),Gc=n(Hj,"P",{});var sCe=s(Gc);kFt=r(sCe,`Note:
Loading a model from its configuration file does `),QUe=n(sCe,"STRONG",{});var c6n=s(QUe);SFt=r(c6n,"not"),c6n.forEach(t),xFt=r(sCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Spe=n(sCe,"A",{href:!0});var f6n=s(Spe);GFt=r(f6n,"from_pretrained()"),f6n.forEach(t),BFt=r(sCe," to load the model weights."),sCe.forEach(t),IFt=i(Hj),M(vC.$$.fragment,Hj),Hj.forEach(t),NFt=i(fd),it=n(fd,"DIV",{class:!0});var hd=s(it);M(RW.$$.fragment,hd),$Ft=i(hd),YUe=n(hd,"P",{});var h6n=s(YUe);jFt=r(h6n,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),h6n.forEach(t),PFt=i(hd),On=n(hd,"P",{});var Kj=s(On);ZFt=r(Kj,"The model class to instantiate is selected based on the "),zUe=n(Kj,"CODE",{});var g6n=s(zUe);VFt=r(g6n,"model_type"),g6n.forEach(t),WFt=r(Kj,` property of the config object (either
passed as an argument or loaded from `),HUe=n(Kj,"CODE",{});var u6n=s(HUe);JFt=r(u6n,"pretrained_model_name_or_path"),u6n.forEach(t),UFt=r(Kj,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),KUe=n(Kj,"CODE",{});var p6n=s(KUe);XFt=r(p6n,"pretrained_model_name_or_path"),p6n.forEach(t),DFt=r(Kj,":"),Kj.forEach(t),qFt=i(hd),Le=n(hd,"UL",{});var Re=s(Le);MC=n(Re,"LI",{});var n4o=s(MC);eXe=n(n4o,"STRONG",{});var _6n=s(eXe);OFt=r(_6n,"bart"),_6n.forEach(t),QFt=r(n4o," \u2014 "),xpe=n(n4o,"A",{href:!0});var b6n=s(xpe);YFt=r(b6n,"FlaxBartForCausalLM"),b6n.forEach(t),zFt=r(n4o," (BART model)"),n4o.forEach(t),HFt=i(Re),TC=n(Re,"LI",{});var s4o=s(TC);oXe=n(s4o,"STRONG",{});var v6n=s(oXe);KFt=r(v6n,"bert"),v6n.forEach(t),eEt=r(s4o," \u2014 "),Gpe=n(s4o,"A",{href:!0});var M6n=s(Gpe);oEt=r(M6n,"FlaxBertForCausalLM"),M6n.forEach(t),rEt=r(s4o," (BERT model)"),s4o.forEach(t),tEt=i(Re),FC=n(Re,"LI",{});var l4o=s(FC);rXe=n(l4o,"STRONG",{});var T6n=s(rXe);aEt=r(T6n,"big_bird"),T6n.forEach(t),nEt=r(l4o," \u2014 "),Bpe=n(l4o,"A",{href:!0});var F6n=s(Bpe);sEt=r(F6n,"FlaxBigBirdForCausalLM"),F6n.forEach(t),lEt=r(l4o," (BigBird model)"),l4o.forEach(t),iEt=i(Re),EC=n(Re,"LI",{});var i4o=s(EC);tXe=n(i4o,"STRONG",{});var E6n=s(tXe);dEt=r(E6n,"bloom"),E6n.forEach(t),mEt=r(i4o," \u2014 "),Ipe=n(i4o,"A",{href:!0});var y6n=s(Ipe);cEt=r(y6n,"FlaxBloomForCausalLM"),y6n.forEach(t),fEt=r(i4o," (BLOOM model)"),i4o.forEach(t),hEt=i(Re),yC=n(Re,"LI",{});var d4o=s(yC);aXe=n(d4o,"STRONG",{});var C6n=s(aXe);gEt=r(C6n,"electra"),C6n.forEach(t),uEt=r(d4o," \u2014 "),Npe=n(d4o,"A",{href:!0});var w6n=s(Npe);pEt=r(w6n,"FlaxElectraForCausalLM"),w6n.forEach(t),_Et=r(d4o," (ELECTRA model)"),d4o.forEach(t),bEt=i(Re),CC=n(Re,"LI",{});var m4o=s(CC);nXe=n(m4o,"STRONG",{});var L6n=s(nXe);vEt=r(L6n,"gpt-sw3"),L6n.forEach(t),MEt=r(m4o," \u2014 "),$pe=n(m4o,"A",{href:!0});var A6n=s($pe);TEt=r(A6n,"FlaxGPT2LMHeadModel"),A6n.forEach(t),FEt=r(m4o," (GPT-Sw3 model)"),m4o.forEach(t),EEt=i(Re),wC=n(Re,"LI",{});var c4o=s(wC);sXe=n(c4o,"STRONG",{});var R6n=s(sXe);yEt=r(R6n,"gpt2"),R6n.forEach(t),CEt=r(c4o," \u2014 "),jpe=n(c4o,"A",{href:!0});var k6n=s(jpe);wEt=r(k6n,"FlaxGPT2LMHeadModel"),k6n.forEach(t),LEt=r(c4o," (OpenAI GPT-2 model)"),c4o.forEach(t),AEt=i(Re),LC=n(Re,"LI",{});var f4o=s(LC);lXe=n(f4o,"STRONG",{});var S6n=s(lXe);REt=r(S6n,"gpt_neo"),S6n.forEach(t),kEt=r(f4o," \u2014 "),Ppe=n(f4o,"A",{href:!0});var x6n=s(Ppe);SEt=r(x6n,"FlaxGPTNeoForCausalLM"),x6n.forEach(t),xEt=r(f4o," (GPT Neo model)"),f4o.forEach(t),GEt=i(Re),AC=n(Re,"LI",{});var h4o=s(AC);iXe=n(h4o,"STRONG",{});var G6n=s(iXe);BEt=r(G6n,"gptj"),G6n.forEach(t),IEt=r(h4o," \u2014 "),Zpe=n(h4o,"A",{href:!0});var B6n=s(Zpe);NEt=r(B6n,"FlaxGPTJForCausalLM"),B6n.forEach(t),$Et=r(h4o," (GPT-J model)"),h4o.forEach(t),jEt=i(Re),RC=n(Re,"LI",{});var g4o=s(RC);dXe=n(g4o,"STRONG",{});var I6n=s(dXe);PEt=r(I6n,"opt"),I6n.forEach(t),ZEt=r(g4o," \u2014 "),Vpe=n(g4o,"A",{href:!0});var N6n=s(Vpe);VEt=r(N6n,"FlaxOPTForCausalLM"),N6n.forEach(t),WEt=r(g4o," (OPT model)"),g4o.forEach(t),JEt=i(Re),kC=n(Re,"LI",{});var u4o=s(kC);mXe=n(u4o,"STRONG",{});var $6n=s(mXe);UEt=r($6n,"roberta"),$6n.forEach(t),XEt=r(u4o," \u2014 "),Wpe=n(u4o,"A",{href:!0});var j6n=s(Wpe);DEt=r(j6n,"FlaxRobertaForCausalLM"),j6n.forEach(t),qEt=r(u4o," (RoBERTa model)"),u4o.forEach(t),OEt=i(Re),SC=n(Re,"LI",{});var p4o=s(SC);cXe=n(p4o,"STRONG",{});var P6n=s(cXe);QEt=r(P6n,"roberta-prelayernorm"),P6n.forEach(t),YEt=r(p4o," \u2014 "),Jpe=n(p4o,"A",{href:!0});var Z6n=s(Jpe);zEt=r(Z6n,"FlaxRobertaPreLayerNormForCausalLM"),Z6n.forEach(t),HEt=r(p4o," (RoBERTa-PreLayerNorm model)"),p4o.forEach(t),KEt=i(Re),xC=n(Re,"LI",{});var _4o=s(xC);fXe=n(_4o,"STRONG",{});var V6n=s(fXe);eyt=r(V6n,"xglm"),V6n.forEach(t),oyt=r(_4o," \u2014 "),Upe=n(_4o,"A",{href:!0});var W6n=s(Upe);ryt=r(W6n,"FlaxXGLMForCausalLM"),W6n.forEach(t),tyt=r(_4o," (XGLM model)"),_4o.forEach(t),ayt=i(Re),GC=n(Re,"LI",{});var b4o=s(GC);hXe=n(b4o,"STRONG",{});var J6n=s(hXe);nyt=r(J6n,"xlm-roberta"),J6n.forEach(t),syt=r(b4o," \u2014 "),Xpe=n(b4o,"A",{href:!0});var U6n=s(Xpe);lyt=r(U6n,"FlaxXLMRobertaForCausalLM"),U6n.forEach(t),iyt=r(b4o," (XLM-RoBERTa model)"),b4o.forEach(t),Re.forEach(t),dyt=i(hd),M(BC.$$.fragment,hd),hd.forEach(t),fd.forEach(t),bBo=i(c),Bc=n(c,"H3",{class:!0});var v$o=s(Bc);IC=n(v$o,"A",{id:!0,class:!0,href:!0});var X6n=s(IC);gXe=n(X6n,"SPAN",{});var D6n=s(gXe);M(kW.$$.fragment,D6n),D6n.forEach(t),X6n.forEach(t),myt=i(v$o),uXe=n(v$o,"SPAN",{});var q6n=s(uXe);cyt=r(q6n,"AutoModelForMaskedLM"),q6n.forEach(t),v$o.forEach(t),vBo=i(c),Ko=n(c,"DIV",{class:!0});var gd=s(Ko);M(SW.$$.fragment,gd),fyt=i(gd),Ic=n(gd,"P",{});var lCe=s(Ic);hyt=r(lCe,`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 `),Dpe=n(lCe,"A",{href:!0});var O6n=s(Dpe);gyt=r(O6n,"from_pretrained()"),O6n.forEach(t),uyt=r(lCe," class method or the "),qpe=n(lCe,"A",{href:!0});var Q6n=s(qpe);pyt=r(Q6n,"from_config()"),Q6n.forEach(t),_yt=r(lCe,` class
method.`),lCe.forEach(t),byt=i(gd),xW=n(gd,"P",{});var M$o=s(xW);vyt=r(M$o,"This class cannot be instantiated directly using "),pXe=n(M$o,"CODE",{});var Y6n=s(pXe);Myt=r(Y6n,"__init__()"),Y6n.forEach(t),Tyt=r(M$o," (throws an error)."),M$o.forEach(t),Fyt=i(gd),Ht=n(gd,"DIV",{class:!0});var eP=s(Ht);M(GW.$$.fragment,eP),Eyt=i(eP),_Xe=n(eP,"P",{});var z6n=s(_Xe);yyt=r(z6n,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),z6n.forEach(t),Cyt=i(eP),Nc=n(eP,"P",{});var iCe=s(Nc);wyt=r(iCe,`Note:
Loading a model from its configuration file does `),bXe=n(iCe,"STRONG",{});var H6n=s(bXe);Lyt=r(H6n,"not"),H6n.forEach(t),Ayt=r(iCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Ope=n(iCe,"A",{href:!0});var K6n=s(Ope);Ryt=r(K6n,"from_pretrained()"),K6n.forEach(t),kyt=r(iCe," to load the model weights."),iCe.forEach(t),Syt=i(eP),M(NC.$$.fragment,eP),eP.forEach(t),xyt=i(gd),mo=n(gd,"DIV",{class:!0});var an=s(mo);M(BW.$$.fragment,an),Gyt=i(an),vXe=n(an,"P",{});var e8n=s(vXe);Byt=r(e8n,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),e8n.forEach(t),Iyt=i(an),Qn=n(an,"P",{});var oP=s(Qn);Nyt=r(oP,"The model class to instantiate is selected based on the "),MXe=n(oP,"CODE",{});var o8n=s(MXe);$yt=r(o8n,"model_type"),o8n.forEach(t),jyt=r(oP,` property of the config object (either
passed as an argument or loaded from `),TXe=n(oP,"CODE",{});var r8n=s(TXe);Pyt=r(r8n,"pretrained_model_name_or_path"),r8n.forEach(t),Zyt=r(oP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),FXe=n(oP,"CODE",{});var t8n=s(FXe);Vyt=r(t8n,"pretrained_model_name_or_path"),t8n.forEach(t),Wyt=r(oP,":"),oP.forEach(t),Jyt=i(an),K=n(an,"UL",{});var ee=s(K);$C=n(ee,"LI",{});var v4o=s($C);EXe=n(v4o,"STRONG",{});var a8n=s(EXe);Uyt=r(a8n,"albert"),a8n.forEach(t),Xyt=r(v4o," \u2014 "),Qpe=n(v4o,"A",{href:!0});var n8n=s(Qpe);Dyt=r(n8n,"AlbertForMaskedLM"),n8n.forEach(t),qyt=r(v4o," (ALBERT model)"),v4o.forEach(t),Oyt=i(ee),jC=n(ee,"LI",{});var M4o=s(jC);yXe=n(M4o,"STRONG",{});var s8n=s(yXe);Qyt=r(s8n,"bart"),s8n.forEach(t),Yyt=r(M4o," \u2014 "),Ype=n(M4o,"A",{href:!0});var l8n=s(Ype);zyt=r(l8n,"BartForConditionalGeneration"),l8n.forEach(t),Hyt=r(M4o," (BART model)"),M4o.forEach(t),Kyt=i(ee),PC=n(ee,"LI",{});var T4o=s(PC);CXe=n(T4o,"STRONG",{});var i8n=s(CXe);e0t=r(i8n,"bert"),i8n.forEach(t),o0t=r(T4o," \u2014 "),zpe=n(T4o,"A",{href:!0});var d8n=s(zpe);r0t=r(d8n,"BertForMaskedLM"),d8n.forEach(t),t0t=r(T4o," (BERT model)"),T4o.forEach(t),a0t=i(ee),ZC=n(ee,"LI",{});var F4o=s(ZC);wXe=n(F4o,"STRONG",{});var m8n=s(wXe);n0t=r(m8n,"big_bird"),m8n.forEach(t),s0t=r(F4o," \u2014 "),Hpe=n(F4o,"A",{href:!0});var c8n=s(Hpe);l0t=r(c8n,"BigBirdForMaskedLM"),c8n.forEach(t),i0t=r(F4o," (BigBird model)"),F4o.forEach(t),d0t=i(ee),VC=n(ee,"LI",{});var E4o=s(VC);LXe=n(E4o,"STRONG",{});var f8n=s(LXe);m0t=r(f8n,"camembert"),f8n.forEach(t),c0t=r(E4o," \u2014 "),Kpe=n(E4o,"A",{href:!0});var h8n=s(Kpe);f0t=r(h8n,"CamembertForMaskedLM"),h8n.forEach(t),h0t=r(E4o," (CamemBERT model)"),E4o.forEach(t),g0t=i(ee),WC=n(ee,"LI",{});var y4o=s(WC);AXe=n(y4o,"STRONG",{});var g8n=s(AXe);u0t=r(g8n,"convbert"),g8n.forEach(t),p0t=r(y4o," \u2014 "),e_e=n(y4o,"A",{href:!0});var u8n=s(e_e);_0t=r(u8n,"ConvBertForMaskedLM"),u8n.forEach(t),b0t=r(y4o," (ConvBERT model)"),y4o.forEach(t),v0t=i(ee),JC=n(ee,"LI",{});var C4o=s(JC);RXe=n(C4o,"STRONG",{});var p8n=s(RXe);M0t=r(p8n,"data2vec-text"),p8n.forEach(t),T0t=r(C4o," \u2014 "),o_e=n(C4o,"A",{href:!0});var _8n=s(o_e);F0t=r(_8n,"Data2VecTextForMaskedLM"),_8n.forEach(t),E0t=r(C4o," (Data2VecText model)"),C4o.forEach(t),y0t=i(ee),UC=n(ee,"LI",{});var w4o=s(UC);kXe=n(w4o,"STRONG",{});var b8n=s(kXe);C0t=r(b8n,"deberta"),b8n.forEach(t),w0t=r(w4o," \u2014 "),r_e=n(w4o,"A",{href:!0});var v8n=s(r_e);L0t=r(v8n,"DebertaForMaskedLM"),v8n.forEach(t),A0t=r(w4o," (DeBERTa model)"),w4o.forEach(t),R0t=i(ee),XC=n(ee,"LI",{});var L4o=s(XC);SXe=n(L4o,"STRONG",{});var M8n=s(SXe);k0t=r(M8n,"deberta-v2"),M8n.forEach(t),S0t=r(L4o," \u2014 "),t_e=n(L4o,"A",{href:!0});var T8n=s(t_e);x0t=r(T8n,"DebertaV2ForMaskedLM"),T8n.forEach(t),G0t=r(L4o," (DeBERTa-v2 model)"),L4o.forEach(t),B0t=i(ee),DC=n(ee,"LI",{});var A4o=s(DC);xXe=n(A4o,"STRONG",{});var F8n=s(xXe);I0t=r(F8n,"distilbert"),F8n.forEach(t),N0t=r(A4o," \u2014 "),a_e=n(A4o,"A",{href:!0});var E8n=s(a_e);$0t=r(E8n,"DistilBertForMaskedLM"),E8n.forEach(t),j0t=r(A4o," (DistilBERT model)"),A4o.forEach(t),P0t=i(ee),qC=n(ee,"LI",{});var R4o=s(qC);GXe=n(R4o,"STRONG",{});var y8n=s(GXe);Z0t=r(y8n,"electra"),y8n.forEach(t),V0t=r(R4o," \u2014 "),n_e=n(R4o,"A",{href:!0});var C8n=s(n_e);W0t=r(C8n,"ElectraForMaskedLM"),C8n.forEach(t),J0t=r(R4o," (ELECTRA model)"),R4o.forEach(t),U0t=i(ee),OC=n(ee,"LI",{});var k4o=s(OC);BXe=n(k4o,"STRONG",{});var w8n=s(BXe);X0t=r(w8n,"ernie"),w8n.forEach(t),D0t=r(k4o," \u2014 "),s_e=n(k4o,"A",{href:!0});var L8n=s(s_e);q0t=r(L8n,"ErnieForMaskedLM"),L8n.forEach(t),O0t=r(k4o," (ERNIE model)"),k4o.forEach(t),Q0t=i(ee),QC=n(ee,"LI",{});var S4o=s(QC);IXe=n(S4o,"STRONG",{});var A8n=s(IXe);Y0t=r(A8n,"esm"),A8n.forEach(t),z0t=r(S4o," \u2014 "),l_e=n(S4o,"A",{href:!0});var R8n=s(l_e);H0t=r(R8n,"EsmForMaskedLM"),R8n.forEach(t),K0t=r(S4o," (ESM model)"),S4o.forEach(t),e5t=i(ee),YC=n(ee,"LI",{});var x4o=s(YC);NXe=n(x4o,"STRONG",{});var k8n=s(NXe);o5t=r(k8n,"flaubert"),k8n.forEach(t),r5t=r(x4o," \u2014 "),i_e=n(x4o,"A",{href:!0});var S8n=s(i_e);t5t=r(S8n,"FlaubertWithLMHeadModel"),S8n.forEach(t),a5t=r(x4o," 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method.`),dCe.forEach(t),f4t=i(ud),$W=n(ud,"P",{});var F$o=s($W);h4t=r(F$o,"This class cannot be instantiated directly using "),vDe=n(F$o,"CODE",{});var q7n=s(vDe);g4t=r(q7n,"__init__()"),q7n.forEach(t),u4t=r(F$o," (throws an error)."),F$o.forEach(t),p4t=i(ud),Kt=n(ud,"DIV",{class:!0});var rP=s(Kt);M(jW.$$.fragment,rP),_4t=i(rP),MDe=n(rP,"P",{});var O7n=s(MDe);b4t=r(O7n,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),O7n.forEach(t),v4t=i(rP),Pc=n(rP,"P",{});var mCe=s(Pc);M4t=r(mCe,`Note:
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model\u2019s configuration. Use `),V_e=n(mCe,"A",{href:!0});var Y7n=s(V_e);E4t=r(Y7n,"from_pretrained()"),Y7n.forEach(t),y4t=r(mCe," to load the model weights."),mCe.forEach(t),C4t=i(rP),M(Sw.$$.fragment,rP),rP.forEach(t),w4t=i(ud),dt=n(ud,"DIV",{class:!0});var pd=s(dt);M(PW.$$.fragment,pd),L4t=i(pd),FDe=n(pd,"P",{});var z7n=s(FDe);A4t=r(z7n,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),z7n.forEach(t),R4t=i(pd),Yn=n(pd,"P",{});var tP=s(Yn);k4t=r(tP,"The model class to instantiate is selected based on the "),EDe=n(tP,"CODE",{});var H7n=s(EDe);S4t=r(H7n,"model_type"),H7n.forEach(t),x4t=r(tP,` property of the config object (either
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falling back to using pattern matching on `),CDe=n(tP,"CODE",{});var eLn=s(CDe);I4t=r(eLn,"pretrained_model_name_or_path"),eLn.forEach(t),N4t=r(tP,":"),tP.forEach(t),$4t=i(pd),pe=n(pd,"UL",{});var Me=s(pe);xw=n(Me,"LI",{});var cCo=s(xw);wDe=n(cCo,"STRONG",{});var oLn=s(wDe);j4t=r(oLn,"albert"),oLn.forEach(t),P4t=r(cCo," \u2014 "),W_e=n(cCo,"A",{href:!0});var rLn=s(W_e);Z4t=r(rLn,"TFAlbertForMaskedLM"),rLn.forEach(t),V4t=r(cCo," (ALBERT model)"),cCo.forEach(t),W4t=i(Me),Gw=n(Me,"LI",{});var fCo=s(Gw);LDe=n(fCo,"STRONG",{});var tLn=s(LDe);J4t=r(tLn,"bert"),tLn.forEach(t),U4t=r(fCo," \u2014 "),J_e=n(fCo,"A",{href:!0});var aLn=s(J_e);X4t=r(aLn,"TFBertForMaskedLM"),aLn.forEach(t),D4t=r(fCo," (BERT model)"),fCo.forEach(t),q4t=i(Me),Bw=n(Me,"LI",{});var hCo=s(Bw);ADe=n(hCo,"STRONG",{});var nLn=s(ADe);O4t=r(nLn,"camembert"),nLn.forEach(t),Q4t=r(hCo," \u2014 "),U_e=n(hCo,"A",{href:!0});var sLn=s(U_e);Y4t=r(sLn,"TFCamembertForMaskedLM"),sLn.forEach(t),z4t=r(hCo," (CamemBERT 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"),O_e=n(_Co,"A",{href:!0});var gLn=s(O_e);pCt=r(gLn,"TFDistilBertForMaskedLM"),gLn.forEach(t),_Ct=r(_Co," (DistilBERT model)"),_Co.forEach(t),bCt=i(Me),Pw=n(Me,"LI",{});var bCo=s(Pw);GDe=n(bCo,"STRONG",{});var uLn=s(GDe);vCt=r(uLn,"electra"),uLn.forEach(t),MCt=r(bCo," \u2014 "),Q_e=n(bCo,"A",{href:!0});var pLn=s(Q_e);TCt=r(pLn,"TFElectraForMaskedLM"),pLn.forEach(t),FCt=r(bCo," (ELECTRA model)"),bCo.forEach(t),ECt=i(Me),Zw=n(Me,"LI",{});var vCo=s(Zw);BDe=n(vCo,"STRONG",{});var _Ln=s(BDe);yCt=r(_Ln,"esm"),_Ln.forEach(t),CCt=r(vCo," \u2014 "),Y_e=n(vCo,"A",{href:!0});var bLn=s(Y_e);wCt=r(bLn,"TFEsmForMaskedLM"),bLn.forEach(t),LCt=r(vCo," (ESM model)"),vCo.forEach(t),ACt=i(Me),Vw=n(Me,"LI",{});var MCo=s(Vw);IDe=n(MCo,"STRONG",{});var vLn=s(IDe);RCt=r(vLn,"flaubert"),vLn.forEach(t),kCt=r(MCo," \u2014 "),z_e=n(MCo,"A",{href:!0});var MLn=s(z_e);SCt=r(MLn,"TFFlaubertWithLMHeadModel"),MLn.forEach(t),xCt=r(MCo," (FlauBERT model)"),MCo.forEach(t),GCt=i(Me),Ww=n(Me,"LI",{});var TCo=s(Ww);NDe=n(TCo,"STRONG",{});var TLn=s(NDe);BCt=r(TLn,"funnel"),TLn.forEach(t),ICt=r(TCo," \u2014 "),H_e=n(TCo,"A",{href:!0});var FLn=s(H_e);NCt=r(FLn,"TFFunnelForMaskedLM"),FLn.forEach(t),$Ct=r(TCo," (Funnel Transformer model)"),TCo.forEach(t),jCt=i(Me),Jw=n(Me,"LI",{});var FCo=s(Jw);$De=n(FCo,"STRONG",{});var ELn=s($De);PCt=r(ELn,"layoutlm"),ELn.forEach(t),ZCt=r(FCo," \u2014 "),K_e=n(FCo,"A",{href:!0});var yLn=s(K_e);VCt=r(yLn,"TFLayoutLMForMaskedLM"),yLn.forEach(t),WCt=r(FCo," (LayoutLM model)"),FCo.forEach(t),JCt=i(Me),Uw=n(Me,"LI",{});var ECo=s(Uw);jDe=n(ECo,"STRONG",{});var CLn=s(jDe);UCt=r(CLn,"longformer"),CLn.forEach(t),XCt=r(ECo," \u2014 "),e1e=n(ECo,"A",{href:!0});var wLn=s(e1e);DCt=r(wLn,"TFLongformerForMaskedLM"),wLn.forEach(t),qCt=r(ECo," (Longformer model)"),ECo.forEach(t),OCt=i(Me),Xw=n(Me,"LI",{});var yCo=s(Xw);PDe=n(yCo,"STRONG",{});var LLn=s(PDe);QCt=r(LLn,"mobilebert"),LLn.forEach(t),YCt=r(yCo," \u2014 "),o1e=n(yCo,"A",{href:!0});var ALn=s(o1e);zCt=r(ALn,"TFMobileBertForMaskedLM"),ALn.forEach(t),HCt=r(yCo," (MobileBERT model)"),yCo.forEach(t),KCt=i(Me),Dw=n(Me,"LI",{});var CCo=s(Dw);ZDe=n(CCo,"STRONG",{});var RLn=s(ZDe);ewt=r(RLn,"mpnet"),RLn.forEach(t),owt=r(CCo," \u2014 "),r1e=n(CCo,"A",{href:!0});var kLn=s(r1e);rwt=r(kLn,"TFMPNetForMaskedLM"),kLn.forEach(t),twt=r(CCo," (MPNet model)"),CCo.forEach(t),awt=i(Me),qw=n(Me,"LI",{});var wCo=s(qw);VDe=n(wCo,"STRONG",{});var SLn=s(VDe);nwt=r(SLn,"rembert"),SLn.forEach(t),swt=r(wCo," \u2014 "),t1e=n(wCo,"A",{href:!0});var xLn=s(t1e);lwt=r(xLn,"TFRemBertForMaskedLM"),xLn.forEach(t),iwt=r(wCo," (RemBERT model)"),wCo.forEach(t),dwt=i(Me),Ow=n(Me,"LI",{});var LCo=s(Ow);WDe=n(LCo,"STRONG",{});var GLn=s(WDe);mwt=r(GLn,"roberta"),GLn.forEach(t),cwt=r(LCo," \u2014 "),a1e=n(LCo,"A",{href:!0});var BLn=s(a1e);fwt=r(BLn,"TFRobertaForMaskedLM"),BLn.forEach(t),hwt=r(LCo," (RoBERTa model)"),LCo.forEach(t),gwt=i(Me),Qw=n(Me,"LI",{});var ACo=s(Qw);JDe=n(ACo,"STRONG",{});var ILn=s(JDe);uwt=r(ILn,"roberta-prelayernorm"),ILn.forEach(t),pwt=r(ACo," \u2014 "),n1e=n(ACo,"A",{href:!0});var NLn=s(n1e);_wt=r(NLn,"TFRobertaPreLayerNormForMaskedLM"),NLn.forEach(t),bwt=r(ACo," (RoBERTa-PreLayerNorm model)"),ACo.forEach(t),vwt=i(Me),Yw=n(Me,"LI",{});var RCo=s(Yw);UDe=n(RCo,"STRONG",{});var $Ln=s(UDe);Mwt=r($Ln,"roformer"),$Ln.forEach(t),Twt=r(RCo," \u2014 "),s1e=n(RCo,"A",{href:!0});var jLn=s(s1e);Fwt=r(jLn,"TFRoFormerForMaskedLM"),jLn.forEach(t),Ewt=r(RCo," (RoFormer model)"),RCo.forEach(t),ywt=i(Me),zw=n(Me,"LI",{});var kCo=s(zw);XDe=n(kCo,"STRONG",{});var PLn=s(XDe);Cwt=r(PLn,"tapas"),PLn.forEach(t),wwt=r(kCo," \u2014 "),l1e=n(kCo,"A",{href:!0});var ZLn=s(l1e);Lwt=r(ZLn,"TFTapasForMaskedLM"),ZLn.forEach(t),Awt=r(kCo," (TAPAS model)"),kCo.forEach(t),Rwt=i(Me),Hw=n(Me,"LI",{});var SCo=s(Hw);DDe=n(SCo,"STRONG",{});var VLn=s(DDe);kwt=r(VLn,"xlm"),VLn.forEach(t),Swt=r(SCo," \u2014 "),i1e=n(SCo,"A",{href:!0});var WLn=s(i1e);xwt=r(WLn,"TFXLMWithLMHeadModel"),WLn.forEach(t),Gwt=r(SCo," (XLM model)"),SCo.forEach(t),Bwt=i(Me),Kw=n(Me,"LI",{});var xCo=s(Kw);qDe=n(xCo,"STRONG",{});var JLn=s(qDe);Iwt=r(JLn,"xlm-roberta"),JLn.forEach(t),Nwt=r(xCo," \u2014 "),d1e=n(xCo,"A",{href:!0});var ULn=s(d1e);$wt=r(ULn,"TFXLMRobertaForMaskedLM"),ULn.forEach(t),jwt=r(xCo," (XLM-RoBERTa model)"),xCo.forEach(t),Me.forEach(t),Pwt=i(pd),M(e6.$$.fragment,pd),pd.forEach(t),ud.forEach(t),FBo=i(c),Zc=n(c,"H3",{class:!0});var E$o=s(Zc);o6=n(E$o,"A",{id:!0,class:!0,href:!0});var XLn=s(o6);ODe=n(XLn,"SPAN",{});var DLn=s(ODe);M(ZW.$$.fragment,DLn),DLn.forEach(t),XLn.forEach(t),Zwt=i(E$o),QDe=n(E$o,"SPAN",{});var qLn=s(QDe);Vwt=r(qLn,"FlaxAutoModelForMaskedLM"),qLn.forEach(t),E$o.forEach(t),EBo=i(c),or=n(c,"DIV",{class:!0});var _d=s(or);M(VW.$$.fragment,_d),Wwt=i(_d),Vc=n(_d,"P",{});var cCe=s(Vc);Jwt=r(cCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the `),m1e=n(cCe,"A",{href:!0});var OLn=s(m1e);Uwt=r(OLn,"from_pretrained()"),OLn.forEach(t),Xwt=r(cCe," class method or the "),c1e=n(cCe,"A",{href:!0});var QLn=s(c1e);Dwt=r(QLn,"from_config()"),QLn.forEach(t),qwt=r(cCe,` class
method.`),cCe.forEach(t),Owt=i(_d),WW=n(_d,"P",{});var y$o=s(WW);Qwt=r(y$o,"This class cannot be instantiated directly using "),YDe=n(y$o,"CODE",{});var YLn=s(YDe);Ywt=r(YLn,"__init__()"),YLn.forEach(t),zwt=r(y$o," (throws an error)."),y$o.forEach(t),Hwt=i(_d),ea=n(_d,"DIV",{class:!0});var aP=s(ea);M(JW.$$.fragment,aP),Kwt=i(aP),zDe=n(aP,"P",{});var zLn=s(zDe);e6t=r(zLn,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),zLn.forEach(t),o6t=i(aP),Wc=n(aP,"P",{});var fCe=s(Wc);r6t=r(fCe,`Note:
Loading a model from its configuration file does `),HDe=n(fCe,"STRONG",{});var HLn=s(HDe);t6t=r(HLn,"not"),HLn.forEach(t),a6t=r(fCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),f1e=n(fCe,"A",{href:!0});var KLn=s(f1e);n6t=r(KLn,"from_pretrained()"),KLn.forEach(t),s6t=r(fCe," to load the model weights."),fCe.forEach(t),l6t=i(aP),M(r6.$$.fragment,aP),aP.forEach(t),i6t=i(_d),mt=n(_d,"DIV",{class:!0});var bd=s(mt);M(UW.$$.fragment,bd),d6t=i(bd),KDe=n(bd,"P",{});var eAn=s(KDe);m6t=r(eAn,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),eAn.forEach(t),c6t=i(bd),zn=n(bd,"P",{});var nP=s(zn);f6t=r(nP,"The model class to instantiate is selected based on the "),eqe=n(nP,"CODE",{});var oAn=s(eqe);h6t=r(oAn,"model_type"),oAn.forEach(t),g6t=r(nP,` property of the config object (either
passed as an argument or loaded from `),oqe=n(nP,"CODE",{});var rAn=s(oqe);u6t=r(rAn,"pretrained_model_name_or_path"),rAn.forEach(t),p6t=r(nP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),rqe=n(nP,"CODE",{});var tAn=s(rqe);_6t=r(tAn,"pretrained_model_name_or_path"),tAn.forEach(t),b6t=r(nP,":"),nP.forEach(t),v6t=i(bd),ke=n(bd,"UL",{});var Pe=s(ke);t6=n(Pe,"LI",{});var GCo=s(t6);tqe=n(GCo,"STRONG",{});var aAn=s(tqe);M6t=r(aAn,"albert"),aAn.forEach(t),T6t=r(GCo," \u2014 "),h1e=n(GCo,"A",{href:!0});var nAn=s(h1e);F6t=r(nAn,"FlaxAlbertForMaskedLM"),nAn.forEach(t),E6t=r(GCo," (ALBERT model)"),GCo.forEach(t),y6t=i(Pe),a6=n(Pe,"LI",{});var BCo=s(a6);aqe=n(BCo,"STRONG",{});var sAn=s(aqe);C6t=r(sAn,"bart"),sAn.forEach(t),w6t=r(BCo," \u2014 "),g1e=n(BCo,"A",{href:!0});var lAn=s(g1e);L6t=r(lAn,"FlaxBartForConditionalGeneration"),lAn.forEach(t),A6t=r(BCo," (BART model)"),BCo.forEach(t),R6t=i(Pe),n6=n(Pe,"LI",{});var ICo=s(n6);nqe=n(ICo,"STRONG",{});var iAn=s(nqe);k6t=r(iAn,"bert"),iAn.forEach(t),S6t=r(ICo," \u2014 "),u1e=n(ICo,"A",{href:!0});var dAn=s(u1e);x6t=r(dAn,"FlaxBertForMaskedLM"),dAn.forEach(t),G6t=r(ICo," (BERT model)"),ICo.forEach(t),B6t=i(Pe),s6=n(Pe,"LI",{});var NCo=s(s6);sqe=n(NCo,"STRONG",{});var mAn=s(sqe);I6t=r(mAn,"big_bird"),mAn.forEach(t),N6t=r(NCo," \u2014 "),p1e=n(NCo,"A",{href:!0});var cAn=s(p1e);$6t=r(cAn,"FlaxBigBirdForMaskedLM"),cAn.forEach(t),j6t=r(NCo," (BigBird model)"),NCo.forEach(t),P6t=i(Pe),l6=n(Pe,"LI",{});var $Co=s(l6);lqe=n($Co,"STRONG",{});var fAn=s(lqe);Z6t=r(fAn,"distilbert"),fAn.forEach(t),V6t=r($Co," \u2014 "),_1e=n($Co,"A",{href:!0});var hAn=s(_1e);W6t=r(hAn,"FlaxDistilBertForMaskedLM"),hAn.forEach(t),J6t=r($Co," (DistilBERT model)"),$Co.forEach(t),U6t=i(Pe),i6=n(Pe,"LI",{});var jCo=s(i6);iqe=n(jCo,"STRONG",{});var gAn=s(iqe);X6t=r(gAn,"electra"),gAn.forEach(t),D6t=r(jCo," \u2014 "),b1e=n(jCo,"A",{href:!0});var uAn=s(b1e);q6t=r(uAn,"FlaxElectraForMaskedLM"),uAn.forEach(t),O6t=r(jCo," (ELECTRA model)"),jCo.forEach(t),Q6t=i(Pe),d6=n(Pe,"LI",{});var PCo=s(d6);dqe=n(PCo,"STRONG",{});var pAn=s(dqe);Y6t=r(pAn,"mbart"),pAn.forEach(t),z6t=r(PCo," \u2014 "),v1e=n(PCo,"A",{href:!0});var _An=s(v1e);H6t=r(_An,"FlaxMBartForConditionalGeneration"),_An.forEach(t),K6t=r(PCo," (mBART model)"),PCo.forEach(t),e8t=i(Pe),m6=n(Pe,"LI",{});var ZCo=s(m6);mqe=n(ZCo,"STRONG",{});var bAn=s(mqe);o8t=r(bAn,"roberta"),bAn.forEach(t),r8t=r(ZCo," \u2014 "),M1e=n(ZCo,"A",{href:!0});var vAn=s(M1e);t8t=r(vAn,"FlaxRobertaForMaskedLM"),vAn.forEach(t),a8t=r(ZCo," (RoBERTa model)"),ZCo.forEach(t),n8t=i(Pe),c6=n(Pe,"LI",{});var VCo=s(c6);cqe=n(VCo,"STRONG",{});var MAn=s(cqe);s8t=r(MAn,"roberta-prelayernorm"),MAn.forEach(t),l8t=r(VCo," \u2014 "),T1e=n(VCo,"A",{href:!0});var TAn=s(T1e);i8t=r(TAn,"FlaxRobertaPreLayerNormForMaskedLM"),TAn.forEach(t),d8t=r(VCo," (RoBERTa-PreLayerNorm model)"),VCo.forEach(t),m8t=i(Pe),f6=n(Pe,"LI",{});var WCo=s(f6);fqe=n(WCo,"STRONG",{});var FAn=s(fqe);c8t=r(FAn,"roformer"),FAn.forEach(t),f8t=r(WCo," \u2014 "),F1e=n(WCo,"A",{href:!0});var EAn=s(F1e);h8t=r(EAn,"FlaxRoFormerForMaskedLM"),EAn.forEach(t),g8t=r(WCo," (RoFormer model)"),WCo.forEach(t),u8t=i(Pe),h6=n(Pe,"LI",{});var JCo=s(h6);hqe=n(JCo,"STRONG",{});var yAn=s(hqe);p8t=r(yAn,"xlm-roberta"),yAn.forEach(t),_8t=r(JCo," \u2014 "),E1e=n(JCo,"A",{href:!0});var CAn=s(E1e);b8t=r(CAn,"FlaxXLMRobertaForMaskedLM"),CAn.forEach(t),v8t=r(JCo," (XLM-RoBERTa model)"),JCo.forEach(t),Pe.forEach(t),M8t=i(bd),M(g6.$$.fragment,bd),bd.forEach(t),_d.forEach(t),yBo=i(c),Jc=n(c,"H3",{class:!0});var C$o=s(Jc);u6=n(C$o,"A",{id:!0,class:!0,href:!0});var wAn=s(u6);gqe=n(wAn,"SPAN",{});var LAn=s(gqe);M(XW.$$.fragment,LAn),LAn.forEach(t),wAn.forEach(t),T8t=i(C$o),uqe=n(C$o,"SPAN",{});var AAn=s(uqe);F8t=r(AAn,"AutoModelForMaskGeneration"),AAn.forEach(t),C$o.forEach(t),CBo=i(c),DW=n(c,"DIV",{class:!0});var RAn=s(DW);M(qW.$$.fragment,RAn),RAn.forEach(t),wBo=i(c),Uc=n(c,"H3",{class:!0});var w$o=s(Uc);p6=n(w$o,"A",{id:!0,class:!0,href:!0});var kAn=s(p6);pqe=n(kAn,"SPAN",{});var SAn=s(pqe);M(OW.$$.fragment,SAn),SAn.forEach(t),kAn.forEach(t),E8t=i(w$o),_qe=n(w$o,"SPAN",{});var xAn=s(_qe);y8t=r(xAn,"TFAutoModelForMaskGeneration"),xAn.forEach(t),w$o.forEach(t),LBo=i(c),QW=n(c,"DIV",{class:!0});var GAn=s(QW);M(YW.$$.fragment,GAn),GAn.forEach(t),ABo=i(c),Xc=n(c,"H3",{class:!0});var L$o=s(Xc);_6=n(L$o,"A",{id:!0,class:!0,href:!0});var BAn=s(_6);bqe=n(BAn,"SPAN",{});var IAn=s(bqe);M(zW.$$.fragment,IAn),IAn.forEach(t),BAn.forEach(t),C8t=i(L$o),vqe=n(L$o,"SPAN",{});var NAn=s(vqe);w8t=r(NAn,"AutoModelForSeq2SeqLM"),NAn.forEach(t),L$o.forEach(t),RBo=i(c),rr=n(c,"DIV",{class:!0});var vd=s(rr);M(HW.$$.fragment,vd),L8t=i(vd),Dc=n(vd,"P",{});var hCe=s(Dc);A8t=r(hCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),y1e=n(hCe,"A",{href:!0});var $An=s(y1e);R8t=r($An,"from_pretrained()"),$An.forEach(t),k8t=r(hCe," class method or the "),C1e=n(hCe,"A",{href:!0});var jAn=s(C1e);S8t=r(jAn,"from_config()"),jAn.forEach(t),x8t=r(hCe,` class
method.`),hCe.forEach(t),G8t=i(vd),KW=n(vd,"P",{});var A$o=s(KW);B8t=r(A$o,"This class cannot be instantiated directly using "),Mqe=n(A$o,"CODE",{});var PAn=s(Mqe);I8t=r(PAn,"__init__()"),PAn.forEach(t),N8t=r(A$o," (throws an error)."),A$o.forEach(t),$8t=i(vd),oa=n(vd,"DIV",{class:!0});var sP=s(oa);M(eJ.$$.fragment,sP),j8t=i(sP),Tqe=n(sP,"P",{});var ZAn=s(Tqe);P8t=r(ZAn,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),ZAn.forEach(t),Z8t=i(sP),qc=n(sP,"P",{});var gCe=s(qc);V8t=r(gCe,`Note:
Loading a model from its configuration file does `),Fqe=n(gCe,"STRONG",{});var VAn=s(Fqe);W8t=r(VAn,"not"),VAn.forEach(t),J8t=r(gCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),w1e=n(gCe,"A",{href:!0});var WAn=s(w1e);U8t=r(WAn,"from_pretrained()"),WAn.forEach(t),X8t=r(gCe," to load the model weights."),gCe.forEach(t),D8t=i(sP),M(b6.$$.fragment,sP),sP.forEach(t),q8t=i(vd),co=n(vd,"DIV",{class:!0});var nn=s(co);M(oJ.$$.fragment,nn),O8t=i(nn),Eqe=n(nn,"P",{});var JAn=s(Eqe);Q8t=r(JAn,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),JAn.forEach(t),Y8t=i(nn),Hn=n(nn,"P",{});var lP=s(Hn);z8t=r(lP,"The model class to instantiate is selected based on the "),yqe=n(lP,"CODE",{});var UAn=s(yqe);H8t=r(UAn,"model_type"),UAn.forEach(t),K8t=r(lP,` property of the config object (either
passed as an argument or loaded from `),Cqe=n(lP,"CODE",{});var XAn=s(Cqe);e7t=r(XAn,"pretrained_model_name_or_path"),XAn.forEach(t),o7t=r(lP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wqe=n(lP,"CODE",{});var DAn=s(wqe);r7t=r(DAn,"pretrained_model_name_or_path"),DAn.forEach(t),t7t=r(lP,":"),lP.forEach(t),a7t=i(nn),ge=n(nn,"UL",{});var be=s(ge);v6=n(be,"LI",{});var UCo=s(v6);Lqe=n(UCo,"STRONG",{});var qAn=s(Lqe);n7t=r(qAn,"bart"),qAn.forEach(t),s7t=r(UCo," \u2014 "),L1e=n(UCo,"A",{href:!0});var OAn=s(L1e);l7t=r(OAn,"BartForConditionalGeneration"),OAn.forEach(t),i7t=r(UCo," (BART model)"),UCo.forEach(t),d7t=i(be),M6=n(be,"LI",{});var XCo=s(M6);Aqe=n(XCo,"STRONG",{});var QAn=s(Aqe);m7t=r(QAn,"bigbird_pegasus"),QAn.forEach(t),c7t=r(XCo," \u2014 "),A1e=n(XCo,"A",{href:!0});var YAn=s(A1e);f7t=r(YAn,"BigBirdPegasusForConditionalGeneration"),YAn.forEach(t),h7t=r(XCo," (BigBird-Pegasus model)"),XCo.forEach(t),g7t=i(be),T6=n(be,"LI",{});var DCo=s(T6);Rqe=n(DCo,"STRONG",{});var zAn=s(Rqe);u7t=r(zAn,"blenderbot"),zAn.forEach(t),p7t=r(DCo," \u2014 "),R1e=n(DCo,"A",{href:!0});var HAn=s(R1e);_7t=r(HAn,"BlenderbotForConditionalGeneration"),HAn.forEach(t),b7t=r(DCo," (Blenderbot model)"),DCo.forEach(t),v7t=i(be),F6=n(be,"LI",{});var qCo=s(F6);kqe=n(qCo,"STRONG",{});var KAn=s(kqe);M7t=r(KAn,"blenderbot-small"),KAn.forEach(t),T7t=r(qCo," \u2014 "),k1e=n(qCo,"A",{href:!0});var eRn=s(k1e);F7t=r(eRn,"BlenderbotSmallForConditionalGeneration"),eRn.forEach(t),E7t=r(qCo," (BlenderbotSmall model)"),qCo.forEach(t),y7t=i(be),E6=n(be,"LI",{});var OCo=s(E6);Sqe=n(OCo,"STRONG",{});var oRn=s(Sqe);C7t=r(oRn,"encoder-decoder"),oRn.forEach(t),w7t=r(OCo," \u2014 "),S1e=n(OCo,"A",{href:!0});var rRn=s(S1e);L7t=r(rRn,"EncoderDecoderModel"),rRn.forEach(t),A7t=r(OCo," (Encoder decoder model)"),OCo.forEach(t),R7t=i(be),y6=n(be,"LI",{});var QCo=s(y6);xqe=n(QCo,"STRONG",{});var tRn=s(xqe);k7t=r(tRn,"fsmt"),tRn.forEach(t),S7t=r(QCo," \u2014 "),x1e=n(QCo,"A",{href:!0});var aRn=s(x1e);x7t=r(aRn,"FSMTForConditionalGeneration"),aRn.forEach(t),G7t=r(QCo," (FairSeq Machine-Translation model)"),QCo.forEach(t),B7t=i(be),C6=n(be,"LI",{});var YCo=s(C6);Gqe=n(YCo,"STRONG",{});var nRn=s(Gqe);I7t=r(nRn,"gptsan-japanese"),nRn.forEach(t),N7t=r(YCo," \u2014 "),G1e=n(YCo,"A",{href:!0});var sRn=s(G1e);$7t=r(sRn,"GPTSanJapaneseForConditionalGeneration"),sRn.forEach(t),j7t=r(YCo," (GPTSAN-japanese model)"),YCo.forEach(t),P7t=i(be),w6=n(be,"LI",{});var zCo=s(w6);Bqe=n(zCo,"STRONG",{});var lRn=s(Bqe);Z7t=r(lRn,"led"),lRn.forEach(t),V7t=r(zCo," \u2014 "),B1e=n(zCo,"A",{href:!0});var iRn=s(B1e);W7t=r(iRn,"LEDForConditionalGeneration"),iRn.forEach(t),J7t=r(zCo," (LED model)"),zCo.forEach(t),U7t=i(be),L6=n(be,"LI",{});var HCo=s(L6);Iqe=n(HCo,"STRONG",{});var dRn=s(Iqe);X7t=r(dRn,"longt5"),dRn.forEach(t),D7t=r(HCo," \u2014 "),I1e=n(HCo,"A",{href:!0});var mRn=s(I1e);q7t=r(mRn,"LongT5ForConditionalGeneration"),mRn.forEach(t),O7t=r(HCo," (LongT5 model)"),HCo.forEach(t),Q7t=i(be),A6=n(be,"LI",{});var KCo=s(A6);Nqe=n(KCo,"STRONG",{});var cRn=s(Nqe);Y7t=r(cRn,"m2m_100"),cRn.forEach(t),z7t=r(KCo," \u2014 "),N1e=n(KCo,"A",{href:!0});var fRn=s(N1e);H7t=r(fRn,"M2M100ForConditionalGeneration"),fRn.forEach(t),K7t=r(KCo," (M2M100 model)"),KCo.forEach(t),eLt=i(be),R6=n(be,"LI",{});var ewo=s(R6);$qe=n(ewo,"STRONG",{});var hRn=s($qe);oLt=r(hRn,"marian"),hRn.forEach(t),rLt=r(ewo," \u2014 "),$1e=n(ewo,"A",{href:!0});var gRn=s($1e);tLt=r(gRn,"MarianMTModel"),gRn.forEach(t),aLt=r(ewo," (Marian model)"),ewo.forEach(t),nLt=i(be),k6=n(be,"LI",{});var owo=s(k6);jqe=n(owo,"STRONG",{});var uRn=s(jqe);sLt=r(uRn,"mbart"),uRn.forEach(t),lLt=r(owo," \u2014 "),j1e=n(owo,"A",{href:!0});var pRn=s(j1e);iLt=r(pRn,"MBartForConditionalGeneration"),pRn.forEach(t),dLt=r(owo," (mBART model)"),owo.forEach(t),mLt=i(be),S6=n(be,"LI",{});var rwo=s(S6);Pqe=n(rwo,"STRONG",{});var _Rn=s(Pqe);cLt=r(_Rn,"mt5"),_Rn.forEach(t),fLt=r(rwo," \u2014 "),P1e=n(rwo,"A",{href:!0});var bRn=s(P1e);hLt=r(bRn,"MT5ForConditionalGeneration"),bRn.forEach(t),gLt=r(rwo," (MT5 model)"),rwo.forEach(t),uLt=i(be),x6=n(be,"LI",{});var two=s(x6);Zqe=n(two,"STRONG",{});var vRn=s(Zqe);pLt=r(vRn,"mvp"),vRn.forEach(t),_Lt=r(two," \u2014 "),Z1e=n(two,"A",{href:!0});var MRn=s(Z1e);bLt=r(MRn,"MvpForConditionalGeneration"),MRn.forEach(t),vLt=r(two," (MVP model)"),two.forEach(t),MLt=i(be),G6=n(be,"LI",{});var awo=s(G6);Vqe=n(awo,"STRONG",{});var TRn=s(Vqe);TLt=r(TRn,"nllb-moe"),TRn.forEach(t),FLt=r(awo," \u2014 "),V1e=n(awo,"A",{href:!0});var FRn=s(V1e);ELt=r(FRn,"NllbMoeForConditionalGeneration"),FRn.forEach(t),yLt=r(awo," (NLLB-MOE model)"),awo.forEach(t),CLt=i(be),B6=n(be,"LI",{});var nwo=s(B6);Wqe=n(nwo,"STRONG",{});var ERn=s(Wqe);wLt=r(ERn,"pegasus"),ERn.forEach(t),LLt=r(nwo," \u2014 "),W1e=n(nwo,"A",{href:!0});var yRn=s(W1e);ALt=r(yRn,"PegasusForConditionalGeneration"),yRn.forEach(t),RLt=r(nwo," (Pegasus model)"),nwo.forEach(t),kLt=i(be),I6=n(be,"LI",{});var swo=s(I6);Jqe=n(swo,"STRONG",{});var CRn=s(Jqe);SLt=r(CRn,"pegasus_x"),CRn.forEach(t),xLt=r(swo," \u2014 "),J1e=n(swo,"A",{href:!0});var wRn=s(J1e);GLt=r(wRn,"PegasusXForConditionalGeneration"),wRn.forEach(t),BLt=r(swo," (PEGASUS-X model)"),swo.forEach(t),ILt=i(be),N6=n(be,"LI",{});var lwo=s(N6);Uqe=n(lwo,"STRONG",{});var LRn=s(Uqe);NLt=r(LRn,"plbart"),LRn.forEach(t),$Lt=r(lwo," \u2014 "),U1e=n(lwo,"A",{href:!0});var ARn=s(U1e);jLt=r(ARn,"PLBartForConditionalGeneration"),ARn.forEach(t),PLt=r(lwo," (PLBart model)"),lwo.forEach(t),ZLt=i(be),$6=n(be,"LI",{});var iwo=s($6);Xqe=n(iwo,"STRONG",{});var RRn=s(Xqe);VLt=r(RRn,"prophetnet"),RRn.forEach(t),WLt=r(iwo," \u2014 "),X1e=n(iwo,"A",{href:!0});var kRn=s(X1e);JLt=r(kRn,"ProphetNetForConditionalGeneration"),kRn.forEach(t),ULt=r(iwo," (ProphetNet model)"),iwo.forEach(t),XLt=i(be),j6=n(be,"LI",{});var dwo=s(j6);Dqe=n(dwo,"STRONG",{});var SRn=s(Dqe);DLt=r(SRn,"switch_transformers"),SRn.forEach(t),qLt=r(dwo," \u2014 "),D1e=n(dwo,"A",{href:!0});var xRn=s(D1e);OLt=r(xRn,"SwitchTransformersForConditionalGeneration"),xRn.forEach(t),QLt=r(dwo," (SwitchTransformers model)"),dwo.forEach(t),YLt=i(be),P6=n(be,"LI",{});var mwo=s(P6);qqe=n(mwo,"STRONG",{});var GRn=s(qqe);zLt=r(GRn,"t5"),GRn.forEach(t),HLt=r(mwo," \u2014 "),q1e=n(mwo,"A",{href:!0});var BRn=s(q1e);KLt=r(BRn,"T5ForConditionalGeneration"),BRn.forEach(t),eAt=r(mwo," (T5 model)"),mwo.forEach(t),oAt=i(be),Z6=n(be,"LI",{});var cwo=s(Z6);Oqe=n(cwo,"STRONG",{});var IRn=s(Oqe);rAt=r(IRn,"umt5"),IRn.forEach(t),tAt=r(cwo," \u2014 "),O1e=n(cwo,"A",{href:!0});var NRn=s(O1e);aAt=r(NRn,"UMT5ForConditionalGeneration"),NRn.forEach(t),nAt=r(cwo," (UMT5 model)"),cwo.forEach(t),sAt=i(be),V6=n(be,"LI",{});var fwo=s(V6);Qqe=n(fwo,"STRONG",{});var $Rn=s(Qqe);lAt=r($Rn,"xlm-prophetnet"),$Rn.forEach(t),iAt=r(fwo," \u2014 "),Q1e=n(fwo,"A",{href:!0});var jRn=s(Q1e);dAt=r(jRn,"XLMProphetNetForConditionalGeneration"),jRn.forEach(t),mAt=r(fwo," (XLM-ProphetNet model)"),fwo.forEach(t),be.forEach(t),cAt=i(nn),W6=n(nn,"P",{});var hwo=s(W6);fAt=r(hwo,"The model is set in evaluation mode by default using "),Yqe=n(hwo,"CODE",{});var PRn=s(Yqe);hAt=r(PRn,"model.eval()"),PRn.forEach(t),gAt=r(hwo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),zqe=n(hwo,"CODE",{});var ZRn=s(zqe);uAt=r(ZRn,"model.train()"),ZRn.forEach(t),hwo.forEach(t),pAt=i(nn),M(J6.$$.fragment,nn),nn.forEach(t),vd.forEach(t),kBo=i(c),Oc=n(c,"H3",{class:!0});var R$o=s(Oc);U6=n(R$o,"A",{id:!0,class:!0,href:!0});var VRn=s(U6);Hqe=n(VRn,"SPAN",{});var WRn=s(Hqe);M(rJ.$$.fragment,WRn),WRn.forEach(t),VRn.forEach(t),_At=i(R$o),Kqe=n(R$o,"SPAN",{});var JRn=s(Kqe);bAt=r(JRn,"TFAutoModelForSeq2SeqLM"),JRn.forEach(t),R$o.forEach(t),SBo=i(c),tr=n(c,"DIV",{class:!0});var Md=s(tr);M(tJ.$$.fragment,Md),vAt=i(Md),Qc=n(Md,"P",{});var uCe=s(Qc);MAt=r(uCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),Y1e=n(uCe,"A",{href:!0});var URn=s(Y1e);TAt=r(URn,"from_pretrained()"),URn.forEach(t),FAt=r(uCe," class method or the "),z1e=n(uCe,"A",{href:!0});var XRn=s(z1e);EAt=r(XRn,"from_config()"),XRn.forEach(t),yAt=r(uCe,` class
method.`),uCe.forEach(t),CAt=i(Md),aJ=n(Md,"P",{});var k$o=s(aJ);wAt=r(k$o,"This class cannot be instantiated directly using "),eOe=n(k$o,"CODE",{});var DRn=s(eOe);LAt=r(DRn,"__init__()"),DRn.forEach(t),AAt=r(k$o," (throws an error)."),k$o.forEach(t),RAt=i(Md),ra=n(Md,"DIV",{class:!0});var iP=s(ra);M(nJ.$$.fragment,iP),kAt=i(iP),oOe=n(iP,"P",{});var qRn=s(oOe);SAt=r(qRn,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),qRn.forEach(t),xAt=i(iP),Yc=n(iP,"P",{});var pCe=s(Yc);GAt=r(pCe,`Note:
Loading a model from its configuration file does `),rOe=n(pCe,"STRONG",{});var ORn=s(rOe);BAt=r(ORn,"not"),ORn.forEach(t),IAt=r(pCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),H1e=n(pCe,"A",{href:!0});var QRn=s(H1e);NAt=r(QRn,"from_pretrained()"),QRn.forEach(t),$At=r(pCe," to load the model weights."),pCe.forEach(t),jAt=i(iP),M(X6.$$.fragment,iP),iP.forEach(t),PAt=i(Md),ct=n(Md,"DIV",{class:!0});var Td=s(ct);M(sJ.$$.fragment,Td),ZAt=i(Td),tOe=n(Td,"P",{});var YRn=s(tOe);VAt=r(YRn,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),YRn.forEach(t),WAt=i(Td),Kn=n(Td,"P",{});var dP=s(Kn);JAt=r(dP,"The model class to instantiate is selected based on the "),aOe=n(dP,"CODE",{});var zRn=s(aOe);UAt=r(zRn,"model_type"),zRn.forEach(t),XAt=r(dP,` property of the config object (either
passed as an argument or loaded from `),nOe=n(dP,"CODE",{});var HRn=s(nOe);DAt=r(HRn,"pretrained_model_name_or_path"),HRn.forEach(t),qAt=r(dP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),sOe=n(dP,"CODE",{});var KRn=s(sOe);OAt=r(KRn,"pretrained_model_name_or_path"),KRn.forEach(t),QAt=r(dP,":"),dP.forEach(t),YAt=i(Td),Ne=n(Td,"UL",{});var De=s(Ne);D6=n(De,"LI",{});var gwo=s(D6);lOe=n(gwo,"STRONG",{});var ekn=s(lOe);zAt=r(ekn,"bart"),ekn.forEach(t),HAt=r(gwo," \u2014 "),K1e=n(gwo,"A",{href:!0});var okn=s(K1e);KAt=r(okn,"TFBartForConditionalGeneration"),okn.forEach(t),eRt=r(gwo," (BART model)"),gwo.forEach(t),oRt=i(De),q6=n(De,"LI",{});var uwo=s(q6);iOe=n(uwo,"STRONG",{});var rkn=s(iOe);rRt=r(rkn,"blenderbot"),rkn.forEach(t),tRt=r(uwo," \u2014 "),ebe=n(uwo,"A",{href:!0});var tkn=s(ebe);aRt=r(tkn,"TFBlenderbotForConditionalGeneration"),tkn.forEach(t),nRt=r(uwo," (Blenderbot model)"),uwo.forEach(t),sRt=i(De),O6=n(De,"LI",{});var pwo=s(O6);dOe=n(pwo,"STRONG",{});var akn=s(dOe);lRt=r(akn,"blenderbot-small"),akn.forEach(t),iRt=r(pwo," \u2014 "),obe=n(pwo,"A",{href:!0});var nkn=s(obe);dRt=r(nkn,"TFBlenderbotSmallForConditionalGeneration"),nkn.forEach(t),mRt=r(pwo," (BlenderbotSmall model)"),pwo.forEach(t),cRt=i(De),Q6=n(De,"LI",{});var _wo=s(Q6);mOe=n(_wo,"STRONG",{});var skn=s(mOe);fRt=r(skn,"encoder-decoder"),skn.forEach(t),hRt=r(_wo," \u2014 "),rbe=n(_wo,"A",{href:!0});var lkn=s(rbe);gRt=r(lkn,"TFEncoderDecoderModel"),lkn.forEach(t),uRt=r(_wo," (Encoder decoder model)"),_wo.forEach(t),pRt=i(De),Y6=n(De,"LI",{});var bwo=s(Y6);cOe=n(bwo,"STRONG",{});var ikn=s(cOe);_Rt=r(ikn,"led"),ikn.forEach(t),bRt=r(bwo," \u2014 "),tbe=n(bwo,"A",{href:!0});var dkn=s(tbe);vRt=r(dkn,"TFLEDForConditionalGeneration"),dkn.forEach(t),MRt=r(bwo," (LED model)"),bwo.forEach(t),TRt=i(De),z6=n(De,"LI",{});var vwo=s(z6);fOe=n(vwo,"STRONG",{});var mkn=s(fOe);FRt=r(mkn,"marian"),mkn.forEach(t),ERt=r(vwo," \u2014 "),abe=n(vwo,"A",{href:!0});var ckn=s(abe);yRt=r(ckn,"TFMarianMTModel"),ckn.forEach(t),CRt=r(vwo," (Marian model)"),vwo.forEach(t),wRt=i(De),H6=n(De,"LI",{});var Mwo=s(H6);hOe=n(Mwo,"STRONG",{});var fkn=s(hOe);LRt=r(fkn,"mbart"),fkn.forEach(t),ARt=r(Mwo," \u2014 "),nbe=n(Mwo,"A",{href:!0});var hkn=s(nbe);RRt=r(hkn,"TFMBartForConditionalGeneration"),hkn.forEach(t),kRt=r(Mwo," (mBART model)"),Mwo.forEach(t),SRt=i(De),K6=n(De,"LI",{});var Two=s(K6);gOe=n(Two,"STRONG",{});var gkn=s(gOe);xRt=r(gkn,"mt5"),gkn.forEach(t),GRt=r(Two," \u2014 "),sbe=n(Two,"A",{href:!0});var ukn=s(sbe);BRt=r(ukn,"TFMT5ForConditionalGeneration"),ukn.forEach(t),IRt=r(Two," (MT5 model)"),Two.forEach(t),NRt=i(De),e8=n(De,"LI",{});var Fwo=s(e8);uOe=n(Fwo,"STRONG",{});var pkn=s(uOe);$Rt=r(pkn,"pegasus"),pkn.forEach(t),jRt=r(Fwo," \u2014 "),lbe=n(Fwo,"A",{href:!0});var _kn=s(lbe);PRt=r(_kn,"TFPegasusForConditionalGeneration"),_kn.forEach(t),ZRt=r(Fwo," (Pegasus model)"),Fwo.forEach(t),VRt=i(De),o8=n(De,"LI",{});var Ewo=s(o8);pOe=n(Ewo,"STRONG",{});var bkn=s(pOe);WRt=r(bkn,"t5"),bkn.forEach(t),JRt=r(Ewo," \u2014 "),ibe=n(Ewo,"A",{href:!0});var vkn=s(ibe);URt=r(vkn,"TFT5ForConditionalGeneration"),vkn.forEach(t),XRt=r(Ewo," (T5 model)"),Ewo.forEach(t),De.forEach(t),DRt=i(Td),M(r8.$$.fragment,Td),Td.forEach(t),Md.forEach(t),xBo=i(c),zc=n(c,"H3",{class:!0});var S$o=s(zc);t8=n(S$o,"A",{id:!0,class:!0,href:!0});var Mkn=s(t8);_Oe=n(Mkn,"SPAN",{});var Tkn=s(_Oe);M(lJ.$$.fragment,Tkn),Tkn.forEach(t),Mkn.forEach(t),qRt=i(S$o),bOe=n(S$o,"SPAN",{});var Fkn=s(bOe);ORt=r(Fkn,"FlaxAutoModelForSeq2SeqLM"),Fkn.forEach(t),S$o.forEach(t),GBo=i(c),ar=n(c,"DIV",{class:!0});var Fd=s(ar);M(iJ.$$.fragment,Fd),QRt=i(Fd),Hc=n(Fd,"P",{});var _Ce=s(Hc);YRt=r(_Ce,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),dbe=n(_Ce,"A",{href:!0});var Ekn=s(dbe);zRt=r(Ekn,"from_pretrained()"),Ekn.forEach(t),HRt=r(_Ce," class method or the "),mbe=n(_Ce,"A",{href:!0});var ykn=s(mbe);KRt=r(ykn,"from_config()"),ykn.forEach(t),ekt=r(_Ce,` class
method.`),_Ce.forEach(t),okt=i(Fd),dJ=n(Fd,"P",{});var x$o=s(dJ);rkt=r(x$o,"This class cannot be instantiated directly using "),vOe=n(x$o,"CODE",{});var Ckn=s(vOe);tkt=r(Ckn,"__init__()"),Ckn.forEach(t),akt=r(x$o," (throws an error)."),x$o.forEach(t),nkt=i(Fd),ta=n(Fd,"DIV",{class:!0});var mP=s(ta);M(mJ.$$.fragment,mP),skt=i(mP),MOe=n(mP,"P",{});var wkn=s(MOe);lkt=r(wkn,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),wkn.forEach(t),ikt=i(mP),Kc=n(mP,"P",{});var bCe=s(Kc);dkt=r(bCe,`Note:
Loading a model from its configuration file does `),TOe=n(bCe,"STRONG",{});var Lkn=s(TOe);mkt=r(Lkn,"not"),Lkn.forEach(t),ckt=r(bCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),cbe=n(bCe,"A",{href:!0});var Akn=s(cbe);fkt=r(Akn,"from_pretrained()"),Akn.forEach(t),hkt=r(bCe," to load the model weights."),bCe.forEach(t),gkt=i(mP),M(a8.$$.fragment,mP),mP.forEach(t),ukt=i(Fd),ft=n(Fd,"DIV",{class:!0});var Ed=s(ft);M(cJ.$$.fragment,Ed),pkt=i(Ed),FOe=n(Ed,"P",{});var Rkn=s(FOe);_kt=r(Rkn,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),Rkn.forEach(t),bkt=i(Ed),es=n(Ed,"P",{});var cP=s(es);vkt=r(cP,"The model class to instantiate is selected based on the "),EOe=n(cP,"CODE",{});var kkn=s(EOe);Mkt=r(kkn,"model_type"),kkn.forEach(t),Tkt=r(cP,` property of the config object (either
passed as an argument or loaded from `),yOe=n(cP,"CODE",{});var Skn=s(yOe);Fkt=r(Skn,"pretrained_model_name_or_path"),Skn.forEach(t),Ekt=r(cP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),COe=n(cP,"CODE",{});var xkn=s(COe);ykt=r(xkn,"pretrained_model_name_or_path"),xkn.forEach(t),Ckt=r(cP,":"),cP.forEach(t),wkt=i(Ed),$e=n(Ed,"UL",{});var qe=s($e);n8=n(qe,"LI",{});var ywo=s(n8);wOe=n(ywo,"STRONG",{});var Gkn=s(wOe);Lkt=r(Gkn,"bart"),Gkn.forEach(t),Akt=r(ywo," \u2014 "),fbe=n(ywo,"A",{href:!0});var Bkn=s(fbe);Rkt=r(Bkn,"FlaxBartForConditionalGeneration"),Bkn.forEach(t),kkt=r(ywo," (BART model)"),ywo.forEach(t),Skt=i(qe),s8=n(qe,"LI",{});var Cwo=s(s8);LOe=n(Cwo,"STRONG",{});var Ikn=s(LOe);xkt=r(Ikn,"blenderbot"),Ikn.forEach(t),Gkt=r(Cwo," \u2014 "),hbe=n(Cwo,"A",{href:!0});var Nkn=s(hbe);Bkt=r(Nkn,"FlaxBlenderbotForConditionalGeneration"),Nkn.forEach(t),Ikt=r(Cwo," (Blenderbot model)"),Cwo.forEach(t),Nkt=i(qe),l8=n(qe,"LI",{});var wwo=s(l8);AOe=n(wwo,"STRONG",{});var $kn=s(AOe);$kt=r($kn,"blenderbot-small"),$kn.forEach(t),jkt=r(wwo," \u2014 "),gbe=n(wwo,"A",{href:!0});var jkn=s(gbe);Pkt=r(jkn,"FlaxBlenderbotSmallForConditionalGeneration"),jkn.forEach(t),Zkt=r(wwo," (BlenderbotSmall model)"),wwo.forEach(t),Vkt=i(qe),i8=n(qe,"LI",{});var Lwo=s(i8);ROe=n(Lwo,"STRONG",{});var Pkn=s(ROe);Wkt=r(Pkn,"encoder-decoder"),Pkn.forEach(t),Jkt=r(Lwo," \u2014 "),ube=n(Lwo,"A",{href:!0});var Zkn=s(ube);Ukt=r(Zkn,"FlaxEncoderDecoderModel"),Zkn.forEach(t),Xkt=r(Lwo," (Encoder decoder model)"),Lwo.forEach(t),Dkt=i(qe),d8=n(qe,"LI",{});var Awo=s(d8);kOe=n(Awo,"STRONG",{});var Vkn=s(kOe);qkt=r(Vkn,"longt5"),Vkn.forEach(t),Okt=r(Awo," \u2014 "),pbe=n(Awo,"A",{href:!0});var Wkn=s(pbe);Qkt=r(Wkn,"FlaxLongT5ForConditionalGeneration"),Wkn.forEach(t),Ykt=r(Awo," (LongT5 model)"),Awo.forEach(t),zkt=i(qe),m8=n(qe,"LI",{});var Rwo=s(m8);SOe=n(Rwo,"STRONG",{});var Jkn=s(SOe);Hkt=r(Jkn,"marian"),Jkn.forEach(t),Kkt=r(Rwo," \u2014 "),_be=n(Rwo,"A",{href:!0});var Ukn=s(_be);eSt=r(Ukn,"FlaxMarianMTModel"),Ukn.forEach(t),oSt=r(Rwo," (Marian model)"),Rwo.forEach(t),rSt=i(qe),c8=n(qe,"LI",{});var kwo=s(c8);xOe=n(kwo,"STRONG",{});var Xkn=s(xOe);tSt=r(Xkn,"mbart"),Xkn.forEach(t),aSt=r(kwo," \u2014 "),bbe=n(kwo,"A",{href:!0});var Dkn=s(bbe);nSt=r(Dkn,"FlaxMBartForConditionalGeneration"),Dkn.forEach(t),sSt=r(kwo," (mBART model)"),kwo.forEach(t),lSt=i(qe),f8=n(qe,"LI",{});var Swo=s(f8);GOe=n(Swo,"STRONG",{});var qkn=s(GOe);iSt=r(qkn,"mt5"),qkn.forEach(t),dSt=r(Swo," \u2014 "),vbe=n(Swo,"A",{href:!0});var Okn=s(vbe);mSt=r(Okn,"FlaxMT5ForConditionalGeneration"),Okn.forEach(t),cSt=r(Swo," (MT5 model)"),Swo.forEach(t),fSt=i(qe),h8=n(qe,"LI",{});var xwo=s(h8);BOe=n(xwo,"STRONG",{});var Qkn=s(BOe);hSt=r(Qkn,"pegasus"),Qkn.forEach(t),gSt=r(xwo," \u2014 "),Mbe=n(xwo,"A",{href:!0});var Ykn=s(Mbe);uSt=r(Ykn,"FlaxPegasusForConditionalGeneration"),Ykn.forEach(t),pSt=r(xwo," (Pegasus model)"),xwo.forEach(t),_St=i(qe),g8=n(qe,"LI",{});var Gwo=s(g8);IOe=n(Gwo,"STRONG",{});var zkn=s(IOe);bSt=r(zkn,"t5"),zkn.forEach(t),vSt=r(Gwo," \u2014 "),Tbe=n(Gwo,"A",{href:!0});var Hkn=s(Tbe);MSt=r(Hkn,"FlaxT5ForConditionalGeneration"),Hkn.forEach(t),TSt=r(Gwo," (T5 model)"),Gwo.forEach(t),qe.forEach(t),FSt=i(Ed),M(u8.$$.fragment,Ed),Ed.forEach(t),Fd.forEach(t),BBo=i(c),ef=n(c,"H3",{class:!0});var G$o=s(ef);p8=n(G$o,"A",{id:!0,class:!0,href:!0});var Kkn=s(p8);NOe=n(Kkn,"SPAN",{});var eSn=s(NOe);M(fJ.$$.fragment,eSn),eSn.forEach(t),Kkn.forEach(t),ESt=i(G$o),$Oe=n(G$o,"SPAN",{});var oSn=s($Oe);ySt=r(oSn,"AutoModelForSequenceClassification"),oSn.forEach(t),G$o.forEach(t),IBo=i(c),nr=n(c,"DIV",{class:!0});var yd=s(nr);M(hJ.$$.fragment,yd),CSt=i(yd),of=n(yd,"P",{});var vCe=s(of);wSt=r(vCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the `),Fbe=n(vCe,"A",{href:!0});var rSn=s(Fbe);LSt=r(rSn,"from_pretrained()"),rSn.forEach(t),ASt=r(vCe," class method or the "),Ebe=n(vCe,"A",{href:!0});var tSn=s(Ebe);RSt=r(tSn,"from_config()"),tSn.forEach(t),kSt=r(vCe,` class
method.`),vCe.forEach(t),SSt=i(yd),gJ=n(yd,"P",{});var B$o=s(gJ);xSt=r(B$o,"This class cannot be instantiated directly using "),jOe=n(B$o,"CODE",{});var aSn=s(jOe);GSt=r(aSn,"__init__()"),aSn.forEach(t),BSt=r(B$o," (throws an error)."),B$o.forEach(t),ISt=i(yd),aa=n(yd,"DIV",{class:!0});var fP=s(aa);M(uJ.$$.fragment,fP),NSt=i(fP),POe=n(fP,"P",{});var nSn=s(POe);$St=r(nSn,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),nSn.forEach(t),jSt=i(fP),rf=n(fP,"P",{});var MCe=s(rf);PSt=r(MCe,`Note:
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model\u2019s configuration. Use `),ybe=n(MCe,"A",{href:!0});var lSn=s(ybe);WSt=r(lSn,"from_pretrained()"),lSn.forEach(t),JSt=r(MCe," to load the model weights."),MCe.forEach(t),USt=i(fP),M(_8.$$.fragment,fP),fP.forEach(t),XSt=i(yd),fo=n(yd,"DIV",{class:!0});var sn=s(fo);M(pJ.$$.fragment,sn),DSt=i(sn),VOe=n(sn,"P",{});var iSn=s(VOe);qSt=r(iSn,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),iSn.forEach(t),OSt=i(sn),os=n(sn,"P",{});var hP=s(os);QSt=r(hP,"The model class to instantiate is selected based on the "),WOe=n(hP,"CODE",{});var dSn=s(WOe);YSt=r(dSn,"model_type"),dSn.forEach(t),zSt=r(hP,` property of the config object (either
passed as an argument or loaded from `),JOe=n(hP,"CODE",{});var mSn=s(JOe);HSt=r(mSn,"pretrained_model_name_or_path"),mSn.forEach(t),KSt=r(hP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),UOe=n(hP,"CODE",{});var cSn=s(UOe);ext=r(cSn,"pretrained_model_name_or_path"),cSn.forEach(t),oxt=r(hP,":"),hP.forEach(t),rxt=i(sn),B=n(sn,"UL",{});var I=s(B);b8=n(I,"LI",{});var Bwo=s(b8);XOe=n(Bwo,"STRONG",{});var fSn=s(XOe);txt=r(fSn,"albert"),fSn.forEach(t),axt=r(Bwo," \u2014 "),Cbe=n(Bwo,"A",{href:!0});var hSn=s(Cbe);nxt=r(hSn,"AlbertForSequenceClassification"),hSn.forEach(t),sxt=r(Bwo," (ALBERT model)"),Bwo.forEach(t),lxt=i(I),v8=n(I,"LI",{});var Iwo=s(v8);DOe=n(Iwo,"STRONG",{});var gSn=s(DOe);ixt=r(gSn,"bart"),gSn.forEach(t),dxt=r(Iwo," \u2014 "),wbe=n(Iwo,"A",{href:!0});var uSn=s(wbe);mxt=r(uSn,"BartForSequenceClassification"),uSn.forEach(t),cxt=r(Iwo," (BART model)"),Iwo.forEach(t),fxt=i(I),M8=n(I,"LI",{});var Nwo=s(M8);qOe=n(Nwo,"STRONG",{});var pSn=s(qOe);hxt=r(pSn,"bert"),pSn.forEach(t),gxt=r(Nwo," \u2014 "),Lbe=n(Nwo,"A",{href:!0});var _Sn=s(Lbe);uxt=r(_Sn,"BertForSequenceClassification"),_Sn.forEach(t),pxt=r(Nwo," (BERT model)"),Nwo.forEach(t),_xt=i(I),T8=n(I,"LI",{});var $wo=s(T8);OOe=n($wo,"STRONG",{});var bSn=s(OOe);bxt=r(bSn,"big_bird"),bSn.forEach(t),vxt=r($wo," \u2014 "),Abe=n($wo,"A",{href:!0});var vSn=s(Abe);Mxt=r(vSn,"BigBirdForSequenceClassification"),vSn.forEach(t),Txt=r($wo," (BigBird model)"),$wo.forEach(t),Fxt=i(I),F8=n(I,"LI",{});var jwo=s(F8);QOe=n(jwo,"STRONG",{});var MSn=s(QOe);Ext=r(MSn,"bigbird_pegasus"),MSn.forEach(t),yxt=r(jwo," \u2014 "),Rbe=n(jwo,"A",{href:!0});var TSn=s(Rbe);Cxt=r(TSn,"BigBirdPegasusForSequenceClassification"),TSn.forEach(t),wxt=r(jwo," (BigBird-Pegasus model)"),jwo.forEach(t),Lxt=i(I),E8=n(I,"LI",{});var Pwo=s(E8);YOe=n(Pwo,"STRONG",{});var FSn=s(YOe);Axt=r(FSn,"biogpt"),FSn.forEach(t),Rxt=r(Pwo," \u2014 "),kbe=n(Pwo,"A",{href:!0});var ESn=s(kbe);kxt=r(ESn,"BioGptForSequenceClassification"),ESn.forEach(t),Sxt=r(Pwo," (BioGpt model)"),Pwo.forEach(t),xxt=i(I),y8=n(I,"LI",{});var Zwo=s(y8);zOe=n(Zwo,"STRONG",{});var 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bIn=s(Fve);vWt=r(bIn,"TFXLMRobertaForSequenceClassification"),bIn.forEach(t),MWt=r(k8o," (XLM-RoBERTa model)"),k8o.forEach(t),TWt=i(ce),bL=n(ce,"LI",{});var S8o=s(bL);KYe=n(S8o,"STRONG",{});var vIn=s(KYe);FWt=r(vIn,"xlnet"),vIn.forEach(t),EWt=r(S8o," \u2014 "),Eve=n(S8o,"A",{href:!0});var MIn=s(Eve);yWt=r(MIn,"TFXLNetForSequenceClassification"),MIn.forEach(t),CWt=r(S8o," (XLNet model)"),S8o.forEach(t),ce.forEach(t),wWt=i(wd),M(vL.$$.fragment,wd),wd.forEach(t),Cd.forEach(t),jBo=i(c),sf=n(c,"H3",{class:!0});var $$o=s(sf);ML=n($$o,"A",{id:!0,class:!0,href:!0});var TIn=s(ML);eze=n(TIn,"SPAN",{});var FIn=s(eze);M(FJ.$$.fragment,FIn),FIn.forEach(t),TIn.forEach(t),LWt=i($$o),oze=n($$o,"SPAN",{});var EIn=s(oze);AWt=r(EIn,"FlaxAutoModelForSequenceClassification"),EIn.forEach(t),$$o.forEach(t),PBo=i(c),lr=n(c,"DIV",{class:!0});var Ld=s(lr);M(EJ.$$.fragment,Ld),RWt=i(Ld),lf=n(Ld,"P",{});var ECe=s(lf);kWt=r(ECe,`This is a generic model class that will be instantiated as one of the model classes of 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with the `),yve=n(ECe,"A",{href:!0});var yIn=s(yve);SWt=r(yIn,"from_pretrained()"),yIn.forEach(t),xWt=r(ECe," class method or the "),Cve=n(ECe,"A",{href:!0});var CIn=s(Cve);GWt=r(CIn,"from_config()"),CIn.forEach(t),BWt=r(ECe,` class
method.`),ECe.forEach(t),IWt=i(Ld),yJ=n(Ld,"P",{});var j$o=s(yJ);NWt=r(j$o,"This class cannot be instantiated directly using "),rze=n(j$o,"CODE",{});var wIn=s(rze);$Wt=r(wIn,"__init__()"),wIn.forEach(t),jWt=r(j$o," (throws an error)."),j$o.forEach(t),PWt=i(Ld),sa=n(Ld,"DIV",{class:!0});var pP=s(sa);M(CJ.$$.fragment,pP),ZWt=i(pP),tze=n(pP,"P",{});var LIn=s(tze);VWt=r(LIn,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),LIn.forEach(t),WWt=i(pP),df=n(pP,"P",{});var yCe=s(df);JWt=r(yCe,`Note:
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model\u2019s configuration. Use `),wve=n(yCe,"A",{href:!0});var RIn=s(wve);DWt=r(RIn,"from_pretrained()"),RIn.forEach(t),qWt=r(yCe," to load the model weights."),yCe.forEach(t),OWt=i(pP),M(TL.$$.fragment,pP),pP.forEach(t),QWt=i(Ld),gt=n(Ld,"DIV",{class:!0});var Ad=s(gt);M(wJ.$$.fragment,Ad),YWt=i(Ad),nze=n(Ad,"P",{});var kIn=s(nze);zWt=r(kIn,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),kIn.forEach(t),HWt=i(Ad),ts=n(Ad,"P",{});var _P=s(ts);KWt=r(_P,"The model class to instantiate is selected based on the "),sze=n(_P,"CODE",{});var SIn=s(sze);eJt=r(SIn,"model_type"),SIn.forEach(t),oJt=r(_P,` property of the config object (either
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falling back to using pattern matching on `),ize=n(_P,"CODE",{});var GIn=s(ize);aJt=r(GIn,"pretrained_model_name_or_path"),GIn.forEach(t),nJt=r(_P,":"),_P.forEach(t),sJt=i(Ad),Se=n(Ad,"UL",{});var Ze=s(Se);FL=n(Ze,"LI",{});var x8o=s(FL);dze=n(x8o,"STRONG",{});var BIn=s(dze);lJt=r(BIn,"albert"),BIn.forEach(t),iJt=r(x8o," \u2014 "),Lve=n(x8o,"A",{href:!0});var IIn=s(Lve);dJt=r(IIn,"FlaxAlbertForSequenceClassification"),IIn.forEach(t),mJt=r(x8o," (ALBERT model)"),x8o.forEach(t),cJt=i(Ze),EL=n(Ze,"LI",{});var G8o=s(EL);mze=n(G8o,"STRONG",{});var NIn=s(mze);fJt=r(NIn,"bart"),NIn.forEach(t),hJt=r(G8o," \u2014 "),Ave=n(G8o,"A",{href:!0});var $In=s(Ave);gJt=r($In,"FlaxBartForSequenceClassification"),$In.forEach(t),uJt=r(G8o," (BART model)"),G8o.forEach(t),pJt=i(Ze),yL=n(Ze,"LI",{});var B8o=s(yL);cze=n(B8o,"STRONG",{});var jIn=s(cze);_Jt=r(jIn,"bert"),jIn.forEach(t),bJt=r(B8o," \u2014 "),Rve=n(B8o,"A",{href:!0});var PIn=s(Rve);vJt=r(PIn,"FlaxBertForSequenceClassification"),PIn.forEach(t),MJt=r(B8o," (BERT model)"),B8o.forEach(t),TJt=i(Ze),CL=n(Ze,"LI",{});var I8o=s(CL);fze=n(I8o,"STRONG",{});var ZIn=s(fze);FJt=r(ZIn,"big_bird"),ZIn.forEach(t),EJt=r(I8o," \u2014 "),kve=n(I8o,"A",{href:!0});var VIn=s(kve);yJt=r(VIn,"FlaxBigBirdForSequenceClassification"),VIn.forEach(t),CJt=r(I8o," (BigBird model)"),I8o.forEach(t),wJt=i(Ze),wL=n(Ze,"LI",{});var N8o=s(wL);hze=n(N8o,"STRONG",{});var WIn=s(hze);LJt=r(WIn,"distilbert"),WIn.forEach(t),AJt=r(N8o," \u2014 "),Sve=n(N8o,"A",{href:!0});var JIn=s(Sve);RJt=r(JIn,"FlaxDistilBertForSequenceClassification"),JIn.forEach(t),kJt=r(N8o," (DistilBERT model)"),N8o.forEach(t),SJt=i(Ze),LL=n(Ze,"LI",{});var $8o=s(LL);gze=n($8o,"STRONG",{});var UIn=s(gze);xJt=r(UIn,"electra"),UIn.forEach(t),GJt=r($8o," \u2014 "),xve=n($8o,"A",{href:!0});var XIn=s(xve);BJt=r(XIn,"FlaxElectraForSequenceClassification"),XIn.forEach(t),IJt=r($8o," (ELECTRA model)"),$8o.forEach(t),NJt=i(Ze),AL=n(Ze,"LI",{});var j8o=s(AL);uze=n(j8o,"STRONG",{});var DIn=s(uze);$Jt=r(DIn,"mbart"),DIn.forEach(t),jJt=r(j8o," \u2014 "),Gve=n(j8o,"A",{href:!0});var qIn=s(Gve);PJt=r(qIn,"FlaxMBartForSequenceClassification"),qIn.forEach(t),ZJt=r(j8o," (mBART model)"),j8o.forEach(t),VJt=i(Ze),RL=n(Ze,"LI",{});var P8o=s(RL);pze=n(P8o,"STRONG",{});var OIn=s(pze);WJt=r(OIn,"roberta"),OIn.forEach(t),JJt=r(P8o," \u2014 "),Bve=n(P8o,"A",{href:!0});var QIn=s(Bve);UJt=r(QIn,"FlaxRobertaForSequenceClassification"),QIn.forEach(t),XJt=r(P8o," (RoBERTa model)"),P8o.forEach(t),DJt=i(Ze),kL=n(Ze,"LI",{});var Z8o=s(kL);_ze=n(Z8o,"STRONG",{});var YIn=s(_ze);qJt=r(YIn,"roberta-prelayernorm"),YIn.forEach(t),OJt=r(Z8o," \u2014 "),Ive=n(Z8o,"A",{href:!0});var zIn=s(Ive);QJt=r(zIn,"FlaxRobertaPreLayerNormForSequenceClassification"),zIn.forEach(t),YJt=r(Z8o," (RoBERTa-PreLayerNorm model)"),Z8o.forEach(t),zJt=i(Ze),SL=n(Ze,"LI",{});var V8o=s(SL);bze=n(V8o,"STRONG",{});var HIn=s(bze);HJt=r(HIn,"roformer"),HIn.forEach(t),KJt=r(V8o," \u2014 "),Nve=n(V8o,"A",{href:!0});var KIn=s(Nve);eUt=r(KIn,"FlaxRoFormerForSequenceClassification"),KIn.forEach(t),oUt=r(V8o," (RoFormer model)"),V8o.forEach(t),rUt=i(Ze),xL=n(Ze,"LI",{});var W8o=s(xL);vze=n(W8o,"STRONG",{});var eNn=s(vze);tUt=r(eNn,"xlm-roberta"),eNn.forEach(t),aUt=r(W8o," \u2014 "),$ve=n(W8o,"A",{href:!0});var oNn=s($ve);nUt=r(oNn,"FlaxXLMRobertaForSequenceClassification"),oNn.forEach(t),sUt=r(W8o," (XLM-RoBERTa model)"),W8o.forEach(t),Ze.forEach(t),lUt=i(Ad),M(GL.$$.fragment,Ad),Ad.forEach(t),Ld.forEach(t),ZBo=i(c),mf=n(c,"H3",{class:!0});var P$o=s(mf);BL=n(P$o,"A",{id:!0,class:!0,href:!0});var rNn=s(BL);Mze=n(rNn,"SPAN",{});var tNn=s(Mze);M(LJ.$$.fragment,tNn),tNn.forEach(t),rNn.forEach(t),iUt=i(P$o),Tze=n(P$o,"SPAN",{});var aNn=s(Tze);dUt=r(aNn,"AutoModelForMultipleChoice"),aNn.forEach(t),P$o.forEach(t),VBo=i(c),ir=n(c,"DIV",{class:!0});var Rd=s(ir);M(AJ.$$.fragment,Rd),mUt=i(Rd),cf=n(Rd,"P",{});var CCe=s(cf);cUt=r(CCe,`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.`),CCe.forEach(t),pUt=i(Rd),RJ=n(Rd,"P",{});var Z$o=s(RJ);_Ut=r(Z$o,"This class cannot be instantiated directly using "),Fze=n(Z$o,"CODE",{});var lNn=s(Fze);bUt=r(lNn,"__init__()"),lNn.forEach(t),vUt=r(Z$o," (throws an error)."),Z$o.forEach(t),MUt=i(Rd),la=n(Rd,"DIV",{class:!0});var bP=s(la);M(kJ.$$.fragment,bP),TUt=i(bP),Eze=n(bP,"P",{});var iNn=s(Eze);FUt=r(iNn,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),iNn.forEach(t),EUt=i(bP),ff=n(bP,"P",{});var wCe=s(ff);yUt=r(wCe,`Note:
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model\u2019s configuration. Use `),Zve=n(wCe,"A",{href:!0});var mNn=s(Zve);LUt=r(mNn,"from_pretrained()"),mNn.forEach(t),AUt=r(wCe," to load the model weights."),wCe.forEach(t),RUt=i(bP),M(IL.$$.fragment,bP),bP.forEach(t),kUt=i(Rd),ho=n(Rd,"DIV",{class:!0});var ln=s(ho);M(SJ.$$.fragment,ln),SUt=i(ln),Cze=n(ln,"P",{});var cNn=s(Cze);xUt=r(cNn,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),cNn.forEach(t),GUt=i(ln),as=n(ln,"P",{});var vP=s(as);BUt=r(vP,"The model class to instantiate is selected based on the "),wze=n(vP,"CODE",{});var fNn=s(wze);IUt=r(fNn,"model_type"),fNn.forEach(t),NUt=r(vP,` property of the config object (either
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falling back to using pattern matching on `),Aze=n(vP,"CODE",{});var gNn=s(Aze);PUt=r(gNn,"pretrained_model_name_or_path"),gNn.forEach(t),ZUt=r(vP,":"),vP.forEach(t),VUt=i(ln),ae=n(ln,"UL",{});var ne=s(ae);NL=n(ne,"LI",{});var J8o=s(NL);Rze=n(J8o,"STRONG",{});var uNn=s(Rze);WUt=r(uNn,"albert"),uNn.forEach(t),JUt=r(J8o," \u2014 "),Vve=n(J8o,"A",{href:!0});var pNn=s(Vve);UUt=r(pNn,"AlbertForMultipleChoice"),pNn.forEach(t),XUt=r(J8o," (ALBERT model)"),J8o.forEach(t),DUt=i(ne),$L=n(ne,"LI",{});var U8o=s($L);kze=n(U8o,"STRONG",{});var _Nn=s(kze);qUt=r(_Nn,"bert"),_Nn.forEach(t),OUt=r(U8o," \u2014 "),Wve=n(U8o,"A",{href:!0});var bNn=s(Wve);QUt=r(bNn,"BertForMultipleChoice"),bNn.forEach(t),YUt=r(U8o," (BERT model)"),U8o.forEach(t),zUt=i(ne),jL=n(ne,"LI",{});var X8o=s(jL);Sze=n(X8o,"STRONG",{});var vNn=s(Sze);HUt=r(vNn,"big_bird"),vNn.forEach(t),KUt=r(X8o," \u2014 "),Jve=n(X8o,"A",{href:!0});var MNn=s(Jve);eXt=r(MNn,"BigBirdForMultipleChoice"),MNn.forEach(t),oXt=r(X8o," (BigBird 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h7o=s(sA);Kze=n(h7o,"STRONG",{});var d$n=s(Kze);eqt=r(d$n,"nystromformer"),d$n.forEach(t),oqt=r(h7o," \u2014 "),cMe=n(h7o,"A",{href:!0});var m$n=s(cMe);rqt=r(m$n,"NystromformerForMultipleChoice"),m$n.forEach(t),tqt=r(h7o," (Nystr\xF6mformer model)"),h7o.forEach(t),aqt=i(ne),lA=n(ne,"LI",{});var g7o=s(lA);eHe=n(g7o,"STRONG",{});var c$n=s(eHe);nqt=r(c$n,"qdqbert"),c$n.forEach(t),sqt=r(g7o," \u2014 "),fMe=n(g7o,"A",{href:!0});var f$n=s(fMe);lqt=r(f$n,"QDQBertForMultipleChoice"),f$n.forEach(t),iqt=r(g7o," (QDQBert model)"),g7o.forEach(t),dqt=i(ne),iA=n(ne,"LI",{});var u7o=s(iA);oHe=n(u7o,"STRONG",{});var h$n=s(oHe);mqt=r(h$n,"rembert"),h$n.forEach(t),cqt=r(u7o," \u2014 "),hMe=n(u7o,"A",{href:!0});var g$n=s(hMe);fqt=r(g$n,"RemBertForMultipleChoice"),g$n.forEach(t),hqt=r(u7o," (RemBERT model)"),u7o.forEach(t),gqt=i(ne),dA=n(ne,"LI",{});var p7o=s(dA);rHe=n(p7o,"STRONG",{});var u$n=s(rHe);uqt=r(u$n,"roberta"),u$n.forEach(t),pqt=r(p7o," \u2014 "),gMe=n(p7o,"A",{href:!0});var p$n=s(gMe);_qt=r(p$n,"RobertaForMultipleChoice"),p$n.forEach(t),bqt=r(p7o," (RoBERTa model)"),p7o.forEach(t),vqt=i(ne),mA=n(ne,"LI",{});var _7o=s(mA);tHe=n(_7o,"STRONG",{});var _$n=s(tHe);Mqt=r(_$n,"roberta-prelayernorm"),_$n.forEach(t),Tqt=r(_7o," \u2014 "),uMe=n(_7o,"A",{href:!0});var b$n=s(uMe);Fqt=r(b$n,"RobertaPreLayerNormForMultipleChoice"),b$n.forEach(t),Eqt=r(_7o," (RoBERTa-PreLayerNorm model)"),_7o.forEach(t),yqt=i(ne),cA=n(ne,"LI",{});var b7o=s(cA);aHe=n(b7o,"STRONG",{});var v$n=s(aHe);Cqt=r(v$n,"roc_bert"),v$n.forEach(t),wqt=r(b7o," \u2014 "),pMe=n(b7o,"A",{href:!0});var M$n=s(pMe);Lqt=r(M$n,"RoCBertForMultipleChoice"),M$n.forEach(t),Aqt=r(b7o," (RoCBert model)"),b7o.forEach(t),Rqt=i(ne),fA=n(ne,"LI",{});var v7o=s(fA);nHe=n(v7o,"STRONG",{});var T$n=s(nHe);kqt=r(T$n,"roformer"),T$n.forEach(t),Sqt=r(v7o," \u2014 "),_Me=n(v7o,"A",{href:!0});var F$n=s(_Me);xqt=r(F$n,"RoFormerForMultipleChoice"),F$n.forEach(t),Gqt=r(v7o," (RoFormer 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method.`),LCe.forEach(t),ROt=i(kd),BJ=n(kd,"P",{});var W$o=s(BJ);kOt=r(W$o,"This class cannot be instantiated directly using "),_He=n(W$o,"CODE",{});var U$n=s(_He);SOt=r(U$n,"__init__()"),U$n.forEach(t),xOt=r(W$o," (throws an error)."),W$o.forEach(t),GOt=i(kd),ia=n(kd,"DIV",{class:!0});var MP=s(ia);M(IJ.$$.fragment,MP),BOt=i(MP),bHe=n(MP,"P",{});var X$n=s(bHe);IOt=r(X$n,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),X$n.forEach(t),NOt=i(MP),uf=n(MP,"P",{});var ACe=s(uf);$Ot=r(ACe,`Note:
Loading a model from its configuration file does `),vHe=n(ACe,"STRONG",{});var D$n=s(vHe);jOt=r(D$n,"not"),D$n.forEach(t),POt=r(ACe,` load the model weights. It only affects the
model\u2019s configuration. Use `),LMe=n(ACe,"A",{href:!0});var q$n=s(LMe);ZOt=r(q$n,"from_pretrained()"),q$n.forEach(t),VOt=r(ACe," to load the model weights."),ACe.forEach(t),WOt=i(MP),M(EA.$$.fragment,MP),MP.forEach(t),JOt=i(kd),ut=n(kd,"DIV",{class:!0});var Sd=s(ut);M(NJ.$$.fragment,Sd),UOt=i(Sd),MHe=n(Sd,"P",{});var O$n=s(MHe);XOt=r(O$n,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),O$n.forEach(t),DOt=i(Sd),ns=n(Sd,"P",{});var TP=s(ns);qOt=r(TP,"The model class to instantiate is selected based on the "),THe=n(TP,"CODE",{});var Q$n=s(THe);OOt=r(Q$n,"model_type"),Q$n.forEach(t),QOt=r(TP,` property of the config object (either
passed as an argument or loaded from `),FHe=n(TP,"CODE",{});var Y$n=s(FHe);YOt=r(Y$n,"pretrained_model_name_or_path"),Y$n.forEach(t),zOt=r(TP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),EHe=n(TP,"CODE",{});var z$n=s(EHe);HOt=r(z$n,"pretrained_model_name_or_path"),z$n.forEach(t),KOt=r(TP,":"),TP.forEach(t),eQt=i(Sd),Fe=n(Sd,"UL",{});var ye=s(Fe);yA=n(ye,"LI",{});var A7o=s(yA);yHe=n(A7o,"STRONG",{});var H$n=s(yHe);oQt=r(H$n,"albert"),H$n.forEach(t),rQt=r(A7o," \u2014 "),AMe=n(A7o,"A",{href:!0});var K$n=s(AMe);tQt=r(K$n,"TFAlbertForMultipleChoice"),K$n.forEach(t),aQt=r(A7o," (ALBERT model)"),A7o.forEach(t),nQt=i(ye),CA=n(ye,"LI",{});var R7o=s(CA);CHe=n(R7o,"STRONG",{});var ejn=s(CHe);sQt=r(ejn,"bert"),ejn.forEach(t),lQt=r(R7o," \u2014 "),RMe=n(R7o,"A",{href:!0});var ojn=s(RMe);iQt=r(ojn,"TFBertForMultipleChoice"),ojn.forEach(t),dQt=r(R7o," (BERT model)"),R7o.forEach(t),mQt=i(ye),wA=n(ye,"LI",{});var k7o=s(wA);wHe=n(k7o,"STRONG",{});var rjn=s(wHe);cQt=r(rjn,"camembert"),rjn.forEach(t),fQt=r(k7o," \u2014 "),kMe=n(k7o,"A",{href:!0});var tjn=s(kMe);hQt=r(tjn,"TFCamembertForMultipleChoice"),tjn.forEach(t),gQt=r(k7o," (CamemBERT 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\u2014 "),BMe=n(B7o,"A",{href:!0});var cjn=s(BMe);GQt=r(cjn,"TFFlaubertForMultipleChoice"),cjn.forEach(t),BQt=r(B7o," (FlauBERT model)"),B7o.forEach(t),IQt=i(ye),SA=n(ye,"LI",{});var I7o=s(SA);SHe=n(I7o,"STRONG",{});var fjn=s(SHe);NQt=r(fjn,"funnel"),fjn.forEach(t),$Qt=r(I7o," \u2014 "),IMe=n(I7o,"A",{href:!0});var hjn=s(IMe);jQt=r(hjn,"TFFunnelForMultipleChoice"),hjn.forEach(t),PQt=r(I7o," (Funnel Transformer model)"),I7o.forEach(t),ZQt=i(ye),xA=n(ye,"LI",{});var N7o=s(xA);xHe=n(N7o,"STRONG",{});var gjn=s(xHe);VQt=r(gjn,"longformer"),gjn.forEach(t),WQt=r(N7o," \u2014 "),NMe=n(N7o,"A",{href:!0});var ujn=s(NMe);JQt=r(ujn,"TFLongformerForMultipleChoice"),ujn.forEach(t),UQt=r(N7o," (Longformer model)"),N7o.forEach(t),XQt=i(ye),GA=n(ye,"LI",{});var $7o=s(GA);GHe=n($7o,"STRONG",{});var pjn=s(GHe);DQt=r(pjn,"mobilebert"),pjn.forEach(t),qQt=r($7o," \u2014 "),$Me=n($7o,"A",{href:!0});var _jn=s($Me);OQt=r(_jn,"TFMobileBertForMultipleChoice"),_jn.forEach(t),QQt=r($7o," (MobileBERT 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"),VMe=n(V7o,"A",{href:!0});var Cjn=s(VMe);gYt=r(Cjn,"TFRobertaPreLayerNormForMultipleChoice"),Cjn.forEach(t),uYt=r(V7o," (RoBERTa-PreLayerNorm model)"),V7o.forEach(t),pYt=i(ye),jA=n(ye,"LI",{});var W7o=s(jA);jHe=n(W7o,"STRONG",{});var wjn=s(jHe);_Yt=r(wjn,"roformer"),wjn.forEach(t),bYt=r(W7o," \u2014 "),WMe=n(W7o,"A",{href:!0});var Ljn=s(WMe);vYt=r(Ljn,"TFRoFormerForMultipleChoice"),Ljn.forEach(t),MYt=r(W7o," (RoFormer model)"),W7o.forEach(t),TYt=i(ye),PA=n(ye,"LI",{});var J7o=s(PA);PHe=n(J7o,"STRONG",{});var Ajn=s(PHe);FYt=r(Ajn,"xlm"),Ajn.forEach(t),EYt=r(J7o," \u2014 "),JMe=n(J7o,"A",{href:!0});var Rjn=s(JMe);yYt=r(Rjn,"TFXLMForMultipleChoice"),Rjn.forEach(t),CYt=r(J7o," (XLM model)"),J7o.forEach(t),wYt=i(ye),ZA=n(ye,"LI",{});var U7o=s(ZA);ZHe=n(U7o,"STRONG",{});var kjn=s(ZHe);LYt=r(kjn,"xlm-roberta"),kjn.forEach(t),AYt=r(U7o," \u2014 "),UMe=n(U7o,"A",{href:!0});var Sjn=s(UMe);RYt=r(Sjn,"TFXLMRobertaForMultipleChoice"),Sjn.forEach(t),kYt=r(U7o," (XLM-RoBERTa 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with the `),DMe=n(RCe,"A",{href:!0});var $jn=s(DMe);VYt=r($jn,"from_pretrained()"),$jn.forEach(t),WYt=r(RCe," class method or the "),qMe=n(RCe,"A",{href:!0});var jjn=s(qMe);JYt=r(jjn,"from_config()"),jjn.forEach(t),UYt=r(RCe,` class
method.`),RCe.forEach(t),XYt=i(xd),PJ=n(xd,"P",{});var U$o=s(PJ);DYt=r(U$o,"This class cannot be instantiated directly using "),UHe=n(U$o,"CODE",{});var Pjn=s(UHe);qYt=r(Pjn,"__init__()"),Pjn.forEach(t),OYt=r(U$o," (throws an error)."),U$o.forEach(t),QYt=i(xd),da=n(xd,"DIV",{class:!0});var FP=s(da);M(ZJ.$$.fragment,FP),YYt=i(FP),XHe=n(FP,"P",{});var Zjn=s(XHe);zYt=r(Zjn,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),Zjn.forEach(t),HYt=i(FP),bf=n(FP,"P",{});var kCe=s(bf);KYt=r(kCe,`Note:
Loading a model from its configuration file does `),DHe=n(kCe,"STRONG",{});var Vjn=s(DHe);ezt=r(Vjn,"not"),Vjn.forEach(t),ozt=r(kCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),OMe=n(kCe,"A",{href:!0});var Wjn=s(OMe);rzt=r(Wjn,"from_pretrained()"),Wjn.forEach(t),tzt=r(kCe," to load the model weights."),kCe.forEach(t),azt=i(FP),M(UA.$$.fragment,FP),FP.forEach(t),nzt=i(xd),pt=n(xd,"DIV",{class:!0});var Gd=s(pt);M(VJ.$$.fragment,Gd),szt=i(Gd),qHe=n(Gd,"P",{});var Jjn=s(qHe);lzt=r(Jjn,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),Jjn.forEach(t),izt=i(Gd),ss=n(Gd,"P",{});var EP=s(ss);dzt=r(EP,"The model class to instantiate is selected based on the "),OHe=n(EP,"CODE",{});var Ujn=s(OHe);mzt=r(Ujn,"model_type"),Ujn.forEach(t),czt=r(EP,` property of the config object (either
passed as an argument or loaded from `),QHe=n(EP,"CODE",{});var Xjn=s(QHe);fzt=r(Xjn,"pretrained_model_name_or_path"),Xjn.forEach(t),hzt=r(EP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),YHe=n(EP,"CODE",{});var Djn=s(YHe);gzt=r(Djn,"pretrained_model_name_or_path"),Djn.forEach(t),uzt=r(EP,":"),EP.forEach(t),pzt=i(Gd),Ue=n(Gd,"UL",{});var He=s(Ue);XA=n(He,"LI",{});var D7o=s(XA);zHe=n(D7o,"STRONG",{});var qjn=s(zHe);_zt=r(qjn,"albert"),qjn.forEach(t),bzt=r(D7o," \u2014 "),QMe=n(D7o,"A",{href:!0});var Ojn=s(QMe);vzt=r(Ojn,"FlaxAlbertForMultipleChoice"),Ojn.forEach(t),Mzt=r(D7o," (ALBERT model)"),D7o.forEach(t),Tzt=i(He),DA=n(He,"LI",{});var q7o=s(DA);HHe=n(q7o,"STRONG",{});var Qjn=s(HHe);Fzt=r(Qjn,"bert"),Qjn.forEach(t),Ezt=r(q7o," \u2014 "),YMe=n(q7o,"A",{href:!0});var Yjn=s(YMe);yzt=r(Yjn,"FlaxBertForMultipleChoice"),Yjn.forEach(t),Czt=r(q7o," (BERT model)"),q7o.forEach(t),wzt=i(He),qA=n(He,"LI",{});var O7o=s(qA);KHe=n(O7o,"STRONG",{});var zjn=s(KHe);Lzt=r(zjn,"big_bird"),zjn.forEach(t),Azt=r(O7o," \u2014 "),zMe=n(O7o,"A",{href:!0});var Hjn=s(zMe);Rzt=r(Hjn,"FlaxBigBirdForMultipleChoice"),Hjn.forEach(t),kzt=r(O7o," (BigBird model)"),O7o.forEach(t),Szt=i(He),OA=n(He,"LI",{});var Q7o=s(OA);eKe=n(Q7o,"STRONG",{});var Kjn=s(eKe);xzt=r(Kjn,"distilbert"),Kjn.forEach(t),Gzt=r(Q7o," \u2014 "),HMe=n(Q7o,"A",{href:!0});var ePn=s(HMe);Bzt=r(ePn,"FlaxDistilBertForMultipleChoice"),ePn.forEach(t),Izt=r(Q7o," (DistilBERT model)"),Q7o.forEach(t),Nzt=i(He),QA=n(He,"LI",{});var Y7o=s(QA);oKe=n(Y7o,"STRONG",{});var oPn=s(oKe);$zt=r(oPn,"electra"),oPn.forEach(t),jzt=r(Y7o," \u2014 "),KMe=n(Y7o,"A",{href:!0});var rPn=s(KMe);Pzt=r(rPn,"FlaxElectraForMultipleChoice"),rPn.forEach(t),Zzt=r(Y7o," (ELECTRA model)"),Y7o.forEach(t),Vzt=i(He),YA=n(He,"LI",{});var z7o=s(YA);rKe=n(z7o,"STRONG",{});var tPn=s(rKe);Wzt=r(tPn,"roberta"),tPn.forEach(t),Jzt=r(z7o," \u2014 "),eTe=n(z7o,"A",{href:!0});var aPn=s(eTe);Uzt=r(aPn,"FlaxRobertaForMultipleChoice"),aPn.forEach(t),Xzt=r(z7o," (RoBERTa model)"),z7o.forEach(t),Dzt=i(He),zA=n(He,"LI",{});var H7o=s(zA);tKe=n(H7o,"STRONG",{});var nPn=s(tKe);qzt=r(nPn,"roberta-prelayernorm"),nPn.forEach(t),Ozt=r(H7o," \u2014 "),oTe=n(H7o,"A",{href:!0});var sPn=s(oTe);Qzt=r(sPn,"FlaxRobertaPreLayerNormForMultipleChoice"),sPn.forEach(t),Yzt=r(H7o," (RoBERTa-PreLayerNorm model)"),H7o.forEach(t),zzt=i(He),HA=n(He,"LI",{});var K7o=s(HA);aKe=n(K7o,"STRONG",{});var lPn=s(aKe);Hzt=r(lPn,"roformer"),lPn.forEach(t),Kzt=r(K7o," \u2014 "),rTe=n(K7o,"A",{href:!0});var iPn=s(rTe);eHt=r(iPn,"FlaxRoFormerForMultipleChoice"),iPn.forEach(t),oHt=r(K7o," (RoFormer model)"),K7o.forEach(t),rHt=i(He),KA=n(He,"LI",{});var eLo=s(KA);nKe=n(eLo,"STRONG",{});var dPn=s(nKe);tHt=r(dPn,"xlm-roberta"),dPn.forEach(t),aHt=r(eLo," \u2014 "),tTe=n(eLo,"A",{href:!0});var mPn=s(tTe);nHt=r(mPn,"FlaxXLMRobertaForMultipleChoice"),mPn.forEach(t),sHt=r(eLo," (XLM-RoBERTa model)"),eLo.forEach(t),He.forEach(t),lHt=i(Gd),M(eR.$$.fragment,Gd),Gd.forEach(t),xd.forEach(t),DBo=i(c),vf=n(c,"H3",{class:!0});var X$o=s(vf);oR=n(X$o,"A",{id:!0,class:!0,href:!0});var cPn=s(oR);sKe=n(cPn,"SPAN",{});var fPn=s(sKe);M(WJ.$$.fragment,fPn),fPn.forEach(t),cPn.forEach(t),iHt=i(X$o),lKe=n(X$o,"SPAN",{});var hPn=s(lKe);dHt=r(hPn,"AutoModelForNextSentencePrediction"),hPn.forEach(t),X$o.forEach(t),qBo=i(c),cr=n(c,"DIV",{class:!0});var Bd=s(cr);M(JJ.$$.fragment,Bd),mHt=i(Bd),Mf=n(Bd,"P",{});var SCe=s(Mf);cHt=r(SCe,`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 `),aTe=n(SCe,"A",{href:!0});var gPn=s(aTe);fHt=r(gPn,"from_pretrained()"),gPn.forEach(t),hHt=r(SCe," class method or the "),nTe=n(SCe,"A",{href:!0});var uPn=s(nTe);gHt=r(uPn,"from_config()"),uPn.forEach(t),uHt=r(SCe,` class
method.`),SCe.forEach(t),pHt=i(Bd),UJ=n(Bd,"P",{});var D$o=s(UJ);_Ht=r(D$o,"This class cannot be instantiated directly using "),iKe=n(D$o,"CODE",{});var pPn=s(iKe);bHt=r(pPn,"__init__()"),pPn.forEach(t),vHt=r(D$o," (throws an error)."),D$o.forEach(t),MHt=i(Bd),ma=n(Bd,"DIV",{class:!0});var yP=s(ma);M(XJ.$$.fragment,yP),THt=i(yP),dKe=n(yP,"P",{});var _Pn=s(dKe);FHt=r(_Pn,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),_Pn.forEach(t),EHt=i(yP),Tf=n(yP,"P",{});var xCe=s(Tf);yHt=r(xCe,`Note:
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model\u2019s configuration. Use `),sTe=n(xCe,"A",{href:!0});var vPn=s(sTe);LHt=r(vPn,"from_pretrained()"),vPn.forEach(t),AHt=r(xCe," to load the model weights."),xCe.forEach(t),RHt=i(yP),M(rR.$$.fragment,yP),yP.forEach(t),kHt=i(Bd),go=n(Bd,"DIV",{class:!0});var dn=s(go);M(DJ.$$.fragment,dn),SHt=i(dn),cKe=n(dn,"P",{});var MPn=s(cKe);xHt=r(MPn,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),MPn.forEach(t),GHt=i(dn),ls=n(dn,"P",{});var CP=s(ls);BHt=r(CP,"The model class to instantiate is selected based on the "),fKe=n(CP,"CODE",{});var TPn=s(fKe);IHt=r(TPn,"model_type"),TPn.forEach(t),NHt=r(CP,` property of the config object (either
passed as an argument or loaded from `),hKe=n(CP,"CODE",{});var FPn=s(hKe);$Ht=r(FPn,"pretrained_model_name_or_path"),FPn.forEach(t),jHt=r(CP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),gKe=n(CP,"CODE",{});var EPn=s(gKe);PHt=r(EPn,"pretrained_model_name_or_path"),EPn.forEach(t),ZHt=r(CP,":"),CP.forEach(t),VHt=i(dn),eo=n(dn,"UL",{});var It=s(eo);tR=n(It,"LI",{});var oLo=s(tR);uKe=n(oLo,"STRONG",{});var yPn=s(uKe);WHt=r(yPn,"bert"),yPn.forEach(t),JHt=r(oLo," \u2014 "),lTe=n(oLo,"A",{href:!0});var CPn=s(lTe);UHt=r(CPn,"BertForNextSentencePrediction"),CPn.forEach(t),XHt=r(oLo," (BERT model)"),oLo.forEach(t),DHt=i(It),aR=n(It,"LI",{});var rLo=s(aR);pKe=n(rLo,"STRONG",{});var wPn=s(pKe);qHt=r(wPn,"ernie"),wPn.forEach(t),OHt=r(rLo," \u2014 "),iTe=n(rLo,"A",{href:!0});var LPn=s(iTe);QHt=r(LPn,"ErnieForNextSentencePrediction"),LPn.forEach(t),YHt=r(rLo," (ERNIE model)"),rLo.forEach(t),zHt=i(It),nR=n(It,"LI",{});var tLo=s(nR);_Ke=n(tLo,"STRONG",{});var APn=s(_Ke);HHt=r(APn,"fnet"),APn.forEach(t),KHt=r(tLo," \u2014 "),dTe=n(tLo,"A",{href:!0});var RPn=s(dTe);eKt=r(RPn,"FNetForNextSentencePrediction"),RPn.forEach(t),oKt=r(tLo," (FNet model)"),tLo.forEach(t),rKt=i(It),sR=n(It,"LI",{});var aLo=s(sR);bKe=n(aLo,"STRONG",{});var kPn=s(bKe);tKt=r(kPn,"megatron-bert"),kPn.forEach(t),aKt=r(aLo," \u2014 "),mTe=n(aLo,"A",{href:!0});var SPn=s(mTe);nKt=r(SPn,"MegatronBertForNextSentencePrediction"),SPn.forEach(t),sKt=r(aLo," (Megatron-BERT model)"),aLo.forEach(t),lKt=i(It),lR=n(It,"LI",{});var nLo=s(lR);vKe=n(nLo,"STRONG",{});var xPn=s(vKe);iKt=r(xPn,"mobilebert"),xPn.forEach(t),dKt=r(nLo," \u2014 "),cTe=n(nLo,"A",{href:!0});var GPn=s(cTe);mKt=r(GPn,"MobileBertForNextSentencePrediction"),GPn.forEach(t),cKt=r(nLo," (MobileBERT model)"),nLo.forEach(t),fKt=i(It),iR=n(It,"LI",{});var sLo=s(iR);MKe=n(sLo,"STRONG",{});var BPn=s(MKe);hKt=r(BPn,"nezha"),BPn.forEach(t),gKt=r(sLo," \u2014 "),fTe=n(sLo,"A",{href:!0});var IPn=s(fTe);uKt=r(IPn,"NezhaForNextSentencePrediction"),IPn.forEach(t),pKt=r(sLo," (Nezha model)"),sLo.forEach(t),_Kt=i(It),dR=n(It,"LI",{});var lLo=s(dR);TKe=n(lLo,"STRONG",{});var NPn=s(TKe);bKt=r(NPn,"qdqbert"),NPn.forEach(t),vKt=r(lLo," \u2014 "),hTe=n(lLo,"A",{href:!0});var $Pn=s(hTe);MKt=r($Pn,"QDQBertForNextSentencePrediction"),$Pn.forEach(t),TKt=r(lLo," (QDQBert model)"),lLo.forEach(t),It.forEach(t),FKt=i(dn),mR=n(dn,"P",{});var iLo=s(mR);EKt=r(iLo,"The model is set in evaluation mode by default using "),FKe=n(iLo,"CODE",{});var jPn=s(FKe);yKt=r(jPn,"model.eval()"),jPn.forEach(t),CKt=r(iLo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),EKe=n(iLo,"CODE",{});var PPn=s(EKe);wKt=r(PPn,"model.train()"),PPn.forEach(t),iLo.forEach(t),LKt=i(dn),M(cR.$$.fragment,dn),dn.forEach(t),Bd.forEach(t),OBo=i(c),Ff=n(c,"H3",{class:!0});var q$o=s(Ff);fR=n(q$o,"A",{id:!0,class:!0,href:!0});var ZPn=s(fR);yKe=n(ZPn,"SPAN",{});var VPn=s(yKe);M(qJ.$$.fragment,VPn),VPn.forEach(t),ZPn.forEach(t),AKt=i(q$o),CKe=n(q$o,"SPAN",{});var WPn=s(CKe);RKt=r(WPn,"TFAutoModelForNextSentencePrediction"),WPn.forEach(t),q$o.forEach(t),QBo=i(c),fr=n(c,"DIV",{class:!0});var Id=s(fr);M(OJ.$$.fragment,Id),kKt=i(Id),Ef=n(Id,"P",{});var GCe=s(Ef);SKt=r(GCe,`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 `),gTe=n(GCe,"A",{href:!0});var JPn=s(gTe);xKt=r(JPn,"from_pretrained()"),JPn.forEach(t),GKt=r(GCe," class method or the "),uTe=n(GCe,"A",{href:!0});var UPn=s(uTe);BKt=r(UPn,"from_config()"),UPn.forEach(t),IKt=r(GCe,` class
method.`),GCe.forEach(t),NKt=i(Id),QJ=n(Id,"P",{});var O$o=s(QJ);$Kt=r(O$o,"This class cannot be instantiated directly using "),wKe=n(O$o,"CODE",{});var XPn=s(wKe);jKt=r(XPn,"__init__()"),XPn.forEach(t),PKt=r(O$o," (throws an error)."),O$o.forEach(t),ZKt=i(Id),ca=n(Id,"DIV",{class:!0});var wP=s(ca);M(YJ.$$.fragment,wP),VKt=i(wP),LKe=n(wP,"P",{});var DPn=s(LKe);WKt=r(DPn,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),DPn.forEach(t),JKt=i(wP),yf=n(wP,"P",{});var BCe=s(yf);UKt=r(BCe,`Note:
Loading a model from its configuration file does `),AKe=n(BCe,"STRONG",{});var qPn=s(AKe);XKt=r(qPn,"not"),qPn.forEach(t),DKt=r(BCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),pTe=n(BCe,"A",{href:!0});var OPn=s(pTe);qKt=r(OPn,"from_pretrained()"),OPn.forEach(t),OKt=r(BCe," to load the model weights."),BCe.forEach(t),QKt=i(wP),M(hR.$$.fragment,wP),wP.forEach(t),YKt=i(Id),_t=n(Id,"DIV",{class:!0});var Nd=s(_t);M(zJ.$$.fragment,Nd),zKt=i(Nd),RKe=n(Nd,"P",{});var QPn=s(RKe);HKt=r(QPn,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),QPn.forEach(t),KKt=i(Nd),is=n(Nd,"P",{});var LP=s(is);eea=r(LP,"The model class to instantiate is selected based on the "),kKe=n(LP,"CODE",{});var YPn=s(kKe);oea=r(YPn,"model_type"),YPn.forEach(t),rea=r(LP,` property of the config object (either
passed as an argument or loaded from `),SKe=n(LP,"CODE",{});var zPn=s(SKe);tea=r(zPn,"pretrained_model_name_or_path"),zPn.forEach(t),aea=r(LP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),xKe=n(LP,"CODE",{});var HPn=s(xKe);nea=r(HPn,"pretrained_model_name_or_path"),HPn.forEach(t),sea=r(LP,":"),LP.forEach(t),lea=i(Nd),HJ=n(Nd,"UL",{});var Q$o=s(HJ);gR=n(Q$o,"LI",{});var dLo=s(gR);GKe=n(dLo,"STRONG",{});var KPn=s(GKe);iea=r(KPn,"bert"),KPn.forEach(t),dea=r(dLo," \u2014 "),_Te=n(dLo,"A",{href:!0});var eZn=s(_Te);mea=r(eZn,"TFBertForNextSentencePrediction"),eZn.forEach(t),cea=r(dLo," (BERT model)"),dLo.forEach(t),fea=i(Q$o),uR=n(Q$o,"LI",{});var mLo=s(uR);BKe=n(mLo,"STRONG",{});var oZn=s(BKe);hea=r(oZn,"mobilebert"),oZn.forEach(t),gea=r(mLo," \u2014 "),bTe=n(mLo,"A",{href:!0});var rZn=s(bTe);uea=r(rZn,"TFMobileBertForNextSentencePrediction"),rZn.forEach(t),pea=r(mLo," (MobileBERT model)"),mLo.forEach(t),Q$o.forEach(t),_ea=i(Nd),M(pR.$$.fragment,Nd),Nd.forEach(t),Id.forEach(t),YBo=i(c),Cf=n(c,"H3",{class:!0});var Y$o=s(Cf);_R=n(Y$o,"A",{id:!0,class:!0,href:!0});var tZn=s(_R);IKe=n(tZn,"SPAN",{});var aZn=s(IKe);M(KJ.$$.fragment,aZn),aZn.forEach(t),tZn.forEach(t),bea=i(Y$o),NKe=n(Y$o,"SPAN",{});var nZn=s(NKe);vea=r(nZn,"FlaxAutoModelForNextSentencePrediction"),nZn.forEach(t),Y$o.forEach(t),zBo=i(c),hr=n(c,"DIV",{class:!0});var $d=s(hr);M(eU.$$.fragment,$d),Mea=i($d),wf=n($d,"P",{});var ICe=s(wf);Tea=r(ICe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),vTe=n(ICe,"A",{href:!0});var sZn=s(vTe);Fea=r(sZn,"from_pretrained()"),sZn.forEach(t),Eea=r(ICe," class method or the "),MTe=n(ICe,"A",{href:!0});var lZn=s(MTe);yea=r(lZn,"from_config()"),lZn.forEach(t),Cea=r(ICe,` class
method.`),ICe.forEach(t),wea=i($d),oU=n($d,"P",{});var z$o=s(oU);Lea=r(z$o,"This class cannot be instantiated directly using "),$Ke=n(z$o,"CODE",{});var iZn=s($Ke);Aea=r(iZn,"__init__()"),iZn.forEach(t),Rea=r(z$o," (throws an error)."),z$o.forEach(t),kea=i($d),fa=n($d,"DIV",{class:!0});var AP=s(fa);M(rU.$$.fragment,AP),Sea=i(AP),jKe=n(AP,"P",{});var dZn=s(jKe);xea=r(dZn,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),dZn.forEach(t),Gea=i(AP),Lf=n(AP,"P",{});var NCe=s(Lf);Bea=r(NCe,`Note:
Loading a model from its configuration file does `),PKe=n(NCe,"STRONG",{});var mZn=s(PKe);Iea=r(mZn,"not"),mZn.forEach(t),Nea=r(NCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),TTe=n(NCe,"A",{href:!0});var cZn=s(TTe);$ea=r(cZn,"from_pretrained()"),cZn.forEach(t),jea=r(NCe," to load the model weights."),NCe.forEach(t),Pea=i(AP),M(bR.$$.fragment,AP),AP.forEach(t),Zea=i($d),bt=n($d,"DIV",{class:!0});var jd=s(bt);M(tU.$$.fragment,jd),Vea=i(jd),ZKe=n(jd,"P",{});var fZn=s(ZKe);Wea=r(fZn,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),fZn.forEach(t),Jea=i(jd),ds=n(jd,"P",{});var RP=s(ds);Uea=r(RP,"The model class to instantiate is selected based on the "),VKe=n(RP,"CODE",{});var hZn=s(VKe);Xea=r(hZn,"model_type"),hZn.forEach(t),Dea=r(RP,` property of the config object (either
passed as an argument or loaded from `),WKe=n(RP,"CODE",{});var gZn=s(WKe);qea=r(gZn,"pretrained_model_name_or_path"),gZn.forEach(t),Oea=r(RP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),JKe=n(RP,"CODE",{});var uZn=s(JKe);Qea=r(uZn,"pretrained_model_name_or_path"),uZn.forEach(t),Yea=r(RP,":"),RP.forEach(t),zea=i(jd),UKe=n(jd,"UL",{});var pZn=s(UKe);vR=n(pZn,"LI",{});var cLo=s(vR);XKe=n(cLo,"STRONG",{});var _Zn=s(XKe);Hea=r(_Zn,"bert"),_Zn.forEach(t),Kea=r(cLo," \u2014 "),FTe=n(cLo,"A",{href:!0});var bZn=s(FTe);eoa=r(bZn,"FlaxBertForNextSentencePrediction"),bZn.forEach(t),ooa=r(cLo," (BERT model)"),cLo.forEach(t),pZn.forEach(t),roa=i(jd),M(MR.$$.fragment,jd),jd.forEach(t),$d.forEach(t),HBo=i(c),Af=n(c,"H3",{class:!0});var H$o=s(Af);TR=n(H$o,"A",{id:!0,class:!0,href:!0});var vZn=s(TR);DKe=n(vZn,"SPAN",{});var MZn=s(DKe);M(aU.$$.fragment,MZn),MZn.forEach(t),vZn.forEach(t),toa=i(H$o),qKe=n(H$o,"SPAN",{});var TZn=s(qKe);aoa=r(TZn,"AutoModelForTokenClassification"),TZn.forEach(t),H$o.forEach(t),KBo=i(c),gr=n(c,"DIV",{class:!0});var Pd=s(gr);M(nU.$$.fragment,Pd),noa=i(Pd),Rf=n(Pd,"P",{});var $Ce=s(Rf);soa=r($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 `),ETe=n($Ce,"A",{href:!0});var FZn=s(ETe);loa=r(FZn,"from_pretrained()"),FZn.forEach(t),ioa=r($Ce," class method or the "),yTe=n($Ce,"A",{href:!0});var EZn=s(yTe);doa=r(EZn,"from_config()"),EZn.forEach(t),moa=r($Ce,` class
method.`),$Ce.forEach(t),coa=i(Pd),sU=n(Pd,"P",{});var K$o=s(sU);foa=r(K$o,"This class cannot be instantiated directly using "),OKe=n(K$o,"CODE",{});var yZn=s(OKe);hoa=r(yZn,"__init__()"),yZn.forEach(t),goa=r(K$o," (throws an error)."),K$o.forEach(t),uoa=i(Pd),ha=n(Pd,"DIV",{class:!0});var kP=s(ha);M(lU.$$.fragment,kP),poa=i(kP),QKe=n(kP,"P",{});var CZn=s(QKe);_oa=r(CZn,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),CZn.forEach(t),boa=i(kP),kf=n(kP,"P",{});var jCe=s(kf);voa=r(jCe,`Note:
Loading a model from its configuration file does `),YKe=n(jCe,"STRONG",{});var wZn=s(YKe);Moa=r(wZn,"not"),wZn.forEach(t),Toa=r(jCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),CTe=n(jCe,"A",{href:!0});var LZn=s(CTe);Foa=r(LZn,"from_pretrained()"),LZn.forEach(t),Eoa=r(jCe," to load the model weights."),jCe.forEach(t),yoa=i(kP),M(FR.$$.fragment,kP),kP.forEach(t),Coa=i(Pd),uo=n(Pd,"DIV",{class:!0});var mn=s(uo);M(iU.$$.fragment,mn),woa=i(mn),zKe=n(mn,"P",{});var AZn=s(zKe);Loa=r(AZn,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),AZn.forEach(t),Aoa=i(mn),ms=n(mn,"P",{});var SP=s(ms);Roa=r(SP,"The model class to instantiate is selected based on the "),HKe=n(SP,"CODE",{});var RZn=s(HKe);koa=r(RZn,"model_type"),RZn.forEach(t),Soa=r(SP,` property of the config object (either
passed as an argument or loaded from `),KKe=n(SP,"CODE",{});var kZn=s(KKe);xoa=r(kZn,"pretrained_model_name_or_path"),kZn.forEach(t),Goa=r(SP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),eeo=n(SP,"CODE",{});var SZn=s(eeo);Boa=r(SZn,"pretrained_model_name_or_path"),SZn.forEach(t),Ioa=r(SP,":"),SP.forEach(t),Noa=i(mn),z=n(mn,"UL",{});var H=s(z);ER=n(H,"LI",{});var fLo=s(ER);oeo=n(fLo,"STRONG",{});var xZn=s(oeo);$oa=r(xZn,"albert"),xZn.forEach(t),joa=r(fLo," \u2014 "),wTe=n(fLo,"A",{href:!0});var GZn=s(wTe);Poa=r(GZn,"AlbertForTokenClassification"),GZn.forEach(t),Zoa=r(fLo," (ALBERT model)"),fLo.forEach(t),Voa=i(H),yR=n(H,"LI",{});var hLo=s(yR);reo=n(hLo,"STRONG",{});var BZn=s(reo);Woa=r(BZn,"bert"),BZn.forEach(t),Joa=r(hLo," \u2014 "),LTe=n(hLo,"A",{href:!0});var IZn=s(LTe);Uoa=r(IZn,"BertForTokenClassification"),IZn.forEach(t),Xoa=r(hLo," (BERT model)"),hLo.forEach(t),Doa=i(H),CR=n(H,"LI",{});var gLo=s(CR);teo=n(gLo,"STRONG",{});var NZn=s(teo);qoa=r(NZn,"big_bird"),NZn.forEach(t),Ooa=r(gLo," \u2014 "),ATe=n(gLo,"A",{href:!0});var $Zn=s(ATe);Qoa=r($Zn,"BigBirdForTokenClassification"),$Zn.forEach(t),Yoa=r(gLo," (BigBird model)"),gLo.forEach(t),zoa=i(H),wR=n(H,"LI",{});var uLo=s(wR);aeo=n(uLo,"STRONG",{});var jZn=s(aeo);Hoa=r(jZn,"biogpt"),jZn.forEach(t),Koa=r(uLo," \u2014 "),RTe=n(uLo,"A",{href:!0});var PZn=s(RTe);era=r(PZn,"BioGptForTokenClassification"),PZn.forEach(t),ora=r(uLo," (BioGpt model)"),uLo.forEach(t),rra=i(H),LR=n(H,"LI",{});var pLo=s(LR);neo=n(pLo,"STRONG",{});var ZZn=s(neo);tra=r(ZZn,"bloom"),ZZn.forEach(t),ara=r(pLo," \u2014 "),kTe=n(pLo,"A",{href:!0});var VZn=s(kTe);nra=r(VZn,"BloomForTokenClassification"),VZn.forEach(t),sra=r(pLo," (BLOOM model)"),pLo.forEach(t),lra=i(H),AR=n(H,"LI",{});var _Lo=s(AR);seo=n(_Lo,"STRONG",{});var WZn=s(seo);ira=r(WZn,"camembert"),WZn.forEach(t),dra=r(_Lo," \u2014 "),STe=n(_Lo,"A",{href:!0});var JZn=s(STe);mra=r(JZn,"CamembertForTokenClassification"),JZn.forEach(t),cra=r(_Lo," (CamemBERT model)"),_Lo.forEach(t),fra=i(H),RR=n(H,"LI",{});var bLo=s(RR);leo=n(bLo,"STRONG",{});var UZn=s(leo);hra=r(UZn,"canine"),UZn.forEach(t),gra=r(bLo," \u2014 "),xTe=n(bLo,"A",{href:!0});var XZn=s(xTe);ura=r(XZn,"CanineForTokenClassification"),XZn.forEach(t),pra=r(bLo," (CANINE model)"),bLo.forEach(t),_ra=i(H),kR=n(H,"LI",{});var vLo=s(kR);ieo=n(vLo,"STRONG",{});var DZn=s(ieo);bra=r(DZn,"convbert"),DZn.forEach(t),vra=r(vLo," \u2014 "),GTe=n(vLo,"A",{href:!0});var qZn=s(GTe);Mra=r(qZn,"ConvBertForTokenClassification"),qZn.forEach(t),Tra=r(vLo," (ConvBERT model)"),vLo.forEach(t),Fra=i(H),SR=n(H,"LI",{});var MLo=s(SR);deo=n(MLo,"STRONG",{});var OZn=s(deo);Era=r(OZn,"data2vec-text"),OZn.forEach(t),yra=r(MLo," \u2014 "),BTe=n(MLo,"A",{href:!0});var QZn=s(BTe);Cra=r(QZn,"Data2VecTextForTokenClassification"),QZn.forEach(t),wra=r(MLo," (Data2VecText model)"),MLo.forEach(t),Lra=i(H),xR=n(H,"LI",{});var TLo=s(xR);meo=n(TLo,"STRONG",{});var YZn=s(meo);Ara=r(YZn,"deberta"),YZn.forEach(t),Rra=r(TLo," \u2014 "),ITe=n(TLo,"A",{href:!0});var zZn=s(ITe);kra=r(zZn,"DebertaForTokenClassification"),zZn.forEach(t),Sra=r(TLo," (DeBERTa 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method.`),PCe.forEach(t),Ysa=i(Zd),cU=n(Zd,"P",{});var ojo=s(cU);zsa=r(ojo,"This class cannot be instantiated directly using "),soo=n(ojo,"CODE",{});var PWn=s(soo);Hsa=r(PWn,"__init__()"),PWn.forEach(t),Ksa=r(ojo," (throws an error)."),ojo.forEach(t),ela=i(Zd),ga=n(Zd,"DIV",{class:!0});var xP=s(ga);M(fU.$$.fragment,xP),ola=i(xP),loo=n(xP,"P",{});var ZWn=s(loo);rla=r(ZWn,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),ZWn.forEach(t),tla=i(xP),Gf=n(xP,"P",{});var ZCe=s(Gf);ala=r(ZCe,`Note:
Loading a model from its configuration file does `),ioo=n(ZCe,"STRONG",{});var VWn=s(ioo);nla=r(VWn,"not"),VWn.forEach(t),sla=r(ZCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),LFe=n(ZCe,"A",{href:!0});var WWn=s(LFe);lla=r(WWn,"from_pretrained()"),WWn.forEach(t),ila=r(ZCe," to load the model weights."),ZCe.forEach(t),dla=i(xP),M(wk.$$.fragment,xP),xP.forEach(t),mla=i(Zd),vt=n(Zd,"DIV",{class:!0});var Vd=s(vt);M(hU.$$.fragment,Vd),cla=i(Vd),doo=n(Vd,"P",{});var JWn=s(doo);fla=r(JWn,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),JWn.forEach(t),hla=i(Vd),cs=n(Vd,"P",{});var GP=s(cs);gla=r(GP,"The model class to instantiate is selected based on the "),moo=n(GP,"CODE",{});var UWn=s(moo);ula=r(UWn,"model_type"),UWn.forEach(t),pla=r(GP,` property of the config object (either
passed as an argument or loaded from `),coo=n(GP,"CODE",{});var XWn=s(coo);_la=r(XWn,"pretrained_model_name_or_path"),XWn.forEach(t),bla=r(GP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),foo=n(GP,"CODE",{});var DWn=s(foo);vla=r(DWn,"pretrained_model_name_or_path"),DWn.forEach(t),Mla=r(GP,":"),GP.forEach(t),Tla=i(Vd),ue=n(Vd,"UL",{});var ve=s(ue);Lk=n(ve,"LI",{});var hAo=s(Lk);hoo=n(hAo,"STRONG",{});var qWn=s(hoo);Fla=r(qWn,"albert"),qWn.forEach(t),Ela=r(hAo," \u2014 "),AFe=n(hAo,"A",{href:!0});var OWn=s(AFe);yla=r(OWn,"TFAlbertForTokenClassification"),OWn.forEach(t),Cla=r(hAo," (ALBERT model)"),hAo.forEach(t),wla=i(ve),Ak=n(ve,"LI",{});var gAo=s(Ak);goo=n(gAo,"STRONG",{});var QWn=s(goo);Lla=r(QWn,"bert"),QWn.forEach(t),Ala=r(gAo," \u2014 "),RFe=n(gAo,"A",{href:!0});var YWn=s(RFe);Rla=r(YWn,"TFBertForTokenClassification"),YWn.forEach(t),kla=r(gAo," (BERT model)"),gAo.forEach(t),Sla=i(ve),Rk=n(ve,"LI",{});var uAo=s(Rk);uoo=n(uAo,"STRONG",{});var zWn=s(uoo);xla=r(zWn,"camembert"),zWn.forEach(t),Gla=r(uAo," \u2014 "),kFe=n(uAo,"A",{href:!0});var HWn=s(kFe);Bla=r(HWn,"TFCamembertForTokenClassification"),HWn.forEach(t),Ila=r(uAo," (CamemBERT model)"),uAo.forEach(t),Nla=i(ve),kk=n(ve,"LI",{});var pAo=s(kk);poo=n(pAo,"STRONG",{});var KWn=s(poo);$la=r(KWn,"convbert"),KWn.forEach(t),jla=r(pAo," \u2014 "),SFe=n(pAo,"A",{href:!0});var eJn=s(SFe);Pla=r(eJn,"TFConvBertForTokenClassification"),eJn.forEach(t),Zla=r(pAo," (ConvBERT model)"),pAo.forEach(t),Vla=i(ve),Sk=n(ve,"LI",{});var _Ao=s(Sk);_oo=n(_Ao,"STRONG",{});var oJn=s(_oo);Wla=r(oJn,"deberta"),oJn.forEach(t),Jla=r(_Ao," \u2014 "),xFe=n(_Ao,"A",{href:!0});var rJn=s(xFe);Ula=r(rJn,"TFDebertaForTokenClassification"),rJn.forEach(t),Xla=r(_Ao," (DeBERTa model)"),_Ao.forEach(t),Dla=i(ve),xk=n(ve,"LI",{});var bAo=s(xk);boo=n(bAo,"STRONG",{});var tJn=s(boo);qla=r(tJn,"deberta-v2"),tJn.forEach(t),Ola=r(bAo," \u2014 "),GFe=n(bAo,"A",{href:!0});var aJn=s(GFe);Qla=r(aJn,"TFDebertaV2ForTokenClassification"),aJn.forEach(t),Yla=r(bAo," (DeBERTa-v2 model)"),bAo.forEach(t),zla=i(ve),Gk=n(ve,"LI",{});var vAo=s(Gk);voo=n(vAo,"STRONG",{});var 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method.`),VCe.forEach(t),$da=i(Wd),pU=n(Wd,"P",{});var tjo=s(pU);jda=r(tjo,"This class cannot be instantiated directly using "),joo=n(tjo,"CODE",{});var UJn=s(joo);Pda=r(UJn,"__init__()"),UJn.forEach(t),Zda=r(tjo," (throws an error)."),tjo.forEach(t),Vda=i(Wd),ua=n(Wd,"DIV",{class:!0});var BP=s(ua);M(_U.$$.fragment,BP),Wda=i(BP),Poo=n(BP,"P",{});var XJn=s(Poo);Jda=r(XJn,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),XJn.forEach(t),Uda=i(BP),Nf=n(BP,"P",{});var WCe=s(Nf);Xda=r(WCe,`Note:
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model\u2019s configuration. Use `),KFe=n(WCe,"A",{href:!0});var qJn=s(KFe);Oda=r(qJn,"from_pretrained()"),qJn.forEach(t),Qda=r(WCe," to load the model weights."),WCe.forEach(t),Yda=i(BP),M(Hk.$$.fragment,BP),BP.forEach(t),zda=i(Wd),Mt=n(Wd,"DIV",{class:!0});var Jd=s(Mt);M(bU.$$.fragment,Jd),Hda=i(Jd),Voo=n(Jd,"P",{});var OJn=s(Voo);Kda=r(OJn,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),OJn.forEach(t),ema=i(Jd),fs=n(Jd,"P",{});var IP=s(fs);oma=r(IP,"The model class to instantiate is selected based on the "),Woo=n(IP,"CODE",{});var QJn=s(Woo);rma=r(QJn,"model_type"),QJn.forEach(t),tma=r(IP,` property of the config object (either
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falling back to using pattern matching on `),Uoo=n(IP,"CODE",{});var zJn=s(Uoo);sma=r(zJn,"pretrained_model_name_or_path"),zJn.forEach(t),lma=r(IP,":"),IP.forEach(t),ima=i(Jd),Xe=n(Jd,"UL",{});var Ke=s(Xe);Kk=n(Ke,"LI",{});var NAo=s(Kk);Xoo=n(NAo,"STRONG",{});var HJn=s(Xoo);dma=r(HJn,"albert"),HJn.forEach(t),mma=r(NAo," \u2014 "),eEe=n(NAo,"A",{href:!0});var KJn=s(eEe);cma=r(KJn,"FlaxAlbertForTokenClassification"),KJn.forEach(t),fma=r(NAo," (ALBERT model)"),NAo.forEach(t),hma=i(Ke),eS=n(Ke,"LI",{});var $Ao=s(eS);Doo=n($Ao,"STRONG",{});var eUn=s(Doo);gma=r(eUn,"bert"),eUn.forEach(t),uma=r($Ao," \u2014 "),oEe=n($Ao,"A",{href:!0});var oUn=s(oEe);pma=r(oUn,"FlaxBertForTokenClassification"),oUn.forEach(t),_ma=r($Ao," (BERT model)"),$Ao.forEach(t),bma=i(Ke),oS=n(Ke,"LI",{});var jAo=s(oS);qoo=n(jAo,"STRONG",{});var rUn=s(qoo);vma=r(rUn,"big_bird"),rUn.forEach(t),Mma=r(jAo," \u2014 "),rEe=n(jAo,"A",{href:!0});var tUn=s(rEe);Tma=r(tUn,"FlaxBigBirdForTokenClassification"),tUn.forEach(t),Fma=r(jAo," (BigBird model)"),jAo.forEach(t),Ema=i(Ke),rS=n(Ke,"LI",{});var PAo=s(rS);Ooo=n(PAo,"STRONG",{});var aUn=s(Ooo);yma=r(aUn,"distilbert"),aUn.forEach(t),Cma=r(PAo," \u2014 "),tEe=n(PAo,"A",{href:!0});var nUn=s(tEe);wma=r(nUn,"FlaxDistilBertForTokenClassification"),nUn.forEach(t),Lma=r(PAo," (DistilBERT model)"),PAo.forEach(t),Ama=i(Ke),tS=n(Ke,"LI",{});var ZAo=s(tS);Qoo=n(ZAo,"STRONG",{});var sUn=s(Qoo);Rma=r(sUn,"electra"),sUn.forEach(t),kma=r(ZAo," \u2014 "),aEe=n(ZAo,"A",{href:!0});var lUn=s(aEe);Sma=r(lUn,"FlaxElectraForTokenClassification"),lUn.forEach(t),xma=r(ZAo," (ELECTRA model)"),ZAo.forEach(t),Gma=i(Ke),aS=n(Ke,"LI",{});var VAo=s(aS);Yoo=n(VAo,"STRONG",{});var iUn=s(Yoo);Bma=r(iUn,"roberta"),iUn.forEach(t),Ima=r(VAo," \u2014 "),nEe=n(VAo,"A",{href:!0});var dUn=s(nEe);Nma=r(dUn,"FlaxRobertaForTokenClassification"),dUn.forEach(t),$ma=r(VAo," (RoBERTa model)"),VAo.forEach(t),jma=i(Ke),nS=n(Ke,"LI",{});var WAo=s(nS);zoo=n(WAo,"STRONG",{});var mUn=s(zoo);Pma=r(mUn,"roberta-prelayernorm"),mUn.forEach(t),Zma=r(WAo," \u2014 "),sEe=n(WAo,"A",{href:!0});var cUn=s(sEe);Vma=r(cUn,"FlaxRobertaPreLayerNormForTokenClassification"),cUn.forEach(t),Wma=r(WAo," (RoBERTa-PreLayerNorm model)"),WAo.forEach(t),Jma=i(Ke),sS=n(Ke,"LI",{});var JAo=s(sS);Hoo=n(JAo,"STRONG",{});var fUn=s(Hoo);Uma=r(fUn,"roformer"),fUn.forEach(t),Xma=r(JAo," \u2014 "),lEe=n(JAo,"A",{href:!0});var hUn=s(lEe);Dma=r(hUn,"FlaxRoFormerForTokenClassification"),hUn.forEach(t),qma=r(JAo," (RoFormer model)"),JAo.forEach(t),Oma=i(Ke),lS=n(Ke,"LI",{});var UAo=s(lS);Koo=n(UAo,"STRONG",{});var gUn=s(Koo);Qma=r(gUn,"xlm-roberta"),gUn.forEach(t),Yma=r(UAo," \u2014 "),iEe=n(UAo,"A",{href:!0});var uUn=s(iEe);zma=r(uUn,"FlaxXLMRobertaForTokenClassification"),uUn.forEach(t),Hma=r(UAo," (XLM-RoBERTa model)"),UAo.forEach(t),Ke.forEach(t),Kma=i(Jd),M(iS.$$.fragment,Jd),Jd.forEach(t),Wd.forEach(t),aIo=i(c),$f=n(c,"H3",{class:!0});var ajo=s($f);dS=n(ajo,"A",{id:!0,class:!0,href:!0});var pUn=s(dS);ero=n(pUn,"SPAN",{});var _Un=s(ero);M(vU.$$.fragment,_Un),_Un.forEach(t),pUn.forEach(t),eca=i(ajo),oro=n(ajo,"SPAN",{});var bUn=s(oro);oca=r(bUn,"AutoModelForQuestionAnswering"),bUn.forEach(t),ajo.forEach(t),nIo=i(c),_r=n(c,"DIV",{class:!0});var Ud=s(_r);M(MU.$$.fragment,Ud),rca=i(Ud),jf=n(Ud,"P",{});var JCe=s(jf);tca=r(JCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
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nDn=s(Hro);Aua=r(nDn,"nezha"),nDn.forEach(t),Rua=r(GRo," \u2014 "),KEe=n(GRo,"A",{href:!0});var sDn=s(KEe);kua=r(sDn,"NezhaForQuestionAnswering"),sDn.forEach(t),Sua=r(GRo," (Nezha model)"),GRo.forEach(t),xua=i(O),ex=n(O,"LI",{});var BRo=s(ex);Kro=n(BRo,"STRONG",{});var lDn=s(Kro);Gua=r(lDn,"nystromformer"),lDn.forEach(t),Bua=r(BRo," \u2014 "),eye=n(BRo,"A",{href:!0});var iDn=s(eye);Iua=r(iDn,"NystromformerForQuestionAnswering"),iDn.forEach(t),Nua=r(BRo," (Nystr\xF6mformer model)"),BRo.forEach(t),$ua=i(O),ox=n(O,"LI",{});var IRo=s(ox);eto=n(IRo,"STRONG",{});var dDn=s(eto);jua=r(dDn,"opt"),dDn.forEach(t),Pua=r(IRo," \u2014 "),oye=n(IRo,"A",{href:!0});var mDn=s(oye);Zua=r(mDn,"OPTForQuestionAnswering"),mDn.forEach(t),Vua=r(IRo," (OPT model)"),IRo.forEach(t),Wua=i(O),rx=n(O,"LI",{});var NRo=s(rx);oto=n(NRo,"STRONG",{});var cDn=s(oto);Jua=r(cDn,"qdqbert"),cDn.forEach(t),Uua=r(NRo," \u2014 "),rye=n(NRo,"A",{href:!0});var 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qRo=s(hx);fto=n(qRo,"STRONG",{});var GDn=s(fto);Upa=r(GDn,"xlm"),GDn.forEach(t),Xpa=r(qRo," \u2014 "),hye=n(qRo,"A",{href:!0});var BDn=s(hye);Dpa=r(BDn,"XLMForQuestionAnsweringSimple"),BDn.forEach(t),qpa=r(qRo," (XLM model)"),qRo.forEach(t),Opa=i(O),gx=n(O,"LI",{});var ORo=s(gx);hto=n(ORo,"STRONG",{});var IDn=s(hto);Qpa=r(IDn,"xlm-roberta"),IDn.forEach(t),Ypa=r(ORo," \u2014 "),gye=n(ORo,"A",{href:!0});var NDn=s(gye);zpa=r(NDn,"XLMRobertaForQuestionAnswering"),NDn.forEach(t),Hpa=r(ORo," (XLM-RoBERTa model)"),ORo.forEach(t),Kpa=i(O),ux=n(O,"LI",{});var QRo=s(ux);gto=n(QRo,"STRONG",{});var $Dn=s(gto);e_a=r($Dn,"xlm-roberta-xl"),$Dn.forEach(t),o_a=r(QRo," \u2014 "),uye=n(QRo,"A",{href:!0});var jDn=s(uye);r_a=r(jDn,"XLMRobertaXLForQuestionAnswering"),jDn.forEach(t),t_a=r(QRo," (XLM-RoBERTa-XL model)"),QRo.forEach(t),a_a=i(O),px=n(O,"LI",{});var YRo=s(px);uto=n(YRo,"STRONG",{});var PDn=s(uto);n_a=r(PDn,"xlnet"),PDn.forEach(t),s_a=r(YRo," \u2014 "),pye=n(YRo,"A",{href:!0});var ZDn=s(pye);l_a=r(ZDn,"XLNetForQuestionAnsweringSimple"),ZDn.forEach(t),i_a=r(YRo," (XLNet model)"),YRo.forEach(t),d_a=i(O),_x=n(O,"LI",{});var zRo=s(_x);pto=n(zRo,"STRONG",{});var VDn=s(pto);m_a=r(VDn,"xmod"),VDn.forEach(t),c_a=r(zRo," \u2014 "),_ye=n(zRo,"A",{href:!0});var WDn=s(_ye);f_a=r(WDn,"XmodForQuestionAnswering"),WDn.forEach(t),h_a=r(zRo," (X-MOD model)"),zRo.forEach(t),g_a=i(O),bx=n(O,"LI",{});var HRo=s(bx);_to=n(HRo,"STRONG",{});var JDn=s(_to);u_a=r(JDn,"yoso"),JDn.forEach(t),p_a=r(HRo," \u2014 "),bye=n(HRo,"A",{href:!0});var UDn=s(bye);__a=r(UDn,"YosoForQuestionAnswering"),UDn.forEach(t),b_a=r(HRo," (YOSO model)"),HRo.forEach(t),O.forEach(t),v_a=i(cn),vx=n(cn,"P",{});var KRo=s(vx);M_a=r(KRo,"The model is set in evaluation mode by default using "),bto=n(KRo,"CODE",{});var XDn=s(bto);T_a=r(XDn,"model.eval()"),XDn.forEach(t),F_a=r(KRo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),vto=n(KRo,"CODE",{});var DDn=s(vto);E_a=r(DDn,"model.train()"),DDn.forEach(t),KRo.forEach(t),y_a=i(cn),M(Mx.$$.fragment,cn),cn.forEach(t),Ud.forEach(t),sIo=i(c),Zf=n(c,"H3",{class:!0});var sjo=s(Zf);Tx=n(sjo,"A",{id:!0,class:!0,href:!0});var qDn=s(Tx);Mto=n(qDn,"SPAN",{});var ODn=s(Mto);M(yU.$$.fragment,ODn),ODn.forEach(t),qDn.forEach(t),C_a=i(sjo),Tto=n(sjo,"SPAN",{});var QDn=s(Tto);w_a=r(QDn,"TFAutoModelForQuestionAnswering"),QDn.forEach(t),sjo.forEach(t),lIo=i(c),br=n(c,"DIV",{class:!0});var Xd=s(br);M(CU.$$.fragment,Xd),L_a=i(Xd),Vf=n(Xd,"P",{});var XCe=s(Vf);A_a=r(XCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
with the `),vye=n(XCe,"A",{href:!0});var YDn=s(vye);R_a=r(YDn,"from_pretrained()"),YDn.forEach(t),k_a=r(XCe," class method or the "),Mye=n(XCe,"A",{href:!0});var zDn=s(Mye);S_a=r(zDn,"from_config()"),zDn.forEach(t),x_a=r(XCe,` class
method.`),XCe.forEach(t),G_a=i(Xd),wU=n(Xd,"P",{});var ljo=s(wU);B_a=r(ljo,"This class cannot be instantiated directly using "),Fto=n(ljo,"CODE",{});var HDn=s(Fto);I_a=r(HDn,"__init__()"),HDn.forEach(t),N_a=r(ljo," (throws an error)."),ljo.forEach(t),$_a=i(Xd),_a=n(Xd,"DIV",{class:!0});var jP=s(_a);M(LU.$$.fragment,jP),j_a=i(jP),Eto=n(jP,"P",{});var KDn=s(Eto);P_a=r(KDn,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),KDn.forEach(t),Z_a=i(jP),Wf=n(jP,"P",{});var DCe=s(Wf);V_a=r(DCe,`Note:
Loading a model from its configuration file does `),yto=n(DCe,"STRONG",{});var eqn=s(yto);W_a=r(eqn,"not"),eqn.forEach(t),J_a=r(DCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Tye=n(DCe,"A",{href:!0});var oqn=s(Tye);U_a=r(oqn,"from_pretrained()"),oqn.forEach(t),X_a=r(DCe," to load the model weights."),DCe.forEach(t),D_a=i(jP),M(Fx.$$.fragment,jP),jP.forEach(t),q_a=i(Xd),Tt=n(Xd,"DIV",{class:!0});var Dd=s(Tt);M(AU.$$.fragment,Dd),O_a=i(Dd),Cto=n(Dd,"P",{});var rqn=s(Cto);Q_a=r(rqn,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),rqn.forEach(t),Y_a=i(Dd),gs=n(Dd,"P",{});var PP=s(gs);z_a=r(PP,"The model class to instantiate is selected based on the "),wto=n(PP,"CODE",{});var tqn=s(wto);H_a=r(tqn,"model_type"),tqn.forEach(t),K_a=r(PP,` property of the config object (either
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falling back to using pattern matching on `),Ato=n(PP,"CODE",{});var nqn=s(Ato);r1a=r(nqn,"pretrained_model_name_or_path"),nqn.forEach(t),t1a=r(PP,":"),PP.forEach(t),a1a=i(Dd),_e=n(Dd,"UL",{});var Te=s(_e);Ex=n(Te,"LI",{});var eko=s(Ex);Rto=n(eko,"STRONG",{});var sqn=s(Rto);n1a=r(sqn,"albert"),sqn.forEach(t),s1a=r(eko," \u2014 "),Fye=n(eko,"A",{href:!0});var lqn=s(Fye);l1a=r(lqn,"TFAlbertForQuestionAnswering"),lqn.forEach(t),i1a=r(eko," (ALBERT model)"),eko.forEach(t),d1a=i(Te),yx=n(Te,"LI",{});var oko=s(yx);kto=n(oko,"STRONG",{});var iqn=s(kto);m1a=r(iqn,"bert"),iqn.forEach(t),c1a=r(oko," \u2014 "),Eye=n(oko,"A",{href:!0});var dqn=s(Eye);f1a=r(dqn,"TFBertForQuestionAnswering"),dqn.forEach(t),h1a=r(oko," (BERT model)"),oko.forEach(t),g1a=i(Te),Cx=n(Te,"LI",{});var rko=s(Cx);Sto=n(rko,"STRONG",{});var mqn=s(Sto);u1a=r(mqn,"camembert"),mqn.forEach(t),p1a=r(rko," \u2014 "),yye=n(rko,"A",{href:!0});var cqn=s(yye);_1a=r(cqn,"TFCamembertForQuestionAnswering"),cqn.forEach(t),b1a=r(rko," (CamemBERT model)"),rko.forEach(t),v1a=i(Te),wx=n(Te,"LI",{});var tko=s(wx);xto=n(tko,"STRONG",{});var fqn=s(xto);M1a=r(fqn,"convbert"),fqn.forEach(t),T1a=r(tko," \u2014 "),Cye=n(tko,"A",{href:!0});var hqn=s(Cye);F1a=r(hqn,"TFConvBertForQuestionAnswering"),hqn.forEach(t),E1a=r(tko," (ConvBERT model)"),tko.forEach(t),y1a=i(Te),Lx=n(Te,"LI",{});var ako=s(Lx);Gto=n(ako,"STRONG",{});var gqn=s(Gto);C1a=r(gqn,"deberta"),gqn.forEach(t),w1a=r(ako," \u2014 "),wye=n(ako,"A",{href:!0});var uqn=s(wye);L1a=r(uqn,"TFDebertaForQuestionAnswering"),uqn.forEach(t),A1a=r(ako," (DeBERTa model)"),ako.forEach(t),R1a=i(Te),Ax=n(Te,"LI",{});var nko=s(Ax);Bto=n(nko,"STRONG",{});var pqn=s(Bto);k1a=r(pqn,"deberta-v2"),pqn.forEach(t),S1a=r(nko," \u2014 "),Lye=n(nko,"A",{href:!0});var _qn=s(Lye);x1a=r(_qn,"TFDebertaV2ForQuestionAnswering"),_qn.forEach(t),G1a=r(nko," (DeBERTa-v2 model)"),nko.forEach(t),B1a=i(Te),Rx=n(Te,"LI",{});var sko=s(Rx);Ito=n(sko,"STRONG",{});var bqn=s(Ito);I1a=r(bqn,"distilbert"),bqn.forEach(t),N1a=r(sko," \u2014 "),Aye=n(sko,"A",{href:!0});var vqn=s(Aye);$1a=r(vqn,"TFDistilBertForQuestionAnswering"),vqn.forEach(t),j1a=r(sko," (DistilBERT model)"),sko.forEach(t),P1a=i(Te),kx=n(Te,"LI",{});var lko=s(kx);Nto=n(lko,"STRONG",{});var Mqn=s(Nto);Z1a=r(Mqn,"electra"),Mqn.forEach(t),V1a=r(lko," \u2014 "),Rye=n(lko,"A",{href:!0});var Tqn=s(Rye);W1a=r(Tqn,"TFElectraForQuestionAnswering"),Tqn.forEach(t),J1a=r(lko," (ELECTRA model)"),lko.forEach(t),U1a=i(Te),Sx=n(Te,"LI",{});var iko=s(Sx);$to=n(iko,"STRONG",{});var Fqn=s($to);X1a=r(Fqn,"flaubert"),Fqn.forEach(t),D1a=r(iko," \u2014 "),kye=n(iko,"A",{href:!0});var Eqn=s(kye);q1a=r(Eqn,"TFFlaubertForQuestionAnsweringSimple"),Eqn.forEach(t),O1a=r(iko," (FlauBERT model)"),iko.forEach(t),Q1a=i(Te),xx=n(Te,"LI",{});var dko=s(xx);jto=n(dko,"STRONG",{});var yqn=s(jto);Y1a=r(yqn,"funnel"),yqn.forEach(t),z1a=r(dko," \u2014 "),Sye=n(dko,"A",{href:!0});var Cqn=s(Sye);H1a=r(Cqn,"TFFunnelForQuestionAnswering"),Cqn.forEach(t),K1a=r(dko," (Funnel Transformer model)"),dko.forEach(t),eba=i(Te),Gx=n(Te,"LI",{});var mko=s(Gx);Pto=n(mko,"STRONG",{});var wqn=s(Pto);oba=r(wqn,"gptj"),wqn.forEach(t),rba=r(mko," \u2014 "),xye=n(mko,"A",{href:!0});var Lqn=s(xye);tba=r(Lqn,"TFGPTJForQuestionAnswering"),Lqn.forEach(t),aba=r(mko," (GPT-J model)"),mko.forEach(t),nba=i(Te),Bx=n(Te,"LI",{});var cko=s(Bx);Zto=n(cko,"STRONG",{});var Aqn=s(Zto);sba=r(Aqn,"layoutlmv3"),Aqn.forEach(t),lba=r(cko," \u2014 "),Gye=n(cko,"A",{href:!0});var Rqn=s(Gye);iba=r(Rqn,"TFLayoutLMv3ForQuestionAnswering"),Rqn.forEach(t),dba=r(cko," (LayoutLMv3 model)"),cko.forEach(t),mba=i(Te),Ix=n(Te,"LI",{});var fko=s(Ix);Vto=n(fko,"STRONG",{});var kqn=s(Vto);cba=r(kqn,"longformer"),kqn.forEach(t),fba=r(fko," \u2014 "),Bye=n(fko,"A",{href:!0});var Sqn=s(Bye);hba=r(Sqn,"TFLongformerForQuestionAnswering"),Sqn.forEach(t),gba=r(fko," (Longformer model)"),fko.forEach(t),uba=i(Te),Nx=n(Te,"LI",{});var hko=s(Nx);Wto=n(hko,"STRONG",{});var xqn=s(Wto);pba=r(xqn,"mobilebert"),xqn.forEach(t),_ba=r(hko," \u2014 "),Iye=n(hko,"A",{href:!0});var Gqn=s(Iye);bba=r(Gqn,"TFMobileBertForQuestionAnswering"),Gqn.forEach(t),vba=r(hko," (MobileBERT model)"),hko.forEach(t),Mba=i(Te),$x=n(Te,"LI",{});var gko=s($x);Jto=n(gko,"STRONG",{});var Bqn=s(Jto);Tba=r(Bqn,"mpnet"),Bqn.forEach(t),Fba=r(gko," \u2014 "),Nye=n(gko,"A",{href:!0});var Iqn=s(Nye);Eba=r(Iqn,"TFMPNetForQuestionAnswering"),Iqn.forEach(t),yba=r(gko," (MPNet model)"),gko.forEach(t),Cba=i(Te),jx=n(Te,"LI",{});var uko=s(jx);Uto=n(uko,"STRONG",{});var Nqn=s(Uto);wba=r(Nqn,"rembert"),Nqn.forEach(t),Lba=r(uko," \u2014 "),$ye=n(uko,"A",{href:!0});var $qn=s($ye);Aba=r($qn,"TFRemBertForQuestionAnswering"),$qn.forEach(t),Rba=r(uko," (RemBERT model)"),uko.forEach(t),kba=i(Te),Px=n(Te,"LI",{});var pko=s(Px);Xto=n(pko,"STRONG",{});var jqn=s(Xto);Sba=r(jqn,"roberta"),jqn.forEach(t),xba=r(pko," \u2014 "),jye=n(pko,"A",{href:!0});var Pqn=s(jye);Gba=r(Pqn,"TFRobertaForQuestionAnswering"),Pqn.forEach(t),Bba=r(pko," (RoBERTa model)"),pko.forEach(t),Iba=i(Te),Zx=n(Te,"LI",{});var _ko=s(Zx);Dto=n(_ko,"STRONG",{});var Zqn=s(Dto);Nba=r(Zqn,"roberta-prelayernorm"),Zqn.forEach(t),$ba=r(_ko," \u2014 "),Pye=n(_ko,"A",{href:!0});var Vqn=s(Pye);jba=r(Vqn,"TFRobertaPreLayerNormForQuestionAnswering"),Vqn.forEach(t),Pba=r(_ko," (RoBERTa-PreLayerNorm model)"),_ko.forEach(t),Zba=i(Te),Vx=n(Te,"LI",{});var bko=s(Vx);qto=n(bko,"STRONG",{});var Wqn=s(qto);Vba=r(Wqn,"roformer"),Wqn.forEach(t),Wba=r(bko," \u2014 "),Zye=n(bko,"A",{href:!0});var Jqn=s(Zye);Jba=r(Jqn,"TFRoFormerForQuestionAnswering"),Jqn.forEach(t),Uba=r(bko," (RoFormer model)"),bko.forEach(t),Xba=i(Te),Wx=n(Te,"LI",{});var vko=s(Wx);Oto=n(vko,"STRONG",{});var Uqn=s(Oto);Dba=r(Uqn,"xlm"),Uqn.forEach(t),qba=r(vko," \u2014 "),Vye=n(vko,"A",{href:!0});var Xqn=s(Vye);Oba=r(Xqn,"TFXLMForQuestionAnsweringSimple"),Xqn.forEach(t),Qba=r(vko," (XLM model)"),vko.forEach(t),Yba=i(Te),Jx=n(Te,"LI",{});var Mko=s(Jx);Qto=n(Mko,"STRONG",{});var Dqn=s(Qto);zba=r(Dqn,"xlm-roberta"),Dqn.forEach(t),Hba=r(Mko," \u2014 "),Wye=n(Mko,"A",{href:!0});var qqn=s(Wye);Kba=r(qqn,"TFXLMRobertaForQuestionAnswering"),qqn.forEach(t),e2a=r(Mko," (XLM-RoBERTa model)"),Mko.forEach(t),o2a=i(Te),Ux=n(Te,"LI",{});var Tko=s(Ux);Yto=n(Tko,"STRONG",{});var Oqn=s(Yto);r2a=r(Oqn,"xlnet"),Oqn.forEach(t),t2a=r(Tko," \u2014 "),Jye=n(Tko,"A",{href:!0});var Qqn=s(Jye);a2a=r(Qqn,"TFXLNetForQuestionAnsweringSimple"),Qqn.forEach(t),n2a=r(Tko," (XLNet model)"),Tko.forEach(t),Te.forEach(t),s2a=i(Dd),M(Xx.$$.fragment,Dd),Dd.forEach(t),Xd.forEach(t),iIo=i(c),Jf=n(c,"H3",{class:!0});var ijo=s(Jf);Dx=n(ijo,"A",{id:!0,class:!0,href:!0});var Yqn=s(Dx);zto=n(Yqn,"SPAN",{});var zqn=s(zto);M(RU.$$.fragment,zqn),zqn.forEach(t),Yqn.forEach(t),l2a=i(ijo),Hto=n(ijo,"SPAN",{});var Hqn=s(Hto);i2a=r(Hqn,"FlaxAutoModelForQuestionAnswering"),Hqn.forEach(t),ijo.forEach(t),dIo=i(c),vr=n(c,"DIV",{class:!0});var qd=s(vr);M(kU.$$.fragment,qd),d2a=i(qd),Uf=n(qd,"P",{});var qCe=s(Uf);m2a=r(qCe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
with the `),Uye=n(qCe,"A",{href:!0});var Kqn=s(Uye);c2a=r(Kqn,"from_pretrained()"),Kqn.forEach(t),f2a=r(qCe," class method or the "),Xye=n(qCe,"A",{href:!0});var eOn=s(Xye);h2a=r(eOn,"from_config()"),eOn.forEach(t),g2a=r(qCe,` class
method.`),qCe.forEach(t),u2a=i(qd),SU=n(qd,"P",{});var djo=s(SU);p2a=r(djo,"This class cannot be instantiated directly using "),Kto=n(djo,"CODE",{});var oOn=s(Kto);_2a=r(oOn,"__init__()"),oOn.forEach(t),b2a=r(djo," (throws an error)."),djo.forEach(t),v2a=i(qd),ba=n(qd,"DIV",{class:!0});var ZP=s(ba);M(xU.$$.fragment,ZP),M2a=i(ZP),eao=n(ZP,"P",{});var rOn=s(eao);T2a=r(rOn,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),rOn.forEach(t),F2a=i(ZP),Xf=n(ZP,"P",{});var OCe=s(Xf);E2a=r(OCe,`Note:
Loading a model from its configuration file does `),oao=n(OCe,"STRONG",{});var tOn=s(oao);y2a=r(tOn,"not"),tOn.forEach(t),C2a=r(OCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Dye=n(OCe,"A",{href:!0});var aOn=s(Dye);w2a=r(aOn,"from_pretrained()"),aOn.forEach(t),L2a=r(OCe," to load the model weights."),OCe.forEach(t),A2a=i(ZP),M(qx.$$.fragment,ZP),ZP.forEach(t),R2a=i(qd),Ft=n(qd,"DIV",{class:!0});var Od=s(Ft);M(GU.$$.fragment,Od),k2a=i(Od),rao=n(Od,"P",{});var nOn=s(rao);S2a=r(nOn,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),nOn.forEach(t),x2a=i(Od),us=n(Od,"P",{});var VP=s(us);G2a=r(VP,"The model class to instantiate is selected based on the "),tao=n(VP,"CODE",{});var sOn=s(tao);B2a=r(sOn,"model_type"),sOn.forEach(t),I2a=r(VP,` property of the config object (either
passed as an argument or loaded from `),aao=n(VP,"CODE",{});var lOn=s(aao);N2a=r(lOn,"pretrained_model_name_or_path"),lOn.forEach(t),$2a=r(VP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nao=n(VP,"CODE",{});var iOn=s(nao);j2a=r(iOn,"pretrained_model_name_or_path"),iOn.forEach(t),P2a=r(VP,":"),VP.forEach(t),Z2a=i(Od),xe=n(Od,"UL",{});var Ve=s(xe);Ox=n(Ve,"LI",{});var Fko=s(Ox);sao=n(Fko,"STRONG",{});var dOn=s(sao);V2a=r(dOn,"albert"),dOn.forEach(t),W2a=r(Fko," \u2014 "),qye=n(Fko,"A",{href:!0});var mOn=s(qye);J2a=r(mOn,"FlaxAlbertForQuestionAnswering"),mOn.forEach(t),U2a=r(Fko," (ALBERT model)"),Fko.forEach(t),X2a=i(Ve),Qx=n(Ve,"LI",{});var Eko=s(Qx);lao=n(Eko,"STRONG",{});var cOn=s(lao);D2a=r(cOn,"bart"),cOn.forEach(t),q2a=r(Eko," \u2014 "),Oye=n(Eko,"A",{href:!0});var fOn=s(Oye);O2a=r(fOn,"FlaxBartForQuestionAnswering"),fOn.forEach(t),Q2a=r(Eko," (BART model)"),Eko.forEach(t),Y2a=i(Ve),Yx=n(Ve,"LI",{});var yko=s(Yx);iao=n(yko,"STRONG",{});var hOn=s(iao);z2a=r(hOn,"bert"),hOn.forEach(t),H2a=r(yko," \u2014 "),Qye=n(yko,"A",{href:!0});var gOn=s(Qye);K2a=r(gOn,"FlaxBertForQuestionAnswering"),gOn.forEach(t),eva=r(yko," (BERT model)"),yko.forEach(t),ova=i(Ve),zx=n(Ve,"LI",{});var Cko=s(zx);dao=n(Cko,"STRONG",{});var uOn=s(dao);rva=r(uOn,"big_bird"),uOn.forEach(t),tva=r(Cko," \u2014 "),Yye=n(Cko,"A",{href:!0});var pOn=s(Yye);ava=r(pOn,"FlaxBigBirdForQuestionAnswering"),pOn.forEach(t),nva=r(Cko," (BigBird model)"),Cko.forEach(t),sva=i(Ve),Hx=n(Ve,"LI",{});var wko=s(Hx);mao=n(wko,"STRONG",{});var _On=s(mao);lva=r(_On,"distilbert"),_On.forEach(t),iva=r(wko," \u2014 "),zye=n(wko,"A",{href:!0});var bOn=s(zye);dva=r(bOn,"FlaxDistilBertForQuestionAnswering"),bOn.forEach(t),mva=r(wko," (DistilBERT model)"),wko.forEach(t),cva=i(Ve),Kx=n(Ve,"LI",{});var Lko=s(Kx);cao=n(Lko,"STRONG",{});var vOn=s(cao);fva=r(vOn,"electra"),vOn.forEach(t),hva=r(Lko," \u2014 "),Hye=n(Lko,"A",{href:!0});var MOn=s(Hye);gva=r(MOn,"FlaxElectraForQuestionAnswering"),MOn.forEach(t),uva=r(Lko," (ELECTRA model)"),Lko.forEach(t),pva=i(Ve),eG=n(Ve,"LI",{});var Ako=s(eG);fao=n(Ako,"STRONG",{});var TOn=s(fao);_va=r(TOn,"mbart"),TOn.forEach(t),bva=r(Ako," \u2014 "),Kye=n(Ako,"A",{href:!0});var FOn=s(Kye);vva=r(FOn,"FlaxMBartForQuestionAnswering"),FOn.forEach(t),Mva=r(Ako," (mBART model)"),Ako.forEach(t),Tva=i(Ve),oG=n(Ve,"LI",{});var Rko=s(oG);hao=n(Rko,"STRONG",{});var EOn=s(hao);Fva=r(EOn,"roberta"),EOn.forEach(t),Eva=r(Rko," \u2014 "),e0e=n(Rko,"A",{href:!0});var yOn=s(e0e);yva=r(yOn,"FlaxRobertaForQuestionAnswering"),yOn.forEach(t),Cva=r(Rko," (RoBERTa model)"),Rko.forEach(t),wva=i(Ve),rG=n(Ve,"LI",{});var kko=s(rG);gao=n(kko,"STRONG",{});var COn=s(gao);Lva=r(COn,"roberta-prelayernorm"),COn.forEach(t),Ava=r(kko," \u2014 "),o0e=n(kko,"A",{href:!0});var wOn=s(o0e);Rva=r(wOn,"FlaxRobertaPreLayerNormForQuestionAnswering"),wOn.forEach(t),kva=r(kko," (RoBERTa-PreLayerNorm model)"),kko.forEach(t),Sva=i(Ve),tG=n(Ve,"LI",{});var Sko=s(tG);uao=n(Sko,"STRONG",{});var LOn=s(uao);xva=r(LOn,"roformer"),LOn.forEach(t),Gva=r(Sko," \u2014 "),r0e=n(Sko,"A",{href:!0});var AOn=s(r0e);Bva=r(AOn,"FlaxRoFormerForQuestionAnswering"),AOn.forEach(t),Iva=r(Sko," (RoFormer model)"),Sko.forEach(t),Nva=i(Ve),aG=n(Ve,"LI",{});var xko=s(aG);pao=n(xko,"STRONG",{});var ROn=s(pao);$va=r(ROn,"xlm-roberta"),ROn.forEach(t),jva=r(xko," \u2014 "),t0e=n(xko,"A",{href:!0});var kOn=s(t0e);Pva=r(kOn,"FlaxXLMRobertaForQuestionAnswering"),kOn.forEach(t),Zva=r(xko," (XLM-RoBERTa model)"),xko.forEach(t),Ve.forEach(t),Vva=i(Od),M(nG.$$.fragment,Od),Od.forEach(t),qd.forEach(t),mIo=i(c),Df=n(c,"H3",{class:!0});var mjo=s(Df);sG=n(mjo,"A",{id:!0,class:!0,href:!0});var SOn=s(sG);_ao=n(SOn,"SPAN",{});var xOn=s(_ao);M(BU.$$.fragment,xOn),xOn.forEach(t),SOn.forEach(t),Wva=i(mjo),bao=n(mjo,"SPAN",{});var GOn=s(bao);Jva=r(GOn,"AutoModelForTextEncoding"),GOn.forEach(t),mjo.forEach(t),cIo=i(c),IU=n(c,"DIV",{class:!0});var BOn=s(IU);M(NU.$$.fragment,BOn),BOn.forEach(t),fIo=i(c),qf=n(c,"H3",{class:!0});var cjo=s(qf);lG=n(cjo,"A",{id:!0,class:!0,href:!0});var IOn=s(lG);vao=n(IOn,"SPAN",{});var NOn=s(vao);M($U.$$.fragment,NOn),NOn.forEach(t),IOn.forEach(t),Uva=i(cjo),Mao=n(cjo,"SPAN",{});var $On=s(Mao);Xva=r($On,"TFAutoModelForTextEncoding"),$On.forEach(t),cjo.forEach(t),hIo=i(c),jU=n(c,"DIV",{class:!0});var jOn=s(jU);M(PU.$$.fragment,jOn),jOn.forEach(t),gIo=i(c),Of=n(c,"H2",{class:!0});var fjo=s(Of);iG=n(fjo,"A",{id:!0,class:!0,href:!0});var POn=s(iG);Tao=n(POn,"SPAN",{});var ZOn=s(Tao);M(ZU.$$.fragment,ZOn),ZOn.forEach(t),POn.forEach(t),Dva=i(fjo),Fao=n(fjo,"SPAN",{});var VOn=s(Fao);qva=r(VOn,"Computer vision"),VOn.forEach(t),fjo.forEach(t),uIo=i(c),a0e=n(c,"P",{});var WOn=s(a0e);Ova=r(WOn,"The following auto classes are available for the following computer vision tasks."),WOn.forEach(t),pIo=i(c),Qf=n(c,"H3",{class:!0});var hjo=s(Qf);dG=n(hjo,"A",{id:!0,class:!0,href:!0});var JOn=s(dG);Eao=n(JOn,"SPAN",{});var UOn=s(Eao);M(VU.$$.fragment,UOn),UOn.forEach(t),JOn.forEach(t),Qva=i(hjo),yao=n(hjo,"SPAN",{});var XOn=s(yao);Yva=r(XOn,"AutoModelForDepthEstimation"),XOn.forEach(t),hjo.forEach(t),_Io=i(c),Mr=n(c,"DIV",{class:!0});var Qd=s(Mr);M(WU.$$.fragment,Qd),zva=i(Qd),Yf=n(Qd,"P",{});var QCe=s(Yf);Hva=r(QCe,`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 `),n0e=n(QCe,"A",{href:!0});var DOn=s(n0e);Kva=r(DOn,"from_pretrained()"),DOn.forEach(t),eMa=r(QCe," class method or the "),s0e=n(QCe,"A",{href:!0});var qOn=s(s0e);oMa=r(qOn,"from_config()"),qOn.forEach(t),rMa=r(QCe,` class
method.`),QCe.forEach(t),tMa=i(Qd),JU=n(Qd,"P",{});var gjo=s(JU);aMa=r(gjo,"This class cannot be instantiated directly using "),Cao=n(gjo,"CODE",{});var OOn=s(Cao);nMa=r(OOn,"__init__()"),OOn.forEach(t),sMa=r(gjo," (throws an error)."),gjo.forEach(t),lMa=i(Qd),va=n(Qd,"DIV",{class:!0});var WP=s(va);M(UU.$$.fragment,WP),iMa=i(WP),wao=n(WP,"P",{});var QOn=s(wao);dMa=r(QOn,"Instantiates one of the model classes of the library (with a depth estimation head) from a configuration."),QOn.forEach(t),mMa=i(WP),zf=n(WP,"P",{});var YCe=s(zf);cMa=r(YCe,`Note:
Loading a model from its configuration file does `),Lao=n(YCe,"STRONG",{});var YOn=s(Lao);fMa=r(YOn,"not"),YOn.forEach(t),hMa=r(YCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),l0e=n(YCe,"A",{href:!0});var zOn=s(l0e);gMa=r(zOn,"from_pretrained()"),zOn.forEach(t),uMa=r(YCe," to load the model weights."),YCe.forEach(t),pMa=i(WP),M(mG.$$.fragment,WP),WP.forEach(t),_Ma=i(Qd),_o=n(Qd,"DIV",{class:!0});var fn=s(_o);M(XU.$$.fragment,fn),bMa=i(fn),Aao=n(fn,"P",{});var HOn=s(Aao);vMa=r(HOn,"Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model."),HOn.forEach(t),MMa=i(fn),ps=n(fn,"P",{});var JP=s(ps);TMa=r(JP,"The model class to instantiate is selected based on the "),Rao=n(JP,"CODE",{});var KOn=s(Rao);FMa=r(KOn,"model_type"),KOn.forEach(t),EMa=r(JP,` property of the config object (either
passed as an argument or loaded from `),kao=n(JP,"CODE",{});var eQn=s(kao);yMa=r(eQn,"pretrained_model_name_or_path"),eQn.forEach(t),CMa=r(JP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Sao=n(JP,"CODE",{});var oQn=s(Sao);wMa=r(oQn,"pretrained_model_name_or_path"),oQn.forEach(t),LMa=r(JP,":"),JP.forEach(t),AMa=i(fn),DU=n(fn,"UL",{});var ujo=s(DU);cG=n(ujo,"LI",{});var Gko=s(cG);xao=n(Gko,"STRONG",{});var rQn=s(xao);RMa=r(rQn,"dpt"),rQn.forEach(t),kMa=r(Gko," \u2014 "),i0e=n(Gko,"A",{href:!0});var tQn=s(i0e);SMa=r(tQn,"DPTForDepthEstimation"),tQn.forEach(t),xMa=r(Gko," (DPT model)"),Gko.forEach(t),GMa=i(ujo),fG=n(ujo,"LI",{});var Bko=s(fG);Gao=n(Bko,"STRONG",{});var aQn=s(Gao);BMa=r(aQn,"glpn"),aQn.forEach(t),IMa=r(Bko," \u2014 "),d0e=n(Bko,"A",{href:!0});var nQn=s(d0e);NMa=r(nQn,"GLPNForDepthEstimation"),nQn.forEach(t),$Ma=r(Bko," (GLPN model)"),Bko.forEach(t),ujo.forEach(t),jMa=i(fn),hG=n(fn,"P",{});var Iko=s(hG);PMa=r(Iko,"The model is set in evaluation mode by default using "),Bao=n(Iko,"CODE",{});var sQn=s(Bao);ZMa=r(sQn,"model.eval()"),sQn.forEach(t),VMa=r(Iko,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Iao=n(Iko,"CODE",{});var lQn=s(Iao);WMa=r(lQn,"model.train()"),lQn.forEach(t),Iko.forEach(t),JMa=i(fn),M(gG.$$.fragment,fn),fn.forEach(t),Qd.forEach(t),bIo=i(c),Hf=n(c,"H3",{class:!0});var pjo=s(Hf);uG=n(pjo,"A",{id:!0,class:!0,href:!0});var iQn=s(uG);Nao=n(iQn,"SPAN",{});var dQn=s(Nao);M(qU.$$.fragment,dQn),dQn.forEach(t),iQn.forEach(t),UMa=i(pjo),$ao=n(pjo,"SPAN",{});var mQn=s($ao);XMa=r(mQn,"AutoModelForImageClassification"),mQn.forEach(t),pjo.forEach(t),vIo=i(c),Tr=n(c,"DIV",{class:!0});var Yd=s(Tr);M(OU.$$.fragment,Yd),DMa=i(Yd),Kf=n(Yd,"P",{});var zCe=s(Kf);qMa=r(zCe,`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 `),m0e=n(zCe,"A",{href:!0});var cQn=s(m0e);OMa=r(cQn,"from_pretrained()"),cQn.forEach(t),QMa=r(zCe," class method or the "),c0e=n(zCe,"A",{href:!0});var fQn=s(c0e);YMa=r(fQn,"from_config()"),fQn.forEach(t),zMa=r(zCe,` class
method.`),zCe.forEach(t),HMa=i(Yd),QU=n(Yd,"P",{});var _jo=s(QU);KMa=r(_jo,"This class cannot be instantiated directly using "),jao=n(_jo,"CODE",{});var hQn=s(jao);eTa=r(hQn,"__init__()"),hQn.forEach(t),oTa=r(_jo," (throws an error)."),_jo.forEach(t),rTa=i(Yd),Ma=n(Yd,"DIV",{class:!0});var UP=s(Ma);M(YU.$$.fragment,UP),tTa=i(UP),Pao=n(UP,"P",{});var gQn=s(Pao);aTa=r(gQn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),gQn.forEach(t),nTa=i(UP),eh=n(UP,"P",{});var HCe=s(eh);sTa=r(HCe,`Note:
Loading a model from its configuration file does `),Zao=n(HCe,"STRONG",{});var uQn=s(Zao);lTa=r(uQn,"not"),uQn.forEach(t),iTa=r(HCe,` load the model weights. It only affects the
model\u2019s configuration. Use `),f0e=n(HCe,"A",{href:!0});var pQn=s(f0e);dTa=r(pQn,"from_pretrained()"),pQn.forEach(t),mTa=r(HCe," to load the model weights."),HCe.forEach(t),cTa=i(UP),M(pG.$$.fragment,UP),UP.forEach(t),fTa=i(Yd),bo=n(Yd,"DIV",{class:!0});var hn=s(bo);M(zU.$$.fragment,hn),hTa=i(hn),Vao=n(hn,"P",{});var _Qn=s(Vao);gTa=r(_Qn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),_Qn.forEach(t),uTa=i(hn),_s=n(hn,"P",{});var XP=s(_s);pTa=r(XP,"The model class to instantiate is selected based on the "),Wao=n(XP,"CODE",{});var bQn=s(Wao);_Ta=r(bQn,"model_type"),bQn.forEach(t),bTa=r(XP,` property of the config object (either
passed as an argument or loaded from `),Jao=n(XP,"CODE",{});var vQn=s(Jao);vTa=r(vQn,"pretrained_model_name_or_path"),vQn.forEach(t),MTa=r(XP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Uao=n(XP,"CODE",{});var MQn=s(Uao);TTa=r(MQn,"pretrained_model_name_or_path"),MQn.forEach(t),FTa=r(XP,":"),XP.forEach(t),ETa=i(hn),le=n(hn,"UL",{});var me=s(le);_G=n(me,"LI",{});var Nko=s(_G);Xao=n(Nko,"STRONG",{});var TQn=s(Xao);yTa=r(TQn,"beit"),TQn.forEach(t),CTa=r(Nko," \u2014 "),h0e=n(Nko,"A",{href:!0});var FQn=s(h0e);wTa=r(FQn,"BeitForImageClassification"),FQn.forEach(t),LTa=r(Nko," (BEiT model)"),Nko.forEach(t),ATa=i(me),bG=n(me,"LI",{});var $ko=s(bG);Dao=n($ko,"STRONG",{});var EQn=s(Dao);RTa=r(EQn,"bit"),EQn.forEach(t),kTa=r($ko," \u2014 "),g0e=n($ko,"A",{href:!0});var yQn=s(g0e);STa=r(yQn,"BitForImageClassification"),yQn.forEach(t),xTa=r($ko," (BiT model)"),$ko.forEach(t),GTa=i(me),vG=n(me,"LI",{});var jko=s(vG);qao=n(jko,"STRONG",{});var CQn=s(qao);BTa=r(CQn,"convnext"),CQn.forEach(t),ITa=r(jko," \u2014 "),u0e=n(jko,"A",{href:!0});var wQn=s(u0e);NTa=r(wQn,"ConvNextForImageClassification"),wQn.forEach(t),$Ta=r(jko," (ConvNeXT model)"),jko.forEach(t),jTa=i(me),MG=n(me,"LI",{});var Pko=s(MG);Oao=n(Pko,"STRONG",{});var LQn=s(Oao);PTa=r(LQn,"convnextv2"),LQn.forEach(t),ZTa=r(Pko," \u2014 "),p0e=n(Pko,"A",{href:!0});var AQn=s(p0e);VTa=r(AQn,"ConvNextV2ForImageClassification"),AQn.forEach(t),WTa=r(Pko," (ConvNeXTV2 model)"),Pko.forEach(t),JTa=i(me),TG=n(me,"LI",{});var Zko=s(TG);Qao=n(Zko,"STRONG",{});var RQn=s(Qao);UTa=r(RQn,"cvt"),RQn.forEach(t),XTa=r(Zko," \u2014 "),_0e=n(Zko,"A",{href:!0});var kQn=s(_0e);DTa=r(kQn,"CvtForImageClassification"),kQn.forEach(t),qTa=r(Zko," (CvT model)"),Zko.forEach(t),OTa=i(me),FG=n(me,"LI",{});var Vko=s(FG);Yao=n(Vko,"STRONG",{});var SQn=s(Yao);QTa=r(SQn,"data2vec-vision"),SQn.forEach(t),YTa=r(Vko," \u2014 "),b0e=n(Vko,"A",{href:!0});var xQn=s(b0e);zTa=r(xQn,"Data2VecVisionForImageClassification"),xQn.forEach(t),HTa=r(Vko," (Data2VecVision model)"),Vko.forEach(t),KTa=i(me),Ji=n(me,"LI",{});var OQ=s(Ji);zao=n(OQ,"STRONG",{});var GQn=s(zao);eFa=r(GQn,"deit"),GQn.forEach(t),oFa=r(OQ," \u2014 "),v0e=n(OQ,"A",{href:!0});var BQn=s(v0e);rFa=r(BQn,"DeiTForImageClassification"),BQn.forEach(t),tFa=r(OQ," or "),M0e=n(OQ,"A",{href:!0});var IQn=s(M0e);aFa=r(IQn,"DeiTForImageClassificationWithTeacher"),IQn.forEach(t),nFa=r(OQ," (DeiT model)"),OQ.forEach(t),sFa=i(me),EG=n(me,"LI",{});var Wko=s(EG);Hao=n(Wko,"STRONG",{});var NQn=s(Hao);lFa=r(NQn,"dinat"),NQn.forEach(t),iFa=r(Wko," \u2014 "),T0e=n(Wko,"A",{href:!0});var $Qn=s(T0e);dFa=r($Qn,"DinatForImageClassification"),$Qn.forEach(t),mFa=r(Wko," (DiNAT model)"),Wko.forEach(t),cFa=i(me),yG=n(me,"LI",{});var Jko=s(yG);Kao=n(Jko,"STRONG",{});var jQn=s(Kao);fFa=r(jQn,"dinov2"),jQn.forEach(t),hFa=r(Jko," \u2014 "),F0e=n(Jko,"A",{href:!0});var PQn=s(F0e);gFa=r(PQn,"Dinov2ForImageClassification"),PQn.forEach(t),uFa=r(Jko," (DINOv2 model)"),Jko.forEach(t),pFa=i(me),Ui=n(me,"LI",{});var QQ=s(Ui);eno=n(QQ,"STRONG",{});var ZQn=s(eno);_Fa=r(ZQn,"efficientformer"),ZQn.forEach(t),bFa=r(QQ," \u2014 "),E0e=n(QQ,"A",{href:!0});var VQn=s(E0e);vFa=r(VQn,"EfficientFormerForImageClassification"),VQn.forEach(t),MFa=r(QQ," or "),y0e=n(QQ,"A",{href:!0});var WQn=s(y0e);TFa=r(WQn,"EfficientFormerForImageClassificationWithTeacher"),WQn.forEach(t),FFa=r(QQ," (EfficientFormer model)"),QQ.forEach(t),EFa=i(me),CG=n(me,"LI",{});var Uko=s(CG);ono=n(Uko,"STRONG",{});var JQn=s(ono);yFa=r(JQn,"efficientnet"),JQn.forEach(t),CFa=r(Uko," \u2014 "),C0e=n(Uko,"A",{href:!0});var UQn=s(C0e);wFa=r(UQn,"EfficientNetForImageClassification"),UQn.forEach(t),LFa=r(Uko," (EfficientNet model)"),Uko.forEach(t),AFa=i(me),wG=n(me,"LI",{});var Xko=s(wG);rno=n(Xko,"STRONG",{});var XQn=s(rno);RFa=r(XQn,"focalnet"),XQn.forEach(t),kFa=r(Xko," \u2014 "),w0e=n(Xko,"A",{href:!0});var DQn=s(w0e);SFa=r(DQn,"FocalNetForImageClassification"),DQn.forEach(t),xFa=r(Xko," (FocalNet model)"),Xko.forEach(t),GFa=i(me),LG=n(me,"LI",{});var Dko=s(LG);tno=n(Dko,"STRONG",{});var qQn=s(tno);BFa=r(qQn,"imagegpt"),qQn.forEach(t),IFa=r(Dko," \u2014 "),L0e=n(Dko,"A",{href:!0});var OQn=s(L0e);NFa=r(OQn,"ImageGPTForImageClassification"),OQn.forEach(t),$Fa=r(Dko," (ImageGPT model)"),Dko.forEach(t),jFa=i(me),Xi=n(me,"LI",{});var YQ=s(Xi);ano=n(YQ,"STRONG",{});var QQn=s(ano);PFa=r(QQn,"levit"),QQn.forEach(t),ZFa=r(YQ," \u2014 "),A0e=n(YQ,"A",{href:!0});var YQn=s(A0e);VFa=r(YQn,"LevitForImageClassification"),YQn.forEach(t),WFa=r(YQ," or "),R0e=n(YQ,"A",{href:!0});var zQn=s(R0e);JFa=r(zQn,"LevitForImageClassificationWithTeacher"),zQn.forEach(t),UFa=r(YQ," (LeViT model)"),YQ.forEach(t),XFa=i(me),AG=n(me,"LI",{});var qko=s(AG);nno=n(qko,"STRONG",{});var HQn=s(nno);DFa=r(HQn,"mobilenet_v1"),HQn.forEach(t),qFa=r(qko," \u2014 "),k0e=n(qko,"A",{href:!0});var KQn=s(k0e);OFa=r(KQn,"MobileNetV1ForImageClassification"),KQn.forEach(t),QFa=r(qko," (MobileNetV1 model)"),qko.forEach(t),YFa=i(me),RG=n(me,"LI",{});var Oko=s(RG);sno=n(Oko,"STRONG",{});var eYn=s(sno);zFa=r(eYn,"mobilenet_v2"),eYn.forEach(t),HFa=r(Oko," \u2014 "),S0e=n(Oko,"A",{href:!0});var oYn=s(S0e);KFa=r(oYn,"MobileNetV2ForImageClassification"),oYn.forEach(t),eEa=r(Oko," (MobileNetV2 model)"),Oko.forEach(t),oEa=i(me),kG=n(me,"LI",{});var Qko=s(kG);lno=n(Qko,"STRONG",{});var rYn=s(lno);rEa=r(rYn,"mobilevit"),rYn.forEach(t),tEa=r(Qko," \u2014 "),x0e=n(Qko,"A",{href:!0});var tYn=s(x0e);aEa=r(tYn,"MobileViTForImageClassification"),tYn.forEach(t),nEa=r(Qko," (MobileViT model)"),Qko.forEach(t),sEa=i(me),SG=n(me,"LI",{});var Yko=s(SG);ino=n(Yko,"STRONG",{});var aYn=s(ino);lEa=r(aYn,"mobilevitv2"),aYn.forEach(t),iEa=r(Yko," \u2014 "),G0e=n(Yko,"A",{href:!0});var nYn=s(G0e);dEa=r(nYn,"MobileViTV2ForImageClassification"),nYn.forEach(t),mEa=r(Yko," (MobileViTV2 model)"),Yko.forEach(t),cEa=i(me),xG=n(me,"LI",{});var zko=s(xG);dno=n(zko,"STRONG",{});var sYn=s(dno);fEa=r(sYn,"nat"),sYn.forEach(t),hEa=r(zko," \u2014 "),B0e=n(zko,"A",{href:!0});var lYn=s(B0e);gEa=r(lYn,"NatForImageClassification"),lYn.forEach(t),uEa=r(zko," (NAT model)"),zko.forEach(t),pEa=i(me),Ta=n(me,"LI",{});var Dg=s(Ta);mno=n(Dg,"STRONG",{});var iYn=s(mno);_Ea=r(iYn,"perceiver"),iYn.forEach(t),bEa=r(Dg," \u2014 "),I0e=n(Dg,"A",{href:!0});var dYn=s(I0e);vEa=r(dYn,"PerceiverForImageClassificationLearned"),dYn.forEach(t),MEa=r(Dg," or "),N0e=n(Dg,"A",{href:!0});var mYn=s(N0e);TEa=r(mYn,"PerceiverForImageClassificationFourier"),mYn.forEach(t),FEa=r(Dg," or "),$0e=n(Dg,"A",{href:!0});var cYn=s($0e);EEa=r(cYn,"PerceiverForImageClassificationConvProcessing"),cYn.forEach(t),yEa=r(Dg," (Perceiver model)"),Dg.forEach(t),CEa=i(me),GG=n(me,"LI",{});var Hko=s(GG);cno=n(Hko,"STRONG",{});var fYn=s(cno);wEa=r(fYn,"poolformer"),fYn.forEach(t),LEa=r(Hko," \u2014 "),j0e=n(Hko,"A",{href:!0});var hYn=s(j0e);AEa=r(hYn,"PoolFormerForImageClassification"),hYn.forEach(t),REa=r(Hko," (PoolFormer model)"),Hko.forEach(t),kEa=i(me),BG=n(me,"LI",{});var Kko=s(BG);fno=n(Kko,"STRONG",{});var gYn=s(fno);SEa=r(gYn,"pvt"),gYn.forEach(t),xEa=r(Kko," \u2014 "),P0e=n(Kko,"A",{href:!0});var uYn=s(P0e);GEa=r(uYn,"PvtForImageClassification"),uYn.forEach(t),BEa=r(Kko," (PVT model)"),Kko.forEach(t),IEa=i(me),IG=n(me,"LI",{});var eSo=s(IG);hno=n(eSo,"STRONG",{});var pYn=s(hno);NEa=r(pYn,"regnet"),pYn.forEach(t),$Ea=r(eSo," \u2014 "),Z0e=n(eSo,"A",{href:!0});var _Yn=s(Z0e);jEa=r(_Yn,"RegNetForImageClassification"),_Yn.forEach(t),PEa=r(eSo," (RegNet model)"),eSo.forEach(t),ZEa=i(me),NG=n(me,"LI",{});var oSo=s(NG);gno=n(oSo,"STRONG",{});var bYn=s(gno);VEa=r(bYn,"resnet"),bYn.forEach(t),WEa=r(oSo," \u2014 "),V0e=n(oSo,"A",{href:!0});var vYn=s(V0e);JEa=r(vYn,"ResNetForImageClassification"),vYn.forEach(t),UEa=r(oSo," (ResNet model)"),oSo.forEach(t),XEa=i(me),$G=n(me,"LI",{});var rSo=s($G);uno=n(rSo,"STRONG",{});var MYn=s(uno);DEa=r(MYn,"segformer"),MYn.forEach(t),qEa=r(rSo," \u2014 "),W0e=n(rSo,"A",{href:!0});var TYn=s(W0e);OEa=r(TYn,"SegformerForImageClassification"),TYn.forEach(t),QEa=r(rSo," (SegFormer model)"),rSo.forEach(t),YEa=i(me),jG=n(me,"LI",{});var tSo=s(jG);pno=n(tSo,"STRONG",{});var FYn=s(pno);zEa=r(FYn,"swiftformer"),FYn.forEach(t),HEa=r(tSo," \u2014 "),J0e=n(tSo,"A",{href:!0});var EYn=s(J0e);KEa=r(EYn,"SwiftFormerForImageClassification"),EYn.forEach(t),eya=r(tSo," (SwiftFormer model)"),tSo.forEach(t),oya=i(me),PG=n(me,"LI",{});var aSo=s(PG);_no=n(aSo,"STRONG",{});var yYn=s(_no);rya=r(yYn,"swin"),yYn.forEach(t),tya=r(aSo," \u2014 "),U0e=n(aSo,"A",{href:!0});var CYn=s(U0e);aya=r(CYn,"SwinForImageClassification"),CYn.forEach(t),nya=r(aSo," (Swin Transformer model)"),aSo.forEach(t),sya=i(me),ZG=n(me,"LI",{});var nSo=s(ZG);bno=n(nSo,"STRONG",{});var wYn=s(bno);lya=r(wYn,"swinv2"),wYn.forEach(t),iya=r(nSo," \u2014 "),X0e=n(nSo,"A",{href:!0});var LYn=s(X0e);dya=r(LYn,"Swinv2ForImageClassification"),LYn.forEach(t),mya=r(nSo," (Swin Transformer V2 model)"),nSo.forEach(t),cya=i(me),VG=n(me,"LI",{});var sSo=s(VG);vno=n(sSo,"STRONG",{});var AYn=s(vno);fya=r(AYn,"van"),AYn.forEach(t),hya=r(sSo," \u2014 "),D0e=n(sSo,"A",{href:!0});var RYn=s(D0e);gya=r(RYn,"VanForImageClassification"),RYn.forEach(t),uya=r(sSo," (VAN model)"),sSo.forEach(t),pya=i(me),WG=n(me,"LI",{});var lSo=s(WG);Mno=n(lSo,"STRONG",{});var kYn=s(Mno);_ya=r(kYn,"vit"),kYn.forEach(t),bya=r(lSo," \u2014 "),q0e=n(lSo,"A",{href:!0});var SYn=s(q0e);vya=r(SYn,"ViTForImageClassification"),SYn.forEach(t),Mya=r(lSo," (ViT model)"),lSo.forEach(t),Tya=i(me),JG=n(me,"LI",{});var iSo=s(JG);Tno=n(iSo,"STRONG",{});var xYn=s(Tno);Fya=r(xYn,"vit_hybrid"),xYn.forEach(t),Eya=r(iSo," \u2014 "),O0e=n(iSo,"A",{href:!0});var GYn=s(O0e);yya=r(GYn,"ViTHybridForImageClassification"),GYn.forEach(t),Cya=r(iSo," (ViT Hybrid model)"),iSo.forEach(t),wya=i(me),UG=n(me,"LI",{});var dSo=s(UG);Fno=n(dSo,"STRONG",{});var BYn=s(Fno);Lya=r(BYn,"vit_msn"),BYn.forEach(t),Aya=r(dSo," \u2014 "),Q0e=n(dSo,"A",{href:!0});var IYn=s(Q0e);Rya=r(IYn,"ViTMSNForImageClassification"),IYn.forEach(t),kya=r(dSo," (ViTMSN model)"),dSo.forEach(t),me.forEach(t),Sya=i(hn),XG=n(hn,"P",{});var mSo=s(XG);xya=r(mSo,"The model is set in evaluation mode by default using "),Eno=n(mSo,"CODE",{});var NYn=s(Eno);Gya=r(NYn,"model.eval()"),NYn.forEach(t),Bya=r(mSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),yno=n(mSo,"CODE",{});var $Yn=s(yno);Iya=r($Yn,"model.train()"),$Yn.forEach(t),mSo.forEach(t),Nya=i(hn),M(DG.$$.fragment,hn),hn.forEach(t),Yd.forEach(t),MIo=i(c),oh=n(c,"H3",{class:!0});var bjo=s(oh);qG=n(bjo,"A",{id:!0,class:!0,href:!0});var jYn=s(qG);Cno=n(jYn,"SPAN",{});var PYn=s(Cno);M(HU.$$.fragment,PYn),PYn.forEach(t),jYn.forEach(t),$ya=i(bjo),wno=n(bjo,"SPAN",{});var ZYn=s(wno);jya=r(ZYn,"TFAutoModelForImageClassification"),ZYn.forEach(t),bjo.forEach(t),TIo=i(c),Fr=n(c,"DIV",{class:!0});var zd=s(Fr);M(KU.$$.fragment,zd),Pya=i(zd),rh=n(zd,"P",{});var KCe=s(rh);Zya=r(KCe,`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 `),Y0e=n(KCe,"A",{href:!0});var VYn=s(Y0e);Vya=r(VYn,"from_pretrained()"),VYn.forEach(t),Wya=r(KCe," class method or the "),z0e=n(KCe,"A",{href:!0});var WYn=s(z0e);Jya=r(WYn,"from_config()"),WYn.forEach(t),Uya=r(KCe,` class
method.`),KCe.forEach(t),Xya=i(zd),eX=n(zd,"P",{});var vjo=s(eX);Dya=r(vjo,"This class cannot be instantiated directly using "),Lno=n(vjo,"CODE",{});var JYn=s(Lno);qya=r(JYn,"__init__()"),JYn.forEach(t),Oya=r(vjo," (throws an error)."),vjo.forEach(t),Qya=i(zd),Fa=n(zd,"DIV",{class:!0});var DP=s(Fa);M(oX.$$.fragment,DP),Yya=i(DP),Ano=n(DP,"P",{});var UYn=s(Ano);zya=r(UYn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),UYn.forEach(t),Hya=i(DP),th=n(DP,"P",{});var ewe=s(th);Kya=r(ewe,`Note:
Loading a model from its configuration file does `),Rno=n(ewe,"STRONG",{});var XYn=s(Rno);e0a=r(XYn,"not"),XYn.forEach(t),o0a=r(ewe,` load the model weights. It only affects the
model\u2019s configuration. Use `),H0e=n(ewe,"A",{href:!0});var DYn=s(H0e);r0a=r(DYn,"from_pretrained()"),DYn.forEach(t),t0a=r(ewe," to load the model weights."),ewe.forEach(t),a0a=i(DP),M(OG.$$.fragment,DP),DP.forEach(t),n0a=i(zd),Et=n(zd,"DIV",{class:!0});var Hd=s(Et);M(rX.$$.fragment,Hd),s0a=i(Hd),kno=n(Hd,"P",{});var qYn=s(kno);l0a=r(qYn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),qYn.forEach(t),i0a=i(Hd),bs=n(Hd,"P",{});var qP=s(bs);d0a=r(qP,"The model class to instantiate is selected based on the "),Sno=n(qP,"CODE",{});var OYn=s(Sno);m0a=r(OYn,"model_type"),OYn.forEach(t),c0a=r(qP,` property of the config object (either
passed as an argument or loaded from `),xno=n(qP,"CODE",{});var QYn=s(xno);f0a=r(QYn,"pretrained_model_name_or_path"),QYn.forEach(t),h0a=r(qP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Gno=n(qP,"CODE",{});var YYn=s(Gno);g0a=r(YYn,"pretrained_model_name_or_path"),YYn.forEach(t),u0a=r(qP,":"),qP.forEach(t),p0a=i(Hd),Ge=n(Hd,"UL",{});var We=s(Ge);QG=n(We,"LI",{});var cSo=s(QG);Bno=n(cSo,"STRONG",{});var zYn=s(Bno);_0a=r(zYn,"convnext"),zYn.forEach(t),b0a=r(cSo," \u2014 "),K0e=n(cSo,"A",{href:!0});var HYn=s(K0e);v0a=r(HYn,"TFConvNextForImageClassification"),HYn.forEach(t),M0a=r(cSo," (ConvNeXT model)"),cSo.forEach(t),T0a=i(We),YG=n(We,"LI",{});var fSo=s(YG);Ino=n(fSo,"STRONG",{});var KYn=s(Ino);F0a=r(KYn,"cvt"),KYn.forEach(t),E0a=r(fSo," \u2014 "),e5e=n(fSo,"A",{href:!0});var ezn=s(e5e);y0a=r(ezn,"TFCvtForImageClassification"),ezn.forEach(t),C0a=r(fSo," (CvT model)"),fSo.forEach(t),w0a=i(We),zG=n(We,"LI",{});var hSo=s(zG);Nno=n(hSo,"STRONG",{});var ozn=s(Nno);L0a=r(ozn,"data2vec-vision"),ozn.forEach(t),A0a=r(hSo," \u2014 "),o5e=n(hSo,"A",{href:!0});var rzn=s(o5e);R0a=r(rzn,"TFData2VecVisionForImageClassification"),rzn.forEach(t),k0a=r(hSo," (Data2VecVision model)"),hSo.forEach(t),S0a=i(We),Di=n(We,"LI",{});var zQ=s(Di);$no=n(zQ,"STRONG",{});var tzn=s($no);x0a=r(tzn,"deit"),tzn.forEach(t),G0a=r(zQ," \u2014 "),r5e=n(zQ,"A",{href:!0});var azn=s(r5e);B0a=r(azn,"TFDeiTForImageClassification"),azn.forEach(t),I0a=r(zQ," or "),t5e=n(zQ,"A",{href:!0});var nzn=s(t5e);N0a=r(nzn,"TFDeiTForImageClassificationWithTeacher"),nzn.forEach(t),$0a=r(zQ," (DeiT model)"),zQ.forEach(t),j0a=i(We),qi=n(We,"LI",{});var HQ=s(qi);jno=n(HQ,"STRONG",{});var szn=s(jno);P0a=r(szn,"efficientformer"),szn.forEach(t),Z0a=r(HQ," \u2014 "),a5e=n(HQ,"A",{href:!0});var lzn=s(a5e);V0a=r(lzn,"TFEfficientFormerForImageClassification"),lzn.forEach(t),W0a=r(HQ," or "),n5e=n(HQ,"A",{href:!0});var izn=s(n5e);J0a=r(izn,"TFEfficientFormerForImageClassificationWithTeacher"),izn.forEach(t),U0a=r(HQ," (EfficientFormer model)"),HQ.forEach(t),X0a=i(We),HG=n(We,"LI",{});var gSo=s(HG);Pno=n(gSo,"STRONG",{});var dzn=s(Pno);D0a=r(dzn,"mobilevit"),dzn.forEach(t),q0a=r(gSo," \u2014 "),s5e=n(gSo,"A",{href:!0});var mzn=s(s5e);O0a=r(mzn,"TFMobileViTForImageClassification"),mzn.forEach(t),Q0a=r(gSo," (MobileViT model)"),gSo.forEach(t),Y0a=i(We),KG=n(We,"LI",{});var uSo=s(KG);Zno=n(uSo,"STRONG",{});var czn=s(Zno);z0a=r(czn,"regnet"),czn.forEach(t),H0a=r(uSo," \u2014 "),l5e=n(uSo,"A",{href:!0});var fzn=s(l5e);K0a=r(fzn,"TFRegNetForImageClassification"),fzn.forEach(t),e5a=r(uSo," (RegNet model)"),uSo.forEach(t),o5a=i(We),eB=n(We,"LI",{});var pSo=s(eB);Vno=n(pSo,"STRONG",{});var hzn=s(Vno);r5a=r(hzn,"resnet"),hzn.forEach(t),t5a=r(pSo," \u2014 "),i5e=n(pSo,"A",{href:!0});var gzn=s(i5e);a5a=r(gzn,"TFResNetForImageClassification"),gzn.forEach(t),n5a=r(pSo," (ResNet model)"),pSo.forEach(t),s5a=i(We),oB=n(We,"LI",{});var _So=s(oB);Wno=n(_So,"STRONG",{});var uzn=s(Wno);l5a=r(uzn,"segformer"),uzn.forEach(t),i5a=r(_So," \u2014 "),d5e=n(_So,"A",{href:!0});var pzn=s(d5e);d5a=r(pzn,"TFSegformerForImageClassification"),pzn.forEach(t),m5a=r(_So," (SegFormer model)"),_So.forEach(t),c5a=i(We),rB=n(We,"LI",{});var bSo=s(rB);Jno=n(bSo,"STRONG",{});var _zn=s(Jno);f5a=r(_zn,"swin"),_zn.forEach(t),h5a=r(bSo," \u2014 "),m5e=n(bSo,"A",{href:!0});var bzn=s(m5e);g5a=r(bzn,"TFSwinForImageClassification"),bzn.forEach(t),u5a=r(bSo," (Swin Transformer model)"),bSo.forEach(t),p5a=i(We),tB=n(We,"LI",{});var vSo=s(tB);Uno=n(vSo,"STRONG",{});var vzn=s(Uno);_5a=r(vzn,"vit"),vzn.forEach(t),b5a=r(vSo," \u2014 "),c5e=n(vSo,"A",{href:!0});var Mzn=s(c5e);v5a=r(Mzn,"TFViTForImageClassification"),Mzn.forEach(t),M5a=r(vSo," (ViT model)"),vSo.forEach(t),We.forEach(t),T5a=i(Hd),M(aB.$$.fragment,Hd),Hd.forEach(t),zd.forEach(t),FIo=i(c),ah=n(c,"H3",{class:!0});var Mjo=s(ah);nB=n(Mjo,"A",{id:!0,class:!0,href:!0});var Tzn=s(nB);Xno=n(Tzn,"SPAN",{});var Fzn=s(Xno);M(tX.$$.fragment,Fzn),Fzn.forEach(t),Tzn.forEach(t),F5a=i(Mjo),Dno=n(Mjo,"SPAN",{});var Ezn=s(Dno);E5a=r(Ezn,"FlaxAutoModelForImageClassification"),Ezn.forEach(t),Mjo.forEach(t),EIo=i(c),Er=n(c,"DIV",{class:!0});var Kd=s(Er);M(aX.$$.fragment,Kd),y5a=i(Kd),nh=n(Kd,"P",{});var owe=s(nh);C5a=r(owe,`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 `),f5e=n(owe,"A",{href:!0});var yzn=s(f5e);w5a=r(yzn,"from_pretrained()"),yzn.forEach(t),L5a=r(owe," class method or the "),h5e=n(owe,"A",{href:!0});var Czn=s(h5e);A5a=r(Czn,"from_config()"),Czn.forEach(t),R5a=r(owe,` class
method.`),owe.forEach(t),k5a=i(Kd),nX=n(Kd,"P",{});var Tjo=s(nX);S5a=r(Tjo,"This class cannot be instantiated directly using "),qno=n(Tjo,"CODE",{});var wzn=s(qno);x5a=r(wzn,"__init__()"),wzn.forEach(t),G5a=r(Tjo," (throws an error)."),Tjo.forEach(t),B5a=i(Kd),Ea=n(Kd,"DIV",{class:!0});var OP=s(Ea);M(sX.$$.fragment,OP),I5a=i(OP),Ono=n(OP,"P",{});var Lzn=s(Ono);N5a=r(Lzn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),Lzn.forEach(t),$5a=i(OP),sh=n(OP,"P",{});var rwe=s(sh);j5a=r(rwe,`Note:
Loading a model from its configuration file does `),Qno=n(rwe,"STRONG",{});var Azn=s(Qno);P5a=r(Azn,"not"),Azn.forEach(t),Z5a=r(rwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),g5e=n(rwe,"A",{href:!0});var Rzn=s(g5e);V5a=r(Rzn,"from_pretrained()"),Rzn.forEach(t),W5a=r(rwe," to load the model weights."),rwe.forEach(t),J5a=i(OP),M(sB.$$.fragment,OP),OP.forEach(t),U5a=i(Kd),yt=n(Kd,"DIV",{class:!0});var em=s(yt);M(lX.$$.fragment,em),X5a=i(em),Yno=n(em,"P",{});var kzn=s(Yno);D5a=r(kzn,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),kzn.forEach(t),q5a=i(em),vs=n(em,"P",{});var QP=s(vs);O5a=r(QP,"The model class to instantiate is selected based on the "),zno=n(QP,"CODE",{});var Szn=s(zno);Q5a=r(Szn,"model_type"),Szn.forEach(t),Y5a=r(QP,` property of the config object (either
passed as an argument or loaded from `),Hno=n(QP,"CODE",{});var xzn=s(Hno);z5a=r(xzn,"pretrained_model_name_or_path"),xzn.forEach(t),H5a=r(QP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Kno=n(QP,"CODE",{});var Gzn=s(Kno);K5a=r(Gzn,"pretrained_model_name_or_path"),Gzn.forEach(t),e3a=r(QP,":"),QP.forEach(t),o3a=i(em),Ms=n(em,"UL",{});var YP=s(Ms);lB=n(YP,"LI",{});var MSo=s(lB);eso=n(MSo,"STRONG",{});var Bzn=s(eso);r3a=r(Bzn,"beit"),Bzn.forEach(t),t3a=r(MSo," \u2014 "),u5e=n(MSo,"A",{href:!0});var Izn=s(u5e);a3a=r(Izn,"FlaxBeitForImageClassification"),Izn.forEach(t),n3a=r(MSo," (BEiT model)"),MSo.forEach(t),s3a=i(YP),iB=n(YP,"LI",{});var TSo=s(iB);oso=n(TSo,"STRONG",{});var Nzn=s(oso);l3a=r(Nzn,"regnet"),Nzn.forEach(t),i3a=r(TSo," \u2014 "),p5e=n(TSo,"A",{href:!0});var $zn=s(p5e);d3a=r($zn,"FlaxRegNetForImageClassification"),$zn.forEach(t),m3a=r(TSo," (RegNet model)"),TSo.forEach(t),c3a=i(YP),dB=n(YP,"LI",{});var FSo=s(dB);rso=n(FSo,"STRONG",{});var jzn=s(rso);f3a=r(jzn,"resnet"),jzn.forEach(t),h3a=r(FSo," \u2014 "),_5e=n(FSo,"A",{href:!0});var Pzn=s(_5e);g3a=r(Pzn,"FlaxResNetForImageClassification"),Pzn.forEach(t),u3a=r(FSo," (ResNet model)"),FSo.forEach(t),p3a=i(YP),mB=n(YP,"LI",{});var ESo=s(mB);tso=n(ESo,"STRONG",{});var Zzn=s(tso);_3a=r(Zzn,"vit"),Zzn.forEach(t),b3a=r(ESo," \u2014 "),b5e=n(ESo,"A",{href:!0});var Vzn=s(b5e);v3a=r(Vzn,"FlaxViTForImageClassification"),Vzn.forEach(t),M3a=r(ESo," (ViT model)"),ESo.forEach(t),YP.forEach(t),T3a=i(em),M(cB.$$.fragment,em),em.forEach(t),Kd.forEach(t),yIo=i(c),lh=n(c,"H3",{class:!0});var Fjo=s(lh);fB=n(Fjo,"A",{id:!0,class:!0,href:!0});var Wzn=s(fB);aso=n(Wzn,"SPAN",{});var Jzn=s(aso);M(iX.$$.fragment,Jzn),Jzn.forEach(t),Wzn.forEach(t),F3a=i(Fjo),nso=n(Fjo,"SPAN",{});var Uzn=s(nso);E3a=r(Uzn,"AutoModelForVideoClassification"),Uzn.forEach(t),Fjo.forEach(t),CIo=i(c),yr=n(c,"DIV",{class:!0});var om=s(yr);M(dX.$$.fragment,om),y3a=i(om),ih=n(om,"P",{});var twe=s(ih);C3a=r(twe,`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 `),v5e=n(twe,"A",{href:!0});var Xzn=s(v5e);w3a=r(Xzn,"from_pretrained()"),Xzn.forEach(t),L3a=r(twe," class method or the "),M5e=n(twe,"A",{href:!0});var Dzn=s(M5e);A3a=r(Dzn,"from_config()"),Dzn.forEach(t),R3a=r(twe,` class
method.`),twe.forEach(t),k3a=i(om),mX=n(om,"P",{});var Ejo=s(mX);S3a=r(Ejo,"This class cannot be instantiated directly using "),sso=n(Ejo,"CODE",{});var qzn=s(sso);x3a=r(qzn,"__init__()"),qzn.forEach(t),G3a=r(Ejo," (throws an error)."),Ejo.forEach(t),B3a=i(om),ya=n(om,"DIV",{class:!0});var zP=s(ya);M(cX.$$.fragment,zP),I3a=i(zP),lso=n(zP,"P",{});var Ozn=s(lso);N3a=r(Ozn,"Instantiates one of the model classes of the library (with a video classification head) from a configuration."),Ozn.forEach(t),$3a=i(zP),dh=n(zP,"P",{});var awe=s(dh);j3a=r(awe,`Note:
Loading a model from its configuration file does `),iso=n(awe,"STRONG",{});var Qzn=s(iso);P3a=r(Qzn,"not"),Qzn.forEach(t),Z3a=r(awe,` load the model weights. It only affects the
model\u2019s configuration. Use `),T5e=n(awe,"A",{href:!0});var Yzn=s(T5e);V3a=r(Yzn,"from_pretrained()"),Yzn.forEach(t),W3a=r(awe," to load the model weights."),awe.forEach(t),J3a=i(zP),M(hB.$$.fragment,zP),zP.forEach(t),U3a=i(om),vo=n(om,"DIV",{class:!0});var gn=s(vo);M(fX.$$.fragment,gn),X3a=i(gn),dso=n(gn,"P",{});var zzn=s(dso);D3a=r(zzn,"Instantiate one of the model classes of the library (with a video classification head) from a pretrained model."),zzn.forEach(t),q3a=i(gn),Ts=n(gn,"P",{});var HP=s(Ts);O3a=r(HP,"The model class to instantiate is selected based on the "),mso=n(HP,"CODE",{});var Hzn=s(mso);Q3a=r(Hzn,"model_type"),Hzn.forEach(t),Y3a=r(HP,` property of the config object (either
passed as an argument or loaded from `),cso=n(HP,"CODE",{});var Kzn=s(cso);z3a=r(Kzn,"pretrained_model_name_or_path"),Kzn.forEach(t),H3a=r(HP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),fso=n(HP,"CODE",{});var eHn=s(fso);K3a=r(eHn,"pretrained_model_name_or_path"),eHn.forEach(t),e9a=r(HP,":"),HP.forEach(t),o9a=i(gn),mh=n(gn,"UL",{});var nwe=s(mh);gB=n(nwe,"LI",{});var ySo=s(gB);hso=n(ySo,"STRONG",{});var oHn=s(hso);r9a=r(oHn,"timesformer"),oHn.forEach(t),t9a=r(ySo," \u2014 "),F5e=n(ySo,"A",{href:!0});var rHn=s(F5e);a9a=r(rHn,"TimesformerForVideoClassification"),rHn.forEach(t),n9a=r(ySo," (TimeSformer model)"),ySo.forEach(t),s9a=i(nwe),uB=n(nwe,"LI",{});var CSo=s(uB);gso=n(CSo,"STRONG",{});var tHn=s(gso);l9a=r(tHn,"videomae"),tHn.forEach(t),i9a=r(CSo," \u2014 "),E5e=n(CSo,"A",{href:!0});var aHn=s(E5e);d9a=r(aHn,"VideoMAEForVideoClassification"),aHn.forEach(t),m9a=r(CSo," (VideoMAE model)"),CSo.forEach(t),c9a=i(nwe),pB=n(nwe,"LI",{});var wSo=s(pB);uso=n(wSo,"STRONG",{});var nHn=s(uso);f9a=r(nHn,"vivit"),nHn.forEach(t),h9a=r(wSo," \u2014 "),y5e=n(wSo,"A",{href:!0});var sHn=s(y5e);g9a=r(sHn,"VivitForVideoClassification"),sHn.forEach(t),u9a=r(wSo," (ViViT model)"),wSo.forEach(t),nwe.forEach(t),p9a=i(gn),_B=n(gn,"P",{});var LSo=s(_B);_9a=r(LSo,"The model is set in evaluation mode by default using "),pso=n(LSo,"CODE",{});var lHn=s(pso);b9a=r(lHn,"model.eval()"),lHn.forEach(t),v9a=r(LSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),_so=n(LSo,"CODE",{});var iHn=s(_so);M9a=r(iHn,"model.train()"),iHn.forEach(t),LSo.forEach(t),T9a=i(gn),M(bB.$$.fragment,gn),gn.forEach(t),om.forEach(t),wIo=i(c),ch=n(c,"H3",{class:!0});var yjo=s(ch);vB=n(yjo,"A",{id:!0,class:!0,href:!0});var dHn=s(vB);bso=n(dHn,"SPAN",{});var mHn=s(bso);M(hX.$$.fragment,mHn),mHn.forEach(t),dHn.forEach(t),F9a=i(yjo),vso=n(yjo,"SPAN",{});var cHn=s(vso);E9a=r(cHn,"AutoModelForMaskedImageModeling"),cHn.forEach(t),yjo.forEach(t),LIo=i(c),Cr=n(c,"DIV",{class:!0});var rm=s(Cr);M(gX.$$.fragment,rm),y9a=i(rm),fh=n(rm,"P",{});var swe=s(fh);C9a=r(swe,`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 `),C5e=n(swe,"A",{href:!0});var fHn=s(C5e);w9a=r(fHn,"from_pretrained()"),fHn.forEach(t),L9a=r(swe," class method or the "),w5e=n(swe,"A",{href:!0});var hHn=s(w5e);A9a=r(hHn,"from_config()"),hHn.forEach(t),R9a=r(swe,` class
method.`),swe.forEach(t),k9a=i(rm),uX=n(rm,"P",{});var Cjo=s(uX);S9a=r(Cjo,"This class cannot be instantiated directly using "),Mso=n(Cjo,"CODE",{});var gHn=s(Mso);x9a=r(gHn,"__init__()"),gHn.forEach(t),G9a=r(Cjo," (throws an error)."),Cjo.forEach(t),B9a=i(rm),Ca=n(rm,"DIV",{class:!0});var KP=s(Ca);M(pX.$$.fragment,KP),I9a=i(KP),Tso=n(KP,"P",{});var uHn=s(Tso);N9a=r(uHn,"Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),uHn.forEach(t),$9a=i(KP),hh=n(KP,"P",{});var lwe=s(hh);j9a=r(lwe,`Note:
Loading a model from its configuration file does `),Fso=n(lwe,"STRONG",{});var pHn=s(Fso);P9a=r(pHn,"not"),pHn.forEach(t),Z9a=r(lwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),L5e=n(lwe,"A",{href:!0});var _Hn=s(L5e);V9a=r(_Hn,"from_pretrained()"),_Hn.forEach(t),W9a=r(lwe," to load the model weights."),lwe.forEach(t),J9a=i(KP),M(MB.$$.fragment,KP),KP.forEach(t),U9a=i(rm),Mo=n(rm,"DIV",{class:!0});var un=s(Mo);M(_X.$$.fragment,un),X9a=i(un),Eso=n(un,"P",{});var bHn=s(Eso);D9a=r(bHn,"Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),bHn.forEach(t),q9a=i(un),Fs=n(un,"P",{});var eZ=s(Fs);O9a=r(eZ,"The model class to instantiate is selected based on the "),yso=n(eZ,"CODE",{});var vHn=s(yso);Q9a=r(vHn,"model_type"),vHn.forEach(t),Y9a=r(eZ,` property of the config object (either
passed as an argument or loaded from `),Cso=n(eZ,"CODE",{});var MHn=s(Cso);z9a=r(MHn,"pretrained_model_name_or_path"),MHn.forEach(t),H9a=r(eZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wso=n(eZ,"CODE",{});var THn=s(wso);K9a=r(THn,"pretrained_model_name_or_path"),THn.forEach(t),e4a=r(eZ,":"),eZ.forEach(t),o4a=i(un),Nt=n(un,"UL",{});var tm=s(Nt);TB=n(tm,"LI",{});var ASo=s(TB);Lso=n(ASo,"STRONG",{});var FHn=s(Lso);r4a=r(FHn,"deit"),FHn.forEach(t),t4a=r(ASo," \u2014 "),A5e=n(ASo,"A",{href:!0});var EHn=s(A5e);a4a=r(EHn,"DeiTForMaskedImageModeling"),EHn.forEach(t),n4a=r(ASo," (DeiT model)"),ASo.forEach(t),s4a=i(tm),FB=n(tm,"LI",{});var RSo=s(FB);Aso=n(RSo,"STRONG",{});var yHn=s(Aso);l4a=r(yHn,"focalnet"),yHn.forEach(t),i4a=r(RSo," \u2014 "),R5e=n(RSo,"A",{href:!0});var CHn=s(R5e);d4a=r(CHn,"FocalNetForMaskedImageModeling"),CHn.forEach(t),m4a=r(RSo," (FocalNet model)"),RSo.forEach(t),c4a=i(tm),EB=n(tm,"LI",{});var kSo=s(EB);Rso=n(kSo,"STRONG",{});var wHn=s(Rso);f4a=r(wHn,"swin"),wHn.forEach(t),h4a=r(kSo," \u2014 "),k5e=n(kSo,"A",{href:!0});var LHn=s(k5e);g4a=r(LHn,"SwinForMaskedImageModeling"),LHn.forEach(t),u4a=r(kSo," (Swin Transformer model)"),kSo.forEach(t),p4a=i(tm),yB=n(tm,"LI",{});var SSo=s(yB);kso=n(SSo,"STRONG",{});var AHn=s(kso);_4a=r(AHn,"swinv2"),AHn.forEach(t),b4a=r(SSo," \u2014 "),S5e=n(SSo,"A",{href:!0});var RHn=s(S5e);v4a=r(RHn,"Swinv2ForMaskedImageModeling"),RHn.forEach(t),M4a=r(SSo," (Swin Transformer V2 model)"),SSo.forEach(t),T4a=i(tm),CB=n(tm,"LI",{});var xSo=s(CB);Sso=n(xSo,"STRONG",{});var kHn=s(Sso);F4a=r(kHn,"vit"),kHn.forEach(t),E4a=r(xSo," \u2014 "),x5e=n(xSo,"A",{href:!0});var SHn=s(x5e);y4a=r(SHn,"ViTForMaskedImageModeling"),SHn.forEach(t),C4a=r(xSo," (ViT model)"),xSo.forEach(t),tm.forEach(t),w4a=i(un),wB=n(un,"P",{});var GSo=s(wB);L4a=r(GSo,"The model is set in evaluation mode by default using "),xso=n(GSo,"CODE",{});var xHn=s(xso);A4a=r(xHn,"model.eval()"),xHn.forEach(t),R4a=r(GSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Gso=n(GSo,"CODE",{});var GHn=s(Gso);k4a=r(GHn,"model.train()"),GHn.forEach(t),GSo.forEach(t),S4a=i(un),M(LB.$$.fragment,un),un.forEach(t),rm.forEach(t),AIo=i(c),gh=n(c,"H3",{class:!0});var wjo=s(gh);AB=n(wjo,"A",{id:!0,class:!0,href:!0});var BHn=s(AB);Bso=n(BHn,"SPAN",{});var IHn=s(Bso);M(bX.$$.fragment,IHn),IHn.forEach(t),BHn.forEach(t),x4a=i(wjo),Iso=n(wjo,"SPAN",{});var NHn=s(Iso);G4a=r(NHn,"TFAutoModelForMaskedImageModeling"),NHn.forEach(t),wjo.forEach(t),RIo=i(c),wr=n(c,"DIV",{class:!0});var am=s(wr);M(vX.$$.fragment,am),B4a=i(am),uh=n(am,"P",{});var iwe=s(uh);I4a=r(iwe,`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 `),G5e=n(iwe,"A",{href:!0});var $Hn=s(G5e);N4a=r($Hn,"from_pretrained()"),$Hn.forEach(t),$4a=r(iwe," class method or the "),B5e=n(iwe,"A",{href:!0});var jHn=s(B5e);j4a=r(jHn,"from_config()"),jHn.forEach(t),P4a=r(iwe,` class
method.`),iwe.forEach(t),Z4a=i(am),MX=n(am,"P",{});var Ljo=s(MX);V4a=r(Ljo,"This class cannot be instantiated directly using "),Nso=n(Ljo,"CODE",{});var PHn=s(Nso);W4a=r(PHn,"__init__()"),PHn.forEach(t),J4a=r(Ljo," (throws an error)."),Ljo.forEach(t),U4a=i(am),wa=n(am,"DIV",{class:!0});var oZ=s(wa);M(TX.$$.fragment,oZ),X4a=i(oZ),$so=n(oZ,"P",{});var ZHn=s($so);D4a=r(ZHn,"Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),ZHn.forEach(t),q4a=i(oZ),ph=n(oZ,"P",{});var dwe=s(ph);O4a=r(dwe,`Note:
Loading a model from its configuration file does `),jso=n(dwe,"STRONG",{});var VHn=s(jso);Q4a=r(VHn,"not"),VHn.forEach(t),Y4a=r(dwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),I5e=n(dwe,"A",{href:!0});var WHn=s(I5e);z4a=r(WHn,"from_pretrained()"),WHn.forEach(t),H4a=r(dwe," to load the model weights."),dwe.forEach(t),K4a=i(oZ),M(RB.$$.fragment,oZ),oZ.forEach(t),eCa=i(am),Ct=n(am,"DIV",{class:!0});var nm=s(Ct);M(FX.$$.fragment,nm),oCa=i(nm),Pso=n(nm,"P",{});var JHn=s(Pso);rCa=r(JHn,"Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),JHn.forEach(t),tCa=i(nm),Es=n(nm,"P",{});var rZ=s(Es);aCa=r(rZ,"The model class to instantiate is selected based on the "),Zso=n(rZ,"CODE",{});var UHn=s(Zso);nCa=r(UHn,"model_type"),UHn.forEach(t),sCa=r(rZ,` property of the config object (either
passed as an argument or loaded from `),Vso=n(rZ,"CODE",{});var XHn=s(Vso);lCa=r(XHn,"pretrained_model_name_or_path"),XHn.forEach(t),iCa=r(rZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Wso=n(rZ,"CODE",{});var DHn=s(Wso);dCa=r(DHn,"pretrained_model_name_or_path"),DHn.forEach(t),mCa=r(rZ,":"),rZ.forEach(t),cCa=i(nm),EX=n(nm,"UL",{});var Ajo=s(EX);kB=n(Ajo,"LI",{});var BSo=s(kB);Jso=n(BSo,"STRONG",{});var qHn=s(Jso);fCa=r(qHn,"deit"),qHn.forEach(t),hCa=r(BSo," \u2014 "),N5e=n(BSo,"A",{href:!0});var OHn=s(N5e);gCa=r(OHn,"TFDeiTForMaskedImageModeling"),OHn.forEach(t),uCa=r(BSo," (DeiT model)"),BSo.forEach(t),pCa=i(Ajo),SB=n(Ajo,"LI",{});var ISo=s(SB);Uso=n(ISo,"STRONG",{});var QHn=s(Uso);_Ca=r(QHn,"swin"),QHn.forEach(t),bCa=r(ISo," \u2014 "),$5e=n(ISo,"A",{href:!0});var YHn=s($5e);vCa=r(YHn,"TFSwinForMaskedImageModeling"),YHn.forEach(t),MCa=r(ISo," (Swin Transformer model)"),ISo.forEach(t),Ajo.forEach(t),TCa=i(nm),M(xB.$$.fragment,nm),nm.forEach(t),am.forEach(t),kIo=i(c),_h=n(c,"H3",{class:!0});var Rjo=s(_h);GB=n(Rjo,"A",{id:!0,class:!0,href:!0});var zHn=s(GB);Xso=n(zHn,"SPAN",{});var HHn=s(Xso);M(yX.$$.fragment,HHn),HHn.forEach(t),zHn.forEach(t),FCa=i(Rjo),Dso=n(Rjo,"SPAN",{});var KHn=s(Dso);ECa=r(KHn,"AutoModelForObjectDetection"),KHn.forEach(t),Rjo.forEach(t),SIo=i(c),Lr=n(c,"DIV",{class:!0});var sm=s(Lr);M(CX.$$.fragment,sm),yCa=i(sm),bh=n(sm,"P",{});var mwe=s(bh);CCa=r(mwe,`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 `),j5e=n(mwe,"A",{href:!0});var eKn=s(j5e);wCa=r(eKn,"from_pretrained()"),eKn.forEach(t),LCa=r(mwe," class method or the "),P5e=n(mwe,"A",{href:!0});var oKn=s(P5e);ACa=r(oKn,"from_config()"),oKn.forEach(t),RCa=r(mwe,` class
method.`),mwe.forEach(t),kCa=i(sm),wX=n(sm,"P",{});var kjo=s(wX);SCa=r(kjo,"This class cannot be instantiated directly using "),qso=n(kjo,"CODE",{});var rKn=s(qso);xCa=r(rKn,"__init__()"),rKn.forEach(t),GCa=r(kjo," (throws an error)."),kjo.forEach(t),BCa=i(sm),La=n(sm,"DIV",{class:!0});var tZ=s(La);M(LX.$$.fragment,tZ),ICa=i(tZ),Oso=n(tZ,"P",{});var tKn=s(Oso);NCa=r(tKn,"Instantiates one of the model classes of the library (with a object detection head) from a configuration."),tKn.forEach(t),$Ca=i(tZ),vh=n(tZ,"P",{});var cwe=s(vh);jCa=r(cwe,`Note:
Loading a model from its configuration file does `),Qso=n(cwe,"STRONG",{});var aKn=s(Qso);PCa=r(aKn,"not"),aKn.forEach(t),ZCa=r(cwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Z5e=n(cwe,"A",{href:!0});var nKn=s(Z5e);VCa=r(nKn,"from_pretrained()"),nKn.forEach(t),WCa=r(cwe," to load the model weights."),cwe.forEach(t),JCa=i(tZ),M(BB.$$.fragment,tZ),tZ.forEach(t),UCa=i(sm),To=n(sm,"DIV",{class:!0});var pn=s(To);M(AX.$$.fragment,pn),XCa=i(pn),Yso=n(pn,"P",{});var sKn=s(Yso);DCa=r(sKn,"Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),sKn.forEach(t),qCa=i(pn),ys=n(pn,"P",{});var aZ=s(ys);OCa=r(aZ,"The model class to instantiate is selected based on the "),zso=n(aZ,"CODE",{});var lKn=s(zso);QCa=r(lKn,"model_type"),lKn.forEach(t),YCa=r(aZ,` property of the config object (either
passed as an argument or loaded from `),Hso=n(aZ,"CODE",{});var iKn=s(Hso);zCa=r(iKn,"pretrained_model_name_or_path"),iKn.forEach(t),HCa=r(aZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Kso=n(aZ,"CODE",{});var dKn=s(Kso);KCa=r(dKn,"pretrained_model_name_or_path"),dKn.forEach(t),ewa=r(aZ,":"),aZ.forEach(t),owa=i(pn),Ar=n(pn,"UL",{});var _n=s(Ar);IB=n(_n,"LI",{});var NSo=s(IB);elo=n(NSo,"STRONG",{});var mKn=s(elo);rwa=r(mKn,"conditional_detr"),mKn.forEach(t),twa=r(NSo," \u2014 "),V5e=n(NSo,"A",{href:!0});var cKn=s(V5e);awa=r(cKn,"ConditionalDetrForObjectDetection"),cKn.forEach(t),nwa=r(NSo," (Conditional DETR model)"),NSo.forEach(t),swa=i(_n),NB=n(_n,"LI",{});var $So=s(NB);olo=n($So,"STRONG",{});var fKn=s(olo);lwa=r(fKn,"deformable_detr"),fKn.forEach(t),iwa=r($So," \u2014 "),W5e=n($So,"A",{href:!0});var hKn=s(W5e);dwa=r(hKn,"DeformableDetrForObjectDetection"),hKn.forEach(t),mwa=r($So," (Deformable DETR model)"),$So.forEach(t),cwa=i(_n),$B=n(_n,"LI",{});var jSo=s($B);rlo=n(jSo,"STRONG",{});var gKn=s(rlo);fwa=r(gKn,"deta"),gKn.forEach(t),hwa=r(jSo," \u2014 "),J5e=n(jSo,"A",{href:!0});var uKn=s(J5e);gwa=r(uKn,"DetaForObjectDetection"),uKn.forEach(t),uwa=r(jSo," (DETA model)"),jSo.forEach(t),pwa=i(_n),jB=n(_n,"LI",{});var PSo=s(jB);tlo=n(PSo,"STRONG",{});var pKn=s(tlo);_wa=r(pKn,"detr"),pKn.forEach(t),bwa=r(PSo," \u2014 "),U5e=n(PSo,"A",{href:!0});var _Kn=s(U5e);vwa=r(_Kn,"DetrForObjectDetection"),_Kn.forEach(t),Mwa=r(PSo," (DETR model)"),PSo.forEach(t),Twa=i(_n),PB=n(_n,"LI",{});var ZSo=s(PB);alo=n(ZSo,"STRONG",{});var bKn=s(alo);Fwa=r(bKn,"table-transformer"),bKn.forEach(t),Ewa=r(ZSo," \u2014 "),X5e=n(ZSo,"A",{href:!0});var vKn=s(X5e);ywa=r(vKn,"TableTransformerForObjectDetection"),vKn.forEach(t),Cwa=r(ZSo," (Table Transformer model)"),ZSo.forEach(t),wwa=i(_n),ZB=n(_n,"LI",{});var VSo=s(ZB);nlo=n(VSo,"STRONG",{});var MKn=s(nlo);Lwa=r(MKn,"yolos"),MKn.forEach(t),Awa=r(VSo," \u2014 "),D5e=n(VSo,"A",{href:!0});var TKn=s(D5e);Rwa=r(TKn,"YolosForObjectDetection"),TKn.forEach(t),kwa=r(VSo," (YOLOS model)"),VSo.forEach(t),_n.forEach(t),Swa=i(pn),VB=n(pn,"P",{});var WSo=s(VB);xwa=r(WSo,"The model is set in evaluation mode by default using "),slo=n(WSo,"CODE",{});var FKn=s(slo);Gwa=r(FKn,"model.eval()"),FKn.forEach(t),Bwa=r(WSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),llo=n(WSo,"CODE",{});var EKn=s(llo);Iwa=r(EKn,"model.train()"),EKn.forEach(t),WSo.forEach(t),Nwa=i(pn),M(WB.$$.fragment,pn),pn.forEach(t),sm.forEach(t),xIo=i(c),Mh=n(c,"H3",{class:!0});var Sjo=s(Mh);JB=n(Sjo,"A",{id:!0,class:!0,href:!0});var yKn=s(JB);ilo=n(yKn,"SPAN",{});var CKn=s(ilo);M(RX.$$.fragment,CKn),CKn.forEach(t),yKn.forEach(t),$wa=i(Sjo),dlo=n(Sjo,"SPAN",{});var wKn=s(dlo);jwa=r(wKn,"AutoModelForImageSegmentation"),wKn.forEach(t),Sjo.forEach(t),GIo=i(c),Rr=n(c,"DIV",{class:!0});var lm=s(Rr);M(kX.$$.fragment,lm),Pwa=i(lm),Th=n(lm,"P",{});var fwe=s(Th);Zwa=r(fwe,`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 `),q5e=n(fwe,"A",{href:!0});var LKn=s(q5e);Vwa=r(LKn,"from_pretrained()"),LKn.forEach(t),Wwa=r(fwe," class method or the "),O5e=n(fwe,"A",{href:!0});var AKn=s(O5e);Jwa=r(AKn,"from_config()"),AKn.forEach(t),Uwa=r(fwe,` class
method.`),fwe.forEach(t),Xwa=i(lm),SX=n(lm,"P",{});var xjo=s(SX);Dwa=r(xjo,"This class cannot be instantiated directly using "),mlo=n(xjo,"CODE",{});var RKn=s(mlo);qwa=r(RKn,"__init__()"),RKn.forEach(t),Owa=r(xjo," (throws an error)."),xjo.forEach(t),Qwa=i(lm),Aa=n(lm,"DIV",{class:!0});var nZ=s(Aa);M(xX.$$.fragment,nZ),Ywa=i(nZ),clo=n(nZ,"P",{});var kKn=s(clo);zwa=r(kKn,"Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),kKn.forEach(t),Hwa=i(nZ),Fh=n(nZ,"P",{});var hwe=s(Fh);Kwa=r(hwe,`Note:
Loading a model from its configuration file does `),flo=n(hwe,"STRONG",{});var SKn=s(flo);e6a=r(SKn,"not"),SKn.forEach(t),o6a=r(hwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Q5e=n(hwe,"A",{href:!0});var xKn=s(Q5e);r6a=r(xKn,"from_pretrained()"),xKn.forEach(t),t6a=r(hwe," to load the model weights."),hwe.forEach(t),a6a=i(nZ),M(UB.$$.fragment,nZ),nZ.forEach(t),n6a=i(lm),Fo=n(lm,"DIV",{class:!0});var bn=s(Fo);M(GX.$$.fragment,bn),s6a=i(bn),hlo=n(bn,"P",{});var GKn=s(hlo);l6a=r(GKn,"Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),GKn.forEach(t),i6a=i(bn),Cs=n(bn,"P",{});var sZ=s(Cs);d6a=r(sZ,"The model class to instantiate is selected based on the "),glo=n(sZ,"CODE",{});var BKn=s(glo);m6a=r(BKn,"model_type"),BKn.forEach(t),c6a=r(sZ,` property of the config object (either
passed as an argument or loaded from `),ulo=n(sZ,"CODE",{});var IKn=s(ulo);f6a=r(IKn,"pretrained_model_name_or_path"),IKn.forEach(t),h6a=r(sZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),plo=n(sZ,"CODE",{});var NKn=s(plo);g6a=r(NKn,"pretrained_model_name_or_path"),NKn.forEach(t),u6a=r(sZ,":"),sZ.forEach(t),p6a=i(bn),_lo=n(bn,"UL",{});var $Kn=s(_lo);XB=n($Kn,"LI",{});var JSo=s(XB);blo=n(JSo,"STRONG",{});var jKn=s(blo);_6a=r(jKn,"detr"),jKn.forEach(t),b6a=r(JSo," \u2014 "),Y5e=n(JSo,"A",{href:!0});var PKn=s(Y5e);v6a=r(PKn,"DetrForSegmentation"),PKn.forEach(t),M6a=r(JSo," (DETR model)"),JSo.forEach(t),$Kn.forEach(t),T6a=i(bn),DB=n(bn,"P",{});var USo=s(DB);F6a=r(USo,"The model is set in evaluation mode by default using "),vlo=n(USo,"CODE",{});var ZKn=s(vlo);E6a=r(ZKn,"model.eval()"),ZKn.forEach(t),y6a=r(USo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Mlo=n(USo,"CODE",{});var VKn=s(Mlo);C6a=r(VKn,"model.train()"),VKn.forEach(t),USo.forEach(t),w6a=i(bn),M(qB.$$.fragment,bn),bn.forEach(t),lm.forEach(t),BIo=i(c),Eh=n(c,"H3",{class:!0});var Gjo=s(Eh);OB=n(Gjo,"A",{id:!0,class:!0,href:!0});var WKn=s(OB);Tlo=n(WKn,"SPAN",{});var JKn=s(Tlo);M(BX.$$.fragment,JKn),JKn.forEach(t),WKn.forEach(t),L6a=i(Gjo),Flo=n(Gjo,"SPAN",{});var UKn=s(Flo);A6a=r(UKn,"AutoModelForSemanticSegmentation"),UKn.forEach(t),Gjo.forEach(t),IIo=i(c),kr=n(c,"DIV",{class:!0});var im=s(kr);M(IX.$$.fragment,im),R6a=i(im),yh=n(im,"P",{});var gwe=s(yh);k6a=r(gwe,`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 `),z5e=n(gwe,"A",{href:!0});var XKn=s(z5e);S6a=r(XKn,"from_pretrained()"),XKn.forEach(t),x6a=r(gwe," class method or the "),H5e=n(gwe,"A",{href:!0});var DKn=s(H5e);G6a=r(DKn,"from_config()"),DKn.forEach(t),B6a=r(gwe,` class
method.`),gwe.forEach(t),I6a=i(im),NX=n(im,"P",{});var Bjo=s(NX);N6a=r(Bjo,"This class cannot be instantiated directly using "),Elo=n(Bjo,"CODE",{});var qKn=s(Elo);$6a=r(qKn,"__init__()"),qKn.forEach(t),j6a=r(Bjo," (throws an error)."),Bjo.forEach(t),P6a=i(im),Ra=n(im,"DIV",{class:!0});var lZ=s(Ra);M($X.$$.fragment,lZ),Z6a=i(lZ),ylo=n(lZ,"P",{});var OKn=s(ylo);V6a=r(OKn,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),OKn.forEach(t),W6a=i(lZ),Ch=n(lZ,"P",{});var uwe=s(Ch);J6a=r(uwe,`Note:
Loading a model from its configuration file does `),Clo=n(uwe,"STRONG",{});var QKn=s(Clo);U6a=r(QKn,"not"),QKn.forEach(t),X6a=r(uwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),K5e=n(uwe,"A",{href:!0});var YKn=s(K5e);D6a=r(YKn,"from_pretrained()"),YKn.forEach(t),q6a=r(uwe," to load the model weights."),uwe.forEach(t),O6a=i(lZ),M(QB.$$.fragment,lZ),lZ.forEach(t),Q6a=i(im),Eo=n(im,"DIV",{class:!0});var vn=s(Eo);M(jX.$$.fragment,vn),Y6a=i(vn),wlo=n(vn,"P",{});var zKn=s(wlo);z6a=r(zKn,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),zKn.forEach(t),H6a=i(vn),ws=n(vn,"P",{});var iZ=s(ws);K6a=r(iZ,"The model class to instantiate is selected based on the "),Llo=n(iZ,"CODE",{});var HKn=s(Llo);e8a=r(HKn,"model_type"),HKn.forEach(t),o8a=r(iZ,` property of the config object (either
passed as an argument or loaded from `),Alo=n(iZ,"CODE",{});var KKn=s(Alo);r8a=r(KKn,"pretrained_model_name_or_path"),KKn.forEach(t),t8a=r(iZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Rlo=n(iZ,"CODE",{});var ees=s(Rlo);a8a=r(ees,"pretrained_model_name_or_path"),ees.forEach(t),n8a=r(iZ,":"),iZ.forEach(t),s8a=i(vn),Ye=n(vn,"UL",{});var $o=s(Ye);YB=n($o,"LI",{});var XSo=s(YB);klo=n(XSo,"STRONG",{});var oes=s(klo);l8a=r(oes,"beit"),oes.forEach(t),i8a=r(XSo," \u2014 "),e3e=n(XSo,"A",{href:!0});var res=s(e3e);d8a=r(res,"BeitForSemanticSegmentation"),res.forEach(t),m8a=r(XSo," (BEiT model)"),XSo.forEach(t),c8a=i($o),zB=n($o,"LI",{});var DSo=s(zB);Slo=n(DSo,"STRONG",{});var tes=s(Slo);f8a=r(tes,"data2vec-vision"),tes.forEach(t),h8a=r(DSo," \u2014 "),o3e=n(DSo,"A",{href:!0});var aes=s(o3e);g8a=r(aes,"Data2VecVisionForSemanticSegmentation"),aes.forEach(t),u8a=r(DSo," (Data2VecVision model)"),DSo.forEach(t),p8a=i($o),HB=n($o,"LI",{});var qSo=s(HB);xlo=n(qSo,"STRONG",{});var nes=s(xlo);_8a=r(nes,"dpt"),nes.forEach(t),b8a=r(qSo," \u2014 "),r3e=n(qSo,"A",{href:!0});var ses=s(r3e);v8a=r(ses,"DPTForSemanticSegmentation"),ses.forEach(t),M8a=r(qSo," (DPT model)"),qSo.forEach(t),T8a=i($o),KB=n($o,"LI",{});var OSo=s(KB);Glo=n(OSo,"STRONG",{});var les=s(Glo);F8a=r(les,"mobilenet_v2"),les.forEach(t),E8a=r(OSo," \u2014 "),t3e=n(OSo,"A",{href:!0});var ies=s(t3e);y8a=r(ies,"MobileNetV2ForSemanticSegmentation"),ies.forEach(t),C8a=r(OSo," (MobileNetV2 model)"),OSo.forEach(t),w8a=i($o),eI=n($o,"LI",{});var QSo=s(eI);Blo=n(QSo,"STRONG",{});var des=s(Blo);L8a=r(des,"mobilevit"),des.forEach(t),A8a=r(QSo," \u2014 "),a3e=n(QSo,"A",{href:!0});var mes=s(a3e);R8a=r(mes,"MobileViTForSemanticSegmentation"),mes.forEach(t),k8a=r(QSo," (MobileViT model)"),QSo.forEach(t),S8a=i($o),oI=n($o,"LI",{});var YSo=s(oI);Ilo=n(YSo,"STRONG",{});var ces=s(Ilo);x8a=r(ces,"mobilevitv2"),ces.forEach(t),G8a=r(YSo," \u2014 "),n3e=n(YSo,"A",{href:!0});var fes=s(n3e);B8a=r(fes,"MobileViTV2ForSemanticSegmentation"),fes.forEach(t),I8a=r(YSo," (MobileViTV2 model)"),YSo.forEach(t),N8a=i($o),rI=n($o,"LI",{});var zSo=s(rI);Nlo=n(zSo,"STRONG",{});var hes=s(Nlo);$8a=r(hes,"segformer"),hes.forEach(t),j8a=r(zSo," \u2014 "),s3e=n(zSo,"A",{href:!0});var ges=s(s3e);P8a=r(ges,"SegformerForSemanticSegmentation"),ges.forEach(t),Z8a=r(zSo," (SegFormer model)"),zSo.forEach(t),V8a=i($o),tI=n($o,"LI",{});var HSo=s(tI);$lo=n(HSo,"STRONG",{});var ues=s($lo);W8a=r(ues,"upernet"),ues.forEach(t),J8a=r(HSo," \u2014 "),l3e=n(HSo,"A",{href:!0});var pes=s(l3e);U8a=r(pes,"UperNetForSemanticSegmentation"),pes.forEach(t),X8a=r(HSo," (UPerNet model)"),HSo.forEach(t),$o.forEach(t),D8a=i(vn),aI=n(vn,"P",{});var KSo=s(aI);q8a=r(KSo,"The model is set in evaluation mode by default using "),jlo=n(KSo,"CODE",{});var _es=s(jlo);O8a=r(_es,"model.eval()"),_es.forEach(t),Q8a=r(KSo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Plo=n(KSo,"CODE",{});var bes=s(Plo);Y8a=r(bes,"model.train()"),bes.forEach(t),KSo.forEach(t),z8a=i(vn),M(nI.$$.fragment,vn),vn.forEach(t),im.forEach(t),NIo=i(c),wh=n(c,"H3",{class:!0});var Ijo=s(wh);sI=n(Ijo,"A",{id:!0,class:!0,href:!0});var ves=s(sI);Zlo=n(ves,"SPAN",{});var Mes=s(Zlo);M(PX.$$.fragment,Mes),Mes.forEach(t),ves.forEach(t),H8a=i(Ijo),Vlo=n(Ijo,"SPAN",{});var Tes=s(Vlo);K8a=r(Tes,"TFAutoModelForSemanticSegmentation"),Tes.forEach(t),Ijo.forEach(t),$Io=i(c),Sr=n(c,"DIV",{class:!0});var dm=s(Sr);M(ZX.$$.fragment,dm),e7a=i(dm),Lh=n(dm,"P",{});var pwe=s(Lh);o7a=r(pwe,`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 `),i3e=n(pwe,"A",{href:!0});var Fes=s(i3e);r7a=r(Fes,"from_pretrained()"),Fes.forEach(t),t7a=r(pwe," class method or the "),d3e=n(pwe,"A",{href:!0});var Ees=s(d3e);a7a=r(Ees,"from_config()"),Ees.forEach(t),n7a=r(pwe,` class
method.`),pwe.forEach(t),s7a=i(dm),VX=n(dm,"P",{});var Njo=s(VX);l7a=r(Njo,"This class cannot be instantiated directly using "),Wlo=n(Njo,"CODE",{});var yes=s(Wlo);i7a=r(yes,"__init__()"),yes.forEach(t),d7a=r(Njo," (throws an error)."),Njo.forEach(t),m7a=i(dm),ka=n(dm,"DIV",{class:!0});var dZ=s(ka);M(WX.$$.fragment,dZ),c7a=i(dZ),Jlo=n(dZ,"P",{});var Ces=s(Jlo);f7a=r(Ces,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),Ces.forEach(t),h7a=i(dZ),Ah=n(dZ,"P",{});var _we=s(Ah);g7a=r(_we,`Note:
Loading a model from its configuration file does `),Ulo=n(_we,"STRONG",{});var wes=s(Ulo);u7a=r(wes,"not"),wes.forEach(t),p7a=r(_we,` load the model weights. It only affects the
model\u2019s configuration. Use `),m3e=n(_we,"A",{href:!0});var Les=s(m3e);_7a=r(Les,"from_pretrained()"),Les.forEach(t),b7a=r(_we," to load the model weights."),_we.forEach(t),v7a=i(dZ),M(lI.$$.fragment,dZ),dZ.forEach(t),M7a=i(dm),wt=n(dm,"DIV",{class:!0});var mm=s(wt);M(JX.$$.fragment,mm),T7a=i(mm),Xlo=n(mm,"P",{});var Aes=s(Xlo);F7a=r(Aes,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),Aes.forEach(t),E7a=i(mm),Ls=n(mm,"P",{});var mZ=s(Ls);y7a=r(mZ,"The model class to instantiate is selected based on the "),Dlo=n(mZ,"CODE",{});var Res=s(Dlo);C7a=r(Res,"model_type"),Res.forEach(t),w7a=r(mZ,` property of the config object (either
passed as an argument or loaded from `),qlo=n(mZ,"CODE",{});var kes=s(qlo);L7a=r(kes,"pretrained_model_name_or_path"),kes.forEach(t),A7a=r(mZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Olo=n(mZ,"CODE",{});var Ses=s(Olo);R7a=r(Ses,"pretrained_model_name_or_path"),Ses.forEach(t),k7a=r(mZ,":"),mZ.forEach(t),S7a=i(mm),Rh=n(mm,"UL",{});var bwe=s(Rh);iI=n(bwe,"LI",{});var exo=s(iI);Qlo=n(exo,"STRONG",{});var xes=s(Qlo);x7a=r(xes,"data2vec-vision"),xes.forEach(t),G7a=r(exo," \u2014 "),c3e=n(exo,"A",{href:!0});var Ges=s(c3e);B7a=r(Ges,"TFData2VecVisionForSemanticSegmentation"),Ges.forEach(t),I7a=r(exo," (Data2VecVision model)"),exo.forEach(t),N7a=i(bwe),dI=n(bwe,"LI",{});var oxo=s(dI);Ylo=n(oxo,"STRONG",{});var Bes=s(Ylo);$7a=r(Bes,"mobilevit"),Bes.forEach(t),j7a=r(oxo," \u2014 "),f3e=n(oxo,"A",{href:!0});var Ies=s(f3e);P7a=r(Ies,"TFMobileViTForSemanticSegmentation"),Ies.forEach(t),Z7a=r(oxo," (MobileViT model)"),oxo.forEach(t),V7a=i(bwe),mI=n(bwe,"LI",{});var rxo=s(mI);zlo=n(rxo,"STRONG",{});var Nes=s(zlo);W7a=r(Nes,"segformer"),Nes.forEach(t),J7a=r(rxo," \u2014 "),h3e=n(rxo,"A",{href:!0});var $es=s(h3e);U7a=r($es,"TFSegformerForSemanticSegmentation"),$es.forEach(t),X7a=r(rxo," (SegFormer model)"),rxo.forEach(t),bwe.forEach(t),D7a=i(mm),M(cI.$$.fragment,mm),mm.forEach(t),dm.forEach(t),jIo=i(c),kh=n(c,"H3",{class:!0});var $jo=s(kh);fI=n($jo,"A",{id:!0,class:!0,href:!0});var jes=s(fI);Hlo=n(jes,"SPAN",{});var Pes=s(Hlo);M(UX.$$.fragment,Pes),Pes.forEach(t),jes.forEach(t),q7a=i($jo),Klo=n($jo,"SPAN",{});var Zes=s(Klo);O7a=r(Zes,"AutoModelForInstanceSegmentation"),Zes.forEach(t),$jo.forEach(t),PIo=i(c),xr=n(c,"DIV",{class:!0});var cm=s(xr);M(XX.$$.fragment,cm),Q7a=i(cm),Sh=n(cm,"P",{});var vwe=s(Sh);Y7a=r(vwe,`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 `),g3e=n(vwe,"A",{href:!0});var Ves=s(g3e);z7a=r(Ves,"from_pretrained()"),Ves.forEach(t),H7a=r(vwe," class method or the "),u3e=n(vwe,"A",{href:!0});var Wes=s(u3e);K7a=r(Wes,"from_config()"),Wes.forEach(t),eLa=r(vwe,` class
method.`),vwe.forEach(t),oLa=i(cm),DX=n(cm,"P",{});var jjo=s(DX);rLa=r(jjo,"This class cannot be instantiated directly using "),eio=n(jjo,"CODE",{});var Jes=s(eio);tLa=r(Jes,"__init__()"),Jes.forEach(t),aLa=r(jjo," (throws an error)."),jjo.forEach(t),nLa=i(cm),Sa=n(cm,"DIV",{class:!0});var cZ=s(Sa);M(qX.$$.fragment,cZ),sLa=i(cZ),oio=n(cZ,"P",{});var Ues=s(oio);lLa=r(Ues,"Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),Ues.forEach(t),iLa=i(cZ),xh=n(cZ,"P",{});var Mwe=s(xh);dLa=r(Mwe,`Note:
Loading a model from its configuration file does `),rio=n(Mwe,"STRONG",{});var Xes=s(rio);mLa=r(Xes,"not"),Xes.forEach(t),cLa=r(Mwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),p3e=n(Mwe,"A",{href:!0});var Des=s(p3e);fLa=r(Des,"from_pretrained()"),Des.forEach(t),hLa=r(Mwe," to load the model weights."),Mwe.forEach(t),gLa=i(cZ),M(hI.$$.fragment,cZ),cZ.forEach(t),uLa=i(cm),yo=n(cm,"DIV",{class:!0});var Mn=s(yo);M(OX.$$.fragment,Mn),pLa=i(Mn),tio=n(Mn,"P",{});var qes=s(tio);_La=r(qes,"Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),qes.forEach(t),bLa=i(Mn),As=n(Mn,"P",{});var fZ=s(As);vLa=r(fZ,"The model class to instantiate is selected based on the "),aio=n(fZ,"CODE",{});var Oes=s(aio);MLa=r(Oes,"model_type"),Oes.forEach(t),TLa=r(fZ,` property of the config object (either
passed as an argument or loaded from `),nio=n(fZ,"CODE",{});var Qes=s(nio);FLa=r(Qes,"pretrained_model_name_or_path"),Qes.forEach(t),ELa=r(fZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),sio=n(fZ,"CODE",{});var Yes=s(sio);yLa=r(Yes,"pretrained_model_name_or_path"),Yes.forEach(t),CLa=r(fZ,":"),fZ.forEach(t),wLa=i(Mn),lio=n(Mn,"UL",{});var zes=s(lio);gI=n(zes,"LI",{});var txo=s(gI);iio=n(txo,"STRONG",{});var Hes=s(iio);LLa=r(Hes,"maskformer"),Hes.forEach(t),ALa=r(txo," \u2014 "),_3e=n(txo,"A",{href:!0});var Kes=s(_3e);RLa=r(Kes,"MaskFormerForInstanceSegmentation"),Kes.forEach(t),kLa=r(txo," (MaskFormer model)"),txo.forEach(t),zes.forEach(t),SLa=i(Mn),uI=n(Mn,"P",{});var axo=s(uI);xLa=r(axo,"The model is set in evaluation mode by default using "),dio=n(axo,"CODE",{});var eos=s(dio);GLa=r(eos,"model.eval()"),eos.forEach(t),BLa=r(axo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),mio=n(axo,"CODE",{});var oos=s(mio);ILa=r(oos,"model.train()"),oos.forEach(t),axo.forEach(t),NLa=i(Mn),M(pI.$$.fragment,Mn),Mn.forEach(t),cm.forEach(t),ZIo=i(c),Gh=n(c,"H3",{class:!0});var Pjo=s(Gh);_I=n(Pjo,"A",{id:!0,class:!0,href:!0});var ros=s(_I);cio=n(ros,"SPAN",{});var tos=s(cio);M(QX.$$.fragment,tos),tos.forEach(t),ros.forEach(t),$La=i(Pjo),fio=n(Pjo,"SPAN",{});var aos=s(fio);jLa=r(aos,"AutoModelForUniversalSegmentation"),aos.forEach(t),Pjo.forEach(t),VIo=i(c),Gr=n(c,"DIV",{class:!0});var fm=s(Gr);M(YX.$$.fragment,fm),PLa=i(fm),Bh=n(fm,"P",{});var Twe=s(Bh);ZLa=r(Twe,`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 `),b3e=n(Twe,"A",{href:!0});var nos=s(b3e);VLa=r(nos,"from_pretrained()"),nos.forEach(t),WLa=r(Twe," class method or the "),v3e=n(Twe,"A",{href:!0});var sos=s(v3e);JLa=r(sos,"from_config()"),sos.forEach(t),ULa=r(Twe,` class
method.`),Twe.forEach(t),XLa=i(fm),zX=n(fm,"P",{});var Zjo=s(zX);DLa=r(Zjo,"This class cannot be instantiated directly using "),hio=n(Zjo,"CODE",{});var los=s(hio);qLa=r(los,"__init__()"),los.forEach(t),OLa=r(Zjo," (throws an error)."),Zjo.forEach(t),QLa=i(fm),xa=n(fm,"DIV",{class:!0});var hZ=s(xa);M(HX.$$.fragment,hZ),YLa=i(hZ),gio=n(hZ,"P",{});var ios=s(gio);zLa=r(ios,"Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration."),ios.forEach(t),HLa=i(hZ),Ih=n(hZ,"P",{});var Fwe=s(Ih);KLa=r(Fwe,`Note:
Loading a model from its configuration file does `),uio=n(Fwe,"STRONG",{});var dos=s(uio);eAa=r(dos,"not"),dos.forEach(t),oAa=r(Fwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),M3e=n(Fwe,"A",{href:!0});var mos=s(M3e);rAa=r(mos,"from_pretrained()"),mos.forEach(t),tAa=r(Fwe," to load the model weights."),Fwe.forEach(t),aAa=i(hZ),M(bI.$$.fragment,hZ),hZ.forEach(t),nAa=i(fm),Co=n(fm,"DIV",{class:!0});var Tn=s(Co);M(KX.$$.fragment,Tn),sAa=i(Tn),pio=n(Tn,"P",{});var cos=s(pio);lAa=r(cos,"Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model."),cos.forEach(t),iAa=i(Tn),Rs=n(Tn,"P",{});var gZ=s(Rs);dAa=r(gZ,"The model class to instantiate is selected based on the "),_io=n(gZ,"CODE",{});var fos=s(_io);mAa=r(fos,"model_type"),fos.forEach(t),cAa=r(gZ,` property of the config object (either
passed as an argument or loaded from `),bio=n(gZ,"CODE",{});var hos=s(bio);fAa=r(hos,"pretrained_model_name_or_path"),hos.forEach(t),hAa=r(gZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),vio=n(gZ,"CODE",{});var gos=s(vio);gAa=r(gos,"pretrained_model_name_or_path"),gos.forEach(t),uAa=r(gZ,":"),gZ.forEach(t),pAa=i(Tn),ks=n(Tn,"UL",{});var uZ=s(ks);vI=n(uZ,"LI",{});var nxo=s(vI);Mio=n(nxo,"STRONG",{});var uos=s(Mio);_Aa=r(uos,"detr"),uos.forEach(t),bAa=r(nxo," \u2014 "),T3e=n(nxo,"A",{href:!0});var pos=s(T3e);vAa=r(pos,"DetrForSegmentation"),pos.forEach(t),MAa=r(nxo," (DETR model)"),nxo.forEach(t),TAa=i(uZ),MI=n(uZ,"LI",{});var sxo=s(MI);Tio=n(sxo,"STRONG",{});var _os=s(Tio);FAa=r(_os,"mask2former"),_os.forEach(t),EAa=r(sxo," \u2014 "),F3e=n(sxo,"A",{href:!0});var bos=s(F3e);yAa=r(bos,"Mask2FormerForUniversalSegmentation"),bos.forEach(t),CAa=r(sxo," (Mask2Former model)"),sxo.forEach(t),wAa=i(uZ),TI=n(uZ,"LI",{});var lxo=s(TI);Fio=n(lxo,"STRONG",{});var vos=s(Fio);LAa=r(vos,"maskformer"),vos.forEach(t),AAa=r(lxo," \u2014 "),E3e=n(lxo,"A",{href:!0});var Mos=s(E3e);RAa=r(Mos,"MaskFormerForInstanceSegmentation"),Mos.forEach(t),kAa=r(lxo," (MaskFormer model)"),lxo.forEach(t),SAa=i(uZ),FI=n(uZ,"LI",{});var ixo=s(FI);Eio=n(ixo,"STRONG",{});var Tos=s(Eio);xAa=r(Tos,"oneformer"),Tos.forEach(t),GAa=r(ixo," \u2014 "),y3e=n(ixo,"A",{href:!0});var Fos=s(y3e);BAa=r(Fos,"OneFormerForUniversalSegmentation"),Fos.forEach(t),IAa=r(ixo," (OneFormer model)"),ixo.forEach(t),uZ.forEach(t),NAa=i(Tn),EI=n(Tn,"P",{});var dxo=s(EI);$Aa=r(dxo,"The model is set in evaluation mode by default using "),yio=n(dxo,"CODE",{});var Eos=s(yio);jAa=r(Eos,"model.eval()"),Eos.forEach(t),PAa=r(dxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Cio=n(dxo,"CODE",{});var yos=s(Cio);ZAa=r(yos,"model.train()"),yos.forEach(t),dxo.forEach(t),VAa=i(Tn),M(yI.$$.fragment,Tn),Tn.forEach(t),fm.forEach(t),WIo=i(c),Nh=n(c,"H3",{class:!0});var Vjo=s(Nh);CI=n(Vjo,"A",{id:!0,class:!0,href:!0});var Cos=s(CI);wio=n(Cos,"SPAN",{});var wos=s(wio);M(eD.$$.fragment,wos),wos.forEach(t),Cos.forEach(t),WAa=i(Vjo),Lio=n(Vjo,"SPAN",{});var Los=s(Lio);JAa=r(Los,"AutoModelForZeroShotImageClassification"),Los.forEach(t),Vjo.forEach(t),JIo=i(c),Br=n(c,"DIV",{class:!0});var hm=s(Br);M(oD.$$.fragment,hm),UAa=i(hm),$h=n(hm,"P",{});var Ewe=s($h);XAa=r(Ewe,`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(Ewe,"A",{href:!0});var Aos=s(C3e);DAa=r(Aos,"from_pretrained()"),Aos.forEach(t),qAa=r(Ewe," class method or the "),w3e=n(Ewe,"A",{href:!0});var Ros=s(w3e);OAa=r(Ros,"from_config()"),Ros.forEach(t),QAa=r(Ewe,` class
method.`),Ewe.forEach(t),YAa=i(hm),rD=n(hm,"P",{});var Wjo=s(rD);zAa=r(Wjo,"This class cannot be instantiated directly using "),Aio=n(Wjo,"CODE",{});var kos=s(Aio);HAa=r(kos,"__init__()"),kos.forEach(t),KAa=r(Wjo," (throws an error)."),Wjo.forEach(t),eRa=i(hm),Ga=n(hm,"DIV",{class:!0});var pZ=s(Ga);M(tD.$$.fragment,pZ),oRa=i(pZ),Rio=n(pZ,"P",{});var Sos=s(Rio);rRa=r(Sos,"Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),Sos.forEach(t),tRa=i(pZ),jh=n(pZ,"P",{});var ywe=s(jh);aRa=r(ywe,`Note:
Loading a model from its configuration file does `),kio=n(ywe,"STRONG",{});var xos=s(kio);nRa=r(xos,"not"),xos.forEach(t),sRa=r(ywe,` load the model weights. It only affects the
model\u2019s configuration. Use `),L3e=n(ywe,"A",{href:!0});var Gos=s(L3e);lRa=r(Gos,"from_pretrained()"),Gos.forEach(t),iRa=r(ywe," to load the model weights."),ywe.forEach(t),dRa=i(pZ),M(wI.$$.fragment,pZ),pZ.forEach(t),mRa=i(hm),wo=n(hm,"DIV",{class:!0});var Fn=s(wo);M(aD.$$.fragment,Fn),cRa=i(Fn),Sio=n(Fn,"P",{});var Bos=s(Sio);fRa=r(Bos,"Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),Bos.forEach(t),hRa=i(Fn),Ss=n(Fn,"P",{});var _Z=s(Ss);gRa=r(_Z,"The model class to instantiate is selected based on the "),xio=n(_Z,"CODE",{});var Ios=s(xio);uRa=r(Ios,"model_type"),Ios.forEach(t),pRa=r(_Z,` property of the config object (either
passed as an argument or loaded from `),Gio=n(_Z,"CODE",{});var Nos=s(Gio);_Ra=r(Nos,"pretrained_model_name_or_path"),Nos.forEach(t),bRa=r(_Z,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Bio=n(_Z,"CODE",{});var $os=s(Bio);vRa=r($os,"pretrained_model_name_or_path"),$os.forEach(t),MRa=r(_Z,":"),_Z.forEach(t),TRa=i(Fn),Ir=n(Fn,"UL",{});var En=s(Ir);LI=n(En,"LI",{});var mxo=s(LI);Iio=n(mxo,"STRONG",{});var jos=s(Iio);FRa=r(jos,"align"),jos.forEach(t),ERa=r(mxo," \u2014 "),A3e=n(mxo,"A",{href:!0});var Pos=s(A3e);yRa=r(Pos,"AlignModel"),Pos.forEach(t),CRa=r(mxo," (ALIGN model)"),mxo.forEach(t),wRa=i(En),AI=n(En,"LI",{});var cxo=s(AI);Nio=n(cxo,"STRONG",{});var Zos=s(Nio);LRa=r(Zos,"altclip"),Zos.forEach(t),ARa=r(cxo," \u2014 "),R3e=n(cxo,"A",{href:!0});var Vos=s(R3e);RRa=r(Vos,"AltCLIPModel"),Vos.forEach(t),kRa=r(cxo," (AltCLIP model)"),cxo.forEach(t),SRa=i(En),RI=n(En,"LI",{});var fxo=s(RI);$io=n(fxo,"STRONG",{});var Wos=s($io);xRa=r(Wos,"blip"),Wos.forEach(t),GRa=r(fxo," \u2014 "),k3e=n(fxo,"A",{href:!0});var Jos=s(k3e);BRa=r(Jos,"BlipModel"),Jos.forEach(t),IRa=r(fxo," (BLIP model)"),fxo.forEach(t),NRa=i(En),kI=n(En,"LI",{});var hxo=s(kI);jio=n(hxo,"STRONG",{});var Uos=s(jio);$Ra=r(Uos,"chinese_clip"),Uos.forEach(t),jRa=r(hxo," \u2014 "),S3e=n(hxo,"A",{href:!0});var Xos=s(S3e);PRa=r(Xos,"ChineseCLIPModel"),Xos.forEach(t),ZRa=r(hxo," (Chinese-CLIP model)"),hxo.forEach(t),VRa=i(En),SI=n(En,"LI",{});var gxo=s(SI);Pio=n(gxo,"STRONG",{});var Dos=s(Pio);WRa=r(Dos,"clip"),Dos.forEach(t),JRa=r(gxo," \u2014 "),x3e=n(gxo,"A",{href:!0});var qos=s(x3e);URa=r(qos,"CLIPModel"),qos.forEach(t),XRa=r(gxo," (CLIP model)"),gxo.forEach(t),DRa=i(En),xI=n(En,"LI",{});var uxo=s(xI);Zio=n(uxo,"STRONG",{});var Oos=s(Zio);qRa=r(Oos,"clipseg"),Oos.forEach(t),ORa=r(uxo," \u2014 "),G3e=n(uxo,"A",{href:!0});var Qos=s(G3e);QRa=r(Qos,"CLIPSegModel"),Qos.forEach(t),YRa=r(uxo," (CLIPSeg model)"),uxo.forEach(t),En.forEach(t),zRa=i(Fn),GI=n(Fn,"P",{});var pxo=s(GI);HRa=r(pxo,"The model is set in evaluation mode by default using "),Vio=n(pxo,"CODE",{});var Yos=s(Vio);KRa=r(Yos,"model.eval()"),Yos.forEach(t),eka=r(pxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Wio=n(pxo,"CODE",{});var zos=s(Wio);oka=r(zos,"model.train()"),zos.forEach(t),pxo.forEach(t),rka=i(Fn),M(BI.$$.fragment,Fn),Fn.forEach(t),hm.forEach(t),UIo=i(c),Ph=n(c,"H3",{class:!0});var Jjo=s(Ph);II=n(Jjo,"A",{id:!0,class:!0,href:!0});var Hos=s(II);Jio=n(Hos,"SPAN",{});var Kos=s(Jio);M(nD.$$.fragment,Kos),Kos.forEach(t),Hos.forEach(t),tka=i(Jjo),Uio=n(Jjo,"SPAN",{});var ers=s(Uio);aka=r(ers,"TFAutoModelForZeroShotImageClassification"),ers.forEach(t),Jjo.forEach(t),XIo=i(c),Nr=n(c,"DIV",{class:!0});var gm=s(Nr);M(sD.$$.fragment,gm),nka=i(gm),Zh=n(gm,"P",{});var Cwe=s(Zh);ska=r(Cwe,`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 `),B3e=n(Cwe,"A",{href:!0});var ors=s(B3e);lka=r(ors,"from_pretrained()"),ors.forEach(t),ika=r(Cwe," class method or the "),I3e=n(Cwe,"A",{href:!0});var rrs=s(I3e);dka=r(rrs,"from_config()"),rrs.forEach(t),mka=r(Cwe,` class
method.`),Cwe.forEach(t),cka=i(gm),lD=n(gm,"P",{});var Ujo=s(lD);fka=r(Ujo,"This class cannot be instantiated directly using "),Xio=n(Ujo,"CODE",{});var trs=s(Xio);hka=r(trs,"__init__()"),trs.forEach(t),gka=r(Ujo," (throws an error)."),Ujo.forEach(t),uka=i(gm),Ba=n(gm,"DIV",{class:!0});var bZ=s(Ba);M(iD.$$.fragment,bZ),pka=i(bZ),Dio=n(bZ,"P",{});var ars=s(Dio);_ka=r(ars,"Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration."),ars.forEach(t),bka=i(bZ),Vh=n(bZ,"P",{});var wwe=s(Vh);vka=r(wwe,`Note:
Loading a model from its configuration file does `),qio=n(wwe,"STRONG",{});var nrs=s(qio);Mka=r(nrs,"not"),nrs.forEach(t),Tka=r(wwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),N3e=n(wwe,"A",{href:!0});var srs=s(N3e);Fka=r(srs,"from_pretrained()"),srs.forEach(t),Eka=r(wwe," to load the model weights."),wwe.forEach(t),yka=i(bZ),M(NI.$$.fragment,bZ),bZ.forEach(t),Cka=i(gm),Lt=n(gm,"DIV",{class:!0});var um=s(Lt);M(dD.$$.fragment,um),wka=i(um),Oio=n(um,"P",{});var lrs=s(Oio);Lka=r(lrs,"Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model."),lrs.forEach(t),Aka=i(um),xs=n(um,"P",{});var vZ=s(xs);Rka=r(vZ,"The model class to instantiate is selected based on the "),Qio=n(vZ,"CODE",{});var irs=s(Qio);kka=r(irs,"model_type"),irs.forEach(t),Ska=r(vZ,` property of the config object (either
passed as an argument or loaded from `),Yio=n(vZ,"CODE",{});var drs=s(Yio);xka=r(drs,"pretrained_model_name_or_path"),drs.forEach(t),Gka=r(vZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),zio=n(vZ,"CODE",{});var mrs=s(zio);Bka=r(mrs,"pretrained_model_name_or_path"),mrs.forEach(t),Ika=r(vZ,":"),vZ.forEach(t),Nka=i(um),mD=n(um,"UL",{});var Xjo=s(mD);$I=n(Xjo,"LI",{});var _xo=s($I);Hio=n(_xo,"STRONG",{});var crs=s(Hio);$ka=r(crs,"blip"),crs.forEach(t),jka=r(_xo," \u2014 "),$3e=n(_xo,"A",{href:!0});var frs=s($3e);Pka=r(frs,"TFBlipModel"),frs.forEach(t),Zka=r(_xo," (BLIP model)"),_xo.forEach(t),Vka=i(Xjo),jI=n(Xjo,"LI",{});var bxo=s(jI);Kio=n(bxo,"STRONG",{});var hrs=s(Kio);Wka=r(hrs,"clip"),hrs.forEach(t),Jka=r(bxo," \u2014 "),j3e=n(bxo,"A",{href:!0});var grs=s(j3e);Uka=r(grs,"TFCLIPModel"),grs.forEach(t),Xka=r(bxo," (CLIP model)"),bxo.forEach(t),Xjo.forEach(t),Dka=i(um),M(PI.$$.fragment,um),um.forEach(t),gm.forEach(t),DIo=i(c),Wh=n(c,"H3",{class:!0});var Djo=s(Wh);ZI=n(Djo,"A",{id:!0,class:!0,href:!0});var urs=s(ZI);edo=n(urs,"SPAN",{});var prs=s(edo);M(cD.$$.fragment,prs),prs.forEach(t),urs.forEach(t),qka=i(Djo),odo=n(Djo,"SPAN",{});var _rs=s(odo);Oka=r(_rs,"AutoModelForZeroShotObjectDetection"),_rs.forEach(t),Djo.forEach(t),qIo=i(c),$r=n(c,"DIV",{class:!0});var pm=s($r);M(fD.$$.fragment,pm),Qka=i(pm),Jh=n(pm,"P",{});var Lwe=s(Jh);Yka=r(Lwe,`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(Lwe,"A",{href:!0});var brs=s(P3e);zka=r(brs,"from_pretrained()"),brs.forEach(t),Hka=r(Lwe," class method or the "),Z3e=n(Lwe,"A",{href:!0});var vrs=s(Z3e);Kka=r(vrs,"from_config()"),vrs.forEach(t),eSa=r(Lwe,` class
method.`),Lwe.forEach(t),oSa=i(pm),hD=n(pm,"P",{});var qjo=s(hD);rSa=r(qjo,"This class cannot be instantiated directly using "),rdo=n(qjo,"CODE",{});var Mrs=s(rdo);tSa=r(Mrs,"__init__()"),Mrs.forEach(t),aSa=r(qjo," (throws an error)."),qjo.forEach(t),nSa=i(pm),Ia=n(pm,"DIV",{class:!0});var MZ=s(Ia);M(gD.$$.fragment,MZ),sSa=i(MZ),tdo=n(MZ,"P",{});var Trs=s(tdo);lSa=r(Trs,"Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration."),Trs.forEach(t),iSa=i(MZ),Uh=n(MZ,"P",{});var Awe=s(Uh);dSa=r(Awe,`Note:
Loading a model from its configuration file does `),ado=n(Awe,"STRONG",{});var Frs=s(ado);mSa=r(Frs,"not"),Frs.forEach(t),cSa=r(Awe,` load the model weights. It only affects the
model\u2019s configuration. Use `),V3e=n(Awe,"A",{href:!0});var Ers=s(V3e);fSa=r(Ers,"from_pretrained()"),Ers.forEach(t),hSa=r(Awe," to load the model weights."),Awe.forEach(t),gSa=i(MZ),M(VI.$$.fragment,MZ),MZ.forEach(t),uSa=i(pm),Lo=n(pm,"DIV",{class:!0});var yn=s(Lo);M(uD.$$.fragment,yn),pSa=i(yn),ndo=n(yn,"P",{});var yrs=s(ndo);_Sa=r(yrs,"Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model."),yrs.forEach(t),bSa=i(yn),Gs=n(yn,"P",{});var TZ=s(Gs);vSa=r(TZ,"The model class to instantiate is selected based on the "),sdo=n(TZ,"CODE",{});var Crs=s(sdo);MSa=r(Crs,"model_type"),Crs.forEach(t),TSa=r(TZ,` property of the config object (either
passed as an argument or loaded from `),ldo=n(TZ,"CODE",{});var wrs=s(ldo);FSa=r(wrs,"pretrained_model_name_or_path"),wrs.forEach(t),ESa=r(TZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ido=n(TZ,"CODE",{});var Lrs=s(ido);ySa=r(Lrs,"pretrained_model_name_or_path"),Lrs.forEach(t),CSa=r(TZ,":"),TZ.forEach(t),wSa=i(yn),ddo=n(yn,"UL",{});var Ars=s(ddo);WI=n(Ars,"LI",{});var vxo=s(WI);mdo=n(vxo,"STRONG",{});var Rrs=s(mdo);LSa=r(Rrs,"owlvit"),Rrs.forEach(t),ASa=r(vxo," \u2014 "),W3e=n(vxo,"A",{href:!0});var krs=s(W3e);RSa=r(krs,"OwlViTForObjectDetection"),krs.forEach(t),kSa=r(vxo," (OWL-ViT model)"),vxo.forEach(t),Ars.forEach(t),SSa=i(yn),JI=n(yn,"P",{});var Mxo=s(JI);xSa=r(Mxo,"The model is set in evaluation mode by default using "),cdo=n(Mxo,"CODE",{});var Srs=s(cdo);GSa=r(Srs,"model.eval()"),Srs.forEach(t),BSa=r(Mxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),fdo=n(Mxo,"CODE",{});var xrs=s(fdo);ISa=r(xrs,"model.train()"),xrs.forEach(t),Mxo.forEach(t),NSa=i(yn),M(UI.$$.fragment,yn),yn.forEach(t),pm.forEach(t),OIo=i(c),Xh=n(c,"H2",{class:!0});var Ojo=s(Xh);XI=n(Ojo,"A",{id:!0,class:!0,href:!0});var Grs=s(XI);hdo=n(Grs,"SPAN",{});var Brs=s(hdo);M(pD.$$.fragment,Brs),Brs.forEach(t),Grs.forEach(t),$Sa=i(Ojo),gdo=n(Ojo,"SPAN",{});var Irs=s(gdo);jSa=r(Irs,"Audio"),Irs.forEach(t),Ojo.forEach(t),QIo=i(c),J3e=n(c,"P",{});var Nrs=s(J3e);PSa=r(Nrs,"The following auto classes are available for the following audio tasks."),Nrs.forEach(t),YIo=i(c),Dh=n(c,"H3",{class:!0});var Qjo=s(Dh);DI=n(Qjo,"A",{id:!0,class:!0,href:!0});var $rs=s(DI);udo=n($rs,"SPAN",{});var jrs=s(udo);M(_D.$$.fragment,jrs),jrs.forEach(t),$rs.forEach(t),ZSa=i(Qjo),pdo=n(Qjo,"SPAN",{});var Prs=s(pdo);VSa=r(Prs,"AutoModelForAudioClassification"),Prs.forEach(t),Qjo.forEach(t),zIo=i(c),jr=n(c,"DIV",{class:!0});var _m=s(jr);M(bD.$$.fragment,_m),WSa=i(_m),qh=n(_m,"P",{});var Rwe=s(qh);JSa=r(Rwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the `),U3e=n(Rwe,"A",{href:!0});var Zrs=s(U3e);USa=r(Zrs,"from_pretrained()"),Zrs.forEach(t),XSa=r(Rwe," class method or the "),X3e=n(Rwe,"A",{href:!0});var Vrs=s(X3e);DSa=r(Vrs,"from_config()"),Vrs.forEach(t),qSa=r(Rwe,` class
method.`),Rwe.forEach(t),OSa=i(_m),vD=n(_m,"P",{});var Yjo=s(vD);QSa=r(Yjo,"This class cannot be instantiated directly using "),_do=n(Yjo,"CODE",{});var Wrs=s(_do);YSa=r(Wrs,"__init__()"),Wrs.forEach(t),zSa=r(Yjo," (throws an error)."),Yjo.forEach(t),HSa=i(_m),Na=n(_m,"DIV",{class:!0});var FZ=s(Na);M(MD.$$.fragment,FZ),KSa=i(FZ),bdo=n(FZ,"P",{});var Jrs=s(bdo);exa=r(Jrs,"Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),Jrs.forEach(t),oxa=i(FZ),Oh=n(FZ,"P",{});var kwe=s(Oh);rxa=r(kwe,`Note:
Loading a model from its configuration file does `),vdo=n(kwe,"STRONG",{});var Urs=s(vdo);txa=r(Urs,"not"),Urs.forEach(t),axa=r(kwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),D3e=n(kwe,"A",{href:!0});var Xrs=s(D3e);nxa=r(Xrs,"from_pretrained()"),Xrs.forEach(t),sxa=r(kwe," to load the model weights."),kwe.forEach(t),lxa=i(FZ),M(qI.$$.fragment,FZ),FZ.forEach(t),ixa=i(_m),Ao=n(_m,"DIV",{class:!0});var Cn=s(Ao);M(TD.$$.fragment,Cn),dxa=i(Cn),Mdo=n(Cn,"P",{});var Drs=s(Mdo);mxa=r(Drs,"Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),Drs.forEach(t),cxa=i(Cn),Bs=n(Cn,"P",{});var EZ=s(Bs);fxa=r(EZ,"The model class to instantiate is selected based on the "),Tdo=n(EZ,"CODE",{});var qrs=s(Tdo);hxa=r(qrs,"model_type"),qrs.forEach(t),gxa=r(EZ,` property of the config object (either
passed as an argument or loaded from `),Fdo=n(EZ,"CODE",{});var Ors=s(Fdo);uxa=r(Ors,"pretrained_model_name_or_path"),Ors.forEach(t),pxa=r(EZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Edo=n(EZ,"CODE",{});var Qrs=s(Edo);_xa=r(Qrs,"pretrained_model_name_or_path"),Qrs.forEach(t),bxa=r(EZ,":"),EZ.forEach(t),vxa=i(Cn),Be=n(Cn,"UL",{});var Je=s(Be);OI=n(Je,"LI",{});var Txo=s(OI);ydo=n(Txo,"STRONG",{});var Yrs=s(ydo);Mxa=r(Yrs,"audio-spectrogram-transformer"),Yrs.forEach(t),Txa=r(Txo," \u2014 "),q3e=n(Txo,"A",{href:!0});var zrs=s(q3e);Fxa=r(zrs,"ASTForAudioClassification"),zrs.forEach(t),Exa=r(Txo," (Audio Spectrogram Transformer model)"),Txo.forEach(t),yxa=i(Je),QI=n(Je,"LI",{});var Fxo=s(QI);Cdo=n(Fxo,"STRONG",{});var Hrs=s(Cdo);Cxa=r(Hrs,"data2vec-audio"),Hrs.forEach(t),wxa=r(Fxo," \u2014 "),O3e=n(Fxo,"A",{href:!0});var Krs=s(O3e);Lxa=r(Krs,"Data2VecAudioForSequenceClassification"),Krs.forEach(t),Axa=r(Fxo," (Data2VecAudio model)"),Fxo.forEach(t),Rxa=i(Je),YI=n(Je,"LI",{});var Exo=s(YI);wdo=n(Exo,"STRONG",{});var ets=s(wdo);kxa=r(ets,"hubert"),ets.forEach(t),Sxa=r(Exo," \u2014 "),Q3e=n(Exo,"A",{href:!0});var ots=s(Q3e);xxa=r(ots,"HubertForSequenceClassification"),ots.forEach(t),Gxa=r(Exo," (Hubert model)"),Exo.forEach(t),Bxa=i(Je),zI=n(Je,"LI",{});var yxo=s(zI);Ldo=n(yxo,"STRONG",{});var rts=s(Ldo);Ixa=r(rts,"sew"),rts.forEach(t),Nxa=r(yxo," \u2014 "),Y3e=n(yxo,"A",{href:!0});var tts=s(Y3e);$xa=r(tts,"SEWForSequenceClassification"),tts.forEach(t),jxa=r(yxo," (SEW model)"),yxo.forEach(t),Pxa=i(Je),HI=n(Je,"LI",{});var Cxo=s(HI);Ado=n(Cxo,"STRONG",{});var ats=s(Ado);Zxa=r(ats,"sew-d"),ats.forEach(t),Vxa=r(Cxo," \u2014 "),z3e=n(Cxo,"A",{href:!0});var nts=s(z3e);Wxa=r(nts,"SEWDForSequenceClassification"),nts.forEach(t),Jxa=r(Cxo," (SEW-D model)"),Cxo.forEach(t),Uxa=i(Je),KI=n(Je,"LI",{});var wxo=s(KI);Rdo=n(wxo,"STRONG",{});var sts=s(Rdo);Xxa=r(sts,"unispeech"),sts.forEach(t),Dxa=r(wxo," \u2014 "),H3e=n(wxo,"A",{href:!0});var lts=s(H3e);qxa=r(lts,"UniSpeechForSequenceClassification"),lts.forEach(t),Oxa=r(wxo," (UniSpeech model)"),wxo.forEach(t),Qxa=i(Je),eN=n(Je,"LI",{});var Lxo=s(eN);kdo=n(Lxo,"STRONG",{});var its=s(kdo);Yxa=r(its,"unispeech-sat"),its.forEach(t),zxa=r(Lxo," \u2014 "),K3e=n(Lxo,"A",{href:!0});var dts=s(K3e);Hxa=r(dts,"UniSpeechSatForSequenceClassification"),dts.forEach(t),Kxa=r(Lxo," (UniSpeechSat model)"),Lxo.forEach(t),eGa=i(Je),oN=n(Je,"LI",{});var Axo=s(oN);Sdo=n(Axo,"STRONG",{});var mts=s(Sdo);oGa=r(mts,"wav2vec2"),mts.forEach(t),rGa=r(Axo," \u2014 "),e9e=n(Axo,"A",{href:!0});var cts=s(e9e);tGa=r(cts,"Wav2Vec2ForSequenceClassification"),cts.forEach(t),aGa=r(Axo," (Wav2Vec2 model)"),Axo.forEach(t),nGa=i(Je),rN=n(Je,"LI",{});var Rxo=s(rN);xdo=n(Rxo,"STRONG",{});var fts=s(xdo);sGa=r(fts,"wav2vec2-conformer"),fts.forEach(t),lGa=r(Rxo," \u2014 "),o9e=n(Rxo,"A",{href:!0});var hts=s(o9e);iGa=r(hts,"Wav2Vec2ConformerForSequenceClassification"),hts.forEach(t),dGa=r(Rxo," (Wav2Vec2-Conformer model)"),Rxo.forEach(t),mGa=i(Je),tN=n(Je,"LI",{});var kxo=s(tN);Gdo=n(kxo,"STRONG",{});var gts=s(Gdo);cGa=r(gts,"wavlm"),gts.forEach(t),fGa=r(kxo," \u2014 "),r9e=n(kxo,"A",{href:!0});var uts=s(r9e);hGa=r(uts,"WavLMForSequenceClassification"),uts.forEach(t),gGa=r(kxo," (WavLM model)"),kxo.forEach(t),uGa=i(Je),aN=n(Je,"LI",{});var Sxo=s(aN);Bdo=n(Sxo,"STRONG",{});var pts=s(Bdo);pGa=r(pts,"whisper"),pts.forEach(t),_Ga=r(Sxo," \u2014 "),t9e=n(Sxo,"A",{href:!0});var _ts=s(t9e);bGa=r(_ts,"WhisperForAudioClassification"),_ts.forEach(t),vGa=r(Sxo," (Whisper model)"),Sxo.forEach(t),Je.forEach(t),MGa=i(Cn),nN=n(Cn,"P",{});var xxo=s(nN);TGa=r(xxo,"The model is set in evaluation mode by default using "),Ido=n(xxo,"CODE",{});var bts=s(Ido);FGa=r(bts,"model.eval()"),bts.forEach(t),EGa=r(xxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Ndo=n(xxo,"CODE",{});var vts=s(Ndo);yGa=r(vts,"model.train()"),vts.forEach(t),xxo.forEach(t),CGa=i(Cn),M(sN.$$.fragment,Cn),Cn.forEach(t),_m.forEach(t),HIo=i(c),Qh=n(c,"H3",{class:!0});var zjo=s(Qh);lN=n(zjo,"A",{id:!0,class:!0,href:!0});var Mts=s(lN);$do=n(Mts,"SPAN",{});var Tts=s($do);M(FD.$$.fragment,Tts),Tts.forEach(t),Mts.forEach(t),wGa=i(zjo),jdo=n(zjo,"SPAN",{});var Fts=s(jdo);LGa=r(Fts,"AutoModelForAudioFrameClassification"),Fts.forEach(t),zjo.forEach(t),KIo=i(c),Pr=n(c,"DIV",{class:!0});var bm=s(Pr);M(ED.$$.fragment,bm),AGa=i(bm),Yh=n(bm,"P",{});var Swe=s(Yh);RGa=r(Swe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the `),a9e=n(Swe,"A",{href:!0});var Ets=s(a9e);kGa=r(Ets,"from_pretrained()"),Ets.forEach(t),SGa=r(Swe," class method or the "),n9e=n(Swe,"A",{href:!0});var yts=s(n9e);xGa=r(yts,"from_config()"),yts.forEach(t),GGa=r(Swe,` class
method.`),Swe.forEach(t),BGa=i(bm),yD=n(bm,"P",{});var Hjo=s(yD);IGa=r(Hjo,"This class cannot be instantiated directly using "),Pdo=n(Hjo,"CODE",{});var Cts=s(Pdo);NGa=r(Cts,"__init__()"),Cts.forEach(t),$Ga=r(Hjo," (throws an error)."),Hjo.forEach(t),jGa=i(bm),$a=n(bm,"DIV",{class:!0});var yZ=s($a);M(CD.$$.fragment,yZ),PGa=i(yZ),Zdo=n(yZ,"P",{});var wts=s(Zdo);ZGa=r(wts,"Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),wts.forEach(t),VGa=i(yZ),zh=n(yZ,"P",{});var xwe=s(zh);WGa=r(xwe,`Note:
Loading a model from its configuration file does `),Vdo=n(xwe,"STRONG",{});var Lts=s(Vdo);JGa=r(Lts,"not"),Lts.forEach(t),UGa=r(xwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),s9e=n(xwe,"A",{href:!0});var Ats=s(s9e);XGa=r(Ats,"from_pretrained()"),Ats.forEach(t),DGa=r(xwe," to load the model weights."),xwe.forEach(t),qGa=i(yZ),M(iN.$$.fragment,yZ),yZ.forEach(t),OGa=i(bm),At=n(bm,"DIV",{class:!0});var vm=s(At);M(wD.$$.fragment,vm),QGa=i(vm),Wdo=n(vm,"P",{});var Rts=s(Wdo);YGa=r(Rts,"Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),Rts.forEach(t),zGa=i(vm),Is=n(vm,"P",{});var CZ=s(Is);HGa=r(CZ,"The model class to instantiate is selected based on the "),Jdo=n(CZ,"CODE",{});var kts=s(Jdo);KGa=r(kts,"model_type"),kts.forEach(t),eBa=r(CZ,` property of the config object (either
passed as an argument or loaded from `),Udo=n(CZ,"CODE",{});var Sts=s(Udo);oBa=r(Sts,"pretrained_model_name_or_path"),Sts.forEach(t),rBa=r(CZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Xdo=n(CZ,"CODE",{});var xts=s(Xdo);tBa=r(xts,"pretrained_model_name_or_path"),xts.forEach(t),aBa=r(CZ,":"),CZ.forEach(t),nBa=i(vm),Ddo=n(vm,"UL",{});var Gts=s(Ddo);dN=n(Gts,"LI",{});var Gxo=s(dN);qdo=n(Gxo,"STRONG",{});var Bts=s(qdo);sBa=r(Bts,"wav2vec2"),Bts.forEach(t),lBa=r(Gxo," \u2014 "),l9e=n(Gxo,"A",{href:!0});var Its=s(l9e);iBa=r(Its,"TFWav2Vec2ForSequenceClassification"),Its.forEach(t),dBa=r(Gxo," (Wav2Vec2 model)"),Gxo.forEach(t),Gts.forEach(t),mBa=i(vm),M(mN.$$.fragment,vm),vm.forEach(t),bm.forEach(t),eNo=i(c),Hh=n(c,"H3",{class:!0});var Kjo=s(Hh);cN=n(Kjo,"A",{id:!0,class:!0,href:!0});var Nts=s(cN);Odo=n(Nts,"SPAN",{});var $ts=s(Odo);M(LD.$$.fragment,$ts),$ts.forEach(t),Nts.forEach(t),cBa=i(Kjo),Qdo=n(Kjo,"SPAN",{});var jts=s(Qdo);fBa=r(jts,"TFAutoModelForAudioFrameClassification"),jts.forEach(t),Kjo.forEach(t),oNo=i(c),Zr=n(c,"DIV",{class:!0});var Mm=s(Zr);M(AD.$$.fragment,Mm),hBa=i(Mm),Kh=n(Mm,"P",{});var Gwe=s(Kh);gBa=r(Gwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio frame (token) classification head) when created
with the `),i9e=n(Gwe,"A",{href:!0});var Pts=s(i9e);uBa=r(Pts,"from_pretrained()"),Pts.forEach(t),pBa=r(Gwe," class method or the "),d9e=n(Gwe,"A",{href:!0});var Zts=s(d9e);_Ba=r(Zts,"from_config()"),Zts.forEach(t),bBa=r(Gwe,` class
method.`),Gwe.forEach(t),vBa=i(Mm),RD=n(Mm,"P",{});var ePo=s(RD);MBa=r(ePo,"This class cannot be instantiated directly using "),Ydo=n(ePo,"CODE",{});var Vts=s(Ydo);TBa=r(Vts,"__init__()"),Vts.forEach(t),FBa=r(ePo," (throws an error)."),ePo.forEach(t),EBa=i(Mm),ja=n(Mm,"DIV",{class:!0});var wZ=s(ja);M(kD.$$.fragment,wZ),yBa=i(wZ),zdo=n(wZ,"P",{});var Wts=s(zdo);CBa=r(Wts,"Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),Wts.forEach(t),wBa=i(wZ),eg=n(wZ,"P",{});var Bwe=s(eg);LBa=r(Bwe,`Note:
Loading a model from its configuration file does `),Hdo=n(Bwe,"STRONG",{});var Jts=s(Hdo);ABa=r(Jts,"not"),Jts.forEach(t),RBa=r(Bwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),m9e=n(Bwe,"A",{href:!0});var Uts=s(m9e);kBa=r(Uts,"from_pretrained()"),Uts.forEach(t),SBa=r(Bwe," to load the model weights."),Bwe.forEach(t),xBa=i(wZ),M(fN.$$.fragment,wZ),wZ.forEach(t),GBa=i(Mm),Ro=n(Mm,"DIV",{class:!0});var wn=s(Ro);M(SD.$$.fragment,wn),BBa=i(wn),Kdo=n(wn,"P",{});var Xts=s(Kdo);IBa=r(Xts,"Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),Xts.forEach(t),NBa=i(wn),Ns=n(wn,"P",{});var LZ=s(Ns);$Ba=r(LZ,"The model class to instantiate is selected based on the "),emo=n(LZ,"CODE",{});var Dts=s(emo);jBa=r(Dts,"model_type"),Dts.forEach(t),PBa=r(LZ,` property of the config object (either
passed as an argument or loaded from `),omo=n(LZ,"CODE",{});var qts=s(omo);ZBa=r(qts,"pretrained_model_name_or_path"),qts.forEach(t),VBa=r(LZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),rmo=n(LZ,"CODE",{});var Ots=s(rmo);WBa=r(Ots,"pretrained_model_name_or_path"),Ots.forEach(t),JBa=r(LZ,":"),LZ.forEach(t),UBa=i(wn),$t=n(wn,"UL",{});var Tm=s($t);hN=n(Tm,"LI",{});var Bxo=s(hN);tmo=n(Bxo,"STRONG",{});var Qts=s(tmo);XBa=r(Qts,"data2vec-audio"),Qts.forEach(t),DBa=r(Bxo," \u2014 "),c9e=n(Bxo,"A",{href:!0});var Yts=s(c9e);qBa=r(Yts,"Data2VecAudioForAudioFrameClassification"),Yts.forEach(t),OBa=r(Bxo," (Data2VecAudio model)"),Bxo.forEach(t),QBa=i(Tm),gN=n(Tm,"LI",{});var Ixo=s(gN);amo=n(Ixo,"STRONG",{});var zts=s(amo);YBa=r(zts,"unispeech-sat"),zts.forEach(t),zBa=r(Ixo," \u2014 "),f9e=n(Ixo,"A",{href:!0});var Hts=s(f9e);HBa=r(Hts,"UniSpeechSatForAudioFrameClassification"),Hts.forEach(t),KBa=r(Ixo," (UniSpeechSat model)"),Ixo.forEach(t),eIa=i(Tm),uN=n(Tm,"LI",{});var Nxo=s(uN);nmo=n(Nxo,"STRONG",{});var Kts=s(nmo);oIa=r(Kts,"wav2vec2"),Kts.forEach(t),rIa=r(Nxo," \u2014 "),h9e=n(Nxo,"A",{href:!0});var eas=s(h9e);tIa=r(eas,"Wav2Vec2ForAudioFrameClassification"),eas.forEach(t),aIa=r(Nxo," (Wav2Vec2 model)"),Nxo.forEach(t),nIa=i(Tm),pN=n(Tm,"LI",{});var $xo=s(pN);smo=n($xo,"STRONG",{});var oas=s(smo);sIa=r(oas,"wav2vec2-conformer"),oas.forEach(t),lIa=r($xo," \u2014 "),g9e=n($xo,"A",{href:!0});var ras=s(g9e);iIa=r(ras,"Wav2Vec2ConformerForAudioFrameClassification"),ras.forEach(t),dIa=r($xo," (Wav2Vec2-Conformer model)"),$xo.forEach(t),mIa=i(Tm),_N=n(Tm,"LI",{});var jxo=s(_N);lmo=n(jxo,"STRONG",{});var tas=s(lmo);cIa=r(tas,"wavlm"),tas.forEach(t),fIa=r(jxo," \u2014 "),u9e=n(jxo,"A",{href:!0});var aas=s(u9e);hIa=r(aas,"WavLMForAudioFrameClassification"),aas.forEach(t),gIa=r(jxo," (WavLM model)"),jxo.forEach(t),Tm.forEach(t),uIa=i(wn),bN=n(wn,"P",{});var Pxo=s(bN);pIa=r(Pxo,"The model is set in evaluation mode by default using "),imo=n(Pxo,"CODE",{});var nas=s(imo);_Ia=r(nas,"model.eval()"),nas.forEach(t),bIa=r(Pxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),dmo=n(Pxo,"CODE",{});var sas=s(dmo);vIa=r(sas,"model.train()"),sas.forEach(t),Pxo.forEach(t),MIa=i(wn),M(vN.$$.fragment,wn),wn.forEach(t),Mm.forEach(t),rNo=i(c),og=n(c,"H3",{class:!0});var oPo=s(og);MN=n(oPo,"A",{id:!0,class:!0,href:!0});var las=s(MN);mmo=n(las,"SPAN",{});var ias=s(mmo);M(xD.$$.fragment,ias),ias.forEach(t),las.forEach(t),TIa=i(oPo),cmo=n(oPo,"SPAN",{});var das=s(cmo);FIa=r(das,"AutoModelForCTC"),das.forEach(t),oPo.forEach(t),tNo=i(c),Vr=n(c,"DIV",{class:!0});var Fm=s(Vr);M(GD.$$.fragment,Fm),EIa=i(Fm),rg=n(Fm,"P",{});var Iwe=s(rg);yIa=r(Iwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a connectionist temporal classification head) when created
with the `),p9e=n(Iwe,"A",{href:!0});var mas=s(p9e);CIa=r(mas,"from_pretrained()"),mas.forEach(t),wIa=r(Iwe," class method or the "),_9e=n(Iwe,"A",{href:!0});var cas=s(_9e);LIa=r(cas,"from_config()"),cas.forEach(t),AIa=r(Iwe,` class
method.`),Iwe.forEach(t),RIa=i(Fm),BD=n(Fm,"P",{});var rPo=s(BD);kIa=r(rPo,"This class cannot be instantiated directly using "),fmo=n(rPo,"CODE",{});var fas=s(fmo);SIa=r(fas,"__init__()"),fas.forEach(t),xIa=r(rPo," (throws an error)."),rPo.forEach(t),GIa=i(Fm),Pa=n(Fm,"DIV",{class:!0});var AZ=s(Pa);M(ID.$$.fragment,AZ),BIa=i(AZ),hmo=n(AZ,"P",{});var has=s(hmo);IIa=r(has,"Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),has.forEach(t),NIa=i(AZ),tg=n(AZ,"P",{});var Nwe=s(tg);$Ia=r(Nwe,`Note:
Loading a model from its configuration file does `),gmo=n(Nwe,"STRONG",{});var gas=s(gmo);jIa=r(gas,"not"),gas.forEach(t),PIa=r(Nwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),b9e=n(Nwe,"A",{href:!0});var uas=s(b9e);ZIa=r(uas,"from_pretrained()"),uas.forEach(t),VIa=r(Nwe," to load the model weights."),Nwe.forEach(t),WIa=i(AZ),M(TN.$$.fragment,AZ),AZ.forEach(t),JIa=i(Fm),ko=n(Fm,"DIV",{class:!0});var Ln=s(ko);M(ND.$$.fragment,Ln),UIa=i(Ln),umo=n(Ln,"P",{});var pas=s(umo);XIa=r(pas,"Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model."),pas.forEach(t),DIa=i(Ln),$s=n(Ln,"P",{});var RZ=s($s);qIa=r(RZ,"The model class to instantiate is selected based on the "),pmo=n(RZ,"CODE",{});var _as=s(pmo);OIa=r(_as,"model_type"),_as.forEach(t),QIa=r(RZ,` property of the config object (either
passed as an argument or loaded from `),_mo=n(RZ,"CODE",{});var bas=s(_mo);YIa=r(bas,"pretrained_model_name_or_path"),bas.forEach(t),zIa=r(RZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),bmo=n(RZ,"CODE",{});var vas=s(bmo);HIa=r(vas,"pretrained_model_name_or_path"),vas.forEach(t),KIa=r(RZ,":"),RZ.forEach(t),eNa=i(Ln),je=n(Ln,"UL",{});var Oe=s(je);FN=n(Oe,"LI",{});var Zxo=s(FN);vmo=n(Zxo,"STRONG",{});var Mas=s(vmo);oNa=r(Mas,"data2vec-audio"),Mas.forEach(t),rNa=r(Zxo," \u2014 "),v9e=n(Zxo,"A",{href:!0});var Tas=s(v9e);tNa=r(Tas,"Data2VecAudioForCTC"),Tas.forEach(t),aNa=r(Zxo," (Data2VecAudio model)"),Zxo.forEach(t),nNa=i(Oe),EN=n(Oe,"LI",{});var Vxo=s(EN);Mmo=n(Vxo,"STRONG",{});var Fas=s(Mmo);sNa=r(Fas,"hubert"),Fas.forEach(t),lNa=r(Vxo," \u2014 "),M9e=n(Vxo,"A",{href:!0});var Eas=s(M9e);iNa=r(Eas,"HubertForCTC"),Eas.forEach(t),dNa=r(Vxo," (Hubert model)"),Vxo.forEach(t),mNa=i(Oe),yN=n(Oe,"LI",{});var Wxo=s(yN);Tmo=n(Wxo,"STRONG",{});var yas=s(Tmo);cNa=r(yas,"mctct"),yas.forEach(t),fNa=r(Wxo," \u2014 "),T9e=n(Wxo,"A",{href:!0});var Cas=s(T9e);hNa=r(Cas,"MCTCTForCTC"),Cas.forEach(t),gNa=r(Wxo," (M-CTC-T model)"),Wxo.forEach(t),uNa=i(Oe),CN=n(Oe,"LI",{});var Jxo=s(CN);Fmo=n(Jxo,"STRONG",{});var was=s(Fmo);pNa=r(was,"sew"),was.forEach(t),_Na=r(Jxo," \u2014 "),F9e=n(Jxo,"A",{href:!0});var Las=s(F9e);bNa=r(Las,"SEWForCTC"),Las.forEach(t),vNa=r(Jxo," (SEW model)"),Jxo.forEach(t),MNa=i(Oe),wN=n(Oe,"LI",{});var Uxo=s(wN);Emo=n(Uxo,"STRONG",{});var Aas=s(Emo);TNa=r(Aas,"sew-d"),Aas.forEach(t),FNa=r(Uxo," \u2014 "),E9e=n(Uxo,"A",{href:!0});var Ras=s(E9e);ENa=r(Ras,"SEWDForCTC"),Ras.forEach(t),yNa=r(Uxo," (SEW-D model)"),Uxo.forEach(t),CNa=i(Oe),LN=n(Oe,"LI",{});var Xxo=s(LN);ymo=n(Xxo,"STRONG",{});var kas=s(ymo);wNa=r(kas,"unispeech"),kas.forEach(t),LNa=r(Xxo," \u2014 "),y9e=n(Xxo,"A",{href:!0});var Sas=s(y9e);ANa=r(Sas,"UniSpeechForCTC"),Sas.forEach(t),RNa=r(Xxo," (UniSpeech model)"),Xxo.forEach(t),kNa=i(Oe),AN=n(Oe,"LI",{});var Dxo=s(AN);Cmo=n(Dxo,"STRONG",{});var xas=s(Cmo);SNa=r(xas,"unispeech-sat"),xas.forEach(t),xNa=r(Dxo," \u2014 "),C9e=n(Dxo,"A",{href:!0});var Gas=s(C9e);GNa=r(Gas,"UniSpeechSatForCTC"),Gas.forEach(t),BNa=r(Dxo," (UniSpeechSat model)"),Dxo.forEach(t),INa=i(Oe),RN=n(Oe,"LI",{});var qxo=s(RN);wmo=n(qxo,"STRONG",{});var Bas=s(wmo);NNa=r(Bas,"wav2vec2"),Bas.forEach(t),$Na=r(qxo," \u2014 "),w9e=n(qxo,"A",{href:!0});var Ias=s(w9e);jNa=r(Ias,"Wav2Vec2ForCTC"),Ias.forEach(t),PNa=r(qxo," (Wav2Vec2 model)"),qxo.forEach(t),ZNa=i(Oe),kN=n(Oe,"LI",{});var Oxo=s(kN);Lmo=n(Oxo,"STRONG",{});var Nas=s(Lmo);VNa=r(Nas,"wav2vec2-conformer"),Nas.forEach(t),WNa=r(Oxo," \u2014 "),L9e=n(Oxo,"A",{href:!0});var $as=s(L9e);JNa=r($as,"Wav2Vec2ConformerForCTC"),$as.forEach(t),UNa=r(Oxo," (Wav2Vec2-Conformer model)"),Oxo.forEach(t),XNa=i(Oe),SN=n(Oe,"LI",{});var Qxo=s(SN);Amo=n(Qxo,"STRONG",{});var jas=s(Amo);DNa=r(jas,"wavlm"),jas.forEach(t),qNa=r(Qxo," \u2014 "),A9e=n(Qxo,"A",{href:!0});var Pas=s(A9e);ONa=r(Pas,"WavLMForCTC"),Pas.forEach(t),QNa=r(Qxo," (WavLM model)"),Qxo.forEach(t),Oe.forEach(t),YNa=i(Ln),xN=n(Ln,"P",{});var Yxo=s(xN);zNa=r(Yxo,"The model is set in evaluation mode by default using "),Rmo=n(Yxo,"CODE",{});var Zas=s(Rmo);HNa=r(Zas,"model.eval()"),Zas.forEach(t),KNa=r(Yxo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),kmo=n(Yxo,"CODE",{});var Vas=s(kmo);e$a=r(Vas,"model.train()"),Vas.forEach(t),Yxo.forEach(t),o$a=i(Ln),M(GN.$$.fragment,Ln),Ln.forEach(t),Fm.forEach(t),aNo=i(c),ag=n(c,"H3",{class:!0});var tPo=s(ag);BN=n(tPo,"A",{id:!0,class:!0,href:!0});var Was=s(BN);Smo=n(Was,"SPAN",{});var Jas=s(Smo);M($D.$$.fragment,Jas),Jas.forEach(t),Was.forEach(t),r$a=i(tPo),xmo=n(tPo,"SPAN",{});var Uas=s(xmo);t$a=r(Uas,"AutoModelForSpeechSeq2Seq"),Uas.forEach(t),tPo.forEach(t),nNo=i(c),Wr=n(c,"DIV",{class:!0});var Em=s(Wr);M(jD.$$.fragment,Em),a$a=i(Em),ng=n(Em,"P",{});var $we=s(ng);n$a=r($we,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),R9e=n($we,"A",{href:!0});var Xas=s(R9e);s$a=r(Xas,"from_pretrained()"),Xas.forEach(t),l$a=r($we," class method or the "),k9e=n($we,"A",{href:!0});var Das=s(k9e);i$a=r(Das,"from_config()"),Das.forEach(t),d$a=r($we,` class
method.`),$we.forEach(t),m$a=i(Em),PD=n(Em,"P",{});var aPo=s(PD);c$a=r(aPo,"This class cannot be instantiated directly using "),Gmo=n(aPo,"CODE",{});var qas=s(Gmo);f$a=r(qas,"__init__()"),qas.forEach(t),h$a=r(aPo," (throws an error)."),aPo.forEach(t),g$a=i(Em),Za=n(Em,"DIV",{class:!0});var kZ=s(Za);M(ZD.$$.fragment,kZ),u$a=i(kZ),Bmo=n(kZ,"P",{});var Oas=s(Bmo);p$a=r(Oas,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),Oas.forEach(t),_$a=i(kZ),sg=n(kZ,"P",{});var jwe=s(sg);b$a=r(jwe,`Note:
Loading a model from its configuration file does `),Imo=n(jwe,"STRONG",{});var Qas=s(Imo);v$a=r(Qas,"not"),Qas.forEach(t),M$a=r(jwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),S9e=n(jwe,"A",{href:!0});var Yas=s(S9e);T$a=r(Yas,"from_pretrained()"),Yas.forEach(t),F$a=r(jwe," to load the model weights."),jwe.forEach(t),E$a=i(kZ),M(IN.$$.fragment,kZ),kZ.forEach(t),y$a=i(Em),So=n(Em,"DIV",{class:!0});var An=s(So);M(VD.$$.fragment,An),C$a=i(An),Nmo=n(An,"P",{});var zas=s(Nmo);w$a=r(zas,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),zas.forEach(t),L$a=i(An),js=n(An,"P",{});var SZ=s(js);A$a=r(SZ,"The model class to instantiate is selected based on the "),$mo=n(SZ,"CODE",{});var Has=s($mo);R$a=r(Has,"model_type"),Has.forEach(t),k$a=r(SZ,` property of the config object (either
passed as an argument or loaded from `),jmo=n(SZ,"CODE",{});var Kas=s(jmo);S$a=r(Kas,"pretrained_model_name_or_path"),Kas.forEach(t),x$a=r(SZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Pmo=n(SZ,"CODE",{});var ens=s(Pmo);G$a=r(ens,"pretrained_model_name_or_path"),ens.forEach(t),B$a=r(SZ,":"),SZ.forEach(t),I$a=i(An),jt=n(An,"UL",{});var ym=s(jt);NN=n(ym,"LI",{});var zxo=s(NN);Zmo=n(zxo,"STRONG",{});var ons=s(Zmo);N$a=r(ons,"pop2piano"),ons.forEach(t),$$a=r(zxo," \u2014 "),x9e=n(zxo,"A",{href:!0});var rns=s(x9e);j$a=r(rns,"Pop2PianoForConditionalGeneration"),rns.forEach(t),P$a=r(zxo," (Pop2Piano model)"),zxo.forEach(t),Z$a=i(ym),$N=n(ym,"LI",{});var Hxo=s($N);Vmo=n(Hxo,"STRONG",{});var tns=s(Vmo);V$a=r(tns,"speech-encoder-decoder"),tns.forEach(t),W$a=r(Hxo," \u2014 "),G9e=n(Hxo,"A",{href:!0});var ans=s(G9e);J$a=r(ans,"SpeechEncoderDecoderModel"),ans.forEach(t),U$a=r(Hxo," (Speech Encoder decoder model)"),Hxo.forEach(t),X$a=i(ym),jN=n(ym,"LI",{});var Kxo=s(jN);Wmo=n(Kxo,"STRONG",{});var nns=s(Wmo);D$a=r(nns,"speech_to_text"),nns.forEach(t),q$a=r(Kxo," \u2014 "),B9e=n(Kxo,"A",{href:!0});var sns=s(B9e);O$a=r(sns,"Speech2TextForConditionalGeneration"),sns.forEach(t),Q$a=r(Kxo," (Speech2Text model)"),Kxo.forEach(t),Y$a=i(ym),PN=n(ym,"LI",{});var eGo=s(PN);Jmo=n(eGo,"STRONG",{});var lns=s(Jmo);z$a=r(lns,"speecht5"),lns.forEach(t),H$a=r(eGo," \u2014 "),I9e=n(eGo,"A",{href:!0});var ins=s(I9e);K$a=r(ins,"SpeechT5ForSpeechToText"),ins.forEach(t),eja=r(eGo," (SpeechT5 model)"),eGo.forEach(t),oja=i(ym),ZN=n(ym,"LI",{});var oGo=s(ZN);Umo=n(oGo,"STRONG",{});var dns=s(Umo);rja=r(dns,"whisper"),dns.forEach(t),tja=r(oGo," \u2014 "),N9e=n(oGo,"A",{href:!0});var mns=s(N9e);aja=r(mns,"WhisperForConditionalGeneration"),mns.forEach(t),nja=r(oGo," (Whisper model)"),oGo.forEach(t),ym.forEach(t),sja=i(An),VN=n(An,"P",{});var rGo=s(VN);lja=r(rGo,"The model is set in evaluation mode by default using "),Xmo=n(rGo,"CODE",{});var cns=s(Xmo);ija=r(cns,"model.eval()"),cns.forEach(t),dja=r(rGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Dmo=n(rGo,"CODE",{});var fns=s(Dmo);mja=r(fns,"model.train()"),fns.forEach(t),rGo.forEach(t),cja=i(An),M(WN.$$.fragment,An),An.forEach(t),Em.forEach(t),sNo=i(c),lg=n(c,"H3",{class:!0});var nPo=s(lg);JN=n(nPo,"A",{id:!0,class:!0,href:!0});var hns=s(JN);qmo=n(hns,"SPAN",{});var gns=s(qmo);M(WD.$$.fragment,gns),gns.forEach(t),hns.forEach(t),fja=i(nPo),Omo=n(nPo,"SPAN",{});var uns=s(Omo);hja=r(uns,"TFAutoModelForSpeechSeq2Seq"),uns.forEach(t),nPo.forEach(t),lNo=i(c),Jr=n(c,"DIV",{class:!0});var Cm=s(Jr);M(JD.$$.fragment,Cm),gja=i(Cm),ig=n(Cm,"P",{});var Pwe=s(ig);uja=r(Pwe,`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 `),$9e=n(Pwe,"A",{href:!0});var pns=s($9e);pja=r(pns,"from_pretrained()"),pns.forEach(t),_ja=r(Pwe," class method or the "),j9e=n(Pwe,"A",{href:!0});var _ns=s(j9e);bja=r(_ns,"from_config()"),_ns.forEach(t),vja=r(Pwe,` class
method.`),Pwe.forEach(t),Mja=i(Cm),UD=n(Cm,"P",{});var sPo=s(UD);Tja=r(sPo,"This class cannot be instantiated directly using "),Qmo=n(sPo,"CODE",{});var bns=s(Qmo);Fja=r(bns,"__init__()"),bns.forEach(t),Eja=r(sPo," (throws an error)."),sPo.forEach(t),yja=i(Cm),Va=n(Cm,"DIV",{class:!0});var xZ=s(Va);M(XD.$$.fragment,xZ),Cja=i(xZ),Ymo=n(xZ,"P",{});var vns=s(Ymo);wja=r(vns,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),vns.forEach(t),Lja=i(xZ),dg=n(xZ,"P",{});var Zwe=s(dg);Aja=r(Zwe,`Note:
Loading a model from its configuration file does `),zmo=n(Zwe,"STRONG",{});var Mns=s(zmo);Rja=r(Mns,"not"),Mns.forEach(t),kja=r(Zwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),P9e=n(Zwe,"A",{href:!0});var Tns=s(P9e);Sja=r(Tns,"from_pretrained()"),Tns.forEach(t),xja=r(Zwe," to load the model weights."),Zwe.forEach(t),Gja=i(xZ),M(UN.$$.fragment,xZ),xZ.forEach(t),Bja=i(Cm),Rt=n(Cm,"DIV",{class:!0});var wm=s(Rt);M(DD.$$.fragment,wm),Ija=i(wm),Hmo=n(wm,"P",{});var Fns=s(Hmo);Nja=r(Fns,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),Fns.forEach(t),$ja=i(wm),Ps=n(wm,"P",{});var GZ=s(Ps);jja=r(GZ,"The model class to instantiate is selected based on the "),Kmo=n(GZ,"CODE",{});var Ens=s(Kmo);Pja=r(Ens,"model_type"),Ens.forEach(t),Zja=r(GZ,` property of the config object (either
passed as an argument or loaded from `),eco=n(GZ,"CODE",{});var yns=s(eco);Vja=r(yns,"pretrained_model_name_or_path"),yns.forEach(t),Wja=r(GZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),oco=n(GZ,"CODE",{});var Cns=s(oco);Jja=r(Cns,"pretrained_model_name_or_path"),Cns.forEach(t),Uja=r(GZ,":"),GZ.forEach(t),Xja=i(wm),qD=n(wm,"UL",{});var lPo=s(qD);XN=n(lPo,"LI",{});var tGo=s(XN);rco=n(tGo,"STRONG",{});var wns=s(rco);Dja=r(wns,"speech_to_text"),wns.forEach(t),qja=r(tGo," \u2014 "),Z9e=n(tGo,"A",{href:!0});var Lns=s(Z9e);Oja=r(Lns,"TFSpeech2TextForConditionalGeneration"),Lns.forEach(t),Qja=r(tGo," (Speech2Text model)"),tGo.forEach(t),Yja=i(lPo),DN=n(lPo,"LI",{});var aGo=s(DN);tco=n(aGo,"STRONG",{});var Ans=s(tco);zja=r(Ans,"whisper"),Ans.forEach(t),Hja=r(aGo," \u2014 "),V9e=n(aGo,"A",{href:!0});var Rns=s(V9e);Kja=r(Rns,"TFWhisperForConditionalGeneration"),Rns.forEach(t),ePa=r(aGo," (Whisper model)"),aGo.forEach(t),lPo.forEach(t),oPa=i(wm),M(qN.$$.fragment,wm),wm.forEach(t),Cm.forEach(t),iNo=i(c),mg=n(c,"H3",{class:!0});var iPo=s(mg);ON=n(iPo,"A",{id:!0,class:!0,href:!0});var kns=s(ON);aco=n(kns,"SPAN",{});var Sns=s(aco);M(OD.$$.fragment,Sns),Sns.forEach(t),kns.forEach(t),rPa=i(iPo),nco=n(iPo,"SPAN",{});var xns=s(nco);tPa=r(xns,"FlaxAutoModelForSpeechSeq2Seq"),xns.forEach(t),iPo.forEach(t),dNo=i(c),Ur=n(c,"DIV",{class:!0});var Lm=s(Ur);M(QD.$$.fragment,Lm),aPa=i(Lm),cg=n(Lm,"P",{});var Vwe=s(cg);nPa=r(Vwe,`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 `),W9e=n(Vwe,"A",{href:!0});var Gns=s(W9e);sPa=r(Gns,"from_pretrained()"),Gns.forEach(t),lPa=r(Vwe," class method or the "),J9e=n(Vwe,"A",{href:!0});var Bns=s(J9e);iPa=r(Bns,"from_config()"),Bns.forEach(t),dPa=r(Vwe,` class
method.`),Vwe.forEach(t),mPa=i(Lm),YD=n(Lm,"P",{});var dPo=s(YD);cPa=r(dPo,"This class cannot be instantiated directly using "),sco=n(dPo,"CODE",{});var Ins=s(sco);fPa=r(Ins,"__init__()"),Ins.forEach(t),hPa=r(dPo," (throws an error)."),dPo.forEach(t),gPa=i(Lm),Wa=n(Lm,"DIV",{class:!0});var BZ=s(Wa);M(zD.$$.fragment,BZ),uPa=i(BZ),lco=n(BZ,"P",{});var Nns=s(lco);pPa=r(Nns,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),Nns.forEach(t),_Pa=i(BZ),fg=n(BZ,"P",{});var Wwe=s(fg);bPa=r(Wwe,`Note:
Loading a model from its configuration file does `),ico=n(Wwe,"STRONG",{});var $ns=s(ico);vPa=r($ns,"not"),$ns.forEach(t),MPa=r(Wwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),U9e=n(Wwe,"A",{href:!0});var jns=s(U9e);TPa=r(jns,"from_pretrained()"),jns.forEach(t),FPa=r(Wwe," to load the model weights."),Wwe.forEach(t),EPa=i(BZ),M(QN.$$.fragment,BZ),BZ.forEach(t),yPa=i(Lm),kt=n(Lm,"DIV",{class:!0});var Am=s(kt);M(HD.$$.fragment,Am),CPa=i(Am),dco=n(Am,"P",{});var Pns=s(dco);wPa=r(Pns,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),Pns.forEach(t),LPa=i(Am),Zs=n(Am,"P",{});var IZ=s(Zs);APa=r(IZ,"The model class to instantiate is selected based on the "),mco=n(IZ,"CODE",{});var Zns=s(mco);RPa=r(Zns,"model_type"),Zns.forEach(t),kPa=r(IZ,` property of the config object (either
passed as an argument or loaded from `),cco=n(IZ,"CODE",{});var Vns=s(cco);SPa=r(Vns,"pretrained_model_name_or_path"),Vns.forEach(t),xPa=r(IZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),fco=n(IZ,"CODE",{});var Wns=s(fco);GPa=r(Wns,"pretrained_model_name_or_path"),Wns.forEach(t),BPa=r(IZ,":"),IZ.forEach(t),IPa=i(Am),KD=n(Am,"UL",{});var mPo=s(KD);YN=n(mPo,"LI",{});var nGo=s(YN);hco=n(nGo,"STRONG",{});var Jns=s(hco);NPa=r(Jns,"speech-encoder-decoder"),Jns.forEach(t),$Pa=r(nGo," \u2014 "),X9e=n(nGo,"A",{href:!0});var Uns=s(X9e);jPa=r(Uns,"FlaxSpeechEncoderDecoderModel"),Uns.forEach(t),PPa=r(nGo," (Speech Encoder decoder model)"),nGo.forEach(t),ZPa=i(mPo),zN=n(mPo,"LI",{});var sGo=s(zN);gco=n(sGo,"STRONG",{});var Xns=s(gco);VPa=r(Xns,"whisper"),Xns.forEach(t),WPa=r(sGo," \u2014 "),D9e=n(sGo,"A",{href:!0});var Dns=s(D9e);JPa=r(Dns,"FlaxWhisperForConditionalGeneration"),Dns.forEach(t),UPa=r(sGo," (Whisper model)"),sGo.forEach(t),mPo.forEach(t),XPa=i(Am),M(HN.$$.fragment,Am),Am.forEach(t),Lm.forEach(t),mNo=i(c),hg=n(c,"H3",{class:!0});var cPo=s(hg);KN=n(cPo,"A",{id:!0,class:!0,href:!0});var qns=s(KN);uco=n(qns,"SPAN",{});var Ons=s(uco);M(eq.$$.fragment,Ons),Ons.forEach(t),qns.forEach(t),DPa=i(cPo),pco=n(cPo,"SPAN",{});var Qns=s(pco);qPa=r(Qns,"AutoModelForAudioXVector"),Qns.forEach(t),cPo.forEach(t),cNo=i(c),Xr=n(c,"DIV",{class:!0});var Rm=s(Xr);M(oq.$$.fragment,Rm),OPa=i(Rm),gg=n(Rm,"P",{});var Jwe=s(gg);QPa=r(Jwe,`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 `),q9e=n(Jwe,"A",{href:!0});var Yns=s(q9e);YPa=r(Yns,"from_pretrained()"),Yns.forEach(t),zPa=r(Jwe," class method or the "),O9e=n(Jwe,"A",{href:!0});var zns=s(O9e);HPa=r(zns,"from_config()"),zns.forEach(t),KPa=r(Jwe,` class
method.`),Jwe.forEach(t),eZa=i(Rm),rq=n(Rm,"P",{});var fPo=s(rq);oZa=r(fPo,"This class cannot be instantiated directly using "),_co=n(fPo,"CODE",{});var Hns=s(_co);rZa=r(Hns,"__init__()"),Hns.forEach(t),tZa=r(fPo," (throws an error)."),fPo.forEach(t),aZa=i(Rm),Ja=n(Rm,"DIV",{class:!0});var NZ=s(Ja);M(tq.$$.fragment,NZ),nZa=i(NZ),bco=n(NZ,"P",{});var Kns=s(bco);sZa=r(Kns,"Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration."),Kns.forEach(t),lZa=i(NZ),ug=n(NZ,"P",{});var Uwe=s(ug);iZa=r(Uwe,`Note:
Loading a model from its configuration file does `),vco=n(Uwe,"STRONG",{});var ess=s(vco);dZa=r(ess,"not"),ess.forEach(t),mZa=r(Uwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Q9e=n(Uwe,"A",{href:!0});var oss=s(Q9e);cZa=r(oss,"from_pretrained()"),oss.forEach(t),fZa=r(Uwe," to load the model weights."),Uwe.forEach(t),hZa=i(NZ),M(e$.$$.fragment,NZ),NZ.forEach(t),gZa=i(Rm),xo=n(Rm,"DIV",{class:!0});var Rn=s(xo);M(aq.$$.fragment,Rn),uZa=i(Rn),Mco=n(Rn,"P",{});var rss=s(Mco);pZa=r(rss,"Instantiate one of the model classes of the library (with a audio retrieval via x-vector head) from a pretrained model."),rss.forEach(t),_Za=i(Rn),Vs=n(Rn,"P",{});var $Z=s(Vs);bZa=r($Z,"The model class to instantiate is selected based on the "),Tco=n($Z,"CODE",{});var tss=s(Tco);vZa=r(tss,"model_type"),tss.forEach(t),MZa=r($Z,` property of the config object (either
passed as an argument or loaded from `),Fco=n($Z,"CODE",{});var ass=s(Fco);TZa=r(ass,"pretrained_model_name_or_path"),ass.forEach(t),FZa=r($Z,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Eco=n($Z,"CODE",{});var nss=s(Eco);EZa=r(nss,"pretrained_model_name_or_path"),nss.forEach(t),yZa=r($Z,":"),$Z.forEach(t),CZa=i(Rn),Pt=n(Rn,"UL",{});var km=s(Pt);o$=n(km,"LI",{});var lGo=s(o$);yco=n(lGo,"STRONG",{});var sss=s(yco);wZa=r(sss,"data2vec-audio"),sss.forEach(t),LZa=r(lGo," \u2014 "),Y9e=n(lGo,"A",{href:!0});var lss=s(Y9e);AZa=r(lss,"Data2VecAudioForXVector"),lss.forEach(t),RZa=r(lGo," (Data2VecAudio model)"),lGo.forEach(t),kZa=i(km),r$=n(km,"LI",{});var iGo=s(r$);Cco=n(iGo,"STRONG",{});var iss=s(Cco);SZa=r(iss,"unispeech-sat"),iss.forEach(t),xZa=r(iGo," \u2014 "),z9e=n(iGo,"A",{href:!0});var dss=s(z9e);GZa=r(dss,"UniSpeechSatForXVector"),dss.forEach(t),BZa=r(iGo," (UniSpeechSat model)"),iGo.forEach(t),IZa=i(km),t$=n(km,"LI",{});var dGo=s(t$);wco=n(dGo,"STRONG",{});var mss=s(wco);NZa=r(mss,"wav2vec2"),mss.forEach(t),$Za=r(dGo," \u2014 "),H9e=n(dGo,"A",{href:!0});var css=s(H9e);jZa=r(css,"Wav2Vec2ForXVector"),css.forEach(t),PZa=r(dGo," (Wav2Vec2 model)"),dGo.forEach(t),ZZa=i(km),a$=n(km,"LI",{});var mGo=s(a$);Lco=n(mGo,"STRONG",{});var fss=s(Lco);VZa=r(fss,"wav2vec2-conformer"),fss.forEach(t),WZa=r(mGo," \u2014 "),K9e=n(mGo,"A",{href:!0});var hss=s(K9e);JZa=r(hss,"Wav2Vec2ConformerForXVector"),hss.forEach(t),UZa=r(mGo," (Wav2Vec2-Conformer model)"),mGo.forEach(t),XZa=i(km),n$=n(km,"LI",{});var cGo=s(n$);Aco=n(cGo,"STRONG",{});var gss=s(Aco);DZa=r(gss,"wavlm"),gss.forEach(t),qZa=r(cGo," \u2014 "),e4e=n(cGo,"A",{href:!0});var uss=s(e4e);OZa=r(uss,"WavLMForXVector"),uss.forEach(t),QZa=r(cGo," (WavLM model)"),cGo.forEach(t),km.forEach(t),YZa=i(Rn),s$=n(Rn,"P",{});var fGo=s(s$);zZa=r(fGo,"The model is set in evaluation mode by default using "),Rco=n(fGo,"CODE",{});var pss=s(Rco);HZa=r(pss,"model.eval()"),pss.forEach(t),KZa=r(fGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),kco=n(fGo,"CODE",{});var _ss=s(kco);eVa=r(_ss,"model.train()"),_ss.forEach(t),fGo.forEach(t),oVa=i(Rn),M(l$.$$.fragment,Rn),Rn.forEach(t),Rm.forEach(t),fNo=i(c),pg=n(c,"H3",{class:!0});var hPo=s(pg);i$=n(hPo,"A",{id:!0,class:!0,href:!0});var bss=s(i$);Sco=n(bss,"SPAN",{});var vss=s(Sco);M(nq.$$.fragment,vss),vss.forEach(t),bss.forEach(t),rVa=i(hPo),xco=n(hPo,"SPAN",{});var Mss=s(xco);tVa=r(Mss,"AutoModelForTextToSpectrogram"),Mss.forEach(t),hPo.forEach(t),hNo=i(c),sq=n(c,"DIV",{class:!0});var Tss=s(sq);M(lq.$$.fragment,Tss),Tss.forEach(t),gNo=i(c),_g=n(c,"H3",{class:!0});var gPo=s(_g);d$=n(gPo,"A",{id:!0,class:!0,href:!0});var Fss=s(d$);Gco=n(Fss,"SPAN",{});var Ess=s(Gco);M(iq.$$.fragment,Ess),Ess.forEach(t),Fss.forEach(t),aVa=i(gPo),Bco=n(gPo,"SPAN",{});var yss=s(Bco);nVa=r(yss,"AutoModelForTextToWaveform"),yss.forEach(t),gPo.forEach(t),uNo=i(c),dq=n(c,"DIV",{class:!0});var Css=s(dq);M(mq.$$.fragment,Css),Css.forEach(t),pNo=i(c),bg=n(c,"H2",{class:!0});var uPo=s(bg);m$=n(uPo,"A",{id:!0,class:!0,href:!0});var wss=s(m$);Ico=n(wss,"SPAN",{});var Lss=s(Ico);M(cq.$$.fragment,Lss),Lss.forEach(t),wss.forEach(t),sVa=i(uPo),Nco=n(uPo,"SPAN",{});var Ass=s(Nco);lVa=r(Ass,"Multimodal"),Ass.forEach(t),uPo.forEach(t),_No=i(c),o4e=n(c,"P",{});var Rss=s(o4e);iVa=r(Rss,"The following auto classes are available for the following multimodal tasks."),Rss.forEach(t),bNo=i(c),vg=n(c,"H3",{class:!0});var pPo=s(vg);c$=n(pPo,"A",{id:!0,class:!0,href:!0});var kss=s(c$);$co=n(kss,"SPAN",{});var Sss=s($co);M(fq.$$.fragment,Sss),Sss.forEach(t),kss.forEach(t),dVa=i(pPo),jco=n(pPo,"SPAN",{});var xss=s(jco);mVa=r(xss,"AutoModelForTableQuestionAnswering"),xss.forEach(t),pPo.forEach(t),vNo=i(c),Dr=n(c,"DIV",{class:!0});var Sm=s(Dr);M(hq.$$.fragment,Sm),cVa=i(Sm),Mg=n(Sm,"P",{});var Xwe=s(Mg);fVa=r(Xwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the `),r4e=n(Xwe,"A",{href:!0});var Gss=s(r4e);hVa=r(Gss,"from_pretrained()"),Gss.forEach(t),gVa=r(Xwe," class method or the "),t4e=n(Xwe,"A",{href:!0});var Bss=s(t4e);uVa=r(Bss,"from_config()"),Bss.forEach(t),pVa=r(Xwe,` class
method.`),Xwe.forEach(t),_Va=i(Sm),gq=n(Sm,"P",{});var _Po=s(gq);bVa=r(_Po,"This class cannot be instantiated directly using "),Pco=n(_Po,"CODE",{});var Iss=s(Pco);vVa=r(Iss,"__init__()"),Iss.forEach(t),MVa=r(_Po," (throws an error)."),_Po.forEach(t),TVa=i(Sm),Ua=n(Sm,"DIV",{class:!0});var jZ=s(Ua);M(uq.$$.fragment,jZ),FVa=i(jZ),Zco=n(jZ,"P",{});var Nss=s(Zco);EVa=r(Nss,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),Nss.forEach(t),yVa=i(jZ),Tg=n(jZ,"P",{});var Dwe=s(Tg);CVa=r(Dwe,`Note:
Loading a model from its configuration file does `),Vco=n(Dwe,"STRONG",{});var $ss=s(Vco);wVa=r($ss,"not"),$ss.forEach(t),LVa=r(Dwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),a4e=n(Dwe,"A",{href:!0});var jss=s(a4e);AVa=r(jss,"from_pretrained()"),jss.forEach(t),RVa=r(Dwe," to load the model weights."),Dwe.forEach(t),kVa=i(jZ),M(f$.$$.fragment,jZ),jZ.forEach(t),SVa=i(Sm),Go=n(Sm,"DIV",{class:!0});var kn=s(Go);M(pq.$$.fragment,kn),xVa=i(kn),Wco=n(kn,"P",{});var Pss=s(Wco);GVa=r(Pss,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),Pss.forEach(t),BVa=i(kn),Ws=n(kn,"P",{});var PZ=s(Ws);IVa=r(PZ,"The model class to instantiate is selected based on the "),Jco=n(PZ,"CODE",{});var Zss=s(Jco);NVa=r(Zss,"model_type"),Zss.forEach(t),$Va=r(PZ,` property of the config object (either
passed as an argument or loaded from `),Uco=n(PZ,"CODE",{});var Vss=s(Uco);jVa=r(Vss,"pretrained_model_name_or_path"),Vss.forEach(t),PVa=r(PZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Xco=n(PZ,"CODE",{});var Wss=s(Xco);ZVa=r(Wss,"pretrained_model_name_or_path"),Wss.forEach(t),VVa=r(PZ,":"),PZ.forEach(t),WVa=i(kn),Dco=n(kn,"UL",{});var Jss=s(Dco);h$=n(Jss,"LI",{});var hGo=s(h$);qco=n(hGo,"STRONG",{});var Uss=s(qco);JVa=r(Uss,"tapas"),Uss.forEach(t),UVa=r(hGo," \u2014 "),n4e=n(hGo,"A",{href:!0});var Xss=s(n4e);XVa=r(Xss,"TapasForQuestionAnswering"),Xss.forEach(t),DVa=r(hGo," (TAPAS model)"),hGo.forEach(t),Jss.forEach(t),qVa=i(kn),g$=n(kn,"P",{});var gGo=s(g$);OVa=r(gGo,"The model is set in evaluation mode by default using "),Oco=n(gGo,"CODE",{});var Dss=s(Oco);QVa=r(Dss,"model.eval()"),Dss.forEach(t),YVa=r(gGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Qco=n(gGo,"CODE",{});var qss=s(Qco);zVa=r(qss,"model.train()"),qss.forEach(t),gGo.forEach(t),HVa=i(kn),M(u$.$$.fragment,kn),kn.forEach(t),Sm.forEach(t),MNo=i(c),Fg=n(c,"H3",{class:!0});var bPo=s(Fg);p$=n(bPo,"A",{id:!0,class:!0,href:!0});var Oss=s(p$);Yco=n(Oss,"SPAN",{});var Qss=s(Yco);M(_q.$$.fragment,Qss),Qss.forEach(t),Oss.forEach(t),KVa=i(bPo),zco=n(bPo,"SPAN",{});var Yss=s(zco);eWa=r(Yss,"TFAutoModelForTableQuestionAnswering"),Yss.forEach(t),bPo.forEach(t),TNo=i(c),qr=n(c,"DIV",{class:!0});var xm=s(qr);M(bq.$$.fragment,xm),oWa=i(xm),Eg=n(xm,"P",{});var qwe=s(Eg);rWa=r(qwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the `),s4e=n(qwe,"A",{href:!0});var zss=s(s4e);tWa=r(zss,"from_pretrained()"),zss.forEach(t),aWa=r(qwe," class method or the "),l4e=n(qwe,"A",{href:!0});var Hss=s(l4e);nWa=r(Hss,"from_config()"),Hss.forEach(t),sWa=r(qwe,` class
method.`),qwe.forEach(t),lWa=i(xm),vq=n(xm,"P",{});var vPo=s(vq);iWa=r(vPo,"This class cannot be instantiated directly using "),Hco=n(vPo,"CODE",{});var Kss=s(Hco);dWa=r(Kss,"__init__()"),Kss.forEach(t),mWa=r(vPo," (throws an error)."),vPo.forEach(t),cWa=i(xm),Xa=n(xm,"DIV",{class:!0});var ZZ=s(Xa);M(Mq.$$.fragment,ZZ),fWa=i(ZZ),Kco=n(ZZ,"P",{});var els=s(Kco);hWa=r(els,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),els.forEach(t),gWa=i(ZZ),yg=n(ZZ,"P",{});var Owe=s(yg);uWa=r(Owe,`Note:
Loading a model from its configuration file does `),efo=n(Owe,"STRONG",{});var ols=s(efo);pWa=r(ols,"not"),ols.forEach(t),_Wa=r(Owe,` load the model weights. It only affects the
model\u2019s configuration. Use `),i4e=n(Owe,"A",{href:!0});var rls=s(i4e);bWa=r(rls,"from_pretrained()"),rls.forEach(t),vWa=r(Owe," to load the model weights."),Owe.forEach(t),MWa=i(ZZ),M(_$.$$.fragment,ZZ),ZZ.forEach(t),TWa=i(xm),St=n(xm,"DIV",{class:!0});var Gm=s(St);M(Tq.$$.fragment,Gm),FWa=i(Gm),ofo=n(Gm,"P",{});var tls=s(ofo);EWa=r(tls,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),tls.forEach(t),yWa=i(Gm),Js=n(Gm,"P",{});var VZ=s(Js);CWa=r(VZ,"The model class to instantiate is selected based on the "),rfo=n(VZ,"CODE",{});var als=s(rfo);wWa=r(als,"model_type"),als.forEach(t),LWa=r(VZ,` property of the config object (either
passed as an argument or loaded from `),tfo=n(VZ,"CODE",{});var nls=s(tfo);AWa=r(nls,"pretrained_model_name_or_path"),nls.forEach(t),RWa=r(VZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),afo=n(VZ,"CODE",{});var sls=s(afo);kWa=r(sls,"pretrained_model_name_or_path"),sls.forEach(t),SWa=r(VZ,":"),VZ.forEach(t),xWa=i(Gm),nfo=n(Gm,"UL",{});var lls=s(nfo);b$=n(lls,"LI",{});var uGo=s(b$);sfo=n(uGo,"STRONG",{});var ils=s(sfo);GWa=r(ils,"tapas"),ils.forEach(t),BWa=r(uGo," \u2014 "),d4e=n(uGo,"A",{href:!0});var dls=s(d4e);IWa=r(dls,"TFTapasForQuestionAnswering"),dls.forEach(t),NWa=r(uGo," (TAPAS model)"),uGo.forEach(t),lls.forEach(t),$Wa=i(Gm),M(v$.$$.fragment,Gm),Gm.forEach(t),xm.forEach(t),FNo=i(c),Cg=n(c,"H3",{class:!0});var MPo=s(Cg);M$=n(MPo,"A",{id:!0,class:!0,href:!0});var mls=s(M$);lfo=n(mls,"SPAN",{});var cls=s(lfo);M(Fq.$$.fragment,cls),cls.forEach(t),mls.forEach(t),jWa=i(MPo),ifo=n(MPo,"SPAN",{});var fls=s(ifo);PWa=r(fls,"AutoModelForDocumentQuestionAnswering"),fls.forEach(t),MPo.forEach(t),ENo=i(c),Or=n(c,"DIV",{class:!0});var Bm=s(Or);M(Eq.$$.fragment,Bm),ZWa=i(Bm),wg=n(Bm,"P",{});var Qwe=s(wg);VWa=r(Qwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created
with the `),m4e=n(Qwe,"A",{href:!0});var hls=s(m4e);WWa=r(hls,"from_pretrained()"),hls.forEach(t),JWa=r(Qwe," class method or the "),c4e=n(Qwe,"A",{href:!0});var gls=s(c4e);UWa=r(gls,"from_config()"),gls.forEach(t),XWa=r(Qwe,` class
method.`),Qwe.forEach(t),DWa=i(Bm),yq=n(Bm,"P",{});var TPo=s(yq);qWa=r(TPo,"This class cannot be instantiated directly using "),dfo=n(TPo,"CODE",{});var uls=s(dfo);OWa=r(uls,"__init__()"),uls.forEach(t),QWa=r(TPo," (throws an error)."),TPo.forEach(t),YWa=i(Bm),Da=n(Bm,"DIV",{class:!0});var WZ=s(Da);M(Cq.$$.fragment,WZ),zWa=i(WZ),mfo=n(WZ,"P",{});var pls=s(mfo);HWa=r(pls,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),pls.forEach(t),KWa=i(WZ),Lg=n(WZ,"P",{});var Ywe=s(Lg);eJa=r(Ywe,`Note:
Loading a model from its configuration file does `),cfo=n(Ywe,"STRONG",{});var _ls=s(cfo);oJa=r(_ls,"not"),_ls.forEach(t),rJa=r(Ywe,` load the model weights. It only affects the
model\u2019s configuration. Use `),f4e=n(Ywe,"A",{href:!0});var bls=s(f4e);tJa=r(bls,"from_pretrained()"),bls.forEach(t),aJa=r(Ywe," to load the model weights."),Ywe.forEach(t),nJa=i(WZ),M(T$.$$.fragment,WZ),WZ.forEach(t),sJa=i(Bm),Bo=n(Bm,"DIV",{class:!0});var Sn=s(Bo);M(wq.$$.fragment,Sn),lJa=i(Sn),ffo=n(Sn,"P",{});var vls=s(ffo);iJa=r(vls,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),vls.forEach(t),dJa=i(Sn),Us=n(Sn,"P",{});var JZ=s(Us);mJa=r(JZ,"The model class to instantiate is selected based on the "),hfo=n(JZ,"CODE",{});var Mls=s(hfo);cJa=r(Mls,"model_type"),Mls.forEach(t),fJa=r(JZ,` property of the config object (either
passed as an argument or loaded from `),gfo=n(JZ,"CODE",{});var Tls=s(gfo);hJa=r(Tls,"pretrained_model_name_or_path"),Tls.forEach(t),gJa=r(JZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ufo=n(JZ,"CODE",{});var Fls=s(ufo);uJa=r(Fls,"pretrained_model_name_or_path"),Fls.forEach(t),pJa=r(JZ,":"),JZ.forEach(t),_Ja=i(Sn),Ag=n(Sn,"UL",{});var zwe=s(Ag);F$=n(zwe,"LI",{});var pGo=s(F$);pfo=n(pGo,"STRONG",{});var Els=s(pfo);bJa=r(Els,"layoutlm"),Els.forEach(t),vJa=r(pGo," \u2014 "),h4e=n(pGo,"A",{href:!0});var yls=s(h4e);MJa=r(yls,"LayoutLMForQuestionAnswering"),yls.forEach(t),TJa=r(pGo," (LayoutLM model)"),pGo.forEach(t),FJa=i(zwe),E$=n(zwe,"LI",{});var _Go=s(E$);_fo=n(_Go,"STRONG",{});var Cls=s(_fo);EJa=r(Cls,"layoutlmv2"),Cls.forEach(t),yJa=r(_Go," \u2014 "),g4e=n(_Go,"A",{href:!0});var wls=s(g4e);CJa=r(wls,"LayoutLMv2ForQuestionAnswering"),wls.forEach(t),wJa=r(_Go," (LayoutLMv2 model)"),_Go.forEach(t),LJa=i(zwe),y$=n(zwe,"LI",{});var bGo=s(y$);bfo=n(bGo,"STRONG",{});var Lls=s(bfo);AJa=r(Lls,"layoutlmv3"),Lls.forEach(t),RJa=r(bGo," \u2014 "),u4e=n(bGo,"A",{href:!0});var Als=s(u4e);kJa=r(Als,"LayoutLMv3ForQuestionAnswering"),Als.forEach(t),SJa=r(bGo," (LayoutLMv3 model)"),bGo.forEach(t),zwe.forEach(t),xJa=i(Sn),C$=n(Sn,"P",{});var vGo=s(C$);GJa=r(vGo,"The model is set in evaluation mode by default using "),vfo=n(vGo,"CODE",{});var Rls=s(vfo);BJa=r(Rls,"model.eval()"),Rls.forEach(t),IJa=r(vGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Mfo=n(vGo,"CODE",{});var kls=s(Mfo);NJa=r(kls,"model.train()"),kls.forEach(t),vGo.forEach(t),$Ja=i(Sn),M(w$.$$.fragment,Sn),Sn.forEach(t),Bm.forEach(t),yNo=i(c),Rg=n(c,"H3",{class:!0});var FPo=s(Rg);L$=n(FPo,"A",{id:!0,class:!0,href:!0});var Sls=s(L$);Tfo=n(Sls,"SPAN",{});var xls=s(Tfo);M(Lq.$$.fragment,xls),xls.forEach(t),Sls.forEach(t),jJa=i(FPo),Ffo=n(FPo,"SPAN",{});var Gls=s(Ffo);PJa=r(Gls,"TFAutoModelForDocumentQuestionAnswering"),Gls.forEach(t),FPo.forEach(t),CNo=i(c),Qr=n(c,"DIV",{class:!0});var Im=s(Qr);M(Aq.$$.fragment,Im),ZJa=i(Im),kg=n(Im,"P",{});var Hwe=s(kg);VJa=r(Hwe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created
with the `),p4e=n(Hwe,"A",{href:!0});var Bls=s(p4e);WJa=r(Bls,"from_pretrained()"),Bls.forEach(t),JJa=r(Hwe," class method or the "),_4e=n(Hwe,"A",{href:!0});var Ils=s(_4e);UJa=r(Ils,"from_config()"),Ils.forEach(t),XJa=r(Hwe,` class
method.`),Hwe.forEach(t),DJa=i(Im),Rq=n(Im,"P",{});var EPo=s(Rq);qJa=r(EPo,"This class cannot be instantiated directly using "),Efo=n(EPo,"CODE",{});var Nls=s(Efo);OJa=r(Nls,"__init__()"),Nls.forEach(t),QJa=r(EPo," (throws an error)."),EPo.forEach(t),YJa=i(Im),qa=n(Im,"DIV",{class:!0});var UZ=s(qa);M(kq.$$.fragment,UZ),zJa=i(UZ),yfo=n(UZ,"P",{});var $ls=s(yfo);HJa=r($ls,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),$ls.forEach(t),KJa=i(UZ),Sg=n(UZ,"P",{});var Kwe=s(Sg);eUa=r(Kwe,`Note:
Loading a model from its configuration file does `),Cfo=n(Kwe,"STRONG",{});var jls=s(Cfo);oUa=r(jls,"not"),jls.forEach(t),rUa=r(Kwe,` load the model weights. It only affects the
model\u2019s configuration. Use `),b4e=n(Kwe,"A",{href:!0});var Pls=s(b4e);tUa=r(Pls,"from_pretrained()"),Pls.forEach(t),aUa=r(Kwe," to load the model weights."),Kwe.forEach(t),nUa=i(UZ),M(A$.$$.fragment,UZ),UZ.forEach(t),sUa=i(Im),xt=n(Im,"DIV",{class:!0});var Nm=s(xt);M(Sq.$$.fragment,Nm),lUa=i(Nm),wfo=n(Nm,"P",{});var Zls=s(wfo);iUa=r(Zls,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),Zls.forEach(t),dUa=i(Nm),Xs=n(Nm,"P",{});var XZ=s(Xs);mUa=r(XZ,"The model class to instantiate is selected based on the "),Lfo=n(XZ,"CODE",{});var Vls=s(Lfo);cUa=r(Vls,"model_type"),Vls.forEach(t),fUa=r(XZ,` property of the config object (either
passed as an argument or loaded from `),Afo=n(XZ,"CODE",{});var Wls=s(Afo);hUa=r(Wls,"pretrained_model_name_or_path"),Wls.forEach(t),gUa=r(XZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Rfo=n(XZ,"CODE",{});var Jls=s(Rfo);uUa=r(Jls,"pretrained_model_name_or_path"),Jls.forEach(t),pUa=r(XZ,":"),XZ.forEach(t),_Ua=i(Nm),kfo=n(Nm,"UL",{});var Uls=s(kfo);R$=n(Uls,"LI",{});var MGo=s(R$);Sfo=n(MGo,"STRONG",{});var Xls=s(Sfo);bUa=r(Xls,"layoutlm"),Xls.forEach(t),vUa=r(MGo," \u2014 "),v4e=n(MGo,"A",{href:!0});var Dls=s(v4e);MUa=r(Dls,"TFLayoutLMForQuestionAnswering"),Dls.forEach(t),TUa=r(MGo," (LayoutLM model)"),MGo.forEach(t),Uls.forEach(t),FUa=i(Nm),M(k$.$$.fragment,Nm),Nm.forEach(t),Im.forEach(t),wNo=i(c),xg=n(c,"H3",{class:!0});var yPo=s(xg);S$=n(yPo,"A",{id:!0,class:!0,href:!0});var qls=s(S$);xfo=n(qls,"SPAN",{});var Ols=s(xfo);M(xq.$$.fragment,Ols),Ols.forEach(t),qls.forEach(t),EUa=i(yPo),Gfo=n(yPo,"SPAN",{});var Qls=s(Gfo);yUa=r(Qls,"AutoModelForVisualQuestionAnswering"),Qls.forEach(t),yPo.forEach(t),LNo=i(c),Yr=n(c,"DIV",{class:!0});var $m=s(Yr);M(Gq.$$.fragment,$m),CUa=i($m),Gg=n($m,"P",{});var e6e=s(Gg);wUa=r(e6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a visual question answering head) when created
with the `),M4e=n(e6e,"A",{href:!0});var Yls=s(M4e);LUa=r(Yls,"from_pretrained()"),Yls.forEach(t),AUa=r(e6e," class method or the "),T4e=n(e6e,"A",{href:!0});var zls=s(T4e);RUa=r(zls,"from_config()"),zls.forEach(t),kUa=r(e6e,` class
method.`),e6e.forEach(t),SUa=i($m),Bq=n($m,"P",{});var CPo=s(Bq);xUa=r(CPo,"This class cannot be instantiated directly using "),Bfo=n(CPo,"CODE",{});var Hls=s(Bfo);GUa=r(Hls,"__init__()"),Hls.forEach(t),BUa=r(CPo," (throws an error)."),CPo.forEach(t),IUa=i($m),Oa=n($m,"DIV",{class:!0});var DZ=s(Oa);M(Iq.$$.fragment,DZ),NUa=i(DZ),Ifo=n(DZ,"P",{});var Kls=s(Ifo);$Ua=r(Kls,"Instantiates one of the model classes of the library (with a visual question answering head) from a configuration."),Kls.forEach(t),jUa=i(DZ),Bg=n(DZ,"P",{});var o6e=s(Bg);PUa=r(o6e,`Note:
Loading a model from its configuration file does `),Nfo=n(o6e,"STRONG",{});var eis=s(Nfo);ZUa=r(eis,"not"),eis.forEach(t),VUa=r(o6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),F4e=n(o6e,"A",{href:!0});var ois=s(F4e);WUa=r(ois,"from_pretrained()"),ois.forEach(t),JUa=r(o6e," to load the model weights."),o6e.forEach(t),UUa=i(DZ),M(x$.$$.fragment,DZ),DZ.forEach(t),XUa=i($m),Io=n($m,"DIV",{class:!0});var xn=s(Io);M(Nq.$$.fragment,xn),DUa=i(xn),$fo=n(xn,"P",{});var ris=s($fo);qUa=r(ris,"Instantiate one of the model classes of the library (with a visual question answering head) from a pretrained model."),ris.forEach(t),OUa=i(xn),Ds=n(xn,"P",{});var qZ=s(Ds);QUa=r(qZ,"The model class to instantiate is selected based on the "),jfo=n(qZ,"CODE",{});var tis=s(jfo);YUa=r(tis,"model_type"),tis.forEach(t),zUa=r(qZ,` property of the config object (either
passed as an argument or loaded from `),Pfo=n(qZ,"CODE",{});var ais=s(Pfo);HUa=r(ais,"pretrained_model_name_or_path"),ais.forEach(t),KUa=r(qZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Zfo=n(qZ,"CODE",{});var nis=s(Zfo);eXa=r(nis,"pretrained_model_name_or_path"),nis.forEach(t),oXa=r(qZ,":"),qZ.forEach(t),rXa=i(xn),Vfo=n(xn,"UL",{});var sis=s(Vfo);G$=n(sis,"LI",{});var TGo=s(G$);Wfo=n(TGo,"STRONG",{});var lis=s(Wfo);tXa=r(lis,"vilt"),lis.forEach(t),aXa=r(TGo," \u2014 "),E4e=n(TGo,"A",{href:!0});var iis=s(E4e);nXa=r(iis,"ViltForQuestionAnswering"),iis.forEach(t),sXa=r(TGo," (ViLT model)"),TGo.forEach(t),sis.forEach(t),lXa=i(xn),B$=n(xn,"P",{});var FGo=s(B$);iXa=r(FGo,"The model is set in evaluation mode by default using "),Jfo=n(FGo,"CODE",{});var dis=s(Jfo);dXa=r(dis,"model.eval()"),dis.forEach(t),mXa=r(FGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Ufo=n(FGo,"CODE",{});var mis=s(Ufo);cXa=r(mis,"model.train()"),mis.forEach(t),FGo.forEach(t),fXa=i(xn),M(I$.$$.fragment,xn),xn.forEach(t),$m.forEach(t),ANo=i(c),Ig=n(c,"H3",{class:!0});var wPo=s(Ig);N$=n(wPo,"A",{id:!0,class:!0,href:!0});var cis=s(N$);Xfo=n(cis,"SPAN",{});var fis=s(Xfo);M($q.$$.fragment,fis),fis.forEach(t),cis.forEach(t),hXa=i(wPo),Dfo=n(wPo,"SPAN",{});var his=s(Dfo);gXa=r(his,"AutoModelForVision2Seq"),his.forEach(t),wPo.forEach(t),RNo=i(c),zr=n(c,"DIV",{class:!0});var jm=s(zr);M(jq.$$.fragment,jm),uXa=i(jm),Ng=n(jm,"P",{});var r6e=s(Ng);pXa=r(r6e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
with the `),y4e=n(r6e,"A",{href:!0});var gis=s(y4e);_Xa=r(gis,"from_pretrained()"),gis.forEach(t),bXa=r(r6e," class method or the "),C4e=n(r6e,"A",{href:!0});var uis=s(C4e);vXa=r(uis,"from_config()"),uis.forEach(t),MXa=r(r6e,` class
method.`),r6e.forEach(t),TXa=i(jm),Pq=n(jm,"P",{});var LPo=s(Pq);FXa=r(LPo,"This class cannot be instantiated directly using "),qfo=n(LPo,"CODE",{});var pis=s(qfo);EXa=r(pis,"__init__()"),pis.forEach(t),yXa=r(LPo," (throws an error)."),LPo.forEach(t),CXa=i(jm),Qa=n(jm,"DIV",{class:!0});var OZ=s(Qa);M(Zq.$$.fragment,OZ),wXa=i(OZ),Ofo=n(OZ,"P",{});var _is=s(Ofo);LXa=r(_is,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),_is.forEach(t),AXa=i(OZ),$g=n(OZ,"P",{});var t6e=s($g);RXa=r(t6e,`Note:
Loading a model from its configuration file does `),Qfo=n(t6e,"STRONG",{});var bis=s(Qfo);kXa=r(bis,"not"),bis.forEach(t),SXa=r(t6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),w4e=n(t6e,"A",{href:!0});var vis=s(w4e);xXa=r(vis,"from_pretrained()"),vis.forEach(t),GXa=r(t6e," to load the model weights."),t6e.forEach(t),BXa=i(OZ),M($$.$$.fragment,OZ),OZ.forEach(t),IXa=i(jm),No=n(jm,"DIV",{class:!0});var Gn=s(No);M(Vq.$$.fragment,Gn),NXa=i(Gn),Yfo=n(Gn,"P",{});var Mis=s(Yfo);$Xa=r(Mis,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),Mis.forEach(t),jXa=i(Gn),qs=n(Gn,"P",{});var QZ=s(qs);PXa=r(QZ,"The model class to instantiate is selected based on the "),zfo=n(QZ,"CODE",{});var Tis=s(zfo);ZXa=r(Tis,"model_type"),Tis.forEach(t),VXa=r(QZ,` property of the config object (either
passed as an argument or loaded from `),Hfo=n(QZ,"CODE",{});var Fis=s(Hfo);WXa=r(Fis,"pretrained_model_name_or_path"),Fis.forEach(t),JXa=r(QZ,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Kfo=n(QZ,"CODE",{});var Eis=s(Kfo);UXa=r(Eis,"pretrained_model_name_or_path"),Eis.forEach(t),XXa=r(QZ,":"),QZ.forEach(t),DXa=i(Gn),Hr=n(Gn,"UL",{});var Bn=s(Hr);j$=n(Bn,"LI",{});var EGo=s(j$);eho=n(EGo,"STRONG",{});var yis=s(eho);qXa=r(yis,"blip"),yis.forEach(t),OXa=r(EGo," \u2014 "),L4e=n(EGo,"A",{href:!0});var Cis=s(L4e);QXa=r(Cis,"BlipForConditionalGeneration"),Cis.forEach(t),YXa=r(EGo," (BLIP model)"),EGo.forEach(t),zXa=i(Bn),P$=n(Bn,"LI",{});var yGo=s(P$);oho=n(yGo,"STRONG",{});var wis=s(oho);HXa=r(wis,"blip-2"),wis.forEach(t),KXa=r(yGo," \u2014 "),A4e=n(yGo,"A",{href:!0});var Lis=s(A4e);eDa=r(Lis,"Blip2ForConditionalGeneration"),Lis.forEach(t),oDa=r(yGo," (BLIP-2 model)"),yGo.forEach(t),rDa=i(Bn),Z$=n(Bn,"LI",{});var CGo=s(Z$);rho=n(CGo,"STRONG",{});var Ais=s(rho);tDa=r(Ais,"git"),Ais.forEach(t),aDa=r(CGo," \u2014 "),R4e=n(CGo,"A",{href:!0});var Ris=s(R4e);nDa=r(Ris,"GitForCausalLM"),Ris.forEach(t),sDa=r(CGo," (GIT model)"),CGo.forEach(t),lDa=i(Bn),V$=n(Bn,"LI",{});var wGo=s(V$);tho=n(wGo,"STRONG",{});var kis=s(tho);iDa=r(kis,"instructblip"),kis.forEach(t),dDa=r(wGo," \u2014 "),k4e=n(wGo,"A",{href:!0});var Sis=s(k4e);mDa=r(Sis,"InstructBlipForConditionalGeneration"),Sis.forEach(t),cDa=r(wGo," (InstructBLIP model)"),wGo.forEach(t),fDa=i(Bn),W$=n(Bn,"LI",{});var LGo=s(W$);aho=n(LGo,"STRONG",{});var xis=s(aho);hDa=r(xis,"pix2struct"),xis.forEach(t),gDa=r(LGo," \u2014 "),S4e=n(LGo,"A",{href:!0});var Gis=s(S4e);uDa=r(Gis,"Pix2StructForConditionalGeneration"),Gis.forEach(t),pDa=r(LGo," (Pix2Struct model)"),LGo.forEach(t),_Da=i(Bn),J$=n(Bn,"LI",{});var AGo=s(J$);nho=n(AGo,"STRONG",{});var Bis=s(nho);bDa=r(Bis,"vision-encoder-decoder"),Bis.forEach(t),vDa=r(AGo," \u2014 "),x4e=n(AGo,"A",{href:!0});var Iis=s(x4e);MDa=r(Iis,"VisionEncoderDecoderModel"),Iis.forEach(t),TDa=r(AGo," (Vision Encoder decoder model)"),AGo.forEach(t),Bn.forEach(t),FDa=i(Gn),U$=n(Gn,"P",{});var RGo=s(U$);EDa=r(RGo,"The model is set in evaluation mode by default using "),sho=n(RGo,"CODE",{});var Nis=s(sho);yDa=r(Nis,"model.eval()"),Nis.forEach(t),CDa=r(RGo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),lho=n(RGo,"CODE",{});var $is=s(lho);wDa=r($is,"model.train()"),$is.forEach(t),RGo.forEach(t),LDa=i(Gn),M(X$.$$.fragment,Gn),Gn.forEach(t),jm.forEach(t),kNo=i(c),jg=n(c,"H3",{class:!0});var APo=s(jg);D$=n(APo,"A",{id:!0,class:!0,href:!0});var jis=s(D$);iho=n(jis,"SPAN",{});var Pis=s(iho);M(Wq.$$.fragment,Pis),Pis.forEach(t),jis.forEach(t),ADa=i(APo),dho=n(APo,"SPAN",{});var Zis=s(dho);RDa=r(Zis,"TFAutoModelForVision2Seq"),Zis.forEach(t),APo.forEach(t),SNo=i(c),Kr=n(c,"DIV",{class:!0});var Pm=s(Kr);M(Jq.$$.fragment,Pm),kDa=i(Pm),Pg=n(Pm,"P",{});var a6e=s(Pg);SDa=r(a6e,`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 `),G4e=n(a6e,"A",{href:!0});var Vis=s(G4e);xDa=r(Vis,"from_pretrained()"),Vis.forEach(t),GDa=r(a6e," class method or the "),B4e=n(a6e,"A",{href:!0});var Wis=s(B4e);BDa=r(Wis,"from_config()"),Wis.forEach(t),IDa=r(a6e,` class
method.`),a6e.forEach(t),NDa=i(Pm),Uq=n(Pm,"P",{});var RPo=s(Uq);$Da=r(RPo,"This class cannot be instantiated directly using "),mho=n(RPo,"CODE",{});var Jis=s(mho);jDa=r(Jis,"__init__()"),Jis.forEach(t),PDa=r(RPo," (throws an error)."),RPo.forEach(t),ZDa=i(Pm),Ya=n(Pm,"DIV",{class:!0});var YZ=s(Ya);M(Xq.$$.fragment,YZ),VDa=i(YZ),cho=n(YZ,"P",{});var Uis=s(cho);WDa=r(Uis,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),Uis.forEach(t),JDa=i(YZ),Zg=n(YZ,"P",{});var n6e=s(Zg);UDa=r(n6e,`Note:
Loading a model from its configuration file does `),fho=n(n6e,"STRONG",{});var Xis=s(fho);XDa=r(Xis,"not"),Xis.forEach(t),DDa=r(n6e,` load the model weights. It only affects the
model\u2019s configuration. Use `),I4e=n(n6e,"A",{href:!0});var Dis=s(I4e);qDa=r(Dis,"from_pretrained()"),Dis.forEach(t),ODa=r(n6e," to load the model weights."),n6e.forEach(t),QDa=i(YZ),M(q$.$$.fragment,YZ),YZ.forEach(t),YDa=i(Pm),Gt=n(Pm,"DIV",{class:!0});var Zm=s(Gt);M(Dq.$$.fragment,Zm),zDa=i(Zm),hho=n(Zm,"P",{});var qis=s(hho);HDa=r(qis,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),qis.forEach(t),KDa=i(Zm),Os=n(Zm,"P",{});var zZ=s(Os);eqa=r(zZ,"The model class to instantiate is selected based on the "),gho=n(zZ,"CODE",{});var Ois=s(gho);oqa=r(Ois,"model_type"),Ois.forEach(t),rqa=r(zZ,` property of the config object (either
passed as an argument or loaded from `),uho=n(zZ,"CODE",{});var Qis=s(uho);tqa=r(Qis,"pretrained_model_name_or_path"),Qis.forEach(t),aqa=r(zZ,` if possible), or when it\u2019s missing, by
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NextImageProcessor"),d(pse,"href","/docs/transformers/main/en/model_doc/sam#transformers.SamImageProcessor"),d(_se,"href","/docs/transformers/main/en/model_doc/segformer#transformers.SegformerImageProcessor"),d(bse,"href","/docs/transformers/main/en/model_doc/vit#transformers.ViTImageProcessor"),d(vse,"href","/docs/transformers/main/en/model_doc/vit#transformers.ViTImageProcessor"),d(Mse,"href","/docs/transformers/main/en/model_doc/swin2sr#transformers.Swin2SRImageProcessor"),d(Tse,"href","/docs/transformers/main/en/model_doc/vit#transformers.ViTImageProcessor"),d(Fse,"href","/docs/transformers/main/en/model_doc/detr#transformers.DetrImageProcessor"),d(Ese,"href","/docs/transformers/main/en/model_doc/videomae#transformers.VideoMAEImageProcessor"),d(yse,"href","/docs/transformers/main/en/model_doc/tvlt#transformers.TvltImageProcessor"),d(Cse,"href","/docs/transformers/main/en/model_doc/segformer#transformers.SegformerImageProcessor"),d(wse,"href","/docs/transformers/main/en/model_doc/convnext#transformers.ConvNextImageProcessor"),d(Lse,"href","/docs/transformers/main/en/model_doc/videomae#transformers.VideoMAEImageProcessor"),d(Ase,"href","/docs/transformers/main/en/model_doc/vilt#transformers.ViltImageProcessor"),d(Rse,"href","/docs/transformers/main/en/model_doc/vit#transformers.ViTImageProcessor"),d(kse,"href","/docs/transformers/main/en/model_doc/vit_hybrid#transformers.ViTHybridImageProcessor"),d(Sse,"href","/docs/transformers/main/en/model_doc/vit#transformers.ViTImageProcessor"),d(xse,"href","/docs/transformers/main/en/model_doc/vit#transformers.ViTImageProcessor"),d(Gse,"href","/docs/transformers/main/en/model_doc/clip#transformers.CLIPImageProcessor"),d(Bse,"href","/docs/transformers/main/en/model_doc/yolos#transformers.YolosImageProcessor"),d(ao,"class","docstring 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Classes"};function Qcs(x){return Tds(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class rfs extends _ds{constructor(h){super();bds(this,h,Qcs,qcs,vds,{})}}export{rfs as default,Ocs as metadata};
